human-induced changes in the hydrological cycle of the western united states tim barnett 1, david...
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Human-induced changes in the hydrological cycle of the
western United States
Tim Barnett1, David Pierce1, Hugo
Hildalgo1, Tapash Das1, Celine Bonfils2, Ben
Santer2, G. Bala2, Art Mirin2, Andy Wood4,
Toru Nozawa3, Dan Cayan1, Mike Dettinger1
1Scripps Institution of Oceanography
2Lawrence Livermore National Lab
3National Inst. Environmental Sciences (Japan)
4Univ. Washington
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Changing hydrological cycle
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The hydrological cycle is changing over the western United States
• Examples of such changes are well documented:
– Changes in snowfall & snow pack
• e.g., Mote 2003; Mote et al. 2005; Knowles et al. 2006
– Changes in streamflow
• e.g., Cayan et al. 2001; Stewart et al. 2005; Maurer et al. 2007
– Warmer air temperatures
• e.g., Dettinger et al. 1995; Easterling 2002
WHY?
Anthro causes? Natural variability? Solar / Volcanic forcing changes?
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WHY? Detection and Attribution (D&A)
• Detection: are the changes inconsistent with natural variability?
• Attribution: are the changes consistent with anthropogenic (or other) forcing?
• Generate a “fingerprint” that encapsulates changes expected (from model runs)
• Match fingerprint in obs and forced models (with and without anthropogenic forcings)
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Novel aspects
• Multivariate Detection and Attribution (D&A)
Analyze snowpack, river flow and air temperature simultaneously
• Regional
– Have to address problems of large amplitude natural variability
• Related to the hydrological cycle
– Rare in formal D&A work
– People can immediately relate to it
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Detection & Attribution: Overall scheme
1. Start with observations
– SWE/P (1 April Snow Water Equv. / Oct-Mar precip)
– Temperature (examined JFM daily minimum temperature)
– River flow (examined JFM fraction and CT, center of timing)
2. Next, use global GCMs: control and anthropogenically forced runs
3. Downscale to region of interest
4. Run hydrological model w/ downscaled data for hydrologic variables (SWE/P, flow)
5. Detection and attribution analysis
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Models and data
• Control model GCM runs– 850 yrs CCSM3-FV (1.25Ox1O; finer resolution than T85)– 750 yrs PCM (T42)
• Anthropogenically forced GCM runs, 1900-1999– PCM (4 members)– MIROC (10 members)
• Regional statistical downscaling of GCM forcing– 2 methods, 12 km resolution, western US domain
• VIC hydrological model (1/8 deg resolution)
• Observations, 1950-1999– Snow courses for SWE– UW, Maurer, PRISM for T and P– Naturalized flow from Colorado R. (Lee’s Ferry), Columbia R. (Dalles),
Sacramento and San Joaquin river
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Climate Model Selection
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Data
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SWE/P TREND COMPONENT
Model basedObs snow course
These time series are the basis for the fingerprint
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P affecting SWE/P?
Dividing by P removes majority
of correlation between SWE
and P
Trend in P (blue) vs. SWE (red),
1950-1999
60% of stations show increasing
P, but 71% show decreasing SWE
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The SWE/P fingerprint
Trend in the leading EOF of 9 SWE/P timeseries (74%)
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D&A
PDFs are assembed by resampling control climate runs
S/N ratio is essentially thestandard normal deviate of the fingerprint given control run statistics.
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SWE/P D&A
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Detection Time Scale
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Extension to Multivariate - Time series of key variables (obs.)
All variables have been normalized (fractionalized) by dividing by the CCSM3-FV control run mean over first 300 yrs.
Necessary for the multivariate detection and attribution (D&A), so have same variance in each variable (the “units problem”).
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Multivariate fingerprint: PCM
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Multivariate D&A: PCM
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Ensemble signal strength & significance
Fingerprint
signal strength
Significance
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Attribution: Why did it take so long?Detection time
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D&A summary
• Natural variability cannot explain obs.
• Solar/volcanic forcing cannot explain obs
• Changes in precipitation cannot explain obs
• ANTHROPOGENIC warming CAN explain obs. changes very well
Q: WHY? ANS: It is ‘US’!
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Conclusions
• Much previous work noting changes in snow cover, temperature, and river flow over the western U.S. -- but no formal D&A, nor multi-variate analysis
• We have performed a formal multivariate detection and attribution analysis of SWE/P, JFM temperatures, and river flow
• The changes in western hydrology over 1950-99 are largely human-induced; PCM captures 74% of low frequency signal
• The PCM, run in forecast mode, shows a grim view of western U.S. water supplies within the next 30 years. If it worked so well over the last 50 years, we have good reason to believe these predictions
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References (a few)
Hidalgo, H. G., M. D. Dettinger, and D. R. Cayan, 2007: Downscaling daily precipitation and temperature fields over the U.S. with constructed analogues. J. Clim., in review.
Wood, A. W., L. R. Leung, V. Sridhar, and D. P. Lettenmaier, 2004: Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change, 62, 189-216.
Barnett, T. P., D. W. Pierce, H. G. Hidalgo, C. Bonfils, B. D. Santer, T. Das, G. Bala, A. W. Wood, T. Nazawa, A. Mirin, D. R. Cayan, M. D. Dettinger, 2008: Human-induced changes in the hydrology of the western United States. Science, published online 31 Jan 2008, doi:10.1126/science.1152538.
Bonfils, C., B. D. Santer, D. W. Pierce, H. G. Hidalgo, G. Bala, T. Das, T. P. Barnett, C. Doutriaux, A. W. Wood, A. Mirin, T. Nozawa, 2008: Detection and attribution of temperature changes in the mountainous western United States. J. Climate, in review.
Hidalgo, H. G., T. Das, M. D. Dettinger, D. R. Cayan, D. W. Pierce, T. P. Barnett, G. Bala, A. Mirin, A. W. Wood, C. Bonfils, B. D. Santer, and T. Nozawa, 2008: Detection and attribution of climate change in streamflow timing of the western United States. J. Climate,iIn review.
Pierce D.W., T.P. Barnett, H.G. Hidalgo. T. Das, C. Bonfils, B. Sander, G. Bala, M. Dettinger, D. Cayan and A. Mirin. 2008. Attribution of declining western US snowpack to human effects. J Climate, in review.
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Do we have time to change directions??
Where are we going? And why are we in this hand-basket? Roger Pulwarty