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Modeled water-mass changes in the North Pacific thermocline, both in the subsurface and at the surface, reveal the impact of the competition between anthropogenic aerosols (AAs) and greenhouse gases (GHGs) over the past 6 decades. The AA effect overwhelms the GHG effect during 1950–1985 in driving salinity changes on density surfaces, while after 1985 the GHG effect dominates. These subsurface water-mass changes are traced back to changes at the surface, of which ~70% stems from the migration of density surface outcrops, equatorward due to regional cooling by AAs and subsequent poleward due to warming by GHGs. Ocean subduction connects these surface outcrop changes to the main thermocline. Both observations and models reveal this transition in climate forcing around 1985 and highlight the important role of AA climate forcing on our oceans’ water masses.

 

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. 

The core Argo array has operated with the design goal of uniform spatial distribution of 3° in latitude and longitude. Recent studies have acknowledged that spatial and temporal scales of variability in some parts of the ocean are not resolved by 3° sampling and have recommended increased core Argo density in the equatorial region, boundary currents, and marginal seas with an integrated vision of other Argo variants. Biogeochemical (BGC) Argo floats currently observe the ocean from a collection of pilot arrays, but recently funded proposals will transition these pilot arrays to a global array. The current BGC Argo implementation plan recommends uniform spatial distribution of BGC Argo floats. For the first time, we estimate the effectiveness of the existing BGC Argo array to resolve the anomaly from the mean using a subset of modeled, full-depth BGC fields. We also study the effectiveness of uniformly distributed BGC Argo arrays with varying float densities at observing the ocean. Then, using previous Argo trajectories, we estimate the Argo array’s future distribution and quantify how well it observes the ocean. Finally, using a novel technique for sequentially identifying the best deployment locations, we suggest the optimal array distribution for BGC Argo floats to minimize objective mapping uncertainty in a subset of BGC fields and to best constrain BGC temporal variability.

 

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. 

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).

 

We analyze 15‐year of observational data and a 5‐year Southern Ocean model simulation to quantify the transformation rates of Circumpolar Deep Water (CDW) and the associated heat loss to the surface. This study finds that over the continental shelves of East Antarctica and the Weddell and Ross Seas, surface buoyancy fluxes transform ∼4.4 Sv of surface waters into CDW, providing a path for CDW to lose heat to the surface. In addition, ∼6.6 Sv of CDW are mixed with surface waters in the Weddell and Ross subpolar gyres. In contrast, enhanced stratification inhibits the outcropping of CDW isopycnals, reducing their transformation rates by a factor of ∼8 over the continental shelf and by a factor of ∼3 over the deeper ocean in the Amundsen and Bellingshausen Seas. The CDW retains its offshore warm properties as it intrudes over the continental shelves, resulting in elevated bottom temperatures there. This analysis demonstrates the importance of processes in subpolar gyres to erode CDW and to facilitate further transformation on the continental shelves, significantly reducing the heat able to access ice shelf fronts. This sheltering effect is strongest in the western Weddell Sea and tends to diminish toward the east, which helps explain the large zonal differences in continental‐shelf bottom temperatures and the melt rates of Antarctic ice shelves.