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Statisticians Contribute to Climate Science, Policy

1 March 2023 556 views No Comment
Philip W. “Bo” Hammer, Executive Director, Institute for Mathematical and Statistical Innovation

    Scientists from across the disciplines, including statisticians, have spent decades making the case that our planet is rapidly warming due to anthropogenic emissions of carbon dioxide and other greenhouse gasses. The catastrophic consequences of global warming are becoming undeniably obvious. Scientists, economists, and social scientists are collaborating to improve our understanding of and predictions for how Earth’s changing climate will affect humanity and the ecological and social systems upon which life relies.

    As a key element of its mission, IMSI, the NSF-funded Institute for Mathematical and Statistical Innovation, held a long program on the mathematics and statistics needed for confronting global climate change September 19 to December 9, 2022. IMSI is designed to convene applied mathematicians, statisticians, and scientists who research topics with major societal implications. This long program was divided into six workshops that explored a range of key scientific issues that have direct bearing on humans’ understanding of our warming planet; the regional impacts on annual weather resulting from this evolving dynamical system; and predicting future risk, hazards, and damages due to extreme weather events and the social cost of carbon.

    To improve some of the current shortcomings in climate models, researchers turn to statistical and machine learning strategies that use existing weather and climate data sets.

    The Earth’s climate is a complex dynamical system whose physics takes place on spatiotemporal scales ranging from nanometers to kilometers and microseconds to centuries. Modeling such a complex system and making predictions about future outcomes (that is, how climate begets weather) is further complicated by unknowns in, for example, the physics of aerosols and clouds and by imperfect data sets requiring novel statistical methods for analysis. To improve some of the current shortcomings in climate models, researchers turn to statistical and machine learning strategies that use existing weather and climate data sets.

    Much of the discussion throughout the program focused on the verifiability, validity, and uncertainty quantification of climate models, particularly when applying these models to future weather and climate prediction at spatiotemporal scales that are useful for risk planning and adaptation to a warming planet. One key technical challenge lies in ‘downscaling’ the relatively low resolution of the global climate models to the high resolution needed to make local climate impact studies of key variables such as precipitation and temperature.

    High-resolution climate models are particularly important to those responsible for managing the socioeconomic risks and impacts of extreme events. For example, events like flooding and heat waves have major effects on farmers, emergency management agencies, insurance companies, and just about everyone else thinking long-term about where to live, build, or work. Spatial downscaling introduces uncertainties that are necessary to quantify (i.e., uncertainty quantification), reduce, and communicate, so the public and policymakers can rely on climate scientists and meteorologists with increasing confidence in their decision-making.

    Generally, peoples’ experiences of climate change are two-fold. They experience the slow evolution of annual patterns in local weather such as droughts becoming a way of life, winters that aren’t as cold, and summers that seem muggier than they used to be. Or they are caught by the seeming onslaught of fearsome extreme events such as freak heat waves in typically cool summer regions (such as the Pacific Northwest or UK), two new seasons of extensive extremes—wildfire (the western US) and hurricane (the southeast)—and massive flooding on the regional scale of states and nations (Pakistan).

    Statisticians are working with climate scientists to explore new approaches to understanding how the frequency, intensity, and global distribution of extreme events may be changing because of climate change. Statisticians have much to contribute here, particularly in developing tools for quantifying and predicting rare and extreme events within a system that is nonstationary and for which there is a shortage of relevant data.

    One major challenge in understanding the dynamics of extreme events is that the climate and weather systems under study are changing due to a warming Earth. As such, the known geographic distribution of phenomena such as wind, temperature, humidity, and precipitation that are based on historical data may not follow its current spatiotemporal statistical distributions in the future. Extremes are the events that occur in the tails of these distributions. Yet there is evidence to support the conclusion that as the distributions change with global warming, phenomena thought to be extremes today may be less extreme, in the statistical sense, in the future—that is, more likely to occur in the future than in the past.

    Karen McKinnon of the University of California, Los Angeles noted that the upper tail of temperature distributions is getting longer than the lower tail; that is, the distribution is skewed toward the hot end. This suggests there will be more heat waves than unusually cold days in the future. In addition, she showed that, globally, very humid areas are getting more humid and dry areas are getting drier. These observations have major implications for human health (impacts of heat waves coupled with high humidity) and so-called “fire weather”—extended hot, dry conditions that amplify the probability of wildfires.

    The impact of hot, humid weather on human health is a major area of scientific debate. Matt Huber of Purdue University is working to reconcile differing opinions among physiologists and epidemiologists about how extremes in heat and humidity contribute to heat stroke and death. Physiologists argue that hot, moist conditions (high wet bulb temperature) increase human health risks, while epidemiologists observe that the data doesn’t back up this conclusion at the population level. Huber takes as a given that there are thresholds in wet bulb temperature above which mammals’ (including humans) ability to function (dissipate metabolic heat from the core of the body) declines precipitously. These types of weather conditions will become more frequent and extreme in tropical and subtropical regions (e.g., in many developing nations), with negative impacts on human health and regional economies due to declines in peoples’ ability to sustain outdoor labor (agriculture and construction, for example).

    Epidemiologist Kristi Ebi of the University of Washington uses statistical tools to quantify excess deaths during heatwaves caused by the statistical extremity of the heat wave and anthropogenic climate change. Attributing excess heatwave deaths directly to climate change is difficult because of the complex set of factors that contribute to each death such as individual vulnerability to heat and humidity, socioeconomic status and access to cooling, and local baseline weather. Her conclusion, after taking these factors into account, is there will be statistically significant excess deaths during heat waves that are attributable to climate change.

    Federal investments in climate research that cut across the disciplines, such as the collaborative opportunities provided by IMSI, are critical to advancing our understanding of the complex interconnectedness of Earth and human systems, anthropogenic impacts, and developing strategies for carbon reduction while also mitigating and adapting to the effects of our warming planet.

    Visit the IMSI long program webpage for detailed information about each workshop. Information about how to propose an activity is also available.

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