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Simulations of Floods and Droughts in the Western U.S. Under Climate Change

Simulations of Floods and Droughts in the Western U.S. Under Climate Change. L. Ruby Leung Pacific Northwest National Laboratory US CLIVAR/NCAR ASP Researcher Colloquium June 13 - 17, 2011 Boulder, CO. Mega-drought of the future ( Gao , Leung, Dominguez, Salath é , Lettenmaier ).

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Simulations of Floods and Droughts in the Western U.S. Under Climate Change

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  1. Simulations of Floods and Droughts in the Western U.S. Under Climate Change L. Ruby Leung Pacific Northwest National Laboratory US CLIVAR/NCAR ASP Researcher Colloquium June 13 - 17, 2011 Boulder, CO

  2. Mega-drought of the future (Gao, Leung, Dominguez, Salathé, Lettenmaier) • IPCC AR4 models projected an imminent transition to warmer and more arid climate in the southwestern U.S. (Seager et al. 2007) E change P - E change P change • Focus on hydrological droughts (R= P - E): • P – E changes derived directly from GCMs • Runoff changes simulated by hydrological models driven by GCMs • Differences among GCM and hydrologic model estimates partly traced to elasticity – %change in flow per %change in precip – differences among land surface models 2

  3. Atmospheric Moisture Convergence (AR4 GCMs) Seager et al. (2010) P – E Change (Oct – Mar) Mean Flow Convergence Transient Eddy Moisture Convergence Mean Flow Advection 3

  4. Changes in P – E in the future • Annual P – E in the SW is primarily controlled by the positive P – E during winter, which sustains a positive annual P – E • Two main factors contribute to the reductions in P – E in the SW: • Areas influenced by mean moisture divergence get drier as atmospheric moisture increases with warming • Reduced transient eddy moisture convergence due to poleward shift of storm tracks • Can GCMs simulate realistic transient moisture flux in mountainous regions? 4

  5. To assess the potential effects of model resolution on P – E changes • Four pairs of GCM-RCM simulations are compared: • CCSM3, CGCM3, HADCM3 (from NARCCAP) and ECHAM5 • WRF simulations driven by CCSM3 and CGCM3 are from NARCCAP (50 km resolution with A2 scenario) • WRF simulations driven by HADCM3 used a different model configuration (35 km resolution, A2 scenario, spectral nudging) (Dominguez and Castro) • WRF simulations driven by ECHAM5 used a nested model configuration (36 km resolution, A1B scenario, nudging on outer domain) (Salathé) 5

  6. Temperature and snowpack change RCMs show less snowpack reduction RCMs show less warming Large differences among GCMs 6

  7. Moisture flux convergence in GCMs and RCMs Drying due to divergence circulation RCMs show larger increase Increase in transient eddy fluxes! 7

  8. Differences between global and regional models • RCMs consistently showed that the SW is less susceptible to climate change than what GCMs suggested (T, snowpack, P – E) • At higher resolution, more transient eddy moisture flux is simulated by the RCMs (compared to the GCMs) and NARR (compared to NCEP/DOE global reanalysis) • Are the changes in transient flux more realistically simulated by RCMs than GCMs? 8

  9. Summary • Although the IPCC AR4 models show that the southwestern US is susceptible to mega droughts in the future, large uncertainties remain in the magnitude of the droughts: • Different models and ensemble members show large differences – could the results be dominated by some members with large changes? • How sensitive are the results to land surface representations – precipitation elasticity? • How sensitive are the results to model resolution – transient eddy moisture flux? 9

  10. Changes in heavy precipitation and floods in the future (Leung and Qian) • Observations and modeling studies have suggested that extreme precipitation increases in a warmer climate • What processes are responsible for extreme precipitation in the western US? How well can regional climate simulations capture extreme precipitation and floods? • How will these processes change in a warmer climate? How will changes in extreme precipitation affect water resources? 10

  11. Numerical Experiments • As part of NARCCAP, WRF simulations have been performed using boundary conditions from CCSM and CGCM for the North American domain at 50 km grid resolution • For each GCM, two simulations are performed for the present (1970-1999) and future (2040-2070) climate under the A2 emission scenario • WRF physics parameterizations: CAM radiation, Grell-Devenyi convection, WSM5 mixed phase microphysics, YSU non-local PBL, Noah LSM • Some NARCCAP model outputs are available from the Earth System Grid 11

  12. Changes in precipitation rate from WRF-CCSM California Pacific Northwest Current Precipitation amount (mm) Future Central Rockies Precipitation rate (2mm/day bin) 12

  13. Changes in mean and extreme precipitation • Changes in heavy and extreme precipitation have different spatial patterns compared to changes in mean precipitation – Are the processes responsible for changes in the mean and extremes different? D Mean D 90% D 95% WRF-CCSM WRF-CGCM 13

  14. Atmospheric rivers and floods • Atmospheric Rivers (ARs) are narrow bands of intense water vapor transport often found in the warm sectors of extratropical cyclones • An atmospheric river was present in all of the floods on the Russian River since 1997, though not all atmospheric rivers are flood producers (Ralph et al. 2005) • Main ingredients for heavy orographic precipitation: LLJ, large moisture content, neutral stability Ralph et al. (2005) 14

  15. Large-scale circulation associated with AR CGCM CCSM Vertically integrated moisture flux 500 hPa height and 850 hPa T 15

  16. AR Frequency AR statistics from observations and global climate simulations Month • CGCM simulated an overall lower frequency of AR compared to observations and CCSM • Both models (75% for CCSM and 85% for CGCM) simulated a higher frequency of AR landfalling in the north coast compared to observations (61%) • Combining the CCSM and CGCM statistics produced the AR seasonal cycle most comparable to observations Normalized AR Frequency NCEP O N D J F M A M J J A S Month CCSM CGCM O N D J F M A M J J A S 16

  17. Atmospheric rivers in regional climate simulations • The downscaled simulations generally captured the wet anomalies associated with the AR • WRF-CGCM has a more dominant wet anomaly to the north WRF-CGCM AR Precipitation Anomaly (October – March) Observed WRF-CCSM 17

  18. GCM simulated AR changes in the future climate • The number of AR days increases by 27% and 132%, respectively, based on the CCSM and CGCM simulations of current (1970-1999) and future (2040-2069) climate • CCSM projected larger increase in AR frequency in the north compared to CGCM • There is a 7 – 12% increase in column water vapor and water vapor flux, with little change in wind speed O N D J F M A M J J A S Change in AR Frequency 18 Month

  19. Changes in AR precipitation and runoff Change in total AR precip Change in total AR runoff WRF-CCSM WRF-CCSM WRF-CGCM WRF-CGCM 19

  20. Contributions of AR to the 95th percentile precipitation Current Future WRF-CCSM WRF-CGCM 20

  21. Changes in runoff/precip for mean and AR conditions Change in runoff/precip for mean Change in runoff/precip for AR WRF-CCSM WRF-CCSM October - March WRF-CGCM WRF-CGCM 21

  22. Summary • Consistent with other studies, the WRF simulations show a shift from lower to higher precipitation rate in the future warmer conditions • Differences in the spatial distribution of mean vs extreme precipitation changes suggest that they are related to different physical/dynamical mechanisms • CCSM and CGCM simulated a 27% and 132% increase in AR frequency and a 10-12% increase in column water vapor flux associated with AR • As a result, precipitation associated with AR generally increases in the western US, particularly over the Sierra Nevada • AR contributes more to heavy precipitation in a warmer climate, particularly in northern CA • Disproportionately more runoff results from heavy precipitation events (with warmer than normal temperature) while mean runoff decreases – challenges for water management 22

  23. Can RCMs add value? (O’Kane et al. 2009) Stationary (time-mean) • Where transient eddy variability plays a role, downscaling adds important information • Where there is strong local forcing (e.g., topography), downscaling also adds value in time mean (stationary) fields 100x • Since extreme events result from interactions between stationary and transient eddy dynamics (in the mid-latitudes), high resolution is important in capturing the characteristics of extreme events Transient 100x Transient Stationary (time-mean) Typical scale range of RCM Fine scales Large scales 2 Dx 5,000 km 23

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