## June global temperature is 0.91C after a fall in May to 0.80C

La Nina continues to dominate global temperatures up to June 2022 bringing cold temperatures to Eastern Australia. The global May temperature (anomaly) was 0.80C and June was 0.91 C. The data used are GHCNV4 and HADSST4 and the method is always based on an integration using Spherical Triangulation.

3D view of temperature anomalies for June 2022 showing a continuing La Nina.

The annual temperature average for 2022 after the first six months is 0.85C which is about 0.1C below the peak year of 2020.

The monthly data updated to June is shown below

The full temperature data back to 1850 is available here.

monthly temperature anomalies

annual temperature anomalies

Posted in Climate Change, climate science | 16 Comments

## AR6 Climate “Muddles”

ATTP has a new post which highlights a fundamental problem with increasing complexity of AR6 climate models. This is based on a new article in Nature by Zeke Hausfather et al . Some of the latest models in AR6 are running far too hot. The more sophisticated they try to become  so the hotter they seem to run. Modellers introduce new complexity through informed guesswork to parameterise it. The article notes however that such “hot models ‘ have problems.

“Numerous studies have found that these high-sensitivity models do a poor job of reproducing historical temperatures over time and in simulating the climates of the distant past. Specifically, they often show no warming over the twentieth century and then a sharp warming spike in the past few decades, and some simulate the last ice age as being much colder than palaeoclimate evidence indicates”

ATTP summarises the article as follows

The Hausfather et al. article was basically suggesting that researchers who use climate model output to assess the impact of climate change should aim to follow a similar practice to what was presented in the most recent IPCC report. Use GWLs, rather than simply focussing on scenarios going to 2100, weight the models if the warming trajectory is relevant, and try to consider which models may be best suited to the problem that is being considered.

So I thought it would be interesting instead to go right back to basics and use the simplest CO2 green house climate “model” possible. From this we can derive the radiative forcing due to rising levels of CO2 giving a logarithmic dependence

Following this we can simply make an estimate for the net change in surface temperature due to CO2.

$S = \epsilon\sigma T^4$
$DS = 4\epsilon\sigma T^3 DT$
$DT = \frac{DS}{4\epsilon\sigma T^3}$

Averaging over clouds($\epsilon = 0.5$), oceans and land($\epsilon = 0.95$) we then get a global averaged $\epsilon = 0.65$

If we assume that each increment in forcing DS is offset by the same increase in Black Body radiation due to a small surface temperature rise DT, then we can iterate through the temperature rise due to increases in CO2 concentrations. We take Pre-industrial CO2 levels as 280 ppm with an associated temperature of 288 K

Here is the result of this “model”.

Simplest possible Climate Model.

We can see that temperatures should rise by ~1C since pre-industrial times which proves to be surprisingly accurate!  A  doubling of CO2 levels would then lead to a net temperature rise of just 1.5C due to the logarithmic increase in forcing. This is not a  “climate  breakdown”.

I once worked on Magnetohydrodynamic (MHD) code for modelling Tokamak plasmas. That too was immensely complicated but recent breakthroughs in fusion have mainly been  experimental. So I suspect the same will apply to our current dependence on ever more complex climate models predicting imminent doom.

Posted in AGW, Climate Change, climate science | 27 Comments

## April global temperature drops to 0.77C

The global average temperature anomaly for April 2022 dropped by 0.2C  since March to 0.77C (1961-1990 baseline). The highest monthly temperature ever recorded was 1.23C  in March 2016.

Monthly temperatures updated for April 2022. Data are based on GHCNV4 and HadSST4

North America shows much cooler temperatures since March with a strong La Nina evident over the Southern Pacific. (Colours shown are always relative to the normalised climate between 1961-1990)

The 2022 annual average temperature anomaly (for what it’s worth)  after the first 4 months is 0.84C . The highest annual temperature anomalies ever calculated were 0.94C in 2016 and 0.96C in 2020.

Posted in Climate Change, climate science | Tagged | 3 Comments