What are the local consequences of a global climate change? This question is important for proper handling of risks associated with weather and climate. It also tacitly assumes that there is a systematic link between conditions taking place on a global scale and local effects. It is the utilization of the dependency of local climate on the global picture that is the backbone of downscaling; however, it is perhaps easiest to explain the concept of downscaling in climate research if we start asking why it is necessary.
Global climate models are our best tools for computing future temperature, wind, and precipitation (or other climatological variables), but their limitations do not let them calculate local details for these quantities. It is simply not adequate to interpolate from model results. However, the models are able to predict large-scale features, such as circulation patterns, El Niño Southern Oscillation (ENSO), and the global mean temperature. The local temperature and precipitation are nevertheless related to conditions taking place over a larger surrounding region as well as local geographical features (also true, in general, for variables connected to weather/climate). This, of course, also applies to other weather elements.
Downscaling makes use of systematic dependencies between local conditions and large-scale ambient phenomena in addition to including information about the effect of the local geography on the local climate. The application of downscaling can involve several different approaches. This article will discuss various downscaling strategies and methods and will elaborate on their rationale, assumptions, strengths, and weaknesses.
One important issue is the presence of spontaneous natural year-to-year variations that are not necessarily directly related to the global state, but are internally generated and superimposed on the long-term climate change. These variations typically involve phenomena such as ENSO, the North Atlantic Oscillation (NAO), and the Southeast Asian monsoon, which are nonlinear and non-deterministic.
We cannot predict the exact evolution of non-deterministic natural variations beyond a short time horizon. It is possible nevertheless to estimate probabilities for their future state based, for instance, on projections with models run many times with slightly different set-up, and thereby to get some information about the likelihood of future outcomes.
When it comes to downscaling and predicting regional and local climate, it is important to use many global climate model predictions. Another important point is to apply proper validation to make sure the models give skillful predictions.
For some downscaling approaches such as regional climate models, there usually is a need for bias adjustment due to model imperfections. This means the downscaling doesn’t get the right answer for the right reason. Some of the explanations for the presence of biases in the results may be different parameterization schemes in the driving global and the nested regional models.
A final underlying question is: What can we learn from downscaling? The context for the analysis is important, as downscaling is often used to find answers to some (implicit) question and can be a means of extracting most of the relevant information concerning the local climate. It is also important to include discussions about uncertainty, model skill or shortcomings, model validation, and skill scores.
For several decades, the Sahelian countries have been facing continuing rainfall shortages, which, coupled with anthropogenic factors, have severely disrupted the great ecological balance, leading the area in an inexorable process of desertification and land degradation. The Sahel faces a persistent problem of climate change with high rainfall variability and frequent droughts, and this is one of the major drivers of population’s vulnerability in the region. Communities struggle against severe land degradation processes and live in an unprecedented loss of productivity that hampers their livelihoods and puts them among the populations in the world that are the most vulnerable to climatic change. In response to severe land degradation, 11 countries of the Sahel agreed to work together to address the policy, investment, and institutional barriers to establishing a land-restoration program that addresses climate change and land degradation. The program is called the Pan-Africa Initiative for the Great Green Wall (GGW). The initiative aims at helping to halt desertification and land degradation in the Sahelian zone, improving the lives and livelihoods of smallholder farmers and pastoralists in the area and helping its populations to develop effective adaptation strategies and responses through the use of tree-based development programs. To make the GGW initiative successful, member countries have established a coordinated and integrated effort from the government level to local scales and engaged with many stakeholders. Planning, decision-making, and actions on the ground is guided by participation and engagement, informed by policy-relevant knowledge to address the set of scalable land-restoration practices, and address drivers of land use change in various human-environmental contexts. In many countries, activities specific to achieving the GGW objectives have been initiated in the last five years.