Simulations of Floods and Droughts in the Western U.S. Under Climate Change L. Ruby Leung Pacific...
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Transcript of Simulations of Floods and Droughts in the Western U.S. Under Climate Change L. Ruby Leung Pacific...
Simulations of Floods and Droughts in the Western U.S. Under Climate Change
L. Ruby LeungPacific Northwest National Laboratory
US CLIVAR/NCAR ASP Researcher Colloquium
June 13 - 17, 2011Boulder, CO
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)
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Focus on hydrological droughts (R = P - E):P – E changes derived directly from GCMsRunoff 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
P change
E change
P - E change
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Transient Eddy Moisture Convergence Mean Flow Advection
P – E Change (Oct – Mar) Mean Flow Convergence
Atmospheric Moisture Convergence (AR4 GCMs)
Seager et al. (2010)
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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?
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)
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Temperature and snowpack change
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Large differences among G
CM
s
RCMs show less warmingRCMs show less snowpack reduction
Moisture flux convergence in GCMs and RCMs
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Increase in transient eddy fluxes!
RCMs show larger increaseDrying due to divergence circulation
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?
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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?
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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?
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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
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Changes in precipitation rate from WRF-CCSM
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 330
200
400
600
800
1000
1200
1400
1600
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 330
500
1000
1500
2000
2500
3000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 300
200
400
600
800
1000
1200
1400
1600
California
Pacific Northwest
Central Rockies
Current
Future
Pre
cip
itat
ion
am
ou
nt
(mm
)
Precipitation rate (2mm/day bin)
Changes in mean and extreme precipitation
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D Mean D 90% D 95%
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?
WRF-CCSM
WRF-CGCM
Atmospheric rivers and floodsAtmospheric Rivers (ARs) are narrow bands of intense water vapor transport often found in the warm sectors of extratropical cyclonesAn 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
14 Ralph et al. (2005)
Large-scale circulation associated with AR
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CCSM CGCMV
erti
cally
inte
gra
ted
m
ois
ture
flu
x50
0 h
Pa
hei
gh
t an
d
850
hP
a T
AR statistics from observations and global climate simulations
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O N D J F M A M J J A S
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
NCEP
CCSM
CGCM
Mean (CCSM/CGCM)
Month
Norm
aliz
ed A
R F
requency
A
R F
requency
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
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
O N D J F M A M J J A S
NCEP
CCSM
CGCM
Atmospheric rivers in regional climate simulations
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The downscaled simulations generally captured the wet anomalies associated with the AR
WRF-CGCM has a more dominant wet anomaly to the north
Observed WRF-CCSM
AR Precipitation Anomaly (October – March)WRF-CGCM
GCM simulated AR changes in the future climate
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O N D J F M A M J J A S
Change in A
R F
requency
Month
-1
0
1
2
3
4
5CCSMCGCM
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) climateCCSM projected larger increase in AR frequency in the north compared to CGCMThere is a 7 – 12% increase in column water vapor and water vapor flux, with little change in wind speed
Changes in AR precipitation and runoff
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Change in total AR precip
WRF-CCSM
WRF-CGCM
Change in total AR runoff
WRF-CCSM
WRF-CGCM
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Changes in runoff/precip for mean and AR conditions
WRF-CGCM WRF-CGCM
WRF-CCSM WRF-CCSM
Change in runoff/precip for mean Change in runoff/precip for AR
Oct
ob
er -
Mar
ch
SummaryConsistent 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
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Can RCMs add value?Where transient eddy variability plays a role, downscaling adds important informationWhere there is strong local forcing (e.g., topography), downscaling also adds value in time mean (stationary) fields
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Transient
Stationary(time-mean)
5,000 km
Typical scale range of RCM
2 Dx
100xTransient
Stationary(time-mean)
100x
Large scales Fine scales
(O’Kane et al. 2009)
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