Plant functional type mapping for earth system models

Abstract. The sensitivity of global carbon and water cycling to climate variability is coupled directly to land cover and the distribution of vegetation. To investigate biogeochemistry-climate interactions, earth system models require a representation of vegetation distributions that are either prescribed from remote sensing data or simulated via biogeography models. However, the abstraction of earth system state variables in models means that data products derived from remote sensing need to be post-processed for model-data assimilation. Dynamic global vegetation models (DGVM) rely on the concept of plant functional types (PFT) to group shared traits of thousands of plant species into usually only 10–20 classes. Available databases of observed PFT distributions must be relevant to existing satellite sensors and their derived products, and to the present day distribution of managed lands. Here, we develop four PFT datasets based on land-cover information from three satellite sensors (EOS-MODIS 1 km and 0.5 km, SPOT4-VEGETATION 1 km, and ENVISAT-MERIS 0.3 km spatial resolution) that are merged with spatially-consistent Koppen-Geiger climate zones. Using a beta (s) diversity metric to assess reclassification similarity, we find that the greatest uncertainty in PFT classifications occur most frequently between cropland and grassland categories, and in dryland systems between shrubland, grassland and forest categories because of differences in the minimum threshold required for forest cover. The biogeography-biogeochemistry DGVM, LPJmL, is used in diagnostic mode with the four PFT datasets prescribed to quantify the effect of land-cover uncertainty on climatic sensitivity of gross primary productivity (GPP) and transpiration fluxes. Our results show that land-cover uncertainty has large effects in arid regions, contributing up to 30% (20%) uncertainty in the sensitivity of GPP (transpiration) to precipitation. The availability of PFT datasets that are consistent with current satellite products and adapted for earth system models is an important component for reducing the uncertainty of terrestrial biogeochemistry to climate variability.

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

[2]  M. Lefsky.  A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System , 2010 .

[3]  Susan L Ustin,et al.  Remote sensing of plant functional types. , 2010, The New phytologist.

[4]  Dan Yakir,et al.  Contribution of Semi-Arid Forests to the Climate System , 2010, Science.

[5]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[6]  Sietse O. Los,et al.  Simulations of global evapotranspiration using semiempirical and mechanistic schemes of plant hydrology , 2009 .

[7]  S. Ganguly,et al.  Amazon forests did not green‐up during the 2005 drought , 2009 .

[8]  B. Poulter,et al.  Satellite remote sensing of tropical forest canopies and their seasonal dynamics , 2009 .

[9]  S. Higgins,et al.  Impacts of climate change on the vegetation of Africa: an adaptive dynamic vegetation modelling approach , 2009 .

[10]  Benjamin Poulter,et al.  Modeling the Sensitivity of the Seasonal Cycle of GPP to Dynamic LAI and Soil Depths in Tropical Rainforests , 2009, Ecosystems.

[11]  Ryan Pavlick,et al.  Simulated geographic variations of plant species richness, evenness and abundance using climatic constraints on plant functional diversity , 2009 .

[12]  Mathias Disney,et al.  Impact of land cover uncertainties on estimates of biospheric carbon fluxes , 2008 .

[13]  A. Ducharne,et al.  Comprehensive data set of global land cover change for land surface model applications , 2008 .

[14]  Taylor H. Ricketts,et al.  Explaining the global pattern of protected area coverage: relative importance of vertebrate biodiversity, human activities and agricultural suitability , 2008 .

[15]  S. Sitch,et al.  The role of fire disturbance for global vegetation dynamics: coupling fire into a Dynamic Global Vegetation Model , 2008 .

[16]  C. Nobre,et al.  A new world natural vegetation map for global change studies. , 2008, Anais da Academia Brasileira de Ciencias.

[17]  Martin Herold,et al.  Some challenges in global land cover mapping : An assessment of agreement and accuracy in existing 1 km datasets , 2008 .

[18]  Steffen Fritz,et al.  Identifying and quantifying uncertainty and spatial disagreement in the comparison of Global Land Cover for different applications , 2008 .

[19]  Hongliang Fang,et al.  Mapping plant functional types from MODIS data using multisource evidential reasoning , 2008 .

[20]  Scott D. Miller,et al.  Seasonal drought stress in the Amazon: Reconciling models and observations , 2008 .

[21]  Frédéric Achard,et al.  GLOBCOVER : The most detailed portrait of Earth , 2008 .

[22]  Steffen Fritz,et al.  A Global Forest Growing Stock, Biomass and Carbon Map Based on FAO Statistics , 2008 .

[23]  Markus Reichstein,et al.  Uncertainties of modeling gross primary productivity over Europe: A systematic study on the effects of using different drivers and terrestrial biosphere models , 2007 .

[24]  A. Huete,et al.  Amazon Forests Green-Up During 2005 Drought , 2007, Science.

[25]  A. Bouwman,et al.  Mapping contemporary global cropland and grassland distributions on a 5 × 5 minute resolution , 2007 .

[26]  T. McMahon,et al.  Updated world map of the Köppen-Geiger climate classification , 2007 .

[27]  H. Haberl,et al.  Quantifying and mapping the human appropriation of net primary production in earth's terrestrial ecosystems , 2007, Proceedings of the National Academy of Sciences.

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

[29]  C. Müller,et al.  Modelling the role of agriculture for the 20th century global terrestrial carbon balance , 2007 .

[30]  R. DeFries,et al.  Cropland expansion changes deforestation dynamics in the southern Brazilian Amazon , 2006, Proceedings of the National Academy of Sciences.

[31]  Vivek K. Arora,et al.  Estimating changes in global vegetation cover (1850–2100) for use in climate models , 2006 .

[32]  S. Kanae,et al.  Global Hydrological Cycles and World Water Resources , 2006, Science.

[33]  R. Dickinson,et al.  The Community Land Model and Its Climate Statistics as a Component of the Community Climate System Model , 2006 .

[34]  Martin Jung,et al.  Exploiting synergies of global land cover products for carbon cycle modeling , 2006 .

[35]  Hideki Kobayashi,et al.  Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index , 2005 .

[36]  S. Carpenter,et al.  Global Consequences of Land Use , 2005, Science.

[37]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

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

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

[40]  P. Legendre,et al.  ANALYZING BETA DIVERSITY: PARTITIONING THE SPATIAL VARIATION OF COMMUNITY COMPOSITION DATA , 2005 .

[41]  Chandra Giri,et al.  A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets , 2005 .

[42]  A. Belward,et al.  GLC 2000 : a new approach to global land cover mapping from Earth observation data , 2005 .

[43]  S. Zaehle,et al.  Contemporary “green” water flows: Simulations with a dynamic global vegetation and water balance model , 2005 .

[44]  S. Fritz,et al.  A land cover map of South America , 2004 .

[45]  M. Castro,et al.  Sensitivity of the Continental Hydrological Cycle to the Spatial Resolution over the Iberian Peninsula , 2004 .

[46]  Julio Soares de Arruda.  A land cover map of South America , 2004 .

[47]  C. Tucker,et al.  Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999 , 2003, Science.

[48]  R. DeFries,et al.  Global distribution of C3 and C4 vegetation: Carbon cycle implications , 2003 .

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

[50]  Shamil Maksyutov,et al.  TransCom 3 CO2 inversion intercomparison: 1. Annual mean control results and sensitivity to transport and prior flux information , 2003 .

[51]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[52]  Keith W. Oleson,et al.  Landscapes as patches of plant functional types: An integrating concept for climate and ecosystem models , 2002 .

[53]  M. Hulme,et al.  A high-resolution data set of surface climate over global land areas , 2002 .

[54]  J. Canadell,et al.  Recent patterns and mechanisms of carbon exchange by terrestrial ecosystems , 2001, Nature.

[55]  S. Plummer,et al.  Perspectives on combining ecological process models and remotely sensed data , 2000 .

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

[57]  N. Ramankutty,et al.  Characterizing patterns of global land use: An analysis of global croplands data , 1998 .

[58]  James S. Clark,et al.  Effects of climate and atmospheric CO2 partial pressure on the global distribution of C4 grasses: present, past, and future , 1998, Oecologia.

[59]  Thomas M. Smith,et al.  Plant functional types : their relevance to ecosystem properties and global change , 1998 .

[60]  Klein Goldewijk Cgm,et al.  A Hundred Year (1890 - 1990) Database for Integrated Environmental Assessments (HYDE, version 1.1) , 1997 .

[61]  Ramakrishna R. Nemani,et al.  A remote sensing based vegetation classification logic for global land cover analysis , 1995 .

[62]  W. Cramer,et al.  A global biome model based on plant physiology and dominance, soil properties and climate , 1992 .

[63]  D. Legates,et al.  Mean seasonal and spatial variability in global surface air temperature , 1990 .

[64]  Norman L. Christensen,et al.  CONVERGENCE DURING SECONDARY FOREST SUCCESSION , 1984 .

[65]  E. Matthews.  Global Vegetation and Land Use: New High-Resolution Data Bases for Climate Studies , 1983 .

[66]  R. Whittaker.  Evolution and measurement of species diversity , 1972 .

[67]  W. Köppen.  Das geographische System der Klimate , 1936 .

[68]  S S I T C H,et al.  Evaluation of Ecosystem Dynamics, Plant Geography and Terrestrial Carbon Cycling in the Lpj Dynamic Global Vegetation Model , 2022 .