https://arxiv.org/abs/2305.07859

*Authors*
Subhashis Hazarika
<https://arxiv.org/search/cs?searchtype=author&query=Hazarika%2C+S>, Haruki
Hirasawa <https://arxiv.org/search/cs?searchtype=author&query=Hirasawa%2C+H>
, Sookyung Kim
<https://arxiv.org/search/cs?searchtype=author&query=Kim%2C+S>, Kalai Ramea
<https://arxiv.org/search/cs?searchtype=author&query=Ramea%2C+K>, Salva R.
Cachay <https://arxiv.org/search/cs?searchtype=author&query=Cachay%2C+S+R>,
Peetak
Mitra <https://arxiv.org/search/cs?searchtype=author&query=Mitra%2C+P>, Dipti
Hingmire <https://arxiv.org/search/cs?searchtype=author&query=Hingmire%2C+D>
, Hansi Singh
<https://arxiv.org/search/cs?searchtype=author&query=Singh%2C+H>, Phil J.
Rasch <https://arxiv.org/search/cs?searchtype=author&query=Rasch%2C+P+J>
*13 May 2023*

Download PDF <https://arxiv.org/pdf/2305.07859>


*Abstract*

Clouds have a significant impact on the Earth's climate system. They play a
vital role in modulating Earth's radiation budget and driving regional
changes in temperature and precipitation. This makes clouds ideal for
climate intervention techniques like Marine Cloud Brightening (MCB) which
refers to modification in cloud reflectivity, thereby cooling the
surrounding region. However, to avoid unintended effects of MCB, we need a
better understanding of the complex cloud to climate response function.
Designing and testing such interventions scenarios with conventional Earth
System Models is computationally expensive. Therefore, we propose a hybrid
AI-assisted visual analysis framework to drive such scientific studies and
facilitate interactive what-if investigation of different MCB intervention
scenarios to assess their intended and unintended impacts on climate
patterns. We work with a team of climate scientists to develop a suite of
hybrid AI models emulating cloud-climate response function and design a
tightly coupled frontend interactive visual analysis system to perform
different MCB intervention experiments.


*Source: arXiv*

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