Comment on acp-2021-610

<p>Investigating the temporal and spatial changes in Arctic sea ice freeboard, thickness and volume is crucial for climate and environmental research. In this study, the freeboard, thickness and volume of the Arctic sea ice between 2011 and 2020 were acquired from CryoSat-2 data and it was compared with NASA Operation IceBridge and Alfred Wegener Institue AWI datasets. The effect of wind field and temperature and itscontribution on Arctic sea ice were also investigated. The main steps of our research are as follows. 1) Selecting data points above 66 degrees north latitude and filtering out sea ice by flag value and mask; Using OSI SAF sea ice concentration (SIC) data to further constrict the area; Interpolating the latest mean sea surface data DTU21 into CryoSat-2 data; Calculating the sea ice freeboard via spatial altimetry relationship. 2) Combining with snow density, snow depth, multi-year ice density and first-year ice density to estimate the sea ice thickness respectively from freeboard according to the assumption of hydrostatic equilibrium. 3) Using the area and extent provided by NSIDC to interpret the volume. 4) Utilizing Seasonal and Trend decomposition using Loess(STL) to analyse the seasonal and interannual variations of sea ice. 5) Dividing the Arctic Ocean by its marginal sea, coupled with the HY-2B microwave scatterometer data and NCEP/NCAR reanalysis data, the impact of the Beaufort Sea, the Chukchi Sea, the East Siberian Sea, the Laptev Sea, the Kara Sea, the Barents Sea, the Greenland Sea, and the Baffin Bay wind field on the sea ice were studied. 6) Exploring the correlation between the sea surface temperature and the sea ice freeboard, thickness and volume. The result indicated that (1) the freeboard and thickness was decreasing about 9.748% and 8.80% during 2011-2020,respectively; (2) there are interlunar variations in sea ice freeboard and thickness, the freeboard and thickness of sea ice reach the minimum in August to September each year and the maximum appears in March to April; (3) Arctic sea ice is affected by both thermodynamics and dynamics, the reduction of Arctic sea ice was largely due to the sea surface wind field, one of the dynamic factors is the wind field on the sea surface. The sea ice changes in the various sea areas of the Arctic Ocean are related to the wind field to varying degrees. The changes in the sea surface wind field in recent years have further promoted the reduction of sea ice, making it possible for the Arctic to open to navigation in the summer. Fully understanding the evolution of sea ice change in the Arctic Ocean is helpful for humans to better protect the earth.</p>

Extreme surface turbulent heat fluxes affecting convective processes in subpolar ocean regions may amount to 1000-3000 W/m2. Their quantitative estimation is critically important for many oceanographic and meteorological applications. Extreme turbulent fluxes are largely responsible for the vertical mixing in the ocean, especially in the subpolar latitudes where deep convection forms intermediate waters. Over western boundary currents and their extension regions very strong turbulent heat fluxes may result in local responses in the lower atmosphere on time scales from several hours to days and spatial scales from several kilometers to several tens of kilometers. Accurate estimation of extreme turbulent fluxes also strongly relates to the sampling problem especially for the poorly and irregularly sampled regions. Estimation of extreme turbulent fluxes is thus requires knowledge of probability distribution of fluxes. We suggest a concept for determination of extreme surface turbulent heat fluxes based upon theoretical probability distributions, which allow for accurate estimation of extreme fluxes. In this concept the absolute extremeness of surface turbulent fluxes is quantified from the Modified Fisher-Tippett (MFT) distribution. Further we extend MFT distribution to a fractional distribution, quantifying the so-called relative extremeness, representing the fraction of surface flux accumulated during continuous time (e.g. months, season) due to most intense surface fluxes (e.g. the strongest 1% of flux events). We provide explicit form of the fractional distribution and effective algorithms for parameter estimation.Further we demonstrate applications of the concept for the global ocean using reanalyses and Voluntary Observing Ship (VOS) data for the period 1979 onwards. Global climatologies reveal that the regions with the strongest relative extremeness are not collocated with the strongest mean fluxes. Moreover, interannual variability of the absolute and relative flux extremes is not necessarily correlated with variability of mean fluxes. Growing mean flux may result in both increase and decrease of absolute and relative extremes that has implications for estimates of linear trends which may have different signs for mean fluxes, absolute and relative flux extremes – the situations found for the western boundary currents and major convections sites. Suggested concept has also profound implications for comparative assessments of surface turbulent fluxes from reanalyses (ERA5, ERA-Interim, CFSR, MERRA2, NCEP-DOE, JRA55) and satellite (IFREMER, J-OFURO, HOAPS, SEAFLUX) products. High mean fluxes in some products are not necessarily associated with the strongest absolute and relative extremeness in the same products and vice versa. Also a new concept allows for accurate estimation and minimization of sampling biases in VOS and satellite flux products. Finally, we analyzed the mechanisms responsible for forming extreme surface turbulent fluxes associated with cold air outbreaks in the rear parts of midlatitudinal cyclones under locally high winds and strong air-sea temperature gradients.

The Arctic Ocean has suffered extreme reductions in sea ice in recent decades, and these observed changes suggest implications in terms of moisture transport. The Arctic region is a net sink of moisture in terms of the total hydrological cycle, however, its role as a moisture source for specific regions has not been extensively studied. Our results show that 80% of the moisture supply from the Arctic contributes to precipitation over itself, representing about 8% of the global moisture supply to the Arctic, the remaining 20% is distributed in the surrounding. A reduction in the sea ice extent could make the Arctic Ocean a slightly higher source of moisture to itself or to the surrounding areas. The analysis of the areas affected by Arctic moisture transport is important for establishing those areas vulnerable to change in a framework of a growing sea ice decline. To this end, the Lagrangian model FLEXPART was used in this work to establish the main sinks for the Arctic Ocean, focusing on the moisture transport from this region. The results suggest that most of the moisture loss occurs locally over the Arctic Ocean itself, especially in summer. Some moisture contribution from the Arctic Ocean to continental areas in North America and Eurasia is also noted in autumn and winter especially from Central Arctic, the East Siberian Sea, the Laptev, Kara, Barents, East Greenland and Bering Seas, and the Sea of Okhotsk.

This paper identified an atmospheric circulation anomaly–dipole structure anomaly in the Arctic atmosphere and its relationship with winter sea ice motion, based on the International Arctic Buoy Program (IABP) dataset (1979–98) and datasets from the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) for the period 1960–2002. The dipole anomaly corresponds to the second-leading mode of EOF of monthly mean sea level pressure (SLP) north of 70°N during the winter season (October–March) and accounts for 13% of the variance. One of its two anomalous centers is stably occupied between the Kara Sea and Laptev Sea; the other is situated from the Canadian Archipelago through Greenland extending southeastward to the Nordic seas. The dipole anomaly differs from one described in other papers that can be attributed to an eastward shift of the center of action of the North Atlantic Oscillation. The finding shows that the dipole anomaly also differs from the “Barents Oscillation” revealed in a study by Skeie. Since the dipole anomaly shows a strong meridionality, it becomes an important mechanism to drive both anomalous sea ice exports out of the Arctic Basin and cold air outbreaks into the Barents Sea, the Nordic seas, and northern Europe. When the dipole anomaly remains in its positive phase, that is, negative SLP anomalies appear between the Kara Sea and the Laptev Sea with concurrent positive SLP over from the Canadian Archipelago extending southeastward to Greenland, there are large-scale changes in the intensity and character of sea ice transport in the Arctic basin. The significant changes include a weakening of the Beaufort gyre, an increase in sea ice export out of the Arctic basin through Fram Strait and the northern Barents Sea, and enhanced sea ice import from the Laptev Sea and the East Siberian Sea into the Arctic basin. Consequently, more sea ice appears in the Greenland and the Barents Seas during the positive phase of the dipole anomaly. During the negative phase of the dipole anomaly, SLP anomalies show an opposite scenario in the Arctic Ocean and its marginal seas when compared to the positive phase, with the center of negative SLP anomalies over the Nordic seas. Correspondingly, sea ice exports decrease from the Arctic basin flowing into the Nordic seas and the northern Barents Sea because of the strengthened Beaufort gyre. The finding indicates that influences of the dipole anomaly on winter sea ice motion are greater than that of the winter AO, particularly in the central Arctic basin and northward to Fram Strait, implying that effects of the dipole anomaly on sea ice export out of the Arctic basin become robust. The dipole anomaly is closely related to atmosphere–ice–ocean interactions that influence the Barents Sea sector.

We examine the effects of a general sea ice-atmosphere feedback (SAF) over the Barents Sea by turning it on and off in a coupled climate model. The SAF is “turned off” by forcing the atmosphere with surface turbulent and longwave heat fluxes and surface temperatures that reflect climatological sea ice cover over the Barents Sea, while allowing the sea ice and sea surface temperature (SST) to freely evolve. Suppressing the SAF reduces the variability of near-surface air temperature (T), sea ice concentration (I) , and SST averaged over the Barents Sea by up to 35 %, confirming the existence of a positive thermodynamically-driven SAF found in prior uncoupled modeling studies. Decreased interannual variability accounts for most of the total reduction in I, T, and SST variability, and the largest reductions in variability occur during the winter sea ice growth and spring melt seasons. In contrast to the results from the coupled model experiment, the total variances of I, T, and SST do not significantly change in response to suppressing the SAF in a simple vector autoregressive model, indicating that the SAF is nonlinear.

<p>The ice/snow melt onset (MO) is a critical triggering signal for ice-albedo positive feedback in the Arctic. Concerning the Northeast Passage (NEP), for 1979-1998, the MO in the East Siberian Sea (ESS) occurred generally earlier than that in the Laptev Sea (LS). However, for 1999-2018, the LS experienced significantly earlier MO than did the ESS in several years. This phenomenon is identified as the MO Seesaw (MOS), i.e., the MO difference between the LS and ESS. For the positive MOS, storm tracks in May tend to cover the ESS rather than the LS and easterly wind prevails and shifts slightly to a northerly wind in the ESS, resulting in higher surface air temperature (SAT) and total-column water vapor (TWV) and earlier MO in the ESS. For the negative MOS, storm tracks are much stronger in the LS than in the ESS and prominent southerly/southwesterly wind brings warm air from coastal land towards the LS. The effect of the Barents Oscillation (BO) on the MOS could be dated back to April. When the Barents Sea is centered with a low SLP in April, sea ice in the LS would be driven away from the coasts, leading to a lower sea ice area (SIA), which increases the surface latent heat flux and humidifies the overlying atmosphere. Along with an enhanced downward sensible heat flux, earlier regional average MO occurs in the LS. For 1999-2018, the MOS was more closely related to both the local variables and the large-scale atmospheric circulation indices.</p>

We used deep learning networks to establish a relationship model among MODIS daily surface reflectance product (MOD09GA) and Arctic melt ponds fraction (MPF), ice fraction (IF), and open water fraction (OWF). We applied this model to MODIS 8-day surface reflectance (MOD09A1) to derive Arctic 8-day MPF and SIF (SIF as the sum of IF and MPF). The results demonstrate that our model improved MPF estimation accuracy to an RMSE of 3.7%, compared with previous models. The characteristics of MPF spatiotemporal changes seen in early summer (May-July) indicate that MPF increased first from May-June, reaching its peak around early July, and then decreased. In addition, early summer MPF was significantly negatively correlated with sea ice extent (SIE) in September. We also found that early summer MPF caused sea ice in the Beaufort Sea, the Chukchi Sea, and the East Siberian Sea to move to warm water. Moreover, the movement of sea ice from the marginal sea to the center of the Arctic was shown to be conducive to the reduction of SIE in September. Early summer MPF was also related to Arctic oscillation (AO) during June to July, and significantly positively related to air temperature in the East Siberian and Chukchi Seas in September. As a consequence, these areas produced more open water and absorbed more heat, reducing the extent of sea ice in September, while increasing their air temperatures. The results also show that early summer MPF has a high negative correlation with air temperature in northern China, and MPF can be used to predict air temperature in northern China. These new findings should be investigated in future studies with additional data collection and field observations.

Recent Arctic winter sea ice loss has been most pronounced in the Barents Sea. Here we explore the spatial structure of Barents Sea ice change as observed over the last 40 years. The dominant mode of winter sea ice concentration interannual variability corresponds to areal change (explains 43% of spatial variance) and has a center of action in the northeastern Barents Sea where the temperate Atlantic inflow meets the wintertime sea ice. Sea ice area import and northerly wind also contribute to this “areal-change mode”; the area increases with more ice import and stronger winds from the north. The remaining 57% variance in sea ice, individually and combined, redistributes the sea ice without changing the total area. The two leading redistribution modes are a dipole of increase in sea ice concentration south of Svalbard with decrease southwest of Novaya Zemlya, and a tripole of increase in the central Barents Sea with decrease east of Svalbard and in the southeastern Barents Sea. Redistribution is mainly contributed by anomalous wind and sea ice area import. Basic predictability (i.e., the lagged response to observed drivers) is predominantly associated with the areal-change mode as influenced by temperature of the Atlantic inflow and sea ice import from the Arctic.

In the Arctic, global warming is 2–3 times faster than over other regions of the globe. As a result, noticeable changes are already being recorded in all areas of the environment. However, there is very little data on such changes in the Russian Arctic. Therefore, to fill the gap in the data on the vertical distribution of the gas and aerosol composition of air in this region, an experiment was carried out on the Tu-134 Optik flying laboratory in September 2020 to sound the atmosphere and water surface over the water areas of all seas in the Russian Arctic. This paper analyzes the spatial distribution of methane. It is shown that during the experiment its concentration was the highest over the Kara Sea (2090 ppb) and the lowest over the Chukchi Sea (2005 ppb). The East Siberian and Bering Seas were slightly different from the Chukchi Sea in terms of the methane concentration. Average values of CH4 are characteristic of the Barents (2030 ppb) and the Laptev Seas (2040 ppb). The difference between the concentrations at an altitude of 200 meters and in the free troposphere attained 150 ppb over the Kara Sea, decreased to 91 and 94 ppb over the Barents and Laptev Seas, and further decreased over the East Siberian, Chukchi, and Bering Seas to 66, 63, and 74 ppb, respectively. Horizontal heterogeneity in the distribution of methane over the Arctic seas is the greatest over the Laptev Sea, where it attained 73 ppb. It is two times higher than over the Barents and Kara Seas, and 5–7 times higher than over the East Siberian and Bering Seas.

The indirect effect of winter Arctic Oscillation (AO) events on the proceeding summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer sea ice forecasts in a large number of coupled climate models show a considerable drop in predictive skill for forecasts initialised prior to the date of melt onset in spring, suggesting that some drivers of sea ice variability on longer time scales may not be well represented in these models. To this end, we introduce an unsupervised learning approach based on cluster analysis and complex networks to establish how well the latest generation of coupled climate models participating in phase 6 of the World Climate Research Programme Coupled Model Intercomparison Project (CMIP6) are able to reflect the spatio-temporal patterns of variability in northern-hemisphere winter sea-level pressure and Arctic summer sea ice concentration over the period 1979–2020, relative to ERA5 atmospheric reanalysis and satellite-derived sea ice observations respectively. Two specific global metrics are introduced as ways to compare patterns of variability between models and observations/reanalysis: the Adjusted Rand Index – a method for comparing spatial patterns of variability, and a network distance metric – a method for comparing the degree of connectivity between two geographic regions. We find that CMIP6 models generally reflect the spatial pattern of variability of the AO relatively well, although over-estimate the magnitude of sea-level pressure variability over the north-western Pacific Ocean, and under-estimate the variability over the north Africa and southern Europe. They also under-estimate the importance of regions such as the Beaufort, East Siberian and Laptev seas in explaining pan-Arctic summer sea ice area variability, which we hypothesise is due to regional biases in sea ice thickness. Finally, observations show that historically, winter AO events (negatively) covary strongly with summer sea ice concentration in the eastern Pacific sector of the Arctic, although now under a thinning ice regime, both the eastern and western Pacific sectors exhibit similar behaviour. CMIP6 models however do not show this transition on average, which may hinder their ability to make skilful seasonal to inter-annual predictions of summer sea ice.

This study explores the reason why strong upward anomalous surface turbulent heat fluxes (STHFs) over the Barents and Kara Seas (BKS) occur for a period of only a few days after wind‐driven sea ice loss, even though anomalously low sea ice persists for more than one month. Composite analysis with ERA5 reanalysis data reveals that the sea ice decline coincides with the poleward advection of warm, moist air on the eastern flank of a synoptic‐scale surface low. This results in the anomalous surface air temperature (SAT) exceeding the anomalous skin temperature (SKT) and a downward anomalous STHF. As the surface low propagates eastward, the wind direction changes, resulting in the advection of cold, dry air, the anomalous SKT exceeding the anomalous SAT and a brief period with a strong upward anomalous STHF. This period of strong upward anomalous STHF is cut short, as the surface low propagates southeastward out of the BKS. The eastward propagation of the surface low is crucial, as it allows for northward driving of sea ice to be followed by cold advection and a strong upward anomalous STHF. These results indicate that when wind‐driven sea ice motion exposes the ocean to the atmosphere, except for a brief episode, the reduction of sea ice does not coincide with an increase in a strong upward anomalous STHF.

Spatio-temporal variability of sea-ice and ocean parameters over the Arctic Ocean in response to a warming climate

Sea ice expansion in the Southern Ocean is believed to have contributed to glacial-interglacial atmospheric CO2 variability by inhibiting air-sea gas exchange and influencing the ocean’s meridional overturning circulation. However, limited data on past sea ice coverage over the last 140 ka (a complete glacial cycle) have hindered our ability to link sea ice expansion to oceanic processes that affect atmospheric CO2 concentration. Assessments of past sea ice coverage using diatom assemblages have primarily focused on the Last Glacial Maximum (~21 ka) to Holocene, with few quantitative reconstructions extending to the onset of glacial Termination II (~135 ka). Here we provide new estimates of winter sea ice concentrations (wSIC) and summer sea surface temperatures (sSSTs) for a full glacial-interglacial cycle from the southwestern Pacific sector of the Southern Ocean using fossil diatom assemblages from deep-sea core TAN1302-96 (59.09° S, 157.05° E, water depth 3099 m). We find that winter sea ice was consolidated over the core site during the latter part of the penultimate glaciation, Marine Isotope Stage (MIS) 6 (from at least 140 to 134 ka), when sSSTs were between ~1 and 1.5 °C. The winter sea ice edge then retreated rapidly as sSSTs increased during the transition into the Last Interglacial Period (MIS 5e), reaching ~4.5 °C by 125 ka. As the Earth entered the early glacial stages, sSSTs began to decline around 112 ka, but winter sea ice largely remained absent until ~65 ka during MIS 4, when it was sporadically present but unconsolidated (< 40 % wSIC). WSIC and sSSTs reached their maximum concentration and coolest values by 24.5 ka, just prior to the Last Glacial Maximum. Winter sea ice remained absent throughout the Holocene, while SSSTs briefly exceeded modern values, reaching ~5 °C by 11.4 ka, before decreasing to ~4 °C and stabilizing. The absence of sea ice coverage over the core site during the early glacial period suggests that sea ice may not have been a major contributor to CO2 drawdown at this time. During MIS 5d, we observe a weakening of meridional SST gradients between 42° to 59° S throughout the region, which may have contributed to early reductions in atmospheric CO2 concentrations through its impact on air-sea gas exchange. Sea ice expansion during MIS 4, however, coincides with observed reductions in Antarctic Intermediate Water production and subduction, suggesting that sea ice may have influenced intermediate ocean circulation changes.

This study assesses the capability of Surface Quasi-Geostrophy (SQG) to reconstruct the three-dimensional (3D) dynamics in four critical areas of the Arctic Ocean: the Nordic, Barents, East Siberian, and Beaufort Seas. We first reconstruct the upper ocean dynamics from TOPAZ4 reanalysis of sea surface height (SSH), surface buoyancy (SSB), and surface velocities (SSV) and validate the results with the geostrophic and total TOPAZ4 velocities. The reconstruction of upper ocean dynamics using SSH fields is in high agreement with the geostrophic velocities, with correlation coefficients greater than 0.8 for the upper 400 m. SSH reconstructions outperform surface buoyancy reconstructions, even in places near freshwater inputs from river discharges, melting sea ice, and glaciers. Surface buoyancy fails due to the uncorrelation of SSB and subsurface potential vorticity (PV). Reconstruction from surface currents correlates to the total TOPAZ4 velocities with correlation coefficients greater than 0.6 up to 200 m. In the second part, we apply the SQG approach validated with the reanalysis outputs to satellite-derived sea level anomalies and validate the results against in-situ measurements. Due to lower water column stratification, the SQG approach’s performance is better in fall and winter than in spring and summer. Our results demonstrate that using surface information from SSH or surface velocities, combined with information on the stratification of the water column, it is possible to effectively reconstruct the upper ocean dynamics in the Arctic and Subarctic Seas up to 400 m. Future remote sensing missions in the Arctic Ocean, such as SWOT, Seastar, WaCM, CIMR, and CRISTAL, will produce enhanced SSH and surface velocity observations, allowing SQG schemes to characterize upper ocean 3D mesoscale dynamics up to 400 m with higher resolutions and lower uncertainties.

The huge warming of the Arctic that started in the early 1920s and lasted for almost two decades is one of the most spectacular climate events of the twentieth century. During the peak period 1930–40, the annually averaged temperature anomaly for the area 60°–90°N amounted to some 1.7°C. Whether this event is an example of an internal climate mode or is externally forced, such as by enhanced solar effects, is presently under debate. This study suggests that natural variability is a likely cause, with reduced sea ice cover being crucial for the warming. A robust sea ice–air temperature relationship was demonstrated by a set of four simulations with the atmospheric ECHAM model forced with observed SST and sea ice concentrations. An analysis of the spatial characteristics of the observed early twentieth-century surface air temperature anomaly revealed that it was associated with similar sea ice variations. Further investigation of the variability of Arctic surface temperature and sea ice cover was performed by analyzing data from a coupled ocean–atmosphere model. By analyzing climate anomalies in the model that are similar to those that occurred in the early twentieth century, it was found that the simulated temperature increase in the Arctic was related to enhanced wind-driven oceanic inflow into the Barents Sea with an associated sea ice retreat. The magnitude of the inflow is linked to the strength of westerlies into the Barents Sea. This study proposes a mechanism sustaining the enhanced westerly winds by a cyclonic atmospheric circulation in the Barents Sea region created by a strong surface heat flux over the ice-free areas. Observational data suggest a similar series of events during the early twentieth-century Arctic warming, including increasing westerly winds between Spitsbergen and Norway, reduced sea ice, and enhanced cyclonic circulation over the Barents Sea. At the same time, the North Atlantic Oscillation was weakening.