How Can AI Help to Predict Climate Change?
Published at January 06, 2023

Climate change is one of the most crucial issues today. As climate change's effects become more apparent, finding ways to accurately predict future climate trends is becoming increasingly important.

Artificial intelligence (AI) has emerged as a powerful tool for helping us better understand and predict climate change. AI-based models are being used to forecast climate patterns, analyse data from satellites and other sources, and even uncover new insights about how our planet is changing.

BCG Climate AI Survey 2022 report states that 87% of business and public sector CEOs with climate and AI decision-making authority think AI is crucial in the battle against climate change.

In the same survey, leaders from the public and commercial sectors ranked mitigation (reduction) as having the most significant business value at 61%, followed by mitigation (monitoring emissions) at 57%. (shown in Exhibit 3 below).

In addition, fundamentals (facilitating climate research, climate finance, and education) come in at 28%, followed by adaptation (forecasting hazards) at 44%, adaptation (managing vulnerabilities and exposure) at 42%, mitigation (removing emissions) at 37%, and adaptation (removing emissions) at 42%.

How can AI help with climate change?

Another interesting example is using AI to forecast and prevent wildfires. It provides an interactive mapping of high-risk areas and uses fire-spread algorithms to track the progression of fires in almost real-time, guiding resource allocation and long-term plans for sustainable forest management, according to the World Economic Forum.

One article from Aiplusinfo.com showed how AI helps climate prediction, here are the details:

1. Data collection

Climate prediction involves gathering and processing temperature and pressure observations. Then incorporating the data collection into numerical computer models, projecting the models into the future, and using the predictions as a starting point for subsequent iterations.

Using IoT, climate sensors and data recorders on land, in the air, and in the oceans all around the world are giving a wealth of weather and climate data in real-time. Even people and conventional computer programs find it impossible to evaluate and search for patterns in the massive amount of information that exists around the world. It is a problem because if we can't make sense of all this information, we won't be able to use it to our benefit.

Fortunately, this is something that can be easily achieved with artificial intelligence systems. For example, massive amounts of data can be sent into the system to train it to recognise lightning and tornadoes in a storm.

2. Data insight

The most recent AI methods use rich data to train neural networks. Instead of using brute-force calculation to anticipate weather based on present conditions, these networks create their understanding of how conditions evolve by studying historical weather data.

Satellite meteorology and atmospheric research provide an excellent training ground that can be used to satisfy the voracious demand for data that artificial intelligence systems have.

Since the first conference on AI was sponsored by the National Oceanic and Atmospheric Administration in 1986, fundamental AI principles have been used to analyse weather and climate.

To accurately “nowcast”, or anticipate the future at a rapid rate that conventional approaches cannot give, a mix of machine learning and forecasting may also prove crucial.

3. Prediction based on historical context

The newly built global weather model uses weather data that has been gathered over the last 40 years to make predictions rather than depending on intricate physics calculations.

Iterative forecasting, which uses similar recurring stages from forecast to forecast but uses less processing power, enables researchers to mimic the weather around the world considerably more quickly and nearly as successfully as conventional weather forecasts. This method works well for identifying severe weather situations that can be exploited to save lives.

Using data gathered from sources including weather stations, weather balloons, weather stations, and satellites, weather forecasters today use numerical weather prediction models to interpret the present weather trends.

Based on equations that calculate airflow, present atmospheric pressure, etc., these numerical models employ observations to determine the current weather conditions. AI can calculate a large number of data points to comprehend weather systems and create predictions.

How can AI combat climate change?

According to Forbes, here are some of the details of how AI can combat climate change.

1. Mitigation

The Framework for Using AI in Combating Climate Change's Mitigation section combines macro- and micro-scale measurement with reduction (intensification reduction of GHG emissions, improvement of energy efficiency, and reduction of greenhouse effects) and removal (technological and environmental removal).

Macro-level measurement: Climate prediction models must include data on overall environmental emissions.

Micro-level measurement: Producers can track their progress toward ESG goals, understand the carbon footprints of their products, or look for ways to cut scope 1, 2, and 3 emissions by using micro-level emissions data.

2. Reduction

Increasing measures to minimise present emissions and the impacts of GreenHouse Gases are required due to the global climatic emergency.

Reduced GHG emissions: AI solutions can help with the transition to alternative energy sources.

Reducing Emissions-Generating Activities: AI can help cut emissions by improving supply chains through better demand forecasting (to counteract overproduction) or effective product transportation (such as shortening delivery times and minimising energy use).

3. Removal

One strategy to lessen the effects of climate change is to remove greenhouse gases from the atmosphere. This can be accomplished through technological advancements like carbon capture and storage or natural mechanisms like greater photosynthesis by plants.

The main removal in using AI such as environmental removal includes monitoring natural ecosystems. Technological removal and industrial procedures can supplement environmental removal, although they are still in the early stages and have scale problems.

Wrapping up

Artificial Intelligence has proven to be a powerful tool in helping us better understand and predict climate change. AI can help identify, monitor, and analyse changes in weather patterns, ocean levels, soil quality, and other environmental factors.

AI models can also be used to simulate the effects of different climate policies and strategies and provide more accurate predictions of future changes to our environment. By leveraging the power of AI, we can make more informed decisions on how best to protect our planet from the impacts of climate change.

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