Skip to main content

Alaskan Regional Climate Changes in Dynamically Downscaled CMIP5 Simulations

  • Conference paper

Abstract

Global models are the most widely used tools for understanding and assessing climatic variability and changes. However, coarse-resolution limits their capability to capture detailed finer-scale meteorological features, including heterogeneous spatial distributions and high-frequency temporal variability. In this study, the mesoscale Weather Research and Forecasting (WRF) model is used to dynamically downscale a CMIP5 global model simulation (CCSM MOAR output) for a portion of the Arctic marginal zone, encompassing Alaska and surrounding areas, with the aim to improve understanding, representation, and future projection of high-resolution climate changes in the area. Dynamic downscaling of the twentieth century simulation was conducted for the period 1991–2005 and validated against in situ observations archived by the NCDC. Downscaled results generally capture observed conditions well. However, cold biases exist across most of the study area, except for a weak warm bias along the western and northern Alaskan coasts. In addition, downscaled winds are stronger than observations and precipitation is overestimated along the Alaskan panhandle. The biases in the downscaled temperature, wind speed, and precipitation are correctable. The downscaled temperature bias exhibits strong seasonality, with a warm bias in the cold months and a cold bias in the warm months, particularly along the western and northern Alaskan coasts. Seasonality in the wind speed and precipitation biases, however, is relatively small. Under the RCP6 scenario, downscaled regional climate over Alaska and the surrounding areas demonstrate a significant warming trend over the entire study area during the twenty-first century, with the strongest warming occurring over the Arctic Ocean. Precipitation is also projected to increase along Alaska’s coastal areas and over the Arctic Ocean. Interior Alaska, on the other hand, becomes drier in the future climate scenario.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Bengtsson, L., Botzett, M., & Esch, M. (1996). Will greenhouse gas-induced warming over the next 50 years lead to higher frequency and greater intensity of hurricanes? Tellus, 48A, 57–73.

    Article  Google Scholar 

  • Chen, F., & Dudhia, J. (2001). Coupling an advanced land-surface hydrology model with the PSU/NCAR MM5 modeling system. Part I: Model description and implementation. Monthly Weather Review, 129, 569–585.

    Article  Google Scholar 

  • Comiso, J. C., Parkinson, C. L., Gersten, R., & Stock, L. (2008). Accelerated decline in the Arctic sea ice cover. Geophysical Research Letters, 35, L01703.

    Article  Google Scholar 

  • Gardner, A. S., Moholdt, G., Cogley, J. G., Wouters, B., Arendt, A. A., Wahr, J., et al. (2013). A reconciled estimate of glacier contributions to sea level rise: 2003 to 2009. Science, 340, 852–857. doi:10.1126/science1234532.

    Article  CAS  Google Scholar 

  • Giorgi, F., Jones, C., & Asrar, G. (2009). Addressing climate information needs at the regional level: The CORDEX framework. WMO Bulletin, 58(V3), 175–183.

    Google Scholar 

  • Grell, G. A., & Devenyi, D. (2002). A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophysical Research. Letters, 29, 1693. doi:10.1029/2002GL015311.

    Article  Google Scholar 

  • Gent, P. R., G. Danabasoglu, L. J. Donner, M. M. Holland, E. C. Hunke, S. R. Jayne et al. (2011). The community climate system model version 4. Journal of Climate, 24(19), 4973–4991.

    Google Scholar 

  • Hock, R., de Woul, M., Radić, V., & Dyurgerov, M. (2009). Mountain glaciers and ice caps around Antarctica make a large sea-level rise contribution. Geophysical Research Letters, 36, L07501. doi:10.1029/2008GL037020.

    Article  Google Scholar 

  • Iacono, M., Delamere, J., Mlawer, E., Shephard, M., Clough, S., & Collins, W. (2008). Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. Journal of Geophysical Research, 113, D13103.

    Article  Google Scholar 

  • Janjic, Z. I. (1994). The step-mountain Eta coordinate model: Further developments of the convection, viscous sublayer and turbulence closure schemes. Monthly Weather Review, 122, 927–945.

    Article  Google Scholar 

  • Janjic, Z. I. (1996). The Mellor-Yamada level 2.5 scheme in the NCEP Eta Model. In 11th Conference on Numerical Weather Prediction, Norfolk, VA, American Meteorological Society, pp. 333–334.

    Google Scholar 

  • Janjic, Z. I. (2002). Nonsingular implementation of the Mellor–Yamada level 2.5 scheme in the NCEP meso model. NCEP Office Note, No. 437, National Centers for Environmental Prediction, 61p.

    Google Scholar 

  • Kalnay, E., et al. (1996). The NCEP/NCAR 40-year reanalysis project. Bulletin of American Meteorological Society, 77, 437–471.

    Article  Google Scholar 

  • Leung, L. R., Mearns, L. O., Giorgi, F., & Wilby, R. (2003). Workshop on regional climate research: Needs and opportunities. Bulletin of American Meteorological Society, 84, 89–95.

    Article  Google Scholar 

  • Lynch, A. H., McGinnis, D. L., & Bailey, D. A. (1998). Snow-albedo feedback and the spring transition in a regional climate system model: Influence of land surface model. Journal of Geophysical Research, 103, 29037–29049.

    Article  Google Scholar 

  • Mearns, L. O., Gutowski, W. J., Jones, R., Leung, L.-Y., McGinnis, S., Nunes, A. M. B., et al. (2009). A regional climate change assessment program for North America. Eos, 90, 311–312.

    Article  Google Scholar 

  • Mellor, G. L., & Yamada, T. (1982). Development of a turbulence closure model for geophysical fluid problems. Review of Geophysics Space Physics, 20, 851–875.

    Article  Google Scholar 

  • Morrison, H. C., Thompson, G., & Tatarskii, V. (2009). Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Monthly Weather Review, 137, 991–1007.

    Article  Google Scholar 

  • Osterkamp, T. E. (2003). A thermal history of permafrost in Alaska. In Proceedings of Eighth International Conference on Permafrost, Zurich, pp. 863–868.

    Google Scholar 

  • Skamarock, W. C., et al. (2008). A description of the advanced research WRF version 3. NCAR Technical Note, NCAR/TN–475+STR, 113pp.

    Google Scholar 

  • Stegall, S. T., & Zhang, J. (2012). Wind field climatology, changes, and extremes in the Chukchi–Beaufort Seas and Alaska North Slope during 1979–2009. Journal of Climate, 25, 8075–8089.

    Article  Google Scholar 

  • Stroeve, J. C., Serreze, M. C., Holland, M. M., Kay, J. E., Malanik, J., & Barrett, A. P. (2012). The Arctic’s rapidly shrinking sea ice cover: A research synthesis. Climatic Change, 110, 1005–1027.

    Article  Google Scholar 

  • Whitfield, J. (2003). Alaska’s climate: Too hot to handle. Nature, 425, 338–339. doi:10.1038/425338a.

    Article  CAS  Google Scholar 

  • Zhang, J., Bhatt, U. S., Tangborn, W. V., & Lingle, C. S. (2007). Climate downscaling for estimating glacier mass balances in northwestern North America: Validation with a USGS benchmark glacier. Geophysical Research Letters, 34, L21505. doi:10.1029/2007GL031139.

    Article  Google Scholar 

  • Zhang, X., & Zhang, J. (2001). Heat and freshwater budgets and pathways in the Arctic Mediterranean in a coupled ocean/sea-ice model. Journal of Oceanography, 57, 207–237.

    Article  Google Scholar 

  • Zhang, X., Zhang, J., Krieger, J., Shulski, M., Liu, F., Stegall, S., et al. (2013). Beaufort and Chukchi Seas mesoscale meteorology modeling study, Final Report. U. S. Department of the Interior, Bureau of Ocean Energy Management. OCS Study BOEM 2013-0119, 204p., www.boem.gov/BOEM-2013-0119.

Download references

Acknowledgement

This work was supported by the NSF Grants EAR-0943742, PLR-1304684, and ARC-1023592. Computing resources were provided by the Arctic Region Supercomputing Center at the University of Alaska Fairbanks.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jing Zhang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Zhang, J., Krieger, J., Bhatt, U., Lu, C., Zhang, X. (2016). Alaskan Regional Climate Changes in Dynamically Downscaled CMIP5 Simulations. In: Uzochukwu, G., Schimmel, K., Kabadi, V., Chang, SY., Pinder, T., Ibrahim, S. (eds) Proceedings of the 2013 National Conference on Advances in Environmental Science and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-19923-8_5

Download citation

Publish with us

Policies and ethics