I wrote a short piece on the debate about the future of climate modeling: Should we pool resources to do km-scale "masterpiece" runs or focus on using ML to build data-driven models that can still do large ensembles? 1/3

Some context on the reduction in NIH overhead rates: In 2021-22 UC's operating budget was ~$47 billion of which $987 mill came from indirect cost recovery (~2%). 20% of this went to grant and contract administration, ~45% to UC General Fund and ~35% to the UC "Opportunity Fund"

Hopefully the damage is just 5-10% staff cuts and “searching high and low for DEI” It’s disappointing this is all a group of tech-minded people can think to do. Now should be an exciting time for thinking about modernizing NOAA: a golden age of earth obs +rapid ML advances in WF

2024 was the warmest year on record and it also had the largest recorded increase in atmospheric CO2 (+3.58ppm). These are related: a strong El Niño drove much of the warming and reduced carbon uptake by drying out Northern Hemisphere land

A good paper for learning about connection between California wildfires and climate change is Williams et al (2019). They look at the historical record, but provide a nice overview of the different types of California fires and their climatic drivers…

There’s a new paper in Science adding to the discussion of the “missing” 0.2K in 2023’s extreme warmth. The authors show that the 2023 featured unusually low planetary albedo, linked to a multi-year trend of declining low clouds, particularly over the North Atlantic and tropical oceans (1/5)