We describe the construction of a 10' latitude/longitude data set of mean monthly surface climate over global land areas, excluding Antarctica. The climatology includes 8 climate elements -precipitation, wet-day frequency, temperature, diurnal temperature range, relative humidity, sunshine duration, ground frost frequency and windspeed -and was interpolated from a data set of station means for the period centred on 1961 to 1990. Precipitation was first defined in terms of the parameters of the Gamma distribution, enabling the calculation of monthly precipitation at any given return period. The data are compared to an earlier data set at 0.5º latitude/longitude resolution and show added value over most regions. The data will have many applications in applied climatology, biogeochemical modelling,

The authors describe the construction of a 0.5° latitude/longitude gridded dataset of monthly terrestrial surface climate over for the period 1901-1996. The dataset comprises a suite of 7 climate elements: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressure, cloud cover and ground-frost frequency. The spatial coverage extends over all land areas, excluding Antarctica. Fields of monthly climate anomalies, relative the 1961-1990 mean, were interpolated from surface climate data. The anomaly grids were then added to a 1961-1990 mean monthly climatology (described in Part I) to arrive at grids of monthly climate. 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, which are described and evaluated. It is argued that this new dataset represents an advance 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 is available from the Climatic Research Unit.

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The world's climate is changing and will continue to change into the coming century at rates projected to be unprecedented in recent human history. The risks associated with these changes are real but highly uncertain. Societal vulnerability to the risks associated with climate change may exacerbate ongoing social and economic challenges, particularly for those parts of societies dependent on resources that are sensitive to changes in climate. Risks are apparent in agriculture, fisheries and many other components that constitute the livelihood of rural populations in developing countries. In this paper we explore the nature of risk and vulnerability in the context of climate change and review the evidence on present-day adaptation in developing countries and on coordinated international action on future adaptation. We argue that all societies are fundamentally adaptive and there are many situations in the past where societies have adapted to changes in climate and to similar risks. But some sectors are more sensitive and some groups in society more vulnerable to the risks posed by climate change than others. Yet all societies need to enhance their adaptive capacity to face both present and future climate change outside their experienced coping range. The challenges of climate change for development are in the present. Observed climate change, present-day climate variability and future expectations of change are changing the course of development strategies -development agencies and governments are now planning for this adaptation challenge. The primary challenge, therefore, posed at both the scale of local natural resource management and at the scale of international agreements and actions, is to promote adaptive capacity in the context of competing sustainable development objectives.

This paper reviews observed and possible future (2000-2100) continentwide changes in temperature and rainfall for Africa. For the historic period we draw upon a new observed global climate data set which allows us to explore aspects of regional climate change related to diurnal temperature range and rainfall variability. The latter includes an investigation of regions where seasonal rainfall is sensitive to El Niño climate variability. This review of past climate change provides the context for our scenarios of future greenhouse gas-induced climate change in Africa. These scenarios draw upon the draft emissions scenarios prepared for the Intergovernmental Panel on Climate Change's Third Assessment Report, a suite of recent global climate model experiments, and a simple climate model to link these 2 sets of analyses. We present a range of 4 climate futures for Africa, focusing on changes in both continental and regional seasonal-mean temperature and rainfall. Estimates of associated changes in global CO 2 concentration and global-mean sea-level change are also supplied. These scenarios draw upon some of the most recent climate modelling work. We also identify some fundamental limitations to knowledge with regard to future African climate. These include the often poor representation of El Niño climate variability in global climate models, and the absence in these models of any representation of regional changes in land cover and dust and biomass aerosol loadings. These omitted processes may well have important consequences for future African climates, especially at regional scales. We conclude by discussing the value of the sort of climate change scenarios presented here and how best they should be used in national and regional vulnerability and adaptation assessments.

An objective scheme, initially developed by Jenkinson and Collison, is used to classify daily circulation types over the British Isles, along the lines of the subjective method devised by Lamb. The scheme uses daily grid-point mean sea-level pressure data for the region. The results of the analysis over the period 1881-1989 are compared with 'true' Lamb weather types. The frequencies of objectively developed types are highly correlated with traditional Lamb types, especially so for synoptic (cyclonic and anticyclonic) types, although still good for wind directional types. Comparison of the two classification schemes reveals negligible differences between the correlations of the counts of circulation types and regional temperature and rainfall. The major difference between the two classification schemes is that the decline of the westerlies since 1940 is less evident with the objective scheme.

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Various methods for combining station temperature and precipitation time series into regional series are examined. Interpolation of the station series on to regular grid‐boxes of some kind reduces the effects of both spatial and temporal changes in station coverage. Regional time series are best produced by using anomaly or standardized anomaly values rather than the raw values. For temperature, and for spatially coherent regions in terms of precipitation variance, the exact method does not seriously affect the resulting time series, provided anomalies are used, although the magnitudes of trends may differ. For regions with large spatial variations in precipitation variance, the additional step of standardizing the anomaly values is recommended. Both anomaly and standardized anomaly series can be easily transformed back to the original units, although the exact method for doing so can alter the resulting time series in non‐trivial ways.