Heatwaves are among the deadliest extreme weather events. As the climate changes, they are increasingly affecting regions that have not historically experienced such extremes with the same frequency or intensity. Such recurrent and prolonged periods of extreme heat can affect a wide range of sectors, particularly human health. However, some of the societal impacts of heatwaves can be mitigated through region-specific planning across timescales ranging from hours to months. Strikingly, heatwaves are among the most predictable extreme weather events, offering the potential for lead times of several weeks and, consequently, early warnings. This talk explores the interplay between the atmospheric drivers of heatwaves, their predictability, and the implications for their societal impacts.

This presentation explores how advanced sensitivity analysis methods within an atmospheric chemical transport model (CTM) can be used to inform air quality management decisions. We do so by developing and applying reverse sensitivity (or adjoint) method in a state-of-the-art CTM to various applications. These applications demonstrate how an adjoint model provides unparalleled granularity across locations and emission sectors. As a prime example, the method is applied to examine the impact of decarbonization policies on air quality by estimating co-benefits of reducing CO2 emissions from a range of combustion sources.  Location-specific sectoral co-benefits will be presented at local, national, and hemispheric resolutions. The adjoint-derived results are novel in two important ways. First, they provide location-specific health co-benefit estimates for various sectors and at different resolutions. Second, we show how the method can be extended to estimate location-specific environmental justice co-benefits at urban scale. Population health and environmental justice co-benefits can also be calculated as monetized estimates (i.e., $/ton-CO2), allowing for direct comparison with the social cost of carbon as a combined measure of societal impact. Health co-benefit estimates for the year 2016, and for the northern hemisphere (108 km), the United States (12 km), and the city of Los Angeles (1 km) will be presented as examples. Environmental justice co-benefits are calculated for Los Angeles and for exposure disparities with respect to race and income. The results demonstrate how location-specific co-benefits may be used to conduct a more holistic, and yet granular accounting of benefits of decarbonization. Furthermore, we discuss how the inclusion of population health and environmental justice in the accounting of mitigation benefits has the potential to expand the narrative and broaden the reach of climate discourse among decision makers and the public alike.

(1) tbd (2) Evaluating Subseasonal NeuralGCM Forecasts of North Atlantic Weather Regimes (3) tbd (4) Hail Risk and Hail Severity in Climate Change

((1) tbd (2) tbd (3) tbd (4) European Hail Risk under Climate Change (HAR-CC)

(1) Elucidating the impact of rain on shallow convective organization using long-term observations of downdrafts (2) tbd (3) tbd (4) The Annual Meeting of the DMG (German Meteorological Society) 1926 in Karlsruhe

Longwave atmospheric cloud-radiative effect (LW ACRE) is important in supporting a variety of spatially organized moist systems in tropical weather and climate, from tropical cyclones, the Madden-Julian oscillation (MJO), to Walker circulation. Our works aim to understand the fundamental properties and the processes that generate the feedback between LW ACRE and tropical precipitating convection. I will discuss two major findings. (1) We explain why LW ACRE only supports larger-scale circulations, as in the MJO but not in mesoscale convective systems (MCSs), by studying a field campaign MCS case. (2) Why does such scale dependency exist? We propose a minimal conceptual recipe to quantitatively describe the scale dependency of LW-convection feedback by simply assuming that anvil clouds, which primarily generate LW ACRE, act as long-term memory of precipitation. The scale dependency of LW-convection feedback is, thus, a natural emerging property of longer memory of anvil clouds than precipitation. Finally, I will discuss how this conceptual model may provide information of how anvil LW ACRE is generated in tropical convective systems.

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The biosphere is a source and sink of gases and particles to the atmosphere. Thus, the biosphere sets the natural or
“preindustrial” chemical composition of the atmosphere. Human activity and climate change can perturb the
emissions to, deposition from, and chemistry within, the troposphere. At the same time, air pollution can impact
global ecosystem health, with implications for climate and food security. In this seminar, I will illustrate these ideas
with some recent studies from my group, highlighting in particular: (1) fire as a source of ozone and aerosols, (2) preindustrial
to future evolution of biogenic organic aerosol, and (3) the impact of ozone and air pollution on natural and
managed ecosystem health.

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(1) Observing mesoscale variability with KITsondes during NAWDIC (2) Improving Subseasonal Weather Forecast using Tropical Weighting: A Fine-Tuned 2D Transformer (3) Callie Maier (4) tbd

Hail and severe convective storms rank among the most damaging weather hazards in Central Europe. Relevant processes span a range of scales: from hailstone growth at the microphysical level over storm dynamics and organization on the mesoscale to larger-scale synoptic environments and long-term climate change. The analysis of societal risk adds complexity, since impacts depend on exposure, vulnerability, and how extremes combine. This talk brings together several lines of research, linking processes to impacts across scales. Firstly, we investigate if we can learn about relevant processes from machine learning models that predict observed hail stone sizes. Secondly, on a regional scale, we explore whether emerging convective hotspots around the European Alps can be connected to large-scale patterns. Finally, we focus on impact relevance, presenting resampling and storyline approaches that allow for process-sensitive risk modeling to address both short-term and long-term impact assessments.

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