Sahel Climate Change in the IPCC AR4 models

Michela Biasutti biasutti@ldeo.columbia.edu

in collaboration with :Alessandra Giannini, Adam Sobel, Isaac Held

OUTLINE

• 20th Century: Was the Sahel drought internal noise? Forced Signal? Anthropogenic? GHG or Aerosols?

• 21st Century: What is the source of model disagreement?Different SST forcing?Different response to the same SST forcing?

Hoerling et al., 2006

Fig. 5. The 1950–99 trends of (left) observed and (middle) atmospheric GCM simulated seasonal African rainfall for JAS. Plotted is the total seasonal rainfall change (mm) over the 50-yr period. (right) The empirical PDFs of JAS 50-yr rainfall trends averaged over the Sahel region. The data given by the red curve are from the 80 individual members of the AGCM simulations forced with the history of global observed SSTs. The data given by the blue curve are from 15 individual members of unforced coupled atmosphere–ocean model simulations. The observed trend value is indicated by the gray bar.

SST-forced Sahel drought: natural?

AMIP

coupledCTL

NASA/GISS

IPCC Simulations

GCMs

20th CenturySimulation (XX)

Global Warming Scenario (A1B)

Pre-Industrial Control (PI)

XXPI A1B

Hoerling et al., 2006

“[The ensamble mean] fails to simulate the pattern or amplitude of the twentieth-century African drying, indicating that the drought conditions were likely of natural origin.”

IPCC Simulations

1950 2000 2050

Importance of Internal Variability60

XX

Sim

ulat

ions

1950-1985 Trend

1950-1999 Trend

1930-1999 Trend

1. reduced variability

2. predominance of drying trend

Forced Signal: (1975-1999 mean) minus (PI mean)

XX-PI Rainfall Change

XX-PI SST Change

OUTLINE

• 20th Century: Was the Sahel drought internal noise? Forced? Anthropogenic? GHG or Aerosols?

• 21st Century: What is the source of model disagreement?Different SST forcing?Different response to the same SST forcing?

Effect of GHG4x(yrs50:70)-PI

Mean Rainfall Change

Robustness of Rainfall Change

Surface Temperature

20

NASA/GISS

SULFATE AEROSOL FORCINGS (1850-1997)

Temp RESPONSE

Effect of Reflective Aerosols

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

ROTSTAYN AND LOHMANN ‘02

Precip RESPONSE

• 20th Century drying of the Sahel is reproduced by almost all IPCC AR4 models it is (partly) externally forced. (But natural, internal variability is substantial.)

• The forcing was anthropogenic, with the most robust signal coming from the sulfate aerosol forcing.

• The response to GHG increase alone is inconsistent across models, which implies an uncertain outlook for the Sahel.

Some Conclusions

GFDL

Precipitation Response in the Sahel

Given the role of SST in simulations of the 20th Century, is it SST?:

different SST anomalies?different sensitivity to same SST anomalies?

What are the possible causes of discrepancy?

Relationship of Sahel rainfall & SST (pre-industrial, not forced)

Biasutti et al., 2007

Linear Multi-RegressiveModel:

from SST (Indo-Pacific & Atlantic Gradient)

to Sahel Rainfall

interannual (=detrended)

XX

A1B

goodness of model

PI(training run)

Linear Multi-RegressiveModel

trained on (detrended) PI:from SST

(Indo-Pacific & Atlantic Gradient) to Sahel Rainfall

interannual

interannual + trend

PI

XX

A1B

goodness of model

nb: same results if NTA & STA are used (3 predictors) and/or ifmodel is trained on XX.

Held & Lu, 2007

Uniform Warming

Nor

th A

tlant

ic obs

AM2CM2

mirocCM2

miroc

Linear Regression Coefficients

Simulated &PredictedSahel Rainfall

• ~30%(?) of 20th Century drying of the Sahel was externally forced. The forcing was anthropogenic, with the most robust signal coming from the sulfate aerosol forcing.

• In the 21st Century, when GHG are the dominant forcing, the Sahel response is inconsistent across models.

• Global SST changes can explain the 20th Century trend, but, in most models, not the 21st Century one (at least not through the same mechanisms active in the past).

• A model’s good representation of the past is no indication of a trustworthy prediction of the future. How can we reduce the uncertainty of our climate outlook?

Conclusions