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1. Quantifying CMIP6 model uncertainties in extreme precipitation projections

Author

John, Amal, Douville, Hervé, Ribes, Aurélien, Yiou, Pascal, Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Horizon 2020 Framework Programme, H2020, H2020 Marie Skłodowska-Curie Actions, MSCA: 813844, Center for Agriculture, Food and the Environment, University of Massachusetts Amherst, CAFE, Horizon 2020, This work is part of the Climate Advanced Forecasting of sub-seasonal Extremes (CAFE) project, which has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 813844. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output to CMIP. We also thank the Institut Pierre-Simon Laplace (ISPL) Mésocentre for Climate Sciences for the CMIP6 data acquisition, storage space, and intensive computing resources for this paper., This work is part of the Climate Advanced Forecasting of sub-seasonal Extremes (CAFE) project, which has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 813844 . We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output to CMIP. We also thank the Institut Pierre-Simon Laplace (ISPL) Mésocentre for Climate Sciences for the CMIP6 data acquisition, storage space, and intensive computing resources for this paper., Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), and Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Atmospheric Science, [SDU]Sciences of the Universe [physics], [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, [SDE.MCG]Environmental Sciences/Global Changes, Geography, Planning and Development, Extremes, Uncertainty, Climate change, Precipitation, [SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology, Management, Monitoring, Policy and Law
Abstract

International audience; Projected changes in precipitation extremes and their uncertainties are evaluated using an ensemble of global climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP). They are scaled by corresponding changes either in global mean surface temperature (ΔGSAT) or in local surface temperature (ΔT) and are expressed in terms of 20-yr return values (RV20) of annual maximum one-day precipitation. Our main objective is to quantify the model response uncertainty and to highlight the regions where changes may not be consistent with the widely used assumption of a Clausius–Clapeyron (CC) rate of ≈7%/K. When using a single realization for each model, as in the latest report from the Intergovernmental Panel on Climate Change (IPCC), the assessed inter-model spread includes both model uncertainty and internal variability, which can be however assessed separately using a large ensemble. Despite the overestimated inter-model spread, our results show a robust enhancement of extreme precipitation with more than 90% of models simulating an increase of RV20. Moreover, this increase is consistent with the CC rate of ≈7%/K over about 83% of the global land domain when scaled by (ΔGSAT). Our results also advocate for producing multiple single model initial condition ensembles in the next CMIP projections, to better filter internal variability out in estimating the response of extreme events.

Published
2022
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2. A high-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with theERA5 reanalysis for climate change impact assessments

Author

Thomas Noël, Mathieu Vrac, Guillaume Levavasseur, Dimitri Defrance, Harilaos Loukos, the Climate Data Factory, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Fondation de l'École Polytechnique, FX Centre National de la Recherche Scientifique, CNRS, The activities described in this paper were funded by the Copernicus Climate Change Service. ECMWF implements the Copernicus Climate Change Service and the Copernicus Atmosphere Monitoring Service on behalf of the European Commission. To process the data, this study benefited from the IPSL mesocenter ESPRI facility that is supported by CNRS , SU , and Ecole Polytechnique partly funded by IS-ENES3 project. We also thank the Institut Pierre Simon Laplace for assistance with the Synda software and the Deutsches Klimarechenzentrum for the Quality Assurance tool., The Climate Data Factory, Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)

Subjects
Science (General), [SDV]Life Sciences [q-bio], Computer applications to medicine. Medical informatics, R858-859.7, Climate change, Wind speed, Q1-390, 03 medical and health sciences, 0302 clinical medicine, Downscaling, CMIP5, Impact modeling, Precipitation, Adaptation, Data Article, 030304 developmental biology, Projections, 0303 health sciences, Multidisciplinary, Impact assessment, 13. Climate action, Greenhouse gas, Climatology, Environmental science, ERA5, Climate model, High-resolution, 030217 neurology & neurosurgery, Quantile
Abstract

International audience; A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP5 experiment using the ERA5 reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.25°x 0.25°, comprises 21 climate models and includes 5 surface daily variables at monthly resolution: air temperature (mean, minimum, and maximum), precipitation, and mean near-surface wind speed. Two greenhouse gas emissions scenarios are available: one with mitigation policy (RCP4.5) and one without mitigation (RCP8.5). The downscaling method is a Quantile Mapping method (QM) called the Cumulative Distribution Function transform (CDF-t) method that was first used for wind values and is now referenced in dozens of peer-reviewed publications. The data processing includes quality control of metadata according to the climate modeling community standards and value checking for outlier detection.

Published
2021
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3. A dedicated robust instrument for water vapor generation at low humidity for use with a laser water isotope analyzer in cold and dry polar regions

Author

C. Leroy-Dos Santos, M. Casado, F. Prié, O. Jossoud, E. Kerstel, M. Farradèche, S. Kassi, E. Fourré, A. Landais, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Glaces et Continents, Climats et Isotopes Stables (GLACCIOS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire Interdisciplinaire de Physique [Saint Martin d’Hères] (LIPhy ), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Agence Nationale de la Recherche, ANR Institut Polaire Français Paul Emile Victor, IPEV: 1110, 1205 Prince Albert II of Monaco Foundation: 2487, Financial support. This research has been supported by the Prince Albert II of Monaco Foundation: 2487, Acknowledgements. The development presented in this paper was largely inspired by the initial PhD work of Janek Landsberg, which we gratefully acknowledge here. The research leading to these results has received funding from the Prince Albert II of Monaco Foundation 'ANTARCTIC-SNOW' program, the ANR 'EAIIST' project and the ADELISE 'CNRS-LEFE' program. The deployment of this instrument in the field was made possible by the logistic support from the 'NIVO2' and ADELISE 'IPEV' programs. We thank the two reviewers for their useful comments which greatly improved the article., ANR-16-CE01-0011,EAIIST,Projet International d'exploration de la calotte polaire de l'Antarctique de l'Est(2016), and Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)

Subjects
[PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph], Atmospheric Science, Spectrum analyzer, 010504 meteorology & atmospheric sciences, δ18O, lcsh:TA715-787, 010401 analytical chemistry, lcsh:Earthwork. Foundations, Humidity, Atmospheric sciences, Laser, 01 natural sciences, 0104 chemical sciences, law.invention, lcsh:Environmental engineering, 13. Climate action, law, Environmental science, Polar, Precipitation, lcsh:TA170-171, Spectroscopy, Water vapor, 0105 earth and related environmental sciences
Abstract

Obtaining precise continuous measurements of water vapor isotopic composition in dry places (polar or high-altitude regions) is an important challenge. The current limitation is the strong influence of humidity on the measured water isotopic composition by laser spectroscopy instruments for low humidity levels (below 3000 ppmv). This problem is addressed by determining the relationships between humidity and measured δ18O and δD of known water standards. Here, we present the development of a robust field instrument able to generate water vapor, down to 70 ppmv, at very stable humidity levels (average 1σ lower than 10 ppmv). This instrument, operated by a Raspberry interface, can be coupled to a commercial laser spectroscopy instrument. We checked the stability of the system as well as its accuracy when expressing the measured isotopic composition of water vapor on the VSMOW–SLAP (Vienna Standard Mean Ocean Water – Standard Light Antarctic Precipitation) scale. It proved to be highly stable during autonomous operation over more than 1 year at the East Antarctic Concordia and Dumont d'Urville stations.

Published
2021
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4. The Pliocene Model Intercomparison Project Phase 2: Large-scale climate features and climate sensitivity

Author

A. M. Haywood, J. C. Tindall, H. J. Dowsett, A. M. Dolan, K. M. Foley, S. J. Hunter, D. J. Hill, W.-L. Chan, A. Abe-Ouchi, C. Stepanek, G. Lohmann, D. Chandan, W. R. Peltier, N. Tan, C. Contoux, G. Ramstein, X. Li, Z. Zhang, C. Guo, K. H. Nisancioglu, Q. Zhang, Q. Li, Y. Kamae, M. A. Chandler, L. E. Sohl, B. L. Otto-Bliesner, R. Feng, E. C. Brady, A. S. von der Heydt, M. L. J. Baatsen, D. J. Lunt, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Modélisation du climat (CLIM), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), 17189 University of Toronto, U of T Vetenskapsrådet, VR: 2013-06476, 2017-04232 Engineering and Physical Sciences Research Council, EPSRC: EP/M008.363/1 University of Leeds Natural Sciences and Engineering Research Council of Canada, NSERC: A9627 Government of Ontario Ministerio de Educación, Cultura y Deporte, MECD: 024.002.001 National Science Foundation, NSF: 1418411, 1852977 National Center for Atmospheric Research, NCAR Seventh Framework Programme, FP7 China Scholarship Council, CSC: 201804910023 China Postdoctoral Science Foundation: 2015M581154 Netherlands Earth System Science Centre, NESSC European Research Council, ERC: 278636 Japan Society for the Promotion of Science, KAKEN: 17H06104 Ministry of Education, Culture, Sports, Science and Technology, Monbusho: 17H06323 Canada Foundation for Innovation U.S. Geological Survey, USGS Engineering and Physical Sciences Research Council, EPSRC: EP/M008.363/1, Acknowledgements. We acknowledge the use of NOAA_ERSST_V5 data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at https://www.esrl.noaa.gov/psd/ (last access: 12 September 2019). Alan M. Haywood, Julia C. Tindall, Aisling M. Dolan, Stephen J. Hunter and Daniel J. Hill acknowledge the FP7 Ideas programme: European Research Council (grant no. PLIO-ESS, 278636), the Past Earth Network (EPSRC grant no. EP/M008.363/1) and the University of Leeds Advanced Research Computing service. Julia C. Tindall was also supported through the Centre for Environmental Modelling and Computation (CEMAC), University of Leeds. Harry J. Dowsett and Kevin M. Foley acknowledge support from the USGS Climate Research and Development Program. This research used samples and/or data provided by the Ocean Drilling Program (ODP) and International Ocean Discovery Program (IODP). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Bette L. Otto-Bliesner, Esther C. Brady and Ran Feng acknowledge that material for their participation is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation (NSF) (cooperative agreement no. 1852977 and NSF OPP grant no. 1418411). The CESM project is supported primarily by the National Science Foundation. Computing and data storage resources, including the Cheyenne supercomputer (https://doi.org/10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. NCAR is sponsored by the National Science Foundation. Ning Tan, Camille Contoux and Gilles Ramstein were granted access to the HPC resources of TGCC under the allocations 2016-A0030107732, 2017-R0040110492 and 2018-R0040110492 (gencmip6) and 2019-A0050102212 (gen2212) provided by GENCI. The IPSL-CM6 team of the IPSL Climate Modelling Centre (https://cmc.ipsl.fr, last access: 16 September 2020) is acknowledged for having developed, tested, evaluated and tuned the IPSL climate model, as well as per- formed and published the CMIP6 experiments. Christian Stepanek acknowledges funding from the Helmholtz Climate Initiative REKLIM. Christian Stepanek and Gerrit Lohmann acknowledge funding via the Alfred Wegener Institute’s research programme Marine, Coastal and Polar Systems. Qiong Zhang acknowledge support from the Swedish Research Council (2013-06476 and 2017-04232). Simulations with EC-Earth were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at the National Supercomputer Centre (NSC). Wing-Le Chan and Ayako Abe-Ouchi acknowledge funding from JSPS (KAKENHI grant no. 17H06104 and MEXT KAKENHI grant no. 17H06323). Their simulations with MIROC4m were performed on the Earth Simulator at JAMSTEC, Yokohama, Japan. The work by Anna S. von der Heydt and Michiel L. J. Baatsen was carried out under the program of the Netherlands Earth System Science Centre (NESSC), financially supported by the Ministry of Education, Culture and Science (OCW grant no. 024.002.001). Simulations with CCSM4-Utr were performed at the SURFsara Dutch national computing facilities and were sponsored by NWO-EW (Netherlands Organisation for Scientific Research, Exact Sciences) (project no. 17189). W. Richard Peltier and Deepak Chandan were supported by Canadian NSERC Discovery Grant A9627, and they wish to acknowledge the support of SciNet HPC Consortium for providing computing facilities. SciNet is funded by the Canada Foundation for Innovation under the auspices of Compute Canada, the Government of Ontario, the Ontario Research Fund – Research Excellence, and the University of Toronto. Xiangyu Li acknowledges financial support from the China Scholarship Council (201804910023) and the China Postdoctoral Science Foundation (project no. 2015M581154). The NorESM simulations benefitted from resources provided by UNINETT Sigma2 – the National Infrastructure for High Performance Computing and Data Storage in Norway. The authors would also like to thank Tim Herbert and an anonymous reviewer for helpful comments on an earlier version of this paper., Financial support. This research has been supported by the Past Earth Network (EPSRC grant no. EP/M008.363/1)., Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), and Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)

Subjects
010504 meteorology & atmospheric sciences, lcsh:Environmental protection, Stratigraphy, Climate change, Zonal and meridional, 010502 geochemistry & geophysics, Atmospheric sciences, 01 natural sciences, lcsh:Environmental pollution, Pliocene climate, lcsh:TD169-171.8, Precipitation, lcsh:Environmental sciences, 0105 earth and related environmental sciences, lcsh:GE1-350, Global and Planetary Change, geography, geography.geographical_feature_category, Paleontology, 13. Climate action, [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, lcsh:TD172-193.5, Polar amplification, Environmental science, Climate sensitivity, Climate model, Ice sheet
Abstract

The Pliocene epoch has great potential to improve our understanding of the long-term climatic and environmental consequences of an atmospheric CO2 concentration near ∼400 parts per million by volume. Here we present the large-scale features of Pliocene climate as simulated by a new ensemble of climate models of varying complexity and spatial resolution based on new reconstructions of boundary conditions (the Pliocene Model Intercomparison Project Phase 2; PlioMIP2). As a global annual average, modelled surface air temperatures increase by between 1.7 and 5.2 ∘C relative to the pre-industrial era with a multi-model mean value of 3.2 ∘C. Annual mean total precipitation rates increase by 7 % (range: 2 %–13 %). On average, surface air temperature (SAT) increases by 4.3 ∘C over land and 2.8 ∘C over the oceans. There is a clear pattern of polar amplification with warming polewards of 60∘ N and 60∘ S exceeding the global mean warming by a factor of 2.3. In the Atlantic and Pacific oceans, meridional temperature gradients are reduced, while tropical zonal gradients remain largely unchanged. There is a statistically significant relationship between a model's climate response associated with a doubling in CO2 (equilibrium climate sensitivity; ECS) and its simulated Pliocene surface temperature response. The mean ensemble Earth system response to a doubling of CO2 (including ice sheet feedbacks) is 67 % greater than ECS; this is larger than the increase of 47 % obtained from the PlioMIP1 ensemble. Proxy-derived estimates of Pliocene sea surface temperatures are used to assess model estimates of ECS and give an ECS range of 2.6–4.8 ∘C. This result is in general accord with the ECS range presented by previous Intergovernmental Panel on Climate Change (IPCC) Assessment Reports.

Published
2020
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5. Forest production efficiency increases with growth temperature

Author

Alessandro Cescatti, Alessio Collalti, Andreas Ibrom, Giorgio Matteucci, Marcos Fernández-Martínez, Ramdane Alkama, Daniel S. Goll, Julia Pongratz, Philippe Ciais, Anders Stockmarr, Vanessa Haverd, Stephen Sitch, Julia E. M. S. Nabel, Iain Colin Prentice, Pierre Friedlingstein, Almut Arneth, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), FOE-2019 College of Natural Resources, University of California Berkeley, CNR: DTA.AD003.474, We thank R.H. Waring, S. Vicca, M. Campioli, F. Pagani and E. Grieco for early constructive comments and thoughtful suggestions, S. Noce for the map of data points. We thank efforts from all site investigators and their funding agencies. This paper contributes to the AXA Chair Programme in Biosphere and Climate Impacts and the Imperial College initiative Grand Challenges in Ecosystems and the Environment. A.C. and G.M. are partially supported by resources available from the Ministry of University and Research (FOE-2019), under the project 'Climate Change' (CNR DTA.AD003.474), M.F.-M. is a postdoctoral fellow of the Research Foundation—Flanders (FWO), AXA Research Fund, and Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)

Subjects
0106 biological sciences, 010504 meteorology & atmospheric sciences, General Physics and Astronomy, adaptation, Atmospheric sciences, 01 natural sciences, Nutrient, Forest production efficiency, ddc:550, SDG 13 - Climate Action, gpp, lcsh:Science, Plant ecology, chemistry.chemical_classification, forest productivity, Carbon dioxide in Earth's atmosphere, Biomass (ecology), Multidisciplinary, Climate-change ecology, Biogeography, [SDE]Environmental Sciences, Engineering sciences. Technology, primary production, biomass production efficienty, Science, carbon use efficiency, chemistry.chemical_element, General Biochemistry, Genetics and Molecular Biology, Article, Latitude, Organic matter, Ecosystem, Precipitation, Biology, 0105 earth and related environmental sciences, Computer. Automation, npp, Primary production, General Chemistry, 15. Life on land, Earth sciences, chemistry, 13. Climate action, Environmental science, lcsh:Q, Forest ecology, Carbon, 010606 plant biology & botany
Abstract

Forest production efficiency (FPE) metric describes how efficiently the assimilated carbon is partitioned into plants organs (biomass production, BP) or—more generally—for the production of organic matter (net primary production, NPP). We present a global analysis of the relationship of FPE to stand-age and climate, based on a large compilation of data on gross primary production and either BP or NPP. FPE is important for both forest production and atmospheric carbon dioxide uptake. We find that FPE increases with absolute latitude, precipitation and (all else equal) with temperature. Earlier findings—FPE declining with age—are also supported by this analysis. However, the temperature effect is opposite to what would be expected based on the short-term physiological response of respiration rates to temperature, implying a top-down regulation of carbon loss, perhaps reflecting the higher carbon costs of nutrient acquisition in colder climates. Current ecosystem models do not reproduce this phenomenon. They consistently predict lower FPE in warmer climates, and are therefore likely to overestimate carbon losses in a warming climate., Many models assume a universal carbon use efficiency across forest biomes, in contrast to assumptions of other process-based models. Here the authors analyse forest production efficiency across a wide range of climates to show a positive relationship with annual temperature and precipitation, indicating that ecosystem models are overestimating forest carbon losses under warming.

Published
2020
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