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about Gavin Schmidt

Gavin Schmidt is a climate modeler, working for NASA and with Columbia University.

Some new CMIP6 MSU comparisons

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We add some of the CMIP6 models to the updateable MSU [and SST] comparisons.

After my annual update, I was pointed to some MSU-related diagnostics for many of the CMIP6 models (24 of them at least) from Po-Chedley et al. (2022) courtesy of Ben Santer. These are slightly different to what we have shown for CMIP5 in that the diagnostic is the tropical corrected-TMT (following Fu et al., 2004) which is a better representation of the mid-troposphere than the classic TMT diagnostic through an adjustment using the lower stratosphere record (i.e. TMT_{corr} = 1.1 TMT - 0.1 TLS).

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References

  1. S. Po-Chedley, J.T. Fasullo, N. Siler, Z.M. Labe, E.A. Barnes, C.J.W. Bonfils, and B.D. Santer, "Internal variability and forcing influence model–satellite differences in the rate of tropical tropospheric warming", Proceedings of the National Academy of Sciences, vol. 119, 2022. http://dx.doi.org/10.1073/pnas.2209431119
  2. Q. Fu, C.M. Johanson, S.G. Warren, and D.J. Seidel, "Contribution of stratospheric cooling to satellite-inferred tropospheric temperature trends", Nature, vol. 429, pp. 55-58, 2004. http://dx.doi.org/10.1038/nature02524

How not to science

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A trip down memory lane and a lesson on scientific integrity.

I had reason to be reviewing the history of MSU satellite retrievals for atmospheric temperatures recently. It’s a fascinating story of technology, creativity, hubris, error, imagination, rivalry, politics, and (for some) a search for scientific consilience – worthy of movie script perhaps? – but I want to highlight a minor little thing. Something so small that I’d never noticed it before, and I don’t recall anyone else pointing it out, but it is something I find very telling.

The story starts in the early 90’s, but what caught my eye was a single line in an op-ed (sub. req.) written two decades later:

… in 1994 we published an article in the journal Nature showing that the actual global temperature trend was “one-quarter of the magnitude of climate model results.”McNider and Christy, Feb 19th 2014, Wall Street Journal

Most of the op-ed is a rather tired rehash of faux outrage based on a comment made by John Kerry (the then Secretary of State) and we can skip right past that. It’s only other claim of note is a early outing of John Christy’s misleading graphs comparing the CMIP5 models to the satellite data but we’ll get back to that later.

First though, let’s dig into that line. The 1994 article is a short correspondence piece in Nature, where Christy and McNider analyzed MSU2R lower troposphere dataset and using ENSO and stratospheric volcanic effects to derive an ‘underlying’ global warming trend of 0.09 K/decade. This was to be compared with “warming rates of 0.3 to 0.4 K/decade” from models which was referenced to Manabe et al. (1991) and Boer et al. (1992). Hence the “one quarter” claim.

But lets dig deeper into each of those elements in turn. First, 1994 was pretty early on in terms of MSU science. The raw trend in the (then Version C) MSU2R record from 1979-1993 was -0.04 K/decade. [Remember ‘satellite cooling’?]. This was before Wentz and Schabel (1998) pointed out that orbital decay in the NOAA satellites was imparting a strong cooling bias (about 0.12 K/decade) on the MSU2R (TLT) record. Secondly, the two cited modeling papers don’t actually give an estimated warming trends for the 1980s and early 90s. The first is a transient model run using a canonical 1% increasing CO<sub>2</sub> – a standard experiment, but not one intended to match the real world growth of CO2 concentrations. The second model study is a simple equilibrium 2xCO2 run with the Canadian climate model, and does not report relevant transient warming rates at all. This odd referencing was pointed out in correspondence with Spencer and Christy by Hansen et al. (1995) who also noted that underlying model SAT trends for the relevant period were expected to be more like 0.1-0.15 K/decade. So the claim that the MSU temperatures were warming at “one quarter” the rate of the models wasn’t even valid in 1994. They might have more credibly claimed “two thirds” the rate, but the uncertainties are such that no such claim would have been robust (for instance, just the uncertainties on the linear regression alone are ~ +/-0.14 K/dec).

This image has an empty alt attribute; its file name is mcnider55-253x600.png
Replication of the Christy and McNider calculation and figure from 1994 but using the UAH v5.5 data.

But it gets worse. In 2014, McNider and Christy were well aware of the orbital decay correction (1998), and they were even aware of the diurnal drift correction that was needed because of a sign error introduced while trying to fix the orbital decay issue (discovered in 2005). The version of the MSU2R product at the beginning of 2014 was version 5.5, and that had a raw trend of -0.01 K/decade 1979-1993 (+/- 0.18 K/dec 95% CI, natch). Using an analogous methodology to that used in 1994 (see figure to the right), the underlying linear trend after accounting for ENSO and volcanic aerosols was…. 0.15 K/dec! Almost identical to the expected trend from models!

So not only was their original claim incorrect at the time, but had they repeated the analysis in 2014, their own updated data and method would have shown that there was no discrepancy at all.

Now in 2014, there was a longer record and more suitable models to compare to. Models had been run with appropriate volcanic forcings and in large enough ensembles that there was a quantified spread of expected trends. Comparisons could now be done in a more sophisticated away, that compared like with like and took account of many different elements of uncertainty (forcings, weather, structural effects in models and observations etc.). But McNider and Christy chose not to do that.

Instead, they chose to hide the structural uncertainty in the MSU retrievals (the TMT trends for 1979-2013 in UAH v5.5 and RSS v3.3 were 0.04 and 0.08 +/- 0.05 K/dec respectively – a factor of two different!), and ignore the spread in the CMIP5 models TMT trends [0.08,0.36] and graph it in a way as to maximise the visual disparity in a frankly misleading way. Additionally, they decided to highlight the slower warming TMT records instead of the TLT record they had discussed in 1994. For contrast, the UAH v5.5 TLT trends for 1979-2013 were 0.14± 0.05 K/dec.

But all these choices were made in the service of rhetoric, not science, to suggest that models are, and had always been, wrong, and that the UAH MSU data had always been right. A claim moreover that is totally backwards.

Richard Feynman often spoke about a certain kind of self-critical integrity as being necessary to do credible science. That kind of integrity was in very short supply in this op-ed.

References

  1. J.R. Christy, and R.T. McNider, "Satellite greenhouse signal", Nature, vol. 367, pp. 325-325, 1994. http://dx.doi.org/10.1038/367325a0
  2. F.J. Wentz, and M. Schabel, "Effects of orbital decay on satellite-derived lower-tropospheric temperature trends", Nature, vol. 394, pp. 661-664, 1998. http://dx.doi.org/10.1038/29267
  3. J. Hansen, H. Wilson, M. Sato, R. Ruedy, K. Shah, and E. Hansen, "Satellite and surface temperature data at odds?", Climatic Change, vol. 30, pp. 103-117, 1995. http://dx.doi.org/10.1007/BF01093228

2022 updates to model-observation comparisons

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Our annual post related to the comparisons between long standing records and climate models.

As frequent readers will know, we maintain a page of comparisons between climate model projections and the relevant observational records, and since they are mostly for the global mean numbers, these get updated once the temperature products get updated for the prior full year. This has now been completed for 2022.

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2022 updates to the temperature records

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Another January, another annual data point.

As in years past, the annual rollout of the GISTEMP, NOAA, HadCRUT and Berkeley Earth analyses of the surface temperature record have brought forth many stories about the long term trends and specific events of 2022 – mostly focused on the impacts of the (ongoing) La Niña event and the litany of weather extremes (UK and elsewhere having record years, intense rainfall and flooding, Hurricane Ian, etc. etc.).

But there are a few things that don’t get covered much in the mainstream stories, and so we can dig into them a bit here.

What influence does ENSO really have?

It’s well known (among readers here, I assume), that ENSO influences the interannual variability of the climate system and the annual mean temperatures. El Niño events enhance global warming (as in 1998, 2010, 2016 etc.) and La Niña events (2011, 2018, 2021, 2022 etc.) impart a slight cooling.

GISTEMP anomalies (w.r.t. late 19th C) coded for ENSO state in the early spring.

Consequently, a line drawn from an El Niño year to a subsequent La Niña year will almost always show a cooling – a fact well known to the climate disinformers (though they are not so quick to show the uncertainties in such cherry picks!). For instance, the trends from 2016 to 2022 are -0.12±0.37ºC/dec but with such large uncertainties, the calculation is meaningless. Far more predictive are the long term trends which are consistently (now) above 0.2ºC/dec (and with much smaller uncertainties ±0.02ºC/dec for the last 40 years).

It’s worth exploring quantitatively what the impact is, and this is something I’ve been looking at for a while. It’s easy enough correlate the detrended annual anomalies with the ENSO index (maximum correlation is for the early spring values), and then use that regression to estimate the specific impact for any year, and to estimate an ENSO-corrected time series.

Correlation of detrended annual anomalies and spring ENSO indexGISTEMP and and ENSO-corrected version of the time series
a) Correlation between an ENSO index (in Feb/Mar) and the detrended annual anomaly. b) An ENSO-corrected version of the GISTEMP record.

The surface temperature records are becoming more coherent

Back in 2013/2014, the differences between the surface indices (HadCRUT3, NOAA v3 and GISTEMP v3) contributed to the initial confusion related to the ‘pause’, which was seemingly evident in HadCRUT3, but not so much in the other records (see this discussion from 2015). Since then all of the series have adopted improved SST homogenization, and HadCRUT5 adopted a similar interpolation across the pole as was used in the GISTEMP products. From next month onwards, NOAA will move to v5.1 which will now incorporate Arctic buoy data (a great innovation) and also provide a spatially complete record. The consequence is that the surface instrument records will be far more coherent than they have ever been. Some differences remain pre-WW2 (lots of SST inhomogeneities to deal with) and in the 19th C (where data sparsity is a real challenge).

Four surface-station based estimate of global warming since 1880.

The structural uncertainty in satellite records is large

While the surface-based records are becoming more consistent, the various satellite records are as far apart as ever. The differences between the RSS and UAH TLT records are much larger than the spread in the surface records (indeed, they span those trends), making any claims of greater precision somewhat dubious. Similarly, the difference in the versions of the AIRS records (v6 vs. v7) of ground temperature anomalies produce quite distinct trends (in the case of AIRS v6, Nov 2022 was exceptionally cold, which was not seen in other records).

1979 trends in surface and satellite records showing a coherent warming in all records, but substantial differences between AIRS and MSU TLT versions.
Differences between surface, MSU TLT and AIRS ground temperature records.

When will we reach 1.5ºC above the pre-industrial?

This was a very common question in the press interviews this week. It has a few distinct components – what is the ‘pre-industrial’ period that’s being referenced, what is the uncertainty in that baseline, and what are the differences in the long term records since then?

The latest IPCC report discusses this issue in some depth, but the basic notion is that since the impacts that are expected at 1.5ºC are derived in large part from the CMIP model simulations that have a nominal baseline of ~1850, ‘pre-industrial’ temperatures are usually assumed to be some kind of mid-19th Century average. This isn’t a universally accepted notion – Hawkins et al (2017) for instance, suggest we should use a baseline from the 18th Century – but it is one that easier to operationalise.

The baseline of 1880-1900 can be calculated for all the long temperature series, and with respect to that 2022 (or the last five years) is between 1.1 and 1.3ºC warmer (with Berkeley Earth showing the most warming). For the series that go back to 1850, the difference between 1850-1900 and 1880-1900 is 0.01 to 0.03ºC, so probably negligible for this purpose.

Linear trends since 1996 are robustly just over 0.2ºC/decade in all series, so that suggests between one and two decades are required to have the mean climate exceed 1.5ºC, that is around 2032 to 2042. The first specific year that breaches this threshold will come earlier and will likely be associated with a big El Niño. Assuming something like 2016 (a +0.11ºC effect), that implies you might see the excedence some 5 years earlier – say 2027 to 2037 (depending a little on the time-series you are following).

2023 is starting the year with a mild La Niña, which is being forecast to switch to neutral conditions by mid-year. Should we see signs of an El Niño developing towards the end of the year, that will heavily favor 2024 to be a new record, though not one that is likely to exceed 1.5ºC however you calculate it.

[Aside: In contrast to my reasoning here, the last decadal outlook from the the UK MetOffice/WMO suggested that 2024 has a 50-50 chance of exceeding 1.5ºC, some 5 or so years early than I’d suggest, and that an individual year might reach 1.7ºC above the PI in the next five years! I don’t know why this is different – it could be a larger variance associated with ENSO in their models, it could be a higher present day baseline (but I don’t think so), or a faster warming rate than the linear trend (which could relate to stronger forcings, or higher effective sensitivity). Any insight on this would be welcome!]

References

  1. E. Hawkins, P. Ortega, E. Suckling, A. Schurer, G. Hegerl, P. Jones, M. Joshi, T.J. Osborn, V. Masson-Delmotte, J. Mignot, P. Thorne, and G.J. van Oldenborgh, "Estimating Changes in Global Temperature since the Preindustrial Period", Bulletin of the American Meteorological Society, vol. 98, pp. 1841-1856, 2017. http://dx.doi.org/10.1175/BAMS-D-16-0007.1

Scafetta comes back for more

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A new paper from Scafetta and it’s almost as bad as the last one.

Back in March, we outlined how a model-observations comparison paper in GRL by Nicola Scafetta (Scafetta, 2022a) got wrong basically everything that one could get wrong (the uncertainty in the observations, the internal variability in the models, the statistical basis for comparisons – the lot!). Now he’s back with a new paper in a different journal (Scafetta, 2022b) that could be seen as trying to patch the holes in the first one, but while he makes some progress, he now adds some new errors while attempting CPR on his original conclusions.

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References

  1. N. Scafetta, "Advanced Testing of Low, Medium, and High ECS CMIP6 GCM Simulations Versus ERA5‐T2m", Geophysical Research Letters, vol. 49, 2022. http://dx.doi.org/10.1029/2022GL097716
  2. N. Scafetta, "CMIP6 GCM ensemble members versus global surface temperatures", Climate Dynamics, 2022. http://dx.doi.org/10.1007/s00382-022-06493-w

Watching the detections

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The detection and the attribution of climate change are based on fundamentally different frameworks and shouldn’t be conflated.

We read about and use the phrase ‘detection and attribution’ of climate change so often that it seems like it’s just one word ‘detectionandattribution’ and that might lead some to think that it is just one concept. But it’s not.

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A CERES of fortunate events…

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The CERES estimates of the top-of-atmosphere radiative fluxes are available from 2001 to the present. That is long enough to see that there has been a noticeable trend in the Earth’s Energy Imbalance (EEI), mostly driven by a reduction in the solar radiation reflected by the planet, while the outgoing long wave radiation does not appear to contribute much. But what can be causing this?

A paper last year (Goode et al., 2021) also reported on a two decade estimate of Earthshine measurements which appear to confirm a small decrease in albedo (and decrease in reflected short wave (SW) radiation). While the two measurements are subtly different due to the distinct geometries, they do show sufficient coherence to give us some confidence that they are real.

Comparison of CERES SWup trends (blue) with inferred changes in Earthshine (black).

Similarly, Loeb et al. (2021) show that the trends in the EEI derived from CERES match what you get from the changes in ocean heat content.

Satellite-derived trends in EEI compared to estimates from changes in ocean heat (Loeb et al., 2021).

A few people have started to interpret the dominance of the SW trends to imply that the overall trends in climate are not (despite copious evidence) being driven by the rise in greenhouse gases (for instance, the rather poorly argued and seemingly un-copyedited Dübal and Vahrenholt (2021)) but these simplistic interpretations are seriously confused.

We can explore the issues and pitfalls of this using the ‘simple model’ of the greenhouse effect we explored back in 2007. At that time, we said:

You should think of these kinds of exercises as simple flim-flam detectors – if someone tries to convince you that they can do a simple calculation and prove everyone else wrong, think about what the same calculation would be in this more straightforward system and see whether the idea holds up. If it does, it might work in the real world (no guarantee though) – but if it doesn’t, then it’s most probably garbage.

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References

  1. P.R. Goode, E. Pallé, A. Shoumko, S. Shoumko, P. Montañes‐Rodriguez, and S.E. Koonin, "Earth's Albedo 1998–2017 as Measured From Earthshine", Geophysical Research Letters, vol. 48, 2021. http://dx.doi.org/10.1029/2021GL094888
  2. N.G. Loeb, G.C. Johnson, T.J. Thorsen, J.M. Lyman, F.G. Rose, and S. Kato, "Satellite and Ocean Data Reveal Marked Increase in Earth’s Heating Rate", Geophysical Research Letters, vol. 48, 2021. http://dx.doi.org/10.1029/2021GL093047
  3. H. Dübal, and F. Vahrenholt, "Radiative Energy Flux Variation from 2001–2020", Atmosphere, vol. 12, pp. 1297, 2021. http://dx.doi.org/10.3390/atmos12101297

Climate impacts of the #IRA

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With the signing of the Inflation Reduction Act (IRA) on Tuesday Aug 16, the most significant climate legislation in US federal history (so far) became law.

Despite the odd name (and greatly overused TLA), the IRA contains a huge number of elements, totalling roughly $350 billion of investment, in climate solutions over the next ten years. This is an historic effort though it falls short of the broader ‘Green New Deal‘ goals that were proposed in 2019, and doesn’t include all of the elements that were in the proposed 2021 reconcilliation package (the American Jobs Plan in “Build Back Better“) that ultimately floundered.

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The CO2 problem in six easy steps (2022 Update)

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One of our most-read old posts is the step-by-step explanation for why increasing CO2 is a significant problem (The CO2 problem in 6 easy steps). However, that was written in 2007 – 15 years ago! While the basic steps and concepts have not changed, there’s 15 years of more data, updates in some of the details and concepts, and (it turns out) better graphics to accompany the text. And so, here is a mildly updated and referenced version that should be a little more useful.

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