Documenting Results of Dynamical Downscaling of Climate Forecasts over the

Equatorial East Africa Using Regional Spectral Model

Drs. Matayo Indeje, L. Sun, J. Mutemi & L.J. Ogallo

11th international RSM workshop, August15-19, 2011, National Central University, Jongli, Taiwan

STUDY AREA

Rainfall Annual Cycle

Equatorial Eastern Africa

Blue – Eastern half of the regionRed – western half of the region

GCM OROGRAPHY ON A T42 GRID RSM OROGRAPHY ON A 55KM GRID

Reason for downscaling

Displacement of Orography in Global Models

Regional Climate Model Challenges Over Eastern Africa

•Complex Topographic features > Land/sea, Land/Lake contrasts•Orographic Forcing > East Africa Highlands, Ethiopian Highlands, Ruwenzoris, Turkana Channeling Effect•Diverse Vegetation Types

EthiopianHighlands

East AfricanHighlands

LakeVictoria

TrukanaChannel

Ruwenzoris

Indian Ocean

CongoForest

NCEP REGIONAL SPECTRAL MODEL (RSM-CVS)

• Grid Spacing: 55km (~ x=108, y=69 grid points)

• Time Step: 200s

• Simplified Arakawa-Schubert Cumulus Scheme

• Simulation Time on IBM-RS6000 Computer ~ 24 minutes a day on a single processor

• Lateral forcing: ECHAM4.5 GCM (Provided by the IRI)

• Completed RSM climatology of 30 Years (1970-1999) based on 10 Ensemble runs (since 2003)

• Operational Real Time RSM downscaled Forecasting since 2005

30-YEAR MODEL CLIMATOLOGY

GCMRSMOBSERVATION

Dynamical downscaling:Nesting a high resolution dynamical model within a global GCM.

AGCM (250km res.)

Regional Spectral Model (55km res.)

OND 1997 OND 1998 OND 1999

RSM CASE STUDY FOR ANOMALOUS YEARS (El Nino/wet 1997 and La Nina/Dry 1999)

Wet 1997 Dry 1999 Difference

Simulation of Regional Circulation Patterns at 850 and 200-hPa over GHA

DAILY RAINFALL (RSM vs OBSERVATION)

02040

6080

100120

140160180

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Days

Rai

nfa

ll (

mm

)

RSM OBSERVATION

INSEASON RAINFALL FREQUENCY

0

2

4

6

8

10

12

14

16

<1mm 1-2.5mm 2.6-3.5mm 3.6-4.5mm >5mm

Frequency

No

. o

f C

ases

RSM

OBSERVATION

DAILY RAINFALL REALIZATION: RSM Vs OBSERVATIONS

MODEL VALIDATION

EOF Analysis: RSM Vs Observation

50 % Variance 46 % VarianceCorr. Coef = 0.78

EXAMPLE OF REAL TIME SEASONAL DYNAMICAL FORECAST: OCTOBER TO DECEMBER 2004

55 KM RESOLUTION

AN

BN

NN

Regional Climate Model Products are Tailored for Application in;

•Crop Modeling (Agriculture and Food Security)

•Disease Monitoring (Malaria, RVF, etc)

•Hydrological Applications (hydro-power)

CLIMATE RELATED RISK MANAGEMENT

. HUNGER - DROUGHT – RAINFALL DEFICIT

. DISEASE – RAINFALL AND TEMPERATURE

Malaria Epidemic Prediction Model For East Africa

Malaria case anomalies

-2000

200400600800

1000Jan-8

0

Jan-8

2

Jan-8

4

Jan-8

6

Jan-8

8

Jan-9

0

Jan-9

2

Jan-9

4

Jan-9

6

Jan-9

8

Time in years

Ca

se

an

om

aly

. Epidemic malaria in the highlands (Altitude: 1500-2500 meters above sea level)

. Malaria cases increased Threfold in the region since 1990

100xRT

RTER

mm

ii

Where ER is the epidemic riskTi is the current mean monthly maximum temperature anomaly Ri is the current mean monthly rainfall above 150 mm threshold for Tm is the maximum intensity index for monthly mean temperature anomaly (Climatology) Rm is the maximum intensity index for monthly mean rainfall anomaly (Climatology)

Rainfall above 300 mm per month takes on negative index values as such rainfall causes flashing of larvae thus reducing transmission.Epidemic Risk (ER) above 50% indicates a high risk of an epidemic.

The model uses climate data to forecast an epidemic risk

Research on Climate Change Downscaled Scenario

• Physical and Dynamical Mechanisms responsible for the projected trend in rainfall and Temperature