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Focal Area(s): (2) Predictive modeling through the use of AI techniques and AI-derived model components: This white paper focuses on using artificial intelligence (AI) to discover connections across processes and scales and to represent those processes in predictive models of anthropogenic impacts on the water cycle.
plexosdb is a lightweight Python library for constructing PLEXOS models using a SQLite-backed data structure. It provides a clear, modular interface that maps relational data directly to model components. By leveraging SQLite and idiomatic Python, it enables fast iteration and reproducible workflows. The result is a performant, composable foundation for scalable PLEXOS model development.
FIR fine structure line and submillimeter line emissions important in the cooling of photodissociation regions (PDRs) as they would arise in clumpy regions are investigated with a simple two-component model for clumpy PDRs consisting of high-density clumps embedded in a lower density interclump medium. The effects of varying the densities of the clump and interclump material in a self-consistent manner are studied. It is found that, while the clump filling factor moderately affects the penetration of the UV field, the density of the interclump material is the most important factor in determining the penetration of the UV field. Observational evidence for the effects of clumpiness in PDRs is found in the spatially extended C II and C I forbidden line emissions which reveal the extent of the UV penetration, and in the observed high intensities of the high CO transitions which indicate the presence of high-density clumps.
GCSS Cirrus Cloud Systems Working Group (WG2) is presently conducting a comparison of cirrus cloud models for idealized initial conditions. The experiments involve binary (off/on) tests of model sensitivity to infrared radiative processes, and thermal stratification, and vertical wind shear for situations of weakly forced (3 cm/s uplift) cold (-60 to -70 C) and warm (-35 to -50 C) cirrus clouds. A range of model types are involved including parcel, SCM, 2-D CRM, 3-D CRM and LES models. The test cases will be described and results from 2-dimensional cirrus cloud models with bulk microphysics (implicit second moment scheme) and explicit bin microphysics will be compared. Vertical ice mass flux (particle fall speed) is a critical model component leading to significant intermodel differences. Efforts are ongoing to better quantify this aspect. Future plans of WG2 will also be briefly described and include model comparisons for a well-observed case of cold (ARM IOP) cirrus and of warm (EUCREX) cirrus, as well as, a joint activity with WG4 to consider the treatment of anvil cirrus in a variety of models.
Earth System Model (ESM) development benefits from attention to the downstream applications for nature and society. The evaluation of ESMs often focuses on a limited set of climatic impact-drivers (CIDs)1 and numerous large-scale parameters that are not directly relevant for impact analysis. To address this limitation, we propose a comprehensive framework for impact-relevant model version comparison. This framework explores both the intended and unintended consequences of shifts in model components, parameters, and resolutions, emphasizing the perspectives of the climate impacts community. We have selected a core set of CIDs as a basis for comparing model versions.
Focal Area(s): Focal Area 2: AI for predictive modeling, including AI-derived model components, and AI-enabled applications including parameter optimization, data assimilation, and uncertainty quantification. Focal Area 1: AI-assisted data assimilation using numerical Earth system models. Science Challenge: Earth system models have structural biases that lead to uncertain predictions, and their complexity and expense makes it difficult constraining the models with data or improved physical understanding.
We advocate for several modeling approaches suitable to prove understanding of and capability to model aerosol behavior in nucleating droplets and ice and to model regional cloud systems at the mesoscale. The full complement of data that is ideal for particular study types is increasingly well understood; the ASR ISDAC Science Plan provides an excellent example of listing the data required for each study type targeted by that campaign. Whereas discussion here focused on the microscale and mesoscale, other studies should obviously focus on the regional, seasonal and global scale. When considering ice microphysics alone, laser focus on unconstrained model components could be usefully intensified (e.g., collision-coalescence kernels). Similarly, a focus on boundary layer structure would be deeply valuable to a wide range of model types. The expense of observations motivates efficient planning for observational data sets to support specific study approaches; the concept of closure is often useful (e.g., the surface energy budget).
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Previous studies have shown that models overestimate the strength of ENSO teleconnections to the North Pacific during springtime, but the underlying reasons for this bias remain unknown. Here, in this work, the relative contributions from basic-state and thermodynamic/dynamic forcing factors are disentangled through idealized experiments with the Community Earth System Model and a range of stationary wave modeling experiments. It is revealed that in CESM1 the diabatic heating biases over the tropical Indian Ocean and tropical central-western Pacific jointly favor a cyclonic (anticyclonic) circulation bias to occur in the North Pacific during the springtime of El Niño (La Niña) events. On one hand, the difference in the modeled and observed climatological basic state does not lead to the bias formation directly, as the diabatic heating biases are the primary cause. On the other hand, the springtime basic state is conducive to a more vigorous stationary wave response to the biased diabatic heating than the wintertime state, and this explains why the teleconnection bias occurs during springtime but not in winter. An iterative bias-correction approach is then implemented in the atmospheric model component of CESM1 to verify the linkage between the tropical diabatic heating bias and the teleconnection bias. Moreover, this explanation is shown to be relevant in other models of phase 5 of the Coupled Model Intercomparison Project (CMIP5) as a strong relationship is found between biases in ENSO-related tropical central-western Pacific/Indian Ocean precipitation and North Pacific circulation across models in spring.
This paper addresses the specter of a September ice-free Arctic in the 21st century using newly available simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). We find that large spread in the projected timing of the September ice-free Arctic in 30 CMIP5 models is associated at least as much with different atmospheric model components as with initial conditions. Here we reduce the spread in the timing of an ice-free state using two different approaches for the 30 CMIP5 models: (i) model selection based on the ability to reproduce the observed sea ice climatology and variability since 1979 and (ii) constrained estimation based on the strong and persistent relationship between present and future sea ice conditions. Results from the two approaches show good agreement. Under a high-emission scenario both approaches project that September ice extent will drop to approx. 1.7 million sq km in the mid 2040s and reach the ice-free state (defined as 1 million sq km) in 2054-2058. Under a medium-mitigation scenario, both approaches project a decrease to approx.1.7 million sq km in the early 2060s, followed by a leveling off in the ice extent.
The resolution sensitivity of the Kain–Fritsch (KF) convection scheme and the role of interactions between the physics and dynamics within the gray zone (<10 km) were investigated using the Separate Physics and Dynamics Experiment (SPADE) framework. Two groups of experiments were conducted using the Weather Research and Forecasting (WRF) model via traditional (Tradition) runs and SPADE runs with resolutions of 1, 2, 4, and 8 km during the wet period of the Tropical Warm Pool–International Cloud Experiment (TWP‐ICE). Results show that the KF scheme simulates the weakened convective processes well as the resolution increases in both groups, and the changes in the convective variables with resolution in SPADE are smaller than in the Tradition group. This indicates the important effects of interactions between model components on convection parameterizations as the resolution changes. Additionally, the microphysics variables remain nearly unchanged with resolution in SPADE and weaken slightly in Tradition as the resolution decreases, suggesting the relatively weaker influences of model interactions for the resolved‐cloud parameterization. Therefore, the scale‐aware behavior of KF scheme is further strengthened in Tradition runs, primarily through inhibiting the strength of stratiform processes through physics–dynamics interactions and physical components.
Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021–22 and 2022–23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021–22 and 12 out of 18 models in 2022–23. Averaging across all forecast targets, the FluSight ensemble is the 2nd most accurate model measured by WIS in 2021–22 and the 5th most accurate in the 2022–23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change.
The California Sacramento-San Joaquin River Delta is the hub for California's water supply, conveying water from Northern to Southern California agriculture and communities while supporting important ecosystem services, agriculture, and communities in the Delta. Changes in climate, long-term drought, and water quality have all been suspected as playing role in the dramatic expansion of invasive aquatic plants and their impact on ecosystems of the San Francisco Bay / California Delta complex. NASA Ames Research Center, USDA-Agricultural Research Service, the State of California, UC Davis, and local governments have partnered under a USDA sponsored project (DRAAWP) to develop science-based, adaptive-management strategies for invasive aquatic plants in Sacramento-San Joaquin Delta. Critical to developing management strategies is to understand how the Delta is affected by both the magnitude of fluctuations in land-use and climate / drought induced altered environments and how the plants respond to these altered environments. We utilize the Soil Water Assessment Tool (SWAT), a watershed-scale model developed to quantify the impact of land management practices in large and complex watersheds on water quality, as the backbone for a customized Delta model - Delta-SWAT. The model uses land-use, soils, elevation, and hydrologic routing to characterize pesticide and nutrient transport from the Sacramento and San Joaquin rivers watersheds and loading into the Delta. Land-use within the Delta, as well as water extraction to supply those functions, and the resulting return of water to Delta waterways are included in Delta-SWAT. Hydrologic transport within the Delta has required significant attention to address the lack of elevation driven transport processes. Delta-SWAT water quality trend estimates are compared with water quality monitoring conducted throughout the Delta. Aquatic plant response to water quality and other environmental factors is carried out using a customized model component. Plant response to the range of water quality factors, response times, and altered temperature and light regimes of the Delta have required gap-filling studies to provide model parameters. Delta-SWAT provides a tool for evaluating temporal and spatial effects of land-use and altered environments in the Delta and contributing watersheds on aquatic weed growth. Using Delta-SWAT for simulation modeling allows evaluation of historic and current conditions as well as consideration potential climate change and management practice outcomes. Delta-SWAT adds to the scientific understanding of dynamics in the Delta and enhances development of science-informed, management strategies and practices.
We analyze high-resolution spectra of the nearby (1.34 pc) stars alpha Cen A (G2 V) and alpha Cen B (K1 V), which were obtained with the Goddard High Resolution Spectrograph on the Hubble Space Telescope. The observations consist of echelle spectra of the Mg II 2800 A and Fe II 2599 A resonance lines and the Lyman-alpha lines of hydrogen and deuterium. The interstellar gas has a velocity (v = - 18.0 +/- 0.2 km/s) consistent with the local flow vector proposed for this line of sight by Lailement & Berlin (1992). The temperature and nonthermal velocity inferred from the Fe II, Mg II, and D I line profiles are T = 5400 +/- 500 K and xi = 1.20 +/- 0.25 km/s, respectively. However, single-component fits to the H I Lyman-alpha lines yield a Doppler parameter (b(sub HI) = 11.80 km/s) that implies a significantly warmer temperature of 8350 K, and the velocity of the H I absorption (v = - 15.8 +/- 0.2 km/s) is redshifted by about 2.2 km/s with respect to the Fe II, Mg II, and D I lines. The one-component model of the interstellar gas suggests natural logarithm N base HI = 18.03 +/- 0.01 and D/H = (5.7 +/- 0.2) x 10(exp -6) . These parameters lead to a good fit to the observed spectra, but this model does not explain the higher temperature and redshift of H I relative to the other interstellar lines. The most sensible way to resolve the discrepancy between H(I) and the other lines is to add a second absorption component to the H(I) lines. This second component is hotter (T approx. equals 30,000 K), is redshifted relative to the primary component by 2-4 km/s, and has a column density too low to be detected in the Fe(II), Mg(II), and D(I) lines. We propose that the gas responsible for this component is located near the heliopause, consisting of the heated H I gas from the interstellar medium that is compressed by the solar wind. This so-called 'hydrogen wall' is predicted by recent multifluid gasdynamical models of the interstellar gas and solar wind interaction. Our data provide the first measurements of the temperature and column density of H(I) in the hydrogen wall. After considering the effects that a corresponding hydrogen wall around alpha Cen would have on our analysis, our best estimates for the parameters of the solar hydrogen wall are natural log N(sup (2))(H(I)) = 14.74 +/- 0.24, b(sup (2))(H(I)) = 21.9 +/- 1.7 km/s (corresponding to T = 29,000 +/- 5000 K), and v(sup (2))(H(I)) greater than -16km/s. Unfortunately, the existence of this heated H(I) reduces our ability to compute the H(I) column density of the interstellar medium accurately because, with slight alterations to our assumed stellar Lyman-alpha profiles, we discovered that acceptable two-component fits also exist with natural log N(H(I))approx. 17.6. We, therefore, quote large error bars for the H I column density along the alpha Cen line of sight, natural log N(H(I)) = 17.80 +/- 0.30. For this range in N(H(I)), n(H(I)) = 0.15 /cu.cm (+/- a factor of 2) and D/H = (0.5-1.9) x 10(exp -5). This is the first direct measurement of the H(I) density in a local cloud and allows us to predict the distance from the Sun to the edge of the local cloud along various lines of sight. This range in D/H is consistent with the value D/H = 1.6 x 10(exp -5) previously derived for the Capella and Procyon lines of sight. We cannot tell whether D/H ratio varies or is constant in the local interstellar medium, but we do find that the D(I)/Mg(II) ratio for the alpha Cen line of sight is about 4 times smaller than for the Capella and Procyon lines of sight. Therefore, either D/H or the Mg depletion varies significantly over distance scales of only a few parsecs.
The shape of the spectral energy distribution of active galaxies in the EUV soft X-ray band (13.6 eV to 1 keV) is uncertain because obscuration by dust and gas can hamper our view of the continuum. To investigate the shape of the spectral energy distribution in this energy band, we have generated a set of photoionization models which reproduce the small dispersion found in correlations between high-ionization mid-infrared emission lines in a sample of hard X-ray selected AGN. Our calculations show that a broken power-law continuum model is sufficient to reproduce the [Ne V]14.32 microns/[Ne III], [Ne V]24.32 microns/[O IV]25.89 micron and [O IV] 25.89 microns/[Ne III] ratios, and does not require the addition of a "big bump" EUV model component. We constrain the EUV-soft X-ray slope, alpha(sub i), to be between 1.5 - 2.0 and derive a best fit of alpha(sub i) approx. 1.9 for Seyfert 1 galaxies, consistent with previous studies of intermediate redshift quasars. If we assume a blue bump model, most sources in our sample have derived temperatures between T(sub BB) = 10(exp 5.18) K to 10(exp 5.7) K, suggesting that the peak of this component spans a large range of energies extending from approx. (Lambda)600 A to > (Lambda)1900 A. In this case, the best fitting peak energy that matches the mid-infrared line ratios of Seyfert 1 galaxies occurs between approx. (Lambda)700-(Lambda)1000 A. Despite the fact that our results do not rule out the presence of an EUV bump, we conclude that our power-law model produces enough photons with energies > 4 Ry to generate the observed amount of mid-infrared emission in our sample of BAT AGN.
The dataset comprises Soil Temperature and Deformation monitoring data gathered from a watershed situated along the Nome-Teller road at Mile 47 in Alaska. Its primary objective is to enhance comprehension of deformation mechanisms in permafrost environments by using 51 probes designed following Wielandt et al. (2022). The dataset comprises a description of Probe_ID, Location, start time, end time and probe length in the "ProbesLocations_StartEndDates.csv" file, the 51 data files encapsulated within "Data_files.zip" and the computed deformation for 2023 and 2022 in "Deformation_T47.zip".Each file in Data_files.zip is named as followed "ProbeId_StartMonth_EndMonth.csv". Within each file, the initial column denotes the timestamp in UTC, followed by the sensor’s battery voltage and temperature and acceleration values (X, Y, Z) in subsequent columns. The data collection frequency is set at 30-minute intervals. Each file in Deformation_T47.zip is named as followed "ProbeId_Temp_def_year.csv". Within each file, the initial column denotes the timestamp in UTC, followed by the temperature at each sensor and cumulative deformation from last to first sensor in subsequent columns. The data collection is daily averaged.This dataset forms an integral component of the Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic). The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
In May 1981, observations of a solar active region at 6 cm and 20 cm were conducted with the aid of a Very Large Array (VLA). It was found that the 6 cm emission was associated with sunspots and transverse magnetic fields between spots. The 20 cm emission came from elongated sources connecting regions of opposite magnetic polarity. The sources of the emission at 20 cm could be identified as coronal loops. A model for hydrostatic loops proposed by Rosner et al. (1978) was employed to determine a likely mechanism for the emission at 20 cm. It was found that a two-component model of bremsstrahlung emission from the feet and gyroresonance emission from the top of the loops can satisfactorily explain the observations.
The large-signal behaviors of a regulator depend largely on the type of power circuit topology and control. Thus, for maximum flexibility, it is best to develop models for each functional block a independent modules. A regulator can then be configured by collecting appropriate pre-defined modules for each functional block. In order to complete the component model generation for a comprehensive spacecraft power system, the following modules were developed: solar array switching unit and control; shunt regulators; and battery discharger. The capability of each module is demonstrated using a simplified Direct Energy Transfer (DET) system. Large-signal behaviors of solar array power systems were analyzed. Stability of the solar array system operating points with a nonlinear load is analyzed. The state-plane analysis illustrates trajectories of the system operating point under various conditions. Stability and transient responses of the system operating near the solar array's maximum power point are also analyzed. The solar array system mode of operation is described using the DET spacecraft power system. The DET system is simulated for various operating conditions. Transfer of the software program CAMAPPS (Computer Aided Modeling and Analysis of Power Processing Systems) to NASA/GSFC (Goddard Space Flight Center) was accomplished.