A new approach to construct representative future forcing data for dynamic downscaling

Abstract

Climate downscaling using regional climate models (RCMs) has been widely used to generate local climate change information needed for climate change impact assessments and other applications. Six-hourly data from individual simulations by global climate models (GCMs) are often used as the lateral forcing for the RCMs. However, such forcing often contains both internal variations and externally-forced changes, which complicate the interpretation of the downscaled changes. Here, we describe a new approach to construct representative forcing for RCM-based climate downscaling and discuss some related issues. The new approach combines the transient weather signal from one GCM simulation with the monthly mean climate states from the multi-model ensemble mean for the present and future periods, together with a bias correction term. It ensures that the mean climate differences in the forcing data between the present and future periods represent externally-forced changes only and are representative of the multi-model ensemble mean, while changes in transient weather patterns are also considered based on one select GCM simulation. The adjustments through the monthly fields are comparable in magnitude to the bias correction term and are small compared with the variations in 6-hourly data. Any inconsistency among the independently adjusted forcing fields is likely to be small and have little impact. For quantifying the mean response to future external forcing, this approach avoids the need to perform RCM large ensemble simulations forced by different GCM outputs, which can be very expensive. It also allows changes in transient weather patterns to be included in the lateral forcing, in contrast to the Pseudo Global Warming (PGW) approach, in which only the mean climate change is considered. However, it does not address the uncertainty associated with internal variability or inter-model spreads. The simulated transient weather changes may also be unrepresentative of other models. This new approach has been applied to construct the forcing data for the second phase of the WRF-based downscaling over much of North America with 4 km grid spacing.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. Bruyère CL, Done JM, Holland GJ, Fredrick S (2014) Bias corrections of global models for regional climate simulations of high-impact weather. Clim Dyn 43:1847–1856. doi:10.1007/s00382-013-2011-6

    Article  Google Scholar 

  2. Collins M et al (2013) Long-term climate change: Projections, commitments and irreversibility. In: Stocker TF et al (eds) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, pp 1029–1136

  3. Dai A (2013) the influence of the inter-decadal pacific oscillation on U.S. precipitation during 1923–2010. Clim Dyn 41:633–646. doi:10.1007/s00382-012-1446-5

    Article  Google Scholar 

  4. Dai A, Bloecker CE (2017a) Impacts of internal variability on temperature and precipitation trends in large ensemble simulations by two climate models. Clim Dyn (submitted)

  5. Dai A, Fyfe JC, Xie S-P, Dai X (2015) Decadal modulation of global surface temperature by internal climate variability. Nat Clim Change 5:555–559. doi:10.1038/nclimate2605

    Article  Google Scholar 

  6. Dai A, Rasmussen RM, Liu C, Ikeda K, Prein AF (2017b) Changes in precipitation characteristics over North America by the late 21st century simulated by a convection-permitting model. Clim Dyn (revised)

  7. Dee DP et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quart J R Meteorol Soc 137:553–597. doi:10.1002/qj.828

    Article  Google Scholar 

  8. Deser C, Knutti R, Solomon S, Phillips AS (2012a) Communications of the role of natural variability in future North American climate. Nat Clim Change 2:775–779. doi:10.1038/nclimate1562

    Article  Google Scholar 

  9. Deser C, Phillips AS, Bourdette V, Teng H (2012b) Uncertainty in climate change projections: the role of internal variability. Climate Dyn 38:527–546. doi:10.1007/s00382-010-0977-x

    Article  Google Scholar 

  10. Deser C, Phillips AS, Alexander MA, Smoliak BV (2014) Projecting North American climate over the next 50 years: uncertainty due to internal variability. J Climate 27:2271–2296. doi:10.1175/JCLI-D-13-00451.1

    Article  Google Scholar 

  11. Dong B, Dai A (2015) The influence of the Inter-decadal Pacific Oscillation on temperature and precipitation over the globe. Clim Dyn 45:2667–2681. doi:10.1007/s00382-015-2500-x

    Article  Google Scholar 

  12. Giogi F, Mearns LO (1991) Approaches to the simulation of regional climate change: a revew. Rev Gephys 29:191–216

  13. Giorgi F, Lionello P (2008) Climate change projections for the Mediterranean region. Global Planet Change 63:90–104

  14. Giorgi F, Jones C, Asrar GR (2006) Addressing climate information needs at the regional level: the CORDEX framework. Bull World Meteorol Organ 58:175–183

  15. Hara M, Yoshikane T, Kawase H, Kimura F (2008) Estimation of the impact of global warming on snow depth in Japan by the pseudo-global warming method. Hydrol Res Lett 2:61–64

    Article  Google Scholar 

  16. Jacob D et al (2014) EUROCORDEX: new high resolution climate change projections for European impact research. Reg Environ Change 4:563–578. doi:10.1007/s1011301304992

    Article  Google Scholar 

  17. Kawase H, Yoshikane T, Hara M, Kimura F, Yasunari T, Ailikun B, Ueda H, Inoue T (2009) Intermodel variability of future changes in the Baiu rainband estimated by the pseudo global warming downscaling method. J Geophys Res 114:D24110. doi:10.1029/2009JD011803

    Article  Google Scholar 

  18. Kendon E, Ban N, Roberts N, Fowler H, Roberts M, Chan S, Evans J, Fosser G, Wilkinson J (2017) Do convection-permitting regional climate models improve projections of future precipitation change? Bull Am Meteor Soc 98:79–93. doi:10.1175/BAMS-D-15-0004.1

    Article  Google Scholar 

  19. Knutti R, Furrer R, Tebaldi C, Cermak J, Meehl GA (2010) Challenges in combining projections from multiple climate models. J Clim 23:2739–2758

    Article  Google Scholar 

  20. Liu C et al (2016) Continental-scale convection-permitting modeling of the current and future climate of North America. Clim Dyn. doi:10.1007/s00382-016-3327-9 (press)

    Article  Google Scholar 

  21. Maraun D et al (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48:RG3003. doi:10.1029/2009RG000314

    Article  Google Scholar 

  22. Mearns LO, Gutowski WJ, Jones R, Leung L-Y, McGinnis S, Nunes AMB, Qian Y (2009) A regional climate change assessment program for North America. EOS 90:311–312

    Article  Google Scholar 

  23. Prein AF et al (2015) A review on regional convection-permitting climate modeling: demonstrations, prospects, and challenges. Rev Geophys 53:323–361. doi:10.1002/2014RG000475

    Article  Google Scholar 

  24. Prein AF, Rasmussen RM, Ikeda K, Liu C, Clark MP, Holland GJ (2017) The future intensification of hourly precipitation extremes. Nat Clim Change 7:48–52. doi:10.1038/NCLIMATE3168

    Article  Google Scholar 

  25. Rasmussen RM et al (2011) High-resolution coupled climate runoff simulations of seasonal snowfall over Colorado: a process study of current and warmer climate. J Clim 24:3015–3048

    Article  Google Scholar 

  26. Rasmussen RM, Ikeda K, Liu C, Gochis D, Clark M, Dai A, Gutmann E, Dudhia J, Chen F, Barlage M, Yates D (2014) Climate change impacts on the water balance of the Colorado Headwaters: high-resolution regional climate model simulations. J Hydrometeorol 15:1091–1116

    Article  Google Scholar 

  27. Schär C, Frie C, Lu¨ thi D, Davies HC (1996) Surrogate climate-change scenarios for regional climate models. Geophys Res Lett 23:669–672

    Article  Google Scholar 

  28. Themeßl MJ, Gobiet A, Leuprecht A (2010) Empirical-statistical downscaling and error correction of daily precipitation from regional climate models. Int J Climatol 31:1530–1544

    Article  Google Scholar 

  29. Vautard R et al (2014) The European climate under a 2 °C global warming. Environ Res Lett 9:034006. doi:10.1088/1748-9326/9/3/034006

  30. Wallace JM, Deser C, Smoliak BV, Phillips AS (2016) Attribution of climate change in the presence of internal variability. In: Chang CP et al (eds) Climate Change: Multidecadal and Beyond. World Scientific Series on Asia-Pacific Weather and Climate, vol 6, World Scientific, pp 1–29

  31. Walton DB, Sun F, Hall A, Capps S (2015) A hybrid dynamical-statistical downscaling technique. Part I: Development and validation of the technique. J Clim 28:4597–4617

    Article  Google Scholar 

  32. Wang J, Kotamarthi VR (2015) High-resolution dynamically downscaled projections of precipitation in the mid and late 21st century over North America. Earth’s Future 3:268–288. doi:10.1002/2015EF000304

    Article  Google Scholar 

  33. Wilby RL, Wigley TML (1997) Downscaling general circulation model output: a review of methods and limitaitons. Progr Phys Geogr 21:530–548

    Article  Google Scholar 

  34. Xu Z, Yang Z-L (2015) A new dynamical downscaling approach with GCM bias corrections and spectral nudging. J Geophys Res Atmos 120:3063–3084. doi:10.1002/2014JD022958

    Article  Google Scholar 

Download references

Acknowledgements

A. Dai acknowledges the supported by the U.S. National Science Foundation (Grant #AGS–1353740), the U.S. Department of Energy’s Office of Science (Award #DE–SC0012602), and the U.S. National Oceanic and Atmospheric Administration (Award #NA15OAR4310086). NCAR is funded by the National Science Foundation. Computer resources were provided by the Computational and Information Systems Laboratory of NCAR.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Aiguo Dai.

Additional information

This paper is a contribution to the special issue on Advances in Convection-Permitting Climate Modeling, consisting of papers that focus on the evaluation, climate change assessment, and feedback processes in kilometer-scale simulations and observations. The special issue is coordinated by Christopher L. Castro, Justin R. Minder, and Andreas F. Prein.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dai, A., Rasmussen, R.M., Ikeda, K. et al. A new approach to construct representative future forcing data for dynamic downscaling. Clim Dyn 55, 315–323 (2020). https://doi.org/10.1007/s00382-017-3708-8

Download citation

Keywords

  • Climate downscaling
  • Forcing data
  • Regional climate change
  • WRF
  • North America