CMIP6 temperature cycles

Nick Stokes has an interesting post comparing a blended version of CMIP6 tos/tas  to compare models to data. The blending is intended to correct for the fact that  Ocean temperatures are measured at the surface (tos) while land temperatures are measured 2m above the surface (tas). He made available the CMIP6 model data in an easy to use csv file. So I thought it would be easy to compare CMIP6 model results to my global temperature monthly data. However I discovered another effect.

CMIP6 monthly temperatures (Univ. Melbourne). Note the regular monthly oscillation in all models . For example the yellow post 2100 model signal (MRI ESM2)

Each model produces a monthly cycle of changing temperatures like a sine wave. The models work in absolute temperatures and you typically find a variation of 4 or 5 degrees during each year. The reason for this is probably 2 fold. Firstly there is an asymmetry between the land/ocean areas in the  Northern Hemisphere to that in the Southern Hemisphere. Secondly there is a slightly elliptical orbit of the earth (perihelion/aphelion). This causes the Southern Hemisphere summer to be about 4.1 million miles closer to the sun than the Northern Hemisphere summer. So why don’t we see this in the measured data?

The  fact is that none of the global temperature data show any sign of such an oscillation, although it is observed in Meteorological ‘reanalysis’ data. The main reason for this is that everyone always work in temperature ‘anomalies’ so they just calculate deviations from a ‘normal’ monthly average. I always take a 30 year normalisation period for weather stations of 1961-1991 which is the same as HadSST3/4. Let’s compare all 102 CMIP6 models with the data from Nick’s csv file where I calculate model “anomalies” on the same baseline.

Global temperature anomaly data compared to 102 CMIP6 model runs with tos/tas blending. The purple arrow shows the normalisation period for both.

The data are up to March 2012. The agreement appears reasonable good partly because they have been normalised to the same baseline. However more recent temperature data favours those models running slightly cooler.

What I find interesting though is that global temperatures actually change by ~5 degrees every year.  This is then superimposed onto an overall global “warming” of just 1 degree over the last 180 years. It is only by using temperature anomalies that this small effect can even be measured.  The above monthly model  temperature data are derived form monthly temperature data that actually looks like this !


Posted in AGW, climate science | 7 Comments

Global Temperatures so far (March 2021)

HadSST3 has finally been updated for February and March so I can calculate global temperatures based on spherical triangulation.

So far 2021 is running much cooler that 2020. The temperatures for the first 3 months of the year are:

Jan 0.65C (down 0.01C from Dec 2020 and 0.31 from Nov 2020)
Feb 0.52C (further drop of 0.16C)
Mar 0.65C (rise of 0.13C)

Monthly Global temperatures

Based on the first 3 months there has also been a large drop in the “annual” temperature of ~0.3C compared to 2020,  back to levels seen 15 years ago (2005).

Annual global temperatures 2021 is based on just the first 3 months.

Here is a spatial distributions for March.

Much of the Southern Hemisphere temperatures are actually even lower than the 1961-1990 average temperature.

I may convert to  HadSST4 once HadCRUT5 becomes operational. However this update yet again increases warming trends (surprised?)

Posted in AGW, Hadley, UK Met Office | 1 Comment

Imperial’s Pessimistic Model

Boris has clearly been spooked by a new gloomy modelling study by the Imperial Group which puts his road map to normality in jeopardy. How is this possible when the UK’s case rate is falling and ~ 50% of the population are vaccinated ?

Well you can trace everything back to one crucial  parameter Imperial are using in their model.

We assume the vaccines prevents to a certain extent, an infected person who is vaccinated from transmitting the virus (optimistic, although only assumed in 1 sensitivity analysis). 30% after 2 jabs and 0% after 1 dose.

So according to Imperial, vaccination only has a minor effect on transmissibility. You can still get infected even if you have been vaccinated and then infect other people without ever noticing. If this were actually true then the pandemic would never end and we would remain in permanent semi-lockdown. If instead vaccinated people can’t catch covid and don’t transmit infection to others then the pandemic will soon be over, once 75% of the population are vaccinated. That is because they assume the Kent variant is now dominant with an R0 = 4. So once  3 out of 4 people are immune R falls below 1 permanently without any social distancing restrictions. This is Herd Immunity !

So where does their low value of 30% reduction in transmission come from? It is all based on this study of  care home workers and their family members:

Effect of vaccination on transmission of COVID-19: an observational study in healthcare workers and their households

The samples from that study were as follows :

194,362 Household members : 3,123 COVID cases
144,525 Healthcare workers : 4,343 COVID cases

The healthcare workers fell into two groups A) those vaccinated = 113,253  and B) those unvaccinated = 31,272

The study found: Household members of vaccinated healthcare workers had a lower risk of COVID-19 case compared to household members of unvaccinated healthcare worker (rate per 100 person-years 9·40 versus 5·93; HR 0·70, 95% confidence interval [CI] 0·63 to 0·78).   I make that a 37% reduction in infections in households with a vaccinated healthcare worker.  Imperial use this result as direct input to their model i.e. assuming that vaccination reduces transmission by just 30% but only after the 2nd dose, and 0% after the first dose of the vaccine ! There are 12 weeks between the 1st and second doses in the UK so this makes a huge difference on Reff.

The basic assumption Imperial makes is that health care workers alone can bring Covid home and then infect others within their household. Yet this is obvious nonsense because their partners and other household members also have jobs, go shopping, and attend school etc. Perhaps some of them even work as care home staff. Imperial ignores the fact that they can catch COVID from hundreds of other people outside the household. In early February 1 in 60 people had COVID in the community and infection rates were high everywhere. So in that context a 30% net reduction in household infection really is an impressively large effect.

We can also check this. The paper was published on March 21 so the data must have been collected over a period ending a couple of weeks earlier. ONS data shows that infection rates fell from  1 in 50 to about 1 in 200 by the end of February so a fair average over January to February would be ~1.0 %.

Therefore you would expect up to 2000 cases from community infection alone in Household members. So a 30% total reduction now translates into a  78% reduction in direct  healthcare worker transmission.

If you remove this baseline of community infections then the correct figure should be more like a 60% reduction in transmission by healthcare workers after the first dose and an 80%  reduction after the second dose. If they use these figures then their gloomy predictions disappear !


Posted in Covid-19 | 2 Comments