By now, most sciÂenÂtists—97 perÂcent of them, to be exact—agree that the temperature of the planet is rising and that the increase is due to human activities such as fossil fuel use and deforÂestaÂtion. But until recently, the jury was still out regarding the variÂability surÂrounding that increase—for example, how much difÂferÂence there will be between the hottest hot days from one year to the next, as well as with each year’s coldest cold days.
Some studies sugÂgested an increase in variÂability, others a decrease. The problem with these results, said Evan Kodra, PhD’14, is that none of them took a sysÂtemÂatic approach to gleaning that answer. Each was examÂining some other phenomenon—such as whether a parÂticÂular region would expeÂriÂence overall warming—and the variÂability data was a secÂondary, but interÂesting, finding.
That’s why Kodra and his adviser Auroop GanÂguly, a cliÂmate change expert and assoÂciate proÂfessor in Northeastern’s DepartÂment of Civil and EnviÂronÂmental EngiÂneering, decided to take a difÂferent approach in their paper pubÂlished online on Wednesday in the journal SciÂenÂtific Reports, pubÂlished by Nature. Their work was perÂformed in Northeastern’s SusÂtainÂability and Data SciÂences LabÂoÂraÂtory run by Ganguly.
What they found may surÂprise some: While global temÂperÂaÂture is indeed increasing, so too is the variÂability in temÂperÂaÂture extremes. For instance, while each year’s average hottest and coldest temÂperÂaÂtures will likely rise, those averÂages will also tend to fall within a wider range of potenÂtial high and low temÂperate extremes than are curÂrently being observed.
This means that even as overall temÂperÂaÂtures rise, we may still conÂtinue to expeÂriÂence extreme cold snaps, said Kodra, who earned the ColÂlege of Engineering’s outÂstanding gradÂuate research award in 2014 and is now leading data anaÂlytics efforts at Energy Points, an innoÂvÂaÂtive Boston area startup.
That is an imporÂtant point in the ongoing effort to accuÂrately inform the public about cliÂmate change. “Just because you have a year that’s colder than the usual over the last decade isn’t a rejecÂtion of the global warming hypothÂesis,” Kodra explained.
The new results proÂvide imporÂtant sciÂenÂtific as well as sociÂetal impliÂcaÂtions, GanÂguly noted. For one thing, knowing that models project a wider range of extreme temÂperÂaÂture behavior will allow secÂtors like agriÂculÂture, public health, and insurÂance planÂning to better preÂpare for the future. For example, Kodra said, “an agriÂculÂture insurÂance comÂpany wants to know next year what is the coldest snap we could see and hedge against that. So, if the range gets wider they have a broader array of poliÂcies to consider.”
With funding from the multi-​​​​university NSF ComÂputer & InforÂmaÂtion SciÂence & EngiÂneering directorate’s ExpeÂdiÂtions in ComÂputing $10 ​​milÂlion grant on underÂstanding cliÂmate change, the duo used comÂpuÂtaÂtional tools from Big Data sciÂence to sysÂtemÂatÂiÂcally examine this aspect of cliÂmate change for the first time.
The research also opens new areas of interest for future work, both in cliÂmate and data sciÂence. It sugÂgests that the natÂural processes that drive weather anomÂalies today could conÂtinue to do so in a warming future. For instance, the team specÂuÂlates that ice melt in hotter years may cause colder subÂseÂquent winÂters, but these hypotheses can only be conÂfirmed in physics-​​based studies.
The study used simÂuÂlaÂtions from the most recent cliÂmate models develÂoped by groups around the world for the InterÂgovÂernÂmental Panel on CliÂmate Change and “reanalysis data sets,” which are genÂerÂated by blending the best availÂable weather obserÂvaÂtions with numerÂical weather models. The team comÂbined a suite of methods in a relÂaÂtively new way to charÂacÂterize extremes and explain how their variÂability is influÂenced by things like the seaÂsons, geoÂgraphÂical region, and the land-​​sea interÂface. The analysis of mulÂtiple cliÂmate model runs and reanalysis data sets was necÂesÂsary to account for uncerÂtainÂties in the physics and model imperfections.
Source: Northeastern University
