How fast is the Earth warming?

This article presents a method for calculating the Earth’s rate of warming, using the existing global temperature series.

Guest essay by Sheldon Walker

It can be difficult to work out the Earth’s rate of warming. There are large variations in temperature from month to month, and different rates can be calculated depending upon the time interval and the end points chosen. A reasonable estimate can be made for long time intervals (100 years for example), but it would be useful if we could calculate the rate of warming for medium or short intervals. This would allow us to determine whether the rate of warming was increasing, decreasing, or staying the same.

The first step in calculating the Earth’s rate of warming is to reduce the large month to month variation in temperature, being careful not to lose any key information. The central moving average (CMA) is a mathematical method that will achieve this. It is important to choose an averaging interval that will meet the objectives. Calculating the average over 121 months (the month being calculated, plus 60 months on either side), gives a good reduction in the variation from month to month, without the loss of any important detail.

Graph 1 shows the GISTEMP temperature series. The blue line shows the raw temperature anomaly, and the green line shows the 121 month central moving average. The central moving average curve has little month to month variation, but clearly shows the medium and long term temperature trend.

Graph 1

The second step in calculating the Earth’s rate of warming is to determine the slope of the central moving average curve, for each month on the time axis. The central moving slope (CMS) is a mathematical method that will achieve this. This is similar to the central moving average, but instead of calculating an average for the points in the interval, a linear regression is done between the points in the interval and the time axis (the x-axis). This gives the slope of the central moving average curve, which is a temperature change per time interval, or rate of warming. In order to avoid dealing with small numbers, all rates of warming in this article will be given in °C per century.

It is important to choose the correct time interval to calculate the slope over. This should make the calculated slope responsive to real changes in the slope of the CMA curve, but not excessively responsive. Calculating the slope over 121 months (the month being calculated plus 60 months on either side), gives a slope with a good degree of sensitivity.

Graph 2 shows the rate of warming curve for the GISTEMP temperature series. The blue line is the 121 month central moving slope (CMS), calculated for the central moving average curve. The y-axis shows the rate of warming in °C per century, and the x-axis shows the year. When the rate of warming curve is in the lower part of the graph ( colored light blue), then it shows cooling (the rate of warming is below zero). When the rate of warming curve is in the upper part of the graph ( colored light orange), then it shows warming (the rate of warming is above zero).

Graph 2

The curve shows 2 major periods of cooling since 1880. Each lasted approximately a decade (1900 to 1910, and 1942 to 1952), and reached cooling rates of about -2.0 °C per century. There is a large interval of continuous warming from 1910 to 1942 (about 32 years). This reached a maximum rate of warming of about +2.8 °C per century around 1937. 1937 is the year with the highest rate of warming since the start of the GISTEMP series in 1880 (more on that later).

There is another large interval of continuous warming from about 1967 to the present day (about 48 years). This interval has 2 peaks at about 1980 and 1998, where the rates of warming were just under +2.4 °C per century. The rate of warming has been falling steadily since the last peak in 1998. In 2015, the rate of warming is between +0.5 and +0.8 °C per century, which is about 30% of the rate in 1998. (Note that all of these rates of warming were calculated AFTER the so‑called “Pause-busting” adjustments were made. More on that later.)

It is important to check that the GISTEMP rate of warming curve is consistent with the curves from the other temperature series (including the satellite series).

Graph 3 shows the rate of warming curves for GISTEMP, NOAA, UAH, and RSS. (Note that the satellite temperature series did not exist before 1979.)

Graph 3

All of the rate of warming curves show good agreement with each other. Peaks and troughs line up, and the numerical values for the rates of warming are similar. Both of the satellite series appear to have a larger change in the rate of warming when compared to the surface series, but both satellite series are in good agreement with each other.

Some points about this method:

1) There is no cherry-picking of start and end times with this method. The entire temperature series is used.

2) The rate of warming curves from different series can be directly compared with each other, no adjustment is needed for the different baseline periods. This is because the rate of warming is based on the change in temperature with time, which is the same regardless of the baseline period.

3) This method can be performed by anybody with a moderate level of skill using a spreadsheet. It only requires the ability to calculate averages, and perform linear regressions.

4) The first and last 5 years of each rate of warming curve has more uncertainty than the rest of the curve. This is due to the lack of data beyond the ends of the curve.  It is important to realise that the last 5 years of the curve may change when future temperatures are added.

There is a lot that could be said about these curves. One topic that is “hot” at the moment, is the “Pause” or “Hiatus”.

The rate of warming curves for all 4 major temperature series show that there has been a significant drop in the rate of warming over the last 17 years. In 1998 the rate of warming was between +2.0 and +2.5 °C per century. Now, in 2015, it is between +0.5 and +0.8 °C per century. The rate now is only about 30% of what it was in 1998.  Note that these rates of warming were calculated AFTER the so-called “Pause-busting” adjustments were made.

I was originally using the GISTEMP temperature series ending with May 2015, when I was developing the method described here. When I downloaded the series ending with June 2015 and graphed it, I thought that there must be something wrong with my computer program, because the rate of warming curve had changed so dramatically. I eventually traced the “problem” back to the data, and then I read that GISTEMP had adopted the “Pause-busting” adjustments that NOAA had devised.

Graph 4 shows the effect on the rate of warming curve, of the GISTEMP “Pause-busting” adjustments. The blue line shows the rates from the May 2015 data, and the red line shows the rates from the June 2015 data.

Graph 4

One of the strange things about the GISTEMP “Pause-busting” adjustments, is that the year with the highest rate of warming (since 1880) has changed. It used to be around 1998, with a warming rate of about +2.4 °C per century. After the adjustments, it moved to around 1937 (that’s right, 1937, back when the CO2 level was only about 300 ppm), with a warming rate of about +2.8 °C per century.

If you look at the NOAA series, they already had 1937 as the year with the highest rate of warming, so GISTEMP must have picked it up from NOAA when they switched to the new NCEI ERSST.v4 sea surface temperature reconstruction.

So, the next time that you hear somebody claiming that Global Warming is accelerating, show them a graph of the rate of warming. Some climate scientists seem to enjoy telling us that things are worse than predicted. Here is a chance to cheer them up with some good news. Somehow I don’t think that they will want to hear it.

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September 2, 2015 1:39 pm

Very interesting, however:
1) IPCC AR5 has no idea how much of the CO2 increase between 1750 and 2011 is due to industrialized man because the contributions of the natural sources and sinks are a massive WAG.
2) At 2 W/m^2 the “unbalanced” RF IPCC attributes to that CO2 increase between 1750 & 2011 is lost in the magnitudes and uncertainties of the major factors in the global heat balance. A third or fourth decimal point bee fart in a hurricane.
3) IPCC admits in text box 9.2 that their GCM’s cannot explain the pause/hiatus/lull/stasis and are consequentially useless.

September 2, 2015 4:30 pm

Hey, Brandon Gates is BACK, baby!

September 4, 2015 12:27 am

Models vs measurement.
It seems to me that temperature + relative humidity (RH) is the proper metric to see if there is some coherence between the models and measurements. If we wanted to make it even more difficult we could add in pressure.
Hind casting temperature is not too difficult. Hind casting temp + RH is going to be trickier. And if they go global with that what exactly does a number for the global average RH mean? Global average air pressure? Well mountains are going to complicate that. And they complicate RH. And of course RH is a proxy for water vapor in the air. Which depends on temperature and pressure.
============
Willis is correct. I can tell the difference between a 10°C day and an 40°C day. But what do I really know about a 10.0°C day and a 10.1°C day if my measurement error in the field is .1° or worse 1°C. And I haven’t even brought in RH and local air pressure. Or all the other variables – like the 10°C day was measured in 1900 and the 10.1°C was measured in 2015. Is it really .1°C hotter in 2015 vs 1900? Or can we just say that we don’t know given the reading error and drift in calibration in 1900. Not to mention all the other effects that could affect the thermometer. The 2015 day could be hotter or colder than the 1900 day. The error bar of the difference has to be at least .5°C and it could be worse. Even if the 2015 thermometer was perfect.
And the error bars don’t decline from taking millions of measurements at thousands of points. They sum as the sq rt of the sum of the squares of the measurement errors on a particular day (if the distribution of the errors is Gaussian). What does this tell you? Well the error bars are HUGE. Lets say we have 10,000 measurements with an error of .5°C for a given anomaly day What is the error bar of the average? about 84°C. Heh. Well that says that the average for a system that is nominally 300°K is useless. Suppose we can get the errors down to .1°C (modern era) the error bar is 56°C. OK We are really good .01C measurement error gives more than 31°C. error bar. To get the error bar down to .1°C for those 10,000 measurements we need an accuracy of measurement that is obviously ridiculous. (temperature error of 1E-6)
But we can improve things with fewer measurements. Now isn’t that funny.
http://ipl.physics.harvard.edu/wp-uploads/2013/03/PS3_Error_Propagation_sp13.pdf

Reply to  M Simon
September 4, 2015 9:58 am

The surface record only has a single dew point measurement a day (well the data I’m using does). But from looking at my weather station it doesn’t change all that fast. At the stations it’s changed a little since the 40’s, but no trend following Co2.
But I did find something else I thought interesting, every night, rel humidity maxes out, and water is condensed out of the air, some of which is lost into the surface, so water is boiled out of the oceans in the tropics, transported poleward, where it cools and is deposited into the water table as it moves. It fits nicely with Willis’s idea that water regulates the tropics temperatures.comment image

September 4, 2015 12:40 am

Well suppose we divide the error by 10,000 to account for averaging. in the .5°C error case we get .01°C error roughly. But maybe the divisor to use is really the sq rt of 10.000. Then the error is .84°C.
So if the difference is less than .84°C we know nothing. And we don’t usually count it as significant unless it is 3X that. And even if .01°C is the correct number only differences of .03°C are significant.
I assume the math guys will correct my understanding. I look forward to that.

September 4, 2015 9:35 am

Per psychrometry the enthalpy of moist air contains two components: dry air at 0.24 Btu/lb-F and water vapor at about 1,000 Btu/lb. Water vapor is measured in grains per lb dry air, 7,000 grains per lb.
At 0% RH it’s all dry air Btus.
At 100% RH, saturated w/ all the vapor the air can hold (more w/ warm than cold), and the same Btus the dry bulb temperature will be much lower. It’s a balancing act, more vapor Btu, fewer dry air Btu. The added vapor cools the air. It’s how evaporative coolers work and it’s also how water vapor moderates the atmospheric heat balance.

Reply to  Nicholas Schroeder
September 4, 2015 9:51 am

Per psychrometry the enthalpy of moist air contains two components: dry air at 0.24 Btu/lb-F and water vapor at about 1,000 Btu/lb. Water vapor is measured in grains per lb dry air, 7,000 grains per lb.
At 0% RH it’s all dry air Btus.
At 100% RH, saturated w/ all the vapor the air can hold (more w/ warm than cold), and the same Btus the dry bulb temperature will be much lower. It’s a balancing act, more vapor Btu, fewer dry air Btu. The added vapor cools the air. It’s how evaporative coolers work and it’s also how water vapor moderates the atmospheric heat balance.

Added vapor might cool (Florida in the 40’s feels pretty cold), It also is responsible for carrying huge amounts of heat out of the tropics.
The difference between Tropical air, and Canadian air is 10-20Fcomment image
And it’s all due to water vapor’s heat carrying capacity.