Fiaz Ahmed, J. David Neelin
Tropical East and West Pacific Oceans display differences in their vertical velocity (or omega) profiles. The East Pacific is characterized by bottom-heavy profiles, while the West Pacific is characterized by top-heavy profiles. Although inter-basin differences in the horizontal SST gradient are known to be important, physical reasons for why these omega structure variants exist are not fully understood. This question is addressed using a steady, linear model on an $f$-plane with $n$ atmospheric layers. Convection and radiation are parameterized as linear responses to thermodynamic perturbations with convective nonlinearity approximated by convection on/off regimes. The free (or eigen) modes of the model yield vertical structures resembling the observed baroclinic modes of the tropical atmosphere, with each mode associated with a characteristic horizontal scale (the eigenvalue). In the standard parameter regime, the first-baroclinic mode has a large spatial scale ($\sim$ 1500 km) while the second-baroclinic mode has a smaller spatial scale ($\sim$ 250 km). When the model is forced with a strong- and weak-gradient surface temperature ($T_s$) patterns, the resulting omega profiles assume bottom- and top-heavy structures respectively -- mimicking the observed differences between East and West Pacific Oceans. Additional dependence on the magnitude of the Coriolis force is also observed. The connection between the vertical structure and the horizontal scale of the baroclinic modes explains why a strong-gradient $T_s$ profile projects strongly onto the second-baroclinic mode yielding bottom-heavy omega profiles in the eastern Pacific, while a weak-gradient $T_s$ profile projects strongly onto the first-baroclinic mode, yielding top-heavy omega profiles typical of the Western Pacific.
Spencer A Hill, Destiny Zamir Meyers, Adam H Sobel, Michela Biasutti, Mark A Cane, Michael K Tippett, Fiaz Ahmed
Extreme rainfall in the Indian summer monsoon can be destructive and deadly. Although El Niño/ events in the equatorial Pacific make dry days and whole summers more likely throughout India, their influence on daily extremes is not well established. Despite this summer-mean drying effect, we show using observational data spanning 1901-2020 that El Niño increases extreme rainfall likelihoods within monsoonal India, especially in the the summer's core rainy areas of central-eastern India and the narrow southwestern coastal band. Conversely, extremes are broadly suppressed in the drier southeast and far northwest, and more moderate accumulations are inhibited throughout the domain. These rainfall signals appear driven by corresponding ones in convective buoyancy, provided both the undilute instability of near-surface air and its dilution by mixing with drier air above are accounted for. When the summer ENSO state is predicted from a seasonal forecast ensemble initialized in May, the extreme rainfall patterns broadly persist, suggesting the potential for skillful seasonal forecasts. The framework of analyzing the full distributions of rainfall and convective buoyancy could be usefully applied to hourly extremes, other tropical regions under ENSO, other variability modes, and to trends in extreme rainfall under climate change.
Sungduk Yu, Zeyuan Hu, Akshay Subramaniam, Walter Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius J. M. Busecke, Nora Loose, Charles I. Stern, Tom Beucler, Bryce Harrop, Helge Heuer, Benjamin R. Hillman, Andrea Jenney, Nana Liu, Alistair White, Tian Zheng, Zhiming Kuang, Fiaz Ahmed, Elizabeth Barnes, Noah D. Brenowitz, Christopher Bretherton, Veronika Eyring, Savannah Ferretti, Nicholas Lutsko, Pierre Gentine, Stephan Mandt, J. David Neelin, Rose Yu, Laure Zanna, Nathan Urban, Janni Yuval, Ryan Abernathey, Pierre Baldi, Wayne Chuang, Yu Huang, Fernando Iglesias-Suarez, Sanket Jantre, Po-Lun Ma, Sara Shamekh, Guang Zhang, Michael Pritchard
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderstorms that occur on the sub-resolution scale. Hybrid methods combining physics with machine learning (ML) offer faster, higher fidelity climate simulations by outsourcing compute-hungry, high-resolution simulations to ML emulators. However, these hybrid ML-physics simulations require domain-specific data and workflows that have been inaccessible to many ML experts. As an extension of the ClimSim dataset (Yu et al., 2024), we present ClimSim-Online, which also includes an end-to-end workflow for developing hybrid ML-physics simulators. The ClimSim dataset includes 5.7 billion pairs of multivariate input/output vectors, capturing the influence of high-resolution, high-fidelity physics on a host climate simulator's macro-scale state. The dataset is global and spans ten years at a high sampling frequency. We provide a cross-platform, containerized pipeline to integrate ML models into operational climate simulators for hybrid testing. We also implement various ML baselines, alongside a hybrid baseline simulator, to highlight the ML challenges of building stable, skillful emulators. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim and https://github.com/leap-stc/climsim-online) are publicly released to support the development of hybrid ML-physics and high-fidelity climate simulations.
Tom Beucler, Pierre Gentine, Janni Yuval, Ankitesh Gupta, Liran Peng, Jerry Lin, Sungduk Yu, Stephan Rasp, Fiaz Ahmed, Paul A. O'Gorman, J. David Neelin, Nicholas J. Lutsko, Michael Pritchard
Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML) algorithms hold promise to improve such process representations, but tend to extrapolate poorly to climate regimes they were not trained on. To get the best of the physical and statistical worlds, we propose a new framework - termed "climate-invariant" ML - incorporating knowledge of climate processes into ML algorithms, and show that it can maintain high offline accuracy across a wide range of climate conditions and configurations in three distinct atmospheric models. Our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency, data efficiency, and generalizability across climate regimes.