Sea Ice Fraction Research Articles

Melt pond fractions on Arctic summer sea ice retrieved from Sentinel-3 satellite data with a constrained physical forward model

Abstract. The presence of melt ponds on Arctic summer sea ice significantly alters its albedo and thereby the surface energy budget and mass balance. Large-scale observations of melt pond coverage and sea ice albedo are crucial to investigate the role of sea ice for Arctic amplification and its representation in global climate models. We present the new Melt Pond Detection 2 (MPD2) algorithm, which retrieves melt pond, sea ice, and open-ocean fractions as well as surface albedo from Sentinel-3 visible and near-infrared reflectances. In contrast to most other algorithms, our method uses neither fixed values for the spectral albedo of the surface constituents nor an artificial neural network. Instead, it aims for a fully physical representation of the reflective properties of the surface constituents based on their optical characteristics. The state vector X, containing the optical properties of melt ponds and sea ice along with the area fractions of melt ponds and open ocean, is optimized in an iterative procedure to match the measured reflectances and describe the surface state. A major problem in unmixing a compound pixel is that a mixture of half open water and half bright ice cannot be distinguished from a homogeneous pixel of darker ice. In order to overcome this, we suggest constraining the retrieval with a priori information. Initial values and constraint of the surface fractions are derived with an empirical retrieval which uses the same spectral reflectances as implemented in the physical retrieval. The snow grain size and optical thickness change with time, and thus the ice surface albedo changes throughout the season. Therefore, field observations of spectral albedo are used to develop a parameterization of the sea ice optical properties as a function of the temperature history of the sea ice. With these a priori data, the iterative optimization is initialized and constrained, resulting in a retrieval uncertainty of below 8 % for melt pond and 9 % for open-ocean fractions compared to the reference dataset. As reference data for evaluation, a 10 m resolution product of melt pond and open-ocean fraction from Sentinel-2 optical imagery is used.

Mapping icebergs in sea ice: An analysis of seasonal SAR backscatter at C- and L-band

Icebergs in the Arctic can pose a threat to maritime traffic and offshore installations and influence the properties of the upper ocean layer. While icebergs in open water are regularly monitored using C-band SAR satellites, less attention has been paid to icebergs in regions with a high areal fraction of sea ice, where detection using traditional methods is more difficult. In this study, we compare the capability of C- and L-band SAR to detect icebergs in level and deformed fast sea ice across various seasons. To this end we use a timeseries of SAR images acquired at HH- and HV-polarization in 2019 and 2020, covering respectively 301 and 356 icebergs. As reference data for validation, we used iceberg polygons derived from Sentinel-2 images. Our results reveal that compared to C-band, L-band SAR is significantly better at separating the backscatter of icebergs and sea ice and thus is preferable for detecting icebergs in ocean regions with a high sea ice concentration. It is further shown that L-band SAR is less affected by melting conditions, suggesting that it can be used for iceberg detections in both summer and winter.

Decadal Changes in the Antarctic Sea Ice Response to the Changing ENSO in the Last Four Decades

Sea ice fraction (SIF) over the Ross/Amundsen/Bellingshausen Sea (RAB) are investigated using the Modern-Era Retrospective Analysis for Research and Application, Version 2 (MERRA-2), focusing on the differences in time-lagged response to ENSO between the late 20th (1980–2000, L20) and the early 21st century (2001–2021, E21). The findings suggest that the typical Antarctic response to ENSO is influenced by changes in ENSO type/intensity, highlighting the need for caution when investigating the Antarctic teleconnection. Time-lagged regressions onto the mature phase of El Niño reveal that the SIF decrease and SST increase over the RAB is relatively weaker in E21 and most pronounced at 0–4 months lag. Conversely, the SIF in L20 continues to decline and reaches its peak at two-season lag (5–7 months). Tropospheric wind, pressure, and wave activity in response to El Niño in L20 show a zonally oriented high/low-pressure areas with two-season lag, enhancing the poleward flow that plays a key role in sea ice melt in the RAB, while this pattern in E21 is insignificant at the same lag. This study suggests that stronger (weaker) and more eastern (central) Pacific ENSOs on average in L20 (E21) are associated with this decadal change in the SIF response to ENSO.

High‐Resolution Thermal Imaging in the Antarctic Marginal Ice Zone: Skin Temperature Heterogeneity and Effects on Heat Fluxes

AbstractInsufficient in situ observations from the Antarctic marginal ice zone (MIZ) limit our understanding and description of relevant mechanical and thermodynamic processes that regulate the seasonal sea ice cycle. Here we present high‐resolution thermal images of the ocean surface and complementary measurements of atmospheric variables that were acquired underway during one austral winter and one austral spring expedition in the Atlantic and Indian sectors of the Southern Ocean. Skin temperature data and ice cover images were used to estimate the partitioning of the heterogeneous surface and calculate the heat fluxes to compare with ERA5 reanalyses. The winter MIZ was composed of different but relatively regularly distributed sea ice types with sharp thermal gradients. The surface‐weighted skin temperature compared well with the reanalyses due to a compensation of errors between the sea ice fraction and the ice floe temperature. These uncertainties determine the dominant source of inaccuracy for heat fluxes as computed from observed variables. In spring, the sea ice type distribution was more irregular, with alternation of sea ice cover and large open water fractions even 400 km from the ice edge. The skin temperature distribution was more homogeneous and did not produce substantial uncertainties in heat fluxes. The discrepancies relative to reanalysis data are however larger than in winter and are attributed to biases in the atmospheric variables, with the downward solar radiation being the most critical.

Effects of variable ice–ocean surface properties and air mass transformation on the Arctic radiative energy budget

Abstract. Low-level airborne observations of the Arctic surface radiative energy budget are discussed. We focus on the terrestrial part of the budget, quantified by the thermal-infrared net irradiance (TNI). The data were collected in cloudy and cloud-free conditions over and in the vicinity of the marginal sea ice zone (MIZ) close to Svalbard during two aircraft campaigns conducted in the spring of 2019 and in the early summer of 2017. The measurements, complemented by ground-based observations available from the literature and radiative transfer simulations, are used to evaluate the influence of surface type (sea ice, open ocean, MIZ), seasonal characteristics, and synoptically driven meridional air mass transports into and out of the Arctic on the near-surface TNI. The analysis reveals a typical four-mode structure of the frequency distribution of the TNI as a function of surface albedo, the sea ice fraction, and surface brightness temperature. Two modes prevail over sea ice and another two over open ocean, each representing cloud-free and cloudy radiative states. Characteristic shifts and modifications of the TNI modes during the transition from winter to spring and early summer conditions are discussed. Furthermore, the influence of warm air intrusions (WAIs) and marine cold-air outbreaks (MCAOs) on the near-surface downward thermal-infrared irradiances and the TNI is highlighted for several case studies. It is concluded that during WAIs the surface warming depends on cloud properties and evolution. Lifted clouds embedded in warmer air masses over a colder sea ice surface, decoupled from the ground by a surface-based temperature inversion, have the potential to warm the surface more strongly than near-surface fog or thin low-level boundary layer clouds because of a higher cloud base temperature. For MCAOs it is found that the thermodynamic profile of the southward-moving air mass adapts only slowly to the warmer ocean surface.

Modelling wintertime sea-spray aerosols under Arctic haze conditions

Abstract. Anthropogenic and natural emissions contribute to enhanced concentrations of aerosols in the Arctic winter and early spring, with most attention being paid to anthropogenic aerosols that contribute to so-called Arctic haze. Less-well-studied wintertime sea-spray aerosols (SSAs) under Arctic haze conditions are the focus of this study, since they can make an important contribution to wintertime Arctic aerosol abundances. Analysis of field campaign data shows evidence for enhanced local sources of SSAs, including marine organics at Utqiaġvik (formerly known as Barrow) in northern Alaska, United States, during winter 2014. Models tend to underestimate sub-micron SSAs and overestimate super-micron SSAs in the Arctic during winter, including the base version of the Weather Research Forecast coupled with Chemistry (WRF-Chem) model used here, which includes a widely used SSA source function based on Gong et al. (1997). Quasi-hemispheric simulations for winter 2014 including updated wind speed and sea-surface temperature (SST) SSA emission dependencies and sources of marine sea-salt organics and sea-salt sulfate lead to significantly improved model performance compared to observations at remote Arctic sites, notably for coarse-mode sodium and chloride, which are reduced. The improved model also simulates more realistic contributions of SSAs to inorganic aerosols at different sites, ranging from 20 %–93 % in the observations. Two-thirds of the improved model performance is from the inclusion of the dependence on SSTs. The simulation of nitrate aerosols is also improved due to less heterogeneous uptake of nitric acid on SSAs in the coarse mode and related increases in fine-mode nitrate. This highlights the importance of interactions between natural SSAs and inorganic anthropogenic aerosols that contribute to Arctic haze. Simulation of organic aerosols and the fraction of sea-salt sulfate are also improved compared to observations. However, the model underestimates episodes with elevated observed concentrations of SSA components and sub-micron non-sea-salt sulfate at some Arctic sites, notably at Utqiaġvik. Possible reasons are explored in higher-resolution runs over northern Alaska for periods corresponding to the Utqiaġvik field campaign in January and February 2014. The addition of a local source of sea-salt marine organics, based on the campaign data, increases modelled organic aerosols over northern Alaska. However, comparison with previous available data suggests that local natural sources from open leads, as well as local anthropogenic sources, are underestimated in the model. Missing local anthropogenic sources may also explain the low modelled (sub-micron) non-sea-salt sulfate at Utqiaġvik. The introduction of a higher wind speed dependence for sub-micron SSA emissions, also based on Arctic data, reduces biases in modelled sub-micron SSAs, while sea-ice fractions, including open leads, are shown to be an important factor controlling modelled super-micron, rather than sub-micron, SSAs over the north coast of Alaska. The regional results presented here show that modelled SSAs are more sensitive to wind speed dependence but that realistic modelling of sea-ice distributions is needed for the simulation of local SSAs, including marine organics. This study supports findings from the Utqiaġvik field campaign that open leads are the primary source of fresh and aged SSAs, including marine organic aerosols, during wintertime at Utqiaġvik; these findings do not suggest an influence from blowing snow and frost flowers. To improve model simulations of Arctic wintertime aerosols, new field data on processes that influence wintertime SSA production, in particular for fine-mode aerosols, are needed as is improved understanding about possible local anthropogenic sources.

Elevated methylmercury in Antarctic surface seawater: The role of phytoplankton mass and sea ice

Methylmercury is a neurotoxin that is biomagnified in marine food webs. Its distribution and biogeochemical cycle in Antarctic seas are still poorly understood due to scarce studies. Here, we report the total methylmercury profiles (up to 4000 m) in unfiltered seawater (MeHgT) from the Ross Sea to the Amundsen Sea. We found high MeHgT levels in oxic unfiltered surface seawater (upper 50 m depth) in these regions. It was characterized by an obviously higher maximum concentration level of MeHgT (up to 0.44 pmol/L, at a depth of 3.35 m), which is higher than other open seas (including the Arctic Ocean, the North Pacific Ocean and the equatorial Pacific), and a high MeHgT average concentration in the summer surface water (SSW, 0.16 ± 0.12 pmol/ L). Further analyses suggest that the high phytoplankton mass and sea-ice fraction are important drivers of the high MeHgT level that we observed in the surface water. For the influence of phytoplankton, the model simulation showed that the uptake of MeHg by phytoplankton would not fully explain the high levels of MeHgT, and we speculated that high phytoplankton mass may emit more particulate organic matter as microenvironments that can sustain Hg in-situ methylation by microorganisms. The presence of sea-ice may not only harbor a microbial source of MeHg to surface water but also trigger increased phytoplankton mass, facilitating elevation of MeHg in surface seawater. This study provides insight into the mechanisms that impact the content and distribution of MeHgT in the Southern Ocean.

Effects of Increasing the Category Resolution of the Sea Ice Thickness Distribution in a Coupled Climate Model on Arctic and Antarctic Sea Ice Mean State

AbstractMany modern sea ice models used in global climate models represent the subgrid‐scale heterogeneity in sea ice thickness with an ice thickness distribution (ITD), which improves model realism by representing the significant impact of the high spatial heterogeneity of sea ice thickness on thermodynamic and dynamic processes. Most models default to five thickness categories. However, little has been done to explore the effects of the resolution of this distribution (number of categories) on sea‐ice feedbacks in a coupled model framework and resulting representation of the sea ice mean state. Here, we explore this using sensitivity experiments in CESM2 with the standard 5 ice thickness categories and 15 ice thickness categories. Increasing the resolution of the ITD in a run with preindustrial climate forcing results in substantially thicker Arctic sea ice year‐round. Analyses show that this is a result of the ITD influence on ice strength. With 15 ITD categories, weaker ice occurs for the same average thickness, resulting in a higher fraction of ridged sea ice. In contrast, the higher resolution of thin ice categories results in enhanced heat conduction and bottom growth and leads to only somewhat increased winter Antarctic sea ice volume. The spatial resolution of the ICESat‐2 satellite mission provides a new opportunity to compare model outputs with observations of seasonal evolution of the ITD in the Arctic (ICESat‐2; 2018–2021). Comparisons highlight significant differences from the ITD modeled with both runs over this period, likely pointing to underlying issues contributing to the representation of average thickness.

A New Zonal Wave-3 Index for the Southern Hemisphere

Abstract Zonal wave 3 (ZW3) is an important feature of the Southern Hemisphere extratropical atmospheric circulation and has strong impacts on meridional heat and momentum transport, regional Antarctic sea ice extent, and Southern Hemisphere blocking events. Attempts have been made in the past to define an index that quantifies the variability in the ZW3 pattern; however, existing methods are based on fixed geographical locations and fail to capture certain ZW3 events because of strong variability in phase. In addition, a fixed spatial index poorly characterizes ZW3 in CMIP models, which can exhibit biases in the mean phase of the ZW3 pattern. In this study, we introduce a new way to characterize ZW3 variability by incorporating two indices, one each for magnitude and phase, based on the combination of the first two empirical orthogonal functions (EOFs) of the 500-hPa meridional wind anomalies. We show that the new ZW3 index provides a clear advantage over past indices because it captures a substantially higher proportion of variance (∼40% compared to ∼16%), and it can be used for both reanalysis datasets and coupled climate models regardless of model biases. A composite analysis associated with the new index reveals a strong relationship between the ZW3 defined by our index and sea ice fraction around Antarctica, with significant regional sea ice anomalies during strong ZW3 events with different phases.

The Impacts of Immersion Ice Nucleation Parameterizations on Arctic Mixed-Phase Stratiform Cloud Properties and the Arctic Radiation Budget in GEOS-5

Abstract The influence of four different immersion freezing parameterizations on Arctic clouds and the top-of-the atmosphere (TOA) and surface radiation fluxes is investigated in the fifth version of the National Aeronautics and Space Administration (NASA) Goddard Earth Observing System (GEOS-5) with sea surface temperature, sea ice fraction, and aerosol emissions held fixed. The different parameterizations were derived from a variety of sources, including classical nucleation theory and field and laboratory measurements. Despite the large spread in the ice-nucleating particle (INP) concentrations in the parameterizations, the cloud properties and radiative fluxes had a tendency to form two groups, with the lower INP concentration category producing larger water path and low-level cloud fraction during winter and early spring, whereas the opposite occurred during the summer season. The stability of the lower troposphere was found to strongly correlate with low-cloud fraction and, along with the effect of ice nucleation, ice sedimentation, and melting rates, appears to explain the spring-to-summer reversal pattern in the relative magnitude of the cloud properties between the two categories of simulations. The strong modulation effect of the liquid phase on immersion freezing led to the successful simulation of the characteristic Arctic cloud structure, with a layer rich in supercooled water near cloud top and ice and snow at lower levels. Comparison with satellite retrievals and in situ data suggest that simulations with low INP concentrations more realistically represent Arctic clouds and radiation.

Airborne measurements of directional reflectivity over the Arctic marginal sea ice zone

Abstract. The directional reflection of solar radiation by the Arctic Ocean is mainly shaped by two dominating surface types: sea ice (often snow-covered) and open ocean (ice-free). In the transitional zone between them, the marginal sea ice zone (MIZ), the surface reflection properties are determined by a mixture of the reflectance of both surface types. Retrieval methods applied over the MIZ need to take into account the mixed directional reflectivity; otherwise uncertainties in the retrieved atmospheric parameters over the MIZ may occur. To quantify these uncertainties, respective measurements of reflection properties of the MIZ are needed. Therefore, in this case study, an averaged hemispherical–directional reflectance factor (HDRF) of the inhomogeneous surface (mixture of sea ice and open ocean) in the MIZ is derived using airborne measurements collected with a digital fish-eye camera during a 20 min low-level flight leg in cloud-free conditions. For this purpose, a sea ice mask was developed to separate the reflectivity measurements from sea ice and open ocean and to derive separate HDRFs of the individual surface types. The respective results were compared with simulations and independent measurements available from the literature. It is shown that the open-ocean HDRF in the MIZ differs from homogeneous ocean surfaces due to wave attenuation. Using individual HDRFs of both surface types and the sea ice fraction, the mixed HDRF describing the directional reflectivity of the inhomogeneous surface of the MIZ was retrieved by a linear weighting procedure. Accounting for the wave attenuation, good agreement between the average measured HDRF and the constructed HDRF of the MIZ was found for the presented case study.

Ship-based estimates of momentum transfer coefficient over sea ice and recommendations for its parameterization

Abstract. A major source of uncertainty in both climate projections and seasonal forecasting of sea ice is inadequate representation of surface–atmosphere exchange processes. The observations needed to improve understanding and reduce uncertainty in surface exchange parameterizations are challenging to make and rare. Here we present a large dataset of ship-based measurements of surface momentum exchange (surface drag) in the vicinity of sea ice from the Arctic Clouds in Summer Experiment (ACSE) in July–October 2014, and the Arctic Ocean 2016 experiment (AO2016) in August–September 2016. The combined dataset provides an extensive record of momentum flux over a wide range of surface conditions spanning the late summer melt and early autumn freeze-up periods, and a wide range of atmospheric stabilities. Surface exchange coefficients are estimated from in situ eddy covariance measurements. The local sea-ice fraction is determined via automated processing of imagery from ship-mounted cameras. The surface drag coefficient, CD10n, peaks at local ice fractions of 0.6–0.8, consistent with both recent aircraft-based observations and theory. Two state-of-the-art parameterizations have been tuned to our observations, with both providing excellent fits to the measurements.

Earth system model parameter adjustment using a Green's functions approach

Abstract. We demonstrate the practicality and effectiveness of using a Green's functions estimation approach for adjusting uncertain parameters in an Earth system model (ESM). This estimation approach has previously been applied to an intermediate-complexity climate model and to individual ESM components, e.g., ocean, sea ice, or carbon cycle components. Here, the Green's functions approach is applied to a state-of-the-art ESM that comprises a global atmosphere/land configuration of the Goddard Earth Observing System (GEOS) coupled to an ocean and sea ice configuration of the Massachusetts Institute of Technology general circulation model (MITgcm). Horizontal grid spacing is approximately 110 km for GEOS and 37–110 km for MITgcm. In addition to the reference GEOS-MITgcm simulation, we carried out a series of model sensitivity experiments, in which 20 uncertain parameters are perturbed. These “control” parameters can be used to adjust sea ice, microphysics, turbulence, radiation, and surface schemes in the coupled simulation. We defined eight observational targets: sea ice fraction, net surface shortwave radiation, downward longwave radiation, near-surface temperature, sea surface temperature, sea surface salinity, and ocean temperature and salinity at 300 m. We applied the Green's functions approach to optimize the values of the 20 control parameters so as to minimize a weighted least-squares distance between the model and the eight observational targets. The new experiment with the optimized parameters resulted in a total cost reduction of 9 % relative to a simulation that had already been adjusted using other methods. The optimized experiment attained a balanced cost reduction over most of the observational targets. We also report on results from a set of sensitivity experiments that are not used in the final optimized simulation but helped explore options and guided the optimization process. These experiments include an assessment of sensitivity to the number of control parameters and to the selection of observational targets and weights in the cost function. Based on these sensitivity experiments, we selected a specific definition for the cost function. The sensitivity experiments also revealed a decreasing overall cost as the number of control variables was increased. In summary, we recommend using the Green's functions estimation approach as an additional fine-tuning step in the model development process. The method is not a replacement for modelers' experience in choosing and adjusting sensitive model parameters. Instead, it is an additional practical and effective tool for carrying out final adjustments of uncertain ESM parameters.

The Impact of Lead Patterns on Mean Profiles of Wind, Temperature, and Turbulent Fluxes in the Atmospheric Boundary Layer over Sea Ice

In the polar regions, the atmospheric boundary layer (ABL) characteristics are strongly influenced by convection over leads, which are elongated channels in the sea ice covered ocean. The effects on the ABL depend on meteorological forcing and lead geometry. In non-convection-resolving models, in which several leads of potentially different characteristics might be present in a single grid cell, such surface characteristics and the corresponding ABL patterns are not resolved. Our main goal is to investigate potential implications for such models when these subgrid-scale patterns are not considered appropriately. We performed non-eddy-resolving microscale simulations over five different domains with leads of different widths separated by 100% sea ice. We also performed coarser-resolved simulations over a domain representing a few grid cells of a regional climate model, wherein leads were not resolved but accounted for via a fractional sea ice cover of 91% in each cell. Domain size and mean sea ice concentration were the same in all simulations. Differences in the domain-averaged ABL profiles and patterns of wind, temperature, and turbulent fluxes indicate a strong impact of both the leads and their geometry. Additional evaluations of different turbulence parameterizations show large effects by both gradient-independent heat transport and vertical entrainment.

Eddies in the Marginal Ice Zone of Fram Strait and Svalbard from Spaceborne SAR Observations in Winter

Here we investigate the intensity of eddy generation and their properties in the marginal ice zone (MIZ) regions of Fram Strait and around Svalbard using spaceborne synthetic aperture radar (SAR) data from Envisat ASAR and Sentinel-1 in winter 2007 and 2018. Analysis of 2039 SAR images allowed identifying 4619 eddy signatures. The number of eddies detected per image per kilometer of MIZ length is similar for both years. Submesoscale and small mesoscale eddies dominate with cyclones detected twice more frequently than anticyclones. Eddy diameters range from 1 to 68 km with mean values of 6 km and 12 km over shallow and deep water, respectively. Mean eddy size grows with increasing ice concentration in the MIZ, yet most eddies are detected at the ice edge and where the ice concentration is below 20%. The fraction of sea ice trapped in cyclones (53%) is slightly higher than that in anticyclones (48%). The amount of sea ice trapped by a single ‘mean’ eddy is about 40 km2, while the average horizontal retreat of the ice edge due to eddy-induced ice melt is about 0.2–0.5 km·d–1 ± 0.02 km·d–1. Relation of eddy occurrence to background currents and winds is also discussed.

Impacts of climate change on methylmercury formation and bioaccumulation in the 21st century ocean

Impacts of climate change on methylmercury formation and bioaccumulation in the 21st century ocean

WAVERYS: a CMEMS global wave reanalysis during the altimetry period

As part of the Copernicus Marine Service, WAVERYS is the multi-year wave reanalysis that provides global wave data with a fine grid resolution of 1/5°. This wave reanalysis covers the period 1993–2019 and disseminates 3-h integrated wave parameters describing the sea state at the ocean surface. The wave model used is the version 4 of the model MFWAM, which is driven by sea ice fraction and wind provided by the atmospheric reanalysis ERA5. The WAVERYS includes the assimilation of altimeter wave data and directional wave spectra provided by Sentinel-1. The wave reanalysis includes also wave-current interactions by using 3-h surface current forcing provided by the ocean reanalysis GLORYS. This paper highlights the assessment of wave parameters provided by the WAVERYS. The validation has been performed with independent altimeter significant wave heights and buoy wave data. The results show the good accuracy of scatter index of SWH (significant wave height) which is 8.7% in comparison with HY-2A altimeter. Moreover, we point out that scatter index of SWH from the WAVERYS is improved by about 9% with respect to the ERA5 wave dataset. We also indicate the good accuracy of swell propagation thanks to the assimilation of directional wave spectra. An analysis has been conducted for wave-current interactions and also discussions about extreme values and trend of time series are suggested.

Prediction of Northern Hemisphere Regional Sea Ice Extent and Snow Depth Using Stratospheric Ozone Information

AbstractThe forecast potential of springtime ozone on April surface temperatures at particular locations in the Northern Hemisphere has been previously reported. Evidence suggests that early springtime Arctic stratospheric ozone acts as a proxy for extreme events in the winter polar vortex. Here, using a state‐of‐the‐art chemistry‐climate model, reanalysis and observations, we extend the forecast potential of ozone on surface temperatures to aspects of the Northern Hemisphere cryosphere. Sea ice fraction and sea ice extent differences between years of March high and low Arctic stratospheric ozone extremes show excellent agreement between an ensemble of chemistry‐climate model simulations and observations, with differences occurring not just in April but extending through to the following winter season in some locations. Large snow depth differences are also obtained in regional locations in Russia and along the southeast coast of Alaska. These differences remain elevated until early summer, when snow cover diminishes. Using a conditional empirical model in a leave‐three‐out cross validation method, March total column ozone is able to accurately predict the sign of the observed sea ice extent and snow depth anomalies over 70% of the time during an ozone extreme year, especially in the region of the Bering strait and the Greenland Sea, which could be useful for shipping routes and for testing climate models.

Consistent Comparison of Remotely Sensed Sea Ice Concentration Products with ERA-Interim Reanalysis Data in Polar Regions

Sea ice concentration (SIC) plays a significant role in climate change research and ship’s navigation in polar regions. Satellite-based SIC products have become increasingly abundant in recent years; however, the uncertainty of these products still exists and needs to be further investigated. To comprehensively evaluate the consistency of the SIC derived from different SIC algorithms in long time series and the whole polar regions, we compared four passive microwave (PM) satellite SIC products with the ERA-Interim sea ice fraction dataset during the period of 2015–2018. The PM SIC products include the SSMIS/ASI, AMSR2/BT, the Chinese FY3B/NT2, and FY3C/NT2. The results show that the remotely sensed SIC products derived from different SIC algorithms are generally in good consistency. The spatial and temporal distribution of discrepancy among satellite SIC products for both Arctic and Antarctic regions are also observed. The most noticeable difference for all the four SIC products mostly occurs in summer and at the marginal ice zone, indicating that large uncertainties exist in satellite SIC products in such period and areas. The SSMIS/ASI and AMSR2/BT show relatively better consistency with ERA-Interim in the Arctic and Antarctic, respectively, but they exhibit opposite bias (dry/wet) relative to the ERA-Interim data. The sea ice extent (SIE) and sea ice area (SIA) derived from PM and ERA-Interim SIC were also compared. It is found that the difference of PM SIE and SIA varies seasonally, which is in line with that of PM SIC, and the discrepancy between PM and ERA-Interim data is larger in Arctic than in Antarctic. We also noticed that different algorithms have different performances in different regions and periods; therefore, the hybrid of multiple algorithms is a promising way to improve the accuracy of SIC retrievals. It is expected that our findings can contribute to improving the satellite SIC algorithms and thus promote the application of these useful products in global climate change studies.

Observations for Model Intercomparison Project (Obs4MIPs): status for CMIP6

Abstract. The Observations for Model Intercomparison Project (Obs4MIPs) was initiated in 2010 to facilitate the use of observations in climate model evaluation and research, with a particular target being the Coupled Model Intercomparison Project (CMIP), a major initiative of the World Climate Research Programme (WCRP). To this end, Obs4MIPs (1) targets observed variables that can be compared to CMIP model variables; (2) utilizes dataset formatting specifications and metadata requirements closely aligned with CMIP model output; (3) provides brief technical documentation for each dataset, designed for nonexperts and tailored towards relevance for model evaluation, including information on uncertainty, dataset merits, and limitations; and (4) disseminates the data through the Earth System Grid Federation (ESGF) platforms, making the observations searchable and accessible via the same portals as the model output. Taken together, these characteristics of the organization and structure of obs4MIPs should entice a more diverse community of researchers to engage in the comparison of model output with observations and to contribute to a more comprehensive evaluation of the climate models. At present, the number of obs4MIPs datasets has grown to about 80; many are undergoing updates, with another 20 or so in preparation, and more than 100 are proposed and under consideration. A partial list of current global satellite-based datasets includes humidity and temperature profiles; a wide range of cloud and aerosol observations; ocean surface wind, temperature, height, and sea ice fraction; surface and top-of-atmosphere longwave and shortwave radiation; and ozone (O3), methane (CH4), and carbon dioxide (CO2) products. A partial list of proposed products expected to be useful in analyzing CMIP6 results includes the following: alternative products for the above quantities, additional products for ocean surface flux and chlorophyll products, a number of vegetation products (e.g., FAPAR, LAI, burned area fraction), ice sheet mass and height, carbon monoxide (CO), and nitrogen dioxide (NO2). While most existing obs4MIPs datasets consist of monthly-mean gridded data over the global domain, products with higher time resolution (e.g., daily) and/or regional products are now receiving more attention. Along with an increasing number of datasets, obs4MIPs has implemented a number of capability upgrades including (1) an updated obs4MIPs data specifications document that provides additional search facets and generally improves congruence with CMIP6 specifications for model datasets, (2) a set of six easily understood indicators that help guide users as to a dataset's maturity and suitability for application, and (3) an option to supply supplemental information about a dataset beyond what can be found in the standard metadata. With the maturation of the obs4MIPs framework, the dataset inclusion process, and the dataset formatting guidelines and resources, the scope of the observations being considered is expected to grow to include gridded in situ datasets as well as datasets with a regional focus, and the ultimate intent is to judiciously expand this scope to any observation dataset that has applicability for evaluation of the types of Earth system models used in CMIP.