Relative Impacts of Sea Ice Loss and Atmospheric Internal Variability on the Winter Arctic to East Asian Surface Air Temperature Based on Large-Ensemble Simulations with NorESM2

To quantify the relative contributions of Arctic sea ice and unforced atmospheric internal variability to the “warm Arctic, cold East Asia”(WACE) teleconnection, this study analyses three sets of large-ensemble simulations carried out by the Norwegian Earth System Model with a coupled atmosphere–land surface model, forced by seasonal sea ice conditions from preindustrial, present-day, and future periods. Each ensemble member within the same set uses the same forcing but with small perturbations to the atmospheric initial state. Hence, the difference between the present-day(or future) ensemble mean and the preindustrial ensemble mean provides the ice-loss-induced response, while the difference of the individual members within the present-day(or future) set is the effect of atmospheric internal variability. Results indicate that both present-day and future sea ice loss can force a negative phase of the Arctic Oscillation with a WACE pattern in winter. The magnitude of ice-induced Arctic warming is over four(ten) times larger than the ice-induced East Asian cooling in the present-day(future) experiment;the latter having a magnitude that is about 30% of the observed cooling. Sea ice loss contributes about 60%(80%) to the Arctic winter warming in the present-day(future) experiment. Atmospheric internal variability can also induce a WACE pattern with comparable magnitudes between the Arctic and East Asia. Ice-lossinduced East Asian cooling can easily be masked by atmospheric internal variability effects because random atmospheric internal variability may induce a larger magnitude warming. The observed WACE pattern occurs as a result of both Arctic sea ice loss and atmospheric internal variability, with the former dominating Arctic warming and the latter dominating East Asian cooling.

Projecting Wintertime Newly Formed Arctic Sea Ice through Weighting CMIP6 Model Performance and Independence

Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Arctic multiyear sea ice,changes in newly formed sea ice indicate more thermodynamic and dynamic information on Arctic atmosphere–ocean–ice interaction and northern mid–high latitude atmospheric teleconnections. Here, we use a large multimodel ensemble from phase 6 of the Coupled Model Intercomparison Project(CMIP6) to investigate future changes in wintertime newly formed Arctic sea ice. The commonly used model-democracy approach that gives equal weight to each model essentially assumes that all models are independent and equally plausible, which contradicts with the fact that there are large interdependencies in the ensemble and discrepancies in models' performances in reproducing observations. Therefore, instead of using the arithmetic mean of well-performing models or all available models for projections like in previous studies, we employ a newly developed model weighting scheme that weights all models in the ensemble with consideration of their performance and independence to provide more reliable projections. Model democracy leads to evident bias and large intermodel spread in CMIP6 projections of newly formed Arctic sea ice. However, we show that both the bias and the intermodel spread can be effectively reduced by the weighting scheme. Projections from the weighted models indicate that wintertime newly formed Arctic sea ice is likely to increase dramatically until the middle of this century regardless of the emissions scenario.Thereafter, it may decrease(or remain stable) if the Arctic warming crosses a threshold(or is extensively constrained).

Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness

In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications.

Arctic Sea Ice Variations in the First Half of the 20th Century:A New Reconstruction Based on Hydrometeorological Data

The shrinking Arctic sea-ice area(SIA) in recent decades is a striking manifestation of the ongoing climate change.Variations of the Arctic sea ice have been continuously observed by satellites since 1979, relatively well monitored since the 1950s, but are highly uncertain in the earlier period due to a lack of observations. Several reconstructions of the historical gridded sea-ice concentration(SIC) data were recently presented based on synthesized regional sea-ice observations or by applying a hybrid model–empirical approach. Here, we present an SIC reconstruction for the period1901–2019 based on established co-variability between SIC and surface air temperature, sea surface temperature, and sea level pressure patterns. The reconstructed sea-ice data for March and September are compared to the frequently used Had ISST1.1 and SIBT1850 datasets. Our reconstruction shows a large decrease in SIA from the 1920 to 1940 concurrent with the Early 20th Century Warming event in the Arctic. Such a negative SIA anomaly is absent in Had ISST1.1 data. The amplitude of the SIA anomaly reaches about 0.8 mln km^(2) in March and 1.5 mln km^(2) in September. The anomaly is about three times stronger than that in the SIBT1850 dataset. The larger decrease in SIA in September is largely due to the stronger SIC reduction in the western sector of the Arctic Ocean in the 70°–80°N latitudinal zone. Our reconstruction provides gridded monthly data that can be used as boundary conditions for atmospheric reanalyses and model experiments to study the Arctic climate for the first half of the 20th century.

A Cross-Seasonal Linkage between Arctic Sea Ice and Eurasian Summertime Temperature Fluctuations

This study explores the linkage between summertime temperature fluctuations over midlatitude Eurasia and the preceding Arctic sea ice concentration (SIC) by utilizing the squared norm of the temperature anomaly, the essential part of local eddy available potential energy, as a metric to quantify the temperature fluctuations with weather patterns on various timescales. By comparing groups of singular value decomposition (SVD) analysis, we suggest a significant linkage between strong (weak) August 10-to-30-day temperature fluctuations over mid-west Asia and enhanced (decreased) Barents-Kara Sea ice in the previous February. We find that when the February SIC increases in the Barents-Kara Sea, a zonal dipolar pattern of SST anomalies appears in the Atlantic subpolar region and lasts from February into the summer months. Evidence suggests that in such a background state, the atmospheric circulation changes evidently from July to August, so that the August is characterized by an amplified meridional circulation over Eurasia, weakened westerlies, and high- pressure anomalies along the Arctic coast. Moreover, the 10-to-30-day wave becomes more active in the North Atlantic-Barents-Kara Sea-Central Asia regions and manifests a more evident southward propagation from the Barents- Kara Sea into the Ural region, which is responsible for the enhanced 10-to-30-day wave activity and temperature fluctuations in the region.

Wintertime Arctic Sea-Ice Decline Related to Multi-Year La Niña Events

Arctic sea ice has undergone a significant decline in the Barents-Kara Sea(BKS)since the late 1990s.Previous studies have shown that the decrease in sea ice caused by increased poleward moisture transport is modulated by tropical sea temperature changes(mainly referring to La Niña events).The occurrence of multi-year La Niña(MYLA)events has increased significantly in recent decades,and their impact on Arctic sea ice needs to be further explored.In this study,we investigate the relationship between sea-ice variation and different atmospheric diagnostics during MYLA and other La Niña(OTLA)years.The decline in BKS sea ice during MYLA winters is significantly stronger than that during OTLA years.This is because MYLA events tend to be accompanied by a warm Arctic-cold continent pattern with a barotropic high pressure blocked over the Urals region.Consequently,more frequent northward atmospheric rivers intrude into the BKS,intensifying longwave radiation downward to the underlying surface and melting the BKS sea ice.However,in the early winter of OTLA years,a negative North Atlantic Oscillation presents in the high latitudes of the Northern Hemisphere,which obstructs the atmospheric rivers to the south of Iceland.We infer that such a different response of BKS sea-ice decline to different La Niña events is related to stratospheric processes.Considering the rapid climate changes in the past,more frequent MYLA events may account for the substantial Arctic sea-ice loss in recent decades.

Acoustic Velocity-Based Inversion of the Physical Properties of Sea Ice in the Central Arctic Region

Studying the Arctic sea ice contributes to a comprehensive understanding of the climate system in polar regions and offers valuable insights into the interplay between polar climate change and the global climate and environment.One of the key research aspects is the investigation of the temperature,salinity,and density parameters of sea ice to obtain essential insights.During the 11th Chinese National Arctic Research Expedition,acoustic velocity was measured on an ice core at a short-term ice station,however,temperature,salinity,and density were not measured.In the present work,we utilized a genetic algorithm to invert these obtained acoustic velocity data to sea ice temperature,salinity,and density parameters on the basis of the relationship between acoustic velocity and the physical properties of Arctic summer sea ice.We validated the effectiveness of this inversion procedure by comparing its findings with those of other researchers.The results indicate that within the normalized depth range of 0.43-0.94,the ranges for temperature,salinity,and density are -0.48--0.29℃,1.63-3.35,and 793.1-904.1 kg m^(-3),respectively.

Spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice in multiple dimensions during 1979 to 2020

Arctic sea ice is broadly regarded as an indicator and amplifier of global climate change.The rapid changes in Arctic sea ice have been widely concerned.However,the spatiotemporal changes in the horizontal and vertical dimensions of Arctic sea ice and its asymmetry during the melt and freeze seasons are rarely quantified simultaneously based on multiple sources of the same long time series.In this study,the spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice were investigated from both the horizontal and vertical dimensions during 1979–2020 based on remote sensing and assimilation data.The results indicated that Arctic sea ice was declining at a remarkably high rate of–5.4×10^(4) km^(2)/a in sea ice area(SIA)and–2.2 cm/a in sea ice thickness(SIT)during 1979 to 2020,and the reduction of SIA and SIT was the largest in summer and the smallest in winter.Spatially,compared with other sub-regions,SIA showed a sharper declining trend in the Barents Sea,Kara Sea,and East Siberian Sea,while SIT presented a larger downward trend in the northern Canadian Archipelago,northern Greenland,and the East Siberian Sea.Regarding to the seasonal trend of sea ice on sub-region scale,the reduction rate of SIA exhibited an apparent spatial heterogeneity among seasons,especially in summer and winter,i.e.,the sub-regions linked to the open ocean exhibited a higher decline rate in winter;however,the other sub-regions blocked by the coastlines presented a greater decline rate in summer.For SIT,the sub-regions such as the Beaufort Sea,East Siberian Sea,Chukchi Sea,Central Arctic,and Canadian Archipelago always showed a higher downward rate in all seasons.Furthermore,a striking freeze-thaw asymmetry of Arctic sea ice was also detected.Comparing sea ice changes in different dimensions,sea ice over most regions in the Arctic showed an early retreat and rapid advance in the horizontal dimension but late melting and gradual freezing in the vertical dimension.The amount of sea ice melting and freezing was disequilibrium in the Arctic during the considered period,and the rate of sea ice melting was 0.3×10^(4) km^(2)/a and 0.01 cm/a higher than that of freezing in the horizontal and vertical dimensions,respectively.Moreover,there were notable shifts in the melting and freezing of Arctic sea ice in 1997/2003 and 2000/2004,respectively,in the horizontal/vertical dimension.

An improved algorithm for retrieving thin sea ice thickness in the Arctic Ocean from SMOS and SMAP L-band radiometer data

The aim of this study was to develop an improved thin sea ice thickness(SIT)retrieval algorithm in the Arctic Ocean from the Soil Moisture Ocean Salinity and Soil Moisture Active Passive L-band radiometer data.This SIT retrieval algorithm was trained using the simulated SIT from the cumulative freezing degree days model during the freeze-up period over five carefully selected regions in the Beaufort,Chukchi,East Siberian,Laptev and Kara seas and utilized the microwave polarization ratio(PR)at incidence angle of 40°.The improvements of the proposed retrieval algorithm include the correction for the sea ice concentration impact,reliable reference SIT data over different representative regions of the Arctic Ocean and the utilization of microwave polarization ratio that is independent of ice temperature.The relationship between the SIT and PR was found to be almost stable across the five selected regions.The SIT retrievals were then compared to other two existing algorithms(i.e.,UH_SIT from the University of Hamburg and UB_SIT from the University of Bremen)and validated against independent SIT data obtained from moored upward looking sonars(ULS)and airborne electromagnetic(EM)induction sensors.The results suggest that the proposed algorithm could achieve comparable accuracies to UH_SIT and UB_SIT with root mean square error(RMSE)being about 0.20 m when validating using ULS SIT data and outperformed the UH_SIT and UB_SIT with RMSE being about 0.21 m when validatng using EM SIT data.The proposed algorithm can be used for thin sea ice thickness(<1.0 m)estimation in the Arctic Ocean and requires less auxiliary data in the SIT retrieval procedure which makes its implementation more practical.

Arctic sea ice in CMIP5 climate model projections and their seasonal variability

This paper is focused on the seasonality change of Arctic sea ice extent （SIE） from 1979 to 2100 using newly available simulations from the Coupled Model Intercomparison Project Phase 5 （CMIP5）. A new approach to compare the simulation metric of Arctic SIE between observation and 31 CMIP5 models was established. The approach is based on four factors including the climatological average, linear trend of SIE, span of melting season and annual range of SIE. It is more objective and can be popularized to other comparison of models. Six good models （GFDL-CM3, CESM1-BGC, MPI-ESM-LR, ACCESS-1.0, HadGEM2-CC, and HadGEM2-AO in turn） are found which meet the criterion closely based on above approach. Based on ensemble mean of the six models, we found that the Arctic sea ice will continue declining in each season and firstly drop below 1 million km2 （defined as the ice-free state） in September 2065 under RCP4.5 scenario and in September 2053 under RCP8.5 scenario. We also study the seasonal cycle of the Arctic SIE and find out the duration of Arctic summer （melting season） will increase by about I00 days under RCP4.5 scenario and about 200 days under RCPS.5 scenario relative to current circumstance by the end of the 21st century. Asymmetry of the Arctic SIE seasonal cycle with later freezing in fall and early melting in spring, would be more apparent in the future when the Arctic climate approaches to ＂tipping point＂, or when the ice-free Arctic Ocean appears. Annual range of SIE （seasonal melting ice extent） will increase almost linearly in the near future 30-40 years before the Arctic appears ice-free ocean, indicating the more ice melting in summer, the more ice freezing in winter, which may cause more extreme weather events in both winter and summer in the future years.

The role of bias correction on subseasonal prediction of Arctic sea ice during summer 2018

Subseasonal Arctic sea ice prediction is highly needed for practical services including icebreakers and commercial ships,while limited by the capability of climate models.A bias correction methodology in this study was proposed and performed on raw products from two climate models,the First Institute Oceanography Earth System Model(FIOESM)and the National Centers for Environmental Prediction(NCEP)Climate Forecast System(CFS),to improve 60 days predictions for Arctic sea ice.Both models were initialized on July 1,August 1,and September 1 in 2018.A 60-day forecast was conducted as a part of the official sea ice service,especially for the ninth Chinese National Arctic Research Expedition(CHINARE)and the China Ocean Shipping(Group)Company(COSCO)Northeast Passage voyages during the summer of 2018.The results indicated that raw products from FIOESM underestimated sea ice concentration(SIC)overall,with a mean bias of SIC up to 30%.Bias correction resulted in a 27%improvement in the Root Mean Square Error(RMSE)of SIC and a 10%improvement in the Integrated Ice Edge Error(IIEE)of sea ice edge(SIE).For the CFS,the SIE overestimation in the marginal ice zone was the dominant features of raw products.Bias correction provided a 7%reduction in the RMSE of SIC and a 17%reduction in the IIEE of SIE.In terms of sea ice extent,FIOESM projected a reasonable minimum time and amount in mid-September;however,CFS failed to project both.Additional comparison with subseasonal to seasonal(S2S)models suggested that the bias correction methodology used in this study was more effective when predictions had larger biases.

The Coordinated Influence of Indian Ocean Sea Surface Temperature and Arctic Sea Ice on Anomalous Northeast China Cold Vortex Activities with Different Paths during Late Summer

The Northeast China cold vortex(NCCV)during late summer(from July to August)is identified and classified into three types in terms of its movement path using machine learning.The relationships of the three types of NCCV intensity with atmospheric circulations in late summer,the sea surface temperature(SST),and Arctic sea ice concentration(SIC)in the preceding months,are analyzed.The sensitivity tests by the Community Atmosphere Model version 5.3(CAM5.3)are used to verify the statistical results.The results show that the coordination pattern of East Asia-Pacific(EAP)and Lake Baikal high pressure forced by SST anomalies in the North Indian Ocean dipole mode(NIOD)during the preceding April and SIC anomalies in the Nansen Basin during the preceding June results in an intensity anomaly for the first type of NCCV.While the pattern of high pressure over the Urals and Okhotsk Sea and low pressure over Lake Baikal during late summer-which is forced by SST anomalies in the South Indian Ocean dipole mode(SIOD)in the preceding June and SIC anomalies in the Barents Sea in the preceding April-causes the intensity anomaly of the second type.The third type is atypical and is not analyzed in detail.Sensitivity tests,jointly forced by the SST and SIC in the preceding period,can well reproduce the observations.In contrast,the results forced separately by the SST and SIC are poor,indicating that the NCCV during late summer is likely influenced by the coordinated effects of both SST and SIC in the preceding months.

Toward Quantifying the Increasing Accessibility of the Arctic Northeast Passage in the Past Four Decades

Sea ice,one of the most dominant barriers to Arctic shipping,has decreased dramatically over the past four decades.Arctic maritime transport is hereupon growing in recent years.To produce a long-term assessment of trans-Arctic accessibility,we systematically revisit the daily Arctic navigability with a view to the combined effects of sea ice thickness and concentration throughout the period 1979−2020.The general trends of Navigable Windows(NW)in the Northeast Passage show that the number of navigable days is steadily growing and reached 89±16 days for Open Water(OW)ships and 163±19 days for Polar Class 6(PC6)ships in the 2010s,despite high interannual and interdecadal variability in the NWs.More consecutive NWs have emerged annually for both OW ships and PC6 ships since 2005 because of the faster sea ice retreat.Since the 1980s,the number of simulated Arctic routes has continuously increased,and optimal navigability exists in these years of record-low sea ice extent(e.g.,2012 and 2020).Summertime navigability in the East Siberian and Laptev Seas,on the other hand,varies dramatically due to changing sea ice conditions.This systematic assessment of Arctic navigability provides a reference for better projecting the future trans-Arctic shipping routes.

Seasonal and inter-annual variations of Arctic cyclones and their linkage with Arctic sea ice and atmospheric teleconnections

The seasonal and inter-annual variations of Arctic cyclone are investigated. An automatic cyclone tracking algorithm developed by University of Reading was applied on the basis of European Center for Medium-range Weather Forecasts（ECMWF） ERA-interim mean sea level pressure field with 6 h interval for 34 a period. The maximum number of the Arctic cyclones is counted in winter, and the minimum is in spring not in summer.About 50% of Arctic cyclones in summer generated from south of 70°N, moving into the Arctic. The number of Arctic cyclones has large inter-annual and seasonal variabilities, but no significant linear trend is detected for the period 1979–2012. The spatial distribution and linear trends of the Arctic cyclones track density show that the cyclone activity extent is the widest in summer with significant increasing trend in CRU（central Russia）subregion, and the largest track density is in winter with decreasing trend in the same subregion. The linear regressions between the cyclone track density and large-scale indices for the same period and pre-period sea ice area indices show that Arctic cyclone activities are closely linked to large-scale atmospheric circulations, such as Arctic Oscillation（AO）, North Atlantic Oscillation（NAO） and Pacific-North American Pattern（PNA）. Moreover,the pre-period sea ice area is significantly associated with the cyclone activities in some regions.

The seasonal foot printing mechanism of spring Arctic sea ice in the Bergen climate models

The influence of spring Arctic sea ice variability on the Pacific Decadal Oscillation （PDO） like sea surface temperature （SST） variability is established and investigated using an Atmosphere Ocean General Circulation Model （AOGCM） of the Bergen Climate Model version 2 （BCM2）. The spring Arctic sea ice variability affects the mid-latitudes and tropics through the propagation of the anomalous Eliassen-Palm （E-P） flux from the polar region to mid- and low-latitudes during boreal spring. The pathway includes anomalous upward wave activity, which propagates to the high troposphere from near the surface of the polar region, turns southward between 500 hPa and 200 hPa and extends downward between 50~N and 70~N, influencing the near surface atmospheric circulation. The alteration of the near surface atmospheric circulation then causes anomalous surface ocean circulation. These circulation changes consequently leads to the SST anomalies in the North Pacific which may persist until the following summer, named seasonal ＂foot printing＂ mechanism （SFPM）.

Separation of Atmospheric Circulation Patterns Governing Regional Variability of Arctic Sea Ice in Summer

In recent decades,Arctic summer sea ice extent(SIE)has shown a rapid decline overlaid with large interannual variations,both of which are influenced by geopotential height anomalies over Greenland(GL-high)and the central Arctic(CA-high).In this study,SIE along coastal Siberia(Sib-SIE)and Alaska(Ala-SIE)is found to account for about 65%and 21%of the Arctic SIE interannual variability,respectively.Variability in Ala-SIE is related to the GL-high,whereas variability in Sib-SIE is related to the CA-high.A decreased Ala-SIE is associated with decreased cloud cover and increased easterly winds along the Alaskan coast,promoting ice-albedo feedback.A decreased Sib-SIE is associated with a significant increase in water vapor and downward longwave radiation(DLR)along the Siberian coast.The years 2012 and 2020 with minimum recorded ASIE are used as examples.Compared to climatology,summer 2012 is characterized by a significantly enhanced GL-high with major sea ice loss along the Alaskan coast,while summer 2020 is characterized by an enhanced CA-high with sea ice loss focused along the Siberian coast.In 2012,the lack of cloud cover along the Alaskan coast contributed to an increase in incoming solar radiation,amplifying ice-albedo feedback there;while in 2020,the opposite occurs with an increase in cloud cover along the Alaskan coast,resulting in a slight increase in sea ice there.Along the Siberian coast,increased DLR in 2020 plays a dominant role in sea ice loss,and increased cloud cover and water vapor both contribute to the increased DLR.

Characteristics of extratropical cyclone variability in the Northern Hemisphere and their response to rapid changes in Arctic sea ice

Extratropical cyclones are critical weather systems that affect large-scale weather and climate changes at mid-high latitudes.However,prior research shows that there are still great difficulties in predicting extratropical cyclones for occurrence,frequency,and position.In this study,mean sea level pressure(MSLP)data from the European Centre for Medium-Range Weather Forecasts(ECMWF)reanalysis(ERA5)are used to calculate the variance statistics of the MSLP to reveal extratropical cyclone activity(ECA).Based on the analysis of the change characteristics of ECA in the Northern Hemisphere,the intrinsic link between ECA in the Northern Hemisphere and Arctic sea ice is explored.The results show that the maximum ECA mainly occurs in winter over the mid-high latitudes in the Northern Hemisphere.The maximum ECA changes in the North Pacific and the North Atlantic,which are the largest variations in the Northern Hemisphere,are independent of each other,and their mechanisms may be different.Furthermore,MSLP is a significant physical variable that affects ECA.The North Atlantic Oscillation(NAO)and North Pacific Index(NPI)are significant indices that impact ECA in the North Atlantic and North Pacific,respectively.The innovation of this paper is to explore the relationship between the activity of extratropical cyclones in the Northern Hemisphere and the abnormal changes in Arctic sea ice for the first time.The mechanism is that the abnormal changes in summer-autumn and winter Arctic sea ice lead to the phase transition of the NPI and NAO,respectively,and then cause the occurrence of ECA in the North Pacific and North Atlantic,respectively.Arctic sea ice plays a crucial role in the ECA in the Northern Hemisphere by influencing the polar vortex and westerly jets.This is the first exploration of ECAs in the Northern Hemisphere using Arctic sea ice,which can provide some references for the in-depth study and prediction of ECAs in the Northern Hemisphere.

Screening and Identification of Amylase-producing Strain from Arctic Ocean and Optimization of Enzyme Producing Conditions

[Objective] The aims were to investigate the screening and identification of amylase-producing marine bacteria from Arctic sea and the optimization of the amylase producing conditions. [Method] A high-yield strain for producing amylase named ArcB84A was isolated from a total of 156 marine bacteria of Arctic sea. Then,the morphological identification of the strain,molecular identification of 16S rRNA and optimization of fermentation conditions were conducted. [Result] ArcB84A strain was a member of Pseudoalteromonas genus. The optimum conditions for enzyme production of B84A strain included that,the initial pH value of the medium was 7.0-8.0,and the best carbon and nitrogen sources respectively were 5‰ glucose and peptone. Surfactants including TritonX-100,Tween20 and Tween80 could increase amylase activity of the strain,in which,the effect of 10‰ Tween80 was the most obvious.

Parameterization, sensitivity, and uncertainty of 1-D thermodynamic thin-ice thickness retrieval

Retrieval of Thin-Ice Thickness(TIT)using thermodynamic modeling is sensitive to the parameterization of the independent variables(coded in the model)and the uncertainty of the measured input variables.This article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice thickness.Moreover,it estimates the uncertainty of the output in response to the uncertainties of the input variables.The parameterized independent variables include atmospheric longwave emissivity,air density,specific heat of air,latent heat of ice,conductivity of ice,snow depth,and snow conductivity.Measured input parameters include air temperature,ice surface temperature,and wind speed.Among the independent variables,the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth,followed ice conductivity.The sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity,atmospheric emissivity,and snow conductivity and depth.The retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved data.From in situ measurements,the uncertainties of the measured air temperature and surface temperature are found to be high.The resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement error.The results show that the overall uncertainty of TIT to air temperature,surface temperature,and wind speed uncertainty is around 0.09 m,0.049 m,and−0.005 m,respectively.

On the Association between Spring Arctic Sea Ice Concentration and Chinese Summer Rainfall:A Further Study

In our previous study, a statistical linkage between the spring Arctic sea ice concentration （SIC） and the succeeding Chinese summer rainfall during the period 1968-2005 was identified. This linkage is demonstrated by the leading singular value decomposition （SVD） that accounts for 19% of the co-variance. Both spring SIC and Chinese summer rainfall exhibit a coherent interannual variability and two apparent interdecadal variations that occurred in the late 1970s and the early 1990s. The combined impacts of both spring Arctic SIC and Eurasian snow cover on the summer Eurasian wave train may explain their statistical linkage. In this study, we show that evolution of atmospheric circulation anomalies from spring to summer, to a great extent, may explain the spatial distribution of spring and summer Arctic SIC anomalies, and is dynamically consistent with Chinese summer rainfall anomalies in recent decades. The association between spring Arctic SIC and Chinese summer rainfall on interannual time scales is more important relative to interdecadal time scales. The summer Arctic dipole anomaly may serve as the bridge linking the spring Arctic SIC and Chinese summer rainfall, and their coherent interdecadal variations may reflect the feedback of spring SIC variability on the atmosphere. The summer Arctic dipole anomaly shows a closer relationship with the Chinese summer rainfall relative to the Arctic Oscillation.