ERSSTExtended Reconstructed Sea Surface Temperature
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Figure 1 shows the correlations between the observed DJF mean daily precipitation anomalies and the November Nino 3 index based on ERSST for the period of 1970 to 2010.
--, and Coauthors, 2016a: Further exploring and quantifying uncertainties for extended reconstructed sea surface temperature (ERSST) version 4 (v4).
(a) Atlantic-mean (0[degrees]-65[degrees]N, 75[degrees]-7.S[degrees]W) sea surface temperatures (red) and global mean excluding the North Atlantic (blue) sea surface temperatures for annual means (thin lines) and decadal mean (thick lines) based on the Extended Reconstructed SST, version 4 (ERSST.v4), dataset (Huang et al.
5: Observational extrapolation (OISST, HadlSST, ERSST v4) Ch.
(Other winter months yielded similar results.) Figure 4.1 shows histograms of monthly ERSST.V5 Nino3 and Nino4 indices, compared with two different probability distribution functions (PDFs) determined not by fitting the histogram, but by fitting two different Markov processes to each index time series: an AR1 process (or red noise; e.g., Frankignoul and Hasselmann 1977) with a memory time scale on the order of several months, yielding a Gaussian (normal) distribution; and a "stochastically generated skewed" process (SGS; Sardeshmukh et al.
The DJF oceanic Nino index (ONI, 3-month running mean of ERSST.v4 SST anomalies in the Nino-3.4 region), based on centered 30-year base periods updated every 5 years, was used as an indicator of the ENSO.
There are currently three families of SST datasets available that take different approaches to bias adjustment [HadSST/Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadlSST), ERSST, and COBE], However, all still use approaches that are essentially adaptations of methods originally developed decades ago.
Some datasets like the NOAA Extended Reconstructed SST (ERSST) opt to not include satellite information in order to preserve the consistency, or homogeneity, of the record.