Fluctuations in the weather can have a significant impact on melting Antarctic ice, and models that do not include this factor can underestimate the global impact of sea level rise, according to CLIMA researchers. “We know ice sheets are melting as global temperatures increase, but uncertainties remain about how much and how fast that will happen,” said Chris Forest, professor of climate dynamics at Penn State. “Our findings shed new light on one area of uncertainty, suggesting climate variability has a significant impact on melting ice sheets and sea level rise.”
According to CLIMA researchers, to assess climate risks, less complex models, with their ability to better sample uncertainties, may be a better choice. “There is a downside to the very detailed, very complex models we often strive for,” said Casey Helgeson, assistant research professor, Earth and Environmental Systems Institute. “Sometimes the complexity of scientific tools constrains what we can learn through science. The choke point isn’t necessarily at the knowledge going into a model, but at the processing.”
EESI, CLIMA and MARISA welcome Benjamin Watson as the new Coastal Climate Extension Specialist for the Chesapeake Bay Region, stationed at the Virginia Institute for Marine Sciences (VIMS). Ben brings a background in science outreach and engagement in local communities. ‘I look forward to continuing to work with stakeholders across the watershed to promote informed environmental decision-making,’ he said, ‘and to making sure Chesapeake communities are prepared for the challenges of climate change.’
Equilibrium climate sensitivity — how sensitive the Earth’s climate is to changes in atmospheric carbon dioxide — may be underestimated in individual climate models, according to a team of climate scientists. “Probabilistic estimates of climate system properties often rely on the comparison of model simulation to observed temperature records and an estimate of the internal climate variability,” the researchers report in Geophysical Research Letters. If the internal climate variability is wrong, then the probabilistic estimates will be wrong and climate predictions could miss the mark.