2018 Temperature Comparisons

The US Government shutdown delayed the release of the December GHCN station data. This also, perhaps surprisingly, also delayed the the Met Office/CRU results. So just how independent are they one from each other? Here is a comparison of the annual results from Berkeley Earth, GIStemp, my own 3d-GHCN, 3D-H4 and HadCRUT4.6,  all on the same baseline of 1961-1990.

Compare temperature series in 2018

You can see that they all have the same shape but that they then begin to diverge after 2004. Why?  GHCN V3 has 7280 valid stations and CRU have 7688 the vast majority of which use the same data, hence the reason for the delay in also releasing CRUTEM . The ocean surface data are  also interdependent between  HADSST3 and ERSST3, with only marginal differences.  So what causes these apparent changes in results and trends ?

The basic difference is simply the way the surface weighted average is made.

  • HadCRUT only average temperatures over locations where there are measurements based on  a 5×5 lat,lon grid.  The area weighted average of each cell is cos(Lat). This method has remained constant since 1990. You can argue that this is the only impartial choice since it avoids any interpolation.
  • GISSTemp  results in  the steepest temperature rise because it assumes that every station is representative of temperature change within a 1200km radius. So the addition of new ‘rapidly’ warming stations in regions like the the Arctic have a far larger effect over adjacent regions when forming the average. My gut feeling is this method,  originated by James Hansen has a warming biased.
  • Berkeley Earth uses a least squares fitting technique based on an  assumption that Temperature is a smooth function of position over the earth’s surface. So they also extrapolate into areas without data. Cowtan & Way use a kriging technique on the raw HadCRUT4 data, essentially doing the same thing.
  • I use spherical triangulation of all station and ocean data over the surface of the sphere to cover all the earth’s surface. Each triangle has one measurement at each vertex and all the earth’s surface is covered. Nick Stokes uses a similar technique for TempLS.

Here is a comparison between the monthly temperature anomalies of HadCrut4 and GHCN V3 when calculated exactly the same way using Spherical Triangulation. Only about 5% of stations are different, but there remains a small difference in data corrections (homogenisation). HadCRUT4.6 uses 7688 stations and GHCN has 7280.

Almost exact agreement between HadCUT4.6 and GHCNV3 when calculated exactly the same way.

The new GHCN V4 provisional data has collected 27315 stations of which 17372 have at least 10y of data between 1961-1990, so eventually V4 should double the number of stations, although not dramatically increasing the geographic coverage. We can expect a bit of extra warming though since Arctic latitudes have yet more data. Here is a preview.

GHCN V4 December temperatures over the Northern Hemisphere from over 17000 stations+HadSST3

With so many new stations mainly in the Northern regions global temperatures will apparently ‘warm again’ once the main groups adopt it.

Comparison of GHCN V3 (blue) and V4 (red) calculated in exactly the same way. Deltas are shown against the right hand y-axis.

The December temperature rises by another ~0.05C above that of GHCN V3.

In conclusions all global temperature indices agree with each other in general, but that is mainly because they all use the same core station and ocean data. The main differences are due to how they spatially average the available data. HadCRUT4.6 is the most conservative because it only averages over (Lat, Lon) cells where there is real data. All the others extrapolate into regions without data. I think Spherical triangulation is the most honest because it works on the surface of a sphere, weighting each measurement equally.

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Zeke’s Wonder Plot

Zeke Hausfather who works for Carbon Brief and Berkeley Earth has produced a plot which shows almost perfect agreement between CMIP5 model projections and global temperature data. This is based on RCP4.5 models and a baseline of 1981-2010. First here is his original plot.

I have reproduced his plot and  essentially agree that it is correct. However, I also found some interesting quirks. Firstly here is my version of his plot where I have added the CMIP5 mean to compare with the new blended TOS/TAS mean. I have also included the latest HadCRUT4.6 annual values in purple.

Original plot with RCP4.5 model ensemble members overlaid and unblended model mean shown in red. HadCRU4.6 annual values have been added in purple. Click to expand

The apples to apples comparison (model SSTs blended with model land 2m temperatures)  reduces the model mean by about 0.06C. Zeke has also smoothed out the temperature data by using a 12 month running average. This has the effect of exaggerating peak values as compared to using the annual averages. To see this simply compare HadCrut4 (annual) in purple with his Hadley/UEA.

So now what happens if you change RCP?

Here is the result for RCP2.6 which has less forcing that RCP4.5

The same plot but now overlaid with RCP2.6 model ensemble and mean. click to expand

The model spread and the mean have increased slightly. So the model mean and grey shading should also  slightly rise.

Next, does the normalisation (baseline) affect the result ?

Effect of changing normalisation period. Cowtan & Way uses kriging to interpolate Hadcrut4.6 coverage into the Arctic and elsewhere.

Yes it does. Shown above is the result for a normalisation from 1961-1990. Firstly look how the lowest 2 model projections now drop further down while the data seemingly now lies below both the blended (thick black) and the original CMIP average (thin black). HadCRUT4 2016 is now below the blended value.

This improved model agreement has nothing to do with the data itself but instead is due to a reduction in warming predicted by the models. So what exactly is meant by ‘blending’?

Measurements of global average temperature anomalies use weather stations on land and sea surface temperatures (SST) over oceans. The land measurements are “surface air temperatures”(SAT)  defined as the temperature 2m above ground level. The CMIP5 simulations however used SAT everywhere. The blended model projections use simulated SAT over land and TOS (temperature at surface) over oceans. This reduces all model predictions slightly, thereby marginally improving agreement with data.  See also Climate-lab-book

The detailed blending calculations were done by Kevin Cowtan using a land mask and ice mask to define where TOS and SAT should be used in forming the global average. I downloaded his python scripts and checked all the algorithm, and they look good to me. His results are based on the RCP8.5 ensemble. These are the results I get using his Python code.

RCP 8.5 ensemble. The original projections are in blue and the blended ones in red. The ensemble mean is reduced by up to 0.07C . Data shown is Cowtan & Way.

Agreement has definitely now improved between the data (Cowtan a& Way) and the models, but they are still running warmer from 1998 to 2014.

Here finally is my 1950-2050 overview, where the blended RCP4.5 result has been added.

The solid blue curve is the CMIP5 RCP4.6 ensemble average after blending. The dashed curve is the original. Click to expand.

Again the models mostly lie above the data after 1999.

This post is intended to demonstrate just how careful you must be when interpreting plots that seemingly demonstrate either full agreement of climate models with data, or else total disagreement.

In summary, Zeke Hausfather writing for Carbon Brief 1) used a clever choice of baseline, 2) of RCP for blended models and 3) by using a 12 month running average, was able to show an almost perfect agreement between data and models. His plot is 100% correct.  However exactly the same data plotted with a different baseline and using annual values (exactly like those in the models), instead of 12 monthly running averages shows instead that the models are still lying consistently above the data. I know which one I think best represents reality.

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A comparison of CMIP5 Climate Models with HadCRUT4.6

Overview: Figure 1. shows a comparison of the latest HadCRUT4.6 temperatures with CMIP5 models for Representative Concentration Pathways (RCPs). The temperature data lies significantly below all RCPs, which themselves only diverge after ~2025.

Fig 1. Model comparisons to data 1950-2050. Spaghetti are individual annual model results for each RCP. Solid curves are model ensemble annual averages.

Modern Climate models originate from Global Circulation models which are used for weather forecasting. These simulate the 3D hydrodynamic flow of the atmosphere and ocean on earth as it rotates daily on its tilted axis, and while orbiting the sun annually. The meridional flow of energy from the tropics to the poles generates convective cells, prevailing winds, ocean currents and weather systems. Energy must be  balanced at the top of the atmosphere between incoming solar  energy and out going infra-red energy. This depends on changes in the solar heating, water vapour, clouds , CO2, Ozone etc. This energy balance determines the surface temperature.

Weather forecasting models use live data assimilation to fix the state of the atmosphere in time and then extrapolate forward one or more days up to a maximum of a week or so.  Climate models however run autonomously from some initial state, stepping  far into the future assuming that they correctly simulate a changing climate due to  CO2 levels, incident solar energy, aerosols, volcanoes etc. These models predict  past and future surface temperatures, regional climates, rainfall, ice cover etc. So how well are they doing?

Fig 2. Global Surface temperatures from 12 different CMIP5 models run with RCP8.5

The disagreement on the global average surface temperature is huge – a spread of 4C. This implies that there must still be a problem relating to achieving overall energy balance at the TOA. Wikipedia tells us that the average temperature should be about 288K or 15C. Despite this discrepancy in reproducing net surface temperature the model trends in warming for  RCP8.5 are similar.

Likewise weather station measurements of temperature have changed with time and place, so they too do not yield a consistent absolute temperature average. The ‘solution’ to this problem is to use temperature ‘anomalies’ instead, relative to some fixed normal monthly period (baseline).  I always use the same baseline as CRU 1961-1990. Global warming is then measured by the change in such global average temperature anomalies. The implicit assumption of this is that nearby  weather station and/or ocean measurements warm or cool coherently, such that the changes in temperature relative to the baseline can all be spatially averaged together. The usual example of this is that two nearby stations with different altitudes will have different temperatures but produce the similar ‘anomalies’. A similar procedure is used on the model results to produce temperature anomalies. So how do they compare to the data?

Figure 3 shows the result for HadCRUT4.6 compared to the CMIP5 model ensembles run with CO2 forcing levels from RCP8.5, RCP4.5, RCP2.4 and where anomalies use the same 30y normalisation period.

Fig 3. HadCRUT4.6 compared to 41 models run with 3 widely different RCP forcing.

Note how all models now converge to the zero baseline (1961-1990) eliminating differences in absolute temperatures. This apparently allows models to be compared directly to measured temperature anomalies, although each use anomalies for different reasons. Those of the data is due to poor coverage while that of the models is due to poor agreement in absolute temperatures.  The various dips seen in Fig 3. before 2000 are due to historic volcanic eruptions whose cooling effect has been included in the models.

Fig 4. Model comparisons to data 1950-2050

Figure 4 shows a close up detail from 1950-2050. This shows how there is a large spread in model trends even within each RCP ensemble. The data falls below the bulk of model runs after 2005 except briefly during the recent el Nino peak in 2016.

Figure 1. shows that the data are now lower than the mean of every RCP, furthermore we won’t be able to distinguish between RCPs until after ~2030.

Method: I have downloaded and processed all CMIP5 runs from KNMI Climate Explorer for each RCP. I then calculated annual averages for the 1961-1990 baseline and combined them in all into a single CSV file.  These can each be download using for example this URL:  RCP85

To retrieve any of the others just change ’85’ to ’60’ or ’45’ or ’26’ in the URL.

Posted in AGW, Climate Change, IPCC | 18 Comments