To better monitor the vertical crustal movements and sea level changes around Greenland, multiple data sources were used in this paper, including global positioning system (GPS), tide gauge, satellite gravimetry, satellite altimetry, glacial isostatic adjustment (GIA). First, the observations of more than 50 GPS stations from the international GNSS service (IGS) and Greenland network (GNET) in 2007–2018 were processed and the common mode error (CME) was eliminated with using the principal component analysis (PCA). The results show that all GPS stations show an uplift trend and the stations in southern Greenland have a higher vertical speed. Second, by deducting the influence of GIA, the impact of current GrIS mass changes on GPS stations was analysed, and the GIA-corrected vertical velocity of the GPS is in good agreement with the vertical velocity obtained by gravity recovery and climate experiment (GRACE). Third, the absolute sea level change around Greenland at 4 gauge stations was obtained by combining relative sea level derived from tide gauge observations and crustal uplift rates derived from GPS observations, and was validated by sea level products of satellite altimetry. The results show that although the mass loss of GrIS can cause considerable global sea level rise, eustatic movements along the coasts of Greenland are quite complex under different mechanisms of sea level changes.

This study explores the ice flow acceleration (21.1%) of Pedersenbreen during 2016–2017 after the extremely warm winter throughout the whole Arctic in 2015/2016 using in situ data and quantitatively analyses the factors contributing to this acceleration. Several data sets, including 2008–2018 air temperature data from Ny-Ålesund, ten-year in situ GPS measurements and Elmer/Ice ice flow modelling under different ice temperature scenarios, suggest that the following factors contributed to the ice flow acceleration: the softened glacier ice caused by an increase in the air temperature (1.5°C) contributed 2.7%–30.5%, while basal lubrication contributed 69.5%–97.3%. The enhanced basal sliding was mostly due to the increased surface meltwater penetrating to the bedrock under the rising air temperature conditions; consequently, the glacier ice flow acceleration was caused mainly by an increase in subglacial water. For Pedersenbreen, there was an approximately one-year time lag between the change in air temperature and the change in glacier ice flow velocity.

Using a gridded array for real-time geostrophic oceanography (Argo) program float dataset, the features of upper-ocean salinity stratification in the tropical Pacific Ocean are studied. The salinity component of the squared Brunt-Väisälä frequency $ {N}^{2} $ ($ {N}_{S}^{2} $) is used to represent salinity stratification. Layer-max $ {N}_{S}^{2} $ (LMN), defined as the $ {N}_{S}^{2} $ maximum over the upper 300 m depth, and halocline depth (HD), defined as the depth where the $ {N}_{S}^{2} $ maximum is located, are used to specifically describe the intensity of salinity stratification. Salinity stratification in the Topical Pacific Ocean has both spatial and temporal variability. Over the western and eastern equatorial Pacific, the LMN has a large magnitude with a shallow HD, and both have completely opposite distributions outside of the equatorial region. An obvious seasonal cycle in the LMN occurs in the north side of eastern equatorial Pacific and freshwater flux forcing dominates the seasonal variations, followed by subsurface forcing. At the eastern edge of the western Pacific warm pool around the dateline, significant interannual variation of salinity stratification occurs and is closely related to the El Niño Southern Oscillation event. When an El Niño event occurs, the precipitation anomaly freshens sea surface and the thermocline shoaling induced by the westerly wind anomaly lifts salty water upward, together contribute to the positive salinity stratification anomaly over the eastern edge of the warm pool. The interannual variations in ocean stratification can slightly affect the propagation of first baroclinic gravity waves.

To assess the performances of state-of-the-art global climate models on simulating the Arctic clouds and surface radiation balance, the 2001–2014 Arctic Basin surface radiation budget, clouds, and the cloud radiative effects (CREs) in 22 coupled model intercomparison project 6 (CMIP6) models are evaluated against satellite observations. For the results from CMIP6 multi-model mean, cloud fraction (CF) peaks in autumn and is lowest in winter and spring, consistent with that from three satellite observation products (CloudSat-CALIPSO, CERES-MODIS, and APP-x). Simulated CF also shows consistent spatial patterns with those in observations. However, almost all models overestimate the CF amount throughout the year when compared to CERES-MODIS and APP-x. On average, clouds warm the surface of the Arctic Basin mainly via the longwave (LW) radiation cloud warming effect in winter. Simulated surface energy loss of LW is less than that in CERES-EBAF observation, while the net surface shortwave (SW) flux is underestimated. The biases may result from the stronger cloud LW warming effect and SW cooling effect from the overestimated CF by the models. These two biases compensate each other, yielding similar net surface radiation flux between model output (3.0 W/m²) and CERES-EBAF observation (6.1 W/m²). During 2001–2014, significant increasing trend of spring CF is found in the multi-model mean, consistent with previous studies based on surface and satellite observations. Although most of the 22 CMIP6 models show common seasonal cycles of CF and liquid water path/ice water path (LWP/IWP), large inter-model spreads exist in the amounts of CF and LWP/IWP throughout the year, indicating the influences of different cloud parameterization schemes used in different models. Cloud Feedback Model Intercomparison Project (CFMIP) observation simulator package (COSP) is a great tool to accurately assess the performance of climate models on simulating clouds. More intuitive and credible evaluation results can be obtained based on the COSP model output. In the future, with the release of more COSP output of CMIP6 models, it is expected that those inter-model spreads and the model-observation biases can be substantially reduced. Longer term active satellite observations are also necessary to evaluate models’ cloud simulations and to further explore the role of clouds in the rapid Arctic climate changes.

An investigation of equatorial near-inertial wave dynamics under complete Coriolis parameters is performed in this paper. Starting from the basic model equations of oceanic motions, a Korteweg de Vries equation is derived to simulate the evolution of equatorial nonlinear near-inertial waves by using methods of scaling analysis and perturbation expansions under the equatorial beta plane approximation. Theoretical dynamic analysis is finished based on the obtained Korteweg de Vries equation, and the results show that the horizontal component of Coriolis parameters is of great importance to the propagation of equatorial nonlinear near-inertial solitary waves by modifying its dispersion relation and by interacting with the basic background flow.

Based on the latest oceanic surface drifter dataset from the global drifter program during 2000–2019, this study investigated the global variation of relative frequency shift (RFS), near-inertial energy (NIE) and inverse excess bandwidth (IEB) of near-inertial motions, and analyzed their relations with oceanic mesoscale dynamics, relative vorticity and strain. Compared with previous works, we have some new findings in this study: (1) the RFS was high with negative values in some regions in which we found a significant blue shift of the RFS in the equatorward of 30°N (S) and from 50°N to 60°N in the Pacific, and a red shift in the western boundary currents and their extension regions, the North Atlantic and the Antarctic Circumpolar Current regions; (2) more peak values of the NIE were found in global regions like the South Indian Ocean, the Luzon Strait and some areas of the South Ocean; (3) the global distribution of the IEB were characterized by clear zonal bands and affected by vorticity and wind field; (4) the RFS was elevated as the absolute value of the gradient of vorticity increased, the IEB did not depend on the gradient of vorticity, and the eddy kinetic energy (EKE) weakened with the decrease of the absolute value of RFS; (5) the NIE decreased with increasing absolute value of the relative vorticity and the gradient of vorticity, but it increased with increasing strain and EKE when EKE was larger than 0.003 2 m²/s².

The present work describes the basic features of super typhoon Meranti (2016) by multiple data sources. We mainly focus on the upper ocean response to Meranti using multiplatform satellites, in situ surface drifter and Argo floats, and compare the results with the widely used idealized wind vortex model and reanalysis datasets. The pre-existing meso-scale eddy provided a favor underlying surface boundary condition and also modulated the upper ocean response to Meranti. Results show that the maximum sea surface cooling was 2.0°C after Meranti. The satellite surface wind failed to capture the core structure of Meranti as the idealized wind vortex model deduced. According to the observation of sea surface drifters, the near-inertial currents were significantly enhanced during the passage of Meranti. The temperature and salinity profiles from Argo floats revealed both the mixed-layer extension and subsurface upwelling induced by Meranti. The comparison results show that the sea surface temperature and surface wind in the reanalysis datasets differs from those in remote sensing system. Sea surface cooling is similar in both satellite and in situ observation, and sea surface salinity response has a lower correlation with the precipitation rate.

The four-dimensional variational assimilation (4D-Var) has been widely used in meteorological and oceanographic data assimilation. This method is usually implemented in the model space, known as primal approach (P4D-Var). Alternatively, physical space analysis system (4D-PSAS) is proposed to reduce the computation cost, in which the 4D-Var problem is solved in physical space (i.e., observation space). In this study, the conjugate gradient (CG) algorithm, implemented in the 4D-PSAS system is evaluated and it is found that the non-monotonic change of the gradient norm of 4D-PSAS cost function causes artificial oscillations of cost function in the iteration process. The reason of non-monotonic variation of gradient norm in 4D-PSAS is then analyzed. In order to overcome the non-monotonic variation of gradient norm, a new algorithm, Minimum Residual (MINRES) algorithm, is implemented in the process of assimilation iteration in this study. Our experimental results show that the improved 4D-PSAS with the MINRES algorithm guarantees the monotonic reduction of gradient norm of cost function, greatly improves the convergence properties of 4D-PSAS as well, and significantly restrains the numerical noises associated with the traditional 4D-PSAS system.

Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed (SSWS) from HH-polarized Sentinel-1 (S1) SAR images. The Polarization Ratio (PR) models combined with the CMOD5.N Geophysical Model Function (GMF) is used for SSWS retrieval from the HH-polarized SAR data. We compared different PR models developed based on previous C-band SAR data in HH-polarization for their applications to the S1 SAR data. The recently proposed CMODH, i.e., retrieving SSWS directly from the HH-polarized S1 data is also validated. The results indicate that the CMODH model performs better than results achieved using the PR models. We proposed a neural network method based on the backward propagation (BP) neural network to retrieve SSWS from the S1 HH-polarized data. The SSWS retrieved using the BP neural network model agrees better with the buoy measurements and ASCAT dataset than the results achieved using the conventional methods. Compared to the buoy measurements, the bias, root mean square error (RMSE) and scatter index (SI) of wind speed retrieved by the BP neural network model are 0.10 m/s, 1.38 m/s and 19.85%, respectively, while compared to the ASCAT dataset the three parameters of training set are –0.01 m/s, 1.33 m/s and 15.10%, respectively. It is suggested that the BP neural network model has a potential application in retrieving SSWS from Sentinel-1 images acquired at HH-polarization.