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DOE OSTI2026

Combined effective field theory interpretation of Higgs boson, electroweak vector boson, top quark, and multijet measurements

Constraints on Wilson coefficients (WCs) corresponding to dimension-6 operators of the standard model effective field theory (SMEFT) are determined from a simultaneous fit to seven sets of CMS measurements probing Higgs boson, electroweak vector boson, top quark, and multijet production. Measurements of electroweak precision observables are also included and provide complementary constraints to those from the CMS experiment. The CMS measurements, using LHC proton-proton collision data at $\sqrt{s}=13\,\text {Te}\text {V} $, corresponding to integrated luminosities of 36.3 or 138$\,\text {fb}^{-1}$, are chosen to provide sensitivity to a broad set of operators, for which consistent SMEFT predictions can be derived. These are primarily measurements of differential cross sections which are parameterized as functions of the WCs. In measurements targeting ${\text {t}} (\bar{\textrm{t}})\text {X} $ production, SMEFT effects are modelled at the detector level. Individual constraints on 64 WCs, and constraints on 43 linear combinations of WCs, are obtained.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Sensitivity of the CUPID experiment to $0\nu \beta \beta$ decay of 100 Mo

CUPID is a next-generation bolometric experiment to search for neutrinoless double-beta decay ($0\nu \beta \beta$ ) of 100 Mo using Li 2 MoO 4 scintillating crystals. It will operate 1596 crystals at ~10 mK in the CUORE cryostat at the Laboratori Nazionali del Gran Sasso in Italy. Each crystal will be facing two Ge-based bolometric light detectors for α rejection. We compute the discovery and the exclusion sensitivity of CUPID to $0\nu \beta \beta$ in a Frequentist and a Bayesian framework. This computation is done numerically based on pseudo-experiments. For the CUPID baseline scenario, with a background and an energy resolution of 1.0 x 10 –4 counts/keV/kg/yr and 5 keV FWHM at the Q-value, respectively, this results in a Bayesian exclusion sensitivity (90% c.i.) of $\hat{T}_{1/2} > 1.6 \times 10^{27} \ \textrm{yr}$, corresponding to the effective Majorana neutrino mass of $\hat{m}_{\beta \beta } < \ 9.6$ – 28 meV. The Frequentist discovery sensitivity (3σ ) is $\hat{T}_{1/2}= 1.0 \times 10^{27} \ \textrm{yr}$, corresponding to $\hat{m}_{\beta \beta }= \ 12$ – 36 meV.

Alfonso, K. [Virginia Polytechnic Institute and St↗

DOE OSTI2025

Interaction of 25 eV electrons with DNA constituents: XPS analysis of calf thymus DNA, nucleosides, and nucleobases

Abstract Understanding the interactions of secondary electrons generated by ionizing radiation provides a fundamental basis for developing strategies in cancer therapy. In this study, we investigated the interactions of 25 eV low-energy electrons (LEEs) with calf thymus DNA and its constituents, four types of nucleosides and nucleobases, using X-ray photoelectron spectroscopy (XPS). Based on the acquisition and analysis of core-level spectra (O 1s, C 1s, N 1s, and P 2p) in DNA, structural changes induced by 25 eV electrons suggest potential site- and base-specific selectivity. These changes may involve hydroxyl (C–OH) group release from the sugar moiety, cleavage of C–N bonds (likely corresponding to N-glycosidic linkages), and phosphate backbone damage in calf thymus DNA. Among the four nucleosides, thymidine and guanosine showed more evident structural modifications, while cytidine and adenosine were relatively stable. In addition, nucleosides displayed greater susceptibility to LEE-induced structural changes than their corresponding nucleobases. This study reveals the selective damage mechanisms of LEEs on various DNA constituents, which may provide mechanistic insights for future developments in precision cancer therapy based on molecular-level damage. Graphical abstract

Pereira-da-Silva, João (ORCID:0000000220661303)↗

DOE OSTI2022

Measuring the electron Yukawa coupling via resonant s-channel Higgs production at FCC-ee

The Future Circular Collider (FCC-ee) offers the unique opportunity of studying the Higgs Yukawa coupling to the electron, $y_\mathrm {e}$ , via resonant s -channel production, $\mathrm {e^+e^-}\rightarrow \mathrm {H}$ , in a dedicated run at $\sqrt{s} = m_\mathrm {H}$ . The signature for direct Higgs production is a small rise in the cross sections for particular final states, consistent with Higgs decays, over the expectations for their occurrence due to Standard Model (SM) background processes involving $\mathrm {Z}^*$ , $\gamma ^*$ , or t -channel exchanges alone. Performing such a measurement is remarkably challenging for four main reasons. First, the low value of the e $^\pm $ mass leads to a tiny $y_\mathrm {e}$ coupling and correspondingly small cross section: $\sigma _\mathrm {ee\rightarrow H} \propto m_\mathrm {e}^2 = 0.57$ fb accounting for initial-state $\gamma $ radiation. Second, the $\mathrm {e^+e^-}$ beams must be monochromatized such that the spread of their centre-of-mass (c.m.) energy is commensurate with the narrow width of the SM Higgs boson, $\varGamma _\mathrm {H} = 4.1$ MeV, while keeping large beam luminosities. Third, the Higgs mass must also be known beforehand with a few-MeV accuracy in order to operate the collider at the resonance peak, $\sqrt{s} = m_\mathrm {H}$ . Last but not least, the cross sections of the background processes are many orders-of-magnitude larger than those of the Higgs decay signals. A preliminary generator-level study of 11 Higgs decay channels using a multivariate analysis, which exploits boosted decision trees to discriminate signal and background events, identifies two final states as the most promising ones in terms of statistical significance: $\mathrm {H}\rightarrow gg$ and $\mathrm {H}\rightarrow \mathrm {W}\mathrm {W}^*\!\rightarrow \ell \nu $ + 2 jets. For a benchmark monochromatization with 4.1-MeV c.m. energy spread (leading to $\sigma _\mathrm {ee\rightarrow H} = 0.28$ fb) and 10 ab $^{-1}$ of integrated luminosity, a $1.3\sigma $ signal significance can be reached, corresponding to an upper limit on the e $^\pm $ Yukawa coupling at 1.6 times the SM value: |y e | < 1.6|y e SM | at 95% confidence level, per FCC-ee interaction point per year. Directions for future improvements of the study are outlined.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

The secret structure of the gravitational vacuum

We argue that the vacuum of quantum gravity must contain a hierarchical structure of correlations spanning all length scales. These correlated domains (called “vecros”) correspond to virtual fluctuations of black hole microstates. Larger fluctuations are suppressed by their larger action, but this suppression is offset by a correspondingly larger phase space of possible configurations. We give an explicit lattice model of these vecro fluctuations, noting how their distribution changes as the gravitational pull of a star becomes stronger. At the threshold of formation of a closed trapped surface, these virtual fluctuations transition into on-shell black hole microstates (fuzzballs). Fuzzballs radiate from their surface like normal bodies, resolving the information paradox. Here, we also argue that any model without vecro-type extended vacuum correlations cannot resolve the paradox.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

A geometric perspective on kinetic matter–radiation interaction and moment systems

Here, in this paper, we provide a geometric perspective on the kinetic interaction of matter and radiation, based on a pair bracket approach. We discuss the interaction of kinetic theories via dissipative brackets, with our fundamental example being the coupling of matter, described by the Boltzmann equation, and radiation, described by the radiation transport equation. We explore the transition from kinetic systems to their corresponding moment systems, provide a Hamiltonian description of such moment systems, and give a geometric interpretation of the moment closure problem for kinetic theories. As an application, we discuss in detail diffusion radiation hydrodynamics as an example of a pair bracket formulation on a space of moments corresponding to kinetic matter–radiation interaction. Additionally, using the variable moment closure framework of Burby [J. W. Burby, Variable-moment fluid closures with Hamiltonian structure, Sci. Rep. 13 (2023) 18286, doi:10.1038/s41598-023-45416-5], we show how to construct Hamiltonian moment closures for kinetic transport equations with arbitrary Hamiltonian. Using this general construction, we derive novel Hamiltonian moment closures for pure radiation transport.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Understanding Chemo-Mechanical Stability of Protonic Ceramic Cells Through Electronic Conduction in the BaCe 0.7 Zr 0.1 Y 0.1 Yb 0.1 O 3-δ Electrolyte

BaCe 0.7 Zr 0.1 Y 0.1 Yb 0.1 O 3-δ (BCZYYb) is a promising ceramic electrolyte for reversible protonic ceramic cells. It is reported that BCZYYb-based cells show excellent long-term durability, particularly in the electrolysis mode, in contrast to the cells based on the conventional electrolyte yttria-stabilized zirconia. In this study, we investigate the chemo-mechanical stability behavior (a low tendency of delamination) of the BCZYYb-based protonic ceramic cells in terms of local electronic conduction in the electrolyte. The local electronic conductivity of the BCZYYb electrolyte is determined using Pt-probe-embedded cells near each electrode interface. The BCZYYb electrolyte exhibits sufficient p-type conductivity (∼10 −3 S cm −1 ) near the oxygen electrode (corresponding p O 2 : 5.27–21 × 10 −2 atm) and n-type conductivity (∼10 −4 S cm −1 ) near the hydrogen electrode (corresponding p O 2 : 0.99–1.30 × 10 −24 atm) at 600 °C. A standard cell is prepared and tested over long-term in the fuel cell (at positive and negative voltages) and electrolysis modes. The cell exhibits stable performance without delamination or cracks in both operating modes, owing to local electronic conduction.

Electrochemistry↗

DOE OSTI2020

Considerations for three-dimensional image reconstruction from experimental data in coherent diffractive imaging

The Datasets folder contains 3 datasets, corresponding to the datasets mentioned in the article with 670, 2691 and 586 images. The Runs folder has all the runs used. Besides the images itself it also contains tags for each image under `/entry_1/image_1/detector_and_photon_corrected/tags`. The `headings` attribute gives the name of each tag. The `Jonas Strict` tag corresponds to the 670 images, the `Redflamingo` tag to the 2691 images and the `Redflamingo_Ida_subset` tag to the 586 images. The final dataset with 260 images can be obtained from `Datasets/amoc6914-Redflamingo-manual-586.h5` and use the `/model/diameterNM` group to select the 260 images with size closest to 214 nm.

AMO↗

DOE OSTI2021

Short-Lived Intermediate in the N2O Generation by P450 NO Reductase Captured by Time-Resolved IR Spectroscopy and XFEL Crystallography

Diffraction images were collected using an MX300-HS CCD detector. For each cryoloop, 2D raster scan with 50 μm step in each direction was performed twice. In the second pass, the raster-scan grid was shifted 25 μm in each direction. In cheetah directory, - {puck_name}/{pin_id}_hits.h5 correspond to the first scan, and - {puck_name}/{pin_id}-2_hits.h5 correspond to the second scan.

BL3↗

DOE OSTI2020

Theory of the tertiary instability and the Dimits shift within a scalar model

The Dimits shift is the shift between the threshold of the drift-wave primary instability and the actual onset of turbulent transport in magnetized plasma. It is generally attributed to the suppression of turbulence by zonal flows, but developing a more detailed understanding calls for consideration of specific reduced models. The modified Terry--Horton system has been proposed by St-Onge [J. Plasma Phys. {\bf 83}, 905830504 (2017)] as a minimal model capturing the Dimits shift. Here, we use this model to develop an analytic theory of the Dimits shift and a related theory of the tertiary instability of zonal flows. We show that tertiary modes are localized near extrema of the zonal velocity $U(x)$, where $x$ is the radial coordinate. By approximating $U(x)$ with a parabola, we derive the tertiary-instability growth rate using two different methods and show that the tertiary instability is essentially the primary drift-wave instability modified by the local $U''$. Then, depending on $U''$, the tertiary instability can be suppressed or unleashed. The former corresponds to the case when zonal flows are strong enough to suppress turbulence (Dimits regime), while the latter corresponds to the case when zonal flows are unstable and turbulence develops. This understanding is different from the traditional paradigm that turbulence is controlled by the flow shear $U'$. Our analytic predictions are in agreement with direct numerical simulations of the modified Terry--Horton system.

fusion plasmas↗

DOE OSTI2023

Aquapv

This project brings together a multidisciplinary team to develop tools and analysis that will help U.S. industry accelerate deployment of floating PV on reservoirs and estuaries through producing: • A tool to assess potential environmental interactions of FPV on reservoirs and estuaries and thresholds for corresponding impacts. • A tailored FPV-specific techno-economic tool to guide users on financial performance and corresponding design considerations. • A synthesized tool, AquaPV, that combines the environmental and economic tools. • A set of case studies on an open-loop reservoir with a traditional hydropower plant (i.e., connected to a natural waterbody), closed-loop reservoir with a proposed pumped storage hydropower plant (i.e., manmade and disconnected from a natural waterbody), and a marine estuary (i.e., where a river meets the ocean), with an FPV system designed and modeled for each site. • A review of regulatory, environmental, and stakeholder opportunities and challenges to identify clear regulatory and environmental pathways to siting FPV on U.S. reservoirs. • A novel, high-fidelity geospatial resource assessment to estimate FPV technical potential on reservoirs in the United States.

Phillips, TylerB. [Idaho National Laboratory (INL)↗

DOE OSTI2024

XRF-XFS-XAS-Auto v1.0 - Beta release

This software allows to analyze XRF maps, XFS spectra and XAS spectra collected at the Advanced Light Source's Beamline 10.3.2. Features include: 1) XRF maps: - process XRF maps, all elemental maps are saved as bmp automatically and labeled with the incident energy used, the scale bar is also labeled and can be controlled. - XRF elemental correlation plots, save the correlation plots automatically - Extract single or multiple transects in XRF maps on one or several regions of interest, each transect profile is numbered and saved in a corresponding folder, along with the corresponding maps showing transect location. 2) XFS spectra - save in log10 scale the XFS spectra, either a single or multiple files all at once. The files are saved as .bmp. - XFS spectra are labeled according to tabulated fluorescence emission lines. 3) XAS spectra - allows to plot individual scalers in the raw data. - allows calibration of the spectra using an Io internal glitch present in all spectra and performing 1st derivative. - Least-square linear combination fitting of XANES or extended XANES spectra using a database of standards using 1, 2 or 3 components maximum. It also provides the 5 top combinations and provide the user for the possibility of saving the 2nd, 3rd, 4th and 5th best combinations in addition to the best one. The processed spectra (pre-edge background substracted, post-edge normalized), the fits and residuals are automatically saved. A table of the component, with fit% and SSN is provided and saved automatically as well.

Fakra, Sirine↗

DOE OSTI2023

ARMing the Edge: Designing Edge Computing–Capable Machine Learning Algorithms to Target ARM Doppler Lidar Processing

Abstract There is a need for long-term observations of cloud and precipitation fall speeds in validating and improving rainfall forecasts from climate models. To this end, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) site at Lamont, Oklahoma, hosts five ARM Doppler lidars that can measure cloud and aerosol properties. In particular, the ARM Doppler lidars record Doppler spectra that contain information about the fall speeds of cloud and precipitation particles. However, due to bandwidth and storage constraints, the Doppler spectra are not routinely stored. This calls for the automation of cloud and rain detection in ARM Doppler lidar data so that the spectral data in clouds can be selectively saved and further analyzed. During the ARMing the Edge field experiment, a Waggle node capable of performing machine learning applications in situ was deployed at the ARM SGP site for this purpose. In this paper, we develop and test four algorithms for the Waggle node to automatically classify ARM Doppler lidar data. We demonstrate that supervised learning using a ResNet50-based classifier will classify 97.6% of the clear-air images and 94.7% of cloudy images correctly, outperforming traditional peak detection methods. We also show that a convolutional autoencoder paired with k -means clustering identifies 10 clusters in the ARM Doppler lidar data. Three clusters correspond to mostly clear conditions with scattered high clouds, and seven others correspond to cloudy conditions with varying cloud-base heights.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know

Remote sensing instruments are heavily used to provide observations for both the operational and research communities. These sensors do not provide direct observations of the desired atmospheric variables, but instead, retrieval algorithms are necessary to convert the indirect observations into the variable of interest. It is critical to be aware of the underlying assumptions made by many retrieval algorithms, including that the retrieval problem is often ill posed and that there are various sources of uncertainty that need to be treated properly. In short, the retrieval challenge is to invert a set of noisy observations to obtain estimates of atmospheric quantities. The problem is often complicated by imperfect forward models, by imperfect prior knowledge, and by the existence of nonunique solutions. Optimal estimation (OE) is a widely used physical retrieval method that combines measurements, prior information, and the corresponding uncertainties based on Bayes’s theorem to find an optimal solution for the atmospheric state. Furthermore, OE also allows the relative contributions of the different sources of error to the uncertainty in the final retrieved atmospheric state to be understood. Here, we provide a novel Python library to illustrate the use of OE for inverse problems in the atmospheric sciences. We introduce two example problems: how to retrieve drop size distribution parameters from radar observations and how to retrieve the temperature profile from ground-based microwave sensors. Using these examples, we discuss common pitfalls, how the various error sources impact the retrieval, and how the quality of the retrieval results can be quantified.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

A Polarimetric Radar Analysis of Ice Microphysical Processes in Melting Layers of Winter Storms Using S-Band Quasi-Vertical Profiles

Quasi-vertical profiles (QVPs) obtained from a database of U.S. WSR-88D data are used to document polarimetric characteristics of the melting layer (ML) in cold-season storms with high vertical resolution and accuracy. A polarimetric technique to define the top and bottom of the ML is first introduced. Using the QVPs, statistical relationships are developed to gain insight into the evolution of microphysical processes above, within, and below the ML, leading to a statistical polarimetric model of the ML that reveals characteristics that reflectivity data alone are not able to provide, particularly in regions of weak reflectivity factor at horizontal polarization Z H . QVP ML statistics are examined for two regimes in the ML data: Z H ≥ 20 dB Z and Z H < 20 dB Z . Regions of Z H ≥ 20 dB Z indicate locations of MLs collocated with enhanced differential reflectivity Z DR and reduced copolar correlation coefficient ρ hv , while for Z H < 20 dB Z a well-defined ML is difficult to discern using Z H alone. Evidence of large Z DR up to 4 dB, backscatter differential phase δ up to 8°, and low ρ hv down to 0.80 associated with lower Z H (from -10 to 20 dB Z ) in the ML is observed when pristine, nonaggregated ice falls through it. Positive correlation is documented between maximum specific differential phase K DP and maximum Z H in the ML; these are the first QVP observations of K DP in MLs documented at S band. Negative correlation occurs between minimum ρ hv in the ML and ML depth and between minimum ρ hv in the ML and the corresponding enhancement of Z H (Δ Z H = Z H max - Z H rain ).

Meteorology & Atmospheric Sciences↗

DOE OSTI2020

Hydrometeor Shape Variability in Snowfall as Retrieved from Polarimetric Radar Measurements

A polarimetric radar–based method for retrieving atmospheric ice particle shapes is applied to snowfall measurements by a scanning Ka-band radar deployed at Oliktok Point, Alaska (70.495°N, 149.883°W). The mean aspect ratio, which is defined by the hydrometeor minor-to-major dimension ratio for a spheroidal particle model, is retrieved as a particle shape parameter. The radar variables used for aspect ratio profile retrievals include reflectivity, differential reflectivity, and the copolar correlation coefficient. The retrievals indicate that hydrometeors with mean aspect ratios below 0.2–0.3 are usually present in regions with air temperatures warmer than approximately from –17° to –15°C, corresponding to a regime that has been shown to be favorable for growth of pristine ice crystals of planar habits. Radar reflectivities corresponding to the lowest mean aspect ratios are generally between –10 and 10 dBZ. For colder temperatures, mean aspect ratios are typically in a range between 0.3 and 0.8. There is a tendency for hydrometeor aspect ratios to increase as particles transition from altitudes in the temperature range from –17° to –15°C toward the ground. Furthermore, this increase is believed to result from aggregation and riming processes that cause particles to become more spherical and is associated with areas demonstrating differential reflectivity decreases with increasing reflectivity. Aspect ratio retrievals at the lowest altitudes are consistent with in situ measurements obtained using a surface-based multiangle snowflake camera. Pronounced gradients in particle aspect ratio profiles are observed at altitudes at which there is a change in the dominant hydrometeor species, as inferred by spectral measurements from a vertically pointing Doppler radar.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Frozen Hydrometeor Terminal Fall Velocity Dependence on Particle Habit and Riming as Observed by Vertically Pointing Radars

Vertically pointing Ka-band radar measurements are used to derive fall velocity–reflectivity factor ($V$ t = $aZ$$^{b}_{e}$) relations for frozen hydrometeor populations of different habits during snowfall events observed at Oliktok Point, Alaska, and at the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC). Case study events range from snowfall with highly rimed particles observed during periods with large amounts of supercooled liquid water path (LWP > 320 g m –2 ) to unrimed snowflakes including instances when pristine planar crystals were the dominant frozen hydrometeor habit. The prefactor a and the exponent b in the observed $V$ t –$Z$ e relations scaled to the sea level vary in the approximate ranges 0.5–1.4 and 0.03–0.13, respectively (reflectivities are in mm 6 m –3 and velocities are in m s –1 ). The coefficient a values are the smallest for planar crystals (a ~ 0.5) and the largest (a > 1.2) for particles under severe riming conditions with high LWP. There is no clear distinction between b values for high and low LWP conditions. The range of the observed $V$ t –$Z$ e relation coefficients is in general agreement with results of modeling using fall velocity–size (υ t = αD β ) relations for individual particles found in literature for hydrometeors of different habits, though there is significant variability in α and β coefficients from different studies even for a same particle habit. Correspondences among coefficients in the $V$ t –$Z$ e relations for particle populations and in the individual particle υ t –$D$ relations are analyzed. Furthermore, these correspondences and the observed $V$ t –$Z$ e relations can be used for evaluating different frozen hydrometeor fall velocity parameterizations in models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Improved Diagnosis of Precipitation Type with LightGBM Machine Learning

Abstract Existing precipitation-type algorithms have difficulty discerning the occurrence of freezing rain and ice pellets. These inherent biases are not only problematic in operational forecasting but also complicate the development of model-based precipitation-type climatologies. To address these issues, this paper introduces a novel light gradient-boosting machine (LightGBM)-based machine learning precipitation-type algorithm that utilizes reanalysis and surface observations. By comparing it with the Bourgouin precipitation-type algorithm as a baseline, we demonstrate that our algorithm improves the critical success index (CSI) for all examined precipitation types. Moreover, when compared with the precipitation-type diagnosis in reanalysis, our algorithm exhibits increased F1 scores for snow, freezing rain, and ice pellets. Subsequently, we utilize the algorithm to compute a freezing-rain climatology over the eastern United States. The resulting climatology pattern aligns well with observations; however, a significant mean bias is observed. We interpret this bias to be influenced by both the algorithm itself and assumptions regarding precipitation processes, which include biases associated with freezing drizzle, precipitation occurrence, and regional synoptic weather patterns. To mitigate the overall bias, we propose increasing the precipitation cutoff from 0.04 to 0.25 mm h −1 , as it better reflects the precision of precipitation observations. This adjustment yields a substantial reduction in the overall bias. Finally, given the strong performance of LightGBM in predicting mixed precipitation episodes, we anticipate that the algorithm can be effectively utilized in operational settings and for diagnosing precipitation types in climate model outputs. Significance Statement Freezing rain can have significant impacts on transportation and infrastructure, making accurate prediction of precipitation types crucial. In this study, we use a machine learning method known as LightGBM to predict precipitation types. We show that the new algorithm performs better than the existing methods for all precipitation types examined. Additionally, we compute a freezing-rain climatology over the eastern United States. Although the resulting climatology pattern corresponds well to observations, the algorithm overpredicts freezing-rain occurrence. We argue that this bias can be substantially reduced by increasing the precipitation cutoff from 0.04 to 0.25 mm h −1 . Overall, this work highlights the potential of the LightGBM algorithm for both weather forecasting and diagnosing precipitation types in climate models.

Meteorology & Atmospheric Sciences↗