Alan F. Hamlet Anthony L. Westerling Tim P. Barnett Dennis P. Lettenmaier JISAO/CSES Climate Impacts Group Dept. of Civil and Environmental Engineering.
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Presentation on theme: "Alan F. Hamlet Anthony L. Westerling Tim P. Barnett Dennis P. Lettenmaier JISAO/CSES Climate Impacts Group Dept. of Civil and Environmental Engineering."— Presentation transcript:
Alan F. Hamlet Anthony L. Westerling Tim P. Barnett Dennis 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 CACRB GB Cool Season Climate of the Western U.S.
Snow Model Schematic of VIC Hydrologic Model and Energy Balance Snow Model PNW CA CR B GB
Evaluation of Streamflow Simulations of the Colorado River at Lee’s Ferry, AZ
R 2 = 0.83 R 2 = 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.
R 2 = 0.56 Colorado River R 2 = 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°C 0.9-2.4°C 1.2-5.5°C Observed 20th century variability +1.7°C +0.7°C +3.2°C
Pacific Northwest % -1 to +3% -1 to +9% -2 to +21% Observed 20th century variability +1% +2% +6%
Regionally Averaged Cool Season Precipitation Anomalies PRECIP
Summary Statistics for Regionally Averaged Cool Season
Regionally Averaged Warm Season Precipitation Anomalies PRECIP
Correlation: CRB-SSJ = 0.07 CRB-PNW = 0.08 SSJ-PNW = 0.36 Correlation: CRB-SSJ = 0.14 CRB-PNW = -0.14 SSJ-PNW = 0.06 Correlation: CRB-SSJ = 0.73 CRB-PNW = 0.51 SSJ-PNW = 0.65 Simulated Changes in System Wide Energy Production in the Western U.S.
DJF Avg Temp (C) 20-year Flood for “1973-2003” Compared to “1916-2003” for a Consistent Late 20 th Century Temperature Regime X 20 ’73-’03 / X 20 ’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 20 th 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)
Changes in Streamflow Variability from Long-Term Observations and Paleo Reconstructions (1858-1977)
Changes in Streamflow Variability from VIC Simulations of Annual Flow (1916-2003)
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?
Natural Flow Columbia River at The Dalles Patterns of ENSO Related Variability About a Shifting Long- Term Mean Seem to be Robust in the 20 th 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 R 2 )
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 20 th century consistent with GCM projections for the PNW?
IPCC AR4 “A2” GCM Simulations Large-Scale Bias Correction at GCM Grid Upscaling To PNW Overview of GCM Data Processing CDFs Match Observations for the Training Period 1915-1964 Simple Aggregation of GCM cells over the PNW
-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*
Change in Pacific Northwest winter precipitation for HadCM3 between 1970-2000 and 2030-2060 Change = - 2% 628 614 *Signal to noise ratio is low*
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 20 th Century Observations
Comparison of 20 th century winter temperature observations and 10 bias-corrected IPCC AR4 GCM simulations for the Pacific Northwest A2 Emissions Scenarios
Comparison of 20 th century winter precipitation observations and 10 bias-corrected IPCC AR4 GCM simulations for the Pacific Northwest A2 Emissions Scenarios
Evaluating Precipitation Changes Using a GCM Super Ensemble Approach
270 years 1930-1959 270 years 1970-1999 A super ensemble approach applied to nine GCM simulations of PNW winter precipitation for two different 30-year periods.
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 21 st 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.