One contribution of 12 to a Special Feature on 'Global change and biodiversity: future challenges'.

Global climate change is a detectable and attributable global phenomenon, yet its manifestation at the regional scale, especially within the rainfall record, can be difficult to identify. This problem is particularly acute over southern Africa, a region characterised by a low density of observations and highly dependent on rural agriculture, where the impact of rainfall changes on maize cultivation critically depends on the timing with respect to the crop phenological cycle. To evaluate changes in rainfall affecting maize cropping, daily rainfall observations from 104 stations across Malawi, Mozambique, Zambia and Zimbabwe were used to detect trends in planting dates, rainfall cessation and duration of the rainfall season, as well as number of dry days, length of dry spells and measures of rainfall intensity during critical periods for growing maize. Correlations with the Southern Oscillation Index (SOI) and Antarctic Oscillation (AAO) were used to infer how large-scale climate variability affects these attributes of rainfall and highlight where (and when) trends may contribute to more frequent crossings of critical thresholds. The El Niño Southern Oscillation (ENSO) was associated with changes in planting and cessation dates as well as the frequency of raindays during the rainfall season (particularly early in the season). AAO mainly affected raindays towards the end of the season when maize was planted late. Trends are discussed relative to changes projected in empirically downscaled scenarios of rainfall from 7 general circulation models for the 2046-2065 period, assuming an SRES A2 emissions scenario.

Subsistence farmers within southern Africa have identified the onset of the maize growing season as an important seasonal characteristic, advance knowledge of which would aid preparations for the planting of rain-fed maize. Onset over South Africa and Zimbabwe is calculated using rainfall data from the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) and the Computing Center for Water Research (CCWR). The two datasets present similar estimates of the mean, standard deviation, and trend of onset for the common period (1979–97) over South Africa. During this period, onset has been tending to occur later in the season, in particular over the coastal regions and the Limpopo valley. However, the CCWR data (1950–97) indicate that this is part of long-term (decadal) variability. Characteristic rainfall patterns associated with late and early onset are estimated using a self-organizing map (SOM). Late onset is associated with heavier rainfall over the subcontinent. When onset is early over Zimbabwe, there is an increased frequency of more intense rainfall over northeast Madagascar during the preceding August. Accompanying these intense events is an increased frequency of positive 500-hPa geopotential height anomalies to the southeast of the continent. Similar positive height anomalies are also frequently present during early onset. The study indicates that onset variability is partly forced by synoptic conditions, and the successful use of general circulation models to estimate onset will depend on their simulation of the zonally asymmetric component of the westerly circulation.

Aim Intercomparison of mechanistic and empirical models is an important step towards improving projections of potential species distribution and abundance. We aim to compare suitability and productivity estimates for a well‐understood crop species to evaluate the strengths and weaknesses of mechanistic versus empirical modelling. Location South Africa. Methods We compared four habitat suitability models for dryland maize based on climate and soil predictors. Two were created using maximum entropy (MAXENT), the first based on national crop distribution points and the second based only on locations with high productivity. The third approach used a generalized additive model (GAM) trained with continuous productivity data derived from the satellite normalized difference vegetation index (NDVI). The fourth model was a mechanistic crop growth model (DSSAT) made spatially explicit. We tested model accuracy by comparing the results with observed productivity derived from MODIS NDVI and with observed suitability based on the current spatial distribution of maize crop fields. Results The GAM and DSSAT results were linearly correlated to NDVI‐measured yield (R2 = 0.75 and 0.37, respectively). MAXENT suitability values were not linearly related to yield (R2 = 0.08); however, a MAXENT model based on occurrences of high‐productivity maize was linearly related to yield (R2 = 0.62). All models produced crop suitability maps of similarly good accuracy (Kappa = 0.73–75). Main conclusions These findings suggest that empirical models can achieve the same or better accuracy as mechanistic models for predicting both suitability (i.e. species range) and productivity (i.e. species abundance). While MAXENT could not predict productivity across the species range when trained on all occurrences, it could when trained with a high‐productivity subset, suggesting that ecological niche models can be adjusted to better correlate with species abundance.

The study focus on the analysis of extreme precipitation events of the present and future climate over southern Africa.Parametric and non-parametric approaches are used to identify and analyse these extreme events in data from the Coordinated Regional Climate Downscaling Experiment (CORDEX) models. The performance of the global climate model (GCM) forced regional climate model (RCM) simulations shows that the models are able to capture the observed climatological spatial patterns of the extreme precipitation. It is also shown that the downscaling of the present climate are able to add value to the performance of GCMs over some areas and depending on the metric used. The added value over GCMs justify the additional computational effort of RCM simulation for the generation relevant climate information for regional application. In the climate projections for the end of twenty-first Century relative to the reference period , annual total precipitation is projected to decrease while the maximum number of consecutive dry days increases. Maximum 5-day precipitation amounts and 95th percentile of precipitation are also projected to increase significantly in the tropical and sub-tropical regions of southern Africa and decrease in the extra-tropical region. There are indications that rainfall intensity is likely to increase. This does not equate to an increase in total rainfall, but suggests that when it does rain, the intensity is likely to be greater. These changes are magnified under the RCP8.5 when compared with the RCP4.5 and are consistent with previous studies based on GCMs over the region.

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[1] Two regional climate models (RCMs) are used to downscale 10 years of control and 10 years of future (2070 -2079)

When will least developed countries be most vulnerable to climate change, given the influence of projected socio-economic development? The question is important, not least because current levels of international assistance to support adaptation lag more than an order of magnitude below what analysts estimate to be needed, and scaling up support could take many years. In this paper, we examine this question using an empirically derived model of human losses to climate-related extreme events, as an indicator of vulnerability and the need for adaptation assistance. We develop a set of 50-year scenarios for these losses in one country, Mozambique, using high-resolution climate projections, and then extend the results to a sample of 23 least-developed countries. Our approach takes into account both potential changes in countries' exposure to climatic extreme events, and socio-economic development trends that influence countries' own adaptive capacities. Our results suggest that the effects of socio-economic development trends may begin to offset rising climate exposure in the second quarter of the century, and that it is in the period between now and then that vulnerability will rise most quickly. This implies an urgency to the need for international assistance to finance adaptation.vulnerability | adaptive capacity | development | natural disasters | natural hazards