{"id":847351,"date":"2022-06-29T09:00:00","date_gmt":"2022-06-29T16:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-group&#038;p=847351"},"modified":"2024-04-17T11:56:07","modified_gmt":"2024-04-17T18:56:07","slug":"microsoft-climate-research-initiative","status":"publish","type":"msr-group","link":"https:\/\/www.microsoft.com\/en-us\/research\/collaboration\/microsoft-climate-research-initiative\/","title":{"rendered":"Microsoft Climate Research Initiative"},"content":{"rendered":"<section class=\"mb-3 moray-highlight\">\n\t<div class=\"card-img-overlay mx-lg-0\">\n\t\t<div class=\"card-background  has-background-plum card-background--full-bleed\">\n\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1920\" height=\"720\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/05\/Climate-Research-Initiative_header-flip_1920x720.jpg\" class=\"attachment-full size-full\" alt=\"Climate Research Initiative - photo of a man with a tripod looking up at the Northern Lights\" style=\"\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/05\/Climate-Research-Initiative_header-flip_1920x720.jpg 1920w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/05\/Climate-Research-Initiative_header-flip_1920x720-300x113.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/05\/Climate-Research-Initiative_header-flip_1920x720-1024x384.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/05\/Climate-Research-Initiative_header-flip_1920x720-768x288.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/05\/Climate-Research-Initiative_header-flip_1920x720-1536x576.jpg 1536w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/05\/Climate-Research-Initiative_header-flip_1920x720-1600x600.jpg 1600w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/05\/Climate-Research-Initiative_header-flip_1920x720-240x90.jpg 240w\" sizes=\"auto, (max-width: 1920px) 100vw, 1920px\" \/>\t\t<\/div>\n\t\t<!-- Foreground -->\n\t\t<div class=\"card-foreground d-flex mt-md-n5 my-lg-5 px-g px-lg-0\">\n\t\t\t<!-- Container -->\n\t\t\t<div class=\"container d-flex mt-md-n5 my-lg-5 align-self-center\">\n\t\t\t\t<!-- Card wrapper -->\n\t\t\t\t<div class=\"w-100 w-lg-col-5\">\n\t\t\t\t\t<!-- Card -->\n\t\t\t\t\t<div class=\"card material-md-card py-5 px-md-5\">\n\t\t\t\t\t\t<div class=\"card-body \">\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\n<h1 class=\"wp-block-heading\" id=\"microsoft-climate-research-initiative\">Microsoft Climate Research Initiative<\/h1>\n\n\n\n<p>Researchers working together to fight climate change<\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n<p>The <strong>Microsoft Climate Research Initiative<\/strong> (MCRI) is a community of multi-disciplinary researchers working together to fight climate change. Collectively, through collaborative research projects, this network of researchers and affiliates aims to advance a shared sustainability research agenda in support of global climate goals.<\/p>\n\n\n\n<p>Microsoft, with our research and computing capabilities, is uniquely positioned to drive transformations in this space. However, deep and continuous engagement with domain experts is necessary in order to discover and develop ways to overcome constraints to decarbonization, carbon accounting,\u202fand to climate risk assessments.\u202f\u202f\u202f&nbsp;<\/p>\n\n\n\n<p>MCRI enables Microsoft to work with leading scientists from academic institutions and other research organizations on shared priorities that address key climate change challenges.<\/p>\n\n\n\n<div class=\"wp-block-media-text has-vertical-padding-none  is-stacked-on-mobile is-style-spectrum is-style-border is-style-offset-media--top is-style-offset-media--offset-\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/MCRI-environment-people-stargazing_1400x788-1024x576.jpg\" alt=\"MCRI - a group of people stargazing the Milky Way\" class=\"wp-image-866223 size-full\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/MCRI-environment-people-stargazing_1400x788-1024x576.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/MCRI-environment-people-stargazing_1400x788-300x169.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/MCRI-environment-people-stargazing_1400x788-768x432.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/MCRI-environment-people-stargazing_1400x788-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/MCRI-environment-people-stargazing_1400x788-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/MCRI-environment-people-stargazing_1400x788-343x193.jpg 343w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/MCRI-environment-people-stargazing_1400x788-240x135.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/MCRI-environment-people-stargazing_1400x788-640x360.jpg 640w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/MCRI-environment-people-stargazing_1400x788-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/MCRI-environment-people-stargazing_1400x788-1280x720.jpg 1280w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/MCRI-environment-people-stargazing_1400x788.jpg 1400w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<h3 class=\"wp-block-heading has-text-align-left is-style-default\" id=\"our-mission-is-to-conduct-targeted-cross-disciplinary-collaborative-research-to-accelerate-solutions-that-help-combat-climate-change\"><em><em>Our mission is to conduct targeted, cross-disciplinary, collaborative research to accelerate solutions that help combat climate change<\/em><\/em>.<\/h3>\n<\/div><\/div>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"goals\">Goals<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Accelerate cutting-edge research and transformative innovation in climate science and technology through collaboration<\/li>\n\n\n\n<li>Develop and sustain a highly collaborative research ecosystem comprising diversity of perspectives across representation, expertise, institution, and geography<\/li>\n\n\n\n<li>Provide strategic direction for climate-related research priorities and investments<\/li>\n<\/ul>\n\n\n\n<blockquote class=\"wp-block-quote is-style-spectrum--green-orange is-layout-flow wp-block-quote-is-layout-flow\">\n<p>&#8220;As researchers, we&#8217;re excited to work together on projects specifically selected for their potential impact on global climate challenges. With Microsoft&#8217;s computational capabilities and the domain expertise from our academic collaborators, our complementary strengths can accelerate progress in incredible ways.&#8221;<\/p>\n<cite>\u2013 Karin Strauss, Microsoft Senior Principal Research Manager<\/cite><\/blockquote>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"our-collaborators\">Our collaborators<\/h3>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"200\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Dept-of-Chemistry-logo_stacked_300x200.png\" alt=\"UC Berkeley Dept of Chemistry logo\" class=\"wp-image-854988\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Dept-of-Chemistry-logo_stacked_300x200.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Dept-of-Chemistry-logo_stacked_300x200-240x160.png 240w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"200\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/IPL_logo_blanco_300x200.png\" alt=\"IPL Image Processing Lab logo\" class=\"wp-image-854991\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/IPL_logo_blanco_300x200.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/IPL_logo_blanco_300x200-240x160.png 240w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"200\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/LSCE_05471_logo_300x200.png\" alt=\"LSCE logo\" class=\"wp-image-856641\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/LSCE_05471_logo_300x200.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/LSCE_05471_logo_300x200-240x160.png 240w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"200\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/MIT_logo_300x200.png\" alt=\"MIT logo\" class=\"wp-image-855654\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/MIT_logo_300x200.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/MIT_logo_300x200-240x160.png 240w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"200\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/noaa-emblem-rgb-2022_logo_300x200.png\" alt=\"MCRI - NOAA logo\" class=\"wp-image-858021\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/noaa-emblem-rgb-2022_logo_300x200.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/noaa-emblem-rgb-2022_logo_300x200-240x160.png 240w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"200\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Tsinghua_University_logo_300x200.png\" alt=\"Tsinghua University logo\" class=\"wp-image-855207\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Tsinghua_University_logo_300x200.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Tsinghua_University_logo_300x200-240x160.png 240w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"200\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Univ-Michigan_logo_300x200.png\" alt=\"University of Michigan logo\" class=\"wp-image-854994\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Univ-Michigan_logo_300x200.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Univ-Michigan_logo_300x200-240x160.png 240w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"200\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Univ-Reading_logo_300x200.png\" alt=\"University of Reading logo\" class=\"wp-image-855210\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Univ-Reading_logo_300x200.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Univ-Reading_logo_300x200-240x160.png 240w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"200\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/07\/University-Washington_logo2_300x200.png\" alt=\"University of Washington logo\" class=\"wp-image-674199\"\/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"200\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Univ-Valencia_logo_transparent_final_300x200.png\" alt=\"University of Valencia logo\" class=\"wp-image-854985\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Univ-Valencia_logo_transparent_final_300x200.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Univ-Valencia_logo_transparent_final_300x200-240x160.png 240w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"200\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Verisk_AER_logo_300x200.png\" alt=\"Verisk logo\" class=\"wp-image-855657\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Verisk_AER_logo_300x200.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Verisk_AER_logo_300x200-240x160.png 240w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"200\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Wuhan_University_logo_300x200.png\" alt=\"Wuhan University logo\" class=\"wp-image-855213\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Wuhan_University_logo_300x200.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/06\/Wuhan_University_logo_300x200-240x160.png 240w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n<\/div>\n\n\n\n\n\n<p>These collaborative projects with academia are focused around materials engineering for carbon reduction and removal, data fusion to improve carbon accounting, and causal machine learning for understanding and predicting climate risk and related interventions.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"carbon-accounting\">Carbon accounting<\/h3>\n\n\n\n\n\n<p><strong>Tsinghua University<\/strong>: Jia Xing (PI)<br><strong>Wuhan University<\/strong>: Siwei Li (Co-PI)<br><strong>Microsoft<\/strong>: <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/shuz\/\">Shuxin Zheng<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/changliu\/\">Chang Liu<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/yushi2\/\">Yu Shi<\/a><\/p>\n\n\n\n<p>Understanding the change in CO<sub>2<\/sub> emissions from the measurement of CO<sub>2<\/sub> concentrations such as that done by satellites is very useful in tracking the real-time progress of carbon reduction actions. Current CO<sub>2<\/sub> observations are relatively limited: numerical model-based methods have very low calculation efficiency. The proposed study aims to develop a novel method that combines atmospheric numerical modeling and machine learning to infer the CO<sub>2<\/sub> emissions from satellite observations and ground monitor sensor data.<\/p>\n\n\n\n<p><strong>Related papers:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/rapid-inference-of-nitrogen-oxide-emissions-based-on-a-top-down-method-with-a-physically-informed-variational-autoencoder\/\" target=\"_blank\" rel=\"noreferrer noopener\">Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed Variational Autoencoder<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/mimicking-atmospheric-photochemical-modeling-with-a-deep-neural-network\/\" target=\"_blank\" rel=\"noreferrer noopener\">Mimicking atmospheric photochemical modeling with a deep neural network<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/deep-learning-for-prediction-of-the-air-quality-response-to-emission-changes\/\" target=\"_blank\" rel=\"noreferrer noopener\">Deep learning for prediction of the air quality response to emission changes<\/a><\/li>\n<\/ul>\n\n\n\n\n\n<p><strong>Tsinghua University<\/strong>: Zhu Liu (PI), Piyu Ke (PhD student)<br><strong>LSCE<\/strong>: Biqing Zhu (Co-PI), Philippe Ciais (Co-PI) <br><strong>UC Irvine<\/strong>: Steven J. Davis (Co-PI)<br><strong>Microsoft<\/strong>: <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/xiaofangui\/\" target=\"_blank\" rel=\"noreferrer noopener\">Xiaofan Gui<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jiabia\/\">Jiang Bian<\/a><\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Project Summary<\/strong>:                                                                                                                                                                                                                                                                                  <\/p>\n\n\n\n<p>The initiative focuses on improving the computation of the carbon budget, which refers to the permissible amount of carbon dioxide (CO2) emissions required to limit global warming to a targeted level. The existing methods for calculating the carbon budget are limited by a lag of 1 to 2 years, and challenges in forecasting regions with sparse data. This project aims to address these problems by enhancing the accuracy and speed of carbon sink forecasts, particularly those related to oceans.<\/p>\n\n\n\n<p>Two key advancements define this project:<\/p>\n\n\n\n<ol class=\"wp-block-list\" start=\"1\" style=\"list-style-type:1\">\n<li><strong><em>Speeding Up Carbon Sink Estimates<\/em><\/strong>: Traditionally, ocean carbon sink estimates, calculated by the state-of-the-art Global Ocean Biogeochemistry Model (GOBM), take 1 to 2 years due to data lag. This project has remarkably reduced this time to 1 to 2 months by leveraging a deep learning approach. By utilizing data with only a maximum lag of 1-2 months, this method bypasses the need for the 1-2 year lag data, achieving near real-time forecasting.<\/li>\n\n\n\n<li><strong><em>Forecasting in Regions with Limited Observational Data<\/em><\/strong><em>: Existing models struggle with limited observational data, hampering accurate forecasts in specific regions. To overcome this, the project introduces a semi-supervised deep learning model that enables generalization into broader regions and extended time frames. This is achieved through a specialized teacher-student learning mechanism that augments limited labeled data.<\/em><\/li>\n<\/ol>\n\n\n\n<p>The ultimate objective of this innovative project is to provide governments and administrative agencies with accurate and timely carbon budget computations. The anticipated outcome of this work would allow for more responsive and informed policy adjustments, directly contributing to global efforts in controlling climate change. By achieving near real-time forecasting and overcoming data sparsity challenges, this project represents a significant step forward in the field of carbon sink\/budget forecasting.<\/p>\n\n\n\n<p><strong>Related papers<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/near-real-time-monitoring-of-global-ocean-carbon-sink\/\">Near-real-time monitoring of global ocean carbon sink<\/a><\/li>\n\n\n\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.nature.com\/articles\/s41467-020-18922-7\" target=\"_blank\" rel=\"noopener noreferrer\">Near-real-time monitoring of global CO<sub>2<\/sub>&nbsp;emissions reveals the effects of the COVID-19 pandemic<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n\n\n\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.nature.com\/articles\/s41597-020-00708-7\" target=\"_blank\" rel=\"noopener noreferrer\">Carbon Monitor, a near-real-time daily dataset of global CO<sub>2<\/sub>&nbsp;emission from fossil fuel and cement production<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n\n\n\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/essd.copernicus.org\/articles\/14\/1639\/2022\/\" target=\"_blank\" rel=\"noopener noreferrer\">Comparing national greenhouse gas budgets reported in UNFCCC inventories against atmospheric inversions<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n\n\n\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/essd.copernicus.org\/articles\/14\/1917\/2022\/\" target=\"_blank\" rel=\"noopener noreferrer\">Global Carbon Budget 2021<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n<\/ul>\n\n\n\n\n\n<hr class=\"wp-block-separator has-text-color has-teal-color has-alpha-channel-opacity has-teal-background-color has-background is-style-dots\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"carbon-reduction-and-removal\">Carbon reduction and removal<\/h3>\n\n\n\n\n\n<p><strong>University of California, Berkeley<\/strong>: Jeffrey Long (PI), Katerina Graf (PhD student), Hiroyasu Furukawa (Research Scientist), Xiang Fu (MIT PhD student), Andrew Rosen (Miller Research Fellow)<br><strong>Microsoft<\/strong>: <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jakesmith\/\">Jake Smith<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/bnguy\/\">Bichlien Nguyen<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/kalifrost\/\">Kali Frost<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/kstrauss\/\">Karin Strauss<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/tianxie\/\">Tian Xie<\/a><\/p>\n\n\n\n<p>Removing CO<sub>2<\/sub> from the environment is expected to be an integral component of keeping temperature rise below 1.5\u00b0C. However, today this is an inefficient and expensive undertaking. This project will tailor design of new metal\u2013organic frameworks (MOFs) that exhibit new structures and mechanisms of CO<sub>2<\/sub> capture relevant to the low-cost removal of CO<sub>2<\/sub> from air and other dilute gas streams.<\/p>\n\n\n\n<p><strong>Related paper:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/mofdiff-coarse-grained-diffusion-for-metal-organic-framework-design\/\">MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design<\/a><\/li>\n<\/ul>\n\n\n\n\n\n<p><strong>North Carolina State<\/strong>: Michael D. Dickey (PI), Dhwanil Vaghani (Graduate Research Assistant), Chemical Engineering Seniors (Senior design team)<br><strong>University of New South Wales<\/strong>: Kourosh Kalantar-Zadeh (Collaborator\/advisor)<br><strong>Microsoft<\/strong>: <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/kalifrost\/\">Kali Frost<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/bnguy\/\">Bichlien Nguyen<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/kstrauss\/\">Karin Strauss<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jakesmith\/\">Jake Smith<\/a><\/p>\n\n\n\n<p>The CO<sub>2<\/sub> reduction process can be used to convert captured carbon into a storable form as well as to manufacture sustainable fuels and materials with lower environmental impacts. This project will evaluate liquid metal-based reduction processes, identifying advantages, pinch-points, and opportunities for improvement needed to reach industrial-relevant scales. It will lay the foundation for improving catalysts and address scaling bottlenecks.<\/p>\n\n\n\n<p><strong>Related papers<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/pubs.rsc.org\/en\/content\/articlelanding\/2018\/cs\/c7cs00043j#!\" target=\"_blank\" rel=\"noopener noreferrer\">Liquid metals: fundamentals and applications in chemistry<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n\n\n\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/onlinelibrary.wiley.com\/doi\/epdf\/10.1002\/adma.202105789\" target=\"_blank\" rel=\"noopener noreferrer\">Liquid-Metal-Enabled Mechanical-Energy-Induced Co<sub>2<\/sub> Conversion<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n<\/ul>\n\n\n\n\n\n<p><strong>University of Michigan<\/strong>: David Kwabi (PI), Bryan Goldsmith (Co-PI), Anne McNeil (Co-PI), Jessica Tami (PhD student), Cameron Gruich (PhD student), Longbang Liu (Undergraduate), Siddhant Singh (PhD student)<br><strong>Microsoft<\/strong>: <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/bnguy\/\">Bichlien Nguyen<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jakesmith\/\">Jake Smith<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/kalifrost\/\">Kali Frost<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/kstrauss\/\">Karin Strauss<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/marwinsegler\/\">Marwin Segler<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/yinxia\/\">Yingce Xia<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/zihenglu\/\">Ziheng Lu<\/a><\/p>\n\n\n\n<p>Energy storage is essential to enable 100% zero-carbon electricity generation. This work will use generative machine learning models and quantum mechanical modeling to drive the discovery and optimization of a new class of organic molecules for energy-efficient electrochemical energy storage and carbon capture.<\/p>\n\n\n\n\n\n<p><strong>University of Washington<\/strong>: Aniruddh Vashisth (PI), Yiwan Zheng (PhD student)<br><strong>Microsoft<\/strong>: <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/bnguy\/\">Bichlien Nguyen<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jakesmith\/\">Jake Smith<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/kalifrost\/\">Kali Frost<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/kstrauss\/\">Karin Strauss<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/zihenglu\/\">Ziheng Lu<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/shuz\/\">Shuxin Zheng<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jiabia\/\">Jiang Bian<\/a><\/p>\n\n\n\n<p>Despite encouraging progress in recycling, many plastic polymers often end up being one-time-use materials. The plastics that compose printed circuit boards (PCBs), ubiquitous in every modern device, are amongst those most difficult to recycle. Vitrimers, a new class of polymers that can be recycled multiple times without significant changes in material properties, present a promising alternative. This project will leverage advances in machine learning to select vitrimer formulations that withstand the requirements imposed by their use in PCBs.<\/p>\n\n\n\n<p><strong>Related paper<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><span style=\"font-size:12.0pt;font-family:\"Aptos\",sans-serif;\nmso-fareast-font-family:Aptos;mso-fareast-theme-font:minor-latin;mso-bidi-font-family:\nAptos;mso-ansi-language:EN-US;mso-fareast-language:EN-US;mso-bidi-language:\nAR-SA\"><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/inverse-design-of-vitrimeric-polymers-by-molecular-dynamics-and-generative-modeling\/\">Inverse Design of Vitrimeric Polymers by Molecular Dynamics and Generative Modeling<\/a><\/span><\/li>\n<\/ul>\n\n\n\n\n\n<p><strong>University of Washington<\/strong>: Eleftheria Roumeli (PI)<br><strong>Microsoft<\/strong>: <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/kseverson\/\">Kristen Severson<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/yuanjc\/\">Yuan-Jyue Chen<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/bnguy\/\">Bichlien Nguyen<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jakesmith\/\">Jake Smith<\/a><\/p>\n\n\n\n<p>The concrete industry is a major contributor to greenhouse gas emissions, the majority of which can be attributed to cement, thereby making the discovery of alternative cements a promising avenue for decreasing the environmental impacts of the industry. This project will employ machine learning methods to accelerate mechanical property optimization of \u201cgreen\u201d cements which meet application quality constraints while minimizing carbon footprint.<\/p>\n\n\n\n<p><strong>Related paper<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/amortized-inference-of-gaussian-process-hyperparameters-for-improved-concrete-strength-trajectory-prediction\/\">Amortized inference of Gaussian process hyperparameters for improved concrete strength trajectory prediction<\/a><\/li>\n<\/ul>\n\n\n\n<p><strong>Related article<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.popsci.com\/environment\/bioplastic-future\/?mkt_tok=nti3lufiui0ynjuaaagrra0kw9ucbktx6b6encbjxcnfsmob9nsx4b4a2myx7zbukezz5xz2edezidbvgiyt2ud2wnd5bxl3fpmm2bzehpm14vgdpob9zh4vrullmsu\" target=\"_blank\" rel=\"noopener noreferrer\">What will it take to make truly compostable plastic? \u2013 Popular Science Knowable Magazine<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n\n\n\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.thetimes.co.uk\/article\/biodegradable-plastic-decomposes-like-fruit-scientists-claim-xpm03rmlr\" target=\"_blank\" rel=\"noopener noreferrer\">Biodegradable plastic decomposes like fruit, scientists claim \u2013 The Times<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> <\/li>\n\n\n\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.washington.edu\/news\/2023\/07\/10\/new-biodegradable-plastics-compostable-in-your-backyard\/\" target=\"_blank\" rel=\"noopener noreferrer\">New biodegradable plastics are compostable in your backyard \u2013 UW News<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n\n\n\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.geekwire.com\/2023\/uw-researchers-develop-promising-bioplastic-that-can-decompose-like-a-banana-peel\/\" target=\"_blank\" rel=\"noopener noreferrer\">UW researchers develop promising &#8216;bioplastic&#8217; that can decompose like a banana peel \u2013 GeekWire<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n<\/ul>\n\n\n\n\n\n<hr class=\"wp-block-separator has-text-color has-teal-color has-alpha-channel-opacity has-teal-background-color has-background is-style-dots\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"environmental-resilience\">Environmental resilience&nbsp;<\/h3>\n\n\n\n\n\n<p><strong>University of California, Irvine<\/strong>: <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/engineering.uci.edu\/users\/julie-m-schoenung\" target=\"_blank\" rel=\"noopener noreferrer\">Julie M. Schoenung<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (PI), <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.faculty.uci.edu\/profile.cfm?faculty_id=2423\" target=\"_blank\" rel=\"noopener noreferrer\">Oladele Ogunseitan<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (Co-PI), Haoyang He (Postdoc)<br><strong>Microsoft<\/strong>: <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/bnguy\/\">Bichlien Nguyen<\/a>, <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.linkedin.com\/in\/winstonsaunders\/\" target=\"_blank\" rel=\"noopener noreferrer\">Winston Saunders<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Maria Viitaniemi, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/kalifrost\/\">Kali Frost<\/a><\/p>\n\n\n\n<p>Materials play a crucial role in meeting global decarbonization and sustainability targets. Leveraging expertise across life-cycle assessment, material science, computational design, and policy, this project aims to accelerate sustainable development of materials, especially to advance data-driven, high throughput discovery of materials properties and sustainability attributes.<\/p>\n\n\n\n\n\n<p><strong>Universitat de Valencia<\/strong>: Gustau Camps-Valls (PI), Gherardo Varando (Co-PI), Jose Maria Tarraga (Scientific Researcher)<br><strong>University of Reading<\/strong>: Ted Shepherd (PI), Ros Cornforth (Co-PI), Elena Saggioro (PhD student\/Researcher), Genevieve Nartey (MSc student)<br><strong>Microsoft<\/strong>: <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/trevordhu\/\">Trevor Dhu<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/emrek\/\">Emre Kiciman<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/lmackey\/\">Lester Mackey<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/eldillon\/\">Eleanor Dillon<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/ranveer\/\">Ranveer Chandra<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/pekumar\/\">Peeyush Kumar<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/swatisharma\/\">Swati Sharma<\/a><\/p>\n\n\n\n<p>The Causal4Africa project will investigate the problem of food security in Africa from a novel\u202fcausal inference standpoint.\u202fThe project will illustrate the usefulness of causal discovery and estimation of effects from observational data by intervention analysis. Ambitiously, it will improve the usefulness of causal ML approaches for climate risk assessment by enabling the interpretation and evaluation of the likelihood and potential consequences of specific interventions.<\/p>\n\n\n\n<p><strong>Related papers:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/arxiv.org\/abs\/2310.11287\" target=\"_blank\" rel=\"noopener noreferrer\">Assessing the Causal Impact of Humanitarian Aid on Food Security<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/climate-risk-assessment-needs-urgent-improvement\/\">Climate risk assessment needs urgent improvement<\/a><\/li>\n\n\n\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/17565529.2020.1808438\" target=\"_blank\" rel=\"noopener noreferrer\">Storylines for decision-making: climate and food security in Namibia<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/quantifying-causal-pathways-of-teleconnections\/\">Quantifying Causal Pathways of Teleconnections<\/a><\/li>\n\n\n\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.nature.com\/articles\/s41467-019-10105-3\" target=\"_blank\" rel=\"noopener noreferrer\">Inferring causation from time series in Earth system sciences<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n\n\n\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"http:\/\/dx.doi.org\/10.1093\/pnasnexus\/pgac009\" target=\"_blank\" rel=\"noopener noreferrer\">Small is beautiful: climate-change science as if people mattered<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n\n\n\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/meetingorganizer.copernicus.org\/EGU23\/EGU23-15000.html\" target=\"_blank\" rel=\"noopener noreferrer\">Abstract EGU23-15000 (copernicus.org)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/11\/wcrp_poster-BP_MCRI.pdf\">Evaluating the Impact of Humanitarian Aid on Food Security<\/a><\/li>\n\n\n\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/isp.uv.es\/slides\/wcrp23_poster_food_insecurity.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Understanding food insecurity in Africa through data-driven causal inference methods<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n\n\n\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/isp.uv.es\/slides\/causal4disasters.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Causal inference for disaster management<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (invited talk at ESA \u201cAI for Disaster Management\u201d 2023)<\/li>\n\n\n\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/arxiv.org\/abs\/2307.10703\" target=\"_blank\" rel=\"noopener noreferrer\">Graphs in state-space models for Granger causality in climate science<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (CausalStats23, Paris, 2023) [<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/isp.uv.es\/slides\/graphEM_causeme_2023_for_web.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">slides<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>]<\/li>\n<\/ul>\n\n\n\n\n\n<p><strong>MIT<\/strong>: Judah Cohen (PI), Dara Entekhabi (Co-PI), Sonja Totz (Postdoc)<br><strong>Microsoft<\/strong>: <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/lmackey\/\">Lester Mackey<\/a><\/p>\n\n\n\n<p>Water and fire managers rely on subseasonal forecasts two to six weeks in advance to allocate water, manage wildfires, and prepare for droughts and other weather extremes. However, skillful forecasts for the subseasonal regime are lacking due to a complex dependence on local weather, global climate variables, and the chaotic nature of weather. To address this need, this project will use machine learning to adaptively correct the biases in traditional physics-based forecasts and adaptively combine the forecasts of disparate models.<\/p>\n\n\n\n<p><strong>Related research:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/adaptive-bias-correction-for-improved-subseasonal-forecasting\/\">Adaptive Bias Correction for Improved Subseasonal Forecasting<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/learned-benchmarks-for-subseasonal-forecasting\/\">Learned Benchmarks for Subseasonal Forecasting<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/online-learning-with-optimism-and-delay\/\">Online Learning with Optimism and Delay<\/a><\/li>\n\n\n\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/github.com\/microsoft\/subseasonal_data\">Subseasonal Data Python Package<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (GitHub)<\/li>\n\n\n\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/blog\/improving-subseasonal-forecasting-with-machine-learning\/\">Improving Subseasonal Forecasting with Machine Learning<\/a> (blog)<\/li>\n<\/ul>\n\n\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-end-mark\"\/>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/group\/environmental-sustainability-research\/projects\/\" target=\"_blank\" rel=\"noreferrer noopener\">More sustainability projects <\/a><\/div>\n<\/div>\n\n\n","protected":false},"excerpt":{"rendered":"<p>The Microsoft Climate Research Initiative (MCRI) is a community of multi-disciplinary researchers working together to fight climate change.<\/p>\n","protected":false},"featured_media":848449,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_group_start":"","footnotes":""},"research-area":[198583],"msr-group-type":[243721],"msr-locale":[268875],"msr-impact-theme":[261670],"class_list":["post-847351","msr-group","type-msr-group","status-publish","has-post-thumbnail","hentry","msr-research-area-ecology-environment","msr-group-type-collaboration","msr-locale-en_us"],"msr_group_start":"","msr_detailed_description":"","msr_further_details":"","msr_hero_images":[],"msr_research_lab":[],"related-researchers":[{"type":"user_nicename","display_name":"Yuan-Jyue Chen","user_id":35057,"people_section":"Research team","alias":"yuanjc"},{"type":"user_nicename","display_name":"Kali Frost","user_id":41284,"people_section":"Research team","alias":"kalifrost"},{"type":"user_nicename","display_name":"Emre Kiciman","user_id":31739,"people_section":"Research team","alias":"emrek"},{"type":"user_nicename","display_name":"Lester Mackey","user_id":36161,"people_section":"Research team","alias":"lmackey"},{"type":"user_nicename","display_name":"Bichlien Nguyen","user_id":35942,"people_section":"Research team","alias":"bnguy"},{"type":"guest","display_name":"Bora Ozaltun","user_id":707479,"people_section":"Research team","alias":""},{"type":"guest","display_name":"Winston Saunders","user_id":611028,"people_section":"Research team","alias":""},{"type":"user_nicename","display_name":"Kristen Severson","user_id":40372,"people_section":"Research team","alias":"kseverson"},{"type":"user_nicename","display_name":"Jake Smith","user_id":40891,"people_section":"Research team","alias":"jakesmith"},{"type":"user_nicename","display_name":"Karin Strauss","user_id":32587,"people_section":"Research team","alias":"kstrauss"},{"type":"guest","display_name":"Maria Viitaniemi","user_id":963792,"people_section":"Research team","alias":""},{"type":"guest","display_name":"Gustau Camps-Valls","user_id":855234,"people_section":"Collaborators","alias":""},{"type":"guest","display_name":"Philippe Ciais","user_id":800080,"people_section":"Collaborators","alias":""},{"type":"guest","display_name":"Judah Cohen","user_id":608778,"people_section":"Collaborators","alias":""},{"type":"guest","display_name":"Ros Cornforth","user_id":855228,"people_section":"Collaborators","alias":""},{"type":"guest","display_name":"Steven J. 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