Timothy D. Mitchell

Ecosystem service supply and vulnerability to global change in Europe

Global change will alter the supply of ecosystem services that are vital for human well-being. To investigate ecosystem service supply during the 21st century, we used a range of ecosystem models and scenarios of climate and land-use change to conduct a Europe-wide assessment. Large changes in climate and land use typically resulted in large changes in ecosystem service supply. Some of these trends may be positive (for example, increases in forest area and productivity) or offer opportunities (for example, “surplus land” for agricultural extensification and bioenergy production). However, many changes increase vulnerability as a result of a decreasing supply of ecosystem services (for example, declining soil fertility, declining water availability, increasing risk of forest fires), especially in the Mediterranean and mountain regions.

Pattern scaling: An examination of the accuracy of the technique for describing future climates

A fully probabilistic, or risk, assessment of future regional climate changeand its impacts involves more scenarios of radiative forcing than can besimulated by a general (GCM) or regional (RCM) circulation model. Additionalscenarios may be created by scaling a spatial response pattern from a GCM bya global warming projection from a simple climate model. I examine thistechnique, known as pattern scaling, using a particular GCM (HadCM2).Thecritical assumption is that there is a linear relationship between the scaler(annual global-mean temperature) and the response pattern. Previous studieshave found this assumption to be broadly valid for annual temperature; Iextend this conclusion to precipitation and seasonal (JJA) climate. However,slight non-linearities arise from the dependence of the climatic response onthe rate, not just the amount, of change in the scaler. These non-linearitiesintroduce some significant errors into the estimates made by pattern scaling,but nonetheless the estimates accurately represent the modelled changes. Aresponse pattern may be made more robust by lengthening the period from whichit is obtained, by anomalising it relative to the control simulation, and byusing least squares regression to obtain it. The errors arising from patternscaling may be minimised by interpolating from a stronger to a weaker forcingscenario.

Climate data for political areas

The origins of the idea that humans might be enhancing the natural greenhouse effect through emissions of carbon dioxide (and other greenhouse gases) stretch back into the nineteenth century (Tyndall 1863; Arrhenius 1896a 1896b), but it did not ‘fire the imagination of the scientific community’ until the 1970s (Kellogg 1987, 113). Now the annual total of climate-related publications is doubling every decade (Stanhill 2001). As the scope of the challenge to societies posed by climate change has become apparent, policymakers have sought a better understanding of the possible consequences of climate change on all spatial scales. In recent years, much emphasis has been placed on the regional variations in climate change, climate impacts, vulnerability and adaptation. In particular, the Inter-governmental Panel on Climate Change (IPCC) has specifically provided:

Climate change (Communication arising): Regional warming and malaria resurgence

Disease outbreaks are known to be often influenced by local weather, but how changes in disease trends might be affected by long-term global warming is more difficult to establish. In a study of malaria in the African highlands, Hay et al. found no significant change in long-term climate at four locations where malaria incidence has been increasing since 1976. We contend, however, that their conclusions are likely to be flawed by their inappropriate use of a global climate data set. Moreover, the absence of a historical climate signal allows no inference to be drawn about the impact of future climate change on malaria in the region.

Predicting regional climate change: living with uncertainty

Regional climate prediction is not an insoluble problem, but it is a problem characterized by inherent uncertainty. There are two sources of this uncertainty: the unpredictability of the climatic and global systems. The climate system is rendered unpredictable by deterministic chaos; the global system renders climate prediction uncertain through the unpredictability of the external forcings imposed on the climate system. It is commonly inferred from the differences between climate models on regional scales that the models are deficient, but climate system unpredictability is such that this inference is premature; the differences are due to an unresolved combination of climate system unpredictability and model deficiencies. Since model deficiencies are discussed frequently and the two sources of inherent uncertainty are discussed only rarely, this review considers the implications of climatic and global system unpredictability for regional climate prediction. Consequently we regard regional climate prediction as a cascade of uncertainty, rather than as a single result process sullied by model deficiencies. We suggest three complementary methodological approaches: (1) the use of multiple forcing scenarios to cope with global system unpredictability; (2) the use of ensembles to cope with climate system unpredictability; and (3) the consideration of the entire response of the climate system to cope with the nature of climate change. We understand regional climate change in terms of changes in the general circulations of the atmosphere and oceans; so we illustrate the role of uncertainty in the task of regional climate prediction with the behaviour of the North Atlantic thermohaline circulation. In conclusion we discuss the implications of the uncertainties in regional climate prediction for research into the impacts of climate change, and we recognize the role of feedbacks in complicating the relatively simple cascade of uncertainties presented here.

Regional warming and malaria resurgence

Disease outbreaks are known to be often influenced by local weather, but how changes in disease trends might be affected by long-term global warming is more difficult to establish. In a study of malaria in the African highlands, Hay et al. found no significant change in long-term climate at four locations where malaria incidence has been increasing since 1976. We contend, however, that their conclusions are likely to be flawed by their inappropriate use of a global climate data set. Moreover, the absence of a historical climate signal allows no inference to be drawn about the impact of future climate change on malaria in the region.