scholarly journals Biogeophysical effects of historical land cover changes simulated by six Earth system models of intermediate complexity

2006 ◽  
Vol 26 (6) ◽  
pp. 587-600 ◽  
Author(s):  
V. Brovkin ◽  
M. Claussen ◽  
E. Driesschaert ◽  
T. Fichefet ◽  
D. Kicklighter ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Changes ◽  
Earth System ◽  
System Models ◽  
Intermediate Complexity ◽  
Earth System Models

scholarly journals Plant functional type mapping for earth system models

2011 ◽  
Vol 4 (3) ◽  
pp. 2081-2121 ◽  
Author(s):  
B. Poulter ◽  
P. Ciais ◽  
E. Hodson ◽  
H. Lischke ◽  
F. Maignan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Climate Variability ◽  
Forest Cover ◽  
Plant Functional Type ◽  
Earth System ◽  
Functional Type ◽  
System Models ◽  
Satellite Sensors ◽  
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 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.

scholarly journals Use of various remote sensing land cover products for PFT mapping over Siberia

2013 ◽  
Vol 6 (1) ◽  
pp. 255-296
Author(s):  
C. Ottlé ◽  
J. Lescure ◽  
F. Maignan ◽  
B. Poulter ◽  
T. Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Land Surface ◽  
Land Cover Classification ◽  
Classification Systems ◽  
Tree Cover ◽  
Surface Model ◽  
Earth System ◽  
System Models ◽  
Earth System Models

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 datasets 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, 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 datasets against one another and with auxiliary data to identify key uncertainties that contribute to variability in Plant Functional Type (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, continuous vegetation fields (MODIS-VCF) and satellite-derived tree heights (to discriminate against sparse, shrub, and forest vegetation). The GlobCover dataset, 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 PFTs maps for the ORCHIDEE land surface model at 1 km scale. Compared to the original PFT dataset, 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 better represent the water and carbon cycles in northern latitudes. Updated land cover datasets 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: doi:10.1594/PANGAEA.810709.

scholarly journals Plant functional type mapping for earth system models

2011 ◽  
Vol 4 (4) ◽  
pp. 993-1010 ◽  
Author(s):  
B. Poulter ◽  
P. Ciais ◽  
E. Hodson ◽  
H. Lischke ◽  
F. Maignan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Climate Variability ◽  
Forest Cover ◽  
Remote Sensing Data ◽  
Earth System ◽  
State Variables ◽  
System Models ◽  
Satellite Sensors ◽  
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 Köppen-Geiger climate zones. Using a beta (ß) 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.

scholarly journals Mapping past human land use using archaeological data: A new classification for global land use synthesis and data harmonization

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0246662 ◽  
Author(s):  
Kathleen D. Morrison ◽  
Emily Hammer ◽  
Oliver Boles ◽  
Marco Madella ◽  
Nicola Whitehouse ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Classification Scheme ◽  
Earth System ◽  
Land Use Systems ◽  
System Models ◽  
The Past ◽  
The Earth ◽  
Earth System Models ◽  
Past Land Use

In the 12,000 years preceding the Industrial Revolution, human activities led to significant changes in land cover, plant and animal distributions, surface hydrology, and biochemical cycles. Earth system models suggest that this anthropogenic land cover change influenced regional and global climate. However, the representation of past land use in earth system models is currently oversimplified. As a result, there are large uncertainties in the current understanding of the past and current state of the earth system. In order to improve representation of the variety and scale of impacts that past land use had on the earth system, a global effort is underway to aggregate and synthesize archaeological and historical evidence of land use systems. Here we present a simple, hierarchical classification of land use systems designed to be used with archaeological and historical data at a global scale and a schema of codes that identify land use practices common to a range of systems, both implemented in a geospatial database. The classification scheme and database resulted from an extensive process of consultation with researchers worldwide. Our scheme is designed to deliver consistent, empirically robust data for the improvement of land use models, while simultaneously allowing for a comparative, detailed mapping of land use relevant to the needs of historical scholars. To illustrate the benefits of the classification scheme and methods for mapping historical land use, we apply it to Mesopotamia and Arabia at 6 kya (c. 4000 BCE). The scheme will be used to describe land use by the Past Global Changes (PAGES) LandCover6k working group, an international project comprised of archaeologists, historians, geographers, paleoecologists, and modelers. Beyond this, the scheme has a wide utility for creating a common language between research and policy communities, linking archaeologists with climate modelers, biodiversity conservation workers and initiatives.

scholarly journals Atmospheric processing of iron in mineral and combustion aerosols: development of an intermediate-complexity mechanism suitable for Earth system models

2018 ◽  
Vol 18 (19) ◽  
pp. 14175-14196 ◽  
Author(s):  
Rachel A. Scanza ◽  
Douglas S. Hamilton ◽  
Carlos Perez Garcia-Pando ◽  
Clifton Buck ◽  
Alex Baker ◽  
...  
Keyword(s):  
Oxalic Acid ◽  
Earth System ◽  
Reference Case ◽  
Iron Solubility ◽  
Soluble Iron ◽  
System Models ◽  
Intermediate Complexity ◽  
Atmospheric Processing ◽  
Combustion Aerosols ◽  
Earth System Models

Abstract. Atmospheric processing of iron in dust and combustion aerosols is simulated using an intermediate-complexity soluble iron mechanism designed for Earth system models. The solubilization mechanism includes both a dependence on aerosol water pH and in-cloud oxalic acid. The simulations of size-resolved total, soluble and fractional iron solubility indicate that this mechanism captures many but not all of the features seen from cruise observations of labile iron. The primary objective was to determine the extent to which our solubility scheme could adequately match observations of fractional iron solubility. We define a semi-quantitative metric as the model mean at points with observations divided by the observational mean (MMO). The model is in reasonable agreement with observations of fractional iron solubility with an MMO of 0.86. Several sensitivity studies are performed to ascertain the degree of complexity needed to match observations; including the oxalic acid enhancement is necessary, while different parameterizations for calculating model oxalate concentrations are less important. The percent change in soluble iron deposition between the reference case (REF) and the simulation with acidic processing alone is 63.8 %, which is consistent with previous studies. Upon deposition to global oceans, global mean combustion iron solubility to total fractional iron solubility is 8.2 %; however, the contribution of fractional iron solubility from combustion sources to ocean basins below 15∘ S is approximately 50 %. We conclude that, in many remote ocean regions, sources of iron from combustion and dust aerosols are equally important. Our estimates of changes in deposition of soluble iron to the ocean since preindustrial climate conditions suggest roughly a doubling due to a combination of higher dust and combustion iron emissions along with more efficient atmospheric processing.

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