Events

 
 

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

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.

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

TBD

El Nino, historically considered a localized regional phenomenon, rose  to the forefront of the scientific research agenda thanks to the extraordinary
impacts of the 1972--1973 event. Widespread droughts led to a decline in global food production, the first time since the advent of the Green revolution. 
A drastic crash in anchoveta catches led to the collapse of the fishing industry in Peru. The 1982--1983 El Nino literally rained out Queen Elizabeth II's royal cruise towards California to meet President Reagan. El Nino catapulted into discourses of environmental politics and public science communication when the human and environmental disasters during the 1997--1998 El Nino was turned into a continuous spectacle by news outlets. 
 
Since 1997 many strong El Ninos have come and gone, but none had the scale and extremity of hydroclimatic impacts associated with the aforementioned events. What makes the 1972, 1982, and 1997 El Ninos distinct is their outstanding character of eastern-ocean intensification, marked by an increasing amplitude of anomalous sea surface temperatures and sea surface heights from the central towards the far-eastern Pacific. It is this remarkable eastern-ocean intensification that leads to its most notable impacts --- devastating floods in Peru and California, as the rain bearing systems break through the dry eastern equatorial Pacific to reach the  far eastern Pacific.

As an extremely strong El Nino rises up in 2026, the spectre looming is of widespread droughts, floods, and ecosystem damage, especially in the tropics.
The speaker would like to make the case why the 2026 El Nino's impact could be less dramatic than anticipated. He bases his reasoning on the
demonstrable consequences of equatorial ocean-atmosphere dynamics in limiting eastern-ocean intensification. Under ENSO dynamics, an atmospheric
Kelvin wave response develops to the east of the primary convective anomalies. The associated surface winds dampen SST variations in the
eastern Pacific, preventing eastern-ocean intensification.
 
In his hypothetical model, when a strong El Nino co-occurs with a positive Indian Ocean Dipole (IOD), this dynamics changes with the IOD inducing powerful surface wind anomalies across the western Pacific. These western Pacific wind anomalies offset the self-limiting damping effect and facilitates eastern-ocean intensification. This paradigm is used to explain the aberrant behavior
of the otherwise strong 2015-2016 El Nino's hydroclimatic impacts, and the features of the ongoing 2026 El Nino. Finally, I discuss the role of decadal variations as a factor in explaining the intensity of the strong El Ninos of 2015 and 2026.

(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

(1) tbd (2) tbd (3) tbd (4) tbd

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.

(1) tbd (2) tbd (3) tbd (4) tbd

(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.

(1) tbd (2) tbd  (3) tbd (4) tbd