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Climate change is already seriously impacting our lives in many ways. Threats to human and natural systems will increase as our planet continues to warm. This program will explore mathematical, statistical and computational strategies to better understand both the changes to the climate system and the associated impacts. A series of workshops will focus on climate models, detection and attribution of climate change, extreme weather and climate events, remote sensing, machine learning, and the economic consequences of climate change. This program aims to foster new multidisciplinary collaborations and integrate young scientists and researchers into industry, private sector, and academic research through these workshops and embedded research projects with affiliated universities, national laboratories, and private industry.
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Program WorkshopsBack to top
Climate models are important tools for understanding past, current and future global climate variability, yet they exhibit key uncertainties that limit their applicability to fine scale analysis and future projections. Some key sources of uncertainty include coarse grid resolution, inadequate representation of relevant physics and interactions, overfitting from downscaling and bias-correction, lack of observations to calibrate and evaluate models, uncertain model parameters, different model structures, and so on. In addition, coupled climate models are computationally expensive and thus difficult to use for uncertainty analysis, while reduced complexity models are fast and flexible but are highly parameterized and lack physics. These computational tradeoffs pose major challenges for evaluating/comparing model results, constructing reliable projections, and quantifying relevant uncertainties. The workshop will bring together researchers from multi-disciplinary fields to highlight new math/stat methods for climate model evaluation and uncertainty quantification across spatial and temporal scales, and to advance our understanding about the physical processes leading to model errors, biases, and uncertainty.
Weather and climate extremes profoundly impact human society and the natural environment across the globe. Recent years have seen an increase in economic losses due to climate and weather extremes, particularly from extremes in different variables that occur simultaneously in space and time, so called compound extremes. Researchers typically study climate and weather extremes from different perspectives. The statistics and applied math communities have focused on theory and methods for extreme values. In contrast, atmospheric scientists have focused on quantifying changes in extremes and understanding the mechanism behind them. Both approaches are crucial for understanding and mitigating the frequency and magnitude of extremes. The workshop will bring together researchers from both communities in order to advance our understanding of the mechanisms causing climate and weather extremes and to find novel approaches to mitigate climate change and its impacts.
Detection and attribution of climate change refers to the procedures used in assessing whether or not climate is changing, and if so, how to pinpoint the causes of any identified changes. Quantification of the uncertainty in attribution statements is of critical importance. Detection and attribution methods inform mankind’s current influence on climate and increase confidence in projections of future climate change. Detection and attribution studies aid climate policy decisions and suggest techniques for adaptation and/or remediation where needed. This summit is intended as a research workshop on current issues related to climate change detection and attribution, including changes in extreme events and the attribution of individual storms and other weather events and their impacts.
The Earth’s climate system is a classical example of a multiscale, multiphysics dynamical system with an extremely large number of active degrees of freedom, exhibiting variability on scales ranging from micrometers and seconds to thousands of kilometers and centuries. Machine learning approaches present a timely opportunity to leverage the information content of large datasets generated by observational systems and models to improve scientific understanding and prediction capability of weather and climate dynamics. The workshop will bring together an interdisciplinary group of researchers in applied mathematics, climate science, and data science to discuss recent advances and future perspectives on machine learning for weather and climate applications, including feature extraction, subgrid-scale modeling, and statistical prediction.
Remote sensing plays a critical role in many aspects of climate science, including real-time and long-term monitoring, forecast initialization, model verification, and statistical analysis. Remote sensing records now span multiple decades and provide information on multiple processes in the climate system. The ever-changing Earth-observing satellite constellation and the development and deployment of new remote sensing capabilities, including NASA’s planned Earth System Observatory and ESA’s Sentinel missions, present a timely opportunity to make advances in these areas, motivating the development of new techniques to analyze and assimilate large volumes of data with high spatial and temporal resolution. This workshop will bring together researchers from the remote sensing, data analysis, and climate science communities to explore applications of current- and next-generation remote sensing products and data analysis techniques to climate analysis and modeling.
Climate change poses serious financial, health and property risks both to major industrial sectors and to the public. Characterizing the economic impacts, and quantifying relevant risk-based uncertainties, are critical for establishing resilient and equitable strategies for the future. This workshop will bring together students, researchers, and stakeholders from diverse disciplinary areas to address economic impacts of climate and weather across a wide range of temporal and spatial scales. We will explore new ideas and approaches to connecting academic research with stakeholder and private sector interests, in particular using applied mathematical, statistical, and econometric methods.