Regional Disparities in the Beneficial Effects of CO2 on Crop Water Productivity
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Regional Disparities in the Beneficial Effects of CO 2 on Crop Water Productivity Delphine Deryng*,1,2, Joshua Elliott 3, Alexander C. Ruane4, Christian Folberth5,6, Christoph Müller7, Thomas A. M. Pugh8, Erwin Schmid9, Kenneth J. Boote10, Declan Conway2, Dieter Gerten7, James W. Jones10, Nikolaï Kabharof6, Stefan Olin11, Sibyll Schapphof 7, Hong Yang5 and Cynthia Rosenzweig4 *[email protected] 1Tyndall Centre for Climate Change, University of East Anglia, Norwich, UK; 2Grantham Research Institute on Climate Change & the Environment, London School of Economics and Political Science, London, UK; 3University of Chicago & ANL Computation Institute, Chicago, USA; 4NASA Goddard Institute for Space Studies, New York, USA; 5Swiss Federal Institute of Aquatic Science and Technology (EAWAG), Dübendorf, Switzerland; 6International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria; 7Potsdam Institute for Climate Impact Research, Potsdam, Germany; 8Karlsruhe Institute of Technology (IMKIFU), Garmisch- Partenkirchen, Germany; 9University of Natural Resources and Life Sciences (Boku), Vienna, Austria; 10University of Florida, Gainesville, USA; 11Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden Uncertainties due to crop model differences dominate Spread in simulated response to CO 2 dominates for C3 crops Figure 2: CWP responses (median standard error) to elevated CO2 (550 ppmv from FACE and corresponding grid-cell values extracted from GGCM simulations in this study) for maize, wheat, rice and soybean at ample and limited soil water. FACE data were collected from references summarised in Table S1. The left and right sides of the box are lower and upper quartiles, respectively, and the band near the middle of the box is the median value across each set of simulations. Open circles are outliers. Figure 1: Map of mean relative change between simulated CWP w/ CO2 and w/o CO2 only (%) in the model ensemble (inc. 6 GGCMs x 5 GCMs) by the 2050s under RCP 8.5. Rainfed simulations are shown for maize (a), wheat (b), rice (c) and soybean (d). Simulated areas are masked by current rainfed areas from the MIRCA dataset. Figure 3: Share of the model ensemble total variance in simulating global CWP resulting from differences in (1) GCMs climate scenarios, (2) GGCMs response, and (3) CO2 effects for maize, wheat, soybean and rice under RCP 8.5. Table 1: Relative change in global average yield, AET and CWP (%): Median values across all GCM–GGCM combinations for w/ CO2 and w/o CO2 simulations for the long time horizon, i.e. 2080s, under RCP 8.5. Degree of agreement in the sign of change is characterised by a background colour (orange: more than 80% agreement in a net decrease; yellow: between 60-80% agreement in a net decrease; green: between 60-80% agreement in a net increase; blue: more than 80% agreement in a net increase; clear: less than 60% agreement in the sign of change). Introduction Elevated atmospheric CO2 concentrations are expected to enhance photosynthesis and reduce water consumption of crops. However, there is high uncertainty about the global implications of these effects for future crop production and agricultural water requirements under climate change. Here we combine results from networks of field experiments and global gridded crop models (GGCMs) to present a spatially explicit global perspective on crop water productivity (CWP, the ratio of crop yield to transpiration) for maize, wheat, rice and soybean under rising CO2 and associated climate change projected for a high-end greenhouse gas emissions scenario. CO2 effects could increase global average CWP by up to 8-25% by the 2080s depending on crop types Regional disparities Large increases in arid regions and for well-fertilised crops. If realised in the fields, effects of elevated CO2 could considerably reduce crop production losses and agricultural consumptive water use. Regional disparities are driven by differences in growing conditions across agro-ecosystems Comparison against FACE observations: More field experiments needed, especially in tropical and semi-arid areas Wheat Maize Rice Soybean CWP
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Transcript of Regional Disparities in the Beneficial Effects of CO2 on Crop Water Productivity
- 1. Regional Disparities in the Beneficial Effects of CO2 on Crop Water Productivity Delphine Deryng*,1,2, Joshua Elliott3, Alexander C. Ruane4, Christian Folberth5,6, Christoph Mller7, Thomas A. M. Pugh8, Erwin Schmid9, Kenneth J. Boote10, Declan Conway2, Dieter Gerten7, James W. Jones10, Nikola Kabharof6, Stefan Olin11, Sibyll Schapphof7, Hong Yang5 and Cynthia Rosenzweig4 *[email protected] 1Tyndall Centre for Climate Change, University of East Anglia, Norwich, UK; 2Grantham Research Institute on Climate Change & the Environment, London School of Economics and Political Science, London, UK; 3University of Chicago & ANL Computation Institute, Chicago, USA; 4NASA Goddard Institute for Space Studies, New York, USA; 5Swiss Federal Institute of Aquatic Science and Technology (EAWAG), Dbendorf, Switzerland; 6International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria;7Potsdam Institute for Climate Impact Research, Potsdam, Germany;8Karlsruhe Institute of Technology (IMKIFU), Garmisch- Partenkirchen, Germany; 9University of Natural Resources and Life Sciences (Boku), Vienna, Austria; 10University of Florida, Gainesville, USA; 11Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden Uncertainties due to crop model differences dominate Spread in simulated response to CO2 dominates for C3 crops Figure 2: CWP responses (median standard error) to elevated CO2 (550 ppmv from FACE and corresponding grid-cell values extracted from GGCM simulations in this study) for maize, wheat, rice and soybean at ample and limited soil water. FACE data were collected from references summarised in Table S1. The left and right sides of the box are lower and upper quartiles, respectively, and the band near the middle of the box is the median value across each set of simulations. Open circles are outliers. Figure 1: Map of mean relative change between simulated CWP w/ CO2 and w/o CO2 only (%) in the model ensemble (inc. 6 GGCMs x 5 GCMs) by the 2050s under RCP 8.5. Rainfed simulations are shown for maize (a), wheat (b), rice (c) and soybean (d). Simulated areas are masked by current rainfed areas from the MIRCA dataset. Figure3:ShareofthemodelensembletotalvarianceinsimulatingglobalCWPresultingfromdifferences in (1) GCMs climate scenarios, (2) GGCMs response, and (3) CO2 effects for maize, wheat, soybean and rice under RCP 8.5. Table 1: Relative change in global average yield, AET and CWP (%): Median values across all GCMGGCM combinations for w/ CO2 and w/o CO2 simulations for the long time horizon, i.e. 2080s, under RCP 8.5. Degree of agreement in the sign of change is characterisedbyabackgroundcolour(orange:morethan80%agreementinanetdecrease;yellow:between60-80%agreement in a net decrease; green: between 60-80% agreement in a net increase; blue: more than 80% agreement in a net increase; clear: less than 60% agreement in the sign of change). Introduction Elevated atmospheric CO2 concentrations are expected to enhancephotosynthesisandreducewaterconsumptionofcrops. However, there is high uncertainty about the global implications of these effects for future crop production and agricultural water requirements under climate change. Here we combine results from networks of field experiments and global gridded crop models (GGCMs) to present a spatially explicit global perspective on crop water productivity (CWP, the ratio of crop yield to transpiration) for maize, wheat, rice and soybean under rising CO2 and associated climate change projected for a high-end greenhouse gas emissions scenario. CO2 effects could increase global average CWP by up to 8-25% by the 2080s depending on crop types Regional disparities Large increases in arid regions and for well-fertilised crops. Ifrealisedinthefields,effectsofelevated CO2 could considerably reduce crop production losses and agricultural consumptive water use. Regional disparities are driven by differences in growing conditions across agro-ecosystems Comparison against FACE observations: More field experiments needed, especially in tropical and semi-arid areas WheatMaize Rice Soybean CWP
