Observations from 1980–2020 of near-surface atmospheric water vapor in arid and semi-arid regions don’t match climate models. This gap between expectations and data has major implications for hydroclimate projections, including fire hazards. In PNAS: ow.ly/JFLG50QmrbP

Less than a week to submit an abstract for #EGU24. If you work on understanding the contributions of internal variability and forced responses to historical or future climate change and climate impacts (e.g., using large ensembles), then please consider submitting to our session!

Really excited to share our new paper on climate-invariant machine learning science.org/doi/10.1126/sc… to solve extrapolation issues under climate change, led by the great Tom Beucler

Meet our Speaker Robert Jnglin Wills Robert Jnglin Wills D-USYS@ETH at the #SGCD24 and discuss what leads some climate change impacts to be robust and others uncertain. ➡️Register now: proclim.ch/id/EdMcf

New paper in PNASNews led with @cristiproist shows that a weird spatial pattern of temperature change has slowed global-mean warming since 1980. Because the pattern could evolve in the future, observed warming doesn’t help us constrain long-term warming. pnas.org/doi/10.1073/pn…

Join us next month for a panel discussion on "Can we rule out internal variability as the main driver of recent tropical SST trends?" w/Peter Huybers & Robert Jnglin Wills

"#Climate models have done well, but they also show some biases. It is essential to improve them in order to understand the impact of #GlobalChange on regional weather", explains Robert Jnglin Wills (D-USYS@ETH). Statistical and machine learning methods offer one solution 🌎 #SGCD24

Is the La-Niña-like warming pattern over the past 40+ years forced or unforced? In this seminar, I argue it is forced & show statistical + hi-res model forced response estimates. Peter Huybers discusses obs. uncertainty & evidence models have too little low-freq. variability

Using AI to understand the Earth System response: The Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP) uses statistical and machine learning methods to try to figure out the true forced response, as reflected in the evolving pattern of surface