The University of Illinois at Urbana-Champaign climate projection for 2080
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The dataset represents an assimilation of global climate change projections of 17 GCMs for six SRES scenarios (A1B, A1FI, A1T, A2, B1, and B2) included in IPCC TAR. The study explicitly considered multiple layers of climate projection uncertainties, including uncertainties associated with GHG emission scenarios, GCM climate sensitivity and regional variability. A model based on maximum entropy concept evaluated the suitability (measure by “skill score”) of each GCM based on its hindcasts of temperature and precipitation (Laurent and Cai, 2006). The analyses were done for each 2-degree grid cell over global land mass. GCM skill scores were used jointly with prescribed climate sensitivity function and pattern scaling method to derive changes of monthly precipitation and temperature from the period 1961-1990 to 2070-2099.
GCM skill scores were also calculated for each 2-degree grid cell based on root mean square error. The linkages between spatial distribution of GCMs’ skill scores and the distributions of global land cover, elevation and climate zones were further explored, to shed light on GCM selection for impact assessment and possibly further GCM development (Cai et al., 2009).
Cai, X., D. Wang, T. Zhu, and C. Ringler (2009), Assessing the regional variability of GCM simulations, Geophys. Res. Lett., 36, L02706, doi:10.1029/2008GL036443.
Laurent, R., and X. Cai (2007), A maximum entropy method for combining AOGCMs for regional intra-year climate change assessment, Clim. Change, 82, 411–435.
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WorldClim (Hijmans et al., 2005) presents the interpolated climate surfaces for global land areas (excluding Antarctica) at very high spatial resolutions of 30 arc-seconds, 2.5 arc-minutes, 5 arc-minutes, and 10 arc-minutes. Above tool visualizes the data retrieved from the 5 arc-minutes (~10 km spatial resolution) dataset. Based on the historic and future climate data from a variety of sources, authors of WorldClim improved the spatial resolution using an interpolation method, called the thin-plate smoothing spline algorithm, with latitude, longitude, and elevation as independent variables. See http://WorldClim.org for more information about the methodology and data download.
Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978
