The View from Down Here Isn’t Much Different

Arguing over mean temperature time series has become very, very stale. I am old enough to recall Gilbert Plass’s many publications about warming trends in northern Europe pre 1950. His graphs were probably sufficient to demonstrate a warming planet, but we know now that the trends these portrayed reversed after the early 1950s for the next several decades. So, time series were sufficient to show how CO2 was warming the planet until they weren’t. Of course, there is a ready explanation, actually several, for why they reversed that preserves “appearances”. The climate system is complicated enough that one can always find an explanation for why a trend does whatever it does.

People making statistical adjustments to time series never mention, perhaps because they aren’t aware, that adjustments come with uncertainties of their own. Without carefully examining each station’s time series, and data about station moves, encroachment, and so forth, one can’t be certain that statistical adjustments are helping matters. Many criteria by which we judge the correctness of time series and our corrections are ad hoc. There is no basis for even believing that corrections resulting in a residual “anomaly” that is a series of random, normally distributed numbers, means the corrections are valid. Weighting criteria are ad hoc, too.

One thing that climate scientists (I am not not one) are trying to accomplish with regard to mean temperature is to demonstrate that the Earth is out of equilibrium with regard to energy balance, and hence that some agent like CO2 or feedback is warming the planet. But this seems a pretty pointless goal when one considers that heat is being carried constantly toward the poles and toward the top of the troposphere–transport means we are always out of equilibrium, perhaps quite far from equilibrium, and we don’t fully understand and cannot model this transport accurately.

The thing activists are trying to accomplish is change to the political and economic structure of the world using a “climate crisis” as justification. Temperatures are in no way fit for this grand and frankly scary purpose.

What one thinks of the mean temperature record is dictated largely by one’s prior beliefs. If someone is predisposed toward belief in climate troubles, then a temperature time series means quite a lot. If one is skeptically inclined, it means much less. Looks much like belief in the paranormal doesn’t it?

James Schrumpf

You seem, if I understand you well, to doubt about the usefulness and correctness of latitude and longitude gridding of absolute or anomaly-based station data before averaging it into time series.

Maybe you look at various graphs showing you why you are wrong.

Here is a graph showing for CONUS the yearly average of daily maxima per station in the GHCN daily data set:
(1) https://drive.google.com/file/d/1qGV5LfKw_lFKNdZMlq15ZHz6sA1CA294/view

And here is the same for the Globe:
(2) https://drive.google.com/file/d/1GMuNs9ptRzDd7KxFQbKv0o5ySR5VNc9b/view

Maybe you think: „Aha. The Globe is like CONUS“.

But… here is the Globe without CONUS:
(3) https://drive.google.com/file/d/1UcLK3usYjICeHeAsAb5ivcusW0Y0EdNe/view

What happens in (2) is that you have in GHCN daily worldwide about 18000 US stations competing with 18000 non-US stations: this means that about 6 % of the Globe’s land surface has the same weight as the remaining 94 %.

When you exclude the US stations, you irremediably get another picture of the Globe.

If you now average, in a preliminary step, all stations worldwide into grid cells of e.g. 2.5 degree (about 70000 km² each) you obtain the following graph:
(4) https://drive.google.com/file/d/1TFdltVVFSyDLPM4ftZUCEl33GmjJnasT/view

Yes: it looks like the preceding graph (3) in which all US stations had been excluded.

This is due to the fact that now, about 200 CONUS grid cells compete with 2000 grid cells outside of CONUS. Thus CONUS’ weight is reduced to about its surface ratio wrt the Globe. Fair enough!

Ironically, the gridding method gives, when applied to CONUS itself, a graph
(5) https://drive.google.com/file/d/16XwogXkjltMuSzCdC5mPKqC1KNev4ayy/view

showing that the number of daily maxima per station per year above 35 °C even decreases. The reason is again that the gridding gives more weight to isolated stations whose ‘voice’ was diluted within the yearly average of the entire CONUS area.

Like an ungridded Globe looks like CONUS, an ungridded CONUS looks like the sum of its West and East coasts.

Regards
J.-P. D.

James: Many scientific fields, we design experiments to determine what we want to know. Let’s imagine we are time travelers and could go back in time and measure temperature anywhere on the planet. For GHG-mediated global warming, we believe that GHGs slow radiative cooling to space, leaving more energy in the environment and therefore a higher temperature. So perhaps we would design an experiment to measure the temperature and average local heat capacity per unit area, and then see how much energy has built up over time in terms of W/m2. The ARGO buoys are already doing a pretty good job of measuring how much energy is accumulating in the ocean, so let’s give ourselves the job of measuring energy accumulation over land. Since there is the same weight of air over every square meter, the heat capacity of the atmosphere is the same over every square meter. If we assume the lapse rate remains constant (a reasonable approximation), then warming everywhere in the atmosphere will be the same as warming 2 m above the surface. So, maybe we would design an experiment where we placed one temperature station in every 1, 10, 100, 1000 or 10,000 km^2. If we intended to compare with a climate model, we might place one temperature station at the center of every land grid cell in bottom layer of the climate model. However, I doubt we would scatter temperature stations at random, or put them mostly in cities, or near the coast, or near airports.

Unfortunately, we aren’t time travelers. We must work with the data we have, despite the fact that the thermometers aren’t ideally distributed. Nevertheless, we can still make a grid and average all of the thermometers in each grid cell, so we can have one temperature for every (say) 10,000 km^2 grid cell. Then we can average all of the grid cells. BEST has a better way of doing this: kriging, which is a way of using the data to determine a temperature “field”. That field is continuous: T(x,y) is the temperature at point x,y. Gridded temperatures are discontinuous.

All of the temperature indices calculate temperature per unit area and average, because that is what climate scientists most want to know from an energy accumulation perspective.

You may have a different perspective. You may care about how much warming is hurting people design an experiment with one thermometer in the center of every 100,000 people. Or put the thermometers where both people and agriculture are. However, whatever you want to know, thermometers probably have not been placed at the right location to give you a MEANINGFUL answer, so simply averaging is unlikely to provide the best answer. As best I can tell, simple averaging serves no purpose.

With the advent of satellite altimetry, we can measure risings sea level everywhere in the ocean. However, I only care about SLR at the coast where people living. That happens to be where tide gauges are located. In this case, simple averaging would be more useful than the global average we get from satellite altimetry. However, a better answer would be to weight the tide gauge data by the value of the land at risk around the tide gauge. (Climate scientists need the global value to prove thermal expansion, ice cap melting, glacier melting, ground water depletion, and reservoir-filling explain the rise we see.)