Engineering topics

Muelmenstaedt, Johannes

Publications and source records attributed to Muelmenstaedt, Johannes.

3 records

DOE OSTI2022

DOE-NOAA Marine Cloud Brightening (Workshop Report 2022)

Marine Cloud Brightening (MCB) refers to the deliberate injection of aerosol particles into marine clouds to increase their reflection of solar radiation to temporarily cool the planet while decarbonization efforts are pursued. A workshop was conducted to assess the state of knowledge in the field of MCB, and to provide a possible research path toward reducing unknowns in key components of the underlying physical science. This three-day workshop took place in April 2022 and was jointly sponsored by Department of Energy (DOE)’s Atmospheric System Research (ASR) program and the National Oceanic and Atmospheric Administration (NOAA). The workshop focused on identifying key physical science knowledge gaps necessary to answer the following driving questions: 1) Is MCB feasible over sufficiently large regions and is implementation practicable for long-enough durations to avert the worst impacts of global warming? 2) If practicable, what will be the regional impacts of such MCB interventions? 3) Do we have adequate systems in place to detect and quantify the effects of such interventions? 4) What physical and engineering science challenges must be resolved satisfactorily before we can consider embarking on MCB?

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) Science Plan

Coastal cities provide the opportunity to characterize marine clouds and the substantial effects of manmade particles on cloud properties and processes. La Jolla lies to the north of San Diego, California, but it is often about a day directly downwind of the major pollution sources located in the ports of Los Angeles and Long Beach. The large dynamic range of aerosol particle concentrations combined with the multi-hour to multi-day persistence of stratocumulus cloud layers makes the site ideal for investigating the seasonal changes in cloud and aerosol properties as well as the quantitative relationships between cloud and aerosol properties.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Facilitating better and faster simulations of aerosol-cloud interactions in Earth system models

Focal Area(s): 1. Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising a hierarchy of models. 2. Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: One major challenge that Earth system models (ESMs) face in providing credible prediction of the Earth system and its water cycle characteristics (e.g., mean state, variability, and extreme events) is to accurately simulate aerosol-cloud interactions (ACI). The physical, chemical, and dynamical processes affecting ACI are extremely complex and they range from nanoscale to planetary scale. In each model development cycle, scientists spend significant efforts investigating model deficiencies and uncertainties associated with aerosols (e.g., emissions, chemical processes, aerosol microphysics, and transport) and clouds (e.g., macrophysics, microphysics, turbulence, and large-scale circulation) in order to develop improved treatments. However, despite decades of active research, ACI is still a major source of uncertainty in climate projections, even though great progress has been made. Specific scientific challenges include: (i) Parameterizations are developed based on limited data; (ii) The complexity of a parameterization required for accurate predictions is not understood; (iii) Incomplete and unknown physics leads to errors in the fully coupled Earth system; and (iv) Complex physics is computationally too expensive to employ in ESMs.

54 ENVIRONMENTAL SCIENCES↗