Intercomparison of Climate Data Sets as a Measure of Observational Uncertainty

Abstract

Uncertainties in climate observations are revealed when alternate observationally based data sets are compared. General circulation model-based "reanalyses" of meteorological observations will yield different results from different models, even if identical sets of raw unanalyzed data form their starting points. We have examined 25 longitude-latitude fields (including selected levels for three-dimensional quantities) encompassing atmospheric climate variables for which the PCMDI observational data base contains two or more high-quality sources. For the most part we compare ECMWF with NCEP reanalysis. In some cases, we compare in situ and / or satellite-derived data with reanalysis. To obtain an overview of the differences for all 25 fields, we use a graphical technique developed for climate model diagnosis: a "portrait diagram" displaying root-mean-square differences between the alternate data sources. With a few exceptions (arising from the requirement that RMS differences be normalized to accommodate different units of variables) the portrait diagrams indicate areas of agreement and disagreement that can be confirmed by examining traditional graphics such as zonal mean plots. In accord with conventional wisdom, the greatest agreement between alternate data sets-hence the smallest implied observational uncertainty-occurs for upper tropospheric zonal wind. We also find fairly good agreement between reanalysis and more direct measures of precipitation, suggesting that modern observational systems are resolving some longstanding problems with its measurement.

Influence of wind speed averaging on estimates of dimethylsulfide emission fluxes

“…It is also not clear that the approach of using a Schmidt number correction factor to expand k w to other gases will hold here, given that under such violently turbulent conditions k w may not be independent of gas solubility [ Frost and Upstill‐Goddard , 1999]. Since such extreme events generally are not well‐simulated in current atmospheric models [e.g., Covey et al , 2002], it is unlikely that the influence of such events on air‐sea exchange processes is captured in current climate and atmospheric chemistry models.…”

North American extreme precipitation events and related large-scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

“…While satellite based products of daily precipitation offer more complete spatial coverage than stations, their ability to reproduce the extreme precipitation derived from station data is severely deficient (Timmermans et al 2018), likely due to complications in the retrieval algorithms at the extreme end of the precipitation distribution as well as the infrequent temporal sampling of polar orbits. These differences form a crude estimate of the observational uncertainty (Covey et al 2002) but do not provide information about common systematic errors. Due to the intermittent nature of precipitation, the gridding process is more challenging than it is for smoothly varying fields like surface air temperature.…”

The effect of horizontal resolution on simulation quality in the Community Atmospheric Model, CAM5.1

“…There is little guidance in the literature as to which of the observational products is superior. Hence, we interpret these differences as a crude measure of observational uncertainty as in Covey et al [2002] and plot the observations in Figure 2 as an envelope encompassing the minimum and maximum values reported. A comprehensive discussion of observational uncertainty would include satellite retrieval algorithms as well as the sparseness of station data in many parts of the world but is outside the scope of this paper.…”