1. Hugo Hidalgo 1, Tapash Das 1, Dan Cayan 1,2, David Pierce 1, Tim Barnett 1, Govindasamy Bala 3, Art Mirin 3, Andrew Wood 4, Celine Bonfils 3, Ben Santer 3. 1) Scripps Institution of Oceanography 2) United States Geological Survey 3) Lawrence Livermore National Laboratory 4) University of Washington Detection and attribution of Climate Change in Streamflow Seasonality of the Western US

2. Acknowledgments This research is supported by grants from the Lawrence Livermore National Laboratory and the California Energy Commission

3. Warming already has driven observable hydroclimatic change Knowles et al., 2006 -2.2 std devs LESS as snowfall +1 std dev MORE as snowfall Observed: Less snow/more rain Observed: Less spring snowpack Observed: Earlier greenup dates Observed: Earlier snowmelt runoff Slide courtesy of M. Dettinger TRENDS (1950-97) in April 1 snow-water content at western snow courses Mote 2003 Cayan et al. 2001 Stewart et al. 2005

4. Optimal detection & attribution ● Detection of climate change is the process of identifying if an observed change is significantly different from what would be expected from natural internal climate variability (Hegerl et al. 2006). ● Attribution of anthropogenic climate change is the process of identifying if the observed change is: a) consistent with the type of changes obtained from climate simulations that include external anthropogenic forcings and internal variability and b) inconsistent with other explanations of climate change (Hegerl et al. 2006).

5. Optimal detection & attribution ● Given a variable for detection, the basic idea is to reduce the problem of multiples dimensions (n) to a univariate or low- dimensional problem (Hegerl et al. 1996). ● In this low-dimensional space, a detection vector is used to compare the observations with the expected climate change pattern.

6. Previous D&A studies ● Ocean heat content (Barnet et al. 2001; Levitus et al. 2001; 2005; Reicher et al. 2002; Wong et al. 2001), ● ocean salinity (Wong et al. 1999; Curry et al. 2003), ● sea level pressure (Gillet et al. 2003), ● changes in sea ice extent (Tett et al. 1996), ● precipitation (Hegerl et al. 2006; Hoerling et al. 2006), ● tropopause height (Santer et al 2003) ● temperature (Santer et al. 1996a, 1996b; Hegerl et al. 1996; 1997; 2000; 2006; 2007; Barnet et al. 1999; Tett et al. 1999; 2001; Mitchel and Karoly 2001; Stott et al. 2001; Stott 2003; Zwiers and Zhang 2003; Kalroy et al. 2003; Kalroy and Braganza 2005; Stone et al. 2007).

7. Modeling ● Downscaled to 1/8 degree resolution using method of constructed analogues (CA) or bias correction followed by spatial disaggregation (BCSD) ● Precipitation, tmax and tmin used as input to the variable infiltration capacity model (VIC; Liang et al. 1994) ● The VIC runoff and baseflow were routed using a computer program by Lohmann et al. (1996) to obtain daily streamflow data for the rivers ● The JFM streamflow fractions were computed ● Fingerprint was computed ● The d parameters were computed for all model runs (d parameter: trends projected into fingerprint).

8. Defining characteristics of VIC are the probabilistic treatment of sub-grid soil moisture capacity distribution, the parameterization of baseflow as a nonlinear recession from the lower soil layer, and that the unsaturated hydraulic conductivity at each particular time step is a function of the degree of saturation of the soil (Sheffield et al. 2004; Campbell 1974; Liang et al. 1994)

9. Modeled vs. “Observed” monthly streamflow

10. “Observed” trends JFM streamflow fraction

11. PCM anthro trends JFM streamflow fraction

12. PCM solar volcanic trends JFM streamflow fraction

13. FINGERPRINT: FIRST PRINCIPAL COMPONENT OF THE AVERAGE JFM FRACTIONS FROM THE ANTHRO RUNS

14. DETECTION PLOT (JFM streamflow fraction) CONTROL CCSM3-FV (CA) ANTHRO PCM (BCSD) VIC NATURALIZED CONTROL PCM (BCSD) SOLAR & VOLCANIC PCM (CA)

15. ENSO and PDO out

16. FINGERPRINT: FIRST PRINCIPAL COMPONENT OF THE AVERAGE JFM FRACTIONS FROM THE ANTHRO RUNS

17. DETECTION PLOT (JFM streamflow fraction) CONTROL CCSM3-FV ANTHRO PCM VIC NATURALIZED CONTROL PCM

18. Center timing of streamflow

19. CT detection CONTROL CCSM3-FV (CA) ANTHRO PCM (BCSD) VIC CONTROL PCM (BCSD) SOLAR & VOLCANIC PCM (CA)

20. Columbia Out

21. Detection plot (JFM streamflow fraction) Columbia out CONTROL CCSM3-FV (CA) ANTHRO PCM (BCSD) VIC NATURALIZED CONTROL PCM (BCSD) SOLAR & VOLCANIC PCM (CA)

22. MIROC runs

23. Detection plot (JFM streamflow fraction) CONTROL CCSM3-FV ANTHRO PCM VIC NATURALIZED CONTROL PCM SOLAR & VOLCANIC PCM ANTHRO MIROC

24. CALI COLO COLU MIROC, RUN 8

25. Summer detection plot CONTROL CCSM3-FV ANTHRO PCM VIC NATURALIZED CONTROL PCM SOLAR & VOLCANIC PCM ANTHRO MIROC

26. CONCLUSIONS ● Detection of climate change was found for the JFM streamflow fractions over the second half of the 20 th century. ● There was a positive detection even in the case when the ENSO and PDO signals were regressed-out. ● Attribution of climate change for JFM streamflow turned out to be model dependant: PCM implies it, but for MIROC the jury is out.

27. Downscaling methods CA – Daily variability is conserved – Tends to produce textured weaker trends BCSD – Daily variability is not conserved – Tends to produce smooth stronger trends