Climate change has already begun to transform planet Earth, and over the next few decades these dramatic transformations are expected to accelerate in an ongoing response to greenhouse gas emissions.
You may have already experienced these changes where you live and may be wondering: What will climate of the future be like where I live? How hot and dry will summers be? Will it still snow in winter? And perhaps: How might things change course if we act to reduce emissions?
This web application helps to provide answers to these questions. We don’t have time machines so we can’t travel to the year 2080. However, we can think about places that are warmer and wetter (or drier) today than we where live. Perhaps you have even traveled to such a place for a holiday or for work and experienced that place’s weather. We can ask: If climate continues to change, how much will my home town feel like some warmer and wetter (or drier) place?
To find the places that have a climate today most similar to the expected future climate in your city, the web app uses a statistical technique to perform climate analog mapping for thousands of cities, towns, and suburbs across the globe. In short, climate analog mapping provides an measurable way to answer to the question: If I wanted to experience what my city’s climate is expected to be like in the future, where should I go? We suspect the answer will surprise you.
For example, if you happen to live in New York City, USA, you would need to travel to northern Mississippi to experience what New York City is expected to feel like by 2080. Say hello to long, hot, humid summers and goodbye to snow in winter. If you live in Shanghai, China, you would need to travel to northern Pakistan to experience what Shanghai’s climate could be like in 2080.
Because the climate analog mapping analyses depend on how climate is expected to change, and because the specific nature of those changes is uncertain, the app allows exploration of results for several different possible futures, including:
You can map the best match for your city for each of these two scenarios. The default setting shows the best match using an average of the five climate models, but by changing the settings, you can also map the individual matches for each of the five climate models as well.
In addition to finding the closest match to your city, be sure to also check out your city’s climate similarity surface to see how similar your city’s future climate is to present-day climates over the entire Earth (note that the climate similarity surfaces are available only for the average of the five forecasts for each emission scenario, not for each of the five climate models). Also, note that we didn’t create the climate data used in our analyses, but you can learn more about the climate data here.
An interesting, but not necessarily surprising finding is that there are no perfect matches. In other words, for no city is there an identical future and present-day climate. In fact, because of the magnitude of expected climate change, for many cities the “best” match is not all that similar, especially for cities closer to the equator. This means that many cities could experience a future climate unlike anything present on Earth today, especially if rates of greenhouse gas emissions are not reduced.
This app includes updated analyses of those described in a 2019 paper published in Nature Communications.
This app was created by Matt Fitzpatrick at the University of Maryland Center for Environmental Science - Appalachian Lab, with substantial coding support provided by Teofil Nakov.
After selecting a city from the list on the left side of the page or by clicking on one of the cities highlighted with a purple circle on the map, you can map two kinds of information:
A line from the selected city to the location with the most similar climate. As described in the Background page, this location will be the best match found on Earth, but not a perfect match. The size of the circle gives a sense of how good of a match that location is, with larger circles indicating poorer matches and smaller circles indicating better matches (kind of like the bullseye in a game of darts).
Or you can add a climate similarity surface to show not just the location of the most similar climate, but also how climate similarity to the selected city varies from place to place. If all locations have low similarity (in other words, all locations are a poor match to that city’s future climate), the climate similarity surface likely will cover a small portion of the Earth’s surface. Mumbai, India is a good example where this is the case.
Note that some cites – mainly those near the equator – are expected to get hot enough that there is no current place on the planet that has a representative climate. For these locations, there is nothing to map, neither a line or a similarity surface and so the app returns only a message stating that there are no mappable analogs for the selected location. Chennai, India is a good example where this is the case.
You can select one of two emission levels:
Lastly, you can select how much detail you want for the line map:
This app was created by Matt Fitzpatrick at the University of Maryland Center for Environmental Science - Appalachian Lab, with substantial coding support provided by Teofil Nakov.
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This app was created by Matt Fitzpatrick at the University of Maryland Center for Environmental Science - Appalachian Lab, with substantial coding support provided by Teofil Nakov.