Detection and attribution of Climate Change in Streamflow Seasonality of the Western US

1. Detection and attribution of Climate Change in Streamflow Seasonality of the Western US Hugo Hidalgo1, Tapash Das1, Dan Cayan1,2, David Pierce1, Tim Barnett1, Govindasamy Bala3, Art Mirin3, Andrew Wood4, Celine Bonfils3, Ben Santer3. 1) Scripps Institution of Oceanography 2) United States Geological Survey 3) Lawrence Livermore National Laboratory 4) University of Washington

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

3. Knowles et al., 2006 -2.2 std devs LESS as snowfall +1 std dev MORE as snowfall Observed: Less spring snowpack Warming already has driven observable hydroclimatic change TRENDS (1950-97) in April 1 snow-water content at western snow courses Observed: Less snow/more rain Mote 2003 Observed: Earlier greenup dates Cayan et al. 2001 Observed: Earlier snowmelt runoff Slide courtesy of M. Dettinger 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) NATURALIZED ANTHROPCM (BCSD) VIC CONTROL CCSM3-FV (CA) CONTROL PCM (BCSD) SOLAR & VOLCANICPCM (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) NATURALIZED ANTHROPCM VIC CONTROL CCSM3-FV CONTROL PCM

18. Center timing of streamflow

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

20. Columbia Out

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

22. MIROC runs

23. Detection plot (JFM streamflow fraction) NATURALIZED ANTHROPCM VIC ANTHROMIROC CONTROL CCSM3-FV CONTROL PCM SOLAR & VOLCANICPCM

24. MIROC, RUN 8 CALI COLO COLU

25. Summer detection plot ANTHROPCM NATURALIZED ANTHROMIROC VIC CONTROL CCSM3-FV CONTROL PCM SOLAR & VOLCANICPCM

26. CONCLUSIONS • Detection of climate change was found for the JFM streamflow fractions over the second half of the 20th 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. CA Daily variability is conserved Tends to produce textured weaker trends BCSD Daily variability is not conserved Tends to produce smooth stronger trends Downscaling methods