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Abstract The Argo array provides nearly 4000 temperature and salinity profiles of the top 2000 m of the ocean every 10 days. Still, Argo floats will never be able to measure the ocean at all times, everywhere. Optimized Argo float distributions should match the spatial and temporal variability of the many societally important ocean features that they observe. Determining these distributions is challenging because float advection is difficult to predict. Using no external models, transition matrices based on existing Argo trajectories provide statistical inferences about Argo float motion. We use the 24 years of Argo locations to construct an optimal transition matrix that minimizes estimation bias and uncertainty. The optimal array is determined to have a 2° × 2° spatial resolution with a 90-day time step. We then use the transition matrix to predict the probability of future float locations of the core Argo array, the Global Biogeochemical Array, and the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) array. A comparison of transition matrices derived from floats using Argos system and Iridium communication methods shows the impact of surface displacements, which is most apparent near the equator. Additionally, we demonstrate the utility of transition matrices for validating models by comparing the matrix derived from Argo floats with that derived from a particle release experiment in the Southern Ocean State Estimate (SOSE). 

Abstract Equatorial islands have distinct oceanographic signatures, including cool sea surface temperature and high productivity immediately to their west. It has long been hypothesized that topographic upwelling is responsible for such characteristics—upward deflection by the islands of the eastward‐flowing equatorial undercurrent (EUC). Using 22 years of in situ measurements by Argo, we provide the first direct observations of this process occurring with consistency at two prominent archipelagos in the equatorial Pacific. Argo measurements resolve a clear subsurface thermal fingerprint of vertical divergence at the depth of the EUC, confined to within 100 km of both the Gilbert (∼175°E) and Galápagos Islands (∼90°W). This signal at the Galápagos is well‐reproduced by a high‐resolution ocean reanalysis, enabling the estimation of vertical velocities balancing the zonal convergence of the EUC upon the islands. This sharpened view of the physics underpinning such important tropical ecosystems has implications for strategies to model and predict them. 

Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR. 

Abstract Atmospheric rivers (ARs) result in precipitation over land and ocean. Rainfall on the ocean can generate a buoyant layer of freshwater that impacts exchanges between the surface and the mixed layer. These “fresh lenses” are important for weather and climate because they may impact the ocean stratification at all time scales. Here we use in situ ocean data, collocated with AR events, and a one-dimensional configuration of a general circulation model, to investigate the impact of AR precipitation on surface ocean salinity in the California Current System (CCS) on seasonal and event-based time scales. We find that at coastal and onshore locations the CCS freshens through the rainy season due to AR events, and years with higher AR activity are associated with a stronger freshening signal. On shorter time scales, model simulations suggest that events characteristic of CCS ARs can produce salinity changes that are detectable by ocean instruments (≥0.01 psu). Here, the surface salinity change depends linearly on rain rate and inversely on wind speed. Higher wind speeds ( U > 8 m s −1 ) induce mixing, distributing freshwater inputs to depths greater than 20 m. Lower wind speeds ( U ≤ 8 m s −1 ) allow freshwater lenses to remain at the surface. Results suggest that local precipitation is important in setting the freshwater seasonal cycle of the CCS and that the formation of freshwater lenses should be considered for identifying impacts of atmospheric variability on the upper ocean in the CCS on weather event time scales. Significance Statement Atmospheric rivers produce large amounts of rainfall. The purpose of this study is to understand how this rain impacts the surface ocean in the California Current System on seasonal and event time scales. Our results show that a greater precipitation over the rainy season leads to a larger decrease in salinity over time. On shorter time scales, these atmospheric river precipitation events commonly produce a surface salinity response that is detectable by ocean instruments. This salinity response depends on the amount of rainfall and the wind speed. In general, higher wind speeds will cause the freshwater input from rain to mix deeper, while lower wind speeds will have reduced mixing, allowing a layer of freshwater to persist at the surface. 

Abstract Since the mid-2000s, the Argo oceanographic observational network has provided near-real-time four-dimensional data for the global ocean for the first time in history. Internet (i.e., the “web”) applications that handle the more than two million Argo profiles of ocean temperature, salinity, and pressure are an active area of development. This paper introduces a new and efficient interactive Argo data visualization and delivery web application named Argovis that is built on a classic three-tier design consisting of a front end, back end, and database. Together these components allow users to navigate 4D data on a world map of Argo floats, with the option to select a custom region, depth range, and time period. Argovis’s back end sends data to users in a simple format, and the front end quickly renders web-quality figures. More advanced applications query Argovis from other programming environments, such as Python, R, and MATLAB. Our Argovis architecture allows expert data users to build their own functionality for specific applications, such as the creation of spatially gridded data for a given time and advanced time–frequency analysis for a space–time selection. Argovis is aimed to both scientists and the public, with tutorials and examples available on the website, describing how to use the Argovis data delivery system—for example, how to plot profiles in a region over time or to monitor profile metadata.