By Andy May
This is the last in my current series of posts on ocean temperatures. In our previous posts we compared land-based measurements to sea-surface temperatures (SSTs) and discussed problems and contradictions in the land-based measurements (see here). This first post was overly long and complicated, but I needed to lay a foundation before getting to the interesting stuff. Next, we covered the basic thermal structure of the ocean and how it circulates its enormous heat content around the world (see here). This was followed by a description of the ocean surface or “skin” layer here. The ocean skin is where evaporation takes place and it is where many solar radiation wavelengths, especially the longer infrared wavelengths emitted by CO2, are absorbed, then evaporated away, or reflected.
The next post covered the mixed layer, which is a layer of uniform temperature just under the skin layer. The mixed layer sits above the thermocline where water temperature begins to drop rapidly. The next post discussed the differences between various estimates of SST, the data used and the problems with the measurements and the corrections. The emphasis was on the two main datasets, HadSST and NOAA’s ERSST. The most recent post discusses SST anomalies, the logic was, if all the measurements are from just below the ocean surface, why are anomalies needed? Why can’t we simply use the measurements corrected to a useful depth, like 20 cm?
The theme of all the posts is to keep the analysis as close to the measurements as possible. Too many corrections and data manipulations confuse the interpretation, remove us from the measurements, and homogenize the measurements to such an extent that we get a false sense of confidence in the result. This illusion of accuracy, due to over-processing data, is discussed by William Brigg’s here. His post is on smoothing data, but the arguments apply to homogenizing temperatures, creating anomalies from the mean, and of bias correcting the measurements with statistical techniques. All these processes make “the data look better” and give us a false sense of confidence. We come away not knowing how much of the resulting graphs and maps are due to the corrections and data manipulation and how much are due to the underlying measurements. We progressed step-by-step though all the processing we could and examined what the temperatures looked like at every step. We are trying to pull back the wizard’s curtain, as much as possible, but probably not as well as Toto.
Area-Weighting
The data used in all these posts, except the earliest posts on the land measurements in CONUS (the conterminous United States, or the “lower 48”), were all from latitude and longitude grids. Gridding the measurements is needed globally because the measurements are mostly concentrated in the Northern Hemisphere, and very sparse elsewhere and in both polar regions. As we’ve previously shown, Northern Hemisphere temperatures are anomalous, the rest of world is much less variable in its surface temperature. See here for a discussion of this and some graphs. See this post by Renee Hannon and this one by the same author, for more detail on hemispheric variations in temperature trends.
While gridding is probably not necessary, and may be misleading, in areas with good coverage like the United States, to produce a global average SST, we need to grid the available data. This is not to say that gridding replaces good measurements or improves the measurements, just that with the data we have it is necessary.
Each grid cell represents a different area of the ocean. The difference is a function only of latitude. Each degree of latitude is 111 km. A degree of longitude at the equator is also 111 km. but decreases to zero at the poles. So, to compute the area of each grid cell we only need to know the latitude of the cell and size of the grid cells in latitude and longitude. The solution is provided and derived by Dr. Math (National Council of Teachers of Mathematics), here. I won’t interrupt the narrative with an equation, but the R code linked at the bottom of the post shows the equation and how it was used.
Normally, especially at night, the mixed layer and the SST are very close to one another, so plotting them all together is valid.
The curves in Figure 1 are all corrected to a depth between 20 cm. to one meter. They are grid averages. The figure is constructed with area-weighted grid cell values. The NOAA ERSST curve is shown twice, after nulling the ERSST values corresponding to null values in the HadSST dataset and using all the ERSST grid values. That way we are comparing ERSST and HadSST over the same global ocean areas. The normal ERSST dataset (the lower brown line in Figure 1) uses interpolation and extrapolation to populate grid cells that have insufficient data, these are null cells in HadSST.
The NOAA ICOADS SST data are closest to the measurements, the “corrections” to the data are simpler and less complicated than those used by HadSST and ERSST. HadSST and ERSST use similar source measurements, but as described previously, they grid the data differently, and they cover different areas. Once the ERSST grid is “masked” to conform with the HadSST grid, the record pops up to 22 degrees, from 18.2. This is logical, since most of the ERSST extrapolation is in the polar regions under the ice caps.
Figure 1. All the grid values in the records are weighted by the grid cell areas they represent.
The NOAA MIMOC and University of Hamburg multiyear grid averages plot on top of the NOAA ERSST record. NOAA MIMOC and the University of Hamburg create their grids using more than 12 years of data, so they populate much more of their grid than the single year datasets. They also weigh Argo and buoy data heavily, just like NOAA ERSST.
The HadSST measured temperatures trend downward. When the ERSST grid is masked with the HadSST nulls, it also trends downward. This is seen in Figure 2.
Figure 2. Declining SSTs over the HadSST covered area are apparent in both the HadSST and ERSST datasets.
The HadSST dataset only has grid values where sufficient measurements exist to compute one, they do not extrapolate data into adjoining grid cells like ERSST. Thus, the HadSST data represents the portion of the ocean with the best data, and this area is clearly declining in temperature, when only actual measurements are used. The ERSST suggests a decline of 2.5 degrees/century. The HadSST decline is 1.6 degrees/century.
The ERSST line, with no mask applied (Figure 1), has an increasing trend of 1.6 degrees/century. Thus, the interpolated and extrapolated areas show warming that does not show up in the cells with the best data. As we saw in the last post and show again in this post as Figure 3, the HadSST and ERSST anomalies show none of the complexity it took six posts to cover. They show a joint trend of warming, of about 1.7 degrees/century, but offset about a tenth of a degree due to different coverage areas.
Figure 3. The HadSST and ERSST anomalies. The baselines for both are 1961-1990.
Conclusions
The best way to analyze data is to use the absolute minimum statistical manipulation required present it in usable form. Every correction, every computation, all smoothing operations, every gridding step must be fully justified. It is an unfortunate fact of scientific and engineering life today that our colleagues are forever coming up with “this must be corrected for,” that must be corrected for,” and so on. Little thought is given to how the corrections affect our perception of the resulting graphs and maps. With enough corrections one can turn a pile of manure into a castle but is it really a castle?
I had a boss once who was very smart, he eventually became the CEO and chairman of the board of the company we worked for. I was much younger then and I was one of the scientists in the peanut gallery who kept asking “What about this?” “What about that, have you corrected for these things?” My boss would say, “Let’s not out-science ourselves. What does the data say, as it is?” After he became CEO, he sold the company for five times the average strike price of my accumulated stock options. I was still a scientist, albeit a wealthy one. He was right, so is Dr. William Briggs. Study the raw data, keep it close to you, don’t “out-science” yourself.
I reprocessed a lot of data to make this post, I think I did it correctly, but I do make mistakes. For those that want to check my work, you can find my new area-weighting R code here.
None of this is in my new book Politics and Climate Change: A History but buy it anyway.
Marcel Crok, of Clintel.org, which also has this post up, asked me:
Here is my answer:
The very best data is represented in that figure. But, it only represents a portion of the world ocean, and the area represented is constantly changing. That constantly changing area is showing declining measured temperatures. The ships, floats, and buoys measuring temperature are constantly moving and changing in each cell. The cells with measurements are constantly changing. The “reference” period, between 1961-2000, has very poor data.
The only way to get a positive anomaly with this crappy data is to create monthly anomalies, from the populated cells, in that month, and then present them as one record. But, it is a record of a constantly changing area, what good is it?
Less than half the ocean has good data and that half is constantly changing as the instruments and ships move about. All I did was do a yearly average, to avoid seasonal anomalies. Thus, the yearly average temperature of the cells with the best data (wherever they are) is declining over the last 20 years. This has been confirmed by Nick Stokes, an Australian climatologist in the WUWT comments in the past few hours.
The creation of anomalies, especially the way HadSST and ERSST have done it, removes all the underlying complexity in the data and presents a misleading graph based on very, very poor and unrepresentative data.
My personal conclusion, after doing all this work? We don’t have a clue what the average temperature of the world ocean is, nor do we know whether it is warming or cooling.