Climatic Change

, Volume 140, Issue 3–4, pp 635–647 | Cite as

Pattern scaling based projections for precipitation and potential evapotranspiration: sensitivity to composition of GHGs and aerosols forcing



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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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.

Supplementary material

10584_2016_1879_MOESM1_ESM.docx (9.6 mb)
ESM 1 Figures 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)


  1. Allen MR, Ingram WJ (2002) Constraints on future changes in climate and the hydrologic cycle. Nature 419:224–232CrossRefGoogle Scholar
  2. Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56, vol 300. FAO, Rome, p 6541Google Scholar
  3. Alley WM (1984) The palmer drought severity index: limitations and assumptions. J Clim Appl Meteorol 23:1100–1109. doi: 10.1175/1520-0450(1984)023<1100:TPDSIL>2.0.CO;2 CrossRefGoogle Scholar
  4. Chung S, Seinfeld J (2005) Climate response of direct radiative forcing of anthropogenic black carbon. J Geophys Res 110:1–25. doi: 10.1029/2004JD005441
  5. Dai A, Trenberth KE, Qian T (2004) A global dataset of palmer drought severity index for 1870–2002: relationship with soil moisture and effects of surface warming. J Hydrometeorol 5:1117–1130. doi: 10.1175/JHM-386.1 CrossRefGoogle Scholar
  6. Feng S, Fu Q (2013) Expansion of global drylands under a warming climate. Atmos Chem Phys 13:10081–10094. doi: 10.5194/acp-13-10081-2013 CrossRefGoogle Scholar
  7. Fu Q, Feng S (2014) Responses of terrestrial aridity to global warming. J Geophys Res B Solid Earth 119:7863–7875. doi: 10.1002/2014JD021608
  8. Gettelman A, Liu X, Ghan SJ et al (2010) Global simulations of ice nucleation and ice supersaturation with an improved cloud scheme in the community atmosphere model. J Geophys Res Atmos 115:D18216. doi: 10.1029/2009JD013797 CrossRefGoogle Scholar
  9. Ghan SJ, Liu X, Easter RC et al (2012) Toward a minimal representation of aerosols in climate models: comparative decomposition of aerosol direct, semidirect, and indirect radiative forcing. J Clim 25:6461–6476. doi: 10.1175/JCLI-D-11-00650.1 CrossRefGoogle Scholar
  10. Hartmann D (1994) Global Physical Climatology. Academic Press, San Diego, pp 411Google Scholar
  11. Held IM, Soden BJ (2006) Robust responses of the hydrological cycle to global warming. J Clim 19:5686–5699. doi: 10.1175/JCLI3990.1 CrossRefGoogle Scholar
  12. Hu A, Xu Y, Tebaldi C et al (2013) Mitigation of short-lived climate pollutants slows sea-level rise. Nat Clim Chang 3:730–734. doi: 10.1038/nclimate1869 CrossRefGoogle Scholar
  13. Hulme M, Wigley TML, Barrow EM et al (2000) Using a climate scenario generator for vulnerability and adaptation assessments: MAGICC and SCENGEN version 2.4 workbook. Climatic Research Unit, Norwich, p 60Google Scholar
  14. Hurrell JW, Holland MM, Gent PR et al (2013) The community earth system model: a framework for collaborative research. Bull Am Meteorol Soc 94:1339–1360. doi: 10.1175/BAMS-D-12-00121.1 CrossRefGoogle Scholar
  15. Hwang YT, Frierson DMW, Kang SM (2013) Anthropogenic sulfate aerosol and the southward shift of tropical precipitation in the late 20th century. Geophys Res Lett 40:2845–2850. doi: 10.1002/grl.50502 CrossRefGoogle Scholar
  16. Ishizaki Y, Yokohata T, Emori S et al (2013) Validation of a pattern scaling approach for determining the maximum available renewable freshwater resource. J Hydrometeorol 15:505–516. doi: 10.1175/JHM-D-12-0114.1 CrossRefGoogle Scholar
  17. Ishizaki Y, Shiogama H, Emori S et al (2014) Dependence of precipitation scaling patterns on emission scenarios for representative concentration pathways. J Clim 26:8868–8879. doi: 10.1175/JCLI-D-12-00540.1 CrossRefGoogle Scholar
  18. Kay JE, Deser C, Phillips A et al (2015) The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bull Am Meteorol Soc 96:1333–1349. doi: 10.1175/BAMS-D-13-00255.1 CrossRefGoogle Scholar
  19. Lin L, Gettelman A, Feng S, Fu Q (2015) Simulated climatology and evolution of aridity in the 21st century. J Geophys Res Atmos. 120:2014JD022912. doi:  10.1002/2014JD022912
  20. Lin L, Gettelman A, Fu Q, Xu Y (2015) Simulated differences in 21st century aridity due to different scenarios of greenhouse gases and aerosols. Clim Change. 1–16. doi:  10.1007/s10584-016-1615-3
  21. Liu X, Easter RC, Ghan SJ et al (2012) Toward a minimal representation of aerosols in climate models: description and evaluation in the community atmosphere model CAM5. Geosci Model Dev 5:709–739. doi: 10.5194/gmd-5-709-2012 CrossRefGoogle Scholar
  22. Meehl G, Washington WM, Arblaster JM et al (2013) Climate change projections in CESM1(CAM5) compared to CCSM4. J Clim 26:6287–6308. doi: 10.1175/JCLI-D-12-00572.1 CrossRefGoogle Scholar
  23. Middleton NJ, Thomas DSG (1992) UNEP: world atlas of desertification. Edward Arnold, SevenoaksGoogle Scholar
  24. Milly PCD, Dunne KA (2016) Potential evapotranspiration and continental drying. Nat Clim Chang 6:946–949. doi: 10.1038/NCLIMATE3046
  25. Ming Y, Ramaswamy V (2009) Nonlinear climate and hydrological responses to aerosol effects. J Clim 22:1329–1339. doi: 10.1175/2008JCLI2362.1 CrossRefGoogle Scholar
  26. Mitchell TD (2003) Pattern scaling—an examination of the accuracy of the technique for describing future climates. Clim Change 60(3):217–242CrossRefGoogle Scholar
  27. Mitchell JFB, Johns TC, Eagles M, Ingram WJ, Davis RA (1999) Towards the construction of climate change scenarios. Clim Change 41:547–581CrossRefGoogle Scholar
  28. Morrison H, Gettelman A (2008) A new two-moment bulk stratiform cloud microphysics scheme in the community atmosphere model, version 3 (CAM3). Part I: description and numerical tests. J Clim 21:3642–3659. doi: 10.1175/2008JCLI2105.1 CrossRefGoogle Scholar
  29. Ocko IB, Ramaswamy V, Ming Y (2014) Contrasting climate responses to the scattering and absorbing features of anthropogenic aerosol forcings. J Clim 27:5329–5345. doi: 10.1175/JCLI-D-13-00401.1 CrossRefGoogle Scholar
  30. Ramanathan V, Xu Y (2010) The Copenhagen accord for limiting global warming: criteria, constraints, and available avenues. Proc Natl Acad Sci U S A 107:8055–8062. doi: 10.1073/pnas.1002293107 CrossRefGoogle Scholar
  31. Richter I, Xie S-P (2008) Muted precipitation increase in global warming simulations: a surface evaporation perspective. J Geophys Res Atmos 113:D24118. doi: 10.1029/2008JD010561 CrossRefGoogle Scholar
  32. Roderick ML, Greve P, Farquhar GD (2015) On the assessment of aridity with changes in atmospheric CO2. Water Resour Res 51:5450–5463. doi: 10.1002/2015WR017031 CrossRefGoogle Scholar
  33. Sanderson BM, Oleson KW, Strand WG, et al. (2015) A new ensemble of GCM simulations to assess avoided impacts in a climate mitigation scenario. Clim Change. 1–16. doi:  10.1007/s10584-015-1567-z
  34. Santer BD, Wigley TML, Schlesinger ME et al (1990) Developing climate scenarios from equilibrium GCM results, technical note 47. Max Planck Institute Meteorologie, Hamburg, p 29Google Scholar
  35. Scheff J, Frierson DMW (2014) Scaling potential evapotranspiration with greenhouse warming. J Clim 27:1539–1558. doi:  10.1175/JCLI-D-13-00233.1
  36. Schlesinger ME, Malyshev S, Rozanov EV, et al. (2000) Geographical Distributions of Temperature Change for Scenarios of Greenhouse Gas and Sulfur Dioxide Emissions. Technol Forecast Soc Change 65:167–193. doi: 10.1016/S0040-1625(99)00114-6
  37. Sherwood S, Fu Q (2014) A drier future? Science (80-) 343(80-):737–739. doi: 10.1126/science.1247620 CrossRefGoogle Scholar
  38. Shindell DT, Voulgarakis A, Faluvegi G, Milly G (2012) Precipitation response to regional radiative forcing. Atmos Chem Phys 12:6969–6982. doi: 10.5194/acp-12-6969-2012 CrossRefGoogle Scholar
  39. Shiogama H, Emori S, Takahashi K et al (2009) Emission scenario dependency of precipitation on global warming in the MIROC3.2 model. J Clim 23:2404–2417. doi: 10.1175/2009JCLI3428.1 CrossRefGoogle Scholar
  40. Tebaldi C, Arblaster JM (2014) Pattern scaling: its strengths and limitations, and an update on the latest model simulations. Clim Change 122:459–471. doi: 10.1007/s10584-013-1032-9 CrossRefGoogle Scholar
  41. Tilmes S, Sanderson BM, O’Neill BC (2016) Climate impacts of geoengineering in a delayed mitigation scenario. Geophys Res Lett 43:8222–8229. doi: 10.1002/2016GL070122
  42. van Vuuren DP, Edmonds J, Kainuma M et al (2011) The representative concentration pathways: an overview. Clim Change 109:5–31. doi: 10.1007/s10584-011-0148-z CrossRefGoogle Scholar
  43. Xu Y, Lamarque J-F, Sanderson BM (2015) The importance of aerosol scenarios in projections of future heat extremes. Clim Change. 1–14. doi:  10.1007/s10584-015-1565-1
  44. Xu Y, Ramanathan V, Washington WM (2016) Observed high-altitude warming and snow cover retreat over Tibet and the Himalayas enhanced by black carbon aerosols. Atmos Chem Phys 16:1303–1315. doi: 10.5194/acp-16-1303-2016 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Department of Atmospheric SciencesTexas A & M UniversityCollege StationUSA
  2. 2.School of Atmospheric SciencesSun Yat-Sen UniversityGuangzhouChina
  3. 3.Guangdong Province Key Laboratory for Climate Change and Natural Disaster StudiesGuangdongChina

Personalised recommendations