Abstract

We develop methods for adjusting grid box average temperature time series for the effects on variance of changing numbers of contributing data. Owing to the different sampling characteristics of the data, we use different techniques over land and ocean. The result is to damp average temperature anomalies over a grid box by an amount inversely related to the number of contributing stations or observations. Variance corrections influence all grid box time series but have their greatest effects over data sparse oceanic regions. After adjustment, the grid box land and ocean surface temperature data 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 temperature data sets and apply a limited spatial interpolation. The effects of our procedures on hemispheric and global temperature anomaly series are small.

Keywords

Interpolation (computer graphics)Anomaly (physics)Sampling (signal processing)Series (stratigraphy)Sea surface temperatureGridEnvironmental scienceVariance (accounting)Multivariate interpolationTime seriesClimatologyMeteorologySurface (topology)StatisticsMathematicsGeologyComputer scienceGeographyGeodesyGeometryPhysics

Affiliated Institutions

Related Publications

Publication Info

Year
2001
Type
article
Volume
106
Issue
D4
Pages
3371-3380
Citations
310
Access
Closed

External Links

View on DOI.org

Social Impact

Altmetric
PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

310
OpenAlex

Cite This

P. D. Jones, Timothy J. Osborn, Keith R. Briffa et al. (2001). Adjusting for sampling density in grid box land and ocean surface temperature time series. Journal of Geophysical Research Atmospheres , 106 (D4) , 3371-3380. https://doi.org/10.1029/2000jd900564

Identifiers

DOI
10.1029/2000jd900564