Mean Temperature
Because the mean temperature set of station data from which the gridded dataset of anomalies is created contains numerous station duplicates (see .. for details), the best method for obtaining reasonable gridded coverage is the First Difference Method. We calculated these anomalies with respect to the period 1961 - 1990 using the First Difference Method, an approach developed to maximize the use of available station records (see, e.g., Peterson et al., 1998, 'The First Difference Method: Maximizing Station Density for the Calculation of Long-term Global Temperature Change', Journal of Geophysical Research). The First Difference Method involves calculating a series of calendar-month differences in temperature between successive years of station data (FDyr = Tyr - Tyr-1). For example, when creating a station's first difference series for mean February temperature, we subtract the station's February 1880 temperature from the station's February 1881 temperature to create a February 1881 first difference value. First difference values for subsequent years are calculated in the same fashion by subtracting the station's preceding year temperature for all available years of station data.

For each year and month we sum the 'first difference' value of all stations located within the appropriate 5 X 5 degree box and divide by the total number of stations in the box to get an unweighted first difference value for each grid box. We then calculate a cumulative sum of these gridded first difference values for all years from 1880 to 2004 to produce a time series for each grid box. The cumulative sum is calculated for each grid box and each month of gridded first difference data independently through time. Each grid box time series is then adjusted to create anomalies with respect to the period 1961 - 1990.

Maximum/Minimum Temperature
These datasets were created from station data using the Anomaly Method, a method that uses station averages during a specified base period (1961-1990 in this case) from which the monthly/seasonal/annual departures can be calculated. Because the maximum and minimum station data do not contain the numerous duplicates as does the mean temperature dataset, the Anomaly Method is a viable alternative to the FD Method discussed above. The Anomaly Method can be considered to be superior to the FD Method when calculating gridbox size averages from the standpoint that its use does not require temporal interpolation to fill in missing values. With the FD Method, missing data can result in the loss of data prior to a gap in data due to the nature of the FD Method. Temporal interpolation can be used to fill in missing data in order to retain the full length of the first difference time series, but assumptions have to be made in doing so. (Note: Prior to February 24, 2004 the maximum and minimum gridded anomalies were calculated using the FD Method.)

Anomalies were calculated on a monthly basis for all adjusted stations having at least 25 years of data in the 1961-1990 base period. Station anomalies were then averaged within each 5 X 5 degree grid box to obtain the gridded anomalies. For those grid boxes without adjusted data, anomalies were calculated from the raw station data using the same technique.

Note