The title question often appears during discussions of global surface temperatures. That is, GISS, Hadley Centre and NCDC only present their global land+ocean surface temperatures products as anomalies. The questions is: why don’t they produce the global surface temperature products in absolute form?
In this post, I’ve included the answers provided by the three suppliers. I’ll also discuss sea surface temperature data and a land surface air temperature reanalysis which are presented in absolute form. And I’ll include a chapter that has appeared in my books that shows why, when using monthly data, it’s easier to use anomalies.
Back to global temperature products:
GISS on their webpage here states:
Anomalies and Absolute Temperatures
Our analysis concerns only temperature anomalies, not absolute temperature. Temperature anomalies are computed relative to the base period 1951-1980. The reason to work with anomalies, rather than absolute temperature is that absolute temperature varies markedly in short distances, while monthly or annual temperature anomalies are representative of a much larger region. Indeed, we have shown (Hansen and Lebedeff, 1987) that temperature anomalies are strongly correlated out to distances of the order of 1000 km. For a more detailed discussion, see The Elusive Absolute Surface Air Temperature.
UKMO-HADLEY CENTRE EXPLANATION
The UKMO-Hadley Centre answers that question…and why they use 1961-1990 as their base period for anomalies on their webpage here.
Why are the temperatures expressed as anomalies from 1961-90?
Stations on land are at different elevations, and different countries measure average monthly temperatures using different methods and formulae. To avoid biases that could result from these problems, monthly average temperatures are reduced to anomalies from the period with best coverage (1961-90). For stations to be used, an estimate of the base period average must be calculated. Because many stations do not have complete records for the 1961-90 period several methods have been developed to estimate 1961-90 averages from neighbouring records or using other sources of data (see more discussion on this and other points in Jones et al. 2012). Over the oceans, where observations are generally made from mobile platforms, it is impossible to assemble long series of actual temperatures for fixed points. However it is possible to interpolate historical data to create spatially complete reference climatologies (averages for 1961-90) so that individual observations can be compared with a local normal for the given day of the year (more discussion in Kennedy et al. 2011).
It is possible to develop an absolute temperature series for any area selected, using the absolute file, and then add this to a regional average in anomalies calculated from the gridded data. If for example a regional average is required, users should calculate a time series in anomalies, then average the absolute file for the same region then add the average derived to each of the values in the time series. Do NOT add the absolute values to every grid box in each monthly field and then calculate large-scale averages.
Also see the NCDC FAQ webpage here. They state:
Absolute estimates of global average surface temperature are difficult to compile for several reasons. Some regions have few temperature measurement stations (e.g., the Sahara Desert) and interpolation must be made over large, data-sparse regions. In mountainous areas, most observations come from the inhabited valleys, so the effect of elevation on a region’s average temperature must be considered as well. For example, a summer month over an area may be cooler than average, both at a mountain top and in a nearby valley, but the absolute temperatures will be quite different at the two locations. The use of anomalies in this case will show that temperatures for both locations were below average.
Using reference values computed on smaller [more local] scales over the same time period establishes a baseline from which anomalies are calculated. This effectively normalizes the data so they can be compared and combined to more accurately represent temperature patterns with respect to what is normal for different places within a region.
For these reasons, large-area summaries incorporate anomalies, not the temperature itself. Anomalies more accurately describe climate variability over larger areas than absolute temperatures do, and they give a frame of reference that allows more meaningful comparisons between locations and more accurate calculations of temperature trends.
SURFACE TEMPERATURE DATASETS AND A REANALYSIS THAT ARE AVAILABLE IN ABSOLUTE FORM
Most sea surface temperature datasets are available in absolute form. These include:
- the Reynolds OI.v2 SST data from NOAA
- the NOAA reconstruction ERSST
- the Hadley Centre reconstruction HADISST
- and the source data for the reconstructions ICOADS
The Hadley Centre’s HADSST3, which is used in the HADCRUT4 product, is only produced in absolute form, however. And I believe Kaplan SST was also only available in anomaly form.
With the exception of Kaplan SST, all of those datasets are available to download through the KNMI Climate Explorer Monthly Observations webpage. Scroll down to SST and select a dataset. For further information about the use of the KNMI Climate Explorer see the posts Very Basic Introduction To The KNMI Climate Explorer and Step-By-Step Instructions for Creating a Climate-Related Model-Data Comparison Graph.
GHCN-CAMS is a reanalysis of land surface air temperatures and it is presented in absolute form. It must be kept in mind, though, that a reanalysis is not “raw” data; it is the output of a climate model that uses data as inputs. GHCN-CAMS is also available through the KNMI Climate Explorer and identified as “1948-now: CPC GHCN/CAMS t2m analysis (land)”. I first presented it in the post Absolute Land Surface Temperature Reanalysis back in 2010.
WHY WE NORMALLY PRESENT ANOMALIES
[Start of Chapter 2.1 – The Use of Temperature and Precipitation Anomalies]
With rare exceptions, the surface temperature, precipitation, and sea ice area data and model outputs in this book are presented as anomalies, not as absolutes. To see why anomalies are used, take a look at global surface temperature in absolute form. Figure 2-1 shows monthly global surface temperatures from January, 1950 to October, 2011. As you can see, there are wide seasonal swings in global surface temperatures every year.
The three producers of global surface temperature datasets are the NASA GISS (Goddard Institute for Space Studies), the NCDC (NOAA National Climatic Data Center), and the United Kingdom’s National Weather Service known as the UKMO (UK Met Office). Those global surface temperature products are only available in anomaly form. As a result, to create Figure 2-1, I needed to combine land and sea surface temperature datasets that are available in absolute form. I used GHCN+CAMS land surface air temperature data from NOAA and the HADISST Sea Surface Temperature data from the UK Met Office Hadley Centre. Land covers about 30% of the Earth’s surface, so the data in Figure 2-1 is a weighted average of land surface temperature data (30%) and sea surface temperature data (70%).
When looking at absolute surface temperatures (Figure 2-1), it’s really difficult to determine if there are changes in global surface temperatures from one year to the next; the annual cycle is so large that it limits one’s ability to see when there are changes. And note that the variations in the annual minimums do not always coincide with the variations in the maximums. You can see that the temperatures have warmed, but you can’t determine the changes from month to month or year to year.
Take the example of comparing the surface temperatures of the Northern and Southern Hemispheres using the satellite-era sea surface temperatures in Figure 2-2. The seasonal signals in the data from the two hemispheres oppose each other. When the Northern Hemisphere is warming as winter changes to summer, the Southern Hemisphere is cooling because it’s going from summer to winter at the same time. Those two datasets are 180 degrees out of phase.
After converting that data to anomalies (Figure 2-3), the two datasets are easier to compare.
Returning to the global land-plus-sea surface temperature data, once you convert the same data to anomalies, as was done in Figure 2-4, you can see that there are significant changes in global surface temperatures that aren’t related to the annual seasonal cycle. The upward spikes every couple of years are caused by El Niño events. Most of the downward spikes are caused by La Niña events. (I discuss El Niño and La Niña events a number of times in this book. They are parts of a very interesting process that nature created.) Some of the drops in temperature are caused by the aerosols ejected from explosive volcanic eruptions. Those aerosols reduce the amount of sunlight that reaches the surface of the Earth, cooling it temporarily. Temperatures rebound over the next few years as volcanic aerosols dissipate.
HOW TO CALCULATE ANOMALIES
For those who are interested: To convert the absolute surface temperatures shown in Figure 2-1 into the anomalies presented in Figure 2-4, you must first choose a reference period. The reference period is often referred to as the “base years.” I use the base years of 1950 to 2010 for this example.
The process: First, determine average temperatures for each month during the reference period. That is, average all the surface temperatures for all the Januaries from 1950 to 2010. Do the same thing for all the Februaries, Marches, and so on, through the Decembers during the reference period; each month is averaged separately. Those are the reference temperatures. Second, determine the anomalies, which are calculated as the differences between the reference temperatures and the temperatures for a given month. That is, to determine the January, 1950 temperature anomaly, subtract the average January surface temperature from the January, 1950 value. Because the January, 1950 surface temperature was below the average temperature of the reference period, the anomaly has a negative value. If it had been higher than the reference-period average, the anomaly would have been positive. The process continues as February, 1950 is compared to the reference-period average temperature for Februaries. Then March, 1950 is compared to the reference-period average temperature for Marches, and so on, through the last month of the data, which in this example was October 2011. It’s easy to create a spreadsheet to do this, but, thankfully, data sources like the KNMI Climate Explorer website do all of those calculations for you, so you can save a few steps.
CHAPTER 2.1 SUMMARY
Anomalies are used instead of absolutes because anomalies remove most of the large seasonal cycles inherent in the temperature, precipitation, and sea ice area data and model outputs. Using anomalies makes it easier to see the monthly and annual variations and makes comparing data and model outputs on a single graph much easier.
[End of Chapter 2.1 from Climate Models Fail]
There are a good number of other introductory discussions in my ebooks, for those who are new to the topic of global warming and climate change. See the Tables of Contents included in the free previews to Climate Models Fail here and Who Turned on the Heat? here.