Human-induced changes in the hydrological cycle of the western United States

1. 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

2. Changing hydrological cycle

3. 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?

4. 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)

5. 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

6. Detection & Attribution: Overall scheme • 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) • Next, use global GCMs: control and anthropogenically forced runs • Downscale to region of interest • Run hydrological model w/ downscaled data for hydrologic variables (SWE/P, flow) • Detection and attribution analysis

7. 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

8. Climate Model Selection

9. Data

11. SWE/P TREND COMPONENT These time series are the basis for the fingerprint Model based Obs snow course

12. 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

13. The SWE/P fingerprint Trend in the leading EOF of 9 SWE/P timeseries (74%)

14. D&A PDFs are assembed by resampling control climate runs S/N ratio is essentially the standard normal deviate of the fingerprint given control run statistics.

15. SWE/P D&A

16. Detection Time Scale

17. 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”).

18. Multivariate fingerprint: PCM

19. Multivariate D&A: PCM

20. Ensemble signal strength & significance Fingerprint signal strength Significance

21. Attribution: Why did it take so long? Detection time

22. 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’!

23. 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

24. 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.

25. Do we have time to change directions?? Where are we going? And why are we in this hand-basket? Roger Pulwarty