Perspectives on the Future of Land Surface Models and the Challenges of Representing Complex Terrestrial Systems

Author(s): Fisher, RA; Koven, CD | Abstract: © 2020. The Authors. Land surface models (LSMs) are a vital tool for understanding, projecting, and predicting the dynamics of the land surface and its role within the Earth system, under global change. Driven by the need to address a set of key questions, LSMs have grown in complexity from simplified representations of land surface biophysics to encompass a broad set of interrelated processes spanning the disciplines of biophysics, biogeochemistry, hydrology, ecosystem ecology, community ecology, human management, and societal impacts. This vast scope and complexity, while warranted by the problems LSMs are designed to solve, has led to enormous challenges in understanding and attributing differences between LSM predictions. Meanwhile, the wide range of spatial scales that govern land surface heterogeneity, and the broad spectrum of timescales in land surface dynamics, create challenges in tractably representing processes in LSMs. We identify three “grand challenges” in the development and use of LSMs, based around these issues: managing process complexity, representing land surface heterogeneity, and understanding parametric dynamics across the broad set of problems asked of LSMs in a changing world. In this review, we discuss progress that has been made, as well as promising directions forward, for each of these challenges.

[1]  R. McMurtrie,et al.  Forest fine‐root production and nitrogen use under elevated CO2: contrasting responses in evergreen and deciduous trees explained by a common principle , 2009 .

[2]  Anja Rammig,et al.  Leaf and stem economics spectra drive diversity of functional plant traits in a dynamic global vegetation model , 2015, Global change biology.

[3]  R. Knutti,et al.  Focus on cumulative emissions, global carbon budgets and the implications for climate mitigation targets , 2018 .

[4]  K. Beven,et al.  Comment on “Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water” by Eric F. Wood et al. , 2012 .

[5]  Richard Fuchs,et al.  Models meet data: Challenges and opportunities in implementing land management in Earth system models , 2017, Global change biology.

[6]  Atul K. Jain,et al.  Where does the carbon go? A model–data intercomparison of vegetation carbon allocation and turnover processes at two temperate forest free-air CO2 enrichment sites , 2014, The New phytologist.

[7]  B. Law,et al.  Use of a simulation model and ecosystem flux data to examine carbon-water interactions in ponderosa pine. , 2001, Tree physiology.

[8]  P. Chesson Mechanisms of Maintenance of Species Diversity , 2000 .

[9]  Michael C. Dietze,et al.  The role of data assimilation in predictive ecology , 2014 .

[10]  S. Gerber,et al.  Thermal acclimation of leaf respiration of tropical trees and lianas: response to experimental canopy warming, and consequences for tropical forest carbon balance , 2014, Global change biology.

[11]  O. Franklin Optimal nitrogen allocation controls tree responses to elevated CO2. , 2007, New Phytologist.

[12]  Thomas Kaminski,et al.  Limiting the parameter space in the Carbon Cycle Data Assimilation System (CCDAS) , 2013 .

[13]  T. M. Bezemer,et al.  Biodiversity increases the resistance of ecosystem productivity to climate extremes , 2015, Nature.

[14]  Elena Shevliakova,et al.  Resolving terrestrial ecosystem processes along a subgrid topographic gradient for an earth-system model , 2014 .

[15]  Pierre Gentine,et al.  Could Machine Learning Break the Convection Parameterization Deadlock? , 2018, Geophysical Research Letters.

[16]  P. Sellers Canopy reflectance, photosynthesis and transpiration , 1985 .

[17]  S. Pacala,et al.  Predicting and understanding forest dynamics using a simple tractable model , 2008, Proceedings of the National Academy of Sciences.

[18]  P. Ciais,et al.  Mortality as a key driver of the spatial distribution of aboveground biomass in Amazonian forest: results from a dynamic vegetation model , 2010 .

[19]  R. Betts,et al.  Representation of fire, land-use change and vegetation dynamics in the Joint UK Land Environment Simulator vn4.9 (JULES) , 2019, Geoscientific Model Development.

[20]  Daniel M. Ricciuto,et al.  Predicting long‐term carbon sequestration in response to CO2 enrichment: How and why do current ecosystem models differ? , 2015 .

[21]  Y. Malhi,et al.  The response of an Eastern Amazonian rain forest to drought stress: results and modelling analyses from a throughfall exclusion experiment , 2007 .

[22]  Randal D. Koster,et al.  The Interplay between Transpiration and Runoff Formulations in Land Surface Schemes Used with Atmospheric Models , 1997 .

[23]  Michael D. Dettinger,et al.  How snowpack heterogeneity affects diurnal streamflow timing , 2005 .

[24]  Atul K. Jain,et al.  Impact of large‐scale climate extremes on biospheric carbon fluxes: An intercomparison based on MsTMIP data , 2013 .

[25]  Stephen Sitch,et al.  A roadmap for improving the representation of photosynthesis in Earth system models. , 2017, The New phytologist.

[26]  C. Tague,et al.  Ecohydrology and Climate Change in the Mountains of the Western USA – A Review of Research and Opportunities , 2010 .

[27]  S. Zaehle,et al.  Evaluating stomatal models and their atmospheric drought response in a land surface scheme: A multibiome analysis , 2015 .

[28]  Natasha MacBean,et al.  Consistent assimilation of multiple data streams in a carbon cycle data assimilation system , 2016 .

[29]  S. Wofsy,et al.  The biophysics, ecology, and biogeochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: the Ecosystem Demography model, version 2.2 – Part 1: Model description , 2019, Geoscientific Model Development.

[30]  Jens Kattge,et al.  Inclusion of ecologically based trait variation in plant functional types reduces the projected land carbon sink in an earth system model , 2015, Global change biology.

[31]  D. Lawrence,et al.  Improving the representation of hydrologic processes in Earth System Models , 2015 .

[32]  Benjamin Smith,et al.  A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis , 2018, Geoscientific Model Development.

[33]  F. Woodward,et al.  Assessing uncertainties in a second-generation dynamic vegetation model caused by ecological scale limitations. , 2010, The New phytologist.

[34]  Dmitri Kavetski,et al.  A unified approach for process‐based hydrologic modeling: 1. Modeling concept , 2015 .

[35]  E. Rastetter,et al.  Incident radiation and the allocation of nitrogen within Arctic plant canopies: implications for predicting gross primary productivity , 2012, Global change biology.

[36]  Ryan Kelly,et al.  Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation , 2016, Biogeosciences.

[37]  C. Ottlé,et al.  Evaluating the performance of land surface model ORCHIDEE-CAN v1.0 on water and energy flux estimation with a single- and multi-layer energy budget scheme , 2016 .

[38]  Scott L. Painter,et al.  Managing complexity in simulations of land surface and near-surface processes , 2016, Environ. Model. Softw..

[39]  Andrea N. Hahmann,et al.  A proposal for a general interface between land surface schemes and general circulation models , 1998 .

[40]  D. Lawrence,et al.  Implementing Plant Hydraulics in the Community Land Model, Version 5 , 2019, Journal of Advances in Modeling Earth Systems.

[41]  Benjamin Smith,et al.  A stand‐alone tree demography and landscape structure module for Earth system models , 2013 .

[42]  Atul K. Jain,et al.  Using ecosystem experiments to improve vegetation models , 2015 .

[43]  Nate G. McDowell,et al.  Toward a Mechanistic Modeling of Nitrogen Limitation on Vegetation Dynamics , 2012, PloS one.

[44]  J. Chave,et al.  An individual-based forest model to jointly simulate carbon and tree diversity in Amazonia: description and applications , 2017 .

[45]  K. Oleson,et al.  Modeling stomatal conductance in the earth system: linking leaf water-use efficiency and water transport along the soil–plant–atmosphere continuum , 2014 .

[46]  D. Medvigy,et al.  Climate, soil organic layer, and nitrogen jointly drive forest development after fire in the North American boreal zone , 2016 .

[47]  K. Oleson,et al.  Comparing optimal and empirical stomatal conductance models for application in Earth system models , 2018, Global change biology.

[48]  Pierre Friedlingstein,et al.  Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks , 2014 .

[49]  William R. Wieder,et al.  Global soil carbon projections are improved by modelling microbial processes , 2013 .

[50]  Yujie He,et al.  Explicitly representing soil microbial processes in Earth system models , 2015 .

[51]  P. Ciais,et al.  The importance of tree demography and root water uptake for modelling the carbon and water cycles of Amazonia , 2018 .

[52]  George C. Hurtt,et al.  Using lidar data and a height-structured ecosystem model to estimate forest carbon stocks and fluxes over mountainous terrain , 2008 .

[53]  G. Bonan,et al.  Temperature acclimation of photosynthesis and respiration: A key uncertainty in the carbon cycle‐climate feedback , 2015 .

[54]  Dmitri Kavetski,et al.  Pursuing the method of multiple working hypotheses for hydrological modeling , 2011 .

[55]  S. Schubert,et al.  Length Scales of Hydrological Variability as Inferred from SMAP Soil Moisture Retrievals , 2019, Journal of Hydrometeorology.

[56]  S. Pacala,et al.  Evolutionarily Stable Strategy Carbon Allocation to Foliage, Wood, and Fine Roots in Trees Competing for Light and Nitrogen: An Analytically Tractable, Individual-Based Model and Quantitative Comparisons to Data , 2011, The American Naturalist.

[57]  Ü. Niinemets,et al.  Global photosynthetic capacity is optimized to the environment , 2019, Ecology letters.

[58]  G. Bonan,et al.  Carbon cycle confidence and uncertainty: Exploring variation among soil biogeochemical models , 2018, Global change biology.

[59]  I. C. Prentice,et al.  Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model , 2003 .

[60]  Michael C. Dietze,et al.  Facilitating feedbacks between field measurements and ecosystem models , 2013 .

[61]  Frank Lunkeit,et al.  Earth system models of intermediate complexity: closing the gap in the spectrum of climate system models , 2002 .

[62]  M. Ek,et al.  Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water , 2011 .

[63]  Andrew Gettelman,et al.  The Art and Science of Climate Model Tuning , 2017 .

[64]  S. Wofsy,et al.  Modelling the soil-plant-atmosphere continuum in a Quercus-Acer stand at Harvard Forest : the regulation of stomatal conductance by light, nitrogen and soil/plant hydraulic properties , 1996 .

[65]  F. Woodward,et al.  Vegetation dynamics – simulating responses to climatic change , 2004, Biological reviews of the Cambridge Philosophical Society.

[66]  Jeffrey L. Anderson,et al.  Evaluation of a Data Assimilation System for Land Surface Models Using CLM4.5 , 2018, Journal of Advances in Modeling Earth Systems.

[67]  J. Gershenzon,et al.  Tree defence and bark beetles in a drying world: carbon partitioning, functioning and modelling. , 2019, The New phytologist.

[68]  R. Betts,et al.  Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model , 2000, Nature.

[69]  Lindsay A. Turnbull,et al.  Coexistence, niches and biodiversity effects on ecosystem functioning. , 2013, Ecology letters.

[70]  S. Pacala,et al.  A METHOD FOR SCALING VEGETATION DYNAMICS: THE ECOSYSTEM DEMOGRAPHY MODEL (ED) , 2001 .

[71]  K. Oleson,et al.  An Urban Parameterization for a Global Climate Model. Part I: Formulation and Evaluation for Two Cities , 2008 .

[72]  Stephen W Pacala,et al.  Optimal stomatal behavior with competition for water and risk of hydraulic impairment , 2016, Proceedings of the National Academy of Sciences.

[73]  E. Wood,et al.  Development of a 50-Year High-Resolution Global Dataset of Meteorological Forcings for Land Surface Modeling , 2006 .

[74]  Paolo De Angelis,et al.  Reconciling the optimal and empirical approaches to modelling stomatal conductance , 2011 .

[75]  Ian N. Harman,et al.  Modeling canopy-induced turbulence in the Earth system: a unified parameterization of turbulent exchange within plant canopies and the roughness sublayer (CLM-ml v0) , 2017 .

[76]  Gautam Bisht,et al.  Development and Verification of a Numerical Library for Solving Global Terrestrial Multiphysics Problems , 2019, Journal of Advances in Modeling Earth Systems.

[77]  T. Berntsen,et al.  Thaw processes in ice-rich permafrost landscapes represented with laterally coupled tiles in a land surface model , 2018, The Cryosphere.

[78]  Klaus Scipal,et al.  The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space , 2019, Remote Sensing of Environment.

[79]  Ann Henderson-Sellers,et al.  Evapotranspiration models with canopy resistance for use in climate models, a review , 1991 .

[80]  W. Post,et al.  The role of phosphorus dynamics in tropical forests – a modeling study using CLM-CNP , 2013 .

[81]  P. Cox,et al.  An improved representation of physical permafrost dynamics in the JULES land-surface model , 2015 .

[82]  F. Chapin,et al.  EFFECTS OF BIODIVERSITY ON ECOSYSTEM FUNCTIONING: A CONSENSUS OF CURRENT KNOWLEDGE , 2005 .

[83]  D. Lawrence,et al.  Representing Intrahillslope Lateral Subsurface Flow in the Community Land Model , 2019, Journal of Advances in Modeling Earth Systems.

[84]  C. Peng,et al.  Towards a universal model for carbon dioxide uptake by plants , 2017, Nature Plants.

[85]  W. Brand,et al.  Optimisation of photosynthetic carbon gain and within-canopy gradients of associated foliar traits for Amazon forest trees , 2010 .

[86]  G. Boer,et al.  Simulating Competition and Coexistence between Plant Functional Types in a Dynamic Vegetation Model , 2006 .

[87]  Gil Bohrer,et al.  Tree level hydrodynamic approach for resolving aboveground water storage and stomatal conductance and modeling the effects of tree hydraulic strategy , 2016 .

[88]  Patrick Meir,et al.  Evidence from Amazonian forests is consistent with isohydric control of leaf water potential. , 2006, Plant, cell & environment.

[89]  Frederick R. Adler,et al.  Limitation of plant water use by rhizosphere and xylem conductance: results from a model , 1998 .

[90]  R. Betts,et al.  The impact of new land surface physics on the GCM simulation of climate and climate sensitivity , 1999 .

[91]  Simon Scheiter,et al.  Next-generation dynamic global vegetation models: learning from community ecology. , 2013, The New phytologist.

[92]  K.,et al.  The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability , 2015 .

[93]  F. Woodward,et al.  Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate , 2010, Science.

[94]  Philippe Ciais,et al.  Modelling forest management within a global vegetation model—Part 1: Model structure and general behaviour , 2010 .

[95]  P. Cox,et al.  Improved representation of plant functional types and physiology in the Joint UK Land Environment Simulator (JULES v4.2) using plant trait information , 2016 .

[96]  R. Giering,et al.  The BETHY/JSBACH Carbon Cycle Data Assimilation System: experiences and challenges , 2013 .

[97]  Alexander J. Winkler,et al.  Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO2 , 2019, Journal of advances in modeling earth systems.

[98]  P. Cox,et al.  Modelling tropical forest responses to drought and El Niño with a stomatal optimization model based on xylem hydraulics , 2018, Philosophical Transactions of the Royal Society B: Biological Sciences.

[99]  P. Cox,et al.  Equilibrium forest demography explains the distribution of tree sizes across North America , 2018, Environmental Research Letters.

[100]  M. Loreau,et al.  Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[101]  Mevin B Hooten,et al.  Iterative near-term ecological forecasting: Needs, opportunities, and challenges , 2018, Proceedings of the National Academy of Sciences.

[102]  H. Hendricks Franssen,et al.  Estimation of Community Land Model parameters for an improved assessment of net carbon fluxes at European sites , 2017 .

[103]  S. Reed,et al.  Incorporating phosphorus cycling into global modeling efforts: a worthwhile, tractable endeavor. , 2015, The New phytologist.

[104]  Dominique Gravel,et al.  Species coexistence in a variable world. , 2011, Ecology letters.

[105]  G. Bonan,et al.  Climate, ecosystems, and planetary futures: The challenge to predict life in Earth system models , 2018, Science.

[106]  T. Berntsen,et al.  A Tiling Approach to Represent Subgrid Snow Variability in Coupled Land Surface–Atmosphere Models , 2017 .

[107]  Fabrice Zaoui,et al.  Reconstruction of Hydraulic Data by Machine Learning , 2019, Advances in Hydroinformatics.

[108]  E. N. Stavros,et al.  ISS observations offer insights into plant function , 2017, Nature Ecology &Evolution.

[109]  M. Dietze,et al.  Scaling Contagious Disturbance: A Spatially-Implicit Dynamic Model , 2019, Front. Ecol. Evol..

[110]  Peter E. Thornton,et al.  DIMENSIONALITY REDUCTION FOR COMPLEX MODELS VIA BAYESIAN COMPRESSIVE SENSING , 2014 .

[111]  Hans Pretzsch,et al.  A vertically discretised canopy description for ORCHIDEE (SVN r2290) and the modifications to the energy, water and carbon fluxes , 2014 .

[112]  A. Pitman,et al.  Nitrogen and phosphorous limitation reduces the effects of land use change on land carbon uptake or emission , 2015 .

[113]  Andrew D. Friend,et al.  Carbon and nitrogen cycle dynamics in the O‐CN land surface model: 1. Model description, site‐scale evaluation, and sensitivity to parameter estimates , 2010 .

[114]  Hisashi Sato,et al.  SEIB–DGVM: A new Dynamic Global Vegetation Model using a spatially explicit individual-based approach , 2007 .

[115]  G. Bohrer,et al.  Trait-based representation of hydrological functional properties of plants in weather and ecosystem models , 2016, Plant diversity.

[116]  M. Dietze,et al.  A general ecophysiological framework for modelling the impact of pests and pathogens on forest ecosystems. , 2014, Ecology letters.

[117]  Benjamin Smith,et al.  Vegetation demographics in Earth System Models: A review of progress and priorities , 2018, Global change biology.

[118]  Chaopeng Shen,et al.  A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists , 2017, Water Resources Research.

[119]  Bertrand Decharme,et al.  Recent Changes in the ISBA‐CTRIP Land Surface System for Use in the CNRM‐CM6 Climate Model and in Global Off‐Line Hydrological Applications , 2019, Journal of Advances in Modeling Earth Systems.

[120]  Roberta E. Martin,et al.  Global variability in leaf respiration in relation to climate, plant functional types and leaf traits. , 2015, The New phytologist.

[121]  David Medvigy,et al.  Diversity in plant hydraulic traits explains seasonal and inter-annual variations of vegetation dynamics in seasonally dry tropical forests. , 2016, The New phytologist.

[122]  P. Ciais,et al.  Seasonal patterns of CO2 fluxes in Amazon forests: Fusion of eddy covariance data and the ORCHIDEE model , 2011 .

[123]  Peter E. Thornton,et al.  Influence of carbon‐nitrogen cycle coupling on land model response to CO2 fertilization and climate variability , 2007 .

[124]  J. Zscheischler,et al.  Modelling carbon sources and sinks in terrestrial vegetation. , 2018, The New phytologist.

[125]  S. Hubbell,et al.  Variation in hydroclimate sustains tropical forest biomass and promotes functional diversity. , 2018, The New phytologist.

[126]  Richard P. Phillips,et al.  Microbe-driventurnoverosetsminer al-mediated storage of soil carbon under elevated CO 2 , 2014 .

[127]  A. Pitman,et al.  Evaluating the Performance of Land Surface Models , 2008 .

[128]  Antonio Donato Nobre,et al.  Acclimation of photosynthetic capacity to irradiance in tree canopies in relation to leaf nitrogen concentration and leaf mass per unit area , 2002 .

[129]  B. Bonan,et al.  A Land Surface Model (LSM Version 1.0) for Ecological, Hydrological, and Atmospheric Studies: Technical Description and User's Guide , 1996 .

[130]  D. Lawrence,et al.  Improving the Representation of Polar Snow and Firn in the Community Earth System Model , 2017 .

[131]  Fabienne Maignan,et al.  A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle , 2016 .

[132]  Yaxing Wei,et al.  Divergence in land surface modeling: linking spread to structure , 2019, Environmental Research Communications.

[133]  S. Twiss,et al.  A physically motivated index of subgrid‐scale pattern , 2006 .

[134]  John H. C. Gash,et al.  Improving the representation of radiation interception and photosynthesis for climate model applications , 2007 .

[135]  P. V. van Bodegom,et al.  A fully traits-based approach to modeling global vegetation distribution , 2014, Proceedings of the National Academy of Sciences.

[136]  Matthew J. McGrath,et al.  Representing anthropogenic gross land use change, wood harvest, and forest age dynamics in a global vegetation model ORCHIDEE-MICT v8.4.2 , 2017 .

[137]  Veronika Eyring,et al.  Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization , 2015 .

[138]  Michael Battaglia,et al.  Acclimation and adaptation components of the temperature dependence of plant photosynthesis at the global scale. , 2019, The New phytologist.

[139]  Andrew K. Skidmore,et al.  Advances in remote sensing of vegetation function and traits , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[140]  Peter E. Thornton,et al.  Simulating the Biogeochemical and Biogeophysical Impacts of Transient Land Cover Change and Wood Harvest in the Community Climate System Model (CCSM4) from 1850 to 2100 , 2012 .

[141]  K.,et al.  Carbon–Concentration and Carbon–Climate Feedbacks in CMIP5 Earth System Models , 2012 .

[142]  Martyn P. Clark,et al.  The Use of Similarity Concepts to Represent Subgrid Variability in Land Surface Models: Case Study in a Snowmelt-Dominated Watershed , 2014 .

[143]  Denis Bastianelli,et al.  TRY plant trait database - enhanced coverage and open access. , 2019, Global change biology.

[144]  David Galbraith,et al.  Linking hydraulic traits to tropical forest function in a size-structured and trait-driven model (TFS v.1-Hydro) , 2016 .

[145]  Liang Feng,et al.  The decadal state of the terrestrial carbon cycle: Global retrievals of terrestrial carbon allocation, pools, and residence times , 2016, Proceedings of the National Academy of Sciences.

[146]  Atul K. Jain,et al.  Global Carbon Budget 2018 , 2014, Earth System Science Data.

[147]  J. McDonnell,et al.  Hillslope Hydrology in Global Change Research and Earth System Modeling , 2019, Water Resources Research.

[148]  Nathan Collier,et al.  The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty , 2019, Journal of Advances in Modeling Earth Systems.

[149]  R. Allard,et al.  Quality control for community-based sea-ice model development , 2018, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[150]  R. Dickinson,et al.  Coupling of the Common Land Model to the NCAR Community Climate Model , 2002 .

[151]  S. Manabe CLIMATE AND THE OCEAN CIRCULATION1 , 1969 .

[152]  Stephanie A. Bohlman,et al.  Dominance of the suppressed: Power-law size structure in tropical forests , 2016, Science.

[153]  A. Dalcher,et al.  A Simple Biosphere Model (SIB) for Use within General Circulation Models , 1986 .

[154]  Ming Ye,et al.  The multi-assumption architecture and testbed (MAAT v1.0): R code for generating ensembles with dynamic model structure and analysis of epistemic uncertainty from multiple sources , 2018, Geoscientific Model Development.

[155]  Alina Barbu,et al.  The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes , 2012 .

[156]  G. J. Collatz,et al.  Comparison of Radiative and Physiological Effects of Doubled Atmospheric CO2 on Climate , 1996, Science.

[157]  Eoin L. Brodie,et al.  Integrating microbial ecology into ecosystem models: challenges and priorities , 2012, Biogeochemistry.

[158]  P. Dirmeyer,et al.  The Plumbing of Land Surface Models: Benchmarking Model Performance , 2015 .

[159]  D. Lawrence,et al.  Permafrost carbon−climate feedback is sensitive to deep soil carbon decomposability but not deep soil nitrogen dynamics , 2015, Proceedings of the National Academy of Sciences.

[160]  M. Lomas,et al.  Decomposing uncertainties in the future terrestrial carbon budget associated with emission scenarios, climate projections, and ecosystem simulations using the ISI-MIP results , 2014 .

[161]  Atul K. Jain,et al.  Evaluation of 11 terrestrial carbon–nitrogen cycle models against observations from two temperate Free-Air CO2 Enrichment studies , 2014, The New phytologist.

[162]  Xiao Yang,et al.  Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network , 2017, 1707.06611.

[163]  Tiina Markkanen,et al.  Parameter calibration and stomatal conductance formulation comparison for boreal forests with adaptive population importance sampler in the land surface model JSBACH , 2019, Geoscientific Model Development.

[164]  Forrest M. Hoffman,et al.  The International Land Model Benchmarking (ILAMB) System: Design, Theory, and Implementation , 2018, Journal of Advances in Modeling Earth Systems.

[165]  George C. Hurtt,et al.  Carbon cycling under 300 years of land use change: Importance of the secondary vegetation sink , 2009 .

[166]  Separating the Impact of Individual Land Surface Properties on the Terrestrial Surface Energy Budget in both the Coupled and Uncoupled Land–Atmosphere System , 2019, Journal of Climate.

[167]  Rosie A. Fisher,et al.  Environmental drivers of drought deciduous phenology in the Community Land Model , 2015 .

[168]  D. Lawrence,et al.  Parametric Controls on Vegetation Responses to Biogeochemical Forcing in the CLM5 , 2019, Journal of Advances in Modeling Earth Systems.

[169]  George Shu Heng Pau,et al.  A reduced-order modeling approach to represent subgrid-scale hydrological dynamics for land-surface simulations: application in a polygonal tundra landscape , 2014 .

[170]  Gil Bohrer,et al.  fStorage and stomatal conductance illuminates the effects of tree hydraulic strategy , 2016 .

[171]  B. Huntley,et al.  Net ecosystem exchange over heterogeneous Arctic tundra: Scaling between chamber and eddy covariance measurements , 2008 .

[172]  G. Bonan,et al.  Influence of Subgrid-Scale Heterogeneity in Leaf Area Index, Stomatal Resistance, and Soil Moisture on Grid-Scale Land–Atmosphere Interactions , 1993 .

[173]  U. Dieckmann,et al.  Multitrait successional forest dynamics enable diverse competitive coexistence , 2017, Proceedings of the National Academy of Sciences.

[174]  S. Pacala,et al.  Predicting vegetation type through physiological and environmental interactions with leaf traits: evergreen and deciduous forests in an earth system modeling framework , 2017, Global change biology.

[175]  Tongren Xu,et al.  Regional and Global Land Data Assimilation Systems: Innovations, Challenges, and Prospects , 2019, Journal of Meteorological Research.

[176]  P. Sellers,et al.  Closing the scale gap between land surface parameterizations and GCMs with a new scheme, SiB3‐Bins , 2017 .

[177]  Q. Zhuang,et al.  Equifinality in parameterization of process‐based biogeochemistry models: A significant uncertainty source to the estimation of regional carbon dynamics , 2008 .

[178]  Martyn P. Clark,et al.  Benchmarking and Process Diagnostics of Land Models , 2018, Journal of Hydrometeorology.

[179]  M. Herold,et al.  The Importance of Consistent Global Forest Aboveground Biomass Product Validation , 2019, Surveys in Geophysics.

[180]  P. Ciais,et al.  A representation of the phosphorus cycle for ORCHIDEE (revision 4520) , 2017 .

[181]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[182]  D. Lawrence,et al.  Process-level model evaluation: A Snow and Heat Transfer Metric , 2016 .

[183]  K. Dahlin,et al.  Global patterns of drought deciduous phenology in semi‐arid and savanna‐type ecosystems , 2017 .

[184]  Nadejda A. Soudzilovskaia,et al.  Mapping local and global variability in plant trait distributions , 2017, Proceedings of the National Academy of Sciences.

[185]  D. Mackay,et al.  Plant hydraulics improves and topography mediates prediction of aspen mortality in southwestern USA. , 2017, The New phytologist.

[186]  J. Pelletier,et al.  A hybrid‐3D hillslope hydrological model for use in Earth system models , 2015 .

[187]  R. Dickinson,et al.  A numerical approach to calculating soil wetness and evapotranspiration over large grid areas , 2007 .