Climate data induced uncertainty in model-based estimations of terrestrial primary productivity
Model-based estimations of historical fluxes and pools of the terrestrial biosphere differ substantially. These differences arise not only from differences between models but also from differences in the environmental and climatic data used as input to the models. Here we investigate the role of unc...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2017-01-01
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| Series: | Environmental Research Letters |
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| Online Access: | https://doi.org/10.1088/1748-9326/aa6fd8 |
| _version_ | 1797748249683034112 |
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| author | Zhendong Wu Anders Ahlström Benjamin Smith Jonas Ardö Lars Eklundh Rasmus Fensholt Veiko Lehsten |
| author_facet | Zhendong Wu Anders Ahlström Benjamin Smith Jonas Ardö Lars Eklundh Rasmus Fensholt Veiko Lehsten |
| author_sort | Zhendong Wu |
| collection | DOAJ |
| description | Model-based estimations of historical fluxes and pools of the terrestrial biosphere differ substantially. These differences arise not only from differences between models but also from differences in the environmental and climatic data used as input to the models. Here we investigate the role of uncertainties in historical climate data by performing simulations of terrestrial gross primary productivity (GPP) using a process-based dynamic vegetation model (LPJ-GUESS) forced by six different climate datasets. We find that the climate induced uncertainty, defined as the range among historical simulations in GPP when forcing the model with the different climate datasets, can be as high as 11 Pg C yr ^−1 globally (9% of mean GPP). We also assessed a hypothetical maximum climate data induced uncertainty by combining climate variables from different datasets, which resulted in significantly larger uncertainties of 41 Pg C yr ^−1 globally or 32% of mean GPP. The uncertainty is partitioned into components associated to the three main climatic drivers, temperature, precipitation, and shortwave radiation. Additionally, we illustrate how the uncertainty due to a given climate driver depends both on the magnitude of the forcing data uncertainty (climate data range) and the apparent sensitivity of the modeled GPP to the driver (apparent model sensitivity). We find that LPJ-GUESS overestimates GPP compared to empirically based GPP data product in all land cover classes except for tropical forests. Tropical forests emerge as a disproportionate source of uncertainty in GPP estimation both in the simulations and empirical data products. The tropical forest uncertainty is most strongly associated with shortwave radiation and precipitation forcing, of which climate data range contributes higher to overall uncertainty than apparent model sensitivity to forcing. Globally, precipitation dominates the climate induced uncertainty over nearly half of the vegetated land area, which is mainly due to climate data range and less so due to the apparent model sensitivity. Overall, climate data ranges are found to contribute more to the climate induced uncertainty than apparent model sensitivity to forcing. Our study highlights the need to better constrain tropical climate, and demonstrates that uncertainty caused by climatic forcing data must be considered when comparing and evaluating carbon cycle model results and empirical datasets. |
| first_indexed | 2024-03-12T16:02:13Z |
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| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T16:02:13Z |
| publishDate | 2017-01-01 |
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| series | Environmental Research Letters |
| spelling | doaj.art-044415c699734ff7927a2952b4da06e32023-08-09T14:33:24ZengIOP PublishingEnvironmental Research Letters1748-93262017-01-0112606401310.1088/1748-9326/aa6fd8Climate data induced uncertainty in model-based estimations of terrestrial primary productivityZhendong Wu0Anders Ahlström1Benjamin Smith2Jonas Ardö3Lars Eklundh4Rasmus Fensholt5Veiko Lehsten6Department of Physical Geography and Ecosystem Science , Lund University, Sölvegatan 12, SE-223 62 Lund, Sweden; Department of Geosciences and Natural Resource Management , University of Copenhagen, 1350 Copenhagen, Denmark; Author to whom any correspondence should be addressed.Department of Physical Geography and Ecosystem Science , Lund University, Sölvegatan 12, SE-223 62 Lund, Sweden; Department of Earth System Science, School of Earth, Energy and Environmental Sciences , Stanford University, Stanford, CA 94305, United States of AmericaDepartment of Physical Geography and Ecosystem Science , Lund University, Sölvegatan 12, SE-223 62 Lund, SwedenDepartment of Physical Geography and Ecosystem Science , Lund University, Sölvegatan 12, SE-223 62 Lund, SwedenDepartment of Physical Geography and Ecosystem Science , Lund University, Sölvegatan 12, SE-223 62 Lund, SwedenDepartment of Geosciences and Natural Resource Management , University of Copenhagen, 1350 Copenhagen, DenmarkDepartment of Physical Geography and Ecosystem Science , Lund University, Sölvegatan 12, SE-223 62 Lund, Sweden; Swiss Federal Institute for Forest , Snow and Landscape research (WSL), Zürcherstr. 11 CH-8903, Birmensdorf, SwitzerlandModel-based estimations of historical fluxes and pools of the terrestrial biosphere differ substantially. These differences arise not only from differences between models but also from differences in the environmental and climatic data used as input to the models. Here we investigate the role of uncertainties in historical climate data by performing simulations of terrestrial gross primary productivity (GPP) using a process-based dynamic vegetation model (LPJ-GUESS) forced by six different climate datasets. We find that the climate induced uncertainty, defined as the range among historical simulations in GPP when forcing the model with the different climate datasets, can be as high as 11 Pg C yr ^−1 globally (9% of mean GPP). We also assessed a hypothetical maximum climate data induced uncertainty by combining climate variables from different datasets, which resulted in significantly larger uncertainties of 41 Pg C yr ^−1 globally or 32% of mean GPP. The uncertainty is partitioned into components associated to the three main climatic drivers, temperature, precipitation, and shortwave radiation. Additionally, we illustrate how the uncertainty due to a given climate driver depends both on the magnitude of the forcing data uncertainty (climate data range) and the apparent sensitivity of the modeled GPP to the driver (apparent model sensitivity). We find that LPJ-GUESS overestimates GPP compared to empirically based GPP data product in all land cover classes except for tropical forests. Tropical forests emerge as a disproportionate source of uncertainty in GPP estimation both in the simulations and empirical data products. The tropical forest uncertainty is most strongly associated with shortwave radiation and precipitation forcing, of which climate data range contributes higher to overall uncertainty than apparent model sensitivity to forcing. Globally, precipitation dominates the climate induced uncertainty over nearly half of the vegetated land area, which is mainly due to climate data range and less so due to the apparent model sensitivity. Overall, climate data ranges are found to contribute more to the climate induced uncertainty than apparent model sensitivity to forcing. Our study highlights the need to better constrain tropical climate, and demonstrates that uncertainty caused by climatic forcing data must be considered when comparing and evaluating carbon cycle model results and empirical datasets.https://doi.org/10.1088/1748-9326/aa6fd8climate datasetsGPPuncertaintyLPJ-GUESSapparent model sensitivityclimate data range |
| spellingShingle | Zhendong Wu Anders Ahlström Benjamin Smith Jonas Ardö Lars Eklundh Rasmus Fensholt Veiko Lehsten Climate data induced uncertainty in model-based estimations of terrestrial primary productivity Environmental Research Letters climate datasets GPP uncertainty LPJ-GUESS apparent model sensitivity climate data range |
| title | Climate data induced uncertainty in model-based estimations of terrestrial primary productivity |
| title_full | Climate data induced uncertainty in model-based estimations of terrestrial primary productivity |
| title_fullStr | Climate data induced uncertainty in model-based estimations of terrestrial primary productivity |
| title_full_unstemmed | Climate data induced uncertainty in model-based estimations of terrestrial primary productivity |
| title_short | Climate data induced uncertainty in model-based estimations of terrestrial primary productivity |
| title_sort | climate data induced uncertainty in model based estimations of terrestrial primary productivity |
| topic | climate datasets GPP uncertainty LPJ-GUESS apparent model sensitivity climate data range |
| url | https://doi.org/10.1088/1748-9326/aa6fd8 |
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