1 / 31

Climate Model Simulations of Extreme C old- A ir O utbreaks (CAOs)

This study evaluates the ability of climate models to represent extreme cold-air outbreaks, their characteristics, and potential changes in the future. It explores the importance of CAOs, their impact on human lives and the economy, and analyzes precursor conditions and forcing factors. The study finds that climate models can reproduce the first-order characteristics of CAOs but further investigation is needed to understand their full representation.

lynn-vang
Download Presentation

Climate Model Simulations of Extreme C old- A ir O utbreaks (CAOs)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Climate Model Simulations of Extreme Cold-Air Outbreaks (CAOs) Steve Vavrus Center for Climatic Research University of Wisconsin-Madison John Walsh International Arctic Research Center University of Alaska-Fairbanks Bill Chapman Diane Portis Department of Atmospheric Sciences University of Illinois

  2. Importance of Cold-Air Outbreaks • CAO: Incursion of extremely cold polar air mass into the middle • -lower latitudes of Eurasia and North America • CAOs cause many deaths each winter (30/year in U. S., over • 1,000 fatalities during 2003 in Bangladesh and India) • Result in billions of dollars of economic losses, especially in • agricultural sector (e.g., Florida citrus industry) • Cold surges strongly affect climatological heat balance in subtropics • and may influence onset of El Niños (via westerly wind bursts)

  3. Cold-Air Outbreaks are complex phenomena . . . • Occurrence of CAOs is highly variable and associated with low- • frequency circulation modes (Arctic Oscillation, PNA pattern) • CAOs may be triggered by many factors, both local and remote • Climate models may help explain CAO behavior, but they have been • underutilized for this purpose • Trend of CAOs in past decades-century has not followed mean • warming trend in either U. S. or Europe

  4. Observed Variability of Extreme Events United States Cold Waves United States Heat Waves Kunkel et al., 2002

  5. Observed Variability of Extreme Events United States Cold Waves Walsh et al., 2001 (J. Climate) United States Heat Waves Kunkel et al., 2002

  6. Observed Variability of Extreme Events United States Cold Waves Walsh et al., 2001 (J. Climate) United States Heat Waves Kunkel et al., 2002

  7. Objectives • Evaluate ability of climate models to represent CAOs • (frequency, magnitude, synoptics, event types) • Use models and NCEP/NCAR Reanalysis to determine the • relative importance of thermodynamic and dynamic processes • Analyze precursor conditions and forcing factors (e.g., SSTs, • snow cover, clouds, circulation) to explain origin of CAOs • Estimate how and why CAOs may change in the future, due to • enhanced greenhouse forcing

  8. Observed and Simulated Extreme Cold in GCMs

  9. Our Definition of a Cold Air Outbreak (CAO) Two or more consecutive days in which the daily mean temperature is at least two standard deviations below the DJF mean temperature

  10. Simulated and Observed CAO Characteristics in CCM3 AMIP Runs (driven by observed SSTs from 1979-1998)

  11. Simulated and Observed CAO Characteristics in CCM3 AMIP Runs (driven by observed SSTs from 1979-1998)

  12. Skewness = -0.37

  13. CAO WAO

  14. Very Cold Days Actual (Expected) T < Tmean- 2s 4.2% (2.3%) CAO WAO Very Warm Days Actual (Expected) T > Tmean+ 2s 1.0% (2.3%)

  15. Wintertime Temperature Skewness (Observed)

  16. Wintertime Temperature Skewness (AMIP runs, CSM1, and Observed)

  17. CCM3 Wintertime Temperature Skewness in Pacific Northwest Modeled Skewness = -1.22 Observed Skewness = -1.19

  18. CSM1 Simulation of a CAO (February 24 - March 8)

  19. Response of CCM3 AMIP Simulation to ENSO

  20. Response of CCM3 AMIP Simulation to ENSO T42 rCAO-Nino3.4= -0.18 X T85 rCAO-Nino3.4= 0.04 OBSERVED X rCAO-Nino3.4= -0.39

  21. Simulated Future CAOs in CSM1 (Transient CO2 Forcing)

  22. Simulated Future Wintertime Circulation Changes CSM1 L L L L H

  23. Simulated Future Wintertime Circulation Changes CSM1 CCSM3 300 hPa Height Anomalies (m) L L Sea Level Pressure Anomalies (hPa) L L H

  24. Conclusions and Future Work • Climate models seem capable of reproducing the first-order • characteristics of cold air outbreaks • Capability of models to represent other major aspects of CAOs • is under investigation • Relative roles of various forcing terms need to be established • (e.g., SST variability, snow cover, circulation modes, etc.) • Future behavior of CAOs may be a complicated function of • mean temperature, circulation changes, and remote forcings

  25. Our Definition of a Cold Air Outbreak (CAO) Two or more consecutive days in which the daily mean temperature is at least two standard deviations below the DJF mean temperature (“standard deviation” defined here as the average daily standard deviation of interannual air temperature variations during winter) Standard Deviation =S (sDec.1 + sDec.2 + sDec.3 + . . . + sFeb. 28)/90

More Related