UPDATE: There was an error in the November 2014 update of the ERSST.v4 sea surface temperature data supplied by NOAA. It impacted the data presented in this post. It was not Nick Stokes’s or my error. It was simply NOAA working out the bugs of updating a new dataset. Things like that happen. As opposed to rewriting this post, I’ve replaced the illustrations with gif animations showing the incorrect data (upon which this post was based) and the correct data. That way the comments on the thread will still make sense, because they were referring to the erroneous data.
(Oops, forgot to note: Subsequent to the correction, KNMI added the new ERSST.v4 dataset to the Monthly observations at their Climate Explorer. The corrections in this post use the data from the KNMI Climate Explorer.)
Thanks to Kevin and Nick for finding the problem and advising me of it.
# # #
In the post NOAA Is Updating Their Sea Surface Temperature Dataset, we introduced a new dataset from NOAA, their ERSST.v4 data, which is an upgrade of their ERSST.v3b dataset. It’s important because ERSST is the sea surface temperature component of the NOAA NCDC merged land + ocean surface temperature data. In this post, we’ll expand the introduction and present a quick look at the data.
NOAA has recently published two papers detailing their new ERSST.v4 Extended Reconstructed Sea Surface Temperature dataset.
- Huang et al. (2014) Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4), Part I. Upgrades and Intercomparisons, and
- Liu et al. (2014) Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4): Part II. Parametric and Structural Uncertainty Estimations.
The papers are paywalled, but I bought both to confirm a few things I suspected. (Merry Christmas to me.)
NOAA has posted its new ERSST.v4 data online, and it can be found easily through Google. NOAA includes ERSST.v4 sea surface temperature-based ENSO and PDO indices here. They also include the monthly global data in ascii format from 1854 through 2014 here.
I do not have the capability to deal with the monthly data in that format. (And I also prefer to download data through the KNMI Climate Explorer so that people can easily replicate my work.) But I wanted to present graphs of ERSST.v4 data in a post, so I turned to someone who comments regularly at WattsUpWithThat and who most people would not consider a climate skeptic, Nick Stokes. On this matter, Nick Stokes is as close to an independent third party as I can think of. Nick was more than happy to examine new sea surface temperature data, and he did so for the period of 1960 to 2014. (Thank you, Nick, for your assistance.) Nick also downloaded ERSST.v3b data in the same ascii format and created time series data for two latitude bands (globally and 60S-60N) so that we could confirm his methods against the data available through the KNMI Climate Explorer. Thus, what is reported in this post should be a correct representation of the ERSST.v4 global data.
SHIP-BUOY BIAS CORRECTIONS IN ERSST.v4
In the ERSST.v4 Part I paper, Huang et al. (2014) write (my boldface):
5.3 Ship-buoy SST adjustment
In addition to the ship SST bias adjustment, the drifting and moored buoy SSTs in ERSST.v4 are adjusted toward ship SSTs, which was not done in ERSST.v3b. Since 1980 the global marine observations have gone from a mix of roughly 10% buoys and 90% ship-based measurements to 90% buoys and 10% ship measurements (Kennedy et al. 2011). Several papers have highlighted, using a variety of methods, differences in the random biases and a systematic difference between ship-based and buoy-based measurements, with buoy observations systematically cooler than ship observations (Reynolds et al. 2002; Reynolds et al. 2010; Kent et al. 2010; amongst others). Here the adjustment is determined by (1) calculating the collocated ship-buoy SST difference over the global ocean from 1982-2012, (2) calculating the global areal weighted average of ship-buoy SST difference, (3) applying a 12-month running filter to the global averaged ship-buoy SST difference, and (4) evaluating the mean difference and its STD of ship-buoy SSTs based on the data from 1990 to 2012 (the data are noisy before 1990 due to sparse buoy observations). The mean difference of ship-buoy between 1990 and 2012 is 0.12°C with a STD of 0.02°C (all rounded to hundredths in precision). The mean difference of 0.12°C is at the lower-end of published values of 0.12°C to 0.18°C (e.g. Reynolds et al. 2002; Reynolds et al. 2010; Kent et al. 2010). Although buoy SSTs are generally more homogeneous than ship SSTs, they are adjusted here because otherwise it would be necessary to adjust ship SSTs before1980 when there were no or very few buoys. As expected, the global averaged SSTA trends between 1901 and 2012 (refer to Table 2) are the same whether buoy SSTs are adjusted to ship SSTs or the reverse. However, the global mean SST is 0.06°C warmer after 1980 in ERSST.v4 because of the buoy adjustments (not shown) and there are therefore impacts on the long-term trends compared to applying no adjustment to account for the change in observational platforms.
Figure 1 presents the difference in global sea surface temperature anomalies (reference 1981 to 2010) between ERSST.v4 and ERSST.v3b for the period of 1960 to present. Due to the volatility of the difference, I’ve also smoothed it with a 12-month running-mean filter. There are many reasons for the differences between the two datasets (different corrections, different references for quality control, etc.), but the period after 1980 is definitely warmer in the ERSST.v4 data than in the ERSST.v3b data. That would, of course, confirm what Huang et al. (2014) noted: (1) the new ERSST.v4 data have been adjusted for ship-buoy bias and (2) those adjustments lead to a higher long-term warming trend in the ERSST.v4 data.
So HADSST3 is no long the only sea surface temperature dataset that’s corrected for ship-buoy bias.
But how do they compare during the satellite era, which has been the focus of my ENSO research and model-data comparisons over the past few years? See Figure 2.
ERSST.v4 has a slightly lower warming rate than the ERSST.v3b data. And as shown in Figure 3, the global sea surface temperature trend of the ERSST.v4 data is comparable to (very slight less than) the Reynolds OI.v2 data.
And if we compare the ERSST.v4 and Reynolds OI.v2 data excluding the polar oceans (60S-60N), we find the ERSST.v4 data has a slightly lower warming rate. See Figure 4.
Some people will find that surprising, because the ERSST.v4 data excludes satellite-based data.
ERSST.V4 DATA USES REYNOLDS OI.V2 DATA FOR QUALITY CONTROL AND AS A BASIS FOR INFILLING THE PAST
In Huang et al. (2014), under the heading of 4.3 SST quality control and SSTA quantification, they begin (where “STD” is “standard deviation” and “QC” is “quality control”) (my boldface):
The SST data are first screened using a QC procedure checking the differences between observations and first guess SSTs from ERSST.v3b. Those observations are rejected when they deviate from the first guess by more than 4 times STD. In ERSST.v4, the monthly SST STD is calculated using the weekly OISST.v2 from 1982 to 2011.
The use of the Reynolds OI.v2 data in the quality control procedure is further mentioned in the next quote.
The Reynolds OI.v2 data are being utilized to “train” the computer program that infills missing data over the full term of the new ERSST.v4 data. The method NOAA uses for infilling is called Empirical Orthogonal Teleconnections (EOT). Under the heading of (d) Spatially complete data to derive EOT patterns, Huang et al. (2014) write:
Monthly SSTs derived from weekly 1°×1° gridded OISST version 2 (OISST.v2; Reynolds et al. 2002), which is based on in situ and satellite observations, are used between 1982 and 2011 in ERSST.v4 to derive SST STD on a 2°×2° grid in the QC procedure and to derive EOTs.
NOAA APPARENTLY STILL CONSIDERS REYNOLDS OI.V2 DATA TO BE “A GOOD ESTIMATE OF THE TRUTH”
In Smith and Reynolds (2004) Improved Extended Reconstruction of SST (1854-1997), the authors stated about the Reynolds OI.v2 data (my boldface):
Although the NOAA OI analysis contains some noise due to its use of different data types and bias corrections for satellite data, it is dominated by satellite data and gives a good estimate of the truth.
We discussed this years ago. It is the primary reason I use Reynolds OI.v2 data in my monthly sea surface temperature data updates, in my ENSO research and in model-data comparisons. Obviously, a sea surface temperature dataset that’s “a good estimate of the truth” should be the dataset of choice in any discussion of sea surface temperatures over the past 3+ decades, the satellite era.
Now NOAA is using Reynolds OI.v2 data for quality control of ERSST.v4 and for EOT training for the infilling of that new dataset.
After ERSST.v4 replaces ERSST.v3b as the standard NOAA long-term sea surface temperature product, I’ll update the detailed introduction to all datasets. I started to write that post over the weekend, got into it a good way, but I realized I was getting the cart before the horse. We don’t yet have a final ERSST.v4 product.
Who knows? If the new ERSST.v4 data rearranges annual rankings in the 21st Century of the combined NOAA land+ocean surface temperature product, NOAA may change the ERSST.v4 data like they did the ERSST.v3 data.
Thank you again, Nick.