Abstract

Large-scale observed precipitation climatologies are needed for a variety of purposes in the fields of climate and environmental modeling. Although new satellite-derived precipitation estimates offer the prospect of near-global climatologies covering the last one or two decades, historical assessments of precipitation and its variability in time remain dependent on conventional gauge observations. A number of questions need to be asked of the existing precipitation climatologies that use such gauge observations. What time period do they sample? What is the spatial density of gauge coverage? What adjustments are made for measurement bias? And what interpolation method is used to convert them to regular grids? Different precipitation climatologies, nominally describing the same variable, can yield very different answers when used as inputs in either the fields of climate model validation or environmental modeling.

This paper explores some of the reasons for these differences by examining the importance of the first two questions listed above—the temporal and spatial sampling of the precipitation normals that form the basis of these types of climatologies. The authors draw upon subcontinental examples from tropical North Africa and Europe and show that, in the presence of significant decadal-scale precipitation variability, climatologies constructed from the same station network, but sampling different 30-yr time periods (i.e., 1931–60 and 1961–90), can vary by 25% or more. Using the same two regions, the authors also examine the influence of different spatiotemporal gauge sampling strategies on the construction of a long-term, “twentieth-century,” precipitation climatology. They show that, in the presence of multidecadal variability in precipitation, a strategy that favors more complete spatial coverage at the expense of temporal fidelity can induce biases of 5%–10% in the resulting climatology. They compare their 30-yr precipitation climatologies with those of and . In Europe, where twentieth-century precipitation exhibits little interdecadal variability at the regional scale, different interpolation methods and station networks are the major cause of variations between these climatologies. Conversely, in tropical North Africa, where historical precipitation shows decadal-scale departures from the long-term mean, differences between climatologies due to temporal sampling strategies are at least as great as those arising from alternative interpolation techniques and station distributions.

The authors argue for careful consideration of the appropriateness of a given climatology for any application, in particular the time period it represents, or at least an awareness of potential pitfalls in its use.

Abstract

This article explores the influence of personal values and ontological beliefs on people’s perceptions of possible abrupt changes in the Earth’s climate system and on their climate change mitigation preferences. The authors focus on four key areas of risk perception: concern about abrupt climate change as distinct to climate change in general, the likelihood of abrupt climate changes, fears of abrupt climate changes, and preferences in how to mitigate abrupt climate changes. Using cultural theory as an interpretative framework, a multimethodological approach was adopted in exploring these areas: 287 respondents at the University of East Anglia (UK) completed a three-part quantitative questionnaire, with 15 returning to participate in qualitative focus groups to discuss the issues raised in more depth. Supporting the predictions of cultural theory, egalitarians’ values and beliefs were consistently associated with heightened perceptions of the risks posed by abrupt climate change. Yet many believed abrupt climate change to be capricious, irrespective of their psychometrically attributed worldviews or “ways of life.” Mitigation preferences—across all ways of life—were consistent with the “hegemonic myth” dominating climate policy, with many advocating conventional regulatory or market-based approaches. Moreover, a strong fatalistic narrative emerged from within abrupt climate change discourses, with frequent referrals to helplessness, societal collapse, and catastrophe.

Abstract

The construction of a 0.5° lat × 0.5° long surface climatology of global land areas, excluding Antarctica, is described. The climatology represents the period 1961–90 and comprises a suite of nine variables: precipitation, wet-day frequency, mean temperature, diurnal temperature range, vapor pressure, sunshine, cloud cover, ground frost frequency, and wind speed. The climate surfaces have been constructed from a new dataset of station 1961–90 climatological normals, numbering between 19 800 (precipitation) and 3615 (wind speed). The station data were interpolated as a function of latitude, longitude, and elevation using thin-plate splines. The accuracy of the interpolations are assessed using cross validation and by comparison with other climatologies.

This new climatology represents an advance over earlier published global terrestrial climatologies in that it is strictly constrained to the period 1961–90, describes an extended suite of surface climate variables, explicitly incorporates elevation as a predictor variable, and contains an evaluation of regional errors associated with this and other commonly used climatologies. The climatology is already being used by researchers in the areas of ecosystem modelling, climate model evaluation, and climate change impact assessment.

The data are available from the Climatic Research Unit and images of all the monthly fields can be accessed via the World Wide Web.

Abstract

The authors describe the construction of a 0.5° lat–long gridded dataset of monthly terrestrial surface climate for the period of 1901–96. The dataset comprises a suite of seven climate elements: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapor pressure, cloud cover, and ground frost frequency. The spatial coverage extends over all land areas, including oceanic islands but excluding Antarctica. Fields of monthly climate anomalies, relative to the 1961–90 mean, were interpolated from surface climate data. The anomaly grids were then combined with a 1961–90 mean monthly climatology (described in Part I) to arrive at grids of monthly climate over the 96-yr period.

The primary variables—precipitation, mean temperature, and diurnal temperature range—were interpolated directly from station observations. The resulting time series are compared with other coarser-resolution datasets of similar temporal extent. The remaining climatic elements, termed secondary variables, were interpolated from merged datasets comprising station observations and, in regions where there were no station data, synthetic data estimated using predictive relationships with the primary variables. These predictive relationships are described and evaluated.

It is argued that this new dataset represents an advance over other products because (i) it has higher spatial resolution than other datasets of similar temporal extent, (ii) it has longer temporal coverage than other products of similar spatial resolution, (iii) it encompasses a more extensive suite of surface climate variables than available elsewhere, and (iv) the construction method ensures that strict temporal fidelity is maintained. The dataset should be of particular relevance to a number of applications in applied climatology, including large-scale biogeochemical and hydrological modeling, climate change scenario construction, evaluation of regional climate models, and comparison with satellite products. The dataset is available from the Climatic Research Unit and is currently being updated to 1998.