Impact of Sea Surface Temperature and Soil Moisture Climate Prediction Center/NCEP/NOAA

Impact of Sea Surface Temperature and Soil Moisture
on Seasonal Rainfall Prediction over the Sahel
Wassila M. Thiaw and Kingtse C. Mo
Climate Prediction Center/NCEP/NOAA
Washington, DC
Objectives
• To use the NCEP coupled forecast system (CFS), observed
precipitation data, and NCEP reanalysis to document the
forecast errors of seasonal rainfall over the Sahel.
• To diagnose the causes of model errors and to understand the
underlying physics that governs rainfall variability over the
Sahel by comparing the CFS forecasts with model seasonal
simulations (SIMs) and the Atmospheric Model
Intercomparison Project (AMIP) runs.
CFS and Model Experiments
Correlation Maps between RPCs and rainfall for observations
Discussions
• CFS: GFS & MOM3 from GFDL (Saha et al., 2005)
• Ensemble Simulations
– GFS T62L64, 5 members, different initial conditions 6 hours
apart
– AGCM forced by observed SST
Fig. 8: Correlation between the
observed rainfall and RPC 1 from
observed SSTs for (a) RPC 1; (b)
RPC 2; (3) RPC 3 for the period
1950-2001. Contour interval 0.1,
values statistically significant at the
5% level are shaded.
• AMIP (continued run from 12/1/1949)
• CFS Corrected
– Ens. Forecasts 1990-2001, GFS T62L128
– 5 members
– Predicted SSTs from the CFS with systematic errors corrected
Time Distribution of Sahel Rainfall
Sahel Rainfall
Rainfall (mm/day)
5
4
3
CFS
2
O.I. Gauge
1
Fig. 1: Interannual Variability of the
JAS Sahel Rainfall in the CFS (blue)
and the O. I. gauge (red) for the
period 1981-2002
0
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
Year
Part of the P errors comes from the SST systematic errors (Fig.5, 6). For the forecasts on
the seasonal time scales, SSTs have dominant influence on rainfall over the Sahel. The
systematic error pattern is similar to the decadal SST mode. It shows positive SSTs over
the North Pacific and the North Atlantic and negative errors in the tropical Pacific and
the southern oceans (Fig. 7, 8).
During the CFS forecasts, the systematic errors are not corrected so they serve as
additional forcing. The persistence of the errors in the SST pattern may cause the errors
in rainfall magnitudes and the suppressed variability. The CFS model does not have
realistic ice model as a subcomponent. The ice information is supplied through the mean
monthly climatology and the ocean coupling is limited to the south of 65N. These model
deficiencies may contribute to the warming over the North Pacific and the North
Atlantic.
In addition to the SST errors, the soil moisture feedback mechanism may also contribute
to the southward shift of the AEJ and rainfall. This is demonstrated by the comparison
between the AMIP run and the simulations (Fig. 9, 11). Both experiments are forced
with the observed SSTs. The main differences are in soil moisture and surface fluxes.
The SIMs are initialized from the R2 in June and has realistic information on soil
moisture and surface fluxes. The AMIP, which is a continuous run, does not have such
information. The AMIP run shows the southward shift of the AEJ, while the SIMs
provide a better representation of the jet location and rainfall spatial pattern.
SST Impacts
Fig. 5 : (a) SST difference between
the ensemble SST forecasts for JAS
with the initial conditions in June
averaged from 1981 to 2001 and the
corresponding SSTs from the CDAS.
Contour interval is 1o C. Positive
values are shaded; (b) and ©
Anomaly correlation between CFS
and CDAS for the Nino 3.4 and the
North Pacific, respectively.
Vertical Profile of Zonal Wind in CDAS, AMIP, and Model Simulation
SST Errors in the CFS
Fig.2: Climatology of the Jul-Sep
Sahel rainfall for the period 19812002.
Precipitation forecasts over the Sahel from the NCEP coupled forecast system (CFS)
model were compared to the gauge rainfall analysis. The CFS ensemble forecasts for
JAS from initial conditions in June show a southward shift in the West African rain band
(Fig. 2). This leaves the Sahel very dry. The southward shift of the rain band is
accompanied by the southward shift of the AEJ. The CFS forecasts also do not capture
the interannual variability in the Sahel rainfall quite adequately (Fig. 1). The suppressed
interannual variability in P suggests the existence of persistent erroneous forcing. The
model simulations and CFS (corrected) have better representation of the position of the
AEJ and the spatial distribution of rainfall across West Africa (Fig. 3 and 4). They also
show more realistic interannual rainfall variability.
Fig. 6: (a) Time-longitude plot of the
CDAS SST JAS mean difference
averaged from 10oS-10oN and the
CDAS mean for the period 19812001. Contour interval is 0.5oC.
Positive values greater than 1oC are
shaded; (b) same as (a), but for the
difference between the CFS ensemble
forecasts and the CDAS mean; (c) for
the CFS(corrected) ensemble
forecasts; (d) same as (a), but for the
SST difference averaged from 3555oN; (e) same as (d) but for the
difference between the CFS forecasts
and the CDAS mean; (f) same as (d),
but for the CFS (corrected) ensemble
forecasts.
Fig. 10: Vertical profile of the zonal
wind averaged from (0-10oE) from
(a) the CDAS; (b) the AMIP; (c) the
SIMs. Contour interval is 2 m s-1.
Values less than 6 m s-1 are shaded;
(d)-(f) same as (a)-(c), but for dT/dy
averaged from (5oW-15oE). Contour
interval 2x10-6 K m-1. Values greater
than 8x10-6 Km-1 are shaded.
As expected, the AMIP has less soil moisture over the Sahel and less E. E contributes to
P directly, but the largest influence is indirect through the temperature gradients. The
radiation differences are smaller so E is balanced by sensible heat. Less E implies more
sensible heat and indeed the AMIP is warmer over the Sahel than the SIMs. This implies
that the largest temperature gradients over West Africa in the AMIP are located further
south than in the SIMs. The temperature gradients reach the middle troposphere (Fig.
10). This serves as a forcing to move the AEJ southward. In response, The African wave
disturbances, which account for much of the rains in the Sahel shift southward resulting
in dryness over the Sahel.
As it is well known, the most important contribution to rainfall variability over the Sahel
is the decadal mode. The AMIP forced with the observed SSTs does not capture the
decadal changes in rainfall. The model does not have interactive vegetation fraction and
does not use the information of the leaf area index (LAI). The vegetation fraction is
supplied to the model through the monthly mean vegetation fraction climatology.
Therefore, it is not able to simulate the changes of albedo and surface fluxes due to the
greenness of vegetation. Vegetation dynamics is a significant process in simulating
rainfall over the Sahel and it has been found that the decadal variability which is the
essential part of the rainfall variability over the Sahel is better produced when the
interactive vegetation is added to the model. Therefore, a land-surface interaction model
coupled with the CFS will improve precipitation forecasts over the Sahel.
Thiaw, W. M., and K. C. Mo, 2005: J. Climate, In press.
[email protected]; [email protected]