Climate Analysis for Enhanced Resilience

Ben ZaitchikJohns Hopkins University

Projected Precipitation Changes

IPCC AR4

-15

-10

-5

0

5

10

15

B1A1BA2

16 Different Climate Models

% P

reci

pita

tion

Chan

ge

Change in Annual Precipitation:2080s – present, Blue Nile Headwaters

NASA’s Project Nile

GCM-based Precipitation Maps• Spatially coarse• Highly uncertain• Underestimate extremes• Systematically wrong in some

regions

So: Are the GCMs Useless?• No: there is useful information in the

projections

• But: precipitation projections applied directly to food/water security planning can be worse than useless

1. Dynamical Downscaling• Physically-based

predictions

• Can handle non-stationarity

• Requires extensive evaluation of RCM and GCM

• Computer and time intensive

2. Statistical Downscaling• Data-based and not

computer intensive

• Does not rely directly on GCM atmospheric dynamics

• Requires 30+ year meteorological station records

• Assumes stationarity1950 1960 1970 1980 1990 2000 2010

010

020

030

040

050

060

070

0

interpolated prate

Jul - NA m a.sl. 37.42 degE 11.6 degNTime

Kg/

m^2

/s

3. Physiographic Interpolation• Physically based

• Highly localized at low computational expense

• Can utilize satellite data

• Derived from a larger scale projection

• Requires calibration and evaluation

Summary• GCM projections require interpretation and

downscaling

• Dynamical downscaling is valuable but resource intensive, and it relies on GCM atmospheric boundary conditions

• Much can be achieved with statistical methods, but data and understanding are required

• Coordinated dynamical-statistical approaches are optimal