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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The Earth Model Column Collaboratory (EMC 2 ) v1.1: an open-source ground-based lidar and radar instrument simulator and subcolumn generator for large-scale models

Abstract. Climate models are essential for our comprehensive understanding of Earth's atmosphere and can provide critical insights on future changes decades ahead. Because of these critical roles, today's climate models are continuously being developed and evaluated using constraining observations and measurements obtained by satellites, airborne, and ground-based instruments. Instrument simulators can provide a bridge between the measured or retrieved quantities and their sampling in models and field observations while considering instrument sensitivity limitations. Here we present the Earth Model Column Collaboratory (EMC2), an open-source ground-based lidar and radar instrument simulator and subcolumn generator, specifically designed for large-scale models, in particular climate models, but also applicable to high-resolution model output. EMC2 provides a flexible framework enabling direct comparison of model output with ground-based observations, including generation of subcolumns that may statistically represent finer model spatial resolutions. In addition, EMC2 emulates ground-based (and air- or space-borne) measurements while remaining faithful to large-scale models' physical assumptions implemented in their cloud or radiation schemes. The simulator uses either single particle or bulk particle size distribution lookup tables, depending on the selected scheme approach, to perform the forward calculations. To facilitate model evaluation, EMC2 also includes three hydrometeor classification methods, namely, radar- and sounding-based cloud and precipitation detection and classification, lidar-based phase classification, and a Cloud Feedback Model Intercomparison Project Observational Simulator Package (COSP) lidar simulator emulator. The software is written in Python, is easy to use, and can be straightforwardly customized for different models, radars, and lidars. Following the description of the logic, functionality, features, and software structure of EMC2, we present a case study of highly supercooled mixed-phase cloud based on measurements from the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE). We compare observations with the application of EMC2 to outputs from four configurations of the NASA Goddard Institute for Space Studies (GISS) climate model (ModelE3) in single-column model (SCM) mode and from a large-eddy simulation (LES) model. We show that two of the four ModelE3 configurations can form and maintain highly supercooled precipitating cloud for several hours, consistent with observations and LES. While our focus is on one of these ModelE3 configurations, which performed slightly better in this case study, both of these configurations and the LES results post-processed with EMC2 generally provide reasonable agreement with observed lidar and radar variables. As briefly demonstrated here, EMC2 can provide a lightweight and flexible framework for comparing the results of both large-scale and high-resolution models directly with observations, with relatively little overhead and multiple options for achieving consistency with model microphysical or radiation scheme physics.

58 GEOSCIENCES↗
DOE OSTI2022

The role of föhn winds in eastern Antarctic Peninsula rapid ice shelf collapse

Ice shelf collapse reduces buttressing and enables grounded glaciers to contribute more rapidly to sea-level rise in a warming climate. The abrupt collapses of the Larsen A (1995) and B (2002) ice shelves on the Antarctic Peninsula (AP) occurred, at least for Larsen B, when long-period ocean swells damaged the calving front and the ice shelf was inundated with melt lakes that led to large-scale hydrofracture cascades. During collapse, field and satellite observations indicate föhn winds were present on both ice shelves. Here we use a regional climate model and machine learning analyses to evaluate the contributory roles of föhn winds and associated melt events prior to and during the collapses for ice shelves on the AP. Föhn winds caused about 25 % ± 3 % of the total annual melt in just 9 d on Larsen A prior to and during collapse and were present during the Larsen B collapse, which helped form extensive melt lakes. At the same time, the off-coast wind direction created by föhn winds helped melt and physically push sea ice away from the ice shelf calving fronts that allowed long-period ocean swells to reach and damage the front, which has been theorized to have ultimately triggered collapse. Collapsed ice shelves experienced enhanced surface melt driven by föhn winds over a large spatial extent and near the calving front, whereas SCAR inlet and the Larsen C ice shelves are affected less by föhn-wind-induced melt and do not experience large-scale melt ponds. These results suggest SCAR inlet and the Larsen C ice shelves may be less likely to experience rapid collapse due to föhn-driven melt so long as surface temperatures and föhn occurrence remain within historical bounds.

58 GEOSCIENCES↗
DOE OSTI2024

Contributions From Cloud Morphological Changes to the Interannual Shortwave Cloud Feedback Based on MODIS and ISCCP Satellite Observations

The surface temperature-mediated change in cloud properties, referred to as the cloud feedback, continues to dominate the uncertainty in climate projections. A larger number of contemporary global climate models (GCMs) project a higher degree of warming than the previous generation of GCMs. This greater projected warming has been attributed to a less negative cloud feedback in the Southern Ocean. Here, we apply a novel “double decomposition method” that employs the “cloud radiative kernel” and “cloud regime” concepts, to two data sets of satellite observations to decompose the interannual cloud feedback into contributions arising from changes within and shifts between cloud morphologies. Our results show that contributions from the latter to the cloud feedback are large for certain regimes. We then focus on interpreting how both changes within and between cloud morphologies impact the shortwave cloud optical depth feedback over the Southern Ocean in light of additional observations. Results from the former cloud morphological changes reveal the importance of the wind response to warming increases low- and mid-level cloud optical thickness in the same region. Results from the latter cloud morphological changes reveal that a general shift from thick storm-track clouds to thinner oceanic low-level clouds contributes to a positive feedback over the Southern Ocean that is offset by shifts from thinner broken clouds to thicker mid- and low-level clouds. Our novel analysis can be applied to evaluate GCMs and potentially diagnose shortcomings pertaining to their physical parameterizations of particular cloud morphologies.

58 GEOSCIENCES↗
DOE OSTI2024

An Overview of Mesoscale Convective Systems: Global Climatology, Satellite Observations, and Modeling Strategies

Deep convection is responsible for redistributing water as well as energy and providing vital water resources for many regions of the world. Cumulonimbus clouds aggregation into a single storm system develop mesoscale convective systems (MCS). MCSs have precipitation covering a horizontal region on the scale of 100?km or more. After more than the last seven decades, MCS has been gradually received wide attention in the scientific community as an important component of the Earth's hydrologic cycle and energy balance. MCSs differ from ordinary deep convection because of the mesoscale circulations consisting of a layer of air overturning on a scale much larger than those created by individual convective updrafts or downdrafts. Moreover, a key feature that separates MCS from ordinary deep convection is the significant portion of stratiform precipitation that results in the production and detrainment of a large number of ice particles from their organized convective cores and continues the mesoscale ascent that supports ice growth and aggregation as the crystals fall. This significant stratiform precipitation gives rise to top-heavy latent heating profiles that have substantial impacts on the upper-level global circulations, especially for those MCS that have a larger portion of stratiform rainfall. Top-heavy latent heating profiles are more frequent over the ocean than over the land. These unique features of MCS suggest that they are important for the hydrologic cycle, general circulation, and radiative balance of the climate system. In this chapter, we focus on the recent advances in the climatological perspective of MCS as well as the research tools used to better understand MCS after the advent of the satellite era. We will discuss both tropical and midlatitude MCS, the influence of modes of tropical climate variability on MCS, and the new insight from satellite observations on MCS interaction with the land surface.

Chakraborty, Sudip↗
DOE OSTI2025

Leveraging Open-Source Satellite-Derived Building Footprints for Height Inference

At a global scale, cities are growing and characterizing the built environment is essential for deeper understanding of human population patterns, urban development, energy usage, climate change impacts, among others. Buildings are a key component of the built environment and significant progress has been made in recent years to scale building footprint extractions from satellite datum and other remotely sensed products. Billions of building footprints have recently been released by companies such as Microsoft and Google at a global scale. However, research has shown that depending on the methods leveraged to produce a footprint dataset, discrepancies can arise in both the number and shape of footprints produced. Therefore, each footprint dataset should be examined and used on a case-by-case study. In this work, we find through two experiments on Oak Ridge National Laboratory and Microsoft footprints within the same geographic extent that our approach of inferring height from footprint morphology features is source agnostic. Regardless of the differences associated with the methods used to produce a building footprint dataset, our approach of inferring height was able to overcome these discrepancies between the products and generalize, as evidenced by 98% of our results being within 3m of the ground-truthed height. This signifies that our approach can be applied to the billions of open-source footprints which are freely available to infer height, a key building metric. This work impacts the broader domain of urban science in which building height is a key, and limiting factor.

Stipek, Clinton [ORNL] (ORCID:0000000280501096)↗
DOE OSTI2020

PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation

Precipitation measurements with high spatiotemporal resolution are a vital input for hydrometeorological and water resources studies; decision-making in disaster management; and weather, climate, and hydrological forecasting. Moreover, real-time precipitation estimation with high precision is pivotal for the monitoring and managing of catastrophic hydroclimate disasters such as flash floods, which frequently transpire after extreme rainfall. While algorithms that exclusively use satellite infrared data as input are attractive owing to their rich spatiotemporal resolution and near-instantaneous availability, their sole reliance on cloud-top brightness temperature (T b ) readings causes underestimates in wet regions and overestimates in dry regions—this is especially evident over the western contiguous United States (CONUS). We introduce an algorithm, the Precipitation Estimations from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain rate model (PDIR), which utilizes climatological data to construct a dynamic (i.e., laterally shifting) T b –rain rate relationship that has several notable advantages over other quantitative precipitation-estimation algorithms and noteworthy skill over the western CONUS. Validation of PDIR over the western CONUS shows a promising degree of skill, notably at the annual scale, where it performs well in comparison to other satellite-based products. Analysis of two extreme landfalling atmospheric rivers show that solely IR-based PDIR performs reasonably well compared to other IR- and PMW-based satellite rainfall products, marking its potential to be effective in real-time monitoring of extreme storms. This research suggests that IR-based algorithms that contain the spatiotemporal richness and near-instantaneous availability needed for rapid natural hazards response may soon contain the skill needed for hydrologic and water resource applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Reconciliation of asynchronous satellite-based $\mathrm{NO_2}$ and $\mathrm{XCO_2}$ enhancements with mesoscale modeling over two urban landscapes

Fossil fuel carbon dioxide (CO 2ff ), the main driver of global warming and climate change, is often co-emitted with nitrogen oxides (NO x ) and precursors to ground-level ozone from anthropogenic sources like power plants or vehicles. In urban and suburban areas, satellite-based NO 2 can be used as a proxy to track the emissions of CO 2ff . Because of NO 2 ’s shorter lifetime, urban NO 2 plumes are more distinguishable from backgrounds and more sensitive to variations in emissions. However, the combination of these two gases is limited by the asynchrony among NO 2 and CO 2 monitoring satellites. We used CO 2ff simulated by the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model to reconcile the tropospheric NO 2 vertical column density (VCD) from the Tropospheric Monitoring Instrument (TROPOMI) and column-averaged dry-air mole fractions of carbon dioxide enhancements (ΔXCO 2 ) from Orbiting Carbon Observatory 3 (OCO-3) Snapshot Area Maps (SAMs) over a multicity area, Washington D.C.-Baltimore (DC-Balt), and a basin city, Mexico City. NO 2 /CO 2ff ratios over DC-Balt are smaller than Mexico City, indicative of stricter emission restrictions, a more combustion-efficient vehicle fleet, and higher combustion efficiency due to lower altitude in DC-Balt. For single-track cases, the spatial correlations between NO 2 and ΔXCO 2 over Mexico City are stronger than DC-Balt because the NO 2 and CO 2 are mostly trapped in the valley of Mexico City, while DC-Balt is severely affected by distant sources (i.e., US East Coast cities). Using multi-track averaging, spatial correlation coefficients increase with the number of days used for averaging. The correlations reached a maximum when averaging >12 continuous images for DC-Balt and > 10 continuous images for Mexico City. This finding indicates that multi-track averaging using modeled CO 2ff as a proxy is helpful to filter the noise in single-track images, to cancel the interference from distant sources, and to magnify correlations between NO 2 and CO 2ff . Mexico City showed stronger spatial correlations but weaker temporal correlations than DC-Balt due to biomass burning hot spots and large transport errors caused by the trapping effects of the surrounding mountains. Tracking the 20-day moving average of CO 2ff emissions using TROPOMI NO 2 seems technically feasible, considering the relationship between correlation coefficients and the number of available satellite images.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Observational Constraints on the Cloud Feedback Pattern Effect

Abstract Model evidence for the “pattern effect” assumes that global climate models (GCMs) faithfully simulate how clouds respond to varying sea surface temperature (SST) patterns and associated meteorological perturbations. We exploit time-invariant satellite-based estimates of the sensitivity of marine low clouds to meteorological perturbations to estimate how these clouds responded to time-varying SST patterns and meteorology between 1870 and 2014. GCMs and reanalyses provide estimates of the historical meteorological changes. Observations suggest that increasing estimated inversion strength (EIS) between 1980 and 2014 produced a negative low cloud feedback, opposite to the positive feedback expected from increasing CO 2 . This indicates that the processes responsible for marine cloud changes from 1980 to the near present are distinct from those associated with an increase in CO 2 . We also observationally constrain the difference between the historical near-global marine low cloud feedback, , and that arising from increasing CO 2 , . We find that this cloud feedback pattern effect depends strongly on time period and reanalysis dataset, and that varying changes in EIS and SST with warming explain much of its variability. Between 1980 and 2014, we estimate that (90% confidence) assuming meteorological changes from the Multiple Reanalysis Ensemble, implying a total pattern effect (that arising from all climate feedbacks) of 1.86 ± 0.45 W m −2 K −1 . This observational evidence corroborates previous quantitative estimates of the pattern effect, which heretofore relied largely upon GCM-based cloud changes. However, disparate historical meteorological changes across individual reanalyses contribute to considerable uncertainty in its magnitude.

58 GEOSCIENCES↗
DOE OSTI2023

HP-LSP: A reference of land surface phenology from fused Harmonized Landsat and Sentinel-2 with PhenoCam data

Abstract Land surface phenology (LSP) products are currently of large uncertainties due to cloud contaminations and other impacts in temporal satellite observations and they have been poorly validated because of the lack of spatially comparable ground measurements. This study provided a reference dataset of gap-free time series and phenological dates by fusing the Harmonized Landsat 8 and Sentinel-2 (HLS) observations with near-surface PhenoCam time series for 78 regions of 10 × 10 km 2 across ecosystems in North America during 2019 and 2020. The HLS-PhenoCam LSP (HP-LSP) reference dataset at 30 m pixels is composed of: (1) 3-day synthetic gap-free EVI2 (two-band Enhanced Vegetation Index) time series that are physically meaningful to monitor the vegetation development across heterogeneous levels, train models (e.g., machine learning) for land surface mapping, and extract phenometrics from various methods; and (2) four key phenological dates (accuracy ≤5 days) that are spatially continuous and scalable, which are applicable to validate various satellite-based phenology products (e.g., global MODIS/VIIRS LSP), develop phenological models, and analyze climate impacts on terrestrial ecosystems.

Science & Technology - Other Topics↗
DOE OSTI2020

Sea-Ice Effects on the Alaskan Wave Climate

This study investigates the uncertainty of wave resource assessments due to sea ice in the nearshore region of Alaska using an innovative approach of combining remotely sensed data and a third-generation phase-averaged wave model. Modeling analysis focused on winter months, when high wave energy is present along with sea ice. This study includes recommendations to improve the accuracy of wave resource assessments and optimize marine and hydrokinetic device siting with consideration of sea-ice effects. Additional recommendations, based on preliminary sea-ice information in Cook Inlet, Alaska, are provided for a trajectory analysis of drifting sea ice and a collision risk assessment of sea ice and tidal turbines. The outcome of this project will provide valuable information about the level of sea-ice impact on the accuracy of wave resource assessments in the Alaska nearshore region, and of sea-ice collision risk with MHK devices and ocean observation systems during winter months when sea ice is present.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Remote sensing from unoccupied aerial systems: Opportunities to enhance Arctic plant ecology in a changing climate

The Arctic is warming at a faster rate than any other biome on Earth, resulting in widespread changes in vegetation composition, structure, and function that have important feedbacks to the global climate system. The heterogeneous nature of arctic landscapes creates challenges for monitoring and improving understanding of these ecosystems, as current efforts typically rely on ground, airborne, or satellite-based observations that are limited in space, time, or pixel resolution. The use of remote sensing instruments on small Unoccupied Aerial Systems (UASs) has emerged as an important tool to bridge the gap between detailed, but spatially limited ground-level measurements, and lower resolution, but spatially extensive high-altitude airborne and satellite observations. UASs allow researchers to view, describe and quantify vegetation dynamics at fine spatial scales (1-10 cm) over areas much larger than typical field plots. UASs can be deployed with a high degree of temporal flexibility, enabling observation across diurnal, seasonal, and annual timescales. In this work, we review how established and emerging UAS remote sensing technologies can enhance arctic plant ecological research by quantifying fine-scale vegetation patterns and processes, and by enhancing the ability to link ground-based measurements with broader-scale information obtained from airborne and satellite platforms. Synthesis: Improved ecological understanding and model representation of arctic vegetation is needed to forecast the fate of the Arctic in a rapidly changing climate. Observations from UASs provide an approach to address this need, however, the use of this technology in the Arctic currently remains limited. Here we share recommendations to better enable and encourage the use of UASs to improve the description, scaling, and model representation of arctic vegetation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Monitoring and modeling hydrologic conditions in Ukraine for hydropower generation

Study region: The Dnieper and Dniester Rivers of Ukraine. Study focus: The ongoing conflict in Ukraine has caused disruptions to electricity generation, of which hydroelectric sources contribute approximately 9 % to the country’s needs. With the takeover of the Zaporizhzhia nuclear power plant by enemy forces, the loss of the Kakhovka hydroelectric dam, and the future impacts of the conflict on electricity generation unclear, it may be valuable for the Ukrainian government to better understand how it could leverage hydroelectric power sources in the near future. Unfortunately, measurements of river discharge throughout Ukraine ceased data collection in the late 1980’s to early 1990’s. To address this data gap, we developed a protocol that combined satellite-based time-series measurements of river width at seven locations throughout Ukraine from 2013 to 2023 with reanalysis data, climate-model predictions, and hydrologic models to both provide a means of monitoring a proxy for near-real-time discharge and also predict near-term (i.e., 2023–2030) hydrologic patterns for the region. New hydrological insights for the region: We ran new algorithms on 144 WorldView-2 and WorldView-3 satellite images to map rivers and extract width, one of which was validated against river gauge data located along the same river but in a neighboring country. Hydrologic models using two climate scenarios found minimal change in annual discharge at all sites, but magnitude and timing of peak discharge showed a moderate trend. The results suggest that hydropower is underutilized in Ukraine.

13 HYDRO ENERGY↗
DOE OSTI2024

Earth System Reanalysis in Support of Climate Model Improvements

Recent climate model developments, established through increased model resolution, have led to substantial improvements in model simulations of the time-evolving, coupled Earth system and its subcomponents. However, regardless of resolution, climate models will always produce climate features and variability that differ from the real world and will be prone to biases. This is due to many remaining uncertainties, such as in parametric and structural model uncertainty, in the initial conditions prescribed, and in the prescribed (scenario) forcing which varies on decadal to centennial timescales. Further model improvements are expected to arise specifically from improved representation of physical processes realized through model-data fusion. This will create an unprecedented opportunity to better exploit a large array of Earth observations, from in situ measurements to weather radars and satellite observations, as the resolved scales of the models approach those of the observations. For this, climate DA will be the central tool to bring models and observations into consistency, by improving initial conditions, inferring uncertain model parameters and structure, and quantifying uncertainty. Generally, there will be advantages and complementarities of adjoint-based smoother approaches, ensemble-based filter approaches, or new ML-inspired approaches. Yet, the ever-increasing model resolution will present growing challenges arising from computational cost, calling for new ways of performing data assimilation and model optimization. Using the complementarity in a hybrid approach, blending tools and concepts from variational, ensemble and ML methods might be what is required in the future. In this context ML could be important to handle non-linear responses, and to better approximate non-Gaussian distributions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Diverging climate response of corn yield and carbon use efficiency across the U.S.

Abstract In this paper, we developed an open-source package to analyze the overall trend and responses of both carbon use efficiency (CUE) and corn yield to climate factors for the contiguous United States. Our algorithm enables automatic retrieval of remote sensing data through the Google Earth Engine (GEE) and U.S. Department of Agriculture (USDA) agricultural production data at the county level through application programming interface (API). Firstly, we integrated satellite products of net primary productivity and gross primary productivity based on the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and climatic variables from the European Centre for Medium-Range Weather Forecasts. Secondly, we calculated CUE and commonly used climate metrics. Thirdly, we investigated the spatial heterogeneity of these variables. We applied a random forest algorithm to identify the key climate drivers of CUE and crop yield, and estimated the responses of CUE and yield to climate variability using the spatial moving window regression across the U.S. Our results show that growing degree days (GDD) has the highest predictive power for both CUE and yield, while extreme degree days (EDD) is the least important explanatory variable. Moreover, we observed that in most areas of the U.S., yield increases or stays the same with higher GDD and precipitation. However, CUE decreases with higher GDD in the north and shows more mixed and fragmented interactions in the south. Notably, there are some exceptions where yield is negatively correlated with precipitation in the Missouri and Mississippi River Valleys. As global warming continues, we anticipate a decrease in CUE throughout the vast northern part of the country, despite the possibility of yield remaining stable or increasing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Enhanced Warming in Global Dryland Lakes and Its Drivers

Lake surface water temperature (LSWT) is sensitive to climate change. Previous studies have found that LSWT warming is occurring on a global scale and is expected to continue in the future. Recently, new global LSWT data products have been generated using satellite remote sensing, which provides an inimitable opportunity to study the LSWT response to global warming. Based on the satellite observations, we found that the warming rate of global lakes is uneven, with apparent regional differences. Indeed, comparing the LSWT warming in different climate zones (from arid to humid), the lakes in drylands experienced more significant warming (0.28 °C decade-1) than those in semi-humid and humid regions (0.19 °C decade-1) during previous decades (1995–2016). By further quantifying the impact factors, it showed that the LSWT warming is attributed to air temperature (74.4%), evaporation (4.1%), wind (9.9%), cloudiness (4.3%), net shortwave (3.1%), and net longwave (4.0%) over the lake surface. Air temperature is the main driving force for the warming of most global lakes, so the first estimate quantification of future LSWT trends can be determined from air temperature projections. By the end of the 21st century, the summer air temperature would warm up to 1.0 °C (SSP1-2.6) and 6.3 °C (SSP5-8.5) over lakes, with a more significant warming trend over the dryland lakes. Combined with their higher warming sensitivity, the excess summer LSWT warming in drylands is expected to continue, which is of great significance because of their high relevance in these water-limited regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Urbanization exacerbates continental- to regional-scale warming

Urbanization is usually ignored when estimating past changes in large-scale climate and for future climate projections since cities historically covered a small fraction of the Earth’s surface. Here, by combining global land surface temperature observations with historical estimates of urban area, we demonstrate that the urban contribution to continental- to regional-scale warming has become non-negligible, especially for rapidly urbanizing regions and countries in Asia. Consequently, expected urban expansion over the next century suggests further increased urban influence on large-scale surface climate in the future (approximately 0.16 K for North America and Europe for high-emission scenario in 2100). Based on these results, also seen for air temperature, we argue that, in line with other forms of land use/land cover change, urbanization should be explicitly included in climate change assessments. This requires incorporation of dynamic urban extent and biophysics in current-generation Earth system models to quantify potential urban feedback on the climate system across scales.

Chakraborty, TC↗
DOE OSTI2019

Antarctic Sea Ice Expansion, Driven by Internal Variability, in the Presence of Increasing Atmospheric CO2

A number of physically-based hypotheses have been proposed to explain the surprising expansion of Antarctic sea ice area (SIA) over the satellite era (1979 to 2015). Here, we use a fully-coupled state-of-the-art global climate model to show that internal variability alone can produce such multidecadal periods of Antarctic SIA expansion even as atmospheric CO2 increases at observed rates and the planet warms. When our model is started from a relatively warm Southern Ocean state, Antarctic SIA sometimes (in one of three ensemble members) expands over multidecadal time scales at a rate comparable to that over the satellite era. SIA expansion occurs concurrently with rising atmospheric CO2 and warming global surface temperatures, and SIA trends by region and sector resemble those over the satellite era. Our results suggest that internal variability, particularly on long time scales over the Southern Ocean, may suffice to explain Antarctic SIA expansion over the satellite era.

Singh, Hansi AK↗
DOE OSTI2026

Observational Data for Next-Generation Climate Model Evaluation: Requirements, Considerations, and Best Practices

Climate model simulations are an important source of information about our planet’s climate system and also enable informed decision-making under different future scenarios. As a new archive of results from the next generation of climate models is anticipated to become available with the Coupled Model Intercomparison Project phase 7 (CMIP7), the need to develop efficient and robust methods to evaluate models is paramount. Observations are an integral part of model evaluation, providing a means to quantify and understand the degree to which climate models can faithfully reproduce Earth system processes. Such analysis is critical for constraining climate projections, identifying areas of focus for model development, and assisting analysts in deciphering the utility of models for specific applications. Observations of Earth system come from a diversity of sources, span different space–time domains, and are produced by different communities, and each dataset features different data structures and formats, metadata standards, and its own unique uncertainties. Uncertainties in an observational dataset may stem from gaps in temporal and spatial coverage, instrumentation errors, or assumptions in retrieval and processing methods. How then does one ensure that observational data are ready for use and utilized in the most appropriate way for robust, rapid, and routine climate model evaluation? The CMIP7 Model Benchmarking Task Team with input from the broader climate modeling, model evaluation, and observational data communities present a vision and considerations for best practices toward the optimal and appropriate use of observational data to support next-generation climate model evaluation.

Climate models↗