2021 Vol. 40, No. 1
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.
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.
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.
A comprehensive analysis of sea ice and its snow cover during the summer in the Arctic Pacific sector was conducted using the observations recorded during the 7th Chinese National Arctic Research Expedition (CHIANRE-2016) and the satellite-derived parameters of the melt pond fraction (MPF) and snow grain size (SGS) from MODIS data. The results show that there were many low-concentration ice areas in the south of 78°N, while the ice concentration and thickness increased significantly with the latitude above the north of 78°N during CHIANRE-2016. The average MPF presented a trend of increasing in June and then decreasing in early September for 2016. The average snow depth on sea ice increased with latitude in the Arctic Pacific sector. We found a widely developed depth hoar layer in the snow stratigraphic profiles. The average SGS generally increased from June to early August and then decreased from August to September in 2016, and two valley values appeared during this period due to snowfall incidents.
This study cross-calibrated the brightness temperatures observed in the Arctic by using the FY-3B/MWRI L1 and the Aqua/AMSR-E L2A. The monthly parameters of the cross-calibration were determined and evaluated using robust linear regression. The snow depth in case of seasonal ice was calculated by using parameters of the cross-calibration of data from the MWRI Tb. The correlation coefficients of the H/V polarization among all channels Tb of the two sensors were higher than 0.97. The parameters of the monthly cross-calibration were useful for the snow depth retrieval using the MWRI. Data from the MWRI Tb were cross-calibrated to the AMSR-E baseline. Biases in the data of the two sensors were optimized to approximately 0 K through the cross-calibration, the standard deviations decreased significantly in the range of 1.32 K to 2.57 K, and the correlation coefficients were as high as 99%. An analysis of the statistical distributions of the histograms before and after cross-calibration indicated that the FY-3B/MWRI Tb data had been well calibrated. Furthermore, the results of the cross-calibration were evaluated by data on the daily average Tb at 18.7 GHz, 23.8 GHz, and 36.5 GHz (V polarization), and at 89 GHz (H/V polarization), and were applied to the snow depths retrieval in the Arctic. The parameters of monthly cross-calibration were found to be effective in terms of correcting the daily average Tb. The results of the snow depths were compared with those of the calibrated MWRI and AMSR-E products. Biases of 0.18 cm to 0.38 cm were observed in the monthly snow depths, with the standard deviations ranging from 4.19 cm to 4.80 cm.
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.
To explore new operational forecasting methods of waves, a forecasting model for wave heights at three stations in the Bohai Sea has been developed. This model is based on long short-term memory (LSTM) neural network with sea surface wind and wave heights as training samples. The prediction performance of the model is evaluated, and the error analysis shows that when using the same set of numerically predicted sea surface wind as input, the prediction error produced by the proposed LSTM model at Sta. N01 is 20%, 18% and 23% lower than the conventional numerical wave models in terms of the total root mean square error (RMSE), scatter index (SI) and mean absolute error (MAE), respectively. Particularly, for significant wave height in the range of 3–5 m, the prediction accuracy of the LSTM model is improved the most remarkably, with RMSE, SI and MAE all decreasing by 24%. It is also evident that the numbers of hidden neurons, the numbers of buoys used and the time length of training samples all have impact on the prediction accuracy. However, the prediction does not necessary improve with the increase of number of hidden neurons or number of buoys used. The experiment trained by data with the longest time length is found to perform the best overall compared to other experiments with a shorter time length for training. Overall, long short-term memory neural network was proved to be a very promising method for future development and applications in wave forecasting.
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.
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.
The evolution of thermohaline structure at the upper ocean during three tropical cyclones (TCs) in the Northwest Pacific was studied in this study based on successive observation by two new-style underwater gliders during fall 2018. These remote-controllable gliders with CTD sensor enabled us to explore high frequency responses of temperature, salinity, mixed and barrier layers in the upper ocean to severe TCs in this area. Results showed that three significant cooling-to-warming and stratification destructing-to-reconstructing processes at the mixed layer occurred during the lives of three TCs. The maximal cooling of SST all reached ≥0.5°C although TCs with different intensities had different minimal distances to the observed area. Under potential impacts of solar radiation, tide and inertial motions, the mixed layer depth possessed significant high-frequency fluctuations during TC periods. In addition, barrier layers appeared and vanished quickly during TCs, accompanied with varied temperature inversion processes.
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
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 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.
Two kinds of regression equations are used to reproduce the sediment flux of the 26 small coastal watersheds in southeastern China. The first kind is the global equations suggested by
Site U1446 (19°50’N, 85°44’E, at water depth 1 430 m) was drilled during Expedition 353 (Indian monsoon rainfall) of the International Ocean Discovery Program (IODP). It is located in the Mahanadi offshore basin, on the northern Bay of Bengal. Sedimentation rates and contents of biocarbonates are high at this relatively shallow site. Using a micropaleontological approach, we examined planktonic and benthic foraminifera in the upper around 40 m of this site, spanning the last around 190 ka. A striking feature of the foraminiferal record is the occurrence of strong but varying dissolution although the site is located well above the modern lysocline. Such strong dissolution has never been reported in this area. We estimated the flux of foraminifera and quantified the ratio of benthic foraminifera over total foraminifera (benthic/total foraminifera) along with the foraminifer fragmentation index in order to characterize past changes in this above-lysocline dissolution. This study reveals a clear glacial-interglacial contrast, with a stronger dissolution during marine isotope stages (MISs) 1 and 5 than during MISs 2–4 and 6. Such a difference in preservation is likely to have a strong impact on geochemical proxies measured on foraminifera. Our new observations call for an in-depth study of the causes of such above-lysocline dissolution in the region, and an evaluation of its impact on the foraminifera-based proxies used for paleoenvironmental reconstruction.
The exchange flow structure was examined in the North Passage of Changjiang River Estuary, where a deep waterway project (DWP) was carried out to improve the navigability. Before the construction of the DWP, the friction effect played a significant role in shaping the transverse structure of the exchange flow. The turbulent eddy viscosity generated near the seabed can be transferred to the upper water column, which facilitated vertical momentum exchange. As a result, the landward inflow extended to –2 m below the water surface and the seaward outflow was concentrated on the shallow shoal on the southern side of the cross section. After the construction of the DWP, the turbulent mixing was suppressed as a result of density stratification. The friction felt by the water was constrained in the lower half of the water column and the vertical momentum exchange was reduced. Meanwhile, the channel became dynamically narrowed with a Kelvin number of 0.52. Therefore, the Coriolis played a minor role in shaping the transverse structure of the exchange flow. As a consequence, the exchange flow featured a vertically-sheared pattern, with outflow at the surface and inflow underneath. Additionally, the gravitational circulation was enhanced due to increase in along-channel density gradient and stratification. The exchange flow components associated with the lateral processes (residual currents induced by eddy viscosity-shear covariance and lateral advective acceleration) were reduced, which suggests that lateral processes played a minor role in modifying the along-channel dynamics when the estuary becomes dynamically-narrowed.