Adjusting for sampling density in grid box land and ocean surface temperature time series

We develop methods for adjusting grid box average temperature time series f or the effects on variance of changing numbers of contributing data. Owing to the different sampling characteristics of the data, we use different tec hniques over land and ocean. The result is to damp average temperature anom alies over a grid box by an amount inversely related to the number of contr ibuting stations or observations. Variance corrections influence all grid b ox time series but have their greatest effects over data sparse oceanic reg ions. After adjustment, the grid box land and ocean surface temperature dat a sets are unaffected by artificial Variance changes which might affect, in particular, the results of analyses of the incidence of extreme values. We combine the adjusted land surface air temperature and sea surface temperat ure data sets and apply a limited spatial interpolation. The effects of our procedures on hemispheric and global temperature anomaly series are small.