No abstract

Argo‐type profiling floats do not receive satellite positioning while under sea ice. Common practice is to approximate unknown positions by linearly interpolating latitude‐longitude between known positions before and after ice cover, although it has been suggested that some improvement may be obtained by interpolating along contours of planetary‐geostrophic potential vorticity. Profiles with linearly interpolated positions represent 16% of the Southern Ocean Argo data set; consequences arising from this approximation have not been quantified. Using three distinct data sets from the Weddell Gyre—10‐day satellite‐tracked Argo floats, daily‐tracked RAFOS‐enabled floats, and a particle release simulation in the Southern Ocean State Estimate—we perform a data withholding experiment to assess position uncertainty in latitude‐longitude and potential vorticity coordinates as a function of time since last fix. A spatial correlation analysis using the float data provides temperature and salinity uncertainty estimates as a function of distance error. Combining the spatial correlation scales and the position uncertainty, we estimate uncertainty in temperature and salinity as a function of duration of position loss. Maximum position uncertainty for interpolation during 8 months without position data is 116 ± 148 km for latitude‐longitude and 92 ± 121 km for potential vorticity coordinates. The estimated maximum uncertainty in local temperature and salinity over the entire 2,000‐m profiles during 8 months without position data is 0.66 ∘C and 0.15 psu in the upper 300 m and 0.16 ∘C and 0.01 psu below 300 m.

A distributed sensor network of over one hundred free-drifting, real-time marine weather sensors was deployed in the Pacific Ocean beginning in early 2019. The Spotter buoys used in the network represent a next generation ocean weather sensor designed to measure surface waves, wind, currents, and sea surface temperature. Large distributed sensor networks like these provide much needed long-dwell sensing capabilities in open ocean regions. Despite the demand for better weather forecasts and climate data in our oceans, direct in situ measurements of marine surface weather (waves, winds, currents) remain exceedingly sparse in the open oceans. Due to the large expanse of our oceans, distributed paradigms are necessary to create sufficient data density at global scale, similar to advances in sensing on land and in space. Here we discuss initial findings from this long-dwell open ocean distributed sensor network. Through triple-collocation analysis, we determine errors in collocated satellite-derived observations and model estimates. The correlation analysis shows that the Spotter network provides wave height data with lower errors than both satellites and models. The wave spectrum was also further used to infer wind speed. Buoy drift dynamics are similar to established drogued drifters, particularly when accounting for windage. We find a windage correction factor for the Spotter buoy of approximately 1%, which is in agreement with theoretical estimates. Altogether, we present a completely new open ocean weather data set and characterize the data quality against other observations and models to demonstrate the broad value for ocean monitoring and forecasting that can be achieved using large-scale distributed sensor networks in our oceans.

The Argo array provides nearly 4000 temperature and salinity profiles of the top 2000 meters 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 timestep. 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).