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Air–Sea fluxes from ICOADS: the construction of a new gridded dataset with uncertainty estimates
Author(s) -
Berry David I.,
Kent Elizabeth C.
Publication year - 2011
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.2059
Subject(s) - environmental science , consistency (knowledge bases) , climatology , longitude , meteorology , autocorrelation , latitude , interpolation (computer graphics) , flux (metallurgy) , residual , grid , statistics , mathematics , computer science , geography , geology , geodesy , animation , materials science , geometry , computer graphics (images) , algorithm , metallurgy
The methods used to calculate a new in situ global dataset of air–sea exchanges, called the NOCS Flux Dataset v2.0, are described. The fluxes have been derived from in situ weather reports from Voluntary Observing Ships (VOS) covering the period 1973–2006. The reports have been adjusted for known biases and residual uncertainties estimated. The dataset is constructed using Optimal Interpolation (OI) using new estimates of random uncertainty in the observations. Daily fields have been calculated on a 1° latitude by 1° longitude grid, each grid box and time step have an associated uncertainty estimate. Monthly fields have been calculated from simple averages of the daily fields and monthly uncertainty estimates from the daily uncertainties, using estimates of the autocorrelation between the daily uncertainty estimates. The uncertainties due to the choice of flux parameterisation have not been accounted for. Bias adjustments applied to the data are shown to reduce trends in the data and to improve the consistency of estimates of air temperature, sea surface temperature (SST) and specific humidity. The bias adjustments also improve the agreement of NOCS v2.0 with independent data from research moorings. Cross‐validation of the dataset suggests that the uncertainty estimates are realistic, but that the uncertainties are probably underestimated in high variability regions and overestimated in regions with lower variability. Copyright © 2009 Royal Meteorological Society