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Effects of Greenhouse Gases andEffects of Greenhouse Gases andAerosols on Regional Hydrologic CycleAerosols on Regional Hydrologic Cycle
L. Ruby LeungPacific Northwest National Laboratory
Collaborators:Warren Washington, NCAR
V. Ramanathan, SIO
Aspen Global Change InstituteWorkshop on Aerosols and the Hydrological Cycle
July 11-17, 2004, Aspen, CO
Regional Hydrologic Response
• Long term trend in water balance: P - E• Trends in terrestrial hydrology: precipitation,
soil moisture, snowpack, runoff• Where, when, how often, how much: spatial
distribution, magnitude, frequency, intensity,timing
• Variability: extreme, floods/droughts
Hypothesis
• The base state matters when it comes toclimate sensitivity
• Representing regional scale forcing isimportant in establishing the base state ofthe terrestrial hydrological cycle andimproving estimates of climate impacts
ΔΤ = (δΤ/δα)c Δα + (δΤ/δβ)c Δβ + ..
Examples• Climate change effects on water
resources in the western US
• Aerosols effects on regional climate inSouth Asia
• Why climate impacts differ at theregional and continental/global scales
Observed Precipitation (DJF)Daily Extreme (95%)Seasonal Mean
1/8 degree data from UW
Interannual Variability (CA)DJF
Sierra Nevada
Snowpack
Correct Timing and Amountare Both Important
0
1
2
3
4
5
6
1 2 3 4 5 6 7 8 9 10 11 12O N D J F M A M J J A S
mm
/day
PrecipRunoffWater Use
Snowmelt flows have been starting earlier
“Center Timing”of many snowmelt
watersheds has advanced by1-4 weeks earlieracross the West
duringlast 3 decades
Iris Stewart, Dan Cayan and Mike Dettinger
Phil Mote, Univ of WashingtonClimate Impacts Group
Trends in April 1 SWE, 1950-2000
RCM Experiments• An MM5-based RCM was configured using a
nested domain with a large domain at 120 kmresolution covering the US and surroundingocean and a fine domain at 40 km resolutioncovering the western US
• The model was driven by lower and lateralboundary conditions from the NCEP/NCARreanalysis (1980-2000) and ECMWFreanalysis (1980-1993)
Seasonal Mean and Daily ExtremePrecipitation (mm/day)
Observation Simulated
DJF
Mea
nD
JF D
aily
Ext
rem
e
Observed and Simulated El NinoPrecipitation Anomaly
Needs to predict changes in circulation andrepresent orographic effects
Observation RCM Simulation NCEP Reanalyses
Sierra
Cascades
Moist
Dry
(Leung et al. JC 2003a&b)
Climate Change Studies
• One control and 3ensemble future PCMsimulations were usedto drive the RCM for1995-2015 and 2040-2060
• Goal: Examine theeffects of climatechange on waterresources in the westernUS
PCM RCM
Absolute Bias (mm/day)
Win
ter
(DJF
)Su
mm
er (J
JA)
Model Precipitation Biases
An Ensemble of Future ClimateConditions Simulated by a GCM
Climate Change SignalsTemperature Precipitation
PCM
RC
M
Global and Regional Simulations ofSnowpack
GCM under-predicted and misplaced snowRegional Simulation Global Simulation
Extreme Precipitation/Snowpack ChangesLead to significant changes in streamflow affecting hydropower
production, irrigation, flood control, and fish protection
(Leung et al. Climatic Change 2004)
Current and Future Snowpack in theYakima River Basin
0
50
100
150
200
250
1 2 3 4 5 6 7 8 9 10 11 12
CurrentFuture 1Future 2Future 3
Month
Bas
in M
ean
Snow
(mm
)
Stre
amflo
w (c
fs)
Current and Future Streamflow in theYakima River Basin
0
2000
3000
4000
5000
6000
7000
8000
9000
Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep
1000
CurrentFuture 1Future 2Future 3
• More information: Special Issue in ClimaticChange, 2004 Jan/Feb
Two 10 years RCM simulations driven by theNCEP/NCAR reanalysis were performed with and
without INDEOX forcing
Aerosol Effects on Regional Climate inSouth Asia
(Chung et al. 2003)
Observed and Simulated TemperatureSi
mul
ated
Obs
erve
dDJF JJA
Observed and Simulated PrecipitationSi
mul
ated
Obs
erve
dDJF JJA
Change in T
Sub-Regions of India and Tibetan Plateau
Change in Sfc Energy Budgets
SW/LW – downward (+); SH/LH – upward (+)
Change in Solar Heating Rate
Change in T
Convective T Tendency
Change in Convective T Tendency
Summary• RCMs can improve simulation of higher order
climate statistics (spatial distribution of seasonalmean and ENSO anomalies, extreme precipitation)
• Realistic representations of the control climate iscritical to projecting future changes in terrestrialhydrology (examples: snowpack, soil moisture/ET)
• Climate predictability: orography improvespredictability by anchoring the location of the climatefeatures and amplifying climate signals?
• However increase in interannual variability canreduce signal-to-noise ratio!
• Fingerprinting the regional signals (e.g., orographiceffects) may be useful in climate change detection
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