Sea surface salinity (SSS) is known to change over time due to the transport of freshwater and the dynamics of the ocean. The relationship between SSS and its main determinant, freshwater forcing minus horizontal advection and vertical entrainment (FMAV), is described by a salinity balance equation (SBE). We investigate the dynamics of these two component terms of SBE using the tools of functional data analysis. Specifically, we explore how quickly changes in FMAV are associated with changes in SSS by estimating the time lag between two components through function registration. While existing studies have assumed a constant time lag between the two components, we allow for a time-varying lag, referred to as phase, which more realistically reflects the temporal dynamics of variables. Adopting the functional data analysis framework, we treat SSS and FMAV as functional objects. We estimate the phase between SSS and FMAV by a function that matches seasonal features between the two variables that explains the continuous time lag between SSS and FMAV. We compare the estimation results to a more traditional approach involving harmonic analysis and show that the presented method is more effective in aligning their seasonal features, as measured by the distance between the aligned variables at multiple spatial locations.
This document reviews the sampling details of the S-MODE (Submesoscale Ocean Dynamics Experiment), a NASA-funded, EVS-3 (Earth Venture Suborbital-3), oceanographic field program. It describes what measurements were collected, when and with what instruments and platforms. For each measurement platform it gives simple plots showing the basic dataset, and describes the sampling in detail. S-MODE in situ and aircraft data are available from the PO.DAAC (Physical Oceanography Distributed Active Archive Center) landing page, and individual datasets are also available at the DOIs listed in the “Data Availability” section of this report.
Abstract. Using data from the Global Tropical Moored Buoy Array we study the validation process for satellite measurement of sea surface salinity (SSS). We compute short-term variability (STV) of SSS, variability on time scales of 5–14 days. It is meant to be a proxy for subfootprint variability as seen by a satellite measuring SSS. We also compute representation error, which is meant to mimic the SSS satellite validation process where footprint averages are compared to pointwise in situ values. We present maps of these quantities over the tropical array. We also look at seasonality in the variability of SSS and find which months have maximum and minimum amounts. STV is driven at least partly by rainfall. Moorings exhibit larger STV during rainy periods than non-rainy ones. The same computations are also done using output from a high-resolution global ocean model to see how it might be used to study the validation process. The model gives good estimates of STV, in line with the moorings, though tending to have smaller values.
Abstract. The seasonal variability of surface layer salinity (SLS), evaporation (E), precipitation (P), E-P, advection and vertical entrainment over the global ocean is examined using in situ salinity data, the National Centers for Environmental Prediction's Climate System Forecast Reanalysis and a number of other ancillary data. Seasonal amplitudes and phases are calculated using harmonic analysis and presented in all areas of the open ocean between 60° S and 60° N. Areas with large amplitude SLS seasonal variations include: the intertropical convergence zone (ITCZ) in the Atlantic, Pacific and Indian Oceans; western marginal seas of the Pacific; and the Arabian Sea. The median amplitude in areas that have statistically significant seasonal cycles of SLS is 0.19. Between about 60° S and 60° N, 37% of the ocean surface has a statistically significant seasonal cycle of SLS and 75% has a seasonal cycle of E-P. Phases of SLS have a bimodal distribution, with most areas in the Northern Hemisphere peaking in SLS in March/April and in the Southern Hemisphere in September/October. The seasonal cycle is also estimated for surface freshwater forcing using a mixed-layer depth climatology. With the exception of areas near the western boundaries of the North Atlantic and North Pacific, seasonal variability is dominated by precipitation. Surface freshwater forcing also has a bimodal distribution, with peaks in January and July, 1–2 months before the peaks of SLS. Seasonal amplitudes and phases calculated for horizontal advection show it to be important in the tropical oceans. Vertical entrainment, estimated from mixed-layer heaving, is largest in mid and high latitudes, with a seasonal cycle that peaks in late winter. The amplitudes and phases of SLS and surface fluxes compare well in a qualitative sense, suggesting that much of the variability in SLS is due to E-P. However, the amplitudes of SLS are somewhat different than would be expected and the peak of SLS comes typically about one month earlier than expected. The differences of the amplitudes of the two quantities is largest in such areas as the Amazon River plume, the Arabian Sea, the ITCZ and the eastern equatorial Pacific and Atlantic.
Sea surface salinity (SSS) can change as a result of surface freshwater forcing (FWF) or internal ocean processes such as upwelling or advection. SSS should follow FWF by ¼ cycle, or 3 months, if FWF is the primary process controlling it at the seasonal scale. In this paper, we compare the phase relationship between SSS and FWF (i.e., evaporation minus precipitation over mixed layer depth) over the global (non-Arctic) ocean using in situ SSS and satellite evaporation and precipitation. We found that, instead of the expected 3-month delay between SSS and FWF, the delay is mostly closer to 1–2 months, with SSS peaking too soon relative to FWF. We then computed monthly vertical entrainment and horizontal advection terms of the upper ocean salinity balance equation and added their contributions to the phase of the FWF. The addition of these processes to the seasonal upper ocean salinity balance leads to the phase difference between SSS and the forcing processes being closer to the expected value. We conducted a similar computation with the amplitude of the seasonal SSS and the forcing terms, with less definitive results. The results of this study highlight the important role that ocean processes play in the global freshwater cycle at the seasonal scale.
Abstract. Using data from the Global Tropical Moored Buoy Array, we study the validation process for satellite measurement of sea surface salinity (SSS). We compute short-term variability (STV) of SSS, variability on timescales of 2â17âd. It is a proxy for subfootprint variability over a 100âkm footprint as seen by a satellite measuring SSS. We also compute representation error, which is meant to mimic the SSS satellite validation process where footprint averages are compared to pointwise in situ values. We present maps of these quantities over the tropical array. We also look at seasonality in the variability of SSS and find which months have maximum and minimum amounts. STV is driven at least partly by rainfall. Moorings exhibit larger STV during rainy periods than during non-rainy ones. The same computations are also done using output from a high-resolution global ocean model to see how it might be used to study the validation process. The model gives good estimates of STV, in line with the moorings, although tending to have smaller values.
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°C, consistent with a surface heat flux estimate of 750 W/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 winds 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°.
The effect of cooling on an eastward-flowing jet is explored using simple quasigeosrophic (QG) theory. The effects are quantified in terms of a cooling-induced turning with depth, similar to that of Schott and Stommel. The turning with depth is explained as changes in the stretching terms of the QG equations. A two-layer QG model of an eastward-flowing jet is formulated and solved numerically. The tuning with depth in the model is influenced by two competing factors, β, the change in Coriolis parameter with latitude, and stretching. When stretching is dominant, the flow turns in the opposite direction. The model is extended to more than two layers with similar results. The model is compared with conditions found in the Kuroshio in wintertime. Because of the weak stratification and resultant short deformation radius, stretching is shown to be the dominant reaction to changes in potential vorticity caused by cooling. Thus, cooling causes water columns to stretch and the flow to turn to the left with depth. This result is consistent with subtropical mode water as a cooling-induced thickening of the surface mixed layer.
