Future Risk of Global Drought from Downscaled, Bias Corrected Climate Projections Eric F. Wood, Justin Sheffield, Haibin Li Princeton University WCRP-UNESCO (GEWEX/CLIVAR/IHP) Workshop on Metrics and methodologies of estimation of extreme climate events 27-29 September 2010 UNESCO, Paris.
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Eric F. Wood, Justin Sheffield, Haibin Li
WCRP-UNESCO (GEWEX/CLIVAR/IHP) Workshop on
Metrics and methodologies of estimation of extreme climate events
27-29 September 2010
Aerosol Optical Thickness August 2010
Land Temperature Anomaly (oC)
Projections of drought from climate models show decreasing soil moisture globally and increases in drought frequency, severity, duration and spatial extent.
But there are large uncertainties in future projections, derived from emission scenarios and climate models, especially at regional scales.
Sheffield and Wood, 2008
The Problem Corrected
The direct use of climate model outputs for analysis of future drought is problematic because of known model biases, particularly model simulated precipitation that has first order impacts on drought.
GCM soil moisture drought statistics (error bars) versus off-line modeling (dots) (Sheffield and Wood, 2008)
GCM precipitation RMS errors in the Northern Hemisphere extra-tropics (Glecker et al., JGR, 2008)
Future drought conditions globally based on simulations using the Variable Infiltration Capacity (VIC) land surface model (LSM)
Forced by downscaled, bias corrected climate projections using a new equidistant quantile matching method, which takes into account changes in the future climate distribution and better represents extreme years that are most associated with drought. An improvement upon traditional quantile matching methods.
We demonstrate the application with a single climate model (Li et al., 2010).
The bias corrected and downscaled climate forcings are used to drive the LSM to generate future projections of the terrestrial water and energy cycles.
These outputs are then analyzed to understand the propagation of projected drought, including frequency and severity, and to compare these projections with analyses based on 20th C observations.
Global Forcing Dataset Corrected
spatial resolution, bias corrected, trend corrected, etc…
Land Surface Hydrologic Model Simulations
1. Observation forced simulation that represents our best estimate of historic hydrology for 1948-2008.
2. Future climate simulation for 1948-2099 forced with bias corrected and downscaled climate model data for the 20C3M (20thC) and SRESA2 (21stC) future scenarios from the NCAR-PCM climate model.
Total Water Storage
(Li, Sheffield and Wood, JGR, 2010)
Climate Model Biases Corrected
The model has a cool bias in temperatures and spatially and seasonally varying high and low biases in precipitation.
Temperature (K) July
Precipitation (mm/month) July
Precipitation (mm/month) January Corrected
Bias Correction of Precip and Temp.
Model - Obs
(right) For the 25, 50, and 75th percentiles the difference between the two methods is indistinguishable.
Temperature (K) January: EDCDFm - CDFm
(left) Differences in the projections for the high and low percentiles are up to 1K in the percentiles for January temperature. EDCDFm has a narrower distribution (warmer lower percentiles and cooler upper percentiles), reflecting the method’s adjustment to the larger variability in the current climate and a reduced variability in future projections.
The bias correction is applied also to monthly radiation, humidity and windspeed to capture changes and interplay among these associated drivers.
(right) Global time series of the original climate projections (black lines) show increases in precipitation, temperature , longwave radiation and humidity.
Bias corrected variables (red lines) reflect the often large biases in many variables.
Down SW (W/m2)
Down LW (W/m2)
(Below) Despite global increases in precipitation, regional drying is evident in many regions
(Annual trend in P (mm/month/y) 2000-2099)
Specific humidy (g/g)
Diurnal T Range (K)
Max Temperature (K)
Min Temperature (K)
Wind speed (m/s)
Surf. Pressure (Pa)
Soil Moisture Percentiles (%), Corrected Drought Spatial Extent (%)
Future Changes in Global and Regional Soil Moisture and Drought Area
Regions used for drought analysis
Severity-Area-Duration (SAD) Analysis Corrected
(see Andreadis et al., 2005 J Hydromet.; Sheffield et al., 2009 J Climate for more details)
1950-1999 Obs. Corrected 2000-2049 Model
1950-1999 Model2050-2090 Model
Future Large Scale Drought Events
Severity-area-duration (SAD) curves for individual large scale drought events for Europe
Individual drought events are identified from a SAD analysis, which follows drought development through time and space and identifies the severest events
Central European Heatwave, 2003 Corrected
Northwest Europe, 1976
Future Large Scale Drought Events
Peak of mid- and late 21st century European drought events cover large part of the continent
Compare with reconstructions of historic events: