Alan F. Hamlet Anthony L. Westerling Tim P. Barnett Dennis P. Lettenmaier JISAO/CSES Climate Impacts...

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)

CA CRB

Cool Season Climate of the Western U.S.

Snow Model

Schematic of VIC Hydrologic Model and Energy Balance Snow Model

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

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.

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

Cool Season Precip.

Warm Season Precip.

Consensus Forecasts of Temperature and Precipitation Changes from IPCC AR4 GCMs

Pacific Northwest

0.4-1.0°C0.9-2.4°C 1.2-5.5°C

rved 2

+1.7°C+0.7°C

+3.2°C

Pacific Northwest

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

rved 2

+1% +2%

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

Regionally Averaged Warm Season Precipitation Anomalies

PRECIP

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.

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

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)

sum lag1 correl

sum inter-regional correl

sum CV

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

sum lag1 correl

sum CV

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

sum lag1 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

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.

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

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

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

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

Change = - 2%

628614

*Signal to noise ratio is low*

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

Change = + 5.4%

*Signal to noise ratio is low*

Lessons Learned from 20th Century Observations

cgcm_3.1

echam5

ipsl_cm4

cnrm_cm3

giss_er

miroc_3.2

csiro_mk3

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

cgcm_3.1

echam5

ipsl_cm4

cnrm_cm3

giss_er

miroc_3.2

csiro_mk3

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

m) cgcm_3.1

echam5

ipsl_cm4

cnrm_cm3

giss_er

miroc_3.2

csiro_mk3

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.

0.45 0.

Probability of Exceedence

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.

Alan F. Hamlet Anthony L. Westerling Tim P. Barnett Dennis P. Lettenmaier JISAO/CSES Climate Impacts...

Documents

!!!OR Lettenmaier v Freddie Mac.pdf

Detecting Regime Shifts in the Mean and Variance: Methods and Specific Examples Sergei Rodionov JISAO, University of Washington, Seattle, WA. JISAO, University

Alan F. Hamlet Ingrid Tohver Se- Yeun Lee JISAO/CSES Climate Impacts Group

Dennis P. Lettenmaier JISAO Center for Science in the Earth System Climate Impacts Group and Department of Civil and Environmental Engineering University

Alan F. Hamlet Andrew W. Wood Dennis P. Lettenmaier

Alan F. Hamlet Dennis P. Lettenmaier Amy K. Snover JISAO Center for Science in the Earth System Climate Impacts Group and Department of Civil and Environmental

Alan F. Hamlet Anthony L. Westerling Tim P. Barnett Dennis P. Lettenmaier

Recap of Water Year 2007 Hydrologic Forecast and Forecasts for Water Year 2008 Alan F. Hamlet Andrew W. Wood Dennis P. Lettenmaier JISAO/CSES Climate Impacts

Alan F. Hamlet Philip W. Mote Martyn Clark Dennis P. Lettenmaier

Kevin O’Brien University of Washington/JISAO NOAA/PMEL

D.P. Lettenmaier 1 and T.J. Bohn 1

Sps Conference Essen 2009 Wi Lettenmaier

Alan F. Hamlet Dennis P. Lettenmaier JISAO Center for Science in the Earth System Climate Impacts Group and Department of Civil and Environmental Engineering

Andy Wood, Alan Hamlet and Dennis P. Lettenmaier University of Washington

Alan F. Hamlet, Philip W. Mote, Dennis P. Lettenmaier JISAO/CSES Climate Impacts Group

University of Washington JISAO Alaska Fisheries Science Center FIT – Fisheries Interaction Team

Erin Rogers Dennis Lettenmaier Jessica Lundquist

Alan F. Hamlet Dennis P. Lettenmaier

Aihui Wang, Kaiyuan Li, and Dennis P. Lettenmaier

Alan Hamlet Dennis P. Lettenmaier JISAO Climate Impacts Group and the Department of Civil Engineering University of Washington March, 2001 CIG Water Resources