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Southern African summer-rainfall variability, and its teleconnections, on interannual to interdecadal timescales in CMIP5 models

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Abstract

This study provides the first assessment of CMIP5 model performances in simulating southern Africa (SA) rainfall variability in austral summer (Nov–Feb), and its teleconnections with large-scale climate variability at different timescales. Observed SA rainfall varies at three major timescales: interannual (2–8 years), quasi-decadal (8–13 years; QDV) and interdecadal (15–28 years; IDV). These rainfall fluctuations are, respectively, associated with El Niño Southern Oscillation (ENSO), the Interdecadal Pacific Oscillation (IPO) and the Pacific Decadal Oscillation (PDO), interacting with climate anomalies in the South Atlantic and South Indian Ocean. CMIP5 models produce their own variability, but perform better in simulating interannual rainfall variability, while QDV and IDV are largely underestimated. These limitations can be partly explained by spatial shifts in core regions of SA rainfall variability in the models. Most models reproduce the impact of La Niña on rainfall at the interannual scale in SA, in spite of limitations in the representation of ENSO. Realistic links between negative IPO are found in some models at the QDV scale, but very poor performances are found at the IDV scale. Strong limitations, i.e. loss or reversal of these teleconnections, are also noted in some simulations. Such model errors, however, do not systematically impact the skill of simulated rainfall variability. This is because biased SST variability in the South Atlantic and South Indian Oceans strongly impact model skills by modulating the impact of Pacific modes of variability. Using probabilistic multi-scale clustering, model uncertainties in SST variability are primarily driven by differences from one model to another, or comparable models (sharing similar physics), at the global scale. At the regional scale, i.e. SA rainfall variability and associated teleconnections, while differences in model physics remain a large source of uncertainty, the contribution of internal climate variability is increasing. This is particularly true at the QDV and IDV scales, where the individual simulations from the same model tend to differentiate, and the sampling error increase.

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Acknowledgements

ERSST.v4, 20CR.v2, GPCC.v7 and COBE SST2 data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at http://www.esrl.noaa.gov/psd/. The CRU TS 3.24.1 rainfall field were available from the Centre for Environmental Data Archival (CEDA) at http://catalogue.ceda.ac.uk/uuid/3f8944800cc48e1cbc29a5ee12d8542d. The authors would like to thank Noel Keenlyside, Thomas Toniazzo and Yushi Morioka for their helpful discussions.

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Authors and Affiliations

  1. Centre for Agroecology, Water and Resilience (CAWR), Coventry University, Coventry, UK

    Bastien Dieppois, Jonathan Eden, Moussa Sidibe & Damian Lawler

  2. Department of Oceanography, MARE Institute, University of Cape Town, Cape Town, South Africa

    Bastien Dieppois & Mathieu Rouault

  3. School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK

    Bastien Dieppois

  4. Centre de Recherches de Climatologie, UMR 6282 Biogéosciences, CNRS/Université de Bourgogne Franche Comté, Dijon, France

    Benjamin Pohl & Julien Crétat

  5. IPSL/Laboratoire des Sciences du Climat et de l’Environnment, CEA-CNRS-UVSQ, Université Paris Saclay, Gif-sur-Yvette, France

    Julien Crétat

  6. African Climate and Development Initiative, University of Cape Town, Cape Town, South Africa

    Mark New

  7. Nansen-Tutu Center for Marine Environmental Research, University of Cape Town, Cape Town, South Africa

    Mathieu Rouault

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  1. Bastien Dieppois
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382_2019_4720_MOESM1_ESM.eps

Fig. A1 Comparison between SST composite anomalies associated with wet and dry conditions in SA. a–c Scatterplot of observed SST composite anomalies during wet (positive SRI anomalies = +1 SD) and dry (negative SRI anomalies = − 1 SD) conditions at the interannual (left), QDV (middle) and IDV (right) timescales. d–f as a–c but for 95 historical runs from 28 CMIP5 models. Scatter plots are smoothed and coloured (low to high probability = blue, yellow to red) using a 2D kernel density estimate. Red and black lines refer to the regression lines between wet and dry SST composite anomalies, and associated correlation coefficients are provided in the lower left corner on each panel. (EPS 173447 KB)

Fig. A2

Step-by-step process of the multi-scale bootstrap clustering. Step-0: 10 simulated patterns of global SST variability are submitted to the clustering approach. Step-1: simulated patterns are resampled ni times, using rj scales (referring to different sizes of the spatial domain). Step-2: ni × rj Agglomerative hierarchical clustering are produced, using Ward’s agglomerative criteria applied to Euclidian distances. Step-3: the probability of each simulations to be clustered with the others (red values) is estimated. Here, only two clusters are significantly robust at p ≥ 0.90. (EPS 1898 KB)

Fig. A3

Percentage of occurrence of significant signals within the interannual, QDV and IDV timescales of SRI variability in all (95) model simulations, in all (28) models and all (16) institutions using the CMIP5 historical experiments. Statistical significance was estimated at p = 0.05 based 1000 Monte Carlo simulations of the red noise background spectrum. (EPS 64 KB)

Fig. A4

Boxplots of the correlations between simulations of the same models (intra-ensembles; blue), and of different models (inter-models; red), of the CMIP5 historical experiments in reproducing spatial patterns of SA rainfall variability at the three different timescales. (EPS 42 KB)

Fig. A5

Distributions of model simulations in clustering patterns of SA rainfall variability, global SST variability, and teleconnections at the interannual to interdecadal timescales. The six selected clusters, which are shown in Figs. 5, 7 and 8, are in bold. (EPS 42663 KB)

Fig. A6

Boxplots of the correlations between simulations of the same models (intra-ensembles; blue), and of different models (inter-models; red), of the CMIP5 historical experiments in reproducing spatial patterns of global SST variability (shaded), and SST composite anomalies associated with SA rainfall variability (not shaded) at the three different timescales. (EPS 48 KB)

Fig. A7

Mean annual cycle of monthly standard deviations of SST anomalies over the Niño3.4 region (i.e. 5°S-5°N and 120-170°W) in the CMIP5 models (coloured contours refer to each individual model; cf. Table 1) and in the CRU TS 3.24.01 observations (grey shaded). SST anomalies are here calculated by subtracting the monthly climatology according to the definition of the Niño3.4 index (Trenberth 1997). To reduce the influence of the global trends, SST anomalies are detrended using a locally weighted linear regressions, with span equal to the length of the data. (EPS 153 KB)

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Dieppois, B., Pohl, B., Crétat, J. et al. Southern African summer-rainfall variability, and its teleconnections, on interannual to interdecadal timescales in CMIP5 models. Clim Dyn 53, 3505–3527 (2019). https://doi.org/10.1007/s00382-019-04720-5

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