Interpolation of monthly mean temperatures using cokriging in spherical coordinates

Abstract

Paleoclimate reconstructions are generally validated on recent periods. To obtain a set of instrumental records at the regional scale, a time series of monthly mean temperatures in Northeastern Canada were interpolated for the 1961-2000 period. Records were provided by 202 meteorological stations. Temperatures derived from a Canadian regional climate model (Climate Model CRCM 4.2.3 from the AMNO domain produced at ∼50 km resolution) were added as secondary information to take into account local heterogeneity and temporal dependencies. Geostatistical interpolation of the measured temperature was calculated using CRCM modelled data as a covariable and then compared to an ordinary kriging performed on a time series of mean temperature anomalies. Spherical distances between locations were calculated taking into account the curvature of the Earth with monthly semivariances being modelled using Cauchy variograms. Mean absolute error values (1.5 ± 1.2°C) were calculated for the whole period using cross-validation procedures. Errors were found to have the same order of magnitude in the central part of the study area where few recorded temperatures were available. Monthly mean temperature grids are publicly available through the Institut National de la Recherche Scientifique

Undocumented migration in response to climate change

“…Cokriging is a method frequently employed to interpolate climate measures and indices (Aznar et al, 2013; Rogelis & Werner, 2013), and it allowed us to account for the correlation between climate and elevation using a Digital Elevation Model (DEM) (Danielson & Gesch, 2011) as covariate in the interpolation model. We employed a bootstrap resampling procedure to cross-validate the interpolation results and found the local estimates to be robust.…”

Cross-correlation modeling of European windstorms: A cokriging approach for optimizing surface wind estimates

“…While kriging proved to be an accurate method of interpolation, high-error stations, defined for this study as modeled values ±1.96 standard errors from station data (i.e., 2 standard deviations, p < 0.05), were observed predominantly in areas of complex terrain. Recent research has noted that cokriging improves the accuracy of wind surface estimates over ordinary kriging (Aznar et al, 2012;Li et al, 2012;Luo et al, 2008Luo et al, , 2011Odeh et al, 1995;Singh et al, 2011;Wang et al, 2011;Wenxia et al, 2010;Zlatev et al, 2010); however, cokriging wind models focused on the use of elevation as the only covariate, while suggesting that other covariates could be important (Luo et al, 2008;Sliz-Szkliniarz and Vogt, 2011). No research has examined the improvement offered by cokriging with multiple variables in estimating extreme winds.…”

Spatial Prediction of Air Temperature in East Central Anatolia of Turkey

“…Rates of many soil processes, which have strong control on plan growth are directly and indirectly related to soil temperature (Campbell and Norman, 1998). Geology and geomorphology, hydrology, landcover and atmospheric circulation patterns may also have a strong influence on spatial distribution of air temperature (Aznar et al, 2013). Different landscape vegetation covers may form at similar topographic conditions, due to difference in air temperature.…”