Physical response of the coastal ocean to Hurricane Isabel near landfall

Abstract. Hurricane Isabel made landfall near Drum Inlet, North Carolina on 18 September 2003. In nearby Onslow Bay an array of 5 moorings captured the response of the coastal ocean to the passage of the storm by measuring currents, surface waves, bottom pressure, temperature and salinity. Temperatures across the continental shelf decreased by 1–3 degrees C, consistent with a surface heat flux estimate of 750W/m2. Salinity decreased at most mooring locations. A calculation at one of the moorings estimates rainfall of 11 cm and a net addition of fresh water at the surface of 8 cm. The low-pass current field shows a shelf-wide movement of water, first to the southwest, with an abrupt reversal to the northeast along the shelf after landfall. Close analysis of this reversal shows it to be a disturbance propagating offshore at a speed somewhat less than the local shallow water wave speed. The high-pass current field at one of the moorings shows a significant increase in kinetic energy at periods between 10 min and 2 h during the approach of the storm. This high-pass flow is isotropic and has a short (<5 m) vertical decorrelation scale. It appears to be closely associated with the winds, Finally we examined the surface wave field at one of the moorings. It shows the swell energy peaking well before the wind waves. At the height of the storm, as the winds rotated rapidly in the cyclonic sense, the wind wave direction rotated as well, with a lag of 45–90 degrees

Distributions of mixed layer properties in North Pacific water mass formation areas: comparison of Argo floats and World Ocean Atlas 2001

ABSTRACT. Winter mixed layer characteristics in the North Pacific Ocean are examined and compared between Argo floats in 2006 and the World Ocean Atlas 2001 (WOA01) climatology for a series of named water masses, North Pacific Tropical Water (NPTW), Eastern Subtropical Mode Water (ESTMW), North Pacific Subtropical Mode Water (NPSTMW), Light Central ModeWater (LCMW) and Dense Central Mode Water (DCMW). The WOA01 is found to be in good agreement with the Argo data in terms of water mass volumes, average temperature-salinity (T-S) properties, and outcrop areas. The exception to this conclusion is for the central mode waters, DCMW and LCMW, whose outcropping is shown to be much more intermittent than is apparent in the WOA01 and whose T-S properties vary from what is shown in theWOA01. Distributions of mixed layer T-S properties measured by floats are examined within the outcropping areas defined by the WOA01 and show some shifting of T-S characteristics within the confines of the named water masses. In 2006, all the water masses were warmer than climatology on average, with a magnitude of about 0.5 degrees C. The NPTW, NPSTMW and LCMW were saltier than climatology and the ESTMW and DCMW fresher, with magnitudes of about 0.05. In order to put these results into context, differences between Argo and WOA01 were examined over the North Pacific between 20 and 45 degrees N. A large-scale warming and freshening is seen throughout this area, except for the western North Pacific, where results were more mixed

Multiscale simulation, data assimilation, and forecasting in support of the SPURS-2 field campaign

A multiscale simulation, data assimilation, forecasting system was developed in support of the SPURS-2 (Salinity Processes in the Upper-ocean Regional Study 2) field campaign. Before the field campaign, a multiyear simulation was produced for characterizing variabilities in upper-ocean salinity, eddy activity, and other parameters and for illustrating major processes that control the region’s upper-ocean salinity at different spatial and temporal scales. This simulation assisted in formulating sampling plans. During the field experiment, the system integrated SPURS-2 measurements with those from routine operational observing networks, including Argo floats and satellite surface temperatures, salinities, and heights, and provided real-time skillful daily forecasts of ocean conditions. Forecast reports were prepared to summarize oceanic conditions and multiscale features and were delivered to the SPURS-2 chief scientist and other SPURS-2 investigators through the SPURS-2 Information System. After the field experiment, the data assimilation system was used to produce a reanalysis product to help quantify contributions of different processes to salinity variability in the region

Patterns of SSS variability in the eastern tropical Pacific: Intraseasonal to interannual timescales from seven years of NASA satellite data

Sea surface salinity (SSS) observations from NASA’s satellite missions, Aquarius/SAC-D and Soil Moisture Active Passive (SMAP), are used to describe spatial patterns of the seasonal cycle, as well as intraseasonal and interannual variability, in the eastern tropical Pacific, the location of the second Salinity Processes in the Upper-ocean Regional Study (SPURS-2) field experiment. The results indicate that the distribution of SSS variance is highly inhomogeneous in both space and time. The seasonal signal is largest in the core of the Eastern Pacific Fresh Pool and in the Gulf of Panama. The interannual signal is highest in a relatively narrow zonal band along approximately 5°N, while the intraseasonal signal appears to be a dominant mode of variability in the zonally stretched near-equatorial region. Located right in the middle of a hotspot of high SSS variance, the SPURS-2 site appears to be at the crossroads of many different processes that shape the distribution of SSS in the eastern tropical Pacific and beyond

The SPURS-2 eastern tropical Pacific field campaign data collection

This paper describes the large, diverse set of in situ data collected during the Salinity Processes in the Upper-ocean Regional Study 2 (SPURS-2) field campaign. The data set includes measurements of the ocean, atmosphere, and fluxes between atmosphere and ocean; measurements of the skin surface layer, bulk mixed layer, and deeper water; (mostly) physical, chemical, and biological measurements; and shipbased, mobile drifting/floating, and moored observations. We include references detailing the methods for collection of each data set, provide DOIs for accessing the data, and note some papers in this special issue that use them. To facilitate broader access to SPURS-2 data and information, we created an online tool that allows users to explore data sets organized by various categories (e.g., instrument type, mobility, depth). This tool will complement content available from the Physical Oceanography Distributed Active Archive Center (PO.DAAC) and will be highly engaging for visual learners

Sea Surface Salinity Subfootprint Variability from a Global High-Resolution Model

Subfootprint variability (SFV) is variability at a spatial scale smaller than the footprint of a satellite, and it cannot be resolved by satellite observations. It is important to quantify and understand, as it contributes to the error budget for satellite data. The purpose of this study was to estimate the SFV for sea surface salinity (SSS) satellite observations. This was performed by using a high-resolution numerical model, a 1/48° version of the MITgcm simulation, from which one year of output has recently become available. SFV, defined as the weighted standard deviation of SSS within the satellite footprint, was computed from the model for a 2° × 2° grid of points for the one model year. We present maps of median SFV for 40 and 100 km footprint size, display histograms of its distribution for a range of footprint sizes and quantify its seasonality. At a 100 km (40 km) footprint size, SFV has a mode of 0.06 (0.04). It is found to vary strongly by location and season. It has larger values in western-boundary and eastern-equatorial regions, as well as in a few other areas. SFV has strong variability throughout the year, with the largest values generally being in the fall season. We also quantified the representation error, the degree of mismatch between random samples within a footprint and the footprint average. Our estimates of SFV and representation error can be used in understanding errors in the satellite observation of SSS

Seasonal and Interannual Variability of the Subtropical South Indian Ocean Sea Surface Salinity Maximum

The sea surface salinity (SSS) maximum of the South Indian Ocean (the SISSS-max) is a high-salinity feature centered at 30°S, 90°E, near the center of the South Indian subtropical gyre. It is located poleward of a region of strong evaporation and weak precipitation. Using several different satellites and in situ data sets, we track changes in this feature since the early 2000s. The centroid of the SISSS-max moves seasonally north and south, furthest north in late winter and farthest south in late summer. Interannually, the SISSS-max has moved on a northeast-southwest path about 1,500 km in length. The size and maximum SSS of the feature vary in tandem with this motion. It gets larger (smaller) and saltier (fresher) as it moves to the northeast (southwest) closer to (further from) the area of strongest surface freshwater flux. The area of the SISSS-max almost doubles from its smallest to largest extent. It was maximum in area in 2006, decreased steadily until it reached a minimum in 2013, and then increased again. The seasonal variability of the SISSS-max is controlled by the changes that occur on its poleward, or southern, side, whereas interannual variability is controlled by changes on its equatorward side. The variations in the SISSS-max are a complex dance between changes in evaporation, precipitation, wind forcing, gyre-scale ocean circulation, and downward Ekman pumping. Its motion correlated with SSS changes throughout the South Indian Ocean and may be an indicator of changes in the basin's subtropical circulation

Matchup Characteristics of Sea Surface Salinity Using a High-Resolution Ocean Model

Sea surface salinity (SSS) satellite measurements are validated using in situ observations usually made by surfacing Argo floats. Validation statistics are computed using matched values of SSS from satellites and floats. This study explores how the matchup process is done using a high-resolution numerical ocean model, the MITgcm. One year of model output is sampled as if the Aquarius and Soil Moisture Active Passive (SMAP) satellites flew over it and Argo floats popped up into it. Statistical measures of mismatch between satellite and float are computed, RMS difference (RMSD) and bias. The bias is small, less than 0.002 in absolute value, but negative with float values being greater than satellites. RMSD is computed using an “all salinity difference” method that averages level 2 satellite observations within a given time and space window for comparison with Argo floats. RMSD values range from 0.08 to 0.18 depending on the space–time window and the satellite. This range gives an estimate of the representation error inherent in comparing single point Argo floats to area-average satellite values. The study has implications for future SSS satellite missions and the need to specify how errors are computed to gauge the total accuracy of retrieved SSS values

Spatial Scales of Sea Surface Salinity Subfootprint Variability in the SPURS Regions

Subfootprint variability (SFV), or representativeness error, is variability within the footprint of a satellite that can impact validation by comparison of in situ and remote sensing data. This study seeks to determine the size of the sea surface salinity (SSS) SFV as a function of footprint size in two regions that were heavily sampled with in situ data. The Salinity Processes in the Upper-ocean Regional Studies-1 (SPURS-1) experiment was conducted in the subtropical North Atlantic in the period 2012–2013, whereas the SPURS-2 study was conducted in the tropical eastern North Pacific in the period 2016–2017. SSS SFV was also computed using a high-resolution regional model based on the Regional Ocean Modeling System (ROMS). We computed SFV at footprint sizes ranging from 20 to 100 km for both regions. SFV is strongly seasonal, but for different reasons in the two regions. In the SPURS-1 region, the meso- and submesoscale variability seemed to control the size of the SFV. In the SPURS-2 region, the SFV is much larger than SPURS-1 and controlled by patchy rainfall

Quantification of Aquarius, SMAP, SMOS and Argo-Based Gridded Sea Surface Salinity Product Sampling Errors

Evaluating and validating satellite sea surface salinity (SSS) measurements is fundamental. There are two types of errors in satellite SSS: measurement error due to the instrument’s inaccuracy and problems in retrieval, and sampling error due to unrepresentativeness in the way that the sea surface is sampled in time and space by the instrument. In this study, we focus on sampling errors, which impact both satellite and in situ products. We estimate the sampling errors of Level 3 satellite SSS products from Aquarius, SMOS and SMAP, and in situ gridded products. To do that, we use simulated L2 and L3 Aquarius, SMAP and SMOS SSS data, individual Argo observations and gridded Argo products derived from a 12-month high-resolution 1/48? ocean model. The use of the simulated data allows us to quantify the sampling error and eliminate the measurement error. We found that the sampling errors are high in regions of high SSS variability and are globally about 0.02/0.03 psu at weekly time scales and 0.01/0.02 psu at monthly time scales for satellite products. The in situ-based product sampling error is significantly higher than that of the three satellite products at monthly scales (0.085 psu) indicating the need to be cautious when using in situ-based gridded products to validate satellite products. Similar results are found using a Correlated Triple Collocation method that quantifies the standard deviation of products’ errors acquired with different instruments. By improving our understanding and quantifying the effect of sampling errors on satellite-in situ SSS consistency over various spatial and temporal scales, this study will help to improve the validation of SSS, the robustness of scientific applications and the design of future salinity missions