3. AGCM Simulations

1. Predicting and attributing recent East African Spring droughts with dynamical-statistical climate model ensembles Chris C Funk1,2, Shraddhanand Shukla2, Martin P Hoerling3, Franklin R Robertson4, Andrew Hoell2, Brant Liebmann3 1U.S. Geological Survey, 2University of California Santa Barbara; 3NOAA, CAB, Boulder, CO, United States, 4NASA, MSFC, Huntsville, AL, United States Contact: cfunk@usgs.gov 1. Introduction During spring, eastern Kenya and Somalia have experienced more frequent droughts. Better predictions of these droughts could provide substantial humanitarian benefits. We show that dynamical-statistical climate forecasts, based on the latest generation of coupled atmosphere-ocean and uncoupled atmospheric models, effectively predict boreal spring rainfall in this area. We also examine possible forecast skill on longer time scales. The final step in the MFR process estimates the regression slope (b) between the first principal component of the filtered data and the centered target time series. These these two vectors (r and e1) and the regression slope (b) can be conveniently expressed as one simple linear operator, the canonical predictor field p, with pi = briei, where i indexes over all the n-locations. This provides a simple way of producing a time series of estimates, given a set of standardized predictors X, yest=ptXeq. 1 4. Results-Seasonal Forecasts and Five-year Hindcasts Fig. 4. a. 1993-2012 seasonal canonical predictor field for CFS March-May forecast precipitation b. same but for CFS surface temperatures c. 1933-2012 canonical predictor field based on observed SST. d-e. scatterplots showing predicted and observed 1993-2012 EA SPI based on CFS precipitation and temperatures. f. scatterplot showing observed and hindcast five-year EA SPI values. We next present cross-validated seasonal 1993-2012 CFS-based forecasts and 1933-2012 five-year SST-based hindcasts. The foundation of these estimates are the local correlations between each predictor field and GHA March-May rainfall, which have been screened for significance at the 0.1 significance level. At the seasonal scale, the CFS precipitation canonical predictor (Fig. 4a) show similar anti-correlations between EA rainfall and western Pacific rainfall (Fig. 3) as the AGCM simulations . One important difference, however, is that Fig. 4a is based on predictedCFS precipitation, potentially indicating opportunities for skillful predictions. Results based on forecast surface temperatures are similar (Fig. 4b, 4e). Panel 4c shows the five-year 1933-2012 canonical predictor field for the five-year averaged EA SPI and observed SST. This structure tends to emphasize off-equatorial regions of the tropics and sub-tropics. A secondary region of modest positive teleconnection is located in the north-central Pacific. The seasonal predictions and five-year hindcasts were subjected to cross-validation, and the results summarized in Fig. 4d-f. Hit, miss and false alarm counts are reported, based on a threshold of 0.0 SPI. At the seasonal scale, normal versus below normal rains are distinguished well (10 hits, 2 misses), but with a fairly high chance of a false alarm. The five-year hindcasts had 5 hits, 3 misses, and 2 false alarms; visual evaluation of the scatterplot suggests that much of the low frequency skill comes from the last fifteen years (corresponding to 1997-2002, 2003-2007, and 2008-2012). 5. Results-Seasonal Forecasts and Five-year Hindcasts We next examine the plausibility of decadal MFR-based CMIP5 EA SPI predictions. The approach explored here uses the MFR model constructed from observed SST (and described in Fig. 4.c&f) but replaces the observed SST with SST from 53 CMIP5 simulations. The CMIP5 March-May SST were regridded to match the dimensions of the ERSST, standardized, and transformed into ten year averages spanning 1933 to 2012. Each SST simulation (X in eq.1) is then multiplied by the canonical predictor pattern shown in Fig. 4f (p in eq.1). We begin examining the 1933-2012 relationships between the observed SST and the CMIP5 ensemble means (Fig. 5a). Since some locations in the north-eastern Pacific have actually cooled, this figure shows the ratio as 100(1.0 – Var(Obs-CMIP5)/Var(Obs)). The variance explained by the CMIP5 ensemble mean simulations varies substantially across the Indo-Pacific. 3. Observed March-May Trends, 1981-2010 Figure 2 shows March-May (MAM) rainfall trends from the CHIRPS, Global Precipitation Climatology Centre and the Climate Prediction Center's CMAP product. The products show similar patterns of rainfall decline across our EA study region. For Ethiopia and Tanzania, the Climate Hazards Group has purchased data at hundreds of stations directly from the national meteorological agencies; in these regions the CHIRPS may provide a more accurate depiction. In this paper, the two questions we address are i) are the individual dry seasons producing these trends predictable? and ii) could CMIP5 simulations have been used to predict the tendency for below normal 2003-2012 rainfall? Fig. 5a. Correlations between CMIP5 ensemble mean SSTs and ERSST b. Estimates of decadal EA SPI based on the CMIP5 simulations c. pdf of 1993-2012 CMIP5-based estimates of EA SPI. The vertical black line c shows the observed 2003-2012 SPI value. 3. AGCM Simulations Recent AGCM simulations provide a key accompaniment to the statistical reformulations presented here (primary evidence of causation to accompany correlation-based predictions). AGCMs provide insights into the SST relationships associated with EA variations. One exciting new result that has emerged from this work (Lyon and DeWitt, 2012, Hoell and Funk, 2013; Liebmannet al., 2013; Funk et al. 2013) is the critical role played by western Pacific SST. We briefly highlight one new AGCM analysis that complements the prediction-oriented results presented here. Liebmannet al. (2013) examine an ensemble of high-resolution 1979-2012 ECHAM5 simulations. This study finds that the March-May drying in ECHAM5 has resulted mostly from a trend in Pacific Ocean SST having west basin warming and east basin cooling. Overall, the study suggests that a stronger west Pacific SST gradient and enhanced Indo-west Pacific (15S-15N, 105E-150E) precipitation are associated with more frequent dry EA ECHAM5 simulations. Figure 3 plots the 1979-2012 ensemble mean ECHAM5 and EA March-May precipitation from this suite of simulations. The ensemble means exhibit a strong anti-correlation (r=-0.78). Liebmann et al. also find, however, that unpredictable internal variability plays an important role in determining EA rainfall. Increasing western Pacific rainfall and SST decrease EA spring rains. 4. Methods – Matched Filter Regression The MFR estimates a single target time series from three-dimensional (longitude, latitude, time) cubes of standardized predictor variables through a linear combination of two filtering processes. The 1st filtering process focuses on regions historically linked to the target. For simplicity, assume that we have an n-point by n-time matrix of predictors. We begin by calculating the absolute value of the correlation between each location and our target time series, storing our results in an n-location vector, r. Statistically insignificant correlation values are set to zero, screening out statistically unrelated areas. We then transform our predictor data by multiplying the standardized data at each location by the absolute value of the associated correlation in r. The variance at each predictor location is now proportional to the square of the associated correlation value. This process is referred to as 'matched filtering' because it resembles the filtering process used to 'match' and identify weak signals in cluttered multivariate datasets. The next MFR step calculates the 1st empirical orthogonal function based on the covariance structure of the match filtered data. This first eigenvector (e1) describes a pattern that maximizes the explained covariance. At this stage, the MFR process has applied two spatial filters to the original data, r and e1. Scaling by r isolates locations related to our target, while the eigenvector rotation identifies the dominant mode of covariability in the scaled predictor data, emphasizing larger scale (potentially predictable) spatio-temporal processes. Figure 3. 1979-2012 ECHAM5 ensemble mean western Pacific MAM precipitation and East Africa ensemble mean precipitation. Fig. 1. a. schema illustrating research components of FEWS NET’s drought early warning system b. seasonal forecasting context and study region. In recent years, scientists from the University of California, Santa Barbara's Climate Hazards Group, the U.S. Geological Survey, the National Ocean and Atmospheric Administration's Climate Analysis Branch, and the National Aeronautics and Space Agency have been working together to improve drought early warning capabilities for Eastern Africa. The components of this evolving multi-agency drought early warning system (Fig. 1a) include the 1981-to-near real time, 0.05 degree gridded Climate Hazards group Infrared Precipitation with Stations (CHIRPS) precipitation data, diagnostic analyses of Atmospheric General Circulation Model (AGCM) simulations, statistical reformulations of coupled Ocean-Atmosphere GCM forecasts (OAGCMs) and bootstrapped hydrologic and crop model simulations. Each of these components contributes an important element: context, process, prediction, and interpretation, supporting earlier and more effective early warning. Fig. 1b shows the East Africa (EA) window used (black polygon), and a context application: the March 15th 2011 Food Security Outlook. Famine soon broke out in Somalia. Could we have been better prepared? 2. Overview We briefly review the observed East African March-May rainfall trends and some relevant AGCM analyses. We then describe a new statistical forecasting technique, matched filter regression (MFR). We then apply MFR to two important contexts - 1993-2012 seasonal predictions, and 1933-2012 decadal rainfall estimation. The seasonal forecasts are based on output from the version 2 Coupled Forecast System (CFSv2). The decadal forecasts are based on a multi-model ensemble of phase 5 Coupled Model Intercomparison Project (CMIP5) climate change projections The tropical and sub-tropical Indian Ocean and western Pacific correlate strongly with the CMIP5 projections, perhaps indicating a dominant radiative SST control. In the north-eastern Pacific the El Niño-Southern Oscillation and other mechanisms play an important role in modifying decadal SST; these regions exhibit a positive, but weaker, relation to the CMIP5 ensemble mean. In the north-eastern Pacific, cooling has dominated, leading to negative correlations. The purple polygon corresponds to regions with canonical predictor pattern loadings of greater than ±0.00003 in Fig. 4c. The left polygon identifies a region of negative teleconnection (warming produces drier conditions in the EA). The right polygon identifies a region of positive teleconnection (warming produces wetter conditions in the EA). These results are consistent with and EA drying caused by a combination of western Pacific centennial warming trends and cooling in the east Pacific associated with natural decadal variations (Hoell and Funk, 2013; Liebmannet al., 2013). Multiplying the CMIP5 SST with the empirical canonical predictor pattern produces the family of time series and the ensemble mean estimate shown in Fig. 5b. Overall, the timing and intensity of the SPI declines appears fairly consistent across the ensemble, although the inter-simulation spread is stationary between 1933 and 2012, and fairly large, with a standard deviation of about 0.1 SPI in each decade from 1933-1942 through 2003-2012. While the CMIP5 mean estimate explains a reasonable portion of the observed EA SPI decadal variance (38%), the spread between the CMIP5 simulations is substantial. Fig. 5c represents this uncertainty as a probability distribution function for the CMIP5-based estimated 2003-2012 EA SPI mean. The mean CMIP5 estimate is -0.36, with 80% confidence bounds of [-0.5, -0.2]. The magnitude of this change (-30 mm) is consistent the results obtained in the TREND CAM5 experiment (-23 mm). The observed 2003-2012 mean (-0.53 SPI) is beyond this bound. It is likely therefore, that internal Pacific decadal variations, associated with cooling in the eastern Pacific and an associated shift in climate (Fig. 3, Lyon et al., 2012), have also contributed to recent EA drying. References Hoell, A., and Funk, C.: Indo-Pacific Sea Surface Temperature Influences on Failed Consecutive Rainy Seasons over Eastern Africa, ClimDyn, in press, 2013. Liebmann, B., Hoerling, M. P., Funk, C., Bladé, I., Dole, R. M., Allured, D., Pegion, P., and Eischeid, J. K.: Understanding Eastern Horn of Africa Rainfall Variability and Change, J. Climate, In Review, 2013. Lyon, B., and DeWitt, D. G.: A recent and abrupt decline in the East African long rains, GEOPHYSICAL RESEARCH LETTERS, 39, 10.1029/2011gl050337, 2012. Funk, C., Husak, G., Michaelsen, J., Shukla, S., Hoell, A., Lyon, B., Hoerling, M. P., Liebmann, B., Zhang, T., Verdin, J., Galu, G., Eilerts, G., and Rowland, J.: Attribution of 2012 and 2003-12 rainfall deficits in eastern Kenya and southern Somalia, Bull. Amer. Meteor. Soc., , 95, 2013a. Figure 2. March-May 1981-2010 trends from the CHIRPS, GPCC and CMAP archives.