It’s encouraging to note the growing interest for regional climate information for society and climate adaptation, such as recent advances in the World Climate Research Programme (WCRP), the climate adaptation summit CAS2021, and the new Digital Europe. These efforts are likely to boost the Global Framework for Climate Services (GFCS) needed as a guide to decision-makers on matters influenced by weather and climate.
These new moves underscore the understanding that we must start to act on mitigation and adaptation now. But I think we also need to keep in mind that there still are some unresolved issues when it comes to adaptation and there is still a need for more research and development.
For instance, one question remains about the physical consistency in regional climate models (RCMs). These models provide a more refined representation of the regional climate than the coarser driving global climate models (GCMs), but that also implies changes to the shortwave and longwave fluxes at the top of the atmosphere compared to the GCMs. This is a mere consequence of a better representation of clouds and rainfall patterns.
The improved simulations by RCMs can result in different moisture contents and fluxes in the RCM and the GCM. There are also different surface representations between the two, and sometimes different representations of aerosols. One question is whether these differences can explain the biases seen in the RCM results and if these biases are stationary during global warming. Another question is whether such biases could be related to different parametrization schemes used in GCMs and RCMs. Or do these differences matter at all?
It is possible to get some answers to these questions, for instance by plotting the differences between aggregated downscaled (“upscaled downscaled results”) and the GCM results used to drive the RCM. I think there is also a need to involve empirical-statistical downscaling (ESD) and compare such results with the RCMs. ESD and RCMs have different strengths and weaknesses, and they use different and independent sources of information; for the RCMs, it is from the code representing the physical processes on a higher resolution, whereas for ESD, the information is taken from empirical observations from the past. There are many examples where regional climate modelling only involves RCMs and not ESD. Including both may help us address uncertainties.
The concept of “cascading uncertainties” is sometimes discussed in the downscaling community, acknowledging additional uncertainties introduced with a new level of models. I think the term itself, and the way it often is presented, are misleading since each new level of models also introduces new information or constraints as well as uncertainties. If each additional level merely added uncertainties, then they would have no added-value and there would be no point with the extra levels. The information-to-uncertainty can be optimised if both RCMs and ESD are used in the downscaling stage.
Models’ minimum skillful scales is another issue important for regional climate modelling. What is it exactly? When I googled “minimum skillful scale” I got 228 hits so it doesn’t appear to be a much discussed topic. Meteorological phenomena such as El Niño Southern Oscillation (ENSO) have certain spatial patterns which the models may not reproduce perfectly. The spatial extent of such oscillations may be slightly too big or too narrow in the model results. Or the models may produce an incorrect shift in storm tracks or the zone of freezing temperatures (e.g. due to smooth topography), or a slight misrepresentation of the ITCZ (“double ITCZ”). Such details may not have a big global effect but are nevertheless important for the regional or local climate where they are located.
The models’ minimum skillful scales may be due to the models’ coarse spatial resolution (e.g. representation of ocean currents), a mix of imperfect representation of land properties, the use of discrete mathematics to solve mathematical expressions, imperfect algorithms, parameterisation of unresolved processes (e.g. clouds), and incomplete understanding of all relevant physical processes. The GCMs are designed to reproduce large-scale climatological phenomena, which they do impressively well, but not the local details.
The models’ minimum skillful scale is the reason for downscaling: we take the scales that the models can skillfully reproduce and the information on the dependency between the large and small-scale climate to infer the local changes. This is different to so-called bias-adjustment for which the scale dependency and the minimum skillful scale aren’t relevant.
Even if the models were perfect, their results are often limited by the law of small numbers. The reason is the pronounced presence of stochastic regional internal variations on decadal time scales (Deser et al., 2012). We can address such limitations though large model ensemble, but one question that has often been discussed is how to interpret multi-model ensembles of available models – an ensemble of opportunity.
There is no doubt a risk of mal-adaptation if one uses results from only one RCM or from downscaling of a small ensemble of distinct GCM simulations. Or if the numbers are interpreted incorrectly. One solution for overcoming the limitations connected to ensemble size may be through the use of ‘hybrid downscaling’ and ‘pseudo reality’ to emulate a large number of GCM projections. However, these results are as good as the RCM simulations at best and may also need bias-adjustment.
Climate adaptation needs to build on reliable, salient and relevant information, which requires a ‘distillation’ process that accounts for the models minimum skillful scales, multi-model ensembles, and different and independent sources of information. It will involve observations, different types of models, ensembles, evaluation, statistical theory (e.g. how numbers “behave” and how to deal with statistical samples) and artificial intelligence. There are also sensible actions such as making use of sensitivity tests to explore what is most important and how different choices (e.g. subselection of ensembles) affect the final outcome.
In summary, climate adaptation is not merely a “plug-and-play” process where data is downloaded from a data portal and somehow ensures optimal decisions. An analogy is that we don’t expect that a drug store is sufficient for people to get the right medicine, we also need ‘doctors’ who can provide proper guidance and consultation. Hence, some of the most important ingredients of climate services are the climate experts with up-to-date skills giving science-based advice to decision-makers.
” An analogy is that we don’t expect that a drug store is sufficient for people to get the right medicine, we also need ‘doctors’ who can provide proper guidance and consultation.”
That is a highly problematic analogy.
Pharmacists, people and doctors are all the lawful prey of patent medicine salesmen and social entrepreneurs as well as purveyors of less-than-ethical pharmaceuticals.
America’s ongoing pandemic of opiod abuse arose from skilled advertising and distilled merchandising. It has led to multibillion dollar judgements against oxycodone manufacturers like Purdue, and the management firms and lobbyists like McKinsey Inc., that provided improper consultation and guidance enough to embroil legions of physicians and medical insurers in a marketing campaign that, in a cautionary failure of skepticism, quickly evolved into a plague.
Thanks for the comment. Perhaps it doesn’t work so well for the US and it’s a better analogy in countries with well-functioning national health service. I’m writing from the perspective of the Nordic countries, but we also have arrangements with the rest of EU. I also guess the analogy probably doesn’t work in much of the developing world, but perhaps in Canada, New Zealand and Australia? -rasmus
1 & 2–
My 2 cents is that rasmus specified “‘doctors’ who can provide proper guidance and consultation.” I suppose that it would have been slightly more on point had it been just “who provide”, as no doubt the bad actors Russell speaks of *could* have done better, had they not chose to betray their professional ethics.
But it’s just an illustrative analogy, after all. I got the point just fine, the failures of the opioid crisis notwithstanding. And my suspicion would be that it would still work to that extent in Mumbai or Lima or even Kinshasa.
Rasmus: it almost sounds like you are considering the US a developing country, which sadly makes sense in light of the fact that one of our major political parties is in thrall to reality deniers and a former “leader” who is as crooked as a snake. Heck some of these people have this odd idea that our former 45th president will on March 4th somehow become our 17th as all the presidents since the sixteenth were “illegitimate”. Is there any wonder we cannot handle complicated actualities like health care and supporting those in need. Let alone climate disasters looming!
it’s a better analogy in countries with well-functioning national health service.
That would be anywhere in the developed world other than the USA it seems. Better constitutions make it harder for companies to buy politicians. Sadly, it seems the USA is also unable to fix its issues either.
I very much agree @1. In the US, your appeal to whether a “drug store is sufficient for people to get the right medicine, we also need ‘doctors’ who can provide proper guidance and consultation” is remarkably tone-deaf. In the US, physicians typically study drugs per se for about 6 months — excluding their study of organic chemistry as pre-med students. Pharmacists study drugs for close to 6 years … although I understand that many drug-stores are not run or staffed by pharmacists. To my best knowledge, medical histories and communications between specialists and primary care physicians in the US are not — “exemplary” — leaving doubts as to how well physicians manage the full spectrum of drugs their patients might be taking. And this ignores the considerable sway Big Pharma has demonstrated in directing the prescription of medications, to say nothing about their truly incredible pricing schemes.
I grow immediately and unduly [I admit this] suspicious of your discussion of “cascading uncertainties”, “minimum skillful scales”, “downscaling” and “examples where regional climate modelling only involves RCMs and not ESD” [though you did very kindly define these acronyms before scattering them through your discussion] … but speaking as an unwashed laymen I grow very suspicious of acronyms. They remind me too well of US DOD-speak.
The best possible knowledge about regional climate is critical. I believe we have no arguments about that. “Climate adaptation needs to build on … reliable information [salient and relevant … gild this lily!]. Selecting a few buzzes on that same theme: “artificial intelligence”, “sensitivity tests”, “subselection of ensembles” — do these references convince insiders — they do not convince this outsider of much past pretty words. We are well past pretty words. As an unwashed layman, I want to know best guesses about future climate and regional climate … and know only too well how much uncertainty, error, and mis-calculation there can be in the best, most carefully constructed climate models. I am not in the choir you are preaching to and I believe and sincerely hope the larger part of the world public — including the benighted US public — is with me in my choir. So model and model more, and collect data, and make best guesses. I support those efforts and their critical importance without further convincing necessary.
If you want to reach the unconvinced — “artificial intelligence”, “sensitivity tests”, “subselection of ensembles”, RCM versus GCM simulations, and other ephemera of climate modeling confuse more than they convince … and confusion of the unconvinced only results in solidifying their contrary convictions.
Jeremy Grimm: “Selecting a few buzzes on that same theme: “artificial intelligence”, “sensitivity tests”, “subselection of ensembles” — do these references convince insiders — they do not convince this outsider of much past pretty words. We are well past pretty words.”
Hrmmm. So, I’m an “outsider” – I got my doctorate in a different physical science field. I want to say that I appreciate the level of technical detail in the post, because it helps me understand better the concepts surrounding the difficulty of making useful regional projections.
I can also appreciate where Jeremy is coming from, at least in terms of finding the verbiage confusing and offputting. But I’d encourage Jeremy to not see this as “pretty words”, but to simply recognize, without any sort of emotional judgment, that there is a fair bit more complexity in the science than you are aware of, and that understanding the subject well means working to understand that complexity. If you want to understand the subject well, you have to get over an antipathy towards concepts and words you don’t yet understand. If you want a simplified presentation, that’s doable too – but it means you’ll lose out on the opportunity for a much greater depth of understanding.
To each their own; just don’t mistake complexity for gatekeeping. The science really is that complex on its own. Scientists just normally simplify away much of that (actually important) complexity when presenting stuff to laypeople.
Jeremy Grimm,
What may sound like “pretty words” to you actually has a meaning in technical discussions. Perhaps if you would take a moment and learn what the “pretty words” mean and how they are used, some of your questions might come into clearer focus.
Jeremy Grimm:
You are well-informed and articulate, Mr. Grimm, so I don’t suspect you of not bathing. You may call yourself an outsider here, because you’re not very familiar with the specialized vocabulary of climate science yet. I for one am confident you can get up to speed quickly with a little effort, just by reading a couple of books. Then you’d feel like less of an outsider, and perhaps more like a student sitting in the back with his hand up. Many RC regulars are like Windchaser:
More bluntly: Why would you expect RC to pitch its posts to people who’ve never taken a science course? The blog’s regular commenters are mostly a choir of climate-science recognizers, who found our way here and stayed to learn. Each of us can take what we learn and talk about it in other venues, where commenters may bath less regularly.
With that off my chest, I’d like to see more discussion on RC of the economic and political climate (yes, yes) in which the transition to carbon-neutrality is occurring, and how best to speed the process. RC sometimes has guest posts dealing with the public’s perception of climate science, but the core group of bloggers are mostly physical scientists. They do provide the Forced Responses thread, which is pretty much a free-for-all, with only hate speech actually banned. And as you know, there are even a few Trumpist culture warriors hectoring on every thread. IOW, there’s plenty for everybody here 8^D!
@Mal A, thanks, and hear hear!
I am surprised to see responses to my comment. However I am not sure how well my comment expressed my concerns. I read realclimate because I want the ‘skinny’ on climate change. I do not expect and hope never to find RC pitching its posts to “people who’ve never taken a science course.” I retract my appeal to RC about reaching out to the unconvinced. I would not like that. However, I hope climate scientists realize how such pretty words fall on layman-ears when climate scientist do reach out to convince the unconvinced.
I should also be more plain in my complaint about “pretty” words. I am familiar with modeling and some of the techniques involved. Speaking more plainly I understand a little about the refinement of models using “artificial intelligence”, “sensitivity tests”, “subselection of ensembles” and regard such refinement as fancy techniques and fancy ways of fudging model inputs to assure that model outputs better fit what the modeler expects — ‘tweaking’. I believe model tweaking goes on, is probably quite necessary in many/most cases. I also know a little about some of the ‘meats’ that go into ‘sausage’. I view RC as a site devoted to ‘sausage’ more than to the details of sausage making. For me, the sausage is best information about regional climate, and a description of the limitations of those predictions. The pretty words are ugly details about ‘sausage making’ that I assume when I regard models of any sort, especially those where multiple models are combined to obtain results in the many ways they are combined for climate modeling.
I must apologize for coming to this particular post already angry. Partly I was disappointed by the content. The title had me hoping to find some more detailed information about regional climate, information I regard as extremely important and practical. But instead of ‘sausage’ I found some acronym encrusted information about ‘sausage making’. This website must indeed discuss matters of ‘sausage making’. In looking for sausage it angered me to find no sausage and too much information about sausage making. But I also brought anger from a more general anger about how climate models and climate science have toadied, under tremendous political and economic pressures, to support calculations of a carbon ‘budget’ to keep the world’s heat increment to a ‘safe’ 1.5 degrees C. I categorically reject the idea of a carbon budget to guide how much fossil fuel we can continue to burn. I apologize for my angry reaction to modeling trivia.
My “growing interest for regional climate information” is strongly motivated by my panic to find the best place I can for establishing the best future foothold for my all too few children. I believe future climates will transition through decadal or centuries long periods of chaotic behavior, something I believe models necessarily smooth over. We can only model what is known. I apologize further and hope my beliefs explain my lack of regard for the fine points of model making and data tweaking. I am far more persuaded by results from paleoclimate studies and the models they use — and tweak — to convert their proxy data to details of the paleoclimate. I feel compelled to repeat — we can only model what is known.
My “growing interest for regional climate information” is strongly motivated by my panic to find the best place I can for establishing the best future foothold for my all too few children.
This is not so important, frankly, because you can deal with this by 1. learning the characteristics of regenerative systems and the underlying ecological engineering principles, 2. knowing your climate zone and how rapidly they are shifting and simply projecting that into the future to year X and 3. studying said future climate zones and transitioning your location over time. This rough guess will be sufficient for most as you should be designing to deal with the extremes.
That said, finer resolution of future climate expectations can only help with such design decisions. That said, these are still just guesses in the end, so the key really lies in knowing how to do ecological engineering. Virtually any farm/foodshed can be adapted, even to a completely new structure, to new realities pretty easily within five years. The hardest part is in choosing trees as they are so long-lived and slow-growing.
Then again, if we all go regenerative rapidly, none of this will matter much, so maybe we should focus on that more than planning for unnecessarily dangerous futures.
Jeremy Grimm,
I think Rasmus, in this and the previous post on the topic, is saying exactly what you are saying: Regional outcomes are a hard thing to model.
What’s puzzling is why you need some kind of fancy prediction to help you decide where to move. Guatemala or Texas is probably a bad idea. The Northeast USA, where I live, might be a good choice, or you could go North to Canada.
(And I keep giving a reference here to a NYT article that discusses how Siberia is supposedly going to turn into a wheat-and-soybean agricultural powerhouse, if you want to be a big-time farmer.)
You used the magic word, chaotic, and if you really understand the physical principles involved in the complex, non-linear system that we call climate, you would have confidence in the generalized predictions that have consistently been made. Hot and dry will get hotter and dryer, hurricanes will drop more rain, and so on and so on for various phenomena, as we increase the energy in the system.
At some point in a truly BAU future, where we have really messed things up, you might get a very different global pattern, but one hopes that will not happen, and it would likely be far off.
What else do you need to know?
Jeremy Grimm #6- Off topic but to correct Mr Grimm’s mis-“understand”-ing. In the U.S., you can buy tylenol from a teen-ager at a truck stop, but if you have a prescription filled at a pharmacy, that “drug store” might be owned by the Walton family, but it is staffed by a pharmacist. No exceptions.
Re: “The hardest part is in choosing trees”
Yes. I have planted many thousand trees in my life, and i have seen many die because i chose badly. The soil character was not as i thought or the environment changed in directions i had not anticipated. But i have replaced many and i forge on.
But perhaps this is a topic for the forced variations thread.
sidd