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Influences of El Nino southern oscillation (ENSO) on weather systems have increased the frequency and amplitude of extreme events over the last century. This even continues to exacerbate the already warming earth, with 2014–2016 which coincided with the strongest El Nino years observed as the warmest period in recent past. This study presents an approach of characterizing droughts at various timescales and establishes teleconnections between ENSO and drought severity in Botswana. The study uses Standardized Precipitation Evaporation Index (SPEI) at timescales of 1, 3, 6, 12 and 24 month to characterize droughts and Pearson’s correlations to study the teleconnections between SPEIs and ENSO. Results from the study reveal that extreme droughts are a rare occurrence in Botswana though it is more prone to moderate droughts at 12 month SPEI with a probability of 19% in the north. The highest severe drought probability was 7% recorded in the east. Linear trends indicate increasing dryness of around 0.8% per decade. These results have demonstrated that warm sea surface temperatures combined with negative Southern Oscillation Index correspond to persistent negative SPEI values and thus are likely to result in dry conditions. Significant correlations were observed in the mid austral summer in December and January. Due to this relationship, the drought early warning systems could use ENSO as one of the instruments for predicting drought over the study area and hence in its management.

Uncertainties in rainfall have increased in the recent past exacerbating climate risks which are projected to be higher in semiarid environments. This study investigates the associated features of rainfall such as rain onset, cessation, length of the rain season (LRS), and dry spell frequency (DSF) as part of climate risk management in Botswana. Their trends were analysed using Mann-Kendall test statistic and Sen’s Slope estimator. The rainfall-evapotranspiration relationships were used in formulating the rain onset and cessation criteria. To understand some of the complexities arising from such uncertainties, artificial neural network (ANN) is used to predict onset and cessation of rain. Results reveal higher coefficients of variation in onset dates as compared to cessation of rain. Pandamatenga experiences the earliest onset on 28th of November while Tsabong the latest on 14th of January. Likewise, earliest cessation is observed at Tshane on 22nd of February and the latest on 30th of March at Shakawe. The shortest LRS of 45 days is registered at Tsabong whereas the northern locations show LRS greater than 100 days. Stations across the country experience strong negative correlation between onset and LRS of − 0.9. DSF shows increasing trends in 50% of the stations but only significant at Mahalapye, Pandamatenga, and Shakawe. Combining the LRS criteria and DSF, Kasane, Pandamatenga, and Shakawe were identified to be suitable for rainfed agriculture in Botswana especially for short to medium maturing cereal varieties. Predictions of onset and cessation indicate the possibility of delayed onset by 2–5 weeks in the next 5 years. Information generated from this study could help Botswana in climate risk management in the context of rainfed farming.

Multiple reanalysis datasets currently exist that can provide boundary conditions for dynamic downscaling and simulating local hydro-climatic processes at finer spatial and temporal resolutions. Previous work has suggested that there are two reanalyses alternatives that provide the best lateral boundary conditions for downscaling over southern Africa. This study dynamically downscales these reanalyses (ERA-I and MERRA) over southern Africa to a high resolution (10 km) grid using the WRF model. Simulations cover the period 1981–2010. Multiple observation datasets were used for both surface temperature and precipitation to account for observational uncertainty when assessing results. Generally, temperature is simulated quite well, except over the Namibian coastal plain where the simulations show anomalous warm temperature related to the failure to propagate the influence of the cold Benguela current inland. Precipitation tends to be overestimated in high altitude areas, and most of southern Mozambique. This could be attributed to challenges in handling complex topography and capturing large-scale circulation patterns. While MERRA driven WRF exhibits slightly less bias in temperature especially for La Nina years, ERA-I driven simulations are on average superior in terms of RMSE. When considering multiple variables and metrics, ERA-I is found to produce the best simulation of the climate over the domain. The influence of the regional model appears to be large enough to overcome the small difference in relative errors present in the lateral boundary conditions derived from these two reanalyses.

Arid and semi-arid environments have been identified with locations prone to impacts of climate variability and change. Investigating long-term trends is one way of tracing climate change impacts. This study investigates variability through annual and seasonal meteorological time series. Possible inhomogeneities and years of intervention are analysed using four absolute homogeneity tests. Trends in the climatic variables were determined using Mann–Kendall and Sen’s Slope estimator statistics. Association of El Nino Southern Oscillation (ENSO) with local climate is also investigated through multivariate analysis. Results from the study show that rainfall time series are fully homogeneous with 78.6 and 50% of the stations for maximum and minimum temperature, respectively, showing homogeneity. Trends also indicate a general decrease of 5.8, 7.4 and 18.1% in annual, summer and winter rainfall, respectively. Warming trends are observed in annual and winter temperature at 0.3 and 1.5% for maximum temperature and 1.7 and 6.5% for minimum temperature, respectively. Rainfall reported a positive correlation with Southern Oscillation Index (SOI) and at the same time negative association with Sea Surface Temperatures (SSTs). Strong relationships between SSTs and maximum temperature are observed during the El Nino and La Nina years. These study findings could facilitate planning and management of agricultural and water resources in Botswana.

Regional climate modelling studies often begin by downscaling a reanalysis dataset in order to simulate the observed climate, allowing the investigation of regional climate processes and quantification of the errors associated with the regional model. To date choice of reanalysis to perform such downscaling has been made based either on convenience or on performance of the reanalyses within the regional domain for relevant variables such as near-surface air temperature and precipitation. However, the only information passed from the reanalysis to the regional model are the atmospheric temperature, moisture and winds at the location of the boundaries of the regional domain. Here we present a methodology to evaluate reanalyses derived lateral boundary conditions for an example domain over southern Africa using satellite data. This study focusses on atmospheric temperature and moisture which are easily available. Five commonly used global reanalyses (NCEP1, NCEP2, ERA-I, 20CRv2, and MERRA) are evaluated against the Atmospheric Infrared Sounder satellite temperature and relative humidity over boundaries of two domains centred on southern Africa for the years 2003–2012 inclusive. The study reveals that MERRA is the most suitable for climate mean with NCEP1 the next most suitable. For climate variability, ERA-I is the best followed by MERRA. Overall, MERRA is preferred for generating lateral boundary conditions for this domain, followed by ERA-I. While a “better” LBC specification is not the sole precursor to an improved downscaling outcome, any reduction in uncertainty associated with the specification of LBCs is a step in the right direction.