{"id":1110150,"date":"2024-12-05T23:23:07","date_gmt":"2024-12-06T07:23:07","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-blog-post&p=1110150"},"modified":"2024-12-09T17:55:38","modified_gmt":"2024-12-10T01:55:38","slug":"low-latency-carbon-budget-analysis-reveals-large-decline-in-land-carbon-sink-2023","status":"publish","type":"msr-blog-post","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/articles\/low-latency-carbon-budget-analysis-reveals-large-decline-in-land-carbon-sink-2023\/","title":{"rendered":"Low latency carbon budget analysis reveals\u00a0large decline in land carbon sink (2023)"},"content":{"rendered":"\n

Since the Industrial Revolution, the burning of fossil fuels and changes in land use, especially deforestation, have driven the rise in atmospheric carbon dioxide (CO2<\/sub>). While terrestrial vegetation and oceans serve as natural carbon sinks, absorbing some of this CO2<\/sub>, emissions have consistently outpaced their annual capacity. This imbalance has led to a continuous rise in atmospheric CO2 <\/sub>concentrations, fueling global warming and extreme weather events. Against this backdrop, accurate and timely carbon budget estimation is essential if we are to achieve carbon neutrality and slow down global warming.<\/p>\n\n\n\n

The carbon budget measures the balance of carbon sources and sinks in the global carbon cycle. It includes data from fossil fuel and cement emissions, land use changes, and natural sources and absorptions of CO2<\/sub>. This helps track changes in atmospheric CO2<\/sub> levels. Accurate carbon budgets are essential for understanding and addressing climate change. With growing climate challenges, monitoring carbon sinks and emissions is crucial. As countries work towards carbon peaking and neutrality, the carbon budget is key for scientific research and creating sustainable policies.<\/p>\n\n\n\n

Decline in land carbon sinks and the need for low-latency carbon budgeting<\/h2>\n\n\n\n

Researchers from Microsoft Research Asia, in collaboration with Tsinghua University (opens in new tab)<\/span><\/a>, the French Laboratory for Climate and Environmental Sciences (opens in new tab)<\/span><\/a>, and other global research organizations, published a study in National Science Review<\/em> revealing a dramatic decline in global land carbon sinks\u2014the Earth\u2019s land ecosystem that absorb CO2 <\/sub>in 2023.  By employing dynamic global vegetation models, satellite fire emissions, OCO-2 satellite measurements, and ocean model emulators, they created a fast-track carbon budget for 2023, identifying unprecedented weakening of these vital land carbon sinks. Figure 1 provides detailed visualizations of these findings.<\/p>\n\n\n\n

\"Atmospheric
Figure 1. Atmospheric CO2<\/sub> growth rate (1960\u20132023) and carbon budget (2010\u20132023). (a) Growth rate data from marine boundary layer surface stations (MBL, blue bars) and Mauna Loa station measurements (MLO, dark blue squares). (b) The global CO2<\/sub> budget, derived from historical fossil fuel and cement CO2<\/sub> emissions, 2023 estimates of land and ocean sinks, and MBL\/MLO CO2<\/sub> annual growth rates. Estimates are based on simulations from ocean sink emulators and land sink simulations using three dynamic vegetation models forced by low-latency climate input data, with their mean sink in 2019\u20132022 adjusted to match the median sink of 16 models used in the latest Global Carbon Budget. Ocean and land sink estimates are based on OCO-2 high-resolution atmospheric inversion. The difference between the stacked bars (bottom) and the red curve (-1 x fossil emissions) represents the budget imbalance.<\/figcaption><\/figure>\n\n\n\n

Traditional carbon budget methods primarily rely on numerical simulations, which, while capable of modeling complex Earth system processes, face significant delays due to high computational demands and slow data updates. For example, the Global Carbon Budget 2023 report, published by the Global Carbon Project in December 2023, includes data only through the end of 2022, resulting in a one-year information lag. This lag not only compromises the accuracy of assessing climate change trends but also hinders timely action to address climate change.<\/p>\n\n\n\n

Significant environmental events in 2023<\/h2>\n\n\n\n

Major environmental events in 2023 revealed the dynamic and unpredictable nature of the carbon cycle, emphasizing the need for near-real-time monitoring. North America experienced widespread wildfires, contributing 0.58 \u00b1 0.10 GtC to atmospheric CO2<\/sub>. At the same time, a shift from La Ni\u00f1a to a moderate El Ni\u00f1o phase led to significant changes in the global carbon sink. GRACE satellite data also recorded a decline in terrestrial water storage across much of the Northern Hemisphere and the Amazon, exacerbating plant water stress and reducing carbon absorption.<\/p>\n\n\n\n

The Amazon rainforest, in particular, faced extreme drought in the second half of the year, resulting in a carbon sink loss of 0.31 \u00b1 0.19 GtC, while tropical Africa experienced wetter-than-usual conditions, altering regional carbon fluxes. These events, which were not captured by traditional methods, underscore the urgency of adopting innovative approaches like the near-real-time AI-based global carbon budget model to provide timely and actionable climate data.<\/p>\n\n\n\n

Near-real-time AI global carbon budget method<\/h2>\n\n\n\n

To address this, Microsoft researchers and its collaborators developed a near-real-time global carbon sink model that incorporates current environmental variable observations, historical data from numerous ocean and land models, and a framework based on convolutional neural networks (CNNs) and semi-supervised models. It can predict near real-time carbon sink data with a loss margin of less than 2%, enabling accurate, low-latency carbon budget predictions. Figure 2 illustrates the methodology behind the near-real-time global carbon sink model, highlighting its integration of data sources and AI-based framework.<\/p>\n\n\n\n

\"Schematic
Figure 2. Schematic overview of the methodology and data sources used in the near-real-time global carbon sink model.<\/figcaption><\/figure>\n\n\n\n

Cross-disciplinary collaboration for timely carbon budgeting<\/h2>\n\n\n\n

Philippe Ciais, a researcher at the French Laboratory for Climate and Environmental Sciences and co-author of the study, remarks, \u201cIn 2023, the accumulation of CO2<\/sub> in the atmosphere was very high, translating into very low absorption by the terrestrial biosphere. In the Northern Hemisphere, which accounts for more than half of CO2<\/sub> uptake, we observed a declining trend in absorption for eight years. There is no good reason to believe it will bounce back because of continuing disturbance.\u201d<\/p>\n\n\n\n

The rapid decline in the land carbon sink raises concerns about future climate stability, as current prediction models may not fully account for abrupt shifts in carbon sinks. This challenge underscores the need for cross-disciplinary collaboration to drive innovative solutions. The near-real-time carbon budget method, powered by AI, exemplifies this collaboration between Microsoft Research Asia, Tsinghua University, and the French Laboratory of Climate and Environmental Sciences. The project combines expertise from environmental science, Earth system science, ecology, atmospheric science, and AI.<\/p>\n\n\n\n

“In our cross-disciplinary collaboration, we deeply value the complementary knowledge each field brings. The data provided by the French Laboratory of Climate and Environmental Sciences laid a solid foundation for our research, while the innovative application of AI enabled real-time monitoring of carbon sinks and emissions,” says Jiang Bian<\/a><\/a><\/a>, senior principal researcher at Microsoft Research Asia.<\/p>\n\n\n\n

“AI researchers must understand the complexities of environmental science, Earth system science, ecology, and atmospheric science, while experts in these fields must grasp the latest advances in AI and its potential applications. This mutual learning has allowed us to apply AI effectively in environmental monitoring, demonstrating the value of cross-disciplinary collaboration in addressing global challenges.”<\/p>\n\n\n\n

Looking forward<\/h2>\n\n\n\n

As technology and data continue to improve, Microsoft Research Asia plans to further integrate ocean and land carbon budget models, advancing the AI-based Near-real-time Global Carbon Budget (ANGCB) initiative<\/a>. Researchers aim to enhance the models\u2019 performance and efficiency, delivering more timely and accurate data to support global climate change research and provide valuable information for policymaking. These efforts will help drive progress in global environmental governance and foster the development of a sustainable, ecological future.<\/p>\n\n\n\n

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Since the Industrial Revolution, the burning of 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 led to a continuous rise […]<\/p>\n","protected":false},"author":34512,"featured_media":1103292,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-content-parent":199560,"msr_hide_image_in_river":null,"footnotes":""},"research-area":[13556],"msr-locale":[268875],"msr-post-option":[269148,269142],"class_list":["post-1110150","msr-blog-post","type-msr-blog-post","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us","msr-post-option-approved-for-river","msr-post-option-include-in-river"],"msr_assoc_parent":{"id":199560,"type":"lab"},"_links":{"self":[{"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-blog-post\/1110150","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-blog-post"}],"about":[{"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-blog-post"}],"author":[{"embeddable":true,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/users\/34512"}],"version-history":[{"count":4,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-blog-post\/1110150\/revisions"}],"predecessor-version":[{"id":1110843,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-blog-post\/1110150\/revisions\/1110843"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/media\/1103292"}],"wp:attachment":[{"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/media?parent=1110150"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=1110150"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=1110150"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=1110150"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}