Sea Ice Concentration Fields Research Articles

The Influence of Time-Varying Sea Ice Concentration on Antarctic and Southern Ocean Numerical Weather Prediction

Abstract Although operational weather forecasting centers are increasingly using global coupled atmosphere–ocean–ice models to replace atmosphere-only models for short- and medium-range (10 day) weather forecasting, the influence of sea ice on such forecasting has yet to be fully quantified, especially in the Southern Ocean. To address this gap, a polar-specific version of the Weather Research and Forecasting Model is implemented with a circumpolar Antarctic domain to investigate the impact of daily updates of sea ice concentration on short- and medium- range weather forecasting. A statistically significant improvement in near-surface atmospheric temperature and humidity is shown from +24 to +192 h when updating the daily sea ice concentration in the model. The forecast skill improvements for 2-m temperature and dewpoint temperature are enhanced from June to September, which is the period of late sea ice advance. Regionally, model improvement is shown to occur in most sea ice regions, although the improvement is strongest in the Ross Sea and Weddell Sea sectors. The surface heat budget also shows remarkable improvement in outgoing radiative heat fluxes and both sensible and latent heat fluxes. This idealized research demonstrates the nonnegligible effect of including more accurate time-varying sea ice concentration in numerical weather forecasting. Significance Statement The purpose of this study is to understand how a more realistic Antarctic sea ice field may influence the skill of short- and medium-range weather forecasts. Many operational atmospheric numerical weather prediction (NWP) models use a static forecast field through the time frame of the model’s forecast—often 3–10 days. In this study, we updated the sea ice concentration field daily and compared the forecast outcomes with those from model runs using a static sea ice concentration field. We found the forecast skill of near-surface temperature and humidity show the most significant improvements in our idealized experiments. This indicates the importance of incorporating improved dynamic sea ice representation in Antarctic short- to medium-range operational weather forecasting.

Challenges in the Description of Sea Ice for a Kilometer-Scale Weather Forecasting System

Abstract Simulation of atmosphere–ocean–ice interactions in coupled Earth modeling systems with kilometer-scale resolution is a new challenge in operational numerical weather prediction. This study presents an assessment of sensitivity experiments performed with different sea ice products in a convective-scale weather forecasting system for the European Arctic. On kilometer-scale resolution sea ice products are challenged by the large footprint of passive microwave satellite observations and issues with spurious sea ice detection of the higher-resolution retrievals based on synthetic aperture radar instruments. We perform sensitivity experiments with sea ice concentration fields of 1) the global ECMWF-IFS forecast system, 2) a newly developed multisensor product processed through a coupled sea ice–ocean forecasting system, and 3) the AMSR2 product based on passive microwave observations. There are significant differences between the products on O(100) km scales in the northern Barents Sea and along the Marginal Ice Zone north of the Svalbard archipelago and toward the Fram Strait. These differences have a direct impact on the modeled surface skin temperature over ocean and sea ice, the turbulent heat flux, and 2-m air temperature (T2M). An assessment of Arctic weather stations shows a significant improvement of forecasted T2M in the north and east of Svalbard when using the new multisensor product; however, south of Svalbard this product has a negative impact. The different sea ice products are resulting in changes of the surface turbulent heat flux of up to 400 W m−2, which in turn results in T2M variations of up to 5°C. Over a 2-day forecast lead time this can lead to uncertainties in weather forecasts of about 1°C even hundreds of kilometers away from the sea ice. Significance Statement Weather forecasting in polar regions requires an accurate description of sea ice properties due to the very important atmosphere–ocean–ice interactions. With the increasing resolution of weather forecasting systems, there is also a need to advance the resolution of the sea ice characteristics in the models. This is, however, not straightforward due to various issues in the sea ice satellite products. This study explores new products and approaches to integrate high-resolution sea ice in a weather prediction system. We find that the model is sensitive to the choice of the sea ice product and that it is still challenging to provide an accurate sea ice field on a kilometer-scale resolution.

Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes

Abstract Reliable predictions of the Arctic sea ice cover are becoming of paramount importance for Arctic communities and industry stakeholders. In this study pan-Arctic and regional September mean sea ice extents are forecast with lead times of up to 3 months using a complex network statistical approach. This method exploits relationships within climate time series data by constructing regions of spatiotemporal homogeneity (i.e., nodes), and subsequently deriving teleconnection links between them. Here the nodes and links of the networks are generated from monthly mean sea ice concentration fields in June, July, and August; hence, individual networks are constructed for each respective month. Network information is then utilized within a linear Gaussian process regression forecast model, a Bayesian inference technique, in order to generate predictions of sea ice extent. Pan-Arctic forecasts capture a significant amount of the variability in the satellite observations of September sea ice extent, with detrended predictive skills of 0.53, 0.62, and 0.81 at 3-, 2-, and 1-month lead times, respectively. Regional forecasts are also performed for nine Arctic regions. On average, the highest predictive skill is achieved in the Canadian Archipelago, Beaufort, Chukchi, East Siberian, Laptev, and Kara Seas, although the ability to accurately predict many of these regions appears to be changing over time.

Climatic changes of thermal condition in the Kara sea at last 40 years

The paper discusses air (Ta) and sea surface temperature (SST) year-to-year variability due to warming of the Kara Sea, using the data from regular observations at the meteorological stations Roshydromet (GMS) in 1978–2017, NOAA optimum interpolation and reanalysis data. We use the methods of cluster, correlation analysis and Empirical Orthogonal Functions (EOF). We investigate possible cause and effect relationships of these changes with the variations of the wind field components, climatic indices and the sea ice concentration field. The cluster analysis of the three main EOF components has allowed us to identify four areas on the basis of the nature of changes of the water temperature anomalies field. The climatic changes in these areas, in the coastal and island zones of the Kara Sea have manifested themselves in the steady increase of the annual air temperature at GMS from 0,47–0,77 °C/10 years on the southwest coast to 1,33–1,49 °C/10 years in the north of the sea. This is equivalent to warming from 1,9 to 6,0 °C in the last 40 years. For the open sea the value of the Ta trend is about 1,22 °C/10 years, which corresponds to an increase in the average Ta by 4,9 °C in the last 40 years. This value is approximately 3 times greater than that for all the Northern hemisphere for the same period.Annualy, the maximal trend was observed in November and April mainly and exceeded 2–3 °C/10 years at some of the stations. We identify anomalously warm (2016 and 2012) and anomalously cold (1978, 1979, 1992 and 1998) years: the warmest year was 2012, the coldest — 1979. Positive SST trends were observed over all the sea area during the warm period of year (to 1 °C/10 years). SST increased to 2,4 °C, which is approximately 1,5 times greater than the corresponding SST values for the Northern hemisphere. The maximum SST trend (0,4 °C/10 years) was observed in the northwest and southwest parts of the sea. From June to August the trends of SST exceed the annual ones 1,5–2 times. Interannual SST and Ta variations are characterized by close correlation links. Until approximately 1998–2004 the warming was rather insignificant, and after that the growth rate of Ta and SST increased many fold. Apparently it indicates changes in the mode and the large-scale atmospheric circulation in the early 2000s. We also observed a trend of strengthening of the southern wind during the cold period of the year and the northern one — in the warm period (0,5–0,6 m/s in 40 years). It is shown that there is a close correlation between the Ta increase and the changes in the meridional component of the wind speed during the cold period of the year for all the sea areas. For the warm period it is statistically insignificant both for Ta and SST. For the cold season we observed a contribution of the large-scale mode of atmospheric circulation into the variability of V component of the wind speed. The conribution was expressed through the indeces NAO, SCAND, Pol/EUR, AZOR, ISL and the differences of ISLSIB. For the warm season this contribution is expressed through the NAO, SCAND and AO only. For the warm period we showed statistically significant correlation between the increase in SST, Ta and the processes parametrized by the AMO, EA/WR and AZOR indeces. For the cold period the indeces are AMO, Pol/Eur, SIB and ISL SIB. The interannual variations of the sea ice concentration field are characterized by close correlation with Ta changes both in the annual cycle and during the periods of ice cover formation and evolution (R = –0,7... –0,9). For these periods we showed statistically significant relationships between the first EOF mode fluctuations and two climatic indeces — AMO (R = 0,5) and Pol/Eur (R = 0,4). The relationships between the temporary variability of the sea ice concentration and the wind field characteristics are weaker and statistically significant only for the meridional component of the wind speed (R = –0,4).

Validation metrics for ice edge position forecasts

Abstract. The ice edge is a simple quantity in the form of a line that can be derived from a spatially varying sea ice concentration field. Due to its long history and relevance for operations in the Arctic, the position of the ice edge should be an essential element in any system that is designed to monitor or provide forecasts for the physical state of the Arctic Ocean and adjacent ocean regions. Users of monitoring and forecast products for sea ice must be provided with complementary information on the expected accuracy of the data or model results. Such information is traditionally available as a set of metrics that provide an assessment of the information quality. In this study we provide a survey of metrics that are presently included in the product quality assessment of the Copernicus Marine Environment Monitoring Service (CMEMS) Arctic Marine Forecasting Center sea ice edge position forecast. We show that when ice edge results from different products are compared, mismatching results for polynya and local freezing at the coasts of continents and archipelagos have a large impact on the quality assessment. Such situations, which occur regularly in the products we examine, have not been properly acknowledged when sets of metrics for the quality of ice edge position results are constructed. We examine the quality of ice edge forecasts using a total of 15 metrics for the ice edge position. These metrics are analysed in synthetic examples, as well as in selected cases of actual forecasts, and finally for a full year of weekly forecast bulletins. Using necessity and simplicity of information as a guideline, we recommend using a set of four metrics that sheds light on the various aspects of product quality that we consider. Moreover, any user is expected to be interested in a limited part of the geographical domain, so metrics derived as domain-wide integrated quantities may be of limited value. Consequently, we recommend that metrics also be made available for an appropriate set of sub-domains. Furthermore, we find that the metrics decorrelation timescales are much longer than the present forecast range. Hence, our final recommendation is to include depictions of gridded mismatching ice edge positions using maps for the integrated ice edge error.

Summer Enhancement of Arctic Sea Ice Volume Anomalies in the September-Ice Zone

Because of its persistence on seasonal time scales, Arctic sea ice thickness (SIT) is a potential source of predictability for summer sea ice extent (SIE). New satellite observations of SIT represent an opportunity to harness this potential predictability via improved thickness initialization in seasonal forecast systems. In this work, the evolution of Arctic sea ice volume anomalies is studied using a 700-yr control integration and a suite of initialized ensemble forecasts from a fully coupled global climate model. This analysis is focused on the September sea ice zone, as this is the region where thickness anomalies have the potential to impact the SIE minimum. The primary finding of this paper is that, in addition to a general decay with time, sea ice volume anomalies display a summer enhancement, in which anomalies tend to grow between the months of May and July. This summer enhancement is relatively symmetric for positive and negative volume anomalies and peaks in July regardless of the initial month. Analysis of the surface energy budget reveals that the summer volume anomaly enhancement is driven by a positive feedback between the SIT state and the surface albedo. The SIT state affects surface albedo through changes in the sea ice concentration field, melt-onset date, snow coverage, and ice thickness distribution, yielding an anomaly in the total absorbed shortwave radiation between May and August, which enhances the existing SIT anomaly. This phenomenon highlights the crucial importance of accurate SIT initialization and representation of ice–albedo feedback processes in seasonal forecast systems.

Analiz glavnykh komponent poley kontsentratsii morskogo l'da v Barentsevom more

Analiz glavnykh komponent poley kontsentratsii morskogo l'da v Barentsevom more

Analysis of Principal Components of the Sea Ice Concentration Fields in the Barents Sea

Analysis of Principal Components of the Sea Ice Concentration Fields in the Barents Sea

Iceland's Great Frost Winter of 1917/1918 and its representation in reanalyses of the twentieth century

Climate variability during the twentieth century is a subject of considerable interest as it represents a way to place the changes that we are currently experiencing into a long‐term context. The characterization of climatic extremes and their representation in models is of particular importance as these events have a significant impact on communities and ecosystems. Much of the focus on these extremes has been on events that occurred after the establishment of the upper‐air observing network in the 1950s. The recent availability of reanalyses that extend throughout the twentieth century now allow extreme events that occurred before this time to be more fully investigated. The winter of 1917/1918 is referred to as the Great Frost Winter in Iceland. It was the coldest winter in the region during the twentieth century. It was remarkable for the presence of sea ice in Reykjavik Harbour as well as for the unusually large number of polar bear sightings in northern Iceland. Here we use observations as well as two reanalyses that span the twentieth century to document this event. We show that throughout much of the region, January 1918 was the coldest winter month on record. The North Atlantic Oscillation (NAO) attained one of its most negative values during January 1918 and the westward shift in its northern centre of action allowed cold Arctic and Greenlandic air to penetrate south towards Iceland. We also show that the two reanalyses diverged in their ability to represent the temperature anomalies during the event. Differences in the sea‐surface temperature (SST) and sea ice concentration fields used to force the underlying models contributed to this divergence. These results stress the important role that boundary conditions play in determining the fidelity with which reanalyses can represent extreme climate events in data‐sparse regions.

Data-driven prediction strategies for low-frequency patterns of North Pacific climate variability

The North Pacific exhibits patterns of low-frequency variability on the intra-annual to decadal time scales, which manifest themselves in both model data and the observational record, and prediction of such low-frequency modes of variability is of great interest to the community. While parametric models, such as stationary and non-stationary autoregressive models, possibly including external factors, may perform well in a data-fitting setting, they may perform poorly in a prediction setting. Ensemble analog forecasting, which relies on the historical record to provide estimates of the future based on past trajectories of those states similar to the initial state of interest, provides a promising, nonparametric approach to forecasting that makes no assumptions on the underlying dynamics or its statistics. We apply such forecasting to low-frequency modes of variability for the North Pacific sea surface temperature and sea ice concentration fields extracted through Nonlinear Laplacian Spectral Analysis. We find such methods may outperform parametric methods and simple persistence with increased predictive skill.

Filling the Polar Data Gap in Sea Ice Concentration Fields Using Partial Differential Equations

The “polar data gap” is a region around the North Pole where satellite orbit inclination and instrument swath for SMMR and SSM/I-SSMIS satellites preclude retrieval of sea ice concentrations. Data providers make the irregularly shaped data gap round by centering a circular “pole hole mask” over the North Pole. The area within the pole hole mask has conventionally been assumed to be ice-covered for the purpose of sea ice extent calculations, but recent conditions around the perimeter of the mask indicate that this assumption may already be invalid. Here we propose an objective, partial differential equation based model for estimating sea ice concentrations within the area of the pole hole mask. In particular, the sea ice concentration field is assumed to satisfy Laplace’s equation with boundary conditions determined by observed sea ice concentrations on the perimeter of the gap region. This type of idealization in the concentration field has already proved to be quite useful in establishing an objective method for measuring the “width” of the marginal ice zone—a highly irregular, annular-shaped region of the ice pack that interacts with the ocean, and typically surrounds the inner core of most densely packed sea ice. Realistic spatial heterogeneity in the idealized concentration field is achieved by adding a spatially autocorrelated stochastic field with temporally varying standard deviation derived from the variability of observations around the mask. To test the model, we examined composite annual cycles of observation-model agreement for three circular regions adjacent to the pole hole mask. The composite annual cycle of observation-model correlation ranged from approximately 0.6 to 0.7, and sea ice concentration mean absolute deviations were of order 10 − 2 or smaller. The model thus provides a computationally simple approach to solving the increasingly important problem of how to fill the polar data gap. Moreover, this approach based on solving an elliptic partial differential equation with given boundary conditions has sufficient generality to also provide more sophisticated models which could potentially be more accurate than the Laplace equation version. Such generalizations and potential validation opportunities are discussed.

Skill metrics for evaluation and comparison of sea ice models

AbstractFive quantitative methodologies (metrics) that may be used to assess the skill of sea ice models against a control field are analyzed. The methodologies are Absolute Deviation, Root‐Mean‐Square Deviation, Mean Displacement, Hausdorff Distance, and Modified Hausdorff Distance. The methodologies are employed to quantify similarity between spatial distribution of the simulated and control scalar fields providing measures of model performance. To analyze their response to dissimilarities in two‐dimensional fields (contours), the metrics undergo sensitivity tests (scale, rotation, translation, and noise). Furthermore, in order to assess their ability to quantify resemblance of three‐dimensional fields, the metrics are subjected to sensitivity tests where tested fields have continuous random spatial patterns inside the contours. The Modified Hausdorff Distance approach demonstrates the best response to tested differences, with the other methods limited by weak responses to scale and translation. Both Hausdorff Distance and Modified Hausdorff Distance metrics are robust to noise, as opposed to the other methods. The metrics are then employed in realistic cases that validate sea ice concentration fields from numerical models and sea ice mean outlook against control data and observations. The Modified Hausdorff Distance method again exhibits high skill in quantifying similarity between both two‐dimensional (ice contour) and three‐dimensional (ice concentration) sea ice fields. The study demonstrates that the Modified Hausdorff Distance is a mathematically tractable and efficient method for model skill assessment and comparison providing effective and objective evaluation of both two‐dimensional and three‐dimensional sea ice characteristics across data sets.

Implementing a variational data assimilation system in an operational 1/4 degree global ocean model

AbstractThis article describes the implementation of an incremental first guess at an appropriate time three‐dimensional variational (3DVAR) data assimilation scheme, NEMOVAR, in the Met Office's operational 1/4 degree global ocean model. NEMOVAR assimilates observations of sea‐surface temperature (SST), sea‐surface height (SSH), in situ temperature and salinity profiles and sea ice concentration. The Met Office is the first centre to implement NEMOVAR at 1/4 degree and the required developments are discussed, with particular focus on the specification of the background‐error covariances.Background‐error correlations in NEMOVAR are modelled using a diffusion operator. The horizontal background‐error correlations for temperature, salinity and sea ice concentration are parametrized using the Rossby radius, which produces relatively short correlation length‐scales at mid to high latitudes, while a flow‐dependent mixed‐layer depth parametrization is used to define the vertical length‐scales for the 3D variables.Results from a one‐year reanalysis with NEMOVAR are presented and compared with the preceding operational data assimilation scheme at the Met Office. NEMOVAR is shown to provide significant improvements to SST, SSH and sea ice concentration fields, with the largest improvements seen in regions of high variability such as eddy shedding and frontal regions and the marginal ice zone. This improvement is associated with shorter correlation length‐scales in the extratropics and an improved fit to observations in NEMOVAR. Some degradation to subsurface temperature and salinity fields where data are sparse is identified and this will be the focus of future improvements to the system.

Atmospheric influence on Arctic marginal ice zone position and width in the Atlantic sector, February–April 1979–2010

Arctic marginal ice zone (MIZ) widths in the Atlantic sector were measured during the months of maximum sea ice extent (February–April) for years 1979–2010 using a novel method based on objective curves through idealized sea ice concentration fields that satisfied Laplace’s equation. Over the record, the Labrador Sea MIZ (MIZL) had an average width of 122 km and narrowed by 28 % while moving 254 km poleward, the Greenland Sea MIZ (MIZG) had an average width of 98 km and narrowed by 43 % while moving 158 km west toward the Greenland coast, and the Barents Sea MIZ (MIZB) had an average width of 136 km and moved 259 km east toward the Eurasian coast without a trend in width. Trends in MIZ position and width were consistent with a warming Arctic and decreasing sea ice concentrations over the record. Beyond the trends, NAO-like atmospheric patterns influenced interannual variability in MIZ position and width: MIZL widened and moved southeast under anomalously strong northerly flow conducive to advection of sea ice into the Labrador Sea, MIZG widened and moved northeast under anomalously weak northerly flow conducive to diminishing the westward component of sea ice drift, and MIZB widened and moved poleward at the expense of pack ice under anomalously strong southwesterly flow conducive to enhancing oceanic heat flux into the Barents Sea. In addition, meridional flow anomalies associated with the NAO per se moved MIZB east and west by modulating sea ice concentration over the Barents Sea.

Daily, Global, High-Resolution SST and Sea Ice Reanalysis for 1985–2007 Using the OSTIA System

Abstract A sea surface temperature (SST) and sea ice reanalysis has been produced at the Met Office based on the Operational SST and Sea Ice Analysis (OSTIA) system. The OSTIA reanalysis produces daily, high-resolution, global foundation SST and sea ice concentration fields from 1 January 1985 to 31 December 2007. The SST reanalysis uses reprocessed satellite and in situ observations that are assimilated using a multiscale optimal-interpolation-type scheme similar to that used in the near-real-time OSTIA system. Validation of the SST analysis using assimilated in situ observation-minus-background statistics shows that the accuracy of the analysis increases throughout the reanalysis period; the global root-mean-square difference is approximately 0.50 K by 2007. This approach to validation is supported in the recent period by results from comparisons with independent near-surface Argo data against which a global standard deviation error of 0.55 K was calculated. Assessment of the OSTIA reanalysis at high latitudes demonstrates that the SST and sea ice fields are more consistent with one another in the Southern Hemisphere than in the Northern Hemisphere. Comparison of the sea ice extents to those in a similar reanalysis shows OSTIA to have larger extents in the Northern Hemisphere, and the Southern Hemisphere extents are similar. The OSTIA reanalysis SSTs are shown to be regionally comparable with similar reanalyses, with the largest differences occurring at high latitudes in the summer hemisphere. Differences are observed around the ice edge and in regions with high SST gradients. The OSTIA reanalysis provides a valuable high-resolution addition to the satellite period SST data record that makes use of the (Advanced) Along-Track Scanning Radiometer [(A)ATSR] multimission data.

Dependence of NAO variability on coupling with sea ice

The variance of the North Atlantic Oscillation index (denoted N) is shown to depend on its coupling with area-averaged sea ice concentration anomalies in and around the Barents Sea (index denoted B). The observed form of this coupling is a negative feedback whereby positive N tends to produce negative B, which in turn forces negative N. The effects of this feedback in the system are examined by modifying the feedback in two modeling frameworks: a statistical vector autoregressive model (F VAR) and an atmospheric global climate model (F CAM) customized so that sea ice anomalies on the lower boundary are stochastic with adjustable sensitivity to the model’s evolving N. Experiments show that the variance of N decreases nearly linearly with the sensitivity of B to N, where the sensitivity is a measure of the negative feedback strength. Given that the sea ice concentration field has anomalies, the variance of N goes down as these anomalies become more sensitive to N. If the sea ice concentration anomalies are entirely absent, the variance of N is even smaller than the experiment with the most sensitive anomalies. Quantifying how the variance of N depends on the presence and sensitivity of sea ice anomalies to N has implications for the simulation of N in global climate models. In the physical system, projected changes in sea ice thickness or extent could alter the sensitivity of B to N, impacting the within-season variability and hence predictability of N.