Biogeochemistry

, Volume 129, Issue 1–2, pp 53–76 | Cite as

Carbon and energy fluxes in cropland ecosystems: a model-data comparison

  • E. Lokupitiya
  • A. S. Denning
  • K. Schaefer
  • D. Ricciuto
  • R. Anderson
  • M. A. Arain
  • I. Baker
  • A. G. Barr
  • G. Chen
  • J. M. Chen
  • P. Ciais
  • D. R. Cook
  • M. Dietze
  • M. El Maayar
  • M. Fischer
  • R. Grant
  • D. Hollinger
  • C. Izaurralde
  • A. Jain
  • C. Kucharik
  • Z. Li
  • S. Liu
  • L. Li
  • R. Matamala
  • P. Peylin
  • D. Price
  • S. W. Running
  • A. Sahoo
  • M. Sprintsin
  • A. E. Suyker
  • H. Tian
  • C. Tonitto
  • M. Torn
  • Hans Verbeeck
  • S. B. Verma
  • Y. Xue
Article

Abstract

Croplands are highly productive ecosystems that contribute to land–atmosphere exchange of carbon, energy, and water during their short growing seasons. We evaluated and compared net ecosystem exchange (NEE), latent heat flux (LE), and sensible heat flux (H) simulated by a suite of ecosystem models at five agricultural eddy covariance flux tower sites in the central United States as part of the North American Carbon Program Site Synthesis project. Most of the models overestimated H and underestimated LE during the growing season, leading to overall higher Bowen ratios compared to the observations. Most models systematically under predicted NEE, especially at rain-fed sites. Certain crop-specific models that were developed considering the high productivity and associated physiological changes in specific crops better predicted the NEE and LE at both rain-fed and irrigated sites. Models with specific parameterization for different crops better simulated the inter-annual variability of NEE for maize-soybean rotation compared to those models with a single generic crop type. Stratification according to basic model formulation and phenological methodology did not explain significant variation in model performance across these sites and crops. The under prediction of NEE and LE and over prediction of H by most of the models suggests that models developed and parameterized for natural ecosystems cannot accurately predict the more robust physiology of highly bred and intensively managed crop ecosystems. When coupled in Earth System Models, it is likely that the excessive physiological stress simulated in many land surface component models leads to overestimation of temperature and atmospheric boundary layer depth, and underestimation of humidity and CO2 seasonal uptake over agricultural regions.

Keywords

Carbon and energy fluxes Cropland ecosystems Land–atmosphere exchange Model-data comparison Cropland carbon and energy exchange 

Abbreviations

CO2

Carbon dioxide

GPP

Gross primary productivity

H

Sensible heat flux

LAI

Leaf area index

LE

Latent heat flux

MARE

Mean absolute relative error

NACP

North American Carbon Program

NCDC

National Climate Data Center

NEE

Net ecosystem exchange

R

Ecosystem respiration

RMSE

Root mean square error

STD

Standard deviation

Notes

Acknowledgments

We would like to thank the North American Carbon Program Site-Level Interim Synthesis team, the Modeling and Synthesis Thematic Data Center, and the Oak Ridge National Laboratory Distributed Active Archive Center for collecting, organizing, and distributing the model output and flux observations required for this analysis. We acknowledge the comments given by Dr. Andrew Richardson during the initial stages of this manuscript. This research was partly funded by the U.S. Department of Energy (DoE; under contract Nos DE-FG02-06ER64317 and DE-AC02-05CH11231) and National Oceanic and Atmospheric Administration Award NA07OAR4310115. Data from the US-ARM site was supported by the Office of Biological and Environmental Research of the U.S. Department of Energy (under grant or contract DE-AC02-05CH11231) as part of the Atmospheric Radiation Measurement Program. We also acknowledge the support from the Center for Multiscale Modeling of Atmospheric Processes (CMMAP; NSF-ATM-0425247). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

References

  1. Amthor JS, Chen JM, Clein JS, Frolking SE, Goulden ML, Grant RF, Kimball JS, King AW, McGuire AD, Nikolov NT, Potter CS, Wang S, Wofsy SC (2001) Boreal forest CO2 exchange and evapotranspiration predicted by nine ecosystem process models: intermodel comparisons and relationships to field measurements. J Geophys Res 106(D24):33623–33648. doi: 10.1029/2000JD900850 CrossRefGoogle Scholar
  2. Arain MA, Yaun F, Black TA (2006) Soil-plant nitrogen cycling modulated carbon exchanges in a western temperate conifer forest in Canada. Agric For Meteorol 140:171–192. doi: 10.1016/j.agrformet.2006.03.02 CrossRefGoogle Scholar
  3. Asseng S et al (2013) Uncertainty in simulating wheat yields under climate change. Nat Clim Change 3:827–832CrossRefGoogle Scholar
  4. Asseng S et al (2015) Rising temperatures reduce global wheat production. Nat Clim Change 5:143–147CrossRefGoogle Scholar
  5. Baker IT, Prihodko L, Denning AS, Goulden M, Miller S, da Rocha HR (2008) Seasonal drought stress in the Amazon: reconciling models and observations. J Geophys Res 113:G00B01. doi: 10.1029/2007JG000644
  6. Barr AG, Ricciuto DM, Schaefer K, Richardson A, Agarwal D, Thornton PE, Davis K, Jackson B, Cook RB, Hollinger DT, van Ingen C, Amiro B, Andrews A, Arain MA, Baldocchi D, Black TA, Bolstad P, Curtis P, Desai A, Dragoni D, Flanagan L, Gu L, Katul G, Law BE, Lafleur P, Margolis H, Matamala R, Meyers T, McCaughey H, Monson R, Munger JW, Oechel W, Oren R, Roulet N, Torn M, Verma S (2013) NACP Site: Tower Meteorology, Flux Observations with Uncertainty, and Ancillary Data, Data set, Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA. doi: 10.3334/ORNLDAAC/1178
  7. Barr AG et al (2004) Inter-annual variability in the leaf area index of a boreal aspen-hazelnut forest in relation to net ecosystem production. Agric For Meteorol 126:237–255CrossRefGoogle Scholar
  8. Bassu S et al (2014) How do various maize crop models vary in their responses to climate change factors? Glob Change Biol 20:2301–2320CrossRefGoogle Scholar
  9. Boryan C, Yang Z, Mueller R, Craig M (2011) Monitoring US agriculture: the US department of agriculture, national agricultural statistics service, cropland data layer program. Geocarto Int 26(5):341–358CrossRefGoogle Scholar
  10. Chen JM, Liu J, Cihlar J, Guolden ML (1999) Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecol Model 124:99–119CrossRefGoogle Scholar
  11. Ciais P et al (2010) The European carbon balance. Part 2: croplands. Glob Change Biol 16:1409–1428CrossRefGoogle Scholar
  12. de La Casinie`re A, Bokoye AI, Cabot T (1997) Direct solar spectral irradiance measurements and updated simple transmittance models. J Appl Meteorol 36:509–520CrossRefGoogle Scholar
  13. de Noblet-Ducoudré N, Gervois S, Ciais P, Biovy N, Brissson N, Seguin B, Perrier A (2004) Coupling the soil-vegetation atmosphere transfer scheme ORCHIDEE to the agronomy model STICS to study the influence of croplands on the European carbon and water budgets. Agronomie 24:397–407CrossRefGoogle Scholar
  14. Denning, A. S., et al. (2005) Science Implementation strategy for the North American Carbon Program. Available online at http://www.nacarbon.org
  15. Dietz MC et al (2012) Characterizing the performance of ecosystem models across time scales: a spectral analysis of the North American Carbon Program site-level synthesis. Journal of Geophysical Research: Biogeosciences 116:G04029. doi: 10.1029/2011JG001661 Google Scholar
  16. El Maayar M, Price DT, Black TA, Humphreys ER, Jork EM (2002) Sensitivity tests of the integrated biosphere simulator to soil and vegetation characteristics in a pacific coastal coniferous forest. Atmos Ocean 40:313–332Google Scholar
  17. ERS USDA (2010) Corn, briefing rooms of economic research service of the United States Department of Agriculture. Available at http://www.ers.usda.gov/Briefing/Corn/
  18. FAO (2010) FAOSTAT, Food and Agriculture Organization of the United Nations. Available at http://faostat.fao.org/site/567/DesktopDefault.aspx?PageID=567#ancor
  19. Fischer ML, Billesbach DP, Riley WJ, Berry JA, Torn MS (2007) Spatiotemporal variations in growing season exchanges of CO2, H2O, and sensible heat in agricultural fields of the southern Great Plains. Earth Interact. 11:1–21CrossRefGoogle Scholar
  20. Foken T (2008) The enegy budget closure: an overview. Ecol Appl 18:1351–1367CrossRefGoogle Scholar
  21. Foley JA, Prentice IC, Ramankutty N, Levis S, Pollard D, Sitch S, Haxeltine A (1996) An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Glob Biogeochem Cycles 10:603–623Google Scholar
  22. Frolking SE et al (1998) Comparison of N2O emissions from soils at three temperate agricultural sites: simulations of year-round measurements by four models. Nutr Cycl Agroecosys 52:77–105CrossRefGoogle Scholar
  23. Grant RF, Arain A, Arora V, Barr A, Black TA, Chen J, Wang S, Yuan F, Zhang Y (2005) Intercomparison of techniques to model high temperature effects on CO2 and energy exchange in temperate and boreal coniferous forests. Ecol Model 188:217–252CrossRefGoogle Scholar
  24. Grant RF, Arkebauer TJ, Dobermann A, Hubbard KG, Schimelfenig TT, Suyker AE, Verma SB, Walters DT (2007a) Net biome productivity of irrigated and rain-fed maize—soybean rotations: modelling vs. measurements. Agron. J. 99:1404–1423CrossRefGoogle Scholar
  25. Grant RF, Barr AG, Black TA, Iwashita H, Kidson J, McCaughey H, Morgenstern K, Murayama S, Nesic Z, Saigusa N, Shashkov A, Zha T (2007b) Net ecosystem productivity of boreal jack pine stands regenerating from clearcutting under current and future climates. Glob Change Biol 13:1423–1440CrossRefGoogle Scholar
  26. Graven HD et al (2013) Enhanced seasonal exchange of CO2 by northern ecosystems since 1960. Science 341(6150):1085–1089CrossRefGoogle Scholar
  27. Gray JM, Frolking S, Kort EA, Ray DK, Kucharik CJ, Ramankutty N, Friedl MA (2014) Direct human influence on atmospheric CO2 seasonality from increased cropland productivity. Nature 515(7527):398–401CrossRefGoogle Scholar
  28. Guanter L et al (2014) Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc Natl Acad Sci 111(14):E1327–E1333CrossRefGoogle Scholar
  29. Hanson PJ, Amthor JS, Wullschleger SD et al (2004) Oak forest carbon and water simulations: model intercomparisons and evaluations against independent data. Ecol Monogr 74:443–489. doi: 10.1890/03-4049 CrossRefGoogle Scholar
  30. Izaurralde RC, Williams JR, McGill WB, Rosenberg NJ, Quiroga Jakas MC (2006) Simulating soil C dynamics with EPIC: model description and testing against long-term data. Ecol Model 192:362–384CrossRefGoogle Scholar
  31. Jain AK, West TO, Yang X, Post WM (2005) Assessing the impact of changes in climate and CO2 on potential carbon sequestration in agricultural soils. Geophys Res Lett 32. doi: 10.1029/2005GL023922
  32. Kothavala Z, Arain MA, Black TA, Verseghy D (2005) Evaluating fluxes of energy, water vapour and carbon dioxide over common crops. Agric For Meteorol 133:89–108CrossRefGoogle Scholar
  33. Krinner G, Viovy N, de Noblet-Ducoudre N, Ogee J, Polcher J, Friedlingstein P, Ciais P, Sitch S, Prentice IC (2005) A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochem. 19:GB1015CrossRefGoogle Scholar
  34. Kucharik CJ (2003) Evaluation of a process-based agro-ecosystem model (Agro-IBIS) across the U.S. cornbelt: simulations of the inter-annual variability in maize yield. Earth Interact. 7:1–33CrossRefGoogle Scholar
  35. Kucharik CJ, Twine TE (2007) Residue, respiration, and residuals: evaluation of a dynamic agroecosystem model using eddy flux measurements and biometric data. Agric For Meteorol 146:134–158. doi: 10.1016/j.agrformet.2007.05.011 CrossRefGoogle Scholar
  36. Li C, Frolking S, Frolking TA (1992) A model of nitrous oxide evolution from soil driven by rainfall events: 1. Model structure and sensitivity. J Geophys Res 97:9759–9776CrossRefGoogle Scholar
  37. Liu J, Chen JM, Cihlar J, Chen W (1999) Net primary productivity distribution in the BOREAS study region from a process model driven by satellite and surface data. J Geophys Res 104(D22):27735–27754CrossRefGoogle Scholar
  38. Liu S, Bliss N, Sundquist E, Huntington T (2003) Modelling carbon dynamics in vegetation and soil under the impact of soil erosion and deposition. Glob Biogeochem 17:1074. doi: 10.1029/2002GB002010 Google Scholar
  39. Lokupitiya E, Denning S, Paustian K, Baker I, Schaefer K, Verma S, Meyers T, Bernacchi CJ, Suyker A, Fischer M (2009) Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands. Biogeosciences 6:969–986CrossRefGoogle Scholar
  40. Lokupitiya E, Paustian K, Easter M, Williams S, Andrén O, Kätterer T (2012) Carbonbalance in US croplands during the last two decades of the 20th century. Biogeochemistry 107:207–225CrossRefGoogle Scholar
  41. Martre P et al (2015) Multimodel ensembles of wheat growth: many models are better than one. Glob Change Biol 21:911–925CrossRefGoogle Scholar
  42. Medvigy D, Wofsy SC, Munger JW, Hollinger DY, Poulton PR, Melillo JM, Borchers J, Chaney J et al (1995) Vegetation ecosystem modeling and analysis project—comparing biogeography and biogeochemistry models in a continental-scale study of terrestrial ecosystem responses to climate-change and CO2 doubling. Global Biogeochem 9:407–437CrossRefGoogle Scholar
  43. Miles NL, Richardson SJ, Davis KJ, Lauvaux T, Andrews AE, West TO, Bandaru V, Crosson ER (2012) Large amplitude spatial and temporal gradients in atmospheric boundary layer CO2 mole fractions detected with a tower-based network in the U.S. upper Midwest. J Geophys Res 117:G01019. doi: 10.1029/2011JG001781 CrossRefGoogle Scholar
  44. Moorcroft PR, Hurtt GC, Pacala SW (2001) A method for scaling vegetation dynamics: the ecosystem demography model (ED). Ecol Monogr 71:557–586CrossRefGoogle Scholar
  45. Ogle SM, Davis K, Lauvaux T, Schuh A, Cooley D, West TO, Heath LS, Miles NL, Richardson S, Jay Breidt F, Smith JE, McCart JL, Gurney KR, Tans P, Denning AS (2015) An approach for verifying biogenic greenhouse gas emissions inventories with atmospheric CO2 concentration data. Environ Res Lett 10(2015):034012. doi: 10.1088/1748-9326/10/3/034012 CrossRefGoogle Scholar
  46. Ren W, Tian H, Liu M, Zhang C, Chen G, Pan S, Felzer B, Xu X (2007) Effects of tropospheric ozone pollution on net primary productivity and carbon storage in terrestrial ecosystems of China. J. Geophys Res 112:D22S09. doi: 10.1029/2007JD008521
  47. Ricciuto DM, Thornton PE, Schaefer K, Cook RB, Davis KJ (2009) How uncertainty in gap-filled meteorological input forcing at eddy covariance sites impacts modeled carbon and energy flu., Eos Trans. AGU, 90(52) Fall Meet. Suppl., Abstract B54A-03Google Scholar
  48. Ricciuto DM, Schaefer K, Thornton PE, Davis K, Cook RB, Liu S, Anderson R, Arain MA, Baker I, Chen JM, Dietze M, Grant R, Izaurralde C, Jain AK, King AW, Kucharik C, Liu S, Lokupitiya E, Luo Y, Peng C, Poulter B, Price D, Riley W, Sahoo A, Tian H, Tonitto C, Verbeeck H (2013) NACP Site: terrestrial biosphere model and aggregated flux data in standard format, data set. Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA. doi: 10.3334/ORNLDAAC/1183
  49. Running SW, Hunt ER Jr (1993) Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an applicationfor global-scale models. In: Ehleringer JR, Field C (eds) Scaling physiological processes: leaf to globe. Academic Press, San Diego, pp 141–158CrossRefGoogle Scholar
  50. Ryan MG, McMurtrie RE, Ågren GI, Hunt ER Jr, Aber JD, Friend AD, Rastetter EB, Pulliam WJ (1996) Comparing models of ecosystem function for coniferous forests. II. Predictions of response to changes in atmospheric CO2 and climate. In: Breymeyer AI, Hall DO, Ågren GI, Melillo JM (eds) Global change: effects on coniferous forests and grasslands (SCOPE). Wiley, New York, pp 363–387Google Scholar
  51. Saleska SR et al (2003) Carbon fluxes in old-growth Amazonian rainforest: seasonality and disturbance-induced net carbon loss. Science 302:1554–1557Google Scholar
  52. Schaefer K, Collatz GJ, Tans P, Denning AS, Baker I, Berry J, Prihodko L, Suits N, Philpott A (2008) Combined simple biosphere/Carnegie-Ames-Stanford Approach terrestrial carbon cycle model. J Geophys Res 113:G03034. doi: 10.1029/2007JG000603 CrossRefGoogle Scholar
  53. Schuh A, Lauvaux T, Denning A, West T, Davis K, Miles N, Richardson S, Uliasz M, Lokupitiya E, Cooley D, Andrews A, Ogle SM (2013) Evaluating atmospheric CO2 inversions at multiple scales over a highly-inventoried agricultural landscape. Glob Change Biol 19:1424–1439CrossRefGoogle Scholar
  54. Schwalm CR et al (2010) A model-data intercomparison of CO2 exchange across North America: results from the North American carbon program site synthesis. J Geophys Res 115:G00H05. doi: 10.1029/2009JG001229
  55. Semenov MA, Wolf J, Evans LG, Eckersten H, Iglesias A (1996) Comparison of wheat simulation models under climate change.2. Application of climate change scenarios. Clim Res 7:271–281CrossRefGoogle Scholar
  56. Stoy PC et al (2013) Evaluating the agreement between measurements and models of net ecosystem exchange at different times and timescales using wavelet coherence: an example using data from the North American Carbon Program Site-Level Interim Synthesis. Biogeosciences 10:6893–6909CrossRefGoogle Scholar
  57. Sun J, Peng C, McCaughey H, Zhou X, Thomas V, Berninger F, St-Onge B, Hua D (2008) Simulating carbon exchange of Canadian boreal forests: II. Comparing the carbon budgets of a boreal mixedwood stand to a black spruce forest stand. Ecol Model 219:276–286CrossRefGoogle Scholar
  58. Suyker AE, Verma SB (2008) Interannual water vapor and energy exchange in an irrigated maize-based agroecosystem. Agric For Meteorol 148(3):417–427CrossRefGoogle Scholar
  59. Suyker AE, Verma SB, Burba GG, Arkebauer TJ, Walters DT, Hubbard KG (2004) Growing season carbon dioxide exchange in irrigated and rainfed maize. Agric For Meteorol 124(1–2):1–13Google Scholar
  60. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106:7183–7192CrossRefGoogle Scholar
  61. Thornton P, Law BE, Gholz HL, Clark KL, Falge E, Ellsworth DS, Goldstein AH, Monson RK, Hollinger D, Falk M, Chen J, Sparks JP (2002) Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agric For Meteorol 113:185–222CrossRefGoogle Scholar
  62. Tian HQ, Xu X, Zhang C, Ren W, Chen G, Liu M, Lu D, Pan S (2008) Forecasting and assessing the large-scale and long-term impacts of global environmental change on terrestrial ecosystems in the United States and China. In: Miao S, Carstenn S, Nungesser M (eds) Real world ecology: large-scale and long-term case studies and methods. Springer-Verlag, New YorkGoogle Scholar
  63. Tucker CJ, Pinzon JE, Brown ME, Slayback DA, Pak EW et al (2005) An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int J Remote Sens 26:4485–4498CrossRefGoogle Scholar
  64. Twine TE, Kustas WP, Norman JM, Cook DR, Houser PR, Meyers TP, Prueger JH, Starks PJ, Wesely ML (2000) Correcting eddy-covariance flux underestimates over a grassland. Agric For Meteorol 103:279–300CrossRefGoogle Scholar
  65. Verma SB, Dobermann A, Cassman KG, Walters DT, Knops JM, Arkebauer TJ, Suyker AE, Burba GG, Amos B, Yang H, Ginting D, Hubbard KG, Gitelson AA, Walter-Shea EA (2005) Annual carbon dioxide exchange in irrigated and rain-fed maize-based agroecosystems. Agric For Meteorol 131:77–96CrossRefGoogle Scholar
  66. Weng E, Luo Y (2008) Soil hydrological properties regulate grassland ecosystem responses to multifactor global change: a modeling analysis. J Geophys Res 113. doi: 10.1029/2007JG000539
  67. Williams JR (1995) The EPIC model. In: Singh VP (ed) Computer Models of Watershed Hydrology. Water Resources Publications, Highlands Ranch, pp 909–1000Google Scholar
  68. Wilson K, Goldstein A, Falge E, Aubinet M, Baldocchi D, Berbigier P, Bernhofer C, Ceulemans R, Dolman H, Field C, Grelle A, Ibrom A, Law BE, Kowalski A, Meyers T, Moncrieff J, Monson R, Oechel W, Tenhunen J, Valentini R, Verma S (2002) Energy balance closure at FLUXNET sites. Agric For Meteorol 113:223–243CrossRefGoogle Scholar
  69. Xiao JF, Zhuang QL, Baldocchi DD, Law BE, Richardson AD, Chen JQ, Oren R, Starr G, Noormets A, Ma SY, Verma SB, Wharton S, Bolstad PV, Burns SP, Cook DR, Curtis PS, Drake BG, Falk M, Foster DR, Gu LH, Hollinger DY, Katul GG, Matamala R, Monson RK, Munger JW, Sun KTPUG, Tom MS (2008) Estimation of net ecosystem carbon exchange for the conterminous United States by combining MODIS and AmeriFlux data. Agric For Meteorol 148:1827–1847CrossRefGoogle Scholar
  70. Xue Y, Sellers PJ, Kinter JL III, Shukla J (1991) A simplified biosphere model for global climate studies. J Climate 4:345–364CrossRefGoogle Scholar
  71. Zeng N, Zhao F, Collatz GJ, Kalnay E, Salawitch RJ, West TO, Guanter L (2014) Agricultural Green Revolution as a driver of increasing atmospheric CO2 seasonal amplitude. Nature 515(7527):394–397CrossRefGoogle Scholar
  72. Zhan X, Xue Y, Collaz GJ (2003) An analytical approach for estimating CO2 and heat fluxes over the Amazonian region. Ecol Model 162:97–117CrossRefGoogle Scholar
  73. Zhou X, Peng C, Dang Q-L, Sun J, Wu H, Hua D (2008) Simulating carbon exchange in Canadian Boreal forests: I. Model structure, validation, and sensitivity analysis. Ecol Model 219:287–299CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • E. Lokupitiya
    • 1
  • A. S. Denning
    • 2
  • K. Schaefer
    • 3
  • D. Ricciuto
    • 4
  • R. Anderson
    • 5
  • M. A. Arain
    • 6
  • I. Baker
    • 2
  • A. G. Barr
    • 7
  • G. Chen
    • 8
  • J. M. Chen
    • 9
  • P. Ciais
    • 10
  • D. R. Cook
    • 11
  • M. Dietze
    • 12
  • M. El Maayar
    • 13
  • M. Fischer
    • 14
  • R. Grant
    • 15
  • D. Hollinger
    • 16
  • C. Izaurralde
    • 17
  • A. Jain
    • 18
  • C. Kucharik
    • 19
  • Z. Li
    • 20
  • S. Liu
    • 21
  • L. Li
    • 22
  • R. Matamala
    • 11
  • P. Peylin
    • 10
  • D. Price
    • 23
  • S. W. Running
    • 5
  • A. Sahoo
    • 24
  • M. Sprintsin
    • 25
  • A. E. Suyker
    • 26
  • H. Tian
    • 8
  • C. Tonitto
    • 27
  • M. Torn
    • 14
  • Hans Verbeeck
    • 28
  • S. B. Verma
    • 26
  • Y. Xue
    • 29
  1. 1.Department of Zoology and Environment Sciences, Faculty of ScienceUniversity of ColomboColombo 03Sri Lanka
  2. 2.Department of Atmospheric ScienceColorado State UniversityFort CollinsUSA
  3. 3.National Snow and Ice Data Center (NSIDC)University of ColoradoBoulderUSA
  4. 4.Environmental Sciences DivisionOak Ridge National LaboratoryOak RidgeUSA
  5. 5.Numerical Terradynamic Simulation GroupUniversity of MontanaMissoulaUSA
  6. 6.School of Geography and Earth Sciences and McMaster Centre for Climate ChangeMcMaster UniversityHamiltonCanada
  7. 7.Science and Technology Branch, Environment Canada, National Hydrology Research CentreInnovation BoulevardSaskatoonCanada
  8. 8.Ecosystem Dynamics and Global Ecology Laboratory, School of Forestry and Wildlife ScienceAuburn UniversityAuburnUSA
  9. 9.Department of GeographyUniversity of TorontoTorontoCanada
  10. 10.Laboratoire des Sciences du Climat et de l’Environnement (LSCE), CEA CNRS UVSQGif-sur-YvetteFrance
  11. 11.Environmental Science DivisionArgonne National LaboratoryLemontUSA
  12. 12.Department of Earth and EnvironmentBoston UniversityBostonUSA
  13. 13.Energy, Environment and Water Research CenterThe Cyprus InstituteNicosiaCyprus
  14. 14.Lawrence Berkley National LaboratoryBerkeleyUSA
  15. 15.Department of Renewable ResourcesUniversity of AlbertaEdmontonCanada
  16. 16.Northern Research StationUSDA Forest ServiceDurhamUSA
  17. 17.Pacific Northwest National Laboratory and University of MarylandCollege ParkUSA
  18. 18.Department of Atmospheric SciencesUniversity of IllinoisUrbanaUSA
  19. 19.Department of Agronomy & Nelson Institute Center for Sustainability and the Global EnvironmentUniversity of Wisconsin - MadisonMadisonUSA
  20. 20.Teleobservation Research LLCColumbiaUSA
  21. 21.U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) CenterSioux FallsUSA
  22. 22.School of Life SciencesUniversity of Technology SydneyBroadwayAustralia
  23. 23.Natural Resources CanadaNorthern Forestry CentreEdmontonCanada
  24. 24.Department of Civil and Environmental EngineeringPrinceton UniversityPrincetonUSA
  25. 25.Forest Management and GIS DepartmentJewish National Fund-Keren Kayemet LeIsraelJerusalemIsrael
  26. 26.School of Natural ResourcesUniversity of NebraskaLincolnUSA
  27. 27.Department of Ecology and Evolutionary BiologyCornell UniversityIthacaUSA
  28. 28.CAVElab – Computational and Applied Vegetation Ecology, Faculty of Bioscience EngineeringGhent UniversityGhentBelgium
  29. 29.Department of GeographyUniversity of California, Los AngelesLos AngelesUSA

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