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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