Sub-Saharan rainfall variability as simulated by the ARPEGE AGCM, Sub-Saharan rainfall variability as simulated by the ARPEGE AGCM, associated teleconnection mechanisms and future changes.associated teleconnection mechanisms and future changes.

Global Change and Climate modelling CERFACS, 42 Avenue G. Coriolis, 31057 Toulouse, France.CERFACS, 42 Avenue G. Coriolis, 31057 Toulouse, France.

Email: caminade@cerfacs.frEmail: caminade@cerfacs.fr

C. Caminade, L. Terray.C. Caminade, L. Terray.

1a) Estimation of the impact of the oceanic external forcing upon Sahelian rainfall variability.1a) Estimation of the impact of the oceanic external forcing upon Sahelian rainfall variability.

ResultsResults

1) The percentage of the rainfall variance due to oceanic forcing is greater over the coastal area (in particular the Guinea region) than over continental zones (Sahel).

2) Considering decadal to multi-decadal time-scales (low frequency) the signal to noise ratio clearly increases, moreover the potential predictability is the strongest during boreal summer.

3) For the ARPEGE model an average of 12 simulations seem to be the minimal estimator to extract the oceanic externally forced rainfall signal over Africa.

Figure 1: a) Percentage of the variance of rainfall (unfiltered data) due to the SST forcing over the period 1951-1999 (JAS). White areas show non significant values with a F-test at the 99% confidence level. b) Same analysis performed at decadal time scale (8 year low pass filtered data).

Percentage of the variance due to oceanic forcing

RAINFALL

All Freq Low Freq (>8y)

SAHEL 18.9% 32.3%

GUINEA 28.5% 41.2%

Figure 2: Correlation coefficients between the observed (CRU data set) Sahelian Rainfall Index (SRI) and a simulated SRI averaged from various combinations of the 19 AGCM experiments; the vertical bar denote correlation coefficients calculated using unfiltered data (solid line) and low-pass (using an 8 year cut-off) filtered data (dashed), each curve represents the average correlation coefficients. Black dots denote correlation coefficient between each simulated SRI and the average of the remaining 18 simulated ones.

Figure 3: Linear trend of JAS summer precipitation from 1950 to 1999 estimated from a) CRU observations and b) SSTF ensemble mean. Unit is mm/day/50years, and the associated climatology is depicted by the black contours. c) Sahelian rainfall index (10˚N-20˚N, 16˚W-45˚E) anomaly (vs the 1950-99 mean) calculated for both observations and SSTF (dashed lines). The filled area highlights the spread (defined as two standard deviations of the 19 simulations with respect to the ensemble mean) in the SSTFi ensemble. The solid line depicts that an 8 year low pass filter has been applied to the time series.

Figure 4: Spatial correlation between observed Sea surface temperature (ERSSTv2 data set) and the SRI (JAS 1950-1999) for both CRU observation (a) and the SSTF ensemble mean (b). The data have been low pass filtered with an 8 year cut-off. The dotted area denotes significant correlations at the 5% significance level as estimated by a student t-test.

ResultsResults

1) The basic structure of the Sahel drought can be simulated when the model only uses the observed SST as lower boundary conditions (however, the simulated precipitation decrease is mainly located over central and eastern Africa and underestimated).

2) A strong contribution of the atmospheric internal variability in driving the decadal to multi-decadal rainfall variability over the sub-Saharan region has been shown.

3) The simulated rainfall trend is associated to a more southward location of the ITCZ, and is significantly linked to an inter-hemispheric SST pattern at global scale.

Figure 5: Left upper view: Regression pattern of JAS summer SST during 1950-99 against the simulated SRI (SSTF). The data have been low-pass filtered (8year cut-off) and the amplitude has been scaled to correspond to the trend during 50 years (unit: K/50 years). a) to e) Summer African rainfall response to SST forcing in different idealized experiments. The CTL climatology is depicted by the black contours. The dotted area denotes significant changes at the 5% significance level as estimated by a student t-test.

Figure 6: Upper views: 200hPa Potential velocity (shading) and divergent winds (vectors) responses in IND (a) and ATL (b) experiments. Lower views: meridional wind response (averaged between 0˚ and 40˚E) in GLOB (c) and PAC (d) experiments. The CTL climatology is depicted by the black contours. The dotted area denotes significant changes at the 5% significance level as estimated by a student t-test.

Figure 7: Upper views: Mean tropospheric temperature (weighted average between 300hPa and 700hPa) response in GLOB (a) and PAC (b) experiments. The dotted area denotes significant changes at the 5% significance level as estimated by a student t-test. Lower views: c) Spatial patterns associated with the first mode of a principal component analysis applied to the SSTF mean tropospheric temperature. A factor 3 has been applied for visualization commodities. The data have been first low-pass filtered with an 8 year cut-off. d) Associated normalized principal component (bar). The black curve depicts the simulated normalized SRI in SSTF.

RAINFALL RESPONSERAINFALL RESPONSE CIRCULATION RESPONSECIRCULATION RESPONSE TROPOSPHERIC TEMPERATURETROPOSPHERIC TEMPERATURE(TT)(TT)

Rainfall JAS

OBS ARPEGE

THE ZOO…THE ZOO…

SRI LINK WITH THE GLOBAL SRI LINK WITH THE GLOBAL SST GRADIENTSST GRADIENT

SRI LINK WITH THESRI LINK WITH THE TT over the TropicsTT over the Tropics

OBS

Figure 8:Scatter diagram of the correlation coefficients between the modeled and observed Sahelian Rainfall (10˚N-20˚N, 16˚W-45˚E) and the correlation between Sahelian rainfall and a SST index characterizing the meridional gradient at global scale (10˚N-70˚N minus 60˚S-10˚N).

Figure 9:Scatter diagram of the correlation coefficients between the modeled and observed Sahelian Rainfall (10˚N-20˚N, 16˚W-45˚E) and the correlation between Sahelian rainfall and the weighted averaged of tropical (30˚N-30˚S) tropospheric temperature (300-700hPa).

Based on two key mechanisms:Based on two key mechanisms:

• The interhemispheric SST gradient The interhemispheric SST gradient that impacts on the ITCZ location.that impacts on the ITCZ location.

• The warming of the free troposphere The warming of the free troposphere over the tropics that impacts on deep over the tropics that impacts on deep convection intensity.convection intensity.

Only one model (GFDL CM2.1) Only one model (GFDL CM2.1) simulates consistent correlations for simulates consistent correlations for both mechanisms and predicts dryer both mechanisms and predicts dryer conditions over the Sahel at the end conditions over the Sahel at the end of the 21st century. Nevertheless, of the 21st century. Nevertheless, this model has been shown to be this model has been shown to be very sensitive to a uniform warming very sensitive to a uniform warming of tropical SST (Held etal, 2005)…of tropical SST (Held etal, 2005)…

FUTURE CHANGES ?FUTURE CHANGES ?

EXPERIMENTAL DESIGN EXPERIMENTAL DESIGN

1a) and 1b)1a) and 1b)

A 19 member ensemble simulation is A 19 member ensemble simulation is performed with the ARPEGE AGCM. The performed with the ARPEGE AGCM. The atmospheric simulations are forced by atmospheric simulations are forced by observed SST (Reynolds data set) over the observed SST (Reynolds data set) over the 1950-1999 period, each realization differing 1950-1999 period, each realization differing from the other by its initial conditions. The from the other by its initial conditions. The ensemble mean is then an estimation of the ensemble mean is then an estimation of the signal which is signal which is forced by the SSTforced by the SST. It will be . It will be referred to as referred to as SSTFSSTF in the following. in the following.

The observed and simulated Sahelian drought are The observed and simulated Sahelian drought are both associated to an interhemispheric SST both associated to an interhemispheric SST

pattern.pattern.

EXPERIMENTAL DESIGNEXPERIMENTAL DESIGN

Idealized experiments are performed to asses the influence of each basin upon the simulated Sahelian drought. A first ensemble of 20 simulations is forced by the mean (1950-1999) seasonal cycle of SST and sea ice concentrations. The ensemble mean will be denoted by CTL. In the second ensemble the SST forcing is computed as the sum of the mean seasonal cycle plus an anomaly depicting the global SST interhemispheric pattern, at global scale (GLOB, see Fig 5.). In order to determine the influence of each oceanic basins considered separately upon sub-Saharan rainfall, three additional ensembles are performed in which the SST anomaly is only prescribed over the Indian, the Atlantic and Pacific basin. It will be denoted by IND, ATL and PAC in the following.

1b) Sub-saharan rainfall decadal variability as simulated by the ARPEGE AGCM.1b) Sub-saharan rainfall decadal variability as simulated by the ARPEGE AGCM.

2) Simulated Rainfall response to prescribed SST anomalies in the different basin.2) Simulated Rainfall response to prescribed SST anomalies in the different basin.

3) 21st century projections using the CMIP3 multimodel dataset3) 21st century projections using the CMIP3 multimodel dataset