Machine Learning for Climate Predictability
Climate models are the gold standard for conducting studies of what the climate might look like in the future. But they are very computationally expensive – running a single simulation out to the end of the century can take weeks on a supercomputer. Is there a faster way to get information that might not be perfect but is good enough?
Uncertainty in Solar Geoengineering Research
Solar geoengineering describes a set of technologies designed to temporarily, deliberately modify the climate to prevent the worst effects of climate change while greenhouse gas reductions ramp up. The idea of stratospheric sulfate aerosol injection, which would mimic the cooling effects seen after large volcanic eruptions, comes with substantial risk and uncertainty. How do we tell which uncertainties are the most important?
Environmental stopgaps are everywhere in science and sustainability: nuclear waste interim storage, salmon fish hatcheries, and the Salton Sea mitigation water and management plan are some examples. These are all temporary measures to “buy time” for a more complex or long-term solution.
