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Noël T, Loukos H, Defrance D, Vrac M, Levavasseur G. A high-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with the ERA5 reanalysis for climate change impact assessments. Data Brief 2021; 35:106900. [PMID: 33748359 PMCID: PMC7960934 DOI: 10.1016/j.dib.2021.106900] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 02/16/2021] [Accepted: 02/17/2021] [Indexed: 11/25/2022] Open
Abstract
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.
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Affiliation(s)
| | | | | | - Mathieu Vrac
- Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL), CEA/CNRS/UVSQ, Université Paris-Saclay Centre d'Etudes de Saclay, Orme des Merisiers, 91191 Gif-sur-Yvette, France
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3
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Toreti A, Deryng D, Tubiello FN, Müller C, Kimball BA, Moser G, Boote K, Asseng S, Pugh TAM, Vanuytrecht E, Pleijel H, Webber H, Durand JL, Dentener F, Ceglar A, Wang X, Badeck F, Lecerf R, Wall GW, van den Berg M, Hoegy P, Lopez-Lozano R, Zampieri M, Galmarini S, O'Leary GJ, Manderscheid R, Mencos Contreras E, Rosenzweig C. Narrowing uncertainties in the effects of elevated CO 2 on crops. NATURE FOOD 2020; 1:775-782. [PMID: 37128059 DOI: 10.1038/s43016-020-00195-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 11/06/2020] [Indexed: 05/03/2023]
Abstract
Plant responses to rising atmospheric carbon dioxide (CO2) concentrations, together with projected variations in temperature and precipitation will determine future agricultural production. Estimates of the impacts of climate change on agriculture provide essential information to design effective adaptation strategies, and develop sustainable food systems. Here, we review the current experimental evidence and crop models on the effects of elevated CO2 concentrations. Recent concerted efforts have narrowed the uncertainties in CO2-induced crop responses so that climate change impact simulations omitting CO2 can now be eliminated. To address remaining knowledge gaps and uncertainties in estimating the effects of elevated CO2 and climate change on crops, future research should expand experiments on more crop species under a wider range of growing conditions, improve the representation of responses to climate extremes in crop models, and simulate additional crop physiological processes related to nutritional quality.
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Affiliation(s)
- Andrea Toreti
- European Commission, Joint Research Centre (JRC), Ispra, Italy.
| | - Delphine Deryng
- NewClimate Institute, Berlin, Germany.
- IRI THESys, Humboldt-Universität zu Berlin, Berlin, Germany.
- Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany.
| | - Francesco N Tubiello
- Statistics Division, Food and Agriculture Organization of the United Nations, Rome, Italy
| | - Christoph Müller
- Potsdam Institute for Climate Impact Research PIK, Member of the Leibniz Association, Potsdam, Germany
| | - Bruce A Kimball
- US Arid-Land Agricultural Research Center, USDA-ARS, Maricopa, AZ, USA
| | - Gerald Moser
- Department of Plant Ecology, Justus Liebig University Giessen, Giessen, Germany
| | | | | | - Thomas A M Pugh
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
- Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK
- Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
| | - Eline Vanuytrecht
- Flemish Institute for Technological Research (VITO), Mol, Belgium
- KU Leuven, Department of Earth and Environmental Science, Leuven, Belgium
| | - Håkan Pleijel
- Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden
| | - Heidi Webber
- Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
| | | | - Frank Dentener
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Andrej Ceglar
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Xuhui Wang
- Laboratoire des Sciences du Climat et de l'Environment LSCE, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
- Sino-French Institute of Earth System Sciences, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Franz Badeck
- Council for Agricultural Research and Agricultural Economics, Research Centre for Genomics and Bioinformatics, CREA-GB, Fiorenzuola d'Arda, Italy
| | - Remi Lecerf
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Gerard W Wall
- US Arid-Land Agricultural Research Center, USDA-ARS, Maricopa, AZ, USA
| | | | | | | | - Matteo Zampieri
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | | | | | | | - Erik Mencos Contreras
- NASA Goddard Institute for Space Studies, New York, NY, USA
- Center for Climate Systems Research, Columbia University, New York, NY, USA
| | - Cynthia Rosenzweig
- NASA Goddard Institute for Space Studies, New York, NY, USA
- Center for Climate Systems Research, Columbia University, New York, NY, USA
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Noce S, Caporaso L, Santini M. A new global dataset of bioclimatic indicators. Sci Data 2020; 7:398. [PMID: 33199736 PMCID: PMC7670417 DOI: 10.1038/s41597-020-00726-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 10/21/2020] [Indexed: 11/17/2022] Open
Abstract
This study presents a new global gridded dataset of bioclimatic indicators at 0.5° by 0.5° resolution for historical and future conditions. The dataset, called CMCC-BioClimInd, provides a set of 35 bioclimatic indices, expressed as mean values over each time interval, derived from post-processing both climate reanalysis for historical period (1960-1999) and an ensemble of 11 bias corrected CMIP5 simulations under two greenhouse gas concentration scenarios for future climate projections along two periods (2040-2079 and 2060-2099). This new dataset complements the availability of spatialized bioclimatic information, crucial aspect in many ecological and environmental wide scale applications and for several disciplines, including forestry, biodiversity conservation, plant and landscape ecology. The data of individual indicators are publicly available for download in the commonly used Network Common Data Form 4 (NetCDF4) format.
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Affiliation(s)
- Sergio Noce
- Division on Impacts on Agriculture, Forests and Ecosystem Services (IAFES), Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Viterbo, Italy.
| | - Luca Caporaso
- Division on Impacts on Agriculture, Forests and Ecosystem Services (IAFES), Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Viterbo, Italy
- Institute of Marine Sciences (ISMAR), Centro Nazionale delle Ricerche (CNR), Rome, Italy
| | - Monia Santini
- Division on Impacts on Agriculture, Forests and Ecosystem Services (IAFES), Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Viterbo, Italy
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The Implication of Different Sets of Climate Variables on Regional Maize Yield Simulations. ATMOSPHERE 2020. [DOI: 10.3390/atmos11020180] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
High-resolution and consistent grid-based climate data are important for model-based agricultural planning and farm risk assessment. However, the application of models at the regional scale is constrained by the lack of required high-quality weather data, which may be retrieved from different sources. This can potentially introduce large uncertainties into the crop simulation results. Therefore, in this study, we examined the impacts of grid-based time series of weather variables assembled from the same data source (Approach 1, consistent dataset) and from different sources (Approach 2, combined dataset) on regional scale crop yield simulations in Ghana, Ethiopia and Nigeria. There was less variability in the simulated yield under Approach 1, ranging to 58.2%, 45.6% and 8.2% in Ethiopia, Nigeria and Ghana, respectively, compared to those simulated using datasets retrieved under Approach 2. The two sources of climate data evaluated here were capable of producing both good and poor estimates of average maize yields ranging from lowest RMSE = 0.31 Mg/ha in Nigeria to highest RMSE = 0.78 Mg/ha under Approach 1 in Ghana, whereas, under Approach 2, the RMSE ranged from the lowest value of 0.51 Mg/ha in Nigeria to the highest of 0.72 Mg/ha in Ethiopia under Approach 2. The obtained results suggest that Approach 1 introduces less uncertainty to the yield estimates in large-scale regional simulations, and physical consistency between meteorological input variables is a relevant factor to consider for crop yield simulations under rain-fed conditions.
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