This paper describes the construction of an updated gridded climate dataset (referred to as CRU TS3.10) from monthly observations at meteorological stations across the world's land areas. Station anomalies (from 1961 to 1990 means) were interpolated into 0.5° latitude/longitude grid cells covering the global land surface (excluding Antarctica), and combined with an existing climatology to obtain absolute monthly values. The dataset includes six mostly independent climate variables (mean temperature, diurnal temperature range, precipitation, wet‐day frequency, vapour pressure and cloud cover). Maximum and minimum temperatures have been arithmetically derived from these. Secondary variables (frost day frequency and potential evapotranspiration) have been estimated from the six primary variables using well‐known formulae. Time series for hemispheric averages and 20 large sub‐continental scale regions were calculated (for mean, maximum and minimum temperature and precipitation totals) and compared to a number of similar gridded products. The new dataset compares very favourably, with the major deviations mostly in regions and/or time periods with sparser observational data. CRU TS3.10 includes diagnostics associated with each interpolated value that indicates the number of stations used in the interpolation, allowing determination of the reliability of values in an objective way. This gridded product will be publicly available, including the input station series (http://www.cru.uea.ac.uk/ and http://badc.nerc.ac.uk/data/cru/). © 2013 Royal Meteorological Society

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,

CRU TS (Climatic Research Unit gridded Time Series) is a widely used climate dataset on a 0.5° latitude by 0.5° longitude grid over all land domains of the world except Antarctica. It is derived by the interpolation of monthly climate anomalies from extensive networks of weather station observations. Here we describe the construction of a major new version, CRU TS v4. It is updated to span 1901-2018 by the inclusion of additional station observations, and it will be updated annually. The interpolation process has been changed to use angular-distance weighting (ADW), and the production of secondary variables has been revised to better suit this approach. This implementation of ADW provides improved traceability between each gridded value and the input observations, and allows more informative diagnostics that dataset users can utilise to assess how dataset quality might vary geographically.

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.

This study is an extensive revision of the Climatic Research Unit (CRU) land station temperature database that has been used to produce a grid‐box data set of 5° latitude × 5° longitude temperature anomalies. The new database (CRUTEM4) comprises 5583 station records of which 4842 have enough data for the 1961–1990 period to calculate or estimate the average temperatures for this period. Many station records have had their data replaced by newly homogenized series that have been produced by a number of studies, particularly from National Meteorological Services (NMSs). Hemispheric temperature averages for land areas developed with the new CRUTEM4 data set differ slightly from their CRUTEM3 equivalent. The inclusion of much additional data from the Arctic (particularly the Russian Arctic) has led to estimates for the Northern Hemisphere (NH) being warmer by about 0.1°C for the years since 2001. The NH/Southern Hemisphere (SH) warms by 1.12°C/0.84°C over the period 1901–2010. The robustness of the hemispheric averages is assessed by producing five different analyses, each including a different subset of 20% of the station time series and by omitting some large countries. CRUTEM4 is also compared with hemispheric averages produced by reanalyses undertaken by the European Centre for Medium‐Range Weather Forecasts (ECMWF): ERA‐40 (1958–2001) and ERA‐Interim (1979–2010) data sets. For the NH, agreement is good back to 1958 and excellent from 1979 at monthly, annual, and decadal time scales. For the SH, agreement is poorer, but if the area is restricted to the SH north of 60°S, the agreement is dramatically improved from the mid‐1970s.