Pattern scaling: Its strengths and limitations, and an update on the latest model simulations
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We review the ideas behind the pattern scaling technique, and focus on its value and limitations given its use for impact assessment and within integrated assessment models. We present estimates of patterns for temperature and precipitation change from the latest transient simulations available from the Coupled Model Inter-comparison Project Phase 5 (CMIP5), focusing on multi-model mean patterns, and characterizing the sources of variability of these patterns across models and scenarios. The patterns are compared to those obtained from the previous set of experiments, under CMIP3. We estimate the significance of the emerging differences between CMIP3 and CMIP5 results through a bootstrap exercise, while also taking into account the fundamental differences in scenario and model ensemble composition. All in all, the robustness of the geographical features in patterns of temperature and precipitation, when computed as multi-model means, is confirmed by this comparison. The intensity of the change (in both the warmer and cooler areas with respect to global temperature change, and the drier and wetter regions) is overall heightened per degree of global warming in the ensemble mean of the new simulations. The presence of stabilized scenarios in the new set of simulations allows investigation of the performance of the technique once the system has gotten close to equilibrium. Overall, the well established validity of the technique in approximating the forced signal of change under increasing concentrations of greenhouse gases is confirmed.
KeywordsAtlantic Meridional Overturning Circulation Precipitation Change Radiative Forcings Internal Variability Multimodel Ensemble
We thank the editors, Dr. Tom Wigley, Dr. Reto Knutti and two anonymous reviewers for their comments and suggestions.
We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison, and the Working Group on Coupled Modeling of the World Climate Research Programme (WCRP) for their roles in making available the WCRP CMIP3 and CMIP5 multimodel datasets. Support of these datasets is provided by the Office of Science, US Department of Energy (DOE). Portions of this study were supported by the Office of Science, Biological, and Environmental Research, US DOE (Grant DE-SC0004956 and Cooperative Agreement No. DE-FC0297ER62402). The National Center for Atmospheric Research is funded by the National Science Foundation.
- Chadwick R, Good P (2013) Understanding non-linear tropical precipitation responses to CO2 forcings. Geophys Res Lett 10:1029Google Scholar
- Holden PB, Edwards NR (2010) Dimensionally reduced emulation of an AOGCM for application to integrated assessment modelling. Geophys Res Lett 37Google Scholar
- IPCC (2013) Climate Change 2013. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USAGoogle Scholar
- Ishizaki Y, Shiogama H, Emori S, Yokohata T, Nozawa T, Ogura T, Abe M, Yoshimori M, Takahashi K (2012) Temperature scaling pattern dependence on representative concentration pathway emission scenarios. Climatic Change, In revisionGoogle Scholar
- Mahlstein I, Portmann RW, Daniel JS, Solomon S, Knutti R (2012) Perceptible changes in regional precipitation in a future climate. Geophys Res Lett 39, L05701Google Scholar
- Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, van Vuuren DP, Carter TR, Emori S, Kainuma M, Kram T, Meehl GA, Mitchell JFB, Nakicenovic N, Riahi K, Smith SJ, Stouffer RJ, Thomson AM, Weyant JP, Wilbanks TJ (2010) The next generation of scenarios for climate change research and assessment. Nature 463(7282):747–756CrossRefGoogle Scholar
- Nakicenovic N, Alcamo J, Davis G, de Vries B, Fenhann J, Gaffin S, Gregory K, Grubler A, Jung T, Kram T, La E, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Riahi K, Roehrl A, Rogner H, Sankovski A, Schlesinger M, Shukla P, Smith S, Swart R, van Victor NS, Dadi Z (2000) Special report on emissions scenarios: A special report of Working Group III of the intergovernmental panel on climate change. C.U. Press, Cambridge, p 599Google Scholar
- Raisanen J, Ruokolainen L (2006) Probabilistic forecasts of near-term climate change based on a resampling ensemble technique. Tellus Ser A Dyn Meteorol Oceanogr 58(4):461–472Google Scholar
- Santer BD, Wigley TML, Schlesinger ME, Mitchell JFB (1990) Developing climate scenarios from equilibrium GCM results, Hamburg, GermanyGoogle Scholar
- Schlesinger ME, Malyshev S, Rozanov EV, Yang FL, Andronova NG, De Vries B, Grubler A, Jiang KJ, Masui T, Morita T, Penner J, Pepper W, Sankovski A, Zhang Y (2000) Geographical distributions of temperature change for scenarios of greenhouse gas and sulfur dioxide emissions. Technol Forecast Soc Chang 65(2):167–193CrossRefGoogle Scholar
- Shiogama H, Stone DA, Nagashima T, Nozawa T, Emori S (2012) On the linear additivity of climate forcing-response relationships at global and continental scales. International Journal of ClimatologyGoogle Scholar
- Solomon S, Plattner GK, Knutti R, Friedlingstein, P (2009) Irreversible climate change due to carbon dioxide emissions. Proc Natl Acad Sci U S A, 106(6):1704–1709Google Scholar
- Watterson IG, Whetton PH (2011) Joint PDFs for Australian climate in future decades and an idealized application to wheat crop yield. Aust Meteorol Oceanogr J 61:221–230Google Scholar