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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

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 lettenmaier
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

atmospheric moisture convergence ar4 gcms
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

changes in p e in the future
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

to assess the potential effects of model resolution on p e changes
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

temperature and snowpack change
Temperature and snowpack change

RCMs show less snowpack reduction

RCMs show less warming

Large differences among GCMs

6

moisture flux convergence in gcms and rcms
Moisture flux convergence in GCMs and RCMs

Drying due to divergence circulation

RCMs show larger increase

Increase in transient eddy fluxes!

7

differences between global and regional models
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

summary
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

changes in heavy precipitation and floods in the future leung and qian
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

numerical experiments
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

changes in precipitation rate from wrf ccsm
Changes in precipitation rate from WRF-CCSM

California

Pacific Northwest

Current

Precipitation amount (mm)

Future

Central Rockies

Precipitation rate (2mm/day bin)

12

changes in mean and extreme precipitation
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

atmospheric rivers and floods
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

large scale circulation associated with ar
Large-scale circulation associated with AR

CGCM

CCSM

Vertically integrated moisture flux

500 hPa height and

850 hPa T

15

ar statistics from observations and global climate simulations

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

atmospheric rivers in regional climate simulations
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

gcm simulated ar changes in the future climate
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

changes in ar precipitation and runoff
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

changes in runoff precip for mean and ar conditions
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

summary1
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

can rcms add value
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