Global Energy and Water Exchanges Project

Integrated Land Ecosystem - Atmosphere Process Study

Issue No. 13- April 2013

GEWEX and iLEAPS land-surface modelling – Bridging the gaps

www.ileaps.org1 A Editorial 4 Bridging the gap between the iLEAPS and GEWEX 4 1021 land-surface modelling communities B Science 6 Modelling the land-atmosphere interface across scales: from atmospheric science to Earth system science 9 Land modelling from the perspective of numerical weather prediction 12 Interconnectivity of processes and timescales in land surface modelling: a UK perspective from the JULES model 16 The Community Land Model Philosophy: model development and science applications 20 iLEAPS and GEWEX activities on benchmarking of land models

C Activities 24 Multi-scale modelling approaches for land- atmosphere interaction and feedback studies 27 ALANIS project results: a joint ESA-iLEAPS 16 Atmosphere-Land Interaction Study over Boreal Eurasia 32 iLEAPS-Japan: FACE

Free-Air CO2Enrichment study for paddy rice on nitrogen cycle (FACE-N) at Tsukuba FACE, Japan 27 33 iLEAPS-Japan Meeting D People 34 E Meetings 35

iLEAPS Newsletter Executive Editor ISSN Printed version 1796-0363 Tanja Suni ISSN On-line version 1796-0401 Newsletter Coordinator For submissions and subscriptions, please contact [email protected] Magdalena Brus Publisher Hardcopy circulation iLEAPS International Project Office (IPO) 3500 Erik Palmenin aukio PO Box 48 iLEAPS IPO is sponsored by the University of Helsinki, the Finnish FI-00014 University of Helsinki Meteorological Institute, and the Ministry of Education, Finland. Tel: +358 (0)9 191 50571 [email protected] www.ileaps.org

2 News and Science Highlights

iLEAPS-China iLEAPS-Eurasia

Phase 1 of iLEAPS (2004-2014) is coming The iLEAPS-China group represents a compre- iLEAPS-Eurasia starts its activities in 2013. to an end, but land-atmosphere research hensive view on land-atmosphere interactions iLEAPS-Eurasia focuses on the boreal and Arctic is expanding and iLEAPS is getting ready with expertise ranging from measurements regions in Eurasia and coordinates the multi- for its 2nd phase (2014-2024) by starting and modelling of trace gases, aerosols, and hy- disciplinary research programme Pan-Eurasian up several new activities. One of the main drology to emissions, land-use and land-cover Experiment (PEEX). For more information on actions is the restructuring or launch of changes, urbanisation, and remote sensing. iLEAPS-Eurasia, please contact: Dr. Hanna Lap- iLEAPS regional nodes, where local scien- The official launch of iLEAPS-China will take palainen ([email protected]) from tists come together to plan and coordi- place in April 2013. For more information, the Division of Atmospheric Sciences at the Uni- nate research based on the specific needs please contact Dr. Aijun Ding (dingaj@nju. versity of Helsinki. More information about the of a country or a region. edu.cn) from Institute for Climate and Global PEEX initiative can be found in the Meetings Change Research at the Nanjing University. section of this issue.

iLEAPS-Japan iLEAPS has identified other regions with cru- cial importance and characteristic needs and is iLEAPS-Japan, a sub-committee of the Science planning regional activities also in South-East Council of Japan, was restructured in 2012. Asia, Mediterranean, Africa and Latin America. Some of the main foci of iLEAPS-Japan are the expansion of ASIAFLUX in Japan and nearby re- gions, large-scale manipulation experiments in

managed ecosystems such as the Free-Air CO2 Enrichment Experiment (FACE), also with nitro- gen (FACE-N), and land-atmosphere-society in- teractions in East Siberia. For more information, please see: http://ileaps-japan.org/.

Seed funding iLEAPS observation community assesses the influence of vegetation on precipitation patterns and acts as a hub for information exchange, but aims In a new Nature publication, Spracklen et al. (2012) used iLEAPS not only facilitates scientific collaboration satellite remote-sensing data of tropical precipitation and iLEAPS offers seed funding to help start new LEAP vegetation combined with simulated atmospheric trans- to directly support actual research as well. In 2013, - port patterns to assess whether forests actually have an come to submit your free-form applications for rel- research, typically 5-7k € per activity. You are wel than 60% of the tropical land surface, air that had passed overinfluence extensive on tropical vegetation rainfall. in the They preceding found that few for days more pro - evant research activities to iLEAPS Executive Officer duced at least twice as much rain as air that has passed Tanja Suni ([email protected]). Call for new SSC members empirical correlation was consistent with evapotranspi- rationover little from vegetation. the forested The areas authors and demonstratedestimated that that defor this- The overall direction and development of the iLEAPS estation in the Amazon will lead to reductions of 12 and - is composed of 16 experts selected from the interna- 21% in wet-season and dry-season precipitation, respec is guided by its Scientific Steering Committee, which currently looking for new SSC members for the pe- tional environmental research community. iLEAPS is tively, by 2050. See: http://www.nature.com/nature/ journal/v489/n7415/full/nature11390.html. gender equality, we particularly encourage applica- tionsriod 2014-2016. from females In fromorder various to promote land-atmosphere regional and

research fields outside EU and US. Please contact Executive Officer Tanja Suni ([email protected]) if you are interested.

3 A Editorial

Gordon B. Bonan1 and Joseph A. Santanello, Jr.2

1. National Center for Atmospheric Research, Boulder, Colorado, USA Center (GSFC), Greenbelt, Maryland, USA 2. Hydrological Sciences Laboratory, NASA Goddard Space Flight

Bridging the gap between the iLEAPS and GEWEX land-surface modelling communities

Models of Earth’s weather and climate in response to climate change or hu- - man activities drive changes in climate ically been biogeochemical processes and moisture across the land-atmos- by altering energy, water, and biogeo- that(IGBP). affect The atmospheric focus of iLEAPS chemistry has histor and phererequire interface fluxes ofto momentum,solve the equations energy, - otic and human processes is part of the activities and associated land model in- Just as atmospheric models can, and do, evolutionchemical cycles.of models Inclusion of Earth’s of thesephysical bi tercomparisonclimate. As a result, projects GLASS have and typically iLEAPS differof atmospheric between weather physics and and climate dynamics. ap- proceeded in parallel as a function of plications, mostly related to issues of In contrast, numerical weather pre- their particular capabilities and appli- scale, resolved or parameterised phys- dictionclimate to(NWP) Earth relies system more models heavily (ESM). on ics, and computational requirements, accurate representation of the terres- Given the current and continued so too can the land models that pro- trial hydrosphere, including the initiali- dissolutioncations. of traditional discipline sation of soil moisture and snow, their boundaries driven by global environ- mental change research, it is now time- vide the required surface fluxes differ and feedbacks with boundary layer ly to survey the two communities to between weather and climate models. time evolution, and their influence on better understand their recent past, Here, however, the issue is less one of term biological processes are not nec- present, and future evolution in order otherscale-dependent minor land model parameterisations. differences, es- essaryprocesses. on these The carbonshort time cycle scales, and long- and, to develop improved prediction models peciallyComputational with respect demands to caninitialisation, influence as a result, the development and scope of land models for NWP has diverged iLEAPS-GEWEX joint Newsletter con- over time from that of the climate com- across scales. To this end, this special modelsdata assimilation, (and their development and forecast and skill. ap- common land-atmosphere research plication)However, is the largely distinction driven by among the differ land- acrosstains five these contributions two diverse that land illustrate mod- ent science and research needs of the scopemunity. of the land-atmosphere inter- elling communities: a perspective on faceThis is embodied distinction in two in theentities scientific with- modelling the land-atmosphere inter- Our understanding of Earth’s cli- - mateweather has and progressed climate communities. to the point that tanello); the parameterisation of land no credible modelling centre would de- Systemin the international Study (GLASS) scientific is a governing scientif- surfaceface across processes scales (G.in numericalBonan and weath J. San- velop a model without representation icbodies. panel Theof the Global Global Land Energy Atmosphere and Wa- of the terrestrial biosphere, the inter- ter EXchanges (GEWEX), a core project model development in an Earth sys- acting physical, chemical, and biological - er prediction models (M. Ek); similar components of the Earth system, and - modelling across weather and climate callywithin been the diurnalWorld Climate to seasonal Research to annual Pro tem model (D. Lawrence and R. Fisher);- A particular focus is the carbon cycle hydrometeorologicalgramme (WCRP). Its couplingfocus has between histori proaches towards land model bench- andhuman its feedbackperturbations with toclimate, the biosphere. but oth- scales (M. Best and C. Jones); and ap er biogeochemical cycles related to re- Land Ecosystem – Atmosphere Process- active gases and atmospheric chemistry esland Study and (iLEAPS) atmosphere. is the The land-atmos Integrated- marking (E. Blyth and D. Lawrence). - phere core project of the Internation- es in ecosystem state and biogeography al Geosphere-Biosphere Programme are also important. In addition, chang

4 GEWEX News

This 13th iLEAPS Newsletter issue has been prepared in cooperation with the Global Energy and Water Exchanges (GEWEX) project. The mission of GEWEX is to observe, understand and model the hydrological cycle and energy fluxes in the Earth’s atmosphere and at the surface.

Save the Date! 7th International Scientific Conference on the Global Energy and Water Cycle, The Netherlands, 2-5 June 2014

The th7 GEWEX Conference is being hosted by Wageningen University, in the Netherlands. The Conference theme will be focused on the WCRP Grand Challenges related to water resources, extremes and climate sensitivity. For updates on the Conference, see: http://www.gewex.org.

Two GEWEX Assessment Reports are now available Assessment of Radiation Flux The assessment evaluated the overall quality of available global, long-term radiative flux data products at the top-of-atmosphere and surface. Special emphasis was placed on evaluating the overall fidelity with which the GEWEX Surface Radiation Budget (SRB) Project data set captures seasonal to interannual variability, as well as longer-term trends. The objectives of this assessment were twofold: 1) to characterise the uncertainties in SRB and similar products from both a quantitative as well as qualitative perspective; and 2) to develop a better understanding of the strengths, weaknesses and assumption that define the SRB product and its uncertainties. Assessment of Global Cloud Data Sets The Cloud Assessment Working Group has completed its evaluation of the overall quality of available global, long-term cloud data products. The Working Group went beyond simple product comparisons at fixed space and time resolutions to provide expert insight into whether or not a specific cloud product is accurate enough to meet a specific application. While all the assessed products were covered, special emphasis was placed on the International Satellite Cloud Climatology Project (ISCCP) product that is the GEWEX standard product for clouds. Summaries of both the Radiation Flux and Global Cloud Data assessments are being submitted for publication in the Bulletin of the American Meteorological Society. The complete reports are available at: http://www.wcrp-climate.org/reports.shtml.

5 B Science

Gordon B. Bonan1 and Joseph A. Santanello, Jr.2

1. National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA 2. Hydrological Sciences Laboratory, NASA Goddard Space Flight Center (GSFC), Greenbelt, Maryland Modelling the land-atmosphere interface across scales: from atmospheric science to Earth system science

The parameterisation of Earth’s land surface for nu- merical models of weather and climate has evolved

andgreatly hydrometeorological over the past three coupling decades. between The initial land focus and was geophysical control of energy and water fluxes - tion,atmosphere. the partitioning Terrestrial of radiation vegetation into regulatessensible and these la- fluxes, for example, through the absorption of radia into evapotranspiration, runoff, and soil water stor- tent heat fluxes, and the partitioning of precipitation- els explicitly recognises many aspects of canopy mi- crometeorology,age. The current generationthough the ofdetails operational can vary land greatly mod

Gordon Bonan is a senior scientist Joe Santanello is a scientist at the The representation of plant canopies, and more at the National Center for Atmos- NASA Goddard Space Flight Center. generallyamong models. recognition that the biogeophysical pro- pheric Research. He specialises in His expertise is in land-atmosphere the development of terrestrial bio- coupling of water and energy cesses that regulate momentum, energy, and water sphere models for climate simula- cycles, and he co-chairs the Global - tion and is a member of the iLEAPS Land/Atmosphere System Study geochemical processes, led to a broad expansion of Scientific Steering Committee. (GLASS) panel of GEWEX (Global fluxes are closely tied to plant physiological and bio Energy and Water Exchanges). the scientific scope of land models. A key part of this- calgrowth components is simulation in the of simulation trace gas fluxesof weather, in addition climate, to energy and water fluxes, so that the models are criti current generation of models can now simulate leaf phenology,and the chemical the carbon composition cycle, community of the atmosphere. composition, The and vegetation dynamics in response to prevailing

linkagesmeteorological among conditions biogeochemical and climate cycles [1, (such 2]. as car- bon,However, nitrogen modeland phosphorus); frontiers exist reactive and theygases include: and at- mospheric chemistry (such as biogenic volatile organ- ic compounds, nitrogen emissions, methane, ozone, and secondary organic aerosols); improved represen- - ospheric processes; managed ecosystems, including tation of wetlands, river flow, groundwater, and cry

cropland and pastureland; and urban areas. The expanding interdisciplinary scientific breadth 6 of the models is part of the growth of deposition, ozone, aerosols, and land the atmospheric sciences towards - all this research is the recognition ity to simulate biotic and biogeochemi- thatuse andEarth’s land ecosystems cover change. and Underlying water- Earth system science. Indeed, the abil- sheds, and their coupling with the pects of the evolution of climate models atmosphere, are critical elements of cal feedbacks is one of the defining as global planetary change and plane- The development and use of land modelsto Earth systemconsequently models. spans a wide Two entities within the in- tary habitability. bodies have addressed various “Land models provide a aspectsternational of thescientific land-atmosphere governing framework to integrate - phere System Study (GLASS) is a sci- theories of physiological, interface. The Global Land Atmos ecological, biogeochemi- Water EXchanges (GEWEX), a core cal, hydrological, and entific panel of the Global Energy and- meteorological function- - cipalproject goal within of GLASS the is World to coordinate Climate Rethe ing.” evaluationsearch Programme and intercomparison (WCRP). The of prin land models and their applications to scien- - The models provide a framework to in- grated Land Ecosystem – Atmosphere - spectrumtegrate theories of research of physiological, communities. eco- Processestific queries Study of broad (iLEAPS) interest. is theThe land-Inte esmodels at diurnal [4, 5]. to annual timescales, and logical, biogeochemical, hydrological, atmosphere core project of the Inter- thereThese is a needevaluations to include focus biogeochemi on flux - and meteorological functioning; global national Geosphere-Biosphere Pro- cal processes and ecosystem states in a models test the generality of these the- systematic evaluation of models across ories in a diverse array of ecosystems iLEAPS is to provide understanding of multiple spatial and temporal scales [6, thegramme interacting (IGBP). physical, The scientific chemical, goal and of - Some researchers apply the models biological processes in the land-atmos- cle must be additionally tested for long toand discover environments and understand across the feedbacks planet. timescale7]. Models (decadalof the terrestrial to centennial) carbon decy- among soil moisture, surface energy these processes, and their effects on cli- mographic processes (such as mortal- matephere and interface, other humanEarth system modification process of- ity), biogeochemical processes (such and precipitation to improve weather as litter decomposition and soil organ- fluxes, boundary layer development,- as diverse as that of the land modelling ic matter formation), and whole-plant ers are interested in longer term pro- es. The science of GLASS and iLEAPS is physiological processes (such as car- prediction and climate simulation. Oth- communityMuch of asour a understandingwhole. However, of someland- data over the course of a day or year cessesWhile (such the as land carbon models cycle) are that designed influ atmospherecommon research interactions needs areis gained evident. from doesbon allocation).not mean that Matching the model flux tower per- forence coupling past and withfuture atmospheric climates. mod- forms appropriately for the transient

- is only as robust as the models them- response to climate change, CO2 ferti- al feedbacks with the atmosphere, an models, and scientific advancement- - emergingels and specifically frontier is simulateto apply land terrestri mod- ation by the hydrometeorological com- sequently, the terrestrial carbon cycle els for climate change impacts, adapta- munityselves. Modelfocuses development on short-term and (diurnal evalu andlisation, its feedback or nitrogen with deposition.climate are Conrou- - to seasonal to annual) energy and wa- ample, the models can be used to study - “An emerging frontier is thetion, impacts and mitigation of extreme research. weather For events ex odologies to critically evaluate the or climate change on water resources, ter fluxes. GLASS has advanced meth to apply land models for biotic resources, and urban climate; so- scale with the Project for the Intercom- climate change impacts, models, both at the local flux tower cietal adaptations to climate change; parison of Land-Surface Parameterisa- adaptation, and mitiga- and land management policies to mit- tion Schemes (PILPS) and at the glob- igate climate change over the twenty- al scale with the Global Soil Wetness tion research.” integrated framework to assess physi- tinely assessed in transient simulations cal,first chemical, century. Theand modelsbiological provide respons an- simulationsProject (GSWP) forced [3]. with PILPS- observed and GSWP- me- over the twentieth century forced with es to the multitude of anthropogenic teorologystyle uncoupled are routinely flux tower used and to evaluglobal- perturbations in the Earth system, in- coupled carbon cycle-climate simula- reconstructed meteorology [8, 9] or in cluding climate change, CO2, nitrogen the land components of Earth system - ate energy, water, and carbon fluxes in tions [10]. These carbon cycle evalua

7 tions must be integrated with the PILPS Lawrence and Fisher, this Newsletter) es arising from our collective activities; and GSWP hydrometeorological evalu- and Earth system stewardship to main- There are also ongoing communi- The distinction between land-at- tyfor projectsclimate simulation. that clearly demonstrate we inhabit – the ecosystems and water- mosphereations. coupling for numerical the connection between the iLEAPS shedstain a sustainablefrom which future.we obtain The resources land that weather prediction models and climate, necessary for habitability – and its re- sponse to planetary pressures is criti- robustand GLASS impacts domains. (LUCID) For study example, [11] andthe “Climate parameterisa- theLand-Use Global and Land-Atmosphere Climate, IDentification Coupling of tions need to be robust Experiment (GLACE) [12] both address cal to that future. across changing environ- land surface effects on climate change [email protected] through hydrological and biospheric in- mental and biological con- [email protected] ditions, whereas simpler, a priority in both groups as well, with teractions. Benchmarking has become empirical parameterisa- the approaches adopted by each clearly connected to their particular model ca- References tions in combination with pabilities and development foci (Blyth data assimilation may be 1. Bonan GB 2008. Forests and climate change: better in the context of In addition, each of the common forcings, feedbacks, and the climate benefits of processesand Lawrence, described this Newsletter). above is already or forests. Science 320, 1444-1449. - weather prediction.” will soon be monitored routinely and at 2. Levis S 2010. Modelling vegetation and land use in models of the Earth System. Wiley In terdisciplinary Reviews:et al Climate Change 6, or Earth system, models is not straight- (such as snow water equivalent and 840–856. high-resolution from satellite. Snow land surface model development over a dec- snow cover), soil moisture, land cover, 3. van den Hurk B . 2011. Acceleration of- affect the land-atmosphere interface and leaf area are all examples of Earth forforward. weather Many prediction of the processesor simulation that monitoring and conditions that are ade of GLASS. Bulletinet al. of the American Mete orological Society 92, 1593-1600. of seasonal-to-interannual variability improvements and functional and structural 4. Lawrence DM 2011. Parameterisation are similarly relevant for climate sim- (parameters, data assimilation, eval- uation)required of asland influential models in components both com- advances in version 4 of the Community Land feedbacks found in climate simulations Model. Journal of Advances etin al Modeling Earth- areulation. a manifestation Many of the of land-atmosphere local-scale feed- Systemsprehensive 3, M03001. set of benchmark tests for a land 5. Blyth E, Clark DB, Ellis R . 2011. A com backs between land and atmosphere, atmospheremunities. interactions across multi- However, a unified science of land- and carbon at both the global and seasonal mediated through the boundary layer, surface model of simultaneous fluxes of water and must be understood in that con- - - eterisationsple scales, codified need to into be robust a model, across may scale. Geoscientificet al Model Development 4,- er and climate scales include: snow changingprove to beenvironmental unwieldy. Climate and biologi param- ment255-269. of terrestrial biogeochemistry in cou- 6. Randerson JT . 2009. Systematic assess andtext. vegetationCommon processes masking acrossof snow weath albe- do; soil moisture-evapotranspiration- may not necessarily improve weather pled climate-carbonet al. models. Global Change- precipitation coupling; leaf area and its prediction;cal conditions. more Such simple, parameterisations empirical pa- Biology 15, 2462-2484. effect on evapotranspiration; and land rameterisations in combination with 7. Luo YQ 2012. A framework for bench data assimilation may be “better” in the marking landet almodels. Biogeosciences 9, 3857-- 3874. An integrated representation of the restrial carbon cycle, future plant geography 8. Sitch S . 2008. Evaluation of the ter physics,cover change. chemistry, and biology of the use of satellite and ground-based ob- land-atmosphere interface must in- servationscontext of weatherin a land prediction. data assimilation Indeed, and climate-carbon cycle feedbacks using five form model development and evalua- - Dynamic Globalet al. Vegetation Models (DGVMs). es (such as soil moisture, snow) is nec- Global Change Biology 14, 2015-2039. - 9. Le Quéré C 2009. Trends in the sources essarysystem toto provideestimate initial land states conditions and flux for tion across weather and climate scales. and sinks of carbon et dioxide. al Nature Geosci- model (the Joint UK Land Environment ence 2, 831-836. The UK Met Office has a unified land bon cycle feedback analysis: results from the Simulator, JULES) for weather predic- - 10. Friedlingstein P . 2006. Climate-car- tion and climate simulation (Best and encenumerical across weather multiple prediction disciplines models. is not How to develop the necessary sci C4MIP model intercomparison.et al Journal of Cli mate 19, 3337-3353. weather prediction models in the US various disciplinary aspects of global climate responses to past land cover change: 11. Pitman AJ . 2009. Uncertainties in- useJones, a land this model Newsletter). (the Noah In model; contrast, Ek, environmentalclear. What is clear, change however, research is that have the this Newsletter) for operational fore- morphed into a broader context of: the first results from the LUCID intercompari casting and research; the Community Earth system and its interconnected son study. Geophysicalet al Research Letters 36, Earth System Model uses a different physical, chemical, biological, and hu- couplingL14814, doi:10.1029/2009GL039076.between soil moisture and precipita- 12. Koster RD . 2004. Regions of strong model (the Community Land Model, man components; the planetary stress- tion. Science 306, 1138-1140.

8 B Science

Michael Ek National Centers for Environmental Prediction (NCEP), Environmental Modeling Center (EMC), Col- lege Park, Maryland, USA Land modelling from the perspective of numerical weather prediction

Numerical weather prediction (NWP) Not all improvements models typically forecast weather for “ a few days (short-range) out to a few in land surface models are relevant for weather pre- of a land-surface model (LSM) coupled dictions.” withweeks a “parent” (medium-range). atmospheric NWP model; consists the LSM provides proper lower boundary

thesefluxes, energy and soil balance heat fluxcomponents (heat going de- exchangeconditions of to heat, the atmosphericmoisture, and model. mo- pendinto or on coming various out surface of the properties ground). (Ta All- mentumSpecifically, between the LSM the accountssurface and for low the- - er atmosphere, which involves many interactive land-atmosphere processes ble In1) conjunctionand, therefore, with a properradiation specifi and cation of the land state is necessary. Dr. Michael Ek leads the Land-Hydrology surface-layer models, in the cold sea- Team at the National Centers for Environ- To provide proper boundary con- son the LSM must also account for the mental Prediction/ Environmental Modeling ditions(Fig. 1). for the relevant temporal and effects of snow and frozen soils such as Center (NCEP/EMC) for the National Oceanic spatial scales of a NWP model, a LSM changes in surface roughness, reduced and Atmospheric Administration/ National must have appropriate physics to rep- plant activity, and heat associated with Weather Service (NOAA/NWS). The Land-Hy- resent land-surface processes (such - drology Team is responsible for all aspects of as evaporation), corresponding land ly account for the surface water budget land-hydrology in NCEP operational regional data sets and associated parameters assnow-melt. part of the The larger LSM hydrological must also proper cycle, and global weather and seasonal climate (such as vegetation and soil types), and with inputs to the land from precipi- models, and includes applications to drought and connections with the hydrology and wa- proper initial land states (such as soil ter resources communities. Prior to joining from evapotranspiration (ET, evapora- tation (and dew/frost), and outputs the NCEP/EMC in 1999, he spent ten years at analogous to initial atmospheric con- tion from surfaces and transpiration by Oregon State University where he worked on ditions,moisture). though These land initial states land statesmay car are- land-atmosphere interaction, and land and ry more “memory” (especially in the atmospheric boundary-layer model develop- deeper soil), similar to the inertia in soil),plants), and surface sub-surface runoff runoff (overland (or base- flow ments. His other activities include involve- sea-surface temperatures because of for water that cannot infiltrate into wet ment with the Global Energy and Water Cy- as changes in the land states: soil mois- cle Exchanges (GEWEX; gewex.org) program, With input from a radiation scheme tureflow, leaving(including the frozen),LSM bottom), snow asdepth well including projects on land-surface (GLASS), inthe the large parent heat model, capacity the of LSM the partitionsocean. atmospheric boundary layer (GABLS) and Hy- the incoming radiation (long-wave and that ET is part of both surface energy droclimatology (GHP), and the American Me- and density, and canopy water. Note teorological Society Hydrology committee. short-wave) into a surface energy bal- He is particularly interested in the role of local ance accounting for short-wave radia- In the history of NWP, the effect of and water budgets. land-atmosphere coupling in the evolution of land (and thus the inclusion of land land-surface fluxes and boundary-layer de- radiation emitted (upward) by the sur- models) was largely ignored by NWP velopment, including clouds, a project under face,tion reflectedturbulent by sensible the surface, and latentlong-wave heat models, for example those used at the GEWEX/GLASS (called “LoCo”).

9 soil physics and patchy snow cover [3], quickly test upgrades prior to coupled which was later implemented in the (NGM)US National included Weather a simple Service. single-layer But by - make use of specialised measurements the late 1980s the “Nested Grid Model” er NWP centres around the world has testing. Uncoupled LSM validation can the OSU (Oregon State University) LSM NCEP global model. Progress at oth - wassoil slabintroduced model, andinto in the the NCEPearly 1990s,global LSM testing and validation is often (surface sensible and latent heat fluxes, forecast model, and had two soil lay- doneevolved in asomewhat coupled mode: similarly. that is, with a Systemfor example). Study (GLASS) Under the leads GEWEX an ongo pro- ers with soil heat diffusion equations NWP model with interactive land and inggramme, effort thein uncoupled Global Land/Atmosphere LSM evaluation, and soil hydraulic properties [1], an ex- atmosphere components, where LSM coordinated with a number of weather plicit annual cycle of vegetation, plant “validation” may use near-surface me- and climate centres, to study LSM per- stomatal control [2], and simple snow teorological variables (such as 2-meter formance for different seasons at vari- - air temperature), though errors may be - graded version of the OSU model (now attributable to a number of non-land “Noah”physics. LSM) This in was the followed NCEP mesoscale by an up throughous locations this worldwidecooperative [4]. project NWP cen in - tres have been involved and benefited ingprocesses the LSM (Fig. by observed 1). An alternative(or model, or is For example, OSU LSM runoff was sim- formulations,model. Most notably, improved the Noahsoil hasthermal four synthetic)uncoupled atmospheric testing. This forcing includes where driv plylearning discarded about (andtheir unbalanced)LSM performance. in its conductivity,soil layers, new and infiltration addition andof frozenrunoff there is no land-atmosphere interac- early phase and participation in this ef- tion, allowing the LSM to be isolated fort, therefore, led to improvement in from other NWP model components to more properly address systematic LSM A parallel activity under GEWEX Figure 1. Schematic showing the many inter- errors, which allows more direct land thatthe OSU extends model into surface the coupled water balance. realm is active processes in the land surface and at- the GLASS local land-atmospheric mod- mospheric boundary layer (ABL). Adapted from [8], courtesy of Mike Ek and Kevin Tren- Uncoupled tests are computation- elling (“LoCo”) project that seeks to un- model validation. berth (their Figure 3.1 from www.gewex.org ally inexpensive, allow for multi-year derstand, model, and predict the role “Draft GEWEX Imperatives”). LSM runs, and thus provide a method to of local land-atmosphere coupling in

10 Energy budget component Land surface property the Table 1. Relationships between energy budg- component depends on et components and land surface properties. Reflective properties: Sun’s short-wave radiation reflected • Soil moisture back upwards from surface • Vegetation cover and density • Emission of long-wave radiation (heat) • Vegetation and soil types from land surface (surface emissivity) (many weeks) and out to seasonal cli-

Turbulent fluxes: Soil moisture Finally, as NWP models move to- mate forecasts. • Surface momentum flux • Vegetation cover and density wards being more fully Earth System • Sensible heat flux (warm air rising) • Vegetation and soil types (ES) models, there is a need for an in- • Latent heat flux (water vapour flux • Surface roughness creasingly improved representation of resulting from evapotranspiration) • Atmospheric stability the land surface, with connections to other ES components such as ground- water hydrology, river-routing (com- Soil heat flux • Soil moisture and soil type pleting the water cycle with freshwater • Overlying vegetation - chemical cycles, plus the required high- - springtime “green-up”, which may be flow to the ocean), ecosystems, biogeo es and state variables, including clouds ahead of or behind the greenness cli- fewer degrees of freedom and, there- [5];the evolutionthe extent of ofland-atmosphere coupling is limited flux matology, remote sensing allows a LSM fore,er spatial a greater resolution. need to This properly will lead repre to- which allows for a more isolated ex- to more appropriately partition availa- sent land surface processes at a range amination of coupled land-atmosphere ble energy at the surface between heat of temporal and spatial scales beyond processes, again, before regional and can provide the atmospheric model Improved weather forecasts de- and moisture flux. This way the LSM those of NWP. global coupled testing. pend on NWP models having proper has a subsequent positive effect on, for [email protected] model physics and good initial condi- with better boundary conditions. This References tions, the latter typically generated by A land-data assimilation system - instance, clouds and convective rainfall. ical equations for some soil hydraulic proper- - (LDAS) that combines satellite and 1. Clapp RB and Hornberger GM 1978. Empir - ground-based observations may yield fore,long-term from “offline”the LSM (‘spin-up’)perspective, simu up- optimal estimates of the current land variationsties. Water in Resources leaf water Research potential 14, and 601-604. stoma- gradeslations to of land the uncoupledphysics and LSM. better There initial 2. Jarvis PG 1976. The interpretation of the - land states will help NWP model per- the NASA Land Information System tal conductance found in canopies in the field. formance, as long as the LSM improve- states and surface fluxes. For instance, Philosophicalet al Transactions of the Royal Soci - may help to assimilate soil moisture, etyNoah of Londonland surface 273, 593-610. model advances in the ple, a LSM component that predicts the (LIS) data assimilation techniques [7]- 3.National Ek MB Centers. 2003. for ImplementationEnvironmental ofPre the- changesments are in relevant ecosystems for NWP. (evolving For exam vege- tionally, the use of an uncoupled LDAS diction operational mesoscale Eta mod- tation types) is not relevant, while an (runsnow, under and otherLIS, for land instance) data sets. as a cycled Addi improved ET calculation may be be- (uncoupled) land model system, may el. Journal of Geophysicalet al Research 108, then provide better initial land states to landdoi:10.1029/2002JD003296. surface model development over a dec- 4. van den Hurk B . 2011. Acceleration of- cause it directly influences the surface of the latter is the introduction of CO2- As the connection between weather energy budget calculation. An example the parent atmospheric model for NWP. ade of GLASS. Bulletin of the American Mete based photosynthesis [6] to replace the and climate becomes more “seamless”, orological Society 92, 1593–1600. empirically based Jarvis [2] formula- that is, for the case where NWP mod- 5. Santanello JA 2011. Results from Local tion that is still widely applied in LSMs els are used for extended-range (sever- Land-Atmosphereet al. Coupling (LoCo) Project. GEWEX Newsletter, 21(4), 7-9. 2- al weeks) to seasonal climate forecasts, land surface model with multiparameteriza- based formulation is a better represen- land models must consider their im- 6. Niu G.-Y 2011. The community Noah- used in NWP models. However, the CO tion and evaluation with local-scale measure- tation of plant stomatal control and - tion options (Noah-MP): 1. Model descrip ple, from the NWP perspective, while pact out to these time scales. For exam Better initial land conditions may vegetation types (ecosystems) remain ments. Journalet al of Geophysical Research 116,- bethus achieved of ET. using remotely sensed ob- static, vegetation coverage and density D12109.logic modeling and data assimilation frame- will vary seasonally depending on pre- 7.work Kumar enabled SV by the. 2008. Land An Information integrated Systemhydro “greenness” climatology accounting cipitation patterns and other variables servations. For instance, a vegetation for seasonal vegetation phenology was important for plant growth and senes- soil(LIS). moisture IEEE Computer on boundary-layer 41(12), 52-59. cloud develop- an improvement at one time for NWP, - 8. Ek M and Holtslag AAM 2004. Influence of but with near real-time observations ic” vegetation is not necessary for NWP available, land models may now use ac- oncence. time So scales although from modelling days to weeks, “dynam it ment. Journal of Hydrometeorology 5, 86-99. becomes important for extended-range tual vegetation greenness. So during 11 B Science

Martin Best and Chris D. Jones

Met Office, Exeter, UK Interconnectivity of processes and timescales in land surface modelling: a UK perspective from the JULES model

Inter-connected processes. Inter-connected timescales. Understanding our environment and being able to predict changes in it are of crucial importance for both

must be able to understand and simulate natural pro- cessesweather in andthe environmentclimate research. as well In orderas our tointeractions do so, we with them and our vulnerability to natural hazards - cles are crucially important processes for our ability across all space and time scales. Carbon and water cy- ence on society through, for example, food and water to predict future weather and climate and their influ

Martin Best has worked in the Met Chris Jones studied Physics at Cam- (numericalavailability. weather prediction) timescales as from Office on land surface science for bridge University and joined the For the purposes of this article we define NWP over 20 years and currently leads Met Office in 1993, working in nu- hours to days with emphasis on quantitative predic- the research and model develop- merical weather prediction (NWP) - ment in this area, which includes research on data assimilation of ra- managing the JULES community dar rainfall data with a focus on its aretion concerned of specific with events changes (“there in willthe long-termbe a heavy average show land surface model. He has been a influence on soil moisture. In 1997, ofer weatherin London conditions at 3 pm on (“Europe Friday”). is Climatelikely to projectionsexperience member of the Global Land/Atmos- he joined the Met Office Hadley - phere System Study (GLASS) panel Centre to work on the first coupled termediate timescales (from months to several years) of GEWEX (Global Energy and Wa- climate-carbon cycle global circula- hotter summers and wetter winters by 2050”). At in- ter EXchanges) for almost a decade tion model (GCM) with Peter Cox. tions from long-term climate (such as a warmer than and has co-chaired the panel for Since then he has pursued research it is becoming possible to try to predict specific devia the last four years. He has worked into climate-carbon cycle interac- on the land surface science on all tions and framing research results averageLand-atmosphere UK summer) interactions although not are specific key processes weather spatial and temporal scales, gaining in ways of relevance to policy mak- on a given day. an understanding of requirements ers, such as the influence of cli- determining the behaviour of our environment and from all perspectives. He was the mate feedbacks on the compat- are at the heart of both iLEAPS and GEWEX scientif- first person to implement an urban ible fossil fuel emissions to achieve - scheme within both weather fore- different levels of climate targets. cess level and faithfully representing them in numeri- cast and climate models, designed In 2005, Chris became manager of calic goals. models Understanding form the foundation these interactionson which weather at a proand and co-ordinated the first inter- the terrestrial carbon cycle group national urban model comparison in the Hadley Centre and since surface models (LSMs), such as JULES (“Joint UK Land project and has served two terms 2011 is now Head of the Earth Sys- climate predictions can be built. Process-based land-- on the American Meteorological tem and Mitigation Science team. Society Board on the Urban Envi- Chris is currently a Lead Author on toEnvironment atmospheric Simulator”, general circulation http://www.jchmr.org/ju models attempt to ronment (BUE). the IPCC AR5 Carbon Cycle chapter. representles/; the UK these community key interactions land surface seamlessly model), to coupled a level

12 Table1. Influence of sur- Surface fluxes face states on surface fluxes at various time- Momentum Moisture Heat Carbon scales: Hours to days (NWP), Seasonal, Cli- mate. LAI = leaf area in- • Surface to atmos- • Surface to atmos- • LAI seasonality dex (area of leaf surface phere humidity phere temperature • per unit ground surface). gradients gradients • Influences land • • • cover changes, • Surface to atmos- • Surface to atmos- • LAI seasonality, phere humidity phere temperature • vegetation gradients gradients • growth and • • • decomposition • Surface to atmos- • Surface to atmos- phere humidity phere temperature Surface temperature Surface gradients gradients

• Water availability • Indirectly influenced • for evaporation by water availabil- LAI seasonality • ity for evaporation, • • • Water availability through the surface Influences land • for evaporation energy balance cover changes, • • • LAI seasonality, • • Water availability • Indirectly influenced vegetation • for evaporation by water availabil- growth and ity for evaporation, • decomposition through the surface energy balance

Soil moisture • • Indirectly influenced by water availabil- ity for evaporation, through the surface

Surface states Surface • energy balance

• Specified or • Specified or • Indirectly influenced • assimilated LAI and assimilated LAI by specified or Turbulent vegetation height and vegetation assimilated LAI and exchanges of CO (uptake • height vegetation height, 2 • Specified • through the surface and respiration) seasonally varying • Specified energy balance from available LAI and vegetation seasonally • vegetation and height varying LAI • Indirectly influenced soil carbon • and vegetation by specified stores • Modelled seasonal height seasonally varying variations in the • LAI and vegetation vegetation LAI and • Modelled height, through the land cover seasonal the surface energy variations on variations in the balance decadal timescales vegetation LAI • and the land • Indirectly influenced cover variations by modelled seasonal

Vegetation and soil carbon stores carbon and soil Vegetation on decadal variations in the timescales vegetation LAI and the land cover variations on decadal timescales, through the surface energy balance

13 Coupled Carbon and Water cycles Figure 1. Simplified schematic of synergies between the water and carbon cycles through Water enters the soil, is sucked up vegetation and soil and how they interact. through the plant, and evaporates through the leaves er, some developments required for the Carbon is assimilated through the CO longer climate timescales are now be- 2 leaves, dropped to the soil and ing adopted by the shorter forecasting respired back to the atmosphere

timescalesThe requirement as well. These for modelling interactions all ofare these summarised time and in Tablespatial 1. scales for a H O 2 at the forecasting timescales through amodel more such detailed as JULES understanding delivers benefits of the of accuracy that allows skilful predic- More generally, the land surface longer-timescale processes important stores of energy, water and carbon in- - Although iLEAPS has more focus teract with each other and are key con- ling developments are gradually be- ontions. biogeochemical processes whilst trollers of land-atmosphere exchange ingfor climate.adopted Asinto such, forecasting climate applica model- GEWEX focuses on the large-scale hy- of energy, water and carbon as well as drological cycle, these are not two sep- the carbon cycle in climate projections not distinguish between time or spa- hastions. been For shown example, [1,2] the and crucial is becoming role of cycles are closely intertwined – in re- tialmomentum. scales, so The physical physical controls system such does as common in climate modelling, but it is arate areas. Carbon, water and energy the carbon cycle play a role at all scales not yet common to include carbon cycle - - waterality and and in carbon LSMs. Figurecycles as1 shows,they move in a - ever, prognostic leaf area could, for ex- invery opposite simplified directions way, synergies but around between a cultfor each by the surface availability flux. However, of data on such which pa ample,processes improve at shorter weather timescales. predictions How in similar pathway across the land-atmos- rameterisations are made more diffi seasons that have had unusually ear-

Although this does not capture the surfaceto base thestates model. of energy (as observed, sensing of vegetation greenness could fullphere carbon interface. or hydrological cycle, it for Theinstance, modelled through influence temperature), of the land bely orused late by leaf data onset. assimilation Similarly, schemes remote forms a simple example of key process- to help initialise soil moisture in places have traditionally involved a different where direct observations of moisture the soil, is sucked up by the plant and emphasiswater, and forcarbon the onvarious the surface spatial fluxes and subsequentlyes. Whereas rainwaterevaporates enters from through the temporal scales as listed below and de- Other trace gases and aerosols both leaves, carbon is taken up through the - affect,content and are are not affected available. by, the land-sur- leaves, is allocated to the plant, depos- ited to the soil and eventually decom- picted in the top part of Fig. 2. Howev posed back into CO2 presence or cycling of water and car- bon affects the presence. At each and stage cycling the of the other – for example soil mois- ture affects plant growth and carbon uptake, whereas CO2 affects stoma- tal opening and evapotranspiration (evaporation from surfaces and tran- affect, and are affected by, energy parti- spiration by plants). Both also strongly tioning and transformation. Figure 2. Flow of benefits in the development cycle of a seamless surface modelling system for all spatial and temporal scales. Pie charts represent schematic of proportional influence of land surface states on fluxes. Evaluations can be applied on diurnal, seasonal and dec- adal to centennial timescales, but with de- creasing data availability.

14 (volatile organic compounds) emis- sionsface and and vegetation aerosol state.formation, Biogenic ozone VOC deposition and subsequent damage to plant cells and vegetation response to diffuse light caused by aerosols are all

“Carbon and water cycles are crucially important processes for our ability to predict future weather and climate and their in- fluence on society through, for example, food and water availability.” important land-atmosphere interac- included in climate models but also havetions applications[3-5]. These at are seasonal beginning and toNWP be timescales especially related to air (driven by prescribed input conditions) et al. Evaluation of a photosynthesis-based biogenic On the other hand, there are more The future JULES developments have isoprene4. Pacifico emission F, Harrison scheme SP, Jones in JULES CD and simu2011.- opportunitiesquality forecasting. to evaluate the land sur- toand pass coupled a series to anof benchmarking atmosphere model. tests lation of isoprene emissions under present- - face scheme on the forecasting time- [10], cover short and long timescales, local and regional spatial scales and day climateet al.conditions. Atmospheric Chemis processes controlling all of energy, wa- tryof climate and Physics change 11, through 4371–4389. ozone effects on the seasonalscales than cycles for the that climate are undertakentimescales. 5. Sitch S 2007. Indirect radiative forcing inRoutine the forecasting evaluation community of the diurnal deliv to- In short, the land-atmosphere sys- et al carbonland-carbon to climate sink. Naturechange 448, constrained 791–794. by car- er insight into the physical processes temter and is a carbon. complex coupled system of in- 6. Cox PM . 2013. Sensitivity of tropical that can not be determined by study- ter-connected processes and inter-con- ing timescales longer than the diurnal bon dioxide variability. Nature, doi:10.1038/ - use, and the data we use to evaluate seasonalnature11882. cycle to constrain snow albedo feed- 7. Hall A and Qu X 2006. Using the current ability are increasingly being used to nected timescales. Both the models we - cycle. Observations of short-term vari relate observable quantities to long- Through this approach, applications back in future climate change. Geophysicalet al. Re term projections with the ultimate goal acrossthem mustall space reflect and these timescales interactions. can Jointsearch UK Letters Land 33, Environment L03502. Simulator (JU- LES),8. Best model MJ, Pryor description M, Clark – DBPart 1: Energy 2011. Theand such, climate modelling processes are bring robustness to our understand- - improvedof constraining using information uncertainty from [6,7]. the As inglearn of and and benefit prediction from of each weather other and water fluxes. Geoscientific Modelet Developal evaluation of the forecasting perfor- Thement Joint 4, 677-699. UK Land Environment Simulator (JULES),9. Clark DB,model Mercado description LM, Sitch – Part S 2: Carbon. 2011. climate. mance (bottom part of Fig. 2). [email protected] The JULES perspective fluxes and vegetationet al dynamics. Geoscientific [email protected] ofModel benchmark Development tests for4, 701-722. a land surface model JULES is the UK community land-sur- 10. Blyth E . 2011. A comprehensive set References face model that is used across all scales - et al from operational NWP to climate mod- of simultaneous fluxes of water and carbon at warming due to carbon-cycle feedbacks in a both the global and seasonal scale. Geoscien - 1. Cox PM . 2000. Acceleration of global tific Model Development 4, 255–269. search tool throughout the UK research et al. elling at the Met Office and is a key re coupled climate model. Nature 408, 184–187. 2. Friedlingstein P Cox PM, Betts RA, - interactions among the carbon, water 2006. Climate–carbon cycle feedback analysis, andcommunity energy cycles [8,9]. Itdescribed includes above the main and results from the et C4 al. MIP model intercompari has a wide user base across space and inson. diffuse Journal radiation of Climate on 19,the 3337–3353.global land carbon 3. Mercado LM 2009. Impact of changes timescales. It is commonly used offline sink. Nature 458, 1014–1018. 15 B Science

David Lawrence and Rosie Fisher

National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA The Community Land Model Philosophy: model development and science applications

The Community Land Model (CLM) is the dynamic land model component of the Community Earth Sys-

originally developed primarily as a lower boundary conditiontem Model for (CESM). the atmosphere, As with many principally land models, the itCom was- munity Atmosphere Model within the CESM (though CLM is also used in several regional climate models

therefore, of early versions of CLM was on the simula- and the Norwegian Earth System Model). The focus,

principaltion of water (but and not energy exclusive) budgets purpose over continues land. to be as theSince terrestrial that time, component CLM has evolved within anconsiderably. Earth System Its Model (ESM) and as a tool to promote understand- David Lawrence is a scientist in Rosie Fisher is a project scientist in ing of the complex land surface contributions and the Terrestrial Sciences Section of the Terrestrial Sciences Section at NCAR’s Climate and Global Dynam- the National Center for Atmospher- end, two central themes drive CLM development and ics Division. He is a co-chair of the ic Research (NCAR) in Boulder, CO. responses to climate variability and change. To this Community Earth System Model She is interested in integrating eco- use: 1) terrestrial ecosystems, through their cycling (CESM) Land Model Working Group logical processes, plant optimal- of energy, water, chemical elements, and trace gases, (LMWG) and is also a member of ity theory, and emerging trait da- are important determinants of weather and climate, the CESM Scientific Steering Com- tabases into land surface models. and 2) the land surface is a critical interface through mittee. As LMWG co-chair he di- Her graduate studies at Edinburgh - rects the development and ap- University focused on measuring tems and through which humans and ecosystems can plication of the Community Land the response of tropical rainfor- which climate change influences humans and ecosys Model, which is improved and uti- ests to low rainfall, and using those When viewed in this light, the utility of CLM is and lised by a diverse array of scientists data to test process-based models. canaffect be global vastly environmental expanded beyond change. its original purpose from NCAR, the Department of En- Since then she has worked in Shef- and in fact there are multitudinous actual and possi- ergy (DOE) national laboratories, field, Los Alamos and NCAR on de- and US and international universi- veloping new methodologies for used as a tool for assessing climate change impacts on ties. His research interests centre predicting and testing vegetation ble applications of CLM. Importantly, it is increasingly around land processes and Earth responses to climate change. ecosystems and ecosystem services, hydrological sys- system modelling, with an empha- sis on Arctic terrestrial climate sys- tem feedbacks. tems (including drought and flooding), agriculture, and urban environments. Development philosophy and science priorities The overarching development strategy for CLM rests on the notion that the land system is highly coupled and that improvements, for example in the represen-

16 tation of biogeochemical cycles, con- - “The Community Land tribute to improved hydrologic and en- cesses in diurnal to interannual weather • To quantify the role of terrestrial pro- Model is increasingly used - as a tool for assessing ticergy perspective cycle simulation, of the terrestrial and vice system versa. and climate variability including influ climate change impacts onThe a modelwide varietythus benefits of time from and aspatial holis resourcesence on droughts, under climate floods, change;and extremes; - • To establish the vulnerability of water on ecosystems and ecosys- geochemical parameterisation devel- change: for instance, permafrost-car- tem services, hydrological opmentscales. is Core complemented biogeophysical by efforts and bio to bon• To feedback,quantify land snow- feedbacks and vegetation-al to climate- systems, agriculture, and bedo feedback; are broadly set to improve and enable - urban environments. “ theexpand capacity model of functionality.the model to be Priorities applied • To prognose anthropogenic and nat to address pressing terrestrial climate Current capabilities, use, and - onural climate; land cover/land use change and trace gas emissions and their influence evaluation modelscience development questions. Examplesinclude the of follow scien- on local climate and the unique impact The most recently released version of ing:tific topics that are driving current CLM of• To climate examine change the impact in urban of areas;urbanisation - geneity affects land-atmosphere inter- modelthe model, performance CLM4 [1,2], and representsfunctionali a- and nitrogen cycle interactions and actions• To assess and howcarbon land cycling, surface including hetero significant improvement in terms of • To improve understanding of carbon scale issues; of the terrestrial carbon sink; ty. In addition to its core functions of their influence on long-term trajectory- increase exploitation of experimental carbon, water and energy cycling, CLM4 • To enable model – data fusion and bility of ecosystems to climate change ecosystem data; Figure 1. Schematic diagram depicting pro- • To assess the response and vulnera and disturbances (human and natural) cesses represented in CLM. Items highlighted and the possibility for ecosystem man- and investigate parameter optimisa- in pink are new or modified for CLM4.5. Note agement to mitigate climate change; tion• To techniques quantify parameter uncertainty that not all processes are depicted.

17 also simulates a suite of more complex of CCSM (Community Climate System During the ongoing development include dynamic vegetation chang- coupled climate model intercompari- esterrestrial that allow processes. plant types Such to processesadapt to sonModel) projects /CESM and in thea prior CMIP3 version and CMIP5 of the hascycle, seen the model transformation improvement of CLM4 and ex to- changing climate conditions, inter- CLM4.5 (scheduled for release in 2013) active nitrogen cycling that restricts - Improved parameterisations are being the ability of the biosphere to seques- tionsmodel have was also used been in the submitted C4MIP carbonto the incorporatedpansion across throughout many fronts the model (Fig. in 1).- ter carbon beyond the limits of nutri- ongoing,cycle feedback biogeochemically analysis. CLM focused simula cluding for canopy physiology and pho- ent supply, crop behaviour, land-use - change (including wood harvest) im- jects and several GEWEX-supported pacts on both carbon cycling and bio- projectsTRENDY such and Permafrostas LUCID, the Carbon series pro of tosynthesis [7], permafrost hydrology- geophysics, urban environments, as Global Land-Atmosphere Coupling Ex- ics[8], snow,including lake dynamicsanthropogenic [9], river triggers flow, well as permafrost dynamics, dust pro- periments (GLACE), which investigate runoff generation [10], and fire dynam duction, aerosol deposition onto snow, - and, last but not least, biogenic volatile ity and trends on weekly to seasonal and suppression [11]. New features weatherthe influence and climate,of soil moisture and the variabilhistoric prognosticslated for inclusion wetland indistribution, CLM4.5 include eco- With increased model complexi- and forthcoming Global Soil Wetness systemmethane demography emissions [12], [13], flooding vertical and- tyorganic comes compound the need emissions.for new, better, and - more comprehensive tools to evaluate multi-layer canopy radiation, crop fer- the behaviour of the coupled land sys- Projects (GSWP). Feedback from par tilisationly resolved [15] soil and biogeochemistry irrigation [16], [14],and “One major challenge questions that drive CLM development facing CLM is an appro- comprehensive development approach focustem. Thoughon longer many timescales, of the fundamental long-term riverine transport of nutrients. The - priate balance across the consistent with past CLM development ly, model behaviour is routinely evalu- processes represented: the experiencehelps maintain that scientific indicates balance that improve and is- atedvalidation at diurnal, data isseasonal, sparse. Consequentand interan- overall model will suffer if ments in one facet of model behaviour nual time-scales, which is reasonable as these are the temporal resolutions excessive attention is paid On the longer term, developments at which the majority of simulated pro- to one set of processes at thatoften are benefit being other pursued coupled for future processes. mod- - the expense of others.” el releases include data assimilation ments to model structure should be within the CESM Data Assimilation evaluatedcesses operate. systematically Ideally, against new develop a suite of validation data at multiple tempo- ticipation in these projects informs interactions with the socio-econom- icResearch processes Testbed, represented enhanced by Integrat two-way- benchmarking system is not in place model that can be addressed in future ed Assessment Models, feedbacks be- andral and therefore spatial CLMscales. validation A comprehensive remains CLM developers of deficiencies in the tween vegetation and canopy airspace

Improved model validation is the underestimatesversions of the the model. 20th Forcentury example, land vegetation, and the capacity to simulate goaloverly of subjective the International and case specific.Land Mod - TRENDYcarbon uptake analysis (excluding revealed carbon that losses CLM properties, the influence of ozone on- el Benchmarking project (ILAMB) and due to land cover change) and has led CLM researchers strongly support and to an intensive effort to improve CLM alongsub-grid with soil further moisture/snow parameterisation distri maintain an active role in this pro- improvementsbutions and lateral to existing groundwater biogeochem flow-

ILAMB will only be part of the model carbon and nitrogen cycling. ject. There is also recognition that Future Directions ical and biogeophysical processes. - Challenges ly exploiting experimental data from Knowledge of model limitations and manipulationevaluation picture. studies We and are process increasing ob- strengths, determined in part through As is clear from the above discussion, servations as powerful constraints on model intercomparisons and the in- CLM is being developed with the over- creasingly numerous applications and arching goal of steady improvement in - science priorities of CLM and CESM, the process-oriented depiction of the model behaviour and structure. Recent has spurred increasingly diverse and global terrestrial system in an Earth ofexamples nitrogen includefertilisation model/experimen on tree growth comprehensive model development - tal-data comparisons on the influence - iad directions in which the model can poisoning of vegetation [5], and snow- ties are within the scope and have ben- beSystem developed Model. Clearly,with ever-increasingthere are myr [3], litter-bag decomposition [4], ozone activities. These development activi - - - tributesshrub-permafrost to many modelinteractions intercompari [6]. - sequently,efited from CLM the researchers expertise of maintain both the a lengecomplexity facing andCLM processis the maintenance fidelity. One of CLM also benefits from and con GEWEX and iLEAPS communities. Con major scientific and management chal son projects. CLM is employed as part presence in both communities. appropriate scientific balance across

18 the processes represented: the overall also drawing from hydrologic, ecosys- model will suffer if excessive attention 2011. Improving canopy processes in the is paid to one set of processes at the ex- Continued progression of these terres- Community Land Model (CLM4) using global - trialtem, systems and human models dimensions will require models. a sus- flux fields empirically inferred from FLUXNET lution should advance in parallel across tained and cooperative effort involving data. Journal ofet Geophysicalal Research 116,- thepense range of others. of the model Ideally, components process reso in the iLEAPS and GEWEX research com- G02014.tion of the terrestrial hydrological cycle in the context of emerging science prior- 8.permafrost Swenson regionsSC .by 2012. the ImprovedCommunity simula Land ities, which has roughly been the case munities and beyond. Systems (in press). Model. Journalet of al. Advances in Modeling Earth development (model improvements [email protected] model for climate simulations: Model struc- (at least partly by design) for CLM4.5 9. Subin ZM 2012. An improved lake [email protected] ture, evaluation, and sensitivity analyses in References CESM1, Journal of Advances in Modeling Earth aspread compromise across model, between Fig. 1).demands for et al. et al. increasedThe existing process CLM resolution structure both reflects from Systems 4, M02001. 1.functional Lawrence and DM, structural Oleson KW, advances Flanner in MG version Evaluating runoff simulations from the Com- 10. Li H, Huang M, Wigmosta MS 2011. ecological and hydrological perspec- 2011. Parameterization improvements and- - munity Land Model 4.0 using observations input is to engage with as wide a com- 4 of the Community Land Model. Journal ofet Ad al. tives. One way of ensuring a diversity of vances in Modeling Earth Systems 3, M03001. from flux towers and a mountainous water 2. Oleson KW, Lawrence DM, Bonan GB shed. Journalet al. of Geophysical Research 116,- and users of the model as possible, so 2010. Technical description of version 4.0 of rameterizationD24120. of intermediate complexity in munity of scientific developers, testers, that inappropriate model structures the Community Land Model (CLM). NCAR 11. Li F 2012. A process-based fire pa- and parameterisations come to light Technical Note NCAR/TN-478+STR, National a Dynamic Global Vegetation Model. Biogeo Center for Atmosphericet al Research, Boulder, CO, et al. sciences 9, 2761-2780. and dynamic modelling environment nitrogen257 pp. limitation: confronting two global 3. Thomas RQ . 2012. Global patterns of 12. Riley WJ, Subin ZM, Lawrence DM requiresquickly. Maintenance broad trans-disciplinary of such a complex par- 2011. Barriers to predicting changes in global Global Change Biology (submitted). methane biogeochemistry model integrated terrestrial methane fluxes: analyses using a ticipation, open-source coding practic- biogeochemicalet al models with observations.- es, and sustained support for software composition in earth system models with et al in CESM. Biogeosciences 8, 1925-1953. development and maintenance as well long-term4. Bonan GB litterbag . 2012. experiments: Evaluating An litterexample de Assessing uncertainties in a second-genera- tion13. Fisher dynamic R, McDowell vegetation N, model Purves caused D by. 2010. eco- Despite these challenges, the future using the Communityet al Land Model version 4- as documentation. logical scale limitations. New Phytologist 187, (CLM4).tosynthesis Global and Change transpiration Biology 19,responses 957-974. to et al. 666–681. The number of problems to which 5.ozone: Lombardozzi Decoupling DL modeled. 2012. Predictingphotosynthesis pho The effect of vertically-resolved soil biogeo- theseof CLM models and land can modellingnow be applied is bright. is chemistry14. Koven onCD, C Riley and NWJ, dynamics Torn MS in a land 2012. sur- (submitted). and stomatal conductance.et al Biogeosciences 9,- et al. - point that it is probably more appro- 3113-3130. face model. Biogeosciences - impressive. CLM has advanced to the sponse to increasing Arctic shrub abundance priate to think of CLM (and compara- 6. Lawrence DM . 2011. Permafrost re 15. Drewniak B 2012.(submitted). Modeling agricul ture in the Community Land Model. Geoscien ble land models) as terrestrial systems local soil cooling versus large-scale climate et al - models which are a result of synthesis depends on relative influence of shrubs on tific Model Development 16. Sacks WJ . 2009. Effects of global ir and integration of existing knowledge rigation on the near-surface climate. Climate warming. Environmental Research Letterset al. 6, manifest in land surface models but 10.1088/1748-9326/6/4/045504. Dynamics 33, 159–175. 7. Bonan GB, Lawrence PJ, Oleson KW

19 B Science

Eleanor Blyth1 and David Lawrence2

1. Centre for Ecology and Hydrology, Wallingford, UK 2. National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA iLEAPS and GEWEX activities on benchmarking of land models

Improvements in land surface models (LSM) are seen in both the GEWEX and the iLEAPS communities as key to further progress towards understanding and predicting the interactions of terrestrial ecosystems

Broad agreement exists that we need improved mech- anismswith weather, to assess climate the quality variability, and suitability and climate of change.current and future generation land models for a wide array of

the vulnerability of water systems and ecosystems to climatestudies andchange, uses. and These carbon include cycle seasonal feedbacks; forecasting, the ap- proach is similar to earlier treatment of coupled cli-

Eleanor Blyth has worked for NERC David Lawrence is a scientist in Several international projects under the general mate models (Fig. 1). (Natural Environment Research the Terrestrial Sciences Section of - Council) for over 20 years and has NCAR’s Climate and Global Dynam- et al. [1] have extensive experience in land sur- ics Division. He is a co-chair of the banner of ‘benchmarking’ have recently been initiat face modelling. Her expertise is Community Earth System Model eda land to try model to improve benchmarking the situation. process Luo is a more sub- in developing land surface mod- (CESM) Land Model Working Group stantive,defined the detailed, term carefully. and systematic Fundamentally, evaluation the ofgoal land of els to include hydrological and soil (LMWG) and is also a member of models and land model processes which will enable hydraulic processes. Recently she the CESM Scientific Steering Com- modellers to track progress, intercompare models, has concentrated on developing mittee. As LMWG co-chair he methods to evaluate models at the directs the development and ap- global scale, pioneering the idea of plication of the Community Land and identify avenues for improvement. Benchmarking- benchmarking the UK community Model, which is improved and uti- should provide a measure of model ‘goodness’ against land surface model: JULES. She is lised by a diverse array of scientists some predefined metrics or thresholds. This is in con a member of several international from NCAR, the Department of En- evaluation’, whereby a single or set of observed vari- committees which foster land sur- ergy (DOE) national laboratories, ablestrast to of the interest more arecommon simply approach measured to againstdate of ‘modelmodel face modelling and evaluation of and US and international universi- the models: the SSG of GEWEX and ties. His research interests centre model strengths and weaknesses to scientists that uti- the SSC of iLEAPS. Her work has around land processes and Earth output. Benchmarking will provide information about been published in peer-reviewed system modelling, with an empha- Over the past few years, the interest in land model scientific journals. sis on Arctic terrestrial climate sys- lise land models in their research. - tem feedbacks. eral meetings and papers focused on scoping out the problem,benchmarking proposing has grown. community This isprojects, evident and in the design sev-

C-LAMP project outlined a set of tests that the Com- munitying prototype Land Model benchmarking (CLM) modelling systems. group Early on,put theto-

20 Figure 1. Schematic diagram of the bench- Model aspects to be evaluated marking framework for evaluating land mod- Process Parameter els. The framework includes four major com- ¥ Biophysics ¥ State variables ponents: (1) defining model aspects to be ¥ Rate variables ¥ Hydrology evaluated, (2) selecting benchmarks as stand- ¥ Responses ¥ Biogeochemistry ardised references to test models, (3) devel- ¥ Vegeta.on dynamics ¥ Feedback oping a scoring system to measure model Benchmarks performance skills, and (4) stimulating model improvement. Adopted from [1]. ¥ Observa.ons ¥ Temporal scale ¥ Experimental results ¥ Spa.al cover ¥ Data-­‐model products ¥ Error structure Model improvement ¥ Rela.onship and paHerns to evaluate more complex land surface ¥ Structure ¥ Parameter models and to guide their further devel- ¥ Ini.al condi.on ¥ Input variables moisture–evapotranspiration (evapo- rationopment. from The surfacesassumption and is transpiration that the soil Metrics of performance skills by plants) and soil moisture–runoff re- • A priori thresholds To determine model’s • Scoring systems • Acceptability and that the simple model can provide considering weights for • Ranking different processes and estimateslationships for are, the to firstunderlying order, universalrelation- • Strength and deficiency data sets ships that operate in nature, which can - gether to help choose between com- sible to get the hourly energy balanc- ilar proposal is made by [1] for the car- peting carbon cycle model versions es right without ensuring that also the then be evaluated against models. A sim - bon cycle function of the land surface. by Cadule et al [3] of how to test cou- ulated correctly; neither is it possible Observations for benchmarking pled[2]. Subsequently, climate-carbon a proposal models waswith made ob- tohourly get the evaporation annual carbon (water balance flux) is rightsim without ensuring that the daily car- Luo et al. [1] give a comprehensive list bon response to sunlight and soil mois- of possible sources of data for bench- waterservations. and theLast carbon but not cycle least, shouldthe JULES be marking the models, ranging from testedmodelling together group and [4] presented proposed athat simple the ture stress are correct. So although the between the carbon and water cycles is “Scientists across a sur- betweentower flux these data, data river is a task flows, in satelliteitself as keysuite to of the data links to betweendo that. Thisthe iLEAPS cross-over and prising number of coun- eachproducts has some and experiments.inherent errors, Choosing may not tries and disciplines have be presented in a suitable time scale A new group was formed to bring or requires some intermediate mod- the GEWEXcommunity benchmarking together: activities.iLAMB (In - expressed the need for el to translate between model output ternational Land-Atmosphere Model a robust and extensible - land model benchmark- tain: despite the improved availabili- discussions and decisions made by that tyand of observation.data, the human But onecost thingof gathering is cer groupBenchmarking) have been (www.ilamb.org). reported in a publica The- ing system.“ and manipulating the data for analy- sis against model output remains high and outline a framework for bench- and is one of the principal reasons that tion that sought to clarify definitions processes work in each timescale looks reasonablyfinal requirement straightforward, that only the the relevant pro- The increase in relevant data avail- marking [1]. Figure 2 summarises the cesses that deliver it are not only com- abilitybenchmarking is a boon systems to land are modellers desirable. and findings. Targets for benchmarking The interrelations of land surface processesplex but interrelated. mean that comparing the ofneeds datasets to be arebetter limited, exploited. the same But datarisks- identifying which processes in the model against a single observation is setdo exist.that is Sinceused tothe develop number a parameterand length- modelsBenchmarking are relevant can befor simplifiedthe different by isation or to calibrate a model is often timescales: the energy balance is im- the model against surface states and used again in the model evaluation pro- portant (and should be benchmarked) almost meaningless. Instead of testing- at the hourly timescale, the water bal- derlying functions of the model, such as even be used a third time to weight or ance at the monthly timescale, and the thefluxes, control we need of soil to test moisture it against on theevapo un- eliminatecess [6]. Inmodels some within cases, aa datasetmulti-mod can- el database based on their skill at rep- - licating some aspect of the climate sys- requiredcarbon balance performance at the annual of the modeltimescale. can anceration model and runoff.in combination Recently, with Koster multi et- tem prior, for example, to using the However, this simplification of the- decadalal. [5] showed observations that a simple can be water utilised bal be deceiving. In practice, it is not pos models to examine climate projections. 21 Figure 2. Performance index I2 for individ- ual models (circles) and model generations (rows). Best performing models have low I2 values and are located toward the left. Circle sizes indicate the length of the 95% confi- dence intervals. Letters and numbers iden- tify individual models (see supplemental online material at doi: 10.1175/BAMS-89-3- Reichler); flux-corrected models are labeled in red. Grey circles show the average I2 of all models within one model group. Black circles indicate the I2 of the multimodel mean taken Using the same data multiple times over one model group. The green circle (REA) during the model development and metrics take into account that the mod- corresponds to the I2 of the NCEP/NCAR rea- evaluation process is clearly a problem, elwater performance nor by energy. may not But depend how can on the nalyses. Last row (PICTRL) shows I2 for the but because available data is so sparse, model itself but be driven by the driv- preindustrial control experiment of the CMIP- 3 project. Adopted from [10]. ing data as in these previous examples? One proposed way to distinguish this is often unavoidable. Metrics for evaluation the role of the model in capturing true response of the land surface to the Useful information about model behav- The benchmarking framework re- weather or climate is to provide an al- iour can be gleaned through analysis quires a set of metrics that quantify the - of models against available and future performance across the full range of pler than the LSM and based on the manipulation experiments including - ternative, statistical ‘model’, that is sim for example, rainfall exclusion, FACE tential metrics, but establishing ones only shows how this can be done, but (Free-Air CO2 Enrichment), nitrogen thatmodel test processes. the performance There are of manythe mod po- alsodriving have data delivered only. Abramowitz a freely available [7] not fertilisation, and snow-fence experi- el rather than the quality of the driving web-based system: PALS (Protocol for Analysis of Land Surface models) that ments. how should one design metrics for riv- will do the tests and load up the models Community response erdata discharge can be challenging.in the face of uncertainFor example, es- timates of basin-scale precipitation? panel of GEWEX is proposing this as Scientists across a surprising num- In other situations, the outcomes of the(www.PALS.unsw.edu.au). international standard and The is GLASSincor- ber of countries and disciplines have expressed the need for a robust and meteorology: for instance, completely extensible land model benchmark- the model are entirely influenced by the poratingA NASA a suite design, of flux-tower the Land data Surface to be - used by everyone. APS (where the focus is on ecosystems “Despite the improved built which can contain several model anding system.carbon exchanges The linkage of betweenthe land sur iLE- availability of data, the asVerification well as all Toolkit the data (LVT) that [8] is hasused been for face to the atmosphere) and GEWEX model testing and hosts a suite of tests (where the focus is on energy and wa- human cost of gathering related to the energy and water bal- ter exchanges between the land and and manipulating the data the atmosphere) is clear; the two com- for analysis against model munities have contrasting expertise ance.As This noted type above, of tool it is kit often may challeng become- in carbon, energy and water balance output remains high and is increasingly useful for modellers. - one of the principal rea- ing that has gone on in this area by the sons that benchmarking intoing to these design models metrics is that gained test a throughspecific internationalstudies. This community article chronicles of land think sur- comparisonland model process. of the model Often, realagainst insight ex- face modellers, and the story is typical systems are desirable.” perimental data or case studies of par- of many cross-discipline, cross-country (and therefore cross-funders) projects dry deserts and very wet regions do not example, Bonan et al. require a complex carbon-water-ener- ofticular litter-bag extreme decomposition climatic events. studies As anto smoothly or always in the same direc- gy model with sophisticated mathe- [9] utilised a set tion,in that but it doesis always not alwayssteered flow by peopleevenly, matical components solving partial dif- loss over time through a controlled set with good will, good humour and good evaluate simulated vs. observed carbon- intentions! through an unsaturated soil in order to ed that “long-term litter decomposition ferential equations of the flow of water- experimentsof model experiments. provide a real-world They conclud pro- cess-oriented benchmark to discrimi- onlycalculate when the the evaporation.land is limited neither Evapora by [email protected] tion is difficult to determine or model [email protected] nate ecological fact from model fantasy.” 22 References et al. - “Using the same data predictions of future climate change? Philo- 1. Luo YQ 2012. A framework for bench 6. Knutti R 2008. Should we believe model multiple times during the marking land models.et al Biogeosciences 9, 3857-- 3874.ment of terrestrial biogeochemistry in cou- sophical Transactions of the Royal Society A model development and 2. Randerson JT . 2009. Systematic assess standardized,366, 4647-4664. diagnostic benchmarking sys- evaluation process is clear- 7. Abramowitz G 2012. Towards a public, pled climate–carbon models. Global Changeet al. ly a problem, but because Biology 15, 2462-2484. tem for landet al. surface models. Geoscientific- available data is so sparse, models3. Cadule against P, Friedlingstein long-term atmospheric P, Bopp L CO2 Modeltion toolkit Development (LVT) – a5, generalised819-827. framework 2010. Benchmarking coupled climate-carbon 8. Kumar SJ 2012. Land surface Verifica- this is often unavoidable.” measurements. Global Biogeochemicalet al Cycles- for land surfaceet al. model evaluation. Geoscien- prehensive24, 1-24. set of benchmark tests for a land compositiontific Model Development in earth system 5, 869-886. models with 4. Blyth E, Clark DB, Ellis R . 2011. A com long-term9. Bonan GB litterbag 2012. experiments: Evaluating an litterexample de and carbon at both the global and seasonal surface model of simultaneous fluxes of water using the Communityet al Land Model version 4 scale, Geoscientific et Model al. Development 4, (CLM4). Global Change Biology 3, 957-974. 255–269. - 10.the ReichlerAmerican T Meteorological. 2008. How wellSociety do coupled3, 303- 5. Koster Randal D 2012. Land Surface models simulate today’s climate. Bulletin of Controls on Hydroclimatic Means and Vari ability. Journal of Hydrometeorolgy 13, 1604– 311. 1620.

Physical and Physiological Forest Ecology

Editors: Prof. Pertti Hari, Prof. Kari Heliövaara, Dr. Liisa Kulmala 2013, 534 pages, 7 colour & 217 b/w photos and illustrations, 3 tables

ISBN 978-94-007-5602-1, Springer-Verlag

Features robust predictions and explanations of the effects of climate change on forests and feedbacks from forests to climate change. Synthesizes a wealth of information from cells in trees and microbes to the ecosystem. Features a wealth of illustrations to clarify ecological phenomena and concepts in the text.

This important contribution is the result of decades of theoretical thinking and high- value data collection by the University of Helsinki examining forest ecosystems in great detail. The ecology is dominated by a qualitative approach, such as species and vegetation zones, but in contrast quantitative thinking is characteristic in the exact sciences of physics and physiology. The editors have bridged the gap between ecology and the exact sci- ences with an interdisciplinary and quantitative approach. This book recognises this discrepancy as a hindrance to fruit- ful knowledge flow between the disciplines, and that physical and physiological knowledge has been omitted from for- est ecology to a great extent. Starting with the importance of mass and energy flows in the interactions between forest ecosystems and their environment, the editors and authors offer a strong contribution to the pioneer H. T. Odum and his work from over 50 years ago. This book introduces a holistic synthesis of carbon and nitrogen fluxes in forest ecosystems from cell to stand level dur- ing the lifetime of trees. Establishing that metabolism and physical phenomena give rise to concentration, pressure and temperature differences that generate the material and energy fluxes between living organisms and their environment. The editors and authors utilize physiological, physical and anatomical background information to formulate theoretical ideas dealing with the effects of the environment and the state of enzymes, membrane pumps and pigments on metab- olism. The emergent properties play an important role in the transitions from detailed to more aggregate levels in the ecosystem. Conservation of mass and energy allow the construction of dynamic models of carbon and nitrogen fluxes and pools at various levels in the hierarchy of forest ecosystems. 23 C Activities

Anil Kumar1,2, Joseph A. Santanello, Jr.1

1. Hydrological Science Laboratory (Code-617), NASA Goddard Space Flight Center, Greenbelt, Maryland, USA 2. Earth System Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, Maryland, USA

Multi-scale modelling approaches for land- atmosphere interaction and feedback studies

Community land surface models (LSMs) it is possible to achieve better under- oxide (CO2) entering or water vapour are similar in many respects, since each standing of soil-vegetation-land-at- exiting through the stomata of a leaf, must serve as a physically reasona- mosphere feedbacks and interactions where plant transpiration of water va- ble lower boundary responding to and (including biophysical, hydrological, pour reaches a maximum rate when - and biogeochemical interactions) be- canopy resistance is at lowest value ergy, moisture, and, in many cases, car- tween the land-surface and the atmos- constraining atmospheric fluxes of en We have investigated two canopy of the physical and biophysical proper- Adopting biophysical approaches resistance(that is, high methods, stomatal conductance).the well-known tiesbon. of In the particular, vegetation some canopy representation is a com- inphere climate at micro- models and is meso-scales not new, but [1,2]. sim - Jarvis approach and a more complex - Gas-Exchange Evapotranspiration Mod- One the major shortcomings of cur- senting biophysical processes can pro- el (GEM) approach based on the Ball- rentmon coupledfeature used land-atmospheric in LSMs. models duceple vs. different complex feedbacks approaches from to changes repre -

is the inability to generate the proper in CO2 proach is function of meteorological canopy turbulence near the surface be- efforts are oriented towards adapting parametersBerry approach. such In as brief, air thetemperature, Jarvis ap cause of the low heat capacity and com- the biophysical levels. Thus, approaches present modellingwithin a ambient vapour pressure, radiation, plex canopies of plants which compli- - and soil moisture availability, where- cate surface exchange and isolate the logical processes within LSMs are pri- as Ball-Berry employs more rigorous surface and canopy layers from the weathermarily represented or climate model.through The a stomatal physio plant gas-exchange responses such as conductance formulation, which de- carbon assimilation rate and respira- photosynthesis component to the LSM, scribes the rate of passage of carbon di- overlaying atmosphere. By including a In comparing both approaches, we foundtion [3,4]. that GEM does a better job in capturing both diurnal and long-term variability in canopy resistance over

anddifferent reasonable vegetation soil moisture types. GEM variabili also- tyreproduces relative to betterthat of surfacethe Jarvis heat approach fluxes

resistance over vegetation has a pro- found[4]. The impact parameterisation on the energy of partition stomatal-

Figure 1. Modelled dry deposition veloocity

(Vd) estimates by coupling GEM in the Noah

LSM. Observed Vd (solid line) and modelled Vd (dashed line).

24 Figure 2. Model-resolved isoprene emission rate (left) in response to soil moisture (right).

modelsthe Weather lack Researchcorrect representation and Forecasting of vegetation(WRF) model), and emission canopy characteristicsand air quality

addressed by bridging the gaps that can effectivelyin the LSM, capturebut such physicaldeficiencies processes can be from leaf scale to planetary boundary

coupling multi-layer vegetation cano- pyscale models (~1 km). with This photosynthesis-based can be achieved by models (such as GEM) within a large- scale modelling system (meso, regional

From an energy and water cycle perspective,and climate scales).land-atmosphere inter- actions play a very crucial role in ex- treme weather events that can lead ing and the prediction of boundary lay- this end, the National Aeronautics and The Model of Emissions of Gas- to drought or flash flood situation. To as temperature and humidity through esogeochemical and Aerosol studies. from Nature (MEGAN) - er fluxes and surface parameters such- is widely used for estimating biogenic Space Administration (NASA) Unified more, by coupling a multi-layer cano- volatile organic compounds (BVOCs) WRF model coupled to Land Informa py-soilthe transpiration model to GEM regulation. we have Further tested tion System (LIS) (NU-WRF; https:// emission estimate of isoprene (a hy- atmodelingguru.nasa.gov/community/ NASA’s Goddard Space Flight Cent- soil properties on atmospheric turbu- drocarbonin global andvolatile regional compound models. emit The- eratmospheric/nuwrf) (GSFC) has been developed modelling with system the lencethe influence exchange of from plant the biophysics leaf level scale and ted in high quantities by many woody goal to integrate satellite- and ground- based observational data products and efforts can therefore be integrated into on atmospheric chemistry) is impor- advanced land surface modelling tech- weatherto planetary and boundary climate layermodels, scale. which Such tant,plant especially species, within climate significant model impact simu- typically cannot resolve the canopy, as lations, because the increasing atmos- niques to produce optimal fields of land a means to account within-canopy pro- pheric CO2 concentration will decrease integrates the hydrology (LSM), biology isoprene emission, increase CCN con- (forsurface instance states MEGAN),and fluxes. aerosol, NU-WRF radia also- The land surface is also important centration, and lead to a cooling of tion, cloud, and chemistry components ascesses a sink on thefor total atmospheric flux [5]. pollutants - - through deposition pathways, which er CCN concentration have more and ic and hydrologic components of NU- GEM is also capable of resolving via the the planet. The clouds formed at high- of the coupled system. The atmospher- - light and are longer-lived and enhance smaller drops, and so reflect more sun favouredWRF have by shown the occurrence that excessive of unu run- air-pollutant(the operational deposition LSM component velocity. By used im MEGAN lacks correct representation sualoff leading heavy torainfall flood overand landslidesLeh city in was In- byplementing the National GEM withinCenters the for NOAH Environ LSM- ofplanetary canopy resistance cooling [7]. and Nevertheless,must include mental Prediction (NCEP) in the Unit- physiological approaches such as Ball- the impact of the LSM on local land- ed States, we are able to capture day-to- Berry canopy resistance and prognos- atmospheredia [9]. NU-WRF interactions has also and shown coupling that day variation of dry deposition velocity Preliminarily analyses conclude dry conditions, while planetary bound- Furthermore, dry deposition velocity thattic soil the moisture coupled processes. system (consisting of ary(known layer as (PBL) ‘LoCo’) schemes is maximized becomes during more isover sensitive different to leafvegetation area index types (leaf (Fig. area 1). per unit land area) and to maximum Berry scheme) is capable of estimat- As these studies have shown, fur- stomatal resistance prescription in the ingMEGAN the isopreneand NOAH emission LSM with and the its Ball-day- therimportant LSM duringdevelopment wet regimes is necessary [10]. model [6], and suggests that GEM can to-day variability in response to soil for better understanding of physical be effectively applied to estimate depo- - processes from leaf-level to the plan- - wise, current mesoscale (for instance, moisture variability (Fig. 2) [8]. Like sition velocity values for air quality/bi etary boundary layer (PBL) scale. 25 References LSM components and coupling are very et al. et al. importantResults continue in all aspects to demonstrate of weather thatand land surface model development over a dec- - 1. Van den Hurk B 2011. Acceleration of- ity6. CharusombatModule in the U, Noah Niyogi land D, Kumarsurface Amodel, climate modelling systems, and are 2010. Evaluating a new Deposition Veloc critical to explaining the role of carbon ade of glass. Bulletin of the American Meteoro et al. and biogenic emission on weather and logical Society 92, 1593–1600. - Boundary-Layer Meteorology 2, 271-290. - 2. Levis S, 2010. Modeling vegetation and land 7. Kiendler-Scharr A, Wildt J, Dal Maso M use in models of the earth system. Wiley In 2009. New particle formation in forests inhib more sophisticated coupled Earth sys- terdisciplinary Reviews: Climate Change 6, climate. This suggests that only with et al - ited by isopreneet al emissions. Nature 461, 381- tem model (such as atmospheric, hy- uation840-856. of a Coupled Photosynthesis-Based Gas hydrological,384. and biogenic emission processes drological, and biological) components Exchange3. Niyogi D Evapotranspiration. 2009. Development Model and (GEM) Eval to8. Kumarimprove A modeling. 2009. of Coupling isoprene vegetation, emission, will we be able to predict extreme for Mesoscale Weather Forecasting Applica- Joint Special Symposium on Biogeochemical events and to provide detailed assess- - Cycling of Trace Gases and Aerosols, AMS An- ments of hydrological aspects of the tions. Journalet ofal Applied Meteorology and Cli- system that have greatest impacts on synthesis-basedmatology 48, 349-368. canopy resistance formula- nual Meeting,et al. 11-15 January 2009, Phoenix, society (such as precipitation amount tion4. Kumar in the A Noah . land2011. surface Evaluation model, of Boundaa photo- Arizona. - 9. Kumar A 2012. Simulation of(in a Flashrevi- et al. Floodingsion). Storm at the Steep Edge of the Hima and surface runoff/flooding). modelry-Layer for Meteorology the simulation 138, of 263 atmospheric – 284. tur- layas. Journal of Hydrometeorologyet al. 5. Patton GE 2008. A coupled canopy-soilth Sym- the Nature of Land-Atmosphere Coupling posium on Boundary Layers and Turbulence, 10. Santanello JA Jr 2012. Diagnosing bulent modified by tall vegetation, 18 - During the 2006-7 Dry/Wet Extremes in the Stockholm, Sweden. U.S. Southern Great Plains. Journal of Hy drometeorology, e-View doi: http://dx.doi. org/10.1175/JHM-D-12-023.

New iLEAPS Research initiative

Interactions among Managed Ecosystems, Climate, and Societies (IMECS)

Jointly organised by iLEAPS, GLP, and AIMES

In order to understand the relative strengths and weaknesses of various land use classification systems and models for use in climate and water assessments, an organised comparison is necessary. In 2013, the research communities focussing on land processes (Global Land Project, GLP), land-atmosphere interactions (iLEAPS), and integrated Earth System modelling (Analysis, Integration, and Modelling of the Earth System, AIMES) are jointly launching a new research programme to address the knowledge gaps and make progress in advancing research in the field of managed ecosystems and their interactions with the atmosphere and societies.

More information: Tanja Suni, iLEAPS Executive Officer ([email protected])

26 C Activities

Diego Fernàndez-Prieto1, Mahmoud El Hajj2, Garry Hayman3, Markku Kulmala4, Gerrit de Leeuw4, Mattia Marconcini1, Simon Pinnock1

1. European Space Agency, ESA-ESRIN, Frascati, Rome, Italy 2. NOVELTIS, Ramonville Saint Agne, France 3. Centre for Ecology and Hydrology, Wallingford, Oxfordshire, United Kingdom 4. Department of Physics, University of Helsinki, Helsinki, Finland ALANIS project results: a joint ESA-iLEAPS Atmosphere-Land Interaction Study over Boreal Eurasia

Determining the role of the Eurasian - - boreal region in the global Earth sys- bon sink, especially as global warming - maynificantly increase perturb the number this important and extent car of Interising particular, fire-plume the dispersionfollowing andsatellite pro er, the size and remoteness of boreal - viding optimised surface CO emissions. tem is of critical importance. Howev- tion of both terrestrial ecosystem pro- fires,The as wellALANIS as the Smoke length Plumes of the fireproject pe Figure 1. A selection of scenes showing the cessesEurasia and pose their a challenge feedbacks to toquantifica regional utilisedriods. multi-mission Earth Observa- capabilities of the algorithm developed in tion (EO) data for improving current ALANIS for smoke detection. The red points on the scene are fire hot spots (FRP) detected Earth Observation (EO) data have dem- large-scale dispersion forecasts of com- and global climate. In the last few years, by the MODIS instrument. The yellow/green onstrated the potential to become a pounds emitted from biomass burning bounded regions represent where smoke major tool for estimating key variables - plumes/clouds have been detected. The up- and for characterising main processes ferent EO products have been integrat- per most scene was taken on the 30th July governing the land-atmosphere inter- edevents into occurringthe global in chemistry boreal Eurasia. Transport Dif 2010, the bottom left image is from the 14th Model, version 5 (TM5) [1], explicitly July 2010 and the bottom right from the 7th In order to utilise this potential, simulating the main processes charac- July 2010. theface. European Space Agency (ESA), as part of the Support To Science Element (STSE), has launched, in collaboration with iLEAPS, the ALANIS (Atmosphere LANd Interaction Study) project to ad- vance towards the development and validation of novel EO-based multi- mission products and their integration into suitable land-atmosphere coupled models that may respond to some of the key challenges of land-atmosphere

The three thematic areas ad- dressed:interactions smoke science plumes, in boreal methane, Eurasia. and aerosols, and their results are summa-

rised in the following sections. ALANIS - Smoke Plumes Boreal forests play a vital role in curb- ing global warming by storing billions of tons of carbon in forest and peat eco- -

systems. However, forest fires can sig

27 derived geo-information have been Figure 2: Inundation extent as derived for the used: 1) burned areas and emissions sets, we were able to calculate the 3D boreal domain for July 2007 from the regional carbonlite data monoxide retrievals. (CO) Using distributions these data inundation product. derived from the MEdium Resolution- A comparison between prior emis- radiometerImaging Spectrometer (MODIS); (MERIS)2) data andsmoke- the over boreal Eurasia. - the combination of global modelling Moderate Resolution Imaging Spectro terior emissions produced by employ- and satellite observations in a consist- height products using stereo retriev- ingsions the from optimised MERIS and TM5 MODIS model and posthat ent framework to improve dispersion alsplume from extent the Advanced (Fig. 1) andAlong-Track injection integrates the ALANIS EO-based prod- forecasts of compounds emitted from - ucts showed that the ALANIS optimi- biomass burning events occurring in ments on board ESA ENVISAT satellite sation improved some of the estimates [2]Scanning and 3) Radiometer CO columns (AATSR) derived instru from near-real-time Infrared Atmospheric ALANIS-Smoke plumes project boreal Eurasia. ALANIS - Methane Sounding Interferometer (IASI) satel- demonstratedsignificantly. the potential offered be Boreal Eurasian lakes and wetlands play an important role in the carbon cycle as they represent both the larg- ) source in

this region and one of the major4 carbon est natural methane (CH - - sinks.sian lakes Modelling and wetlands the natural is an important variabili cross-cuttingty of methane topic, fluxes linking from boreal climate, Eura hy- - er, the high spatial and temporal vari- drology and biogeochemistry.emissions combined Howev with

patchy and incomplete4 information on theirability geographical of CH distribution makes

The ALANIS methane project inves- ittigated difficult the to potential obtain reliable of EO estimates.data to re- duce current uncertainties in methane emissions from boreal lakes and wet- lands through the synergistic use of EO-based products in a coupled land - ject has produced a number of new or extendedsurface-atmosphere EO-based model.products The for probo- real Eurasia, which are highly relevant to the surface characterisation of wet- lands and their emissions of methane -

(Figs. 2-4): (i) wetland/inundation dy namics for the period July 2007 to June 2008 from a combination of active and

Figure 3: Example of the wetlands extension product derived from ASAR Envisat data over the Ob river watershed.

28 passive microwave measurements, Figure 4. The classification of open water supplemented with vegetation indices bodies and peatlands from the higher resolu- derived from infrared data (the earlier thatspecific the locationsJULES model where had measurements been evaluat- tion product for the Ob and Lena rivers. global product is described in [3]; (ii) edhad over been a largermade. spatial This was domain, the first and time the - er spatial resolution using active mi- number of limitations in the JULES land - wetland/inundation dynamics at high ALANIS Methane project identified a- itations were related to the treatment in RussiaDetailed or aerosol Central properties Europe is can period only (iii)crowave surface measurements state (frozen, for the unfrozen, spring/ ofsurface wetland and hydrology HadGEM2 and models. biogeochem The lim- beically measured transported in situ to northernat point locations, Eurasia. summer months of 2007 and 2008 [4]; istry (in JULES) and the atmospheric but remote sensing with satellite-based chemistry and physics of methane (in instruments can provide aerosol infor- melting) for the years 2007-2010 [5], - mation over large spatial areas, albeit andThe (iv) EOatmospheric products CH4were columns then used for tential of EO data to test, validate and - tothe evaluate years 2003-2009 the wetland [6]. hydrology and HadGEM2), thus demonstrating the po mation is limited to particles in the opti- methane production schemes in the callywith active significant size range, limitations: larger than the about infor enhance model performances. JULES land surface model (JULES, the ALANIS – Aerosols 100 nm in diameter; furthermore, to- Joint UK Land Environment Simula- day’s satellites are not able to measure tor, development led by the UK Centre The contribution of atmospheric aer- osols is the largest uncertainty in cur- To obtain spatial information on a state-of-the-art land surface-atmos- rent estimates of the Earth’s radiation the aerosolconcentrations chemical of composition. smaller particles, pherefor Ecology model, and which Hydrology). can simulate JULES meth is- - especially nucleation-mode particles ane emissions from boreal lakes and genic aerosol dynamics are important smaller than about 25–30 nm in diam- balance. Both natural and anthropo eter, the ALANIS-Aerosols project in- JULES land surface model was used to emitting biogenic volatile organic com- vestigated the possibility of developing derivewetlands a number(and wetlands of wetland globally). emission The poundsover boreal (BVOC) Eurasia: important firstly, in besidesatmos- proxies (parameterisations) in terms of - pheric chemistry, boreal Eurasian for- a combination of satellite-observable mate-chemistry model (Note: JULES est sites regularly produce bursts of - isscenarios also the for land use surface in the componentHadGEM2 cli of new secondary organic aerosol parti- ies were developed based on the cur- rentquantities understanding to determine. of the Theseatmospher prox- The wetland emission scheme in produced from, such as energy produc- ic nucleation and growth processes in JULESHadGEM2). had previously been evaluated at cles. Secondly, anthropogenic aerosol,

tion, industry, road traffic, forest fires continental boundary-layers. Regional

29 nucleation is driven by photochemistry As an example of aged long-range and surface network observations, Atmospher- and occurs typically over spatial scales transported pollution, we also investi- et al - gated a biomass-burning episode ob- ic Chemistry and Physics 11, 4705-4723. servable parameters affecting this pro- served over Finland during 2–6 May 2. Fisher DN . 2013. Automated retrieval- cessof hundreds are UV ofradiation, kilometres. concentrations Satellite-ob reoof Smoke for mapping Plume Injectionaerosol and Heights trace (SPIH) gas injec and- Smoke Plume Masks (SPM) from AATSR ste- of trace gases such as NO2 and SO2, and - 2006 [8]. The main origin of pollution plied Earth Observation and Geoinformatics aerosol optical depth (the extinction of - tion into the free troposphere. Journal of Ap during this period were forest/agricul (submitted). solar radiation due to scattering and - tural fires in Eastern Europe and Rus et al. absorption by aerosol particles, inte- instruments like ESA’s Advanced Along- - grated over the atmospheric column) sia. The study showed that satellite- 3. Prigent C 2012. Changes in land sur which is used as a proxy for the con- ble of detecting the presence of long- face water dynamics since the 1990s and re Track Scanning Radiometer are capa lation to populationet al. pressure. Geophysical densation sink: the aerosol surface on range transported pollution aerosols Research Letters 39, L08403. 4. Bartsch A 2012. Detection of wetland- The behaviour of the proxy was in- aerosol optical depth (AOD) exceeds a dynamics with ENVISAT ASAR in support of which gases can condense [7]. - over the boreal environment. When methane modelling et al. at high latitudes. Biogeo las in Finland during nine days in May on the presence of pollution aerosols in Flagsciences (SSF): 9, 703-714.Extracting information on surface 5. Naeimi V 2012. ASCAT Surface State vestigated in detail at Hyytiälä and Pal- additionvalue of aboutto natural 0.1, this boreal is an forest indication aero- using an empirical threshold-analysis algo- taneous data from satellites, ground- freeze/thaw conditions from backscatter data basedand July observations 2006. The analysis at these used two simul sites, sol particles. rithm. IEEE Transactionset al. on Geoscience and proxies, when calculated based sole- Acknowledgements ofRemote carbon Sensing dioxide 50, and 2566-2582. methane column-aver- 6. Schneising O 2011. Long-term analysis lyand on GLOMAP input from model model simulations. simulations, The The authors wish to thank all the ALANIS team aged mole fractions retrieved from SCIAMA- performed quite satisfactorily in pre- - members for their contribution: CEH (UK),- CHY. Atmosphericet al Chemistry and Physics 11, cleation-mode particles at the two in IUP Bremen (DE), Estellus (FR), UK Met Office of2863–2880. global nucleation mode aerosol concentra- dictingsitu the presence/absence of nu (UK), NOVELTIS (FR), UCL (UK), Univ. of Wa- 7. Kulmala M . 2011. The first estimates- geningen UR (NDL), LATMOS (FR), JRC (EC), that supporting the satellite proxies de- Vienna Univ. of Technology (AU), Univ. of Hel tions based on satellite measurements. At signedmeasurement for predicting sites. nucleation We concluded mode sinki (FI), Lund University (SE) and FMI (FI). mospheric Chemistryet al. and Physics 11, 10791-- particle number concentrations with lites10801. to obtain information on the occurrence model simulation data is very likely to References of8. denatural Leeuw and G anthropogenic 2011. On the aerosols use of satelover et al improve the predictive power of such global CO emission estimates using a four-di- mensional1. Hooghiemstra variational PB data assimilation. 2011. Optimizing system the boreal Eurasian forest. Biogeosciences Discuss 8, 8451-8483. proxies.

New iLEAPS Research initiative Interdisciplinary Biomass Burning Initiative (IBBI)

Jointly organised by iLEAPS, IGAC, and WMO

Biomass burning changes the land surface drastically and leads to the release of large amounts of trace gases and aerosol particles that play important roles in atmospheric chemistry and climate. This coordinated international activity organised by IGAC (International Global Atmospheric Chemistry), iLEAPS, and WMO (World Meteorological Organisation) will help better quantify the present and future influence of biomass burning emissions on the composition and chemistry of the Earth’s atmosphere.

More information: http://www.igacproject.org/BiomassBurning

30 4th iLEAPS Science Conference Terrestrial ecosystems, atmosphere, and people in the Earth system

12-16 May 2014 Nanjing, China www.ileaps-sc2014.org

Conference themes: · Dynamic processes in the land- atmosphere-society continuum · Sustainable management of human- dominated environments · Topical regions: high latitudes and developing countries · Multidisciplinary observations and modelling of land-atmosphere-society interactions

Integrated Land Ecosystem - 31 Atmosphere Processes Study C Activities, ILEAPS-Japan

Hayashi K.1, Hasegawa T.2, Tokida T.1, Ono K.2, Matsuda K.3, Toyoda S.4, Yano M.4, Sudo S.1, Wagai R.1, Matsushima M.5, Minamikawa K.1, Katata G.6, Katayanagi N.1, Fumoto T.1, Iwasaki N.7

1. Carbon and Nutrient Cycles Division, National Institute for Agro-Environmental Sciences (NIAES), Tsukuba, Japan 2. Agro-Meteorology Division, NIAES, Tsukuba, Japan 3. Faculty of Agriculture Field Science Center, Tokyo University of Agriculture and Technology, Fuchu, Japan 4. Department of Environmental Science and Technology, Tokyo Institute of Technology, Yokohama, Japan 5. Graduate School of Horticulture, Chiba University, Matsudo, Japan 6. Nuclear Science and Engineering Directorate, Japan Atomic Energy Agency, Tokai, Japan 7. Ecosystem Informatics Division, NIAES, Tsukuba, Japan

Free-Air CO2Enrichment study for paddy rice on nitrogen cycle (FACE-N) at Tsukuba FACE, Japan

The National Institute for Agro-Envi- [2]; N2 - This study was supported by the Ja- ronmental Sciences, Japan, established ture at the atmosphere-paddy interface pan Society for the Promotion of Sci- O fluxes and their isotopic signa a new Free-Air CO2 Enrichment (FACE) and in the soil [3]; and nitrogen rele- ence. Tsukuba FACE was established and facility for paddy rice in central Japan vant processes such as mineralization maintained by a project, “Development - of mitigation and adaptation techniques FACE studies were originally designed bination with long-term investigations to global warming in the sectors of agri- to(Tsukuba investigate FACE) changes in April in plant 2010. growth, Early and biological nitrogen fixation in com culture, forestry, and fisheries”, provided crop yield, and carbon cycle under ele- have also been developing a multi-lay- by the Ministry of Agriculture, Forestry vated CO2 eron modelsoil organic for an matteratmosphere-soil-vege abundance. We- and Fisheries, Japan. to these research agenda, a three-year project at andTsukuba temperature. FACE that In assessesaddition the transfer of water, heat, and gase- tation system (SOLVEG) [4] to simulate the changes in nitrogen cycle due to cli- ous and particulate matters between [email protected] mate manipulation (FACE-N) started in http://www.niaes.affrc.go.jp/outline/ hourly basis, and another mechanis- face/english/index.html following themes: (i) atmosphere-pad- ticpaddy model fields to simulateand the atmosphere the soil and on pad an- References dyApril exchange 2010. The of nitrogen;FACE-N project (ii) nitrogen- has the et al. related processes in an atmosphere- - 1. Nakamura and Tokida 2012. Journal of dy rice related processes (DNDC-Rice) et al. - soil-rice system; and (iii) development 3 - Agricultural Meteorology 68:15-23. of nitrogen cycling model at a plot scale ing[5]. theOur croppingresults show season; emission however, tenden the 2. Hayashiet al.K 2011. Agriculture, Ecosys- and of regional nitrogen cycling mod- cies of NH from the paddy field dur tems & Environment 144, 117-123. 3. Yano et al. 2012. Abstract of Japan Geosci- el using remote-sensing technique and reactive nitrogen with a net deposition ence Union Meeting 2012, MIS21-P09. geographic information system (GIS) paddy field was a sink of atmospheric–1 yr–1 4. Katata et2011. al Progress in Nuclear Sci ence and Technology 2, 530-537. flux of approximately 15 kg N ha . 5. Katayanagi . 2012. Soil Science and in occasional N2 based on the plot-scale model. theDrainage SOLVEG of theusing paddy micrometeorologi fields resulted- Plant Nutrition 58, 360-372. Tsukuba FACE (35°58’27”N, cal dataset obtainedO emissions. at a nearby We paddytested 139°59’32”E, 10 m asl) has four bays of paddy fields, each of which has one FACE plots are exposed under CO2 lev- a regional monitoring network for the ambientels elevated and oneby averagely FACE plot 200(Fig. ppm 1). The to exchangesfield as a registered of carbon site dioxide, of the waterAsiaFlux, va- pour, and energy between terrestrial themes, we have monitored several ecosystems and the atmosphere, and variablesthe ambient for levelsthree years [1]. For such the as three wet deposition and air concentrations of ni- confirmed well reproducibility of the 3 emission po- Figure 1: Tsukuba FACE fields. model for the atmosphere-paddy field trogen compounds; NH exchanges of water and energy. tentials from flag leaves of paddy rice 32 iLEAPS-Japan meeting at the 3rd International Symposium for Arctic Research (ISAR-3)

15 January 2013 Tokyo, Japan

iLEAPS Executive Officer Tanja Suni and leading scientists in fields very relevant phere research activities in Japan especially iLEAPS-Eurasia Executive officer Hanna to either the new iLEAPS theme Sustain- around AsiaFlux, where the leader of Lappalainen travelled to the 3rd Internation- able Managed Ecosystems or to the Pan- iLEAPS-Japan, iLEAPS SSC member Dr al Symposium of Arctic Research (ISAR-3) Eurasian Experiment or both; Drs Takeshi Nobuko Saigusa and iLEAPS-Japan coordi- in mid-January with the aim to create new Ohta, Tetsuya Hiyama, and Ayumi Kotani nator Sawako Tanaka work actively to wid- collaboration between iLEAPS, iLEAPS-Eur- have more than 15 years of experience with en the flux measurement network in Japan, asia, and Japanese and Russian scientists. land-atmosphere-society interactions in Korea, and other parts of Asia. iLEAPS- One of the main points of collaboration Eastern Siberia (http://www.chikyu.ac.jp/ Japan and coordinator Joni Kujansuu will was the Pan-Eurasian Experiment (PEEX), rihn_e/project/C-07.html) whereas Dr Ken- also conduct enquiries in the Philippines a new iLEAPS project coordinated by iLE- taro Hayashi is a core member of a large in order to organise regional land-atmos- APS-Eurasia at the Division of Atmospheric manipulation experiment on Japanese rice phere-society activities there; one of the Sciences in the University of Helsinki. The paddies in Tsukuba, looking at the influ- first steps will be an iLEAPS-AsiaFlux early-

Finnish delegation also included Joni Ku- ence of CO2 enrichment on carbon cycles, career scientist workshop in 2014. jansuu, the Finland-Asia coordinator of the and, uniquely in Japan, also on nitrogen

Division working part-time for iLEAPS. cycles throughout the year (Free Air CO2 iLEAPS IPO and iLEAPS-Eurasia would like Enrichment experiment FACE http://www. to extend a warm thank you for the entire The delegation met five members of the niaes.affrc.go.jp/outline/face/english/index. iLEAPS-Japan group for a very pleasant and Science Committee of iLEAPS-Japan in a html; with nitrogen, FACE-N). The website fruitful meeting! small satellite meeting in the second even- of iLEAPS-Japan is now available in English ing of the conference. All the Japanese as well; this will enable European scien- researchers present at the meeting are tists to keep track of the many land-atmos-

iLEAPS-Japan

Dr Nobuko Saigusa (Chair, iLEAPS SSC member) Ms Sawako Tanaka (coordinator) Centre for Global Environmental Research, National Institute for Environmental Studies (NIES)

Tsukuba, Ibaraki, Japan [email protected] [email protected] ILEAPS-Japan committee members can be found at: http://ileaps-japan.org/

Figure 1: Tsukuba FACE fields.

33 D People

Hans-Christen Hansson Jason Shogren Sirkku Juhola New Co-chair, Executive Committee New SSC member New SSC member member [email protected] [email protected] [email protected]

Jason Shogren is the Stroock Professor

- Management and Chair of the Depart- andDr. JuholaGeoinformatics is a visiting at Aalto scholar Universi at the- mentHans-Christen of Applied Hansson Environmental is professor Sci in- mentof Natural of Economics Resource and Conservation Finance at andthe Departmentty, Assistant Professor of Real Estate, in urban Planning envi- enceAir pollution (ITM) atand Stockholm Head of theUniversity, Depart ronmental policy at the Department of economics of environmental and natu- Environmental Sciences, University of the life cycle of atmospheric particles, ralUniversity resource of policy, Wyoming. focussing He works on the on thebe- - andSweden. especially His present on how work atmospheric focuses on havioural underpinnings of choice, the cial and public policy at the University - integration of economics and ecology, Helsinki, and Adjunct Professor of so et, both directly through scattering of and the design of incentives for con- in climate change adaptation in both radiationparticles influenceand indirectly the radiation through budg their developedof Jyväskylä, and Finland. in developing She is interested coun- - the American Applied Economics Asso- ciationservation. and Prof.the Beijer Shogren Institute is a Fellow for Eco of- for several years in Japan and different oninfluence health onis a thegrowing clouds concern, and their which ef tries. Dr. Juhola has previously worked drivesfect on histhe involvementradiation balance. connecting Influence ur- include adaptation, agricultural eco- ban research with the regional focused oflogical Sciences, Economics. and has servedHe is also as professor a foreign nomics,parts of biodiversity, Africa. Dr. Juhola’s and governance interests tomember the King of theof Sweden, Royal Swedish as a lead Academy author issues related to climate change in founding partners in the major EU pro- for the Intergovernmental Panel on Cli- both developed and developing coun- research. Prof. Hansson was one of the mate Change, and as a senior econo- - - mist on the Council of Economic Advis- aljects projects EUCAARI focused and EUSAAR.on air quality He is alsoand Juholatries. In is addition a member to ofworking the Finnish and leadMin- a scientific leader in several nation Sjogren will provide an applied econo- ing several international projects, Dr. - mist’sers in theview White for House.global sustainabilityIn iLEAPS, Prof. – Dr Juhola’s aim in iLEAPS is to bring an velopingclimate effectsregional and and interaction. global networks Prof. adaptationistry of Environment’s and policy view Climate to iLEAPS Panel. ofHansson observation is especially stations interested within the in iLE de- research on land-atmosphere-society related research.

APS community. interactions.

34 E Meetings

Several iLEAPS-relevant meetings and - workshops took place in 2012 and in circulated within the wider ACPC com- tic Change under the Global Warming” munitytific questions. and discussed The draft at thereport upcoming will be symposium followed on ISAR-1 “Dras- iLEAPS SSC meeting in Vienna togeth- the beginning of 2013. er with ACPC, Sat-ACPC, and ESA (Eu- and ISAR-2 “Arctic System in a Chang 18th iLEAPS Scientific Steering Com- gaveing Earth”. presentations iLEAPS EOabout Tanja iLEAPS Suni and mittee meeting More information on ACPC and Sat- theiLEAPS-Eurasia Pan-Eurasian EO Experiment Hanna Lappalainen (PEEX), 30-31 October 2012, Helsinki, Finland ropean Space Agency) representatives.-

th meeting, the iLEAPS SSC ACPC: http://www.ileaps.org/multi respectively. decided sites/acpc/. 1.In itsto focus18 on developing the regional Pan-Eurasian Experiment (PEEX) iLEAPS-Japan and iLEAPS IPO meetings News for additional information); meeting 2-4 October 2012, Helsinki, Finland 15 January 2013, Tokyo, Japan 2.nodes to accept in the thekey offerregions by identifiediLEAPS-China (see 12-14 February 2013, Moscow, Russia - Five members of the iLEAPS-Japan Sci- PEEX is a multidisciplinary climate ference in Nanjing University, China, in ence Committee participated at the change, air quality, environment and to host the next iLEAPS Scientific Con meeting together with iLEAPS Execu- research infrastructure program fo- 3 cused on the Northern Eurasian, par- May 2014; . to confirm Dr. Alex Guenther and tive Officer (EO) Tanja Suni, iLEAPS- Kujansuu, the Finland-Asia coordinator a bottom up initiative by several Euro- Professor HC Hansson as iLEAPS co- Eurasia EO Hanna Lappalainen and Joni at Division of Atmospheric Sciences, ticularly Arctic and boreal regions. It is- chairs for 2013-2015. Former co-chair Prof. Markku Kulmala continues as a The SSC started planning the The meeting concentrated on pre- pean,The Russian 1st PEEX and workshop Chinese research was coor or- member of the Executive Committee. sentingUniversity the of Helsinki.current activities with- ganizations and institutes. in iLEAPS-Japan and sharing ideas of and Finnish Meteorological Institute williLEAPS be regional Action issues Plan for and the land-atmos next 3-4- dinated by the University of Helsinki years. Emerging themes in the Plan activities iLEAPS-Japan is concentrat- SSC meeting will take place on 12-13 ingpossible on at futurethe moment directions. are (1) The widen main- (FMI) and was held in Helsinki on 2-4 phere-society interactions. The next ing the AsiaFlux network in Japan and andOct 2012.11 European Over 80 countriesparticipants participat from 42- near regions; (2) land-atmosphere-so- research institutes from Russia, China April 2014 in Vienna, Austria. ciety interactions research in eastern

Sat-ACPC final workshop Siberia; (3) free-air CO2 enrichment ex- ed in the workshop. The main outcome 14-15 February 2013, Bern, Switzerland periments on Japanese rice paddies; of theThe WS 2nd was PEEX the workshop first outline was of held PEEX in - Science Plan. - - lated scientists in Japan and also in the tions in Aerosol-Cloud-Precipitation- Philippines(4) creating and a network other near of iLEAPS-re regions; Moscow on 12-14 Feb 2013. The par ClimateSat-ACPC Interactions) (Remote Sensingis one of applicathe key Plan in spring 2013 and to produce a (5) developing outreach activities: detailedticipants decidedImplementation to finalise Plan the Scienceby the expanding the English website and of the Sat-ACPC team is to make signif- iLEAPS-Japan mailing list and possi- icantprojects strides of iLEAPS. in understanding The main objective the in- PEEX is an important project un- bly starting a Japanese Newsletter or end of 2013. terplay among the aerosol, clouds and der the iLEAPS umbrella and is coor- precipitation, and the way these inter- More information on iLEAPS-Japan - canbulletin. be found in the News section and on dinated by iLEAPS-Eurasia EO Hanna Eight representatives of the Sat- Lappalainen and Senior Researcher Tu ACPCactions community are forcing together the climate with system. iLEAPS Atmospheric Sciences at University of ukka Petäjä, both from the Division of - the website: http://ileaps-japan.org/. - Helsinki. The preparatory Committee ExecutiveThe workshop Officer aimed Tanja toSuni edit participat and ad- Third International Symposium on of PEEX is represented by Prof. Mark vanceed in the a largefinal workshopreview and of recommenthe project.- Arctic Research (ISAR-3) ku Kulmala (Univ. Helsinki) and Prof. dation report regarding aerosol-cloud- 14-18 January 2013, Tokyo, Japan PEEX is open for other institutes to Sergej Zilitinkevich (FMI). climate-precipitation interactions and the observation campaigns and next the change in the Arctic System and join in. More information on the project steps required to solve the open scien- The theme of ISAR-3 was “Detecting can be found here: www.atm.helsinki. searching the global influence.” The fi/peex. 35 iLEAPS SCIENTIFIC STEERING COMMITTEE (SSC) MEMBERS

Hans–Christen Hansson (Co-Chair), Stockholm University, Hans Peter Schmid, Karlsruhe Institute of Technology (KIT), Insti- Department of Applied Environmental Science, tute for Meteorology and Climate Research (IMK–IFU), Garmisch– Stockholm, Sweden Partenkirchen, Germany

Alex Guenther (Co–Chair), Atmospheric Chemistry Division, Sonia I. Seneviratne, Institute for Atmospheric and Climate National Center for Atmospheric Research (NCAR), Boulder, Science, ETH Zurich, Switzerland Colorado, USA Jason Shogren, Department of Economics and Finance, Markku Kulmala (Co–Chair), Dept. Physics, University of Wyoming, Wyoming, USA University of Helsinki, Helsinki, Finland Hanwant B. Singh, NASA Ames Research Center, USA Eleanor Blyth, Centre for Ecology and Hydrology, UK Dan Yakir, Weizmann Institute of Science, Israel Gordon Bonan, Climate and Global Dynamics Division, National Center for Atmospheric Research (NCAR), Sergey Afanasievich Zimov, North–East Scientific Station of Pacific Boulder, Colorado, USA Institute of Geography, Chersky, Yakutia, Russia

Aijun Ding, Institute for Climate and Global Change Research Honorary members (ICGCR), School of Atmospheric Sciences, Nanjing University, China Meinrat O. Andreae, Biogeochemistry Department, Max Planck Institute for Chemistry, Mainz, Germany Sirkku Juhola, Department of Environmental Sciences, Universi- ty of Helsinki; Department of Real Estate, Planning and Almut Arneth, Dept. Physical Geography and Ecosystems Analysis, Geoinformatics, Aalto University, Helsinki, Finland Lund University, Lund, Sweden Francesco Loreto, National Research Council of Italy (CNR), Paulo Artaxo, Dept. Applied Physics, Institute of Physics, Firenze, Italy University of São Paulo, São Paulo, Brazil Paul I. Palmer, Quantitative Earth Observation, School of Geo- Laurens Ganzeveld, Dept. Environmental Sciences, Earth System Sciences, University of Edinburgh, Edinburgh, UK Sciences Group, Wageningen University and Research Centre, Wageningen, Netherlands Markus Reichstein, Biogeochemical Model–Data Integration Group, Max Planck Institute for Biogeochemistry, Jena, Germany Nathalie de Noblet–Ducoudré, Laboratoire des Sciences du Climat et de l’Environnement (LSCE), Gif–sur–Yvette cedex, France Nobuko Saigusa, Office for Terrestrial Monitoring, Center for Global Environmental Research, National Institute for Environ- Daniel Rosenfeld, The Institute of Earth Sciences, The Hebrew mental Studies, Tsukuba, Japan University, Israel

iLEAPS-ENDORSED projects AND RESEARCH INITIATIVES

ABBA GLACE -CMIP5 Advancing the Integrated Monitoring of Trace Gas Exchange Global Land-Atmosphere Coupling Experiment between Biosphere and Atmosphere HENVI Forests and Climate Change ACPC IBBI Aerosols, Clouds, Precipitation and Climate Research Program Interdisciplinary Biomass Burning Initiative Sat-ACPC IMECS Remote Sensing Aerosols, Clouds, Precipitation and Interactions among Managed Ecosystems, Climate, Climate Interactions and Societies LULCC LUCID Land Use and Land Cover Change Land-Use and Climate, Identification of robust impacts ALANIS LULCC Atmosphere-LANd Integrated Study in the boreal zone Land Use and Land Cover Change AMMA METHANE LOSS FROM THE ARCTIC African Monsoon Multidisciplinary Analyses NEESPI FLUXNET Northern Eurasia Earth Science Partnership Initiative International Network Measuring Terrestrial Carbon, Water and Energy Fluxes PEEX Pan-Eurasian Experiment FIRE Task TAITA GEIA Multidisciplinary Research Station in Kenya Global Emissions InitiAtive 36 WELGEGUND Observation Platform in South Africa