Thermocline Model for Estimating Argo Sea Surface Temperature

Argo has become an important constituent of the global ocean observation system. However, due to the lack of sea surface measurements from most Argo profiles, the application of Argo data is still limited. In this study, a thermocline model was constructed based on three key thermocline parameters, i.e, thermocline upper depth, the thermocline bottom depth, and thermocline temperature gradient. Following the model, we estimated the sea surface temperature of Argo profiles by providing the relationship between sea surface and subsurface temperature. We tested the effectiveness of our proposed model using statistical analysis and by comparing the sea surface temperature with the results obtained from traditional methods and in situ observations in the Pacific Ocean. The root mean square errors of results obtained from thermocline model were found to be significantly reduced compared to the extrapolation results and satellite retrieved temperature results. The correlation coefficient between the estimation result and in situ observation was 0.967. Argo surface temperature, estimated by the thermocline model, has been theoretically proved to be reliable. Thus, our model generates theoretically feasible data present the mesoscale phenomenon in more detail. Overall, this study compensates for the lack surface observation of Argo, and provides a new tool to establish complete Argo data sets.

Deep chlorophyll maximum and nutricline in the Mediterranean Sea: emerging properties from a multi-platform assimilated biogeochemical model experiment

Data assimilation has led to advancements in biogeochemical modelling and scientific understanding of the ocean. The recent operational availability of data from BGC-Argo (biogeochemical Argo) floats, which provide valuable insights into key vertical biogeochemical processes, stands to further improve biogeochemical modelling through assimilation schemes that include float observations in addition to traditionally assimilated satellite data. In the present work, we demonstrate the feasibility of joint multi-platform assimilation in realistic biogeochemical applications by presenting the results of 1-year simulations of Mediterranean Sea biogeochemistry. Different combinations of satellite chlorophyll data and BGC-Argo nitrate and chlorophyll data have been tested, and validation with respect to available independent non-assimilated and assimilated (before the assimilation) observations showed that assimilation of both satellite and float observations outperformed the assimilation of platforms considered individually. Moreover, the assimilation of BGC-Argo data impacted the vertical structure of nutrients and phytoplankton in terms of deep chlorophyll maximum depth, intensity, and nutricline depth. The outcomes of the model simulation assimilating both satellite data and BGC-Argo data provide a consistent picture of the basin-wide differences in vertical features associated with summer stratified conditions, describing a relatively high variability between the western and eastern Mediterranean, with thinner and shallower but intense deep chlorophyll maxima associated with steeper and narrower nutriclines in the western Mediterranean.

Ocean Observation Data Prediction for Argo Data Quality Control Using Deep Bidirectional LSTM Network

With the rapid development of maritime technologies, a huge amount of ocean data has been acquired through the state-of-the-art ocean equipment to get better understanding and development of ocean. The prediction and correction of oceanic observation data play a fundamental and important role in the oceanic relevant applications, including both civilian and military fields. On the basis of Argo data, aiming at predicting and correcting the oceanic observation data, we propose an ocean temperature and salinity prediction approach in this paper. In our approach, firstly, bounded nonlinear function is utilized for dataset quality control, which can effectively eliminate the influence of spikes or outliers in Argo data. Then, RBF neural network is used for high-resolution Argo dataset construction. Finally, a bidirectional LSTM framework is proposed to predict and analyze the ocean temperature and salinity on the basis of BOA Argo data. Experimental results demonstrate that the proposed bidirectional LSTM framework can accurately predict the ocean temperature and salinity and enable outstanding performance in oceanic observation data prediction and correction. The proposed approach is also important for the realization of Argo dataset automatic quality control.

Filtering method based on cluster analysis to avoid salinity drifts and recover Argo data in less time

Currently there is a huge amount of freely available hydrographic data, and it is increasingly important to have easy access to it and to be provided with as much information as possible. Argo is a global collection of around 4000 active autonomous hydrographic profilers. Argo data go through two quality processes, real time and delayed mode. This work shows a methodology to filter profiles within a given polygon using the odd–even algorithm; this allows analysis of a study area, regardless of size, shape or location. The aim is to offer two filtering methods and to discard only the real-time quality control data that present salinity drifts. This takes advantage of the largest possible amount of valid data within a given polygon. In the study area selected as an example, it was possible to recover around 80 % in the case of the first filter that uses cluster analysis and 30 % in the case of the second, which discards profilers with salinity drifts, of the total real-time quality control data that are usually discarded by the users due to problems such as salinity drifts. This allows users to use any of the filters or a combination of both to have a greater amount of data within the study area of their interest in a matter of minutes, rather than waiting for the delayed-mode quality control that takes up to 12 months to be completed. This methodology has been tested for its replicability in five selected areas around the world and has obtained good results.

Reduced misclosure of global sea-level budget with updated Tongji-Grace2018 solution

The global sea-level budget is studied using the Gravity Recovery and Climate Experiment (GRACE) solutions, Satellite Altimetry and Argo observations based on the updated budget equation. When the global ocean mass change is estimated with the updated Tongji-Grace2018 solution, the misclosure of the global sea-level budget can be reduced by 0.11–0.22 mm/year compared to four other recent solutions (i.e. CSR RL06, GFZ RL06, JPL RL06 and ITSG-Grace2018) over the period January 2005 to December 2016. When the same missing months as the GRACE solution are deleted from altimetry and Argo data, the misclosure will be reduced by 0.06 mm/year. Once retained the GRACE C20 term, the linear trends of Tongji-Grace2018 and ITSG-Grace2018 solutions are 2.60 ± 0.16 and 2.54 ± 0.16 mm/year, closer to 2.60 ± 0.14 mm/year from Altimetry–Argo than the three RL06 official solutions. Therefore, the Tongji-Grace2018 solution can reduce the misclosure between altimetry, Argo and GRACE data, regardless of whether the C20 term is replaced or not, since the low-degree spherical harmonic coefficients of the Tongji-Grace2018 solution can capture more ocean signals, which are confirmed by the statistical results of the time series of global mean ocean mass change derived from five GRACE solutions with the spherical harmonic coefficients truncated to different degrees and orders.

Characterizing the impact of underwater glider observations on the Navy Coastal Ocean Model (NCOM) in the Gulf Stream Region

As the western boundary current of the North Atlantic, the Gulf Stream is a well established area of interest for the United States Navy, predominately due to its proximity to the continental shelf and the associated challenges of acoustic propagation across large property gradients. Autonomous underwater gliders conduct routine, high-resolution surveys along the U.S. East Coast, including within the Gulf Stream. These observations are assimilated into the operational Navy Coastal Ocean Model (NCOM). An investigation of the forecast-to-nowcast changes in the model for 2017 demonstrates the impact of the observations on the model. The magnitude of model change as a function of distance from nearest new observation reveals relatively large impact of glider observations within a radius of 𝒪(100) km. Glider observations are associated with larger local impact than Argo data, likely due to glider sampling focusing on large spatial gradients. Due to the advective nature of the Gulf Stream system, the impact of glider observations in the model is anisotropic with larger impacts extending downstream from observation locations. Forecast-to-nowcast changes in modeled temperature, salinity, and density result in improved agreement between observed and modeled ocean structure within the upper 200 m over the 24 hours between successive model runs.

Bridging the gaps between particulate backscattering measurements and modeled particulate organic carbon in the ocean

Oceanic particulate organic carbon (POC) is a relatively small (~4 Pg C) but dynamic component of the global carbon cycle with fast mean turnover rates compared to other oceanic, continental and atmospheric carbon stocks. Biogeochemical models historically focused on reproducing the sinking flux of POC driven by large fast-sinking particles (bPOC). However, suspended and slow-sinking particles (sPOC) typically represent 80–90 % of the POC stock, and can make important seasonal contributions to vertical fluxes through the mesopelagic layer (200–1000 m). Recent developments in the parameterization of POC reactivity in the PISCES model (PISCESv2_RC) have greatly improved its ability to capture sPOC dynamics. Here we evaluated this model by matching 3D and 1D simulations with BGC-Argo and satellite observations in globally representative ocean biomes, building on a refined scheme for converting particulate backscattering profiles measured by BGC-Argo floats to POC. We show that PISCES captures the major features of sPOC and bPOC as seen by BGC-Argo floats across a range of spatiotemporal scales, from highly resolved profile time series to biome-aggregated climatological profiles. Our results also illustrate how the comparison between the model and observations is hampered by (1) the uncertainties in empirical POC estimation, (2) the imperfect correspondence between modelled and observed variables, and (3) the bias between modelled and observed physics. Despite these limitations, we identified characteristic patterns of model-observations misfits in the mesopelagic layer of subpolar and subtropical gyres. These misfits likely result from both suboptimal model parameters and model equations themselves, pointing to the need to improve the model representation of processes with a critical influence on POC dynamics, such as sinking, remineralization, (dis)aggregation and zooplankton activity. Beyond model evaluation results, our analysis identified inconsistencies between current estimates of POC from satellite and BGC-Argo data, as well as POC partitioning into phytoplankton, heterotrophs and detritus deduced from in situ bio-optical data. Our approach can help constrain POC stocks, and ultimately budgets, in the epipelagic and mesopelagic ocean.

Monitoring the ocean heat content change and the Earth energy imbalance from space altimetry and space gravimetry

The Earth energy imbalance (EEI) at the top of the atmosphere is responsible for the accumulation of heat in the climate system. Monitoring the EEI is therefore necessary to better understand the Earth’s warming climate. Measuring the EEI is challenging as it is a globally integrated variable whose variations are small (0.5–1 W m−2) compared to the amount of energy entering and leaving the climate system (~ 340 W m−2). Since the ocean absorbs more than 90 % of the excess energy stored by the Earth system, estimating the ocean heat content (OHC) provides an accurate proxy of the EEI. This study provides a space geodetic estimation of the OHC changes at global and regional scales based on the combination of space altimetry and space gravimetry measurements. From this estimate, the global variations in the EEI are derived with realistic estimates of its uncertainty. The mean EEI value is estimated at +0.74 ± 0.22 W m−2 (90 % confidence level) between August 2002 and August 2016. Comparisons against independent estimates based on Argo data and on CERES measurements show good agreement within the error bars of the global mean and the time variations in EEI. Further improvements are needed to reduce uncertainties and to improve the time series especially at interannual and smaller time scales. The space geodetic OHC-EEI product is freely available at https://doi.org/10.24400/527896/a01-2020.003.