Sea-ice thickness on the northern Canadian polar shelf: A second look after 40 years

This paper presents a systematic record of multi-year sea-ice thickness on the northern Canadian polar shelf, acquired during the winter of 2009–10. The data were acquired by submerged sonar positioned within Penny Strait where they measured floes drifting south from the notional “last ice area”. Ice was moving over the site until 10 December and fast thereafter. Old ice comprised about half of the 1669-km long survey. The average old-ice thickness within 25-km segments of the survey track was 3–4 m; maximum keels were 12–16 m deep. Floes with high average draft were of two types, one with interspersed low draft intervals and one without. The presence or absence of thin patches apparently distinguished aggregate floes comprised of sub-units of various ages and deformation states from units of more homogeneous age and deformation state. The former were larger and of somewhat lower mean thickness (1–5 km; 3.5–4.5 m) than the latter (400–600 m; 6.5–14 m). Calculated ice accretion onto the multi-year ice measured in autumn 2009 was used to seasonally adjust the observations to a date in late winter, when prior data are available. The adjusted mean thickness for all 25-km segments with 4 tenths or more old ice was 3.6 m (sample deviation of 0.4 m), a value indistinguishable within sampling error from values measured in the same area during the 1970s. The recently measured ice-draft distributions were also very similar to those from the 1970s.

Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM)

The feasibility of assimilating sea ice thickness (SIT) observations derived from CryoSat-2 along-track measurements of sea ice freeboard is successfully demonstrated using a 3D-Var assimilation scheme, NEMOVAR, within the Met Office's global, coupled ocean–sea-ice model, Forecast Ocean Assimilation Model (FOAM). The CryoSat-2 Arctic freeboard measurements are produced by the Centre for Polar Observation and Modelling (CPOM) and are converted to SIT within FOAM using modelled snow depth. This is the first time along-track observations of SIT have been used in this way, with other centres assimilating gridded and temporally averaged observations. The assimilation leads to improvements in the SIT analysis and forecast fields generated by FOAM, particularly in the Canadian Arctic. Arctic-wide observation-minus-background assimilation statistics for 2015–2017 show improvements of 0.75 m mean difference and 0.41 m root-mean-square difference (RMSD) in the freeze-up period and 0.46 m mean difference and 0.33 m RMSD in the ice break-up period. Validation of the SIT analysis against independent springtime in situ SIT observations from NASA Operation IceBridge (OIB) shows improvement in the SIT analysis of 0.61 m mean difference (0.42 m RMSD) compared to a control without SIT assimilation. Similar improvements are seen in the FOAM 5 d SIT forecast. Validation of the SIT assimilation with independent Beaufort Gyre Exploration Project (BGEP) sea ice draft observations does not show an improvement, since the assimilated CryoSat-2 observations compare similarly to the model without assimilation in this region. Comparison with airborne electromagnetic induction (Air-EM) combined measurements of SIT and snow depth shows poorer results for the assimilation compared to the control, despite covering similar locations to the OIB and BGEP datasets. This may be evidence of sampling uncertainty in the matchups with the Air-EM validation dataset, owing to the limited number of observations available over the time period of interest. This may also be evidence of noise in the SIT analysis or uncertainties in the modelled snow depth, in the assimilated SIT observations, or in the data used for validation. The SIT analysis could be improved by upgrading the observation uncertainties used in the assimilation. Despite the lack of CryoSat-2 SIT observations available for assimilation over the summer due to the detrimental effect of melt ponds on retrievals, it is shown that the model is able to retain improvements to the SIT field throughout the summer months due to prior, wintertime SIT assimilation. This also results in regional improvements to the July modelled sea ice concentration (SIC) of 5 % RMSD in the European sector, due to slower melt of the thicker sea ice.

Perspectives on future sea ice and navigability in the Arctic

The retreat of sea ice has been found to be very significant in the Arctic under global warming. It is projected to continue and will have great impacts on navigation. Perspectives on the changes in sea ice and navigability are crucial to the circulation pattern and future of the Arctic. In this investigation, the decadal changes in sea ice parameters were evaluated by the multi-model from the Coupled Model Inter-comparison Project Phase 6, and Arctic navigability was assessed under two shared socioeconomic pathways (SSPs) and two vessel classes with the Arctic transportation accessibility model. The sea ice extent shows a high possibility of decreasing along SSP5-8.5 under current emissions and climate change. The decadal rate of decreasing sea ice extent will increase in March but decrease in September until 2060, when the oldest ice will have completely disappeared and the sea ice will reach an irreversible tipping point. Sea ice thickness is expected to decrease and transit in certain parts, declining by −0.22 m per decade after September 2060. Both the sea ice concentration and volume will thoroughly decline at decreasing decadal rates, with a greater decrease in volume in March than in September. Open water ships will be able to cross the Northern Sea Route and Northwest Passage between August and October during the period from 2045 to 2055, with a maximum navigable percentage in September. The time for Polar Class 6 (PC6) ships will shift to October–December during the period from 2021 to 2030, with a maximum navigable percentage in October. In addition, the central passage will be open for PC6 ships between September and October during 2021–2030.

Recovering and monitoring the thickness, density and elastic properties of sea ice from seismic noise recorded in Svalbard

Due to global warming, the decline in the Arctic sea ice has been accelerating over the last four decades, with a rate that was not anticipated by climate models. To improve these models, there is the need to rely on comprehensive field data. Seismic methods are known for their potential to estimate sea-ice thickness and mechanical properties with very good accuracy. However, with the hostile environment and logistical difficulties imposed by the polar regions, seismic studies have remained rare. Due to the rapid technological and methodological progress of the last decade, there has been a recent reconsideration of such approaches. This paper introduces a methodological approach for passive monitoring of both sea-ice thickness and mechanical properties. To demonstrate this concept, we use data from a seismic experiment where an array of 247 geophones was deployed on sea ice in a fjord at Svalbard, between March 1 and 24, 2019. From the continuous recording of the ambient seismic field, the empirical Green's function of the seismic waves guided in the ice layer was recovered via the so-called 'noise correlation function'. Using specific array processing, the multi-modal dispersion curves of the ice layer were calculated from the noise correlation function, and then inverted for the thickness and elastic properties of the sea ice via Bayesian inference. The evolution of sea-ice properties was monitored for 24 days, and values are consistent with the literature, as well as with measurements made directly in the field.

NorCPM1 and its contribution to CMIP6 DCPP

The Norwegian Climate Prediction Model version 1 (NorCPM1) is a new research tool for performing climate reanalyses and seasonal-to-decadal climate predictions. It combines the Norwegian Earth System Model version 1 (NorESM1) – which features interactive aerosol–cloud schemes and an isopycnic-coordinate ocean component with biogeochemistry – with anomaly assimilation of sea surface temperature (SST) and T/S-profile observations using the ensemble Kalman filter (EnKF). We describe the Earth system component and the data assimilation (DA) scheme, highlighting implementation of new forcings, bug fixes, retuning and DA innovations. Notably, NorCPM1 uses two anomaly assimilation variants to assess the impact of sea ice initialization and climatological reference period: the first (i1) uses a 1980–2010 reference climatology for computing anomalies and the DA only updates the physical ocean state; the second (i2) uses a 1950–2010 reference climatology and additionally updates the sea ice state via strongly coupled DA of ocean observations. We assess the baseline, reanalysis and prediction performance with output contributed to the Decadal Climate Prediction Project (DCPP) as part of the sixth Coupled Model Intercomparison Project (CMIP6). The NorESM1 simulations exhibit a moderate historical global surface temperature evolution and tropical climate variability characteristics that compare favourably with observations. The climate biases of NorESM1 using CMIP6 external forcings are comparable to, or slightly larger than those of, the original NorESM1 CMIP5 model, with positive biases in Atlantic meridional overturning circulation (AMOC) strength and Arctic sea ice thickness, too-cold subtropical oceans and northern continents, and a too-warm North Atlantic and Southern Ocean. The biases in the assimilation experiments are mostly unchanged, except for a reduced sea ice thickness bias in i2 caused by the assimilation update of sea ice, generally confirming that the anomaly assimilation synchronizes variability without changing the climatology. The i1 and i2 reanalysis/hindcast products overall show comparable performance. The benefits of DA-assisted initialization are seen globally in the first year of the prediction over a range of variables, also in the atmosphere and over land. External forcings are the primary source of multiyear skills, while added benefit from initialization is demonstrated for the subpolar North Atlantic (SPNA) and its extension to the Arctic, and also for temperature over land if the forced signal is removed. Both products show limited success in constraining and predicting unforced surface ocean biogeochemistry variability. However, observational uncertainties and short temporal coverage make biogeochemistry evaluation uncertain, and potential predictability is found to be high. For physical climate prediction, i2 performs marginally better than i1 for a range of variables, especially in the SPNA and in the vicinity of sea ice, with notably improved sea level variability of the Southern Ocean. Despite similar skills, i1 and i2 feature very different drift behaviours, mainly due to their use of different climatologies in DA; i2 exhibits an anomalously strong AMOC that leads to forecast drift with unrealistic warming in the SPNA, whereas i1 exhibits a weaker AMOC that leads to unrealistic cooling. In polar regions, the reduction in climatological ice thickness in i2 causes additional forecast drift as the ice grows back. Posteriori lead-dependent drift correction removes most hindcast differences; applications should therefore benefit from combining the two products. The results confirm that the large-scale ocean circulation exerts strong control on North Atlantic temperature variability, implying predictive potential from better synchronization of circulation variability. Future development will therefore focus on improving the representation of mean state and variability of AMOC and its initialization, in addition to upgrades of the atmospheric component. Other efforts will be directed to refining the anomaly assimilation scheme – to better separate internal and forced signals, to include land and atmosphere initialization and new observational types – and improving biogeochemistry prediction capability. Combined with other systems, NorCPM1 may already contribute to skilful multiyear climate prediction that benefits society.

Bacterial Community Structure, Function and Diversity in Antarctic Sea Ice

<p>Antarctic sea ice is an important feature of the southern ocean where at its maximum it can cover 8 % of the Southern Hemisphere. It provides a stable environment for the colonisation of diverse and highly specialised microbes which play a central role in the assimilation and regulation of energy through the Antarctic food web. Polar environments are sensitive to changes in the environment. Small changes in temperature can have large effects on sea ice thickness and extent and Antarctic sea ice cover is expected to shrink by 25 % over the next century. It is unknown how the sea ice microbiota will respond. In order to understand the effects of climate change on the sea ice ecosystem it is necessary to obtain information about the community structure, function and diversity and their reactions with the environment. Studies have focused on algal diversity and physiology in Antarctic sea ice and in comparison studies on the prokaryotic community are few. Although prokaryotic diversity has been investigated using clone libraries and culture based methods, it is likely that certain species have still not been described. Almost nothing is known about the Antarctic sea ice bacterial community spatial and temporal dynamics under changing abiotic and biotic conditions or their role in biogeochemical cycles. This is the first study linking Antarctic bacterial communities to function by using statistics to investigate the relationships between environmental variables and community structure. Bacterial community structure was investigated by extracting both the DNA and RNA from the environment to understand both the metabolically active (RNA) and total (DNA) bacterial community. The thickness of the sea ice and nutrient concentrations were key factors regulating bacterial community composition in Antarctic sea ice. Sea ice thickness is likely to have an effect on the physiological responses of algae leading to changes in photosynthate concentrations and composition of dissolved organic matter (DOM). Further investigations into the relationships between enzymatic activity and community structure revealed that the composition of the DOM drove variation between bacterial communities. There was no relationship between bacterial abundance and chlorophyll-a (as a measure of algal biomass), suggesting a un-coupling of the microbial loop. However bacteria were actively involved in the hydrolysis of polymers throughout the sea ice core. Investigations using quantitative PCR (qPCR) found that the functional genes involved in denitrification and light energy utilisation were in low abundance therefore these processes are minor in Antarctic sea ice. These results confirm that sea ice bacteria are predominantly heterotrophs and have a major role in the cycling of carbon and nitrogen through the microbial loop ...</p>

Bacterial Community Structure, Function and Diversity in Antarctic Sea Ice

<p>Antarctic sea ice is an important feature of the southern ocean where at its maximum it can cover 8 % of the Southern Hemisphere. It provides a stable environment for the colonisation of diverse and highly specialised microbes which play a central role in the assimilation and regulation of energy through the Antarctic food web. Polar environments are sensitive to changes in the environment. Small changes in temperature can have large effects on sea ice thickness and extent and Antarctic sea ice cover is expected to shrink by 25 % over the next century. It is unknown how the sea ice microbiota will respond. In order to understand the effects of climate change on the sea ice ecosystem it is necessary to obtain information about the community structure, function and diversity and their reactions with the environment. Studies have focused on algal diversity and physiology in Antarctic sea ice and in comparison studies on the prokaryotic community are few. Although prokaryotic diversity has been investigated using clone libraries and culture based methods, it is likely that certain species have still not been described. Almost nothing is known about the Antarctic sea ice bacterial community spatial and temporal dynamics under changing abiotic and biotic conditions or their role in biogeochemical cycles. This is the first study linking Antarctic bacterial communities to function by using statistics to investigate the relationships between environmental variables and community structure. Bacterial community structure was investigated by extracting both the DNA and RNA from the environment to understand both the metabolically active (RNA) and total (DNA) bacterial community. The thickness of the sea ice and nutrient concentrations were key factors regulating bacterial community composition in Antarctic sea ice. Sea ice thickness is likely to have an effect on the physiological responses of algae leading to changes in photosynthate concentrations and composition of dissolved organic matter (DOM). Further investigations into the relationships between enzymatic activity and community structure revealed that the composition of the DOM drove variation between bacterial communities. There was no relationship between bacterial abundance and chlorophyll-a (as a measure of algal biomass), suggesting a un-coupling of the microbial loop. However bacteria were actively involved in the hydrolysis of polymers throughout the sea ice core. Investigations using quantitative PCR (qPCR) found that the functional genes involved in denitrification and light energy utilisation were in low abundance therefore these processes are minor in Antarctic sea ice. These results confirm that sea ice bacteria are predominantly heterotrophs and have a major role in the cycling of carbon and nitrogen through the microbial loop ...</p>

Probabilistic Forecasts of Arctic Sea Ice Thickness

In recent decades, warming temperatures have caused sharp reductions in the volume of sea ice in the Arctic Ocean. Predicting changes in Arctic sea ice thickness is vital in a changing Arctic for making decisions about shipping and resource management in the region. We propose a statistical spatio-temporal two-stage model for sea ice thickness and use it to generate probabilistic forecasts up to three months into the future. Our approach combines a contour model to predict the ice-covered region with a Gaussian random field to model ice thickness conditional on the ice-covered region. Using the most complete estimates of sea ice thickness currently available, we apply our method to forecast Arctic sea ice thickness. Point predictions and prediction intervals from our model offer comparable accuracy and improved calibration compared with existing forecasts. We show that existing forecasts produced by ensembles of deterministic dynamic models can have large errors and poor calibration. We also show that our statistical model can generate good forecasts of aggregate quantities such as overall and regional sea ice volume. Supplementary materials accompanying this paper appear on-line.