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about Gavin Schmidt

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

The Scafetta Saga

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It has taken 17 months to get a comment published pointing out the obvious errors in the Scafetta (2022) paper in GRL.

Back in March 2022, Nicola Scafetta published a short paper in Geophysical Research Letters (GRL) purporting to show through ‘advanced’ means that ‘all models with ECS > 3.0°C overestimate the observed global surface warming’ (as defined by ERA5). We (me, Gareth Jones and John Kennedy) wrote a note up within a couple of days pointing out how wrongheaded the reasoning was and how the results did not stand up to scrutiny.

[Read more…] about The Scafetta Saga

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

As Soon as Possible

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The latest contrarian crowd pleaser from Soon et al (2023) is just the latest repetition of the old “it was the sun wot done it” trope[1] that Willie Soon and his colleagues have been pushing for decades. There is literally nothing new under the sun.

[Read more…] about As Soon as Possible

References

  1. W. Soon, R. Connolly, M. Connolly, S. Akasofu, S. Baliunas, J. Berglund, A. Bianchini, W.M. Briggs, C.J. Butler, R.G. Cionco, M. Crok, A.G. Elias, V.M. Fedorov, F. Gervais, H. Harde, G.W. Henry, D.V. Hoyt, O. Humlum, D.R. Legates, A.R. Lupo, S. Maruyama, P. Moore, M. Ogurtsov, C. ÓhAiseadha, M.J. Oliveira, S. Park, S. Qiu, G. Quinn, N. Scafetta, J. Solheim, J. Steele, L. Szarka, H.L. Tanaka, M.K. Taylor, F. Vahrenholt, V.M. Velasco Herrera, and W. Zhang, "The Detection and Attribution of Northern Hemisphere Land Surface Warming (1850–2018) in Terms of Human and Natural Factors: Challenges of Inadequate Data", Climate, vol. 11, pp. 179, 2023. http://dx.doi.org/10.3390/cli11090179

Back to basics

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You can tell how worried the climate deniers are by how many fields of science they have to trash to try and have people not see what’s happening.

it will not have escaped most people’s notice that global temperatures are heading into uncharted territory. The proximate cause of this week’s headlines is the Climate Reanalyzer website at the U. Maine which provides a nice front end to the NOAA NCEP CFS forecast system and reanalysis and shows absolute daily temperatures in early July clearly exceeding the highest pre-existing temperatures from August 2016. It’s an arresting graphic, and follows in from the record high ocean surface temperatures that were being reported a month ago.

surface temperature as a function of day since 1979, showing 2023 exceeding the warmest temperatures seen in the previous record.

This is however a relatively new resource and was not online the last time that we set absolute temperature records (in summer 2016). So this has both salience and novelty – a potent combination!

The ultimate cause of these patterns is of course the ongoing global warming, driven almost entirely by human activities.

What are we looking at?

As we’ve explained before, all global temperature products are based on some kind of model – statistical, physical etc. There is no direct measurement of the global temperature – not from satellites, stations, or from the one random person who happens to be in most average place on Earth (where might that even be?). But that doesn’t mean the products aren’t useful!

In this particular instance we are looking at the output of a weather forecast model (NCEP CFS) that ingests multiple sources of in situ and satellite data every 3 hours which is then averaged over a day and over the surface of the planet. These calculations are precise reflections of what is in the model, but for multiple reasons this might not be a perfect reflection of what the real world is doing.

We looked at the coherence of different products, including the reanalyses, before and found that while they are highly correlated in terms of annual anomalies, they differ in their absolute magnitude (graphic from 2017).

Global mean temperature variations from different reanalyses.

Differences will depend on resolution – higher resolution models have better (and higher topography) and then will have slightly cooler temperatures (all else being equal – which it isn’t!), tuning, model structure etc. and can’t really be discriminated using the pure (sparse) observations.

Coherence at the monthly scale is also quite good (though a little noisier), and I haven’t (yet) seen a good comparison of the coherence of the different products at the daily scale (note that the standard products (like GISTEMP, HadCRUT5 and NOAAv5) don’t produce a daily product). One might anticipate that there is a similarity, but perhaps not a one-to-one correspondence on exactly which days were the warmest.

Monthly anomalies since 2010 from ten different products showing a broad correlation between products but with offsets in the global mean.

What are we seeing?

For the global temperature, it’s well established that the maximum is during the Northern Hemisphere summer. This sometimes comes as a surprise to people (why doesn’t the opposing seasonality in the Southern Hemisphere cancel this out?), but it relates to the fact that there is a lot more land in the Northern Hemisphere. Since the seasonal cycle over land is much larger than over the ocean (smaller heat capacity, and less evaporation), that means that the seasonal variations in the north outweigh the variations in the south.

Thus the months of July and August are generally the warmest in the year, and consequently we expect the warmest days during those months – and this is reflected in the CFS output (and in the ERA5 output also). The monthly variations are also reflected in the GISTEMP product which allows you to see the shifts from 1880 onward (about a 1ºC warming in each month since the late 19th C):

The station-based products are a little delayed with respect to the reanalyses, but they generally reflect the same patterns – thus one should expect the June temperatures in NOAA, GISTEMP and HadCRUT5 to be the warmest June on record. Given too, that these temperatures are being driven by persistent warming in the oceans, increasingly juiced by the growing El Niño event in the tropical Pacific, records in July and August are also likely. This is of course increasing the odds for 2023 to be a record year (I would estimate about 50% at this point).

But the WSJ Opinion page says that there’s no such thing as the global temperature!

Well, they would say that wouldn’t they. [Narrator: there is, in fact, a perfectly well defined global mean of any two-dimensional field defined on the sphere, including temperature].

More generously, one might think that their argument (such as it is), is that the global mean isn’t directly relevant for anyone. That is, no-one lives in the global mean, all impacts are local and driven by weather variations. But we’ve known for decades that the global mean change is a really good predictor (not perfect, but pretty good) of local impacts on heat waves, intense rainfall, drought intensity etc.

But let’s be honest, it’s basically pure distraction and attempts to complicate something that is pretty basic:

The climate is warming, records are being broken, and we are increasingly seeing the impacts.

I know why the WSJ doesn’t want you to realise this, but it’s not hard to see past their obfuscation.

Turning a new page[s]

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The world is full of climate dashboards (and dashboards of dashboards), and so you might imagine that all datasets and comparisons are instantly available in whatever graphical form you like. Unfortunately, we often want graphics to emphasize a particular point or comparison, and generic graphs from the producers of the data often don’t have the same goal in mind. Dashboards that allow for more flexibility (like WoodForTrees) are useful, but aren’t as visually appealing as they could be. Thus, I find myself creating bespoke graphics of climate and climate model data all the time.

Some of these are maintained on the Climate model-observations comparison page but many of the graphs that I make (often to make a point on twitter) aren’t saved there and often their provenance is a bit obscure. Given that twitter will not last forever (though it might be around for slightly longer than a head of lettuce), it’s probably useful to have a spot to upload these graphics to, along with some explanation, to serve as a reference.

I have therefore created a couple of ‘pages’ (in wordpress speak) with fixed URLs where I will be curating relevant graphics I make (and findable at the bottom of the page under “DATA AND GRAPHICS”). The first is focused on the surface temperature records. I often update relevant graphics associated with this in early January (when we get another dot on the graphs), but there are associated graphs that I’ve made that don’t make it into those updates, so this is a place for them too. This includes the impacts of ENSO, comparisons across different platforms, or the impact of homogenization.

Comparison of four instrumental records which all coherently show warming since 1880.

The second page is bit more eclectic. These are graphs that are relevant to some trope or talking point that often pops up, and my graphs are an attempt to provide context (usually), or to debunk it entirely. This is where you’ll find maps of where the climate is warming faster than the global average, time-series of river ice break-up dates, and an example of sensible scaling of CO2 changes and temperature.

Map showing all the areas where trends from 1971-2022 are greater than the global mean trend. Almost all of the northern hemisphere landmass, and much of the SH land too.

To start with, I’m just going to upload some graphs I’ve made recently (with any updates that are needed), and I’ll add content as I make something new. If there are any other ideas (that aren’t too involved!), I’ll be happy to look at adding those too. Let me know if this is useful.

CMIP6: Not-so-sudden stratospheric cooling

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As predicted in 1967 by Manabe and Wetherald, the stratosphere has been cooling.

A new paper by Ben Santer and colleagues has appeared in PNAS where they extend their previous work on the detection and attribution of anthropogenic climate change to include the upper stratosphere, using observations from the Stratospheric Sounding Units (SSUs) (and their successors, the AMSU instruments) that have flown since 1979.

[Read more…] about CMIP6: Not-so-sudden stratospheric cooling

References

  1. B.D. Santer, S. Po-Chedley, L. Zhao, C. Zou, Q. Fu, S. Solomon, D.W.J. Thompson, C. Mears, and K.E. Taylor, "Exceptional stratospheric contribution to human fingerprints on atmospheric temperature", Proceedings of the National Academy of Sciences, vol. 120, 2023. http://dx.doi.org/10.1073/pnas.2300758120

A NOAA-STAR dataset is born…

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What does a new entrant in the lower troposphere satellite record stakes really imply?

At the beginning of the year, we noted that the NOAA-STAR group had produced a new version (v5.0) of their MSU TMT satellite retrievals which was quite a radical departure from the previous version (4.1). It turns out that v5 has a notable lower trend than v4.1, which had the highest trend among the UAH and RSS retrievals. The paper describing the new version (Zou et al., 2023) came out in March, and with it the availability of not only updated TMT and TLS records (which had existed in the version 4.1), but also a new TLT (Temperature of the Lower Troposphere) record (from 1981 to present). The updated TMT series was featured in the model data comparison already, but we haven’t yet shown the new TLT data in context.

[Read more…] about A NOAA-STAR dataset is born…

References

  1. C. Zou, H. Xu, X. Hao, and Q. Liu, "Mid‐Tropospheric Layer Temperature Record Derived From Satellite Microwave Sounder Observations With Backward Merging Approach", Journal of Geophysical Research: Atmospheres, vol. 128, 2023. http://dx.doi.org/10.1029/2022JD037472

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).

[Read more…] about Some new CMIP6 MSU comparisons

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.

[Read more…] about 2022 updates to model-observation comparisons

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