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As part of the Agricultural Model Intercomparison Project (AgMIP), the Global Gridded Crop Model Intercomparison group (fast track phase) have estimated historical and future changes in yields for several crops under several distinct climate change scenarios. For a detailed description of these data and its uses please refer to Rosenzweig, C. et al. (2014), Elliott, J. et al. (2014), Nelson, J. et al. (2014), and Muller and Robertson (2014).
The output archive comprises time series (1971-2099) generated by seven crop models (EPIC, GEPIC, pDSSAT, LPJmL, IMAGE-LEITAP, PEGASUS, LPJ-GUESS), under a number of temperature trajectories from a suite of five global climate models (HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, GFDL-ESM2M, NorESM1-M) and four representative concentration pathways. The output of this modeling endeavor consists of more than 36,000 global grids (with spatial resolution of 0.5 x 0.5) freely available to the public using the transfer services from Globus Online.
While public access ensures these important data can be used by many interested parties, access of the native formats as well as the software skills needed for pre-processing of these data can in practice represent an important barrier for using this information. Thus, in order to facilitate access to the AgMIP gridded archive, the AgMIP Tool aggregates the outputs from the Global Gridded Crop Model Intercomparison project (fast track phase) to any user-defined level.
Elliott, J., D. Deryng, C. Mller, K. Frieler, M. Konzmann, D. Gerten, M. Glotter, M. Flrke, Y. Wada, N. Best, S. Eisner, B.M. Fekete, C. Folberth, I. Foster, S.N. Gosling, I. Haddeland, N. Khabarov, F. Ludwig, Y. Masaki, S. Olin, C. Rosenzweig, A.C. Ruane, Y. Satoh, E. Schmid, T. Stacke, Q. Tang, and D. Wisser. 2014. “Constraints and potentials of future irrigation water availability on agricultural production under climate change.” Proceedings of the National Academy of Sciences 111:3239–3244.
Müller, C., and R.D. Robertson. 2014. “Projecting future crop productivity for global economic modeling.” Agricultural Economics 45:37–50.
Nelson, G.C., H. Valin, R.D. Sands, P. Havlk, H. Ahammad, D. Deryng, J. Elliott, S. Fujimori, T. Hasegawa, E. Heyhoe, P. Kyle, M.V. Lampe, H. Lotze-Campen, D.M. dCroz, H.v. Meijl, D.v.d. Mensbrugghe, C. Mller, A. Popp, R. Robertson, S. Robinson, E. Schmid, C. Schmitz, A. Tabeau, and D. Willenbockel. 2014. “Climate change effects on agriculture: Economic responses to biophysical shocks.” Proceedings of the National Academy of Sciences 111:3274–3279.
Rosenzweig, C., J. Elliott, D. Deryng, A.C. Ruane, C. Müller, A. Arneth, K.J. Boote, C. Folberth, M. Glotter, N. Khabarov, K. Neumann, F. Piontek, T.A.M. Pugh, E. Schmid, E. Stehfest, H. Yang, and J.W. Jones. 2014. “Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison.” Proceedings of the National Academy of Sciences 111:3268–3273.
Villoria, NB, Elliott, J, Müller, C, Shin, J, Zhao, L, and Song, C., 2016, January. Rapid aggregation of global gridded crop model outputs to facilitate cross-disciplinary analysis of climate change impacts in agriculture. Environmental Modelling & Software. 75, pp.193-201. doi:10.1016/j.envsoft.2015.10.016