Betting on the best case: higher end warming is underrepresented in research

by FJehn16 min read2nd Aug 202112 comments

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Climate changeCause prioritization
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[We the authors decided to put this paper up on the EA Forum as we think it is a good addition to the discussion of climate change in effective altruism. It shows that parts of climate change are still neglected even though the field overall receives lots of attention and funding. This gap in extreme climate change research might be a valuable opportunity for people in the EA community with relevant skills to contribute.

This paper is published in Environmental Research Letters in open access (CC-BY)]

 

Betting on the best case: higher end warming is underrepresented in research

Florian U Jehn1, Marie Schneider1, Jason R Wang2, Luke Kemp3 and Lutz Breuer1,4

Abstract

We compare the probability of different warming rates to their mentions in IPCC reports through text mining. We find that there is a substantial mismatch between likely warming rates and research coverage. 1.5 °C and 2 °C scenarios are substantially overrepresented. More likely higher end warming scenarios of 3 °C and above, despite potential catastrophic impacts, are severely neglected.

1. Introduction

Temperature rise of 3 °C above pre-industrial temperature is more likely than not by the end of the century, on most business as usual scenario. One recent estimate projected a rise of 2.0 °C–4.9 °C with a median of 3.2 °C (Raftery et al 2017). This covers likely ranges; however, climate change is heavy-tailed with surprisingly high likelihoods for high levels of warming. GHG concentrations of 700 ppm could produce a 10% chance of exceeding a temperature rise of 6 °C (Wagner and Weitzman 2015). Such concentrations would be passed by 2100 under six of the nine of the CMIP6 SSP-RF baseline and 6.0 W/m2 forcing scenarios (Riahi et al 2017, Gidden et al 2019). The most recent estimates of equilibrium climate sensitivity show a similar distribution, narrowing the range of outcomes to exclude rises below 2 °C but not ruling out warming above 4.5 °C (Sherwood et al 2020).

Lower concentrations could still end in higher end warming outcomes due to tipping points and uncertainty over Earth system feedbacks and nonlinearities (Oreskes et al 1994, Booth et al 2012, Bodman et al 2013, Lenton et al 2019). These uncertainties make higher end warming climate change even more dangerous, as they leave the option open for far worse outcomes (Weitzman 2012). While concerning, such probabilistic distributions are not the same as the spread of risk. The impacts of higher temperature ranges are both more uncertain but also likely to be disproportionately more severe (New et al 2011). Given that eventual temperature outcomes also depend on deeply uncertain factors like the amount of global cooperation to mitigate climate change, scientists can support policymakers to craft robust, long-term responses to climate change by exploring a wide range of plausible futures, through approaches like Robust Decision-Making (Lempert et al 2003). This makes understanding the effects and consequences of warming of 3 °C and above imperative.

While there have been some valuable attempts to summarize available research (Wagner and Weitzman 2015, Wallace-Wells 2019), calls for action (Lenton et al 2019) and research projects focused on higher end climate impacts such as the HELIX project, it remains unclear if existing research coverage matches either the probabilistic or risk distribution of climate change. The best synthesis of climate change research are the assessment and special reports of the Intergovernmental Panel on Climate Change (IPCC 2013, 2014a, 2014b, 2018, 2019a, 2019b). They reflect expert consensus and are a reliable proxy for the current state of knowledge. To assess the focus of the IPCC on different warming scenarios in temperatures we text-mine available IPCC reports (IPCC 2013, 2014a, 2014b, 2018, 2019a, 2019b) and count how often the different temperatures are mentioned in comparison to the probability of the temperatures projected for an increase of atmospheric CO2 concentrations of 550 and 700 ppm based on the research of Wagner and Weitzman (Wagner and Weitzman 2015). We focus on those concentrations as 700 ppm are commonly exceeded end-of-century throughout many of the CMIP6 analytical scenarios, while 550 ppm would be reached if all currently stated climate policies and plans would be implemented (IEA 2020).

2. Results and discussion

Our results show a large mismatch between the amount of research and the probability of warming (figure 1(a)). 3 °C is the peak of probability for 700 ppm, but accounts for less than 3% of mentions. Temperatures of 3 °C or above (figure 1(c)) account for around two-thirds of the probabilistic mass for 700 ppm, but just over 10% of mentions. Similarly, a more dramatic temperature rise of 6 °C and above (figure 1(d)) is a 10% probability and only less than 1% of mentions. The picture is slightly better for 550 ppm, but higher end warming climate changes are underrepresented there as well. In addition, those numbers are likely to be underestimates of the neglectedness of higher end temperature rise, as many of the textual references do not refer to a change in global mean surface temperature. Overall, the percentage of true positive findings that actually relate to a change in global mean air temperature change varies substantially depending on the temperature (between 5 °C and 10 °C), ranging from 0% for 10 °C to 57% for 7 °C. However, even those mostly refer to the temperature change since the last glacial maximum and possible values for the equilibrium climate sensitivity. For example, the AR5 report of working group II (IPCC 2014a) mentions 8 °C only three times, two of which relate to local temperature increases in the arctic and the thermal optimum of salmon.

Figure 1. Comparison of the probability of temperatures rises and occurrences of those temperatures in the IPCC for (a) all AR5 working group reports and all special reports until 2020; (b) all reports of (a) except the Special Report on 1.5 °C warming; (c) the sum of all temperatures of 3 °C and above (based on a)); (d) the sum of all temperatures of 6 °C and above (based on a)). Subfigures (c) and (d) show the cumulative probability (black) of exceeding 3 °C change (c) and 6 °C (d) as substantially higher than the relative occurrence (orange) of those temperatures in the IPCC reports. The probability curves and the cumulative probability are based on the estimates of Wagner and Weitzman (2015).

 

There is a stark difference between warming probabilities and our knowledge. Over half of the textual references focus on a warming of 1.5 °C. This may be skewed by the 2018 'The Special Report on Global Warming of 1.5 °C (SR15)' (IPCC 2018) which was requested by the Conference of the Parties (COP) to the UNFCCC at the 2015 Paris Climate Summit. When we remove the special report on 1.5 °C warming (IPCC 2018) (figure 1(b)) from our analysis, textual references to 1.5 °C drop, but are still markedly higher than the probability and mentions of scenarios higher than 4 °C. Our text mining results suggest that there is at least some research focused on 4 °C of warming. This is supported by its coverage in well-known grey literature, such as the World Bank's 'Turn Down the Heat' series as well as the 2009 '4 Degrees and Beyond International Climate Conference'.

Our study indicates possible knowledge gaps in higher end warming climate change research. It is not a definitive conclusion, but a useful starting point to discuss the divergence between probability and risk distributions and climate change research focus to date. Our method only delivers a snapshot into the current climate change research and has some limitations. Mentions in IPCC reports do not neatly map onto the exact frequency of research. Furthermore, the frequency of mentions says little about the quality or extent of individual studies. The subject of IPCC special reports are often as reflective of political requests as research needs. Moreover, we use one prominent 2015 estimate of the probabilistic distribution of concentrations and temperatures, but future studies could look to use others (or even a combination of them). There are also other ways to approach our analysis that might result in different numbers (Brown and Caldeira 2017). We searched in Web of Science and Google Scholar for 'Climate Change' and 'X °C', The results have a very similar distribution to our IPCC-based results, but it is difficult to settle on concrete numbers, due to a high, but hard to quantify rate of false positive results, especially at higher temperatures. To address the rate of false positives in our analysis we looked at all temperature mentions in IPCC reports between 5 °C and 10 °C and checked if they were referring to global mean temperature rise or something else. We found no discernible trend in the rate of false positives.

There are multiple reasons to believe that the results presented here are robust. First, given the sheer difference in magnitude of our results, especially for 700 ppm. Second, the gap we highlight in our results is relatively insensitive to the CO2 concentration used. Even at CO2 concentrations of 600 ppm there is still a large research gap higher end warming. The research focus and the probability of warming only overlap around 450 ppm if we include all reports or at 550 ppm if we exclude the 1.5 °C special report (figures 1(a), (b) and 2). However, given that currently CO2 concentrations are already above 410 ppm it seems unlikely that we will be able to limit our greenhouse gas emissions to such levels without radical systemic socioeconomic changes. Higher end temperature rise is neglected while research that is 'betting on the best case' of 1.5 °C or 2 °C proliferates. Third, the IPCC itself has previously noted in the Fifth Assessment Report (AR5) that quantitative estimates of aggregate impacts above 3 °C are rare (IPCC 2014b). This is echoed in summaries of climate science in popular literature, which has to rely on less literature for scenarios above 3 °C and just a few, more speculative geological studies for 6 °C and higher (Lynas 2020). However, it should be noted that the research gap is a larger issue in the impact reports.

Figure 2. Probabilities of warming for CO2 concentrations from 400 to 1000 ppm (based on Wagner and Weitzman (2015)) and the relative occurrence of this warming in the IPCC reports for all AR5 working groups and all special reports until 2020 (both in %). The probability for the temperatures refer to the shown value ±0.25 °C.

 

There are several potential reasons for this divergence between probability and risk in relation to actual research. The most obvious is that the research community is simply meeting the demands of policy-makers. The goal of limiting warming to 2 °C first was formally adopted under the 2010 Cancun Accords, before being enshrined in the 2015 Paris Agreement on Climate Change, alongside the aspirational goal of holding warming to 1.5 °C. Both of these temperature goals are significantly overrepresented in existing research according to our study. The divergence may also partially reflect the conservative outcomes of the consensus procedures of the IPCC, and the tendency of climate science to err on the side of 'least drama'. This in turn is likely shaped by a history of climate scientists being accused of alarmism by well-funded misinformation campaigns (Oreskes and Conway 2012).

Regardless of the explanation, the problem of a misalignment between research coverage and probability and risk persists. Why is there a special report for 1.5 °C warming, but none for a warming of 3 °C and above, even though the latter currently seems more probable and would be more impactful? We hope this paper provides a starting point for discussion on how research should coincide with future probabilities and risks. Such a conversation and realignment of priorities is overdue. It is also direly needed given that the heavy-tails of climate change could constitute a threat of global catastrophe or even human extinction (Lynas 2020, Ord 2020).

3. Methods

To count the temperature mentioned in the IPCC reports (IPCC 2013, 2014a, 2014b, 2018, 2019a, 2019b) all text was extracted from the PDFs of the full reports. The text was then mined for the mentions of the temperatures in the format ' X°C'. The warming probability was calculated from the probability density function of figure 3.2 in Climate Shock (Wagner and Weitzman 2015), which was graciously provided by Gernot Wagner. The probability of the temperatures in figure 2 of this paper refers to the probability of the temperature ±0.25 °C. All code and data used can be found at the repository of this paper (Jehn 2021). This also contains lists that show how often the temperatures were found in the individual IPCC reports.

Acknowledgments

LB acknowledges support from the SDGnexus Network (Grant No. 57526248) funded by the German Academic Exchange Service (DAAD) from funds of Federal Ministry for Economic Cooperation (BMZ) in the frame of the program 'exceed—Hochschulexzellenz in der Entwicklungszusammenarbeit'. We would further like to thank Lukas Barth for giving valuable feedback on an earlier draft of this paper, Gernot Wagner for providing us with code and data that allowed us to do this analysis in the first place and the reviewers for their constructive feedback.

Data availability statement

All data, code, and materials (except the IPCC reports) used in the analyses is available in the repository of this paper (Jehn 2021).

The data that support the findings of this study are openly available at the following URL/DOI: 10.5281/zenodo.4311470.

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Author affiliations

1 Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26, 35390 Giessen, Germany

2 Independent Researcher, Edmonton, Canada

3 Centre for the Study of Existential Risk, University of Cambridge, 14 Mill Lane, Cambridge, United Kingdom

4 Centre for International Development and Environmental Research (ZEU), Justus Liebig University Giessen, Senckenbergstrasse 3, 35392 Giessen, Germany

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Cool stuff!

I don't think this changes the fundamental conclusion, but there are a couple of choices here that seem to make this imbalance larger than it really is:
 

  1. The Wagner and Weitzman probabilities are pre-Paris-Agreement era and newer estimates would probably put quite a bit lower probability on extreme warming levels (e.g. see here).
  2. Because policy targets are focused on 1.5 and 2 degrees, a lot of sections of the IPCC report will study those scenarios more, whereas the real concern seems the understudy of climate impacts (the problem is not that we don't have enough IAMs showing us how to get from 4 to 3.5 degrees but that we don't understand well what 3.5 and 4 degree worlds look like, right?).  If that is right, it seems a bit surprising you are looking at entire IPCC reports rather than only the relevant working groups. 




     
  1. We settled on Wagner and Weitzman because it is well known and also because they were kind enough to provide us with their data and code. It is indeed true that other probability curves might paint a more optimistic picture. However, the differences are so large that I would be surprised if it would change the conclusion of our paper.
  2. We looked at the complete IPCC reports, because we wanted to understand the overall focus of policy relevant research. But it is indeed true that the research gap differs between the working groups and the research gap is larger when it comes to the impact focused reports.

Thanks for that paper Johannes, it was mildly reassuring to read.

Liu and Raftery (2021) show that countries must increase their decarbonization rates by 80% relative to Paris commitments to limit warming to 2°C by 2100. Similarly, if the pace of global decarbonization fails to keep up with IEA’s (2020) STEPS projections (decarbonization has exceeded IEA projections in recent years; see IEA 2019, 2020), we find that several scenarios having greater than 3°C warming by 2100 become plausible (Fig. S4B).

One thing I was struck by is this section in the discussion (bold emphasis mine) is that an 80% increase in decarbonisation rates relative to Paris commitments seems quite large and slightly ironically, not very plausible. Is there any evidence that countries are making or planning to make such a radical step up in their decarbonisation, as it seems like their policies don't even reflect this? 

Paris Agreement targets are till 2030, so I'd be less deterministic wrt what is possible till 2100, looking at Liu and Raftery it sounds as though they are just extrapolating current trends.

In worlds where we would keep temperature <2C by 2100, I would expect large structural breaks, not getting there by trend extrapolation/incremental steps (e.g. decarbonization getting really cheap and easy at some point, or negative emissions becoming very affordable and scaleable etc.).

 

Somewhat related: Robert S. Pindyck on The Use and Misuse of Models for Climate Policy.

In short, his take (a) seems consistent with the claim that research and policy attention is being misallocated and (b) suggests a mechanism that might partly explain the misallocation.

Abstract (my emphasis):

In recent articles I have argued that integrated assessment models (IAMs) have flaws that make them close to useless as tools for policy analysis. IAM-based analyses of climate policy create a perception of knowledge and precision that is illusory and can fool policymakers into thinking that the forecasts the models generate have some kind of scientific legitimacy. However, some economists and climate scientists have claimed that we need to use some kind of model for policy analysis and that IAMs can be structured and used in ways that correct for their shortcomings. For example, it has been argued that although we know very little about key relationships in the model, we can get around this problem by attaching probability distributions to various parameters and then simulating the model using Monte Carlo methods. I argue that this would buy us nothing and that a simpler and more transparent approach to the design of climate change policy is preferable. I briefly outline what such an approach would look like.

A few highlights:

I believe that we need to be much more honest and up-front about the inherent limitations of IAMs. I doubt that the developers of IAMs have any intention of using them in a misleading way. Nevertheless, overselling their validity and claiming that IAMs can be used to evaluate policies and determine the SCC can end up misleading researchers, policymakers, and the public, even if it is unintentional. If economics is indeed a science, scientific honesty is paramount.

...

Yes, the calculations I have just described constitute a “model,” but it is a model that is exceedingly simple and straightforward and involves no pretense that we know the damage function, the feedback parameters that affect climate sensitivity, or other details of the climate–economy system. And yes, some experts might base their opinions on one or more IAMs, on a more limited climate science model, or simply on their research experience and/or general knowledge of climate change and its impact.

...

Some might argue that the approach I have outlined here is insufficiently precise. But I believe that we have no choice. Building and using elaborate models might allow us to think that we are approaching the climate policy problem more scientifically, but in the end, like the Wizard of Oz, we would only be drawing a curtain around our lack of knowledge

...

I have argued that the best we can do at this point is to come up with plausible answers to these questions, most likely by relying at least in part on numbers supplied by climate scientists and environmental economists, that is, utilize expert opinion. This kind of analysis would be simple, transparent, and easy to understand. It might not inspire the kind of awe and sense of scientific legitimacy conveyed by a large-scale IAM, but that is exactly the point.

Hi Peter,  I think there is a nuance two disentangle – IAMs are confusingly used in two contexts: 1) models that try to optimize for some economically efficient social cost of carbon (and by proxy, climate policies), and 2) those that attempt to simulate plausible futures. Where Pindyck's writing was mostly about the first, most IPCC work regards the second. Still, I absolutely agree with Pindyck's criticisms – they translate well over to the second category. We tried to cover that massive topic in the section about deeply uncertain factors and Robust Decision-Making, but with so few words, it is difficult to fully address those points. 

A further tricky aspect is that of the second type of models, the scenarios that are explored can themselves be misleading or they can limit analysis. Lamontagne et al. (2018) show how a full factorial of input scenarios illustrates that many combinations can lead to the same outcomes. When we don't know how the future will actually unfold, the chosen archetypes clout our assessment.

Another aspect is that the inputs themselves are actually outputs of others. Pielke Jr. and Ritchie (2021) discuss that in Distorting the view of our climate future: The misuse and abuse of climate pathways and scenarios.

All of this is to say: yes, I agree that all models are wrong, but some are useful. Our argument is mainly that through various approaches, we have some understanding of plausible temperature outcomes. We should prepare for all of these to be robustly prepared.

Thanks for posting this! Curious about your thoughts on whether there are highly promising opportunities for mitigating higher end warming, which governments/energy researchers won't take if they focus on lower end warming? (Maybe we don't know because, as you say, relevant research has been neglected?)

(As you might agree, I think that's important for concluding that there's much low-hanging fruit here.)

I would say that there are several promising research directions when it comes to higher end warming:

  1. Exploring in detail what the effects of higher end warming even are. E.g. what are the possible effects on crops, livestock, habitable zones for humans, but also the economy?
  2. How could we cope with such possibly massive changes? Are there precedents in history where we had to undergo such massive changes and did it work?
  3. Taking a deeper look into geo-engineering, especially the potentially very effective, but more out there approaches like e.g. Project Vesta

And those are just a few suggestions from the top of my head. Basically, it boils down to finding out what the effects might really be and how we could cope.

Thanks! Good points.

Would you have more specific hypotheses? :-) Ex: Within geoengineering, carbon capture is one possibility. Do you know of neglected areas regarding other types of geoengineering?

Or within exploring the effects of higher end warming, which crops/livestock do we need more research in? Which industries in the economy?

Thank you for any clarifications!

Unfortunately, I cannot really provide conclusive answers here, as our paper only looked into the amount of research overall, but not into specific topics. Getting an overview of this is basically a major research project itself. However, a student of mine looked into this a bit and her preliminary results seem like it is more of a general problem and not specific to certain fields of research.

It's good to be aware of where you're not an expert. Thank you for being honest!