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

[1] We compare versions of six interpolation methods for the interpolation of daily precipitation, mean, minimum and maximum temperature, and sea level pressure from station data over Europe from 1961 to 1990. The interpolation methods evaluated are global and local kriging, two versions of angular distance weighting, natural neighbor interpolation, regression, 2D and 3D thin plate splines, and conditional interpolation. We first evaluated, using station cross-validation and several skill scores, relative skill of each method at estimating point values, looking at spatial and temporal patterns and the frequency distribution of the variables. We then compared, for precipitation, gridded area averages from the candidate interpolation methods against existing high-resolution gridded data sets for the UK and the Alps, which are derived from a much denser network of stations. In both point and area-average cases, differences in skill between interpolation methods at any one point are smaller than the range in skill for a single method either across the domain, or in different seasons. The main control on spatial patterns of interpolation skill is density of the station network, with topographic complexity a compounding factor. The relative skill of different methods remains relatively constant through time, despite a varying station network. Skill in interpolating extreme events is lower than for average days, but relative skill of different methods remains the same. We select global kriging as the best performing method overall, for use in the development of a daily, high-resolution, long-term, European data set of climate variables as part of the EU funded ENSEMBLES project.

Abstract. We present the first analysis of global and hemispheric surface warming trends that attempts to quantify the major sources of uncertainty. We calculate global and hemispheric annual temperature anomalies by combining land surface air temperature and sea surface temperature (SST) through an optimal averaging technique. The technique allows estimation of uncertainties in the annual anomalies resulting from data gaps and random errors. We add independent uncertainties due to urbanisation, changing land-based observing practices and SST bias corrections. We test the accuracy of the SST bias corrections, which represent the largest source of uncertainty in the data, through a suite of climate model simulations. These indicate that the corrections are likely to be fairly accurate on an annual average and on large space scales. Allowing for serial correlation and annual uncertainties, the best linear fit to annual global surface temperature gives an increase of 0.61 ñ 0.16øC between 1861 and 2000. Estimating Uncertainties in Temperature DataLand surface-air temperature (LAT) and SST observations are taken from a new global data set (HadCRUTv, Jones et al., 2001) whose variance is homogenised for local temporal variations in data density. To assess the uncertainties in annual global and hemispheric average surface temperature anomalies due to data gaps, random data representivity errors and measurement errors, we employ a two-step optimal averaging (OA) method. The OA method provides a better estimate of the true mean than does a simple average and a consistent flamework on which to add independent uncertainties due to other factors described below.Step 1

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