Abstract Machine learning approaches are increasingly used to extract patterns and insights
from the ever-increasing stream of geospatial data, but current approaches may not be …

Deep neural network models have become ubiquitous in recent years and have been
applied to nearly all areas of science, engineering, and industry. These models are …

Geophysicists examine and document the repercussions for the earth's climate induced by
alternative emission scenarios and model specifications. Using simplified approximations …

Climate change and resource exploitation have been shown to modify the importance of
bottom-up and top-down forces in ecosystems. However, the resulting pattern of trophic …

The social cost of carbon (SC-CO2) is an economic metric intended to provide a
comprehensive estimate of the net damages-that is, the monetized value of the net impacts …

Climate models have been key for assessing the impact of climate change and simulating
future climate scenarios. The machine learning (ML) community has taken an increased …

Decision making in uncertain scenarios is an ubiquitous challenge in real world systems.
Tools to deal with this challenge include simulations to gather information and statistical …

Many different emission pathways exist that are compatible with the Paris climate
agreement, and many more are possible that miss that target. While some of the most …

LongRunMIP: Motivation and Design for a Large Collection of Millennial-Length AOGCM
Simulations in: Bulletin of the American Meteorological Society Volume 100 Issue 12 (2019) …

Understanding and estimating regional climate change under different anthropogenic
emission scenarios is pivotal for informing societal adaptation and mitigation measures …