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Climate Scenarios

In climate change research, scenarios are tools used to explore different possible futures. They consider how trends in world population, economic growth, energy sources, land use, and other factors could affect humanity's climate-warming greenhouse gas emissions. To create a “climate scenario,” these emissions are then run through a climate model, revealing how they might change the Earth's climate, with physical effects like global warming, sea level rise, and shifts in extreme weather.

Some scenarios are widely shared and analyzed—especially the “RCP” and “SSP” scenarios highlighted by the Intergovernmental Panel on Climate Change (more on those below)—but researchers create many different scenarios for different purposes.

What scenarios do (and don’t) tell us

Climate scenarios are not predictions. They are “what-ifs” to help us think more clearly about how our choices affect the planet. Ideally, researchers and policymakers would consider a range of scenarios, asking how different policy choices and social and technological changes might play out, and the climate risks that might result.

The simplest scenarios do no more than posit different levels of greenhouse gases humans might add to the atmosphere. These are useful for asking questions like, “What risks would we face if emissions stay the same for the next 100 years?” or “What limits on emissions would likely keep global warming under 2° C?”

More comprehensive scenarios develop rich stories that link socioeconomic changes to emissions to climate outcomes. These scenarios ask questions about how different economic sectors will evolve, how countries will relate to each other, or how quickly new technologies might deploy at scale. Using socioeconomic and climate models, they also translate these trends into patterns of energy and land use, economic change, and resulting emissions and climate outcomes.

Climate scenarios are most useful for thinking about the long-term future, since it takes time for decisions made today to greatly affect greenhouse gas levels and the Earth’s climate. However, that long view can inform decisions we make today about reducing and adapting to climate change, by illuminating outcomes we want to achieve or avoid and the actions needed to do so. 

 

Comparing climate scenarios (infographic). A chart compares two climate scenarios. In one scenario, greenhouse gas emissions change very little over the next century. In another, emissions begin to fall quickly in the near future, and plateau at a low level around 2100. The chart shows the global warming that might result in each scenario by 2150. Each scenario has a most likely level of warming (represented by a bold line) and a range of possible warming (represented by a shaded area). Note that it takes 15 or 20 years for the two scenarios to noticeably diverge. Adapted from 2025 Global Change Outlook, MIT Center for Sustainability Science and Strategy.
Uncertainty and risk

Uncertainty is baked into every climate scenario, as its designers must make assumptions about factors like population and economic growth and technology costs—things that don’t follow mathematical laws and could evolve in many ways. Climate models add their own uncertainties as they simplify Earth’s enormously complex climate into something a computer can simulate.

This means that, while scenarios can provide central estimates of future warming (a “most likely” outcome), they also show a much wider range of plausible warming. Climate scientists pay a lot of attention to the “tail risks” at the extreme end of these ranges, and encourage planning for them, the same way we buy insurance against unlikely disasters like house fires.

This uncertainty does not mean we know nothing. Comparing different climate scenarios shows that the range of possible futures depends greatly on actions we take now. And tail risks can show us the worst-case scenarios we need to plan for as we adapt to a changing planet—and what choices would tilt the odds in our favor.

The RCP and SSP scenarios

The best-known scenarios are developed by the scientific community in support of the Intergovernmental Panel on Climate Change (IPCC) and its work summarizing the state of climate science. A key player in this effort is the Coupled Model Intercomparison Project (CMIP), which arranges for the same scenarios to be run in standardized ways through multiple climate models, providing well-vetted information on a few shared scenarios that can be used in IPCC reports.

In 2010, CMIP began using the “representative concentration pathways” (RCPs).1 The RCPs are the simplest kind of scenarios: just future levels of greenhouse gases, without any storylines about the conditions that would drive those levels. Ranging from the mildest RCP2.6 to the most extreme RCP8.5,2 they were chosen to span a range of plausible climate outcomes by the year 2100.

In 2017, scientists supplemented the RCPs with “shared socioeconomic pathways” (SSPs).3 These scenarios tell larger stories about global politics, policy, and socioeconomic development. Under CMIP, SSPs are paired with the RCPs most consistent with the economic changes they describe.

Today, the latest CMIP effort is developing new, updated scenarios that build on the SSP storylines, translate their assumptions into emissions and climate risks, draw plausible pathways from the present state of the world, and extend to the year 2150 and beyond.4 

The shared socioeconomic pathways (click to expand table)
NameDescription5Paired with RCP(s)Very likely range of global warming by 21006
SSP1: SustainabilityThe world responds more to climate change and other environmental challenges. Countries cooperate more and people consume less. More investment goes to clean energy, education, and healthcare.1.9 and 2.61.0-2.4° C
SSP2: Middle of the RoadThe world largely follows recent historical trends. Modest but uneven improvements are made in global living conditions, clean technologies, and access to basic necessities. 4.52.1-3.5° C
SSP3: Regional RivalryRising nationalism, conflict, and authoritarianism lead countries to turn inward. There is less trade and economic development, and weaker global cooperation.7.02.8-4.6° C
SSP4: InequalityGaps in wealth and income grow both within and between countries. Conflict and unrest rise, and investments in development fall. Advanced technologies, including in clean energy, flourish in some pockets of the world.3.4 and 6.0N/A7
SSP5: Fossil-fueled DevelopmentRapid, worldwide economic growth lifts living conditions and investments in healthcare and education, powered by rising exploitation of natural resources, including fossil fuels. Societies largely choose to adapt to climate change and environmental challenges rather than prevent them.8.53.3-5.7° C
“Business-as-usual” and “worst-case” scenarios

RCP8.5 has met with particular attention and controversy. Although designed to be a high-end or “worst-case” scenario, some have incorrectly labeled it a “business-as-usual” scenario, noting it was only consistent with futures in which the world took no concerted action to stop climate change. In fact, when the RCPs were created, the warming in RCP8.5 was on the very high end of existing “baseline” scenarios—so even in the absence of major climate action, it was never the most likely case.8

As facts on the ground have changed—clean energy sources like wind and solar have grown cheaper, many countries have enacted new climate policies, and the rise in global emissions has slowed—RCP8.5, never the most likely scenario, now looks highly implausible. Even the most extreme scenario in CMIP’s next set will be somewhat more moderate.4 (There will also be no parallel to the most optimistic SSP1-1.9 scenario. Real events have narrowed the range of plausible climate futures.)

What can we learn from this?

One lesson is that “business-as-usual” is constantly evolving. For example, older “business-as-usual” scenarios reflected a world with very few climate- or energy-related policies and did not anticipate the rise of wind and solar. This is one good reason to look at a range of scenarios, and not fixate on just one.

We can also learn to make clearer use of high-risk, low-probability scenarios. Until humans zero out our greenhouse gas emissions, we can only delay, not avert, ever greater warming. And the uncertainty in climate models implies that even moderate emissions may lead to extreme climate outcomes (and vice versa). Both facts keep “worst-case” scenarios relevant to our planning. We can see this in CMIP’s new highest-emission scenario, which, while more moderate than RCP8.5, has large overlap in the climate risks it projects, though shifted further into the future.4

But perhaps the most important lesson is that our actions matter. In 2010, more serious and immediate climate risks were on the table than seem plausible today. Since then, policymakers, scientists, engineers, and citizens the world over have made choices that set the world on a different path—and the scenarios we explore for the future must change, too.

 

Published June 24, 2026

 

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license (CC BY-NC-SA 4.0).
Photo Credit
Dominik Jbstl via Pexels
Footnotes

1 Moss, Richard, et al. "The next generation of scenarios for climate change research and assessment." Nature 463 (2010). https://doi.org/10.1038/nature08823.

2 The names correspond to the most likely level of warming associated with each scenario. RCP8.5, for example, corresponds with 8.5 watts per square meter of “radiative forcing,” a measure of the energy absorbed by the Earth’s surface from incoming sunlight, in the year 2100.

3 Riahi, Keywan, et al. "The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview." Global Environmental Change 42 (2017). https://doi.org/10.1016/j.gloenvcha.2016.05.009.

4 van Vuuren, Detlef, et al. "The Scenario Model Intercomparison Project for CMIP7 (ScenarioMIP-CMIP7)." Geoscientific Model Development 19 (2026). https://doi.org/10.5194/gmd-19-2627-2026.

5 Descriptions of the SSPs are adapted from International Institute for Applied Systems Analysis: The SSP Framework (2018).

6 Lee, June-Yi, et al. "2021: Future Global Climate: Scenario-Based Projections and Near-Term Information." In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press (2023). https://doi.org/10.1017/9781009157896.006. See Table 4.5 for details.

7 The IPCC designated SSP4-3.4 and SSP4-6.0 as “Tier 2” scenarios and did not request that contributing scientists run these scenarios as many times through as many models as their counterparts shown in this table. Directly comparable figures therefore do not exist for these scenarios.

8 van Vuuren, Detlef, et al. "The representative concentration pathways: An overview." Climatic Change 109 (2011). https://doi.org/10.1007/s10584-011-0148-z.

Jennifer Morris
by Jennifer Morris, Principal Research Scientist, MIT Center for Sustainability Science and Strategy and MIT Energy Initiative
Aaron Krol
and Aaron Krol, MIT Climate Portal Writing Team
Related MIT Groups
More Resources for Learning
Carbon Brief: "How ‘Shared Socioeconomic Pathways’ explore future climate change"
Out World in Data: "IPCC Scenarios Data Explorer" (Interactive Tool)
International Institute for Applied Systems Analysis: "Shared socioeconomic pathways"
Coupled Model Intercomparison Project
MIT Center for Sustainability Science and Strategy: "Emissions and concentration scenarios"
Environmental Economics and Policy Studies: "Future energy: in search of a scenario reflecting current and future pressures and trends" (Scientific Paper)
Intergovernmental Panel on Climate Change: "Future Global Climate: Scenario-based Projections and Near-term Information" (Report)
Topics
Adaptation
Climate Modeling
Humanities & Social Science

Want to learn more?

Listen to this episode of the Ask MIT Climate podcast on uncertainty and risk in preparing for climate change.

KE: [00:00:00] [00:00:00] If we have a temperature increase of about 6 degrees Centigrade it may be catastrophic. The probability is not large, but it's not zero, and it's not tiny either. And so you have to think about that.

LHF: [00:00:16] Welcome to TIL Climate, the show where you learn about climate change from real scientists. My name is Laur Hesse Fisher, and today we’re talking about … risk and uncertainty.

If you’re anything like me, then you want an easy answer about climate change. How will it affect my family, my business, my country? When will this happen? How much do I really need to change?

When we interviewed MIT Prof. Kerry Emanuel about hurricanes, we also spoke about where the uncertainty is in climate change, and he told us how he speaks with business leaders and politicians about risk.

To really get into this, there’s two things you should [00:01:00] know. The first is -- the Earth’s climate is complicated.

KE: [00:01:06] We're dealing with a very complex system. Many many interacting components: the transmission of radiation through the atmosphere, wind, the coupling with the ocean, ocean circulation, the land surface, the whole biosphere, which interacts with all of that…

I would say it rivals say the human body in complexity. We have all these different subsystems, you know, like we have hearts, and livers, and intestines.

And if those parts start to function differently or, God forbid fail, it's going to affect the whole organism in ways that even today in some ways medical sciences doesn't perfectly understand.

Just as the earth's climate has these subsystems, and how they interact with each other's complex.

LHF: [00:01:54] The second thing you should know is, that it’s hard to map that complexity.

KE: [00:01:59] When we [00:02:00] talk about a climate model, we’re really talking about an algorithm, which is a way of solving a very complex set of equations that govern the behavior of the system. And those equations are not just pulled out of thin air. They're actually the equations that we know govern the behavior of physics.

LHF: [00:02:20] If we wanted to create a perfect model how every part of the human body works, you’d have to know what’s happening on the nano level, like, the subatomic level, for everywhere in your body. It’s kind of the same for our climate system:

KE: [00:02:35] To make a perfect model of the system, you would have to be able to calculate things as small as a cubic centimeter or so or maybe less to do that. And we have nowhere near the computational firepower to do that.

LHF: [00:02:51] A good example of this is clouds. As we know from our previous episode with Prof. Dan Cziczo, the particles that help clouds [00:03:00] form behave very differently depending on where they are in the world. But, as powerful as computer climate models are, even clouds are, for the moment, just too small for the models to take into consideration.

KE: [00:03:12] Clouds are very very important. But the clouds may be 10 kilometers across you can't resolve that with today's climate models. They’re too small, and yet if the climate model didn't have some representation of them, it wouldn't work. And so we have to tell the climate model that they're there.

LHF: [00:03:33] You had written that there are roughly 40 climate models used by different organizations around the world, and they all give somewhat different predictions on climate change. Why do they differ from each other?

KE: [00:03:44] They all make different assumptions about what's happening on scales that are too small for them to actually compute. And it is a way of dealing with, it's not by any means a perfect way, but it is a way of dealing [00:04:00] with uncertainty. So you have different groups making different assumptions about how to do this, running different models, and comparing them.

LHF: [00:04:09] What’s neat is that this is an essential part of science. If we don’t know something for sure, then we want scientists to take different assumptions of what could be, and run them through their models, so we can see what the most common outcome would be. It’s like getting quotes from different contractors, or advice from multiple consultants: you hear what each of them say, compare them, and then use that to build a picture of what to do. This is like what the scientific community does, and they’re really transparent about it.

KE: [00:04:40] One of the most fascinating and interesting and useful parts of science is actually quantifying our own ignorance, quantifying the level to which we’re uncertain.

Let me take an everyday example: if I were to tell you as an atmospheric scientist that the temperature tomorrow, the high temperature, in Boston would be 50 [00:05:00] degrees, but it might be as warm as 53 or as cold as 47, most people understand that the you know you can't make a perfect weather forecast, that there's uncertainty in it. And that doesn't mean that we don't know right? It will be somewhere in that range.

LHF: [00:05:17] As a side note, Prof. Emanuel isn’t saying that climate change is like weather. Weather is like your mood, while climate is like your personality; you might generally have a sunny disposition, but you’re going to feel grouchy sometimes. In the same way, weather may change day to day, but it’s guided by something much larger and more constant, the climate.

OK back to Prof. Emanuel.

KE: [00:05:41] Good scientists are careful to quantify the uncertainty whenever they say anything about the future, whether it's a weather forecast or climate projection. It's absolutely essential to the final step that everybody really needs and wants, which is an assessment of the risks [00:06:00] associated with climate.

LHF: [00:06:02] Risk… if we aren’t sure if something really bad is going to happen, we think of it in terms of a risk. Like our house flooding or us getting an expensive medical bill. It’s why we buy insurance.

Because climate change also comes with a level of uncertainty, it’s helpful looking at it in terms of risk.

This next part is less about the science of climate change and more about how decision makers, and really all of us, can think about risk… What Prof. Emanuel says here might stick with you more than anything else in this podcast series so far.

KE: [00:06:38] When we make decisions about risk, we rarely make decisions based on the most probable outcome. Let me take a really simple example, you're walking your daughter to school, you got come to a busy intersection across which is the school bus, which has just pulled in, and you’re little bit late.

Now, you can let your little girl run for [00:07:00] the bus, and let's say in your own mind there's a 2% probability she'll be run over on the way.

LHF: [00:07:06] OK I know that’s a little dark, but we’ll continue with the example…

KE: [00:07:10] If she doesn't run you'll have to take her to school because she's going to miss the bus. Now the most probable outcome is that she could be fine, and yet that's the last thing you do. And all that illustrates is that to get the risk you have to take into account two things: the probability of the outcome and how expensive, not necessarily in monetary terms, the various outcomes are.

LHF: [00:07:37] So so how likely it's going to happen, and how bad it would be if it did happen?

KE: [00:07:40] Yeah, that's right. Both you have to take into account both.

Well, that's a metaphor for the climate system too. The most probable outcome the way we see it is if we double carbon dioxide will have a temperature increase of about 3 degrees C.

LHF: [00:07:56] An increase like this comes with some really dramatic impacts.

KE: [00:08:00] [00:08:00] You have to start moving structures that are right on the coast Inland or putting them up on pilings. You have to change your agricultural practices. You have to deal with huge immigration pressures because there are parts of the world which are already agriculturally marginal who will cease to be able to do any agriculture at all. So those people are going to want to move. So you have to deal with that. We're already dealing with it. And it's disruptive, but it's not so far catastrophe.

LHF: [00:08:30] The thing is, a catastrophe is inside the realm of possibility.

KE: [00:08:35] If it's five degrees Centigrade or 6 degrees Centigrade it may be catastrophic.

Catastrophic is going to kill you, or it's going to really harm civilization if we're talking about the whole world.

The probability of it being 6 degrees centigrade is not large, but it's not zero, and it's not tiny either. It's somewhere down there. Maybe it's low probability, but it's also a low probability that your daughter will be [00:09:00] run over if she runs for the school bus. You still have to think about it.

...

LHF: [00:09:09] Scientists have created a range of scenarios of what may happen with climate change.

Some people who look at the data think that our society should prepare for what scientists say is most likely to happen. And some people think that we should look at the best or worst that could happen, even if it’s unlikely.

When reading about climate change or listening to advocates or policymakers, you can try to understand which scenario they’re planning for here. Because what we do and how quickly we act, will differ a lot depending on which future we’re planning for.

So, what about you? What world do you think our society should prepare for? The unlikely one where climate change doesn’t really impact much at all? The likely disruptive future? Or the [00:10:00] unlikely catastrophe?

You can tweet us @tilclimate. And if you’re interested in how scientists talk about these different scenarios check out our show notes on tilclimate.mit.edu. Thanks for joining us today on TILclimate, and thanks to Prof. Emanuel for speaking with us. I’m your host Laur Hesse Fisher from the MIT Environmental Solutions Initiative, and I’ll see you next time.

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