ERSST

AcronymDefinition
ERSSTExtended Reconstructed Sea Surface Temperature
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2014) and National Climatic Data Center (ERSST.v4; Huang et al.
THE IMPORTANCE OF UNRESOLVED BIASES IN TWENTIETH-CENTURY SEA SURFACE TEMPERATURE OBSERVATIONS: Biases in sea surface temperature observations lead to larger uncertainties in our understanding of mid- to late-twentieth-century climate variability than previously thought
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.
ENSO teleconnection pattern changes over the southeastern United States under a climate change scenario in CMIP5 models
--, and Coauthors, 2016a: Further exploring and quantifying uncertainties for extended reconstructed sea surface temperature (ERSST) version 4 (v4).
REFERENCES
Indices are determined from area-averaged DJF SST anomalies, relative to 1950-2016, from the Extended Reconstructed SST (ERSST), version 3b, dataset.
ADVANCING SCIENCE AND SERVICES DURING THE 2015/16 EL NINO: The NOAA El Nino Rapid Response Field Campaign: Acting on climate forecasts in summer 2015, NOAA rapidly designed and executed the first field campaign to intensively observe atmospheric conditions over the tropical Pacific during a strong El Nino
(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.
ATLANTIC MULTIDECADAL VARIABILITY AND THE U.K. ACSIS PROGRAM
5: Observational extrapolation (OISST, HadlSST, ERSST v4) Ch.
I. INTRODUCTION TO EXPLAINING EXTREME EVENTS OF 2016 FROM A CLIMATE PERSPECTIVE
(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.
4. THE EXTREME 2015/16 EL NINO, IN THE CONTEXT OF HISTORICAL CLIMATE VARIABILITY AND CHANGE
Huang, B., and Coauthors, 2014: Extended reconstructed sea surface temperature version 4 (ERSST.v4): Part I.
5. ECOLOGICAL IMPACTS OF THE 2015/16 EL NINO IN THE CENTRAL EQUATORIAL PACIFIC: Coral reef and seabird communities in the central equatorial Pacific were disrupted by record-setting sea surface temperatures, linked to an anthropogenically forced trend, during the 2015/16 El Nino
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.
20. EXTREME RAINFALL (R20MM, RX5DAY) IN YANGTZEHUAI, CHINA, IN JUNE-JULY 2016: THE ROLE OF ENSO AND ANTHROPOGENIC CLIMATE CHANGE
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.
A call for new approaches to quantifying biases in observations of sea surface temperature: bias estimation for sea surface temperature is discussed and recommendations for improving data, observational metadata, and uncertainty modeling are given
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.
Observing and predicting the 2015/16 El Nino
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