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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Overestimated Eddy Kinetic Energy in the Eddy‐Rich Regions Simulated by Eddy‐Resolving Global Ocean–Sea Ice Models

Abstract The performance of eddy‐resolving global ocean–sea ice models in simulating mesoscale eddies is evaluated using six eddy‐resolving experiments forced by different atmospheric reanalysis products. Interestingly, eddy‐resolving ocean general circulation models (OGCMs) tend to simulate more (less) energetic eddy‐rich (eddy‐poor) regions with a smaller (larger) spatial extent than satellite observation, which finally shows that larger (smaller) mesoscale energy intensity (EI) is simulated in the eddy‐rich (eddy‐poor) regions. Quantitatively, there is an approximately 27%–60% overestimation of EI in the eddy‐rich regions, which are mainly located in the Kuroshio–Oyashio Extension, the Gulf Stream, and the Antarctic Circumpolar Currents regions, although the global mean EI is underestimated by 25%–45%. Apparently, the eddy kinetic energy in the eddy‐poor region is underestimated. Further analyses based on coherent mesoscale eddy properties show that the overestimation in the eddy‐rich regions is mainly attributed to mesoscale eddies’ intensity and is more prominent when mesoscale eddies are in their growth stage.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1980

Modeling a variable thickness sea ice cover

A numerical model simulating a variable thickness sea ice cover over a seasonal cycle is presented. The model includes a fixed depth mixed-layer formulation with open water heat absorption and lateral melting terms, and a mechanical distribution function consistent with the physics of the ridging process. The equibrium simulation results in realistic geographical ice thickness variations of April ice along the Canadian Archipelago which exceed 7 m, and thicknesses of about 2 m along the Alaskan North Slope. Ice velocity fields were realistic in shape but 25% larger than the net ice station drift over a year; sensitivity simulations indicated a reduced average annual ice export rate of 0.04 Sv as compared to 0.09 Sv for the equilibrium simulation.

Hibler, W. D., III↗
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↗
NASA NTRS2021

A multi-model CMIP6-PMIP4 study of Arctic sea ice at 127 ka: sea ice data compilation and model differences

The Last Interglacial period (LIG) is a period with increased summer insolation at high northern latitudes, which results in strong changes in the terrestrial and marine cryosphere. Understanding the mechanisms for this response via climate modelling and comparing the models' representation of climate reconstructions is one of the objectives set up by the Paleoclimate Modelling Intercomparison Project for its contribution to the sixth phase of the Coupled Model Intercomparison Project. Here we analyse the results from 16 climate models in terms of Arctic sea ice. The multi-model mean reduction in minimum sea ice area from the pre industrial period (PI) to the LIG reaches 50 % (multi-model mean LIG area is 3.20×10^6 sq.km, compared to 6.46×10^6 sq.km for the PI). On the other hand, there is little change for the maximum sea ice area (which is 15–16×10^6 sq.km for both the PI and the LIG. To evaluate the model results we synthesise LIG sea ice data from marine cores collected in the Arctic Ocean, Nordic Seas and northern North Atlantic. The reconstructions for the northern North Atlantic show year-round ice-free conditions, and most models yield results in agreement with these reconstructions. Model–data disagreement appear for the sites in the Nordic Seas close to Greenland and at the edge of the Arctic Ocean. The northernmost site with good chronology, for which a sea ice concentration larger than 75 % is reconstructed even in summer, discriminates those models which simulate too little sea ice. However, the remaining models appear to simulate too much sea ice over the two sites south of the northernmost one, for which the reconstructed sea ice cover is seasonal. Hence models either underestimate or overestimate sea ice cover for the LIG, and their bias does not appear to be related to their bias for the pre-industrial period. Drivers for the inter-model differences are different phasing of the up and down short-wave anomalies over the Arctic Ocean, which are associated with differences in model albedo; possible cloud property differences, in terms of optical depth; and LIG ocean circulation changes which occur for some, but not all, LIG simulations. Finally, we note that inter-comparisons between the LIG simulations and simulations for future climate with moderate (1 %/yr) CO2 increase show a relationship between LIG sea ice and sea ice simulated under CO2 increase around the years of doubling CO2. The LIG may therefore yield insight into likely 21st century Arctic sea ice changes using these LIG simulations.

Arctic sea ice↗
NASA NTRS1986

An introduction to three-dimensional climate modeling

The development and use of three-dimensional computer models of the earth's climate are discussed. The processes and interactions of the atmosphere, oceans, and sea ice are examined. The basic theory of climate simulation which includes the fundamental equations, models, and numerical techniques for simulating the atmosphere, oceans, and sea ice is described. Simulated wind, temperature, precipitation, ocean current, and sea ice distribution data are presented and compared to observational data. The responses of the climate to various environmental changes, such as variations in solar output or increases in atmospheric carbon dioxide, are modeled. Future developments in climate modeling are considered. Information is also provided on the derivation of the energy equation, the finite difference barotropic forecast model, the spectral transform technique, and the finite difference shallow water waved equation model.

Washington, W. M.↗
DOE OSTI2025

Arctic sea-ice loss drives a strong regional atmospheric response over the North Pacific and North Atlantic on decadal scales

Previous studies have suggested that Arctic sea-ice loss can have a profound influence on atmospheric circulation far away from the Arctic. However, there is little scientific consensus on the features of these remote responses, with the opposite impacts reported. Here we present a multi-model analysis of the decadal climate response to Arctic sea-ice loss using state-of-the-art energy conserving methodologies to isolate the impacts of sea-ice decline. We observe weakening of the Aleutian Low and development of a geopotential ridge in the North Pacific, associated with drier winter conditions over the southwest United States. Over the Atlantic, a negative NAO-like response drives wetter winter conditions across the western Mediterranean. These decadal-scale impacts substantially differ from reported centennial-scale responses to Arctic sea-ice loss simulated using non-energy conserving methodologies. Factors such as the timescale of the response and methodologies used to isolate the impacts of disappearing sea-ice cover should be carefully considered when consolidating scientific understanding on the future impacts of changing Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Hierarchy of Global Ocean Models Coupled to CESM1

Abstract We develop a hierarchy of simplified ocean models for coupled ocean, atmosphere, and sea ice climate simulations using the Community Earth System Model version 1 (CESM1). The hierarchy has four members: a slab ocean model, a mixed‐layer model (MLM) with entrainment and detrainment, an Ekman MLM, and an ocean general circulation model (OGCM). Flux corrections of heat and salt are applied to the simplified models ensuring that all hierarchy members have the same climatology. We diagnose the needed flux corrections from auxiliary simulations in which we restore the temperature and salinity to the daily climatology obtained from a target CESM1 simulation. The resulting three‐dimensional corrections contain the interannual variability fluxes that maintain the correct vertical gradients of temperature and salinity in the tropics. We find that the inclusion of mixed‐layer entrainment and Ekman flow produces sea surface temperature and surface air temperature fields whose means and variances are progressively more similar to those produced by the target CESM1 simulation. We illustrate the application of the hierarchy to the problem of understanding the response of the climate system to the loss of Arctic sea ice. We find that the shifts in the positions of the mid‐latitude westerly jet and of the Inter‐tropical Convergence Zone (ITCZ) in response to sea‐ice loss depend critically on upper ocean processes. Specifically, heat uptake associated with the mixed‐layer entrainment influences the shift in the westerly jet and ITCZ. Moreover, the shift of ITCZ is sensitive to the form of Ekman flow parameterization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A hierarchy of global ocean models coupled to CESM1

We develop a hierarchy of simplified ocean models for coupled ocean, atmosphere, and sea ice climate simulations using the Community Earth System Model version 1 (CESM1). The hierarchy has four members: a slab ocean model, a mixed-layer model (MLM) with entrainment and detrainment, an Ekman MLM, and an ocean general circulation model (OGCM). Flux corrections of heat and salt are applied to the simplified models ensuring that all hierarchy members have the same climatology. We diagnose the needed flux corrections from auxiliary simulations in which we restore the temperature and salinity to the daily climatology obtained from a target CESM1 simulation. The resulting three-dimensional corrections contain the interannual variability fluxes that maintain the correct vertical gradients of temperature and salinity in the tropics. We find that the inclusion of mixed-layer entrainment and Ekman flow produces sea surface temperature and surface air temperature fields whose means and variances are progressively more similar to those produced by the target CESM1 simulation. We illustrate the application of the hierarchy to the problem of understanding the response of the climate system to the loss of Arctic sea ice. We find that the shifts in the positions of the mid-latitude westerly jet and of the Inter-tropical Convergence Zone (ITCZ) in response to sea-ice loss depend critically on upper ocean processes. Specifically, heat uptake associated with the mixed-layer entrainment influences the shift in the westerly jet and ITCZ. Moreover, the shift of ITCZ is sensitive to the form of Ekman flow parameterization.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

A Comparison of Snow Depth on Sea Ice Retrievals Using Airborne Altimeters and an AMSR-E Simulator

A comparison of snow depths on sea ice was made using airborne altimeters and an Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) simulator. The data were collected during the March 2006 National Aeronautics and Space Administration (NASA) Arctic field campaign utilizing the NASA P-3B aircraft. The campaign consisted of an initial series of coordinated surface and aircraft measurements over Elson Lagoon, Alaska and adjacent seas followed by a series of large-scale (100 km ? 50 km) coordinated aircraft and AMSR-E snow depth measurements over portions of the Chukchi and Beaufort seas. This paper focuses on the latter part of the campaign. The P-3B aircraft carried the University of Colorado Polarimetric Scanning Radiometer (PSR-A), the NASA Wallops Airborne Topographic Mapper (ATM) lidar altimeter, and the University of Kansas Delay-Doppler (D2P) radar altimeter. The PSR-A was used as an AMSR-E simulator, whereas the ATM and D2P altimeters were used in combination to provide an independent estimate of snow depth. Results of a comparison between the altimeter-derived snow depths and the equivalent AMSR-E snow depths using PSR-A brightness temperatures calibrated relative to AMSR-E are presented. Data collected over a frozen coastal polynya were used to intercalibrate the ATM and D2P altimeters before estimating an altimeter snow depth. Results show that the mean difference between the PSR and altimeter snow depths is -2.4 cm (PSR minus altimeter) with a standard deviation of 7.7 cm. The RMS difference is 8.0 cm. The overall correlation between the two snow depth data sets is 0.59.

Cavalieri, D. J.↗
DOE OSTI2022

A modified thermodynamic sea ice model and its application

This paper presents a modified thermodynamic sea ice model based on the Winton’s three-layer model framework. Several improvements focused on the vertical thermodynamics have been made in the model. Results from a series of one-dimensional experiments show that equilibrium ice thickness in our modified model is increased by about 45 cm when compared with the original Winton’s model. Sensitivity tests indicate that all modifications mentioned above contribute to this change of ice thickness, among which the increase of ice layer has the most significant effect followed by the sea ice conductivity parameterization. The modified thermodynamic sea ice model is then coupled into the sea ice component SIS in GFDL MOM4 to exert long-term integration experiments forced by CORE2.0 data. Results indicate that the modified model shows significant improvement in simulating the Arctic sea ice, including an increase in both the sea ice thickness and volume over the whole Arctic region. This confirms the above founding from 1-D simulations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

Evaluation of the Simulation of Arctic and Antarctic Sea Ice Coverages by Eleven Major Global Climate Models

Comparison of polar sea ice results from 11 major global climate models and satellite-derived observations for 1979-2004 reveals that each of the models is simulating seasonal cycles that are phased at least approximately correctly in both hemispheres. Each is also simulating various key aspects of the observed ice cover distributions, such as winter ice not only throughout the central Arctic basin but also throughout Hudson Bay, despite its relatively low latitudes. However, some of the models simulate too much ice, others too little ice (in some cases varying depending on hemisphere and/or season), and some match the observations better in one season versus another. Several models do noticeably better in the Northern Hemisphere than in the Southern Hemisphere, and one does noticeably better in the Southern Hemisphere. In the Northern Hemisphere all simulate monthly average ice extents to within +/-5.1 x 10(exp 6)sq km of the observed ice extent throughout the year; and in the Southern Hemisphere all except one simulate the monthly averages to within +/-6.3 x 10(exp 6) sq km of the observed values. All the models properly simulate a lack of winter ice to the west of Norway; however, most do not obtain as much absence of ice immediately north of Norway as the observations show, suggesting an under simulation of the North Atlantic Current. The spread in monthly averaged ice extents amongst the 11 model simulations is greater in the Southern Hemisphere than in the Northern Hemisphere and greatest in the Southern Hemisphere winter and spring.

Parksinson, Claire↗
NASA NTRS1991

A finite mixtures algorithm for finding classes in images

The authors describe an algorithm for estimating the proportions of classes in a SAR (synthetic aperture radar) image without any user interaction. The method assumes that the image is a mixture of a known number of different pixel types. A maximum likelihood estimate of the parameters of the resulting mixture distribution is then used to find the proportions for the various classes. The technique was successfully applied to aircraft SAR images of sea ice. Computer simulations were used to determine the relative errors of the technique. The technique performs well even with extremely noisy images.

Samadani, Ramin↗
NASA NTRS1997

The Role of Sea Ice in 2 x CO2 Climate Model Sensitivity: Hemispheric Dependencies - Part 2

How sensitive are doubled CO2 simulations to GCM control-run sea ice thickness and extent? This issue is examined in a series of 10 control-run simulations with different sea ice and corresponding doubled CO2 simulations. Results show that with increased control-run sea ice coverage in the Southern Hemisphere, temperature sensitivity with climate change is enhanced, while there is little effect on temperature sensitivity of (reasonable) variations in control-run sea ice thickness. In the Northern Hemisphere the situation is reversed: sea ice thickness is the key parameter, while (reasonable) variations in control-run sea ice coverage are of less importance. In both cases, the quantity of sea ice that can be removed in the warmer climate is the determining factor. Overall, the Southern Hemisphere sea ice coverage change had a larger impact on global temperature, because Northern Hemisphere sea ice was sufficiently thick to limit its response to doubled CO2, and sea ice changes generally occurred at higher latitudes, reducing the sea ice-albedo feedback. In both these experiments and earlier ones in which sea ice was not allowed to change, the model displayed a sensitivity of -0.02 C global warming per percent change in Southern Hemisphere sea ice coverage.

Rind, D.↗
NASA NTRS2012

Improvement in Simulation of Eurasian Winter Climate Variability with a Realistic Arctic Sea Ice Condition in an Atmospheric GCM

The present study investigates how much a realistic Arctic sea ice condition can contribute to improve simulation of the winter climate variation over the Eurasia region. Model experiments are set up using different sea ice boundary conditions over the past 24 years (i.e., 1988-2011). One is an atmospheric model inter-comparison (AMIP) type of run forced with observed sea-surface temperature (SST), sea ice, and greenhouse gases (referred to as Exp RSI), and the other is the same as Exp RSI except for the sea ice forcing, which is a repeating climatological annual cycle (referred to as Exp CSI). Results show that Exp RSI produces the observed dominant pattern of Eurasian winter temperatures and their interannual variation better than Exp CSI (correlation difference up to approx. 0.3). Exp RSI captures the observed strong relationship between the sea ice concentration near the Barents and Kara seas and the temperature anomaly across Eurasia, including northeastern Asia, which is not well captured in Exp CSI. Lagged atmospheric responses to sea ice retreat are examined using observations to understand atmospheric processes for the Eurasian cooling response including the Arctic temperature increase, sea-level pressure increase, upper-level jet weakening and cold air outbreak toward the mid-latitude. The reproducibility of these lagged responses by Exp RSI is also evaluated.

sea-ice↗
NASA NTRS2003

Fresh Water Content Variability in the Arctic Ocean

Arctic Ocean model simulations have revealed that the Arctic Ocean has a basin wide oscillation with cyclonic and anticyclonic circulation anomalies (Arctic Ocean Oscillation; AOO) which has a prominent decadal variability. This study explores how the simulated AOO affects the Arctic Ocean stratification and its relationship to the sea ice cover variations. The simulation uses the Princeton Ocean Model coupled to sea ice. The surface forcing is based on NCEP-NCAR Reanalysis and its climatology, of which the latter is used to force the model spin-up phase. Our focus is to investigate the competition between ocean dynamics and ice formation/melt on the Arctic basin-wide fresh water balance. We find that changes in the Atlantic water inflow can explain almost all of the simulated fresh water anomalies in the main Arctic basin. The Atlantic water inflow anomalies are an essential part of AOO, which is the wind driven barotropic response to the Arctic Oscillation (AO). The baroclinic response to AO, such as Ekman pumping in the Beaufort Gyre, and ice meldfreeze anomalies in response to AO are less significant considering the whole Arctic fresh water balance.

Hakkinen, Sirpa↗
NASA NTRS2018

Melt Pond Conditions on Declining Arctic Sea Ice Over 1979-2016: Model Development, Validation, and Results

A melt pond (MP) distribution equation has been developed and incorporated into the Marginal Ice-Zone Modeling and Assimilation System to simulate Arctic MPs and sea ice over 1979-2016. The equation differs from previous MP models and yet benefits from previous studies for MP parameterizations as well as a range of observations for model calibration. Model results show higher magnitude of MP volume per unit ice area and area fraction in most of the Canada Basin and the East Siberian Sea and lower magnitude in the central Arctic. This is consistent with Moderate Resolution Imaging Spectroradiometer observations, evaluated with Measurements of Earth Data for Environmental Analysis (MEDEA) data, and closely related to top ice melt per unit ice area. The model simulates a decrease in the total Arctic sea ice volume and area, owing to a strong increase in bottom and lateral ice melt. The sea ice decline leads to a strong decrease in the total MP volume and area. However, the Arctic-averaged MP volume per unit ice area and area fraction show weak, statistically insignificant downward trends, which is linked to the fact that MP water drainage per unit ice area is increasing. It is also linked to the fact that MP volume and area decrease relatively faster than ice area. This suggests that overall the actual MP conditions on ice have changed little in the past decades as the ice cover is retreating in response to Arctic warming, thus consistent with the Moderate Resolution Imaging Spectroradiometer observations that show no clear trend in MP area fraction over 2000-2011.

sea ice↗
NASA NTRS1992

The role of sea ice dynamics in global climate change

The topics covered include the following: general characteristics of sea ice drift; sea ice rheology; ice thickness distribution; sea ice thermodynamic models; equilibrium thermodynamic models; effect of internal brine pockets and snow cover; model simulations of Arctic Sea ice; and sensitivity of sea ice models to climate change.

Hibler, William D., III↗
NASA NTRS2024

Predictability of the Minimum Sea Ice Extent from Winter Fram Strait Ice Area Export: Model vs Observations

Observations show predictive skill of the minimum sea ice extent (Min SIE) from late winter anomalous off-shore ice drift along the Eurasian coastline – leading to local ice thickness anomalies at the onset of the melt season – a signal then amplified by the ice-albedo feedback. We assess whether the observed seasonal predictability of September sea ice extent (Sept SIE) from Fram Strait Ice Area Export (FSIAE, a proxy for Eurasian coastal divergence) is present in Global Climate Model (GCM) large ensembles, namely the CESM2-LE, GISS-E2.1-G, FLOR-LE, CNRM-CM6-1, and CanESM5. All models show distinct periods where winter FSIAE anomalies are negatively correlated with the May sea ice thickness (May SIT) anomalies along the Eurasian coastline, and the following Sept Arctic SIE, as in observations. Counter-intuitively, several models show occasional periods where winter FSIAE anomalies are positively correlated with the following Sept SIE anomalies when the mean ice thickness is large or late in the simulation when the sea ice is thin, and/or when internal variability increases. More importantly, periods with weak correlation between FSIAE and the Sept SIE dominate, suggesting that summer melt processes dominate over late winter preconditioning and May SIT anomalies. In general, we find that the coupling between the FSIAE and ice thickness anomalies along the Eurasian coastline is an ubiquitous feature of GCMs and that the relationship with the following Sept SIE is dependent on the mean Arctic sea ice thickness.

off-shore ice drift↗