Observed trends Annex 1.1
The UK climate data set is presented as a grid of 5 km by 5 km cells containing month-by-month and year-by-year statistics for 28 climate variables or their derivatives. The grids are based on the GB national grid, extended to cover Northern Ireland and the Isle of Man. Data for the Channel Islands are not currently available. Table A1.1 shows the variables available at a monthly resolution and Table A1.2 those available at an annual resolution. The tables also include further details of each of the available variables and their derivatives, including the year from which values are available. Currently the data set includes variables updated to the end of 2005 with subsequent years expected to be periodically added.
|Climate variable||Definition||First year available|
|Maximum air temperature ||Average of the daily highest air temperatures (ºC) ||1914|
|Minimum air temperature||Average of the daily lowest air temperature (ºC)||1914|
|Mean air temperature||Average of mean daily maixmum and mean daily minimum temperatures||1914|
|Days of air frost ||Count of days when the air minimum temperature is below 0 ºC||1961|
|Days of ground frost||Count of days when the grass minimum temperature is below 0 ºC||1961|
|Mean vapour pressure ||Hourly (or 3 hourly) vapour pressure (hPa) averaged over the month||1961|
|Mean relative humidity ||As above but in units of % || 1961 |
|Mean relative wind speed at 10 m||Hourly mean wind speed (knots) at a height of 10 m above ground level averaged over the month|| 1969 |
|Mean sea level pressure||Hourly (or 3 hourly) mean sea level pressure (hPa) averaged over the month||1961|
|Total hours of sunshine||Total hours of bright sunshine during the month, based on the Campbell-Stokes recorder ||1929|
|Total precipitation|| |
Total precipitation amount (mm) during the month
|Days of rain ≥ 1 mm ||Number of days with ≥ 1 mm precipitation||1961|
|Days fo rain ≥ 10 mm ||Number of days with ≥ 10 mm precipitation || 1961 |
|Days of sleet or snow falling||Number of days with sleet or snow falling || 1971 |
|Days of snow cover ||Number of days with greater than 50% of the ground covered by snow at 0900 ||1971|
|Mean cloud cover||Hourly (or 3 hourly) cloud cover (%) averaged over the month||1961|
|Climate variable||Definition||First year available|
|Heating degree days ||∑ (15.5 – daily mean temperature) whenever mean temperature < 15.5 ºC. This assumes both the daily Tmax and Tmin are < 15.5 ºC and in other cases weighted increments are used|| 1961 |
|Cooling degree days||∑ (daily mean temperature – 22) for Tmean > 22 ºC. This assumes both the daily Tmax and Tmin are > 22 ºC and in other cases weighted increments are used||1961|
|Growing degree days ||∑ (daily mean temperature – 5.5) whenever daily mean temperature > 5.5 ºC||1961|
|Extreme temperature range ||Annual maximum temperature minus annual minimum temperature||1961|
|Growing season length||Period bounded by daily mean temperature > 5 ºC for > 5 consecutive days and daily mean temperature < 5 ºC for > 5 consectutive days ||1961|
|Summer 'heatwave' duration||∑ days with daily maximum > 3 ºC above 1961-1990 daily normal temperature for ≥ 5 consecutive days (May to October) ||1961|
|Winter 'heatwave' duration||As summer heatwave but for November to April||1961|
|Summer 'cold wave' duration ||∑ days with daily minimum > 3 ºC below 1961-1990 daily normal temperature for ≥ 5 consecutive days (May to October) ||1961|
|Winter 'cold wave' duration||As sumer cold wave but for November to April ||1961|
|Maximum number of consecutive dry days||Longest spell of consecutive days with precipitation ≤ 0.2 mm during the year||1961|
|Greatest 5-day precipitation total ||Greatest total precipitation amount (mm) for 5 consecutive days during the year||1961|
|Rainfall intensity ||Total precipitation on days with ≥1 mm rainfall divided by count of days with ≥ 1mm during the year||1961|
|Climate variable||Before 1961||1961 onwards |
|Total precipitation ||650||4400|
|Days of rain, annual precipitation indices||n/a||4000|
|Air temperature (max)||320||550|
|Days of ground frost ||n/a||420|
|Degree days, growing season length||n/a||440|
|Heat and cold wave duration||n/a||200|
|Snow (from 1971)||n/a||420|
|Humidity, pressure, wind speed, cloud ||n/a||100|
A challenge in the gridding process is to remove the effects of the constantly varying pool of stations. This could be overcome by only using stations with a complete record but the sparseness of the network that this would lead to would introduce much greater uncertainty due to the spatial interpolation required. Instead, all stations believed to have a good record in any month are used, and every effort made to compensate for missing stations during the gridding process.
The gridding process is accomplished in several stages. Firstly, for most parameters, the monthly average or total values are turned into differences from or percentages of the 1961–1990 long period average (termed anomalies). This generally produces a field that is smoother than the raw observations (termed actuals) and is therefore easier to interpolate. This assumes that a grid of the 1961–1990 average has already been generated. For most parameters, this has been done on a 1 km x 1 km grid. To do this, gaps in the monthly or annual station data are first filled in with estimates generated using relationships with well-correlated neighbour stations. The resulting station averages are then gridded using a combination of multiple regression and spatial interpolation of the regression residuals.
The regression equation is fitted to the station averages using a range of different factors. These include latitude, longitude, altitude, terrain shape, coastal and urban effects. Different combinations of factors are used for different parameters. It is not appropriate to use all geographic factors for all parameters, as there may not be a plausible reason for such a relationship, leading to the possibility of generating spurious correlations that only add noise to the regression surface. The fit of the regression surface to station values will not be perfect, the differences being known as regression residuals. At stations where the residuals are large they tend to be indicative of spurious values and so the residuals are used to help with quality control checks.
The same process is used to generate the monthly and annual gridded datasets. The same range of factors is available for the regression fitting, but since the data being analysed are usually anomalies most of the factors are already accounted for. Often, only a cross-polynomial of latitude and longitude is required to account for broad spatial patterns in the anomalies.
The regression residuals are then interpolated on to a 5 km x 5 km grid using inverse-distance weighting (IDW). This ensures that local variations in the climate are incorporated into the final grid, which is produced when the regression and interpolated residual surfaces are added together. If anomalies were analysed the long term average field is added back on to produce a field of the original parameter.
Testing of different regression models and interpolation methods and settings is carried out by leaving out a set of 10% of the station data. Error statistics of the actual values at these stations compared with values estimated by the grid are calculated and compared. Different settings of the IDW interpolation have been tested, for example varying the power and radius parameters. Spline surfaces have also been tested but were not found to give as good a result. The full method is described in Perry and Hollis (2005a).