Challenges in climate pathway modelling

New research models the likelihoods of different climate scenarios. It shows that 3°C isn’t a worst-case. It’s the most likely. Up until now, climate scenarios have been treated as narrative pathways without assigned probabilities. Climate scientists have resisted giving scenarios a likelihood because of deep uncertainty. That is, the full range of outcomes due to physical, social, political and technological changes can't be known, and therefore, probabilities cannot be reliably estimated. Climate scenarios were described as exploratory tools, not forecasts and were designed to illuminate plausible pathways, not predict them. But... intuitively, we know that some climate futures are more likely than others. This information is helpful for business decision-making. This new paper from the EDHEC Climate Institute challenges the idea that probabilities can't be assigned to climate scenarios and provides two robust, data-driven methods to do it. The first is an 'informative method', which starts with economists’ views on the social cost of carbon (SCC). In effect, it converts wishful thinking into plausible expectations. The second is a 'maximum entropy method'. It makes as few assumptions as possible, using current carbon prices and basic policy constraints as the only inputs. What’s remarkable is that both approaches produce results that are very similar. Does this mean that some climate pathways are more locked in than we think? Model outputs: 🔸 The most likely temperature anomaly in 2100 is between 2.8–3.0ºC 🔸 There is a 35–40% chance of exceeding 3.0ºC 🔸 There is just a 1% chance of staying below 1.5ºC The model was also tested using Oxford Economics scenarios. The results were even more shocking. 🔸 The ‘Climate Catastrophe’ carries a likelihood of 57.5%. 🔸 The ‘Climate Distress’ scenario carries a likelihood of 35% 🔸 Together, they make up 92.5% of the total These high temperatures increase the likelihood of triggering irreversible tipping points, for which standard damage functions no longer apply. This is dangerous territory. 𝗠𝘆 𝗧𝗮𝗸𝗲 Most companies use climate scenarios that treat all futures as exploratory scenarios. But this doesn't allocate future risk efficiently. Without probabilities, we cannot optimise capital allocation between mitigation (transition risk) and adaptation (physical risk). Assigning probabilities to scenarios changes the conversation. It equips firms to weigh investment in risk reduction not just by severity but also by likelihood. Personally, I believe this is a critical next step in climate risk planning. Assigned likelihoods should be accompanied by uncertainty bounds—so decision-makers can assess not just what’s likely, but how confident we can be in those estimates. Source: https://lnkd.in/exy5TDS8 _____________ 𝘍𝘰𝘭𝘭𝘰𝘸 𝘮𝘦 𝘰𝘯 𝘓𝘪𝘯𝘬𝘦𝘥𝘐𝘯: Scott Kelly

Climate models rely on weak data for durable #CarbonRemoval, yet these same models shape today’s climate policy Most climate policy experts tend to focus on the #NDCs as the fundamental tool for creating political buy-in to scale up durable removals. But what informs the NDCs? The #IPCC reports. What informs the IPCC reports? The Integrated Assessment Models (IAMs). The IPCC’s Sixth Assessment Report (AR6) illustrates the problem well. Of the 121 model runs in AR6 scenarios aligned with “well below 2°C” and “above 1.5°C” pathways: 120 deployed BECCS, 28 (!) deployed DACCS None (!) represented biochar or ERW. Carbon Direct has just published an in-depth analysis of the problem and potential solutions. The narrow scope of novel and durable carbon removals in IAMs also shapes many countries' NDCs and long-term strategies. I'd add that there is another important element - the IPCC guidelines for the national greenhouse gas inventories (the GHG accounting rules for the governments), which have also suffered from the same shortcomings. It's great to learn that Carbon Direct is collaborating with three leading research institutions with well-established IAMs: Pacific Northwest National Laboratory, Utrecht University, and the International Institute for Applied Systems Analysis, to close this gap and represent removals more accurately in climate modelling: updating the latest cost assumptions, learning curves, and growth constraints for existing carbon removal technologies, while adding new representations of DACCS, biochar, and ERW. Have a look at their short blog post laying out the key issues: https://lnkd.in/eEczTaW2 There's a link to a longer white paper at the end of the blog. It's well worth the read!

Can climate models reproduce observed trends? The answer can be challenging. Our new review paper in Science Advances led by Isla Simpson and Tiffany Shaw discusses challenges and ways forward in confronting climate models and observations. It's tricky. Climate models and observations may disagree (1) by chance, due to unforced internal variability, (2) due to error in the model response, (3) due to inaccurate prescribed external forcings, (4) due to incomplete or uncertain observations or (5) due to inappropriate comparison methods. The paper discusses ways forward in disentangling the reasons for potential mismatches between observed and simulated trends. It provides a long catalogue of examples of success, discrepancies and unclear situations that require further attention. https://lnkd.in/dHrEJfDh Let by Isla Simpson and Tiffany Shaw with Paulo Ceppi, Amy Clement, Erich Fischer, Kevin Grise, Angeline Pendergrass, James Screen, Robert Jinglin Wills, Tim Woollings, Russell Blackport, Joonsuk Kang, and Stephen Po-Chedley supported by US CLIVAR

The Network for Greening the Financial System (NGFS) has been working with leading climate modelers to develop reference scenarios for the financial sector. These scenarios are commonly used by supervisors in climate scenario exercises, and financial actors for climate risk reporting and assessment. However, many challenges exist in translating the outputs of these scenarios into decision-useful financial information. The NGFS sought feedback from financial actors with a survey earlier this year. Last month they published the results. In United Nations Environment Programme Finance Initiative (UNEP FI)'s climate risk program, we've identified a series of questions that IAM-generated scenarios need to address to be used effectively by financial actors. They are: -Time horizons: how can we apply long-term scenarios with multiyear timesteps to our financial decision-making? -Impacts: are these scenarios really exploring stressful transition scenarios or just constrained best-case scenarios? Are economic and business cycle impacts being considered? -Variables: does the model produce the variables we need? Do we trust the outputs of the models for variables that we need for financial assessments? (e.g., commodity prices, demand variables) -Validation: how do we assess or validate the outputs we are seeing from these climate scenario models? What assumptions are being used to generate the pathways we see, especially around policies and technologies? -Understanding: how do we explain these scenarios to our businesspeople and our executives? Do we fully understand the implications and appropriate use of these scenarios? https://lnkd.in/eTSdtHS4 #climate #climatescenarios #ngfs #climatestresstesting #sustainability #tcfd #issb #esg #netzero #climatemodels #iams #ipcc #physicalrisk #transitionrisk #risk #climatefinance #environment #finance

Treasury's IGR (part 1): modelling climate change impacts, selectively and illustratively Treasury’s Intergenerational Report, the government’s flagship report on the future of the economy, covers climate change and the low-emissions transition. This should unremarkable, but last two IGRs, of 2015 and 2021, largely ignored the issue. The IGR covers climate change impacts in a selective and illustrative way. Australia faces great risks from climate change impacts, given geography and the nature of our economy, and proximity to regions where large populations will be heavily affected by climate change. This has been known for a long time and was clearly spelt out for governments for example in the 2008 Garnaut Climate Change Review. The IGR spells out some of the future impacts and economic risks clearly. It also models some selected impacts – but that modelling is only an illustration of some selected types of impacts. The modelling covers heat effects on labour productivity, heat effects on some crop yields, public expenditure on increasing natural disasters, and possible impacts on international inbound tourism. That list is only a small slice of likely future climate change impacts. A broader list would include a wide range of impacts on agriculture including water availability and impacts outside broadacre crops; more intensive fire seasons; pressure on natural systems including species losses and their flow-on effects no human systems; sea-level rise and the need for re-investment in coastal infrastructure and dwellings; risk to urban water availability in droughts; wider health impacts; systemic effects when natural disasters, stresses on infrastructure and people combine; flow-on effects on Australia of climate disruptions in other countries including on trade and migration pressure; and much more. It is also important to be aware that long-term economic effects of climate change are highly uncertain, and that the quantitative modelling of modelling done for the IGR – like just about any such modelling – does not and perhaps cannot represent those uncertainties in a satisfactory way, neither on the physical impacts nor the economic effects side of things. Ultimately it is a question of risks, and how to manage them. In economics there is a strong tradition of focussing on the average expectation, namely what is thought to be the most likely future outcome. In the field of climate change impacts however, what is of much greater concern than the middle of the probability distribution is the tail risk of highly adverse outcomes. A great deal still needs to be done to better understand these issues, globally and for Australia. This can then be reflected in future economic modelling that is much more comprehensive, wholistic in representing interactions across systems and the economy, unafraid of acknowledging deep uncertainties, and inclusive of both worst-case and best-case scenarios that are far away from a median model output.