Revealing the impact of global warming on climate modes using transparent machine learning and a suite of climate models (Papers Track) Spotlight
Maike Sonnewald (Princeton University); Redouane Lguensat (LSCE-IPSL); Aparna Radhakrishnan (Geophysical Fluid Dynamics Laboratory); Zoubero Sayibou (Bronx Community College); Venkatramani Balaji (Princeton University); Andrew Wittenberg (NOAA)
The ocean is key to climate through its ability to store and transport heat and carbon. From studies of past climates, it is clear that the ocean can exhibit a range of dramatic variability that could have catastrophic impacts on society, such as changes in rainfall, severe weather, sea level rise and large scale climate patterns. The mechanisms of change remain obscure, but are explored using a transparent machine learning method, Tracking global Heating with Ocean Regimes (THOR) presented here. We investigate two future scenarios, one where CO2 is increased by 1% per year, and one where CO2 is abruptly quadrupled. THOR is engineered combining interpretable and explainable methods to reveal its source of predictive skill. At the core of THOR, is the identification of dynamically coherent regimes governing the circulation, a fundamental question within oceanography. Three key regions are investigated here. First, the North Atlantic circulation that delivers heat to the higher latitudes is seen to weaken and we identify associated dynamical changes. Second, the Southern Ocean circulation, the strongest circulation on earth, is seen to intensify where we reveal the implications for interactions with the ice on Antarctica. Third, shifts in ocean circulation regimes are identified in the tropical Pacific region, with potential impacts on the El Nino Southern Oscillation, Earth’s dominant source of year-to-year climate variations affecting weather extremes, ecosystems, agriculture, and fisheries. Together with revealing these climatically relevant ocean dynamics, THOR also constitutes a step towards trustworthy machine learning called for within oceanography and beyond because its predictions are physically tractable. We conclude with by highlighting open questions and potentially fruitful avenues of further machine learning applications to climate research.