CLMcrop yields and water requirements: avoided impacts by choosing RCP 4.5 over 8.5
- 648 Downloads
We perform CLMcrop simulations of the 20th and 21st centuries to assess potential avoided impacts in (a) crop yield losses and (b) water demand increases if humanity were to choose the representative concentration pathway (RCP) 4.5 instead of 8.5. RCP 8.5 imposes more extreme climatic changes on CLMcrop, while simultaneously exposing the crops to higher CO2 fertilization than RCP 4.5. As a result CLMcrop simulates global to regional scale changes in yield and water requirements for RCP 8.5 that exceed and sometimes more than double the RCP 4.5 changes relative to today. Under RCP 4.5 then, human societies may confront easier adaptation to changes in crop yields and water requirements. Under both RCPs, CLMcrop projects declining global yields for C3 crops (e.g., wheat, soybean, rice) without CO2 fertilization and C4 crops (corn, sugarcane) without irrigation. Yield declines of 3 t ha−1 stand out in parts of tropical and subtropical Africa and South America (presently areas of rapid agricultural expansion) and are due to increasing plant respiration and decreasing soil moisture, both due to rising temperatures. Irrigation and CO2 fertilization mitigate yield losses and in some cases lead to gains, so irrigation may help maintain or increase current yields through the 21st century. However, simulated global irrigation requirements increase: as much as 23 % for C4 crops without CO2 fertilization under RCP 8.5 and as little as 3 % for C4 crops with CO2 fertilization under RCP4.5. Nitrogen fertilized crops display greater vulnerability to climate and environmental change than unfertilized crops in our simulations; still relative to unfertilized crops, they deliver significantly higher yields and remain indispensable in supporting a more populous and affluent humanity. These CLMcrop results broadly agree with previously published outcomes for the 21st century. We describe in this article a new version of CLMcrop that represents prognostic crop behavior not only in the mid-latitudes but also the tropics.
This material is based upon work supported by the National Science Foundation (NSF) under Grant Number AGS-1243095. The authors thank Brian O’Neill, Peter Lawrence, and three anonymous reviewers for helpful comments. The CESM project is supported by the NSF and the Office of Science (BER) of the U.S. Department of Energy. The National Center for Atmospheric Research (NCAR) is sponsored by the NSF. Computing resources were provided by the Climate Simulation Laboratory at NCAR’s Computational and Information Systems Laboratory (CISL), sponsored by the NSF and other agencies. CLM simulations were driven with CESM output that relied on CISL compute and storage resources allocated to the CMIP5 project. Bluefire, a 4,064-processor IBM Power6 resource with a peak of 77 TeraFLOPS, provided more than 7.5 million computing hours, the GLADE high-speed disk resource provided 0.4 PetaBytes of dedicated disk and CISL’s 12-PB HPSS archive provided over 1 PetaByte of storage in support of the CMIP5 project.
- Ciais P, Sabine C, Bala G, Bopp L, Brovkin V, Canadell J, et al. (2014) Carbon and other biogeochemical cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, pp. 465–570Google Scholar
- Dzotsi K, Agboh-Noameshie A, Struif Bontkes TE, Singh U, and Dejean P (2003) Using DSSAT to derive optimum combinations of cultivar and sowing date for maize in Southern Togo. In Decision Support Tools for Smallholder Agriculture in Sub- Saharan Africa: a practical guide, Struif Bontkes TE and Wopereis MCS (eds.), IFDC and CTA, Muscle Shoals, AL., 100–113Google Scholar
- Hurrell JW, Holland MM, Gent PR, Ghan S, Kay JE, Kushner PJ, Lamarque J-F, Large WG, Lawrence D, Lindsay K, Lipscomb WH, Long MC, Mahowald N, Marsh DR, Neale RB, Rasch P, Vavrus S, Vertenstein M, Bader D, Collins WD, Hack JJ, Kiehl J, Marshall S (2013) The community earth system model: a framework for collaborative research. B Am Meteorol Soc 94:1339–1360. doi: 10.1175/BAMS-D-12-00121.1 CrossRefGoogle Scholar
- Levis S, Badger A, Drewniak BA, O’Neill BC, Ren X (2014a) CESM-simulated 21st Century Changes in Large Scale Crop Water Requirements and Yields. AGU Fall Meeting GC41B-0547, San Francisco, CaliforniaGoogle Scholar
- Lobell DB, Field CB (2007) Global scale climate-crop yield relationships and the impacts of recent warming. Environ Res Lett 2. doi: 10.1088/1748-9326/2/1/014002
- Monfreda C, Ramankutty N, Foley JA (2008) Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochem Cycles 22. doi: 10.1029/2007GB002947
- Olesen JE, Carter TR, Díaz-Ambrona CH, Fronzek S, Heidmann T, Hickler T, Hold T, Minguez MI, Morales P, Palutikof JP, Quemada M, Ruiz-Ramos M, Ruvæk GH, Sau F, Smith B, Sykes MT (2007) Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models. Clim Chang 81:123–143CrossRefGoogle Scholar
- Oleson KW, Lawrence DM, Bonan GB, Drewniak B, Huang M, Koven CD, Levis S, et al. (2013) Technical Description of version 4.5 of the Community Land Model (CLM), NCAR Technical Note NCAR/TN-503 + STR, 434 pp.Google Scholar
- O'Neill BC, Gettelman A. The Benefits of Reduced Anthropogenic Climate changE (BRACE): Introduction to the special issue (this issue)Google Scholar
- Porter JR, Xie L, Challinor AJ, Cochrane K, Howden SM, Iqbal MM, Lobell DB, and Travasso MI (2014) Food security and food production systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, and White LL (eds.)]. Cambridge University Press, Cambridge, pp. 485–533Google Scholar
- Rosenzweig C, Elliott J, Deryng D, Ruane AC, Müller C, Arneth A, Boote KJ, Folberth C, Glotter M, Khabarov N, Neumann K, Piontek F, Pugh TAM, Schmid E, Stehfest E, Yang H, Jones JW (2014) Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc Natl Acad Sci 111:3268–3273. doi: 10.1073/pnas.1222463110 CrossRefGoogle Scholar