Articles | Volume 13, issue 3
https://doi.org/10.5194/gmd-13-1545-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-1545-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
26 Mar 2020
Model evaluation paper |
| 26 Mar 2020
| 26 Mar 2020
P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production
CREAF, Campus UAB, 08193 Bellaterra, Catalonia, Spain
Earth System Science, Stanford University, Stanford, CA 94305, USA
Institute of Agricultural Sciences, Department of Environmental Systems Science, ETH, Universitätsstrasse 2, 8092 Zürich, Switzerland
Department of Earth System Science, Tsinghua University, Haidian, Beijing, 100084, China
Nicholas G. Smith
Department of Biological Sciences, Texas Tech University, Lubbock, TX 79409, USA
Sandy P. Harrison
Geography and Environmental Science, Reading University, Reading, RG6 6AH, UK
Trevor F. Keenan
Earth and Environmental Sciences Area, Lawrence Berkeley National Lab, Berkeley, CA 94709, USA
Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley, CA 94720, USA
David Sandoval
AXA Chair of Biosphere and Climate Impacts, Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, Berkshire, SL5 7PY, UK
Tyler Davis
AXA Chair of Biosphere and Climate Impacts, Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, Berkshire, SL5 7PY, UK
Center for Geospatial Analysis, The College of William & Mary, Williamsburg, VA 23185, USA
I. Colin Prentice
AXA Chair of Biosphere and Climate Impacts, Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, Berkshire, SL5 7PY, UK
Department of Earth System Science, Tsinghua University, Haidian, Beijing, 100084, China
Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia
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Earth Syst. Sci. Data, 14, 1109–1124, https://doi.org/10.5194/essd-14-1109-2022, https://doi.org/10.5194/essd-14-1109-2022, 2022
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Along with current climate, vegetation, and human influences, long-term accumulation of biomass affects fires. Here, we find that including the influence of antecedent vegetation and moisture improves our ability to predict global burnt area. Additionally, the length of the preceding period which needs to be considered for accurate predictions varies across regions.
Sarah E. Parker, Sandy P. Harrison, Laia Comas-Bru, Nikita Kaushal, Allegra N. LeGrande, and Martin Werner
Clim. Past, 17, 1119–1138, https://doi.org/10.5194/cp-17-1119-2021, https://doi.org/10.5194/cp-17-1119-2021, 2021
Short summary
Short summary
Regional trends in the oxygen isotope (δ18O) composition of stalagmites reflect several climate processes. We compare stalagmite δ18O records from monsoon regions and model simulations to identify the causes of δ18O variability over the last 12 000 years, and between glacial and interglacial states. Precipitation changes explain the glacial–interglacial δ18O changes in all monsoon regions; Holocene trends are due to a combination of precipitation, atmospheric circulation and temperature changes.
Masa Kageyama, Sandy P. Harrison, Marie-L. Kapsch, Marcus Lofverstrom, Juan M. Lora, Uwe Mikolajewicz, Sam Sherriff-Tadano, Tristan Vadsaria, Ayako Abe-Ouchi, Nathaelle Bouttes, Deepak Chandan, Lauren J. Gregoire, Ruza F. Ivanovic, Kenji Izumi, Allegra N. LeGrande, Fanny Lhardy, Gerrit Lohmann, Polina A. Morozova, Rumi Ohgaito, André Paul, W. Richard Peltier, Christopher J. Poulsen, Aurélien Quiquet, Didier M. Roche, Xiaoxu Shi, Jessica E. Tierney, Paul J. Valdes, Evgeny Volodin, and Jiang Zhu
Clim. Past, 17, 1065–1089, https://doi.org/10.5194/cp-17-1065-2021, https://doi.org/10.5194/cp-17-1065-2021, 2021
Short summary
Short summary
The Last Glacial Maximum (LGM; ~21 000 years ago) is a major focus for evaluating how well climate models simulate climate changes as large as those expected in the future. Here, we compare the latest climate model (CMIP6-PMIP4) to the previous one (CMIP5-PMIP3) and to reconstructions. Large-scale climate features (e.g. land–sea contrast, polar amplification) are well captured by all models, while regional changes (e.g. winter extratropical cooling, precipitations) are still poorly represented.
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Short summary
Estimating terrestrial photosynthesis relies on satellite data of vegetation cover and models simulating the efficiency by which light absorbed by vegetation is used for CO2 assimilation. This paper presents the P-model, a light use efficiency model derived from a carbon–water optimality principle, and evaluates its predictions of ecosystem-level photosynthesis against globally distributed observations. The model is implemented and openly accessible as an R package (rpmodel).
Estimating terrestrial photosynthesis relies on satellite data of vegetation cover and models...
