understanding eastern africa rainfall variability and change (towards improved prediction of

Understanding Eastern Africa Rainfall Variability and Change

(Towards Improved Prediction of Seasonal Precipitation)

Brant LiebmannUniversity of Colorado, Boulder, Colorado, USA

Chris FunkU.S. Geological Survey, Sioux Falls, South Dakota, USA

Martin Hoerling, Randall DoleNOAA, Boulder, Colorado, USA

Ileana BladéUniversity of Barcelona, Barcelona, Spain

Model:ECHAM5 Atmospheric Model (Roeckner et al. 2006)

~ 0.75-degree resolution (T159) 40-member ensemble

Run with Specified Sea Surface Temperatures

Precipitation “Data”

Observations: Global Precipitation Climatology Centre (GPCC)

From Station Data – 1-degree resolution(Recently as few as 5 monthly reports in region)

Also GPCP (Station data augmented with Satellite)

Period of Study: 1979-2012

“Change” = Trend (mm/yr) * length of record (34 years)

Seasonal Horn Precipitation Change

October - December

Trend removed

Sign of Eastern Pacific SSTs correctly predicts Horn precipitation in 76.5% of years

Ensemble Average

March - May

March - May

Gradient in SST producesLow-level convergenceover Indonesia

Enhanced convection over Indonesia

Upper-level outflow (westward shift of ‘Walker’ circulation)

Enhanced subsidence over East Africa

Percent correct prediction of observed Horn precipitation based

on sign of model Indonesia precipitation

Ensemble Average Ensemble Average

40-member ensemble of ECHAM 5 atmospheric Model run with specified SSTs

The interannual anomaly of October-December Horn precipitation is well-simulated by the model ensemble-average,

although knowing SSTs in the east Pacific gives almost as good a result

The ensemble-average correctly predicts the sign of precipitation anomaly in March-May in two-thirds of years

(mostly from precipitation over Indonesia)

Model simulates observed change of 1979-2012 Horn precipitation for both March-May (decrease) and October-December (increase)