[AI for the Sustainable Future] Climate Modeling
AI-powered climate models are increasingly instrumental in presenting accurate forecasts of future climate scenarios.
Climate change analysis, modeling, and prediction are the first and foremost parts of AI applications in the climate and sustainability arena. By analyzing vast datasets to develop more accurate and predictive climate models, AI aids in crafting detailed future climate scenarios, leading to strategic planning and decision making. In essence, AI enhances our capacity to accurately predict natural disasters such as floods, wildfires, and storms, ultimately enabling more effective preparation.
This post, the first in the series, delves into the critical role of AI in climate modeling, a key component in our fight against climate change.
What is climate modeling?
One of the best ways to understand climate modeling is to compare it with weather forecasting, the concept that we are already aware of. Simply put, climate modeling examines trends and patterns in the Earth's climate system over longer periods, typically spanning decades to centuries, while weather forecasting focuses on predicting atmospheric conditions over a short period of time.
Climate models incorporate comprehensive data, encompassing a wide range of variables such as atmospheric conditions, oceanic currents and ice melt, terrestrial factors, human influences like greenhouse gas emissions, and even solar radiation. On the other hand, weather forecasts rely on real-time data from satellites, radar, weather stations, and other sources.
In a nutshell, climate modeling entails understanding and projecting long-term trends in the Earth's climate system with more three-dimensional approaches, while weather forecasts are about predicting short-term atmospheric conditions, which can be seen as rather linear. These are two different practices, even though an elaborate climate model can contribute to more accurate weather forecasts.
How can AI enhance climate models?
As climate models require the analysis of vast datasets encompassing both centuries-long historical trends and real-time, multi-dimensional dynamics of the Earth, the role of AI becomes not just significant but increasingly essential. AI enhances these models by accurately interpreting data, recognizing patterns, and projecting future climate scenarios for the coming decades.
In this TED talk, Dr. Raffaele Ferrari, a faculty member specializing in physical oceanography at MIT's Earth, Atmospheric and Planetary Sciences Department, discusses the limitations of past climate models, which failed to include all necessary datasets and variables, and struggled with the complexities of Earth's physics. Dr. Ferrari highlights how the integration of AI and machine learning can dramatically enhance these models, armed with the capability to process vast amounts of data and gain deeper insights into the physics of our planet.
Meanwhile, the European Commission is spearheading a flagship initiative for a sustainable future, known as Destination Earth (DestinE). This initiative aims to create a highly accurate digital model of the Earth on a global scale, It takes the form of a digital twin – a virtual Earth that simulates both natural processes and human activities.
By observing this digital twin of Earth, we can gain a deeper understanding of climate change, anticipate its impacts, and shape policies aimed at mitigating extreme climate-related risks to society.
Speaking of digital twins, let me share an informative video by Nvidia. (Don’t get me wrong. My intention is not to promote any specific company.) This video stands out as an exceptional resource for effectively visualizing how an AI-powered digital Earth twin works as a simulation-based climate scenario.
It’s time to manage the irreversible climate crisis
According to the recent CNN article below, a heated debate is going on among scientists about the feasibility of restricting global warming to 1.5 degrees Celsius above pre-industrial levels, a goal set by the 2015 Paris Agreement. Despite contrasting scientific opinions over the achievability of the goal, the article references the extraordinary heat and extreme weather events of 2023, including El Niño's contribution to making it the hottest year on record, almost breaching the 1.5-degree mark.
I’d like to give an analogy to summarize my takeaways from the article. Imagine you're a doctor treating a patient with a chronic, potentially fatal disease that is incurable but treatable. What would you do? The best approach would be to enhance the patient's overall health while striving to slow the disease's progression as much as possible.
Acknowledging that climate change may be irreversible and resetting our goals does not mean we are giving up on climate action. The article raises concerns that deeming the 1.5-degree target unachievable might foster a defeatist attitude, hindering efforts to limit further warming. However, clinging to an unrealistic climate goal could be seen as another type of escapism, no better than denying the reality of catastrophic climate change.
In this context, it's crucial to challenge the notion that climate issues are solely a liberal political agenda. With a strong foundation in advanced technologies, particularly AI, managing the climate crisis is evolving into a significant industrial sector, impacting lives regardless of political orientations, whether you are a conservative or liberal.
So, stay tuned for the next post in the series, on energy optimization!