[
{
"id": "authors:avera-s5s35",
"collection": "authors",
"collection_id": "avera-s5s35",
"cite_using_url": "https://authors.library.caltech.edu/records/avera-s5s35",
"type": "article",
"title": "Online learning of eddy-viscosity and backscattering closures for geophysical turbulence using ensemble Kalman inversion",
"author": [
{
"family_name": "Guan",
"given_name": "Yifei",
"orcid": "0000-0003-2070-3654"
},
{
"family_name": "Hassanzadeh",
"given_name": "Pedram",
"orcid": "0000-0001-9425-8085"
},
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
},
{
"family_name": "Dunbar",
"given_name": "Oliver",
"orcid": "0000-0001-7374-0382",
"clpid": "Dunbar-Oliver-R-A"
},
{
"family_name": "Huang",
"given_name": "Daniel Zhengyu"
},
{
"family_name": "Wu",
"given_name": "Jinlong",
"orcid": "0000-0001-7438-4228"
},
{
"family_name": "Lopez-Gomez",
"given_name": "Ignacio",
"orcid": "0000-0002-7255-5895"
}
],
"abstract": "Different approaches to using data-driven methods for subgrid-scale closure modeling of geophysical turbulence have emerged recently. Most of these approaches are data hungry and lack interpretability and out-of-distribution generalizability. Here, we use a hybrid approach that combines turbulence theory, physics-based modeling, and data-driven methods to overcome these challenges. Specifically, we address the parametric uncertainty of well-known physics-based large-eddy simulation (LES) closures: the Smagorinsky (Smag) and Leith eddy-viscosity models (one free parameter) and the Jansen-Held (JH) backscattering model (two free parameters). For various cases of two-dimensional turbulence, optimal parameters are first learned online from data via ensemble Kalman inversion (EKI), such that for each case, the LES energy spectrum matches that of direct numerical simulation (DNS). We quantify the uncertainties on these parameters using a modern machine-learning-accelerated Bayesian workflow, \", , \" Only a small training dataset is needed (to calculate the DNS spectra); i.e., the approach is data-efficient. We find the optimized parameter(s) and their associated uncertainty for each closure to be constant across broad flow regimes that differ in dominant length scales, eddy/jet structures, and dynamics, suggesting that these closures are generalizable. Next, we show that the online learned constants agree with the predictions of a recent semianalytical derivation, providing further interpretability. In both and tests that include examining the extreme events, LES with optimized closures, especially with JH, outperforms the baselines (LES with standard Smag, dynamic Smag, or Leith). This work shows the promise of combining advances in theory, physics-based modeling (e.g., JH), and data-driven modeling (e.g., online learning with EKI) to develop data-efficient frameworks for accurate, interpretable, and generalizable closures for geophysical turbulence, with ultimate applications in weather and climate prediction.
",
"doi": "10.1103/mnbm-3g56",
"issn": "2643-1564",
"publisher": "American Physical Society",
"publication": "Physical Review Research",
"publication_date": "2026-05-27",
"series_number": "2",
"volume": "8",
"issue": "2",
"pages": "023215"
},
{
"id": "authors:4jy0k-xx805",
"collection": "authors",
"collection_id": "4jy0k-xx805",
"cite_using_url": "https://authors.library.caltech.edu/records/4jy0k-xx805",
"type": "article",
"title": "Thank You to Our 2025 Peer Reviewers",
"author": [
{
"family_name": "Griffies",
"given_name": "Stephen M.",
"orcid": "0000-0002-3711-236X"
},
{
"family_name": "Fan",
"given_name": "Jiwen",
"orcid": "0000-0001-5280-4391"
},
{
"family_name": "MacBean",
"given_name": "Natasha",
"orcid": "0000-0001-6797-4836"
},
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
},
{
"family_name": "Grooms",
"given_name": "Ian",
"orcid": "0000-0002-4678-7203"
}
],
"abstract": "The editors of Journal of Advances in Modeling Earth Systems thank the 1,035 reviewers who provided 1,649 reviews during 2025. Their hard work and insights, typically done anonymously, benefits authors, readers, and the broader science community.
",
"doi": "10.1029/2026ms006024",
"issn": "1942-2466",
"publisher": "American Geophysical Union",
"publication": "Journal of Advances in Modeling Earth System (JAMES)",
"publication_date": "2026-05",
"series_number": "5",
"volume": "18",
"issue": "5",
"pages": "e2026MS006024"
},
{
"id": "authors:ktk32-whm45",
"collection": "authors",
"collection_id": "ktk32-whm45",
"cite_using_url": "https://authors.library.caltech.edu/records/ktk32-whm45",
"type": "article",
"title": "Reconciling Jupiter's vertical motions with the observed cloud structure in the upper troposphere",
"author": [
{
"family_name": "Mendon\u00e7a",
"given_name": "Jo\u00e3o M.",
"orcid": "0000-0002-6907-4476"
},
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
},
{
"family_name": "Liu",
"given_name": "Junjun",
"clpid": "Liu-Junjun"
},
{
"family_name": "Lian",
"given_name": "Yuan",
"orcid": "0000-0003-1776-291X"
}
],
"abstract": "\n
\n
\n
The eddy fluxes of angular momentum in Jupiter’s upper troposphere are known to converge in prograde jets and diverge in retrograde jets. Away from the equator, this implies convergence of the Eulerian mean meridional flow in zones (anticyclonic shear) and divergence in belts (cyclonic shear). It indicates lower-tropospheric downwelling in zones and upwelling in belts because the mean meridional circulation almost certainly closes at depth. Yet the observed banded structure of Jupiter’s clouds and hazes suggests that there is upwelling in the brighter zones and downwelling in the darker belts. Here, we show that this apparent contradiction can be resolved by considering not the Eulerian but the transformed Eulerian mean circulation, which includes a Stokes drift owing to eddies and is a better approximation of the Lagrangian mean transport of tracers such as ammonia. The potential vorticity structure inferred from observations paired with mixing length arguments suggests that there is transformed Eulerian mean upwelling in zones and downwelling in belts. Simulations with a global circulation model of Jupiter’s upper atmosphere demonstrate the plausibility of these inferences and allow us to speculate on the band structure at deeper levels.
\n
\n
\n
Land surface models (LSMs) are essential tools for simulating the coupled climate system, representing the dynamics of water, energy, and carbon fluxes on land and their interaction with the atmosphere. However, parameterizing sub\u2010grid processes at the scales relevant to climate models (~10–100 km) remains a considerable challenge. The parameterizations typically have a large number of unknown and often correlated parameters, making calibration and uncertainty quantification difficult. Moreover, many existing LSMs are not readily adaptable to the incorporation of modern machine learning (ML) parameterizations trained with in situ and satellite data. This article presents the first version of ClimaLand, a new LSM designed for overcoming these limitations, including a description of the core equations underlying the model, the results of an extensive set of validation exercises, and an assessment of the computational performance of the model. We show that ClimaLand can leverage graphics processing units for computational efficiency, and that its modular architecture and high\u2010level programming language, Julia, allows for integration with ML libraries. In the future, this will enable efficient simulation, calibration, and uncertainty quantification with ClimaLand.
",
"doi": "10.1029/2025ms005118",
"issn": "1942-2466",
"publisher": "American Geophysical Union",
"publication": "Journal of Advances in Modeling Earth System (JAMES)",
"publication_date": "2026-01",
"series_number": "1",
"volume": "18",
"issue": "1",
"pages": "e2025MS005118"
},
{
"id": "authors:ksr6w-qkg20",
"collection": "authors",
"collection_id": "ksr6w-qkg20",
"cite_using_url": "https://authors.library.caltech.edu/records/ksr6w-qkg20",
"type": "article",
"title": "An Energetic Framework for Understanding Hadley Circulation Width Variations: Seasonal Cycle, ENSO, and Global Warming",
"author": [
{
"family_name": "Khapikova",
"given_name": "Polina",
"clpid": "Khapikova-Polina"
},
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
}
],
"abstract": "The Hadley Circulation (HC), a dominant driver of energy and moisture transport in low latitudes, varies in meridional extent on timescales ranging from seasonal to multidecadal. For example, the subtropical termini of the descending HC branches shift latitudinally by 10° over the seasonal cycle, contrasting with a 34° seasonal shift of the Intertropical Convergence Zone (ITCZ). While diagnostic energetic frameworks have successfully provided insight into ITCZ migrations, this study applies a similar framework to understand HC extent variations across timescales. The framework relates the HC extent to the tropical net energy input (NEI) and the eddy energy export from the HC to the extratropics. Variations in HC extent can therefore be decomposed into contributions from NEI and eddy energy export changes, which play different roles on different timescales. Analysis of observations and climate simulations shows that HC contraction during El Niño is primarily related to increased tropical NEI, while HC expansion under global warming is primarily related to increased eddy energy export. This energetic framework, while not fully predictive because it excludes factors such as the angular momentum balance, offers a new perspective for understanding variations in HC extent.
",
"doi": "10.1175/jas-d-25-0052.1",
"issn": "0022-4928",
"publisher": "American Meteorological Society",
"publication": "Journal of the Atmospheric Sciences",
"publication_date": "2025-11-01",
"series_number": "11",
"volume": "82",
"issue": "11",
"pages": "2569-2580"
},
{
"id": "authors:2p7vy-b4x13",
"collection": "authors",
"collection_id": "2p7vy-b4x13",
"cite_using_url": "https://authors.library.caltech.edu/records/2p7vy-b4x13",
"type": "article",
"title": "A Physics-Constrained Neural Differential Equation Framework for Data-Driven Snowpack Simulation",
"author": [
{
"family_name": "Charbonneau",
"given_name": "Andrew",
"clpid": "Charbonneau-Andrew"
},
{
"family_name": "Deck",
"given_name": "Katherine",
"clpid": "Deck-Katherine"
},
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
}
],
"abstract": "This paper presents a physics-constrained neural differential equation framework for parameterization and employs it to model the time evolution of seasonal snow depth given hydrometeorological forcings. When trained on data from multiple SNOTEL sites, the parameterization predicts daily snow depth with under 9% median error and Nash–Sutcliffe efficiencies over 0.94 across a wide variety of snow climates. The parameterization also generalizes to new sites not seen during training, which is not often true for calibrated snow models. Requiring the parameterization to predict snow water equivalent in addition to snow depth only increases the error to ∼12%. The structure of the approach guarantees the satisfaction of physical constraints, enables these constraints during model training, and allows modeling at different temporal resolutions without additional retraining of the parameterization. These benefits hold potential in climate modeling and could extend to other dynamical systems with physical constraints.
",
"doi": "10.1175/aies-d-24-0040.1",
"issn": "2769-7525",
"publisher": "American Meteorological Society",
"publication": "Artificial Intelligence for the Earth Systems",
"publication_date": "2025-07",
"series_number": "3",
"volume": "4",
"issue": "3",
"pages": "e240040"
},
{
"id": "authors:fa0bx-54d29",
"collection": "authors",
"collection_id": "fa0bx-54d29",
"cite_using_url": "https://authors.library.caltech.edu/records/fa0bx-54d29",
"type": "article",
"title": "Observational constraints imply limited future Atlantic meridional overturning circulation weakening",
"author": [
{
"family_name": "Bonan",
"given_name": "David B.",
"orcid": "0000-0003-3867-6009",
"clpid": "Bonan-David-B"
},
{
"family_name": "Thompson",
"given_name": "Andrew F.",
"orcid": "0000-0003-0322-4811",
"clpid": "Thompson-A-F"
},
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
},
{
"family_name": "Zanna",
"given_name": "Laure",
"orcid": "0000-0002-8472-4828"
},
{
"family_name": "Armour",
"given_name": "Kyle C.",
"orcid": "0000-0002-6833-5179"
},
{
"family_name": "Sun",
"given_name": "Shantong"
}
],
"abstract": "The degree to which the Atlantic meridional overturning circulation (AMOC) weakens over the twenty-first century varies widely across climate models, with some predicting substantial weakening. Here we show that this uncertainty can be greatly reduced by using a thermal-wind expression that relates the AMOC strength to the meridional density difference and the overturning depth in the Atlantic. This expression captures the intermodel spread in AMOC weakening, with most of the spread arising from overturning depth changes. The overturning depth also establishes a crucial link between the present-day and future AMOC strength. Climate models with a stronger and deeper present-day overturning tend to predict larger weakening and shoaling under warming because the present-day North Atlantic is less stratified, allowing for a deeper penetration of surface buoyancy flux changes, larger density changes at depth and, consequently, larger AMOC weakening. By incorporating observational constraints, we conclude that the AMOC will experience limited weakening of about 3–6 Sv (about 18–43%) by the end of this century, regardless of emissions scenario. These results indicate that the uncertainty in twenty-first-century AMOC weakening and the propensity to predict substantial AMOC weakening can be attributed primarily to climate model biases in accurately simulating the present-day ocean stratification.
",
"doi": "10.1038/s41561-025-01709-0",
"issn": "1752-0894",
"publisher": "Nature Publishing Group",
"publication": "Nature Geoscience",
"publication_date": "2025-06",
"series_number": "6",
"volume": "18",
"issue": "6",
"pages": "479-487"
},
{
"id": "authors:wz2n2-t4m23",
"collection": "authors",
"collection_id": "wz2n2-t4m23",
"cite_using_url": "https://authors.library.caltech.edu/records/wz2n2-t4m23",
"type": "article",
"title": "Impacts of leaf traits on vegetation optical properties in Earth system modeling",
"author": [
{
"family_name": "Wang",
"given_name": "Yujie",
"orcid": "0000-0002-3729-2743",
"clpid": "Wang-Yujie"
},
{
"family_name": "Braghiere",
"given_name": "Renato K.",
"orcid": "0000-0002-7722-717X",
"clpid": "Braghiere-Renato-K"
},
{
"family_name": "Fischer",
"given_name": "Woodward W.",
"orcid": "0000-0002-8836-3054",
"clpid": "Fischer-W-W"
},
{
"family_name": "Yao",
"given_name": "Yitong",
"orcid": "0000-0002-1713-6719",
"clpid": "Yao-Yitong"
},
{
"family_name": "Shen",
"given_name": "Zhaoyi",
"orcid": "0000-0002-0444-4720",
"clpid": "Shen-Zhaoyi"
},
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
},
{
"family_name": "Bloom",
"given_name": "A. Anthony",
"orcid": "0000-0002-1486-1499",
"clpid": "Bloom-A-Anthony"
},
{
"family_name": "Schimel",
"given_name": "David",
"orcid": "0000-0003-3473-8065",
"clpid": "Schimel-David"
},
{
"family_name": "Croft",
"given_name": "Holly"
},
{
"family_name": "Winkler",
"given_name": "Alexander J.",
"orcid": "0000-0001-6574-4471"
},
{
"family_name": "Reichstein",
"given_name": "Markus",
"orcid": "0000-0001-5736-1112"
},
{
"family_name": "Frankenberg",
"given_name": "Christian",
"orcid": "0000-0002-0546-5857",
"clpid": "Frankenberg-C"
}
],
"abstract": "\n
\n
Quantifying surface energy and carbon budgets is essential for projecting Earth’s climate. Earth System Models (ESMs) typically simulate land surface processes based on plant functional types (PFTs), neglecting the diversity of plant functional traits or characteristics (PFCs; e.g., chlorophyll content and leaf mass per area). Here, we demonstrate substantial differences in modeled leaf optical properties (LOP) and surface albedo between traditional PFT-based and PFC-based approaches, particularly in tropical and boreal forests. We configure the canopy radiative transfer scheme in the Community Earth System Model using PFC-based LOP. This new configuration produces lower shortwave surface albedo in the tropics but higher albedo in boreal regions (>5 W m−2 radiative flux differences), and a weaker tropical but stronger boreal carbon sink. Through land-atmosphere coupling, the PFC-based configuration further alters atmospheric processes, leading to different temperature, cloud cover, and precipitation patterns. Our findings highlight the need to move beyond traditional PFT-based approaches in ESMs.
\n
\n
Extreme heat poses significant threats to human life and ecosystems. Quantifying the effects of anthropogenic climate change on extreme heat has remained challenging, in part due to the short observational record. Here, we isolate the most slowly varying component of the frequency at which the historical 90th and 99th percentiles were exceeded in observational records from 1955 to 2021 by using a statistical method called low\u2010frequency component analysis. The emerging spatiotemporal signal in the changing frequency of temperature extremes can be attributed to a shift of the temperature distribution by local warming of the annual\u2010mean daily maximum temperature. The shift explains over 80% of the interannual variability in the frequency at which the historical 90th percentile is exceeded in the tropics and up to 50% in higher latitudes. This work connects variability in the frequency of extreme surface temperatures to variability in mean local warming.
",
"doi": "10.1029/2024gl110707",
"issn": "0094-8276",
"publisher": "American Geophysical Union",
"publication": "Geophysical Research Letters",
"publication_date": "2024-12-28",
"series_number": "24",
"volume": "51",
"issue": "24",
"pages": "e2024GL110707"
},
{
"id": "authors:2x0kp-pkz30",
"collection": "authors",
"collection_id": "2x0kp-pkz30",
"cite_using_url": "https://authors.library.caltech.edu/records/2x0kp-pkz30",
"type": "article",
"title": "Online Learning of Entrainment Closures in a Hybrid Machine Learning Parameterization",
"author": [
{
"family_name": "Christopoulos",
"given_name": "Costa",
"orcid": "0000-0002-8552-465X",
"clpid": "Christopoulos-Costa"
},
{
"family_name": "Lopez\u2010Gomez",
"given_name": "Ignacio",
"orcid": "0000-0002-7255-5895",
"clpid": "Lopez\u2010Gomez-Ignacio"
},
{
"family_name": "Beucler",
"given_name": "Tom",
"orcid": "0000-0002-5731-1040"
},
{
"family_name": "Cohen",
"given_name": "Yair",
"orcid": "0000-0002-9615-2476",
"clpid": "Cohen-Yair"
},
{
"family_name": "Kawczynski",
"given_name": "Charles",
"clpid": "Kawczynski-Charles-N"
},
{
"family_name": "Dunbar",
"given_name": "Oliver R. A.",
"orcid": "0000-0001-7374-0382",
"clpid": "Dunbar-Oliver-R-A"
},
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
}
],
"abstract": "This work integrates machine learning into an atmospheric parameterization to target uncertain mixing processes while maintaining interpretable, predictive, and well-established physical equations. We adopt an eddy-diffusivity mass-flux (EDMF) parameterization for the unified modeling of various convective and turbulent regimes. To avoid drift and instability that plague offline-trained machine learning parameterizations that are subsequently coupled with climate models, we frame learning as an inverse problem: Data-driven models are embedded within the EDMF parameterization and trained online in a one-dimensional vertical global climate model (GCM) column. Training is performed against output from large-eddy simulations (LES) forced with GCM-simulated large-scale conditions in the Pacific. Rather than optimizing subgrid-scale tendencies, our framework directly targets climate variables of interest, such as the vertical profiles of entropy and liquid water path. Specifically, we use ensemble Kalman inversion to simultaneously calibrate both the EDMF parameters and the parameters governing data-driven lateral mixing rates. The calibrated parameterization outperforms existing EDMF schemes, particularly in tropical and subtropical locations of the present climate, and maintains high fidelity in simulating shallow cumulus and stratocumulus regimes under increased sea surface temperatures from AMIP4K experiments. The results showcase the advantage of physically constraining data-driven models and directly targeting relevant variables through online learning to build robust and stable machine learning parameterizations.
",
"doi": "10.1029/2024ms004485",
"issn": "1942-2466",
"publisher": "American Geophysical Union",
"publication": "Journal of Advances in Modeling Earth System (JAMES)",
"publication_date": "2024-11",
"series_number": "11",
"volume": "16",
"issue": "11",
"pages": "e2024MS004485"
},
{
"id": "authors:ranze-1vf44",
"collection": "authors",
"collection_id": "ranze-1vf44",
"cite_using_url": "https://authors.library.caltech.edu/records/ranze-1vf44",
"type": "article",
"title": "Learning about structural errors in models of complex dynamical systems",
"author": [
{
"family_name": "Wu",
"given_name": "Jin-Long",
"orcid": "0000-0001-7438-4228",
"clpid": "Wu-Jin-Long"
},
{
"family_name": "Levine",
"given_name": "Matthew E.",
"clpid": "Levine-Matthew-E"
},
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
},
{
"family_name": "Stuart",
"given_name": "Andrew",
"orcid": "0000-0001-9091-7266",
"clpid": "Stuart-A-M"
}
],
"abstract": "\n
\n
Complex dynamical systems are notoriously difficult to model because some degrees of freedom (e.g., small scales) may be computationally unresolvable or are incompletely understood, yet they are dynamically important. For example, the small scales of cloud dynamics and droplet formation are crucial for controlling climate, yet are unresolvable in global climate models. Semi-empirical closure models for the effects of unresolved degrees of freedom often exist and encode important domain-specific knowledge. Building on such closure models and correcting them through learning the structural errors can be an effective way of fusing data with domain knowledge. Here we describe a general approach, principles, and algorithms for learning about structural errors. Key to our approach is to include structural error models inside the models of complex systems, for example, in closure models for unresolved scales. The structural errors then map, usually nonlinearly, to observable data. As a result, however, mismatches between model output and data are only indirectly informative about structural errors, due to a lack of labeled pairs of inputs and outputs of structural error models. Additionally, derivatives of the model may not exist or be readily available. We discuss how structural error models can be learned from indirect data with derivative-free Kalman inversion algorithms and variants, how sparsity constraints enforce a “do no harm” principle, and various ways of modeling structural errors. We also discuss the merits of using non-local and/or stochastic error models. In addition, we demonstrate how data assimilation techniques can assist the learning about structural errors in non-ergodic systems. The concepts and algorithms are illustrated in two numerical examples based on the Lorenz-96 system and a human glucose-insulin model.
\n
\n
\n
Idealized general circulation models (GCMs) suggest global-mean precipitation ceases to increase with warming in hot climates because evaporation is limited by the available solar radiation at the surface. We investigate the extent to which this generalizes in comprehensive GCMs. We find that in the Community Atmosphere Model, global-mean precipitation increases approximately linearly with global-mean surface temperatures up to about 330 K, where it peaks at 5 mm day−1. Beyond 330 K, global-mean precipitation decreases substantially despite increasing surface temperatures because of increased atmospheric shortwave absorption by water vapor, which decreases the shortwave radiation available for evaporation at the surface. Precipitation decreases in the tropics and subtropics but continues to increase in the extratropics because of continuously strengthening poleward moisture transport. Precipitable water increases everywhere, resulting in longer water-vapor residence times and implying more episodic precipitation. Other GCMs indicate global-mean precipitation might exhibit a smaller maximum rate and begin to decrease at lower surface temperatures.
\n
Cloud microphysics is a critical aspect of the Earth's climate system, which involves processes at the nano\u2010 and micrometer scales of droplets and ice particles. In climate modeling, cloud microphysics is commonly represented by bulk models, which contain simplified process rates that require calibration. This study presents a framework for calibrating warm\u2010rain bulk schemes using high\u2010fidelity super\u2010droplet simulations that provide a more accurate and physically based representation of cloud and precipitation processes. The calibration framework employs ensemble Kalman methods including Ensemble Kalman Inversion and Unscented Kalman Inversion to calibrate bulk microphysics schemes with probabilistic super\u2010droplet simulations. We demonstrate the framework's effectiveness by calibrating a single\u2010moment bulk scheme, resulting in a reduction of data\u2010model mismatch by more than 75% compared to the model with initial parameters. Thus, this study demonstrates a powerful tool for enhancing the accuracy of bulk microphysics schemes in atmospheric models and improving climate modeling.
",
"doi": "10.1029/2023ms004028",
"issn": "1942-2466",
"publisher": "American Geophysical Union",
"publication": "Journal of Advances in Modeling Earth System (JAMES)",
"publication_date": "2024-07",
"series_number": "7",
"volume": "16",
"issue": "7",
"pages": "e2023MS004028"
},
{
"id": "authors:cvzmm-fmb67",
"collection": "authors",
"collection_id": "cvzmm-fmb67",
"cite_using_url": "https://authors.library.caltech.edu/records/cvzmm-fmb67",
"type": "article",
"title": "Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence",
"author": [
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
},
{
"family_name": "Leung",
"given_name": "L. Ruby",
"orcid": "0000-0002-3221-9467"
},
{
"family_name": "Wills",
"given_name": "Robert C. J.",
"orcid": "0000-0002-7776-2076"
}
],
"abstract": "Accelerated progress in climate modeling is urgently needed for proactive and effective climate change adaptation. The central challenge lies in accurately representing processes that are small in scale yet climatically important, such as turbulence and cloud formation. These processes will not be explicitly resolvable for the foreseeable future, necessitating the use of parameterizations. We propose a balanced approach that leverages the strengths of traditional process-based parameterizations and contemporary artificial intelligence (AI)-based methods to model subgrid-scale processes. This strategy employs AI to derive data-driven closure functions from both observational and simulated data, integrated within parameterizations that encode system knowledge and conservation laws. In addition, increasing the resolution to resolve a larger fraction of small-scale processes can aid progress toward improved and interpretable climate predictions outside the observed climate distribution. However, currently feasible horizontal resolutions are limited to O(10 km) because higher resolutions would impede the creation of the ensembles that are needed for model calibration and uncertainty quantification, for sampling atmospheric and oceanic internal variability, and for broadly exploring and quantifying climate risks. By synergizing decades of scientific development with advanced AI techniques, our approach aims to significantly boost the accuracy, interpretability, and trustworthiness of climate predictions.
",
"doi": "10.5194/acp-24-7041-2024",
"issn": "1680-7324",
"publisher": "European Geosciences Union",
"publication": "Atmospheric Chemistry and Physics",
"publication_date": "2024-06-19",
"series_number": "12",
"volume": "24",
"issue": "12",
"pages": "7041-7062"
},
{
"id": "authors:cdg1t-5mm33",
"collection": "authors",
"collection_id": "cdg1t-5mm33",
"cite_using_url": "https://authors.library.caltech.edu/records/cdg1t-5mm33",
"type": "article",
"title": "Toward a US Framework for Continuity of Satellite Observations of Earth's Climate and for Supporting Societal Resilience",
"author": [
{
"family_name": "Frankenberg",
"given_name": "Christian",
"orcid": "0000-0002-0546-5857",
"clpid": "Frankenberg-C"
},
{
"family_name": "Michalak",
"given_name": "Anna M.",
"orcid": "0000-0002-6152-7979",
"clpid": "Michalak-Anna-M"
},
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
},
{
"literal": "KISS Continuity Study Team"
}
],
"abstract": "There is growing urgency for improved public and commercial services to support a resilient, secure, and thriving United States (US) in the face of mounting decision\u2010support needs for environmental stewardship and hazard response, as well as for climate change adaptation and mitigation. Sustained space\u2010based Earth observations are critical infrastructure to support the delivery of science and decision\u2010support information with local, national, and global utility. This is reflected in part through the United States' sustained support of a suite of weather and land\u2010imaging satellites. However, outside of these two areas, the US lacks an overarching, systematic plan or framework to identify, prioritize, fund, and implement sustained space\u2010based Earth observations to meet the Nation's full range of needs for science, government policy, and societal support. To aid and accelerate the discussion on our nation's needs, challenges and opportunities associated with sustained critical space\u2010based Earth observations, the Keck Institute for Space Studies (KISS) sponsored a multi\u2010week think\u2010tank study to offer ways forward. Based on this study, the KISS study team suggests the establishment of a robust coordination framework to help address US needs for sustained Earth observations. This coordination framework could account for: (a) approaches to identify and prioritize satellite observations needed to meet US needs for science and services, (b) the rapidly evolving landscape of space\u2010based Earth viewing architecture options and technology improvements with increasing opportunities and lower cost access to space, and (c) the technical and programmatic underpinnings required for proper and comprehensive data stewardship to support a wide range of research and public services.
",
"doi": "10.1029/2023ef003757",
"issn": "2328-4277",
"publisher": "American Geophysical Union",
"publication": "Earth's Future",
"publication_date": "2024-02",
"series_number": "2",
"volume": "12",
"issue": "2",
"pages": "e2023EF003757"
},
{
"id": "authors:0677f-qrc81",
"collection": "authors",
"collection_id": "0677f-qrc81",
"cite_using_url": "https://authors.library.caltech.edu/records/0677f-qrc81",
"type": "article",
"title": "Accelerating Large-Eddy Simulations of Clouds With Tensor Processing Units",
"author": [
{
"family_name": "Chammas",
"given_name": "Sheide",
"orcid": "0000-0001-9001-629X",
"clpid": "Chammas-Sheide"
},
{
"family_name": "Wang",
"given_name": "Qing",
"orcid": "0000-0002-9414-5184",
"clpid": "Wang-Qing"
},
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
},
{
"family_name": "Ihme",
"given_name": "Matthias",
"orcid": "0000-0002-4158-7050",
"clpid": "Ihme-Matthias"
},
{
"family_name": "Chen",
"given_name": "Yi-fan",
"orcid": "0000-0001-9751-8049",
"clpid": "Chen-Yi-fan"
},
{
"family_name": "Anderson",
"given_name": "John",
"orcid": "0009-0000-5341-8919",
"clpid": "Anderson-John"
}
],
"abstract": "Clouds, especially low clouds, are crucial for regulating Earth's energy balance and mediating the response of the climate system to changes in greenhouse gas concentrations. Despite their importance for climate, they remain relatively poorly understood and are inaccurately represented in climate models. A principal reason is that the high computational expense of simulating them with large\u2010eddy simulations (LES) has inhibited broad and systematic numerical experimentation and the generation of large data sets for training parametrization schemes for climate models. Here we demonstrate LES of low clouds on tensor processing units (TPUs), application\u2010specific integrated circuits that were originally developed for machine learning applications. We show that TPUs in conjunction with tailored software implementations can be used to simulate computationally challenging stratocumulus clouds in conditions observed during the Dynamics and Chemistry of Marine Stratocumulus (DYCOMS) field study. The TPU\u2010based LES code successfully reproduces clouds during DYCOMS and opens up the large computational resources available on TPUs to cloud simulations. The code enables unprecedented weak and strong scaling of LES, making it possible, for example, to simulate stratocumulus with 10× speedup over real\u2010time evolution in domains with a 34.7 km × 53.8 km horizontal cross section. The results open up new avenues for computational experiments and for substantially enlarging the sample of LES available to train parameterizations of low clouds.
",
"doi": "10.1029/2023ms003619",
"issn": "1942-2466",
"publisher": "American Geophysical Union",
"publication": "Journal of Advances in Modeling Earth System (JAMES)",
"publication_date": "2023-10",
"series_number": "10",
"volume": "15",
"issue": "10",
"pages": "e2023MS003619"
},
{
"id": "authors:z0c0r-2jt68",
"collection": "authors",
"collection_id": "z0c0r-2jt68",
"cite_using_url": "https://authors.library.caltech.edu/records/z0c0r-2jt68",
"type": "article",
"title": "Harnessing AI and computing to advance climate modelling and prediction",
"author": [
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
},
{
"family_name": "Behera",
"given_name": "Swadhin",
"orcid": "0000-0001-8692-2388",
"clpid": "Behera-Swadhin"
},
{
"family_name": "Boccaletti",
"given_name": "Giulio",
"orcid": "0009-0008-1072-7672",
"clpid": "Boccaletti-Giulio"
},
{
"family_name": "Deser",
"given_name": "Clara",
"orcid": "0000-0002-5517-9103",
"clpid": "Deser-Clara"
},
{
"family_name": "Emanuel",
"given_name": "Kerry",
"orcid": "0000-0002-2066-2082",
"clpid": "Emanuel-Kerry"
},
{
"family_name": "Ferrari",
"given_name": "Raffaele",
"orcid": "0000-0003-1895-4294",
"clpid": "Ferrari-Raffaele"
},
{
"family_name": "Leung",
"given_name": "L. Ruby",
"orcid": "0000-0002-3221-9467",
"clpid": "Leung-L-Ruby"
},
{
"family_name": "Lin",
"given_name": "Ning",
"orcid": "0000-0002-5571-1606",
"clpid": "Lin-Ning"
},
{
"family_name": "M\u00fcller",
"given_name": "Thomas",
"orcid": "0000-0003-1225-1483",
"clpid": "M\u00fcller-Thomas"
},
{
"family_name": "Navarra",
"given_name": "Antonio",
"clpid": "Navarra-Antonio"
},
{
"family_name": "Ndiaye",
"given_name": "Ousmane",
"orcid": "0000-0002-5048-4731",
"clpid": "Ndiaye-Ousmane"
},
{
"family_name": "Stuart",
"given_name": "Andrew",
"orcid": "0000-0001-9091-7266",
"clpid": "Stuart-A-M"
},
{
"family_name": "Tribbia",
"given_name": "Joseph",
"orcid": "0000-0003-1639-9688",
"clpid": "Tribbia-Joseph"
},
{
"family_name": "Yamagata",
"given_name": "Toshio",
"orcid": "0000-0003-1267-2149",
"clpid": "Yamagata-Toshio"
}
],
"abstract": "There are contrasting views on how to produce the accurate predictions that are needed to guide climate change adaptation. Here, we argue for harnessing artificial intelligence, building on domain-specific knowledge and generating ensembles of moderately high-resolution (10\u201350 km) climate simulations as anchors for detailed hazard models.
",
"doi": "10.1038/s41558-023-01769-3",
"issn": "1758-678X",
"publisher": "Nature Publishing Group",
"publication": "Nature Climate Change",
"publication_date": "2023-09",
"series_number": "9",
"volume": "13",
"issue": "9",
"pages": "887-889"
},
{
"id": "authors:8dvrz-1qp80",
"collection": "authors",
"collection_id": "8dvrz-1qp80",
"cite_using_url": "https://authors.library.caltech.edu/records/8dvrz-1qp80",
"type": "article",
"title": "Accelerating Scientific Discovery With AI-Aided Automation",
"author": [
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
},
{
"family_name": "Altintas",
"given_name": "Ilkay",
"orcid": "0000-0002-2196-0305"
},
{
"family_name": "Atkins",
"given_name": "Daniel",
"orcid": "0000-0001-9002-9613",
"clpid": "Atkins-Daniel"
}
],
"abstract": "\n
\n
\n
AI-aided design of experiments and observations, together with robotic instrumentation and automated learning from data, has the potential to transform science and propel it forward with unprecedented speed.
\n
\n
\n
We introduce ClimateMachine, a new open-source atmosphere modeling framework which uses the Julia language and is designed to be scalable on central processing units (CPUs) and graphics processing units (GPUs). ClimateMachine uses a common framework both for coarser-resolution global simulations and for high-resolution, limited-area large-eddy simulations (LESs). Here, we demonstrate the LES configuration of the atmosphere model in canonical benchmark cases and atmospheric flows using a total energy-conserving nodal discontinuous Galerkin (DG) discretization of the governing equations. Resolution dependence, conservation characteristics, and scaling metrics are examined in comparison with existing LES codes. They demonstrate the utility of ClimateMachine as a modeling tool for limited-area LES flow configurations.
",
"doi": "10.5194/gmd-15-6259-2022",
"issn": "1991-9603",
"publisher": "European Geosciences Union",
"publication": "Geoscientific Model Development",
"publication_date": "2022-08-12",
"series_number": "15",
"volume": "15",
"issue": "15",
"pages": "6259-6284"
},
{
"id": "authors:1q6gn-mvc46",
"collection": "authors",
"collection_id": "1q6gn-mvc46",
"cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20220810-402975000",
"type": "article",
"title": "Training physics\u2010based machine\u2010learning parameterizations with gradient\u2010free ensemble Kalman methods",
"author": [
{
"family_name": "Lopez-Gomez",
"given_name": "Ignacio",
"orcid": "0000-0002-7255-5895",
"clpid": "Lopez-Gomez-Ignacio"
},
{
"family_name": "Christopoulos",
"given_name": "Costa",
"orcid": "0000-0002-8552-465X",
"clpid": "Christopoulos-Costa-D"
},
{
"family_name": "Ervik",
"given_name": "Haakon Ludvig Langeland",
"orcid": "0000-0003-2912-5774",
"clpid": "Ervik-Haakon-Ludvig-Langeland"
},
{
"family_name": "Dunbar",
"given_name": "Oliver R. A.",
"orcid": "0000-0001-7374-0382",
"clpid": "Dunbar-Oliver-R-A"
},
{
"family_name": "Cohen",
"given_name": "Yair",
"orcid": "0000-0002-9615-2476",
"clpid": "Cohen-Yair"
},
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
}
],
"abstract": "Most machine learning applications in Earth system modeling currently rely on gradient-based supervised learning. This imposes stringent constraints on the nature of the data used for training (typically, residual time tendencies are needed), and it complicates learning about the interactions between machine-learned parameterizations and other components of an Earth system model. Approaching learning about process-based parameterizations as an inverse problem resolves many of these issues, since it allows parameterizations to be trained with partial observations or statistics that directly relate to quantities of interest in long-term climate projections. Here we demonstrate the effectiveness of Kalman inversion methods in treating learning about parameterizations as an inverse problem. We consider two different algorithms: unscented and ensemble Kalman inversion. Both methods involve highly parallelizable forward model evaluations, converge exponentially fast, and do not require gradient computations. In addition, unscented Kalman inversion provides a measure of parameter uncertainty. We illustrate how training parameterizations can be posed as a regularized inverse problem and solved by ensemble Kalman methods through the calibration of an eddy-diffusivity mass-flux scheme for subgrid-scale turbulence and convection, using data generated by large-eddy simulations. We find the algorithms amenable to batching strategies, robust to noise and model failures, and efficient in the calibration of hybrid parameterizations that can include empirical closures and neural networks.",
"doi": "10.1029/2022ms003105",
"issn": "1942-2466",
"publisher": "American Geophysical Union",
"publication": "Journal of Advances in Modeling Earth Systems",
"publication_date": "2022-08-11",
"pages": "Art. No. e2022MS003105"
},
{
"id": "authors:s3q2y-tpm38",
"collection": "authors",
"collection_id": "s3q2y-tpm38",
"cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20220815-504980000",
"type": "article",
"title": "An Efficient Bayesian Approach to Learning Droplet Collision Kernels: Proof of Concept Using \"Cloudy,\" a New n-Moment Bulk Microphysics Scheme",
"author": [
{
"family_name": "Bieli",
"given_name": "Melanie",
"orcid": "0000-0002-2056-9486",
"clpid": "Bieli-Melanie"
},
{
"family_name": "Dunbar",
"given_name": "Oliver R. A.",
"orcid": "0000-0001-7374-0382",
"clpid": "Dunbar-Oliver-R-A"
},
{
"family_name": "De Jong",
"given_name": "Emily K.",
"orcid": "0000-0002-5310-4554",
"clpid": "De-Jong-Emily-K"
},
{
"family_name": "Jaruga",
"given_name": "Anna",
"orcid": "0000-0003-3194-6440",
"clpid": "Jaruga-Anna"
},
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
},
{
"family_name": "Bischoff",
"given_name": "Tobias",
"orcid": "0000-0003-3930-2762",
"clpid": "Bischoff-Tobias"
}
],
"abstract": "The small-scale microphysical processes governing the formation of precipitation particles cannot be resolved explicitly by cloud resolving and climate models. Instead, they are represented by microphysics schemes that are based on a combination of theoretical knowledge, statistical assumptions, and fitting to data (\"tuning\"). Historically, tuning was done in an ad hoc fashion, leading to parameter choices that are not explainable or repeatable. Recent work has treated it as an inverse problem that can be solved by Bayesian inference. The posterior distribution of the parameters given the data\u2014the solution of Bayesian inference\u2014is found through computationally expensive sampling methods, which require over O(10\u2075) evaluations of the forward model; this is prohibitive for many models. We present a proof of concept of Bayesian learning applied to a new bulk microphysics scheme named \"Cloudy,\" using the recently developed Calibrate-Emulate-Sample (CES) algorithm. Cloudy models collision-coalescence and collisional breakup of cloud droplets with an adjustable number of prognostic moments and with easily modifiable assumptions for the cloud droplet mass distribution and the collision kernel. The CES algorithm uses machine learning tools to accelerate Bayesian inference by reducing the number of forward evaluations needed to O(10\u00b2). It also exhibits a smoothing effect when forward evaluations are polluted by noise. In a suite of perfect-model experiments, we show that CES enables computationally efficient Bayesian inference of parameters in Cloudy from noisy observations of moments of the droplet mass distribution. In an additional imperfect-model experiment, a collision kernel parameter is successfully learned from output generated by a Lagrangian particle-based microphysics model.",
"doi": "10.1029/2022ms002994",
"issn": "1942-2466",
"publisher": "American Geophysical Union",
"publication": "Journal of Advances in Modeling Earth Systems",
"publication_date": "2022-08",
"series_number": "8",
"volume": "14",
"issue": "8",
"pages": "Art. No. e2022MS002994"
},
{
"id": "authors:4xm8c-sh061",
"collection": "authors",
"collection_id": "4xm8c-sh061",
"cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20220325-220336231",
"type": "article",
"title": "Epidemic management and control through risk-dependent individual contact interventions",
"author": [
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
},
{
"family_name": "Dunbar",
"given_name": "Oliver R. A.",
"orcid": "0000-0001-7374-0382",
"clpid": "Dunbar-Oliver-R-A"
},
{
"family_name": "Wu",
"given_name": "Jinlong",
"orcid": "0000-0001-7438-4228",
"clpid": "Wu-Jinlong"
},
{
"family_name": "B\u00f6ttcher",
"given_name": "Lucas",
"orcid": "0000-0003-1700-1897",
"clpid": "B\u00f6ttcher-Lucas"
},
{
"family_name": "Burov",
"given_name": "Dmitry",
"orcid": "0000-0002-5060-6794",
"clpid": "Burov-Dmitry"
},
{
"family_name": "Garbuno-I\u00f1igo",
"given_name": "Alfredo",
"orcid": "0000-0003-3279-619X",
"clpid": "Garbuno-I\u00f1igo-Alfredo"
},
{
"family_name": "Wagner",
"given_name": "Gregory L.",
"orcid": "0000-0001-5317-2445",
"clpid": "Wagner-Gregory-L"
},
{
"family_name": "Pei",
"given_name": "Sen",
"orcid": "0000-0002-7072-2995",
"clpid": "Pei-Sen"
},
{
"family_name": "Daraio",
"given_name": "Chiara",
"orcid": "0000-0001-5296-4440",
"clpid": "Daraio-C"
},
{
"family_name": "Ferrari",
"given_name": "Raffaele",
"orcid": "0000-0003-1895-4294",
"clpid": "Ferrari-Raffaele"
},
{
"family_name": "Shaman",
"given_name": "Jeffrey",
"orcid": "0000-0002-7216-7809",
"clpid": "Shaman-Jeffrey"
}
],
"abstract": "Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City are used to establish proof-of-concept. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population (\u2273 75%) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption.",
"doi": "10.1371/journal.pcbi.1010171",
"pmcid": "PMC9223336",
"issn": "1553-734X",
"publisher": "Public Library of Science",
"publication": "PLoS Computational Biology",
"publication_date": "2022-06-23",
"series_number": "6",
"volume": "18",
"issue": "6",
"pages": "Art. No. e1010171"
},
{
"id": "authors:pfx6k-bp417",
"collection": "authors",
"collection_id": "pfx6k-bp417",
"cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20220609-798289200",
"type": "article",
"title": "Thank You to Our 2021 Reviewers",
"author": [
{
"family_name": "Griffies",
"given_name": "Stephen M.",
"orcid": "0000-0002-3711-236X",
"clpid": "Griffies-Stephen-M"
},
{
"family_name": "Blyth",
"given_name": "Eleanor Mary",
"orcid": "0000-0002-5052-238X",
"clpid": "Blyth-Eleanor-Mary"
},
{
"family_name": "Fan",
"given_name": "Jiwen",
"orcid": "0000-0001-5280-4391",
"clpid": "Fan-Jiwen"
},
{
"family_name": "MacBean",
"given_name": "Natasha",
"orcid": "0000-0001-6797-4836",
"clpid": "MacBean-Natasha"
},
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
}
],
"abstract": "As the editors of a scientific journal, we have the honor of front-row seats to the peer review process. We watch as reviewers bring fresh and frank perspectives to authors' work, identifying weakness in conception or expression, suggesting refinements, and urging authors to clarify a point or to better support an argument. The process is occasionally uncomfortable but almost always respectful, constructive, and productive. The final papers are uniformly better for this input, thus exemplifying the power of collaboration in the scientific process. At the Journal of Advances in Modeling Earth Systems (JAMES), we are fortunate to have a pool of reviewers who volunteer their time, knowledge, and intelligence to improve the work of their colleagues and peers. It is no small task: In 2021, we received 1,088 reviews by 689 individuals. Speaking for ourselves as editors, the 43 devoted associate editors, and the 696 authors, we thank each of you for your selfless contributions to strengthening our community.\n\nThe editors of Journal of Advances in Modeling Earth Systems thank the 689 reviewers who provided 1,088 reviews during 2021. Their hard work and insights, typically done anonymously, benefits authors, readers, and the broader science community.",
"doi": "10.1029/2022ms003133",
"issn": "1942-2466",
"publisher": "American Geophysical Union",
"publication": "Journal of Advances in Modeling Earth Systems",
"publication_date": "2022-04",
"series_number": "4",
"volume": "14",
"issue": "4",
"pages": "Art. No. e2022MS003133"
},
{
"id": "authors:by16d-5rc82",
"collection": "authors",
"collection_id": "by16d-5rc82",
"cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20210528-094924009",
"type": "article",
"title": "A Library of Large-Eddy Simulations Forced by Global Climate Models",
"author": [
{
"family_name": "Shen",
"given_name": "Zhaoyi",
"orcid": "0000-0002-0444-4720",
"clpid": "Shen-Zhaoyi"
},
{
"family_name": "Sridhar",
"given_name": "Akshay",
"orcid": "0000-0002-2642-8246",
"clpid": "Sridhar-Akshay"
},
{
"family_name": "Tan",
"given_name": "Zhihong",
"orcid": "0000-0002-7422-3317",
"clpid": "Tan-Zhihong"
},
{
"family_name": "Jaruga",
"given_name": "Anna",
"orcid": "0000-0003-3194-6440",
"clpid": "Jaruga-Anna"
},
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
}
],
"abstract": "Advances in high-performance computing have enabled large-eddy simulations (LES) of turbulence, convection, and clouds. However, their potential to improve parameterizations in global climate models (GCMs) is only beginning to be harnessed, with relatively few canonical LES available so far. The purpose of this paper is to begin creating a public LES library that expands the training data available for calibrating and evaluating GCM parameterizations. To do so, we use an experimental setup in which LES are driven by large-scale forcings from GCMs, which in principle can be used at any location, any time of year, and in any climate state. We use this setup to create a library of LES of clouds across the tropics and subtropics, in the present and in a warmer climate, with a focus on the transition from stratocumulus to shallow cumulus over the East Pacific. The LES results are relatively insensitive to the choice of host GCM driving the LES. Driven with large-scale forcing under global warming, the LES simulate a positive but weak shortwave cloud feedback, adding to the accumulating evidence that low clouds amplify global warming.",
"doi": "10.1029/2021MS002631",
"issn": "1942-2466",
"publisher": "American Geophysical Union",
"publication": "Journal of Advances in Modelling Earth Systems",
"publication_date": "2022-03",
"series_number": "3",
"volume": "14",
"issue": "3",
"pages": "Art. No. e2021MS002631"
},
{
"id": "authors:w8jdm-e8b55",
"collection": "authors",
"collection_id": "w8jdm-e8b55",
"cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20210823-173258629",
"type": "article",
"title": "Parameter Uncertainty Quantification in an Idealized GCM With a Seasonal Cycle",
"author": [
{
"family_name": "Howland",
"given_name": "Michael F.",
"orcid": "0000-0002-2878-3874",
"clpid": "Howland-Michael-F"
},
{
"family_name": "Dunbar",
"given_name": "Oliver R. A.",
"orcid": "0000-0001-7374-0382",
"clpid": "Dunbar-Oliver-R-A"
},
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
}
],
"abstract": "Climate models are generally calibrated manually by comparing selected climate statistics, such as the global top-of-atmosphere energy balance, to observations. The manual tuning only targets a limited subset of observational data and parameters. Bayesian calibration can estimate climate model parameters and their uncertainty using a larger fraction of the available data and automatically exploring the parameter space more broadly. In Bayesian learning, it is natural to exploit the seasonal cycle, which has large amplitude compared with anthropogenic climate change in many climate statistics. In this study, we develop methods for the calibration and uncertainty quantification (UQ) of model parameters exploiting the seasonal cycle, and we demonstrate a proof-of-concept with an idealized general circulation model (GCM). UQ is performed using the calibrate-emulate-sample approach, which combines stochastic optimization and machine learning emulation to speed up Bayesian learning. The methods are demonstrated in a perfect-model setting through the calibration and UQ of a convective parameterization in an idealized GCM with a seasonal cycle. Calibration and UQ based on seasonally averaged climate statistics, compared to annually averaged, reduces the calibration error by up to an order of magnitude and narrows the spread of the non-Gaussian posterior distributions by factors between two and five, depending on the variables used for UQ. The reduction in the spread of the parameter posterior distribution leads to a reduction in the uncertainty of climate model predictions.",
"doi": "10.1029/2021MS002735",
"issn": "1942-2466",
"publisher": "American Geophysical Union",
"publication": "Journal of Advances in Modelling Earth Systems",
"publication_date": "2022-03",
"series_number": "3",
"volume": "14",
"issue": "3",
"pages": "Art. No. e2021MS002735"
},
{
"id": "authors:pjjee-1w296",
"collection": "authors",
"collection_id": "pjjee-1w296",
"cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20201027-125955966",
"type": "article",
"title": "Seasonal Cycle of Idealized Polar Clouds: Large Eddy Simulations Driven by a GCM",
"author": [
{
"family_name": "Zhang",
"given_name": "Xiyue",
"orcid": "0000-0002-6031-7830",
"clpid": "Zhang-Xiyue"
},
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
},
{
"family_name": "Shen",
"given_name": "Zhaoyi",
"orcid": "0000-0002-0444-4720",
"clpid": "Shen-Zhaoyi"
},
{
"family_name": "Pressel",
"given_name": "Kyle G.",
"orcid": "0000-0002-4538-3055",
"clpid": "Pressel-Kyle-G"
},
{
"family_name": "Eisenman",
"given_name": "Ian",
"orcid": "0000-0003-0190-2869",
"clpid": "Eisenman-Ian"
}
],
"abstract": "The uncertainty in polar cloud feedbacks calls for process understanding of the cloud response to climate warming. As an initial step toward improved process understanding, we investigate the seasonal cycle of polar clouds in the current climate by adopting a novel modeling framework using large eddy simulations (LES), which explicitly resolve cloud dynamics. Resolved horizontal and vertical advection of heat and moisture from an idealized general circulation model (GCM) are prescribed as forcing in the LES. The LES are also forced with prescribed sea ice thickness, but surface temperature, atmospheric temperature, and moisture evolve freely without nudging. A semigray radiative transfer scheme without water vapor and cloud feedbacks allows the GCM and LES to achieve closed energy budgets more easily than would be possible with more complex schemes. This enables the mean states in the two models to be consistently compared, without the added complications from interaction with more comprehensive radiation. We show that the LES closely follow the GCM seasonal cycle, and the seasonal cycle of low-level clouds in the LES resembles observations: maximum cloud liquid occurs in late summer and early autumn, and winter clouds are dominated by ice in the upper troposphere. Large-scale advection of moisture provides the main source of water vapor for the liquid-containing clouds in summer, while a temperature advection peak in winter makes the atmosphere relatively dry and reduces cloud condensate. The framework we develop and employ can be used broadly for studying cloud processes and the response of polar clouds to climate warming.",
"doi": "10.1029/2021MS002671",
"issn": "1942-2466",
"publisher": "American Geophysical Union",
"publication": "Journal of Advances in Modelling Earth Systems",
"publication_date": "2022-01",
"series_number": "1",
"volume": "14",
"issue": "1",
"pages": "Art. No. e2021MS002671"
},
{
"id": "authors:jpqx4-32a63",
"collection": "authors",
"collection_id": "jpqx4-32a63",
"cite_using_url": "https://authors.library.caltech.edu/records/jpqx4-32a63",
"type": "article",
"title": "Top-of-Atmosphere Albedo Bias from Neglecting Three-Dimensional Cloud Radiative Effects",
"author": [
{
"family_name": "Singer",
"given_name": "Clare E.",
"orcid": "0000-0002-1708-0997",
"clpid": "Singer-Clare-E"
},
{
"family_name": "Lopez-Gomez",
"given_name": "Ignacio",
"orcid": "0000-0002-7255-5895",
"clpid": "Lopez-Gomez-Ignacio"
},
{
"family_name": "Zhang",
"given_name": "Xiyue",
"orcid": "0000-0002-6031-7830",
"clpid": "Zhang-Xiyue"
},
{
"family_name": "Schneider",
"given_name": "Tapio",
"orcid": "0000-0001-5687-2287",
"clpid": "Schneider-T"
}
],
"abstract": "\n
\n
\n\n\n
Clouds cover on average nearly 70% of Earth’s surface and regulate the global albedo. The magnitude of the shortwave reflection by clouds depends on their location, optical properties, and three-dimensional (3D) structure. Due to computational limitations, Earth system models are unable to perform 3D radiative transfer calculations. Instead they make assumptions, including the independent column approximation (ICA), that neglect effects of 3D cloud morphology on albedo. We show how the resulting radiative flux bias (ICA-3D) depends on cloud morphology and solar zenith angle. We use high-resolution (20–100-m horizontal resolution) large-eddy simulations to produce realistic 3D cloud fields covering three dominant regimes of low-latitude clouds: shallow cumulus, marine stratocumulus, and deep convective cumulonimbus. A Monte Carlo code is used to run 3D and ICA broadband radiative transfer calculations; we calculate the top-of-atmosphere (TOA) reflected flux and surface irradiance biases as functions of solar zenith angle for these three cloud regimes. Finally, we use satellite observations of cloud water path (CWP) climatology, and the robust correlation between CWP and TOA flux bias in our LES sample, to roughly estimate the impact of neglecting 3D cloud radiative effects on a global scale. We find that the flux bias is largest at small zenith angles and for deeper clouds, while the albedo bias is most prominent for large zenith angles. In the tropics, the annual-mean shortwave radiative flux bias is estimated to be 3.1 ± 1.6 W m−2, reaching as much as 6.5 W m−2 locally.
\n\n
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