A new mathematical model of the overturning of the Atlantic meridian (a system of ocean currents) shows greater complexity than previously thought. An international group of scientists has warned against relying on nature to provide direct “early warning” indicators of climate disaster, as new mathematical modeling reveals fascinating new aspects of the complexity of climate dynamics. This suggests that the climate system may be more unpredictable than previously thought.

By modeling the Atlantic Meridional Circulation, one of the oceans’ major current systems, a team of mathematicians from the University of Leicester found that the stability of the system is much more complex than the simple on-off states previously assumed. Moving between these states could lead to major changes in the regional climate of the North Atlantic region, but it is a far cry from the massive consequences of moving between qualitatively different states.

But some of these small transitions can ultimately lead to a major transition between qualitatively different states with huge impacts on global climate. Early warning signals may not determine the severity of later tipping points. Like a tower of Jenga blocks, removing some blocks may affect the stability of the system, but we cannot be sure which block will bring down the entire system.

Publication and significance of findings

Their findings were recently published Science Developments In a paper under the direction of the Niels Bohr Institute at the University of Copenhagen.

The overturning circulation in the Atlantic meridian is one of the most important fundamental features of the climate system. In the North Atlantic, it transports heat from low latitudes to high latitudes, thus contributing to positive thermal anomalies in northern and western Europe and the downwind North Atlantic region. The slowdown of circulation will lead to relative cooling in this region.

Problems in climate prediction

Predicting the behavior of our climate, such as that of the Atlantic meridional circulation, is difficult due to its incredible complexity. Scientists either need a model with the highest possible resolution or try to understand its behavior with a model that consumes fewer resources and allows for rigorous statistical analysis.

Professor Valerio Lucarini, from the School of Mathematics and Computer Science at the University of Leicester, said: “Every state has many neighboring states. Depending on where or what you observe, you may find some signs of an imminent collapse. However, since the indicators reflect only the local characteristics of the system, it is not clear whether this collapse will be limited to neighboring countries or will cause a major turmoil.

“These provinces are different ways in which the Atlantic meridional overturning circulation is organized at large scales, which has important implications for global climate and regionally, particularly in the North Atlantic. In some scenarios, the circulation may reach a “tipping point” where the system is no longer stable and collapses. Early warning indicators It tells us that the system can transition to a different state, but we don’t know how different it will be.

“In a separate study, we observed something similar in the paleoclimatic record: When you change the time scale of interest (like a magnifying glass), you can find different features at smaller and smaller scales that indicate competing global climate regimes. The paleoclimatic records of the past 65 million years over this time period This allowed us to make a new interpretation of climate evolution and reveal these multiple competing states.

“This research paves the way to look at climate through the lens of statistical mechanics and complexity theory. It really encourages a new way of looking at climate, where you have to combine complex numerical simulations, observational data and theory into an inevitable mix. You have to appreciate and acknowledge this complexity. Any change in our understanding of climate “There are no interruptions or free lunches, but we learn a lot from it.”