WMO Reasoning behind Two Sets of “Normals” a.k.a. Two Periods of Base Years for Anomalies

Most of us are familiar with the World Meteorological Organization (WMO)-recommended 30-year period for “normals”, which are also used as base years against which anomalies are calculated. Most, but not all, climate-related data are referenced to 30-year periods. Presently the “climatological standard normals” period is 1981-2010. These “climatological standard normals” are updated every ten years after we pass another year ending in a zero.  That is, the next period for “climatological standard normals” will be 1991-2020, so the shift to new “climatological standard normals” will take place in a few years. 

But were you aware that the WMO also has another recommended 30-year period for “normals”, against which anomalies are calculated? It’s used for the “reference standard normals” or “reference normals”.  The WMO-recommended period for “reference normals” is 1961-1990.     And as many of you know, of the primary suppliers of global mean surface temperature data, the base years of 1961-1990 are only used by the UKMO.

Basically, in simple terms, “climatological standard normals” are for what we might expect at a given time in a given location. On the other hand, the “reference normals” are for how things have changed since the reference period.  That way politicians, activists, and eco-profiteers, etc., can whine about how climate has changed and ask you to pay for it…as if we should expect climate not to change.

We can find a simple and easy-to-understand discussion of the reasons for two sets of “normals” in the WMO document titled WMO Guidelines on the Calculation of Climate Normals (2017 edition), linked here, 725 KB .pdf.  There they write (my boldface and brackets):

5. APPLICATION ASPECTS

5.1 Why calculate both climatological standard normals and reference normals?

As mentioned above, climate normals serve two major functions: as an implicit predictor of the conditions most likely to be experienced in the near future at any given location, and as a stable benchmark against which long-term changes in climate observations can be compared.

In a stable climate, these two purposes can both be served by a common reference period. However, as discussed in The Role of Climatological Normals in a Changing Climate (WMO, 2007), for elements where there is now a clear and consistent trend (most notably temperature), the predictive skill of climate normals is greatest if they are updated as frequently as possible. A 1981–2010 averaging period is much more likely to be representative of conditions in 2017 than the 1961–1990 period. On the other hand, there are clear benefits of using a stable benchmark as a reference point for long-term datasets, both in practical terms (not having to recalculate anomaly-based datasets every 10 years), and in terms of communication – an “above average” year does not suddenly become “below average” because of a change in reference period.

[Wouldn’t that be terrible? To have to list and show the global surface temperature anomaly for a recent year as being “below normal”, as if that would’ve happened in a while.  See Figure 1 below.]

The quote continues:

As these two primary purposes of climate normals have become mutually inconsistent in terms of their requirements for a suitable averaging period, WMO has decided that both should be calculated (subject to availability of data). While the best predictive skill would be achieved from updating climatological standard normals every year, it is recognized that this would be impractical for many countries, and hence it has been decided that these should be updated every 10 years, with the next update due after the end of 2020.

Figure 1 includes Berkeley Earth annual global surface temperature anomalies from 1988 to 2017 using the “climatological standard normals” base years of 1981-2010; and the “reference normals” base years of 1961-1990, which is used by the UK Met Office; and also the base years of 1951-1980, which have been adopted by NASA GISS and Berkeley Earth as their standard base years; and, last but not least, 1901-2000, which is used by NOAA NCEI for their global surface temperature data.

Figure 1

As you can plainly see, the base years used for anomalies do not impact which year was warmest or coolest. The base years only impact how far above “normal” the supplier can claim a year has been.  Not surprisingly, NOAA NCEI uses base years, 1901-2000, that provide the highest values above “normal”.

And that brings to mind something that will cause a slight shift in topics to global surface temperatures in absolute form:

“To be clear, no particular absolute global temperature provides a risk to society, it is the change in temperature compared to what we’ve been used to that matters.”

Of course, the above quote comes from Gavin Schmidt, who is the Director of the NASA Goddard Institute of Space Studies. It is from a 2014 post at the “Climate science from real climate scientists” blog RealClimate, and that quote comes from the blog post Absolute temperatures and relative anomalies (Archived here, just in case.). So not to be accused of quoting Gavin out of context, I’ll present the full paragraph. The topic of discussion for the post was the wide span of absolute global mean temperatures [GMT, in the following quote] found in climate models.  Gavin wrote (my boldface):

Most scientific discussions implicitly assume that these differences aren’t important i.e. the changes in temperature are robust to errors in the base GMT value, which is true, and perhaps more importantly, are focussed on the change of temperature anyway, since that is what impacts will be tied to. To be clear, no particular absolute global temperature provides a risk to society, it is the change in temperature compared to what we’ve been used to that matters.

Anyone with the slightest bit of common sense knows that, annually, the local ambient temperatures where they live vary much more than the 1-deg C change in global surface temperatures that data show we’ve experienced since preindustrial times and way much more than the 0.5-deg C additional change in global mean surface temperatures the UN has set its sights on trying to prevent in the future.

So, to put things in perspective, as a simple example—for a well-known country—let’s plot the long-term monthly variations in surface temperatures, not anomalies, and compare them with global surface temperature anomalies, which is how global mean surface temperatures are normally presented. The country I’ve chosen for this example is China, the most-populated country on Earth. The data we need are available from Berkeley Earth. Their monthly global mean land+ocean surface temperature anomaly data are here, and the near-surface land air temperature anomaly data for China are here along with the all-important monthly surface temperature factors for converting the anomalies into absolute form. (Thank you, Berkeley Earth!) The comparison runs from January 1900 to August 2013, when the data for China ends at Berkeley Earth. See Figure 2. The annual variations in surface temperatures in China average 28.2-deg C for the period of 1951-1980 and those annual variations dwarf the long-term rise in global surface temperatures, about 1-deg C.

But, but, but, I can hardly see the changes in the red curve, the global surface temperature anomalies.

Exactly.

Figure 2

In other words, the gazillion people living in China have been used to annual variations in temperatures that are far, far greater than the wimpy little 1-deg C warming the Earth has experienced since the end of the pre-industrial period—far, far greater.  Also, the average annual variation in monthly surface temperatures for China is more than 56 times greater than the additional 0.5 deg C rise in global surface temperatures the UN is now pushing to avoid.

Hmmm, I really feel a series of posts coming on with lots of reference graphs.

NOTE REGARDING MY USE OF THE TERM ABSOLUTE TEMPERATURE

The term Absolute is commonly used by the climate science community when discussing Earth’s surface temperature when they aren’t using anomalies. See the quote from Gavin Schmidt above or refer to the FAQ webpages of the global surface temperature data suppliers.

[End note]

That’s it for now. Have fun in the comments and enjoy the rest of your day.

STANDARD CLOSING REQUEST, WITH AN ADDITION

Please purchase my recently published ebooks. As many of you know, this year I published 2 ebooks that are available through Amazon in Kindle format:

• Dad, Why Are You A Global Warming Denier? (For an overview, the blog post that introduced it is here.)
• Dad, Is Climate Getting Worse in the United States? (See the blog post here for an overview.)

And please purchase Anthony Watts’s et al. Climate Change: The Facts – 2017.

To those of you who have purchased them, thank you very much. To those of you who will purchase them, thank you, too.

Regards,

Bob