The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty

Author(s): Lawrence, DM; Fisher, RA; Koven, CD; Oleson, KW; Swenson, SC; Bonan, G; Collier, N; Ghimire, B; van Kampenhout, L; Kennedy, D; Kluzek, E; Lawrence, PJ; Li, F; Li, H; Lombardozzi, D; Riley, WJ; Sacks, WJ; Shi, M; Vertenstein, M; Wieder, WR; Xu, C; Ali, AA; Badger, AM; Bisht, G; van den Broeke, M; Brunke, MA; Burns, SP; Buzan, J; Clark, M; Craig, A; Dahlin, K; Drewniak, B; Fisher, JB; Flanner, M; Fox, AM; Gentine, P; Hoffman, F; Keppel-Aleks, G; Knox, R; Kumar, S; Lenaerts, J; Leung, LR; Lipscomb, WH; Lu, Y; Pandey, A; Pelletier, JD; Perket, J; Randerson, JT; Ricciuto, DM; Sanderson, BM; Slater, A; Subin, ZM; Tang, J; Thomas, RQ; Val Martin, M; Zeng, X | Abstract: ©2019. The Authors. The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time-evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.

Nathan Collier | Gautam Bisht | Mariana Vertenstein | William H. Lipscomb | Michael A. Brunke | Pierre Gentine | Joshua B. Fisher | L. Ruby Leung | Daniel M. Ricciuto | Andrew Slater | Andrew M. Badger | Keith W. Oleson | Forrest Hoffman | Sean P. Burns | Mingjie Shi | Xubin Zeng | Jon D. Pelletier | Benjamin M. Sanderson | Chonggang Xu | David M. Lawrence | Ashehad A. Ali | William J. Riley | R. Quinn Thomas | Erik Kluzek | Charles D. Koven | James T. Randerson | Rosie A. Fisher | William R. Wieder | Anthony Craig | Gordon Bonan | William J. Sacks | R. Q. Thomas | Bardan Ghimire | Maria Val Martin | Danica Lombardozzi | Martyn Clark | Jinyun Tang | Beth Drewniak | J. Randerson | J. Pelletier | D. Lawrence | K. Oleson | M. Vertenstein | W. Lipscomb | G. Bonan | Chonggang Xu | W. Wieder | S. Swenson | X. Zeng | M. Clark | A. Slater | J. Fisher | P. Gentine | B. Ghimire | F. Hoffman | L. Leung | C. Koven | D. Ricciuto | Sanjiv Kumar | A. Craig | M. Flanner | B. Sanderson | E. Kluzek | N. Collier | W. Riley | W. Sacks | K. Dahlin | Hongyi Li | R. Fisher | M. Broeke | S. Burns | D. Lombardozzi | Z. Subin | P. Lawrence | G. Keppel‐Aleks | Jinyun Tang | D. Kennedy | J. Lenaerts | M. Brunke | R. Knox | G. Bisht | A. Fox | M. Shi | M. Val Martin | B. Drewniak | Fang Li | Sanjiv Kumar | Yaqiong Lu | Peter J. Lawrence | Mark Flanner | J. Buzan | A. Badger | Yaqiong Lu | Daniel Kennedy | Justin Perket | Sean C. Swenson | Andrew M. Fox | Fang Li | Zachary M. Subin | Leo Kampenhout | Hongyi Li | Michiel Broeke | Jonathan Buzan | Kyla Dahlin | Gretchen Keppel‐Aleks | Ryan Knox | Jan Lenaerts | Ashutosh Pandey | L. Leung | J. Perket | L. Kampenhout | Ashutosh Pandey | A. Ali | Maria Val Martin

[1]  D. L. Nelson,et al.  Smoke injection heights from fires in North America: analysis of 5 years of satellite observations , 2009 .

[2]  D. S. Ward,et al.  Quantifying the role of fire in the Earth system – Part 1: Improved global fire modeling in the Community Earth System Model (CESM1) , 2012 .

[3]  W. Post,et al.  The North American Carbon Program Multi-Scale Synthesis and Terrestrial Model Intercomparison Project – Part 1: Overview and experimental design , 2013 .

[4]  A. Pitman,et al.  A test of an optimal stomatal conductance scheme within the CABLE land surface model , 2014 .

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

[6]  R. Hill,et al.  Description and evaluation , 1976 .

[7]  P. Döll,et al.  MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new high‐resolution data set for agricultural and hydrological modeling , 2010 .

[8]  S. Zaehle,et al.  Improved understanding of drought controls on seasonal variation in Mediterranean forest canopy CO2 and water fluxes through combined in situ measurements and ecosystem modelling. , 2009 .

[9]  P. Thornton,et al.  Ecosystem model spin-up: Estimating steady state conditions in a coupled terrestrial carbon and nitrogen cycle model , 2005 .

[10]  Y. Malhi,et al.  Carbon cost of plant nitrogen acquisition: A mechanistic, globally applicable model of plant nitrogen uptake, retranslocation, and fixation , 2010 .

[11]  N. Holbrook,et al.  Global Relationships between Cropland Intensification and Summer Temperature Extremes over the Last 50 Years , 2017 .

[12]  Pierre Gentine,et al.  Tall Amazonian forests are less sensitive to precipitation variability , 2018, Nature Geoscience.

[13]  Zong‐Liang Yang,et al.  Multi-sensor land data assimilation: Toward a robust global soil moisture and snow estimation , 2018, Remote Sensing of Environment.

[14]  D. Herridge,et al.  Global inputs of biological nitrogen fixation in agricultural systems , 2008, Plant and Soil.

[15]  Yinghai Ke,et al.  A Physically Based Runoff Routing Model for Land Surface and Earth System Models , 2013 .

[16]  P. Reich,et al.  Ecosystem responses to elevated CO2 governed by plant-soil interactions and the cost of nitrogen acquisition. , 2018, The New phytologist.

[17]  The Community Land Model underestimates land-use CO 2 emissions by neglecting soil disturbance from cultivation , 2014 .

[18]  Atul K. Jain,et al.  Comprehensive ecosystem model‐data synthesis using multiple data sets at two temperate forest free‐air CO2 enrichment experiments: Model performance at ambient CO2 concentration , 2014 .

[19]  X. Zeng,et al.  Improving the treatment of the vertical snow burial fraction over short vegetation in the NCAR CLM3 , 2009 .

[20]  E. Stehfest,et al.  Anthropogenic land use estimates for the Holocene – HYDE 3.2 , 2016 .

[21]  S. Saatchi,et al.  Active microwave observations of diurnal and seasonal variations of canopy water content across the humid African tropical forests , 2017 .

[22]  Yiqi Luo,et al.  Elevated CO2 stimulates net accumulations of carbon and nitrogen in land ecosystems: a meta-analysis. , 2006, Ecology.

[23]  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 .

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

[25]  F. Li,et al.  Quantifying the role of fire in the Earth system – Part 1 : Improved global fire modeling in the Community Earth System Model ( CESM 1 ) , 2012 .

[26]  William J. Sacks,et al.  Effects of global irrigation on the near-surface climate , 2009 .

[27]  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 .

[28]  Martin Wild,et al.  A new snow cover fraction parametrization for the ECHAM4 GCM , 2001 .

[29]  P. Reich,et al.  Carbon-Nitrogen Interactions in Terrestrial Ecosystems in Response to Rising Atmospheric Carbon Dioxide , 2006 .

[30]  P. Cox,et al.  Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability , 2013, Nature.

[31]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

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

[33]  Evan H. DeLucia,et al.  Forest carbon use efficiency: is respiration a constant fraction of gross primary production? , 2007 .

[34]  W. Schlesinger,et al.  Transpiration in the global water cycle , 2014 .

[35]  Ü. Niinemets,et al.  Photosynthetic responses to stress in Mediterranean evergreens: Mechanisms and models , 2014 .

[36]  J. Canadell,et al.  Recent increases in terrestrial carbon uptake at little cost to the water cycle , 2017, Nature Communications.

[37]  S. Higgins,et al.  TRY – a global database of plant traits , 2011, Global Change Biology.

[38]  J. Canadell,et al.  The Northern Circumpolar Soil Carbon Database: spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions , 2012 .

[39]  G. Bonan,et al.  Cover Crops May Cause Winter Warming in Snow‐Covered Regions , 2018, Geophysical Research Letters.

[40]  D. Lawrence,et al.  Higher climatological temperature sensitivity of soil carbon in cold than warm climates , 2017 .

[41]  L. Emmons,et al.  The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions , 2012 .

[42]  D. M. Lawrence,et al.  Climate change and the permafrost carbon feedback , 2014, Nature.

[43]  Y. Malhi,et al.  What controls variation in carbon use efficiency among Amazonian tropical forests? , 2018 .

[44]  M. Torn,et al.  The effect of vertically resolved soil biogeochemistry and alternate soil C and N models on C dynamics of CLM4 , 2013 .

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

[46]  Daniel S. Karp,et al.  Hydraulic diversity of forests regulates ecosystem resilience during drought , 2018, Nature.

[47]  T. Chase,et al.  Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0) , 2007 .

[48]  K. Oleson,et al.  Model Structure and Climate Data Uncertainty in Historical Simulations of the Terrestrial Carbon Cycle (1850–2014) , 2019, Global Biogeochemical Cycles.

[49]  G. Balsamo,et al.  The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA‐Interim reanalysis data , 2014 .

[50]  D. Lawrence,et al.  Parameterization improvements and functional and structural advances in Version 4 of the Community Land Model , 2011 .

[51]  Lingli Liu,et al.  A global perspective on belowground carbon dynamics under nitrogen enrichment. , 2010, Ecology letters.

[52]  H. Marschner.  Mineral Nutrition of Higher Plants , 1988 .

[53]  U. Schneider,et al.  GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle , 2013, Theoretical and Applied Climatology.

[54]  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.

[55]  P. Ciais,et al.  Biomass production efficiency controlled by management in temperate and boreal ecosystems , 2015 .

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

[57]  D. Lawrence,et al.  A Comparison of the Diel Cycle of Modeled and Measured Latent Heat Flux During the Warm Season in a Colorado Subalpine Forest , 2018 .

[58]  Markus Reichstein,et al.  Reduction of forest soil respiration in response to nitrogen deposition , 2010 .

[59]  Dmitri Kavetski,et al.  Adaptive time stepping and error control in a mass conservative numerical solution of the mixed form of Richards equation | NOVA. The University of Newcastle's Digital Repository , 2001 .

[60]  I. C. Prentice,et al.  Optimal stomatal behaviour around the world , 2015 .

[61]  Pierre Gentine,et al.  Global variations in ecosystem‐scale isohydricity , 2017, Global change biology.

[62]  Peter A. Troch,et al.  Implementing and Evaluating Variable Soil Thickness in the Community Land Model, Version 4.5 (CLM4.5) , 2016 .

[63]  Assessing the use of subgrid land model output to study impacts of land cover change , 2016 .

[64]  D. Lombardozzi,et al.  Integrating O 3 influences on terrestrial processes: photosynthetic and stomatal response data available for regional and global modeling , 2013 .

[65]  Niklaus E. Zimmermann,et al.  Water-use efficiency and transpiration across European forests during the Anthropocene , 2015 .

[66]  W. J. Shuttleworth,et al.  Creation of the WATCH Forcing Data and Its Use to Assess Global and Regional Reference Crop Evaporation over Land during the Twentieth Century , 2011 .

[67]  Chris Derksen,et al.  Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes , 2010 .

[68]  E. Davin,et al.  Evaluating and improving the Community Land Model's sensitivity to land cover , 2018, Biogeosciences.

[69]  J. Townshend,et al.  A new global 1‐km dataset of percentage tree cover derived from remote sensing , 2000 .

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

[71]  C. Heald,et al.  Future Fire Impacts on Smoke Concentrations, Visibility, and Health in the Contiguous United States , 2018, GeoHealth.

[72]  W. J. Riley,et al.  Barriers to predicting changes in global terrestrial methane fluxes: analyses using CLM4Me, a methane biogeochemistry model integrated in CESM , 2011 .

[73]  C. Koven,et al.  Multiple soil nutrient competition between plants, microbes, and mineral surfaces: model development, parameterization, and example applications in several tropical forests , 2015 .

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

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

[76]  G. Bonan,et al.  Stomatal Function across Temporal and Spatial Scales: Deep-Time Trends, Land-Atmosphere Coupling and Global Models1[OPEN] , 2017, Plant Physiology.

[77]  N. McDowell,et al.  Estimating Global Ecosystem Isohydry/Anisohydry Using Active and Passive Microwave Satellite Data , 2017 .

[78]  T. Foken.  50 Years of the Monin–Obukhov Similarity Theory , 2006 .

[79]  E. Bai,et al.  Representation of nitrogen in climate change forecasts , 2015 .

[80]  Steven M Quiring,et al.  Confronting weather and climate models with observational data from soil moisture networks over the United States. , 2016, Journal of hydrometeorology.

[81]  T. D. Mitchell,et al.  An improved method of constructing a database of monthly climate observations and associated high‐resolution grids , 2005 .

[82]  H. Velthuizen,et al.  Harmonized World Soil Database (version 1.2) , 2008 .

[83]  David M. Lawrence,et al.  A new fractional snow-covered area parameterization for the Community Land Model and its effect on the surface energy balance , 2012 .

[84]  Y. Malhi,et al.  The allocation of ecosystem net primary productivity in tropical forests , 2011, Philosophical Transactions of the Royal Society B: Biological Sciences.

[85]  J. Bagley,et al.  Biophysical impacts of climate-smart agriculture in the Midwest United States. , 2015, Plant, cell & environment.

[86]  R. Q. Thomas,et al.  Global patterns of nitrogen limitation: confronting two global biogeochemical models with observations , 2013, Global change biology.

[87]  Maria Val Martin,et al.  A Global Analysis of Wildfire Smoke Injection Heights Derived from Space-Based Multi-Angle Imaging , 2018, Remote. Sens..

[88]  C. Kottmeier,et al.  The Stable Atmospheric Boundary Layer over an Antarctic ice Sheet , 1999 .

[89]  W. Knorr,et al.  Quantifying photosynthetic capacity and its relationship to leaf nitrogen content for global‐scale terrestrial biosphere models , 2009 .

[90]  Zong-Liang Yang,et al.  Assimilation of MODIS snow cover through the Data Assimilation Research Testbed and the Community Land Model version 4 , 2014 .

[91]  T. Hoar,et al.  Estimating snow water storage in North America using CLM4, DART, and snow radiance data assimilation , 2016 .

[92]  Carl C. Trettin,et al.  The Response of Belowground Carbon Allocation in Forests to Global Change , 2005 .

[93]  Sandy P. Harrison,et al.  The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model , 2010 .

[94]  Urs Wegmüller,et al.  Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR , 2015 .

[95]  D. Lawrence,et al.  Dependence of the evolution of carbon dynamics in the northern permafrost region on the trajectory of climate change , 2018, Proceedings of the National Academy of Sciences.

[96]  Jeffrey L. Anderson,et al.  The Data Assimilation Research Testbed: A Community Facility , 2009 .

[97]  Philip J. Rasch,et al.  Present-day climate forcing and response from black carbon in snow , 2006 .

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

[99]  Peter E. Thornton,et al.  The Partitioning of Evapotranspiration into Transpiration, Soil Evaporation, and Canopy Evaporation in a GCM: Impacts on Land–Atmosphere Interaction , 2007 .

[100]  M. Kanamitsu,et al.  Dynamical Global Downscaling of Global Reanalysis , 2008 .

[101]  D. Medvigy,et al.  Soil Moisture Stress as a Major Driver of Carbon Cycle Uncertainty , 2018, Geophysical Research Letters.

[102]  D. Lawrence,et al.  Permafrost thaw and resulting soil moisture changes regulate projected high-latitude CO2 and CH4 emissions , 2015 .

[103]  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.

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

[105]  S. Levis,et al.  A process-based fire parameterization of intermediate complexity in a Dynamic Global Vegetation Model , 2012 .

[106]  Daniel W. Goldberg,et al.  The North American Soil Moisture Database: Development and Applications , 2016 .

[107]  Benjamin Smith,et al.  Using models to guide field experiments: a priori predictions for the CO2 response of a nutrient‐ and water‐limited native Eucalypt woodland , 2016, Global change biology.

[108]  P. Dirmeyer,et al.  Pairing FLUXNET sites to validate model representations of land-use/land-cover change , 2017 .

[109]  Robert Jacob,et al.  Modeling agriculture in the Community Land Model , 2012 .

[110]  Jesse W. Campbell,et al.  Large historical growth in global terrestrial gross primary production , 2017, Nature.

[111]  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.

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

[113]  J. Chambers,et al.  Observed allocations of productivity and biomass, and turnover times in tropical forests are not accurately represented in CMIP5 Earth system models , 2015 .

[114]  David M. Lawrence,et al.  A GRACE‐based assessment of interannual groundwater dynamics in the Community Land Model , 2015 .

[115]  Dennis P. Lettenmaier,et al.  Measurement of snow interception and canopy effects on snow accumulation and melt in a mountainous maritime climate, Oregon, United States , 2002 .

[116]  M. Andreae,et al.  Emission of trace gases and aerosols from biomass burning , 2001 .

[117]  Ashehad A. Ali,et al.  A global scale mechanistic model of photosynthetic capacity (LUNA V1.0) , 2016 .

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

[119]  Zong-Liang Yang,et al.  Technical description of version 4.5 of the Community Land Model (CLM) , 2013 .

[120]  J. Fisher,et al.  Carbon cost of plant nitrogen acquisition: global carbon cycle impact from an improved plant nitrogen cycle in the Community Land Model , 2016, Global change biology.

[121]  Andrew E. Suyker,et al.  Improving maize growth processes in the community land model: Implementation and evaluation , 2018 .

[122]  Filipe Aires,et al.  Water, Energy, and Carbon with Artificial Neural Networks (WECANN): A statistically-based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence. , 2017, Biogeosciences.

[123]  F. Hoffman,et al.  A Functional Response Metric for the Temperature Sensitivity of Tropical Ecosystems , 2018 .

[124]  P. Jones,et al.  The Twentieth Century Reanalysis Project , 2009 .

[125]  D. Schimel,et al.  Global patterns of terrestrial biological nitrogen (N2) fixation in natural ecosystems , 1999 .

[126]  A. Baccini,et al.  Mapping forest canopy height globally with spaceborne lidar , 2011 .

[127]  R. Q. Thomas,et al.  Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions , 2019, Global biogeochemical cycles.

[128]  Nate G. McDowell,et al.  Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes, CLM4.5(ED) , 2015 .

[129]  Zong-Liang Yang,et al.  Development of a simple groundwater model for use in climate models and evaluation with Gravity Recovery and Climate Experiment data , 2007 .

[130]  David S. Lee,et al.  Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application , 2010 .

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

[132]  D. Lawrence,et al.  Role of Fire in the Global Land Water Budget during the Twentieth Century due to Changing Ecosystems , 2017 .

[133]  J. Fisher,et al.  Modeling the carbon cost of plant nitrogen acquisition: Mycorrhizal trade‐offs and multipath resistance uptake improve predictions of retranslocation , 2014 .

[134]  Taotao Qian,et al.  Changes in Continental Freshwater Discharge from 1948 to 2004 , 2009 .

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

[136]  K. Oleson,et al.  Parameterization and Surface Data Improvements and New Capabilities for the Community Land Model Urban (CLMU) , 2020, Journal of advances in modeling earth systems.

[137]  Andrew R. Bennett,et al.  Description and evaluation of the Community Ice Sheet Model (CISM) v2.1 , 2018, Geoscientific Model Development.

[138]  Xubin Zeng,et al.  Improving the Numerical Solution of Soil Moisture-Based Richards Equation for Land Models with a Deep or Shallow Water Table , 2009 .

[139]  X. Zeng,et al.  Effects of soil wetness, plant litter, and under‐canopy atmospheric stability on ground evaporation in the Community Land Model (CLM3.5) , 2009 .

[140]  S. J. Birks,et al.  Terrestrial water fluxes dominated by transpiration , 2013, Nature.

[141]  S. Long,et al.  What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. , 2004, The New phytologist.

[142]  Allison M. Leach,et al.  The global nitrogen cycle in the twenty-first century , 2013, Philosophical Transactions of the Royal Society B: Biological Sciences.

[143]  S. Reed,et al.  Biological nitrogen fixation: rates, patterns and ecological controls in terrestrial ecosystems , 2013, Philosophical Transactions of the Royal Society B: Biological Sciences.

[144]  Gordon B. Bonan,et al.  Quantifying carbon‐nitrogen feedbacks in the Community Land Model (CLM4) , 2010 .

[145]  Yan Sun,et al.  Change in terrestrial ecosystem water‐use efficiency over the last three decades , 2015, Global change biology.

[146]  David M. Lawrence,et al.  Improved simulation of the terrestrial hydrological cycle in permafrost regions by the Community Land Model , 2012 .

[147]  S. Seneviratne,et al.  Present‐day irrigation mitigates heat extremes , 2017 .

[148]  T. Hoar,et al.  Global Soil Moisture Estimation by Assimilating AMSR-E Brightness Temperatures in a Coupled CLM4-RTM-DART System , 2016 .

[149]  S. K. Akagi,et al.  Emission factors for open and domestic biomass burning for use in atmospheric models , 2010 .

[150]  Peter Troch,et al.  Observed timescales of evapotranspiration response to soil moisture , 2006 .

[151]  David M. Lawrence,et al.  Incorporating organic soil into a global climate model , 2008 .

[152]  Hans Peter Schmid,et al.  Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise , 2013, Nature.

[153]  Paul Dijkstra,et al.  CO2 Elicits Long-Term Decline in Nitrogen Fixation , 2004, Science.

[154]  Yadvinder Malhi,et al.  Confronting model predictions of carbon fluxes with measurements of Amazon forests subjected to experimental drought. , 2013, The New phytologist.

[155]  J. O H N,et al.  Forest carbon use efficiency : is respiration a constant fraction of gross primary production ? , 2007 .

[156]  Markus Reichstein,et al.  Improving canopy processes in the Community Land Model version 4 (CLM4) using global flux fields empirically inferred from FLUXNET data , 2011 .

[157]  R. Vogel,et al.  Estimation of baseflow recession constants , 1996 .

[158]  P. Dirmeyer,et al.  Climate response to Amazon forest replacement by heterogeneous crop cover , 2015 .

[159]  P. Ciais,et al.  Fertile forests produce biomass more efficiently. , 2012, Ecology letters.

[160]  G. Danabasoglu,et al.  The Community Climate System Model Version 4 , 2011 .

[161]  Brendan Choat,et al.  Measurement of vulnerability to water stress-induced cavitation in grapevine: a comparison of four techniques applied to a long-vesseled species. , 2010, Plant, cell & environment.

[162]  Dmitrii Mironov,et al.  An improved lake model for climate simulations: Model structure, evaluation, and sensitivity analyses in CESM1 , 2012 .

[163]  Peter E. Thornton,et al.  Improvements to the Community Land Model and their impact on the hydrological cycle , 2008 .

[164]  Mariana Vertenstein,et al.  Implementation and Initial Evaluation of the Glimmer Community Ice Sheet Model in the Community Earth System Model , 2013 .

[165]  George C. Hurtt,et al.  The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6:rationale and experimental design , 2016 .

[166]  K. Treseder,et al.  Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed. , 2008, Ecology.

[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]  Dmitri Kavetski,et al.  Ancient numerical daemons of conceptual hydrological modeling: 1. Fidelity and efficiency of time stepping schemes , 2010 .

[170]  Chris Derksen,et al.  LS3MIP (v1.0) Contribution to CMIP6: The Land Surface, Snow and Soil Moisture Model Intercomparison Project Aims, Setup and Expected Outcome. , 2016 .

[171]  P. Atkinson,et al.  Large‐scale prerain vegetation green‐up across Africa , 2018, Global change biology.

[172]  A. Arneth,et al.  Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations , 2011 .

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

[174]  David M. Lawrence,et al.  Assessing a dry surface layer‐based soil resistance parameterization for the Community Land Model using GRACE and FLUXNET‐MTE data , 2014 .

[175]  Tao Wang,et al.  Preferential cooling of hot extremes from cropland albedo management , 2014, Proceedings of the National Academy of Sciences.

[176]  S. Lhermitte,et al.  Improving satellite-retrieved surface radiative fluxes in polar regions using a smart sampling approach , 2016 .

[177]  W. Collins,et al.  The Community Earth System Model: A Framework for Collaborative Research , 2013 .

[178]  Xubin Zeng,et al.  Global Vegetation Root Distribution for Land Modeling , 2001 .

[179]  D. Lawrence,et al.  Effects of model structural uncertainty on carbon cycle projections: biological nitrogen fixation as a case study , 2015 .

[180]  Maosheng Zhao,et al.  Improvements of the MODIS terrestrial gross and net primary production global data set , 2005 .

[181]  K. Oleson,et al.  Reconciling leaf physiological traits and canopy flux data: Use of the TRY and FLUXNET databases in the Community Land Model version 4 , 2012 .

[182]  Arief Wijaya,et al.  An integrated pan‐tropical biomass map using multiple reference datasets , 2016, Global change biology.

[183]  K. Oleson,et al.  Implementation and comparison of a suite of heat stress metrics within the Community Land Model version 4.5 , 2014 .

[184]  R. Q. Thomas,et al.  Insights into mechanisms governing forest carbon response to nitrogen deposition: A model–data comparison using observed responses to nitrogen addition , 2013 .

[185]  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 .

[186]  G. Bonan,et al.  The Influence of Chronic Ozone Exposure on Global Carbon and Water Cycles , 2015 .

[187]  P. Gentine,et al.  Implications of Nonlocal Transport and Conditionally Averaged Statistics on Monin–Obukhov Similarity Theory and Townsend’s Attached Eddy Hypothesis , 2018, Journal of the Atmospheric Sciences.

[188]  R. B. Jackson,et al.  A global analysis of root distributions for terrestrial biomes , 1996, Oecologia.

[189]  David M. Lawrence,et al.  Sensitivity of a model projection of near‐surface permafrost degradation to soil column depth and representation of soil organic matter , 2008 .

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

[191]  J. Randerson,et al.  Representing leaf and root physiological traits in CLM improves global carbon and nitrogen cycling predictions , 2016 .

[192]  J. Randerson,et al.  Causes and implications of persistent atmospheric carbon dioxide biases in Earth System Models , 2013 .

[193]  J. Pelletier,et al.  A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling , 2016 .

[194]  Steven M. Quiring,et al.  The North American Soil Moisture Database , 2012 .

[195]  S. Levis,et al.  CLMcrop yields and water requirements: avoided impacts by choosing RCP 4.5 over 8.5 , 2018, Climatic Change.

[196]  C. Field,et al.  A unifying framework for dinitrogen fixation in the terrestrial biosphere , 2008, Nature.

[197]  S. Uhlenbrook,et al.  Partitioning of evaporation into transpiration, soil evaporation and interception: a comparison between isotope measurements and a HYDRUS-1D model , 2012 .

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

[199]  R. N. Juárez.  Observed allocations of productivity and biomass, and turnover times in tropical forests are not accurately represented in CMIP5 Earth system models , 2015 .

[200]  Zong-Liang Yang,et al.  An observation-based formulation of snow cover fraction and its evaluation over large North American river basins , 2007 .

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

[202]  D. Rooij,et al.  Comments on “Improving the Numerical Simulation of Soil Moisture–Based Richards Equation for Land Models with a Deep or Shallow Water Table” , 2010 .

[203]  Atul K. Jain,et al.  Global carbon budget 2013 , 2013 .