1 Climate Scenarios for Perú to 2030 2 Climate Scenarios for Perú to 2030 Vulnerability Vulnerability ofthe The present publication ispart Edition: SENAMHI Year: 2009 Authors: http://www.senamhi.gob.pe PredictionNumerical Center –CPN National Meteorology andHydrology Service SENAMHI TO 2030 CLIMATE SCENARIOSFORPERU by theMinistryofEnvironment Peru. UNFCCC, financedbytheGEF and Coordinated Communication onClimateChange tothe within theframeworkofSecond National Richard Miguel Alan Llacza Clara Oria AcuñaDelia AvalosGrinia Rosas Gabriela Amelia Díaz Obregón,Guillermo and Adaptation Component source consentoftheauthors. orwithprior The content ofthispublicationmay bereproduced Statingthe 2009 Printed inPerú First Issue First Edition Printing Style Digital edition Graphic Director Design Legal Deposit: http://www.senamhi.gob.pe Telephone: (51–1) 6141414 (central) and6141408(CPN) Jr. CahuideMaría 785Jesús –SENAMHI National Meteorology Service andHidrology http://www.minam.gob.pe Telephone: (51–1)2255370-Fax:2255369 Av. Guardia Lima. Civil205,SanBorja, Environment –MINAM Ministry : 2009 : January : xxxxxxxxxxx Canales: Carlos Zubizarreta : HugoNegreiros Bezada : RicardoEslava Escobar : Q&PImpresores Climate Scenarios for Peru to 2030 ON CLIMATE CHANGE SECOND NATIONAL COMMUNICATION Climate Scenarios Environment -MInAM Ministry for Peru to 2030 Communications Coordinator: Luisa Elías/Jenny Gómez Chimayco Ortega General DirectorGeneral and for Climate Change, Desertification WaterResources Vulnerability andAdaptationVulnerability Coordinator: Laura Avellaneda Huamán Vice-minister forVice-minister Strategic andNaturalResources Development Inventories and Mitigation Coordinator: R Inventories andMitigation rbegoso Contreras KelvinOrbegoso Administrator: General Coordinator:General Jorge Álvarez Lam Mag. F EXECUTING UNIOF Eduardo Durand López-Hurtado Hidrology Service -SENAMHI Hidrology Service National Meteorology and Assistant: RuthCamayo Suárez Ministry of Executive President SENAMHI Dr. Antonio JoséBrack Egg Alternate NationalDirector Eng. AP (ret.) Wilar Molina Gamarra Project NationalDirector V anessa Minister nment enviro V ereau Ladd JEC THE PRO afael Millán García afael Millán T

3 Climate Scenarios for Perú to 2030 4 Climate Scenarios for Perú to 2030 PRESENTATION

5 Climate Scenarios for Perú to 2030 6 Climate Scenarios for Perú to 2030 T ACKNOWLEDGEMENTS of theSNCCC. of Environment andto theimplementingagencies The group ofthe Project Execution UnitoftheMinistry development ofthestudyonSantariverbasin. to the contributions of Ancash,for hisvaluableandimportant Eng. JuanGuerrero, advisorfrom theRegional Government CenterModeling of Venezuela. Director oftheScientific The ofZulia, University Deputy in Venezuela. Modeling Center The Scientific of theUniversity Zulia, form informationclimate scenario from theECHAM model. –MPIfrom PlanckThe Max Institute Germany, for providing information to develop thisstudy. of regional scenarios, whichhave beentheessential for providing theinformation usedasbasisfor thegeneration Atmospheric Research – NCAR of the United States of America, Reiterate ourappreciation to theNationalCenter for for done. thework encourage comments andsuggestionspermanent develop thepresent studyandfor providing hisimportant andinvaluable to experience hisknowledge sharing Tiempo yPesquisas Espaciales–CPTECBrazil, for outstanding career from theCentro dePrevisión del o Dr. Obregón, Guillermo prominent scientist,whohasan team wishesto expressThe working itsappreciation: National Meteorology and Hydrology Service NationalMeteorology andHydrology Service Technical-Administrative Director of the Project Amelia DíazPabló 1.1 INTRODUCTION CHAPTER 1 ACKNOWLEDGEMENTS PRESENTATION 1.2

2.1 PERÚ: PHYSICAL ANDSOCIALCONTEXT CHAPTER 2. 2.2 2.3. 2.4 2.5 2.6 3.1 NATIONAL CLIMATE TRENDS CHAPTER 3 3.2 3.3 Results

Objectives Theoretical Framework r 1.2.3 1.2.2 1.2.1. Current situation of climate change at global level Location 1.2.4 General characteristics Latitudinal location Population anddemographic growth Economical activities andGDPinPeruEconomical activities Characteristics ofClimate inPeru Data 2.6.3 2.6.2 2.6.1 Methodology 3.1.2 3.1.1 3.3.3. Lineartrends ofminimumtemperature 3.3.2. 3.3.1 3.2.7. 3.2.6. 3.2.5. 3.2.4. 3.2.3. 3.2.2. 3.2.1 t 3.3.4. 3.3.5. t Climate prospective at globallevel upto theendofXXI Century withuncertainty Dealing precipitations inextremeevents: Niña ElNiño/La variationMultiannual ofextremetemperatures andprecipitation Classification ofclimate in Peru Quality controlQuality andfillingindata Used basicdata Linear trends ofmaximumtemperature Linear trends ofprecipitation Wavelet analysis Analysis ofthePrincipal Components Drought inPeru teleconnections Standardized Precipitation (SPI) Index Determination ofclimate extremeindices Determination ofthestatistical significance ofthelineartrend Estimation oflineartrend Linear 3.3.5.3 Climate extremeindices for minimumtemperature 3.3.5.2 Climate extremeindices for maximumtemperature 3.3.5.1 Climate extremeindices for precipitation emporal and spatial variation of extreme temperature and rends of diurnal cycle inPerurends cycle ofdiurnal eliability of numerical modelsto future ofnumerical project eliability climate change T rends ofclimate extremeindices INDEX

11 12 15 13 12 21 19 17 22 22 23 23 24 29 26 25 25 25 31 31 31 48 44 44 42 42 40 38 38 37 35 35 52 56 66 64 59 59 6 5

7 Climate Scenarios for Perú to 2030 8 Climate Scenarios for Perú to 2030 3.3.6.3 Seasonal distribution of drought occurred 4.1 CLIMATE SCENARIOSFOR 2020-2030 THE DECADES CHAPTER 4 Concepts andglossary Climate scenarios mapsto 2020 and2030 Multiannual averages events maps–Extreme andcurrent trends Hydrometeorological Network -Reliefmapsandclimate classification BIBLIOGRAPHY 5.2 4.2

5.1 Conclusions CONCLUSIONS ANDRECOMENDATIONS CHAPTER 5 4.4 4.3

a a ap ap P P p p PE PE E E ndi ndi ndi ndi 3.3.6 Intercomparison ofglobalclimate models 4.1.1 Recomendations downscaling Dynamical 4.1.2 5.1.2 Onclimate to projections 2030 r 4.2.2 4.2.1 oncurrent climate trends 5.1.1. 4.4.8 Scenarios to projected 2020and2030 Analyzed variables 4.4.7 Spring: Quarter September–November Quarter 4.4.7 Spring: 4.4.1 4.4.6 June–AugustWinter: Quarter 4.4.2 4.4.5 Autumn: Quarter March –May 4.4.5 Autumn: Quarter December –F Quarter 4.4.4 Summer: 4.4.3 X 4 X 3 X 2 X 1 X t

3.3.6.2 T 3.3.6.1 Analysis oftheStandardized Precipitation (SPI) Index Drought analysis based onglobalmodelsto 2030 Projections of precipitation for Peru to 2050 on globalmodels Preliminary data Projections to 2030ofthe90percentile ofprecipitation Maximum Maximum Minimum Minimum 4.4.1.3 Projection to 2030ofthe90percentile ofmaximum temperature 4.4.1.2 4.4.1.1 Precipitacions emperature projections for Peru to 2050 based egional simulation during warmeventsduring projection Seasonal Annual projection eleconnections indroughteleconnections distribution T T emperature emperature 97 ebruary 243 193 131 126 124 123 122 118 118 117 115 114 113 112 103 102 100 111 108 103 68 67 89 90 93 92 95 95 96 98 98 75 84 9 Climate Scenarios for Perú to 2030 10 Climate Scenarios for Perú to 2030 form these GCMs have a very low spatial resolution that ranges form 100 to 300 Km which representswhich a Km 300 tospatialresolutionlow 100rangesform thatvery a have GCMstheseform evaluationsvulnerabilityof necessaryisitsometake tointermediate steps, sincetheinformation coming the in them use to able orderbe toCentersin have; important World but most Modeling Climatethe only time different climate scenarios. To carry out these processes a solid capacity indynamics computing isof required,the climatethat system, the consideringsimulate GCMs The theimpacts. change differentclimate to vulnerable regions in GHGsadaptation and emissions vulnerability of scenarios; generating at the same general circulation models) and nowadays, it allows us to haveProgress has been made in improvementsavailable by incorporating physics-mathematic into models (atmosphere basic information for the assessment and estimate itsfuture situationinamore reliable way. our understanding and modeling of climate system, which will allow us to know better its current situationLately, however there has been made significant progress in the scientific and technological field to improve does climate athighmountainregions works ortheAmazon conclusive. region isnotvery does not have observations of meteorological variables for long periods territory our of of time, part significant a that the considering besides, different ways; severalcertainty in change climate toglobal know how MountaindiversityvastgeneratesclimatesRangeterritoryaofourmicroclimates and in respondthatto Andeancharacter.itsforcountry ourAndes The of case the large inclimateknowledge soaretheof to uncertaintiesassociatedthe completelyknown, not arechange climateglobal of regionalimpactsThe power,generation ofelectric etc. agriculture, industry socio-economicserieswateroffora surrounding the demandsof all in forhumanbasins: consumption, them,besidesvanishingofbeingmostuniquethatmountain high is ecosystems, sourcemainlyofare a glacierthis of part criticalThe Arequipa. in Misti andCoropunaChachani, range,mountaincentral the Pastoruri,level,nationalin atglaciers important most the of someaccelerated retreatwitnessingofthe glaciers,tropical the of 75% than more has Perulevel, global a At retreat.glacial temperature: global increaseof thedirectlyrelated areto know processesweother thatsomematter, alsothis are there On indirectly related (PRAA,2007) to globalwarming or directlyare thatpatterns climate in changespossible indicatorsof records,are of years 40 almost of Riverbasin,with values that rangemm/decade;between 85to 7 these results, basedobservationtheon from 5 to 54mm/decade; or opposite situations, such as the increase of rainfall observed in the Urubamba precipitationin regionssomeMantarocountry,thetheinRiverbasin,in ofsuch registers that diminish a matter, this On national studiespronounced indicate some there modified.are that diminishing trends can’t directly relate them to climate change, they give us a good perception that climate variability is been loss, or floods in San Martin in December 2006, or the early frosts in Huancayo inlast 2007; decade, that sucheven as thoughthe snowfalls we in 2004 in the southern part of the country, that caused severePeru economic is aware of this problem, more and more intense extreme events have been frequently observed in the inthefuture. activities societies, affecting thesustainabledevelopment oftheirsocio-economic vulnerability.levelof Globalclimate changeundoubtedly will generatepreparedsevereless impactson these processes of global scale will cause at a regional and local level in eachgases emissions country,(GHG). with a The highercertainty of orthis lowerstatement implies a series of questions about the consequences is warming up; also, this anomaly is the response to human activity because of the increase of greenhouse unlike the Third Assessment Report (TAR, as per its acronym in English, out, points unequivocally 2007) IPCC,IPCCacronym, English its per as 2001),(AR4 Report Assessment FourthIPCC that The the climate systems INTRODUCTION INTRODUCCIÓN 11 Climate Scenarios for Perú to 2030 12 Climate Scenarios for Perú to 2030 Evaluate • 1.1 Objectives. Development way to contribute theMillennium to guide policy makers on adequate adaptation measures to face climate change at national level and this emission. Finally, these results will allow to improve our knowledge on the vulnerability of adequatelyclimatedramaticourusedprojections,greenhouseas be assumptiona countrymust the gasesunderof and regionallevel, which turninadds some errors stepin theofprocess, for thisreason, thegenerated results generateinformationtomethodsused at the climateforvariability limited knowledgeandon the both for aspect, processstepelaboration each the that in regional of uncertainty an considers climate scenarios on CO2 emissions scenarios, considering the A2 scenario as the most pessimist of all. extreme temperatures It is importantand precipitation to based stateon the last 40 years and their behavior in the future, based nationalprovidelevel,willinformationindicatorsextremeclimate events, trendsconcerningat and ofthe made analysis, present The etc. economy,power, electric of i.e., availability sectors, transportation, own agriculture, their in in changes the determines it how and conditions climate future and current the on information need that sectors socio-economic national different the requirementsof the attend to change,climate of impactspossible the informationondetailed generating at aims studypresent The ofgeneratingregional information.importance recognizesitwhich thein AR4, its statesIPCC incharacteristicslocalstudy,the areasunderthe the as of determinethe present and future vulnerability inspecific regions; andadaptation measures according to informationtouseful generate toEnvironment, allowed haveof Ministry current (CONAM),theCouncil Andean Program for the Adaptation to Climate Change Regional(PRAA-2007)theregional studies,Pollutionasand 2005)(PROCLIM- Air and executedClimateChange of Impacts theby the National Environment In this sense, inter-institutional studies, such as the National Program for Capacity Strengthening to Manage adaptation measures for planning socio-economic activities that might be affected adequate most inthe the establish future.to and assessments vulnerability better develop to important is information generatedFinally,this district. a or department a basin, a suchstudy, under area the characteristicsof properthe associatedtochangeclimate of impactsthe describe would regionalthatscenarios climate requiredthe details,globaltheinformation basedon Global theCirculation of Models(GCMs),obtaining andlocal scales are required. Theintermediate steps imply the use of aseries of methodologies regional at to obtainanalysis topography, its of complexity the to due countries, Andean the for limitation serious Theymaintain naturala dynamics generating variations differentat time scales, eventsNiño/La as El Niña the climate system: the atmosphere, the biosphere, the hydrosphere, the cryosphere and the land surface. Meteorological Organization. Also, it is the result Worldtheofyears, according toa 30 complex as such time, interactionof relatively periodlong a region,between in locationor certain the a five components of of variability, wind and rainfall humidity, temperature, of values mean of terms in description a is Climate 1.2 Estimate • thecurrent data. climate Determine trends atnationallevel basedonobserved • The ofthepresent objectives studyare:

2030. using statisticalanddynamicdownscaling techniques for (highemissions). theA2emissionscenario

Theoretical ves Objecti

the extreme

climate indicators Framework

projections for

rainfall at

national and

level temperature,

TO

the

year both

2030, observed

based

on and

global simulated

climate to

scenarios, the year concentration. the increase of greenhouse gas emissions into the atmosphere, originating the intensification of the GHGsbythe ice caps (see figure 1). Fossil-fuel burning and the change in land use, are the main drivers behind dioxide in the year 2005 exceeds by far, the natural range in the lastcaused only by 650climate variability without forcings.000 It has years, been that observed the concentrations of carbon as it has been determined is it that and XXcentury, “extremelythe of middle been unlikely”the has temperaturesince it average that greenhouse gases generated by human activities of haveincrease caused most the of the probability)thatincreases observed in ofthe global 90% thanlikely” (higher “veryis it thatconcluded IPCC the 2007, In based ontheIPCC (2007). Fourth Assessment Report fuels. ThisChapter gives readerthe someinformation currenttheon future and trendsglobal ofclimate, greenhousegasesemissions (GHGs) causedmainly industrializedby societies becausefossiluseofthe of increase in the and climate relation change between a evidence of scientificfound community has the decadeslast the in activity,and human indirectlyto orattributeddirectly is whichclimate of change a as change climate defines (UNFCCC) Change Climate on Convention Framework Nations United The insolaremissions or by humanactivity.variations possiblyaremillionsyearsandinterruptedorof natural volcanoescauses,by onlyas such eruptions and thousandsas time ofperiodslonger muchlasts thatglacialage, the asevents orseveralyears laststhat has warmed at least 1 ºC in the last century, at a faster pace than the global average. century, the arctic temperatures increased twice as much the rate of the global average index and Europe specifythat the warming rate does not uniformly rise over the whole planet. For example, during the last Hemisphere.50-year-period However, atleastthelast1300 years during intheNorthern to it isimportant 1995. Further scientific analysis have confirmed that the second half of the 20th Century was the since occurred years warmest11 and years 20 last the during occurredhave years 15 warmest the that determined (seeFigure From2). theverybeginning, when information higherrecords getting started tobeismade (1850) rateithasbeen warming the and ºC, 0,74 inincreased temperaturehas (1906-2005) globalyears 100 last the in estimatethatcomprehensiveallowedmoreto havestudies and observationsBetterand FIGURE 1 a sampleofatmosphere. Source IPCC, 2007. ppb, indicate the number of GHGs molecules per million or billion of air molecules, respectively, in and ppm in year.units 2000 The last the in GHGs long-duration of concentrations Atmospheric 1.2.1. Current situation ofclimate changeat globallevel

CO2 (ppm), N2O (ppb) 250 300 350 400 0 500 Carbone Dioxidede(CO Nitrous Oxide(N Methane (CH 1000 Years 4 ) 2 O) 1500 2 ) 2000 1000 600 800 1200 1400 1600 1800 2000 INTRODUCTION

CH4 (ppb) 13 Climate Scenarios for Perú to 2030 14 Climate Scenarios for Perú to 2030 Figure 4). and Greenland, as well as it is the reduction of Pole mountain glaciers and snow coverage at global level North (see the in mainly significant, very is coverage ice of processes melting the in acceleration The the20thCentury.17 cmduring either the increase of the ocean water volume or the ice melting in the poles, determining and increase of climatetheheataddedthetosystem,of than80% contributing risinglevelstosea(seeFigure todue 3), more absorbing been have oceans the that confirming temperature, the surface causingsea of increase is continuous warming ocean and temperature air the 1961, since made observations to According FIGURE 2 100 yearsand150 (purple) (red) (IPCC, 2007). current temperatures, both expressed in ºC. The linear trends are shown during the last 25 years (yellow), 50 year (orange), of estimates the observed is it axis right the in and 1990 to 1961 period the fortemperature average in anomalies shows axis left information.The the to adjustment linear of application including dots) (black temperature mean annual Global

Difference in (°C) from 1981 - 1990 FIGURE 3 -0.8 -0.6 -0.4 -0.2 0.6 0.0 0.2 0.4 Anomalies at sea level, observed by satelliteAnomalies atsealevel, anddirectdata.Source observed IPCC, 2007 Sea Level (mm) -150 -100 100 -50 50 0 1860 1880 5-95% decennialerrorbars Smooth series Mean Annual 1880 1900 1900 1920 1920 Years 1940 1940 1960 1960 Period Rate 1980 Years °Cfordecenies 1980 2000 2000

13.2 13.4 13.6 13.8 14.0 14.2 14.4 14.6

temperature (ºC) temperature Global Annual estimated mean mean estimated Annual Global Special Report on Emissions Scenarios – See summary inSENAMHI, 2005 http/www.ipcc.ch summary –See onEmissionsScenarios Special Report Hemisphere, anincrease (seeFigure ofless that1ºCisexpected 6). the Northern Hemisphere, where it could exceed 4 ºC, while in the large ocean extensions of the Southern Also it has been estimated that the increase in temperature will be higher over large continental masses in as well as, information thenew aboutclimate system. developed some improvements in their climate estimation processes with aassessment newIPCC,previousthethe considerslargerclimate greaterofnumber reportmodelsofhave a that realism and complexity, (B1 scenario) and up to 4 ºC (for a range of 2,4 to 6,4 forºC) the most pessimistic (A1F1 scenario). Unlike the warming,globalaat level, rangefrom (for1,8 ºCrangefor 1,1a2,9to theºC) ofmore optimistic scenario estimationscalculatebestaveragesurfacereducedemissionsThetotheairGHGs notused are if ºC 0,2 IPCCTheprojects as likely”“verythree nextdecadestheapproximate thatintherean willbe warmingof pessimist oneinwhichcurrent emissionstrends are maintained. scenarios that range form the more optimistic one that considers a stop in global emissions up to CO2theconcentrations, most in response to human activities in relation to its environment. thisIn sense, there are scenariosEmission(SpecialpossiblerangeonofScenarios)ReportIPCC (2000),athewhichthere is ofin will increase 1,1 between ºC and 6,4 ºC (see Figure 5). The climate projections consider the SRES emissions climate projections. These projections indicate that during the XXI century, mean temperature in thedifferent Earth climate models that were considered the by First IPCC Working Group its in last report futureon up. willcontinueto thefuture, warm In theEarth This statement isbasedontheresults obtainedfrom the 1.2.2. Climate prospective at globallevel until theendofXXI Century can benow explained, inaretrospective way, becauseofclimate changecausedby humanactivities. weather events, such as the variations in the atmospheric circulation models, Extreme stormsclimate events have trajectoriesincreased, and climate and patterns have rainfall,changed: heat waves and other extreme FIGURE 4 32 35 38 41 and satellite information. Hemisphere,Changes insnow coverfrom March intheNorthern to April, basedonthesnow cover indexinastation 1920 1930 1940 1950 1960 Years 1970 1980 1990 2000 INTRODUCTION 2010 15 Climate Scenarios for Perú to 2030 16 Climate Scenarios for Perú to 2030 FIGURE 6 FIGURE 5 correspond to projectionsupto 2025.Source IPCC, 2007. Projections of global warming models compared to some made observations up to 2005, shown in black dots the lines over decadesform 2020-2029 and2090to 2099.Source IPCC, 2007 averaged scenarios SRES (down) A2 and (middle) A1B and (up) B1 the for AOGCM multi-model projections Average CSurface warming (º) 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 1 1985 1990 0 2020 -2029 0.5 1995 1 1.5 2000 2 2.5 2005 3 Years 3.5 (°C) 4 2010 4.5 5 2015 5.5 2090 -2099 6 2020 6.5 7 7.5 2025 higher for certain climate (forhigher forvariables instance: certain temperature) than for others. (for instance: precipitation). is estimates model in confidence changes.The climate past and climate current featuresof observed the confidence This originates from above. the andfact that scale continentalmodels are at particularly,based change,on accepted climate futurephysical aboutprinciples estimates and their ability to reproduce provide credible quantitativemodels climate that considerable fact confidence isconcerning the There decrease inmostofthesubtropical regions. Regardingprecipitation,increasewilllikely itlatitudes,thathigh very atpossible is willis it whileitthat it According to the IPCC, it is likely (66% of probability) that typhoons and hurricanes will increase in intensity. consensus.the icefluxatagloballevel andup to thismomentthere isnotthenecessary the projected global warming. However, current climate models cannot explain the dynamics observed rise.of Current models indicate that the loss of ice-mass will occur more rapidly than anticipated, because of glaciers and snow cover. Ice cover likemountain the oneas wellin shrinking,continueasGreenland willRegionArctic the willin cover continueice Century,the XXI to the During shrink, contributing to sea level expansion. thermal of estimation the improve to helped has that mass glacier of loss the about information registered more theseprojections smalleris thanthose thein ThirdAssessment Report(TAR) theofIPCC, because there is expansionitself, due to ocean warming, contributes thermalwhere in 2100,70 to 75% to(see Figure up onepessimist7). Uncertainty favorableor associated least the to for cm 59 to 28 andfavorablescenario most thefor cm 37 to 18 fromrangesincreasethat anproject models the of mostrise,level sea About FIGURE 7 between theyearsbetween 1980to 1999andithasbeenestimated independentlyfrom theobservation. the range of the projections of the models for representsthe A1B scenario of the SRES in the XXI Century, comparedportion to the mean shaded blue altimeter.The satellite a with observed level sea mean global shows line green The curve. the at starting range variation the shows portion shaded red the and measurements, tide on based level sea mean global of reconstruction a is line red The term. long a at estimated level, sea of rate variations the concerning uncertainty shows futureportion grey-shaded The 1870. beforeits level sea of and measurements global no past are Thereprojection. the in 1999) to 1980 from mean the of (deviations level sea mean global of series Chronological Sea level variations (mm) 1.2.3. Reliability ofnumericalmodelstofuture project climate1.2.3. Reliability change 200 100 100 200 300 400 500 1800 0 Past Estimations 1850 1900 Instrumental Records Years 1950 2000 Future Proyections 2050

INTRODUCTION 2100 17 Climate Scenarios for Perú to 2030 18 Climate Scenarios for Perú to 2030 be represented explicitlyinmodels. Madden-Julian and Oscillation Southern Niña LaOscillation. / The main reason Niño for most Elof these precipitation errors tropical is that several of important simulation small-scale the processes cannotin smaller at greater scales,are generally there arestill someimportant problems they largeraat scale. For example, Although, there areerrors. some deficiencies significant some show still models Nevertheless, The • The • climate for models of use the on confident be to projections. reasons three are there that indicates IPCC The A • FIGURE 8 the Holocene Warm Period 6,000years ago, of21,000years ago. ortheLastGlacial Maximum changes.Models have been used tosimulate theclimate inancient times, such asthemiddle ofpart physical laws: conservation ofmass, energyphysical andmomentum laws: conservation observed global warming during thelast50years during (seefigure globalwarming 8) observed average climate. Also, these patterns have been used to evaluate therepresentcharacteristics maintoimportantabilitymany implication anofhave proved havetoModels climate. of GHGs forcing in the

third Source IPCC ,2007 appreciated. be can model climate the of effectiveness the so observations, to corresponds line thick black the and factors anthropogenic and natural using lines pink the factors, natural using model the of results the show of lines blue means by simulated results with temperature surface climate models using natural and anthropogenic factors. in Averages from 1906 to 2005 are observed, changesin which the light observed on made comparison Continental

first second

reason

reason

reason

originates

comes

is

based

from

from

the

on

the

fact the

ability

that ability

fundamental of

of models

these

to models

reproduce principles

to

simulate

of

features

these

important climate

of

past

models climates

aspects

are

and

of

based

climate current

on probabilistic way, are: The universal terms used to define the likelihood of a result, provided that the result can be calculated in a guidance note: uncertainty the in described ones the on based are confidence of scale the define to used terms The different go they together. concepts;but,inthepractice is as likely as it is unlikely (for example, to toss a coin and get heads or tails). Confidence and probabilitysituationisunlikely are to happen (for example, to throw the dices two times and obtain sixa twice) orthat it a that results. certainty specific with express of probabilities way, can the This authors and the knowledge IPCCTheprovided uncertainty anguidance note distinguishesthat levelsconfidence ofscientific the on result ofalltheauthors. ismadeonconfidencecriteria interpretalltheprocesses thatregulate thevalues resultsor andthedetermination theonexactness a of estimates are obtained in a probabilistic way. The uncertainty when in structure arises occursvalue when itin is not possible uncertainty to The values or specificstructure. and results value cannot be totally interpretuncertainty: andof they arekinds calculated primary using two statisticalare methodologies,There results onclimate change. thatwould beobtained intheassessmentreports of specific probabilities the and knowledge scientific on confidence of levels different the distinguish to able be toorder intransparent way a in IPCCthe addressedbysubject a wasuncertainty withDealing Very lowVery confidence Low confidence confidence Medium confidence High highconfidence Very Terminology ofconfidence levels Exceptionally unlikely unlikely Extremely unlikely Very Unlikely asnot About aslikely thannot More likely Likely likely Very likely Extremely certain Virtually Terminology oflikelihood 1.2.4 Dealingwithuncertainty 1 outof10 2 outof10 5 outof10 8 outof10 9 outof10 ofconfidenceScale < 1%probability < 5%probability < 10%probability < 33%probability To 33of66%probability > 50%probability > 66%probability > 90%probability > 95%probability > 99%probability Likelihood oftheresult INTRODUCTION 19 Climate Scenarios for Perú to 2030 20 Climate Scenarios for Perú to 2030 21 Climate Scenarios for Perú to 2030 22 Climate Scenarios for Perú to 2030 b. a. of Peru, consistingof: The presence of this large mountain range, along the territory determines the geographical heterogeneity • • • • The presence ofthesecurrents upto determines four zones marine offourcoastal region: south. further and temporarily country the of coast northern the influences permanently that Niño El called currentwater warm a and region coastal the in climate non-tropical temperate a determining latitude, south flows 5º to up that northward, onewith current water cold a each Current, currents, Humboldt or sea Current Peruvian two The of characteristics. confluence different the of because heterogeneous is sea Peruvian The CHARACTERISTICS GENERAL 2.2. sovereigntymaritime ofPeru upto 200milesoffshore. extends and to the west by the PacificOcean. It is worth mentioning that according to our new Constitution, the Chile by south the to Bolivia, by southeast the to Brazil, by east the to Colombia, and Ecuador by north borderedis to the It America. thirdSouth the in and one planet the in largest country twentieth the is It longitude andithasatotal (Ponce area of1’285,215.6Km2 deLeón, 2000). immediately America, South of 0º01’48” between line equatorial 18º21’03”the and below part 68º39’27”latitude, south 81º19’34,5”and western west central the in located is Peru speaking, Geographically LOCATION 2.1. converging withagreat ofnaturalresources. variety into aterritory Punathe plateautropicalAndean the high or or region jungle dessert, Amazonthem, the of in all basin, coastal in the reflected are that landscapes, and different climates constitute which of all PacificOcean, a with country a at looking are we the of currents the and Range Mountain Andes that the geography,of complex elevations he to due mainly mentioning avoid to inevitable is it Peru, to refer we When PERU: PHYSICAL ANDSOCIALCONTEXT Three hydrographic basins:thePacific, the Atlantic andthe Titicaca. the Mountains; Andes the and sea mountain region, as amountainmass;andtheAmazon region, eastoftheAndes. the between coast, the landmasses: continental large Three of Piura andLambayeque. coasts the off meet waters cold and warm the where sea, tropical Transitioncool the between zone, zone,The Ocean west ofthePeruvian Current, temperatures withwarm The Tropical of5ºsouthlatitude, temperatures withwarm sea,north with relatively low temperatures. The Cold Sea of the Peruvian Current, its location at starts 5º south latitude to the central of part Chile, 2.2.2 Andes MountainRange 2.2.1. Peruvian Sea * NationalStatisticsandComputing Institute. 2.4 P 2.4 inthecoast,slopes,from andover southto thePeruvian north Amazon region. Mountains and the Sea Currents, determine an variation important of climate conditions and vegetation hours to (shorter the south in the winter). The conjunction of the latitude with the altitude of the Andes sunlight and day of duration the as such variations ecological causing forresponsible is Latitude, South 18º up further little a to line Equatorial the almost fromPeruvian territory, the of location latitudinal The L 2.3 ranges. of precipitations, originating some arid zones in the inter-Andean valleys that lay parallel to the mountain The complex division of the Andes mountain range causes a huge local heterogeneity in the distribution humid area towards theeast. a and west the towardsregion mountain the in originated is area arid an region, southern the towards the Amazon jungle up to the high mountain range, in the western and eastern slopes. Because it is wider The altitudinal distribution of the Andes Mountain determines different vertical layers, from sea level and c. in the inter-census period between 1972-1981(2,6% peryear). between period in theinter-census years. Between the 1981 and 1993 census, the population growth was 2,0% per year, this rate was higher 46 last the in observed trend diminishing the confirms which % 1,6 of 1993-2007 period growthforthe average annual an shown has it that refers rate, growth annual average the by measured increase, Its Total populationinPeru amountsto 28million220thousand764inhabitants(INEI2007)*. of the basin. Titicaca Lake Andean plateau in the central and southern the region, ravine, and Porculla the the high of Andean north plateau, upland, in bleak the surroundings the as: such region mountain the in zones Different opulation atitudinal Miles 10 000 15 000 20 000 25 000 30 000 35 000 Fuente: INEI-Censos NacionalesdePoblación y Vivienda, 1940,1961,1972,1981,1993y2007. 5 000 0 TOTAL POPULATION AND AVERAGE ANNUAL GROWTHRATE 1940 7 023,1

and

location 1,9 D emographic 10 420,4 1951 Total Population 2,1 14 121,6 G 1972 rowth 1940-2007 2,6 17 762,2 1981 Grow rate 2,0 22 639,4 1993 PERÚ: PHYSICAL ANDSOCIALCONTEXT 1,6 28 220,8 2007 (%) 0,0 0,5 1,0 1,5 2,0 2,5 3,0 23 Climate Scenarios for Perú to 2030 24 Climate Scenarios for Perú to 2030 closer to theadequate rate. head and towardsto quickly development; if these estimates are would reached be getting the country poverty reduce substantially to order in 7% of rate average annual an requires Peru rates. growth high registering keep might economy that expected is it variations, matter,small forthis except On 2010. in GDP,the Concerning 7,3% and 2009 in 6.6% 2008, in 6,2% 2007, growthyear 7,2% the a in expected is it annualperiods. in thenext reachwould variables macro-economical main the that rates the shows which 2007) (MMM, 2008-2010 Recently, the of Ministry Economy and Finance presented the Multi-annual FrameworkMacro-economic exceeds 60billiondollars. For example, thegrowth in2003was4%withregard to theprevious year. already it currently and activity intensified an shows years 10 last the in Product Domestic Gross the of informationthe provided Peruin (INEI), byComputing and Statistics National the Institute evolutionthe of a country, and as such, it serves as a reference how the economic life of a nation is going. According to The GDP is an economical indicator that involves the addition of all the productive activities and services production(0,9%). tertiary secondary by followed (69.5%), material elaborated elaboratedproducts or industrial products (29,6%) and a minimum percentage for of or exports services primary or material raw are exportations the Most of trade. and construction agriculture, fisheries, mining, on based mainly is Peru of economy The 2.5 ECONOMICAL ACTI ECONOMICAL 2.5 1 2 3 4 5 6 7 8 Source: Ministry ofEconomy andFinance,Source: Ministry 2007 2000 Estimated 2001 V 2002 ITIES AND GDP IN PERU IN GDP AND ITIES 2003 GDP GROWTH 2000-2010 GDP GROWTH 2004 2005 2006 2007 2008 2009 2010 year. map03Appendix 2). (See mm/ 800 5 with Gabán San and mm/year 500 mm/year,3 3780 (Loreto)Orellanawith with María Tingo highest accumulated values registered in Pongo de with Caynará4 300 (San mm/year,Martin) Francisco precipitation in one year, show values that oscillate between 1 000 mm/year to 5 800 mm/year, being the accumulated jungle, the In Mountains. Andes the of part eastern the in occur rainfall year,heaviest the mm/ 870 to mm/year 250 between oscillate values these region mountain southern the in year,while mm/ 200 1 to mm/year mm/ 270 between varies 370 rainfall, accumulated region, mountain 1 central the in to year; mm/year 360 between oscillates rainfall accumulated In region, mm/year. mountain 10 northern exceed the not does rainfall annual coast southern and central the in Conversely, year. mm/ 260 to mm/year 22 from vary that values reaches coast northern the in rainfall Totalmultiannual oscillate values map02Appendix 2). 16ºCand22(See between jungle the in while ºC, 10 to ºC -7 between oscillate temperatures average minimum regionmountain the of part southern the in ºC; 10 and ºC region3 mountain the between varies values of part northern the in ºC; 17 and ºC 12 between oscillate values coast the of part southern and central ºC in the northern part of the jungle. In the northern part of the coast, on average, it reaches 21 ºC; in the 22 plateauand Andean averagehigh temperaturethe Multi-annual minimum in ºC -7 reachesof values 01 Appendix 2). map (See Ilave and Pillones in ºC 12 to up reaching registered, are values temperature maximum lowest the region mountain the of part southern and central the in while jungle, low the of part most and (Ricaplaya) coast northern the in ºC 32 to up reachestemperature maximum average Multi-annual is there region mountain the in rainfall; no ofthejunglethere rainfall. isheavy part andsouthern moderatealmost rainfallandinthenorthern or scarce is there coast the of part southern and lowest temperatures are registered in the highlands, the especially Also,in the jungle.high low Andean plateau.and coast the In the of central part northern the in occur temperatures highest the general, In 2.6.2. Multiannualvariation ofextreme temperatures andprecipitation ofrainfalloriginated fromwith plenty convective clouds. map03Appendix 1). (See vegetation, exuberant a having for characterized region plain almost an jungle, the is there finally and its altitudeanduneven topography ofclimates ithasa diversity thatrangefrom thetemperate to polar; of because that Mountain, Andes the to due region steep a is mountain the events; Niño El the during zone northern the for except rainfall, scarce with region dry a is foothills, Andean the and coastline the regions.jungle the and mountain the coast, the distinguished: areaplain Thea coast, situated between part of the country. However, in general, three geomorphological and climatological units can be clearly part of the country; up to rainy climate zones, warm with plenty of rainfall such as in Iquitos in climate in Puno zones, like in the territory, southern cold that with range rainfall fromshortage semi-dry the northern Werren Thornthwaite. According to this classification, Peru has 27 different types of climate in the whole data, on which all the climate indices were established according to the Climate Classification System of meteorological of (1965-1984) records years 20 on based is classification this of information basic The PacificSouth andthecontinentality. Anticyclone the climate, such as latitude, altitude, the Andes Mountain Range, the Peruvian Current (cold waters), the The climate classification of Peru (SENAMHI 1988) has been elaborated considering factors that determine 2.6.1 Classification ofclimate in Peru PERU IN CLIMATE OF CHARACTERISTICS 2.6 PERÚ: PHYSICAL ANDSOCIALCONTEXT 25 Climate Scenarios for Perú to 2030 26 Climate Scenarios for Perú to 2030 large economic loss because its direct or indirect impacts. In the following paragraphs the main impacts cause which it, to associated events extreme the ENSO,and the of presence the by determined mostly climatein Peruin occur that The variations from yearone to another, is interannualas variability known (2007). etc.) and Higgin according to Kousky storms, tropical systems, pressure high and low blockings, latitudes mean Oscillation, (Madden-Julian transition from La toNiña El tendsthe Niño to be gradually.while Both transitions are influenced by intraseasonal variability quick, be to tends Niña La to Niño El the from Transitionvalues. mean climate its the close values temperature surface sea by characterized periods transition also are there Niña); (La phase negative or cold a and years 4-5 of intervals in occurs which Niño) (El phase positive or warm a phases:two Pacific. has Equatorialcycle the in ENSO anomalies ocean-atmosphere The the by induced Aceituno 2007) through the that tele-connections, are the changes in the global atmospheric circulation and (Garreaud America South of area subtropical and tropical large a over influence direct and strong a since it alters the global patters of atmospheric circulation, precipitation and temperature. The ENSO, has Atmosphere Systems in the Equatorial Pacific, with consequences important for climate at a global level, Ocean- the of perturbation a is (ENSO) Oscillation Southern Niño- El that says 2007) (SENAMHI A. Diaz. Niña Niño/La El events:extreme in rainfall temperatureand extreme Temporal2.6.3. of variation spatial and 13,14 and15Appendix2). Nº map (See quarter previous the to regard with regions jungle and mountain the in increases slightly November,rainfall – September period the In mm. 900 to up jungle the in and mm 100 to up region mountain the in mm, 5 than less is coast the for rainfall accumulated the where , August – June from northern the for quarter the to corresponds exceptperiod, driest the hand, other the quarter,On values. its keeps rainfall where previous jungle, the to relation in territory Peruvian whole the in decrease rainfall for March-May,the period Multi-quarterly registers accumulated rainfall values that significantly map Nº12Appendix2). Andes the of reach accumulatedwith Mountains,jungle the forin that to valuesup slope mm/year.400 2 quarter this (See western the in occur rainfall lowest the mm, 500 and 50mm between varies that values accumulated with period, same this in occurs rainfall heaviest the regions jungle and the mountain In quarter. whole the for mm 110 to up amounts it coast northern the in while mm, 5 reach that For the period December-February, rainfall in the central and southern coast, shows accumulated values mapsNº8,9,10and11Appendix2). oscillate 16and21ºC.(See between values temperature minimum jungle the in and ºC, 7 and ºC -13 between change values these region, southern coast, temperature varies between 9 ºC and 14 ºC, while in the central and southern mountain 18 ºC and 22 ºC in the summer. On the other hand, for the period June-August (winter) in the central and seasonal the summer occurs or in (Arequipa)Imata with -2,4 ºC; also, the jungle shows values that oscillate between in multi-quarterly registered value lowest (summer) the while ºC; February 20 and ºC to 15 between oscillate December values temperature minimum period the for coast, Peruvian the In and 7Appendix2). mountain and jungle region are slightly lower than the values showed during the spring (See maps 4,5,6 33 ºC in the meteorological temperaturesstation Maximum of Iñapan. registered in the summer for the Ricaplaya and in the jungle in the months for September to November (spring) with a value that reaches meteorologicalthe in ºC 33 of value a with (summer) of station January and December between period average maximum temperature, with the difference in the highest values for the northern coast form the multiannual to similar configuration show temperatures maximum average seasonal or Multi-quarterly of 16 ºC. See mapsNº36,37,38,39and40 Appendix2. of 16ºC.See effect of the lake. For the between the June-August quarter southern jungle showed and average value thermo-regulating the to due moderate was temperature in decrease the where Lake, Titicaca the to June-August was lower than -13 ºC and in locations situated above 4000 masl, except for the areas close values slightly lower than their multiannual averages. In the mountain, minimum temperature between its initial phase. In the jungle, minimum temperatures were between 18 ºC and 21 ºC, which represented average temperature reached 7 ºC in Ica for the quarter between June –August and when La Niña was in Minimum temperature showed significant variations in the central and southern coast, where minimum 34 and35Appendix2. and September-November, maximum temperature reached an average of 34 ºC. See maps Nº 31, 32, 33, June-August between quarters the during jungle, the In November. and September between quarter the in winter.,the werein values regionobserved ºC highest mountain 19 the the and in summer while the in ºC 33 to up reached values average where coast, the in observed was variation significant most differences with regard of maximum to variation climatetemperature, values. quarterly the Concerning significant no werethere region jungle and 29 mountain the In and normal. than lower slightly ºC was which ºC, 23 between oscillated averages coast, southern coast and central northern the the in While In averages. multi- annual to close 1989. were that May values ºC, in 32 and ended ºC 28 and between 1988 oscillated temperature average June maximum in started Niña La that considered is It maps Nº26,27,28,29and30Appendix2. an 1982/83, period the See period. same forthe normal than higher region,precipitationwas jungle and mountain the of of part months same the northern the In during 1,860%. of anomaly and representingregistered; mm, was mm 5,051 of value accumulated 257 is May and June between average multiannual significantthe where station, country. the Ricaplaya way,of the This in part showed northern the in anomalies 1983 May to 1982 June starting period the between precipitations Accumulated mapsNº21,22,23,24and25Appendix2. See showed values between 28 ºC and 32 ºC, with very slight variation with respect to multiannual estimates. average, on temperature, minimum jungle, the In June-August. to corresponding quarter the for ones the also (Imata) ºC -14 than lower were region mountain southern the in registered values the values, in especially and average multiannual summer and its autumn seasons. above values In the reached mountain region, coast the isotherm the settings are in similar temperature to the Minimum multiannual September andNovember. Nº16,17,18,19and20Appendix2. Maps See between quarter the in occurred values highest the jungle, and mountain the In coast. northern the in ºC 35 of average an reaching 1983, May to 1982 December in occurred values temperature maximum highest the average quarterly the considering When region. jungle the in the ºC 32 in and ºC region 22 mountain to ºC 14 coast; southern and central the in ºC 32 to ºC 24 coast; northern the in ºC 33 to up reached 1983 May and 1982 June between period the during temperature maximum average The intense of thenegative phaseoftheENSO. intense most most the of one as considered the 1988/89, of Niña La considered event: cold one also are century; last the during events both analyzed; been have 1997/98, Niño El and 1982/83 of events warm regimens level, national pluvimetric forat and Niño reason El this the thermal the on ENSO the of b. 1988/89 LaNiña 1982/1983 a. ElNiño PERÚ: PHYSICAL ANDSOCIALCONTEXT 27 Climate Scenarios for Perú to 2030 28 Climate Scenarios for Perú to 2030 See mapsNº56,57,58,59and60,Appendix2. See precipitations in the whole territory. The period between June and August registered less precipitations. heaviest the occurred May, to Marcho and February to December quarters the In values. multi-annual (between Gabán San in mm Puno,Cusco 8700 and of Madre de value Dios). accumulated In the rainfall remainder part jungle, of zonethe the country, (high precipitations in were Choclococha, and lower in than Huancavelica); total mm of 000 2 amounted rainfall region, Atico, mountain Punta central in the mm In 45 to Arequipa. up registered values accumulated coast southern the In Piura. Domingo, Santo in mm 000 4 to up values region, mountain northern the in Lancones; in 900mm 3 to up values with coast, were1988 northern May Accumulatedthe and intensein 1997 June precipitations between mapsNº51toSee 55,Appendix2. jungle minimum temperatures lower than 15 ºC were registered, for the quarter from June to September. the In averages. multi-annual the to similar temperatureswas minimum of behavior regionsthe jungle and mountain the in coast, the in ºC) 4 to (up normal than values higher showtemperatures Minimum maps Nºs46to 50inAppendix2. climate, and in the jungle, a larger area was observed with maximum temperatures exceeding 32 ºC. See normal regardto with variation important any show not did temperatures maximum of values average The values of maximum temperatures in the coast were higher than 28 ºC; while in the mountain region maximum of temperature inthesummer.value average an as registered was ºC 33 coast central the in ºC, 4 to up values average multi-annual their exceeded that temperatures minimum and maximum showed region coastal The quarter.precipitation theJune-August during mapsNº41,42,43,44and45Appendix2. See values showed the highest precipitation during the quarter between December-February and the lowest 100mm. In the other regions, precipitation 1 was of lowervalues registered (Capachica) than region mountain multiannual southern values. the in Also respectively. mm The jungle quarterly northern accumulated 100 2 (Ricaplaya) mm 650 reaching regionmountain and value,northern coast, (Pongomm 700 northern 6 Caynarachi)the (Quiruvilca)and de in multiannual total its than higher was Precipitation c. 1997/98 ElNiño TENDENCIAS CLIMÁTICAS 29 Climate Scenarios for Perú to 2030 30 Climate Scenarios for Perú to 2030 climate changeandconsequentlyfor planninganddevelopment management. variability at long term, from a global point of view, as well as a tool for identifying vulnerable regions to climate complex the knowing of basis fundamental the as serve will Chapter present the of results The Analyze thedrought asaclimate extreme. 2. indices. extreme and trends their determining changes, climate Detect 1. objectives: following the has 1965-2006 period the for level national at data observed historical on based chapter present The ofoscillationlarger thaninterannual variability.kind some by modulated are they that observed is it term, long a at precipitation, in trends extreme climate the of analysis the Conversely,in (Imata). Arequipa in regions high the for (1940.2006) observations of the confirm research this time, by trendsobserved Vincental.temperature,et climateof the in extremes (2005), same largea with but record the At trends. the of regionalization a show does it but values, significant show not does precipitation Similarly, autumn. of months the in temperature minimum for registered values higher with significant, non positive, are (1971-2006) trends temperature that results show the general, In 2008). al. et Obregón and 2006 al. et (Marengo Arequipa of Department the in out carried were extremes climate as well as trends, precipitation and temperature on studies Recent found noresults onclimate extremes. therewereamplitude for thermal Peru,1960-2000 daily the period forin the and (TN10P) nights cold of frequency the in trends negative and (TN90P) nights warm of frequency the in trends positive a of ce extreme indices for temperature. The results of this research et (Vincent al. 2005) identified the occurren- climate on studies several out (CLIVAR) Predictability carried and Climate Variability of subprogram the As part of the World Climate Research Program of (WCRP) the World Meteorological Organization (WMO), Huancavelica andAyacucho itdecreased 0,2mm/decade. the coast northern and in the high Andean plateau increased from 0,1 to 0,3 mm/decade. Conversely, in rature they are from 0,2 to 0,5 ºC/decade. Also, they show that annual precipitation in the high basins in coastal zone for maximum temperature these values are 0,2 to 0,3 ºC /decade and for minimum tempe maximum and minimum temperature between 3 to 4 ºC/decade in the high Andean plateau and in the in variations some indicates Peru, of region mountain southern and coast the in distributed zones five for(2006), Zevallos and Sanabria by out carried (1964-2000), data of amount large a with study Another (AAO) respectively. tem and/or the circulation,atmospheric Pacificsuch as the North Oscillation (NPO), Antarctic Oscillation sys- ocean (gradual).the of monotonicvariations long-term agreevariability oscillations the These with of presence the to than rather Peru, in regions different the in precipitation modulate that oscillations series used in any the study of this kind. all The main reasontemporally is due to the homogenize presence of almost decadal to or longer need a is there that out points (2006), Obregón issue, same this on diminish; with higher some values in the southern was mountain region there than regionsin the central mountain other region. in Another study and precipitations in increase an was there regions jungle and mountain northern the in and level, 5% the at significant statistically not are trends registered the mountain and jungle regions of Peru, for the period 1951-2000 (Obregón and Marengo, 2006), show that Several studies on the climate trends of total annual precipitation, in some representative stations in the of thedifferent outinoursociety. carried activities effects of climate change in Peru. These parameters will constitute fundamental basis for future planning portant scientific result, is a real need in order to establish some basic climate parameters toevaluate the Detecting climate change in climate series and indices of climate extremes in Peru, besides being an im- NATIONAL CLIMATE TRENDS - fulfill the objectives ofthepresentfulfill theobjectives report. to obtain the temporal series of the climate variables to make the estimates and analysis of the trends to the estimates and facilitate the corresponding analysis on the climate extreme indices and subsequently, of data to be used. This also meets the need to have temporally homogeneous records in order to make the stations had recordsobservational corresponding to this period, so it was chosen as the basic period from1965 determined was period common of the most analysis because of 2006, type to first the Using analysis,the make the to stations ofkey selection and following were principles considered: process control the quality in step first a As records, etc. data discontinuities,the trendssome in of because detected be can they and practices observation the in changes are there when occur mainly non-homogenity of errors the Finally, noticed. be to difficult times some are reasons,which other and instruments measuring the of sensors of deterioration cation, communi- electronic the in mistake a to due occur may errors Contingent 53mm). for 35mm instance The internal consistency errors can be, for example, when there is a transposition of the (forobservations 3.1.2. Appendix 1. 1 Map in is stations climatological and pluviometric the from used was that information detailed The lot. a quite vary periods observation territory.The whole the for SENAMHI, – Peru of Service Hydrology and Meteorology National the of database the from temperature maximum and minimum and tation precipi- of observations daily the on based is report present the in used was that information basic The 3.1.1. withintheareatial distribution ofstudy. forof study which is made tothis kind highlight regional features is to be sure to have an adequate spa- most adequately that will be used,the data series bycontrol. means of a detailed quality Another point It is essential to have good quality data for this kind of estimates and analysis, so it is necessary to prepare tion ofclimate ofanalysis related trends orany to otherkind climate change. morephenomena accurately. Thisway, aredata into anyof base the turn tentative observed detecthe - climate characterize to possible be will it them with and obtained, be will representativestatistics most are enough long, continuous and homogenous. This premise will provide great advantage because the used series climate temporal that necessary is change, climate of detection the on studies out Tocarry 3.1. 4) 3) 2) 1) 3) Non-homogeneous and; 2) Random consistency 19 Internal The source ofdatacanbeclassifiedinthree oferrors categories: inany kind data a. Daily controlQuality andhow to fillindata Used basicdata DATA Analysis ofoutliervalues. Daily, monthlyandseasonalanalysisoftheaverage oftheseries. and variance variable.Graphic analysisoftheevolution intimeoftheselected from eachother. ofeachtimeseries, forDaily continuity precipitation andtemperature, independently CLIMATE TRENDS 31 Climate Scenarios for Perú to 2030 32 Climate Scenarios for Perú to 2030 the homogeneity ofeachone theclimate variables: the homogeneity determine order to in analysis, followingfinal the to weresubject data, completed with series, time The using thecloseststationfor correlation purposes. without or with completed are data total the way this temperature; of case in analysis harmonic the as data are estimated. In the case, the total missing data are not completed, other criteria can be used; such missing the again Figure12, in shown series, new Fromthis information. existing total the of 2,8% ting represencompleted,wereinformation - missing of pieces 7 which Figurein in 11, shown is example An is reconstructed. This methodcanbemade several timesuntilthemissingdataistotally completed. nals as the and annual linear cycle trends as well; and the missing data is estimated, then the time series nuous missing data. In order to apply this method, the data is initially standardized conti- to (3) remove three to certain sig- up fill to used was method auto-regression the with, correlated be to near one have didn’tstation a when or data, missing the relatedto º, 0,5 ± of radius a within data no was thereWhere indicating themissingdata,asshown inFigure 10. precipitation, for series complete the and 9, Figure in shown as data, missing the for determined was a radius of ± 0,5º latitude/longitude. this case, In from the existing data, an estimated and specific value within grid) the in points 3 than more (for data enough was there when used was method Kriging The statistical method(AR1) auto-regressive were used. first-order the and Kriging called method interpolation the studies these make to So, The stepnext was to complete the monthly data for each one of the selected series, as aforementioned. incomplete records to estimate theclimate indices. werethere cases most in because selected, be to order in crucial was data monthly of lack the stations, many For characteristics. required the with comply didn’t initially that and Iquitos, of city the in tation precipi- of information of lack with stations some of case the was this information, no or little very was where there data. monthly missing showedof todepartments 15% up some in specially done Thiswas it was to necessary increase the number of stations with precipitation and temperature series, those that However, wereconsidered. 1965-2006, period the for series monthly whole the from months) (60 12% than less were data monthly discontinued and missing the which in stations those initially,perature,all orderIn to climatologicalthe key select stations for total monthly precipitation and monthly mean tem- really isinPeru,variability dataseries. usingthehistorical climate what of results realistic and reliable obtain to order reality,in to close get to trying them, plete discontinuity, on, that in need orderto be worked to get representative data, and where possible, com- of kind some recordsshow observed the All preparethem. to used data the of quality the about study comprehensive a not is there Peru, in climate the about made been have studies many though Even data were selected. precipitation daily with stations 99 way, This period. the of end and beginning the to corresponding period between 1965 and 2006 (42 years) or, up to 85% of discontinued data, but they had to have data the for data daily continuous valid, of 90% was than more have should stations variable each key of series time the the that of selection final the for condition main the Then, indices. climate extreme the of estimates the make to data daily with selected, were stations key the analysis, these all Considering exceeded showed 2,5timesthevariance intheadjacentstationwere separated. stations. that Finally, adjoining values in the observed ones different the distributions, inadequate form some showed stations the when separated and detected were errors data assumed al- the period chosen, ready the for stations selected previously the in made analysis of kinds two other the from Then, b. data Monthly FIGURE 9 FIGURE 10 data basedontheothers(bluedots) representdots estimated Red data. representdots observed method.Blue Kriging the using interpolation of Example based on the adjacent observed dataseries.. based ontheadjacentobserved Example of interpolation using the Kriging method in precipitation series. Red dots represent complete data estimated mm 100 150 200 250 300 50

1965 Latitude 0 -16.5 -15.5 -14.5 -17 -16 -15 -73 1970 1975 -72.5 1980 -72 Complete data 1985 Longitude Years -71.5 1990 1995 -71 2000 -70.5 2005 CLIMATE TRENDS 2010 33 Climate Scenarios for Perú to 2030 34 Climate Scenarios for Perú to 2030 described in described Tables 2.5and2.6,respectively. and b-c 13 Figure in shown is series data two last the of distribution The data. temperature minimum and maximum average monthly with stations 29 Also in Table described 2.4. and Figure13a in shown When all the adjustments were finished, 64 stations for total monthly precipitation were selected. This is whole timeseries. the in deviation the eliminate or reduce fairly they also but, removeinconsistencies; only not described just adjustments The series. long in noticed easily be can average the in changes addition, In period. lues can be easily found, since they are related to discontinuity, trends and singularities for a determined of temporal evolution of the climate can series be done by a graphics analysis. Also, va- some uncertain detected by means of the double mass curve. Following this procedure, deduction of the heterogeneity is heterogeneity which in part the correcting of consists observed, is it heterogeneity,when Removing FIGURE 11

is estimate basedontheother(bluepoints). represents the size of the series used to generate the AR1 parameter to estimate the data represented in red. The data The series. temperature in data black dots represent data. the observed complete The crosses in blue indicate the amount of missing data. to The red dashed line method (AR) auto-regression the using interpolation an of Example °C 22 24 26 28 30 32 34 36 1965 3) Applicationofthedoublemassmethod ofthebestcorrelated (homogenous) series 2) Selection averages andaccumulative from deviations 1) Analysisofpartial theaverage. 1970 1975 1980 1985 Years 1990 1995 2000 2005 2010 slopes using: estimated N’’n(n-1)/2 estimating = obtained is trends series temporal the of slope the method, this In etal.by (1982). Hisch extended and (1968) Sen by developed used is procedure simple very a reason Forthis 1987). (Gilbert, slope value whenever there are some discrepancy values “outliers” in the time data series to be estimated However,regression method. when “ß” real the from biased significantly be way,can this estimatedit is per slope estimated the of squares least the base “ß”a variable as using estimated be can unit), linear time the a using of (ratio slope called series, temporal a of trend linear estimated the of magnitude The dardization in dealing with these estimates, basically to analyze the spatial distribution of trends. This trends. of distribution spatial the analyze to basically estimates, these with dealing in dardization stan- of kind some necessary is it precipitation; of spatial, and temporal both variability high the to Due the timeseries, calledSen’s slope. of value slope estimated the is N’Se, these of of values median The i. > is j which in series the of pairs Where, xj and xi are the values of the data at time j and i, respectively, and j > i. N’ is the number of data METHODOLOGY FIGURE 12

Final ofmaximumtemperature, dataseries completed usingtheauto-regression method(AR1) °C 22 24 26 28 30 32 34 36 1965 3.2.1. Estimation oflineartrend 1970 1975 1980 S 1985 e = Years x j j − − 1990 i x i 1995 2000 2005 CLIMATE TRENDS 2010 35 Climate Scenarios for Perú to 2030 36 Climate Scenarios for Perú to 2030 corresponding under study. to theperiod average the to relation in series time each for trend linear the of percentage the shows report present precipitationof trends, distribution way,spatial the the of understanding and betteranalysis a toobtain FIGURE 13 15°S 10°S 15°S 10°S 5°S 5°S 0° 0° c) Monthlyminimumtemperature 80°W 80°W a) Monthlyprecipitation 75°W 75°W 70°W 70°W 15°S 10°S 5°S 0° rm 9520 (2 er) a Ttl monthly c) Total and, a) minimum temperatures. temperatures maximum years), b) (42 precipitation, 1965-2006 from Distribution of base stations with climate information b) Monthlymaximumtemperature 80°W 75°W 70°W Where, se is the estimated slope of the time series done by means of the Sen method, This parameter iscomputed asfollows: And with the correction, dueto repetitions,And withthecorrection, stays as: thevariance isdefined as: of(S),[Var(S)] the variance series, the in values of repetition no is there and hypothesis null the Considering (S). S-sgn S= made is For the series larger than 10, the statistic (S) is close to normal distribution, when the following correction =−1,síxi-xj<0 =0,síxi-xj0 sgn(xi -xj)=1,síxixj >0, Where, is: thesgn function (iid). cally distributed (S)isgivenThe statisticaltest by: ofMann-Kendall identi- and variables random independent are xn ….. x3 x2, x1, the that states (HO) hypothesis null The (1983). andSlack(1984)Gilbert tails onthismethodologycanbefound (1975),Hirsch inKendall More de considered independent. be as can that month specific some or averagesstation totalor a of series, annual total the forprecipitation, as such series, climatological to applied be to going is it when test this of characteristics the account into taken be must it Then, dependence. seasonal and monthly strong the to due precipitation, as such series, climatological in application its restrict conditions These conclusion,theautocorrelation needsto benull. oftheseries In certain conditions, such as: they must be random variables, independent and identically distributed (iid). with comply to needs data the test this in that mind in keep to necessary is it time, same the At values. use therelative magnitude ofthevaluestimeseries, values, extreme filtering instead ofusing real to how know to is test this using of advantage The used. is Mann-Kendall test non-parametric the ces, indi- climate and temperatures precipitation, of trends linear of significance statistical the Determining trends ofmaximumandminimumtemperatures were expressed inºC/decade. that are being considered. For example, nd is equal to 42 years for precipitations (1965-2006). The linear series data the of months) or (years data of number the is nd and period, whole the forprecipitation of 3.2.2. Determination ofthestatistical significance ofthelineartrend Var ( S ) =    n ( Var n S L T − = 1 ( ∑ = ( ) n S i = − 1 ) 1 2 e s n = j ∑ = n i x * + + n 1 d n i sgn( ( 5 n ) − − * 1 100 ∑ 8 1 x p 8 1 ( ) g = i 1 2 − t n p x ( + t j p ) 5 − ) 1 ( ) 2 t p + 5 )   

x i

is the average CLIMATE TRENDS - 37 Climate Scenarios for Perú to 2030 38 Climate Scenarios for Perú to 2030 very versatile conditions of precipitation. tool to report very time scale, on while certain it humid experiments conditions on another time scale. Thisde SPI makes a conditions drought experiment to possible is it methodology; same term the with short captured be or may events term long that allows it SPI, the of feature important another is invariance temporal Its drought ofthewater ontheavailability resources. the of impact the reflects value This scale. time any for occurrence, of probability the of measure a as drought, the of intensity the measure to value simple a provides it because used is SPI The (SPI). Index The methodology used in the analysis of drought in the present is report, the Standardized Precipitation indicesare basedonpercentilesseasons. between impacts. Other whichallowscomparisons to make their understand to easy it making value, threshold fixed a on based are indices these of Some record. events of registered number historical small the the trends in due to the significance of the and cessing These indices do not represent extremes considered as rare which can possible affect the statistical pro are described. report present the for estimated indices the of definitions the of one each 1 table In climate. future of (2002) and are used in the IPCC report (AR4) et (Trenberth al. 2006) to define variations of extreme values al.Frisch et of methodology the on based are report present the in used.areperatureused indices The tem- minimum and maximum and precipitation of data daily indices, extreme climate the Toestimate direction ofthetrend. This value, α,inallthetest madefor is0,05(5%). thepresent report, value of Z is lower than Z1-α /2 (two-side test). The value of Z1-α /2 is chosen a priori once we know the absolute the if accepted is HO α, scale significance the at trends, descending the as well as trends ding the If null hypothesis (HO) is truth, the statistical Z has a normal standard distribution. To test, the ascen- tumn (MAM) andwinter (JJA). au- (DJF), summer (SON), spring follow: as defined were year the of seasons the study,present the For considered.were values annual the only trends, indices climate the Regarding well. as estimated were tion, total annual and seasonal were estimated; and for temperatures, the annual and seasonal averages interpretation andto deepeninthelineartrend analysis, thetrends ofprecipita oftheclimate variables - the improve to order In trend. (descending) ascending the indicate Z of values (negative) positive The Then Sand Var (S)are usedto standardized estimate statisticalZ,withnormal distribution: group.worst the in data of number the is tp and data repeated with groups of number the is g Where 3.2.4. Standardized Precipitation Index (SPI) 3.2.3. Determination ofclimate extreme indices Z =0,ifS Z Z = = [ [ Var Var S S ( ( + − S S 1 ) 1 ) ] ] 1/2 1/2 ,ifS<0 ,ifS>0, - periods areperiods for characterized showing negative 2) SPIvalues(Table droughtSo, average. the than less is precipitation that indicate values negative average,and the ceeds ex precipitation that indicate SPI of values positive The period. analyzed the for precipitation average the relative precipitation, of values the in deviation no was there that indicates zero to equal index an The SPI values correspond to the standardization of the total gama-transformed precipitations for which isstandard,and thedeviation i.e. theunit. and annual values. This distribution is then transformed in normal standard, so that the mean equals zero monthly,order determine toin 1972) Stegun, and quarterly Abramowitz 1993, Kee, (Mc SPI the apply to necessary is that condition a probabilities, of distribution Gama a to adjusted were they then, annually; and quarterly accumulatedwere station each forprecipitation of values monthly totalPeru, overthe all distributed stations 64 for years) (42 2006 to 1965 from records precipitation monthly Fromthe years). 50 than more (preferably time of periods long relatively over precipitation of distribution the on based is values SPI the of estimate The 1999). al, et (Hayes, duration short or long either foravailability, water the conditions of droughtet al, (McKee 1993 and 1995), then it became a valid tool for all the studies on The Standardized Precipitation (SPI) was originally established as a Index method to quantify and define TN90p TN10p TX90p TX10 RX5day RX1day R99p R95p R20mm R10mm CWD CDD Indicator Warm nights Cold nights Warm days Cold days precipitation in5days maximum Monthly precipitación enundía amount Maximum days humid Extremely days humid Very precipitation Intense Very Number ofdays with precipitationIntense Number ofdays with days Consecutive humid days Consecutive dry Name ofindicator Table 1Indicators ofclimate extremes to theclimatology >90thPercentileTX inrelation Annual percentage inwhich climatology from 1971-2000 percentile inrelation to the minimum temperature is 10th TX< Annual percentage inwhich to theclimatology >90thPercentileTX inrelation Annual percentage inwhich climatology from 1971-2000 percentile inrelation to the maximum temperature is 10th TX< Annual percentage inwhich in 5consecutive days maximumprecipitationMonthly precipitation in1day maximum Monthly in whichRR>99percentile Total annualprecipitation in whichRR>95percentile Total annualprecipitation a year inwhichPRCP>=20mm Number ofdays in a year inwhichPRCP>=10mm Number days in days with RR>=1mm numberconsecutive Maximum days with RR<1mm numberconsecutive Maximum Definition CLIMATE TRENDS Unit days days days days days days days days mm mm mm mm - 39 Climate Scenarios for Perú to 2030 40 Climate Scenarios for Perú to 2030 producing negative anomalies in precipitation, and determining the variability in the intensity of these of intensity the in variability the determining and precipitation, in anomalies negativeproducing in ENSO the of events warm the of effect the confirm to intends event, ENSO the of index an is which 3.4, Niño El the of temperature surface sea of anomaly monthly the and SPI the between relation sonal sea- this way, This 1992). Neal, and (Quinn coast northern the in floods and Peru, of part southern the overprecipitation in anomalies causes phenomenon ENSO the of phase warm the that known well is It precipitation. observed of regimens different the in resulting conditions, geographical local to associated and diverse are Peru in precipitation produce and climate and weather modulate that systems atmospheric scale large The annual and way theSPIisobtained. six-monthly monthly, the this probability, same the for determined was precipitation) x (standardized Z of value being the precipitation, G(x), probability accumulated the estimating After (Lloyd-Hughes andSaunders, 2002). ,beingnthenumberofobservations The parameters αandβare estimated and. Where: following equation: the of means by established is and 2005) al. et (Wu publication the in defined is distribution gama The A = ln( 3.2.5. Tele-connections ofdroughts inPeru Г [α]–gamafunction (monthlyprecipitation)x –randomvariable a –parameter ofform β– parameter ofscale G (x)–accumulated probability Where: x ) − ∑ Source: McKee, 1993. Source: McKee, <=-2 -1,99 a-1.5 -1,49 a-1 -0,99 a0 > 0 SPI ( ln( n x ) G ) ( x ) Table 2 The SPIandintensity ofdrought = b Γ Extreme Severe Moderate Slight Humid Severity ofthedrought 1 ( a a ) = ∫ 0 x 4 x 1 A a −     1 e 1 − + x / b 1 x d + 4 3 A     Probability b 34,1% 2,3% 4,4% 9,2% 50% = a x series, whichisobtainedby themethodsuggested by Davies (1976),asfollows: time the of DOF of number effective the using estimated is correlations of significance statistical The Sx andSy respectively following way: the in defined is correlation The physics relation. inherent an of possibility the is there significant, cally rate of in the which arevariability both variables associated. case the correlation In coefficient is statisti- measure a useful indicates ( r) coefficient correlation the of magnitude(N). The time of (t), period a during y and x(t) variables, two between exists that relation linear the indicates correlation Simultaneous dized anomalies, computed from themeanmonthlyvaluesfor eachseasonofthe year. seasonal series for each one of the ocean/atmosphere indices and for the SPI, were estimated as standar the standardized andsouthern and difference(TSA-TNA). theSSTofnorthern between Tropical Atlantic (PDO) The Index Oscillation Decadal Pacific the event, (ENSO) Oscillation Southern Niño El the of indices the and SPI the of series seasonal and temporal between made were analysis correlation neous rrence of precipitation during the seasons of the year, and consequently cause drought in Peru, simulta- In order to determine these possible connections between large scale patterns, that modulate the occu- ofprecipitationsome effect onthedistribution anddroughtevents in Peru. to the interannual of variability the SST dipole between the tropicalsouthern and northern Atlantic; causes is the Atlantic Ocean, it is expected that the transport of humidity from the Tropical Atlantic mainly related decadal variability. If the main source of atmospheric humidity for the junglelatitudinal the andpositionInter theof Tropicalmountain Convergence regionsZone(ITCZ)shows inter-annualhigh it and in andPeru Southern and Northern Tropical Atlantic (TSA – TNA) defined byRajagopalan et al (1998), is associated- tele-con nections; withpossible both spatialconsistent and seasonal.of existence Also, thethe difference determine in tothe sea surfaceand temperatureresults between (SST) these the disseminate to is purpose southern mountain region is strongly modulated in a decadal scale by the Pacific Decadal Oscillation. The Theresults workthedoneObregónofby Marengoand (2006)show thatprecipitation centralthein and precipitations. on PDO of impact the on studies some are there hand, other the On Peru. in connections In the presentIn study, to estimate timescale. is thecharacteristic Where, Where, x(t) and y (t) are the temporal series of a N size with averages cesses AR1orred-noise processes, inaway that: is the effective N is the size number of independent of observations, and the time series Td b) Statistical Significance of Correlations a) Simultaneouscorrelations N e f r = T N 1 d ∑

t = T N = 1 d 1 1 ( N

x it is assumed that arethe series the results of auto-regression pro + − ( f e t r r ) = s x x − ( ( x N T 1 x 1 s ).( d ) ) y r r y y y ( ( ( t 1 1 ) ) ) − y )

x and y ,and standard deviation CLIMATE TRENDS - - 41 Climate Scenarios for Perú to 2030 42 Climate Scenarios for Perú to 2030 Where: fluctuations. episodic or signals period. throughoutthe non-stationary way, harmonic This detect toOT useful the is each of phase mean and ampleness the gives which transform,Fournier’s traditional the what to trary con- harmonic, each of phase and amplitude the of assessment local a facilitates analysis wavelet The are ofthetrueEOFs. amixture degenerated the of “multiplet”EOFs part their are and way, they This ½). (2/n) λα = (λ∂α error sampling mixing, the separation between an autovalue and the adjacent ones, Aβ = λα –λβ must be larger that its to subject be to not proximities, the in autovalues for that determines rule This used. is (1984) rule th representativemost of number the determine and diagnosisanalysis,a the in unity EOFs Nor the make mixed. are EOFs To more or two which in degeneration effective an occurs it magnitudes, similar have neighborhood the in autovalues the when that demonstrated also was It . auto-values corresponding their separating by determined is that (EOF), autovalue each of unit statistical the are “noise”also and In any PCA it is toimportant determine how many EOF/PCA represent the signal and how many of them for residual this. dispersionanditcontinueslike combined dispersion of the X’s. The second e2 is the linear combination that explains the larger fraction gonal function e1 is the linear combination of the input variables xj that explain the larger fraction of the ortho Itmustbeemphasized thatthefirstempirical Z. ofthecoefficientmatrix –PC ortemporal series components” as “principal to referred are coefficients expansion the and pattern, spatial the represent they case, this in and denominated functions”.areauto-vectors) “empirical (orpatterns orthogonal EOF orthogonal. The mutually are columns whose (NxM), Z coefficients expansion of matrix corresponding the and (MxM) E matrix the include that orthogonal mutually determined are patterns of groups C trix ma- the From (1965-2006). study present the for used period the of months of number the is 504 N= and stations) (pluviometric series SPI of number the to corresponds M=64 where X(NxM) normalized. and monthly both (SPI), Index Precipitation Standardized the of matrix data the from obtained (MxM) C correlation or matrix dispersion the of diagonalization the from done was PCA the of computing The periods. values thathave alotofnullvaluesasconsequenceprolonged dry 3.1. have they because values,is continued differentseries Thereason for monthly quarterly using form sed on temporal series of 64 SPI quarterly (agrometeorological droughts) computed as indicated in item In the present report, the PCA is used to determine the spatial/temporal patterns of droughts in Peru, ba- 1986;PreisendorferRichman ,1998; Von Storch 1995; andNavarra, Von Storch andZwiers, 1999). the PCA is quite known and frequently used. More details can be found in many references (for example, climatological, and meteorological both modeling, from data and data observed with made studies In canberepresented.proportion variability the of most which in ones, smaller to dimensions these reduce to necessary and convenient is it so dimensions, several have that variables /atmospheric climate the of structure spatial-phase the examine to used technique statistical multivariate a is (PCA) components principal the of analysis The the nullhypothesis, zero correlation. Then, the significance of the correlations at 95% is obtained by means oftwo-tailed the t-test, based on series Y. r 3.2.7. Wavelet Analysis 3.2.6. Analysis ofthePrincipal Components (PCA) x ( 1 )

is the autocorrelation of the base series X and r y ( 1 )

is the autocorrelation of the second - - The wavelet follows the (change scale) f, allowing to obtain the relation the obtain to allowing f, For a given wave, there is usually a relation that transfers r for the time t, and the scale s for the frequency energy of each wave. This equation essentially shows the transformed of the of transformed the shows essentially equation This wave. each of energy is derived from a basic function Where: basic function isexpressedbasic function as: standardizedmonthly the of PCA the in determined precipitationpattern for(SPI) indices Peru. Morlet’s significant each of series time the of oscillations the of analysis the for adequate particularly is wavelet this of waves to pattern similar waves.The Morlet called are waves generated envelope.The Gaussian a by modulated wave plain a of consists function This b). 1982 1982a, al. et (Morlet Morlet of function There are a lot of basic functions used to generate several waves. In the present study the most complex temporal domain, for the The Wavelet decomposes a continuous function ofthedroughttemporal series inPeru patterns are to expected be, asresult ofthePCA. the as multi-scale, and characteristics episodic with non-stationary highly are that series the of analysis the signal analysis with multi-scale components. For this reason, the OT is considered to be ideal for the on high frequency oscillations and it shrinks for low frequencies. These features are the most adequate in Also, the OT provides an adaptive window to time-frequency that automatically enlarges when it focuses of eachonethemand, with thisestimate ofdroughts. theirrelation withtheinterannual variability the to subject typical different are the scales, which in oscillations the be determine to purpose Waveletthe with analysis will PCA the by determined SPI, the of patterns spatial each of series temporal The are: 1998) Compo, and (Torrenco wave Morlet the for t-f space the and r-s space the between relation The thegenerated wavesand frequency) are: time spaces: both in located be to has it and value mean the zerohaveas must function (Morlet’sbasic Where, wo is the non-dimensional frequency and it ha a value of 6 to meet the condition of admissibility function y y t f = ( r = , t s r ( ) w t , y; = y ) 0 e = + f ∗ w i

( is the conjugated complex of conjugatedcomplex the is 4 t 0 1 p t ) s w e in the time-frequency space, t-f. inthetime-frequency s − 0 2 e t 2 w i + / 0 2 2 (

t − s

W r ) f W e ( ( r t − ( , ) ( r s t function, definedastheintegral function, of: − s , )

r y s = ) y ) 2 W ( /

function function in the transfer domain and of the scale , or in the r-s domain. t r 2 ) 1 , ( s

s

r ( by means of the transference (r) (change position) and expansion t , ∫ ) s y y ) = ∗ → ( t 1 . The s − W s y r f ( ) ( t ( 1 t , t f ) f − ( s

in terms of a group of waves t ) s ) r . t d )

In conclusion, the wavelet decomposes the decomposes wavelet the conclusion, In factor of the integral is used to normalize the tonormalize integralused the is of factor f ( y t ) r , , s function, of the of function, ( t ) CLIMATE TRENDS . Each wave 43 Climate Scenarios for Perú to 2030 44 Climate Scenarios for Perú to 2030 de Dios. Madre Puno,and of Arequipa Departments the in especially pattern, precipitationclimatological gional reof kind some jungle,apparentlythereis southern and mountain the about observed been has what For significant. statistically are which -84%, and -82% between values negative intense reachingtively, and Arequipa of Tacnanega- emphasizedare trends part these southern the between that mentioning is worth It visible non-significant a negativeregional positivewithout trends and pattern. are observed, some region mountain the of side western the and coast the of part southern and central the in Also, jungle (Tambopata) the trends and southern Conversely,are María) positive. in the central jungle (Tingo of San Martin. In the jungle northern (Loreto) negative trends are observed, focused on Juancito station. the middleofotherstationwithnegative trends, speciallythoseinthejungleregion oftheDepartment extension of these positive trends towards the jungle northern are observed in some isolated stations, in reach the southern mountain region, in Puno, especially in the highest areas of Arequipa and and region Tacna.mountain central entire The the in continue trends positive the Then, Peru. of jungle northern the and Lima) of part (northern coast central towardsthe extends (1965-2006). yearscharacteristic 42 This last the for observed is precipitation annual total the in increase an Peru of coast northern the For Loreto, of upto station,Department +70%intheRicaplaya Tumbes. of Department station, Juancito the in -94%, from percentages reach values extreme observed The 14. Figurein shown are country, the throughout distributed station 64 for (1965-2006) period analyzed the foraveragevalue percentages,the relation to in precipitation,estimated in annual total trendsof Linear zed indetail. the present itemIn of climate the detection change and the national climateextreme indices are analy- R 3.3 FIGURE 14 esults 3.3.1 Lineartrends ofprecipitation 15°S 10°S 5°S 0° 80°W TOTAL ANNUAL 75°W 70°W

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10

0 10 20 30 40 50 60 70 % are indicated in black less than the Test Mann-Kendall, 5%or of a rate at significance statistical with Trends 2006). (1965- average multiannual tothe relative %, annual in precipitation total of Trend Linear - in total annualvalues). observed is what to (opposite negative are and Tacnawheretrends Arequipa precipitation of part high ger differences are observed, where negative trends prevail, and in the souther mountain, specially in the some isolated stations of in Cuscothe Departments and Abancay. mountain region, the In northern lar Huancavelicaof and regionDepartments mountain the in southern the in and Lima) (Ancashand coast central the in Martin, Loreto,San of Departments the including jungle, northern the in 14) (Figure tion in distribution (SON) spring several of the keeps characteristics the totalobserved annual trend- distribu during pattern spatial respectively. The (Lima), Huaros in and (Tacna) the Grande Sama in as observed values +87%, and -146% between values extreme with trends shows a) 16 (Figure season spring The average understudy(1965-2006). ofthewholeperiod (MAM) and winter (JJA), are shown in Figure 16 a-d. The trends are estimated in % relative to the seasonal autumn (DJF), summer (SON), spring Peru,the forin precipitation of trends seasonal of distribution The instance computingthetrends oftheseasonaltotal valuesfor precipitation. time. This characteristic requires seasonal making analysis to detect climate change for precipitation, for winter the in recorded values minimum and months summer during recorded values maximum with seasonality, is Peru of part most over precipitation of distribution annual the of characteristic main The oftotal precipitation, theinterannualintensity andthevaluestrends variability are relatively low. higher with modulate to seems event ENSO the of effect the south, and central both region, mountain a sharp increase related to occurred the ENSO event, it then it seems to when gradually go down century, . On the other last hand, in the the of end to up decade 60’s the from precipitation in diminish tant impor the be to seems characteristic main jungle,low, the is precipitation of northern variability interannual the the where In station. Sondorillo the in observed been has what to according years, 42 last therethe increaseapparentlycoast, precipitation constant is of a during Peru.in northern occurs the In The temporal distribution of the total annual precipitation in the four stations, show the that complexity associated to thepositive ENSOevents andhighprecipitations in1984related coldphase. to theLaNiña stations register identical interannual variability, withThe lowHuayao precipitations and Puno stationsin the showyears inverse 1983 andtrends 1992; with apparently lower values that are not statistically significant.to anotherevent ofalongerperiod. Both in 2002. The interannual does variability not show any modulation related to the ENSO phenomenon or from an average value of approximately 1 000 mm from 1995 to 2000, to a peak of more than 3 000 mm goes that precipitation in increase1990’s. intense the an of is end there Thenthe to 70’sup the decade tense diminish in precipitation; statistically significant of -40mm/year (-93% of the average), mainly form related to El Niño / Southern Oscillation (ENSO). The station located at Juancito is characterized for an in- signal clear any present not does variability Interannual average). total the of (17,7% years 42 the hout increasethrougconstant - slight a show Total they and 700mm than less is precipitationoverSondorillo mountainregion(Huayao) and southern (Puno). region mountain central (Juancito) jungle northern (Sondorillo), coast northern the in located are they tions distributed in the country that may be representative of the regional characteristics of precipitation; period under study. Figure 15 shows the interannual the distribution of during precipitation precipitation and annual its total trend of in fourdistribution temporal sta- a of means by is trends linear to relation its and precipitation annual total of variability interannual between idea clear a obtain to way best The lack ofobservations. matter,this Cusco,on of made the any conclusion of since argumenthavebecause no will Department any of kind relation between the recorded negative trends in the jungle northern or down further to the make to nor get to neither allow not does Peru, in region jungle and mountain central the to ponding corres - data of lack La -68%. shows trend, negative it significant a with location a is (Cusco)Colquepata CLIMATE TRENDS - - 45 Climate Scenarios for Perú to 2030 46 Climate Scenarios for Perú to 2030 some differences in the northern jungle, Department of San Martin, and in thesome centraldifferences of mountainMartin, San jungle,region,Department in the northern with summer, the in one the to close very is season autumn the during trends the of distribution The -152% inCarania (Lima)to +98%inChoclococha(Huancavelica). from range values months winter The (Huancavelica). Choclococha in registered +72% to (Arequipa) summer season. The the extreme values in and the autumn are fromobserved annual -98%, registeredtotal in Pampathe Blanca in observed been has what to relationprecipitation in total change substantially the not of do trends patterns the c-d) 16 (Figure seasons (JJA) winter and (MAM) autumn the In show valuesintotal extreme annualrecords. that station same the are which (Tumbes), Playa Rica in 72% of maximum the and (Loreto) Juancito in of the trends than in the direction, increase or diminish. The extreme trends reach -88% minimum values values absolute the in morenoticeable be differenceto seem ones summer the and patterns annual the between The country. whole the for value annual total the to proportional are they and registered, are values precipitation highest the season this during because explained be can slopes Theseannual. total the to similar very are b) (Figuretrends16 precipitation seasonal (DJF) season summer the During FIGURE 15 mm mm nrhr jnl) uyo cnrl onan ein ad uo suhr muti rgo) ttos Te best The stations. region) mountain (southern Puno and region)adjustment oflineartrends isshown inthered lines. mountain (central Juancito Huayao coast) (northern jungle) Sondorillo the (northern at mm in (1965-2006) precipitation annual total of distribution Temporal 1000 1500 1000 2000 3000 4000 500 0 0 1965 1965 1970 1970 1975 1975 1980 1980 Annual precipitation Annual precipitation Years Years 1985 1985 1990 1990 1995 1995 2000 2000 2005 2005 and northern jungle (Juancito) (Figure 16 a) has similar characteristics to the distributions of the total the of distributions the to characteristics similar has a) 16 (Figure (Juancito) jungle northern and The temporal distribution of seasonal precipitation in the spring (SON) in the coast northern (Sondorillo) are someothersthatare scattered, withnegative trends. significant, especially in the western side of the AndesMountains. In the middle of these locations there prevailing pattern, positive trends arecharacteristic some and statistically showvery mountain a central the and Conversely,coast the months. summer the in as pattern similar a is there also region southern d) seems to be similar to the one in the spring along the coast, mountain and northern jungle; and, in the where there happens to be some opposite trends in places.certain The spatial winter pattern (Figure 16 10°S 15°S 10°S 15°S FIGURE 16 5°S 5°S 0° 0° Mann-Kendall) are indicated inblackcircles.Mann-Kendall) (test less or level 5% a at significance statistical with trends The (JJA). winter d) and (MAM) autumn c) (DEF), summer b) Linear trend of total seasonal precipitation in %, relative to the multiannual seasonal average (1965-2006) a) spring (SON), 80°W 80°W c) Total in Autumn (MAM) a)Total Spring(SON) 75°W 75°W 70°W 70°W

-150 -140 -130 -120 -110 -100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90 -100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80

% % 15°S 10°S 15°S 10°S 5°S 5°S 0° 0° 80°W 80°W b)Total inSummer(DJF) d)Total inWinter (JJA) 75°W 75°W 70°W 70°W CLIMATE TRENDS

-160 -150 -140 -130 -120 -110 -100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80

% % 47 Climate Scenarios for Perú to 2030 48 Climate Scenarios for Perú to 2030 jungle (San Martin) thenegative trend isstatistically significant. jungle (SanMartin) was registered, as in a location in Puno, coastal zone of Piura, San and Martin Tingo María. In the northern trends are significant. In some very regionalized places, some diminish in annual the maximum where temperature jungle, southern the in apparently and, mountain the in location every in almost significant was an increase therein the Peruannual maximum of mean temperatures, and territory their trends whole showed values the statistically almost in that observed be can it years) (42 2006 and 1965 Between San in registered Marcos (Cajamarca).ºC/decade +0,52 and (Puno) Pampahuta in registered /decade, ºC -0,26 are: values extreme The country. whole the in % 5 at significant, statistically values, positive of predominance a is The spatial distribution of the trends of annual maximum mean temperature (Figure 18) shows that there tions thatbasicallymodulate oftheinterannual theintensity variability. oscilla- these to associated be to seems (Puno) Huayao in trends (positive) negative the for reason the before 1975 and after 1985, an inverse behavior to the one observed during the spring months. This way, oscillation that modulates precipitation in these two regions, that becomes evident in the largerhigh some variability is there that seems it But, 1990. and 1981 in ones the as such events intense other to due weakened signalseems ENSO the and regionmountain precipitationinterannualvariability showshigh of 2002,whenprecipitation reached (Puno) thecentral(Huayao) more andsouthern than1000 mm.In winter the in especially and, 1982 in case the in as such ENSO, the of events extreme the with relation no particular characteristic. The distribution in Juancito registers little interannual variability and a strong In the winter months (Figure 17 d) precipitations over the coast northern are practically none, and shows ofprecipitationthe temporal distribution inthemonthsofautumn(MAM). shows Figure17c 1991/92. and 1982/83 of events strong the in seen be can it as jungle, northern and coast ENSO,the the locatedin of eventsstations relation tothe in warm tothe moretoshowsensibility seem jungle, southern and central the in stations the months, those during Also, study. under period the during especially variability, interannual high with spring, of months the during observed ones the to similar are region mountain southern and central the in located stations the in and total, annual the Thejungle showin stations locatedtosimilar distributions in the the ones coast observed and northern inprecipitation. a shortage causing stations, both in evident are 1991/92 and 1982/83 of phenomenon ENSO the of phase positive the in occurred that events The interannual. the than scale longer a at oscillation apparently an seen months. different spring from the be There can very and total annual the to similar is variability rannual inte the region, mountain southern and central the of stations the In . signal evident no practically is there , events ENSO other In Sondorillo. in increase slight a and Juancito in is diminish Theresubstantial a also stations. two in 1982/83 of phenomenon ENSO the of signals some observe to possible is it months these In totals. annual the tojungle similar very trends northern and variability and interannual show b) coast (Figure17 the in located stations the of distributions total (DJF) summer the During changesquite alotbeforetion becausetheinterannual variability 1985. 1975andafter the ENSO phenomenon. However, seem they to be modulated these months during by a larger oscilla- The behavior of these temporal series does not have any evident and continuous signals of the effects of tion ofthetotal annualvalues. direc- same followthe trends the but 1985, to 1975 from particularly high, very is variability interannual analysis.under period Conversely,whole (Puno) (Huayao)central the in southern and region,mountain the and,moderateevents during apparently, is ENSO the of effects variability the theresignalsof are no interannual The Sondorillo. in weak quite and Juancito in trends negative intense with values, annual 3.3.2. Lineartrends ofmaximumtemperature - FIGURE 17

mm mm mm mm (DJF), c)autumn(MAM) Abetter adjustmentofthelineartrends and d)winter isindicated (JJA). inred lines. jungle), Huayao (central (northern mountain region) and Puno (southern mountain region). a) Spring (SON), b) summer Juancito coast), (northern Sondorillo of stations the in (1965-2006) precipitation seasonal total Temporalof distribution 1000 1500 1000 1500 500 500 100 200 300 200 400 600 800 0 0 0 0 1965 1965 1965 1965 1970 1970 1970 1970 1975 1975 1975 1975 b) SummerPrecipitation a) SpringPrecipitation 1980 1980 1980 1980 1985 1985 1985 1985 Years Years Years Years 1990 1990 1990 1990 1995 1995 1995 1995 2000 2000 2000 2000 2005 2005 2005 2005 CLIMATE TRENDS 49 Climate Scenarios for Perú to 2030 50 Climate Scenarios for Perú to 2030 FIGURE 17

mm mm mm mm (DJF), c)autumn(MAM) Abetter adjustmentofthelineartrends and d)winter isindicated (JJA). inred lines. jungle), Huayao (central (northern mountain region) and Puno (southern mountain region). a) Spring (SON), b) summer Juancito coast), (northern Sondorillo of stations the in (1965-2006) precipitation seasonal total Temporalof distribution 1000 1500 1000 1500 100 500 500 100 200 300 400 20 40 60 80 0 0 0 0 1965 1965 1965 1965 1970 1970 1970 1970 1975 1975 1975 1975 c) Autumn Precipitation d) Winter Precipitation 1980 1980 1980 1980 1985 1985 1985 1985 Years Years Years Years 1990 1990 1990 1990 1995 1995 1995 1995 2000 2000 2000 2000 2005 2005 2005 2005 (San Martin and (San Martin Tingo María). with the winter pattern, except for the negative trends observed in all the stations of the jungle northern occurs thing same The one. annual the to identical is pattern spatial autumn The negative. are trends Peru,of Puno and where the except for isolatedregions,Piura,some of rritory coast Martin the San in as te whole the include trends, significant positive the (DJF) summer the in and (SON) time spring the In inPiura,the winter time(JJA), inthelocationcalledSausaldeChulucan, with+0.67ºC/decade. Moyobamba with 0,39 and -0,33 ºC/decade respectively. The maximum seasonal value was registered in seasonal trends are registered in the summer season the (DJF) and autumn (MAM) jungle, in the in northern of values lowest The 18). (Figure temperature mean maximum annual the of one the to patterns The spatial distribution of the seasonal maximum mean temperature trends (Figure 20 a-d) shows similar and 1997/98. Figure 19, where it can be observed slight alterations in temperature during the warm events of 1982/83 The only station that seem to be affected by the impact of the ENSO is the Puno station, as it is shown in inthesestationsisindependentoneform eachother.variability temperature maximum that seems It phase. the within not are them among variations the and riods pe longer by modulated not apparently,is and variability, interannual low to associated is featurechange). This continuous (gradual alterations monotonic presenting for characterize stations three the in in the jungle region (Moyobamba) does. The temporal variation of annual maximum mean temperature region.mountain Twostation the towhat showpositivetrends,(Puno stations Matucana) and contrary maximum temperaturesannual of three selected stations that jungle,better characterize central la and northern southern the of distribution temporal the shows 19 Figure (1965-2006). study under period the for obtained, be could trends temperature and variability of idea clear a which from chosen, were As it was done for precipitation, for maximum and minimum temperatures some representative stations FIGURE 18 with statisticalsignificanceMann-Kendall at5%orlessinthe Test are indicated inblackcircles. Spatial distribution of annual maximum mean temperature ( ºC/decade) estimated for the period 1965-2006. The trends 15°S 10°S 5°S 0° 80°W Annual MaximumTemperature 75°W 70°W -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6

°C/década CLIMATE TRENDS - - 51 Climate Scenarios for Perú to 2030 52 Climate Scenarios for Perú to 2030 the middle of the low interannual variability, without the presence of any anomaly due to the presence the to due anomaly any of presence the variability,without interannual low the of middle the of the annual maximum temperatures (Figure 19). Minimum temperature in Puno gradually increases in distribution the in observed those to opposite exactly are trends These 23. Figure see (Matucana) tain thern (Moyobamba) and southern (Puno) jungle, shows opposite directions regarding the central moun - nor representativetemperaturesthe in mean in minimum stations annual of distribution temporalThe the station inareas closeto the registerTiticaca Lake of significant negative trends. majority the where Puno, in observed is difference largest The Peru. of part most over pattern a mean term, a distribution of the trends with values in the same direction of the maximum temperature Piura,in value maximum the /decade.ºC +0.48 with location, Esperanza La the in Thisshows, pattern in The minimum Lake. value of Titicaca the trendthe was registeredsurrounding in areas Puno, in the the El in ProgresoPuno, location with in -0.28 ºC and /decade and region mountain central the in gistered re are values Conversely,negative jungle. northern and coast northern the Tacna,in and Arequipa of Department region, mountain southern the in observed are inclinations significant statistically positive 2006, is shown 1965- in Figure period 22. the Also forit Peru,can be observed in the predominance trends of the temperature positive trends.mean minimum Also, some annual the of distribution spatial A theevents of1982/83and1997/98. during as itisobserved the autumn, and and summer the during particularly temperaturesmaximum Matucana, Punoin in slightly and ENSO the of events warm the between existing relation direct the is characteristic observed other season and it occurs in a synchronized way throughout the whole under period analysis. Another any in than larger is variability interannual the summers during that shows region mountain southern decade and negative anomalies at the end of the 90’s. The of distribution the stations in the central and the autumn and winter seasons, it shows little cycles with slight positive anomalies at the end of the 80’s all the seasons of the year and it does not register any signal of the ENSO phenomenon. However, during in variability low shows jungle) (northern station Moyobamba seasons.four The the during trend tonic ristic of the jungle northern and central and southern mountain region (Figure 21 showsa-d) the mono arecharacte which three temperaturesstations the maximum of seasonal of distribution temporalThe FIGURE 19 °C 15 20 25 30 1965 indicated inred lines. (northern station Moyobamba is trends linear the of adjustment better the A jungle) (southern Puno and región) mountain (central of Matucana jungle), (1965-2006) temperatura mean máximum anual of distribution Temporal 3.3.3. Lineartrends ofminimumtemperatures 1970 1975 Annual MaximumTemperature 1980 1985 Years 1990 1995 2000 2005 - - - - negative anomaliesoccurred in1999. ciated to the warm event of 1982/83 that caused positive anomalies, and to the presence of very intense asso is 1980’s.apparentlythis, de All of decade before the jungle, northern the in observed one the to weakvery interannual variability from the beginning up to the end of the 1980’s, then it becomes similar during the ENSO event of 1976/77, and in the central mountain region this characteristic reverses, with a study,under period beginningthe the of apparently associatedstrongtooccurred a negative anomaly since observed scale, larger a of oscillation apparent and after 2006, to 1980’sup the of beginning the between observed is variability interannual los jungle northern the in way, well.This as variability nual interan- of periods opposite show they trends, opposite having besides region, mountain central and jungle northern the temperaturein minimum series annual the hand, other the On event. ENSO the of 10°S 15°S 10°S 15°S FIGURE 20 5°S 5°S 0° 0° 5% or less (Test Mann-Kendall) are indicated5% orless(Test inblackcircle. Mann-Kendall) at significance statistical with trends The (JJA). winter d) and (MAM) autumn c) (DJF), summer b) (SON), spring a) 2006), 1965- period the for estimated decade) / (ºC temperatue mean maximum seasonal of trends the of distribution Spatial c) MaximumTemp. Autumn (MAM) a)Maximum Temp. Spring(SON) 80°W 80°W 75°W 75°W 70°W 70°W

-0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6

°C/Decade °C/Decade 15°S 10°S 15°S 10°S 5°S 5°S 0° 0° b) MaximumTemp. Summer(DEF) d) MaximumTemp. Winter (JJA) 80°W 80°W 75°W 75°W 70°W 70°W CLIMATE TRENDS

-0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5

°C/Decade °C/Decade - 53 Climate Scenarios for Perú to 2030 54 Climate Scenarios for Perú to 2030 FIGURE 21 (SON), b) summer (DJF), c) autumn and d) winter (JJA). (SON), b)summer(DJF), c)autumnandd)winter (JJA). The better adjustment ofthelineartrends isindicated inred lines. Moyobamba (northern jungle), Matucana (central mountain region) andTemporal Punodistribution (seasonal southern maximumtemperatureofmean /decade), mountain(ºC estimatedperiod1965-2006) thethe forof region) stations, a) spring

°C °C °C °C 15 20 25 30 15 20 25 30 15 20 25 30 15 20 25 30 1965 1965 1965 1965 1970 1970 1970 1970 B) SummerMaximumTemperature (DEF) a) SpringMaximumTemperature (SON) c) Autumn MaximumTemperature(MAM) 1975 1975 1975 1975 d) Winter Maximum Temperature (JJA) 1980 1980 1980 1980 Years Years Years Years 1985 1985 1985 1985 1990 1990 1990 1990 1995 1995 1995 1995 2000 2000 2000 2000 2005 2005 2005 2005 and negative thefour anomaliesduring seasonsrelated to theENSO 1997/98. na) shows positive anomalies, similar very in the four seasons related to the effects of the ENSO 1991/92 year, with a intensevery value during spring (SON). Also in the station located in the mountain - (Matuca rrence of negative anomalies during the warm event of the ENSO of 1977/78, is present throughout the The high interannual variability in observed Moyobamba before the years 1980 , associated to the occu- an summermonths. spring the during totals,particularly annual the to areidentical region (Matucana) mountain central the (Figure 25), shows that the variability of minimum temperature in the northern jungle (Moyobamba) and The temporal distributions of seasonal minimum mean temperatures of the three representative stations trends inPuno inthehighlandsofArequipa. are lesspersistent thantheobserved the year, the during Also, significant. statistically and negative are trends that be to happens Puno in ca Lake. While in the high areas of Arequipa and Tacna the trends are positive and statistically significant, in the highlands of Arequipa and those located in Puno, particularly in the northeastern part of the Titica- Another characteristic that is necessary to point out is the bipolar behavior between the stations situated (Puno) upto +0.91ºC/decade, registered inLaAngostura(Arequipa). a result of the extreme values. These values are fromobserved -0.63 ºC/decade, registered in El Progreso the higher range of seasonal variation is observed in the winter months, when it reaches 1 ºC /decade, as autumn, and spring in greater.approximatelyºC/decade is 1,5 aroundyear vary range trends the While the during persistence the where region, mountain southern and jungle northern the in than region, mountain central and coast the in noticeable more are differences These station. after station trends, temperature maximum the of distribution seasonal the in observed the than behavior persistent less a shows a-d) 24 (Figuretemperature mean minimum of trends seasonal the of distribution spatial The FIGURE 22 trends with statistical significance at 5% or less (Testtrends are withstatisticalsignificanceMann-Kendall) indicated at5%orless inblackcircles. 1965-2006. fortemperatureestimatedperiod mean /decade) the trendminimum (ºCThe annual of distribution Spatial 15°S 10°S 5°S 0° 80°W Annual MinimumTemperature 75°W 70°W -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5

°C/década CLIMATE TRENDS 55 Climate Scenarios for Perú to 2030 56 Climate Scenarios for Perú to 2030 summer months (DJF) in the location of Moyobamba (San Martin) with -0,92 ºC/decade (Figure 27 b) 27 (Figure ºC/decade -0,92 with Martin) (San Moyobamba of location the in (DJF) months summer the registeredin was value minimum (FigureThe26). cycle diurnal annual the of trends the of one the to pattern similar very a show a-d) 27 (Figure cycle diurnal the of trends of distributions seasonal The inthestations negative trends observed Tacna. the explain also can mechanism same This cycle. annual the of trends negative of occurrence the for reason main the temperatures,are minimum of trends positive intense the to temperatures,compared In the high zones of Arequipa and Puno, the weak positive trend and the negative trend of the maximum negative trends orweak positive trends oftheannualminimummeantemperatures. the and temperature mean maximum annual the of trends positive intense the between mixture a be to seem jungle, northern the in observed ones the to opposite Puno, of part trends central the maximum in registeredThe zone. this in observed temperatures mean minimum annual of trend positive higher the to due basically are Lake, Titicacathe to adjacent areas the in Puno, of part central the over located ones the except for cycle, diurnal the in increases significant. The statistically are them of most In the mountain region, except for the highlands in Arequipa and Puno, positive trends are observed and jungle. inthenorthern trend registered coastisdueto thesamemechanismobserved inthenorthern negative the Also temperatures. mean minimum annual the of trends positive the to due than region this in temperature mean maximum annual the in observed trend negative or trend positive weak the of result the more be to seems which temperatures; of amplitude diurnal the in diminish a is, that ved, obser are trends negative jungle northern the in located stations the In values. significant statistically show stations the of ProgresoregisteredEl Most /decade in (Puno). ºC +0,62 to up Martin) (San bamba The slopes (Figurein the annual cycle mean diurnal 26) show values -0,74 between ºC/decade in Moyo stations are shown inFigure 26and27,respectively. 23 forthe values seasonal and annual mean fortemperatures)estimated extreme of amplitude (diurnal cycle diurnal called temperature, minimum and maximum the between difference the in trends, The FIGURE 23 °C 10 15 5 1965 is indicated inred lines jungle), Matucana ( (northern central mountain region) Moyobamba and Puno (southern mountain the region). A betterin adjustment of the linear (1965-2006) trends temperature mean minimum annual of distribution Temporal 3.3.4 Trends inPeru ofthe diurnal cycle 1970 1975 Annual MinimumTemperature 1980 Years 1985 1990 1995 2000 2005 - - na, statistically non-significant positive values areIn observed. of the Department Tacna occurs because the case of the annual cycle. In the winter (JJA), see Figure 27 d, in the southern zone of Puno and in Tacin - explained reason same the for also cycle, annual the of ones the to similar are trends the of patterns the seasons, (MAM) autumn and (SON) spring the in especially regions, coast the and mountain the In temperature. due to the occurrence of intense negative temperatures or weak positive trends of the mean maximum (Figure 27 d). In the northern jungle the negative trends observed during the four seasons of the year are ºC/decade +0,85 with (Arequipa) Angostura La in winter the registeredin was value maximum the and 10°S 15°S 10°S 15°S FIGURE 24 5°S 5°S c) Autumn Minimum Temperature (MAM) 0° 0° a)Spring MinimumTemperature (SON) Mann-Kendall) are indicated inblackcircles.Mann-Kendall) spring (SON), b) summer (DJF), c) autumn (MAM)y, d) winter (JJA). The trends with statistical significance at 5%a) 1965-2006. or(Test period lessthe for estimated (ºC/decade), trends temperature mean minimum seasonal of distribution Spatial 80°W 80°W 75°W 75°W 70°W 70°W

-0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6

°C/Década °C/Década 15°S 10°S 15°S 10°S 5°S 5°S b)Summer MinimumTemperature (DJF) 0° 0° d) Winter MinimumTemperature (JJA) 80°W 80°W 75°W 75°W 70°W 70°W CLIMATE TRENDS

-0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6

°C/Década °C/Década 57 Climate Scenarios for Perú to 2030 58 Climate Scenarios for Perú to 2030 FIGURE 25 summer DJF, c)autumn(MAM) andwinter (JJA). The bestadjustmentofthelinear trend isindicated inred lines of Moyobambajungle), Matucana (northern (central mountain region and Puno ( southern mountain region), a)spring (SON), b) Temporal distribution of seasonal mean minimum temperature (ºC /decade) estimated for the period 1965-2006 for the stations

°C °C °C °C 10 10 15 10 15 12 14 16 18 10 15 6 8 0 5 5 5 1965 1965 1965 1965 1970 1970 1970 1970 B) SummerMinimumTemperature (DEF) c) Autumn MinimumTemperature (MAM) a) SpringMinimumTemperature (SON) d) Winter Minimum Temperature(JJA) 1975 1975 1975 1975 1980 1980 1980 1980 Years Years Years Years 1985 1985 1985 1985 1990 1990 1990 1990 1995 1995 1995 1995 2000 2000 2000 2000 2005 2005 2005 2005 jungle and in the southern mountainregionjungle andinthesouthern (PacificOcean DrainageArea) tive values, statistically significant are grouped in the coast and central mountain region, in the northern northern mountain region, and in the high zones of Arequipa, Tacna and Puno. On the other hand, nega- characteristics. For example, the positive values, statistically significant, are scattered along the coast and regional own orographic the to joined effect scale large the reflect possibly trends,that the of signs the on based is This Peru. in precipitation of regionalization apparent an characterize to seams index This occurred inthelast42years. precipitation of intensity the in 8mm of diminish of increase a means mm/day/year,which 0,2 ± ween bet ranginglow, relatively are trends the of values The terms. total in rainfall the of intensity the of (-) one year and the trend shows the possible at variation a long term, the increase SDII (+) or diminish SDII during occurred precipitation the of day per intensity average the indicates terms, simple in index, This The distribution of the trend of the annual precipitation daily intensity index (SDII) is shown in Figure 28. negative, whiletheminimumtemperature trend ispositive. is trendtemperature mean maximum the because occurs Puno of part southernmost the in and trend, temperature mean minimum the than higher are winter the in trendtemperature mean maximum the FIGURE 26 statistical significance at 5% or less (Test arestatistical significanceMann-Kendall) indicated at5%orless inblackcircles. Spatial distribution of the annual trendsdiurnal cycle (ºC / decade) estimated for the period 1965-2006. The trends with 3.3.5.1. Extreme Climate Indices 3.3.5.1.Extreme ofprecipitation 3.3.5. Lineartrends Climate oftheExtreme Indices 15°S 10°S 5°S 0° 80°W Annual DiurnalCicle 75°W 70°W -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8

°C/década CLIMATE TRENDS - 59 Climate Scenarios for Perú to 2030 60 Climate Scenarios for Perú to 2030 in the intensity ofprecipitationsin theintensity there isalsoanincrease ofdays withoutany intheperiod rainfall. ria and Loreto the CDD trends, are opposite to the SDII index, which indicated that besides being a decrease prolonged the period is,dry the intensity of the precipitation is higher. Conversely, in San Martin, Tingo - Ma This relation indicates that the higher the CDD is, the SDII is higher too; or viceversa, that is to say; the more ofdirectrelation these indices. between kind some of existence the as interpreted be can which region, mountain northern and coast the in index respectively. The trends of the CDD index show similar a tospatial distribution very the trend of the SDII days with precipitations consecutive (CWD), shown and in Figure(CDD) 29 days a-b, can dry be interpreted consecutive as indicators of of droughtnumber and floods,maximum of indexes the of trends The 10°S 15°S 10°S 15°S FIGURE 27 5°S 5°S 0° 0° Kendall) areKendall) indicated inblackcircle. (TestMann- less or 5% at significance statistical with trends The (JJA). winter d) and autumn c) (DJF), summer b) (SON), spring a) 1965-2006 estimatedforperiod /decade) the ºC ( cycle diurnal seasonal the trendsof the of distribution Spatial c) Autumn DiurnalCycle(MAM) a) SpringDiurnalCycle(SON) 80°W 80°W 75°W 75°W 70°W 70°W

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8

°C/Década °C/Década 15°S 10°S 15°S 10°S 5°S 5°S 0° 0° b) SummerDiurnalCycle(DJF) d) Winter DiurnalCycle(JJA) 80°W 80°W 75°W 75°W 70°W 70°W

1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.9 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6

°C/Década °C/Década respectively. These indexes indirectly indicate the intensity of precipitations registered and are related,are and registeredprecipitations of intensity the indicate indirectly indexes respectively. These a-b) (Figure30 index day RX5 the and index day RX1 the byrepresented are days accumulatedconsecutive 5 during total maximum the and day one in registered precipitation maximum the of trend The in theseplaces. trends observed in the southern mountain region indicate that the local characteristics are predominant (a total of 8 days in 42 years), which has practically a non-consistent significance. On the other hand, the se the trends are not statistically significant, and most of the trends show values lower than 0,2 days/year central mountain region, where apparently there is an increase in the days without precipitation, becau- and northern the includes that regional pattern a (Arequipa). of existence the indicates index Thus,this observed, is values, without any defined spatialnegative pattern, registering an extreme value of 0,48and days/year, in positive Pampa de Arrieros with trends, of mixture a region mountain southern the In is registered trend Maria). significance (Tingo statistically a where jungle, central and northern the towards extend jungle (San Martin). The positive values prevail over the and northern central mountain region, and they zero. These values are distributed more frequently in the southern mountain region and in the northern practically is trend the where values null of prevailing a shows trend index CWD the of distribution The inthehighzones ofArequipamountain region, andPuno. particularly of positive values of CDD and SDII, which seems to have the same behavior as in the coast and northern prevailing a shows region southern The precipitation. extreme of existence the as such analysis, this in of the item 3.3.1. (Figure 14). This result is because there are other parameters that need to be considered infall, originating as a result a decrease in total precipitation; but this is what it is not verify in the analysis the in decrease ra- without days of number increaseof an of occurrence the the time same the precipitation,at of intensity as interpreted be could characteristics These index. SDII the to opposite trends, significance statistically some with CDD the of trends positive prevails region mountain central the In FIGURE 28 1965-2006. (TestThe trends are withstatisticalsignificanceMann-Kendall) indicated at 5% inblackcircles. period the for estimated (mm/day/year) (DSII) index intensity daily precipitation the of trend the of distribution Spatial 15°S 10°S 5°S 0° 80°W 75°W SDII 70°W -0.2 -0.1 0 0.1 0.2

mm/día CLIMATE TRENDS 61 Climate Scenarios for Perú to 2030 62 Climate Scenarios for Perú to 2030 that generate theseprecipitations. some typical regional factors (orography, geographical location) that respond to large scale mechanisms precipitations,mentioning,of intensity the is arewhich worth is probably originated of byinfluence the that fact, Another regionalized. quite arePeru of region coast and mountain the over precipitation the of intensity the that indicate also they region, jungle the over precipitation generate that mechanisms certain of differences stressed some indicating besides indices, two these for trend the of patterns The the mountain region. over mainly observed are trends significant statistically positive and negative where distribution, RX1day the in occur not does this region; mountain the over negative and jungle the in significant lly statistica - trends positive shows day RX5 the that observed is it Also values. higher slightly with day), a pattern similar to the forone observed the maximum precipitations trends registered in one day - (RX1 The trend of the index of maximum accumulated precipitation in five days (RX5day) (Figure 30 b) shows role oftheregistered in theintensity seems hattheorography important precipitations. plays avery it Martin, San different of two least at the at but mechanisms; of types regional scale,Department the in there is an opposite action from some mechanism that generates the precipitations over these region or there is a station with statistical significance) which is related to the CDD index trends. This indicates that jungle (although they are not statistically significant) and a decrease in he part northern of jungle (where te. In these regions, on one side, there is an increase in the intensity of daily precipitation in the southern are in observed the stations located in San and Martin Tingo Maria, where the trends are just the opposi- trend served in the SDII index are basically due to the intensity of the daily precipitation. The exceptions ob the hat showsindices, which two these country. whole relationbetween the direct a indicatesThis daytrend RX1 (Figurethe of trenddistribution index Thespatial (FigureSDII to the similar is 30) over28) basic conditionsfor soil saturation. are precipitation of amount and persistency the since floods, and/or landslides possible of indicator an as important very is index day RX5 the Particularly, floods. of occurrence probable the way,with some 10°S 15°S FIGURE 29 5°S 0° significant at5% Mann-Kendall, areTest indicated inblackcircles. The white circles indicate void trends (0,0day). statistically trends The 1965-2006. period the for estimated (CWD) days rainy consecutive of number Maximum b) and Spatial distribution of the trends of the indexes of : a) Maximum number or consecutive days without precipitation (CDD) 80°W a) CDD 75°W 70°W

-0.2 -0.1 0 1 2 3 4 Día 15°S 10°S 5°S 0° 80°W b) CWD 75°W 70°W

-0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 Día - stations located in the western side of the Andes Mountains do not show any kind of trend. This index trend. This of kind any show not do Mountains Andes the of side western the in located stations the of most that observed is it trend index R10mm the of distribution the In Figureb. in a 31 observed The trends of moderate precipitations (R10mm) and the trend of intense precipitations (R20mm) can be five or day days. one in precipitations accumulated in trends the modulate to seem not do characteristics southern the in and mountain observed, a predominance of the positive is trends. In the southern mountain region,day apparently the local RX1 the of trends negative the of predominance a region tain creases mountain region over the and northern jungle.a decreasethe central moun- In in the northern The trends show a polarity between the mountain and the jungle northern in both distributions, with in- Also the positive trends registered in the southern part of Peru are more homogeneous that the ones the that homogeneous more are Peru of part southern the in registered trends positive the Also regions. both in trends significant positive some are there since distribution, this in noticeable less are region, mountain northern the and martin San in located The stations the of Mountains. trends the Andes between the contrast of slope western the along mainly trends, null present that stations with re 32 a-b. The distribution of trends of very rainy days follows a pattern similar to the index trend R10mm The trends of the number of very rainy days (R95p) and extremely rainy days (R99p) are described in Figu- ofevent intheseregions.ones thatmodulate thiskind significant values are present in both distributions. All this indicates that theare local characteristics the tions, except for that arethey the fact jungle,more also consistentbecause the statistical in he northern or diminish in the days with moderate rainfall and heavy rainfall over the same regions, in similar propor values of the maximum trends of these two indexes are very close, which means that there is an increase as well as in the mountain southern region, aresimilar, very with some stressed regionalized trends. The is It also that observed the patterns of the two distributions in the mountain region and jungle, northern ofthepresentthe period analysisfor thewholecountry. increases in the distribution of the Rx5day, there is a predominance of stations without any trend during 15°S 10°S FIGURE 30 5°S 0° trends (0.0mm/day). void indicate circles white circles. The blank in indicated are Mann-Kendall, test less or 5% at significance statistical with b) , and trends The 2006. (R1day) – 1965 period day the for estimated (RX5day), one days consecutive 5 in in precipitation accumulated maximum precipitation accumulated maximum a) indexes: the of trends the of distribution Spatial 80°W a) RX1day 75°W 70°W

1 -0.5 0 0.5 1 mm 15°S 10°S 5°S 0° 80°W b) RX5day 75°W 70°W CLIMATE TRENDS

1 -0.5 0 0.5 1 1.5 2 mm - 63 Climate Scenarios for Perú to 2030 64 Climate Scenarios for Perú to 2030 jungle. southern and region mountain the all over similar is intensity The values. significant statistically show index this of trends positive the of most that is difference The days. colds the exactly for one is the tothat opposite model a describes 33) (Figure (TX90p) days warm to corresponding pattern trend The mountainregion.of analysis, inthesouthern colddays gradually decreased particularly period the during that means and 10% percentile the than lowertemperatures maximum with days of and in San observed Martin Tingo Maria. This pattern indicates that there was a decrease in the number are values positive region jungle the In Puno. in two and Cusco in located one follows: as distributed values positive shows which region, mountain southern the in locations three forPeru, except of gions re jungle southern and mountain whole the over distributed significant, statistically values, negative of predominance a shows (TX10p) days cold very of number the indicated that index the of trend The 3.3.5.2. same thinghappenedinthecentralzone ofPuno. western side of the jungle northern there were some significant increases in extreme precipitations. The one station is in the observed central mountain region. The trends indicate that in mountain and in the Only region. mountain southern and jungle southern and central region mountain northern the over distributed are they and prevail, trends positive where stations few very are there country, whole the Concerning the trends in the extremely rainy days (Figure 32 b) there is predominance of null values over apparently modulated by large scaleeffects, more thanitisbecauseofgeographical factors. characteristics, own its presents that region another also is this that evidently more indicating region, Another characteristic observed is the presence of negative significant trends in all the central mountain the of orographicregardless factors. precipitations of regionalization a indicates which distributions, others in observed 10°S 15°S FIGURE 31 5°S 0° higher Test are indicated inblackcircles, Mann-Kendall thewhite circles indicate void trends (0,0mm/day). and b) heavy precipitations (R20mm), estimated for the period 1965-2006. The trends with statistical significance at 5% or Spatial distribution of the trend of the indexes of the amount of days with occurrence of: moderate precipitation (R10mm) 80°W Extreme Climate IndexesExtreme for/OFmaximum temperature a) R10mm 75°W 70°W

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 Día 15°S 10°S 5°S 0° 80°W b) R20mm 75°W 70°W

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 Día - 10°S 15°S 10°S 15°S FIGURE 33 FIGURE 32 5°S 5°S 0° 0° 2000. The trends withstatisticalsignificance at5%orless areMann-Kendall indicated inblackcircles.Test 1971- of climatology the on based 1965-2006, period the for estimated 90% percentile (TX90p), days warm b) and 10% percentile (TX10p), days cold a) temperature, maximum for indices extreme climate of trends the of distribution Spatial circles indicate void trend (0,0mm/day). of climatology the on based 1965-2006, 1971-2000. period The trends with the statistical significancefor at 5% orestimated less 99% TestMann-Kendall, percentile are indicated(R99p), in black circles.days rainy The white extremely b) and 95% percentile (R95p), days rainy very a) precipitation for indices extreme climate of trends the of distribution Spatial 80°W 80°W a) Tx10p a) R95p 75°W 75°W 70°W 70°W

-10 -8 -6 -4 -2 0 2 4 6 8 10 12

-0.6 -0.4 -0.2 0 0.2 0.4 0.6

Día mm 15°S 10°S 15°S 10°S 5°S 5°S 0° 0° 80°W 80°W b) TX90p b) R99p 75°W 75°W 70°W 70°W CLIMATE TRENDS

-1 0 1 2 3 4 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

Día mm 65 Climate Scenarios for Perú to 2030 66 Climate Scenarios for Perú to 2030 in theselocations. it happens to be truth, it could be relevant for the agrometeorological conditions for of some cropskind This apparently half). effect of the Titicaca Lake on this index needs to be verified by other studies, and if a and month one to close (very year / days 1,2 to up increased they Puno in Similarly, period). years 42 the in months two (approximatelydays/yearin 1,6 to up of ratio a at decreasedArequipa of zones high the in frostsmeteorological of number The Lake. the Titicaca to zones adjacent the in increase an and the in locations isolated other meteorological frost with days of number and the in decrease significant a Peruis thereregionof mountain Arequipa of zones high the in that indicate to seems Everything ofthe ones intheadjacentzones north Titicaca whichare Lake, statisticallysignificant positive trends. negativevalues,with Arequipa,significant, high of statistically distributed over differ the zones the from trends The(Figure Peru34). in region mountain southern and central the to circumscribed are analysis this in ºC) 0,0 than less or equal temperatures (minimum meteorologicalfrosts with days for trends The energy balanceday day, after from whichthemaximumtemperature isaresult. jungle, aswell asinPuno,central andnorthern are apparently due to regional effects thatmodulate the the thermo-regulating influence of the Titicaca Lake. The values of the observed trends in Cusco and the is toIt important highlight that both indices of extreme maximum temperature do not show any sign of FIGURE 34 meteorological frost. The trends with statistical significance at 5% or less (Test Mann-Kendall) are indicated in black in indicated are Mann-Kendall) (Test less circles. or with 5% days at of significance number statistical with of trends The Indexes frost. temperature.meteorological minimum for trends Indexes Climate Extreme of distribution Spatial 3.3.5.3. Extreme Climate Indexes3.3.5.3. Extreme forMinimum Temperature 15°S 10°S 5°S 0° 80°W FD 75°W 70°W

-1.6 -1.2 -0.8 -0.4 0 0.4 0.8 1.2 Día from the one observed in the remainder part ofthecountry. intheremainder part from theoneobserved temperatures the decrease number (TN10p), of also cold show days this (TX10p) same inverse behaviors extreme with days of number region,the mountain central Punoand of zone region,central jungle the sitive/negative trends of cold nights over these Andean zones. Also, it is worth mentioning, that while in frosts between the central zone of Puno and the high zones in Arequipa between the po is the polarity meteorological of decrease/increase of difference the strengthens that characteristic One significant. southern mountain and isolated location in the mountain northern and central coast are predominantly and Maria) and TIngo Martin (San jungle of northern the for period values trend negativestudy. presentThe the the during warmer becoming gradually were nights cold the time each that indicates This a). 35 (Figure Lake Titacaca the of north zones adjacent the and (Marcapomacocha) region mountain cold with days central of the in located number stations, isolated some forThe except country, whole the almost a-b. in decreased nights 35 Figure in seen be can analysis, of period the during (TN90p), nights warm of number and (TN10p) nights cold of number the of distribution spatial the in trend The SPI, is used a basis, on mean terms for the whole territory, to estimate a) temporal variability of the SPI the of variability temporal a) estimate to territory, whole the for terms mean on basis, a used is SPI, A comprehensive the spatial/temporal analysis of drought in Peru, considering 64 temporal series of the 3.3.6. Drought analysis low negative value.by avery jungle there(San Martin) is a contrast in the trends, with a statistically significant positive value, followed negativetrendsand Lima trendsand Ancash regionin mountain the in Similarly, Ica. in northern the in Pamapaand therecoast central Arequipathe are (Imata showingIn stations some positive Arrieros). de registeredregionis Punomountain in nights of and cold west with days of number the increasein hest the in observed polarity the trends in this areas and in the central of part Puno. On the contrary, the hig - Cusco. This pattern is inverse to the one forobserved cold nights in the high zones of Arequipa, without to extend to seems that Puno and Arequipa of zones high the over significant, statistically values, sitive (Figurenights po with warm pattern of homogeneous number Thetrendalmost the showsan in b) 35 15°S 10°S FIGURE 35 5°S 0° of 1966-1995. The trends withstatisticalsignificance at5%orless Mann-Kendall, areTest indicated inblackcircles. (TN10p), nights cold very a) temperature minimum percentile for10% and b) warm indexespercentile nights 90% (TX90p) estimated climatefor the period 1971-2000, Extreme based on the climatology of trends the of distribution Spatial 80°W a) Tx10p 75°W 70°W

-0.6 -0.4 -0.2 0 0.2 0.4 0.6 Día 15°S 10°S 5°S 0° 80°W b) TX90p 75°W 70°W CLIMATE TRENDS

-0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Día - - 67 Climate Scenarios for Perú to 2030 68 Climate Scenarios for Perú to 2030 estimated temporal scale, asitisthecaseofannualSPI. of intreseasonal variability. This becomes variability simultaneously more stable with the increase of the signs some observe to possible even is it variability, interannual high shows The SPI monthly of year.distribution or quarter month, one in precipitation accumulated of amount the to related month month, that after is that variability, temporal the distinguish can we droughts of types three these Among it may beassumed. as floods, with regions other and events drought with regions between compensation simultaneous a complexity of precipitation in the Peruvian territory. At the same time, other distributions do not indicate of drought or floodevents are fundamentally regional or maybe local; due partly to the spatial/temporal occurrence the whole, a as country, the over because explained be can it but, controversial; seems fact scales.threeThis the zero,in SPI the around oscillates time, whole the during value, mean the because Peru, in types different three its of any in occurrence drought of decrease or increase trends, temporal the During present of period study, by means of the SPI method, it seems that there was no evidence of involving intense hydrological intense, causingavery ofthecountry, itwasvery drought.most part besides event, This 1991/1992. in occurred event the be to seems it as country, the of part most or involvedall that event an occurred apparently has study,it of period the during occasions,rare very in regionalizedand areclimate, of characteristic natural a events,drought that indicated This tern. pat any without apparently years, the through lot a vary that them of one each of deviations standard the in expressed variability, spatial high the is distributions three these in characteristic common One the ENSOevents isshown. hydrological(Figureof scale typical droughts.one-year c) of Additionally,36 phases cold and warm the agrometeorologicaland droughts,of typical b) (Figure36 scale three-month the fordroughts; rological meteo of (Figuretypical scale is that a) 36 one-month the for: presentstudy the in used is (64) stations the all from SPI the averagesof the of (1965-2006) distribution temporal the AccordingFigureto a-c, 36 Peruvian territory. one will have a more comprehensive on the occurrence spatial/temporal of droughts knowledge in the patterns, these of series temporal the for analysis OT the complement as patterns using and Peruspatial/temporal in drought the of show ACPs the end, the At droughts. produce mainly events extreme whose and scales, longer and annual an at precipitations of variability the modulate that forcings scale large the recognize to source the as serve analysis, correlation the by determined teleconnections The ofdrought.one ofthecategories each for risk greater a is there where regions or places the shows categories aforementioned three the in intensities of types three these of occurrence the of (%) variability temporal the of analysis the Then, previously madedonotshow significant differences both ofthem. between (1997), with the purpose to consider only the most intense events of the ENSO, since some experiments bydifferent is recommended bit from what little a 3.4, Trenberth Niño El temperaturein surface sea the of ºC 1 than (higher) lower anomalies negative from determined are which highlighted, are ENSO the of events (warm) cold the distributions these in Also, Peru. in drought of occurrence the of case in risk the of idea the givesus station). variability This (pluviometric place foreach estimatedare extreme and severe moderate, intensity: of category the of one each drought for of kind different the of frequencies meteorological,droughts:variability,the where hydrologicalspatial agrometeorologicalthe and b) and; and the occurrence in terms of percentage, in the three categories for each one of the 3 differenttypes of a) Average temporal distributionofdrought intensity 3.3.6.1. Analysis of the Standardized Precipitation oftheStandardized Index(SPI) Analysis - - confirm that some of the very intense warm events of the ENSO, do caused some intense droughts, as droughts, intense some caused do ENSO, the of events warm intense very the of some that confirm be can it But, events. cold of presence the in contrary the happen not does it also (ENSO), Oscillation average, Southern / phenomenon Niño El the of events warm in the all by caused aredirectly associatedor are not territory, Peruvian the over events drought the observed be can it graphics this in Finally, FIGURE 36 SPI SPI SPI -2 -1 -2 -1 -2 -1 0 1 2 0 1 2 0 1 2 eprl itiuin f h Sadrie Peiiain ne (P) vrg vle o te eid 9520. a) 1965-2006. period Oscillation(ENSO) phenomenon.El Niño/Southern (cold) ofthe occurrence events ofwarm theElperiod Niño the the indentify (blue) red in areas shaded for The SPI. the of deviation standard value the indicate grey in areas shaded averageThe SPI). (SPI) Index Precipitation Standardized meteorological droughts (monthly SPI), b) agrometeorological droughtsSPI) c) (quarterly hydrological droughts (annual the of distribution Temporal 1965 1965 1965 c) SPI: 12 Months b) SPI:03Months a) SPI:01Month 1970 1970 1970 1975 1975 1975 1980 1980 1980 1985 1985 1985 Años Años Años 1990 1990 1990 1995 1995 1995 2000 2000 2000 CLIMATE TRENDS 2005 2005 2005 69 Climate Scenarios for Perú to 2030 70 Climate Scenarios for Perú to 2030 where agrometeorological (hydrological) droughts were registered. Other periods in which intensevery affected by droughts between moderate and extreme, with a value that reached 20% (30%) of locations study,wereunder locations the of 60% which in 1991/92, of one the Peru,regionsspecially in vast over ENSO,toindicatedpreviousseem droughtsthe 1991/92, cause as in item1982/83, such 1965/66, of the of events warm The . stations) the of (20%) 40% reached that droughts ) (agrometeorological cal (meteorologi - 1984/1985 1973/74, summer the of case the is such events, cold occur by affected ones, periods meterological in the specially droughts, that indications some are there since events ENSO, warm the the to of related directly are events drought all not that observed is it hand, other the On tation shortage. and agrometeorological droughtsmeteorological can be interpreted as the most sensitive scales in response to anythe precipi- of variability interannual high the that shows it time, same the at 36), (Figure SPI average the normal of distribution the temporal the of in observed part characteristics the strengthensare it and these climate the proves droughts, of types three these of distribution temporal The such isthecaseoffirstquinquennial1970’s and2000. observed; is locations few very in drought occurred which in 90’s the Also,periods of decade.quennial 70’sthe 80’s,the of middle tothe up extended it and decade quin- first the occurredin one last the and of quinquennial last the in started that case the is it as locations; analyzed the of majority the in periods drought three to up occurred which in stages werethere that observed be can (annual),it scale logical hydro the at mainly droughts, of frequency the of distribution temporal the In c). 37 (Figure drought hydrologicalthe of case the in droughts,as the of scale temporal the to state proportionally decreases it that be can it intensities, drought of types different the by affected stations of percentage the From more noticeableinthehydrological drought scale(annual scale). arearevalues repeated; maximum they same but, the scales two other the Figure in shownis it In a. 37 as 1991/92, of summer the in observed value secondary maximum a and 1989/90 of redsummer the in nuous or occasional peaks, and a maximum value reaching 60% of disconti- the stations used, which was registe some with variability interannual high very a shows scale meteorological the in droughts of Also, it is observed that the distribution of the addition of the frequencies of the three different intensities andtheareathe intensity affected by thedroughttypes. ineachoneofthese is shown the periods of occurrence of cold and warm events of the ENSO, with the purpose to associate (meteorological,drought of agrometeorologicalit hydrological). Also (types) and scales temporal three the for a-c, Figure37 in shown is 1965-2006, period the during stations) (64 territory whole the in tered frequencies,the percentage,of extreme,severein moderatedroughts the regisand distribution of The - ofthesoil/climate system thatisbeingaffected. conclusion,thisisduetoprecipitation. thememory In logicaldroughts goes beyond theending oftheevent itself, probably due.to the accumulative effect of increase in the SPI scale, as it in is the observed SPI annual distribution (Figure 36 c). The duration of hydro rarilylocalized, but this characteristics intensify and involve alonger period of time, in accordance with an tempoare and intensity less show that ones meteorologicalthe arethe drought, of Fromtypes three the ofthenaturalclimate variability. areoccur again,sincethey part but, undoub- tedly, the circulation anomalies at large scale need to be studied, in case these patterns may thelast five-year period of the 1960’s decade, among other, are not associated to anykind of ENSO event; the 1970’s decade and beginning of 1980; starting of the last five years of the 1990’s of end the at occurred decade,one the as: events Also,drought many values. positive totrend slight aa with SPI fewan years of 1999/20001999/2000 seemedhavepersistencetocausedof some kind heavyprecipitations, of favoring ofevent coldhand,theother the 1965/66, On1982/83,eventsof 1991/92.warm the of case the was it b) Temporal distributionofthearea affected by droughts in Peru - - - - intensity intensity may occur with the same continuity in the moderate whole territory , apparently of more droughts feasible frequent in the high the which, in scale, annual the at disappear practically difference This agrometeorological, where the frequencies seem to be more homogeneous in most of part the stations. This difference in the whole Peruvian territory seems to be greater in the meteorological scale than in the cated the western side of the Andes Mountains, and the rest of the mountain and jungle region of Peru. apparently is small, it difference the from although in 4%, regions 2% to lo occurrencefrequency the in This type of drought intensity (moderate) in the temporal scale of one month and a quarter, show a clear of Arequipa (Pampa jungle(Juancito). Cusco deArrieros), (Colquepata) andnorthern temporal scales. frequenciesHigh of this of type drought are scarcely registered in the mountain region previous two other the of droughtsmoderate the in observed is no it as regionalany characteristic, of sign is there Also, jungle. and mountain the in observed are 12% and 6% between values while coast, moderate of frequencies southern and of northern the in 2%) (lowerthan values low hydrological droughts(Figurevery show distribution c) 38 The 18%. and 16% between frequency a with (Cusco), pata Colque of location the in observed was frequency maximum The jungle. northern the of part most in the mountain and jungle in region, and registeredhigher values, with a arefrequency between 10% and 12% were 10% observed and 6% between values frequency Also Arequipa. of region mountain and coast the in locations some in lowermeteorological2% tovalues the than milar with month) (one scale si- distribution spatial an show b) 38 (Figure scale agrometeorological the in droughts moderate Also, mountainregion. (Mayo riverbasin)and somedispersevaluesinthecentralandsouthern San Martin in as registered are 12% and 10% between extremes some where jungle, the in 10% and 8% between droughts become more frequent in all the mountain region with values between 6% and 8% and values regionmountain Pacific (Figurethe located basin in drainage places Ocean and coast a) 38 These the all in 2% than lower frequencies show intensity moderate of SPI) (monthly drought Meteorological mountainregion. andsouthern slopeofthenorthern tern agrometeorological) in each one of the intensities and are restricted to isolated spots located in the wes- and (meteorological scales temporal two other the in registered frequencies the times two as high as frequency.occurrence the specially,of scales, and intensity the in differenceThis values, higher reaches three the in distribution, spatial the in (annual) patterns drought hydrological the from differ they and intensities, of types three the of one each in similar quite percentage occurrence an with patterns tial spa - show droughts agrometeorological(quarterly) and droughts (monthly) meteorological general, In (Figure andextreme a-c) 40a-c). 39 (Figuresevere a-c) 38 (Figuremoderate, intensity: their on depending grouped were They territory. Peruvianwhole the in distributed station 64 the for independently wereconsideredintensities drought threedrought(meteorological, scales agrometeorological hydrological)and threeof the categories and the study,in of period whole the to relation in occurrence of percentage occurrence, of frequency The areas affected by droughts, ofthe1970/1975and1998/2004. suchastheperiods and the first quinquenniel of the 1990’s decade, with some inserted periods showing low percentages of 1980 of beginning the and 1970’sdecade the of end the last between 1960’sdecade, the the of quinquennial are: they and studied, were that locates include mostly that periods intense with noticeable very is drought hydrological the of frequencies of distribution The ENSO. the to related directly be to not seems which interannual, the just by than scale affected higher a of (territory) modulation a stations show apparently droughts, of percentage the of distributions temporal the of characteristics The that are notdirectlyassociated to any ofanENSOevent. period 1990, and 1979/80 1966/67, between was hydrologicalones, the registered,particularly were droughts c) Spatial distributionofdrought frequencies CLIMATE TRENDS - - 71 Climate Scenarios for Perú to 2030 72 Climate Scenarios for Perú to 2030 follow the model of moderate droughts in the same temporal scales. The pattern of severe droughts severe of pattern The scales. temporal same the in droughts moderate of model the follow a-b) (Figure39 quarter and monthly scales, temporal the at droughtssevere of pattern distribution The region, except for three locationsinthe mountainandjungle. mountain the of parts higher other and Puno of zone central the in locations some Arequipa, of zones FIGURE 37 Sequías (%) Sequías (%) Sequías (%) 10 20 30 40 50 60 10 20 30 40 50 60 10 20 30 40 50 warm (cold) events of El Niño / Southern Oscillation(ENSO). /Southern (cold) eventswarm ofEl Niño c) (quarterly), hydrological intense droughts (annual SPI). The of more shaded areas in green (blue) color identify period of occurrence of categories three by affected stations) 64 droughts registered for of the period 1965-2006. (percentage a) meteorological droughts Peru (monthly SPI), b) of agrometeorological droughts area the of distribution Temporal 1965 1965 1965 1970 1970 1970 1975 1975 1975 1980 1980 1980 c) SPI:12Months b) SPI:03Months a) SPI:01Month Years 1985 1985 1985 Years Years 1990 1990 1990 1995 1995 1995 2000 2000 2000 2005 2005 2005 (quarterly), is the apparently regionalization of the drought occurrence. These regions seem to group to seem regions These occurrence. drought the of regionalization apparently the is (quarterly), scale this in droughts severe of frequency of distribution the in characteristic important Another Peru. tes a of distribution the frequencies slightly more homogenous in all the mountain and jungle region of At scale the (Figurequarterly 39 b) the contrast in observed the previous scale intensifies, which origina- jungle. poral scaleespeciallyinthenorthern tem- this in drought of occurrence the to pronemore be to seem regionsregion. two jungle last These in the mountain region, over the western side of the Andes Mountains and the rest of the mountain and stations the in droughtsobserved frequenciesof the between contrast morepreciselythe out points c) (Figure39 scale monthly the in frequencies of distribution quarterly.The the in than drought)rological (meteoscale monthly the in ratio region,higher coastal a frequenciestothe at areminimum restricted The c). 39 (Figure droughts moderate hydrological of model the from different very is intensity this in 15°S 10°S 15°S 10°S FIGURE 38 5°S 5°S 0° 0° a) SP101:Moderatedrought(%) c) SP112: Moderatedrought(%) 80°W 80°W 75°W 75°W 70°W 70°W

2 4 6 8 10 12 0 0 2 4 6 8 10 12 14 16 18 20 22 24 26

% % 15°S 10°S 5°S 0° b) SP103:Moderatedrought(%) droughts (annual). hydrological b) c) and (monthly), (quarterly) agrometeorological drought the meteorological a) temporal for the scales. of one percentages each for 1965-2006, in period occurrence drought moderate of frequency the of distribution Spatial 80°W 75°W 70°W CLIMATE TRENDS

0 2 4 6 8 10 12 14 16 18 % - 73 Climate Scenarios for Perú to 2030 74 Climate Scenarios for Perú to 2030 sities (moderate and severe), where the annual scale (hydrological) has a large number of stations that stations of number large a has (hydrological) scale annual the where severe), and (moderate sities inten- drought two other the from differs behavior This 6%. - 5% of maximum a with -3% 2% between values registered which jungle northern the for except 1%, than lower frequencies drought registered locations the of most years 42 the during that show a-c) 40 (Figure droughts meteorological Extreme oftherainfallperiod. variability the to that regions these of aridness the to associated more possibly are values These Arequipa. and region(Piuraand mountain Tumbes)northern the in observed are values extreme the Also drought. of annual Finally, two. the type this in frequency between occurrence of values homogeneous and interdependencelow show c) (Figuredroughtssevere39 strong a is there intensity of type this in that us indicate can which similar, are scales two the for frequencies the of values The Peru. of part northern the regionsin the all and Lima, and Junin Huancavelica,to extends Puno,it and and Arequipa between 15°S 10°S 15°S 10°S FIGURE 39 5°S 5°S 0° 0° a) SP101:Severedroughts(%) c) SP112: Severedroughts(%) 80°W 80°W 75°W 75°W 70°W 70°W

0 2 4 6 8 10 12 14 16 18 20 2 3 4 5 6 1 0

% % 15°S 10°S 5°S 0° b) SP103:Severedroughts(%) hydrological (annual). c) and, agrometeorological b) (quarterly) (monthly), for each one of the temporal scales a) meteorological frequency, in percentages, for the period 1965-2006, Spatial distribution of the severe drought occurrence 80°W 75°W 70°W

0 1 2 3 4 5 6 7 8 % hydrological event, suchasthemeteorological drought occursinstead ofashort drought. the as event, extreme prolonged a that possibility great is there characteristics, regionalhaving besides occurrence its places, these in that indicatedrought Loreto. extreme in of crease patterns spatial These de a and stations of number the in increase slight a is regionthere mountain northern the in and,area Arequipa,Punoand of same areconsideredforthe Tacna,stations region other mountain central the in similar frequencies shows for the same c) areas, but 40 a (Figurelittle more extended.scale annual Tothe Finally,the south it includes the Andahuaylas.high zones and Ancash of part southern the between region,mountain central the in appears area another and prominentmore are areas these scale month three- the At Loreto. and Martin San between jungle, northern the Puno,and and Arequipa in located stations with frequencies between 1% and 4% are more or less put together in groups. These the wherePeru, stations in areareas two show droughts Meteorological c). (Figure40 stations of number higher a is putting together the stations with similar values, being the annual scale the most noticeable and with in showof this droughttype low observed values. frequency characteristic intensity Another important 15°S 10°S 15°S 10°S FIGURE 40 5°S 5°S 0° 0° a) SP101:Extremedrought(%) c) SP112: Extremedrought(%) 80°W 80°W 75°W 75°W 70°W 70°W

2 3 4 5 6 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 0

% % 15°S 10°S 5°S 0° b) SP103:Extremedrought(%) goeerlgcl ruhs qatry ad c) hydrological droughts (annual). and, (quarterly) b) droughts (monthly), agrometeorological droughts meteorological temporal a) the scales of the one drought each for for 1965-2006, extreme percentages, period in the frequency, of occurrence distribution Spatial 80°W 75°W 70°W CLIMATE TRENDS

0 1 2 3 4 5 6 7 8 % - 75 Climate Scenarios for Perú to 2030 76 Climate Scenarios for Perú to 2030 the rainy station, which differs from what occurs at the beginning of the rainy period, especially in the in especially period, rainy the of beginning the at occurs what from differs which station, rainy the of end the at and precipitation maximum of months these during Peruvianterritory the of part most in Thus, this two last distribution patterns indicate that the warm events of the ENSO could cause droughts tation in the mountain northern region and negative anomalies in the southern of part this Department. over and this at Department the same time, shows a division between the positive anomalies of precipi- region of Ancash. This reference clearly indicates that the ENSO events originate precipitation anomalies tumn months (MAM), in which cc values statistically significant are registered, at 95% in all the mountain au- the during persists (DJF), summer the during observed cc, the of distribution spatial of pattern The region ofPeru,the northern possiblyrelated to the directeffect ofthe circulation.Walker tion over most part of Peru, causing droughts and at the same time they generate positive anomalies over (positive anomaly affectof directlySST) by means of large scale circulation patterns that generate precipita- in three locations that show statistically significant correlations, indicates observed only is Loreto.it thatof although distribution, Thispart southwestern theand Martin warmSan of eventspart eastern of the ENSO mountain regionnorthernandjungle, northern except forsomelocations and mountainthein western region Piuraof Lambayeque,and the to limited mostly are values Positive 0,2). than (lower values lower of predominance with country, the of part most over values negative shows b) 41 (Figure 3,4 Niño El the theIn summer season (DJF) the spatial pattern ofthe ccbetween the anomalies ofthe SPI and the SST of time (SON)inthisregion. which suggests that warm events of the ENSO could favor the occurrence of droughts during the spring are3.4 inverse, ElNiño Ayucuchothe of anomalies the and SPI tothe up where Ancash, between cc the Apurimac, of Departments region,the mountain in central the of part overmost happen to expected is opposite The region. jungle and mountain northern and region mountain southern the over specially rainfall), (excessive precipitation in anomalies positive cause would ENSO the of event warm some of are high and not statistically significant, this distribution indicates that during this season the occurrence registers inverse relations that and in some Apurimac, places they are and very intense, but not Ancash significant. Even between though the cczone values mountain central the and anomalies, both ween bet relations direct presents that Peru of regions northern the jungle; the over extending apparently Arequipa and Puno of Departments the of consisting region, mountain southern the them: between relations opposite show that Peru of regions major three on focus to seem a) 41 (Figure events ENSO An the beginning of the rainy overperiod of Peru most part (SON), the relation between the SPI and the tions, directorinverse, andare located preferably over themountainregion. sta- three the during remain coefficients the of sign the although stations, astronomic continuous two for even not persist not do and isolated scarce, very are they that shows 95%, at significance statistical with (cc) coefficients correlation the of distribution The a-c. 41 Figure in shown are May) (September- period rainy the for country, the throughout distributed stations 64 the of one each for scale) rological that represents the ENSO index phenomenon, and the Standardizedan Precipitation Index (SPI) 3,4, monthly (meteo Niño El the in SST the of anomalies standardized the between correlations seasonal The lation patterns. circu- these of one each torelated scale, large a at characteristics (meteorological)and SPI monthly the of series temporal the between exist that teleconnections possible about us indicate may analysis This SPI. the of anomalies standardized the and (TSA-TNA), Atlantic Tropical North the and AtlanticTropical South the between temperatures surface sea of difference the and (PDO); Oscillation Decadal Pacific the 3.4), Niño ( 3,4 Niño El the of SST the of anomalies standardized of indices the and MAM) and DJF (SON; of: consists territory Peruvian the of part most for period rainy the that seasons three the for ted estima- analysis correlation quarterly the on made is discussion and study,analysis the an of part this In a) Correlation between theSPIandOcean/Atmospheric Indexes 3.3.6.2. Tele-connections indrought distribution - - greatest differences are located in the north-western jungle (San Martin), where in both season inverse season both in where Martin), (San jungle north-western the in located are differences greatest ween the SPI and the El3,4 (FigureNiño 41 a and c), specially regions.in the southern and northern The bet observed models seasonal the to similar very are autumn and spring of patterns seasonal the that show a-c) 42 (Figure index PDO the and SPI the between cc the of distribution temporal seasonal The these places. in droughts of non-occurrence the favor ENSO the of events warm of the Tacna,locations that indicate the in seasons the cc,all registeredthe of during value persistent the that indicateto important is It malies would beregistered. region, which indicates that in presence of an eventual El phenomenon Niño positive precipitation ano mountain north-western and coast northern the in continues relation direct the Also, zone. southern 15°S 10°S 15°S 10°S FIGURE 41 5°S 5°S 0° 0° c) MAM a) SON 80°W 80°W 75°W 75°W 70°W 70°W

-0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 -0.1 0 0.1 0.2 0.3 -0.3 -0.2 -0.4

% % 15°S 10°S 5°S 0° are statisticallysignificant at95%. autumn (MAM), the black circles indicate that the cc c) and (DJF) summer b) (SON), spring a) 1965-2006. period the for index, 3,4 Niño El the and drought) the between (meteorological SPI monthly the of series cc temporal of distribution seasonal Spatial b) DEF 80°W 75°W 70°W CLIMATE TRENDS

0 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 % - - 77 Climate Scenarios for Perú to 2030 78 Climate Scenarios for Perú to 2030 patters varies (El Niño interannual, from 2-7 year and the PDO is decadal) it can be expected , depending effectsame the caused dices over circulation this of locations, same the period oscillation the since but, of these seasons over the same regions of Peru. This means, that anomalies of the same sign for both in- at the end of the rainy period, indicates that the two phenomenon act in the same direction in each one of the patterns of two the cc of 3.4 Thethe and SPI the with PDO similarity Elat the beginningNiño and the PDO(positive phase)tends to causepositive ofPeru. anomaliesofprecipitation over mostpart summer the during that indicates significant. This statistically is cc the of none but Martin), (San jungle are observed, with some negative isolated values, mainly in the southern mountain values region and positive northern cc of predominance and 3,4, Niño El the of SST the with patterns correlation the from different is b) (Figure41 season summer the of pattern the hand, other the On observed. are relations 15°S 10°S 15°S 10°S FIGURE 42 5°S 5°S 0° 0° 80°W 80°W c) MAM a) SON 75°W 75°W 70°W 70°W

-0.3 -0.2 -0.1 -0.4 0 0.1 0.2 0.3 0.4 0.5 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5

% % 15°S 10°S 5°S 0° cc isstatisticallysignificant at95%orhigher. and (DJF) (MAM).autumn summer circlesTheblack thick indicate that b) SON, spring a) 1965-2006, period the for (TSA-TNA) Atlantic South Northern the and Atlantic South Tropicalthe of temperature surface sea in difference the of anomalies the and drought) (meteorological SPI monthly the of series Seasonal spatial distribution of cc between temporal 80°W b) DEF 75°W 70°W

-0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 % with an inverse relation (negative values) over most part of Peru, with some exceeding positive values positive exceeding some with Peru, of part most over values) (negative relation inverse an with regionalization, apparent an show anomalies, TSA-TNAthe and SPI the between cc the months, those the during hemisphere northern thedifference the of the TSA-TNA, in localized torelated is intrinsically ITCZ, Convergence Zone Intertropical the when a), (Figure 43 spring During period. rainy the in fferent di- quite patterns cc show a-c) 43 (FigureTSA-TNAthe and SPI the between correlations seasonal The on thephaseofENSOorPDO depending droughts floods, effect: same the cause would they where region, mountain northern and occur for of most Peru,part where both phenomenon would oppose to each other, except for the coast more sensible during the beginning of the rainy period (SON). In the summer season the opposite would are mountain central and southern the in situated locations the that seems it and are, they which in se on the modulation of these indices, that the drought periods may in vary intensity according to the pha- 15°S 10°S 15°S 10°S FIGURE 43 5°S 5°S 0° 0° c) MAM a) SON 80°W 80°W 75°W 75°W . 70°W 70°W

-0.3 -0.2 -0.1 -0.4 0 0.1 0.2 0.3 0.4 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6

% % 15°S 10°S 5°S 0° that ccisstatisticallysignificant at95%orhigher. (MAM). autumn circlesindicate and black thick The (DJF) summer b) SON, spring a) 1965-2006, period the for (TSA-TNA) Atlantic South Northern the and Atlantic South Tropical the of temperature surface differencethe sea of in anomalies the drought)and between cc of distribution (meteorological SPI monthly spatial the of series temporal Seasonal b) DEF 80°W 75°W 70°W CLIMATE TRENDS

-0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 % 79 Climate Scenarios for Perú to 2030 80 Climate Scenarios for Perú to 2030 mountain, probablydue to subsidenceconditions. central the of slope westerns the ITCZ,and the of position the by produced subsidence the to due is it areas of the jungle northern and possibly mountain northern , where there is a in shortage precipitation The circulation. regional by facilitated flux, vapor water the to due region, northern and jungle the for to the south, mainly during the summer season, precipitation will be abundant in all the country, except ITCZthe showedcloser it the because inversethat, autumn is is this relations. of explanation possible A jungle and southern jungle, in the two seasons, and in the coast and north-western mountain during the northern the for except shortage, rainfall show to probabilities less is there seasons, astronomic two se These patterns are the expected ones, because thecloser the ITCZ is positioned to the south during the mountain showing inverse relations.central the to up extend they mountain, northern and coast the over change summer the in patterns observed the part, southernmost the reaches ITCZ the of position the when 3) 43 (Figure autumn In value. significant statistically no is there although region, mountain the all almost over lues va- high very with (DJF), period rainfall maximum the registeredin are country the all almost for values positive CC seasons.(MAM) autumn and (DJF) summer the for changes completely pattern spring The region. mountain southern the of tip southern the and slope) (western region mountain central and northern the in (droughts) occur might thing opposite the that possibility jungle. the is There northern and tains inhibition of droughts over almost the whole country, preferable in the eastern of part the Andes - Moun therefore, and; precipitation, of anomalies positive of occurrence the for favorable more becomes tern pat the position, and normal its of Tacna.north ITCZ the of positioning (TNA>TSA) the that means This specially in the northern mountain region, western slop of the central mountains, high zones of Arequipa FIGURE 44 indicates that the two first patterns are independents and starting thethird ofpatterns. componentthissomemixture indicates are firstpatterns thatthetwo independentsand starting Distribution of the PCA autovectors of the SPI and the percentage explained variance by each one of them. The North Test

Var. exp. 15 20 15 10 10 0 5 1 2 Teste: North Autovector 3 4 5 - - lues in: 1967, 1983, and 1997/1998 that combined with the values of the spatial pattern, means that in that means pattern, spatial the of values the with combined that 1997/1998 and 1983, 1967, in: lues va- positive intense very with variability interannual high show component this of series temporal The mountains. mountainandthewestern sideofthe centralandsouthern northern regionsother the arepositive values some overregistered intense values the jungle.toIn the very with over Puno and the highest zones of Arequipa, where the up highest values are and it extends observed, specially region, mountain the of part southern and east-central the of part most over values negative The second spatial pattern of the PCA accounts for the 11,3% of the total variance (Figure 46 a) and shows Oscillation Decadal (PDO). Pacific the of reflect a are apparently that years 12-13 of periods with decadal oscillations almost by modulated and years) (2-3 ENSO of oscillations high-frequency the )1991) Barnett by called ENSO, the of spectrum oscillation the of part oscillation, intense very biennial a to related are poral series, indicate that the most intense droughts during observed all the period of the present study, tem- the of analysis wavelet the as well as component, first the of distribution temporal and spatial The attheendof1960’s from weak oscillationsobserved 1989 and1994,apart and1990’s decades. between occurred oscillation the of result a as years, 2-3 of peak significant intense,but less a observed is it Also years. 12/13 between peak a shows that oscillation decadal almost the as same significant are those years was in its warm phase. The during global that spectrum of 1997), the al., wavelet et shows (Mantua that the Oscillation annual Decadal oscillations Pacific the to related be to seems oscillation This associated with an oscillation almost decadal with statistically significant values between 1975 and 1995. be to seem significant and wereintense which 1994, and 1974 during observed oscillations annual The the summersof1989/90and1991/92. in territory Peruvian the over precipitations of shortage extreme causing for responsible was 1994, and 1989 between observed significant, statistically years), (2-3 oscillation biennial intense very The 1990. and 1984 1974, as humid) positiveor (very 1983 and years),1966 as dry amplitude,negativeeither (very study until the starting of 1990. These oscillations of the annual cycle are related to extreme values of the of period the of beginning the from significant, statistically and contrasting oscillations, annual intense very of consists pattern first this of series temporal the that suggests c) (Figure45 TheWaveletanalysis mainly intheyears 1974and1990. precipitations, high of occurrence the indicates also pattern this time, same the At series. negative red registethat years the followedby 1991/92, and 1989/90 of summers the region,in mountain southern and central the in intensity more with especially country, whole the in occurred years, 42 patter,in this under registered droughts intense most the Thus, both. of result the to proportional intensity an with The result of the amplitude of these series with negative auto-values indicates the occurrence of drought 1976, 1966, in Similarly, 1987. 1983, 1980, positivehigh arevalues registered 2002. and 1999 1997, 1990, 1984, 1974, in values high registered been has it Also, 1991/92. and 1989/90 of summers the during The amplitude of the time series of this first component (Figure 45 b) intense show negative very values mountain regionvalues inthesouthern (Arequipa ofthecentralmountainregion. andPuno) andpart highest positive auto-values are located in all the mountain region, focusing the highest density of auto- except for two locations in Loreto and one in Tacna, with very small negative auto-values, over -0,05. The accounts for 24.7% of the total variance, shows some positive values over almost all the Peruvian territory, The spatial pattern of the first component of the Standardized PrecipitationIndex SPI (Figure 45-a ) which occur here inPeru, according to theestimates. themethodusedto make it is shown in Figure 44. Also these two patterns account for 36% of the total variance of the drought that the PCA of the In SPI,using the North Test, it was determined that only patterns two are independent, as b) Analysis oftheprincipalcomponents oftheSPI CLIMATE TRENDS - 81 Climate Scenarios for Perú to 2030 82 Climate Scenarios for Perú to 2030 FIGURE 45 statically significant valuesal95%. indicates analysis wavelet the in line red. The spectrum global side: energy, right spectral side: (left series temporal the First component of the SPI quarterly a) spatial distribution of the autovalues, b) temporal series and , c) wavelet analysis of Períod (years) 14 12 10 9 7 5 3 1 1965

Amplitude -2 0 2 1965 1970 1975 1970 15°S 10°S 5°S 0° 1980 1975 b) Temporal SeriesSP103:P01 80°W Time (years) a) SP103:P01(24.7%) 1985 1980 c) SP103:P01 1990 75°W Years 1985 1995 1990 70°W 2000 1995 2005 -0.05 0 0.05 0.1 0.15 0.2 0.25 2000 Variance 5 2005 10 15 14 12 10 9 7 5 3 1

Períod (years) FIGURE 46 statically significant valuesat95%. of the temporal series (left side: spectral energy, right side: global spectrum . The red line in the wavelet analysis indicates Second component of the quarterly SPI a) spatial distribution of the autovalues, b) temporal series and , c) wavelet analysis Period (years) 14 12 10 9 7 5 3 1 1965

Amplitude -2 -1 0 1 2 1965 1970 1975 1970 15°S 10°S 5°S 0° 1980 1975 b) Temporal SeriesSP103:P02 80°W Time (years) a) SP103:P02(11.3%) 1985 1980 c) SP103:P02 1990 75°W Years 1985 1995 1990 70°W 2000 1995 2005 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 2000 Variance 5 2005 10 15 CLIMATE TRENDS 14 12 10 9 7 5 3 1

Period (years) 83 Climate Scenarios for Perú to 2030 84 Climate Scenarios for Perú to 2030 of Peru and extreme droughts in the northern jungle (Loreto). In the winter of 1991/92 drought starts starts drought 1991/92 of winter the In (Loreto). jungle northern the in droughts extreme and Peru of par southern the in values positive of predominance a registers SPI annual the but regions, jungle and mountain the in zones isolated in droughts extreme and severe shows SPI quarterly the of distribution the 1991, of spring the In event. 1982/83 the in observed one the from pattern different very a scale, quarterly the at show a-c) 48 (Figure SPI the of distribution the 1991/1992, of event ENSO the During slopetowardsthe eastern thecentralmountainregion. to extends that observed is region mountain northern the in precipitations high scales, both in hand, other the On jungle. northern and region mountain central the towards expands its and year, the of season this during scales, two the Puno,in of Tacnaand Departments the in droughts extreme curring oc- of Peru, part southern the in intensified get droughts c) (Figure(MAM)47 autumn of months the In predominates theexcess mountainregion. ofprecipitations focused inthecoastandnorthern it regionsremaining the In (annual), scale quarterly a at (Arequipa)Puno of Department the in focused droughts, severe and moderate with Peru, of part southern and central the in regions the all involving b) (Figure47 1982/83 of summer the during appears event drought a of signal Some factores. local to due possible territory, national the over pattern the alters apparent which station, this during observed is droughtsevere Maritn San of department the In scale. annual the at conditions, normal with but 3.4, and Puno at the scale,quarterly similar to the correlations between the Arequipa SPI and the anomalies of of EL Niño zones high the over intensities maximum with registered, are country the all almost over precipitation high (Figure47) season spring the in that shows , scale annual and quarterly at ENSO the by caused 1982/83, components for principal two first the using SPI, the of reconstruction seasonal The study. They are: 1982/83,1991/92and1997/98. the occurrence of the most intense warm events of the ENSO occurred during the period of the present during described, is SPI the of March) to (September period rainy the of behavior quarterly averageThe statistically significant, occurred in1982 to 1989. OT analysis indicates that these drought events followed by floods, are relatedthe to theand 3-71984/1985, years oscillation, in anomalies negative subsequently and 1982/1983 between occurred anomalies thern jungle. The typical spatial distribution was registered between 1982 and 1985, when some positive the southern mountain region, that extends to the central mountain region and some places in the nor in droughts intense very study,causing of period the throughtout persists years. pattern 3-7 This ween bet oscillation of periods with Peru, in precipitation modulates that 1991), (Barnett, events frequency low ENSO the of pattern variability the represents SPI quarterly the of pattern second the summary, In temporal series, respectively. the in anomalies positive and negative 1960’sintense 19890’sthe to and associatedof decade, end the intense peak of 4 to 6 years, but it is not statistically significant. Also, biennial oscillations are at observed very a with spectrum wavelet global the in reflected is oscillation This decade.1980’s the throughout tion with a period of 3 to 7 years, almost during the whole period of study and it is statistically significant The wavelet analysis of the temporal series of this second pattern (Figure 46 c), has an interannual oscilla- events oftheENSOandin1985isassociated to acoldevent. drought in the remainder of part Peru. From all these years, 1983 and 1987 are associated with the warm regionand mountain central and southern registeredthe areprecipitation in of anomalies positive and reverse values positive with periods the during patterns observed the that indicates which 1988, and 1985 1979, 1978, 1968, during observed were values intense negative Also region. mountain northern 1997/98 in the southern and central mountain region of Peru; and, high precipitation over the coast and and 1983 during registered,mainly were pattern, this followed that droughts intense very years those 3.3.6.3. Seasonal distributionofdroughts3.3.6.3. Seasonal occurred duringthewarm events oftheENSO - - FIGURE 47 15°S 10°S 15°S 10°S 15°S 10°S spring 1982,b)summer1982/83andc)autumn1983. spring barindicates ofthedrought. theintensity The vertical a) (inferior) annual and (superior) SPI quarterly the for 1982/83 of ENSO the during occurred SPI average the of Reconstruction 5°S 5°S 5°S 0° 0° 0° b) DEF-1982/83(sp112) 80°W 80°W 80°W c) MAM-1983(sp103) a) SON-1982(sp103) 75°W 75°W 75°W 70°W 70°W 70°W

-0.5 -0.5 -0.5 -1.5 -0.1 -1.5 -1 -2.5 -1.5 -1 -2 -2.5 -2 -2 0 0.5 1 1.5 2 2.5 0 0.5 1 1.5 2 2.5 0 0.5 1 1.5 2 2.5

SPI SPI SPI 15°S 10°S 15°S 10°S 15°S 10°S 5°S 5°S 5°S 0° 0° 0° b) DEF-1982/83(sp103) 80°W 80°W 80°W c) MAM-1983(sp112) a) SON-1982(sp112) 75°W 75°W 75°W 70°W 70°W 70°W CLIMATE TRENDS

-1.5 -1 -0.5 -0.5 -0.5 -1.5 -0.1 -2.5 -1.5 -1 -2.5 -2 -2 -2 0 0.5 1 1.5 2 2.5 0 0.5 1 1.5 2 2.5 0 0.5 1 1.5 2 2.5

SPI SPI SPI 85 Climate Scenarios for Perú to 2030 86 Climate Scenarios for Perú to 2030 FIGURE 48 15°S 10°S 15°S 10°S 15°S 10°S September 1991 ,b)summer1991/92andc)autumn1992. barindicates oftheregistered theintensity The vertical drought a) (inferior) annual and (superior) SPI quarterly the for 1991/92 of ENSO the during occurred SPI average the of Reconstruction 5°S 5°S 5°S 0° 0° 0° b) DEF-1991/92(sp112) 80°W 80°W 80°W c) MAM-1992(sp103) a) SON-1991(sp103) 75°W 75°W 75°W 70°W 70°W 70°W

-1 0 -2 -1.5 -1.5 -0.5 -1.5 -0.1 -2.5 -2 -2.5 -2 -0.5 0.5 1 1.5 2 2.5 -1 -0.5 0 0.5 1 1.5 0 0.5 1

SPI SPI SPI 15°S 10°S 15°S 10°S 15°S 10°S 5°S 5°S 5°S 0° 0° 0° b) DEF-1991/92(sp103) 80°W 80°W 80°W c) MAM-1992(sp112) a) SON-1991(sp112) 75°W 75°W 75°W 70°W 70°W 70°W

-0.5 -1 -0.5 -1.5 -2 -1.5 -1 -1.5 -0.1 -2.5 -2.5 -2 -2.5 -2 0 0.5 1 1.5 2 -0.5 0 0.5 1 1.5 1 0 0.5

SPI SPI SPI FIGURE 49 15°S 10°S 15°S 10°S 15°S 10°S September 1997, b)summer1997/98andc)autumn1998. barindicates oftheregistered theintensity The vertical drought. a) (inferior) annual and (superior) SPI quarterly the for 1997/98 of ENSO the during occurred SPI average the of Reconstruction 5°S 5°S 5°S 0° 0° 0° b) DEF-1997/98(sp112) 80°W 80°W 80°W c) MAM-1998(sp103) a) SON-1997(sp103) 75°W 75°W 75°W 70°W 70°W 70°W

-1 -0.5 0 0.5 1 1.5 2 2.5 -2 -1.5 -0.5 -1.5 -0.1

-2 0 0.5 1.5 2 2.5 -2 -1.5 1 -1 -0.5 0 0.5 1 1.5 2 2.5

SPI SPI SPI 15°S 10°S 15°S 10°S 15°S 10°S 5°S 5°S 5°S 0° 0° 0° b) DEF-1997/98(sp103) 80°W 80°W 80°W c) MAM-1998(sp112) a) SON-1997(sp112) 75°W 75°W 75°W 70°W 70°W 70°W CLIMATE TRENDS

-1.5 -0.1 -0.5

-1 -1.5 -2.5 -2 -0.5 -1.5 -1 0 0.5 1 1.5 2 -2.5 -2 -0.5 0 0.5 1 1.5 2 2.5 0 0.5 1 1.5 2 2.5

SPI SPI SPI 87 Climate Scenarios for Perú to 2030 88 Climate Scenarios for Perú to 2030 in the intensity of the positive values, especially in the central and northern mountainregion. ofthepositivein theintensity values, especiallyinthecentralandnorthern observed are variations small some country the of rest the in while persists,drought moderate a jungle northern the in Also, scales. both at locations isolated some in region southern to the in moderate drought severe a of occurrence the indicating signs some appears it months, autumn the during Then, junglecontinueoccurring. thern registeredand regionpositivevalues intomountain nor change central the drought the in droughts in observed The precipitation. any indicate not do they but season, previous the to respect with crease, de slightly region southern the over values SPI the scale, annual the At region. mountain central and northern the in higher being intensify, values positive country the of rest the in while jungle; northern the of zones some in as wellPuno, as in locations isolated some in droughts recordmoderate they and In the summer months of 1997/98 the SPI patterns at the scale, quarterly in the southern region, change intheprevious events (1982/83,1991/92). theopposite toalmost exactly observed thepatterns and central jungle,and northern while in all the southern region positive values of the SPI are observed, precipitation. At the annual scale it can be distinguished moderate and severe droughts in the mountain and in of most Perupart some positive values of the PSI is registered which means positive anomalies of observed, aredroughts slight some jungle, the of points isolated some in 1997, of spring the IN region. northern the in conditions contrary Peruand regionof southern wereregisteredthe droughtstreme in ex which in events, previous two the in regions observed the in registered was drought no event this the quarterly, annual are differentvery from the in observed theevents of 1982/83 and 1991/92. During During the warm event of the ENSO in 1997/98 (Figure the 49 spatial a-c) patterns SPI of at the quarterly andcentralmountainregion. inthesummersouthern places thatwere observed same the on focused scales, two the at Peru of part most in intensify droughts season, autumn the In scale, butwithsomelow values(Figurequarterly 48b). the in as trend same the follows pattern the scale, annual the At scale. quarterly a at region mountain and affects the whole country, with maximum values focused in the southern region (Puno) and central - - - 89 Climate Scenarios for Perú to 2030 90 Climate Scenarios for Perú to 2030 90% withrespectto theyear 2000,while for theyear 2100from 90%upto 250%(IPCC 2007). to 25 from increase consistent a show 2030, year the for sources, industrialized and/or energetic from A2 (4), B2 (5) emission scenarios. The change in the emission of greenhouse gas effect (GHG) originating the are they a); (2007 IPCC the of (SRES) Scenarios Emission on Report Special the in defined emissions, gas greenhouse of trajectories two to related are that 1), table (see analyzed been have GCMs six work present the In future. the in change can climate the how as scenarios feasible (3), scenarios emission for this reason the GCMs are of great importance, because they are capable of simulating under different futuretrendsclimate,of and scenarios possible the on projections tomake necessary is matechange,it cli- to vulnerability and impacts potential the on studies making of importance the considering When that have great processing capacity. past climate, and as well current as the generation of of future simulation climate scenarios, the for which is in necessary the use of importance computers fundamental of tools are patterns This 2001). IPCC, 2005; SENAMHI, 2007; (Castro,energy and mass between interactions the in and system the of ponent laws and principles on mass conservation energy and momentum, that rule such processes in each com- relatedclimatesystems,equations processesthe the the physicaldynamical of bysolving main and the Global climate models or General Circulation Models (GCMs) are mathematical algorithms that represent GLO OF INTERCOMPARISON 4.1 treme temperature. ex for percentile 90 the and precipitation for percentile 95 the as such evaluated, been have extremes climate the of projections improved.the is scenarios, Also,details obtained fromspatial the climate the regional small-scale information to the climate signal of the global model, dynamical and adds this way the processsimulation of the This temperatures. of extreme and means precipitation of by variables the elaborated for method, been downscaling have below, presented be will that scenarios Climate complicatedbecome extremely for andcharacterized ahighdegree Giorgi, ofuncertainty. 2008. which reason they must forbe considered region,in the analysis of a signals of climate of change. However, characteristics this process own might the determine can that teleconnections, or circulation cale such as topography, of type soil, vegetation coverage, etc., up to the large scale forcings, such as mesos- The climate of a region is the result of a series of factors at different spatial scales, from the local forcings, FOR 2020–2030 THE DECADE CLIMATE SCENARIOS (5) B2 Describes a world in which local solutions prevail over economic, social and environmental sustainability. It is a world preser and self-satisfaction the are characteristics distinguished more Its world.heterogeneous very a Describes A2: (4) (3) Trajectory of greenhouse gases emissions, ondefined Emission(SRES) inScenarios IPCC, the SpecialReport depending and a technological change not as fast as the other and more diverse. Although this scenario is also oriented to the to oriented protection oftheenvironment andsocialequity,also itfocuses mainlyonlocaland regional levels. is scenario this Although diverse. more and other the as fast as not change technological a and levelsdevelopment increases progressivelyeconomic population intermediate whose with A2, slowerin a ratethan at growth perperson,aswell asthetechnological change, are dividedand are slower thaninotherevolution lines. vation of local identities. Therate birth the economic development is basically oriented to regions and the economical thestructurethoughtofpopulation. thatdescribe scenarios on socio-economic B AL CLIMATE MODELS CLIMATE AL - - information inSENAMHI, 2005): The GCMs used by the IPCC and analyzed in the present study are listed in the following table (see more this sameregion. bias,cold temperatureminimum of over time, same the at particularly peratureand a sub-tropics the in tem- maximum of bias warm predominant its is model CSIRO the about feature outstanding most The Amazon (4ºC). Peruvian-Bolivianthe in bias cold a shows also and Andes stressedoverthe gets that bias warm a show and ºC) a warm bias over the Andes and adjacent western area (4 ºC). The NCAR-PCM model, in general, in the tropical region (5 while ºC), the CCCma and the CSIRO show cold bias east of the tropical Andes (5 temperatureminimum Concerning (Figure CCSR/NIESthe and HADCM3 the showed52), warmer bias a mountainregion. ofthecentral southern biasinasector and awarm PeruvianºC) regionthe Amazon(3 in sector a to restricted bias cold a Brazil.show with NCAR-PCM The Peruvian the Andes (5 followedºC) over by the CSIRO, warm this last one quite with a warmer bias in bias the southeastern border a shows CCSR/NIES the (4ºC), tropics the in specially America, South of part With reference to maximum temperature (Figure 51), the CCCma model shows a bias quite cold in most over Peru, showing lower differences inprecipitation -1 between to 1mmday-1. rainfall of presentation the in skill better had patterns CSIRO and HadCM3 CCSR/NIES, the that showing period, 1990 to 1961 the between America South for models global the evaluated he when (2007), al., et Marengo by obtained were results Similar (15-30%). region mountain southern and region Amazon rainy bias in the western coast (70%). While the HadCM3 shows bias a of dry a low range in the Peruvian more or humid more a presents however,it region, the of part most in (dry) bias negative more show GFDL the and NCAR-PCM the models, analyzed six the fromregion, American South the of level the At region andtheAmazon region intheAndesregion drier in Peru (90%). itisparticularly Andean the in bias dry a present models the All 50. Figure in observed be can percentage of terms in (CRU) climatology observed the and models the of precipitation mean annual between difference The mical regionalization ordownscaling. (New et al., 1999), aimed at detecting possible systematic errors or bias to be taken into account in dyna- observed data or current climate from the Climate Research Unit (CRU) is the made for with the period 3 Table1961-1990 in mentioned models the of climatology the of comparison a Chapter present the In Geophysical FluidDynamicsLaboratory National InstituteforEnvironmentalStudies(NIES) Center forClimateSystemResearch(CCSR)- Canadian CenterforClimateModelingand Analysis National Centrefor Atmospheric Research Research Organization Australia’s CommonwealthScientificandIndustrial Hadley CentreforClimatePredictionandResearch Max PlanckInstitutefürMeteorology

General Atmospheric Circulation jointly Models usedinthesimulations doneby theIPCC Center with theinstitutionsinwhichthey were processed. Country Table 3 EEUU Japan/EEUU Canada EEUU Australia England Germany Acronyms Model GFDL CCSR/ NIES CCCma NCAR CSIRO HCCPR MPIfM

R30 +CCSROGCM CCSR/NIES AGCM CGCM2 NCAR-PCM CSIRO-Mk2 HADCM3 ECHAM5/OPYC3 CLIMATE SCENARIOS 91 Climate Scenarios for Perú to 2030 92 Climate Scenarios for Perú to 2030 temperature andintheA2scenario. maximum of case the for only but 1997/98, and 1982/83 1972/73, events Niño El the reproduced that one only the is model mentioned last however,the cooling); and (warming negative as well as malies ano positive project and fluctuate they say, to is that variability, high show they models other the to shows more stability, it almost follows an average projection, while the CSIRO and CCSR/NIES, compared cooling or at least lower values than the average in the A2 starting 2010 of up to -1 ºC. a The shows model CCCmaNCAR-PCM The 2030. in model starting dispersion larger a showing up scenario, B2 the and in ºC 5 scenario to A2 extreme the in ºC 6 to up of warming extreme an projects that one the is model HADCM3 The scenarios. both in 2050, of end the by ºC 2 to up and 2030, to ºC 1 to up of temperature maximum in increase average and expected is it models these to According warming. means, which five models,the global of The study,projections analyzed this in indicate, in average, positive anomalies thick blacklinerepresents themeanoraverage temperature from allthe5models. five in global models of the IPCCtemperatures up to 2050, for the grid air points over Peru in emission scenarios A2 minimum and B2. The and maximum of increase the on projections the shows 53 Figure 4.1.1. Temperature for projections Peru to 2050,basedonglobalmodels. in alower degree. the only followedbut NCAR-PCM the byfor temperature ºC), bias minimum (4 cold show CCCma CSIRO the and region, Amazon the About model. CSIRO the in even predominate, temperature minimum for bias positive where region Andes the being temperature, maximum to respect with areas, larger in All themodels(except for biasfor show warmer theCSIRO), minimumtemperature FIGURE 50 models, climatology inrelationby CRUfor to 1961-1990. themeanobserved theperiod The unitsare inpercentages. GFDL and NCAR-PCM HADCM3, CSIRO, CCSR/NIES, CCCma, the by simulated precipitation annual between Difference - models show increase ofupto 15%. 6 of out 3 jungle the In HADCM3). (model region jungle southern the of slope western the over 45% to up reach that shortages the prevailing 15% to up of increase an show 6, the of out models two Only The models of the mountain and jungle region do not agree, showing certain differences between them. for 2041to 2100. theperiod Similar results found Marengo 2007 temperaturewith increasea great of the (SST). sea surface intensity the to associated is that situation 15%, to up of increases on agree models the all coast northern the in models is shown in Figure 54. All the models show different distributions throughout the country. Only The variation in the percentage of precipitation in Peru for the period 2025-2035 simulated by the global 4.1.2. Projections ofprecipitation over Peru basedonglobalmodels to 2030 1982/32, butonlyintheB2scenario. Niño El the simulates ability certain with that model, HADCM3 the for except years, Niño El the during biased towards cooling in both scenarios. None of the models simulates the high temperatures detected fluctuations, higher show which model.model HADCM3 CSIRO the Theshowedby variability high The temperature. maximum of that to similar is increase average the temperature, minimum Concerning FIGURE 51 models, inrelationby CRUfor to 1961-1990. meanclimatology theperiod observed The unitsare inºC. Difference between maximum temperature simulated by the CCCma, CCSR/NIES, CSIRO, HADCM3 and the NCAR-PCM Maxime temperature Annual CLIMATE SCENARIOS 93 Climate Scenarios for Perú to 2030 94 Climate Scenarios for Perú to 2030 FIGURE 53 FIGURE 52 Anomalías de las temperaturas máxima (arriba) y mínima (abajo) en ºC, para los puntos de grilla sobre Perú en los los en Perú sobre grilla de puntos los para ºC, de emisiónA2yB2,paraelaño2050. extremos escenarios en (abajo) mínima y (arriba) máxima temperaturas las de Anomalías models, inrelationby CRUfor to 1961-1990. meanclimatology theperiod observed The unitsare inºC. Difference between minimum temperature simulated the by CCCma, CCSR/NIES, CSIRO, HADCM3 and the NCAR-PCM Minime temperature

Anomalía (ºC) -4 -2 0 2 4 6 1950 A n o m a l í a d e l a t e m p e r a t u r a M á x i m a - Es c e n a r i o B2 1970 1990 2010 2030 Annual 2050 Promedio 2 B - M3 C AD H o-B2 Ir S C C C S r/ N ICCCma-B2 E S - B 2 ter for Research Atmospheric –NCARoftheUnited (SENAMHI, States 2005). ofAmerica The RAMS regional model was initialized using the NCAR-PCM T42 global model from the National Cen- inourcountry. modulates theinterannual variability that event climate a ENSO, the of phase warm the to associated Peru, of coast northern the in rainfall forced from the NCAR global model, because it showed a better in performance the simulation of heavy En the present study, the dynamical downscaling was made using the regional RAMS model, which was suchastopography, variables Besides, aregional modelusesothersurface ofsoil, type etc. grid. matrix the than resolution better a of i.e., grid, fine a on but, GCMs; the to related ocean, the and model conditions (boundary and initial data from a GCM), and it solves the equations of the atmosphere downscalingapproach isan in whicharegional Dynamical valuesfrom matrix takes athick-grid pattern DOWNSCALING DYNAMICAL 4.2 coveragethe effectsinalocalclimate. onthecoastline, water andsurface regionalization or dynamical downscaling is an alternative that it is used to simulate in a small-scale grid, dynamical case such in basins; regions, mountain high coast, the in like areas in changes climate know to us allow not do they Km) 500 and 300 between resolutions(normally low their to due that truth also Although, it is truth that GCMs allow us to make projections on how the climate will be in the future, it is FIGURE 54 models, for the2030decadeinrelation to theclimatology 1961-1990 Variation in percentage (%) of annual precipitation simulated by the CCCma, CCSR/NIES, CSIRO, HADCM3 and NCAR-PCM 4.2.1 Preliminary data4.2.1 Preliminary CLIMATE SCENARIOS 95 Climate Scenarios for Perú to 2030 96 Climate Scenarios for Perú to 2030 The characteristics ofthesimulationwere:The characteristics b) a) thepresent study,In three domainswere establishedfor thesimulation: from historical periods: 1983to 2003,andaprojectionfrom 2004to 2035. two in divided 2005), (SENAMHI, Km 60 of resolution horizontal a at domain Peru for model, RAMS the kicenovic and Swart, 2000), with a six-hour frequency, previously processed for the PROCLIM project with (Na- A2 Scenarios corresponding Emission the to format forTheused data arestudy binary this a in files ......

the input data was the scenarios forthe inputdatawasscenarios Peru andaresolution of60km. km, 20 of resolution a with FigureNN, Santa, and Mayo basins: two for out carried domain: Basin 55. Figure km, 60 of resolution a with 2005) (SENAMHI, study PROCLIM forthe designed Peru domain: priate valuesforpriate (SENAMHI(, 2005). thelasthalfofXX century appro with invariable, was model regional the of (SST) temperature surface sea simulation this In were removed from theresults. 2005). This re-initialization would affect the first 1-2 days for each 10 days of simulation; so, the data (SENAMHI,Peru of domain Km 60 the frontier soil and atmospheric an as taking grids, internal the in days 10 every re-initialized were basins), Mayo and Santa the (over 20 x 20 domains nested The model, (temperatureof thelarge-scale to profiles. initialize andhumidity) thesoilhumidity condition initial atmosphere the using i.e.,scheme, similar a using initialized was humidity soil The climate values oftheRAMS model, thatcomefrom amonthlydatabaseatgloballevel. standard the from days 10 every initialized were (SST) data temperature surface sea The integrate theequationsofatmosphere was50seconds. from extended to step1983 time the and 2035 byThe used period RAMSsimulation the tomodel climate signal.is addedto thelarge-scale regional model does not return into the global model and this way small-scale regional information RAMS the from information the which through simulation, regional unidirectional nested a was It 4.2.2 Regional Simulation - * The results ofthe studiesare publishedseparately from thepresent study. VARCLIM 1983-2003. the period climatology ofthemodelduring =Monthly VARSCENR comingfrom valueofthescenario themodel =Monthly 1970-2000 theperiod climatology during observed CLIMOBS =Monthly MAP = Value ofthemodelincludingclimate variability Where: MAP /VARCLIM) =CLIMOBS*(VARSCENR For precipitation inthecostalregion 2007) (Lenderkin, MAP= CLIMOBS+ VARSCENR – VARCLIM) For temperature andprecipitation (Hulmeetal., 2000) withthefollowingclimate variability) relations: seasonal of (incorporation eliminated was model the of bias the then , method interpolation distance inverse the of means by point, station the at interpolated were they basis, monthly a at then and basis tion, for the Peru domain y for the Santa and Mayo basins *. These variables after being averaged at daily Two variables were obtained: extreme temperatures (maximum, minimum) and accumulated precipita- ANALYZED 4.3 FIGURE 55 Area ofPeru andareas oftheSantaandMayo at60Km basins at20Km. Representation ofthesimulationdomainsintopography (meters) variable V ARIA B LES CLIMATE SCENARIOS 97 Climate Scenarios for Perú to 2030 98 Climate Scenarios for Perú to 2030 4.4 SCENARIOS PRO SCENARIOS 4.4 jungle, highcentraljungle, jungle. low centraljungleandsouthern northern south-western mountain, mountain south-eastern and the high plateau. In the jungle there are 4 zones: north-western mountain, mountain, north-eastern central-western mountain, central-eastern mountain, regionmountain the in while there southern, areand central zones: 7 threezones denominated:north, region coastal the for showing region, geographical each per division the show 56 Figure conditions. hydrometeorologicalsimilar show regions which in 2008 SENAMHI, by out carried 56, Figure in is shown that revision climatological regional the using quarterly, and annually made were descriptions The precipitation mapswere anddigitalized. manuallydrafted the while altitude, the with variables these related which used, was interpolation the temperatures me Finally,weremaps the all geographic the using finished information systems, and extre of maps the for the intensity. 1996). (Hyndman, to only respect with changes indicating stations, meteorological the of one each for climatology is obtained for temperature and the 95 percentile, for precipitation to 2030, with respect to the observed climatologicalAdditionally,toits presentednormal.respect percentageis with as this percentile90 the precipitation, of case the in and temperatures extreme for 2030, to anomalies the showing maps Also values, dependingonthevariables. year,the throughoutaccumulated averagesand distributions annual seasonal groupedare in they Also respectively. 2030, and 2020 year the for 2025-2035 and 2015-2025 years the to correspond they and Lu,2000), and (Hulme middle, the at starting decades per shownare scenarios climate the of maps The of South America (report presented (report by America Alves,of South Letal, 2008). part central and region Andean the in ºC 2,5 and ºC 1,0 be will Atlantic South Pacific and the in century the of end the toward temperature of values positive in variations that determine analysis other Thus, for themountainzone ~10ºS(inPeru) passingacross upto Bolivia ~40ºS(inChile/Argentina). ºC >2.5 are temperature in increase projected the Hemisphere Southern the in that indicate to portant glacial retreat; an special attentiondeserves of its potential climate change (Bradley et al., is 2004). im- It existing the areaand the in density, population agriculturepractices its to due zone; intertropical the of of Chile (Bradley et part al.,via and 2006). northern This change in temperature in the mountain regions The increase in maximum temperature is expected to occur in the high mountains of Ecuador, Peru, - Boli A Framework for aproposed IAIinitiative ACCORD/CIMA initiative Members. climate policy (Rauscher et al., et 2008; al., Karmalkar 2008; Urrutia and Vuille 2008, and others) quoted in: effective make to order in makers decision to instruments adequate the provide to necessary are dels ocean-atmosphere arethat toand models hydrologicalcoupled connected eventoand models ecologicalglacial and mo involve that are models climate Range) Regional (Mountain topography. Cordilleraown its American by hindered South seriously the over change climate on projections Detailed 2004). al., et (Miro changes. those better out point do that parameters thermal other in hidden be may that trends, change other trends.However,reflect change not evaluate todoes temperatureitself bymean On the subject about thermal evolution, one often resorts to mean temperature and its behavior in order 4.4.1 Maximum 4.4.1 Maximum Temperature J ECTED 2020 TO ECTED and 2030 - - scenario (pessimist) and between 3 to 5 ºC in the B2 scenario (optimist). 3to (pessimist)andbetween 5ºC intheB2scenario scenario highest warming rate in the Amazon region with an increase in temperature between 4 to 8 ºC in the A2 the be will there 2071-2100, period the within that concludes model, climate global Center Hadley the from data initial using America, forSouth integratedmodels regionalthree numerically used (2007), was there al., et Ambrizzi by made study the in region, Amazon the on impact thermal the to relation In FIGURE 56 Regional climate division(SENAMHI, 2008) CLIMATE SCENARIOS 99 Climate Scenarios for Perú to 2030 100

Climate Scenarios for Perú to 2030 are notsignificant (see Table 4). changes coast southern the in while ºC; 1,2 to 0,4 form range they coast, northern the in occur mainly coastal temperature this Regardingmaximum regionsignificant in most projected2030 changes the to a) Coast results for01,06,11inAppendix3. seeMaps the2020decadeare similar; the values areof variations expressed in terms of ºC, this has 3), only been made for the 2030 decade, since the Appendix in 10 – 01 Maps (see level national at regions by 2030 to 2020 period the for shown Accordingtemperaturestudies,in toaremaximum in changes and Tablesprojections annual 4 and 3 2, analyzed: temperature 90percentile for theyear 2030,theresults are thefollowing: is climate extreme the also and 1983-2003 from climate mean the on based ºW) 68 – ºW 82 ºS, 18 – ºS futureforpresentof 2030 elaboration for– the 2020 includes climatestudy Peruscenarios period the (0 we really at what need ranges toare know the aforementioned values evident;finally for this reason the country, the to important is this although done; been are America temperature overSouth of behavior the of detection the on works many that observed be can it describe, previously been have what For will rangefrom 0,4to 0,8ºCto 2030. region the one with the highest value, up to 1,6 ºC (see Table 5). Regarding the high plateau warming, it sis for the projected changes to 2030 oscillate between 0,4 and 1,6 ºC, being the mountain north-eastern Between 2020 and 2030 maximum temperatures will not show any significant variations, thus, the analy- b) Mountain Northern

Coast Maximum AnnualMaximum Temperature projectedto 2020and2030,thechangeprojected Southern Regiones Central Coast Coast 4.4.1.1 Annual projection North North Top to 2030inrelation thecurrent climate inthecoastal region average to 18,0 -28,0 20,0 -28,0 22,0 -32,0 28,0 -34,0 2020 °C Annual average to 20,0 -28,0 22,0 -28,0 24,0 -32,0 28,0 -34,0 2030 °C Annual Table 4: Projected changes 0,0 -0,4 0,0 -0,4 0,4 -1,2 0,8 -1.2 2030 °C Sama Grande,Calana Pampa Blanca,PuntaColes, Caravelí, Punta Atico, Ica, Ocucaje,Copará, Callao, Cañete Paramonga, Huayán,Lima, Trujillo, Laredo,BuenaVista Esperanza, Chusis,Reque El Salto,RicaPlaya,La locations Main

configuration between the periods 2020and2030shows more theperiods configuration between amplitudein thelatter. Spatial ºC. 34 to 20 between vary will it jungle the in and ºC 28 to 12 between fluctuate will it region coastal region between 20 to 32 ºC and up to 34 ºC in the top northern part of the coast; in the mountain mer up to 1,6 ºC compared to the current climate in almost the whole territory. With some values in the war be will temperature air maximum 2030, to projections annual the for estimation the to According northern and southern jungle(see andsouthern northern Table 6). the projected change to 2030 is 1,6 ºC mainly in the jungle northern and temperatures up to 34 ºC in the changes, significant any show not do temperatures maximum 2030, and 2020 between region Forthis c) Jungle Maximum annualtemperatureMaximum projectedto 2020and2030,theprojectedchange to 2030 Mountain Mountain Mountain Southern Northern

Central Central Jungle Southern Jungle Northern Jungle High Plateau Maximum annualtemperatureMaximum projectedto 2020and2030theprojectedchange Regions Regions 2020 °C Westerb Westerb Westerb Eastern Eastern Eastern High Low to 2030in relation to thecurrent climate inthemountain region

in relation to thecurrent climate inthejungle average to average to 12,0 -18,0 16,0 -24,0 14,0 -18,0 12,0 -24,0 10,0 -22,0 16,0 -26,0 20,0 -32,0 18,0 -28,0 20,0 -30,0 28,0 -32,0 20,0 -34,0 2020 °C 2030 °C Annual Annual average to average to 12,0 -18,0 16,0 -24,0 12,0 -18,0 to 2030°C 12,0 -24,0 12,0 -22,0 16,0 -26,0 20,0 -34,0 18,0 -28,0 20,0 -30,0 28,0 -32,0 20,0 -34,0 2030 °C Annual Annual Table 5 Table 6 to 2030°C Projected Projected changes changes 0,4 -0,8 0,4 -1,2 0,4 -0,8 0,4 -1,2 0,0 -0,4 0,4 -1,6 0,0 -0,8 0,4 -1,2 0,0 -0,8 0,4 -0,8 0,4 -1,6 Mazo Cruz. Capachica, Puno,Sacuce, Ayaviri, Arapa, Huancane, Cuyo Cuyo,Progreso,Muñani, Kayra,Chalhuanca, Cusco. Curahuasi, Granja Yacango, Candarave,Tarata. Arequipa, ElFraile,Omate, Cotahuasi, Chuquibamba, Puquio, Coracora,Orcopampa, Huánuco, Tarma, Acobamba. Huancayo, Marcapomacocha. Santiago deChocorvos, Huarochiri, Cusicancha, Sihuas, Recuay,Oyon, Cajabamba, Celendín. Weberbauer, SanMarcos, Contumazá. Cachicadan, Callancas, San Pablo,Llapa,Salpo, Gabán, Tambopata. Puerto Maldonado,San Merced, Tingo María. Oxapampa, Satipo,La Pucallpa. San Ramón. Santa MaríadeNanay, locations locations Main Main CLIMATE SCENARIOS

- 101 Climate Scenarios for Perú to 2030 102

Climate Scenarios for Perú to 2030 Appendix 3andtable7). 15 – 12 maps (See (DJF). time summer the in changes significant any Conversely,not are there north). (top coast northern the in specially respectively; ºC 1,6 – 1,2 and ºC 2,0 – 1,2 from values with (SON), time spring and (JJA) time winter the in variation intense more show would Peruviancoast, the in 2030 and 2020 to basis seasonal a temperatureon projectedmaximum of values and distribution spatial The a) Coast jungle region are 12to show 15,Appendix3andin inMaps Tables 5,6and7atseasonallevel. Maximum temperatures to 2020-2030 and their anomalies projected to 2030 in the coast, mountain and the summertime(DJF) withvaluesthatreach 1,2ºC,see Table 9. during jungle low central the forstressed,except very not are variations jungle, central the In ºC. 1,6 to the southern jungle, the periods with larger variations are the winter and spring, similarly, with values up forjungle seasons,other the showForto up values ºC. projected 1,6 variations areathis northern the of jungle, with values up to 2,4 ºC, which agree with the studies made by Ambrizzi et al., (2007). Similarly, in northern the in mainly (SON), spring the during observed are variations intense most the jungle the In 9 Jungle significant, plateau, except are theHigh variation notvery In for autumnwithvaluesthat reach 1,2ºC. mountainregionmountain region winter. during andcentral-eastern north-eastern the in and autumn in region mountain south-eastern the in mainly ºC, 1,6 to up seasons, Accordingto in analysis Tablelargestarethe 8, variations shown for (MAM)autumn the winterand (JJA) b) Mountain

Coast North Maximum seasonaltemperatureMaximum projectedto 2020and2030,thechangeprojected Southern Central Coast Coast Regions Top North North

4.4.1.2 Seasonal projections 4.4.1.2 Seasonal 2020 2020 2020 2030 2030 2030 2020 2030 to 2030in relation to thecurrent climate inthecoast (ºC)

Average 26 -34 28 -34 22 -28 26 -32 28 -32 22 -30 22 -32 22 -34 Summer -0,4 -0,4 -0,4 -0,0 -0,4 -0,8 0,0 -0,8 r Average 26 -32 26 -32 20 -28 26 -30 26 -32 20 -30 20 -30 20 -32 Table 7: Autumn

-1,2 -0,4 -0,8 -0,0 0,0 -0,8 0,0 -0,8 r Average 24 -28 24 -30 24 -30 18 -26 26 -32 18 -26 20 -26 20 -26 Winter

-0,4 -0,4 0,4 -2,0 1,2 -2,0 0,0 -0,4 r Average 22 -34 26 -34 22 -34 22 -30 28 -34 20 -28 22 -28 22 -28 Spring

-0,4 -0,0 0,4 -1,6 1,2 -1,6 0,0 -0,8 r 2030 atannualandseasonallevel. to projected changes the as well as temperature, minimum for analyzed are 2030, and 2020 to NAMHI) the presentIn item, the projections, resulting form the dynamical downscaling CCSM (NCAR)-RAMS (SE- ters: temperature andprecipitation; butnotonminimumtemperature. Studies of future climate changes at regional scale in South America have been focused on two parame andsouth-western mountainregion (south-eastern andthehighplateau). national territory the areaof southern the daysarein increasing specially warm forvery projectedperiod,that the means it and days warm very to associated is 2030. yearindex forThisthe shown, temperatureis maximum of percentile 90 the of variation index, climate extreme the 3, Appendix of 16 map in Accordingstudies to Mountain Mountain Mountain Southern Northern Table seasonaltemperature 9:Maximum projectedto 2020and2030,theprojectedchan- Central Jungle Southern Jungle Central

Northern Jungle High Plateau Table seasonaltemperature 8.Maximum projectedto 2020and2030,theprojected Regions Regions 4.4.2 Minimum 4.4.2 Minimum Temperature 4.4.1.3 Projection to 2030ofthe90percentile ofmaximum temperature Western Eastern High Western Low Eastern Western Eastern

change to 2030inrelation to thecurrent climate inthemountain (ºC)

ge to 2030inrelation to thecurrent climate inthejungle(ºC) 2020 2020 2020 2020 2030 2030 2020 2020 2030 2020 2030 2030 2020 2030 2030 2020 2030 2020 2030 2020 2030 2030

Average Average 26 -34 16 -32 14 -24 30 -32 26 -36 16 -32 24 -30 10 -24 14 -26 28 -32 30 -34 24 -32 12 -24 12 -24 30 -34 10 -22 10 -24 12 -22 10 -22 10 -18 20 -22 10 -18 Summer Summer -0,4 -0,8 0,8 -1,6 0,4 -0,8 0,4 -1,2 0,8 -1,2 0,4 -0,8 0,0 -0,8 0,4 -1,2 0,4 -0,8 0,4 -0,8 0,4 -0,8 r r Average Average 24 -32 16 -30 14 -24 28 -32 24 -34 18 -30 24 -30 10 -22 16 -24 22 -32 28 -34 24 -30 14 -20 10 -22 22 -32 12 -20 14 -22 14 -20 12 -20 12 -18 14 -20 12 -18 Autumn Autumn

-0,4 -0,4 -0,8 -0,0 -0,4 -0,0 -0,8 -0,0 0,4 -1,6 0,4 -1,2 0,4 -1,2 0,0 -0,8 0,4 -1,2 0,8 -1,6 0,4 -1,2 r r Average Average 24 -32 18 -24 16 -26 18 -26 28 -32 24 -32 22 -30 10 -20 16 -26 28 -32 22 -32 22 -30 12 -20 10 -20 22 -32 10 -18 12 -22 14 -18 10 -18 10 -16 14 -20 12 -16 Winter Winter

-1,2 -1,6 0,8 -1,2 0,4 -1,6 0,0 -0,4 0,4 -0,8 0,4 -0,8 0,0 -1,6 0,8 -1,6 0,4 -1,2 0,0 -0,8 0,4 -0,8 r r Average Average 26 -34 20 -26 16 -26 20 -26 30 -34 28 -36 28 -34 10 -20 16 -26 30 -36 22 -34 28 -34 14 -22 10 -20 22 -34 12 -18 14 -22 14 -20 14 -18 12 -18 14 -22 14 -18 CLIMATE SCENARIOS Spring Spring

0,8 -1,2 0,4 -2,4 0,4 -1,2 1,2 -2,0 0,0 -1,2 0,0 -0,8 0,4 -1,6 0,0 -1,2 0,0 -0,8 0,0 -1,2 0,0 -0,4 r r - 103 Climate Scenarios for Perú to 2030 104

Climate Scenarios for Perú to 2030 (see maps17,22,27inAppendix3and Table 10). significant too not are changes south, the in While ºC. 1,6 to 0.8 from range to , region coast northern To 2020 and 2030 changes in the distribution of minimum temperature are projected, for the central and a) Coast thehighplateau temperatures2020. In willincrease upto 0,82ºCwithrespectto 2020. cover more spatial amplitude. In the coast, the area for 20 to 22 ºC will show a reduction, with respect to Also, to 2030inthejungleregion, itisprojected thatthearea ofminimumtemperatures 22to 24ºCwill south Andeanregion (Ayacucho, Abancay). the of part and Huancavelica) Huancayo, Pasco, de (Cerro region central Iquitos) of east and Chiclayo (Piura, jungle northern and coast the in mainly ºC, 1,4 and 0,4 between climate current the to respect The results show that minimum temperature of air near to surface 2030 will increase in the country, with - 0,39 ºC/decade between 1951and1999. - 0,39ºC/decadebetween that in the central region of Peru (9º - 11 ºS) the increasing trend of mean temperature is at a ratio of 0,35 altitude,lowhigh at and stations 29 observed on based who a), Seltzer(2005 and Mark and (2002) Mark most increaseimportant occurred at the middle of the 70’s decade between 0,32 – 0,34 ºC/decade. Also the 1939, years,from sixty last the in ºC/decade 0,11 – 0.10 between surface to temperatureclose air of increase significant a observed Andes) (tropical ºS 23 and N 1º between station 268 of recollection the be consistent with the current trend and with what Vuille and Bradley investigated (2000) who based on On the other hand, the increase projected for maximum and minimum temperatures, on average, would rature would be2,5ºCintheAndeanregion. towards theendofcentury tent as it was bydescribed Alves, L et al., (2008) where it is determined that positive in variations tempe The projection of the increase of minimum and maximum temperature in the mountain region is consis- in Appendix3and Table 11). western region and mainly in the central Andean region, with respect to the current climate (see map 27 dix 3, and Table 20). To 2030, the sustained increase of minimum temperatures will be up to 1,22 ºC in the central western and southern region of the country, and in the high plateau (see maps, the 17, 22 in Appen- in mainly 2020, to relation in warmer slightly be to projected are 2030 to temperatures Minimum b) Mountainregion Northern Minimum annualtemperatureMinimum projectedto 2020and2030,theprojectedchangeto 2030

Coast Southern Central Regions Coast Coast 2020 °C 4.4.2.1 Annual projection North North Top average to 10,0 -18,0 10,0 -20,0 12,0 -22,0 16,0 -22,0 in relation to thecurrent climate inthecoast 2030 °C Annual average to 10,0 -18,0 12,0 -18,0 14,0 -20,0 16,0 -24,0 2030 °C Annual Table 10 changes to Projected 0,0 -0,4 0,4 -0,8 0,4 -1,2 1,2 -1.6 Sama Grande,Calana. Pampa Blanca,PuntaColes, Caravelí, Punta Atico, Ica, Ocucaje,Copará, Callao, Cañete. Paramonga, Huayán,Lima, Trujillo, Laredo,BuenaVista. Esperanza, Chusis,Reque. El Salto,RicaPlaya,La locations Main

- anomalies ofupto 1,6(seemap27Appendix3and Table 12). with jungle, northern the in observed are climate current to respect with changes important most The jungle(seemaps17,22Appendix3and mainly inthenorthern Table 10). 2030, to ºC 24 to 22 and 2020 to ºC 22 to 20 from values temperature an of wider much amplitude spatial project they but increase, significant any show not will 2030 and 2020 to temperatures Minimum c) Jungle Minimum annualtemperatureMinimum projectedto 2020and2030theprojectedchangeto 2030, Mountain Mountain Mountain Southern Northern

Central Central Region Region Region Jungle Southern Jungle High Plateau Selva Norte Minimum annualtemperatureMinimum projectedto 2020and2030projectedchange Regions Regions Westernl Westernl Westernl Eastern Eastern Eastern High Low in relation to thecurrent climate inthemountain region

to 2030inrelation to current climate intheJungle -10,0 -10,0 average to average to 18,0 -20,0 16,0 -22,0 18,0 -22,0 18,0 -24,0 -8,0 -12,0 -2,0 -12,0 14,0 -4,0 0,0 -14,0 4,0 -16,0 2,0 -14,0 2020 °C 2020 °C Annual Annual -12,0 -12,0 average to average to 18,0 -22,0 18,0 -24,0 18,0 -22,0 20,0 -24,0 -6,0 -10,0 -2,0 -12,0 14,0 -6,0 2,0 -12,0 6,0 -16,0 4,0 -14,0 2030 °C 2030 °C Annual Annual Table 12 Table 11 changes to changes to Projected Projected 2030 °C 2030 °C 0,4 -0,8 0,4 -0,8 0,4 -1,2 0,4 -1,2 0,4 -1,2 0,4 -0,8 0,0 -0,8 0,4 -1,2 0,0 -0,8 0,4 -0,8 0,4 -1,6 Mazo Cruz. Capachica, Puno,Sacuce, Ayaviri, Arapa, Huancané, Cuyo Cuyo,Progreso,Muñani, Kayra,Chalhuanca, Cusco. Curahuasi, Granja Yacango, Candarave, Tarata. Arequipa, ElFraile,Omate, Cotahuasi, Chuquibamba, Puquio, Coracora,Orcopampa, Huánuco, Tarma, Acobamba. Santiago deChocorvos. Huarochiri, Cusicancha, Sihuas, Recuay, Oyón, Cajabamba, Celendín. Weberbauer, SanMarcos, Contumazá. Cachicadan, Callancas, San Pablo,Llapa,Salpo, Gabán, Tambopata. Puerto Maldonado,San Merced, Tingo María. Oxapampa, Satipo,La Pucallpa. San Ramón. Santa MaríadeNanay, locations locations Main Main CLIMATE SCENARIOS

105 Climate Scenarios for Perú to 2030 106

Climate Scenarios for Perú to 2030 23-25 and28-31inAppendix3 Table 13). perature will range between values of - 0,8 and 0.8 ºC, in relation to the current climate (see maps 18-21, and southern coast, the increase would be around 0,4 ºC. In the summer, the changes in minimum tem - central the in spring in While climate. current the above ºC 2 to up winter, and autumn in coast thern nor the in observed be will temperature, minimum in changes important most the 2030, year Tothe a) Coast south ofAyacucho). Abancayof and Moyobamba,of region, mountain central Cusco north-east and north-east coast, the of part northern (top ºC 1,6 to up summer the in and Iquitos) of east and Martin San of north-west and yo bote and of north-west Iquitos. While in the spring, temperature would reach up to 1,2 ºC (Piura, Chicla- Chiclayo,in Chim- climate,mainly current to respect with ºC, 2 to up reachingincreases projected,with are temperature minimum in changes important most the 2030 to seasons, winter and autumn the In oscillation (seemaps, 18-21,23-25and28-31inAppendix3 Table 14). thermal daily strong a have that regions MASL, 4000 than higher zones the in frosts,meteorological of diminish the in reflected be could increase this cloudiness; more showing skies to attributed probably be would and increase would temperatures region), Andean the in season (dry winter, the In regions. gion, that showingwould more be associated with cloudiness,skies rainfall but in not these necessarily re mountain southern and central the in observed, be would ºC 1,6 to up of summer,increases the In and south-westernºC and1,2inthehighplateau, mountainregions. central-eastern mountain region where it will reach up to 1,6 ºC. While in spring, the increases will be lower between 0,4 south-western and north-western the plateau, high the for Except basis. seasonal on values current to of the Andean region, most part in autumn, minimum temperatureIn will increase up to 2 ºC in relation b) Mountainregion Minimum seasonaltemperatureMinimum projectedto 2020and2030theprojectedchangeto 2030

Coast North Southern Central Coast Coast Regions 4.4.2.2 Seasonal Projection 4.4.2.2 Seasonal Top North North

2020 2020 2020 2030 2030 2030 2020 2030

Average 16 -22 16 -24 16 -20 16 -22 16 -24 16 -22 10 -18 10 -18 in relation to current climate inthecoast (ºC) Summer -0,4 -1,2 -0,8 -0,8 0,8 -1,6 0,0 -0,8 r(°C) Average Table 13 12 -22 16 -24 12 -20 12 -20 20 -24 12 -18 10 -20 08 -18 Autumn

0,8 -1,6 1,2 -2,0 0,4 -1,2 0,0 -1,2 r(°C) Average 12 -20 14 -22 10 -20 12 -18 16 -22 08 -18 08 -16 08 -16 Winter

1,2 -1,6 0,8 -2,0 0,8 -2,0 0,4 -1,2 r(°C) Average 12 -22 16 -22 12 -20 08 -18 16 -22 10 -18 10 -18 08 -18 Spring

0,4 -1,2 0,4 -1,6 0,0 -0,4 0,0 -0,4 r(°C) - - 25 and28-31inAppendix3, Table 15. 23- 18-21, maps (see jungle. the in rainfall necessarily not and cloudiness more with skies to associated be could temperature in increases These ºC. 1,6 and 0,4 between increase would increases the region, the of part remaining the In values. current to relation in ºC 2,0 to up country, the of jungle northern the in mainly 2030, to winter and autumn in increase would temperatures minimum level, seasonal At c) Jungle mum temperatures higher than percentile 90% and means that by 2030, warmer nights would show a show would nights warmer 2030, by that means and 90% percentile than higher temperatures mum mini- with nights warm of variation the projects index this 2030, for temperature minimum for 90 tile According to examinations, map 32 Appendix 34, shows the extreme climate index, of variation percen-

Mountain Mountain Mountain Southern Central Jungle Northern Southenr Jungle Central

Region Region Region Northern Jungle Minimum seasontemperatureMinimum projectedto 2020and2030projectedchangeto 2030in Minimum seasonaltemperatureMinimum projectedto 2020and2030,theprojectedchange Altiplano Regions Regions 4.4.2.3 Projection to 2030ofthe90percentile forminimum temperature Western High Eastern Low Western Eastern Eastern Western

to 2030in relation to thecurrent climate inthemountain region (ºC) 2020 2020 2030 2030 2020 2020 2020 2020 2030 2030 2030 2030 2020 2020 2030 2030 2030 2020 2030 2020 2030 2020

Average Average relation to thecurrent climate inthejungle(ºC) 16 -24 16 -24 18 -20 16 -20 16 -22 16 -22 16 -22 16 -22 -12 -8 -12 -8 -2 -12 -2 -12 -4 -12 -4 -12 6 -20 6 -20 2 -18 2 -16 0 -12 0 -10 0 -12 2 -12 Summer Summer -0,4 -1,6 0,4 -1,6 0,4 -1,2 0,0 -0,8 0,4 -0,8 0,4 -0,8 0,0 -0,4 0,4 -0,8 0,4 -1,6 0,4 -1,6 0,0 -1,6 r(°C) r(°C) Average Average Table 15: Table 14 20 -24 16 -24 16 -24 16 -22 16 -22 18 -22 16 -22 16 -22 -14 -4 -14 -4 6 -20 6 -20 2 -24 2 -24 2 -12 2 -12 2 -12 4 -12 6 -12 2 -12 2 -12 6 -12 Autumn Autumn

0,8 -2,0 1,2 -1,6 0,4 -1,2 0,4 -1,6 1,2 -2,0 1,2 -2,0 0,4 -0,8 1,2 -1,6 0,8 -2,0 0,4 -1,6 0,8 -2,0 r(°C) r(°C) Average Average -10 -10 -12 -12 14 -24 16 -24 14 -22 14 -22 14 -20 14 -22 10 -18 10 -18 -4 -12 -4 -12 -14 -6 -8 -10 -14 -8 -2 -12 -14 -0 2 -16 2 -16 0 -22 0 -24 2 -12 Winter Winter

0,0 -2,0 0,4 -1,2 0,8 -1,6 0,4 -1,2 0,4 -1,6 0,4 -1,6 0,4 -0,8 0,4 -1,2 0,4 -1,2 0,4 -1,2 0 -0,8 r(°C) r(°C) Average Average -10 -12 20 -24 20 -26 18 -22 16 -22 16 -22 18 -22 16 -22 14 -22 -4 -12 -2 -12 -14 -8 -4 -12 -8 -10 -14 -2 -4 -12 2 -16 8 -18 0 -22 2 -24 0 -12 2 -12 CLIMATE SCENARIOS Spring Spring

0,4 -1,6 0,4 -1,2 0,4 -0,8 0,0 -0,8 0,0 -1,2 0,0 -1,2 0,4 -1,2 0,0 -0,8 0,0 -0,4 0,4 -1,2 0,0 -0,8 r(°C) r(°C) 107 Climate Scenarios for Perú to 2030 108

Climate Scenarios for Perú to 2030 The description and variation to 2030,perzones andvariation The description inthecoast,are shown in Table 16. of above average intheDepartments Tumbes andPiura. since precipitations in the Peruvian coast are normally very low. It is estimated and increase of up to 20% number,in large too not are and Tacna.shortages These Libertad La between 30% to 10% about from shortages be would there that show region, this in 2030, to precipitations of percentage in Variations Lambayeque andPiura.La Libertad, While in Tumbes itwould have rainfallover 200mm/year. 10mm/year between Ancash and Moquegua, 50 mm/year in Tacna and between 10 and 100 mm/year in The coast would continue to be mostly a with dessert rainfall values that in both decades do not exceed a) Coast onthisarea.impacts the mountain region, which would be probably associated to a of less that humidity transportation only in mostly decreases and jungle, the of part eastern the in occurring increases register scenarios These showsimilar perspective theregionalized withtheRAMS model. scenarios precipitations; in increases the also and presentare models the Peru,all where coast, northern of the forexcept regions jungle and mountain the in rainfall of distribution the about configuration common a not is there but values, similar some observed also is it models, global about chapter previous the in of +-0,2 mm/day, representing approximately or increases. up to the analysis made 15% of shortages In global models, whose average of the 6 global models for the period 2010-2040 show anomalies for Peru gions of the country, these are similar to the ones obtained by Marengo, 2007 about South America with The scenarios projected to 2030 show values between +10% and -10% over the mountain and jungle re inpercentageslies orvariations for theyear 2030,withrespectto theirclimatological average. climatology, and in their both intensity.in their distribution This, it has only been estimated the anoma- their to similar very behavior a have would precipitations that estimated been has it decades, both For inalltheregions.and shortages marize in one line, the distribution of rainfall at national level; but also on the that fact it shows increases sum- to difficult very topography,is on it reasononly which fornot depending distribution a show also Futurescenarios topography. to associated behavior,mostly complex very a shows rainfall present, At 4.4.3 Precipitation nights. currentincreasing the trend with temperature warm minimum of in positiveconfirmed changes of and projections the with regionconsistent this increase,being sustainably regionwould Andean the in and regionalized behavior in the country. This, in most part of the coast warm nights would tend to decrease 4.4.3.1 Annual projection - The description and variation to 2030perzones andvariation The description inthejungle are shown in Table 18. of Loreto andtheprovinces of Padre AbadandCoronel Portillointhecentraljungle. up shortages to 10% in most of part the of Department San Huánuco,Martin, western and part northern Cusco.of Also, some therebe will Department the with boundaries Maldonado,the in Puerto of jungle of the of Department San and Martin southeastern zone of Loreto, Increases that exceed 10% in the high Rioja and Moyobamba provincesof jungle, the of area central the of part jungle, southern the in to10% increaseup and projected is it 43, Nº map 2030, yearpercentagesfor the in Withregardvariation the to climatological average. nucleus of similar value in the southern zone of Ucayali. This last nucleus would show values above their of Iquitos 3 000 mm/year part over the jungle overof Huánuco and Ucayali the north-eastern and other mm/year 500 3 and Caynarachi) de (Pongo Loreto and Martin San of Departments the of boundaries overthe mm/year 000 4 Gabán), San Punoand in area (jungle mm/year 500 5 with jungle southern the in located be would quantities annual Maximum mm/year. 500 5 to 000 1 amount will Precipitations tively. These mapsindicate thatthenucleusofmaximumrainfallwould continueinthisregion. The total rainfallperyear representing theyears 2020and2030,isshown inthe maps 33and38respec- c) Jungle to 2030 perzones andvariation The description inthemountainregion are shown intable17. (Huanuco andPasco). regionmountain central-eastern of part (Ayacucho,northern zone Apurimac) and south-eastern the in 20% 10% between shortages largest the (Cajamarca), region mountain north-eastern he in be would 10% to up of Shortages and Tacna). Moquegua (Arequipa, region mountain south-western the of part (Lima, Ica and Huancavelica); of southern part the mountain central-eastern region and (Junin) southern central-westernthe of region mountain region,mountain part southern north-western projectedthe in are values normal above 10% of Increases increases. some as well as shortages some be will there ral, gene in trend; similar a show not does 2030 year the for precipitations of percentage the in Variation would 200and1000mm/year. amountbetween nes in Piura and Lambayeque, where rainfall would amount 1 000 mm/year. the eastern slope rainfall In of the most westernpart slope, rainfall will amount between 50 to 200 mm/year, except for the high zo climate averages, i.e., more rainfall in the zones located in the eastern slope than in the western slope. In it as behavior same the with continue precipitationwill that observed be can it 38 and 33 maps Also,in b) Mountain region Table 16:Annual accumulated precipitation projectedto 2030,andvariation inpercentage to

Southern Coast Northern Coast Central Coast Regions 2030 inrelation to thecurrent climate inthecoastal region

annually to2030 PP accumulated (mm/year) 5 -200 5 -50 5 -50

Between +10and+20% Projected changes to 2030(Variation in percentage%) Up to-20% Up to-30% - 10% Ica and Arequipa All theregion of PiuraandLaLibertad Most partofthenorthern locations Main CLIMATE SCENARIOS

- - 109 Climate Scenarios for Perú to 2030 110

Climate Scenarios for Perú to 2030 level are theonesundernumbers 19,20and21respectively. regionsjungle regardand with mountain forseasonal toprecipitationcoast, tables at the summary The inboth decades. andinthe spatialdistribution quarters tion in percentage for the year 2030, because it a lot of similarity in the total amounts for each one of the Precipitation for each one of the seasons will be shown for Peru to the 2020 and 2030 and only the - varia Table 17.Annual accumulated precipitation projectedto 2030,andvariation inpercentage to Table 18.Annual accumulated precipitation projectedto 2030,andvariation inpercentage to Mountain Mountain Mountain Southern Northern

Central Central Region Region Region Jungle Northern Jungle Southern Jungle High Plateau Regions Regions 4.4.3.2. Seasonal projection 4.4.3.2. Seasonal Western Western Western the year 2030inrelation to thecurrent climate inthemountain region Eastern Eastern Eastern the year 2030enrelation to thecurrent climate inthejungleregion High Low

annually to2030 annually to2030 PP accumulated PP accumulated 2 000a3 1 000a4 1 500a5 2 000a3 (mm/year) (mm/year) 500 -1000 200 -1000 500 -1000 500 -1000 500 -1000 100 -1000 100 -500

Between +10and10% Projected changes Projected changes to 2030(Variation to 2030(Variation in percentage%) in percentage%) + 10%y20% Up to+10% Up to-20% Up to-20% Up to+20% Up to-10% Up to20% + 10% + 20% + 10% + 10% + 10% - 10% - 20% - 20% - 10% - 10% - 10% Cusco jungle, MadredeDiosand Most partofthesouthern Pasco andJunin Coronel PortilloinUcayali, provinces ofPadre Abad and Northern part:Huánucoand Ucayali Region. Eastern part Western part Northern partoftheLake Titicaca Lake Southwestern partofthe Apurímac andpartofCusco. and Tacna Southern part:Moquegua Arequipa. Northern part: Ayacucho, Junín andHuancavelica. Huánuco, Pasco. and Huancavelica. Southern Part:Junin,Lima Lima andPasco Northern part(Ancash- Over thewesternzone Over theeasternzone All theregion locations locations Main Main

jungle c) b) Mountainregion a) Coast go 2007,withtheHADCM3modelfor 2010-2040. theperiod impacting over the central and southern jungle in Peru, with a lot of rainfall. Similar results found Maren- inorderamount ofhumidity to getsaturated andoriginate rainfall, astheSACZ would bemore intense, region,mountain the specially country,includes largerthe that a of necessar is it part most in shortages decade,for2030 that the have determined they but climatologicalvalues; their to amounts similar with Also, level. national at decades both in similar very be to projected are 2030 and 2020 in Precipitations is shown ismap44. 2030 to percentage in variation while 39, and 34 maps in shown are 2030 and 2020 Futureto scenarios national level. at significant is summer the in Precipitation etc.). humidity, of transport the to associated winds trade High, Bolivian the Zone, Convergence Intertropical the as such ( rainfall generate that systems pheric atmos- diverse the of interaction the to due amounts, largest the totalize precipitations quarter this In rabaya and Sandia). Another nucleus of 1 500 mm/quarter, will also occur en the northeaster part of in quarter the boundaries of Madre de Dios and Puno (province of Tambopata and provinces mm/ of Ca- 300 2 to up of nucleus a way, this region, this over occur to continue will rainfall of nucleus Maximum decades. both for 300mm/quarter 2 and 200 between vary rainfall total quarter this In be would Shortages Piura. in Ayabaca of up to ofAncash. 20%over thesouthwestern slopeandtheDepartment province the as well as 20% to 10 between increase an show would that locations (Lima), Yauyosof province the and Huancavelica and Junin of Departments the for except region, this in shortages shows 2030 to percentage in variation The (Ayacucho) Sucre Vilcashuaman, between and and Andahuaylas withrainfallexceeding (Apurimac) 500mm/quarter. (Junin) Yauli (Piura), Ayabaca of show provinces the they over located general, rainfall, maximum of in nucleus three shows It similar, mm/quarter. 500 to 100 very between ring are quarter this in 2030 amounts that vary according to and the altitudinal layers, especially over 2020 the western slope, registe to precipitation Total coast, while in the northern coast, only part of the Department of Piura and Tumbes show show Tumbes and Piura of 2 to 10 Department between shortages the show Talara and of Paita of provinces part the while 10%, only southern to up and increases coast, central the northern in the 20% in to 10 while between coast, deficiencies show percentages in Variations intensity and frequency the in increase to of rainfall. contributing be would that coast, the near Tumbes, SST and the Piura over in especially increase an to associated coast, be would results These averages. their overnorthern values shows he which in mm/quarter 200 to 50 between and te similar values to its climatology, i.e., 5 between to 10mm for the coast,central and southern coast northern the in only precipitations amount between 50 and 100 mm/quarter.scarce, Projections to 2020 and 2030 indica- normally are coast central and southern the in Precipitations 0%. 4.4.4 Summer: Quarter December –February 4.4.4 Summer:Quarter CLIMATE SCENARIOS - 111 Climate Scenarios for Perú to 2030 112

Climate Scenarios for Perú to 2030 jungle c) b) a) coast,associated to theincrease ofSST.rainfall inthenorthern located south, further which generates instability and advection be of moist air. will Also, there Zoneis an increase of Covergence Intertropical the because maybe country, the of part most in precipitations in increase an indicate Scenarios region. mountain the in rainfall of amount less and region, jungle the in concentrate rainfall intense most the that climatology,i.e., their to similar values show decades Both vely. inpercentage 45shows thevariation Map for thisquarter. respecti- 2030, for2020and 40 decades and the 35 aremaps forseason shownin autumn the Scenarios March –May4.4.5. Autumn: Quarter vinces of San Martin, that will show some shortages ofupto 10%. thatwillshow someshortages vinces ofSanMartin, pro region,eastern the in as whole well Loreto;as and Mayothe Alto of provinces the in between zone the for10% except to up of increase and show will precipitations 2030, to quarter this In willbelocated. mm/quarter the boundaries with San and Martin the province of Maynas; of northeast Iquitos a nucleus of 1 000 Dios; rainfall will show a nucleus of up to 1 300 mm/quarter, over the province of Alto Amazonas, in provincePunothe of of and department Tambopataregionthe jungle in the includes de Madre in that jungle,front-forest southern the over thus, quarter; previous the of zones the in values lower between 500 and 1 000 mm/quarter. Also, the nucleus of maximum rainfall continue to occur, with amount will rainfall which this in 2030 for and 2020 years important the for projected be is behavior same to This region. continue will rainfall climatology, the to relation in quarter, this During 50tobetween 200mm/quarter, withhigher valuesover slope. theeastern range will region the all in rainfall region, the all In mm/quarter. 500 reach only will they 2020 the in while mm/quarter, 700 of rainfall show projections decade 2030 the In decade. 2020 the that except for the northwester mountain region (province of Ayabaca). The 2030 decade will be rainier To the year 2020 and 2030, rainfall will occur very similar in amount and distribution in all this region, Mountain 5towill bebetween 200mm/quarter. zone, coast will only showthe northern a large amount of rainfall. Total accumulated in this quarter Scenarios agree with the climatology, that is to say, less amount of rainfall in the central and southern Coast moderate, exceeding 10%. and especially in the provinces of Huallaga, Cáceres,Mariscal Bellavista and Picota will shortages be averages that represent a decade, which is really significant, especially theIn shortages. San Martin are projections these but period, rainfall the during variability normal the of part as considered be could shortages and increasesregion. These the of part remainder the in 10% exceed not do that shortages some and jungle) Loretoin province Maynas the of (northern of par northern the in and defined trend, it does show some increases that do not exceed 10%, especially in the central jungle a show not regiondoes the that observed be can it 44, percentage,map in variation jungle, the In and theprovince ofCoronel PortilloinUcayali. will quarter be located over the province of Alto Amazonas in Loreto, in the province of Padre Abad the of Department Loreto, in the province of Maynas and Caballococha. Also, nucleus of 1 000mm/ - R a) the year 2030. decades of 2020 and the 2030, respectively, while map for46 shows the variation in percentage with respect to quarterly accumulated precipitations that show 41, and 36 maps investigations, to According increase inrainfallover thiszone. an to leads which Peru, of part southern the affect will which ocean, Pacific the in warming increased a of because frequent more be will that (trough) instability atmosphere mean the of interactions with fronts of entrance the to associated mainly be will context this in Rainfall summer. the in than amount pitation in the coast, small amounts in the mountain and significant rainfall in the jungle region; but less Future scenarios for both decades have represented the climatology of the country, with very little preci- jungle c) b) Coast occur inthecentralcoast. will Shortages and Tacna).(Arequipa coast southern the in less and coast northern the in decade 2030 the for 30% to up of increase significant a be will there that indicate percentage Variationsin the increases willoccurinthe highforest.andincreases -10%to willbebetween +10%. Shortages jungle, northern while in the central and will southern shortages occur part in the lower jungle and The variations in the percentage of rainfall for this showquarter some in shortages most of part the 200 mm/quarter, thehighestvalueswillberegistered inthehighforest. Over the central and southern jungle, rainfall will be more uniform, showing values between 100 to oftheprovince ofLams (SanMartin). part and over thenorthern (Loreto) Maynas of province the in jungle northern the jungle, front-forest southern the in occur will rainfall maximum mm/quarter, 700 to 100 between values show will decades both in Rainfall highest valuesinthewestern slope. the showing 30%, to 10 between be will Shortages increases. localized with alternated is bution - distri spatial the region, southern and central the in while region, mountain northern the in cially deficiencies, espe some show will 2030 precipitationto percentageof in variations general,the In lowest valueswillberegistered alongthewestern slope. the mm/quarter, 50 to 5 between oscillating be will amounts The quarters. previous the to pect res- with values lower with uniform more be will rainfall that 46) and 41 (maps show decades The Mountain region small amounts, values. fordecrease inrainfallshows whichreason highshortage acertain values corresponding to areshortages not intense, since their normal values for this period are very the in especially 40%, to coast (provinces of and Sechura Paita)northern and central coast 10 (from Trujillo down to Ica). between These be will general, in shortages, distribution spatial this With values are slightlylower behavior. thanitsnormal ainfall in the decades 2020 and 2030 will show amounts of up to 5 mm/quarter in all the coast; the 4.4.6. Winter: June–August Quarter CLIMATE SCENARIOS - 113 Climate Scenarios for Perú to 2030 114

Climate Scenarios for Perú to 2030 jungle c) b) a) inpercentagesAnomalies orvariations to 2030are shown inmap47. 2020 ad2030,respectively. years the in quarter spring the for rainfall accumulated represent which 42, and 37 maps in shown rios increase both in and frequency intensity in the jungle. Similar spatial is distribution shown in the scena - will rainfall and mountain the in especially climate; regardto with period, rainy the starts quarter this In eastern zoneeastern jungle. willbeupto ofthesouthern Increases 10%mainlyover thecentraljungle. part western the over 10% to up Variation in percentage for the year 2030, in this region, are not uniform, they show some shortages central jungle, rainfallwillbeunder itsaverage. the in decade 2020 the in that assumed is It 2030. year the in occur will that ones the than lower values are which mm/quarter, 700 amount will rainfall forest, central high the in 2020 year the In mm/quarter. 200 1 and 200 between are projections rainfall average, On (Loreto). Maynas, in as well Padreas and Abad) Prado Leoncio of (provinces jungle central the in mm/quarter 800 Lamas), and zonas Ama- Alto of (province jungle northern the in of (province Tambopata), mm/quarter quarter 000 1 mm/ 200 1 with jungle front-forest southern the in located be will precipitation of nucleus mum maxi - The season. winter the to respect with increase will 2030 and 2020 decades both in Rainfall associated to theorography oftheregion. -20to rangebetween +30%. The variations and localized be will it that and rainfall or distribution irregular an indicates way,which alternated Variations in percentage to 2030 in this region are not uniform, increases occur and in shortages an jungle.and centraleastern Precipitationsnorthern the toin 5 mm/quarter.occur 300will values between amount will Higher the to associated entrance ordirectexposureamounts to moisture over theslopes(orographic rainfall). higher amount will rainfall slope eastern the in while slope, western the in precipitations of amount less showing region, the all in similar are decades both for Rainfall Mountain region ge values. Piura and Chiclayo with values of -30%. Tumbes will show some increases up to 30% over its avera- of part southern the in located be will shortages coast, northern the In Ica. over -20% register will that shortages some show will coast southern and central the in 2030 percentagestoVariations in a rainy decadeinthiszone in relation to the2020decade. during 2030 will show higher values of rainfall that will reach up to 10 mm/quarter, which indicates below its average values, during the decades 2020 and 2030. The northern coast, especially Tumbes Rainfall in this will quarter be between 0 to 5 in mm/quarter most of part the coast, showing values Coast September–November4.4.7 Spring:Quarter of the northern jungle, the high forest zone in Huanuco and the and Huanuco in zone forest high the jungle, northern the of Southern Northern Regions Central Coast Coast Coast statistical significance, isdifficult tobe shown over SouthAmerica. tropical pography as there is in Peru. Additionally, Marengo 2007, says that a consensus among the models, with to complex specially characteristics, representlocal cannot they reason this formodels,regional the to but, it has to be considered that these results from the global models have un thick resolution compared XXIcentury; the of end the percentile,by 95 the in increasesPeru,shows in it especially America, South Tebaldi, et al, 2006; in which in the average scenario from all the different global scenarios, for the zone of byfound ones the agreewith resultsjungle. These northern the of zones in as Yunguyowell as (Puno), cavelica),Huarochiriand YauyosPunoin y locations Azángaro,some Lampa, Huaraya(Lima), in Moho as (Huan- Castrovirreyna (Junin), Concepción and Chupaca of provinces the in especially way, isolated an in occur will precipitations these of values maximum the in increase an mean that variations Positive inmap48. what itisobserved associatedbe todecreasingwill the bution trend averageof precipitations for decade,this according to level have a decreasing trend with respect the maximum (averageobserved from 1971-2000), this - distri The variation in the 95 percentile for the year 2030, indicates us that maximum precipitations at national climatology wasanalyzed pereachmeteorologicalobserved station. tothe respect with intensity the in change the and fordecade p95th estimated2030 the was the also it stations, meteorological the for precipitation observed the of (p95th) percentile 95 the of estimate the made been has it level, national at precipitation to relation in climate extreme evaluate to sense, this In do, especiallywithrespectto precipitation. tions and temperatures do not occur in the same proportion that the change with the mean (or average) as well asinthefuture ofprecipita projections, thatthechangesintails ofthedistributions - confirm change, become a fundamental piece to be analyzed. Tebaldi et al., 2006, says the data, in the observed climate this to associated be may that climate) extreme (or events extreme the reason this for riability, va- climate in changes potential the are change climate of impacts the estimating in problem main A 4.4.8 Projections to 2030,90percentile for precipitation Table accumulated 19:Seasonal precipitation to 2030andthevariation inpercentage

Acc PP Annual 5 -100 5 -50 5 -50

Talara andPaita+10% Libertad, Lambayeque Summer and theprovincesof projected to 2030inrelation to current climate inthecoast (ºC) Up to-20%overLa Between -20%and of PiuraySechura over theprovinces 30% speciallyin Up to-20% Áncash r(%)

Acc PP Annual 5 -200 5 -10 5 -50

Between -10%a20% from Camanáto Tacna - 10%overChincha, Autumn Nasca andCaravelí province ofIcaand + 30%specially + 10%overthe in Piura r(%)

Acc PP Annual 0 -5 0 -5 0 -5

Winter Up to-40% Up to-40% Up to-40% r(%)

Acc PP Annual 5 -10 5 -10 0 -5

CLIMATE SCENARIOS PiuraandChiclayo Spring westernpartof Upto-30% over Tumbes Up to-20% + 10%over overthe Moquegua and Tacna over Ica + 20% - 10% r(%) - 115 Climate Scenarios for Perú to 2030 116

Climate Scenarios for Perú to 2030 Tabla accumulated 20:Seasonal precipitation projectedto 2030andthevariation inpercentage projected Mountain Mountain Mountain Southern Northern Central High Andean Central Jungle Regions Southern Northern Regions Plateau Jungle Jungle Table Accumulated 21:Seasonal precipitation projected to 2030andthevariation inpercentage Eastern Western Eastern Western Eastern Western W G O H H L I

200 -1500 500-2300 200-1000 200-1000 Acc PP Annual

200 -700 200 -500 100 -200 200 -500 100 -500 100 -500 100 -500 Acc PP Annual to 2030in relation to thecurrent climate inthemountain region (ºC) projected to 2030inrelation to thecurrent climate inthejungle(ºC)

Between -10%and-20%

-10% inMadredeDios Summer Atalaya andsouthern most partofthezone Up to+10%northern +10% inprovinceof -10% inallthezone province ofMaynas zone andsouthern part ofprovince +10% provinceof (between +10a+20%) -10% northernof except forJunín Summer Coronel Portillo Huánuco, Pascoand La Convención Purús province - 10%theremainder and Requena. Pucallpa and Apurímac andpart provinces of Yauli of westernCusco of Yauyos -Lima Up to-20%inall +20% intherest Up to-20%over part ofthezone zone exceptfor eastern Cusco. (Huancavelica) + 10%inPiura in Cusco Up to-20%in (+ 10%) the Province Up to-20% Up to-10% r(%) of thezone +10% over and Junín. - 10% r(%)

200-1300 500 -700 200 -700 200-1000 Acc PP Annual 100 -700 100 -500 100 -500 200 -500 200 -700 50 -200 50 -200 Acc PP Annual

Autumn and easternpartofl and Alto Amazonas provinces ofLoreto Up to+10%mostly and provinceof Yauyos the provinceofSatipo southern of Ayacucho +10% overmostpart Autumn northeast ofHuaraz the southernpartof in allthezone for zonesnorthand +20% overPaucar- tambo andCusco. of LaConvención. provinces of Anta San Martín. and westernpart the zone,except Up to-20%inall Up to-10%over Up to-20%over -10% over Up to20%over Apurímac and and Arequipa. Between +10 Between +10 + 10% Up to+20% Up to+40%, + 10% + 10% of thezone. r(%) Moquegua. specially in and +20% and +20% (+10%) r(%)

100 -700 100 -200 100 -200 100 -700 Acc PP Annual 10 -100 50 -100 10 -100 Acc PP Annual 10 -50 5 -50 5 -10

5-10

Winter and LaConvención province ofManú,

Tambopata and Winter Madre deDios in allthezone, Tiahuamanú. between Arequipa Moyobamba and Huancavelica provinces of Aplao andChivay Up to-10% Pasco andJunín +10% over except for Ríoja and over Huánuco, zone. Western -10% inallthe southern Lima in Áncashand Ayacucho and over southern Huancavelica -10% in (+10%) and northern Increases of of Ayacucho zone except provinces of Up to+30% with Cusco. - 10% - 10% - 10%most r(%) Up to-30% Up to-40% boundaries central and and Tacna. northern of +10% over +20% over Amazonas for zonein Up to20% up to30% part ofthe (+ 10%) part of - 20% r(%)

Lima

200-1200 500 -800 200-1000 Acc PP Annual 100 -300 500 50 -300 50 -300 50 -300 Acc PP Annual 5 -100 5 -100 5 -50

Spring + 10%overPasco province ofManú Spring Huánuco (-10%) and highjungle. boundary zone eastern zone. western zone Libertad andPiura and Huancavelica northern ofLima. Up to-10% the zone,except with Brasil. +10% over +10% over Up to-20%over over middlepart -10% over boundaries with except for and Junín Cajamarca and Moquegua and and Ayacucho. + 20%overLa Cusco (+10%) -10% intheall southern Lima Up to-10%in Up to-10%in Huancavelica over thehigh + 10% Áncash and La Liberttad Up to+30% + 10%over r(%) Amazonas. over Junín, central and Cajamarca - 20%over +20% over for zonein Up to20% up to20% Increases Arequipa. Huánuco. and -10% parts of r(%) 117 Climate Scenarios for Perú to 2030 118

Climate Scenarios for Perú to 2030 5.1.1. The conclusionsare the following: CONCLUSIONS 5.1 tool for posedby climate theanalysisofvulnerability change. the basis for the development and generation of future projections of regional climate as a fundamental as CPTEC from Brazil, MPI from Germany, the MRI from Japan, among others, have provided our a Service such studies, change climate on background well-known a with centers international of specialists and researchers with coordination permanent the as well as scenarios, climate national of generation The ofEnvironment. the Ministry with projects regional and national of framework the within SENAMHI, at Center Prediction Numerical A2 of the IPCC. These methodologies have been developed in different studies by the work team of the scenario emissions extreme the of context regionalthe model,the within RAMS and circulationmodels global six of outputs the analyzeto approach. downscaling dynamical Tonecessary the was it end, this using scenarios temperature and precipitation purpose, this for elaborated having 2030, and 2020 to information, establishment of indices and climate trends in the last forty years and the future climate projections of analysis the from obtained results the on based stated, are Peru in change climate on sions presentthe chapter, level.In national at conclu- main the poverty of reduction and plans development improve national capacities, facilitating the process of integrating climate change concerns into national and develop to efforts national support to Change Climate on Convention Framework Nations United the to Communication National Second the of framework the within developed was study presentThe CONCLUSIONS ANDRECOMMENDATIONS . . oten onan ein hw vr smlr neana vraiiy bt ih poie trends, opposite with where theENSOevents seemto but bethemain dymanicalsource modulatingtheseregions. variability, interannual similar very a show region and mountain central southern the hand, other the On ENSO. the of events intense most the to restricted minimal, is influence its but it, for responsible are events ENSO the variability, interannual the of case the In of large mechanisms scale circulation modulatethat precipitation in indicated periods, mainly yearsdecadal or longer 40 for these regions.last the of analysis temporal The century. last the of end the until 1960’sdecade the from trend) (negative decreases some shows it jungle northern the in while coast, northern the over trend) (positive increases stressed shows precipitation Totalannual 1965and2006(42years). between restricted are analysis the which in results, partial obtain to not and variability the homogenized to select data the temporarily to need the is there besides minimized), are decadal, as such periods, long of oscillations the which in trends, linear strong a obtain to (limited information short relatively with the stations acrossof the regionsmost part in Peru (few stations and a bad spatial and distribution) of density spatial low the of because occurs different periods. with occurrence shortage tions This nal data, the ones that represent the complex interactions of climate system that has different- varia change and climate extremes that are occurring in the climate last on decades in reliability Peru,and is quality the lack higher of a observatio with information, obtain to limitations main the of One Ab out

current

climate

trends - ......

scattered across the territory, because in certain places it decreased and on others it increased; it others on possibly dueto theorographic effects ofthe region. and decreased it places certain in because territory, the across scattered quite is that intensity mean with but day; daily after recording day heavier days becoming with precipitation accumulated significant, statistically not is it although days, rainy of number the in herent mountain region. trends, such as the northern This would indicate that there is an increase index show besides a it predominanceshows regionsof positive values with in co all the country; Peruvian territory, show significant positive values.the Onacross thespread other hand,places thesome trend ofwhere theregions, rainysmall periods to restricted patterns, local shows it teristics, charac- coherent regional with direction a have not does (DD) index periods dry the of trend The it because shows anincrease rainyregion, inthenumberofextreme days. northern the in mainly events, hydroclimatic of kind this to prone become to high zones in Arequipa and the central western region in the Puno, especially are the region, ones that have mountain the potential southern and northern the Conversely, landslides. and floods of through the years, the central mountain region is the one that will be less prone to the occurrence the index that indicates the amount of accumulated precipitation in fivehand, days (RX5day), shows that other the On precipitation. total of trends the in continued observed patterns the region, temperature index registered for on day (Rx1day) and showscentral mountain that in the northern can be proved in the Similarly,distribution westjungle (San of Martin). it the northern the maximum as characteristics, geographical prevailing local the by influenced is index this that observed is it Also region. mountain southern and northern the of one the to respect with one mogeneous ho most the is region mountain central the in (SDII) index intensity average precipitation the of trend the mentioning worth is it indices, precipitation extreme the of characteristics the Among intheenvironment,to disturbance suchasdeforestation, etc. such as the heat islands, due to the urban development and the alteration of the stations, soil some properties due in others some be might there influence anthropogenic the besides possibly that, decade. As it was previously indicated, the temperature trends show regionalparticular very values ºC/ 0,18 is which 2005, and 1981 between AR4, IPCCthe the by planet within whole the forare estimated range values These ºC/decade. 0,2 to ºC/decade 0,1 is analyzed, were that (1965-2006) years 41 the forPeru in temperature minimum and maximum of trends observed the average, On thanmaximumtemperatures.in aslightlylessproportion average, on increased,temperatures mean minimum that highlight to important is It observed. is wheretemperatureplace the the of location the and intensity variability,its rannual on depending shows that these temperatures affected by the stages of the ENSO phenomenon, which alters inte in the observed maximum temperatures. Also, the temporal distribution of minimum temperature the than moreintensity one,interannualwith the than ratureslargeroscillations arebymodulated tempe these that shown clearly is it and analysis present the of period whole the in (monotonic) gradual is variable this of decrease or increase either that show Temporal variations Lake. Titicaca the of part northern the arein locatedthat ºC/decade, exceptforstations 0,2 several and 0,1 ween Annual and seasonal trends of average minimum temperatures are mostly positive, with values bet interannual variability. there is evidence of a possible modulation of these temperatures due to larger oscillations than just differentof anomalies addition, acrossIn interannual country,variability. determining intensity the positive generating events, ENSO intense by affected are temperatures maximum that observed is it Also, Peru. of part southern the of areas high the in significant statistically are these general, in and average, on /decade, ºC +0,2 of values with territory entire the over (increase) values sitive po of predominance a show temperature mean maximum seasonal and annual of trends Linear where this characteristics is just the opposite.the just is trend the of where characteristics this of characteristic outstanding Themost jungle, northern the for except study, present the in indicated stations the of most in days warm The indices of temperature climate extrems show a diminish in the cold days and an increase in the CONCLUSIONS ANDRECOMENDATIONS ------119 Climate Scenarios for Perú to 2030 120

Climate Scenarios for Perú to 2030 . . . .

tain, whichisconsidered a regiontwo oftransitionthathastheinfluence other regions. thern mountain region that in some cases prolonged up to the jungle northern and central moun- nor and coast northern the mountain, central the to up extend Puno,that and Arequipa or zones features,special with focusedhigh , the droughts,mountain on forof occurrence the southern the regions three identify to allow patterns teleconnection these between interaction the of tribution mountain of Peru, in the northern mountain the same sign persists. The characteristics of spatial dis- southern the in period) rainy the of (beginning spring the to inverse are autumn and summer the which, in 3,4, Niño El the in anomalies with patterns correlation the in seen clearly is characteristic This regions. some in least at signs changes pattern correlation the year, a during station one in least at and intensity, the in variation seasonal a showPeru, for indices precipitation the and TNA) of teleconnections (correlations) between circulation patterns at a large scale (ENSO, PDO and TSA- The physical causes of the droughts determined for the rainy period (September – March) by means regions. similarto arid characteristics certain These places may show this behavior are because they little precipitation areas that have with very are distributed in the mountain northern region and the top end of the southern mountain region. that characteristics local only patterns, regional show not do intensities three the (hydrological)in threeall scales, moderatethe is one, followed by severethe drought. Droughts scale annual the at are considered as extreme. the In southern mountain region, the highest frequency of droughts, in or months with low precipitation, that after the adjustment to normal distribution, minimum values relatively high maximum precipitations in the monthly distribution, recording less periods dry and / teorological scales is registered. This is probably due to low variability of the hydrological cycle, with the highest frequency of moderate, severe and extreme drought at the meteorological and agrome In the jungle region it is observed a very peculiar characteristic found in the drought analysis, where littlepersistency. values withvery mean their in fluctuations only events, drought of decrease, nor increase neither observed, been have trends no study of period the during that emphasized be must It circulation. scale large of determining, if possible, circulation effects at regional scale and/or synoptic scale with the variability characteristics, local emphasizing comprehensivestudies,more some out carry to necessity the to like the warm event of the ENSO 1977/98 that didn’t cause any droughts. These characteristics lead other some or 1990, and 1980 of cases the as such events, ENSO the torelated not are that events drought several are there that emphasize to necessary is it But, Peru. of regions several over tions precipita- of distribution normal the altering phenomenon, ENSO the of phase positive the during The most intense droughts that occurred in the period of the present study (1965-2006) took place information. harmful of limitations the to view, due of point a practical a analyzed form society, are on haveeffect that phenomena hydrometeorological complex most the of one droughts, The described described in the following way: at the beginning of the rainy season the positive anomalies of the El of the A behaviorsummary of the ENSO in the spatial/temporal distribution of precipitation can be where interannual and long-term variability ofthelake’swhere interannual variability andlong-term temperature needsto beconsidered. studies, regional some doing matter this on deepen to necessary is matter,it this On Lake, the of effect thermal the of because affected are values temperature minimum Apparently Arequipa. of zones high the and Lake Titicacathe to zones adjacent the between observed bipolarity of ristics characte without but, region, mountain southern the in values significant with increase an shows ( index night warm of TN90p) pattern hand,the other the wateraresourceOn caps.snowof main that would affect agriculture and water for human consumption in many regions of Peru; where the the in highlands. process This will melting first cause an increase of water amountcap in therivers and then a drasticsnow diminish peak the of acceleration the cause will it which because problemwarmer a becoming is are regions higher the that indicates pattern This Lake. Titicaca the of Arequipa,zonesof comparedadjacent tothe parts high frosts)the dayswith in of (diminish values number of days with meteorological frosts (FD) and cold nights ( TN10p) is the presence of negative - - - . . .

spatial andtemporal. different both scales, the Peru,at in variability climate about uncertainties many about knowledge the improve can which monitoring, climate permanent a make to exclusively network servation hydrometeorologicalplanned ob with have to necessary is it Also, change. climate to adaptation and processes planning for useful vulnerability, climate and risk the on studies assessment further for basis as serve will and satisfactory are information, of lack of problems the despite results, The precipitation over patterns Peru have adifferent response event.for eachENSO oscillation period of each one of them is different and their non linear interaction causes that these tion patterns do not independently,act complex in way,they act a very because de amplitude and circula- different to associated mechanisms These anomalies. these generates that phenomenon main the is which ENSO, the of events warm the to related ones the Peru,particularly in droughts generate that patterns circulation large-scale different the of mechanisms performance the tect de to possible is it conclusion, In severe. or moderate were droughts where regions, jungle and mountain northern the in places scattered some for except territory, the of part most in droughts to a very intense biennial oscillation, with a weak modulation of the 3- to- 7- year cycle caused weak associated 1997/98, of event Finally,frequently.ENSO less the occurred that ones the and intense agrometeorologicalthe hydrological1982/83, most and of werethe droughtpatterns period rainy the during Also, Peru. over rainfall of occurrence the for adverse is which pattern Atlantic,Tropical the of temperatures the by conditioned also was It Oscillation. Decadal Pacific the by dulated that ENSO,mo seems the of phase warm the of occurrence the characteristics besides period, this during together all unfavorable acted of set A 1991/92. period rainy the in occurred droughts the in occurred they which intensemost reveals presentthe (1965-2006) the that study of period the during Peruvian in territory intensity the and droughts of types the between difference The droughts oftheseevents. biennial oscillation of 1997/98 that formed a warm event of the ENSO, but did not the originated totypical associated was pattern this also, and pattern, this by modulated were 1982/83 in occurred of study, all the period ENSO) during with intense values in the decade of the 1980’s. The droughts intensenot very and mainly an oscillation of 3 to 7 years (low frequency oscillation spectrum of the prominenthas annual also cycles, apparently pattern as a This complement of region.the firstmountain pattern, some biennialcentral oscillations the reaches it until mountain Andes the of slope western the through extend with region,that mountain opposite, northern the in concentrated rainfall,the of plenty just is behavior the territory the of rest the in while jungle, northern and tain regionspreadmoun- towardsmountain central (Puno) they the and southern the in concentrated droughts intense very presenting for characterized is pattern second The 1991/92. in registered drought the is pattern this of example An variations. decadal intense very by modulated are that phenomenon, ENSO the of oscillation frequency high the of spectrum oscillation the of part are that (2-3years) cycles sporadic biennial besides, cycles, annual prominent and intense showing for characterized Lima) regionand (Huancavelicamountain central the over extends it Puno)and and (Arequipa region mountain southern the in centered territory, whole the involves that patterns A show rritory two spatial/temporal patterns of drought occurrence with definedvery characteristics. The results of the multi-varied analysis of the Principal Component of droughts over the Peruvian te while in the northern coastandmountainregion precipitations arewhile inthenorthern heavier thannormal. toup region,mountain central the extends that precipitationmountain, of pattern southern the in followsit shortage and seasons a two region),zoneother reversesmountain (centralthe it in then Niño 3.4 produce heavier precipitation than normal over most of part Peru, except for the transition CONCLUSIONS ANDRECOMENDATIONS - - - - 121 Climate Scenarios for Perú to 2030 122

Climate Scenarios for Perú to 2030 . . 5.1.2 About theclimate to projections 2030 ...... maximum precipitation to 2030 it tends to decrease in most part of the country and only in a in only and country the of part localized way willincrease they withrespectto current values. most in decrease to tends it 2030 to precipitation of case maximum the In to mountain. southern trend the in current especially a nights, warmer with of number and the 2030 in increase to temperature the minimum with in consistent changes been positive increase, of to trend projections a is there region mountain the in while diminish a be will there coast the of part most behavior,in regionalizedwhere a but pattern, a not is there nights, warm to respect With territory. mountain southern the in ones intense most the being There will also be an stressed trend to the increase days in the number of warm at national level, -30%and+20%above between in thespatialdistribution theiraverages. intercalated are decreases and increases some spring and winter the In values. normal its occur above will rainfall autumn in while season, summer the in country the of part most in significant At seasonal level there will be some irregularities in the behavior of rainfall, will shortages be very and coast northern the in occur will increases +10%and+20%. junglebetween southern important most The -10%. to up jungle jungle) central and (High northern the in and -20% and -10 between region shortages mountain some the in show 2030 specially to precipitations Annual climatology. their to related very are they and rainfall of distribution spatial the in changes great of evidence not is there 2030 and To 2020 Cusco region, mountain central ofAyacucho). part ofAbancay andsouthern Moyobamba, and northeast of northeast coast, the in north (top ºC 1,6 to up summer the in and Iquitos) of east and Marting San of northeast Chiclayo, (Piura, ºC 1,2 reach will they season, spring the current in to While Iquitos. of respect northeast with and ºC, Chimbote Chiclayo, 2 in to mainly up climate, increases important some occur with to winter, projected and are autumn 2030 to during temperature minimum in changes largest the level seasonal At with reduction, a shows Andeanplateau,ºC theHigh temperaturesrespect to 2020.In willincrease upto 1,2ºCto 2020. 20-22 of area the coast, the In amplitude. spatial more show will ºC 24 to 22 from temperatures minimum of area the region jungle the in that projected is To it 2030 (Ayacucho,Andean sector Abancay). and Chiclayo east of Iquitos),(Piura, central region jungle (Cerro de Pasco,northern Huancayo,and Huancavelica coast and part of the the southin mainly ºC, 1,4 and 0,4 between climate current Minimum air temperature close to surface to 2030 will increase in the country, with respect to the to +1,2ºC. up of values with season (DJF) summer the during jungle central low for the except stressed, very are no variations jungle central ºC. of the up to In +1,6 values with spring, and winter in the occur will variations higher with periods the jungle southern the In ºC. +1,6 to up of values show will variations seasons other the in ºC, to up of 2,4 values with jungle, northern the over mainly (SON), season spring the during occur will variations intense more region jungle the In ºC. +1,2 to up of values with autumn the in except significant, very not are variations plateau Andean High the In winter. the during region mountain eastern central and region mountain northeastern the in and autumnregion in mountain southeastern the in mainly ºC, +1,6 to up of (JJA) winter(MAM) and season autumn the in occur will region mountain the in variations important most The . coast northern top the in specially respectively; ºC, 1,6 to +1,2 from and ºC 2,0 to +1,2 from range that values with (SON) spring and (JJA) period in winter the intense more variations, positive show will From the seasonal point of view, maximum temperature over the coastal region to 2020 and 2030 tology in almost alltheterritory. toprojections Annual for2030 temperaturemaximum torespect with ºC, 1,6 currentis its clima- jected inthe presentjected technical document. are they propreventive as intensificationclimate of extremes evident the take of and measures view in trends,toclimatechange these vulnerabilities on the evaluatebased and decades; next the in take will precipitation and temperature extreme that trends possible the indicate which values, absolute the in approximationtrendsidentified futureclimate,toan stressingthe consideredas be must presentstudy the of results the projections; climate the and model the with associated uncertainties inherent some When considering the limitations of the available historical information, and also knowing that there are another levelat suchasontheAmazon ecosystems. alterations some causing be could it and such, as perceived not is activities socioeconomic the on impacts its since examined, carefully be should used, was that methodology the by such as identified vulnerability may extend to certain sectors.socio-economic The drought analysis in the northern jungle, context this in that and years forty last the in observed changes climate the face to indispensable really is This policies. governmental and technologies the in changes as such droughts,that of areas occurrence the show of vulnerability decrease to actions and measures necessary the take to important is It other. among soil, of properties physical circulation, mesoscale orientation, valleys the of effects the considers that information physical adequate most the have not do they because simply longitude; and altitude the reveal to realwhereplaces the of the conditions thereconsider interpolations information;these no is although able not are conditions, climate and weather regional and/or local day every modulate that kilometers) of (hundreds level regional at characteristics geographical mesoscale the because is scales information. of amount larger Thisa with areobtained these distributions, with maps smaller presentedas and for interpolated be cannot they that and used were that stations the of representative indicators,arethe and analysis trends the fromresults, obtained the that mind in keep to necessary is It inthecountry. ofenergy information security onthesubject since itallows to generate important change, climate to related study every of part be to needs variable this of analysis the So, basin. whole the for terms global in land of use the in changes the as such change climate of factors anthropogenic involves that information of source important an are years, the through discharges, of series historical the in occurred changes the addition, In it. in place take different that hydrometeorological processes the of also and basin the all in precipitation of measure indirect representan discharges because basin, a of limits the within information important very provides variable hydrological This discharge. river of A variable that needs to be evaluated without exception, as the other climate variables, is the time series thropogenic effects inseveral regions of Peru. an- to attributed variability long-term of ratio the models, climate through determine, to basis as serve will futureit near feedback the processes,the in identify toand and climate physicalprocesses of the of climate scenarios. Also, the information of this system will allow to evaluate and improve understanding future in and climate current on oscillations these of effect the of knowledge better and clear a haveto tions that would allow us to have enough climate series to cover of most the part country. This will help sta- more of implementation the term, medium a at promote, to as well as Peru; in climate long-term study and explain in the most feasible way, the decadal that characteristics and modulatemulti-decadal present the in developed analysis the complement to order in quality their verify then, time; of periods compile as much information as possible from some private stations with records corresponding to long is evident the It lack of climateso, information tozonesit in is some of necessary important the country; 5.2 RECOMMENDATIONS CONCLUSIONS ANDRECOMENDATIONS - 123 Climate Scenarios for Perú to 2030 124

Climate Scenarios for Perú to 2030 Mann, H.B., testMann, againsttrend. 1945:Non-parametric 13:245-249. Econometrica, methodologies:scenarío directversus 11(3),1145-1159. deltaapproach.Syst. Sci., Hydrol. Earth Lenderink G.; Buishand A. y Deursen W., 2007: Estimates of future discharges of the river using Rhine two Weather andForecasting. 22,353-371. ENSO Cycle. the and Assessing Monitoring Kousky, System classification for V.,Alert An 2007: R., Higgins, Kendall, M.G.,1975: correlation methods”,“Rank 4thEd., Griffin, Charles London. Eds., Press, University Cambridge UK. IPCC, 2007: Cuarto Informe de Evaluación. Climate Change 2007: The Physical Science Basis. Alley, R. et al. Eds., Press, University Cambridge 881pp. UK, IPCC,2001: Basis. 2001: J.TercerChange Scientific ClimateThe Evaluación. al.de et Houghton Informe T. 50, 361-367. Statistician, American packages”.The statistical in quantiles “Sample 1996. Fan,Y. y R.J., Hyndman, prepared Fast-Track for theDETR Group. Impacts note A Scenaríos. Climate into variability climate interannual factor to How 2000: X., Lu, and M. Hulme, Water quality”. water in trend for tests Resources Research, 18:107-121. “Nonparametric (1982). R.A. SMITH, J.R.; SLACK, R.M.; HIRSCH, 7, 85-95. Hewitson, B.C. and R.G. Crane, 1996: Climate downscaling: techniques and application. Climate Research, and features Mean America: South variability. Chapter over 2in The Physical Geography Eds. America. ofSouth Oxford Press. University circulation Atmospheric 2007: P., Aceituno, D., R. Garreaud, Climate century. twentieth the of half second the Research, 19,193-212. during extremes climatic in changes coherent Frich, P., Alexander L. V., P.,Della-Marta Gleason B., Haylock M., Tank AMGK and Peterson T. 2002: Observed Andes. Tropical the in Science, 312,5781,1755-1756. supplies wáter to Threats (2006): W.F.;Vergara, H. Diaz, M.; Vuille, S.; R. Bradley, Geophys.and theplannedGCOSRes. Lett., network. 31,L16210,doi:10.1029/2004GL020229. Bradley, R. S.; Keimig, F. T.; Diaz, H. F. (2004): Projected temperatura changes along the American cordillera Energética, unreto paraelPerú enelSigloXXI. Eds. Colegio delPerú, deIngenieros 256–281. Avalos, G., 2008: Cambio climático y seguridad energética en el Perú. En: Chiri A. F. y Luyo J. E., La Seguridad ) AGCM. presentado Informe alMRI. as simulated by Brazil mesh 20km MRI-JMA over(Meteorological Research Institute and climate Japan Meteorological Agency in changes Future (2008): A. C. Nobre, A., J. F.,Marengo, J. Pesquero, M., L. Alves, BILIOGRAPHY Statistical Association, 63: 1379-1389. P.K. Sen, tau”. Kendall´s (1968). on based regression American coefficient the the of of “Estimates Journal del técnico Estudio diagnóstico). (estudio 2006 NacionaldeMeteorología eHidrología delPerúServicio al –SENAMHI, s/p. 1960 de Perú del costeras zonas y sur sierra la en Sanabria, J., y Zevallos L, 2006: tendencias térmicas y pluviométricas indicadores de un cambio climático Climate Variability, Geophys. Res. Lett., 25(21),3967–3970 Atlantic Tropicaland Midlatitude Decadal Observed TourreY.(1998), and Y.B.,M. Kushnir, Rajagopalan, S. R. 1500, AD since Climate Bradley, P.in events. D. Jones, Eds. (Routledge, London, 1992),pp.Niño 623-648. El of record Historical The 1992. T.V. Neal, y H. W. Quinn, Sciences des - Journal - Sciences Hydrological Hydrologiques 50,999-1021. Blanche. Cordillère des la Avenir de (2005). P. galciaire Ribstein, eau and W., en Suárez, ressources G., Rosas, J., Gómez, J., Yerren, M., Zapata, B., Pouyaud, Ponce deLeón, R.G.2000:Perú: visiónglobalydesíntesis. Auge Editores SA.Lima–Perú. 69-9. Meteorol.ComprehensiveRAMS.Atmos.SystemPhys. A Meteorological modeling 1992. 49, al., et Pielke Santiago deCali, Colombia. 8-10denoviembre de2006. 2006. RUPSUR Encuentro IV milenio. del metas las de loro el en Impacto climático, Cambio y Climática Perú.el VariabilidadPrecipitaciónen 2006: Tendenciasla A. J.de y Variabilidad O.,Marengo G. Obregón, v.59, p22-27. Nobre,Sampaio,A.; C. Salazar,G.; F.;L. CulturaAmazonia.e e (SBPC), Ciencia climáticas Mudancas (2007): Panel onClimate Change. Press. University Cambridge Intergovernmental the of Report Special 2000, Scenaríos. Emissions 2000. R., Swart, and N. Nakicenovic, of Glaciersthe World: U.S. Professional Geological Survey Paper 1386(GlaciersAmerica). ofSouth Peru. of Glaciers In: Jr.,B.,1998. Williams, Morales-Arnao,R.S., Ferrigno,and Atlas J.G.,eds.,SatelliteImage Climatología A,Nº4,Santander, yUniversidad Serie deCantabria, 389-398. de Española Asociación calurosos. días de frecuencia la en Cambio décadas: ultimas las en Valenciana Miro, J. J.; Estrela, M. J. (2004): Tendencia de la temperatura en los meses de julio y agosto en la comunidad reducción minero Santa(Online). oeliminacióndelacontaminaciónorigen enlacuencadelrío Ministerio de Energía y Minas, 1998. Estudio de evaluación ambiental territorial y de planeamiento para la – 236. 233 Pp. 1995. PREPRINTS, Boston: Society, Meteorological American Boston. 9, Climatology, Applied on Conference In: scales. times multiple with monitoring Drought 1995. Mckee, Kleist,J. T.B.;N.J.e Doesken, Boston: Society, Meteorological American Boston. 8, PREPRINTS, 1993.p.179 –184. Climatology, Applied on Conference In: scale. times to duration and frequency drought of relationship The1993. J. Kleist, e N.J. T.B.; Doesken, McKee, implications, GlobalPlanet Change. Mark, B. G., 2007: Tracing Tropical Andean Glaciers, over space and time: some lessons and transdiciplinary Región la para climáticos escenarios de Elaboración Arequipa. 2007: Piloto“Medidas de Adaptación alCambio climáticoenelPerú”. M., GTZ. Valverde, y G. Obregón, J., Marengo, BIBLIOGRAPHY 125 Climate Scenarios for Perú to 2030 126

Climate Scenarios for Perú to 2030 water resources. for Part I,IIandIII.Areport CONAM andthe World Bank. and glaciation for consequences and Impacts – Andes Tropicalthe in change Climate 2007: M., Vuille, 18: Climate, of Journal 1960-2000. 5011-5023. 2005. America South in extremes temperature daily of indices in trends Quintana, J.L. Santos, J. Baez, G. Coronel, J. Garcia, I. Trebejo, M. Bidegain, M.R. Haylock, D. Observed Karoly: T. Alves, L.M. Ambrizzi, M.A. Ramirez, Berlato, A.M. Grimm, E. J.A. Marengo, Carrasco, L. Molion, D.F.G. Rusticucci, Moncunill, E. Rebello, M. Y.M.T.Marino, Barros,M.B. Anunciação, V.R.J. Peterson, T.C. L.A., Vincent, andNew Cambridge, United Kingdom York, NY, USA. Press, University Cambridge (eds.)]. Miller H.L. and Tignor M. Averyt, K.B. Marquis, M. Chen, Z. Manning, M. Qin, D. S., [Solomon, Change Climate on Panel Intergovernmental the of Report the Assessment Fourth to I Group Working of Contribution Basis. Science Physical The 2007: Change Climate In: Change. Climate Atmospheric and Surface P.Observations: and 2007: Zhai, Soden B. Rusticucci, M. Renwick, J.A. Rahimzadeh, F.Parker, D. Tank, Klein A. Easterling, D. Bojariu, R. P.Ambenje,P.D. Jones, K.E., Trenberth, Ávalos G.,Acuña R.,Eds. D., SENAMHI L.,LlaczaA.yMiguel –MINAM, C.,Metzger Perú, Oria 100pp. climáticosenelPerúSENAMHI, 2008:Escenarios paraelaño2030.Autores: Obregón G., G.,DíazA,Rosas Ávalos C.,Acuña G.,DíazA.,Oria D., R.,PRAA.Eds. SENAMHI G.yMiguel L.,Rosas Perú, Metzger 124pp. Autores:2100. año el para Mantaro río del cuenca la en climático cambio de Escenarios SENAMHI,2007: Perú, 170pp. Ávalos G., Oria C., Acuña D., A., Cornejo A., Metzger L., FanoDíaz G., G., Carrillo M., Miguel R., PROCLIM. Eds. Rosas SENAMHI Autores: basin. river Piura 2050: to Peru in scenaríos change Climate 2005: SENAMHI, 50 pp. del deMétodo Climática Perú. Clasificación de Thornthwaite. Eds. SENAMHI Mapa 1988: SENAMHI,Perú, departamentos. Agroclimático Boletín Perú.1986. del OSE, y SENAMHI Proyecto PADI por climáticas Características EIA. – APÉNDICE 1 HYDROMETEOROLOGICAL NETWORK RELIEF MAPS ANDCLIMATE CLASSIFICATION Map N°3: Map N°2: Map N°1 Nª Map APPENDIX 1 APENDICES Name oftheMap Climate classificationmap Relief map Hydrometeorological networkmapatnationallevel 127 Climate Scenarios for Perú to 2030 128

Climate Scenarios for Perú to 2030 Map Nº60: Accumulated precipitation forseptember-november (ElNiño1997/98) Map Nº59: Accumulated precipitation forjune-augustquarter (El Niño1997/98) Map Nº58: Accumulated precipitationformarch-may quarter(ElNiño1997/98) Map Nº57: Accumulated precipitationfordecember-february quarter(ElNiño1997/98) Map Nº56: Accumulated precipitationjune1997 –may1998(ElNiño1997/98) Map Nº55: Average minimumtemperatureforseptember-novemberquarter (ElNiño1997/98) Map Nº54: Average minimumtemperatureforjune-augustquarter(ElNiño 1997/98) Map Nº53: Average minimumtemperatureformarch-mayquarter(ElNiño 1997/98) Map Nº52: Average minimumtemperaturefordecember-februaryquarter (ElNiño1997/98) Map Nº51: Average minimumtemperaturejune1997–may1998(ElNiño1997/98) Map Nº50: Average maximumtemperatureforseptember-novemberquarter(ElNiño1997/98) Map Nº49: Average maximumtemperatureforjune-augustquarter(ElNiño1997/98) Map Nº48: Average maximumtemperatureformarch-mayquarter(ElNiño1997/98) Map Nº47: Average maximumtemperaturefordecember-februaryquarter(ElNiño1997/98) Map Nº46: Average maximumtemperaturejune1997–may(ElNiño1997/98) Map Nº45: Accumulated precipitationforseptember-novemberquarter(LaNiña 1988/89) Map Nº44: Accumulated precipitationforjune-augustquarter(LaNiña1988/89) Map Nº43: Accumulated precipitationformarch-mayquarter(LaNiña1988/89) Map Nº42: Accumulated precipitationfordecember-februaryquarter(LaNiña 1988/89) Map Nº41: Accumulated precipitationjune1988–may1989(LaNiña1988/89) Map Nº40: Average minimumtemperatureforseptember-novemberquarter(LaNiña1988/89) Map Nº39: Average minimumtemperatureforjune-augustquarter(LaNiña1988/89) Map Nº38: Average minimumtemperature formarch-mayquarter(LaNiña1988/89) Map Nº37: Average minimumtemperature fordecember-februaryquarter(LaNiña1988/89) Map Nº36: Average minimumtemperature june1988–may1989(LaNiña1988/89) Map Nº35: Average maximumtemperature forseptember-novemberquarter(LaNiña1988/89) Map Nº34: Average maximumtemperature forjune-augustquarter(LaNiña1988/89) Map Nº33: Average maximumtemperature formarch-mayquarter(LaNiña1988/89) Map Nº32: Average maximumtemperature fordecember-februaryquarter(LaNiña1988/89) Map Nº31: Average maximumtemperature june1988–may1989(LaNiña1988/89) Map Nº30: Accumulated precipitationforseptember-novemberquarter(ElNiño1982/83) Map Nº29: Accumulated precipitationforjune-augustquarter(ElNiño1982/84) Map Nº28: Accumulated precipitationformarch-mayquarter(ElNiño1982/83) Map Nº27: Accumulated precipitationfordecember-februaryquarter(ElNiño1982/83) Map Nº26: Accumulated precipitationjune1982–may1983(ElNiño1982/83) Map Nº25: Average minimumtemperature forseptember-novemberquarter(ElNiño1982/83) Map Nº24: Average minimumtemperature forjune-augustquarter(ElNiño1982/83) Map Nº23: Average minimumtemperature formarch-mayquarter(ElNiño1982/83) Map Nº22: Average minimumtemperature fordecember-februaryquarter(ElNiño1982/83) Map Nº21: Average minimumtemperature june1982–may1983(ElNiño1982/83) Map Nº20: Average maximumtemperature forseptember-novemberquarter(ElNiño1982/83) Map Nº19: Average maximumtemperature forjune-augustquarter(ElNiño1982/83) Map Nº18: Average maximumtemperature formarch-mayquarter(ElNiño1982/83) Map Nº17: Average maximumtemperature fordecember-februaryquarter(ElNiño1982/83) Map Nº16: Average maximumtemperature june1982-may1983(ElNiño1982/83) Map Nº15:Multiquarterlyaverageprecipitationforseptember-november Map Nº14:Multiquarterlyaverageprecipitationforjune-august Map Nº13:Multiquarterlyaverageprecipitationformarch-may Map Nº12:Multiquarterlyaverageprecipitationfordecember-february Map Nº11: Multiquarterlyaverageminimumtemperatureforseptember-november Map Nº10:Multiquarterlyaverageminimumtemperatureforjune-august Map Nº9:Multiquarterlyaverageminimumtemperatureformarch-may Map Nº8:Multiquarterlyaverageminimumtemperaturefordecember-february Map Nº7:Multiquarterlyaveragemaximumtemperatureforseptember-november Map Nº6:Multiquarterlyaveragemaximumtemperatureforjune-august Map Nº5:Multiquarterlyaveragemaximumtemperatureformarch-may Map Nº4:Multiquarterlyaveragemaximumtemperaturefordecember-february Map Nº3: Total multiannualprecipitation Map Nº2:Multiannualaverageminimumtemperature Map Nº1:Multiannualaveragemaximumtemperature Nª Map MULTIANNUAL AVERAGES MAPS EXTREME EVENTS ANDCURRENT EXTREME TRENDS Name oftheMap APPENDIX 2 129 Climate Scenarios for Perú to 2030 130

Climate Scenarios for Perú to 2030 Map Nª Map Map Nº48: Map Variation ofthe95percentile ofprecipitation to 2030. Nº47: Map Variation inpercentage ofprecipitation for september-november to 2030 quarter Nº46: Map Variation to 2030 inpercentage ofprecipitation quarter for june-august Nº45: Map Variation inpercentage ofprecipitation for to march-may 2030 quarter Nº44: Map Variation inpercentage to ofprecipitation 2030 for quarter december-february Nº43: Map Variation inpercentage ofprecipitation to 2030 Nº42:AccumulatedMap precipitation for september-november to 2030 quarter to Nº41:Accumulated 2030 Map precipitationquarter for june-august Nº40:AccumulatedMap precipitation for to march-may 2030 quarter Nº39:AccumulatedMap to precipitation 2030 for quarter december-february Nº38:AccumulatedMap precipitation to 2030 Nº37:AccumulatedMap precipitation for september-november to 2020 quarter to Nº36:Accumulated 2020 Map precipitationquarter for june-august Nº35:AccumulatedMap precipitation for to march-may 2020 quarter Nº34:AccumulatedMap to precipitation 2020 for quarter december-february Nº33:AccumulatedMap precipitation to 2020 Nº32: Map Variation of90percentile ofminimumtemperature to 2030 Nº31: Map Variation ofminimumtemperature for september-november to 2030 quarter Nº30: Map Variation to 2030 ofminimumtemperature quarter for june-august Nº29: Map Variation ofminimumtemperature for to march-may 2030 quarter Nº28: Map Variation ofminimumtemperature to 2030 for quarter december-february Nº27: Map Variation ofannualminimumtemperature to 2030 Nº26:AverageMap minimum temperature for september-november to 2030 quarter Nº25:AverageMap to 2030 minimum temperature quarter for june-august Nº24:AverageMap minimum temperature for to march-may 2030 quarter Nº23:AverageMap minimum temperature to 2030 for quarter december-february Nº22.AnnualaverageMap minimumtemperature to 2030 Nº21:AverageMap minimum temperature for september-november to 2020 quarter Nº20:AverageMap to 2020 minimum temperature quarter for june-august Nº19:AverageMap minimum temperature for to march-may 2020 quarter Nº18:AverageMap minimum temperature to 2020 for quarter december-february Nº17:AnnualaverageMap minimumtemperature to 2020 Nº16: Map Variation ofthe90percentile ofmaximumtemperature to 2030 Nº15: Map Variation ofmaximumtemperature for september-november to 2030 quarter Nº14: Map Variation to 2030 ofmaximumtemperature quarter for june-august Nº13: Map Variation ofmaximumtemperature for to march-may 2030 quarter Nº12: Map Variation ofmaximumtemperature to 2030 for quarter december-february Nº11: Map Variation ofannualmaximumtemperature to 2030 Nº10:AverageMap maximum temperature for september-november to 2030 quarter Nº9:AverageMap to 2030 maximum temperature quarter for june-august Nº8:AverageMap maximumtemperature for to march-may 2030 quarter Nº7:AverageMap maximumtemperature to 2030 for quarter december-february Nº6:AnnualaverageMap maximumtemperature to 2030 Nº5:AverageMap maximumtemperature for september-november to 2020 quarter Nº4:AverageMap to 2020 maximumtemperature quarter for june-august Nº3:AverageMap maximumtemperature for to march-may 2020 quarter Nº2:AverageMap maximumtemperature to 2020 for quarter december-february Nº1:AnnualaverageMap maximumtemperature to 2020 Name oftheMap CLIMATE SCENARIOSMAPS APPENDIX 3 TO 2020AND2030 131 Climate Scenarios for Perú to 2030 132

Climate Scenarios for Perú to 2030 glaciers, whichisoriginating several problems such asthelossofsweet water for humanconsumption. of caps, ice global the of melting is glacier causing is that causes and year after main year temperature rising is the that warming of One increase. temperature of consequence a as snow of fusion the is It retreatGlacier anthropogenic. among these agentsaimsatattainingbalanceandharmony.The interaction or natural be can they together; get life daily in participate that agents and processes the all where is It Environment involves thedifferent hydrological processes, and droughts. suchasfloods, (Salaset shortages al., 2005). mean the to respect during with a determined precipitation period of of time. deviation On the the other hand, of managing expressionand planning an water resources as systems drought meteorological the defined they Also socio-economic. and agricultural hydrological, meteorological, classified groups: were four which in drought, of definitions hundred a than more found (1985) Glants and Whilhite Drought also astheregimens, modesorteleconnections. is it longer, or circulationatmospheric and scales the interactions with the land and Such ocean patterns surface. are known time seasonal at particularly system, preferably climatespatial and totemporalsubject certain scales, because of the dynamical of characteristics the the of variability natural the It’s Climate variability provideeconomic scenarios thebasisashow thefuture may be. scenarios the fields, strategic planning.for military years, socio- and These business in governments the by used havebeen many For purpose. evaluation the to to according used varies methodology scenarios The these elaborate future. the in change may cliamte how of descriptions possible are They Climate scenarios of itscomponents, andthatgathersallorsomeofitsproperties. initsinteractions Numerical representation of the climate system based on the physical, biological and chemical properties Climate model isdueto naturalcausesand,others. inthelastcenturies, to It humanactivities. at different time scales and affect all the climate parameters: temperature, precipitations, cloudiness and is It an variation of important climate that persists for a prolonged period of time. Such variation occurrs Climate change oftheIPCCto thesubject. isbasedonthelastreport (2007). This glossary In order to better understand this document it is tonecessary know certain terms and definitions related CONCEPTS ANDBASIC TERMS APPENDIX 4 133 Climate Scenarios for Perú to 2030 134

Climate Scenarios for Perú to 2030 and the index of climate variability to which a systems is exposed to, its sensibility and its capacity of capacity adaptation. its and sensibility its to, exposed is systems a which to variability climate of index the and change, conditions climate the magnitude, the of on effects depends Vulnerability negative episodes. extreme the or face variability climate to even unable or able is system a which in measure the is It V that isknown. something to respect with disagreement a or information of lack the to due be can It system). climate the of state future the example (for value determined a of knowledge of lack the of expression the is It Uncertainty ofanon-linearsystem asitisclimate.predictability essentially predictability makes which concerning limitations chaotic,some are there used, are observations precise and models and though even limited; non-linear essentially is system climate the and imperfect, or faulty usually is system climate the of conditions previous and current the of knowledge previousstate.currentand its systemknowing a future of the tocondition predict capacity Thethe is It Predictability natural conditions, orthatmay affect health,sanitationor welfare ofpeople. their alter adversely that bodies, receptive into substances gaseous or liquid solid, of incorporation the to also is be harmful It life. may vegetal or that animal to way harmful be may a that or population the of insuch welfare or security health, concentrations, and forms different the in places, in them of combination the environmentor the in biological) or (physical,chemical agent any presenceof the is It Pollution withinfra-redinteraction radiation. their and properties physical their to due effect, greenhouse the to contribute that ones the are They Greenhouse gases to space, surface after it heats up due to solar radiation and it prevents the sun energy from returning immediately It is a phenomenon through which the greenhouse gases effect trap part of the energy coming form the Greenhouse effect withrespect to CO2.25, whichmeanstheCH4has25timespower theearth ofwarming the carbon dioxide CO2, which has a proportional value of global warming power equal to 1 and CH4 to is It a referential value that allows to compare the power that have the greenhouse gases with respect to Global Warming Potential others; itisthemaincauseofclimate change. an showing scale, global increase in the Earth’s at temperature.effect greenhouseThis phenomenon causes a the warming in some zones of and a cooling in consequence as phenomenon generalized a Is warmingGlobal ulnerability 135 Climate Scenarios for Perú to 2030 136

Climate Scenarios for Perú to 2030