(Disclaimer: This post is also featured on the Data Culture blog)
This summer has been full of heatwaves and new heat records. Europe has had sweltering temperatures, China is experiencing its worst heatwave on record, the US had a major heatwave, and earlier this year heatwaves hit the Arctic and Antarctic at the same time.
Our climate is getting warmer, and it is undeniably linked to our large-scale and continued emissions of green-house gasses. However, how will our current heatwaves compare to the heatwaves of the future? What will our future climate look like, if our societies and policy makers continue on their current course? Intrigued by this question, and inspired by the recent opinion piece in PNAS by Kemp et al. on the Climate Endgame: Exploring catastrophic climate change scenarios, this blogpost looks into future future heatwaves might look.
The Climate Data
To understand the future we use data from the CMIP6 climate models, which are state-of-the-art climate models (CMIP = Coupled Model Intercomparison Projects). For instance, the IPCC sixth assessment report used the CMIP6 models (IPCC = United Nations Intergovernmental Panel on Climate Change). I wont go into detail about the CMIP6 models, as I’m not a climate scientist, but more info can be found here.
The CMIP6 models focus on estimating the climate given 4 different future climate scenarios, also called Shared Socio-economic Pathways (SSP). Each SSP scenario explores how the world might change over the rest of the 21st century, modeling how factors such as population, technological, and economic growth, can lead to very different future emissions and warming outcomes.
We focus on two scenarios, the SSP3-7.0 (or SSP370) scenario which is a middle of the road scenario, and the SSP5-8.5 (SSP585) scenario which is a worst case scenario. We download data from the worldclim website for the two scenarios for years 2021-2100. The site contains rasters for different climate properties, such as the monthly average maximum temperature in Celcius (called tx on the site) , the monthly average minimum temperature (called tn), the monthly total precipitation in mm (pr), and many others. Because we are interested in future heatwaves we only focus on the max temperature (tx) rasters.
The worldclim database contains predictions from 25 models. I am not a climate scientist so I do not know the strengths and weaknesses of each individual model. Instead I have chosen to download the predictions from 4 random models and take their average predictions to smooth out any noise or outlier values. (The models I have selected to work with are: ACCESS-CM2, GISS-E2-1-G, INM-CM5-0, and MIROC6.) If you are a climate scientist, I would love to hear back from you regarding these choices.
So how warm are future heatwaves going to be? Starting with the mid-range scenario, for Europe the situations looks something like this:
The gif shows the average temperature during the month of July for 4 20-year time-periods, 2021-2040, 2041-2060, 2061-2080, and 2081-2100. The color denotes how warm each region will be, with the color bar on the right showing the temperature. The brighter the color the warmer the temperature. Here we are not talking about the maximum temperature during a day, the bar shows the expected average temperature over a 24-hour period. For instance, this means that in 2081-2100 a heatwave in Rome will result in a daily average temperature of 36°C (or approx. 97°F). Please note that this temperature is average over all 24-hours in a day. So a temperature of 36°C means that is is going to be very hot! To compare, during the July heatwave of 2022 Rome had an average temperature of 30.5°C (according to the weatherspark site, approx 87°F). This is only the mid-range scenario.
For the worst-case scenario, if we continue business as usual, our future looks much more bleak / warm. People living in Rome during 2081-2100 can expect average daily heatwave temperatures of almost 38°C (100.5°F). The average temperature will feel like you are having a continual fever. It is difficult to get a grasp of the temperature from the gifs so I have also plotted expected temperatures for specific European capitals. Here we can see that Rome is not even the warmest European city. Madrid, for instance, will experience average heatwave temperatures of 41°C (approx. 106°F) during the worst-case scenario in 2081-2100.
Our cities are not designed to be livable under such extreme temperatures. According to WHO heatwaves are among the most dangerous of natural hazards, but rarely receive adequate attention because their death tolls and destruction are not always immediately obvious. In fact, numbers show that the recent heatwave in Spain and Portugal killed more than 2000 people. Future heatwaves will be much worse.
Strange attractors are capable of generating amazingly diverse shapes from abstract to concrete, and to butterflies - see more here. However, all these images are single realizations of specific parameters and initial conditions. And they are static, so I wondered: is it possible to add some movements, some dynamic to strange attractors?
Cellular art-omata, or cellular automata as they usually are called, demonstrate how simple mathematical rules can lead to astonishing complexity.
2018 has been a really exciting year, scientifically speaking a lot of new interesting studies have been published this year (so many that I have had a hard time keeping up with my to-read-list), and personally it has been a fruitful year where I was lucky to publish in PNAS and Nature Human Behaviour. Here i have included my favorite scientific papers of 2018.
As Denmark is getting closer to the next elections the debate about refugees, migrants and their descendants has yet again resurfaced and is beginning to turn sour. Disillusioned with this development and trying to get my mind off the issue, i wondered what the average politician looked like. I was thinking about something along the lines of this work or something like this piece by Soumitra Agarwal. After a quick online search, where i was unable to find much work on faces of politicians, i decided to create my own. The basic idea is to take lot of portrait pictures, overlay them, and take their median.
Trying to come up with a cool visualization for a small side-project, i was contemplating how to draw, or approximate, an object using networks. During my creative process i remembered my colleague and friend Piotr Sapiezynski once told me how he once did something similar (see here and here). Thinking his visualizations look absolutely stunning i tried to do my own version.
Trying to kill some time on a 4-hour long train ride I played around with simulating random walk in two dimensions. Coloring each walker with it’s own unique colors, the motion of individual walkers will more or less look like confused ants moving around on a piece of paper. Resembling the behavior illustrated below – see code below.
I like to keep track of my life; collecting data about random things–one of them happens to be my travel patterns. While visualizing my own travels I started to wonder what the global airport network might look like. I remember reading about the structure of the airport network in the architecture of complex weighted networks by A. Barrat et al. but the paper never visualized the network. To figure it out, I first needed some data, luckily OpenFlights.org has a database of routes as well as airports, which allows us to create some pretty nice looking visualizations (see above figure).
This Sunday while surfing the web I came across a figure depicting the Rössler attractor and while looking at it, it suddenly struck me that I have always seen it depicted from this specific angle. But what does it look like from other angles? Curious, I sat down, quickly wrote a python script to generate the dynamics, used Matplotlib to plot the figure from multiple angles, and ffmpeg to aggregate them into an animation (see below). One thing lead to another and soon I found myself reading about other strange attractors, such as Clifford attractors, and writing code to generate the figures you see above.
I received questions from a couple of people asking me how I drew the network featured on the cover of PNAS (read about it here). Well, this blogpost is for you, and anybody else.
We (Sune Lehmann, Arek Stopczynski and yours truly) recently published a paper in PNAS where we give our two cents on how to uncover meaningful, “fundamental”, social structures from temporal complex networks. In addition to submitting the paper we also sent some pictures along which we felt would look good on the cover of PNAS. As it turns out one of them was actually selected!
I was watching the season finale of Game of Thrones the other day and wondered—with so many characters in the series what does the interaction network look like? Well, as it turns out I was not the first person to get this thought. In fact A. Beveridge and J. Shan read through Storm Of Swords (third book in the series) and mapped all the interactions between characters, and released the data. You can read more about their cool project here. They are, further, planning to release data regarding the others books as well.
While finalizing my PhD I was asked, alongside Sune Lehmann, to author a popular article about networks by the magazine Kvant (danish journal for physics and astronomy). We wrote and submitted the piece and were fairly confident in our work. Nonetheless we were surprised when we were contacted by the editor who asked us for permission to use one of my figures for the cover! This is my first cover, and I gotta say, it feels awesome, next stop …. Nature :)
Have you ever wondered which areas of New York City are the most popular? You need not worry anymore, this little movie will answer your questions. The video shows the dynamics of pick-ups and drop-offs within a representative week. It is interesting to see how the popularity of areas changes over the course of a day, and how certain areas attract more attention during nighttime. To me the circadian patterns resembles a heartbeat.
One of the most iconic sights in New York are its Yellow cabs. They are ubiquitous and an important lifeline that tie the city and its inhabitants together. Understanding how cabs move around can give us new insights into how people travel within the city, how people use the city, and which neighborhoods are popular.
Is just around the corner! We have some cool results that hopefully should be published soon. Until then here are two teaser pics.
Since I as a kid watched my first world cup (1994), I have been hooked on football (or soccer as the Americans call it). Back then l I remember that almost every player used to wear Adidas Copa Mundials - a stylish, yet simple black leather boots with 3 white stripes.
Things are moving fast now. We just uploaded another paper to ArXiv. Check it out! Measuring large-scale social networks with high resolution
Just submitted a paper - Wohoo! Meanwhile until it is published you can find it on arXiv. The paper investigates usability of the Bluetooth sensor is as a proxy for real life face-to-face interactions. You can learn more about the data on the SensibleDTU homepage.
I will be giving a talk at the Niels Bohr Institute on December 4th. Topic will be “Social Contacts and Commnities”. It is based on the results and finding from the SensibleDTU project.
How do we as humans interact over the course of a day? The video shows proximity interactions for student participating in the SensibleDTU project for a randomly chosen 24-hour interval.