Soil moisture (SM) is an important parameter of global water cycle and play a vital role in partitioning surface fluxes, ecosystem dynamics, and biogeochemical cycles. Global SM mapping at high spatial and temporal using various methods with Global Navigation Satellite System-Reflectometry (GNSS-R) observations have been proposed. In previous studies, variations in SM retrieval and analysis tend to be treated as a whole, which however ignores the possible influence of monsoon or local land–ocean interactions on coastal SM. In this work, the Cyclone Global Navigation Satellite System (CYGNSS) observations and ancillary data are used to retrieve SM based on XGBoost model. Based on CYGNSS and Soil Moisture Active Passive (SMAP) SM against ISMN SM, we found that CYGNSS SM demonstrates a slight advantage in root mean square error (RMSE). Moreover, CYGNSS SM show no significant difference in accuracy between coastal and inland sites and SMAP SM show better performance in coastal sites relative to inland sites according to comparing SMAP and CYGNSS SM with ISMN SM at 293 inland and 68 coastal sites. In order to provide complementary insights for the accuracy of SM retrieval, Triple Collocation (TC) analysis demonstrate that inland SM have higher TC-based correlation than coastal SM. According to the contrast with SMAP SM in latitudinal, longitudinal and temporal aspects, coastal CYGNSS SM have relatively high values and bias and generally underestimate SM. Thus, CYGNSS have better performance in inland regions. Based on the SM retrieved by CYGNSS and FY-3 data against ISMN SM, different spaceborne GNSS-R data varies over coastal and inland regions. Furthermore, the bias between CYGNSS and SMAP SM have strong connections with net precipitation (precipitation minus evapotranspiration).

The Arctic is a climate-sensitive region where Arctic amplification (AA) is influenced by aerosol radiative forcing. Aerosol optical depth (AOD), quantifying aerosol extinction, is essential for assessing its effects on the Arctic climate and environment. However, single-satellite AOD products exhibit large uncertainties and data gaps in the Arctic due to sensor limitations and complex surface conditions. The conventional Bayesian maximum entropy (BME) framework's dependence on least-squares covariance modeling restricts its ability to model complex, nonstationary high-dimensional parameter spaces. To address these limitations, we incorporated the particle swarm optimization (PSO) algorithm with global search capability into the BME framework, creating a PSO-BME hybrid model that enhances both the stability and accuracy of Arctic AOD fusion. To optimize Arctic AOD fusion, we tested 24 combinations of four key parameters, using iterative fusion and sensitivity analysis to assess accuracy impacts. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied to identify the optimal parameter configuration. Results indicate that PSO-BME effectively integrates AOD data from MODIS and Multiangle Imaging Spectroradiometer (MISR). In overlapping regions, the fusion AOD achieves a root-mean-squared error (RMSE) of 0.056, an expected error (EE) of 76.7%, and a correlation coefficient of 0.66, while in unobserved regions, the accuracy remains acceptable. The annual coverage of the fusion AOD increases to 41.61% (compared to 14.49% of Moderate Resolution Imaging Spectroradiometer (MODIS) and 1.40% of MISR). Spatiotemporal analyses indicate that the fusion product exhibits a smoother spatial pattern and better captures overall variability, while its evolution suggests a dual-influence mechanism driven by Arctic meteorology and long-range transport from mid to low latitudes. Enhanced AOD coverage and accuracy materially advance Arctic climate science by providing stronger observational constraints for atmospheric research and related studies in the Arctic.

The growth and development of marine plants are largely dependent on nutrient input and solar radiation. Aerosols, through atmospheric deposition, can directly or indirectly influence the growth and distribution of marine organisms, while changes in cloud cover and aerosol presence can alter solar radiation levels. Gaining insight into aerosol-cloud interactions in the Northwest Pacific and their effects on marine plants can support the sustainable development and utilization of ocean resources. This study utilized aerosol optical depth (AOD) data from MODIS to examine the spatiotemporal distribution and potential sources of aerosols over the Northwest Pacific. Additionally, the study integrated MODIS data on cloud optical thickness of ice and liquid (COTI/COTL), cloud water path of ice and liquid (CWPI/CWPL), effective particle radii of ice and liquid clouds (CERI/CERL) and ocean net primary productivity (NPP) data from Oregon State University to analyze aerosol-cloud interaction patterns and their effects on marine phytoplankton. The results show that in the fishing grounds of the northwest Pacific (38 degrees-46 degrees N, 150 degrees-175 degrees E), the sustained northward migration and intensification of the subtropical high during summer weaken the upper-level atmosphere from Asia. This results in aerosols in the local marine area predominantly originating from the clean marine (CM) aerosols of the Pacific region, which typically peak from June to August each year (0.9). Aerosols in both the fishing and spawning grounds (20 degrees-30 degrees N, 130 degrees-170 degrees E) significantly affect the CWPI and CWPL, with those in the fishing grounds exerting a stronger influence. Furthermore, as the net primary productivity (NPP) in the fishing grounds is primarily influenced by clouds, aerosols indirectly impact local phytoplankton photosynthesis by suppressing cloud formation. In contrast, in the spawning grounds, the proximity to the East Asian continent leads to greater influence from human activities, with aerosol deposition and river inputs playing a dominant role in the local marine environment.

Tropospheric aerosols play an important role in the notable warming phenomenon and climate change occurring in the Arctic. The accuracy of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol optical depth (AOD) and the distribution of Arctic AOD based on the CALIOP Level 2 aerosol products and the Aerosol Robotic Network (AERONET) AOD data during 2006–2021 were analyzed. The distributions, trends, and three-dimensional (3D) structures of the frequency of occurrences (FoOs) of different aerosol subtypes during 2006–2021 are also discussed. We found that the CALIOP AOD exhibited a high level of agreement with AERONET AOD, with a correlation coefficient of approximately 0.67 and an RMSE of less than 0.1. However, CALIOP usually underestimated AOD over the Arctic, especially in wet conditions during the late spring and early summer. Moreover, the Arctic AOD was typically higher in winter than in autumn, summer, and spring. Specifically, polluted dust (PD), dust, and clean marine (CM) were the dominant aerosol types in spring, autumn, and winter, while in summer, ES (elevated smoke) from frequent wildfires reached the highest FoOs. There were increasing trends in the FoOs of CM and dust, with decreasing trends in the FoOs of PD, PC (polluted continental), and DM (dusty marine) due to Arctic amplification. In general, the vertical distribution patterns of different aerosol types showed little seasonal variation, but their horizontal distribution patterns at various altitudes varied by season. Furthermore, locally sourced aerosols such as dust in Greenland, PD in eastern Siberia, and ES in middle Siberia can spread to surrounding areas and accumulate further north, affecting a broader region in the Arctic.

Extracting offshore aquaculture areas from high-resolution remote sensing images is significant for rational planning and dynamic management of aquaculture. However, the complex and changeable characteristics of aquaculture areas will lead to difficulty during high-resolution image processing. In this paper, an improved U-Net deep learning model is proposed to extract offshore aquaculture areas from high-resolution images. This model is improved by the pyramid pooling module (PPM) and up-sampling structure based on the original U-Net model. PPM and the up-sampling structure can better mine semantic and position information in high-resolution images, reduce the interference and adhesion of information in images, and improve the extraction accuracy of unclear aquaculture areas. Based on Gaofen-2 remote sensing image data of Rongcheng Bay in the eastern waters of Weihai City, Shandong Province, the extraction of offshore aquaculture areas is studied. The results show that the F1 score and MIoU of the proposed network model are 93.63% and 87.93%, respectively. Compared with several commonly used deep learning models, the improved U-Net model has a better extraction effect.

Nutrients are critical in assessing water quality, so understanding their distribution and variability is essential for effective marine environmental protection. This study focuses on the Yangtze River estuary and surrounding waters, where suspended solids show a strong correlation with active phosphates and silicates. Using GOCI imagery and measured nutrient concentrations, such as active phosphate and silicate, remote sensing models were developed to investigate the seasonal and daily changes in surface water nutrients. The results showed the following key findings: Temporally, active phosphate (PO4-P) and silicate (SiO3-Si) concentrations exhibited distinct seasonal patterns, with the highest values observed in winter (1.692 μmol/L and 16.386 μmol/L, respectively) and the lowest in summer (0.503 μmol/L and 10.645 μmol/L, respectively). Little difference was found between spring and autumn. Spatially, elevated phosphate and silicate concentrations were found near the northern Jiangsu Shoal, the Yangtze River estuary, and Hangzhou Bay, and decreased towards the outer coastal waters. This suggests that the freshwater inflow from the Yangtze River is an important driver of the observed nutrient patterns. Diurnal variations in phosphate and silicate concentrations were observed in the surface waters of the Yangtze River estuary and adjacent areas. Significant diurnal variations in nutrient concentrations were observed in Hangzhou Bay, the northern part of the Yangtze River estuary and the southern part of the Yangtze River estuary. Slight diurnal variations were observed in the inland channels of the estuary. These results help to facilitate the study of the complex process of spatial and temporal dynamics of nutrients in the coastal waters of eastern China.

利用GNSS-R(全球导航卫星系统反射测量)技术进行准确的雪深监测已成为传统雪深测量的重要补充手段.本文使用GNSS-R技术反演了2012—2018年美国阿拉斯加州4个GPS观测站附近的雪深结果,结合加拿大气象中心(Canadian Meteorological Centre,CMC)提供的雪深模型数据产品,以PBO(Plate Boundary Observatory)H2O项目组提供的雪深资料为参考值,分析了不同手段获取的雪深值在不同时间尺度上的变化特征,同时评估了GNSS-R反演雪深结果作为独立数据集验证CMC模型数据的能力.结果表明:GNSS-R、CMC和PBO得到的长时间序列雪深结果均具有较为一致的明显周期性变化,整体上GNSS-R反演结果比CMC数据精度更高,更能反映雪深的年际变化情况.GNSS-R反演值和CMC模拟值均能够反映各测站PBO雪深值的逐月变化规律,但GNSS-R反演值的精度和稳定性总体上优于CMC模拟值.GNSS-R反演结果比CMC模拟值与PBO雪深值的季节性变化更具一致性,且对于本文研究的4个测站,GNSS-R反演雪深的精度和稳定性在雪深值较大的春季和冬季较高,雪深值较小的秋季略差.此外,本文还证实了GNSS-R反演的雪深结果可用于评估CMC模拟雪深值的精度,且评估效果在冬春季优于秋季.

Accurately building the relationship between the oceanographic environment and the distribution of neon flying squid（Ommastrephes bartramii） is very important to understand the potential habitat pattern of O. bartramii.However, when building the prediction model of O. bartramii with traditional oceanographic variables（e.g.,chlorophyll a concentration（Chl a） and sea surface temperature（SST）） from space-borne observations, part of the important spectrum characteristics of the oceanic surface could be masked by using the satellite data products directly. In this study, the neglected remote sensing information（i.e., spectral remote sensing reflectance（Rrs）and brightness temperature（BT）） is firstly incorporated to build the prediction model of catch per unit effort（CPUE） of O. bartramii from July to December during 2014–2018 in the Northwest Pacific Ocean. Results show that both the conventional oceanographic variables and the neglected remote sensing data are suitable for building the prediction model, whereas the overall root mean square error（RMSE） of the predicted CPUE of O.bartramii with the former is typically less accurate than that with the latter. Hence, the Rrs and BT could be a more suitable data source than the Chl a and SST to predict the distribution of O. bartramii, highlighting that the potential value of the neglected variables in understanding the habitat suitability of O. bartramii.

Using the quality-controlled radio occultation (RO) refractivity profiles from nine missions, this study extracts the planetary boundary layer height (PBLH) with the minimum refractivity gradient method during December 2006-November 2019 over the Arctic, investigates the spatiotemporal variations in the PBLH, and discusses the potential relationships of PBLH to sea ice concentration (SIC) and related atmospheric parameters. We find that the RO-derived Arctic PBLH is typically deeper in summer than in autumn, spring, and winter. Moreover, PBLH displays clearly synchronous seasonal cycle in all latitude zones over both land and ocean, whereas the PBLH seasonality in different latitude zones over ocean exhibits much different patterns to that over land. Significant increases in PBLH are typically detected in all latitude zones over the entire Arctic except for the 78 degrees N-84 degrees N over ocean. In general, PBLH over the Arctic Ocean is consistently negatively (positively) correlated with SIC [surface air temperature (SAT) and precipitable water vapor (PWV)] in each season. However, the response of PBLH to the variations in these parameters varies with the sea ice conditions. PBLH tends to be affected by the moisture advection and the surface radiative cooling in the open ocean and the solid ice pack, respectively, while the PBLH in the marginal ice zone typically has transitional features between the open ocean and the solid ice pack. Furthermore, variations in low cloud fraction (CF) appear to have no direct effect on PBLH over the Arctic Ocean.

对表层水体硝酸盐、磷酸盐和硅酸盐浓度之间的相关性进行分析,利用GOCI影像与实测表层水体的营养盐浓度(包括磷酸盐和硅酸盐)建立长江口及附近海域的表层水体营养盐遥感反演模型,并利用实测数据和营养盐之间的相关关系对反演模型进行验证.验证结果表明:磷酸盐和硅酸盐反演模型的平均绝对百分比误差分别是21.65％和6.73％.将建立的营养盐反演模型应用于GOCI影像进行表层水体营养盐日内变化的研究,结果显示:整个长江口及附近海域表层水体磷酸盐和硅酸盐浓度的分布呈现出由近岸向外海递减的趋势,且在苏北浅滩、长江口及杭州湾出现明显的高值区;长江口及附近海域表层磷酸盐和硅酸盐浓度日变化明显,其中杭州湾和长江口外南部营养盐受潮汐影响显著,即在涨潮时表层水体营养盐浓度降低,在落潮时浓度升高,长江口外北部海域营养盐浓度波动较大.

基于监督式学习算法的BP神经网络模型,综合多源卫星遥感观测获取得到的海表面温度(sea surface temperature,SST)、叶绿素 a 质量浓度(chlorophyll-a mass concentration,Chl.a)、海表面高度距平值(sea surface height anomaly,SSHA)、海水质量变化和地转流等海洋环境因子,对西北太平洋柔鱼资源丰度的时空分布进行模拟和预测.以上海海洋大学中国远洋渔业数据中心2004-2017年的西北太平洋海域的柔鱼历史渔业捕捞数据为参考值,对基于多源卫星遥感观测的多海洋环境因子的柔鱼资源丰度的模拟和预测结果进行精度评定.结果表明:与仅采用SST、Chl.a和SSHA等进行柔鱼资源丰度时空分布预测的传统方案相比,进一步加入海水质量变化和地转流后,可有效提高利用BP神经网络对西北太平洋柔鱼资源丰度进行模拟和预测的精度:改进方法模拟的标准差(standard deviation,STD)和均方根误差(root mean square error,RMSE)均减少了 22％,且预测的STD减少了 31％,RMSE减少了 26％.

Using the precipitable water vapor (PWV) and cloud fraction (CF) measurements from the atmospheric infrared sounder (AIRS), this study investigates the potential feedback on Arctic sea ice concentration (SIC) caused by variations in PWV and total CF, as well as CFs at low, middle and high levels during December 2002–November 2020. We find that increases in PWV and total CF generally coincide with the Arctic sea ice decline in each season, and both PWV and total CF anomalies are typically correlated with SIC anomalies in all seasons except for the PWV anomalies in summer. In addition, during all seasons except summer, the low CF generally exhibits closer connection than the middle and high CFs with the trend in total CF and sea ice, and the correlation of SIC with total CF anomalies is governed by that with low CF anomalies. The sensitivities of SIC to changes in PWV and total CF are typically negative, and they are generally consistent with each other in each season. Moreover, the negative sensitivities of SIC to low, middle, and high CFs are greatest and most extensive in summer. The contribution of PWV is typically much greater and more extensive than that of total CF to Arctic sea ice change in all seasons. Among the CFs at different levels, the positive contribution of low CF is typically most extensive in winter and spring, while the contributions of high and middle CFs are relatively greater in summer and autumn.

The Arctic Oscillation (AO) has important effects on the sea ice change in terms of the dynamic and thermodynamic processes. However, while the dynamic processes of AO have been widely explored, the thermodynamic processes of AO need to be further discussed. In this paper, we use the fifth state-of-the-art reanalysis at European Centre for Medium-Range Weather Forecasts (ERA5) from 1979 to 2020 to investigate the relationship between AO and the surface springtime longwave (LW) cloud radiative forcing (CRF), summertime shortwave (SW) CRF in the Arctic region (65°–90°N). In addition, the contribution of CRF induced by AO to the sea ice change is also discussed. Results indicate that the positive (negative) anomalies of springtime LW CRF and summertime SW CRF are generally detected over the Arctic Ocean during the enhanced positive (negative) AO phase in spring and summer, respectively. Meanwhile, while the LW (SW) CRF generally has a positive correlation with AO index (AOI) in spring (summer) over the entire Arctic Ocean, this correlation is statistically significant over 70°–85°N and 120°W–90°E (i.e., region of interest (ROI)) in both seasons. Moreover, the response of CRF to the atmospheric conditions varies in spring and summer. We also find that the positive springtime (summertime) AOI tends to decrease (increase) the sea ice in September, and this phenomenon is especially prominent over the ROI. The sensitivity study among sea ice extent, CRF and AOI further reveals that decreases (increases) in September sea ice over the ROI are partly attributed to the springtime LW (summertime SW) CRF during the positive AOI. The present study provides a new pattern of AO affecting sea ice change via cloud radiative effects, which might benefit the sea ice forecast improvement.

春季云属性的分布和变化对北极9月的海冰变化具有重要的预调节作用.但是,在北极及全球气候变暖加剧的背景下,春季云和9月海冰之间的关系及其在北极不同海域的特征需要进一步更新.本文基于ERA5云辐射、MODIS云量和云水路径以及美国国家冰雪中心的海冰密集度(sea ice concentration,SIC)数据,首先分析了2000—2017年北极地区春季云量、云水路径两种云宏观属性与云长波辐射效应、云短波辐射效应两种云辐射属性气候学尺度的空间分布特征,然后探讨了云宏观属性和辐射属性的相关关系,以及在不同感兴趣区(region of interest,ROI)内的海冰变化对云属性的响应特征.结果表明,在北极的春季,云量的分布随海冰密集度的升高而递减,云水路径的分布随纬度的升高而增大.云长波辐射效应的分布不连续,未见显著规律,在巴伦支海和东格陵兰海域及其以北的北冰洋海域(ROI2)以外的其他区域,云短波辐射效应差异较小.北极云量和云水路径与云长波辐射效应主要存在正相关关系,与云短波辐射效应存在负相关关系,但在相关性的强度和范围上云水路径均不如云量.在冰边缘区占比最高的拉普捷夫海和喀拉海域及其以北的北冰洋海域(ROI1)和波弗特、楚科奇、东西伯利亚海域及其以北的北冰洋海域(ROI4),春季云长波辐射效应倾向于增强9月海冰的融化,云短波辐射效应作用相反,这种响应的滞后时长约为4个月.在多元回归模型中,决定系数可用于表征自变量对因变量的解释程度,决定系数的结果表明,ROI1的云量变率占海冰退化成因的约18.53％,ROI4的云量和云水路径的重要性在海冰减退机制中没有得到体现.

During the northeast monsoon season, Zhe-Min Coastal Current (ZMCC) travels along the Chinese mainland coast and carries fresh, cold, and eutrophic water. ZMCC is significantly important for the hydrodynamic processes and marine ecosystems along its path. Thus, this bottom-trapped plume deserves to be further discussed in terms of the major driving factor, for which different opinions exist. For this purpose, in this study, a high resolution Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM) is established and validated. High correlation coefficients exist between along-shelf wind speeds and seasonal variations of both ZMCC volume transport and the freshwater signal. These coefficients imply that the wind is important in regulating ZMCC. However, for similar annual mean ZMCC volume transports, the extreme south boundaries of Zhe-Min Coastal Water (ZMCW) are different among different years. This difference is attracting attention and is explored in this study. According to the low wind/discharge experiment, it was found that although the volume transport of ZMCC is more sensitive to the variation of local wind speeds, the carried freshwater is limited by the Changjiang River discharge, which ultimately determines the south boundary of ZMCW. The momentum analysis at transects I and II shows that, for driving ZMCC, the along-shore wind forcing is as important as the buoyancy forcing. Note that this conclusion is supported by a zero-discharge experiment. It was also found that the buoyancy forcing varies with respect to time and space, which is due to variations of the discharge of Changjiang River. In addition, a particle tracking experiment shows that the substance carried by the Changjiang River diluted water would distribute along the Zhe-Min coastal region during the northeast monsoon season and it may escape due to the wind relaxation.

水汽输送可以通过直接或间接的方式影响北极地区(60°N—90°N)的水文、辐射和动力等气候因子.了解水汽输送的分布和变化特征,对研究北极气候变化具有重要意义.本文基于ERA5、JRA-55和MERRA-2再分析资料,分析了1980—2018年沿70°N水汽通量的水平分布、垂直分布和月平均分布,以及北极地区整层水汽通量在不同季节的变化特征和线性趋势.基于三种再分析资料的分析结果均表明,沿70°N的水汽输送在东北极主要为向北,在西北极为向南和向北相间分布且相对于东北极更活跃;沿70°N的向北水汽输送在垂直方向上的900 hPa高度附近较为强烈.此外,沿70°N的向北水汽输送通量平均高于向南,说明了北极地区整体体现为水汽汇入地.北极地区的向北水汽输送在夏秋季比冬春季节更活跃,且夏秋季的水汽通量在向北水汽输送通道附近和北大西洋区域的增速更为明显.对沿70°N的水汽入侵的时间变化和水平分布结果进行分析后发现,水汽入侵主要发生在向北水汽输送通道附近,且水汽入侵受比湿的影响比经向风更大,这使得水汽入侵的年平均次数在2008年之后出现剧烈波动.整体上,相比ERA5和JRA-55,MERRA-2与IGRA2的正相关性更强且精度更高,更适用于北极水汽输送研究.

北极是地球上的气候敏感区,近年来其温度升高是全球平均水平的两倍,而大气气溶胶对北极变暖有显著影响.为探究北极大气气溶胶的光学特性,基于MODIS(Moderate-resolution Imaging Spectroradi-ometer)10 km和3 km气溶胶光学厚度AOD(Aerosol Optical Depth)产品,分析了2000年3月—2018年10月北极地区AOD的时空分布特征,并与气溶胶自动观测网AERONET(Aerosol Robotic Network)地基站点的气溶胶观测结果进行了比较验证.结果表明,MODIS 10 km和3 km AOD与AERONET地基站点的相关系数分别为0.840和0.853,均方根误差均小于0.1,MODIS 10 km和3 km两种AOD产品与北极地面现场观测结果均具有较好的一致性.MODIS两种AOD产品与AERONET地基站点的相关系数、均方根误差、相对平均偏差和期望误差的结果均显示,北极地区MODIS 10 km AOD产品精度整体上略高于3 km AOD产品,且两者在北极东半球较其他地区的精度更高;MODIS 10 km AOD产品在西半球的误差呈现出纬度越高精度越高的特点,但3 km AOD产品没有显示这一规律;在时间上,MODIS两种AOD产品精度均在5、9和10月份较高,在秋季比春夏季略高.此外,基于北极地区MODIS AOD产品还发现,不同纬度的AOD之间存在显著差异,AOD随纬度降低不均匀增大.北极地区AOD呈现明显的季节变化,春季3—5月份AOD值普遍介于0.1～0.3,且在4月的北欧区域达到最高值;在夏季6—8月份,AOD高值范围逐渐增大;在秋季9—10月份,AOD普遍在0.1以下.两种MODIS AOD产品在北极洋面和陆面上呈现不同的特征:洋面上10 km的AOD通常更大,而陆面上3 km的AOD更大.

The Antarctic affects the global climate change, and the temperature inversion (TI) and specific humidity inversion (SHI) are the dominant features of the Antarctic atmosphere. In this study, Integrated Global Radiosonde Archive 2 (IGRA2) was used to compare and verify the characteristics of TI and SHI calculated by Atmospheric Infrared Sounder (AIRS) V6 and V7 products under cloudy conditions, and analyzed the spatial and temporal distribution of the characteristics of TI and SHI in the Antarctic. Results show that the accuracy of AIRS temperature and specific humidity profile is better of V7 than it of V6 in most case and is weaker as the cloud fractions (CF) increases. The comparison of TI and SHI characteristics (strength and frequency) between IGRA2 and AIRS under cloudy conditions further indicates that the STD and RMSE between IGRA2 and AIRS of the TI and SHI characteristics decreases gradually with the increase of CF, and the AIRS products under cloudy conditions are also suitable for studying the characteristics of TI and SHI over the Antarctic. Based on AIRS V7 products, the frequency and strength of TI and SHI in the Antarctic for 2004–2020 are lower (weaker) in spring and summer than in autumn and winter. In spatial distribution, the overall TI frequency and strength are lower (weaker) over the ocean than over the land, while the frequency and strength of SHI are opposite.

Sea ice conditions in the Canadian Arctic Archipelago (CAA) play a key role in the navigation of the Northwest Passage (NWP). Limited by observed sea ice thickness data, the research of temporal and spatial variation of sea ice in the NWP is insufficient. Based on the observed sea ice concentration and simulated thickness data, the temporal and spatial characteristics of sea ice concentration, extent and thickness from 1979 to 2017 in the NWP of the CAA were studied. The more specific pathways of the northern and southern routes of the NWP were evaluated. Against the background of the rapid retreat of Arctic sea ice, the 39-year observed sea ice concentration and extent of the NWP exhibited a relatively large decreasing trend in summer and fall, while heavy sea ice conditions were maintained in winter and spring, with slightly increasing trend in some subregions. The sea ice thickness in most subregions of the NWP showed a decreasing trend, with exception of Lancaster Sound. The sea ice thickness was larger along the northern route than the southern routes. The significant correlation (p < 0.05) between sea ice and surface air temperature (SAT) and sea surface temperature (SST) in the NWP suggested that the surface thermodynamic factors had a greater impact on sea ice in the summer and fall, and the variations of sea ice concentration were more closely correlated with the surface thermodynamic factors than sea ice thickness. The SST had a higher correlation with sea ice concentration than SAT, while SAT exhibited a higher correlation with sea ice thickness than SST. The remaining sea ice concentration and thickness in the fall, associated with the summer and fall SAT and SST, contributed to the formation of sea ice in the following winter and spring. The heat content and mixed layer depth were also be considered as the vertical thermodynamic factors to the sea ice condition in the NWP.