CGD's Dr. Warren Washington

Warren M. Washington, Senior Scientist at the National Center for Atmospheric Research in Boulder, Colorado, was among the first scientists to pioneer the development of climate models that are used for evaluation of humankind's impact on the global environment. His modeling work has helped understand climate change including global warming. Over the last 30 years, he has had Presidential Appointments under the Carter, Reagan, Clinton, and G.W. Bush administrations and he has served on many science committees and the including National Science Board, which he chaired from 2002 to 2006. He is a former President of the American Meteorological Society and a member of both the National Academy of Engineering and the American Philosophical Society. This autobiography provides information about how he became a scientist and his insights into science policy. Throughout the book, footnotes and internet web sites are used were more information is provided.

Support: National Science Foundation and the U. S. Department of Energy.

 

 

 

 

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B. D. Santer, C. Mears, F. J. Wentz, K. E. Taylor, P. J. Gleckler, T. M. L. Wigley, T. P. Barnett, J. S. Boyle, W. Brüggemann, N. P. Gillett, S. A. Klein, G. A. Meehl, T. Nozawa, D. W. Pierce, P. A. Stott, W. M. Washington, and M. F. Wehner, 2007: Identification of human-induced changes in atmospheric moisture content, PNAS, vol. 104, No. 39, 15248-15253.

Figure caption: Precipitable water (total atmospheric water vapor) changes in model single-forcing runs. Shown are column-integrated changes in monthly mean _Wo_ in experiments performed with the Parallel Climate Model (PCM) (A) and the MIROC3.2 (medres) model (B). For each model, there are a total of six experiments. In the first five, climate forcings were varied individually according to estimates of their historical changes over the 20th century. The five forcings considered were changes in well mixed GHGs, anthropogenic aerosol effects, tropospheric and stratospheric ozone, solar irradiance, and volcanic aerosols. These forcings were varied simultaneously in the sixth experiment (ALL). In PCM, the anthropogenic aerosol forcing involves only the direct (scattering) effects of sulfate aerosols. The MIROC model anthropogenic aerosol experiment considers forcing by both the direct and indirect effects of sulfate and carbonaceous aerosols (29). All changes in _Wo_ were defined relative to climatological monthly means over 1900-1909. Results are ensemble averages and were decadally filtered (K _ 145 months) to damp high-frequency noise. The ensemble size was 10 for the MIROC ALL integration and 4 for the PCM ALL experiment and for each PCM and MIROC single-forcing run (except The PCM volcanic forcing case, for which only two realizations were available).

Support: National Science Foundation and the U. S. Department of Energy.

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Haiyan Teng, Warren M. Washington, and Gerald A. Meehl, 2007: Interannual Variations and Future Change of Wintertime Extratropical Cyclone Count over North America in CCSM3, submitted to Climate Dynamics.

Abstract

Extratropical cyclone count in one CCSM3 20th-century simulation is compared with NCEP/NCAR reanalysis regarding both climatology and interannual variations during January-March 1950-1999. CCSM3 can simulate the storm tracks reasonably well, albeit there are some discrepancies regarding the cyclone numbers and shape of the storm tracks.

Principal modes of interannual variability of the cyclone count in the North Pacific and the North Atlantic regions are identified respectively by empirical orthogonal function (EOF). Similar to the observations, EOF1 of the model cyclone count in the Pacific is closely linked to Pacific/North American (PNA) teleconnection pattern, whereas EOF1 in the Atlantic sector has a significant correlation with the North Atlantic Oscillation (NAO). Composite maps are constructed for opposite phases of El Nino-Southern Oscillation (ENSO) and NAO and they show consistent anomalous patterns.

One CCSM3 21st-century A1B scenario realization indicates there is significant increase in extratropical cyclone count on the US west coast and decrease in Alaska. Meanwhile, cyclone count increases from the Great Lakes region to Quebec and decreases over the US east coast, suggesting a possible northward shift of the Atlantic storm tracks under the warmer climate. The cyclone count anomalies are closely linked to changes in seasonal mean states of the upper-troposphere zonal wind and baroclinicity in the lower troposphere.

Due to lack of 6-hourly outputs, we cannot apply the cyclone tracking algorithm to other 8 CCSM3 realizations. Yet based on the linkage between the mean state change and the cyclone count anomalies, it is likely a common feature among other ensemble members that cyclone activity is reduced in East Coast and Alaska as a result of global warming.

Figure caption: January, February, March cyclone count averaged over 1950-1999 in (a) NCEP/NCAR Reanalysis; and (b) CCSM3. The unit is number per 2.5x2.5 degree grid box.

Support: National Science Foundation and the U. S. Department of Energy.

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Washington, W.M., 2007: Modeling future climate change. In Proceedings of Bridging the Gap Between Science and Society: The Relationship between Policy and Research in National Laboratories, Universities, Government and Industry. November 1-2, 2003, James A. Baker, III Institute for Public Policy and Rice University, pp 60-64.

Abstract

I just want to mention one thing before I start my formal talk. In my early tenure on the National Science Board, I chaired the committee that looked at the second proposal criteria for the National Science Foundation (NSF). The first criteria is scientific excellence. This second criteria took into account the broader impacts of the proposed research. I worked with Neal Lane, the NSF Director; the rest of the NSF management, and the Board to get the second criteria approved. I think my talk will be an excellent example of societal relevant research that has broader impacts.

I have several questions to try an answer. Can climate models help understand climate change? I believe the answer is yes. Can we verify climate models with observations? We are doing that, but there are still many difficulties in being able to see if our models are doing the right things, but on the whole, the models are agreeing well with observations. We have enormous amounts of historical in situ and satellite data to compare with our models. Why do model projections of future climate change differ? I will get into that in a little bit, but I can tell you that we are working with our colleagues both in the United States and internationally to sort out why computer models give different projections of future climate change.

Support: Ensemble simulations from DOE supported Parallel Climate Model. Shown are observed 1870 to 2000 global observed temperatures, natural climate forcing from volcanic eruptions and solar changes; and anthropogenic climate forcing from increasing greenhouse gases.