The field of climate scenarios has recently been under scrutiny, which has exposed a widespread misunderstanding of the purpose of these scenarios.
Climate scenarios are structured representations of possible future climate outcomes, used to assess risks, impacts, and potential economic losses associated with climate change. One such scenario, the high-emission SSP5-8.5 pathway, was recently deemed to be “implausible” by the CMIP7 committee because climate action, such as the deployment of renewable energy, has successfully averted this trajectory. However, climate change sceptics have claimed that this is proof that scientists have been alarmist about climate change and that decarbonisation measures are not needed. This response fundamentally misunderstands climate change science and the purpose of climate scenarios.
The EDHEC Climate Institute (ECI) has developed several data visualisation tools to help avoid such misinterpretations, making climate information accessible and actionable for all stakeholders. The EXCITE emulator, for instance, allows users to construct customised emissions trajectories and explore how global surface temperatures could evolve under a wide range of greenhouse gas emissions scenarios. Moreover, the EDHEC CLIRMAP platform leverages the latest information on sub-national economic output and highly localised climate change simulation data to show how climate change–induced shifts in average temperature may affect gross regional economic product under various warming scenarios.
In this article, Lionel Melin of the ECI’s dedicated climate scenarios team addresses some of the big questions and misconceptions surrounding this dynamic research field.
Why do you think the debate around SSP5-8.5, and climate scenarios more generally, has sparked such a broad public conversation?
Climate scenarios are no longer confined to academic papers; they directly guide decision-making across global finance, politics, and corporate strategy. As global warming is unfolding, with anxious uncertainty, the broad public spotlight is focused on potential pointers to where the planet is headed.
For context, in the newly published CMIP7 experimental design, the highest-emissions scenario was officially lowered compared to CMIP6. Said otherwise, CMIP7’s new high scenario is less extreme than SSP5-8.5. Scientists of the CMIP7 committee deemed the extreme emissions trajectory of SSP5-8.5 “implausible” because real-world emissions have tracked closer to mid-range scenarios and renewable energy has become substantially cheaper.
The formal shift away from SSP5-8.5 in CMIP7 sparked confusion, leading some corporate and political actors to mistakenly conclude that worst-case climate risks were no longer a threat or that previous adaptation strategies should be abandoned.
The conversation expanded beyond scientific circles because journalists and external commentators sometimes presented the high-emissions scenario (SSP5-8.5) as a standard, baseline "business as usual" pathway. This fundamentally distorted its original scientific purpose -of a high-emission, worst-case boundary linked to substantial coal expansion under specific socioeconomic assumptions (SSP5).
What are climate scenarios actually designed to do, and why are they so often misunderstood as predictions?
Climate scenarios and Model Intercomparison Projects (MIPs) are built to assess the broad range of plausible climate system responses and to evaluate structural differences across various models. They are explicitly designed to span the range of possible futures, including climate-change resolution but also severe outcomes we would prefer not to think about. Scenarios are exploratory "what-if" tools designed to test how the climate and global economy might respond under specific, hypothetical conditions (e.g., varying policy stringency, technological development, and economic growth).
A seductive but dangerous methodological shortcut often takes hold where risk analysts try to identify a single emissions scenario to treat as a baseline. In reality, MIP scenarios are not forecasts, are not structured from formal inherent likelihood, and have never been endorsed by scientific bodies to be treated as definitive "base case" predictions. Furthermore, the public and media naturally crave certainty and discrete forecasts. When a scenario is published, the conditional premise ("if X happens, then Y") is frequently dropped, leaving only the outcome ("Y will happen"). This transforms a methodological stress test into a perceived prediction.
Is SSP5-8.5 actually “wrong,” or is it largely misunderstood?
SSCP5-8.5 is not mathematically or scientifically "wrong." Scientifically, the scenario (and its modern successor, SSP5-8.5) remains entirely valid and relevant when used for its intended purpose: acting as a high-impact, "worst-case" stress test.
The error occurred in its application. It became problematic when analysts and commentators discussed it as the predicted trajectory for the 21st century. The controversy stems entirely from it being publicly mischaracterised as a highly probable central trajectory rather than a severe tail-risk envelope.
What makes a climate scenario scientifically useful, and separately, plausible?
A scenario is scientifically useful if it helps successfully span the full space of potential futures, enables direct comparison of climate sensitivities across different global models, or identifies vulnerabilities in extreme tail-risk situations—even if the scenario itself is unlikely to materialise.
Plausibility is a rather subjective characterisation, embedding an undisclosed probability assessment. Typically, plausibility is determined by how well a scenario aligns with real-world geopolitical and structural constraints, such as the inherent inertia of fossil fuels, slower-than-expected energy transitions, and rising regional rivalries. For illustration, because of these real-world frictions, highly optimistic pathways (like Net Zero 2050) are currently considered neither probable nor plausible.
Why does assigning probabilities to climate scenarios matter for decision-making?
Governments, investors, and insurers must allocate capital effectively. Treating all scenarios as equally likely leads to poor planning, either under-preparing for most likely outcomes and potential risks or over-insuring against highly improbable realisations.
Probabilities allow decision-makers to calculate probability-weighted (therefore scenario-unconditional) expectations. This provides the only robust, decision-ready foundation for managing long-term assets, pricing insurance, and planning infrastructure cycles, rather than picking a single scenario lane.
They also allow political and financial decision-makers to calculate expected impacts and size the scale of risks. Assigning explicit probabilities ensures that extreme tail risks are neither mistaken for baseline expectations nor completely ignored as impossible. This enables them to prioritise core mitigation and adaptation strategies based on what is most likely to occur, while insuring -or preferably acting- against worst-case potential realisations.
That’s why we established a dedicated climate scenarios research programme that accounts for the dynamic nature of emission trajectories. Rather than assigning static probabilities to a fixed set of NGFS pathways, the ECI treats climate scenarios and probability weightings as living inputs, revisited as policy, technology, and geopolitical conditions evolve and new data emerges, so that decision-makers are never anchored to an outdated picture of climate risk.
How should uncertainty be handled and communicated responsibly?
The controversy exposed a divide over how to communicate risk. Some warned that anchoring expectations to improbable extremes misrepresents science and fuels climate doomism, while others argued that highlighting worst-case outcomes is necessary to anticipate potential acute risks and spur action.
Instead of succumbing to the trap of selecting a single "middle-of-the-road" scenario, analysts should draw impact metrics from the full distribution of climate model outputs across all pathways.
Models that display higher-than-expected climate sensitivity -in both directions- should not be discarded as outliers, as doing so actively erases the precise information required to characterise severe tail risks.
Uncertainty should be communicated as an essential piece of information for decision-makers, not a weakness of the science. Communicators must clearly distinguish between central, most probable baselines (the expected trajectory) and lower-probability, yet very high-impact, tail risks (the extremes).
Do you think this controversy will impact the climate scenarios field going forward?
The field is already moving toward utilising mid-range, policy-driven pathways (such as those aligning with current policies or moderate fragmentation) as the central reference points for future projections. This is a massive weakening of the analytic framework. Relying on a narrow band of selected scenarios is threatening to downplay, if not overlook completely, substantial risks that lie in distributional tails.
Because CMIP7 will introduce even greater model variation and shift to emissions-driven carbon cycles, these advancements will inherently widen the uncertainty envelope around every single scenario, and traditional scenario labels will become significantly less informative regarding exact impact magnitudes. As a result, we expect the field to be forced to move away from simplistic scenario selection and fully evolve toward evaluating comprehensive, probabilistic risk distributions.
Taking a step back, the fundamental issue is not whether any one scenario is scientifically valid, but which scenarios are most likely, which are plausible but less likely, and how probability mass is distributed across the remaining set of scenarios.
This is precisely why the EDHEC Climate Institute's Climate Scenarios research programme focuses on complementing existing climate scenarios with probabilistic information and greater regional and sectoral granularity, enabling more robust climate-risk assessments and better-informed decision-making.
