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2020 vision

A meeting of smoke and storms (NASA Earth Observatory)

No-one needs another litany of all the terrible things that happened this year, but there are three areas relevant to climate science that are worth thinking about:

  • What actually happened in climate/weather (and how they can be teased apart). There is a good summary on the BBC radio Discover program covering wildfires, heat waves, Arctic sea ice, the hurricane season, etc. featuring Mike Mann, Nerlie Abram, Sarah Perkins-Kilpatrick, Steve Vavrus and others. In particular, there were also some new analyses of hurricanes (their rapid intensification, slowing, greater precipitation levels etc.), as well as the expanding season for tropical storms that may have climate change components. Yale Climate Connections also has a good summary.
  • The accumulation of CMIP6 results. We discussed some aspects of these results extensively – notably the increased spread in Equilibrium Climate Sensitivity, but there is a lot more work to be done on analyzing the still-growing database that will dominate the discussion of climate projections for the next few years. Of particular note will be the need for more sophisticated analyses of these model simulations that take into account observational constraints on ECS and a wider range of future scenarios (beyond just the SSP marker scenarios that were used in CMIP). These issues will be key for the upcoming IPCC 6th Assessment Report and the next National Climate Assessment.
  • The intersection of climate and Covid-19.
    • The direct connections are clear – massive changes in emissions of aerosols, short-lived polluting gases (like NOx) and CO2 – mainly from reductions in transportation. Initial results demonstrated a clear connection between cleaner air and the pandemic-related restrictions and behavioural changes, but so far the impacts on temperature or other climate variables appear to be too small to detect (Freidlingstein et al, 2020). The impact on global CO2 emissions (LeQuere et al, 2020) has been large (about 10% globally) – but not enough to stop CO2 concentrations from continuing to rise (that would need a reduction of more like 70-80%). Since the impact from CO2 is cumulative this won’t make a big difference in future temperatures unless it is sustained through post-pandemic changes.
    • The metaphorical connections are also clear. The instant rise of corona virus-denialism, the propagation of fringe viewpoints from once notable scientists, petitions to undermine mainstream epidemiology, politicized science communications, and the difficulty in matching policy to science (even for politicians who want to just ‘follow the science’), all seem instantly recognizable from a climate change perspective. The notion that climate change was a uniquely wicked problem (because of it’s long term and global nature) has evaporated as quickly as John Ioannidis’ credibility.

I need to take time to note that there has been human toll of Covid-19 on climate science, ranging from the famous (John Houghton) to the families of people you never hear about in the press but whose work underpins the data collection, analysis and understanding we all rely on. This was/is a singular tragedy.

With the La Niña now peaking in the tropical Pacific, we can expect a slightly cooler year in 2021 and perhaps a different character of weather events, though the long-term trends will persist. My hope is that the cracks in the system that 2020 has revealed (across a swathe of issues) can serve as an motivation to improve resilience, equity and planning, across the board. That might well be the most important climate impact of all.

A happier new year to you all.


  1. P.M. Forster, H.I. Forster, M.J. Evans, M.J. Gidden, C.D. Jones, C.A. Keller, R.D. Lamboll, C.L. Quéré, J. Rogelj, D. Rosen, C. Schleussner, T.B. Richardson, C.J. Smith, and S.T. Turnock, "Current and future global climate impacts resulting from COVID-19", Nature Climate Change, vol. 10, pp. 913-919, 2020.
  2. C. Le Quéré, R.B. Jackson, M.W. Jones, A.J.P. Smith, S. Abernethy, R.M. Andrew, A.J. De-Gol, D.R. Willis, Y. Shan, J.G. Canadell, P. Friedlingstein, F. Creutzig, and G.P. Peters, "Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement", Nature Climate Change, vol. 10, pp. 647-653, 2020.

Denial and Alarmism in the Near-Term Extinction and Collapse Debate

Guest article by Alastair McIntosh,  honorary professor in the College of Social Sciences at the University of Glasgow in Scotland. This is an excerpt from his new book, Riders on the Storm: The Climate Crisis and the Survival of Being

cover art for Riders on the StormMostly, we only know what we think we know about climate science because of the climate science. I have had many run-ins with denialists, contrarians or climate change dismissives as they are variously called. Over the past two years especially, concern has also moved to the other end of the spectrum, to alarmism. Both ends, while the latter has been more thinly tapered, can represent forms of denial. In this abridged adaptation I will start with denialism, but round on the more recent friendly fire on science that has emerged in alarmism.
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Climate Sensitivity: A new assessment

Filed under: — gavin @ 22 July 2020

Not small enough to ignore, nor big enough to despair.

There is a new review paper on climate sensitivity published today (Sherwood et al., 2020 (preprint) that is the most thorough and coherent picture of what we can infer about the sensitivity of climate to increasing CO2. The paper is exhaustive (and exhausting – coming in at 166 preprint pages!) and concludes that equilibrium climate sensitivity is likely between 2.3 and 4.5 K, and very likely to be between 2.0 and 5.7 K.

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  1. S.C. Sherwood, M.J. Webb, J.D. Annan, K.C. Armour, P.M. Forster, J.C. Hargreaves, G. Hegerl, S.A. Klein, K.D. Marvel, E.J. Rohling, M. Watanabe, T. Andrews, P. Braconnot, C.S. Bretherton, G.L. Foster, Z. Hausfather, A.S. Heydt, R. Knutti, T. Mauritsen, J.R. Norris, C. Proistosescu, M. Rugenstein, G.A. Schmidt, K.B. Tokarska, and M.D. Zelinka, "An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence", Reviews of Geophysics, vol. 58, 2020.

Sensitive but unclassified: Part II

Filed under: — gavin @ 13 June 2020

The discussion and analysis of the latest round of climate models continues – but not always sensibly.

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BAU wow wow

How should we discuss scenarios of future emissions? What is the range of scenarios we should explore? These are constant issues in climate modeling and policy discussions, and need to be reassessed every few years as knowledge improves.

I discussed some of this in a post on worst case scenarios a few months ago, but the issue has gained more prominence with a commentary by Zeke Hausfather and Glen Peters in Nature this week (which itself partially derives from ongoing twitter arguments which I won’t link to because there are only so many rabbit holes that you want to fall into).

My brief response to this is here though:

Mike Mann has a short discussion on this as well. But there are many different perspectives around – ranging from the merely posturing to the credible and constructive. The bigger questions are certainly worth discussing, but if the upshot of the current focus is that we just stop using the term ‘business-as-usual’ (as was suggested in the last IPCC report), then that is fine with me, but just not very substantive.


  1. Z. Hausfather, and G.P. Peters, "Emissions – the ‘business as usual’ story is misleading", Nature, vol. 577, pp. 618-620, 2020.

Update day 2020!

Following more than a decade of tradition (at least), I’ve now updated the model-observation comparison page to include observed data through to the end of 2019.

As we discussed a couple of weeks ago, 2019 was the second warmest year in the surface datasets (with the exception of HadCRUT4), and 1st, 2nd or 3rd in satellite datasets (depending on which one). Since this year was slightly above the linear trends up to 2018, it slightly increases the trends up to 2019. There is an increasing difference in trend among the surface datasets because of the polar region treatment. A slightly longer trend period additionally reduces the uncertainty in the linear trend in the climate models.

To summarize, the 1981 prediction from Hansen et al (1981) continues to underpredict the temperature trends due to an underestimate of the transient climate response. The projections in Hansen et al. (1988) bracket the actual changes, with the slight overestimate in scenario B due to the excessive anticipated growth rate of CFCs and CH4 which did not materialize. The CMIP3 simulations continue to be spot on (remarkably), with the trend in the multi-model ensemble mean effectively indistinguishable from the trends in the observations. Note that this doesn’t mean that CMIP3 ensemble means are perfect – far from it. For Arctic trends (incl. sea ice) they grossly underestimated the changes, and overestimated them in the tropics.

CMIP3 for the win!

The CMIP5 ensemble mean global surface temperature trends slightly overestimate the observed trend, mainly because of a short-term overestimate of solar and volcanic forcings that was built into the design of the simulations around 2009/2010 (see Schmidt et al (2014). This is also apparent in the MSU TMT trends, where the observed trends (which themselves have a large spread) are at the edge of the modeled histogram.

A number of people have remarked over time on the reduction of the spread in the model projections in CMIP5 compared to CMIP3 (by about 20%). This is due to a wider spread in forcings used in CMIP3 – models varied enormously on whether they included aerosol indirect effects, ozone depletion and what kind of land surface forcing they had. In CMIP5, most of these elements had been standardized. This reduced the spread, but at the cost of underestimating the uncertainty in the forcings. In CMIP6, there will be a more controlled exploration of the forcing uncertainty (but given the greater spread of the climate sensitivities, it might be a minor issue).

Over the years, the model-observations comparison page is regularly in the top ten of viewed pages on RealClimate, and so obviously fills a need. And so we’ll continue to keep it updated, and perhaps expand it over time. Please leave suggestions for changes in the comments below.


  1. J. Hansen, D. Johnson, A. Lacis, S. Lebedeff, P. Lee, D. Rind, and G. Russell, "Climate Impact of Increasing Atmospheric Carbon Dioxide", Science, vol. 213, pp. 957-966, 1981.
  2. J. Hansen, I. Fung, A. Lacis, D. Rind, S. Lebedeff, R. Ruedy, G. Russell, and P. Stone, "Global climate changes as forecast by Goddard Institute for Space Studies three-dimensional model", Journal of Geophysical Research, vol. 93, pp. 9341, 1988.
  3. G.A. Schmidt, D.T. Shindell, and K. Tsigaridis, "Reconciling warming trends", Nature Geoscience, vol. 7, pp. 158-160, 2014.

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

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.

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Sensitive But Unclassified

The US federal government goes to quite a lot of effort to (mostly successfully) keep sensitive but unclassified (SBU) information (like personal data) out of the hands of people who would abuse it. But when it comes to the latest climate models, quite a few are SBU as well.

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Koonin’s case for yet another review of climate science

We watch long YouTube videos so you don’t have to.

In the seemingly endless deliberations on whether there should be a ‘red team’ exercise to review various climate science reports, Scott Waldman reported last week that the original architect of the idea, Steve Koonin, had given a talk on touching on the topic at Purdue University in Indiana last month. Since the talk is online, I thought it might be worth a viewing.

[Spoiler alert. It wasn’t].

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Unforced Variations vs Forced Responses?

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.

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  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.