Keywords: climate-informed decision-making, arctic, arctic sea ice, permafrost, climate tipping points, forecasting, AI-enabled predictive analytics
Abstract: The Johns Hopkins University/Applied Physics Laboratory
(JHU/APL) is developing machine learning, visualization,
and modeling algorithms to enable climate-informed decision-
making tools for the Arctic. These tools will help planners
develop resilient future Arctic capabilities and infrastructure
by allowing them to explore the evolving short-term
and long-term Arctic environment. We touch here on two recent
JHU/APL research efforts related to Arctic decisionmaking
tools: one on forecasting short-term Arctic sea ice extent
and another on climate-informed decision-making given
potential longer-term Arctic changes, including permafrost
thaw. We highlight the need to incorporate climate tipping
points into future Arctic decision-making tools.
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