Climate & Water in a Changing Africa: Uncertainty, Adaptation & the Social Construction of Fragile Environments

Discussions of climate change and water security in Africa are often simplistic and indeed deterministic. They overlook not only ecological complexities but also the multitude of ways in which various population groups across the continent approach climatological variability, thereby challenging positivist modeling and external adaptation agendas. The current state of affairs for many often-silenced citizens is already one of hunger, uncertainty, and marginalization; the self-appointed lead actors on climate adaptation–states, markets, NGOs–have, from their vantage point, deeply troubling track records of dealing with people and their environments. For plenty of communities around Africa, it might therefore not so much be only the worsening climate that is increasingly exposing people to disease, displacement, and water insecurity, but the very policies adopted in the name of preparing for, and living with, worsening weather. This essay explores how understanding climate adaptation as a fundamentally social and political process points to possibilities for imagining and working toward futures with greater emancipatory potential. There is no scenario in which African societies adapt successfully to climatic change and do not simultaneously radically reimagine both their relationship with the outside world and with each other, including institutions of control and mechanisms of exclusion at home.

Brief communication: Drought likelihood for East Africa

The East Africa drought in autumn of year 2016 caused malnutrition, illness and death. Close to 16 million people across Somalia, Ethiopia and Kenya needed food, water and medical assistance. Many factors influence drought stress and response. However, inevitably the following question is asked: are elevated greenhouse gas concentrations altering extreme rainfall deficit frequency? We investigate this with general circulation models (GCMs). After GCM bias correction to match the climatological mean of the CHIRPS data-based rainfall product, climate models project small decreases in probability of drought with the same (or worse) severity as 2016 ASO (August to October) East African event. This is by the end of the 21st century compared to the probabilities for present day. However, when further adjusting the climatological variability of GCMs to also match CHIRPS data, by additionally bias-correcting for variance, then the probability of drought occurrence will increase slightly over the same period.

Climatological Variability of Fire Weather in Australia

Long-term variations in fire weather conditions are examined throughout Australia from gridded daily data from 1950 to 2016. The McArthur forest fire danger index is used to represent fire weather conditions throughout this 67-yr period, calculated on the basis of a gridded analysis of observations over this time period. This is a complementary approach to previous studies (e.g., those based primarily on model output, reanalysis, or individual station locations), providing a spatially continuous and long-term observations-based dataset to expand on previous research and produce climatological guidance information for planning agencies. Long-term changes in fire weather conditions are apparent in many regions. In particular, there is a clear trend toward more dangerous conditions during spring and summer in southern Australia, including increased frequency and magnitude of extremes, as well as indicating an earlier start to the fire season. Changes in fire weather conditions are attributable at least in part to anthropogenic climate change, including in relation to increasing temperatures. The influence of El Niño–Southern Oscillation (ENSO) on fire weather conditions is found to be broadly consistent with previous studies (indicating more severe fire weather in general for El Niño conditions than for La Niña conditions), but it is demonstrated that this relationship is highly variable (depending on season and region) and that there is considerable potential in almost all regions of Australia for long-range prediction of fire weather (e.g., multiweek and seasonal forecasting). It is intended that improved understanding of the climatological variability of fire weather conditions will help lead to better preparedness for risks associated with dangerous wildfires in Australia.

Optimising the Australian Wave Observation Network

The Australian national in situ wave data network currently consists of 35 platforms distributed around the Australian coastline. At present, all except for five are directional waverider buoys. The spatial density of the observation locations is variable – at a glance, density is higher on the east coast compared to the rest of the coastline. This variability has resulted in some areas of the coastline being well observed and well accounted for in models and wave climate studies and other areas not being observed at all. This work aims to identify potential gaps in the existing wave observing network in order to provide guidance for prospective future deployments. In addition, the technique used allows us to easily identify which are the key locations in the existing network. The method is based on considering the spatial coherence of the wave field determined from a multi-decadal hindcast wave data set. For each modelled data point, correlations between monthly statistics (means and 95th percentiles) of modelled variables (significant wave height, mean period and mean direction) at that location and corresponding modelled variables at each observation site are calculated. Areas of low correlation provide an indication of the key network gaps, i.e. areas where climatological variability of the wave fields is poorly captured by existing observations. Removing locations individually from the network and repeating the analysis can also provide an indication of which are the most important locations in the network (and conversely, which are the least important) to capture the regional climatological variability. Several key gaps are identified, suggesting that most value can be gained by placing additional buoys in these areas. However, it is noted that other factors such as accessibility, areas of maritime industry, and population distribution are also important in selecting sites for new buoy deployments.