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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

A deep dive into the IPCC’s updated carbon budget numbers

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Guest post by Joeri Rogelj (Twitter: @joerirogelj)

Since temperature targets became international climate goals, we have been trying to understand and quantify the implications for our global emissions. Carbon budgets play an important role in this translation.

Carbon budgets tell us how much CO2 we can emit while keeping warming below specific limits. We can estimate the total carbon budget consistent with staying below a given temperature limit. If we subtract the CO2 emissions that we emitted over the past two centuries, we get an estimate of the remaining carbon budget.

I have been involved in the estimation of carbon budgets since the IPCC Fifth Assessment Report in the early 2010s. And since the first IPCC estimates published in 2013, we have learned a lot and have gotten much better at estimating remaining carbon budgets. In the 2018 IPCC Special Report on Global Warming of 1.5°C (SR1.5), the latest insights were integrated in a simple framework that allowed to estimate, track, and understand updates to these carbon budgets.

The most recent Working Group 1 Report of the IPCC Sixth Assessment Cycle (WG1 AR6) provides an updated assessment of the remaining carbon budget. Here’s an insider’s view providing a deep dive into how they differ from previous reports.

The scientific basis underlying a carbon budget is our robust scientific understanding that global warming is near-linearly proportional to the total amount of CO2 we ever emit as a society. This is illustrated in Fig. SPM10 of the WG1 AR6 report, both for the past and for future projections.

Source: Figure SPM.10 from IPCC (2021)

The estimates of remaining carbon budgets also made it into the Summary for Policymakers – the most prominent place that can be given for any finding of the report. Table SPM.2 gives an overview of the latest estimates, for different temperature limits and different probability levels.

Source: Table SPM.2 from IPCC (2021)

How have these estimates changed since previous reports?

IPCC reported carbon budgets for the first time in 2013. And since, important advances have been made in how we estimate these. Five puzzle pieces combine to give carbon budget estimates, and allow us now to understand subsequent updates.

Source: Figure 5.31 in Canadell et al (2021)

Starting with the key message of the AR6 carbon budget update: carbon budget estimates in AR6 are very similar to those published in the SR1.5 in 2018, but they represent a significant update since AR5 in 2013.

When adjusting for the emissions since AR5 and SR1.5, AR6 remaining carbon budget for limiting warming to 1.5C with 50% chance is about 300 GtCO2 larger than in AR5, but virtually the same as in SR1.5.

Source: Data from IPCC (2014), Rogelj et al (2018), and IPCC (2021)

For 66% probability, the AR6 budget is about 60 GtCO2 larger than in SR1.5.

Source: Data from IPCC (2014), Rogelj et al (2018), and IPCC (2021)

The budget is so much larger than in AR5, because since 2013 more accurate methods have been published that ensure that model uncertainties over the historical period are not accumulated into the future. This is best illustrated by this technical figure from SR1.5.

Source: Figure 2.3 from Rogelj et al (2018) – note how the red dot marked 2010 moves to the purple dot marked 2010, once historical modelling uncertainties are corrected for.

Between SR1.5 and AR6 every piece of the carbon budget was reassessed:

  • warming to date
  • how much warming we expect to get per tonne of CO2
  • how much warming would still occur once we reach net zero CO2
  • how much non-CO2 warming we can expect
  • Earth system feedback otherwise not covered

Let’s dive into each piece of this puzzle to understand what has changed between SR1.5 and AR6.

Warming to date – SR1.5 used a 0.97°C warming estimate between 1850-1900 and 2006-2015. This estimate already included corrections for the incomplete global coverage of observations and the different ways in which global surface temperature can be estimated. The AR6, based on a full reassessment of all available data, assesses 0.94°C of global surface temperature increase for the same period.

In isolation, this update results in central estimates being about 65 GtCO2 larger in AR6 than in SR15. For the 33% and 67% estimates that’s about 110 and 50 GtCO2 higher, respectively.

Warming per tonne of CO2 – The next piece of the puzzle is the warming we project per tonne of CO2. SR1.5 used an estimate of 0.8-2.5°C per 1000 GtC (=3664 GtCO2). AR6 assessed this quantity, also known as the Transient Climate Response to Cumulative Emissions of CO2 (or TCRE), to fall in the 1.0-2.3°C range.

Having the same central estimate, the update in TCRE causes no shift in 50% estimates, but the higher and lower percentiles are narrowed. For a 67% chance, AR6 estimates are about 50 and 100 GtCO2 larger compared to SR1.5 for 1.5°C and 2°C of global warming, respectively.

Warming after net zero CO2 – The third piece of the puzzle is the how much warming is expected to still occur once global CO2 emissions reach (and remain at) net zero. This is known as the Zero Emissions Commitment to emissions of CO2 (or ZEC).

The AR6 estimate confirms the SR1.5 estimate of no further CO2-induced warming or cooling once global CO2 emissions reach and stay at next zero. The uncertainty surrounding this value are reported separately. ZEC therefore causes no changes between SR1.5 and AR6.

Non-CO2 warming contribution – The fourth puzzle piece is the projected warming from non-CO2 emissions. As SR1.5, AR6 uses deep mitigation pathways assessed by SR1.5 (Rogelj et al, 2018; Huppmann et al, 2018), but with climate projections updated entirely with dedicated climate emulators that integrate the scientific information across chapter.

By coincidence (and it is really coincidence), the updates in radiative forcing from tens of different gases, climate sensitivity, and carbon-cycle uncertainties result in no net shift in the estimate of non-CO2 warming for the remaining carbon budget.

Pure luck, given the many updated pieces of scientific knowledge that were integrated in AR6, but convenient for explaining differences in carbon budget estimates.

Updated non-CO2 warming estimates lead to no change in remaining carbon budget estimates compares to SR1.5.

Other Earth system feedbacks – The last piece is to account for Earth system feedbacks that would otherwise not be covered. SR1.5 assumed an additional blanket reduction of 100 GtCO2 for this century for these feedbacks. This was a crude estimate and therefore not included as a central part of the remaining carbon budget numbers in SR1.5 AR6 updates this assessment entirely and includes this contribution in its main estimates.

Taking into account not only permafrost thaw, but also a host of other biogeochemical and atmospheric feedbacks, the AR6 estimates to appropriately include the effect of all these feedbacks, remaining carbon budgets have to be reduced by 26 ± 97 GtCO2 per degree Celsius of additional warming.

Altogether these updates mean AR6 remaining carbon budget estimates are very similar compared to SR1.5, while they additionally include the effect of Earth system feedbacks that would otherwise not be covered.

Selecting a remaining carbon budget requires two normative choices as a minimum: the global warming level that is to be avoided, and the likelihood or chance with which this is achieved. Further choices involve how deeply non-CO2 emissions can be reduced.

In addition to updates to science underlying carbon budget estimates, the AR6 also provides a larger set of likelihood levels for its remaining carbon budget estimates (see Table SPM.2 above). As in previous reports, AR6 provides remaining carbon budget estimates for a 33%, 50%, and 67% chance of keeping warming to a given temperature limit. In addition, however, the AR6 also provides the bracketing percentiles for the central 66% range (the range covered between 17% and 83%), so that the uncertainty of the central estimate can be adequately understood.

These values can be used in a variety of ways. For example, the central estimate for the remaining carbon budget for keeping warming to 1.5°C is now 500 GtCO2 starting from the beginning of 2020, with a 66% uncertainty range of 300–900 GtCO2.

Designing a policy for limiting warming to 1.5°C with this global 500 GtCO2 number in mind means that in 1-out-of-2 cases warming will end up below and in 1-out-of-2 cases it will end up above 1.5°C. Alternatively, it can also be understood to mean that in 1-out-of-2 cases policy measures will have to be sharpened beyond the policies consistent with a 500 GtCO2 budget over the coming decades if warming is effectively to be kept to 1.5°C. Similar examples can be given for 1.7°C or other levels (see Table 5.8 in the underlying chapter; Canadell et al (2021)).

A last item affecting the selection of remaining carbon budgets is the expectation of how deeply non-CO2 emissions can be reduced. All remaining carbon budget estimates in AR6 assume that non-CO2 emissions such as methane are reduced consistent with a deep decarbonisation pathway that reaches net zero CO2 emissions. Depending on how effectively these non-CO2 emissions can be reduced, the remaining carbon budgets can vary by 220 GtCO2 or more.

Bottom line of this technical explanation remains, however, that these budgets are small, our current annual global CO2 emissions of about 40 GtCO2/yr are reducing them rapidly, and all budgets require CO2 to decline to net zero while global emissions have not yet shown to decline.

It’s nice to have remaining carbon budgets, but now we need to get on with it and make sure that global CO2 emissions start to decline.

If you would like to know all the ins and outs of AR6 remaining carbon budgets have a look at Section 5.5 in Canadell et al (2021). The entire section describes the assessment of TCRE and remaining carbon budgets, while Box 5.2 presents a more technical comparison with carbon budget estimates from previous reports.

Joeri Rogelj is Director of Research, Grantham Institute Climate Change & Environment, Imperial College London, UK, and Senior Research Scholar, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria

Parts of this post have been published earlier as a twitter thread.

References

Huppmann, D., Rogelj, J., Kriegler, E., Krey, V., et al. (2018) A new scenario resource for integrated 1.5 °C research. Nature Climate Change. [Online] 8 (12), 1027–1030. Available from: doi:10.1038/s41558-018-0317-4.

Josep G. Canadell, J. G., P. M.S. Monteiro, M. H. Costa, L. Cotrim da Cunha, P. M. Cox, A. V. Eliseev, S. Henson, M. Ishii, S. Jaccard, C. Koven, A. Lohila, P. K. Patra, S. Piao, J. Rogelj, S. Syampungani, S. Zaehle, K. Zickfeld, 2021, Global Carbon and other Biogeochemical Cycles and Feedbacks. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu and B. Zhou (eds.)]. Cambridge University Press. In Press.

IPCC (2014) Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [MassonDelmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu and B. Zhou (eds.)]. Cambridge University Press. In Press

Rogelj, J., Shindell, D., Jiang, K., Fifita, S., et al. (2018) Mitigation pathways compatible with 1.5°C in the context of sustainable development. In: Greg Flato, Jan Fuglestvedt, Rachid Mrabet, & Roberto Schaeffer (eds.). Global Warming of 1.5 °C: an IPCC special report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. [Online]. Geneva, Switzerland, IPCC/WMO. pp. 93–174. Available from: http://www.ipcc.ch/report/sr15/.

A Tale of Two Hockey Sticks

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Two decades ago, the so-called “Hockey Stick” curve, published in 1999 by me and my co-authors (Mann, Bradley and Hughes, 1999), was featured in the all-important “Summary for Policy Makers” (SPM) of the 2001 IPCC Third Assessment report. The curve, which depicted temperature variations over the past 1000 years estimated from “proxy data such as tree rings, corals, ice cores, and lake sediments”, showed the upward spiking of modern temperatures (the “blade”) as it dramatically ascends, during the industrial era, upward from the “handle” that describes the modest, slightly downward steady trend that preceded it.

The Hockey Stick became an icon in the case for human-caused climate change, and I found myself at the center of the contentious climate debate (I’ve described my experiences in “The Hockey Stick and the Climate Wars”).

Featured two decades later now in the AR6 SPM is a longer Hockey Stick with an even sharper blade. And no longer just for the Northern Hemisphere, it now covers the whole globe. The recent warming is seen not only to be unprecedented over the past millennium, but tentatively, the past hundred millennia.

Side-by-side comparison of the (left) original Mann et al (1999) “Hockey Stick” reconstruction as featured in the Summary for Policy Makers of the IPCC 3rd Assessment report (2001) and the (right) longer, sharper “Hockey Stick” as featured in the Summary for Policy Makers of the IPCC 6th Assessment report (2021).

The relevant statements in the SPM and Technical Summary are:

A.2.2 Global surface temperature has increased faster since 1970 than in any other 50-year period over at least the last 2000 years (high confidence). Temperatures during the most recent decade (2011–2020) exceed those of the most recent multi-century warm period, around 6500 years ago13 [0.2°C to 1°C relative to 1850– 1900] (medium confidence). Prior to that, the next most recent warm period was about 125,000 years ago when the multi-century temperature [0.5°C to 1.5°C relative to 1850–1900] overlaps the observations of the most recent decade (medium confidence). {Cross-Chapter Box 2.1, 2.3, Cross-Section Box TS.1}

SPM AR6

Global surface temperature has increased by 1.09 [0.95 to 1.20] °C from 1850–1900 to 2011–2020, and the last decade was more likely than not warmer than any multi-centennial period after the Last Interglacial, roughly 125,000 years ago.

Cross Section Box TS.1

As the new IPCC report lays bare (you can find my full commentary about the new report at Time Magazine), we are engaged in a truly unprecedented and fundamentally dangerous experiment with our planet.

References

  1. M.E. Mann, R.S. Bradley, and M.K. Hughes, "Northern hemisphere temperatures during the past millennium: Inferences, uncertainties, and limitations", Geophysical Research Letters, vol. 26, pp. 759-762, 1999. http://dx.doi.org/10.1029/1999GL900070

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.

Coronavirus and climate

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As we collectively reel from the changes wrought by the current pandemic, people are being drawn by analogy to climate issues – but analogies can be tricky and often distort as much as they illuminate.

For instance, in the Boston Globe, Jeff Jacoby’s commentary was not particularly insightful and misquoted Mike Mann pretty egregiously. Mike’s response is good:

I am relieved to see policy makers treating the coronavirus threat with the urgency it deserves. They need to do the same when it comes to an even greater underlying threat: human-caused climate change.

In a recent column (“I’m skeptical about climate alarmism, but I take coronavirus fears seriously,” Ideas, March 15), Jeff Jacoby sought to reconcile his longstanding rejection of the wisdom of scientific expertise when it comes to climate with his embrace of such expertise when it comes to the coronavirus.

In so doing, Jacoby took my words out of context, mischaracterizing my criticisms of those who overstate the climate threat “in a way that presents the problem as unsolvable, and feeds a sense of doom, inevitability, and hopelessness.”

As I have pointed out in past commentaries, the truth is bad enough when it comes to the devastating impacts of climate change, which include unprecedented floods, heat waves, drought, and wildfires that are now unfolding around the world, including the United States and Australia, where I am on sabbatical.

The evidence is clear that climate change is a serious challenge we must tackle now. There’s no need to exaggerate it, particularly when it feeds a paralyzing narrative of doom and hopelessness.

There is still time to avoid the worst outcomes, if we act boldly now, not out of fear, but out of confidence that the future is still largely in our hands. That sentiment hardly supports Jacoby’s narrative of climate change as an overblown problem or one that lacks urgency.

While we have only days to flatten the curve of the coronavirus, we’ve had years to flatten the curve of CO2 emissions. Unfortunately, thanks in part to people like Jacoby, we’re still currently on the climate pandemic path.

Michael E. Mann

State College, Pa.

The writer is a professor at Penn State University, where he is director of the Earth System Science Center.

Direct connections

There are some direct connections too. The lockdowns and travel restrictions are having a material effect on emissions of short-lived air pollutants (like NOx, SO2 etc.), water discharges and carbon dioxide as well. The impacts on air and water quality are already being seen – perhaps allowing people to reset their shifted baselines for what clean air and water are like.

Business-as-usual is kaput

Obviously, nothing is going to be quite the same after this. We will soon be describing prior norms and behaviours as “that is so BC” (before coronavirus). Already, when watching pre-recorded TV shows, I internally cringe when seeing the handshaking and hugging.

But it should also be obvious that for worst-case scenarios to materialise, it is a combination of factors that drive the results. Luck, good or bad, and decisions, wise or unwise, combine to create the future. Luck drives the specific potency of the virus, it’s incubation period and lethality, but societal decisions determined the preparation (or lack thereof), the health care system design or capacity (or lack thereof), and governmental responses (adequate or not).

Indeed, every possible future can only be reached by a specific track of what is (the science) and what we do about it (the policy). That is no different with climate as it is with pandemics. There is no possible future in which no-one made any decisions.

How good have climate models been at truly predicting the future?

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A new paper from Hausfather and colleagues (incl. me) has just been published with the most comprehensive assessment of climate model projections since the 1970s. Bottom line? Once you correct for small errors in the projected forcings, they did remarkably well.

[Read more…] about How good have climate models been at truly predicting the future?

IPCC Special Report on Land

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Thread for discussions of the new special report. [Boosting a comment from alan2102].

Climate Change and Land
An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems


Land degradation accelerates global climate change. Al Jazeera English
Published on Aug 8, 2019

New UN report highlights vicious cycle of climate change, land degradation. CNA
Published on Aug 8, 2019

New IPCC Report Warns of Vicious Cycle Between Soil Degradation and Climate Change. The Real News Network
Published on Aug 8, 2019

Unforced Variations vs Forced Responses?

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Guest commentary by Karsten Haustein, U. Oxford, and Peter Jacobs (George Mason University).

One of the perennial issues in climate research is how big a role internal climate variability plays on decadal to longer timescales. A large role would increase the uncertainty on the attribution of recent trends to human causes, while a small role would tighten that attribution. There have been a number of attempts to quantify this over the years, and we have just published a new study (Haustein et al, 2019) in the Journal of Climate addressing this question.

Using a simplified climate model, we find that we can reproduce temperature observations since 1850 and proxy-data since 1500 with high accuracy. Our results suggest that multidecadal ocean oscillations are only a minor contributing factor in the global mean surface temperature evolution (GMST) over that time. The basic results were covered in excellent articles in CarbonBrief and Science Magazine, but this post will try and go a little deeper into what we found.

[Read more…] about Unforced Variations vs Forced Responses?

References

  1. K. Haustein, F.E.L. Otto, V. Venema, P. Jacobs, K. Cowtan, Z. Hausfather, R.G. Way, B. White, A. Subramanian, and A.P. Schurer, "A Limited Role for Unforced Internal Variability in Twentieth-Century Warming", Journal of Climate, vol. 32, pp. 4893-4917, 2019. http://dx.doi.org/10.1175/JCLI-D-18-0555.1

New Ocean Heat Content Histories

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Guest commentary from Laure Zanna (U. Oxford) and G. Jake Gebbie (WHOI)

Two recent papers, Zanna et al. (2019) (hereafter ZKGIH19) and Gebbie & Huybers (2019) (hereafter GH19), independently reconstructed ocean heat content (OHC) changes prior to the instrumentally-based records (which start ~1950). The goals (and methodologies) of the two papers were quite different – ZKGIH19 investigated regional patterns of ocean warming and thermal sea level rise, while GH19 analyzed the long-term memory of the deep ocean – but they both touch on the same key questions of climate forcing and response.

[Read more…] about New Ocean Heat Content Histories

References

  1. L. Zanna, S. Khatiwala, J.M. Gregory, J. Ison, and P. Heimbach, "Global reconstruction of historical ocean heat storage and transport", Proceedings of the National Academy of Sciences, vol. 116, pp. 1126-1131, 2019. http://dx.doi.org/10.1073/pnas.1808838115
  2. G. Gebbie, and P. Huybers, "The Little Ice Age and 20th-century deep Pacific cooling", Science, vol. 363, pp. 70-74, 2019. http://dx.doi.org/10.1126/science.aar8413