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Making predictions with the CMIP6 ensemble

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The CMIP6 multi-model ensemble is a unique resource with input from scientists and modeling groups from around the world. [CMIP stands for the Coupled Model Intercomparison Project, and it is now in its 6th Phase]. But as we’ve discussed before (#NotAllModels) there are some specific issues that require users to be cautious in making predictions. Fortunately, there are useful ‘best practices’ that can help avoid the worst pitfalls.

A new paper by McCrystall et al that has just appeared in Nature Communications illustrates these issues clearly by having some excellent analyses of the changes in Arctic precipitation regimes at different global warming levels, and examining the sensitivity of their metrics to both local and Arctic warming, but unfortunately relying on the CMIP6 multi-model mean for their headline statements and press release.

The problem with the CMIP6 ensemble

Any use of CMIP future projections (either in CMIP6 or previously) requires some idea of what the scenarios represent (plausible storylines) of course, but in CMIP6 there is the (somewhat awkward) spread in climate sensitivities that is both greater than in previous CMIP iterations and also extends beyond the values constrained by multiple observations.

Histogram of CMIP6 climate sensitivities

There are notably eight models that have climate sensitivities greater than 5ºC (and more than a dozen greater than 4.5ºC) (and for fairness, two models with ECS under 2ºC). This is sufficient to skew the multi-model mean of the global mean surface temperature higher than the assessed projections seen in the latest IPCC report (Chapter 4).

Fig 4.11 from AR6 WG1
Projected temperatures (2081-2100 w.r.t. 1850-1900) from different scenarios in a) the ‘raw’ multi-model mean, and b) constrained mean using a weighted set of models. (Figure 4.11a,b AR6 WG1).

This was not the case for CMIP5. There, the spread of the models was similar to the constrained limits, and the multi-model mean was very close to the assessed projections, and so it was used widely (even by us) as a best guess. The bottom line is that for CMIP6 the multi-model mean is not the same as the expected prediction.

What difference does CMIP6 make for Arctic precipitation?

McCrystall et al do a lot of work in their paper that is great and appropriate. They look at the impacts at specific Global Warming Levels (GWLs):

Fig 7 McCrystall et al (2021)
Increase in rainfall in each season at specific GWLs in CMIP6 and CMIP5.

and in more detail at the sensitivity of the precipitation change per degree over the year:

Fig 4 from McCrystall et al (2021)
Sensitivity of model percentage change in Arctic precipitation per degree of Arctic (a) and Global (b) temperature change. (McCrystall et al, fig 4).

Both of these analyses incorporate all the models but don’t explicitly depend on the overall climate sensitivity. While there are some differences between the CMIP5 and CMIP6 models (particularly at the tails), the means are pretty close. That is, once you abstract out the global or Arctic level of warming, there isn’t that much difference between the projections from the CMIP5 or CMIP6 models (one could test that more formally of course).

There is a slightly clearer difference in their fig. 5 in the SON period where the sensitivity of precipitation to the open water amount seems stronger in CMIP6 than in CMIP5, but mostly everywhere else the sensitivities seem comparable (even though the magnitude of change in some of the CMIP6 models is much larger). So far so good.

However, the main conclusion that the authors highlight in the press release and which was picked up in the newspaper coverage was the claim that rain would dominate Arctic precipitation ‘decades earlier’ than previously thought. This is based on the last row of figure 8:

The decade of transition from a snow-dominated precipitation regime to a rain-dominated regime for CMIP6 [first column] and CMIP5 [second column] multi-model ensemble means for Sep/Oct/Nov. (McCrystall et al. (2021), Fig 8)

This shows the results from the raw ensemble means, and uses the absolute times in the output. Given the excessive warming in some models, which will have an earlier transition than the other CMIP6 or CMIP5 models, this result is mostly due to the inclusion of the high ECS models, not anything fundamental about the processes involved. There is some evidence of differences in this metric in the GWL diagnostics, but the signal is much smaller than in fig 8. Ideally, one might like to compare this result to the constrained subset of the CMIP6 models that were used in the IPCC AR6 Fig. 4.11 or those that match the constrained ECS values, to disentangle the impact of (presumably) better process modeling in CMIP6 from the impact of a wider spread in ECS. However, that is not (yet) possible (since what was done is not yet clear). So, unfortunately, we have a widely telegraphed conclusion that purports to be because of some advances in the science, but mostly relies on the excessive and unrealistic ECS in some of the models used.

Lessons to be learned

As I stated when the IPCC report came out, the CMIP6 ensemble has killed the notion of model democracy (‘one model, one vote’). It wasn’t really strictly correct in CMIP5 or previously, but it was close enough to serve as a useful heuristic. This is no longer the case in CMIP6. The default behavior in the community has to move away from considering the raw model ensemble mean as meaningful. The analyses that make sense are the impacts related to GWL, the sensitivity of any impact to the global mean, and various flavours of weighted or subsetted means that would allow the impacts to be coherent with the assessed projections of global mean temperature from AR6.

Providing these recipes to the broader community should be a high priority for the CMIP community or for any projects that are using the CMIP6 data as input.

References

  1. M.R. McCrystall, J. Stroeve, M. Serreze, B.C. Forbes, and J.A. Screen, "New climate models reveal faster and larger increases in Arctic precipitation than previously projected", Nature Communications, vol. 12, 2021. http://dx.doi.org/10.1038/s41467-021-27031-y

Net Zero/Not Zero

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At the COP26 gathering last week much of the discussion related to “Net-Zero” goals. This concept derives from important physical science results highlighted in the Special Report on 1.5ºC and more thoroughly in the last IPCC report that future warming is tied to future emissions, and that warming will effectively cease only once anthropogenic CO2 emissions are balanced by anthropogenic CO2 removals. But some activists have (rightly) pointed out that large-scale CO2 removals are as yet untested, and so reliance on them to any significant extent to balance out emissions is akin not really committing to net zero at all. Their point is that “net-zero” is not zero and hence will serve as a smokescreen for insufficient climate action. To help sort this out some background might be helpful.

[Read more…] about Net Zero/Not Zero

A Nobel pursuit

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Klaus Hasselmann and Suki Manabe

Last week, the Nobel physics prize was (half) awarded to Suki Manabe and Klaus Hasselmann for their work on climate prediction and the detection and attribution of climate change. This came as quite a surprise to the climate community – though it was welcomed warmly. We’ve discussed the early climate model predictions a lot (including some from Manabe and his colleagues), and we’ve discussed detection and attribution of climate change as well, though with less explicit discussion of Hasselmann’s contribution. Needless to say these are big topics which have had many inputs from many scientists over the years.

But RC has a more attuned audience to these topics than most, and so it might be fun to dive into the details of their early work to see what has stood the test of time and what has not, and how that differs (if it does) from their colleagues and rivals at the time.

Manabe’s Climate Modeling

Fortunately, Manabe recently wrote a retrospective on his early work in response to receiving the Crafoord prize in 2018. That paper (Manabe, 2019) gives a good overview of Manabe’s particular philosophy of climate modeling which was very much focused on getting things to work, and not worrying too much about the details. He makes a eloquent argument for a hierarchy of modeling where simpler, functional, models can contribute a lot to understanding in advance of the more complete and more detailed versions turning up. In this, he is in violent agreement with Isaac Held, his colleague at GFDL, and indeed most climate scientists.

But let’s go back to the beginning. Manabe’s early focus was on radiative-convective equilibrium, and his seminal 1967 paper (with his longtime collaborator Richard Wetherald, who passed in 2011). The Manabe and Wetherald (1967) paper has been described as the most influential climate paper ever.

The key aspects were the inclusion of water vapour feedback as temperatures increased, and the use of ‘convective adjustment’ to maintain stability of the lower atmospheric column. While not a great parameterization of the complexity of real convection, it served to keep the troposphere and surface linked in ways that match what happens in the real world. In practice, it was a big advance towards realism over the work of Plass or Möller from a few years before (despite the lack of cloud feedback). Two examples of the sensitivity of their model (which have mostly held up) are useful to look at at:

Two results from Manabe and Wetherald (1967)

What they showed are the distinct fingerprints of two kinds of forcing; increasing solar activity which warms all parts of the atmosphere, and carbon dioxide increases which warm the surface and troposphere, but cool the stratosphere and above. The source of this result is the spectral resolution of the radiative transfer model they were using, but oddly enough they don’t discuss it at all. In a subsequent short paper Manabe (1970), Manabe extends this result to predict a temperature increase by 2000 of 0.8ºC based on a 25% increase in CO2, which was pretty close. (Funnily enough, this paper appeared in volume about environmental risks that was edited by a young(er) S. Fred Singer, before his turn to the dark side).

Manabe’s subsequent work led to the development of the GFDL GCM, initially just including the atmosphere, but eventually with an ocean, and then the transient results shown in Manabe and Stouffer (1993). Famously, these early results were half the input into the Charney report‘s estimate of climate sensitivity in 1979 (the other half being the preliminary results from Jim Hansen’s model at GISS). Both these predictions have been evaluated in recent years to see how well they did. The time series were included in the Hausfather et al (2020) paper and in the latest IPCC report:

Evaluation of old projections of global temperature, including Manabe (1970) and Manabe and Stouffer (1993)

The next step in climate modeling was to couple dynamic ocean models to the atmospheric models, and again, Manabe and his colleagues were pioneers (notably Manabe and Bryan (1969), but more comprehensively in Manabe et al. (1975), and Bryan et al. (1975)). But as expectations increased that coupled models could help climate predictions, there was a growing realization that there was a problem with how they were being designed.

The basic issue stems from the different timescales of the ocean and atmosphere. Given the ocean temperatures, an atmospheric model will equilibriate in a year or so of model time (maybe a decade if you care about the water vapour distribution in the upper stratosphere). However, given information from the atmosphere, an ocean model takes centuries to millennia to equilibriate the deep ocean. The tail of the age distribution for water parcels in the deep Pacific can reach 10,000 years or so. But back in the day, running a coupled model anything like that long was prohibitively expensive. So in order to get a coupled model simulation for near-present, the ocean needed to be ‘spun-up’ for a good while on it’s own, and then, once it was in equilibrium, the coupling was turned on, and voila! a coupled simulation of the present-day climate. Except…

… it didn’t generally work. The newly coupled model would drift away from today’s climate, sometimes with a collapse of the Atlantic overturning circulation, other times just towards a much warmer or cooler climate, or with a terrible ‘double ITCZ’. This was a problem because it’s not at all clear that the sensitivity of the simulated climate (which was off in serious ways) would be the same as the sensitivity of the real world. This stymied progress for a while (maybe a decade or so) as people worked to understand why the models drifted so much, and to find ways to fix it.

When I started in climate modeling (in the early 1990s), this was still a relevant issue, though two approaches had been adopted. One, advocated by Manabe’s group (and Hasselmann’s!), was the imposition of ‘flux corrections’ or ‘flux adjustments’ (Manabe and Stouffer, 1988; Sausen et al, 1988) which added artificial fluxes at the ocean-atmosphere boundary that gave the ocean and atmosphere what they both needed to stay stable, correcting for what would have been calculated, and then keeping that fixed in all future sensitivity experiments. This (by design) produced a good climatology, but effectively buried the models’ poor physics. The other approach was to work with models that had offsets from the real world (which you would keep trying to reduce) but would have sensitivities that were more physically coherent.

The implications of the two approaches are difficult to assess without a perfect model simulation to compare against, and if we had that, there’d be no need to worry about drifts. Thus during the early 90s there was a fair bit of unresolved religious-like discussions about what should be done. Manabe was vocal that you needed a reasonable model to play with and make progress, while others were of the opinion that the sensitivity of a flux-corrected model wasn’t informative of the real world, and that using flux corrections as a crutch, was actually holding back work on the physics that would (eventually) remove the need for the corrections in the first place. (Minor aside, I was a co-author on a paper that assessed this concept for a slightly simpler class of model, and found that the ‘flux-corrected’ version was not predictive of the ‘true’ sensitivity Bjornsson et al., 1997).

Over time the issue more or less resolved itself as models got incrementally better and computational resources increased so that longer coupled model simulations could be done more routinely. Occasionally, the issue still comes up (i.e. Gnanadesikan et al., 2018), but I think it’s fair to say that few modelers think it’s a useful tool anymore. For context, 10 out of 17 models in CMIP2 (~1995) used flux adjustments, and 6 out of 24 in the CMIP3 ensemble (~2001), but none in CMIP5 or CMIP6, while each generation has greater skill than the previous one. In his 2018 retrospective, Manabe doesn’t discuss the issue at all.

The proof of the pudding in climate model terms though are the quality and skill of the predictions. A recent paper Stouffer and Manabe (2017), assessed how good the Manabe and Stouffer (1989) predictions were. These came from an idealized 1% increasing CO2 experiment after 70 years, when CO2 has approximately doubled, and so is warmer than we would expect for 2020, but the pattern is quite robust:

Comparison of the pattern of warming from Manabe and Stouffer (1989) compared to the subsequent observations.

Not too shabby!

Hasselmann’s Statistical Insights

[I have to admit to not knowing Hasselmann’s oeuvre as well as Manabe’s, and to my recollection I don’t think we’ve met, so this might need some amendment…]

I think the key paper to look at is Hasselmann (1979), which really set the stage for formal methods of detection and attribution of climate change. Later papers, notably Hasselmann (1997)(pdf) extended this to multi-variate attribution problems (written in tensor notation no less, so that probably helped 😉). The basic idea is that although there are a vast number of degrees of freedom in the atmosphere/climate system, you can make a lot of progress by reducing the degrees of freedom and looking just at the dominant modes of variability changes and comparing them with expected patterns from simulations. A key insight is that depending on how the noise and the forced patterns line up, the ‘optimal’ pattern to detect might not be what you first thought. But note that this was written when “continuous model time series of comparable length to analyzed global or hemispheric data [were] not available”, so the paper is mainly conceptual. It really is only in the late 1980s, and more clearly and with more models, the mid-1990s, that the data became available to really apply these methods.

The only figure in Hasselmann’s 1979 paper.

The challenge with all detection & attribution (D&A) work is that it must rely on counter-factuals – i.e. estimates of how the climate would behave in special cases – for instance, if the only forcing was greenhouse gases, or if there was only natural forcings or only internal variability. Since the real world has all of these things going on at the same time, it’s hard to extract them from the observations, particularly since good direct observations don’t stretch back more than a century or so, and proxy climate observations have their own, increased, uncertainties. But even with perfect observations, getting a full characterization of internal variability would be hard, and perhaps impossible. So in practice, the ‘noise ellipsoid’ in the above figure is almost always taken from control runs of coupled climate models which, as Hasselmann acknowledged, were not available in 1979.

Hasselmann’s work before this paper was heavily related to measurements and understanding of ocean waves and the role of ‘random’ weather forcing on long term ocean variability, and that has been widely cited, and afterwards, he played a key role in developing the MPI climate model (i.e. Cubasch et al. (1992). But much of later well-cited work built off the 1979 paper and involved further refinements on the theme of D&A, often working with Gabi Hegerl (i.e. Hegerl et al., 1996; Hegerl et al., 1997).

These were very much the ideas that set the discussions in climate science in the 1990s. As you will recall, Hansen had declared in 1988 that “the greenhouse effect is here!”, based on a 3-sigma signal detected with the original GISS model. But the ocean model used there was a simple ‘Q-flux’, no-dynamics, module, and so had no ENSO, or other coupled modes of variability. The implicit estimate of the internal variability here was, to be clear, not widely accepted. There are a couple of articles and responses at the time that give a flavor, for instance “Hansen vs. the World” by Richard Kerr reporting from a workshop where Manabe, and Hasselmann’s coauthors (notably Cubasch and Barnett) were present, and the responses from Wally Broecker and James Risbey.

Hasselmann himself commented on this in Science in 1997, after the 1995 Second Assessment Report from IPCC declared that “the balance of evidence” suggested that the greenhouse gas signal had indeed been detected. The figure he showed there:

The D&A of forced climate change as it stood in 1997 (10-year smoothed data).

… supported the IPCC conclusion, and his last line is worth repeating:

It would be unfortunate if the current debate over this ultimately transitory issue should distract from the far more serious problem of the long-term evolution of global warming once the signal has been unequivocally detected above the background noise.

Klaus Hasselmann (1997)

Today, 24 years later, the detection and attribution of anthropogenic climate change is “unequivocal”, but we are still being distracted by ultimately transitory issues…

What if the prize had been given a decade ago?

The two restrictions on the award of disciplinary Nobel prizes are that the awardees must still be alive, and that there is a limit of three laureates per prize. For advances made in the 1960s and 1970s, the first is extremely relevant, and makes the second condition somewhat less so. But without wishing to take anything away from the two awardees this year, ten years ago the decision would have been much tougher. Norman Philips published what is recognised as the first GCM in 1955 – he died in 2019. Akio Arakawa was the conceptual leader of climate modeling directly influencing both Manabe and Hansen – he died earlier this year. Of the published papers predicting global warming in the 1970s (as catalogued in the Hausfather et al paper), the authors Rasool, Schneider, Benton, Sawyer, Broecker and Mitchell have all passed. Only Nordhaus and Manabe are still alive – though now both have won Nobel prizes.

But the building of climate models and their application is broader than can be recognized like this. There are no prizes for the people that actually wrote the code for the models – people like Gary Russell or Ernst Maier-Reimer (nicely eulogized by Hasselmann), the specialists who designed the parameterizations, or the teams that developed the inputs and processed the outputs or the technicians that kept the old supercomputers running. In recent papers documenting model development, it’s not unusual to have dozens of authors – not the level of the CERN collaborations, but significantly beyond the Nobel limit. The huge advances in understanding we’ve seen since the 1970s have been the work of thousands of smart and dedicated people all around the world, only a few of which will ever be recognized as widely as this. We should always remember this while we celebrate the winners.

Finally, while it is many scientists’ dream to win a Nobel Prize, Hasselmann’s statement that he would rather have “no global warming and no Nobel Prize” captures the ambiguity that many of us feel in successfully predicting events and trends that we don’t want to come true.

References

  1. S. Manabe, "Role of greenhouse gas in climate change**", Tellus A: Dynamic Meteorology and Oceanography, vol. 71, pp. 1620078, 2019. http://dx.doi.org/10.1080/16000870.2019.1620078
  2. S. Manabe, and R.T. Wetherald, "Thermal Equilibrium of the Atmosphere with a Given Distribution of Relative Humidity", Journal of the Atmospheric Sciences, vol. 24, pp. 241-259, 1967. http://dx.doi.org/10.1175/1520-0469(1967)024<0241:TEOTAW>2.0.CO;2
  3. S. Manabe, "The Dependence of Atmospheric Temperature on the Concentration of Carbon Dioxide", Global Effects of Environmental Pollution, pp. 25-29, 1970. http://dx.doi.org/10.1007/978-94-010-3290-2_4
  4. S. Manabe, and R.J. Stouffer, "Century-scale effects of increased atmospheric C02 on the ocean–atmosphere system", Nature, vol. 364, pp. 215-218, 1993. http://dx.doi.org/10.1038/364215a0
  5. Z. Hausfather, H.F. Drake, T. Abbott, and G.A. Schmidt, "Evaluating the Performance of Past Climate Model Projections", Geophysical Research Letters, vol. 47, 2020. http://dx.doi.org/10.1029/2019GL085378
  6. S. Manabe, and K. Bryan, "Climate Calculations with a Combined Ocean-Atmosphere Model", Journal of the Atmospheric Sciences, vol. 26, pp. 786-789, 1969. http://dx.doi.org/10.1175/1520-0469(1969)026<0786:CCWACO>2.0.CO;2
  7. S. Manabe, K. Bryan, and M.J. Spelman, "A Global Ocean-Atmosphere Climate Model. Part I. The Atmospheric Circulation", Journal of Physical Oceanography, vol. 5, pp. 3-29, 1975. http://dx.doi.org/10.1175/1520-0485(1975)005<0003:AGOACM>2.0.CO;2
  8. K. Bryan, S. Manabe, and R.C. Pacanowski, "A Global Ocean-Atmosphere Climate Model. Part II. The Oceanic Circulation", Journal of Physical Oceanography, vol. 5, pp. 30-46, 1975. http://dx.doi.org/10.1175/1520-0485(1975)005<0030:AGOACM>2.0.CO;2
  9. S. Manabe, and R.J. Stouffer, "Two Stable Equilibria of a Coupled Ocean-Atmosphere Model", Journal of Climate, vol. 1, pp. 841-866, 1988. http://dx.doi.org/10.1175/1520-0442(1988)001<0841:TSEOAC>2.0.CO;2
  10. R. Sausen, K. Barthel, and K. Hasselmann, "Coupled ocean-atmosphere models with flux correction", Climate Dynamics, vol. 2, pp. 145-163, 1988. http://dx.doi.org/10.1007/BF01053472
  11. H. Bjornsson, L.A. Mysak, and G.A. Schmidt, "Mixed Boundary Conditions versus Coupling with an Energy–Moisture Balance Model for a Zonally Averaged Ocean Climate Model", Journal of Climate, vol. 10, pp. 2412-2430, 1997. http://dx.doi.org/10.1175/1520-0442(1997)010<2412:MBCVCW>2.0.CO;2
  12. A. Gnanadesikan, R. Kelson, and M. Sten, "Flux Correction and Overturning Stability: Insights from a Dynamical Box Model", Journal of Climate, vol. 31, pp. 9335-9350, 2018. http://dx.doi.org/10.1175/JCLI-D-18-0388.1
  13. R.J. Stouffer, and S. Manabe, "Assessing temperature pattern projections made in 1989", Nature Climate Change, vol. 7, pp. 163-165, 2017. http://dx.doi.org/10.1038/nclimate3224
  14. R.J. Stouffer, S. Manabe, and K. Bryan, "Interhemispheric asymmetry in climate response to a gradual increase of atmospheric CO2", Nature, vol. 342, pp. 660-662, 1989. http://dx.doi.org/10.1038/342660a0
  15. K. Hasselmann, "Multi-pattern fingerprint method for detection and attribution of climate change", Climate Dynamics, vol. 13, pp. 601-611, 1997. http://dx.doi.org/10.1007/s003820050185
  16. U. Cubasch, K. Hasselmann, H. Höck, E. Maier-Reimer, U. Mikolajewicz, B.D. Santer, and R. Sausen, "Time-dependent greenhouse warming computations with a coupled ocean-atmosphere model", Climate Dynamics, vol. 8, pp. 55-69, 1992. http://dx.doi.org/10.1007/BF00209163
  17. G.C. Hegerl, H. von Storch, K. Hasselmann, B.D. Santer, U. Cubasch, and P.D. Jones, "Detecting Greenhouse-Gas-Induced Climate Change with an Optimal Fingerprint Method", Journal of Climate, vol. 9, pp. 2281-2306, 1996. http://dx.doi.org/10.1175/1520-0442(1996)009<2281:DGGICC>2.0.CO;2
  18. G.C. Hegerl, K. Hasselmann, U. Cubasch, J.F.B. Mitchell, E. Roeckner, R. Voss, and J. Waszkewitz, "Multi-fingerprint detection and attribution analysis of greenhouse gas, greenhouse gas-plus-aerosol and solar forced climate change", Climate Dynamics, vol. 13, pp. 613-634, 1997. http://dx.doi.org/10.1007/s003820050186
  19. K. Hasselmann, "Are We Seeing Global Warming?", Science, vol. 276, pp. 914-915, 1997. http://dx.doi.org/10.1126/science.276.5314.914

The definitive CO2/CH4 comparison post

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There is a new push to reduce CH4 emissions as a possible quick ‘win-win’ for climate and air quality. To be clear this is an eminently sensible idea – as it has been for decades (remember the ‘Methane-to-markets’ initiative from the early 2000s?), but it inevitably brings forth a mish-mash of half-remembered, inappropriate or out-of-date comparisons between the impacts of carbon dioxide and methane. So this is an attempt to put all of that in context and provide a hopefully comprehensive guide to how, when, and why to properly compare the two greenhouse gases.

Historical comparisons

First of all, let’s be clear about the relative magnitude of the gas concentrations. In 2020, CO2 was at ~410 parts per million, while CH4 was around 1870 parts per billion (or 1.87 ppm, a factor of more than 200 smaller). However the relative rise since the pre-industrial is three times larger for CH4, around 150%, compared to the 50% increase in CO2.

The radiative forcing from these changes in concentrations can be easily calculated using standard formulas (from Etminan et al, 2016 which supersede the slightly simpler ones from IPCC TAR), as about 2 W/m2 for the CO2 change and 0.65 W/m2 for CH4.

But methane’s role in atmospheric chemistry and as a source of stratospheric water vapour means that it has a bigger effect on climate than just the direct effect of its concentration. Methane emissions have a feedback on its own lifetime, serve as an ozone precursor, and reduce the production of sulphate and nitrate aerosols (and consequently indirect cloud-aerosol effects), all of which amplify its net warming effect to about 1.2 W/m2 (to about 60% of the CO2 effect since 1750). There is also a very small impact of the CH4 oxidation to CO2 itself for any fossil-fuel derived methane.

(a) Historical radiative forcing by emissions (W/m2) (IPCC fig TS.15a).(b) Historical concentrations of CO2, CH4 and N2O (IPCC AR6 fig TS.9b)

This implies that if you convert the impacts of each set of emissions into temperatures, as was done in the IPCC AR6 report, you get about 0.75ºC from the changes in CO2 and 0.5ºC for CH4 (from the late 19th C, see figure below) or 1ºC and 0.6ºC, respectively, from 1750. Thus despite the smaller concentrations and changes in methane compared to carbon dioxide, the impacts are comparable.

Historical contributions to global warming by emissions (IPCC AR6 SPM)

Stocks and flows

Before we go any further though, we need to understand that the effective perturbation time for CO2 and CH4 in the atmosphere are very different. CO2 emissions embed themselves in the atmosphere/biosphere/upper-ocean carbon cycle and have very long-term impacts (under natural conditions, some 15% of the CO2 perturbation will still be in the atmosphere thousands of years from now). In contrast, methane has a perturbation time-scale of about 12 years. This implies that the impact of CO2 on temperature is cumulative (a function of the total emitted CO2 or stock), while the impact of CH4 is a function of current (~decadal) emissions (the flows). Stabilizing temperature effects from CO2 means getting down to net-zero anthropogenic emissions, while stabilizing temperature effects from CH4 means simply stabilizing emissions.

The impacts of emissions of CH4 compared to CO2 then will have a time-varying component. Over a short time, the enhanced effectiveness of methane will be important but on very long time scales the effects of CO2 will be dominant. This is the source of the difference between the “Global Warming Potential” (GWP) numbers calculated at 20 years or 100 years which have been used for decades. You might recall that GWP is defined as the ratio on per-kg basis of the temperature impact of other greenhouse gases compared to CO2 over a specific time period. But as is clearly stated in AR6, the suitability of comparative emission metrics depends on your end goal or values.

For instance, if you use GWP-100 to trade off emissions on the way to a temperature stabilization scenario, it simply doesn’t work (since you can’t balance any net CO2 emissions with a particular level of CH4 emissions – you would need to have constantly decreasing CH4). Hence, newer concepts like GWP* have been developed that take that into account. Nonetheless, the UNFCCC (and the EPA) use the GWPs from IPCC AR4 for calculating CO2eq emissions and have not updated them as the science has progressed.

Forward-facing comparisons

People tend to be most interested in comparisons related to future choices, and it’s worth bearing in mind that while there are many ways to do this, most don’t relate to real choices that people have, nor do they clearly match up with a consistent set of values. I’ll return to that issue below. So let’s go:

  • Molecule-to-molecule concentrations: On a per-ppm basis, methane is 25 times more effective as a direct greenhouse gas. Including the indirect effects, increases that to 45 times as effective.
  • kg-to-kg: On a mass basis, methane is 70 times more effective as a greenhouse gas. This takes into account of the different molecular weights of the molecules. That would mean 126 times as effective including indirect effects.
  • kgC-to-kgC: an equal amount of kgC as CH4 or CO2 gives rise to the same ppm change, so kgC-to-kgC, methane is again 45 times more effective as a greenhouse gas.
  • kg to kg emitted: This is where it starts to get hairy because of the different timescales. Current (AR6) estimates for fossil-sourced methane are ~83 for GWP-20 and ~30 for GWP-100 (AR6 Table 7.15). (It’s slightly smaller than this for biogenic (non-fossil) methane since the oxidation product of CO2 in that case is carbon neutral). The assessed uncertainties in these values (largely related to direct and indirect aerosol effects) are ±25 and ±11. The AR4 value for methane GWP-100 was 25.
  • kgC emitted to kgC emitted: For some applications, for instance judging the impact of flaring natural gas vs. releasing it directly into the atmosphere, the kg-to-kg comparisons are not relevant, since the same amount of carbon is being emitted, rather than the same total mass. For that, the GWP-like value over 100 years, choosing to release methane directly would be 30*16/44 = 11 times worse than flaring [Corrected 9/20/21].
  • Emissions for temperature stabilization: Each additional GtC of carbon dioxide contributes to about 0.00165ºC of eventual warming (the TCRE), while a sustained TgCH4/yr of methane emissions (0.00075 GtC/yr), leads to ~3 ppb increase of methane concentrations (AR6 Table 5.2), about 0.0024 W/m2 in total radiative forcing, and, assuming a median climate sensitivity of 3ºC for 2xCO2, roughly 0.002ºC of equilibrium global warming. That implies you need a sustained reduction of 0.8 TgCH4/yr (0.0006 GtC/yr of methane) to compensate for a one-off GtC pulse of CO2.

Whatever way you slice this it implies that CH4 reductions have an outsize effect on climate, as well as an undeniably positive impact on air pollution, crop yields and public health (mainly through ozone reductions). It is therefore not a complicated decision to pursue methane reductions, taking care not to assume that doing so gets you off the hook for reducing CO2, whatever the EPA says.

I’d like this page to be useful and current, so if you think I should add an additional comparison, or use case, or if you think I’ve got something wrong, please let me know in the comments.

References

  1. M. Etminan, G. Myhre, E.J. Highwood, and K.P. Shine, "Radiative forcing of carbon dioxide, methane, and nitrous oxide: A significant revision of the methane radiative forcing", Geophysical Research Letters, vol. 43, 2016. http://dx.doi.org/10.1002/2016GL071930

We are not reaching 1.5ºC earlier than previously thought

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Guest commentary by Malte Meinshausen, Zebedee Nicholls, and Piers Forster

Of all the troubling headlines emerging from the release of the Intergovernmental Panel on Climate Change (IPCC) WG1 report, one warning will surely dominate headlines in the next days and weeks: Earth is likely to reach the crucial 1.5℃ warming limit in the early 2030s.

In 2018, the IPCC Special Report on 1.5C warming  stated in its summary for policy makers that the world was likely to cross the 1.5℃ threshold between 2030 and 2052, if current warming trends continue. 

In this latest AR6, a more comprehensive assessment was undertaken to estimate when a warming level of 1.5℃ might be reached. As a result, some early media reports suggest 1.5ºC warming is now anticipated 10-years earlier than previously assumed (AFR, THE TIMES). 

We want to explain here why that is not backed up by a rigorous comparison of the SR1.5 and AR6 reports. In fact, the science in the previous SR1.5 report and the new AR6 report are remarkably consistent.

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The IPCC Sixth Assessment Report

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Climate scientists are inordinately excited by the release of a new IPCC report (truth be told, that’s a bit odd – It’s a bit like bringing your end-of-(seven)-year project home and waiting anxiously to see how well it will be received). So, in an uncharacteristically enthusiastic burst of effort, we have a whole suite of posts on the report for you to read.

If/when we add some more commentary as we digest the details and we see how the report is being discussed, we’ll link it from here. Feel free to discuss general issues with the report in the comments here, and feel free to suggest further deep dives we might pursue.

Climate adaptation should be based on robust regional climate information

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Climate adaptation steams forward with an accelerated speed that can be seen through the Climate Adaptation Summit in January (see previous post), the ECCA 2021 in May/June, and the upcoming COP26. Recent extreme events may spur this development even further (see previous post about attribution of recent heatwaves). 

To aid climate adaptation, Europe’s Climate-Adapt programme provides a wealth of resources, such as guidance, case studies and videos. This is a good start, but a clear and transparent account on how to use the actual climate information for adaptation seems to be missing. How can projections of future heatwaves or extreme rainfall help practitioners, and how to interpret this kind of information? 

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Rapid attribution of PNW heatwave

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Summary: It was almost impossible for the temperatures seen recently in the Pacific North West heatwave to have occurred without global warming. And only improbable with it.

It’s been clear for at least a decade that global warming has been in general increasing the intensity of heat waves, with clear trends in observed maximum temperatures that match what climate models have been predicting. For the specific situation in the Pacific NorthWest at the end of June, we now have the first attribution analysis from the World Weather Attribution group – a consortium of climate experts from around the world working on extreme event attribution. Their preprint (Philip et al.) is available here.

Trends in Tmax globally
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The Rise and Fall of the “Atlantic Multidecadal Oscillation”

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Two decades ago, in an interview with science journalist Richard Kerr for the journal Science, I coined the term the “Atlantic Multidecadal Oscillation” (AMO) to describe an internal oscillation in the climate system resulting from interactions between North Atlantic ocean currents and wind patterns. These interactions were thought to lead to alternating decades-long intervals of warming and cooling centered in the extra-tropical North Atlantic that play out on 40-60 year timescales (hence the name). Think of the purported AMO as a much slower relative of the El Niño/Southern Oscillation (ENSO), with a longer timescale of oscillation (multidecadal rather than interannual) and centered in a different region (the North Atlantic rather than the tropical Pacific).

Today, in a research article published in the same journal Science, my colleagues and I have provided what we consider to be the most definitive evidence yet that the AMO doesn’t actually exist.

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