A dataset of hourly sea surface temperature from drifting buoys

A dataset of sea surface temperature (SST) estimates is generated from the temperature observations of surface drifting buoys of NOAA’s Global Drifter Program. Estimates of SST at regular hourly time steps along drifter trajectories are obtained by fitting to observations a mathematical model representing simultaneously SST diurnal variability with three harmonics of the daily frequency, and SST low-frequency variability with a first degree polynomial. Subsequent estimates of non-diurnal SST, diurnal SST anomalies, and total SST as their sum, are provided with their respective standard uncertainties. This Lagrangian SST dataset has been developed to match the existing and on-going hourly dataset of position and velocity from the Global Drifter Program

Measuring global mean sea level changes with surface drifting buoys

Combining ocean model data and in-situ Lagrangian data, I show that an array of surface drifting buoys tracked by a Global Navigation Satellite System (GNSS), such as the Global Drifter Program, could provide estimates of global mean sea level (GMSL) and its changes, including linear decadal trends. For a sustained array of 1250 globally distributed buoys with a standardized design, I demonstrate that GMSL decadal linear trend estimates with an uncertainty less than 0.3 mm yr$^{-1}$ could be achieved with GNSS daily random error of 1.6 m or less in the vertical direction. This demonstration assumes that controlled vertical position measurements could be acquired from drifting buoys, which is yet to be demonstrated. Development and implementation of such measurements could ultimately provide an independent and resilient observational system to infer natural and anthropogenic sea level changes, augmenting the on-going tide gauge and satellites records.Comment: resubmitted to AGU Geophysical Research Letter

Investigating the relationship between volume transport and sea surface height in a numerical ocean model

The Agulhas Current Time-series Experiment mooring array (ACT) measured transport of the Agulhas Current at 34∘&thinsp;S for a period of 3 years. Using along-track satellite altimetry data directly above the array, a proxy of Agulhas Current transport was developed based on the relationship between cross-current sea surface height (SSH) gradients and the measured transports. In this study, the robustness of the proxy is tested within a numerical modelling framework using a 34-year-long regional hindcast simulation from the Hybrid Coordinate Ocean Model (HYCOM). The model specifically tested the sensitivity of the transport proxy to (1) changes in the vertical structure of the current and to (2) different sampling periods used to calculate the proxy. Two reference proxies were created using HYCOM data from 2010 to 2013 by extracting model data at the mooring positions and along the satellite altimeter track for the box (net) transport and the jet (southwestward) transport. Sensitivity tests were performed where the proxy was recalculated from HYCOM for (1) a period where the modelled vertical stratification was different compared to the reference proxy and (2) different lengths of time periods: 1, 3, 6, 12, 18, and 34 years. Compared to the simulated (native) transports, it was found that the HYCOM proxy was more capable of estimating the box transport of the Agulhas Current compared to the jet transport. This was because the model is unable to resolve the dynamics associated with meander events, for which the jet transport algorithm was developed. The HYCOM configuration in this study contained exaggerated levels of offshore variability in the form of frequently impinging baroclinic anticyclonic eddies. These eddies consequently broke down the linear relationship between SSH slope and vertically integrated transport. Lastly, results showed that calculating the proxy over shorter or longer time periods in the model did not significantly impact the skill of the Agulhas transport proxy. Modelling studies of this kind provide useful information towards advancing our understanding of the sensitivities and limitations of transport proxies that are needed to improve long-term ocean monitoring approaches.</p

Regional versus remote atmosphere‐ocean drivers of the rapid projected intensification of the East Australian Current

Like many western boundary currents, the East Australian Current extension is projected to get stronger and warmer in the future. The CMIP5 multi‐model mean (MMM) projection suggests up to 5°C of warming under an RCP85 scenario by 2100. Previous studies employed Sverdrup balance to associate a trend in basin wide zonally integrated wind stress curl (resulting from the multi‐decadal poleward intensification in the westerly winds over the Southern Ocean) with enhanced transport in the EAC extension. Possible regional drivers are yet to be considered. Here, we introduce the NEMO‐OASIS‐WRF coupled regional climate model as a framework to improve our understanding of CMIP5 projections. We analyse a hierarchy of simulations in which the regional atmosphere and ocean circulations are allowed to freely evolve subject to boundary conditions that represent present day and CMIP5 RCP8.5 climate change anomalies. Evaluation of the historical simulation shows an EAC extension that is stronger than similar ocean‐only models and observations. This bias is not explained by a linear response to differences in wind stress. The climate change simulations show that regional atmospheric CMIP5 MMM anomalies drive 73% of the projected 12 Sv increase in EAC extension transport whereas the remote ocean boundary conditions and regional radiative forcing (greenhouse gases within the domain) play a smaller role. The importance of regional changes in wind stress curl in driving the enhanced EAC extension is consistent with linear theory where the NEMO‐OASIS‐WRF response is closer to linear transport estimates compared to the CMIP5 MMM

Long memory estimation for complex-valued time series

Long memory has been observed for time series across a multitude of fields and the accurate estimation of such dependence, e.g. via the Hurst exponent, is crucial for the modelling and prediction of many dynamic systems of interest. Many physical processes (such as wind data), are more naturally expressed as a complex-valued time series to represent magnitude and phase information (wind speed and direction). With data collection ubiquitously unreliable, irregular sampling or missingness is also commonplace and can cause bias in a range of analysis tasks, including Hurst estimation. This article proposes a new Hurst exponent estimation technique for complex-valued persistent data sampled with potential irregularity. Our approach is justified through establishing attractive theoretical properties of a new complex-valued wavelet lifting transform, also introduced in this paper. We demonstrate the accuracy of the proposed estimation method through simulations across a range of sampling scenarios and complex- and real-valued persistent processes. For wind data, our method highlights that inclusion of the intrinsic correlations between the real and imaginary data, inherent in our complex-valued approach, can produce different persistence estimates than when using real-valued analysis. Such analysis could then support alternative modelling or policy decisions compared with conclusions based on real-valued estimation

Atlantic Meridional Overturning Circulation: Observed Transport and Variability

The Atlantic Meridional Overturning Circulation (AMOC) extends from the Southern Ocean to the northern North Atlantic, transporting heat northwards throughout the South and North Atlantic, and sinking carbon and nutrients into the deep ocean. Climate models indicate that changes to the AMOC both herald and drive climate shifts. Intensive trans-basin AMOC observational systems have been put in place to continuously monitor meridional volume transport variability, and in some cases, heat, freshwater and carbon transport. These observational programs have been used to diagnose the magnitude and origins of transport variability, and to investigate impacts of variability on essential climate variables such as sea surface temperature, ocean heat content and coastal sea level. AMOC observing approaches vary between the different systems, ranging from trans-basin arrays (OSNAP, RAPID 26°N, 11°S, SAMBA 34.5°S) to arrays concentrating on western boundaries (e.g., RAPID WAVE, MOVE 16°N). In this paper, we outline the different approaches (aims, strengths and limitations) and summarize the key results to date. We also discuss alternate approaches for capturing AMOC variability including direct estimates (e.g., using sea level, bottom pressure, and hydrography from autonomous profiling floats), indirect estimates applying budgetary approaches, state estimates or ocean reanalyses, and proxies. Based on the existing observations and their results, and the potential of new observational and formal synthesis approaches, we make suggestions as to how to evaluate a comprehensive, future-proof observational network of the AMOC to deepen our understanding of the AMOC and its role in global climate

Ekman layers in the Southern Ocean: spectral models and observations, vertical viscosity and boundary layer depth

Spectral characteristics of the oceanic boundary-layer response to wind stress forcing are assessed by comparing surface drifter observations from the Southern Ocean to a suite of idealized models that parameterize the vertical flux of horizontal momentum using a first-order turbulence closure scheme. The models vary in their representation of vertical viscosity and boundary conditions. Each is used to derive a theoretical transfer function for the spectral linear response of the ocean to wind stress. The transfer functions are evaluated using observational data. The ageostrophic component of near-surface velocity is computed by subtracting altimeter-derived geostrophic velocities from observed drifter velocities (nominally drogued to represent motions at 15-m depth). Then the transfer function is computed to link these ageostrophic velocities to observed wind stresses. The traditional Ekman model, with infinite depth and constant vertical viscosity is among the worst of the models considered in this study. The model that most successfully describes the variability in the drifter data has a shallow layer of depth O(30–50 m), in which the viscosity is constant and O(100–1000m2 s−1), with a no-slip bottom boundary condition. The second best model has a vertical viscosity with a surface value O(200m2 s−1), which increases linearly with depth at a rate O(0.1–1 cm s−1) and a no-slip boundary condition at the base of the boundary layer of depth O(103 m). The best model shows little latitudinal or seasonal variability, and there is no obvious link to wind stress or climatological mixed-layer depth. In contrast, in the second best model, the linear coefficient and the boundary layer depth seem to covary with wind stress. The depth of the boundary layer for this model is found to be unphysically large at some latitudes and seasons, possibly a consequence of the inability of Ekman models to remove energy from the system by other means than shear-induced dissipation. However, the Ekman depth scale appears to scale like the climatological mixed-layer depth