Long-term planning for many sectors of society—including infrastructure, human health, agriculture, food security, water supply, insurance, conflict, and migration—requires an assessment of the range of possible futures which the planet might experience. Unlike short-term forecasts for which validation data exists for comparing forecast to observation, long-term forecasts have almost no validation data. As a result, researchers must rely on supporting evidence to make their projections. A review of methods for quantifying the uncertainty of climate predictions is given. The primary tool for quantifying these uncertainties are climate models, which attempt to model all the relevant processes that are important in climate change. However, neither the construction nor calibration of climate models is perfect, and therefore the uncertainties due to model errors must also be taken into account in the uncertainty quantification. Typically, prediction uncertainty is quantified by generating ensembles of solutions from climate models to span possible futures. For instance, initial condition uncertainty is quantified by generating an ensemble of initial states that are consistent with available observations and then integrating the climate model starting from each initial condition. A climate model is itself subject to uncertain choices in modeling certain physical processes. Some of these choices can be sampled using so-called perturbed physics ensembles, whereby uncertain parameters or structural switches are perturbed within a single climate model framework. For a variety of reasons, there is a strong reliance on so-called ensembles of opportunity, which are multi-model ensembles (MMEs) formed by collecting predictions from different climate modeling centers, each using a potentially different framework to represent relevant processes for climate change. The most extensive collection of these MMEs is associated with the Coupled Model Intercomparison Project (CMIP). However, the component models have biases, simplifications, and interdependencies that must be taken into account when making formal risk assessments. Techniques and concepts for integrating model projections in MMEs are reviewed, including differing paradigms of ensembles and how they relate to observations and reality. Aspects of these conceptual issues then inform the more practical matters of how to combine and weight model projections to best represent the uncertainties associated with projected climate change.
Benjamin Mark Sanderson
Biomass burning is widespread in sub-Saharan Africa, which harbors more than half of global biomass burning activity. These African open fires are mostly induced by humans for various purposes, ranging from agricultural land clearing and residue burning to deforestation. They affect a wide variety of land ecosystems, including forests, woodlands, shrublands, savannas, grasslands, and croplands. Satellite observations show that fires are distributed almost equally between the northern and southern hemispheres of sub-Saharan Africa, with a dipole-type annual distribution pattern, peaking during the dry (winter) season of either hemisphere. The widespread nature of African biomass burning and the tremendous amounts of particulate and gas-phase emissions the fires produce have been shown to affect a variety of processes that ultimately impact the earth’s atmospheric composition and chemistry, air quality, water cycle, and climate in a significant manner. However, there is still a high level of uncertainty in the quantitative characterization of biomass burning, and its emissions and impacts in Africa and globally. These uncertainties can be potentially alleviated through improvements in the spatial and temporal resolutions of satellite observations, numerical modeling and data assimilation, complemented by occasional field campaigns. In addition, there is great need for the general public, policy makers, and funding organizations within Africa to recognize the seriousness of uncontrolled biomass burning and its potential consequences, in order to bring the necessary human and financial resources to bear on essential policies and scientific research activities that can effectively address the threats posed by the combined adverse influences of the changing climate, biomass burning, and other environmental challenges in sub-Saharan Africa.
The warming of the global climate is expected to continue in the 21st century, although the magnitude of change depends on future anthropogenic greenhouse gas emissions and the sensitivity of climate to them. The regional characteristics and impacts of future climate change in the Baltic Sea countries have been explored since at least the 1990s. Later research has supported many findings from the early studies, but advances in understanding and improved modeling tools have made the picture gradually more comprehensive and more detailed. Nevertheless, many uncertainties still remain. In the Baltic Sea region, warming is likely to exceed its global average, particularly in winter and in the northern parts of the area. The warming will be accompanied by a general increase in winter precipitation, but in summer, precipitation may either increase or decrease, with a larger chance of drying in the southern than in the northern parts of the region. Despite the increase in winter precipitation, the amount of snow is generally expected to decrease, as a smaller fraction of the precipitation falls as snow and midwinter snowmelt episodes become more common. Changes in windiness are very uncertain, although most projections suggest a slight increase in average wind speed over the Baltic Sea. Climatic extremes are also projected to change, but some of the changes will differ from the corresponding change in mean climate. For example, the lowest winter temperatures are expected to warm even more than the winter mean temperature, and short-term summer precipitation extremes are likely to become more severe, even in the areas where the mean summer precipitation does not increase. The projected atmospheric changes will be accompanied by an increase in Baltic Sea water temperature, reduced ice cover, and, according to most studies, reduced salinity due to increased precipitation and river runoff. The seasonal cycle of runoff will be modified by changes in precipitation and earlier snowmelt. Global-scale sea level rise also will affect the Baltic Sea, but will be counteracted by glacial isostatic adjustment. According to most projections, in the northern parts of the Baltic Sea, the latter will still dominate, leading to a continued, although decelerated, decrease in relative sea level. The changes in the physical environment and climate will have a number of environmental impacts on, for example, atmospheric chemistry, freshwater and marine biogeochemistry, ecosystems, and coastal erosion. However, future environmental change in the region will be affected by several interrelated factors. Climate change is only one of them, and in many cases its effects may be exceeded by other anthropogenic changes.
S.C. Pryor and A.N. Hahmann
Winds within the atmospheric boundary layer (i.e., near to Earth’s surface) vary across a range of scales from a few meters and sub-second timescales (i.e., the scales of turbulent motions) to extremely large and long-period phenomena (i.e., the primary circulation patterns of the global atmosphere). Winds redistribute momentum and heat, and short- and long-term predictions of wind characteristics have applications to a number of socioeconomic sectors (e.g., engineering infrastructure). Despite its importance, atmospheric flow (i.e., wind) has been subject to less research within the climate downscaling community than variables such as air temperature and precipitation. However, there is a growing comprehension that wind storms are the single biggest source of “weather-related” insurance losses in Europe and North America in the contemporary climate, and that possible changes in wind regimes and intense wind events as a result of global climate non-stationarity are of importance to a variety of potential climate change feedbacks (e.g., emission of sea spray into the atmosphere), ecological impacts (such as wind throw of trees), and a number of other socioeconomic sectors (e.g., transportation infrastructure and operation, electricity generation and distribution, and structural design codes for buildings). There are a number of specific challenges inherent in downscaling wind including, but not limited to, the fact that it has both magnitude (wind speed) and orientation (wind direction). Further, for most applications, it is necessary to accurately downscale the full probability distribution of values at short timescales (e.g., hourly), including extremes, while the mean wind speed averaged over a month or year is of little utility. Dynamical, statistical, and hybrid approaches have been developed to downscale different aspects of the wind climate, but have large uncertainties in terms of high-impact aspects of the wind (e.g., extreme wind speeds and gusts). The wind energy industry is a key application for right-scaled wind parameters and has been a major driver of new techniques to increase fidelity. Many opportunities remain to refine existing downscaling methods, to develop new approaches to improve the skill with which the spatiotemporal scales of wind variability are represented, and for new approaches to evaluate skill in the context of wind climates.