Environment-sensitivity functions for gross primary productivity in light use efficiency models
The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bao, Shanning [verfasserIn] Wutzler, Thomas [verfasserIn] Koirala, Sujan [verfasserIn] Cuntz, Matthias [verfasserIn] Ibrom, Andreas [verfasserIn] Besnard, Simon [verfasserIn] Walther, Sophia [verfasserIn] Šigut, Ladislav [verfasserIn] Moreno, Alvaro [verfasserIn] Weber, Ulrich [verfasserIn] Wohlfahrt, Georg [verfasserIn] Cleverly, Jamie [verfasserIn] Migliavacca, Mirco [verfasserIn] Woodgate, William [verfasserIn] Merbold, Lutz [verfasserIn] Veenendaal, Elmar [verfasserIn] Carvalhais, Nuno [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Agricultural and forest meteorology - Amsterdam [u.a.] : Elsevier, 1984, 312 |
|---|---|
| Übergeordnetes Werk: |
volume:312 |
| DOI / URN: |
10.1016/j.agrformet.2021.108708 |
|---|
| Katalog-ID: |
ELV007020996 |
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| 520 | |a The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products. | ||
| 650 | 4 | |a Carbon assimilation | |
| 650 | 4 | |a Radiation use efficiency | |
| 650 | 4 | |a Model comparison | |
| 650 | 4 | |a Model equifinality | |
| 650 | 4 | |a Diffuse fraction | |
| 650 | 4 | |a Sensitivity formulations | |
| 650 | 4 | |a Randomly sampled sites | |
| 650 | 4 | |a Temporal scales | |
| 700 | 1 | |a Wutzler, Thomas |e verfasserin |4 aut | |
| 700 | 1 | |a Koirala, Sujan |e verfasserin |4 aut | |
| 700 | 1 | |a Cuntz, Matthias |e verfasserin |4 aut | |
| 700 | 1 | |a Ibrom, Andreas |e verfasserin |4 aut | |
| 700 | 1 | |a Besnard, Simon |e verfasserin |4 aut | |
| 700 | 1 | |a Walther, Sophia |e verfasserin |4 aut | |
| 700 | 1 | |a Šigut, Ladislav |e verfasserin |4 aut | |
| 700 | 1 | |a Moreno, Alvaro |e verfasserin |4 aut | |
| 700 | 1 | |a Weber, Ulrich |e verfasserin |4 aut | |
| 700 | 1 | |a Wohlfahrt, Georg |e verfasserin |4 aut | |
| 700 | 1 | |a Cleverly, Jamie |e verfasserin |4 aut | |
| 700 | 1 | |a Migliavacca, Mirco |e verfasserin |4 aut | |
| 700 | 1 | |a Woodgate, William |e verfasserin |4 aut | |
| 700 | 1 | |a Merbold, Lutz |e verfasserin |4 aut | |
| 700 | 1 | |a Veenendaal, Elmar |e verfasserin |4 aut | |
| 700 | 1 | |a Carvalhais, Nuno |e verfasserin |4 aut | |
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10.1016/j.agrformet.2021.108708 doi (DE-627)ELV007020996 (ELSEVIER)S0168-1923(21)00394-4 DE-627 ger DE-627 rda eng 630 640 550 DE-600 38.84 bkl 48.99 bkl Bao, Shanning verfasserin aut Environment-sensitivity functions for gross primary productivity in light use efficiency models 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products. Carbon assimilation Radiation use efficiency Model comparison Model equifinality Diffuse fraction Sensitivity formulations Randomly sampled sites Temporal scales Wutzler, Thomas verfasserin aut Koirala, Sujan verfasserin aut Cuntz, Matthias verfasserin aut Ibrom, Andreas verfasserin aut Besnard, Simon verfasserin aut Walther, Sophia verfasserin aut Šigut, Ladislav verfasserin aut Moreno, Alvaro verfasserin aut Weber, Ulrich verfasserin aut Wohlfahrt, Georg verfasserin aut Cleverly, Jamie verfasserin aut Migliavacca, Mirco verfasserin aut Woodgate, William verfasserin aut Merbold, Lutz verfasserin aut Veenendaal, Elmar verfasserin aut Carvalhais, Nuno verfasserin aut Enthalten in Agricultural and forest meteorology Amsterdam [u.a.] : Elsevier, 1984 312 Online-Ressource (DE-627)320500608 (DE-600)2012165-9 (DE-576)094504067 1873-2240 nnns volume:312 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.84 Meteorologie: Sonstiges 48.99 Land- und Forstwirtschaft: Sonstiges AR 312 |
| spelling |
10.1016/j.agrformet.2021.108708 doi (DE-627)ELV007020996 (ELSEVIER)S0168-1923(21)00394-4 DE-627 ger DE-627 rda eng 630 640 550 DE-600 38.84 bkl 48.99 bkl Bao, Shanning verfasserin aut Environment-sensitivity functions for gross primary productivity in light use efficiency models 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products. Carbon assimilation Radiation use efficiency Model comparison Model equifinality Diffuse fraction Sensitivity formulations Randomly sampled sites Temporal scales Wutzler, Thomas verfasserin aut Koirala, Sujan verfasserin aut Cuntz, Matthias verfasserin aut Ibrom, Andreas verfasserin aut Besnard, Simon verfasserin aut Walther, Sophia verfasserin aut Šigut, Ladislav verfasserin aut Moreno, Alvaro verfasserin aut Weber, Ulrich verfasserin aut Wohlfahrt, Georg verfasserin aut Cleverly, Jamie verfasserin aut Migliavacca, Mirco verfasserin aut Woodgate, William verfasserin aut Merbold, Lutz verfasserin aut Veenendaal, Elmar verfasserin aut Carvalhais, Nuno verfasserin aut Enthalten in Agricultural and forest meteorology Amsterdam [u.a.] : Elsevier, 1984 312 Online-Ressource (DE-627)320500608 (DE-600)2012165-9 (DE-576)094504067 1873-2240 nnns volume:312 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.84 Meteorologie: Sonstiges 48.99 Land- und Forstwirtschaft: Sonstiges AR 312 |
| allfields_unstemmed |
10.1016/j.agrformet.2021.108708 doi (DE-627)ELV007020996 (ELSEVIER)S0168-1923(21)00394-4 DE-627 ger DE-627 rda eng 630 640 550 DE-600 38.84 bkl 48.99 bkl Bao, Shanning verfasserin aut Environment-sensitivity functions for gross primary productivity in light use efficiency models 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products. Carbon assimilation Radiation use efficiency Model comparison Model equifinality Diffuse fraction Sensitivity formulations Randomly sampled sites Temporal scales Wutzler, Thomas verfasserin aut Koirala, Sujan verfasserin aut Cuntz, Matthias verfasserin aut Ibrom, Andreas verfasserin aut Besnard, Simon verfasserin aut Walther, Sophia verfasserin aut Šigut, Ladislav verfasserin aut Moreno, Alvaro verfasserin aut Weber, Ulrich verfasserin aut Wohlfahrt, Georg verfasserin aut Cleverly, Jamie verfasserin aut Migliavacca, Mirco verfasserin aut Woodgate, William verfasserin aut Merbold, Lutz verfasserin aut Veenendaal, Elmar verfasserin aut Carvalhais, Nuno verfasserin aut Enthalten in Agricultural and forest meteorology Amsterdam [u.a.] : Elsevier, 1984 312 Online-Ressource (DE-627)320500608 (DE-600)2012165-9 (DE-576)094504067 1873-2240 nnns volume:312 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.84 Meteorologie: Sonstiges 48.99 Land- und Forstwirtschaft: Sonstiges AR 312 |
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10.1016/j.agrformet.2021.108708 doi (DE-627)ELV007020996 (ELSEVIER)S0168-1923(21)00394-4 DE-627 ger DE-627 rda eng 630 640 550 DE-600 38.84 bkl 48.99 bkl Bao, Shanning verfasserin aut Environment-sensitivity functions for gross primary productivity in light use efficiency models 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products. Carbon assimilation Radiation use efficiency Model comparison Model equifinality Diffuse fraction Sensitivity formulations Randomly sampled sites Temporal scales Wutzler, Thomas verfasserin aut Koirala, Sujan verfasserin aut Cuntz, Matthias verfasserin aut Ibrom, Andreas verfasserin aut Besnard, Simon verfasserin aut Walther, Sophia verfasserin aut Šigut, Ladislav verfasserin aut Moreno, Alvaro verfasserin aut Weber, Ulrich verfasserin aut Wohlfahrt, Georg verfasserin aut Cleverly, Jamie verfasserin aut Migliavacca, Mirco verfasserin aut Woodgate, William verfasserin aut Merbold, Lutz verfasserin aut Veenendaal, Elmar verfasserin aut Carvalhais, Nuno verfasserin aut Enthalten in Agricultural and forest meteorology Amsterdam [u.a.] : Elsevier, 1984 312 Online-Ressource (DE-627)320500608 (DE-600)2012165-9 (DE-576)094504067 1873-2240 nnns volume:312 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.84 Meteorologie: Sonstiges 48.99 Land- und Forstwirtschaft: Sonstiges AR 312 |
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10.1016/j.agrformet.2021.108708 doi (DE-627)ELV007020996 (ELSEVIER)S0168-1923(21)00394-4 DE-627 ger DE-627 rda eng 630 640 550 DE-600 38.84 bkl 48.99 bkl Bao, Shanning verfasserin aut Environment-sensitivity functions for gross primary productivity in light use efficiency models 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products. Carbon assimilation Radiation use efficiency Model comparison Model equifinality Diffuse fraction Sensitivity formulations Randomly sampled sites Temporal scales Wutzler, Thomas verfasserin aut Koirala, Sujan verfasserin aut Cuntz, Matthias verfasserin aut Ibrom, Andreas verfasserin aut Besnard, Simon verfasserin aut Walther, Sophia verfasserin aut Šigut, Ladislav verfasserin aut Moreno, Alvaro verfasserin aut Weber, Ulrich verfasserin aut Wohlfahrt, Georg verfasserin aut Cleverly, Jamie verfasserin aut Migliavacca, Mirco verfasserin aut Woodgate, William verfasserin aut Merbold, Lutz verfasserin aut Veenendaal, Elmar verfasserin aut Carvalhais, Nuno verfasserin aut Enthalten in Agricultural and forest meteorology Amsterdam [u.a.] : Elsevier, 1984 312 Online-Ressource (DE-627)320500608 (DE-600)2012165-9 (DE-576)094504067 1873-2240 nnns volume:312 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.84 Meteorologie: Sonstiges 48.99 Land- und Forstwirtschaft: Sonstiges AR 312 |
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Enthalten in Agricultural and forest meteorology 312 volume:312 |
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Enthalten in Agricultural and forest meteorology 312 volume:312 |
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Carbon assimilation Radiation use efficiency Model comparison Model equifinality Diffuse fraction Sensitivity formulations Randomly sampled sites Temporal scales |
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Agricultural and forest meteorology |
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Bao, Shanning @@aut@@ Wutzler, Thomas @@aut@@ Koirala, Sujan @@aut@@ Cuntz, Matthias @@aut@@ Ibrom, Andreas @@aut@@ Besnard, Simon @@aut@@ Walther, Sophia @@aut@@ Šigut, Ladislav @@aut@@ Moreno, Alvaro @@aut@@ Weber, Ulrich @@aut@@ Wohlfahrt, Georg @@aut@@ Cleverly, Jamie @@aut@@ Migliavacca, Mirco @@aut@@ Woodgate, William @@aut@@ Merbold, Lutz @@aut@@ Veenendaal, Elmar @@aut@@ Carvalhais, Nuno @@aut@@ |
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2021-01-01T00:00:00Z |
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320500608 |
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3630 |
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ELV007020996 |
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englisch |
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Bao, Shanning Wutzler, Thomas Koirala, Sujan Cuntz, Matthias Ibrom, Andreas Besnard, Simon Walther, Sophia Šigut, Ladislav Moreno, Alvaro Weber, Ulrich Wohlfahrt, Georg Cleverly, Jamie Migliavacca, Mirco Woodgate, William Merbold, Lutz Veenendaal, Elmar Carvalhais, Nuno |
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environment-sensitivity functions for gross primary productivity in light use efficiency models |
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Environment-sensitivity functions for gross primary productivity in light use efficiency models |
| abstract |
The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products. |
| abstractGer |
The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products. |
| abstract_unstemmed |
The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products. |
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Environment-sensitivity functions for gross primary productivity in light use efficiency models |
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We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Carbon assimilation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Radiation use efficiency</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Model comparison</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Model equifinality</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Diffuse fraction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sensitivity formulations</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Randomly sampled sites</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Temporal scales</subfield></datafield><datafield 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| score |
7.402135 |