Pattern scaling is a computationally efficient method to generate global projections of future climate changes, such as temperature and precipitation, under various emission scenarios. In this study, we apply the pattern-scaling method to project future changes of potential evapotranspiration (PET), a metric highly relevant to hydroclimate research. While doing so, this study tests the basic assumption of pattern-scaling methods, which is that the underlying scaling pattern is largely identical across all emission scenarios. We use a pair of the large-ensemble global climate model (GCM) simulations and obtain the two separate scaling patterns, one due to greenhouse gasses (GHGs) and the other due to aerosols, which show substantial regional differences. We also derive a single combined pattern, encapsulating the effects of both forcings. Using an energy balance climate model, future changes in temperature, precipitation, and PET are projected by combining the separate GHGs and aerosols scaling patterns (“hybrid-pattern” approach) and the performance of this “hybrid-pattern” approach is compared to the conventional approach (“single-pattern”) by evaluating both approaches against the GCM direct output. We find that both approaches provide reasonably good emulations for the long-term projection (end of the twenty-first century). However, the “hybrid-pattern” approach provides better emulations for the near-term climate changes (2020–2040) when the large changes in aerosol emissions occur.
Emission Scenario Ensemble Member Pattern Correlation Global Climate Model Internal Variability
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We thank Andrew Gettelman and Claudia Tebaldi for comments on an earlier draft. We thank two anonymous reviewers for careful review and constructive comments. Y. Xu acknowledges supports from US Department of Energy’s Office of Science (BER, DE-FC02-97ER62402) and Advanced Study Programme (ASP) postdoctoral fellowship while he worked at NCAR. L. Lin is supported by the National Basic Research Program of China (2016YFA0602700) and National Natural Science Foundation of China (41330527). Computing resources were provided by the Climate Simulation Laboratory at NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation and other agencies. The National Center for Atmospheric Research is supported by the US National Science Foundation.
ESM 1Figures S1 (a) Temperature simulated by the simple EBM and CESM1 (anomaly with respect to 2006–2015 mean). (b) Total radiative forcing under RCP2.6, RCP4.5, RCP6.0 and RCP8.5. (c) GHG radiative forcing. (d) Aerosol radiative forcing. (e) The ratio between (d) and (b), which shows that the aerosol forcing diminishes toward the end of 21st century. Figure S2 The same as Fig. 1 except for absolute changes in precipitation (mm/day/°C). Figure S3 The same as Fig. 2 except for surface air temperature. Figure S4 The same as Fig. 3 except for the absolute changes of precipitation and PET. Figure S5 Similar to Fig. 4, but showing the pattern correlation and RMSE for each approach (dash lines for the “hybrid-pattern” and solid lines for the “single-pattern”) and each CESM1 projection used as the benchmark (RCP2.6, RCP4.5, and RCP6.0). RMSE of precipitation indeed behave differently from other metrics; that is, single-pattern scaling approach achieves a smaller (better) RMSE. We suspect that it is because by combining two temperature estimates from the energy balance model, the “hybrid-pattern” approach fails to minimize the precipitation absolute value bias. PET, on the other hand, is formulated to be more like T. (DOCX 9837 kb)
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