Evaluating the Interplay Between Biophysical Processes and Leaf Area Changes in Land Surface Models

Land Surface Models (LSMs) are essential to reproduce biophysical processes modulated by vegetation and to predict the future evolution of the land‐climate system. To assess the performance of an ensemble of LSMs (JSBACH, JULES, ORCHIDEE, CLM, and LPJ‐GUESS) a consistent set of land surface energy fluxes and leaf area index (LAI) has been generated. Relationships of interannual variations of modeled surface fluxes and LAI changes have been analyzed at global scale across climatological gradients and compared with those obtained from satellite‐based products. Model‐specific strengths and deficiencies were diagnosed for tree and grass biomes. Results show that the responses of grasses are generally well represented in models with respect to the observed interplay between turbulent fluxes and LAI, increasing the confidence on how the LAI‐dependent partition of net radiation into latent and sensible heat are simulated. On the contrary, modeled forest responses are characterized by systematic bias in the relation between the year‐to‐year variability in LAI and net radiation in cold and temperate climates, ultimately affecting the amount of absorbed radiation due to LAI‐related effects on surface albedo. In addition, for tree biomes, the relationships between LAI and turbulent fluxes appear to contradict the experimental evidences. The dominance of the transpiration‐driven over the observed albedo‐driven effects might suggest that LSMs have the incorrect balance of these two processes. Such mismatches shed light on the limitations of our current understanding and process representation of the vegetation control on the surface energy balance and help to identify critical areas for model improvement.

[1]  A. Arneth,et al.  Biophysics and vegetation cover change: a process-based evaluation framework for confronting land surface models with satellite observations , 2018, Earth System Science Data.

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

[3]  Hongliang Fang,et al.  Inconsistencies of interannual variability and trends in long‐term satellite leaf area index products , 2017, Global change biology.

[4]  Daniel A. Bishop,et al.  Evaluating the effect of alternative carbon allocation schemes in a land surface model (CLM4.5) on carbon fluxes, pools, and turnover in temperate forests , 2017 .

[5]  A. Cescatti,et al.  Patterns and controls of inter-annual variability in the terrestrial carbon budget , 2017 .

[6]  Alessandro Cescatti,et al.  Satellites reveal contrasting responses of regional climate to the widespread greening of Earth , 2017, Science.

[7]  Ranga B. Myneni,et al.  Climate mitigation from vegetation biophysical feedbacks during the past three decades , 2017 .

[8]  Christian H. Reick,et al.  Robust Identification of Local Biogeophysical Effects of Land-Cover Change in a Global Climate Model , 2017 .

[9]  Markus Reichstein,et al.  Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms , 2016 .

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

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

[12]  Niko E. C. Verhoest,et al.  Improving terrestrial evaporation estimates over continental Australia through assimilation of SMOS soil moisture , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[13]  P. Dirmeyer,et al.  The plumbing of land surface models: is poor performance a result of methodology or data quality? , 2016, Journal of hydrometeorology.

[14]  J. Canadell,et al.  Greening of the Earth and its drivers , 2016 .

[15]  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.

[16]  Chris Funk,et al.  Projections of leaf area index in earth system models , 2016 .

[17]  Yi Y. Liu,et al.  Multi-decadal trends in global terrestrial evapotranspiration and its components , 2016, Scientific Reports.

[18]  P. Ciais,et al.  Using satellite data to improve the leaf phenology of a global terrestrial biosphere model , 2015 .

[19]  A. House.  Physical Hydrology , 2015 .

[20]  P. Ciais,et al.  Spatiotemporal patterns of terrestrial gross primary production: A review , 2015 .

[21]  R. B. Jackson,et al.  Quantifying surface albedo and other direct biogeophysical climate forcings of forestry activities , 2015, Global change biology.

[22]  R. Betts,et al.  Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative , 2015 .

[23]  C. Jones,et al.  Quantifying the relative importance of land cover change from climate and land use in the representative concentration pathways , 2015 .

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

[25]  P. Cox,et al.  Observing terrestrial ecosystems and the carbon cycle from space , 2015, Global change biology.

[26]  Ranga B. Myneni,et al.  Recent trends and drivers of regional sources and sinks of carbon dioxide , 2015 .

[27]  R. Schnur,et al.  Separation of the Effects of Land and Climate Model Errors on Simulated Contemporary Land Carbon Cycle Trends in the MPI Earth System Model version 1 , 2015 .

[28]  K. Thonicke,et al.  Identifying environmental controls on vegetation greenness phenology through model–data integration , 2014 .

[29]  B. Felzer,et al.  Effects of nitrogen limitation on hydrological processes in CLM4‐CN , 2013 .

[30]  Benjamin Smith,et al.  Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model , 2013 .

[31]  Shilong Piao,et al.  Evaluation of Land Surface Models in Reproducing Satellite-Derived LAI over the High-Latitude Northern Hemisphere. Part I: Uncoupled DGVMs , 2013, Remote. Sens..

[32]  C. Jones,et al.  CO2 Emissions Determined by HadGEM2-ES to be Compatible with the Representative Concentration Pathway Scenarios and Their Extensions , 2013 .

[33]  M. Lomas,et al.  Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends , 2013, Global change biology.

[34]  Alessandro Anav,et al.  Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011 , 2013, Remote. Sens..

[35]  Joseph H. A. Guillaume,et al.  Characterising performance of environmental models , 2013, Environ. Model. Softw..

[36]  Marc Simard,et al.  A comprehensive benchmarking system for evaluating global vegetation models , 2012 .

[37]  Philippe Ciais,et al.  A framework for benchmarking land models , 2012 .

[38]  Kenneth W. Harrison,et al.  Land surface Verification Toolkit (LVT) – a generalized framework for land surface model evaluation , 2012 .

[39]  Gab Abramowitz,et al.  Towards a public, standardized, diagnostic benchmarking system for land surface models , 2012 .

[40]  Victor Brovkin,et al.  Determining robust impacts of land-use induced land-cover changes on surface climate over North America and Eurasia; Results from the first set of LUCID experiments , 2012 .

[41]  Fabienne Maignan,et al.  Evaluation of a Global Vegetation Model using time series of satellite vegetation indices , 2011 .

[42]  Steven J. Phipps,et al.  Importance of background climate in determining impact of land-cover change on regional climate , 2011 .

[43]  P. Cox,et al.  The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics , 2011 .

[44]  P. Cox,et al.  The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes , 2011 .

[45]  Alvaro Montenegro,et al.  Small temperature benefits provided by realistic afforestation efforts , 2011 .

[46]  Shenfeng Fei,et al.  Ecological forecasting and data assimilation in a data-rich era. , 2011, Ecological applications : a publication of the Ecological Society of America.

[47]  K. Denman,et al.  Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases , 2011 .

[48]  S. Seneviratne,et al.  Evaluation of global observations‐based evapotranspiration datasets and IPCC AR4 simulations , 2011 .

[49]  S. Seneviratne,et al.  Global intercomparison of 12 land surface heat flux estimates , 2011 .

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

[51]  T. Holmes,et al.  Global land-surface evaporation estimated from satellite-based observations , 2010 .

[52]  S. Los,et al.  A comprehensive set of benchmark tests for a land surface model of simultaneous fluxes of water and carbon at both the global and seasonal scale , 2010 .

[53]  W. Landman.  Climate change 2007: the physical science basis , 2010 .

[54]  Jason Beringer,et al.  Ecological Climatology: Concepts and Applications, 2nd Edition , 2010 .

[55]  Eleanor Blyth,et al.  Evaluating the JULES Land Surface Model Energy Fluxes Using FLUXNET Data , 2010 .

[56]  A. Bondeau,et al.  Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model , 2009 .

[57]  Peter E. Thornton,et al.  Systematic assessment of terrestrial biogeochemistry in coupled climate–carbon models , 2009 .

[58]  M. Williams,et al.  Improving land surface models with FLUXNET data , 2009 .

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

[60]  Renee A. McPherson,et al.  A review of vegetation—atmosphere interactions and their influences on mesoscale phenomena , 2007 .

[61]  Jens Kattge,et al.  Will the tropical land biosphere dominate the climate–carbon cycle feedback during the twenty-first century? , 2007 .

[62]  B. Rudolf,et al.  World Map of the Köppen-Geiger climate classification updated , 2006 .

[63]  Gabriel Abramowitz,et al.  Towards a benchmark for land surface models , 2005 .

[64]  I. C. Prentice,et al.  A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system , 2005 .

[65]  R. Dickinson,et al.  The Common Land Model , 2003 .

[66]  W. Oechel,et al.  FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities , 2001 .

[67]  Wolfgang Knorr,et al.  Uncertainties in global terrestrial biosphere modeling: 1. A comprehensive sensitivity analysis with a new photosynthesis and energy balance scheme , 2001 .

[68]  Soroosh Sorooshian,et al.  Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration , 1999 .

[69]  Darrel L. Williams,et al.  BOREAS in 1997: Experiment overview, scientific results, and future directions , 1997 .

[70]  Gusev,et al.  Cabauw Experimental Results from the Project for Intercomparison of Land-Surface Parameterization Schemes , 1997 .

[71]  B. Barkstrom,et al.  Clouds and the Earth's Radiant Energy System (CERES): An Earth Observing System Experiment , 1996 .

[72]  A. Robock,et al.  Scales of temporal and spatial variability of midlatitude soil moisture , 1996 .