RealClimate
@realclimate.bsky.social
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www.realclimate.org is a commentary site on climate science by working climate scientists for the public and journalists. We aim to provide a quick response to developing stories and provide the context sometimes missing in mainstream commentary.
![If you think you know why NASA did not report the discovery of the Antarctic polar ozone hole in 1984 before the publication of Farman et al in May 1985, you might well be wrong.
One of the most fun things in research is what happens when you try and find a reference to a commonly-known fact and slowly discover that your “fact” is not actually that factual, and that the real story is more interesting than you imagined…
Joe Farman and colleagues (BAS)Here is the standard story (one I’ve told repeatedly myself): The publication in 1985 by scientists from the British Antarctic Survey working at Halley Station (right) of observations of extremely low ozone values in Oct 1983 (SH springtime) came as a huge shock to the scientific community. Given that NASA had been monitoring ozone by satellite using the NIMBUS instruments since the late 1970s, people were surprised that this had not been reported already. NASA scientists went back to the satellite data and found that anomalously low values had been rejected as bad data and were not included in the analyses. After reprocessing the data with this flag removed, the trends became clear and the confirmation of ground-based data was reported in the NY Times in Nov 1985 and published formally the next year (Stolarski et al., 1986).
This is mostly true, but not quite…
It is true that the Quality Control (QC) flag on the retrieval was set whenever the inferred ozone level dropped below 180 Dobson Units [1 DU is equivalent to a 0.01mm thick pure ozone layer at standard temperature and pressure]. Prior to 1983, there had never been an observation below 200 DU and so values lower than 180 DU were out of calibration range for the sensor.
However, it wasn’t true that no-one at NASA had noticed.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:iyqug43fzwdjysg3ppwcbphl/bafkreicropj3mj7wptce2thukvkirfbtflqh5azzf2jyacwjt66d6gq2xu)
![Fig. 1 Tipping elements in the Earth system, in relation to past global temperature evolution since the last Ice Age 20,000 years ago as well as future warming scenarios[iii]. The Paris range of 1.5 – 2 °C warming is shown in grey; the bars show increasing risk of crossing tipping points from yellow to red.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:iyqug43fzwdjysg3ppwcbphl/bafkreibpziwv3kx4t2vo3yi33riweitusfuqx6v5uppsmfnfhqrzahkcbi)
![1) Models are NOT tuned [for the late 20th C/21st C warming] and using them for attribution is NOT circular reasoning.
Curry’s claim is wrong on at least two levels. The “models used” (otherwise known as the CMIP5 ensemble) were *not* tuned for consistency for the period of interest (the 1950-2010 trend is what was highlighted in the IPCC reports, about 0.8ºC warming) and the evidence is obvious from the fact that the trends in the individual model simulations over this period go from 0.35 to 1.29ºC! (or 0.84±0.45ºC (95% envelope)).
Ask yourself one question: Were these models tuned to the observed values?
Second, this is not how the attribution is done in any case. What actually happens is that the fingerprint of different forcings are calculated independently of the historical runs (using subsets of the drivers) and then matched to the observations using scalings for the patterns generated. Scaling factors near 1 imply that the models’ expected fingerprints fit reasonably well to the observations. If the models are too sensitive or not enough, that will come out in the factors, since the patterns themselves are reasonably robust. So models that have half the observed trend, or twice as much, can still help determine the pattern of change associated with the drivers. The attribution to the driver is based on the best fits of that pattern and others, not on the mean or trend in the historical runs.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:iyqug43fzwdjysg3ppwcbphl/bafkreihloapwbzagkiq2etw7viwvnmzwwhhnea2pjusa4qyjgv6dimhpgy)
![CMIP6 (circa 2021)
The latest phase of CMIP has been ongoing since around 2019, and now has sufficient models to provide a basis for projections going forward. These models used observed boundary conditions (GHG levels, deforestation, solar, volcanoes etc.) up to 2014, and projections based on the Shared Socioeconomic Pathways (SSPs) from 2015 onward. The same caveat with respect to the comparison to the blended SAT/SST observations stands as with CMIP5, but this is a relatively small effect (i.e. it’s expected to be around 0.05ºC in 2022). Note however, that some CMIP6 models have climate sensitivities outside both the CMIP5 range and the range constrained by observations. Thus in these figures, we plot the full mean (1 ensemble member per model) and the mean of a subset of the models that have a transient climate response (TCR) within the likely constrained range [1.4,2.2]ºC as assessed by IPCC AR6 (Hausfather et al., 2022).
Time series from 1979 of CMIP6 climate model hindcasts to 2014, and projections beyond, compared to observed temperatures. The long term trends in models screened for a likely range of sensitivity are a good fit to the actual temperatures.
Surface temperature changes in CMIP6 models, using a screened ensemble and a fuller ensemble (37 models). Last updated: 23 Jan 2025.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:iyqug43fzwdjysg3ppwcbphl/bafkreiduq4cwom3vlehmubfo6afgh3twlcnofmxoj5poofyv4v6hvkrfve)
