Understanding and Monitoring Climate Extremes: Insights on Droughts and Hazard Prediction

1. Climate Extremes The Drought Hazard Bradfield Lyon International Research Institute for Climate and Society The Earth Institute, Columbia University US CLIVAR Summit on Climate Extremes Denver, CO 7-9 July 2010

2. US CLIVAR Drought Working Group Drought in Coupled Models Project – “DRICOMP”

3. Drought Interest Group

4. Drought – An Extreme Challenge • Difficult to define. Fundamentally, an insufficient supply of water to meet demand • but demands are many, vary with region and sector, and supply can be of • non-local origin (e.g., Tucson, AZ and the CO River) • When does “drought” start? Terminate? As measured by what? Relevant to? • Occurs on multiple timescales – often simultaneously (consider its impacts) • In all cases, ultimately tied to “extended” periods of deficient precipitation relative • to the “expected” value for a particular location but that includes: • - Late onset or early demise of monsoon rainfall • - Monsoon breaks • - Sub-seasonal  SI  multi-year  multi-decadal  CC • Multiple causes. Linked to regional and large scale atmospheric circulation • anomalies (some related to SSTs) and land surface-atmosphere interactions • Enhanced drought prediction depends fundamentally on improved predictions • of precipitation (and other variables related to surface fluxes of water and energy)

5. Monitoring Drought – What Aspect? Figure: UN World Water Development Report-2, Chapter 4, Part 1. Global Hydrology and Water Resources

6. Monitoring Drought – Characteristic Time Scales Correlation Top Layer VIC Soil Moisture and SPI-3 (1950-2000) All Months May-Sep only Hudson Valley NY Std. VIC SM1 Anomalies (monthly, 5-yr) VIC data Courtesy of Justin Sheffield, Princeton Univ.

7. Monitoring Drought Numerous “drought” indices in use each with its own “intrinsic” time scale - PRCP -- monthly, past 90 days, water year, standardized indices (SPI) - Water balance indices: “P-E”, PDSI, etc. - Soil Moisture (typically modeled, experimental satellite products) - Snow Water Equivalent, Surface Water Supply Index - Streamflow - Vegetation Condition (satellite estimates) … Challenges: - Observational data are imperfect; scale issues (information, decisions) - Lack of real time updates for monitoring and prediction (“preliminary”) - Lack of long historical records for satellite-derived (and other) products - Higher frequency (daily) precipitation also of interest but often unavailable - Derived quantities (e.g. model soil moisture) subject to input uncertainties and observations for calibration/comparison are sparse - Relevance of indices (and predictions) to specific applications -- the “best” drought index is the one most closely associated with the specific application of interest (ag, rangeland condition, streamflow, etc.)

8. RMS Difference in Monthly PRCP GPCC – UEA as a Fraction of GPCC Annual Mean (1971-2000) To = e-folding time for run durations in SPI-12 CPC SPI-12 > +1.0 CPC SPI-12 < -1.0 e-folding time (months)

9. Modeled Soil Moisture Estimates (Runoff, ET, Soil Saturation) • Derived variables influenced by • uncertainties in model inputs and • different model designs • Use model-relative measures of • variability (e.g., percentiles) for • comparisons across models in • near real time • Need for enhanced observations • of soil moisture • Better flux measurements for • comparison with models (Ameriflux) Slide Courtesy of Kingtse Mo CPC Drought Briefing for May 2010

10. Estimates of “Soil Moisture” from Satellite SMOS – ESA; SMAP – NASA SMOS Figure: ESA

11. Prolonged Drought -- The Role of SSTs Schubert et al., 2004

12. Prolonged Drought – The Role of SSTs POGA-ML (similar to GOGA) Seager et al., 2005

13. Prolonged Drought – “ENSO +” Protracted Drought 1998-2002 Hoerling and Kumar, 2002

14. Trends: Coupled Models vs. AMIP Shin and Sardeshmukh, 2010

15. 1988 Drought AMJ SST Anomaly AMJ PRCP & 250 hPa Std. Hght. Anomalies

16. AMJ 1988 Anomalous Stationary Waves Reanalysis ECHAM4.5 CFS

17. Observations & AMIP Simulations: Drought of 1988 SPI-6 OBS Jun 1988 SPI-6 NSIPP Jun 1988 SPI-6 ECHAM4.5 Jun 1988 SPI-6 GFDL 2.14 Jun 1988

18. Role of the Land Surface • GLACE-2 (GEWEX, CLIVAR) • Used best estimate of soil moisture from offline (similar to GSWP-2) • Compared control with initialized land sfc. runs across multiple GCMS (10) Koster et al., 2010

19. Role of the Land Surface Koster et al., 2010

20. Role of the Land Surface – Model Biases (GLACE) • Overall (global scale) soil moisture memory reasonably simulated • Regional biases important for the practical application of model output Seneviratne et al., 2006

21. Towards Probabilistic Prediction of Meteorological Drought • Predictive information from both persistence • and GCM • AMIP -- Does not include the role of land • surface condition

22. Towards Probabilistic Prediction of Meteorological Drought

23. Observed yield (kg ha-1) Observed soybean yields (GA, USA yield trials) vs. seasonal rainfall, temperature, simulated yields Rainfall (mm day-1) Mean max temperature (°C) Simulated yield (kg ha-1) Importance of the Sub-seasonal Time Scale:Dynamic Crop Models • Account for dynamic, nonlinear crop-soil-weather interactions • Need DAILY weather inputs in crop models • Requires disaggregation of seasonal forecasts to obtain daily sequences of T, P Slide Courtesy of James Hansen, IRI

24. Bias-Corrected Daily Rainfall from a GCM Raw GCM Daily Rainfall: Amplitude, Frequency Bias Observed Rainfall Bias-Corrected GCM: (Amplitude, Frequency) • GCM over-estimates the OBS • autocorrelation of daily PRCP • Changes in higher-frequency • precipitation events of much • interest to ag. and water sectors • (including under CC) Ines and Hansen (2006), Hansen et al. (2006)

25. Demand to Move Beyond One’s Means Annual DJF JJA Figure 11.12, IPCC AR4

26. “Near-Normal” Monthly Precipitation in Central Park (within +/- 5% of long term median value) Cum. Days in Category No. Days with Precipitation

27. DRAFT TOC, “DIG” Whitepaper on Drought

28. ~ finis ~

29. FIG. 2. Scatterplot of the percentage change in global-mean column-integrated (a),(c) water vapor and (b),(d) precipitation vs the global-mean change in surface air temperature for the PCMDI AR4 models under the (a),(b) Special Report on Emissions Scenarios (SRES) A1B forcing scenario and (c),(d) 20C3M forcing scenario. The changes are computed as differences between the first 20 yr and last 20 yr of the twenty-first (SRES A1B) and twentieth (20C3M) centuries. Solid lines depict the rate of increase in column-integrated water vapor (7.5% K-1). The dashed line in (d) depicts the linear fit of P to T, which increases at a rate of 2.2% K-1.

30. From: UN World Water Development Report, 2003

31. Graphic: Third UN Water Development Report, World Water Assessment Report, 2009

32. http://upload.wikimedia.org/wikipedia/commons/f/f2/World_population_growth_%28lin-log_scale%29.pnghttp://upload.wikimedia.org/wikipedia/commons/f/f2/World_population_growth_%28lin-log_scale%29.png

33. Graphic from -- Groundwater: A global assessment of scale and significance, IWMI, 2007

34. FIG. 2. Scatterplot of the percentage change in global-mean column-integrated (a),(c) water vapor and (b),(d) precipitation vs the global-mean change in surface air temperature for the PCMDI AR4 models under the (a),(b) Special Report on Emissions Scenarios (SRES) A1B forcing scenario and (c),(d) 20C3M forcing scenario. The changes are computed as differences between the first 20 yr and last 20 yr of the twenty-first (SRES A1B) and twentieth (20C3M) centuries. Solid lines depict the rate of increase in column-integrated water vapor (7.5% K-1). The dashed line in (d) depicts the linear fit of P to T, which increases at a rate of 2.2% K-1.

36. US CLIVAR DWG -- Idealized SST Runs Drought Working Group • Work done in parallel with the • Drought in Coupled Models • Project (DRICOMP)