Reanalyses trends
One
of the most important topics and calculations in climate science is
trend, aiming to determine long term changes. Significant issues exist
in the observational record, and methods correcting the problems
themselves need to be explained and verified. In a recent update to the
U.S. Historical Climate Network (HCN) station data, Vose et al. compare
the observational record against several reanalyses near surface air
temperature and their ensemble. In looking at the continental United
States, their Figure 1 shows the revised HCN trend is larger than the
uncorrected data, but also remarkably close to the ensemble of the
reanalyses. Also, despite the differences in trends of the reanalyses,
there is very good agreement in the reanalyses interannual variability
around the trends (their figure 3). The bottom line is that the
corrections to HCN are in agreement with reanalyses (all are
statistically significant warm trends), but it is noted that this is not
a validation of the corrections.
The spatial distribution of reanalysis trend relative to the HCN trend shows substantial local variations among the reanalyses (their figure 4). So, while the observational forcing imposed on reanalyses can influence the large scale features, the model predictions used to make the analyses impart some uncertainty related to the model physical parameterizations. If the model errors are random, the ensemble should then minimize the error. Errors that are systematic among all reanalyses would persist in the ensemble.
This paper demonstrates some important points about reanalyses. Any one reanalysis may have uncertainty in any given research project. Multiple reanalyses can help identify these uncertainties and perhaps the background model biases in the reanalysis. However, the reanalyses output variables being compared must have equivalent formulations to take advantage of the availability of the current modern reanalyses through such intercomparisons. Likewise, on hourly surface output in MERRA and CFSR were useful in this study.
The spatial distribution of reanalysis trend relative to the HCN trend shows substantial local variations among the reanalyses (their figure 4). So, while the observational forcing imposed on reanalyses can influence the large scale features, the model predictions used to make the analyses impart some uncertainty related to the model physical parameterizations. If the model errors are random, the ensemble should then minimize the error. Errors that are systematic among all reanalyses would persist in the ensemble.
This paper demonstrates some important points about reanalyses. Any one reanalysis may have uncertainty in any given research project. Multiple reanalyses can help identify these uncertainties and perhaps the background model biases in the reanalysis. However, the reanalyses output variables being compared must have equivalent formulations to take advantage of the availability of the current modern reanalyses through such intercomparisons. Likewise, on hourly surface output in MERRA and CFSR were useful in this study.
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