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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 OSTI2025

Elucidating Processes Controlling Arctic Atmospheric Aerosol Sources, Aging, and Mixing States (Final Report)

Atmospheric aerosols play critical roles in the Earth’s energy budget, directly by scattering or absorbing solar and terrestrial radiation and indirectly by serving as seeds (nuclei) for cloud droplet and ice crystal formation and by depositing on snow and ice surface, thereby changing the surface albedo. These effects are dependent on aerosol particle size and chemical composition and impact the hydrological cycle as well. This project provided single-particle size and chemical composition measurements across the entire annual cycle in the high Arctic and in the Alaskan Arctic during fall – winter, addressing the most significant gaps in Arctic aerosol observational data. These needs were based on recent rapid sea ice loss across the entire Arctic, as well as the major annual delays in sea ice freeze-up during fall in the Chukchi Sea and increased wintertime sea ice fracturing in the Beaufort Sea, both off the North Slope of Alaska. Two DOE Atmospheric Radiation Measurement (ARM) field campaigns were conducted for atmospheric aerosol sampling. The Aerosols during the Polar Utqiagvik Night (APUN – ‘snow on ground’ in Iñupiaq) ARM field campaign at Utqagivik, Alaska was conducted from Oct. 28 – Dec. 22, 2018. Aerosol sizing instrumentation and a single-particle mass spectrometer were successfully deployed for size-resolved number concentration measurements and measurements of individual particle size and chemical composition, respectively. These results show the influence of locally-produced sea spray aerosol, with high cloud-forming potential, due to delayed sea ice freeze-up in the fall. During the 2019‐2020 international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, daily atmospheric aerosol particles were collected aboard the German icebreaker Polarstern in the Central Arctic from Nov. 2019 – Oct. 2020. Sea salt aerosol and marine organics were observed year-round during MOSAiC with varying morphologies and sources. These findings are important because most Arctic models do not include a sea spray aerosol source, despite this source increasing with declining sea ice extent. In addition to collecting new samples and data, this project also conducted further analysis of previously collected single-particle chemical composition measurements within the North Slope of Alaska oil fields and at Utqiaġvik, AK, during Aug. – Sep. 2015 and 2016 field campaigns. This work resulted in the discovery of chemical reactions of oil field combustion emissions occurring within fog droplets across the North Slope of Alaska and forming secondary aerosol, showing the impact of Arctic oil field emissions beyond black carbon aerosol and greenhouse gases. In addition, the distribution of chemical species across the aerosol population within the oil fields was quantified, using these data and a previously development framework. We also presented the first ambient evidence of the collision of two atmospheric particles resulting in formation of an organic-coated ammonium sulfate particle of marine origin, which has implications for cloud formation with declining sea ice extent. Overall, this project has elucidated connections between seawater biogeochemistry, resource extraction activities, atmospheric composition, clouds, and the energy budget of the Arctic region. The results of this project are expected to improve weather and sea ice forecasting for security and development in the Arctic and beyond.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Weather and climate extremes in a changing Arctic

Weather and climate extremes are increasingly occurring in the Arctic. Here, in this Review, we evaluate historical and projected changes in rare Arctic extremes across the atmosphere, cryosphere and ocean and elucidate their driving mechanisms. Clear shifts occur in mean and extreme distributions after ~2000. For instance, pre-2000 to post-2000 observational probabilities of 1.5 standard deviation events increase by 20% for atmospheric heat waves, 76.7% for Atlantic layer warm events, 83.5% for Arctic sea ice loss and 62.9% for Greenland Ice Sheet melt extent — in many cases, low probability, rare extreme events in the early period become the norm in the latter period. These observed changes can be explained using a ‘pushing and triggering’ concept, representing interplay between external forcing and internal variability: long-term warming destabilizes the climate system and ‘pushes’ it to a new state, allowing subsequent variability associated with large-scale atmosphere–ocean–ice interactions and synoptic systems to ‘trigger’ extreme events over different timescales. Ongoing anthropogenic warming is expected to further increase the frequency and magnitude of extremes, such that simulated probabilities of 1.5 standard deviation events increase by 72.6% for atmospheric heat waves, 68.7% for Atlantic layer warm events and 93.3% for Greenland Ice Sheet melt rate between historic (1984–2014) and future (2069–2099) periods under a very high emission scenario. Future research should prioritize the development of physically based metrics, enhance high-resolution observation and modelling capabilities and improve understanding of multiscale Arctic climate drivers.

atmospheric dynamics↗
DOE OSTI2021

Climate Change Projection in the Twenty-First Century Simulated by NIMS-KMA CMIP6 Model Based on New GHGs Concentration Pathways

Abstract The National Institute of Meteorological Sciences-Korea Meteorological Administration (NIMS-KMA) has participated in the Coupled Model Inter-comparison Project (CMIP) and provided long-term simulations using the coupled climate model. The NIMS-KMA produces new future projections using the ensemble mean of KMA Advanced Community Earth system model (K-ACE) and UK Earth System Model version1 (UKESM1) simulations to provide scientific information of future climate changes. In this study, we analyze four experiments those conducted following the new shared socioeconomic pathway (SSP) based scenarios to examine projected climate change in the twenty-first century. Present day (PD) simulations show high performance skill in both climate mean and variability, which provide a reliability of the climate models and reduces the uncertainty in response to future forcing. In future projections, global temperature increases from 1.92 °C to 5.20 °C relative to the PD level (1995–2014). Global mean precipitation increases from 5.1% to 10.1% and sea ice extent decreases from 19% to 62% in the Arctic and from 18% to 54% in the Antarctic. In addition, climate changes are accelerating toward the late twenty-first century. Our CMIP6 simulations are released to the public through the Earth System Grid Federation (ESGF) international data sharing portal and are used to support the establishment of the national adaptation plan for climate change in South Korea.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A spatial evaluation of Arctic sea ice and regional limitations in CMIP6 historical simulations

The Arctic sea ice response to a warming climate is assessed in a subset of models participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6), using several metrics in comparison with satellite observations and results from the Pan-Arctic Ice Ocean Modeling and Assimilation System and the Regional Arctic System Model. Our study examines the historical representation of sea ice extent, volume, and thickness using spatial analysis metrics, such as the integrated ice-edge error, Brier score, and Spatial Probability Score. We find that the CMIP6 multi-model mean captures the mean annual cycle and 1979-2014 sea ice trends remarkably well. However, individual models experience a wide range of uncertainty in the spatial distribution of sea ice when compared against satellite measurements and reanalysis data. Our metrics expose common and individual regional model biases, which sea ice temporal analyses alone do not capture. We identify large ice edge and ice thickness errors in Arctic sub-regions, implying possible model specific limitations in or lack of representation of some key physical processes. We postulate that many of them could be related to the oceanic forcing, especially in the marginal and shelf seas, where seasonal sea ice changes are not adequately simulated. We therefore conclude that an individual model’s ability to represent the observed/reanalysis spatial distribution still remains a challenge. We propose the spatial analysis metrics as useful tools to diagnose model limitations, narrow down possible processes affecting them, and guide future model improvements critical to the representation and projections of Arctic climate change.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Arctic Aerosol Sources and Mixing States Field Campaign Report

The Arctic is warming at a faster rate than anywhere else on Earth, with rapidly shrinking sea ice extent transforming the region. Depending on chemical and physical properties, atmospheric aerosols directly scatter and/or absorb radiation, serve as cloud droplet and/or ice crystal nuclei, and/or reduce the reflectiveness of the snow surface, thereby altering the atmospheric energy budget. There is a wide spread in the magnitude of simulated arctic aerosol radiative forcing, and significant differences in aerosol concentration levels and seasonal cycles often exist between models and observations. Increasing local natural and anthropogenic emissions are significant, with uncertain climate impacts due to complex feedbacks. Model evaluations, however, are limited by the dearth of arctic aerosol observations available and an inadequate understanding of arctic aerosol processes. The majority of previous arctic aerosol observations have been made through intensive spring/summer field campaigns, with few intensive measurement studies focused on the fall-winter transition, a period when freeze-up is occurring later and thinning sea ice is resulting in wintertime ice fracturing. Aerosol monitoring at arctic coastal stations has provided knowledge of long-term seasonal trends in aerosol concentrations. The completed ARM field campaign addresses observational and knowledge gaps through detailed aerosol size and chemical composition measurements during the fall-winter transition in the coastal Arctic and through the entire annual cycle in the central Arctic. The observations are improving our understanding of the sources and processes controlling the aerosol population in the rapidly changing Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

On the variability of the Bering Sea Cold Pool and implications for the biophysical environment

The Bering Sea experiences a seasonal sea ice cover, which is important to the biophysical environment found there. A pool of cold bottom water (<2°C) is formed on the shelf each winter as a result of cooling and vertical mixing due to brine rejection during the predominately local sea ice growth. The extent and distribution of this Cold Pool (CP) is largely controlled by the winter extent of sea ice in the Bering Sea, which can vary considerably and recently has been much lower than average. The cold bottom water of the CP is important for food security because it delineates the boundary between arctic and subarctic demersal fish species. A northward retreat of the CP will likely be associated with migration of subarctic species toward the Chukchi Sea. We use the fully-coupled Regional Arctic System Model (RASM) to examine variability of the extent and distribution of the CP and its relation to change in the sea ice cover in the Bering Sea during the period 1980–2018. RASM results confirm the direct correlation between the extent of sea ice and the CP and show a smaller CP as a consequence of realistically simulated recent declines of the sea ice cover in the Bering Sea. In fact, the area of the CP was found to be only 31% of the long-term mean in July of 2018. In addition, we also find that a low ice year is followed by a later diatom bloom, while a heavy ice year is followed by an early diatom bloom. Finally, the RASM probabilistic intra-annual forecast capability is reviewed, based on 31-member ensembles for 2019–2021, for its potential use for prediction of the winter sea ice cover and the subsequent summer CP area in the Bering Sea.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Assessing the Robustness of Arctic Sea Ice Bi‐Stability in the Presence of Atmospheric Feedbacks

Abstract Arctic sea‐ice loss is influenced by multiple positive feedbacks, sparking concerns of accelerated loss in the coming years or even a tipping point, where a sea‐ice equilibrium disappears at a given CO 2 value and sea ice rapidly evolves to a new steady state. Such a tipping point would imply a bi‐stability of the Arctic climate—where multiple steady‐state Arctic climates are possible at the same CO 2 value. Previous works have sought to establish the existence of bi‐stability using a range of models, from zero‐dimensional sea ice thermodynamic models to fully coupled global climate models, with conflicting results. Here, we present a new model of the Arctic that includes both sea‐ice thermodynamics and key atmospheric feedbacks in a simple framework. We exploit the model's simplicity to identify physical mechanisms that control the timing and extent of sea‐ice bi‐stability, and the abruptness of ice loss. We show that longwave radiation feedbacks can have a strong influence on Arctic surface climate from atmospheric temperature increases alone, even without major contributions from clear‐sky moisture or convective clouds suggested previously. While winter sea‐ice bi‐stability is robust to changes in uncertain model parameters in this study, summer sea ice is more sensitive. Finally, our model indicates that positive feedbacks may modulate the CO 2 threshold of sea‐ice loss and the width of bi‐stability much more strongly than the abruptness of loss. These results lead to a comprehensive understanding of the conditions that favor Arctic sea‐ice bi‐stability, particularly the role of atmospheric feedbacks, in both future and past climates.

Meteorology & Atmospheric Sciences↗
DOE OSTI2023

More frequent atmospheric rivers slow the seasonal recovery of Arctic sea ice

In recent decades, Arctic sea ice coverage experienced a drastic decline in winter, when sea ice is expected to recover following the melting season. It is unclear to what extent atmospheric processes such as atmospheric rivers (ARs), intense corridors of moisture transport, contribute to this reduced recovery of sea ice. Here, in this study, using observations and climate model simulations we find a robust frequency increase in ARs in early winter over the Barents-Kara Seas and the central Arctic (ABK) between 1979 and 2021. The moisture carried by more frequent ARs has intensified surface downward longwave radiation and rainfall, caused stronger melting of thin, fragile ice cover, and slowed the seasonal recovery of sea ice, accounting for 34% of the sea ice cover decline in ABK. A series of model ensemble experiments suggests that, in addition to a uniform AR increase in response to anthropogenic warming, tropical Pacific variability also contributes to the observed Arctic AR changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Sea ice evolution along the Northern Sea Route and implications for trans-Arctic shipping from 2021 through 2060

Arctic surface temperatures warmed at twice the global average in the second half of the 20 th century due to Arctic amplification (AA), a phenomenon predominantly caused by regional polar changes, like the melting of perennial sea ice and reduced sea ice extent (leading to more solar radiation being absorbed by the ocean surface as opposed to being reflected back to space by the ice surface). AA is projected to reach a factor of three even if the climate is stabilized by the mid 21 st century by reduced greenhouse gas emissions. In all emissions scenarios, AA is projected to lead to temperature changes at least 2.4 times larger than global mean surface temperature changes occurring between 2070 and 2100. The ice-albedo feedback, which occurs when the polar-marine surface absorbs more radiation as highly reflective sea ice melts, is reversible such that the premise of a runaway process is no longer accepted as a realistic possibility. No matter what actions are taken to reduce CO 2 concentrations in the atmosphere from now on, two different methods of predicting an ice-free Arctic suggest that perennial sea ice will mostly disappear in September by the year 2050. If there is no reduction in anthropogenic CO 2 and methane emissions, that scenario could occur sooner than 2030. Defining Arctic navigability as safe and economic passage of Polar Class 7 cargo ships without need of an escorting icebreaker, no single trans Arctic ship route will be navigable year-round in the first half of the 21 st century, including in the strong emission scenarios. However, seasonal trans-Arctic navigability will increase this century. An estimate on the number of days per year that the Northern See Route (NSR) will be navigable in the future is beyond the scope of this report. Along Northern Sea Routes 5 and 6, which run close to the Russian coast and Yamal LNG plant, an ARC 7 ice class LNG tanker, the equivalent of a Polar Class 3 (PC3) vessel, will be at low risk in December through April at some point during the current decade. May will continue to entail some risk (more than April) along relatively short segments of these routes through the end of the next decade (2030-2039). Come June, snow rapidly melts away, and thereafter the underlying sea ice begins becomes thinner and less concentrated, greatly reducing risk. However, neither path is desirable for ARC 7 tankers due to shallow bathymetry in Sannikov Strait, and a more desirable path for these ships passes to the north of the New Siberian Islands (route 20, discussed further below). Conventional LNG tankers (i.e., non-ice-strengthened vessels according to the IMO classification), without icebreaker escorts, will continue to encounter dangerous or impassable conditions along many sections of the NSR through the end of this decade for many months of the year. Through 2039, these conventional tankers will be able to operate safely along NSR 6 from August through October. By 2040-2049, the span of safe operation increases to August through November, and by 2050-2059 it increases to July through November, assuming a northward deviation to avoid the Sannikov Strait.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Warm Arctic, Cold Siberia Pattern: Role of Full Arctic Amplification Versus Sea Ice Loss Alone

The effect of future Arctic amplification (AA) on the extratropical atmospheric circulation remains unclear in modeling studies. Using a collection of coordinated atmospheric and coupled global climate model perturbation experiments, we find an emergent relationship between the high-latitude 1,000–500 hPa thickness response and an enhancement of the Siberian High in winter. This wave number-1-like sea level pressure anomaly pattern is linked to an equatorward shift of the eddy-driven jet and a dynamical cooling response in eastern Asia. Additional simulations, where AA is imposed directly into the model domain by nudging, demonstrate how the sea ice forcing is insufficient by itself to capture the vertical extent of the warming and by extension the amplitude of the response in the Siberian High. Additionally, this study demonstrates the importance of the vertical extent of the tropospheric warming over the polar cap in revealing the “warm Arctic, cold Siberia” anomaly pattern in future projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill

Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Retreat and Regrowth of the Greenland Ice Sheet During the Last Interglacial as Simulated by the CESM2‐CISM2 Coupled Climate–Ice Sheet Model

Abstract During the Last Interglacial, approximately 129 to 116 ka (thousand years ago), the Arctic summer climate was warmer than the present, and the Greenland Ice Sheet retreated to a smaller extent than its current state. Previous model‐derived and geological reconstruction estimates of the sea‐level contribution of the Greenland Ice Sheet during the Last Interglacial vary widely. Here, we conduct a transient climate simulation from 127 to 119 ka using the Community Earth System Model (CESM2), which includes a dynamic ice sheet component (the Community Ice Sheet Model, CISM2) that is interactively coupled to the atmosphere, land, ocean, and sea ice components. Vegetation distribution is updated every 500 years based on biomes simulated using a monthly climatology to force the BIOME4 equilibrium vegetation model. Results show a substantial retreat of the Greenland Ice Sheet, reaching a minimum extent at 121.9 ka, equivalent to a 3.0 m rise in sea level relative to the present day, followed by gradual regrowth. In contrast, a companion simulation employing static vegetation based on pre‐industrial conditions shows a much smaller ice‐sheet retreat, highlighting the importance of the changes in high‐latitude vegetation distribution for amplifying the ice‐sheet response.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Surface temperature comparison of the Arctic winter MOSAiC observations, ERA5 reanalysis, and MODIS satellite retrieval

Atmospheric model systems, such as those used for weather forecast and reanalysis production, often have significant and systematic errors in their representation of the Arctic surface energy budget and its components. The newly available observation data of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition (2019/2020) enable a range of model analyses and validation in order to advance our understanding of potential model deficiencies. In the present study, we analyze deficiencies in the surface radiative energy budget over Arctic sea ice in the ERA5 global atmospheric reanalysis by comparing against the winter MOSAiC campaign data, as well as, a pan-Arctic level-2 MODIS ice surface temperature remote sensing product. We find that ERA5 can simulate the timing of radiatively clear periods, though it is not able to distinguish the two observed radiative Arctic winter states, radiatively clear and opaquely cloudy, in the distribution of the net surface radiative budget. The ERA5 surface temperature over Arctic sea ice has a conditional error with a positive bias in radiatively clear conditions and a negative bias in opaquely cloudy conditions. The mean surface temperature error is 4°C for radiatively clear situations at MOSAiC and up to 15°C in some parts of the Arctic. The spatial variability of the surface temperature, given by 4 observation sites at MOSAiC, is not captured by ERA5 due to its spatial resolution but represented in the level-2 satellite product. The sensitivity analysis of possible error sources, using satellite products of snow depth and sea ice thickness, shows that the positive surface temperature errors during radiatively clear events are, to a large extent, caused by insufficient sea ice thickness and snow depth representation in the reanalysis system. A positive bias characterizes regions with ice thickness greater than 1.5 m, while the negative bias for thinner ice is partly compensated by the effect of snow.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Tethered balloon-borne profile measurements of atmospheric properties in the cloudy atmospheric boundary layer over the Arctic sea ice during MOSAiC: Overview and first results

The tethered balloon-borne measurement system BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) was deployed over the Arctic sea ice for 4 weeks in summer 2020 as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition. Using BELUGA, vertical profiles of dynamic, thermodynamic, aerosol particle, cloud, radiation, and turbulence properties were measured from the ground up to a height of 1,500 m. BELUGA was operated during an anomalously warm period with frequent liquid water clouds and variable sea ice conditions. Three case studies of liquid water phase, single-layer clouds observed on 3 days (July 13, 23, and 24, 2020) are discussed to show the potential of the collected data set to comprehensively investigate cloud properties determining cloud evolution in the inner Arctic over sea ice. Simulated back-trajectories show that the observed clouds have evolved within 3 different air masses (“aged Arctic,” “advected over sea ice,” and “advected over open ocean”), which left distinct fingerprints in the cloud properties. Strong cloud top radiative cooling rates agree with simulated results of previous studies. The weak warming at cloud base is mostly driven by the vertical temperature profile between the surface and cloud base. In-cloud turbulence induced by the cloud top cooling was similar in strength compared to former studies. From the extent of the mixing layer, it is speculated that the overall cloud cooling is stronger and thus faster in the warm oceanic air mass. Larger aerosol particle number concentrations and larger sizes were observed in the air mass advected over the sea ice and in the air mass advected over the open ocean.

Arctic↗
DOE OSTI2020

Climate change and the future productivity and distribution of crab in the Bering Sea

Crab populations in the eastern Bering Sea support some of the most valuable fisheries in the United States, but their future productivity and distribution are uncertain. Here, we explore observed changes in the productivity and distribution for snow crab, Tanner crab, and Bristol Bay red king crab. We link historical indices of environmental variation and predator biomass with observed time series of centroids of abundance and extent of crab stock distribution; we also fit stock–recruit curves including environmental indices for each stock. We then project these relationships under forcing from global climate models to forecast potential productivity and distribution scenarios. Our results suggest that the productivity of snow crab is negatively related to the Arctic Oscillation (AO) and positively related to ice cover; Tanner crab’s productivity and distribution are negatively associated with cod biomass and sea surface temperature. Aspects of red king crab distribution and productivity appear to be related to bottom temperature, ice cover, the AO, and/or cod biomass. Projecting these relationships forward with available forecasts suggests that Tanner crab may become more productive and shift further offshore, red king crab distribution may contract and move north, and productivity may decrease for snow crab as the population contracts northward.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Reducing uncertainty of polar to midlatitude linkages using DOE’s E3SM in a coordinated model-experiment setting

This project brought DOE’s climate modeling effort with the Energy Exascale Earth System Model into the Polar Amplification Model Intercomparison Project (PAMIP), which is part of the sixth and latest Coupled Model Intercomparison Project, CMIP6. PAMIP examines the causes and consequences of polar amplification, when external forcing results in a larger temperature increase in high latitudes than the global average, in a coordinated set of model experiments with a common modeling protocol. Our teams from UC Irvine and the University of Toronto have designed, carried out, analyzed, and disseminated PAMIP output from the Energy Exascale Earth System Model (E3SM) and the Community Earth System Model (CESM). PAMIP’s ongoing stream of significant new results have advanced progress in the community’s understanding and led to new outstanding research questions that have motivated further work. PAMIP has led to improved consensus on the atmospheric response to sea ice loss. The important finding is that for a similar sea ice anomaly forcing, the simulated atmospheric response in the troposphere is remarkably consistent among the 16 models’ runs analyzed. The zonal-mean tropospheric response consists of a very robust equatorward shift of the westerly flow in mid-latitudes. However, while the multi-model mean response is robust, it has a weak amplitude relative to internal variability. We identified a weakness in the models (including E3SM) in terms of their response to sea-ice forcing that is related to eddy forcing (or nonlinear dynamical effects) at mid- to high latitudes. In fact, E3SM is an outlier in terms of the models participating, and in that sense, it turned out to be a vital participant model. We found that reductions in energy transport due sea-ice loss and involving dry air only are compensated by increases in moist energy transport from warmer sea surface temperature in midlatitudes. This leads to a large spread in energy transport into the Arctic and is a potential source of spread in Arctic amplification. We identified an important role that climate modes, including tropical modes of variability (El Nino and the Southern Oscillation (ENSO); the Quasi-biennial Oscillation (QBO)) play in the response to sea-ice anomalies, including in ocean coupled experiments. Similarly, we identified and quantified the contribution of sea-ice thickness to the atmospheric response compared to the response to sea-ice extent only. We found that it is important to run large ensembles and even with an ensemble size of 100 simulations the response is largely influenced by internal variability. We demonstrated convincingly that Ural blocking, not sea-ice loss, provides the weakening of the stratospheric polar vortex in fall/early winter and a negative phase of the North Atlantic Oscillation that can last for up to two months. However, sea-ice anomalies can influence the background flow so that the response to Ural blocking is more persistent under low sea-ice conditions in the Barents/Kara Sea than high sea ice Atmospheric model hierarchies that progressively add individual processes have a long history in providing dynamical insight for modeling the atmosphere. Similarly, coupled model hierarchies that progressively add individual ocean processes can provide insights into the workings of the coupled climate system, however such hierarchies have not been available except for a non-dynamical slab ocean model. Because of the missing processes, surface flux corrections must be added to produce a target climate. In this project, we managed to overcome this problem and develop a globally coupled ocean model hierarchy in CESM that can turn on and off the processes of mixed-layer entrainment and Ekman flow. We used the hierarchy to study the impact of Arctic sea-ice loss on the climate system. We find that the effect of mixed-layer entrainment on ocean heat uptake influences the atmospheric circulation by shifting the latitudinal positions of the mid-latitude westerly jet and the Intertropical Convergence Zone (ITCZ). In quadrupled CO 2 experiments, we studied how air-sea coupling affects the response of tropical rainfall under global warming. In order to identify the importance of individual ocean processes, we used the hierarchy of ocean models to separate the effects of seasonal mixed-layer entrainment, wind-driven Ekman flows, and frictional flows. We showed that including Ekman and frictional flows allows our simulation to produce the Pacific Ocean's enhanced equatorial warming pattern and equatorward ITCZ contraction noted in previous climate simulations. We also showed that the frictional flow, which has yet to receive much attention, is as important as the Ekman flow in generating equatorial heat convergence.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluating the Trustworthiness of Explainable Artificial Intelligence (XAI) Methods Applied to Regression Predictions of Arctic Sea Ice Motion

Abstract Recent advances in explainable artificial intelligence (XAI) methods show promise for understanding predictions made by machine learning (ML) models. XAI explains how the input features are relevant or important for the model predictions. We train linear regression (LR) and convolutional neural network (CNN) models to make 1-day predictions of sea ice velocity in the Arctic from inputs of present-day wind velocity and previous-day ice velocity and concentration. We apply XAI methods to the CNN and compare explanations to variance explained by LR. We confirm the feasibility of using a novel XAI method [i.e., global layerwise relevance propagation (LRP)] to understand ML model predictions of sea ice motion by comparing it to established techniques. We investigate a suite of linear, perturbation-based, and propagation-based XAI methods in both local and global forms. Outputs from different explainability methods are generally consistent in showing that wind speed is the input feature with the highest contribution to ML predictions of ice motion, and we discuss inconsistencies in the spatial variability of the explanations. Additionally, we show that the CNN relies on both linear and nonlinear relationships between the inputs and uses nonlocal information to make predictions. LRP shows that wind speed over land is highly relevant for predicting ice motion offshore. This provides a framework to show how knowledge of environmental variables (i.e., wind) on land could be useful for predicting other properties (i.e., sea ice velocity) elsewhere. Significance Statement Explainable artificial intelligence (XAI) is useful for understanding predictions made by machine learning models. Our research establishes trustability in a novel implementation of an explainable AI method known as layerwise relevance propagation for Earth science applications. To do this, we provide a comparative evaluation of a suite of explainable AI methods applied to machine learning models that make 1-day predictions of Arctic sea ice velocity. We use explainable AI outputs to understand how the input features are used by the machine learning to predict ice motion. Additionally, we show that a convolutional neural network uses nonlinear and nonlocal information in making its predictions. We take advantage of the nonlocality to investigate the extent to which knowledge of wind on land is useful for predicting sea ice velocity elsewhere.

Hoffman, Lauren [Scripps Institution of Oceanograp↗
DOE OSTI2021

A multimillion-year-old record of Greenland vegetation and glacial history preserved in sediment beneath 1.4 km of ice at Camp Century

Understanding the history of the Greenland Ice Sheet (GrIS) is critical for determining its sensitivity to warming and contribution to sea level; however, that history is poorly known before the last interglacial. Most knowledge comes from interpretation of marine sediment, an indirect record of past ice-sheet extent and behavior. Subglacial sediment and rock, retrieved at the base of ice cores, provide terrestrial evidence for GrIS behavior during the Pleistocene. Here, we use multiple methods to determine GrIS history from subglacial sediment at the base of the Camp Century ice core collected in 1966. This material contains a stratigraphic record of glaciation and vegetation in northwestern Greenland spanning the Pleistocene. Enriched stable isotopes of pore-ice suggest precipitation at lower elevations implying ice-sheet absence. Plant macrofossils and biomarkers in the sediment indicate that paleo-ecosystems from previous interglacial periods are preserved beneath the GrIS. Cosmogenic 26 Al/ 10 Be and luminescence data bracket the burial of the lower-most sediment between <3.2 ± 0.4 Ma and >0.7 to 1.4 Ma. In the upper-most sediment, cosmogenic 26 Al/ 10 Be data require exposure within the last 1.0 ± 0.1 My. The unique subglacial sedimentary record from Camp Century documents at least two episodes of ice-free, vegetated conditions, each followed by glaciation. The lower sediment derives from an Early Pleistocene GrIS advance. 26 Al/ 10 Be ratios in the upper-most sediment match those in subglacial bedrock from central Greenland, suggesting similar ice-cover histories across the GrIS. We conclude that the GrIS persisted through much of the Pleistocene but melted and reformed at least once since 1.1 Ma.

58 GEOSCIENCES↗