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, 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)
Seager et al. (2010)
P – E Change (Oct – Mar)
Transient Eddy Moisture Convergence
3
Mean Flow Convergence
Mean Flow Advection
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
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
RCMs show less snowpack reduction
RCMs show less warming
Large differences among GCMs
6
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
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)
8
Are the changes in
transient flux more
realistically simulated
by RCMs than GCMs?
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)
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
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
1600
California
1400
1200
1000
800
600
Precipitation amount (mm)
400
200
0
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 33
3000
2500
Pacific Northwest
Current
2000
1500
Future
1000
500
0
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 33
1600
1400
1200
1000
800
600
400
200
0
Central Rockies
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
12
Precipitation rate (2mm/day bin)
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?
WRF-CCSM
WRF-CGCM
13
D Mean
D 90%
D 95%
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
14
Ralph et al. (2005)
500 hPa height and
850 hPa T
Vertically integrated
moisture flux
Large-scale circulation associated with AR
CCSM
15
CGCM
AR statistics from observations and global
climate simulations
AR Frequency
0.4
0.35
NCEP
0.3
CCSM
0.25
CGCM
0.2
0.15
0.1
0.05
0
O
N
D
J
F
M
A
M
J
J
A
S
Month
Normalized AR Frequency
0.4
16
CGCM simulated an overall
lower frequency of AR
compared to observations
and CCSM
NCEP
0.35
0.3
CCSM
0.25
CGCM
0.2
Combining the CCSM and
CGCM statistics produced
the AR seasonal cycle most
comparable to observations
Mean
(CCSM/CGCM)
0.15
0.1
0.05
0
O
N
D
J
F
M
Month
A
M
J
J
A
Both models (75% for
CCSM and 85% for CGCM)
simulated a higher
frequency of AR landfalling
in the north coast compared
to observations (61%)
S
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
17
WRF-CCSM
GCM simulated AR changes in the future climate
4.5
CCSM
CGCM
Change in AR Frequency
4
3.5
3
2.5
2
1.5
1
0.5
0
-0.5
O
N
D
J
F
M
A
M
J
J
A
S
-1
Month
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
18
Changes in AR precipitation and runoff
Change in total AR precip
19
Change in total AR runoff
WRF-CCSM
WRF-CCSM
WRF-CGCM
WRF-CGCM
Contributions of AR to the 95th percentile
precipitation
WRF-CGCM
WRF-CCSM
Current
20
Future
Changes in runoff/precip for mean and AR conditions
October - March
Change in runoff/precip for mean
21
Change in runoff/precip for AR
WRF-CCSM
WRF-CCSM
WRF-CGCM
WRF-CGCM
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?
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
(O’Kane et al. 2009)
100x
Transient
100x
Transient
Stationary
(time-mean)
Typical scale range of RCM
Fine scales
Large scales
23
5,000 km
2 Dx
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