Robert Jnglin Wills

These simulations were only possible by a university researcher like me with a lot of technical help (and computer time) from @NCAR_Science , especially from co-authors Adam Herrington & Isla Simpson. This project has been a slow cooker we've been working on since late 2020 (6/6)

Our paper and its implications for ongoing high-resolution modeling efforts is nicely summarized in an Editor's Highlight by Hannah Chistensen. (5/6) eos.org/editor-highlig…

Promoting papers feels a bit strange this week considering everything going on, but I am excited about our new paper showing a larger influence of Gulf Stream anomalies on the atmospheric circulation over Europe in models that resolve weather fronts (1/6) doi.org/10.1029/2023MS…

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 temperature anomalies. From the presentation by @ClimateAnomaly in the April 2024 ECS & Climate Sensitivity Symposium. sites.google.com/tamu.edu/ecs-s…

(3) discussion of the tradeoffs of high resolution and the lack of a specific scale where all of the relevant processes are resolved, (4) discussion of new ML approaches to optimize parameters directly, and (5) discussion of a balanced approach to bring this all together. (3/3)

Included is (1) an evaluation of how climate skill has improved over the last 20 years and is continuing to (mostly) improve with efforts to increase resolution, (2) considerations for successful process-informed parameterizations, ... (2/3)

To optimize climate models, how do we prioritize between resolution & ensemble size, between physical param. development & incorporating AI? In a perspective paper out this week, Tapio Schneider, Ruby Leung, and myself argue we need a balanced approach: acp.copernicus.org/articles/24/70…

@AndrewIWilliams @rshivpriyam Congrats Priyam!

Huge!! Congrats @rshivpriyam , well deserved 💪💪💪

@CColose I suspect the quicker reversal in CESM1-HR is instead due to aerosols, ozone, etc. (and timing supports this). Peter's shot noise explanation of the low-frequency discrepancy provides a reason there might not be a real mismatch, but it warrants caution and work on improving OBS

@ClimateAnomaly I wonder how long this can last in the transient in the few ensembles that simulate this historic pattern? I wasn’t very convinced by the relevance of Peter’s argument. There are other possible reasons for model-proxy mismatches at very low frequencies.

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

@CColose Maher et al. 2023 (doi.org/10.5194/esd-14…) Fig. 7 shows that the few models that show an ocean themostat response show it until 2100 (note, different boxes than Wills et al. 2022). The case of GFDL-ESM2M is discussed in Kohyama et al. 2017 (doi.org/10.1175/JCLI-D…) (1/2)

@RiboldiJacopo Took almost the same two pictures a few hours earlier

The first part between Zurich and Buchs SG is probably my absolute favourite train line in 🇨🇭 First it borders the lake of Zürich, then peeks into the mountains of Kanton Glarus...

The ~8h #traintoEGU journey of the ETH Zürich Atmospheric Dynamics group begins now! Glad to have a direct connection to Vienna :) Hit me up if you also are on the RailJet Express 165 🚆 ! Looking forward to a week full of new science at #EGU24! 🌦❄️

"#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 @ClimateAnomaly (@usys_ethzh). Statistical and machine learning methods offer one solution 🌎 #SGCD24

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 & @ClimateAnomaly

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…

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

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