Separating the influences of low-latitude warming and sea-ice loss on Northern Hemisphere climate change

Analyzing a multi-model ensemble of coupled climate model simulations forced with Arctic sea-ice loss using a two-parameter pattern-scaling technique to remove the cross-coupling between low- and high-latitude responses, the sensitivity to high-latitude sea-ice loss is isolated and contrasted to the sensitivity to low-latitude warming. In spite of some differences in experimental design, the Northern Hemisphere near-surface atmospheric sensitivity to sea-ice loss is found to be robust across models in the cold season; however, a larger inter-model spread is found at the surface in boreal summer, and in the free tropospheric circulation. In contrast, the sensitivity to low-latitude warming is most robust in the free troposphere and in the warm season, with more inter-model spread in the surface ocean and surface heat flux over the Northern Hemisphere. The robust signals associated with sea-ice loss include upward turbulent and longwave heat fluxes where sea-ice is lost, warming and freshening of the Arctic ocean, warming of the eastern North Pacific relative to the western North Pacific with upward turbulent heat fluxes in the Kuroshio extension, and salinification of the shallow shelf seas of the Arctic Ocean alongside freshening in the subpolar North Atlantic. In contrast, the robust signals associated with low-latitude warming include intensified ocean warming and upward latent heat fluxes near the western boundary currents, freshening of the Pacific Ocean, salinification of the North Atlantic, and downward sensible and longwave fluxes over the ocean.

Diurnal Oxidation for Manganese Minerals in the Arctic Ocean

The relative abundance of different oxidation states for this important micronutrient varies on the basis of how much available sunlight there is.

An Assessment of Sea Ice Motion Products in the Robeson Channel Using Daily Sentinel-1 Images

Sea ice motion is an essential parameter when determining sea ice deformation, regional advection, and the outflow of ice from the Arctic Ocean. The Robeson Channel, which is located between Ellesmere Island and northwest Greenland, is a narrow but crucial channel for ice outflow. Only three Eulerian sea ice motion products derived from ocean/sea ice reanalysis are available: GLORYS12V1, PSY4V3, and TOPAZ4. In this study, we used Lagrangian ice motion in the Robeson Channel derived from Sentinel-1 images to assess GLORYS12V1, PSY4V3, and TOPAZ4. The influence of the presence of ice arches, and wind and tidal forcing on the accuracies of the reanalysis products was also investigated. The results show that the PSY4V3 product performs the best as it underestimates the motion the least, whereas TOPAZ4 grossly underestimates the motion. This is particularly true in regimes of free drift after the formation of the northern arch. In areas with slow ice motion or grounded ice floes, the GLORYS12V1 and TOPAZ4 products offer a better estimation. The spatial distribution of the deviation between the products and ice floe drift is also presented and shows a better agreement in the Robeson Channel compared to the packed ice regime north of the Robeson Channel.

The Distribution of pCO2W and Air-Sea CO2 Fluxes Using FFNN at the Continental Shelf Areas of the Arctic Ocean

A feed-forward neural network (FFNN) was used to estimate the monthly climatology of partial pressure of CO2 (pCO2W) at a spatial resolution of 1° latitude by 1° longitude in the continental shelf of the European Arctic Sector (EAS) of the Arctic Ocean (the Greenland, Norwegian, and Barents seas). The predictors of the network were sea surface temperature (SST), sea surface salinity (SSS), the upper ocean mixed-layer depth (MLD), and chlorophyll-a concentration (Chl-a), and as a target, we used 2 853 pCO2W data points from the Surface Ocean CO2 Atlas. We built an FFNN based on three major datasets that differed in the Chl-a concentration data used to choose the best model to reproduce the spatial distribution and temporal variability of pCO2W. Using all physical–biological components improved estimates of the pCO2W and decreased the biases, even though Chl-a values in many grid cells were interpolated values. General features of pCO2W distribution were reproduced with very good accuracy, but the network underestimated pCO2W in the winter and overestimated pCO2W values in the summer. The results show that the model that contains interpolating Chl-a concentration, SST, SSS, and MLD as a target to predict the spatiotemporal distribution of pCO2W in the sea surface gives the best results and best-fitting network to the observational data. The calculation of monthly drivers of the estimated pCO2W change within continental shelf areas of the EAS confirms the major impact of not only the biological effects to the pCO2W distribution and Air-Sea CO2 flux in the EAS, but also the strong impact of the upper ocean mixing. A strong seasonal correlation between predictor and pCO2W seen earlier in the North Atlantic is clearly a yearly correlation in the EAS. The five-year monthly mean CO2 flux distribution shows that all continental shelf areas of the Arctic Ocean were net CO2 sinks. Strong monthly CO2 influx to the Arctic Ocean through the Greenland and Barents Seas (>12 gC m−2 day−1) occurred in the fall and winter, when the pCO2W level at the sea surface was high (>360 µatm) and the strongest wind speed (>12 ms−1) was present.

Climate changes modulated the history of Arctic iodine during the Last Glacial Cycle

Iodine has a significant impact on promoting the formation of new ultrafine aerosol particles and accelerating tropospheric ozone loss, thereby affecting radiative forcing and climate. Therefore, understanding the long-term natural evolution of iodine, and its coupling with climate variability, is key to adequately assess its effect on climate on centennial to millennial timescales. Here, using two Greenland ice cores (NEEM and RECAP), we report the Arctic iodine variability during the last 127,000 years. We find the highest and lowest iodine levels recorded during interglacial and glacial periods, respectively, modulated by ocean bioproductivity and sea ice dynamics. Our sub-decadal resolution measurements reveal that high frequency iodine emission variability occurred in pace with Dansgaard/Oeschger events, highlighting the rapid Arctic ocean-ice-atmosphere iodine exchange response to abrupt climate changes. Finally, we discuss if iodine levels during past warmer-than-present climate phases can serve as analogues of future scenarios under an expected ice-free Arctic Ocean. We argue that the combination of natural biogenic ocean iodine release (boosted by ongoing Arctic warming and sea ice retreat) and anthropogenic ozone-induced iodine emissions may lead to a near future scenario with the highest iodine levels of the last 127,000 years.

Distribution and Driving Mechanism of N2O in Sea Ice and Its Underlying Seawater during Arctic Melt Season

Nitrous oxide (N2O) is the third most important greenhouse gas in the atmosphere, and the ocean is an important source of N2O. As the Arctic Ocean is strongly affected by global warming, rapid ice melting can have a significant impact on the N2O pattern in the Arctic environment. To better understand this impact, N2O concentration in ice core and underlying seawater (USW) was measured during the seventh Chinese National Arctic Research Expedition (CHINARE2016). The results showed that the average N2O concentration in first-year ice (FYI) was 4.5 ± 1.0 nmol kg−1, and that in multi-year ice (MYI) was 4.8 ± 1.9 nmol kg−1. Under the influence of exchange among atmosphere-sea ice-seawater systems, brine dynamics and possible N2O generation processes at the bottom of sea ice, the FYI showed higher N2O concentrations at the bottom and surface, while lower N2O concentrations were seen inside sea ice. Due to the melting of sea ice and biogeochemical processes, USW presented as the sink of N2O, and the saturation varied from 47.2% to 102.2%. However, the observed N2O concentrations in USW were higher than that of T-N2OUSW due to the sea–air exchange, diffusion process, possible N2O generation mechanism, and the influence of precipitation, and a more detailed mechanism is needed to understand this process in the Arctic Ocean.

The Predictability of Ocean Environments that Contributed to the 2020/21 Extreme Cold Events in China: 2020/21 La Niña and 2020 Arctic Sea Ice Loss

Several consecutive extreme cold events impacted China during the first half of winter 2020/21, breaking the low-temperature records in many cities. How to make accurate climate predictions of extreme cold events is still an urgent issue. The synergistic effect of the warm Arctic and cold tropical Pacific has been demonstrated to intensify the intrusions of cold air from polar regions into middle-high latitudes, further influencing the cold conditions in China. However, climate models failed to predict these two ocean environments at expected lead times. Most seasonal climate forecasts only predicted the 2020/21 La Niña after the signal had already become apparent and significantly underestimated the observed Arctic sea ice loss in autumn 2020 with a 1–2 month advancement. In this work, the corresponding physical factors that may help improve the accuracy of seasonal climate predictions are further explored. For the 2020/21 La Niña prediction, through sensitivity experiments involving different atmospheric-oceanic initial conditions, the predominant southeasterly wind anomalies over the equatorial Pacific in spring of 2020 are diagnosed to play an irreplaceable role in triggering this cold event. A reasonable inclusion of atmospheric surface winds into the initialization will help the model predict La Niña development from the early spring of 2020. For predicting the Arctic sea ice loss in autumn 2020, an anomalously cyclonic circulation from the central Arctic Ocean predicted by the model, which swept abnormally hot air over Siberia into the Arctic Ocean, is recognized as an important contributor to successfully predicting the minimum Arctic sea ice extent.

Arctic sea level variability from high-resolution model simulations and implications for the Arctic observing system

Two high-resolution model simulations are used to investigate the spatiotemporal variability of the Arctic Ocean sea level. The model simulations reveal barotropic sea level variability at periods of < 30 d, which is strongly captured by bottom pressure observations. The seasonal sea level variability is driven by volume exchanges with the Pacific and Atlantic oceans and the redistribution of the water by the wind. Halosteric effects due to river runoff and evaporation minus precipitation ice melting/formation also contribute in the marginal seas and seasonal sea ice extent regions. In the central Arctic Ocean, especially the Canadian Basin, the decadal halosteric effect dominates sea level variability. The study confirms that satellite altimetric observations and Gravity Recovery and Climate Experiment (GRACE) could infer the total freshwater content changes in the Canadian Basin at periods longer than 1 year, but they are unable to depict the seasonal and subseasonal freshwater content changes. The increasing number of profiles seems to capture freshwater content changes since 2007, encouraging further data synthesis work with a more complicated interpolation method. Further, in situ hydrographic observations should be enhanced to reveal the freshwater budget and close the gaps between satellite altimetry and GRACE, especially in the marginal seas.

On the circulation, water mass distribution, and nutrient concentrations of the western Chukchi Sea

Substantial amounts of nutrients and carbon enter the Arctic Ocean from the Pacific Ocean through the Bering Strait, distributed over three main pathways. Water with low salinities and nutrient concentrations takes an eastern route along the Alaskan coast, as Alaskan Coastal Water. A central pathway exhibits intermediate salinity and nutrient concentrations, while the most nutrient-rich water enters the Bering Strait on its western side. Towards the Arctic Ocean, the flow of these water masses is subject to strong topographic steering within the Chukchi Sea with volume transport modulated by the wind field. In this contribution, we use data from several sections crossing Herald Canyon collected in 2008 and 2014 together with numerical modelling to investigate the circulation and transport in the western part of the Chukchi Sea. We find that a substantial fraction of water from the Chukchi Sea enters the East Siberian Sea south of Wrangel Island and circulates in an anticyclonic direction around the island. This water then contributes to the high-nutrient waters of Herald Canyon. The bottom of the canyon has the highest nutrient concentrations, likely as a result of addition from the degradation of organic matter at the sediment surface in the East Siberian Sea. The flux of nutrients (nitrate, phosphate, and silicate) and dissolved inorganic carbon in Bering Summer Water and Winter Water is computed by combining hydrographic and nutrient observations with geostrophic transport referenced to lowered acoustic Doppler current profiler (LADCP) and surface drift data. Even if there are some general similarities between the years, there are differences in both the temperature–salinity and nutrient characteristics. To assess these differences, and also to get a wider temporal and spatial view, numerical modelling results are applied. According to model results, high-frequency variability dominates the flow in Herald Canyon. This leads us to conclude that this region needs to be monitored over a longer time frame to deduce the temporal variability and potential trends.