Evaluation of climate model performance at regional scales is essential in determining confidence in simulations of present and future climate. Here we developed a process-based approach focussing on the South Indian Ocean Convergence Zone (SIOCZ), a large-scale, austral summer rainfall feature extending across southern Africa into the southwest Indian Ocean. Simulation of the SIOCZ was evaluated for the Coupled Model Intercomparison Project (CMIP5).Comparison was made between CMIP5 and Atmospheric Model Intercomparison Project (AMIP) models to diagnose sources of biases associated with coupled ocean−atmosphere processes. Models were assessed in terms of mean SIOCZ characteristics and processes of interannual variability. Most models simulated a SIOCZ feature, but were typically too zonally oriented. A systematic bias of excessive precipitation was found over southern Africa and the Indian Ocean, but not particularly along the SIOCZ. Excessive precipitation over the continent may be associated with excessively high low-level moisture flux around the Angola Low found in most models, which is almost entirely due to circulation biases in models. AMIP models represented precipitation more realistically over the Indian Ocean, implying a potential coupling error. Interannual variability in the SIOCZ was evaluated through empirical orthogonal function analysis, where results showed a clear dipole pattern, indicative of a northeast−southwest movement of the SIOCZ. The drivers of this shift were significantly related to the El Niño Southern Oscillation and the subtropical Indian Ocean dipole in observations. However, the models did not capture these teleconnections well, limiting our confidence in model representation of variability.KEY WORDS: CMIP5 · ENSO · Ensemble · Teleconnection · Model evaluation · South Indian Ocean Convergence Zone · SIOCZ · Southern Africa · December-January-February · DJF OPEN PEN ACCESS CCESS

Future projections of precipitation at regional scales are vital to inform climate change adaptation activities. Therefore, is it important to quantify projected changes and associated uncertainty, and understand model processes responsible. This paper addresses these challenges for southern Africa and the adjacent Indian Ocean focusing on the local wet season. Precipitation projections for the end of the twenty-first century indicate a pronounced dipole pattern in the CMIP5 multimodel mean. The dipole indicates future wetting (drying) to the north (south) of the climatological axis of maximum rainfall, implying a northward shift of the ITCZ and south Indian Ocean convergence zone that is not consistent with a simple ''wet get wetter'' pattern. This pattern is most pronounced in early austral summer, suggesting a later and shorter wet season over much of southern Africa. Using a decomposition method we determine physical mechanisms underlying this dipole pattern of projected change, and the associated intermodel uncertainty. The projected dipole pattern is largely associated with the dynamical component of change indicative of shifts in the location of convection. Over the Indian Ocean, this apparent northward shift in the ITCZ may reflect the response to changes in the north-south SST gradient over the Indian Ocean, consistent with a ''warmest get wetter'' mechanism. Over land subtropical drying is relatively robust, particularly in the early wet season. This has contributions from dynamical shifts in the location of convection, which may be related to regional SST structures in the southern Indian Ocean, and the thermodynamic decline in relative humidity. Implications for understanding and potentially constraining uncertainty in projections are discussed.

An assessment of probabilistic prediction skill of seasonal temperature extremes over Southern Africa is presented. Verification results are presented for six run-on seasons; September to November, October to December, November to January, December to February, January to March, and February to April over a 15-year retroactive period. Comparisons are drawn between downscaled seasonal 850 hPa geopotential height field forecasts of a two-tiered system versus downscaled height forecasts from a coupled ocean-atmosphere system. The ECHAM4.5 atmospheric general circulation model (GCM) is used for both systems; in the one-tiered system the ECHAM4.5 is directly coupled to the ocean model Modular Ocean Model version three (MOM3), and in the two-tiered system the ECHAM4.5 is coupled with Van den Dool sea surface temperature (SST) hindcasts. Model output statistical equations are developed using canonical correlation analysis (CCA) to reduce system deficiencies. Probabilistic verification is conducted using the relative operating characteristic (ROC) and reliability diagram. The coupled model performs best in capturing seasonal maximum temperature extremes. Seasons demonstrating the highest ROC scores coincide with the period of highest seasonal temperatures found over Southern Africa. The above-normal category of the one-tiered system indicates the highest skill in predicting maximum temperature extremes, implying the coupled model predicts skilfully when there is a high likelihood of experiencing extremely high seasonal maximum temperatures during mid to late summer. The downscaled coupled maximum temperature hindcasts are evaluated additionally in terms of their monetary value and quality to the general public. The seasonal forecast system presented in this study should be able to reduce risks in decision making by the health industry in Southern Africa.

Abstract. This article proposes a novel methodology for reconstructing past climatic conditions in regions and time-periods for which there is limited evidence from documentary and natural proxy sources. Focusing on present-day inland Tanzania during the period 1856–1890, it integrates evidence from qualitative documentary sources with quantitative outputs from climate reanalysis and global circulation models (GCMs), which enables the creation of interdisciplinary seasonal time-series of rainfall variability for three distinct locales. It does so by indexing each dataset to the same 7-point scale and weighting each output according to a predefined level of confidence in the documentary data. This process challenges the subjectivity of nineteenth-century Europeans in Africa, whose reports form the basis of the documentary material, and adds evidence from the region, which is currently lacking from the latest reanalysis products and GCMs. The result is a more scientifically grounded interpretation of documentary materials and a more locally grounded estimation of rainfall that would otherwise be gained from referring to reanalysis or GCMs alone. The methodology is validated with reference to observed long-term trends gathered from (paleo)limnological studies, and it is shown to provide marked insights into four periods of environmental stress in the region’s late-nineteenth-century past. Future challenges may involve integrating evidence from oral traditions and adapting the methodology for other regions and time-periods.