The Experts below are selected from a list of 243 Experts worldwide ranked by ideXlab platform

B. Poulter - One of the best experts on this subject based on the ideXlab platform.

  • Plant Functional Type classification for earth system models results from the european space agency s land cover climate change initiative
    Geoscientific Model Development, 2015
    Co-Authors: B. Poulter, N. Macbean, A. Hartley, I. Khlystova, O. Arino, R. Betts, S. Bontemps, M. Boettcher, C. Brockmann
    Abstract:

    Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land cover data sets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as 1 of 13 essential climate variables targeted for research development. The LC_CCI was implemented in three phases: first responding to a survey of user needs; developing a global, moderate-resolution land cover data set for three time periods, or epochs (2000, 2005, and 2010); and the last phase resulting in a user tool for converting land cover to Plant Functional Type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to Plant Functional Types (PFTs). The translation was performed as part of consultative process among map producers and users, and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three earth system modeling teams shows significant differences between the LC_CCI PFT data set and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land–atmosphere interactions. The main difference between the new LC_CCI product and PFT data sets used currently by three different dynamic global vegetation modeling teams is a reduction in high-latitude grassland cover, a reduction in tropical tree cover and an expansion in temperate forest cover in Europe. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as phase 2 of the European Space Agency CCI program continues.

  • Plant Functional Type classification for Earth System Models: results from the European Space Agency's Land Cover Climate Change Initiative
    Geoscientific Model Development Discussions, 2015
    Co-Authors: B. Poulter, N. Macbean, A. Hartley, I. Khlystova, O. Arino, R. Betts, S. Bontemps, M. Boettcher, C. Brockmann, P. Defourny
    Abstract:

    Abstract. Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land-cover datasets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as one of thirteen Essential Climate Variables targeted for research development. The LC_CCI was implemented in three phases, first responding to a survey of user needs, then developing a global, moderate resolution, land-cover dataset for three time periods, or epochs, 2000, 2005, and 2010, and the last phase resulting in a user-tool for converting land cover to Plant Functional Type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to Plant Functional Types (PFT). The translation was performed as part of consultative process among map producers and users and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three-earth system modeling teams shows significant differences between the LC_CCI PFT dataset and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land–atmosphere interactions. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as Phase 2 of the European Space Agency CCI program continues.

  • use of various remote sensing land cover products for Plant Functional Type mapping over siberia
    Earth System Science Data, 2013
    Co-Authors: C. Ottlé, Fabienne Maignan, B. Poulter, J. Lescure, T Wang, N. Delbart
    Abstract:

    High-latitude ecosystems play an important role in the global carbon cycle and in regulating the climate system and are presently undergoing rapid environmental change. Accurate land cover data sets are required to both document these changes as well as to provide land-surface information for benchmarking and initializing Earth system models. Earth system models also require specific land cover classification systems based on Plant Functional Types (PFTs), rather than species or ecosystems, and so post-processing of existing land cover data is often required. This study compares over Siberia, multiple land cover data sets against one another and with auxiliary data to identify key uncertainties that contribute to variability in PFT classifications that would introduce errors in Earth system modeling. Land cover classification systems from GLC 2000, GlobCover 2005 and 2009, and MODIS collections 5 and 5.1 are first aggregated to a common legend, and then compared to high-resolution land cover classification systems, vegetation continuous fields (MODIS VCFs) and satellite-derived tree heights (to discriminate against sparse, shrub, and forest vegetation). The GlobCover data set, with a lower threshold for tree cover and taller tree heights and a better spatial resolution, tends to have better distributions of tree cover compared to high-resolution data. It has therefore been chosen to build new PFT maps for the ORCHIDEE land surface model at 1 km scale. Compared to the original PFT data set, the new PFT maps based on GlobCover 2005 and an updated cross-walking approach mainly di er in the characterization of forests and degree of tree cover. The partition of grasslands and bare soils now appears more realistic compared with ground truth data. This new vegetation map provides a framework for further development of new PFTs in the ORCHIDEE model like shrubs, lichens and mosses, to represent the water and carbon cycles in northern latitudes better. Updated land cover data sets are critical for improving and maintaining the relevance of Earth system models for assessing climate and human impacts on biogeochemistry and biophysics. The new PFT map at 5 km scale is available for download from the PANGAEA website at

  • Use of various remote sensing land cover products for Plant Functional Type mapping over Siberia
    Earth System Science Data, 2013
    Co-Authors: C. Ottlé, Fabienne Maignan, B. Poulter, J. Lescure, T Wang, N. Delbart
    Abstract:

    High-latitude ecosystems play an important role in the global carbon cycle and in regulating the climate system and are presently undergoing rapid environmental change. Accurate land cover data sets are required to both document these changes as well as to provide land-surface information for benchmarking and initializing Earth system models. Earth system models also require specific land cover classification systems based on Plant Functional Types (PFTs), rather than species or ecosystems, and so post-processing of existing land cover data is often required. This study compares over Siberia, multiple land cover data sets against one another and with auxiliary data to identify key uncertainties that contribute to variability in PFT classifications that would introduce errors in Earth system modeling. Land cover classification systems from GLC 2000, GlobCover 2005 and 2009, and MODIS collections 5 and 5.1 are first aggregated to a common legend, and then compared to high-resolution land cover classification systems, vegetation continuous fields (MODIS VCFs) and satellite-derived tree heights (to discriminate against sparse, shrub, and forest vegetation). The GlobCover data set, with a lower threshold for tree cover and taller tree heights and a better spatial resolution, tends to have better distributions of tree cover compared to high-resolution data. It has therefore been chosen to build new PFT maps for the ORCHIDEE land surface model at 1 km scale. Compared to the original PFT data set, the new PFT maps based on GlobCover 2005 and an updated cross-walking approach mainly differ in the characterization of forests and degree of tree cover. The partition of grasslands and bare soils now appears more realistic compared with ground truth data. This new vegetation map provides a framework for further development of new PFTs in the ORCHIDEE model like shrubs, lichens and mosses, to represent the water and carbon cycles in northern latitudes better. Updated land cover data sets are critical for improving and maintaining the relevance of Earth system models for assessing climate and human impacts on biogeochemistry and biophysics.©Author(s) 2013.

  • Plant Functional Type mapping for earth system models
    Geoscientific Model Development Discussions, 2011
    Co-Authors: B. Poulter, Fabienne Maignan, Stephen Plummer, Heike Lischke, Elisabeth Hodson, Philippe Ciais, N. E. Zimmermann
    Abstract:

    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 just several 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 Köppen-Geiger climate zones. Using a beta (β) 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 Plant Functional Type 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.

S. Bontemps - One of the best experts on this subject based on the ideXlab platform.

  • uncertainty in Plant Functional Type distributions and its impact on land surface models
    Remote Sensing of Environment, 2017
    Co-Authors: A. Hartley, N. Macbean, G Georgievski, S. Bontemps
    Abstract:

    Abstract The spatial distribution and fractional cover of Plant Functional Types (PFTs) is a key uncertainty in land surface models (LSMs) that is closely linked to uncertainties in global carbon, hydrology and energy budgets. Land cover is considered to be an Essential Climate Variable because changes in it can result in local, regional or global scale impacts on climate. In LSMs, land cover (LC) class maps are converted to PFT fractional maps using a cross-walking (CW) table by prescribing the fraction of each PFT that occurs within each LC class. In this study we assess the largest plausible range of PFT uncertainty derived from remotely sensed LC maps produced under the European Space Agency Land Cover Climate Change Initiative on simulations of land surface fluxes using 3 leading LSMs. We evaluate the impact of uncertainty due to both LC classification algorithms, and CW procedure, on energy, moisture and carbon fluxes in LSMs. We investigate the maximum plausible range of uncertainty deriving from both LC and CW within the context of a potential biomass scale (bare ground-grass-shrub-tree), representing a gradient from low to high biomass PFTs. More specifically, plausible alternative land cover maps and associated PFT fractional distributions were produced to prioritise low or high biomass vegetation in the LC classification (uncertainty in LC), and subsequently in the assignment of PFT fractions for each LC class (uncertainty in CW), relative to a reference PFT distribution. We examined the impact of PFT uncertainty on 3 key variables in the carbon, water and energy cycles (gross primary production (GPP), evapo-transpiration (ET), and albedo), for 3 LSMs (JSBACH, JULES and ORCHIDEE) at global scale. Results showed a greater uncertainty in PFT fraction due to CW as opposed to LC uncertainty, for all three variables. CW uncertainty in tree fraction was found to be particularly important in the northern boreal forests for simulated LSM albedo. Uncertainty in the balance between grass and bare soil fraction in arid parts of Africa, central Asia, and central Australia was also found to influence albedo and ET in all models. The spread due to PFT uncertainty for albedo was between 30 and 105% of inter-model uncertainty, for GPP between 20 and 90%, and for ET 0–30%. Each model had a different sensitivity to PFT uncertainty, for example, GPP in JSBACH was found to have a much higher sensitivity to PFT uncertainty in the tropics than JULES and ORCHIDEE, whereas the inverse was true for ET. These results show that inter-model uncertainty for key variables in LSMs can be reduced by more accurate representation of PFT distributions. Future efforts in land cover mapping should therefore be focused on reducing CW uncertainty through better understanding of the fractional cover of PFTs within a land cover class. Efforts to reduce LC uncertainty should particularly be focused on more accurate mapping of grass and bare soil fractions in arid areas. In the context of Land Surface Models, these results demonstrate that prescribed vegetation distribution in models is a key source of uncertainty that is comparable to the spread between models for key model state variables.

  • Plant Functional Type classification for earth system models results from the european space agency s land cover climate change initiative
    Geoscientific Model Development, 2015
    Co-Authors: B. Poulter, N. Macbean, A. Hartley, I. Khlystova, O. Arino, R. Betts, S. Bontemps, M. Boettcher, C. Brockmann
    Abstract:

    Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land cover data sets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as 1 of 13 essential climate variables targeted for research development. The LC_CCI was implemented in three phases: first responding to a survey of user needs; developing a global, moderate-resolution land cover data set for three time periods, or epochs (2000, 2005, and 2010); and the last phase resulting in a user tool for converting land cover to Plant Functional Type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to Plant Functional Types (PFTs). The translation was performed as part of consultative process among map producers and users, and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three earth system modeling teams shows significant differences between the LC_CCI PFT data set and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land–atmosphere interactions. The main difference between the new LC_CCI product and PFT data sets used currently by three different dynamic global vegetation modeling teams is a reduction in high-latitude grassland cover, a reduction in tropical tree cover and an expansion in temperate forest cover in Europe. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as phase 2 of the European Space Agency CCI program continues.

  • Plant Functional Type classification for Earth System Models: results from the European Space Agency's Land Cover Climate Change Initiative
    Geoscientific Model Development Discussions, 2015
    Co-Authors: B. Poulter, N. Macbean, A. Hartley, I. Khlystova, O. Arino, R. Betts, S. Bontemps, M. Boettcher, C. Brockmann, P. Defourny
    Abstract:

    Abstract. Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land-cover datasets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as one of thirteen Essential Climate Variables targeted for research development. The LC_CCI was implemented in three phases, first responding to a survey of user needs, then developing a global, moderate resolution, land-cover dataset for three time periods, or epochs, 2000, 2005, and 2010, and the last phase resulting in a user-tool for converting land cover to Plant Functional Type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to Plant Functional Types (PFT). The translation was performed as part of consultative process among map producers and users and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three-earth system modeling teams shows significant differences between the LC_CCI PFT dataset and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land–atmosphere interactions. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as Phase 2 of the European Space Agency CCI program continues.

N. Macbean - One of the best experts on this subject based on the ideXlab platform.

  • uncertainty in Plant Functional Type distributions and its impact on land surface models
    Remote Sensing of Environment, 2017
    Co-Authors: A. Hartley, N. Macbean, G Georgievski, S. Bontemps
    Abstract:

    Abstract The spatial distribution and fractional cover of Plant Functional Types (PFTs) is a key uncertainty in land surface models (LSMs) that is closely linked to uncertainties in global carbon, hydrology and energy budgets. Land cover is considered to be an Essential Climate Variable because changes in it can result in local, regional or global scale impacts on climate. In LSMs, land cover (LC) class maps are converted to PFT fractional maps using a cross-walking (CW) table by prescribing the fraction of each PFT that occurs within each LC class. In this study we assess the largest plausible range of PFT uncertainty derived from remotely sensed LC maps produced under the European Space Agency Land Cover Climate Change Initiative on simulations of land surface fluxes using 3 leading LSMs. We evaluate the impact of uncertainty due to both LC classification algorithms, and CW procedure, on energy, moisture and carbon fluxes in LSMs. We investigate the maximum plausible range of uncertainty deriving from both LC and CW within the context of a potential biomass scale (bare ground-grass-shrub-tree), representing a gradient from low to high biomass PFTs. More specifically, plausible alternative land cover maps and associated PFT fractional distributions were produced to prioritise low or high biomass vegetation in the LC classification (uncertainty in LC), and subsequently in the assignment of PFT fractions for each LC class (uncertainty in CW), relative to a reference PFT distribution. We examined the impact of PFT uncertainty on 3 key variables in the carbon, water and energy cycles (gross primary production (GPP), evapo-transpiration (ET), and albedo), for 3 LSMs (JSBACH, JULES and ORCHIDEE) at global scale. Results showed a greater uncertainty in PFT fraction due to CW as opposed to LC uncertainty, for all three variables. CW uncertainty in tree fraction was found to be particularly important in the northern boreal forests for simulated LSM albedo. Uncertainty in the balance between grass and bare soil fraction in arid parts of Africa, central Asia, and central Australia was also found to influence albedo and ET in all models. The spread due to PFT uncertainty for albedo was between 30 and 105% of inter-model uncertainty, for GPP between 20 and 90%, and for ET 0–30%. Each model had a different sensitivity to PFT uncertainty, for example, GPP in JSBACH was found to have a much higher sensitivity to PFT uncertainty in the tropics than JULES and ORCHIDEE, whereas the inverse was true for ET. These results show that inter-model uncertainty for key variables in LSMs can be reduced by more accurate representation of PFT distributions. Future efforts in land cover mapping should therefore be focused on reducing CW uncertainty through better understanding of the fractional cover of PFTs within a land cover class. Efforts to reduce LC uncertainty should particularly be focused on more accurate mapping of grass and bare soil fractions in arid areas. In the context of Land Surface Models, these results demonstrate that prescribed vegetation distribution in models is a key source of uncertainty that is comparable to the spread between models for key model state variables.

  • Plant Functional Type classification for earth system models results from the european space agency s land cover climate change initiative
    Geoscientific Model Development, 2015
    Co-Authors: B. Poulter, N. Macbean, A. Hartley, I. Khlystova, O. Arino, R. Betts, S. Bontemps, M. Boettcher, C. Brockmann
    Abstract:

    Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land cover data sets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as 1 of 13 essential climate variables targeted for research development. The LC_CCI was implemented in three phases: first responding to a survey of user needs; developing a global, moderate-resolution land cover data set for three time periods, or epochs (2000, 2005, and 2010); and the last phase resulting in a user tool for converting land cover to Plant Functional Type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to Plant Functional Types (PFTs). The translation was performed as part of consultative process among map producers and users, and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three earth system modeling teams shows significant differences between the LC_CCI PFT data set and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land–atmosphere interactions. The main difference between the new LC_CCI product and PFT data sets used currently by three different dynamic global vegetation modeling teams is a reduction in high-latitude grassland cover, a reduction in tropical tree cover and an expansion in temperate forest cover in Europe. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as phase 2 of the European Space Agency CCI program continues.

  • Plant Functional Type classification for Earth System Models: results from the European Space Agency's Land Cover Climate Change Initiative
    Geoscientific Model Development Discussions, 2015
    Co-Authors: B. Poulter, N. Macbean, A. Hartley, I. Khlystova, O. Arino, R. Betts, S. Bontemps, M. Boettcher, C. Brockmann, P. Defourny
    Abstract:

    Abstract. Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land-cover datasets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as one of thirteen Essential Climate Variables targeted for research development. The LC_CCI was implemented in three phases, first responding to a survey of user needs, then developing a global, moderate resolution, land-cover dataset for three time periods, or epochs, 2000, 2005, and 2010, and the last phase resulting in a user-tool for converting land cover to Plant Functional Type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to Plant Functional Types (PFT). The translation was performed as part of consultative process among map producers and users and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three-earth system modeling teams shows significant differences between the LC_CCI PFT dataset and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land–atmosphere interactions. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as Phase 2 of the European Space Agency CCI program continues.

N. Delbart - One of the best experts on this subject based on the ideXlab platform.

  • use of various remote sensing land cover products for Plant Functional Type mapping over siberia
    Earth System Science Data, 2013
    Co-Authors: C. Ottlé, Fabienne Maignan, B. Poulter, J. Lescure, T Wang, N. Delbart
    Abstract:

    High-latitude ecosystems play an important role in the global carbon cycle and in regulating the climate system and are presently undergoing rapid environmental change. Accurate land cover data sets are required to both document these changes as well as to provide land-surface information for benchmarking and initializing Earth system models. Earth system models also require specific land cover classification systems based on Plant Functional Types (PFTs), rather than species or ecosystems, and so post-processing of existing land cover data is often required. This study compares over Siberia, multiple land cover data sets against one another and with auxiliary data to identify key uncertainties that contribute to variability in PFT classifications that would introduce errors in Earth system modeling. Land cover classification systems from GLC 2000, GlobCover 2005 and 2009, and MODIS collections 5 and 5.1 are first aggregated to a common legend, and then compared to high-resolution land cover classification systems, vegetation continuous fields (MODIS VCFs) and satellite-derived tree heights (to discriminate against sparse, shrub, and forest vegetation). The GlobCover data set, with a lower threshold for tree cover and taller tree heights and a better spatial resolution, tends to have better distributions of tree cover compared to high-resolution data. It has therefore been chosen to build new PFT maps for the ORCHIDEE land surface model at 1 km scale. Compared to the original PFT data set, the new PFT maps based on GlobCover 2005 and an updated cross-walking approach mainly di er in the characterization of forests and degree of tree cover. The partition of grasslands and bare soils now appears more realistic compared with ground truth data. This new vegetation map provides a framework for further development of new PFTs in the ORCHIDEE model like shrubs, lichens and mosses, to represent the water and carbon cycles in northern latitudes better. Updated land cover data sets are critical for improving and maintaining the relevance of Earth system models for assessing climate and human impacts on biogeochemistry and biophysics. The new PFT map at 5 km scale is available for download from the PANGAEA website at

  • Use of various remote sensing land cover products for Plant Functional Type mapping over Siberia
    Earth System Science Data, 2013
    Co-Authors: C. Ottlé, Fabienne Maignan, B. Poulter, J. Lescure, T Wang, N. Delbart
    Abstract:

    High-latitude ecosystems play an important role in the global carbon cycle and in regulating the climate system and are presently undergoing rapid environmental change. Accurate land cover data sets are required to both document these changes as well as to provide land-surface information for benchmarking and initializing Earth system models. Earth system models also require specific land cover classification systems based on Plant Functional Types (PFTs), rather than species or ecosystems, and so post-processing of existing land cover data is often required. This study compares over Siberia, multiple land cover data sets against one another and with auxiliary data to identify key uncertainties that contribute to variability in PFT classifications that would introduce errors in Earth system modeling. Land cover classification systems from GLC 2000, GlobCover 2005 and 2009, and MODIS collections 5 and 5.1 are first aggregated to a common legend, and then compared to high-resolution land cover classification systems, vegetation continuous fields (MODIS VCFs) and satellite-derived tree heights (to discriminate against sparse, shrub, and forest vegetation). The GlobCover data set, with a lower threshold for tree cover and taller tree heights and a better spatial resolution, tends to have better distributions of tree cover compared to high-resolution data. It has therefore been chosen to build new PFT maps for the ORCHIDEE land surface model at 1 km scale. Compared to the original PFT data set, the new PFT maps based on GlobCover 2005 and an updated cross-walking approach mainly differ in the characterization of forests and degree of tree cover. The partition of grasslands and bare soils now appears more realistic compared with ground truth data. This new vegetation map provides a framework for further development of new PFTs in the ORCHIDEE model like shrubs, lichens and mosses, to represent the water and carbon cycles in northern latitudes better. Updated land cover data sets are critical for improving and maintaining the relevance of Earth system models for assessing climate and human impacts on biogeochemistry and biophysics.©Author(s) 2013.

A. Hartley - One of the best experts on this subject based on the ideXlab platform.

  • uncertainty in Plant Functional Type distributions and its impact on land surface models
    Remote Sensing of Environment, 2017
    Co-Authors: A. Hartley, N. Macbean, G Georgievski, S. Bontemps
    Abstract:

    Abstract The spatial distribution and fractional cover of Plant Functional Types (PFTs) is a key uncertainty in land surface models (LSMs) that is closely linked to uncertainties in global carbon, hydrology and energy budgets. Land cover is considered to be an Essential Climate Variable because changes in it can result in local, regional or global scale impacts on climate. In LSMs, land cover (LC) class maps are converted to PFT fractional maps using a cross-walking (CW) table by prescribing the fraction of each PFT that occurs within each LC class. In this study we assess the largest plausible range of PFT uncertainty derived from remotely sensed LC maps produced under the European Space Agency Land Cover Climate Change Initiative on simulations of land surface fluxes using 3 leading LSMs. We evaluate the impact of uncertainty due to both LC classification algorithms, and CW procedure, on energy, moisture and carbon fluxes in LSMs. We investigate the maximum plausible range of uncertainty deriving from both LC and CW within the context of a potential biomass scale (bare ground-grass-shrub-tree), representing a gradient from low to high biomass PFTs. More specifically, plausible alternative land cover maps and associated PFT fractional distributions were produced to prioritise low or high biomass vegetation in the LC classification (uncertainty in LC), and subsequently in the assignment of PFT fractions for each LC class (uncertainty in CW), relative to a reference PFT distribution. We examined the impact of PFT uncertainty on 3 key variables in the carbon, water and energy cycles (gross primary production (GPP), evapo-transpiration (ET), and albedo), for 3 LSMs (JSBACH, JULES and ORCHIDEE) at global scale. Results showed a greater uncertainty in PFT fraction due to CW as opposed to LC uncertainty, for all three variables. CW uncertainty in tree fraction was found to be particularly important in the northern boreal forests for simulated LSM albedo. Uncertainty in the balance between grass and bare soil fraction in arid parts of Africa, central Asia, and central Australia was also found to influence albedo and ET in all models. The spread due to PFT uncertainty for albedo was between 30 and 105% of inter-model uncertainty, for GPP between 20 and 90%, and for ET 0–30%. Each model had a different sensitivity to PFT uncertainty, for example, GPP in JSBACH was found to have a much higher sensitivity to PFT uncertainty in the tropics than JULES and ORCHIDEE, whereas the inverse was true for ET. These results show that inter-model uncertainty for key variables in LSMs can be reduced by more accurate representation of PFT distributions. Future efforts in land cover mapping should therefore be focused on reducing CW uncertainty through better understanding of the fractional cover of PFTs within a land cover class. Efforts to reduce LC uncertainty should particularly be focused on more accurate mapping of grass and bare soil fractions in arid areas. In the context of Land Surface Models, these results demonstrate that prescribed vegetation distribution in models is a key source of uncertainty that is comparable to the spread between models for key model state variables.

  • Plant Functional Type classification for earth system models results from the european space agency s land cover climate change initiative
    Geoscientific Model Development, 2015
    Co-Authors: B. Poulter, N. Macbean, A. Hartley, I. Khlystova, O. Arino, R. Betts, S. Bontemps, M. Boettcher, C. Brockmann
    Abstract:

    Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land cover data sets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as 1 of 13 essential climate variables targeted for research development. The LC_CCI was implemented in three phases: first responding to a survey of user needs; developing a global, moderate-resolution land cover data set for three time periods, or epochs (2000, 2005, and 2010); and the last phase resulting in a user tool for converting land cover to Plant Functional Type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to Plant Functional Types (PFTs). The translation was performed as part of consultative process among map producers and users, and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three earth system modeling teams shows significant differences between the LC_CCI PFT data set and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land–atmosphere interactions. The main difference between the new LC_CCI product and PFT data sets used currently by three different dynamic global vegetation modeling teams is a reduction in high-latitude grassland cover, a reduction in tropical tree cover and an expansion in temperate forest cover in Europe. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as phase 2 of the European Space Agency CCI program continues.

  • Plant Functional Type classification for Earth System Models: results from the European Space Agency's Land Cover Climate Change Initiative
    Geoscientific Model Development Discussions, 2015
    Co-Authors: B. Poulter, N. Macbean, A. Hartley, I. Khlystova, O. Arino, R. Betts, S. Bontemps, M. Boettcher, C. Brockmann, P. Defourny
    Abstract:

    Abstract. Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land-cover datasets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as one of thirteen Essential Climate Variables targeted for research development. The LC_CCI was implemented in three phases, first responding to a survey of user needs, then developing a global, moderate resolution, land-cover dataset for three time periods, or epochs, 2000, 2005, and 2010, and the last phase resulting in a user-tool for converting land cover to Plant Functional Type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to Plant Functional Types (PFT). The translation was performed as part of consultative process among map producers and users and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three-earth system modeling teams shows significant differences between the LC_CCI PFT dataset and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land–atmosphere interactions. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as Phase 2 of the European Space Agency CCI program continues.