Since the Industrial Revolution, burning fossil fuels and changes in land use, especially deforestation, have driven the rise in atmospheric carbon dioxide (CO2). While terrestrial vegetation and oceans serve as natural carbon sinks, absorbing some of this CO2, emissions have consistently outpaced their annual capacity. This imbalance has continuously increased atmospheric CO2 concentrations, fueling global warming and extreme weather events. Understanding the carbon budget—how CO2 is sourced and absorbed—has become essential in combating climate change, especially as countries strive for carbon neutrality.
The primary challenge lies in accurately estimating the carbon budget and its environmental impact. The carbon budget measures the balance between emissions from fossil fuels, cement production, land use changes, and natural sources of CO2 against the absorption capacity of carbon sinks. Addressing the growing climate crisis with accurate and timely data on CO2 levels and carbon sinks is easier. Existing methods fail to track the shifts in global carbon sinks quickly enough, especially when environmental disturbances—such as wildfires or El Niño—alter carbon dynamics unpredictably.
Traditional methods for carbon budgeting typically rely on numerical simulations of the Earth’s carbon cycle. While these models can simulate complex Earth system processes, they often face significant delays. For instance, the Global Carbon Budget 2023 report, which uses data until the end of 2022, illustrates the one-year lag in carbon budget information. This delay limits the effectiveness of current models in providing timely climate data that can guide real-world actions. Researchers need a faster and more reliable way to capture sudden carbon dynamics shifts affecting global warming.
To address these limitations, researchers from Microsoft Research Asia, in collaboration with Tsinghua University, the French Laboratory for Climate and Environmental Sciences, and other global research organizations, introduced an AI-powered method for near-real-time carbon budgeting. By integrating satellite data, dynamic global vegetation models, and ocean model emulators, the research team developed a near-instantaneous carbon sink model capable of predicting carbon budgets with unprecedented speed and accuracy. This model harnesses the power of convolutional neural networks (CNNs) and semi-supervised learning techniques to deliver low-latency results.
The proposed AI-based model utilizes environmental variable observations and historical data to predict global carbon sink levels. The model integrates 12 months of historical data, monthly features, and target outputs. CNNs process this data to compute predictions, while semi-supervised learning provides an unsupervised loss function to improve prediction accuracy. The model processes environmental data from ocean and land sinks and satellite fire emissions to provide real-time updates on CO2 sinks. This methodology ensures that predictions are made with a margin of error of less than 2%, offering a fast, responsive alternative to traditional carbon budgeting methods.
The results of this near-real-time carbon sink model showed promising accuracy. In particular, the model was able to track a dramatic decline in the land carbon sink in 2023. The Amazon rainforest, severely affected by drought, showed a carbon sink loss of 0.31 ± 0.19 GtC. The model also accurately predicted carbon emissions from the 2023 wildfires in North America, contributing 0.58 ± 0.10 GtC to atmospheric CO2. In addition, the model detected a shift from La Niña to a moderate El Niño phase, significantly impacting global carbon dynamics. These findings highlight the effectiveness of the AI model in capturing dynamic environmental changes and producing actionable data in near real-time.
In conclusion, the rapid decline in land carbon sinks poses a serious threat to the effectiveness of global carbon neutrality efforts. The AI-based carbon budget model introduced by the research team from Microsoft Research Asia, Tsinghua University, and the French Laboratory for Climate and Environmental Sciences provides an innovative solution to the challenges of carbon budget estimation. This model’s ability to produce real-time predictions and track environmental shifts more accurately than traditional methods is a crucial step forward in global efforts to combat climate change. By reducing the delay in carbon data updates, this approach enables more effective climate action and policymaking in response to urgent environmental threats.
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Nikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.
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