Category: Climate

AI in Climate Sci

RealClimate has a nice article on emerging uses of AI in climate modeling:

To summarise, most of the near-term results using ML will be in areas where the ML allows us to tackle big data type problems more efficiently than we could do before. This will lead to more skillful models, and perhaps better predictions, and allow us to increase resolution and detail faster than expected. Real progress will not be as fast as some of the more breathless commentaries have suggested, but progress will be real.

I think a key point is that AI/ML is not a silver bullet:

Climate is not weather

This is all very impressive, but it should be made clear that all of these efforts are tackling an initial value problem (IVP) – i.e. given the situation at a specific time, they track the evolution of that state over a number of days. This class of problem is appropriate for weather forecasts and seasonal-to-sub seasonal (S2S) predictions, but isn’t a good fit for climate projections – which are mostly boundary value problems (BVPs). The ‘boundary values’ important for climate are just the levels of greenhouse gases, solar irradiance, the Earth’s orbit, aerosol and reactive gas emissions etc. Model systems that don’t track any of these climate drivers are simply not going to be able to predict the effect of changes in those drivers. To be specific, none of the systems mentioned so far have a climate sensitivity (of any type).

But why can’t we learn climate predictions in the same way? The problem with this idea is that we simply don’t have the appropriate training data set. …

I think the same reasoning applies to many problems that we tackle with SD: the behavior of interest is way out of sample, and thus not subject to learning from data alone.

Better Documentation

There’s a recent talk by Stefan Rahmstorf that gives a good overview of the tipping point in the AMOC, which has huge implications.

I thought it would be neat to add the Stommel box model to my library, because it’s a nice low-order example of a tipping point. I turned to a recent update of the model by Wei & Zhang in GRL.

It’s an interesting paper, but it turns out that documentation falls short of the standards we like to see in SD, making it a pain to replicate. The good part is that the equations are provided:

The bad news is that the explanation of these terms is brief to the point of absurdity:

This paragraph requires you to maintain a mental stack of no less than 12 items if you want to be able to match the symbols to their explanations. You also have to read carefully if you want to know that ‘ means “anomaly” rather than “derivative”.

The supplemental material does at least include a table of parameters – but it’s incomplete. To find the delay taus, for example, you have to consult the text and figure captions, because they vary. Initial conditions are also not conveniently specified.

I like the terse mathematical description of a system because you can readily take in the entirety of a state variable or even the whole system at a glance. But it’s not enough to check the “we have Greek letters” box. You also need to check the “serious person could reproduce these results in a reasonable amount of time” box.

Code would be a nice complement to the equations, though that comes with it’s own problems: tower-of-Babel language choices and extraneous cruft in the code. In this case, I’d be happy with just a more complete high-level description – at least:

  • A complete table of parameters and units, with values used in various experiments.
  • Inclusion of initial conditions for each state variable.
  • Separation of terms in the RhoH-RhoL equation.

A lot of these issues are things you wouldn’t even know are there until you attempt replication. Unfortunately, that is something reviewers seldom do. But electrons are cheap, so there’s really no reason not to do a more comprehensive documentation job.

 

Morons Controlling Weather

For the last 30 years, I’ve been hearing from climate skeptics that man can’t possibly affect the climate. Now MTG says it’s all a lie!

Hilarious that this reverses the usual conflation of weather and climate. I’d say this is so dumb it beggars the imagination, but I’ve heard so much dumb climate denial, this is barely top-10.

Still waiting for that new Maunder Minimum, by the way.

Climate Policy Effectiveness, Pricing and Causality

A new paper by Stechemesser et al. in Science evaluates the large suite of climate policies in

Editor’s summary

It is easy for countries to say they will reduce their emissions of greenhouse gases, but these statements do not mean that the policies they adopt will be effective. Stechemesser et al. evaluated 1500 climate policies that have been implemented over the past 25 years and identified the 63 most successful ones. Some of those successes involved rarely studied policies and unappreciated combinations. This work illustrates the kinds of policy efforts that are needed to close the emissions gaps in various economic sectors. —Jesse Smith

Abstract

Meeting the Paris Agreement’s climate targets necessitates better knowledge about which climate policies work in reducing emissions at the necessary scale. We provide a global, systematic ex post evaluation to identify policy combinations that have led to large emission reductions out of 1500 climate policies implemented between 1998 and 2022 across 41 countries from six continents. Our approach integrates a comprehensive climate policy database with a machine learning–based extension of the common difference-in-differences approach. We identified 63 successful policy interventions with total emission reductions between 0.6 billion and 1.8 billion metric tonnes CO2. Our insights on effective but rarely studied policy combinations highlight the important role of price-based instruments in well-designed policy mixes and the policy efforts necessary for closing the emissions gap.

Effective policies from Stechemesser et al.

Emil Dimanchev has a useful critique on the platform formerly known as Twitter. I think there are two key points. First, the method may have a hard time capturing gradual changes. This is basically a bathtub statistics problem: policies are implemented as step changes, but the effect may involve one or more integrations (from implementation lags, slow capital turnover, R&D pipeline delays, etc.). The structural problem is probably exacerbated by a high level of noise. The bottom line is that some real effects may not be readily detectable.

ED’s second key point is essentially a variant of “correlation is not causation”:

To understand effectiveness of a policy (or of a policy mix), we are interested in the probability (P) that it reduces emissions (our hypothesis, H) when implemented (our condition, E). In statistics, we denote that P(H|E). But the authors do something very different.

Instead, the authors take all cases (arbitrarily) defined as effective (E) and then estimate how often a policy was implemented around that time. That’s P(E|H). The two shouldn’t be conflated (though Tversky and Kahneman showed people often make that mistake).

An example of this conflation is when people conclude from the paper that a policy mix needs CO2 pricing to be effective. But the data merely show that in most of their historical emission breaks, pricing was part of the policy mix, a statement of P(E|H).

Concluding from the paper that pricing increases the probability of a break in emissions, or P(H|E), is exactly like saying that you should play a musical instrument to increase your chances of winning a Nobel prize because most Nobel laureates play a musical instrument.

I have mixed feelings about this argument. It’s correct in principle, but I think it’s incomplete, because there are strong mechanistic arguments for the effectiveness of some climate policies, that should complement the probabilistic reasoning here. I think getting to this mechanistic view is actually what ED drives at in his prescription:

An empirical investigation of effectiveness would involve looking at all cases an instrument or a mix was implemented, and estimating the CO2 reductions it caused while controlling for all confounding variables. That’s obviously very hard. Again, that’s not what the paper does.

To me, this means use an a priori energy model to detect policy effects, rather than an abstract ML method. When the IEA climate policy database first came out, I thought that would be a really cool project. Then I thought about how much work it would be, so that will have to wait for another time. But given the abundance of mechanistic arguments favoring some policies, I can’t help leaning towards the view that “correlation is not causation – but it’s a good start”. Here’s a suggestive view of the results, stratifying the price and non-price initiatives, with the pies roughly sized by absolute numbers of policies:

Lets suppose, in the worst case, that the effects observed in Stechemesser et al. are simply random luck. Why should the non-price policies be associated with effects so much less frequently? The paper may be weak evidence, but I think it’s not inconsistent with the idea – backed by models – that a lot of climate and energy  policies are seriously flawed (CAFE standards and Energy Star come to mind) or simply fluffy feel-good greenwash.

More reasons to love emissions pricing

I was flipping through a recent Tech Review, and it seemed like every other article was an unwitting argument for emissions pricing. Two examples:

Job title of the future: carbon accountant

We need carbon engineers who know how to make emissions go away more than we need bean counters to tally them. Are we also going to have nitrogen accountants, and PFAS accountants, and embodied methane in iridium accountants, and … ? That way lies insanity.

The fact is, if carbon had a nontrivial price attached at the wellhead, it would pervade the economy, and we’d already have carbon accountants. They’re called accountants.

More importantly, behind those accountants is an entire infrastructure of payment systems that enforces conservation of money. You can only cheat an accounting system for so long, before the cash runs out. We can’t possibly construct parallel systems providing the same robustness for every externality we’re interested in.

Here’s what we know about lab-grown meat and climate change

Realistically, now matter how hard we try to work out the relative emissions of natural and laboratory cows, the confidence bounds on the answer will remain wide until the technology is used at scale.

We can’t guide that scaling process by assessments that are already out of date when they’re published. Lab meat innovators need a landscape in which carbon is priced into their inputs, so they can make the right choices along the way.

Climate Causality Confusion

A newish set of papers (1. Theory (preprint); 2. Applications (preprint); 3. Extension) is making the rounds on the climate skeptic sites, with – ironically – little skepticism applied.

The claim is bold:

… According to the commonly assumed causality link, increased [CO2] causes a rise in T. However, recent developments cast doubts on this assumption by showing that this relationship is of the hen-or-egg type, or even unidirectional but opposite in direction to the commonly assumed one. These developments include an advanced theoretical framework for testing causality based on the stochastic evaluation of a potentially causal link between two processes via the notion of the impulse response function. …. All evidence resulting from the analyses suggests a unidirectional, potentially causal link with T as the cause and [CO2] as the effect.

Galileo complex seeps in when the authors claim that absence of correlation or impulse response from CO2 -> temperature proves absence of causality:

Clearly, the results […] suggest a (mono-directional) potentially causal system with T as the cause and [CO2] as the effect. Hence the common perception that increasing [CO2] causes increased T can be excluded as it violates the necessary condition for this causality direction.

Unfortunately, these claims are bogus. Here’s why.

The authors estimate impulse response functions between CO2 and temperature (and back), using the following formalism:


where g(h) is the response at lag h. As the authors point out, if

the IRF is zero for every lag except for the specific lag ℎ0, then Equation (1) becomes y(t)=bx(t-h0) +v(t). This special case is equivalent to simply correlating  y(t) with x(t-h0) at any time instance . It is easy to find (cf. linear regression) that in this case the multiplicative constant is the correlation coefficient of y(t) and  x(t-h0) multiplied by the ratio of the standard deviations of the two processes.

Now … anyone who claims to have an “advanced theoretical framework for testing causality” should be aware of the limitations of linear regression. There are several possible issues that might lead to misleading conclusions about causality.

Problem #1 here is bathtub statistics. Temperature integrates the radiative forcing from CO2 (and other things). This is not debatable – it’s physics. It’s old physics, and it’s experimental, not observational. If you question the existence of the effect, you’re basically questioning everything back to the Enlightenment. The implication is that no correlation is expected between CO2 and temperature, because integration breaks pattern matching. The authors purport to avoid integration by using first differences of temperature and CO2. But differencing both sides of the equation doesn’t solve the integration problem; it just kicks the can down the road. If y integrates x, then patterns of the integrals or derivatives of y and x won’t match either. Even worse differencing filters out the signals of interest.

Problem #2 is that the model above assumes only equation error (the term v(t) on the right hand side). In most situations, especially dynamic systems, both the “independent” (a misnomer) and dependent variables are subject to measurement error, and this dilutes the correlation or slope of the regression line (aka attenuation bias), and therefore also the IRF in the authors’ framework. In the case of temperature, the problem is particularly acute, because temperature also integrates internal variability of the climate system (weather) and some of this variability is autocorrelated on long time scales (because for example oceans have long time constants). That means the effective number of data points is a lot less than the 60 years or 720 months you’d expect from simple counting.

Dynamic variables are subject to other pathologies, generally under the heading of endogeneity bias, and related features with similar effects like omitted variable bias. Generalizing the approach to distributed lags in no way mitigates these. The bottom line is that absence of correlation doesn’t prove absence of causation.

Admittedly, even Nobel Prize winners can screw up claims about causality and correlation and estimate dynamic models with inappropriate methods. But causality confusion isn’t really a good way to get into that rarefied company.

I think methods purporting to assess causality exclusively from data are treacherous in general. The authors’ proposed method is provably wrong in some cases, including this one, as is Granger Causality. Even if you have pretty good assumptions, you’ll always find a system that violates them. That’s why it’s so important to take data-driven results with a grain of salt, and look for experimental control (where you can get it) and mechanistic explanations.

One way to tell if you’ve gotten causality wrong is when you “discover” mechanisms that are physically absurd. That happens on a spectacular scale in the third paper:

… we find Δ=23.5 and 8.1 Gt C/year, respectively, i.e., a total global increase in the respiration rate of Δ=31.6 Gt C/year. This rate, which is a result of natural processes, is 3.4 times greater than the CO2 emission by fossil fuel combustion (9.4 Gt C /year including cement production).

To put that in perspective, the authors propose a respiration flow that would put the biosphere about 30% out of balance. This implies a mass flow of trees harvested, soils destroyed, etc. 3.4 times as large as the planetary flow of fossil fuels. That would be about 4 cubic kilometers of wood, for example. In the face of the massive outflow from the biosphere, the 9.4 GtC/yr from fossil fuels went where, exactly? Extraordinary claims require extraordinary evidence, but the authors apparently haven’t pondered how these massive novel flows could be squared with other lines of evidence, like C isotopes, ocean Ph, satellite CO2, and direct estimates of land use emissions.

This “insight” is used to construct a model of the temperature->CO2 process:

In this model, the trend in CO2 is explained almost exclusively by the mean temperature effect mu_v = alpha*(T-T0). That effect is entirely ad hoc, with no basis in the impulse response framework.

How do we get into this pickle? I think the simple answer is that the authors’ specification of the system is incomplete. As above, they define a causal system,

y(t) = ∫g1(h)x(t-h)dh

x(t) = ∫g2(h)y(t-h)dh

where g(.) is an impulse response function weighting lags h and the integral is over h from 0 to infinity (because only nonnegative lags are causal). In their implementation, x and y are first differences, so in their climate example, Δlog(CO2) and ΔTemp. In the estimation of the impulse lag structures g(.), the authors impose nonnegativity and (optionally) smoothness constraints.

A more complete specification is roughly:

Y = A*X + U

dX/dt = B*X + E

where

  • X is a vector of system states (e.g., CO2 and temperature)
  • Y is a vector of measurements (observed CO2 and temperature)
  • A and B are matrices of coefficients (this is a linear view of the system, but could easily be generalized to nonlinear functions)
  • E is driving noise perturbing the state, and therefore integrated into it
  • U is measurement error

My notation could be improved to consider covariance and state-dependent noise, though it’s not really necessary here. Fred Schweppe wrote all this out decades ago in Uncertain Dynamic Systems, and you can now find it in many texts like Stengel’s Optimal Control and Estimation. Dixit and Pindyck transplanted it to economics and David Peterson brought it to SD where it found its way into Vensim as the combination of Kalman filtering and optimization.

How does this avoid the pitfalls of the Koutsoyiannis et al. approach?

  • An element of X can integrate any other element of X, including itself.
  • There are no arbitrary restrictions (like nonnegativity) on the impulse response function.
  • The system model (A, B, and any nonlinear elements augmenting the framework) can incorporate a priori structural knowledge (e.g., physics).
  • Driving noise and measurement error are recognized and can be estimated along with everything else.

Does the difference matter? I’ll leave that for a second post with some examples.

 

 

Held v Montana

The Montana climate case, Held vs. State of Montana, has just turned in a win for youth.

The decision looks pretty strong. I think the bottom line is that the legislature’s MEPA exclusions preventing consideration of climate in state regulation are a limitation of the MT constitutional environmental rights, and therefore require strict scrutiny. The state failed to show that the MEPA Limitation serves a compelling government interest.

Not to diminish the accomplishments of the plaintiffs, but the state put forth a very weak case. The Montana Supreme Court tossed out AG Knudsen’s untimely efforts to send the case back to the drawing board. The state’s own attorney, Thane Johnson, couldn’t get acronyms right for the IPCC and RCPs. That’s perhaps not surprising, given that the Director of Montana’s alleged environmental agency admitted unfamiliarity with the largest scientific body related to climate,

Montana’s top witnesses — state employees who are responsible for permitting fossil fuel projects — however, acknowledged they are not well-versed in climate science and at times struggled with the many acronyms used in the case.

Chris Dorrington, director of the Montana Department of Environmental Quality, told an attorney for the youth that he had been unaware of the U.N. Intergovernmental Panel on Climate Change (IPCC) — which has issued increasingly dire assessments since it was established more than 30 years ago to synthesize global climate data.

“I attended this trial last week, when there was testimony relevant to IPCC,” Dorrington said. “Prior to that, I wasn’t familiar, and certainly not deeply familiar with its role or its work.”

As noted by Judge Seeley, the state left much of the plaintiffs’ evidence uncontested. They also declined to call their start witness on climate science, Judith Curry, who reflects:

MT’s lawyers were totally unprepared for direct and cross examination of climate science witnesses. This was not surprising, since this is a very complex issue that they apparently had not previously encountered. One lawyer who was cross-examining the Plaintiffs’ witnesses kept getting confused by ICP (IPCC) and RPC (RCP). The Plaintiffs were very enthusiastic about keeping witnesses in reserve to rebut my testimony, with several of the Plaintiffs’ witnesses who were leaving on travel presenting pre-buttals to my anticipated testimony during their direct questioning – all of this totally misrepresented what was in my written testimony, and can now be deleted from the court record since I didn’t testify. I can see that all of this would have turned the Hearing into a 3-ring climate circus, and at the end of all that I might not have managed to get my important points across, since I am only allowed to respond to questions.

On Thurs eve, I received a call from the lead Montana lawyer telling me that they were “letting me off the hook.” I was relieved to be able to stay home and recapture those 4 days I had scheduled for travel to and from MT.

The state’s team sounds pretty dysfunctional:

Montana’s approach to the case has evolved since 2020, has evolved rapidly in the last 6 months since a new legal team was brought in, and even evolved rapidly during the course of the trial.  The lawyers I spoke to in Sept 2022 were gone by the end of Oct, with an interim team brought in from the private sector, and then a new team that was hired for the Montana’s State Attorney’s Office in Dec.

MT’s original expert witnesses were apparently tossed, and I and several other expert witnesses were brought on board in the 11th hour, around Sept 2022. Note:  instructions for preparing our written reports were received from lawyers two generations removed from the actual trial lawyers.  As per questioning during my Deposition, I gleaned that the state originally had a collection of witnesses that were pretty subpar (I don’t know who they were).  The new set of witnesses was apparently much better.

If the state has such a compelling case, why can’t they get their act together?

In any case, I find one argument in all of this really disturbing. Suppose we accept Curry’s math:

With regards to Montana’s CO2 emissions, based on 2019 estimates Montana produces 0.63% of U.S. emissions and 0.09% of global emissions.  For an anticipated warming of 2oC, Montana’s 0.09% of emissions would account for 0.0018oC of warming.  There are other ways to frame this calculation (and more recent numbers), but any way you slice it, you can’t come up with a significant amount of global warming that is caused by Montana’s emissions.

Never mind that MT is also only .0135% of global population. If you get granular enough, every region is a tiny fraction of the world in all things. So if we are to imagine that “my contribution is small” equates to “I don’t have to do anything about the problem,” then no one has to do anything about climate, or any other global problem for that matter. There’s no role for leadership, cooperation or enlightened self-interest. This is a circular firing squad for global civilization.

Computer Collates Climate Contrarian Claims

Coan et al. in Nature have an interesting text analysis of climate skeptics’ claims.

I’ve been at this long enough to notice that a few perennial favorites are missing, perhaps because they date from the 90s, prior to the dataset.

The big one is “temperature isn’t rising” or “the temperature record is wrong.” This has lots of moving parts. Back in the 90s, a key idea was that satellite MSU records showed falling temperatures, implying that the surface station record was contaminated by Urban Heat Island (UHI) effects. That didn’t end well, when it turned out that the UAH code had errors and the trend reversed when they were fixed.

Later UHI made a comeback when the SurfaceStations project crowdsourced an assessment of temperature station quality. Some turned out to be pretty bad. But again, when the dust settled, it turned out that the temperature trend was bigger, not smaller, when poor sites were excluded and TOD was corrected. This shouldn’t have been a surprise, because windy day analsyses and a dozen other things already ruled out UHI, but …

I consider this a reminder of the fact that part of the credibility of mainstream climate science arises not from the fact that models are so good, but because so many alternatives have been tried, and proved so bad, only to rise again and again.

Climate Catastrophe Loops

PNAS has a new article on climate catastrophe mechanisms, focused on the social side, not natural tipping points. The article includes a causal loop diagram capturing some of the key feedbacks:

The diagram makes an unconventional choice: link polarity is denoted by dashed lines, rather than the usual + and – designations at arrowheads. Per the caption,

This is a causal loop diagram, in which a complete line represents a positive polarity (e.g., amplifying feedback; not necessarily positive in a normative sense) and a dotted line denotes a negative polarity (meaning a dampening feedback).

Does this new convention work? I don’t think so. It’s not less visually cluttered, and it makes negative links look tentative, though in fact there’s no reason for a negative link to have any less influence than a positive one. I think it makes it harder to assess loop polarity by following reversals from – links. There’s at least one goof: increasing ecosystem services should decrease food and water shortages, so that link should have negative polarity.

The caption also confuses link and loop polarity: “a complete line represents a positive polarity (e.g., amplifying feedback”. A single line is a causal link, not a loop, and therefore doesn’t represent feedback at all. (The rare exception might be a variable with a link back to itself, sometimes used to indicate self-reinforcement without elaborating on the mechanism.)

Nevertheless, I think this is a useful start toward a map of the territory. For me, it was generative, i.e. it immediately suggested a lot of related effects. I’ve elaborated on the original here:

  1. Food, fuel and water shortages increase pressure to consume more natural resources (biofuels, ag land, fishing for example) and therefore degrade biodiversity and ecosystem services. (These are negative links, but I’m not following the dash convention – I’m leaving polarity unlabeled for simplicity.) This is perverse, because it creates reinforcing loops worsening the resource situation.
  2. State fragility weakens protections that would otherwise protect natural resources against degradation.
  3. Fear of scarcity induces the wealthy to protect their remaining resources through rent seeking, corruption and monopoly.
  4. Corruption increases state fragility, and fragile states are less able to defend against further corruption.
  5. More rent seeking, corruption and monopoly increases economic inequality.
  6. Inequality, rent seeking, corruption, and scarcity all make emissions mitigation harder, eventually worsening warming.
  7. Displacement breeds conflict, and conflict displaces people.
  8. State fragility breeds conflict, as demagogues blame “the other” for problems and nonviolent conflict resolution methods are less available.
  9. Economic inequality increases mortality, because mortality is an extreme outcome, and inequality puts more people in the vulnerable tail of the distribution.

#6 is key, because it makes it clear that warming is endogenous. Without it, the other variables represent a climate-induced cascade of effects. In reality, I think we’re already seeing many of the tipping effects (resource and corruption effects on state fragility, for example) and the resulting governance problems are a primary cause of the failure to reduce emissions.

I’m sure I’ve missed a bunch of links, but this is already a case of John Muir‘s idea, “When we try to pick out anything by itself, we find it hitched to everything else in the Universe.”

Unfortunately, most of the hitches here create reinforcing loops, which can amplify our predicament and cause catastrophic tipping events. I prefer to see this as an opportunity: we can run these vicious cycles in reverse, making them virtuous. Fighting corruption makes states less fragile, making mitigation more successful, reducing future warming and the cascade of side effects that would otherwise reinforce state fragility in the future. Corruption is just one of many places to start, and any progress is amplified. It’s just up to us to cross enough virtuous tipping points to get the whole system moving in a good direction.

Lytton Burning

By luck and a contorted Jet Stream, Montana more or less escaped the horrific heat that gripped the Northwest at the end of June. You probably heard, but this culminated in temperatures in Lytton BC breaking all-time records for Canada and the globe north of latitude 50 by huge margins. The next day, the town burned to the ground.

I wondered just how big this was, so when GHCN temperature records from KNMI became available, I pulled the data for a quick and dirty analysis. Here’s the daily Tmax for Lytton:

That’s about 3.5 standard deviations above the recent mean. Lytton’s records are short and fragmented, so I also pulled Kamloops (the closest station with a long record):

You can see how bizarre the recent event was, even in a long term context. In Kamloops, it’s a +4 standard deviation event, which means a likelihood of 1 in 16,000 if this were simply random. Even if you start adjusting for selection and correlations, it still looks exceedingly rare – perhaps a 1000-year event in a 70-year record.

Clearly it’s not simply random. For one thing, there’s a pretty obvious long term trend in the Kamloops record. But a key question is, what will happen to the variance of temperature in the future? The simplest thermodynamic argument is that energy in partitions of a system has a Boltzmann distribution and therefore that variance should go up with the mean. However, feedback might alter this.

This paper argues that variance goes up:

Extreme summertime temperatures are a focal point for the impacts of climate change. Climate models driven by increasing CO2 emissions project increasing summertime temperature variability by the end of the 21st century. If credible, these increases imply that extreme summertime temperatures will become even more frequent than a simple shift in the contemporary probability distribution would suggest. Given the impacts of extreme temperatures on public health, food security, and the global economy, it is of great interest to understand whether the projections of increased temperature variance are credible. In this study, we use a theoretical model of the land surface to demonstrate that the large increases in summertime temperature variance projected by climate models are credible, predictable from first principles, and driven by the effects of warmer temperatures on evapotranspiration. We also find that the response of plants to increased CO2 and mean warming is important to the projections of increased temperature variability.

But Zeke Housfather argues for stable variance:

summer variability, where extreme heat events are more of a concern, has been essentially flat. These results are similar to those found in a paper last fall by Huntingford et al published in the journal Nature. Huntingford and colleagues looked at both land and ocean temperature records and found no evidence of increasing variability. They also analyzed the outputs of global climate models, and reported that most climate models actually predict a slight decline in temperature variability over the next century as the world warms. The figure below, from Huntingford, shows the mean and spread of variability (in standard deviations) for the models used in the latest IPCC report (the CMIP5 models).

This is good news overall; increasing mean temperatures and variability together would lead to even more extreme heat events. But “good news” is relative, and the projected declines in variability are modest, so rising mean temperatures by the end of this century will still push the overall temperature distribution well outside of what society has experienced in the last 12,000 years.

If he’s right, stable variance implies that the mean temperature of scenarios is representative of what we’ll experience – nothing further to worry about. I hope this is true, but I also hope it takes a long time to find out, because I really don’t want to experience what Lytton just did.