documenting results of dynamical downscaling of climate forecasts over the equatorial east africa...
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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