Active Layer Thermal Monitoring at Fildes Peninsula, King George

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent ff , 2 4 , Francelino M. R. 2 1424 1423 , F. N. B. Simas C), arranged vertically with probes at di 2 ◦ , and J. G. Bockheim 3 0.2 ± , G. B. Lyra 2 , C. E. G. R. Schaefer erent annual weather conditions, reaching a maximum of 117.5 cm in 1 ff This discussion paper is/has beenPlease under refer review to for the the corresponding journal final Solid paper Earth in (SE). SE if available. Universidade Estadual de Santa Cruz,Universidade Ilhéus, Federal Brazil de Viçosa, Viçosa, Brazil Universidade Federal Rural do RioUniversity de of Janeiro, Seropedica, Wisconsin, Brazil Madison, USA intense modeling ofsearching climate the scenarios sources and were trends carried of out these changes by (Mora scientific et. investigations al, 2013; IPCC, 2012; 2009. The ARIMA modelmore conclusive was analysis and considered predictions appropriatethe when longer variability to data when treat sets comparing are thethe available. temperature studied Despite dataset, period, readings enabling no and warming active trend layer was detected. thickness over 1 Introduction International attention on the climate change phenomena has grown in the last decade, providing insights about thetive influence layer of thermal climate regime chancewith in over extreme the the variation studied permafrost. at period Thethaw was ac- the cycles. typical surface The of during active periglacial layer summerity thickness environment, resulting related (ALT) over to in the di frequent studied freeze period and showed variabil- Peninsula, King George Island,(2008–2012). Maritime The Antarctica monitoring over site ansists was fifth of installed seven thermistors during month (accuracy thedepths, period of summer recording of data 2008 atstatistical and hourly analysis con- were intervals performed in toing describe a a the high linear soil temperature fit capacity time inmoving data series, order average includ- to logger. (ARIMA) identify models A global were series tested trendThe in of and controls order a of to series weather define of on the autoregressive the best integrated thermal fit for regime the of data. the active layer have been identified, International attention to the climatethe change phenomena active has layer grown in andand the permafrost last future decade; are trends of duepaper great to is importance to their present role in active in layer understanding temperature energy processes data flux for one regulation. CALM-S The site located objective at of Fildes the this Abstract 1 2 3 4 Received: 31 March 2014 – Accepted: 7Correspondence April to: 2014 R. – F. M. Published: Michel 1 ([email protected]) JulyPublished 2014 by Copernicus Publications on behalf of the European Geosciences Union. E. I. Fernandes-Filho Active layer thermal monitoring atPeninsula, Fildes King George Island, Maritime Antarctica R. F. M. Michel Solid Earth Discuss., 6, 1423–1449,www.solid-earth-discuss.net/6/1423/2014/ 2014 doi:10.5194/sed-6-1423-2014 © Author(s) 2014. CC Attribution 3.0 License. 5 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | C S, ◦ 00 27 0 11 ◦ erent depths down ff W, and 60 m of altitude) was 00 37 0 57 C) (model 107 Temperature Probe, ◦ ◦ 0.2 S, 58 ± 00 12 1426 1425 0 12 ◦ ects, in consequence of which permafrost degradation is ff C (data from 2000 through 2012, Marsh met station – 62 ◦ 2.2 W, and 10 m of altitude) and mean summer air temperatures above 0 − 00 12 erentiation of horizons. Air temperature was obtained from Marsh automatic met 0 ff 59 ◦ The characteristics of the monitored site and the exact depth of the probes are pre- The region experiences a sub-antarctic maritime climate, according to the Köppen The objective of the this paper is to present active layer temperature data for one station, located at Teniente Marsh Air Port. Campbell Scientific Inc, Utah, USA) arrangedto in the a vertical permafrost array table, at the di siteTurbic Cryosols was occurring selected at during the soil survey peninsula.(model and All CR represents 1000, probes most Campbell were of Scientific connected Inc,from to Utah, 1 a USA) March data recording logger 2008 data until at 30 hourly November intervals 2012. sented in Tables 1 and 2,cal the di depth of the probes was established respecting pedologi- 3 Methods The active layer monitoring site (–installed 62 (10.5 cm (F1),2008, 32.5 cm and consist (F2), of 67.5 cm thermistors (F3), (accuracy 83.5 cm (F4)) in the summer of tures of 58 up to four monthsranges (Rakusa-Suszczewski between et 350 and al., 500 1993; mm(Øvstedal per Wen and year, et with Smith, rainfall al., 2001). occurringanalyzing 1994); in Ferron data the precipitation et from summer 1947 al. period to (2004) 1995followed and by found identified 9.6 great years cycles of climate of warmer 5.3 variability years conditions. of when colder conditions Fildes Strait, andcover Maxwell only Bay. the Most extreme northeastern of part the (Simonov, 1977). Fildes Peninsulaclimate is classification, free the of Southspheric ice; Shetland Polar glaciers Islands Oceanic have (Köppen, an 1936), ET characterized climate, by South mean Hemi- annual air tempera- long and 2–4 km wide. It is washed on three sides by the waters of Drake Passage, South America by Drake Passage. Kingand George Fildes Island Peninsula is is the at largest its in southwestern the end archipelago (Fig. 1). This peninsula is about 10 km 2 Material and methods 2.1 Studied site The archipelago ofsouthwest the to South northeast, Shetlandarchipelago is lies Islands, separated near extending from the more the Antarctic Peninsula than northern by the 400 Bransfield km tip Strait from and of from the Antarctic Peninsula. The tensity of climate changeobserved e (Beyer et al., 1999). CALM-S site located at Fildesa Peninsula, King fifth George seven Island, month Maritime period Antarctica (2008–2012). over of the globe, ourto understanding its of thermal Antarctic state andogy, permafrost geomorphic evolution, is its dynamics poor, physical and especially properties, responseglobal links in to climate to relation atmosphere, change pedogenesis, and (Bockheim, hydrol- thus,the 1995; the distribution Bockheim variability et and and al., propertiessciences, 2008). of but An Antarctic also understanding permafrost for of life isification sciences, essential following since for it climate the will inducedscientific cryospheric be interest changes a in major and King control variability George on Island ecosystem (Vieira mod- has et. grown al., in 2009). the last The few years due to the investigations, being recognized as aactual key (climate component variability) of and future climate trends forBroeke (Ledley, 2012; the et. Flanner al., understanding et. 2008). of al., The 2011; active vanare layer den and of permafrost, great part importance of due themate terrestrial change to cryosphere, (Kane their et role al., 2001; in Smith energy and flux Brown, regulation 2009). Compared and with sensitivity other to regions cli- Moss et. al., 2010). The cryosphere and its energy flux became the focus of many in- 5 5 25 15 20 10 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | th i (2) (4) (3) (1) C); ◦ = space i = Z ∆ lag operator, . Then it can be = error and d L C), freezing days, = ◦ ε 0.5 depth position. Nelson erent seasons from the + ff = i C); the freezing degree days ◦ real number, soil temperature in time increments (s), i = 1 = + x C) and the number of freeze–thaw i t j ◦ T ∆ + ), 0.5 i t j 1 ± T ε − 2 s d moving-average terms, ! ) 2 i − time and 1428 1427 L = 1 L = i − − t q θ t temporal position and has a unitary root of multiplicity ε 1 moving average parameter, (1 = T ji q = i ! i X j = i   L C); number of isothermal days (days in which all the i i / L ◦ + θ usivity (m i L φ ff 1 i θ C); the thawing degree days (TDD, obtained by the cu- 1 1 , where ◦ 1 0 0.5 φ t − =

q j p i usivity (ATD) was estimated for di = i q x − i 1 X ﬀ T = − = P 0 0.5 i t X + p − ± − x 1 1 ) model expresses this polynomial factorization property with − d 1 ) + q j

1 i L temperature, T , =   − = d erence between consecutive hourly temperature readings) were per- t h = ff T x , (1 × p ]     ! t i i autoregressive terms, i L L ∆ L = i i i apparent thermal di 2 0 , and is express by: φ φ / φ = p d 2 1 1 1 0 0 α p = = p p = − Z C depth by extrapolating the thermal gradient from the two deepest temperature i i i X X X 0 ◦ ∆ autoregressive parameter, [ p − − − erence (the di = Whereas the term An ARIMA ( For a time series of data A series of statistical analysis were performed to describe the soil temperature time The apparent thermal di We calculated the thawing days (days in which all hourly soil temperature measure- 1 1 1 = = ff rewritten as:   p where, φ term. The error arepled assumed from to a be normal independent, distribution identically with distributed zero variables mean sam- and one standard deviation [0,1]. used to judge suitabilityafter the of best the models were model. selected Considering a the seasonal component seasonalthe was nature added. present case, of then the a ARMA data, model:  integrated moving average (ARIMA) modelsfound. were In tested order until to satisfactory results defineplots were the to best determine fit the forStandardized appropriate the Residuals, model, data Autocorrelation then we plot, first amation Ljung-Box examined series Criterion the statistics of (AICc) ACF and combinations and (Burnham Akaike’s were PACF and Infor- tested; Anderson, 2002) where the major parameters to the time series in order to identify global trend and finally a series of autoregressive formed and the time seriesby was locally decomposed weighted into smoothing it’s (Loess) seasonal and using trend a components window of 25. A linear fit was applied et al. (1985),estimative Outcalt to and assess Hinkel, thewere (1989), made resistance Hinkel for to intermediate et depths energyand al. of plotted flux (1990, for both each in profiles, 2001) day. and the have mean profile. used values were Hourly this calculated series. estimations The Box–Pierceto test confirm and the Augmentedshown). stationarity Plotting Dickey–Fuller it’s and histogram tests (frequency independent distributiondi of where distribution temperature of readings) performed and the first time series (data not α where increments (m), one value greater than mulative sum of the(FDD, mean obtained daily by temperatures theaccording above cumulative Guglielmin 0 et sum al. of (2008).the the The 0 active mean layer daily thicknessmeasurements (ALT) temperatures was (Guglielmin, below calculated 2006). as 0 equation of McGaw et al. (1978): ments are positive and(days at in least which oneone all reading reading hourly is is soil warmer colderhourly than than temperature measurements measurements range are onlydays negative (days between and in at which least there are both negative and positive temperatures with at least 5 5 20 15 10 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | C ◦ ers ff C day 2.3 C day ◦ ◦ C) con- − C at the ◦ ◦ 7.3 5.4 0.46) in early − − 2433.3 ± − C ◦ C, in early January ◦ ) process having the C and ◦ q , d 21.20 + − C day (F3) and 64.0 p ◦ C day (F2), C and avg. min ◦ ◦ C day at F4; in contrast 2011 ◦ C and 0.5 ◦ C and ◦ C zone are rare, only one day was 516 ◦ 9.8 erent years was significant, in 2011 2623.1 − ff − − C for 2011). The freezing season started ◦ 0.24) occurs in late March and the minimum 1.36) between late July and early August. At C to 0.5 C day (F2), 95.6 ◦ 1430 1429 18.6 ± ◦ ± C (avg. max 2.1 ◦ − C C ◦ 0.5 ◦ 382 at F4. There is a clear preponderance of neg- erent periods (Fig. 3). Most of Thawing Days occur − 1.2 C day (F1), erence between maximum and minimum daily aver- ff − ◦ − unit roots. ff ered temperature change due to freezing and thaw- 8.03 ff C and minimum d − ◦ 3229.0 C day in surface and − ◦ erent years; 2008 had a mild winter (21 freezing days and 80,00 freezing degree days at 83.5 cm in July), the summer ect (bu ff ff − C for 2009 and ◦ erent years, daily temperatures records are presented in Fig. 2. C day (F1), 448.9 819 ◦ ff 0.98) was recorded around mid August. Disparities can be noticed − ± 17.4 C − C day (F4). Contrast between di ◦ 527 in surface and ◦ − 4.06 − 2040.8 − 4.1), reaching a maximum and a minimum of 5.75 The cumulative sum of the daily averages reached an maximum in 2009 and a mini- The grouping of days in Freezing, Thawing, Isothermal and Freeze Thaw o ALT was estimated for every season, results summarized in Table 1 (2008 and Daily Air temperature at Fildes Peninsula over the studied period averaged 0,88 freezing degree days at 83.5 cm in July) contrasted by a severe winter in 2011 ± mum in 2011 for all layers,being values varied the greatly hottest over months the and years,the December June TDD and always January were the 901.7 coldest(F4) (Fig. 4). and Over the all the FDD(F3), 57 were months FDD accumulated accumulated ative soil temperatures in the studied profile, despite the percolation of liquid water and a strong zero curtaining e of soil moisture); Freezefrequent Thaw Days freeze are thaw more cycles commonchanges in in in F1, depth, surface, the specially site speciallyrecorded on experiences in during the 2011 summer; for F4. great temperature accumulation of water over the permafrost table. a quick parameter for comparing di between January and March, more frequentlywere for recorded the for upper F4 most layers, in onlyNovember, March these eight 2009. are days more Freeze evenly Days distributed are infor concentrated depth longer between although April F1 periods and and in F2between where 2008, frozen December 2011 and and May, 2012. Isothermal Isothermal periods and are Freeze long Thaw for Days F3 occur and F4 which show 2012 being incomplete), maximum ALT101.0 ranged cm; between the 89.0 totality cm of andperiod. the 105.8 During active cm, layer 2010 mean froze curious during phenomenaative winter the occurred, every whole temperatures year year, at over reaching F3 theyear values studied remained ATL above neg- was zero estimated at the slightly bottom below most the layer deepest (F4), probe, in this this is probably due to the hourly measurements was 8.7 bottom most layer (F4). ( and late June, respectively. The di ages was greater for 2009 and 2011,were in recorded this ( years more extreme minimumin temperatures late May and the thawingyears. season Soil in temperature mid averaged Decembersidering with small all variability layers, maximum between soil the temperature for the upper most layer (F1) considering − (31 freezing days and of 2009 was considerablyat warmer 10.5 cm (31 in thawing January)thawing days compared degree and days to 64.77 at the 10.5 thawing summer cm degree in of January). 2010 days (17 thawing days and 21,15 4.1 Results and discussion Interannual variability of thecontrasts active between layer di temperature showsThe parallel temperature behavior at 10.5 despite cm reachesJanuary, reaching a a maximum minimum daily average ( (4.06 83.5 cm maximum temperature (0.30 reading ( when comparing the di autoregressive polynomial with 4 Results and discussion and thus can be thought as a particular case of an ARMA( 5 5 25 20 10 15 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | q and for the 1 d − , s p 2 (F3), values ects oppose ect is shifted ); m ff 1 ff Q 7 − , s − 2 D 10 , m × 5 P erentiating parameter − usivity can experience ff ff 1.8 10 values for the Ljung–Box ± × ρ and 5.3 1 − s 2 (F2) and 1.1 erentiating parameter whereas the best ff 1 m − 7 s − 2 1432 1431 erences of the hourly measurements give us 10 m ff 5 × − 0.7 10 ± × C) (Fig. 5). The plot of first di ◦ ering capacity, negative ATD values suggests that nonconductive e ff Although the studied period is limited to 57 months and any forecast is not suitable, Histograms for the studied layers show a predominance of temperatures around zero, ATD was calculated for F3 and F4 considering hourly readings and then average for namics and frost conditions in a densely vegetated Maritime Antarctic site. The controls two seasonal autoregressive parameters. The diagnosticssatisfactory, the of standardized the residuals models did (Fig. not 9)auto shows where clusters correlation of was volatility, no found significant betweenstatistics are the all residuals large, and indicating the that the residuals are pattern less. 5 Conclusions Monitoring of the CALM-S site on Fildes Peninsula provided data on the thermal dy- autocorrelations. The best model forter, one F1 seasonal and autoregressive parameter F3 and one included(Table 4), seasonal one di the autoregressive best parame- modeltoregressive for parameter F2 included and only one seasonalfit seasonal parameters, for di one F4 seasonal included au- one autoregressive parameter, two moving average parameters and data from the previous month,after this the correlation forth is smaller month for itimum the starts at subsequent to period the show and seventh increasingPartial month; negative autocorrelations after correlation, expresses reaching three a a yearsvidual max- significant “cleaner” lags, correlation the picture plot can of shows still a serialfor strong be the dependencies correlation subsequent seen. for for period the indi- previous andthat month not which fitted significant is the after negative monthly the fifth averagesfactors. lag. included Its The seasonal seasonal best components components ARIMA were ( model always small or null, as indicated by the partial negative for all layers and intercept is always negative (Fig.an 7). ARIMA model was testedthe data. to Autocorrelation each and layer partial in autocorrelationthe plots order selection were to of estimated the evaluate as ARIMA which a parameters. guide model The for better plots (Fig. fits 8) shows strong correlation with readings in order to have an estimation of over all trend, the slope of the line is slightly F3 expressed the largestF1 frequency (15 in this region;an the idea greatest of amplitude periods is withassociated great found with temperature summer for for oscillation, F1 mostfor and of F4 F2, (Fig. the being 5). strong evenly The variations distributed decompositionassociated for are of F3 with the and time summer very series for limited revealswarmer F1 great seasonal months; and component F3 F2, behaves mostahead, more of while erratically the and noise noise at is is F4 also reduced the concentrated (Fig. seasonal in e the 6). Linear regression was performed on hourly at 32.5 cm. Values for F3table, are negative consistently values smaller due are toand more its winter common, proximity of to occurring 2009 the in andbu permafrost winter winter and of 2010 spring andand of 2011. overwhelm 2008, the This fall conductive behavior trend indicates (Outcalt temperature and Hinkel, 1989). to water content, moist onthe one other hand hand enhances absorbs energy andATD emits flux for energy through the on percolation freezing 57 and and months on are thawing consistent was processes. with Average 4.2 other findings fromet wet al. soil (2013) profiles found of values Maritimesummer of Antarctica, De period 4.7 Pablo in afor profile F2 from has Byers tendency2009, Peninsula, of when Livingstone abnormally smaller Island. cold values temperatures ATD for calculated occurred and winter the being thaw season negative was for reduced fall and winter of profile. the climatic seasons, results areconsiderable seasonal shown variations in when Table 3. thawing1997). Thermal and The freezing di ability processes of the occur profile (Hinkel, in transmitting energy varies during the year mostly due above freeze temperatures during summer, positive temperatures are mild in the soil 5 5 25 20 10 15 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent erent ff ff , 2013. usivity in thawed and frozen soils using ff , 2011. usivity suggests strong influence of wa- ff 10.1017/S0954102012000818 1434 1433 , 2001. 10.1038/ngeo1062 This study was supported technically and logistically by the Brazil- 10.1016/S0921-8181(01)00095-9 The active layer thermal regime invironments, the with studied extreme period variation was in typicalfreeze surface of and during periglacial thaw summer en- cycles. resulting in frequent ter content and snowpack to the soil thermalThe regime. ARIMA model istions an when important longer tool data sets for are more available. conclusiveDespite analysis and the predic- variabilitythickness when over the comparing studied temperature period no readings trend can and be active identified. layer The ALT over the studiedannual period weather shows conditions, a reaching degree a of maximum of variability 117.5 relatedThe cm to calculated in ATD di 2009. apparent thermal di usivity in the refreezing active layer, Toolik Lake, Alaska, Permafrost Periglac., 14, 265– ff – – – – – conductive heat transfer associateddoi: with frozen soils, Global Planet. Change, 29, 275–292, vegetation conditions in permafrost areas, aGeoderma, case 144, study 73–85, at Signy 2008. Island (Maritime Antarctica), tation, a Special Report of WorkingChange, Groups edited I and by: II Field, ofMastrandrea, the C. Intergovernmental M. B., Panel D., on Barros, Mach, Climate V., K.Cambridge Stocker, J., T. 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CALM Sci., site, 25, Byers 167–180, Peninsula, doi: Livingston Island, the environment: A reviewProcess., and 6, recommendations 27–45, for 1995. further research, Permafrost Periglac. derma, 144, 43–49, 2008. sensitivity of Antarctic Gelisols, Antarct. Sci., 11, 387–398, 1999. formation on how climate changes influence active layer and permafrost, as follows: of weather on the thermal regime of the active layer have been identified, providing in- Kane, D. L., Hinkel, K. M., Goering, D. J., Hinzman, L. D., and Outcalt, S. I.: Non- IPCC: Managing the Risks of Extreme Events and Disasters to Advance limate Change Adap- Guglielmin, M.: Ground surface temperature (GST), active layer, andGuglielmin, permafrost M., monitoring Evans, in C. J. E., and Cannone, N.: Active layer thermal regime under di Hinkel, K. M., Paetzold, F., Nelson, F. E., and Bockheim, J. G.: Patterns of soil temperature Hinkel, K. M., Outcalt, S. I., and Nelson, F. E.: Temperature variation and apparent thermal Hinkel, K. M.: Estimating seasonal values of thermal di Flanner, M. G., Shell, K. M., Barlage, M., Perovich, D. K., and Tschudi, M. A.: Radiative forcing Ferron, F. A., Simões, J. C., Aquino, F. E., and Setzer, A. W.: Air temperature time series for De Pablo, M. A., Blanco, J. J., Molina, A., Ramos, M., Quesada, A., and Vieira, G.: Interannual Burnham, K. P. and Anderson, D. R.: Model selection and multimodel inference: a practical Bockheim, J. G.: Permafrost distribution in the southern circumpolar region and its relation to Bockheim, J. G. and Mcleod, M.: Soil distribution in the McMurdo Dry Valleys, Antarctica, Geo- Beyer, L., Bockheim, J. G., Campbell, I. B., and Claridge, G. G. C.: Genesis, properties and References ties. This istute a of Cryospheric contribution Science and of Technology. the Terrantar laboratory, part of the Brazilian National Insti- Acknowledgements. ian Navy, MMA,Chilean UFV Antarctic Institute and (INACH) FEAM-MG; for grants technical received and from logistical CNPq. support We during field also activi- thank the 5 5 30 25 20 15 10 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , R., ffi , 2008. 10.5194/tc-2-179-2008 1435 1436 er, R. J., Thomson, A. M., Weyant, J. P., and Wilbanks, T. J.: ff , L. M., and Giambelluca, T. 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I., and Ng, E.: Thermal properties and regime of wet tundra soils at Ledley, T. S.: Sea ice: multiyear cycles and white ice, J. Geophys. Res.-Atmos., 90, 2156–2202, 5 5 30 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Thermistor depth ∗ 105.8 cm (2011);89.0 cm (2012) 83.5 cm (F4) Class d ) 90.2 cm (2010); 67.5 cm (F3), and 117.5 (2009); 32.5 cm (F2), Clay c Usnea sp. Silt Himantormia sp. b 1438 1437 FS 1 − a 2 mm), 0.2 mm), ≤ g kg ≤ 0.05 mm, ≤ Aquic Haploturbels ( 0.002 mm. Coarse Sand (0.2 Fine Sand (0.05 < 0.002 Fildes – Turbic Haplic CryosolAB (Eutric) 0–20B 20–50C 28 50–100 29 14 18 17 34 30 36 20 47 18 9 Loam Loam Loam Depth (cm) CS a b c d Soil texture of the studied profile. General characteristics of the monitored site. SiteFildes Altitude/Slope/Aspect 65 m/3 % Soil max./S Class WRB/Soil Taxonomy Vegetation Cover Turbic Cryosol (Eutric)/ ALT Mosses and Lichens 102.5 cm (2008); 10.5 cm (F1), ALT – Active Layer Thickness. ∗ Table 2. Table 1. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3.4E-06 8.8E-06 1.3E-05 4.0E-06 1.0E-06 5.9E-06 0.0322 − − − − − − − 50.88 ma2 sar1 00.2536 sar2 0.6152 − 1.9E-04 9.3E-07 − − 0.7224 ma1 0.2355 − 0.212.45 1.5E-04 8.4E-06 1.6E-05 1.230.02 2.9E-05 2.9E-05 2.9E-05 3.51 1.9E-06 4.8E-08 1.99 6.3E-060.200.09 4.4E-05 1.6E-05 3.04 1.0E-06 5.0E-05 2.33 6.8E-06 1.1E-05 3.4E-05 7.3E-07 1.2E-05 1.10 4.2E-05 1.1E-05 1.64 1.0E-05 0.77 1.3E-04 2.86 1.870.27 1.1E-05 2.3E-04 1.1E-05 5.9E-06 − − − − − − − − − − − − − − − − 50.01 − 3.52 2.02 0.33 0.43 2.92 1.30 0.124.19 0.09 8.6E-05 2.9E-05 2.04 0.18 0.36 3.55 2.47 1.29 0.102.19 0.09 1.4E-04 6.3E-05 0.82 0.11 0.11 − − − − − − − − − − − − − − − − − − 0.7355 sar1 − 1440 1439 0.38 0.48 4.19 1.79 0.62 3.27 0.87 0.60 4.93 1.60 0.72 3.88 2.19 1.14 0.32 2.69 64.91 − − − − − − − − − − − − − − − − − 1.18 1.12 3.23 1.49 4.95 1.66 1.25 3.70 0.38 1.75 5.69 1.14 1.66 4.33 2.08 − − − − − − − − − − − − − − − 0.5507 sar1 − cients sigma squared and log of likelihood for F1, F2, F3, and F4. ffi 83.53 2.18 2.36 3.54 0.28 0.38 2.49 6.40 1.96 2.24 4.09 0.63 4.28 7.91 1.21 3.91 5.47 3.14 2.17− 0.85 0.44 0.25 sar1 − − − − − − − − − − − − − − − − 2 cients ar1 0.2540 ar1 0.2568 ar1 0.3534 ar1 0.5030 ffi ARIMA order, coe Average season temperatures for Air, F1, F2, F3, and F4; and ATD for F2 and F3. AICcBIC 173.65 178.49 136.41 141.25 106.6 111.44 118.06 130.07 Sigma log likelihood ARIMAOrder (seasonal)Coe (1,0,0) (1,1,0) (0,0,0) (1,1,0) F1 (1,0,0) (1,1,0) (1,0,2) (2,0,0) F2 F3 F4 Spring_2008 Summer_2009Fall_2009 1.64 2.55 1.55 0.37 0.11 3.5E-05 2.6E-07 Winter_2009 Spring_2009 Summer_2010Fall_2010 0.41 1.44 0.49 Winter_2010 Spring_2010 Summer_2011Fall_2011 Winter_2011 1.63 2.22 1.34 0.21 Spring_2011 Summer_2012Fall_2012 Winter_2012 1.35Spring_2012 1.86 0.94 AVG Standard Deviation 2.75 2.45 1.90 1.45 1.25 8.3E-05 1.9E-05 AveragesFall_2008 Air F1 F2 F3 F4 ATD_F2 ATD_F3 Winter_2008 Table 4. Table 3. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | .

(e)

62º 63ºS 20km and F4 62º15´S (d)

KingGeorgeIsland , F3 (c) 0 59ºW

59ºW

58º00´W rnfedStrait Bransfield 01020km , F2 Fildes 686m Peninsula (b) , F1

(a) 1442 1441 rk Passage Drake SouthShetlandIslands 58º30´W 61ºW Collins Glacier Antarctica Ardley Island : Daily temperatures records for Air(a), F1(b), F2(c), F3(d)and F4(e).

11 KingGeorgeIsland : Location of Fildes Peninsula within Antarctica and the South Shetland Islands. Figure Figure 10 62º00´S 59º00´W Fildes Peninsula

Figure Figure FIGURES 394 395 396

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: Thaw Degree Days and Freeze Degree Days for F1, F2, F3 and F4. 13 : Thaw days, Freeze days, Isothermal days and Freeze Thaw days for F1, F2, F3 and F4. 12 Figure Figure Thaw days, freeze days, isothermal days and freeze thaw days for F1, F2, F3 and Thaw degree days and freeze degree days for F1, F2, F3 and F4.

Figure Figure 403 401 402

3 and F4. and 3 1446 1445

erence for F1, F2, F3 and F4. ﬀ

: Histograms and First Difference for F1, F2, F3 and F4. 14 Figure Figure Loess time series decomposition for F1, F2, F3 and F4. Histograms and ﬁrst di

: Loess time series decomposition for F1, F2, F 404 405 406 407 408 409 410 411 412 15 Figure 6. Figure 5. Figure Figure

1448 1447

: Autocorrelation and Partial Autocorrelation plots for F1, F2, F3 and F4. 17 Figure Figure

Autocorrelation and partial autocorrelation plots for F1, F2, F3 and F4. Linear Regression for F1, F2, F3 and F4. : Linear Regression for F1, F2, F3 and F4. 423 424 425 426 422 16 Figure Figure Figure 8. Figure 7.

418 419 420 421 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Box statistics for ARIMA models fitted to F1, F1, to fitted models ARIMA for statistics Box - 1449

Standardized residuals, autocorrelation plot and Ljung-Box statistics for ARIMA mod- : Standardized Residuals, Autocorrelation plot and Ljung 18 Figure 9. els ﬁtted to F1, F2, F3 and F4. Figure Figure F4. and F3 F2,

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