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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
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 OSTI2022

An evaluation of the E3SMv1 Arctic ocean and sea-ice regionally refined model

The Energy Exascale Earth System Model (E3SM) is a state-of-the-science Earth system model (ESM) with the ability to focus horizontal resolution of its multiple components in specific areas. Regionally refined global ESMs are motivated by the need to explicitly resolve, rather than parameterize, relevant physics within the regions of refined resolution, while offering significant computational cost savings relative to the respective cost of configurations with high-resolution (HR) everywhere on the globe. In this paper, we document results from the first Arctic regionally refined E3SM configuration for the ocean and sea-ice components (E3SM-Arctic-OSI), while employing data-based atmosphere, land, and hydrology components. Our aim is an improved representation of the Arctic coupled ocean and sea-ice state, its variability and trends, and the exchanges of mass and property fluxes between the Arctic and the sub-Arctic. We find that E3SM-Arctic-OSI increases the realism of simulated Arctic ocean and sea-ice conditions compared to a similar low-resolution E3SM simulation without the Arctic regional refinement in ocean and sea-ice components (E3SM-LR-OSI). In particular, exchanges through the main Arctic gateways are greatly improved with respect to E3SM-LR-OSI. Other aspects, such as the Arctic freshwater content variability and sea-ice trends, are also satisfactorily simulated. Yet, other features, such as the upper-ocean stratification and the sea-ice thickness distribution, need further improvements, involving either more advanced parameterizations, model tuning, or additional grid refinements. Overall, E3SM-Arctic-OSI offers an improved representation of the Arctic system relative to E3SM-LR-OSI, at a fraction (15 %) of the computational cost of comparable global high-resolution configurations, while permitting exchanges with the lower-latitude oceans that cannot be directly accounted for in Arctic regional models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Unprecedented Beaufort Sea ice loss in late summer 2021 and its relationship to an extended period of unusually stormy weather

Previous case studies have linked cyclone-induced atmospheric forcing and/or upper-ocean processes to notable Arctic sea ice loss events in the summers of 2012 and 2016. This study examines a more recent and noteworthy case in late summer 2021 in which substantial sea ice loss followed a period of surface meteorological extremes in the Beaufort Sea region of the Arctic. We focus on the period from mid-August to mid-September 2021 that coincided with the Office of Naval Research THINICE Pilot Field Campaign and investigate stormy and windy conditions with respect to air-sea processes impacting sea ice conditions. We find that during the stormy first half of the campaign, cyclone-induced energy fluxes into the marginal ice zone and surrounding waters preconditioned the ice pack for more rapid melt later in the campaign. The second half of the campaign, in contrast, was marked by non-cyclone wind events that enhanced turbulent (namely sensible) heat fluxes into the ice and upper ocean that increased melt. Moreover, this latter period had enhanced advection of the Beaufort Sea ice pack into above-freezing waters, increasing bottom melt to >1 cm d −1 over the remainder of the campaign. While findings are shown to vary by surface type and at relatively small (i.e. ice-floe) scales, insights are offered on the roles of late summer coupled processes on rapid ice loss events in today’s Arctic environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Rain on snow (ROS) understudied in sea ice remote sensing: a multi-sensor analysis of ROS during MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate)

Abstract. Arctic rain on snow (ROS) deposits liquid water onto existing snowpacks. Upon refreezing, this can form icy crusts at the surface or within the snowpack. By altering radar backscatter and microwave emissivity, ROS over sea ice can influence the accuracy of sea ice variables retrieved from satellite radar altimetry, scatterometers, and passive microwave radiometers. During the Arctic Ocean MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition, there was an unprecedented opportunity to observe a ROS event using in situ active and passive microwave instruments similar to those deployed on satellite platforms. During liquid water accumulation in the snowpack from rain and increased melt, there was a 4-fold decrease in radar energy returned at Ku- and Ka-bands. After the snowpack refroze and ice layers formed, this decrease was followed by a 6-fold increase in returned energy. Besides altering the radar backscatter, analysis of the returned waveforms shows the waveform shape changed in response to rain and refreezing. Microwave emissivity at 19 and 89 GHz increased with increasing liquid water content and decreased as the snowpack refroze, yet subsequent ice layers altered the polarization difference. Corresponding analysis of the CryoSat-2 waveform shape and backscatter as well as AMSR2 brightness temperatures further shows that the rain and refreeze were significant enough to impact satellite returns. Our analysis provides the first detailed in situ analysis of the impacts of ROS and subsequent refreezing on both active and passive microwave observations, providing important baseline knowledge for detecting ROS over sea ice and assessing their impacts on satellite-derived sea ice variables.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Role of Intense Arctic Storm in Accelerating Summer Sea Ice Melt: An In Situ Observational Study

Abstract Intense storms have been more frequently observed in the Arctic during recent years, in coincidence with extreme sea ice loss events. However, it is still not fully understood how storms drive such events due to deficient observations and modeling discrepancies. Here we address this problem by analyzing in situ observations acquired during an Arctic expedition, which uniquely captured an intense storm in summer 2016. The result shows a pronounced acceleration of sea ice loss during the storm process. Diagnostic analysis indicates a net energy loss at the ice surface, not supporting the accelerated melting. Although the open water surface gained net heat energy, it was insufficient to increase the mixed‐layer temperature to the observed values. Dynamic analysis suggests that storm‐driven increase in ocean mixing and upward Ekman pumping of the Pacific‐origin warm water tremendously increased oceanic heat flux. The thermal advection by the Ekman pumping led to a warmed mixed layer by 0.05°C–0.12°C and, in consequence, an increased basal sea ice melt rate by 0.1–1.7 cm day −1 .

58 GEOSCIENCES↗
DOE OSTI2024

The correlation between Arctic sea ice, cloud phase and radiation using A-Train satellites

Abstract. Climate warming has a stronger impact on Arctic climate and sea ice cover (SIC) decline than previously thought. Better understanding and characterization of the relationship between sea ice and clouds and the implications for surface radiation is key to improving our confidence in Arctic climate projections. Here we analyze the relationship between sea ice, cloud phase and surface radiation over the Arctic, defined as north of 60° N, using active- and passive-sensor satellite observations from three different datasets. We find that all datasets agree on the climatology of and seasonal variability in total and liquid-bearing (liquid and mixed-phase) cloud covers. Similarly, our results show a robust relationship between decreased SIC and increased liquid-bearing clouds in the lowest levels (below 3 km) for all seasons (strongest in winter) but summer, while increased SIC and ice clouds are positively correlated in two of the three datasets. A refined map correlation analysis indicates that the relationship between SIC and liquid-bearing clouds can change sign over the Bering, Barents and Laptev seas, likely because of intrusions of warm air from low latitudes during winter and spring. Finally, the increase in liquid clouds resulting from decreasing SIC is associated with enhanced radiative cooling at the surface. Our findings indicate that the newly formed liquid clouds reflect more shortwave (SW) radiation back to space compared to the surface, generating a cooling effect of the surface, while their downward longwave (LW) radiation is similar to the upward LW surface emission, which has a negligible radiative impact on the surface. This overall cooling effect should contribute to dampening future Arctic surface warming as SIC continues to decline.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

MPAS-Seaice (v1.0.0): sea-ice dynamics on unstructured Voronoi meshes

Abstract. We present MPAS-Seaice, a sea-ice model which uses the Model for Prediction Across Scales (MPAS) framework and spherical centroidal Voronoi tessellation (SCVT) unstructured meshes. As well as SCVT meshes, MPAS-Seaice can run on the traditional quadrilateral grids used by sea-ice models such as CICE. The MPAS-Seaice velocity solver uses the elastic–viscous–plastic (EVP) rheology and the variational discretization of the internal stress divergence operator used by CICE, but adapted for the polygonal cells of MPAS meshes, or alternatively an integral (“finite-volume”) formulation of the stress divergence operator. An incremental remapping advection scheme is used for mass and tracer transport. We validate these formulations with idealized test cases, both planar and on the sphere. The variational scheme displays lower errors than the finite-volume formulation for the strain rate operator but higher errors for the stress divergence operator. The variational stress divergence operator displays increased errors around the pentagonal cells of a quasi-uniform mesh, which is ameliorated with an alternate formulation for the operator. MPAS-Seaice shares the sophisticated column physics and biogeochemistry of CICE and when used with quadrilateral meshes can reproduce the results of CICE. We have used global simulations with realistic forcing to validate MPAS-Seaice against similar simulations with CICE and against observations. We find very similar results compared to CICE, with differences explained by minor differences in implementation such as with interpolation between the primary and dual meshes at coastlines. We have assessed the computational performance of the model, which, because it is unstructured, runs with 70 % of the throughput of CICE for a comparison quadrilateral simulation. The SCVT meshes used by MPAS-Seaice allow removal of equatorial model cells and flexibility in domain decomposition, improving model performance. MPAS-Seaice is the current sea-ice component of the Energy Exascale Earth System Model (E3SM).

58 GEOSCIENCES↗
DOE OSTI2022

Sensitivity of the Arctic Sea Ice Cover to the Summer Surface Scattering Layer

Here, the ‘surface scattering layer’ (SSL) is the highly-scattering, coarse-grained ice layer that forms on the surface of melting, drained sea ice during spring and summer. Ice of sufficient thickness with an SSL has an observed persistent broadband albedo of ~0.65, resulting in a strong influence on the regional solar partitioning. Experiments during the MOSAiC expedition showed that the SSL re-forms in approximately one day following manual removal. Coincident spectral albedo measurements provide insight into the SSL evolution, where albedo increased on sunny days with higher solar insolation. Comparison with experiments in radiative transfer and global climate models show that the sea ice albedo is greatly impacted by the SSL thickness. The presence of SSL is a significant component of the ice-albedo feedback, with an albedo impact of the same order as melt ponds. Changes in SSL and implications for Arctic sea ice within a warming climate are uncertain.

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 OSTI2020

Influence of sea-ice anomalies on Antarctic precipitation using source attribution in the Community Earth System Model

Abstract. We conduct sensitivity experiments using a general circulation model that has an explicit water source tagging capability forced by prescribed composites of pre-industrial sea-ice concentrations (SICs) and corresponding sea surface temperatures (SSTs) to understand the impact of sea-ice anomalies on regional evaporation, moisture transport and source–receptor relationships for Antarctic precipitation in the absence of anthropogenic forcing. Surface sensible heat fluxes, evaporation and column-integrated water vapor are larger over Southern Ocean (SO) areas with lower SICs. Changes in Antarctic precipitation and its source attribution with SICs have a strong spatial variability. Among the tagged source regions, the Southern Ocean (south of 50° S) contributes the most (40 %) to the Antarctic total precipitation, followed by more northerly ocean basins, most notably the South Pacific Ocean (27%), southern Indian Ocean (16 %) and South Atlantic Ocean (11 %). Comparing two experiments prescribed with high and low pre-industrial SICs, respectively, the annual mean Antarctic precipitation is about 150 Gt yr-1 (or 6 %) more in the lower SIC case than in the higher SIC case. This difference is larger than the model-simulated interannual variability in Antarctic precipitation (99 Gt yr-1). The contrast in contribution from the Southern Ocean, 102 Gt yr-1, is even more significant compared to the interannual variability of 35 Gt yr-1 in Antarctic precipitation that originates from the Southern Ocean. The horizontal transport pathways from individual vapor source regions to Antarctica are largely determined by large-scale atmospheric circulation patterns. Vapor from lower-latitude source regions takes elevated pathways to Antarctica. In contrast, vapor from the Southern Ocean moves southward within the lower troposphere to the Antarctic continent along moist isentropes that are largely shaped by local ambient conditions and coastal topography. This study also highlights the importance of atmospheric dynamics in affecting the thermodynamic impact of sea-ice anomalies associated with natural variability on Antarctic precipitation. Our analyses of the seasonal contrast in changes of basin-scale evaporation, moisture flux and precipitation suggest that the impact of SIC anomalies on regional Antarctic precipitation depends on dynamic changes that arise from SIC–SST perturbations along with internal variability. The latter appears to have a more significant effect on the moisture transport in austral winter than in summer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The disproportionate impact of enhanced evaporation from melting arctic sea ice on cold-season land precipitation trends

Diminishing Arctic sea ice has led to enhanced evaporation from the Arctic marginal seas (AMS), which is expected to alter precipitation over land. In this work, AMS evaporation is numerically tracked to quantify its contribution to cold-season (October–March) precipitation over land in the Northern Hemisphere during 1980–2021. Results show a significant 32% increase in AMS moisture contribution to land precipitation, corresponding to a 16% increase per million square km loss of sea ice area. Especially over the high-latitude land, despite the fractional contribution of AMS to precipitation being relatively low (8%), the augmented AMS evaporation contributed disproportionately (42%) to the observed upward trend in precipitation. Notably, northern East Siberia exhibited a substantial rise in both the amount and fraction of extreme snowfall sourced from the AMS. Our findings underscore the importance of the progressively ice-free Arctic as an important contributor to the escalating levels of cold-season precipitation and snowfall over northern high-latitude land.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Uncertain role of clouds in shaping summertime atmosphere-sea ice connections in reanalyses and CMIP6 models

Downwelling longwave radiation (DLR) driven by the atmospheric and cloud conditions in the troposphere is suggested to be a dominant factor to determine the summertime net surface energy budget over the Arctic Ocean and thus plays a key role to shape the September sea ice. We use reanalyses and the self-organizing map (SOM) method to distinguish CMIP6 model performance in replicating the observed strong atmosphere-DLR connection. We find all models can reasonably simulate the linkage between key atmosphere variables and the clear sky DLR but behave differently in replicating the atmosphere-DLR connection due to cloud forcing. In ERA5 and strongly coupled models, tropospheric high pressure is associated with decreased clouds in the mid- and high-levels and increased clouds near the surface. This out-of-phase structure indicates that DLR cloud forcing is nearly neutral, making the clear sky DLR more important to bridge JJA circulation to late-summer sea ice. In MERRA-2 and weakly coupled models, tropospheric clouds display a vertically homogeneous reduction; the cloud DLR is thus strongly reduced due to the cooling effect, which partially cancels out the clear sky DLR and makes the total DLR less efficient to translate circulation forcing to sea ice. The differences of cloud vertical distribution in CMIP6 appear to be differentiated by circulation related relative humidity. Therefore, a better understanding of the discrepancy of different reanalyses and remote sensing products is critical to comprehensively evaluate simulated interactions among circulation, clouds, sea ice and energy budget at the surface in summer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Atmospheric Turbulent Intermittency Over the Arctic Sea‐Ice Surface During the MOSAiC Expedition

Turbulent motions in the Arctic stable boundary layer are characterized by intermittency, but they are rarely investigated due to limited observations, in particular over the sea‐ice surface. In the present study, we explore the characteristics of turbulent intermittency over the Arctic sea‐ice surface using data collected during the Multidisciplinary drifting Observation for the Study of Arctic Climate expedition from October 2019 to September 2020. We first develop a new algorithm, which performs well in identifying the spectral gap over the Arctic sea‐ice surface. Then the characteristics of intermittency are investigated. It is found that the strength of intermittency increases under the conditions of light surface wind speed, small surface wind speed gradient, and strong surface air temperature gradient. The momentum flux, sensible heat flux, and latent heat flux calculated by raw eddy‐covariance fluctuations are overestimated by 3%, 10%, and 24%, respectively, because submesoscale motions are included. Furthermore, the characteristics of the atmospheric boundary layer structure under various intermittency conditions reveal that strong low‐level jets are favorable to surface turbulent motions that result in weak intermittency, while strong temperature inversions above the surface layer suppress surface turbulent motions and lead to strong intermittency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The importance of sub-meter-scale snow roughness on conductive heat flux of Arctic sea ice

Abstract The conductive heat flux through the snow and ice is a critical component of the mass and energy budgets in the Arctic sea ice system. We use high horizontal resolution (3–15 cm) measurements of snow topography to explore the impacts of sub-meter-scale snow surface roughness on heat flux as simulated by the Finite Element method. Simulating horizontal heat flux in a variable snow cover modestly increases the total simulated heat flux. With horizontal heat flux, as opposed to simple 1D-vertical heat flux modeling, the simulated heat flux is 10% greater than that for uniform snow with the same mean snow thickness for a 31.5 × 21 m region of sea ice (the largest region we studied). Vertical-only (1D) heat flux simulates just a 6% increase for the same region. However, this is highly dependent on observation resolution. Had we measured the snow cover at 1 m horizontal spacing or greater, simulating horizontal heat flux would not have changed the net heat flux from that simulated with vertical-only heat flux. These findings suggest that measuring and modeling snow roughness at sub-meter horizontal scales may be necessary to accurately represent horizontal heat flux on level Arctic sea ice.

Geology↗
DOE OSTI2023

Thin and transient meltwater layers and false bottoms in the Arctic sea ice pack—Recent insights on these historically overlooked features

The rapid melt of snow and sea ice during the Arctic summer provides a significant source of low-salinity meltwater to the surface ocean on the local scale. The accumulation of this meltwater on, under, and around sea ice floes can result in relatively thin meltwater layers in the upper ocean. Due to the small-scale nature of these upper-ocean features, typically on the order of 1 m thick or less, they are rarely detected by standard methods, but are nevertheless pervasive and critically important in Arctic summer. Observations during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in summer 2020 focused on the evolution of such layers and made significant advancements in understanding their role in the coupled Arctic system. Here we provide a review of thin meltwater layers in the Arctic, with emphasis on the new findings from MOSAiC. Both prior and recent observational datasets indicate an intermittent yet long-lasting (weeks to months) meltwater layer in the upper ocean on the order of 0.1 m to 1.0 m in thickness, with a large spatial range. The presence of meltwater layers impacts the physical system by reducing bottom ice melt and allowing new ice formation via false bottom growth. Collectively, the meltwater layer and false bottoms reduce atmosphere-ocean exchanges of momentum, energy, and material. The impacts on the coupled Arctic system are far-reaching, including acting as a barrier for nutrient and gas exchange and impacting ecosystem diversity and productivity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Sea Ice and Cloud Processes Mediating Compensation between Atmospheric and Oceanic Meridional Heat Transports across the CMIP6 Preindustrial Control Experiment

Abstract Bjerknes compensation (BJC) refers to the anticorrelation observed between atmospheric and oceanic heat transport (AHT/OHT) variability, particularly on decadal to longer time scales that may be important to the predictability of the climate system. This study investigates the spread in BJC across fully coupled simulations of phase 6 of the Coupled Model Intercomparison Project (CMIP6) and critical processes (particularly related to sea ice and clouds) that may contribute to that spread. BJC on decadal to longer time scales is confirmed across all the simulations evaluated, and it is strongest in the Northern Hemisphere (NH) between 60° and 70°N. At these latitudes, BJC appears to be primarily driven by the exchange of turbulent fluxes (sensible and latent) in the Greenland, Iceland, and Barents Seas. Metrics to break down how sea ice and clouds uniquely modify the radiative balance of the polar atmosphere during anomalous OHT events are presented. These metrics quantify the impacts of sea ice and clouds on surface and top of atmosphere (latent, sensible, longwave, and shortwave radiative) energy fluxes. Cloud responses tend to counter the clear sky impacts over the Marginal Ice Zone (MIZ). It is further shown that the degree of BJC present in a simulation at high latitudes is heavily influenced by the sensitivity of the sea ice to OHT, which is most influential over the MIZ. These results are qualitatively robust across models and explain the intermodel spread in NH BJC in the preindustrial control experiment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Explicitly determined sea ice emissivity and emission temperature over the Arctic for surface–sensitive microwave channels

Data assimilation of satellite microwave measurements is one of the important keys to improving weather forecasting over the Arctic region. However, the use of surface-sensitive microwave-sounding channel measurements for data assimilation or retrieval has been limited, especially during winter, due to the poorly constrained sea ice emissivity. In this study, aiming at more use of those channel measurements in the data assimilation, we propose an explicit method for specifying the surface radiative boundary conditions (namely emissivity and emitting layer temperature of snow and ice). These were explicitly determined with a radiative transfer model for snow and ice and with snow/ice physical parameters (i.e., snow/ice depths and vertical distributions of temperature, density, salinity, and grain size) simulated from the thermodynamically-driven snow/ice growth model. Here we conducted 1-D Var experiments in order to examine whether this approach can help to use the surface-sensitive microwave temperature channel measurements over the Arctic sea ice region for data assimilation. Results show that (1) the surface-sensitive microwave channels can be used in the 1-D Var retrieval, and (2) the specification of the radiative boundary condition at the surface using the snow/sea ice emission model can significantly improve the atmospheric temperature retrieval, especially in the lower troposphere (500 hPa to surface). The successful retrieval suggests that useful information can be extracted from surface-sensitive microwave-sounding channel radiances over sea ice surfaces through the explicit determination of snow/ice emissivity and emitting layer temperature.

1-D Var retrieval↗