14 research outputs found
Best practices for Core Argo floats - part 1: getting started and data considerations
Argo floats have been deployed in the global ocean for over 20 years. The Core mission of the Argo program (Core Argo) has contributed well over 2 million profiles of salinity and temperature of the upper 2000 m of the water column for a variety of operational and scientific applications. Core Argo floats have evolved such that the program currently consists of more than eight types of Core Argo float, some of which belong to second or third generation developments, three unique satellite communication systems (Argos, Iridium and Beidou) and two types of Conductivity, Temperature and Depth (CTD) sensor systems (Seabird and RBR). This, together with a well-established data management system, delayed mode data quality control, FAIR and open data access, make the program a very successful ocean observing network. Here we present Part 1 of the Best Practices for Core Argo floats in terms of how users can get started in the program, recommended metadata parameters and the data management system. The objective is to encourage new and developing scientists, research teams and institutions to contribute to the OneArgo Program, specifically to the Core Argo mission. Only by leveraging sustained contributions from current Core Argo float groups with new and emerging Argo teams and users who are eager to get involved and are actively encouraged to do so, can the OneArgo initiative be realized. This paper presents a list of best practices to get started in the program, set up the recommended metadata, implement the data management system with the aim to encourage new scientists, countries and research teams to contribute to the OneArgo Program
The importance of monitoring the Greater Agulhas Current and its inter-ocean exchanges using large mooring arrays
The 2013 Intergovernmental Panel on Climate Change report, using CMIP5 and EMIC model outputs suggests that the Atlantic Meridional Overturning Circulation (MOC) is very likely to weaken by 11â34% over the next century, with consequences for global rainfall and temperature patterns. However, these coupled, global climate models cannot resolve important oceanic features such as the Agulhas Current and its leakage around South Africa, which a number of studies have suggested may act to balance MOC weakening in the future. To properly understand oceanic changes and feedbacks on anthropogenic climate change we need to substantially improve global ocean observations, particularly within boundary current regions such as the Agulhas Current, which represent the fastest warming regions across the worldâs oceans. The South African science community, in collaboration with governing bodies and international partners, has recently established one of the worldâs most comprehensive observational networks of a western boundary current system, measuring the Greater Agulhas Current System and its inter-ocean exchanges south of Africa. This observational network, through its design for long-term monitoring, collaborative coordination of resources and skills sharing, represents a model for the international community. We highlight progress of the new Agulhas System Climate Array, as well as the South African Meridional Overturning Circulation programme, which includes the Crossroads and GoodHope hydrographic transects, and the South Atlantic MOC Basin-wide Array. We also highlight some of the ongoing challenges that the programmes still face
Downstream evolution of ocean properties and associated fluxes in the Greater Agulhas Current System: Ad hoc Argo experiments and modeling
The evolution of cyclonic eddies across the Southern Mozambique Chanel and the downstream evolution of the Agulhas Current was investigated using Argo floats, in combination with output from ocean general circulation reanalysis models. Two dedicated experiments were undertaken in April and July 2013, whereby eight floats were deployed within two separate cyclonic eddies. Floats were set to either daily and five-daily profiling from 1000 db to the surface, with park depths ranging from 300 db to 1000 db. The two cyclonic eddies propagated southwestward across the Mozambique Channel from southwest Madagascar to the KwaZulu-Natal Bight, a distance of approximately 1300 km, in approximately 130 days at a mean speed of 0.13 m sâ1 . Estimates indicate the April (July) eddy showed mean trapped depths of 595 ± 294 m (914 ± 107 m), volume transport of 13.4 ± 5.2 Sv (21.2 ± 9.1 Sv), heat flux of -0.07 ± 0.06 PW (-0.2 ± 0.09 PW) and freshwater flux of 0.04 ± 0.04 Sv (0.09 ± 0.05 Sv). These results highlight the role of Madagascar cyclonic eddies as transporters of cooled and freshened source waters into the Agulhas Current. During a third experiment, six floats were deployed in the Agulhas Current, and exited the current within 9 - 12 days at mean speeds of 0.51 â 0.76 m sâ1 . An evolution of properties was shown from north to south for both Argo data and model output; for volume transport (16.76 â 38.18 Sv; 17.70 â 32.51 Sv), heat fluxes (0.85 â 1.79 PW; 0.99 â 1.91 PW) and salt fluxes (0.60 â 1.37 x 1012 kg sâ1 ; 0.63 â 1.17 x 1012 kg sâ1 ). This study illustrates the first near-real time survey of the Agulhas Current, and a potential method of quasi-synoptic surveys using Argo float technology. These experiments highlight alternative methods of studying regions of turbulence by altering the mission parameters of Argo floats. Increased observations of eddies and Western Boundary Currents are critical to our understanding of the global oceans and impacts on the earths climate. Even more so for the understudied Indian Ocean
Physical oceonography of Sodwana Bay and its effect on larval transport and coral bleaching
Thesis (MTech (Oceanography))--Cape Peninsula University of Technology, 2009A collaborative study between Marine and Coastal Management (MCM) and the Oceanographic Research Institute (ORI) was initiated in March 2001 to investigate the physical oceanography of Sodwana Bay, South Africa, and the affects on coral communities resident to the area. A bottom-mounted Acoustic Doppler Current Profiler (ADCP) and three Underwater Temperature Recorders (UTR) were deployed to complement the long-term monitoring UTR deployed on Nine-Mile Reef (NMR) in 1994. The study was terminated after 30 months, whereby all instruments were removed except for the long-term monitoring UTR
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CORRIGENDUM: The impact of meanders, deeping and broadening, and seasonality on Agulhas Current temperature variability (v.50, pg3529, 2020)
Importantly, the error had negligible impact on the variability of the velocity, volume transport, and temperature transport and thus does not affect the main conclusions of the paper. Colors and thin contours show temperature, with each contour corresponding to 1°C. Thick contours show cross-track velocity, with each contour corresponding to 0.25 m sâ1. (c) Meander time series, as measured by sea level anomaly at 33.6°S, 28°E. Meanders are defined as times during which the SLA is lower than â0.2 m (gray shading). Colors and thin contours show temperature, with each contour corresponding to 1°C. Thick contours show cross-track velocity, with each contour corresponding to 0.25 m sâ1. (c) Meander time series, as measured by sea level anomaly at 33.6°S, 28°E. Meanders are defined as times during which the SLA is lower than â0.2 m (gray shading). Colors and thin contours show temperature, with each contour corresponding to 1°C. Thick contours show cross-track velocity, with each contour corresponding to 0.25 m sâ1. (c) Meander time series, as measured by sea level anomaly at 33.6°S, 28°E. Meanders are defined as times during which the SLA is lower than â0.2 m (gray shading). Fig. 7. (a) Composite temperature (colors) and velocity (thick contours) of the strongest 10% of southward box temperature transport times, excluding meander times. (b) Composite of the weakest 10% of southward box temperature transport times, excluding meander times. (c) The difference between the composites of strong minus weak temperature transport. (d) Time series of the box volume transport above 1000 m [Sv; blue (left axis)] and the square of the difference of depth between the 10°C isotherm at 20 km and at 219 km offshore [m2; red (right axis)]. Fig. 7. (a) Composite temperature (colors) and velocity (thick contours) of the strongest 10% of southward box temperature transport times, excluding meander times. (b) Composite of the weakest 10% of southward box temperature transport times, excluding meander times. (c) The difference between the composites of strong minus weak temperature transport. (d) Time series of the box volume transport above 1000 m [Sv; blue (left axis)] and the square of the difference of depth between the 10°C isotherm at 20 km and at 219 km offshore [m2; red (right axis)]. Fig. 7. (a) Composite temperature (colors) and velocity (thick contours) of the strongest 10% of southward box temperature transport times, excluding meander times. (b) Composite of the weakest 10% of southward box temperature transport times, excluding meander times. (c) The difference between the composites of strong minus weak temperature transport. (d) Time series of the box volume transport above 1000 m [Sv; blue (left axis)] and the square of the difference of depth between the 10°C isotherm at 20 km and at 219 km offshore [m2; red (right axis)]. The corresponding principal component time series are shown in Fig. 11, below. (a) The first EOF of the temperature field, describing 40% of the variance. (b) The second EOF of the temperature field, describing 25% of the variance. (c) The third EOF of the temperature field, describing 15% of the variance. (d) The pointwise correlation coefficient between principal component 2 and the cross-track velocity. The corresponding principal component time series are shown in Fig. 11, below. (a) The first EOF of the temperature field, describing 40% of the variance. (b) The second EOF of the temperature field, describing 25% of the variance. (c) The third EOF of the temperature field, describing 15% of the variance. (d) The pointwise correlation coefficient between principal component 2 and the cross-track velocity. The corresponding principal component time series are shown in Fig. 11, below. (a) The first EOF of the temperature field, describing 40% of the variance. (b) The second EOF of the temperature field, describing 25% of the variance. (c) The third EOF of the temperature field, describing 15% of the variance. (d) The pointwise correlation coefficient between principal component 2 and the cross-track velocity
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The Impact of Meanders, Deepening and Broadening, and Seasonality on Agulhas Current Temperature Variability
AbstractFor the first time, the temperature transport of the Agulhas Current is quantified in a time series. Over a 25-month mooring deployment at 34°S, seven tall moorings were instrumented to measure current velocity, temperature, and salinity. Current- and pressure-recording inverted echosounders were used to extend geostrophic velocity, temperature, and salinity records to 300 km offshore. In the mean, the current transports 3.8 PW of heat southward relative to 0°C: â76 Sv (1 Sv ⥠106 m3 sâ1) at a transport-weighted temperature of 12.3°C. A 0.9-PW standard deviation in temperature transport is due to variability in both volume transport and the temperature field. Meandering of the current core dominates variability in the temperature field by warming temperatures offshore and cooling temperatures near the coast. However, meandering has a limited impact on the temperature transport, which varies more closely with a deepening and broadening of the current associated with an inshore isotherm shoaling and an offshore isotherm deepening. Stronger southward temperature transports correspond to a deeper current transporting more volume, yet at a cooler transport-weighted temperature. Seasonality is not observed in the temperature transport time series, possibly because of the offsetting effects of cooler temperatures during times of seasonally stronger volume transports. Although volume transport and temperature transport are highly correlated, the large variability in transport-weighted temperature means that using volume transport alone to infer temperature transport results in an error that could be as large as 24% of the southern Indian Ocean heat transport
First Observations of Seasonal Variability in Water Mass Properties Across the Agulhas Current
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The impact of meanders, deepening and broadening, and seasonality on Agulhas current temperature variability
For the first time, the temperature transport of the Agulhas Current is quantified in a time series. Over a 25-month mooring deployment at 34°S, seven tall moorings were instrumented to measure current velocity, temperature, and salinity. Current- and pressure-recording inverted echosounders were used to extend geostrophic velocity, temperature, and salinity records to 300 km offshore. In the mean, the current transports 3.8 PW of heat southward relative to 0°C: â76 Sv (1 Sv ⥠106 m3 sâ1) at a transport-weighted temperature of 12.3°C. A 0.9-PW standard deviation in temperature transport is due to variability in both volume transport and the temperature field. Meandering of the current core dominates variability in the temperature field by warming temperatures offshore and cooling temperatures near the coast. However, meandering has a limited impact on the temperature transport, which varies more closely with a deepening and broadening of the current associated with an inshore isotherm shoaling and an offshore isotherm deepening. Stronger southward temperature transports correspond to a deeper current transporting more volume, yet at a cooler transport-weighted temperature. Seasonality is not observed in the temperature transport time series, possibly because of the offsetting effects of cooler temperatures during times of seasonally stronger volume transports. Although volume transport and temperature transport are highly correlated, the large variability in transport-weighted temperature means that using volume transport alone to infer temperature transport results in an error that could be as large as 24% of the southern Indian Ocean heat transport