Alan F. HamletAnthony L. WesterlingTim P. BarnettDennis P. Lettenmaier

•JISAO/CSES Climate Impacts Group•Dept. of Civil and Environmental Engineering

University of Washington•Scripps Institute of Oceanography•School of Engineering, University of California, Merced

Late 20th Century Precipitation Variability in the Western U.S. in the Context of Long-Term Climate Variability and Global Change

DJF Temp (°C) NDJFM Precip (mm)

PNW

CA CRB

GB

Cool Season Climate of the Western U.S.

Snow Model

Schematic of VIC Hydrologic Model and Energy Balance Snow Model

PNW

CACRB

GB

Evaluation of Streamflow Simulations of the Colorado River at Lee’s Ferry, AZ

2000

3000

4000

5000

6000

7000

8000

9000

1916

1921

1926

1931

1936

1941

1946

1951

1956

1961

1966

1971

1976

1981

1986

1991

1996

2001

Str

eam

flo

w (

cms)

OBS

REG

0

50

100

150

200

250

300

350

400

450

1916

1921

1926

1931

1936

1941

1946

1951

1956

1961

1966

1971

1976

1981

1986

1991

1996

2001

Str

eam

flo

w (

cms)

OBS

REG

R2 = 0.83

R2 = 0.91

Columbia River

Sacramento River

Cool Season Precipitation Explains Most of the Variability in Annual Flow in the PNW and CA

Relationship Between Annual Flow and

Cool Season Precip.

Relationship Between Annual Flow and

Cool Season Precip.

200

300

400

500

600

700

800

900

1000

1100

1916

1921

1926

1931

1936

1941

1946

1951

1956

1961

1966

1971

1976

1981

1986

1991

1996

2001

Str

eam

flo

w (

cms)

OBS

REG

200

300

400

500

600

700

800

900

1000

1100

1916

1921

1926

1931

1936

1941

1946

1951

1956

1961

1966

1971

1976

1981

1986

1991

1996

2001

Str

eam

flo

w (

cms)

OBS

REG

R2 = 0.56

Colorado River

R2 = 0.18

Colorado River

Cool Season Precip Explains Most of the Variability in Annual Flow in the CRB, but the Summer Monsoon Also Plays a Role

Relationship Between Annual Flow and

Cool Season Precip.

Relationship Between Annual Flow and

Warm Season Precip.

Consensus Forecasts of Temperature and Precipitation Changes from IPCC AR4 GCMs

Pacific Northwest

°C

0.4-1.0°C0.9-2.4°C 1.2-5.5°C

Obse

rved 2

0th

centu

ry v

ari

abili

ty

+1.7°C+0.7°C

+3.2°C

Pacific Northwest

%

-1 to +3%-1 to +9% -2 to +21%

Obse

rved 2

0th

centu

ry v

ari

abili

ty

+1% +2%

+6%

-3

-2

-1

0

1

2

3

419

16

1920

1924

1928

1932

1936

1940

1944

1948

1952

1956

1960

1964

1968

1972

1976

1980

1984

1988

1992

1996

2000

Std

An

om

alie

s R

elat

ive

to 1

961-

1990

PNW

CA

CRB

Regionally Averaged Cool Season Precipitation Anomalies

PRECIP

PNW SSJ CRB 1916-1946 mean (mm) 574.7 443.9 174.7 variance 88.8 100.1 30.6 CV 0.15 0.23 0.17 lag 1auto corr -0.15 0.06 0.11 trend (% per decade) -1.1 6.9 -3.5 1947-1976 mean (mm) 640.3 477.1 168.6 variance 84.4 99.3 34.0 CV 0.13 0.21 0.20 lag 1auto corr -0.42 0.12 -0.29 trend (% per decade) 1.5 2.8 3.8 1977-2003 mean (mm) 594.3 488.1 190.8 variance 126.2 141.9 50.8 CV 0.21 0.29 0.27 lag 1auto corr 0.22 0.12 0.15 trend (% per decade) 4.2 2.4 -9.7

Summary Statistics for Regionally Averaged Cool Season

-3

-2

-1

0

1

2

319

16

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1924

1928

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1984

1988

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1996

2000

Std

An

om

alie

s R

elat

ive

to 1

961-

1990

PNW

CA

CRB

Regionally Averaged Warm Season Precipitation Anomalies

PRECIP

-2

-1

0

1

2

3

1917

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Sys

tem

Wid

e H

ydro

po

wer

P

rod

uct

ion

(S

td A

no

mal

ies)

CRB

SSJ

PNW

Correlation:CRB-SSJ = 0.07CRB-PNW = 0.08SSJ-PNW = 0.36

Correlation:CRB-SSJ = 0.14CRB-PNW = -0.14SSJ-PNW = 0.06

Correlation:CRB-SSJ = 0.73CRB-PNW = 0.51SSJ-PNW = 0.65

Simulated Changes in System Wide Energy Production in the Western U.S.

DJF

Avg

Tem

p (

C)

20-year Flood for “1973-2003” Compared to “1916-2003” for a Consistent Late 20th Century Temperature Regime

X20 ’73-’03 / X20 ’16-’03

X20 ’73-’03 / X20 ’16-’03

Hamlet A.F., Lettenmaier D.P., 2007: Effects of 20th Century Warming and Climate Variability on Flood Risk in the Western U.S., Water Resour.

Res., 43, W06427

Are the changes in variability that have been observed in the last third of the 20th century consistent with normal patterns

of variability?

Long-Term Comparison of Annual Flow Records from Observations and Paleo Reconstructions

PNW:Observed (naturalized) annual flow in the Columbia River at The Dalles, OR

1858-1877 (reconstructed from observed peak river stage)1878-2003 (naturalized from observed monthly records)

CA:Reconstructed combined annual flow in the Sacramento/San Joaquin basin from tree-ring records.

(Overlapping period 1858-1977)

(Meko, D.M., 2001: Reconstructed Sacramento River System Runoff From Tree Rings, Report prepared

for the California Department of Water Resources, July)

Colorado River Basin:Reconstructed annual flow in the Colorado River at Lees Ferry, AZ from tree ring records.

(Overlapping period 1858-1977)

(Woodhouse, C.A., S.T. Gray, and D.M. Meko, 2006: Updated Streamflow Reconstructions for the Upper

Colorado River Basin, Water Resources Research, Vol. 42, W05415)

-1.5

-1

-0.5

0

0.5

1

1.5

1868

1874

1880

1886

1892

1898

1904

1910

1916

1922

1928

1934

1940

1946

1952

1958

1964

1970

1976

1982

1988

sum lag1 correl

sum inter-regional correl

sum CV

Changes in Streamflow Variability from Long-Term Observations and Paleo Reconstructions (1858-1977)

-1.5

-1

-0.5

0

0.5

1

1.5

1868

1874

1880

1886

1892

1898

1904

1910

1916

1922

1928

1934

1940

1946

1952

1958

1964

1970

1976

1982

1988

sum lag1 correl

sum inter-regional correl

sum CV

Changes in Streamflow Variability from VIC Simulations of Annual Flow (1916-2003)

-1.5

-1

-0.5

0

0.5

1

1.5

1868

1874

1880

1886

1892

1898

1904

1910

1916

1922

1928

1934

1940

1946

1952

1958

1964

1970

1976

1982

1988

sum lag1 correl

sum inter-regional correl

sum CV

Changes in Streamflow Variability from Combined Paleo Reconstructions and VIC Simulations of Annual Flow (1916-2003)

All three metrics high together

What about changes in ENSO and PDO as possible explanations?

150000

200000

250000

300000

350000

400000

450000

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

Ap

r-S

ept F

low

(cfs

)

Natural Flow Columbia River at The Dalles

Patterns of ENSO Related Variability About a Shifting Long-Term Mean Seem to be Robust in the 20th Century

Could ENSO explain the lag1 and interregional metrics being anti-correlated?

What about the most recent behavior?

In periods of especially strong (weak) controls on cool season storm track behavior associated with ENSO (i.e. strong or weak NW/SW bipole), both interregional and lag1 autocorrelation would tend to be LOW (HIGH) at the same time.

The data, however, show that typically lag1 autocorrelation and interregional correlation are anti-correlated for the West as a whole. So it would seem that variations in the strength of the ENSO related NW/SW dipole does not provide an explanation of the typical behavior over most the record.

Coupled with the fact that there is little compelling evidence to suggest a systematic change in ENSO telleconnections, it seems that both the explanation for the typical behavior and the most recent changes in variability must lie elsewhere.

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119

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1932

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1964

1968

1972

1976

1980

1984

1988

1992

1996

2000

Std

An

om

alie

s R

elat

ive

to 1

961-

1990

(sm

oo

thed

)

PNW

CA

CRB

GB

PDO

Cool Season Precipitation Anomalies Compared to the PDO(Pattern is not robust)

-0.845-0.264-0.438-0.053

(Regional to PDO Correlation R2 )

A working hypothesis:

The most recent changes suggest:

1) Increasingly unstable storm track in cool season (increased interregional correlation)

2) Increased lag1 autocorrelation and variation in storm intensity at the scale of the Pacific Rim

Are the changes in cool season precipitation variability in the 20th

century consistent with GCM projections for the PNW?

IPCC AR4 “A2”GCM

Simulations

Large-ScaleBias Correction at

GCM Grid

UpscalingTo PNW

Overview of GCM Data Processing

CDFs Match Observations for the

Training Period1915-1964

Simple Aggregation of GCM cells over

the PNW

-3.5

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-2

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-1

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0

0.5

118

65

1875

1885

1895

1905

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1925

1935

1945

1955

1965

1975

1985

1995

2005

2015

2025

2035

2045

2055

2065

2075

2085

2095

Oc

t-M

ar

Av

g T

em

p (

C)

-2.5

-1.64

Change in Pacific Northwest winter temperatures for HadCM3 between 1970-2000 and 2030-2060

Change = + 0.86 C *Signal to noise ratio is high*

500

550

600

650

700

75018

65

1875

1885

1895

1905

1915

1925

1935

1945

1955

1965

1975

1985

1995

2005

2015

2025

2035

2045

2055

2065

2075

2085

2095

Oc

t-M

ar

Av

g T

ota

l Pre

cip

(m

m)

Change in Pacific Northwest winter precipitation for HadCM3 between 1970-2000 and 2030-2060

Change = - 2%

628614

*Signal to noise ratio is low*

500

520

540

560

580

600

620

640

660

68018

65

1875

1885

1895

1905

1915

1925

1935

1945

1955

1965

1975

1985

1995

2005

2015

2025

2035

2045

2055

2065

2075

2085

2095

Oc

t-M

ar

Av

g T

ota

l Pre

cip

(m

m)

Change in Pacific Northwest winter precipitation for ECHAM5 between 1970-2000 and 2030-2060

Change = + 5.4%

571

602

*Signal to noise ratio is low*

Lessons Learned from 20th Century Observations

-4

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0

1903

1910

1917

1924

1931

1938

1945

1952

1959

1966

1973

1980

1987

1994

Oct

-Mar

Avg

Tem

per

atu

re (

C)

cgcm_3.1

echam5

ipsl_cm4

cnrm_cm3

giss_er

miroc_3.2

ccsm3

csiro_mk3

hadcm

pcm1

obs

ensemble mean

Linear (ensemble mean)

Linear (obs)

Comparison of 20th century winter temperature observations and 10 bias-corrected IPCC AR4 GCM simulations for the

Pacific Northwest

A2 Emissions Scenarios

500

550

600

650

700

750

800

1903

1909

1915

1921

1927

1933

1939

1945

1951

1957

1963

1969

1975

1981

1987

1993

Oct

-Mar

To

tal

Pre

cip

itat

ion

(m

m)

cgcm_3.1

echam5

ipsl_cm4

cnrm_cm3

giss_er

miroc_3.2

ccsm3

csiro_mk3

hadcm

pcm1

obs

ensemble mean

Comparison of 20th century winter precipitation observations and 10 bias-corrected IPCC AR4 GCM simulations for the

Pacific Northwest

A2 Emissions Scenarios

Evaluating Precipitation ChangesUsing a GCM Super Ensemble Approach

500

550

600

650

700

750

1903

1909

1915

1921

1927

1933

1939

1945

1951

1957

1963

1969

1975

1981

1987

1993

Oct

-Mar

To

tal

Pre

cip

itat

ion

(m

m) cgcm_3.1

echam5

ipsl_cm4

cnrm_cm3

giss_er

miroc_3.2

ccsm3

csiro_mk3

hadcm

ensemble mean

270 years1930-1959

270 years1970-1999

A super ensemble approach applied to nine GCM simulations of PNW winter precipitation for two different 30-year periods.

400

500

600

700

800

900

1000

11000

0.06

0.11

0.17

0.22

0.28

0.34

0.39

0.45 0.

5

0.56

0.61

0.67

0.72

0.78

0.83

0.89

0.95

Probability of Exceedence

Oct

-Mar

Pre

cip

itat

ion

(m

m)

1970-1999

2010-2039

2030-2059

2060-2089

Sample Size = 270 years

Super ensemble CDFs of PNW winter precipitation for four 30 year time slices from nine GCM simulations

Conclusions•Cool season precipitation is a major driver of annual river flow, hydropower production, and flood risk in the West.

•Substantial and persistent changes in cool season precipitation variability have emerged over the West since about 1975, including increased CV, within-region persistence, and inter-regional correlation.

•Long-term streamflow reconstructions show that the current changes in variability are very unusual in the context of natural variations over the last 150 years or so, and the changes are broadly consistent with GCM projections of cool season precipitation in the PNW.

•Are these systematic changes? Can they be related to changes in circulation associated with greenhouse-forced warming?

Thoughts on Planning Implications:

Even if the current precipitation regime in the West is not a systematic change, it is clear that this is something that can emerge suddenly and persist for a long time. I.e. we can expect that there may be analogous periods in the 21st century that we should be prepared to cope with.

Given the relative performance of GCMs in predicting precipitation and the inherently greater noise that is present in precipitation records, I think it is doubtful that we will have any conclusive information about whether these observed changes are related to greenhouse forcing or not.

This suggests to me that flexible approaches based on monitoring may be the only workable options.