Metadata Report for BODC Series Reference Number 1760592
Metadata Summary
Problem Reports
Data Access Policy
Narrative Documents
Project Information
Data Activity or Cruise Information
Fixed Station Information
BODC Quality Flags
SeaDataNet Quality Flags
Metadata Summary
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Problem Reports
No Problem Report Found in the Database
Data Access Policy
Public domain data
These data have no specific confidentiality restrictions for users. However, users must acknowledge data sources as it is not ethical to publish data without proper attribution. Any publication or other output resulting from usage of the data should include an acknowledgment.
The recommended acknowledgment is
"This study uses data from the data source/organisation/programme, provided by the British Oceanographic Data Centre and funded by the funding body."
Narrative Documents
ACCACIA RV Lance ACCACIA Underway Cruise Document
Cruise details
| Dates | 2013-03-15 — 2014-03-31 |
|---|---|
| Principal Scientific Officer | James Lee, University of York |
| Data supplied by | James Lee, University of York |
RV Lance departed from Tromsø, Norway on 15 March 2013 and docked in Tromsø, Norway on 31 March 2013.
This cruise was the first cruise of the NERC funded project called Aerosol-Cloud Coupling and Climate interactions in the Arctic (ACCACIA). The aims of the cruise were to:
- obtain surface based in-situ measurements of marine aerosol composition and properties and aerosol precursos gases (DMS, VOC, halocarbon)
- Collect standard oceanographic measurements such as salinity, chlorophyll-a and nutrient concentrations
- Conduct bubble tank experiments to develop a primary multicomponent seaspray aerosol flux parameterisation
The data provided to BODC includes the entire cruise period from 2013-03-15 — 2013-03-31. Date and time were supplied in UT. The span of the data covers all scientific activities.
Thermo Scientific Ozone analyser 49i
The Ozone analyser model 49i is a dual cell photometer ozone analyser that uses UV photometric technology to measure the amount of ozone in the air. The instrument allows for dual and auto range and allows for the sample and reference flowing at the same time. Temperature and pressure correction are available as a standard feature. The measuring range spans 0.05 ppb to 200 ppm.
General specifications are presented in the table below:
| Parameter | specs |
|---|---|
| Preset ranges | 0-0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50, 100 and 200 ppm 0-0.1, 0.2, 1, 2, 5, 10, 20, 50, 100, 200 and 400 mg m-3 |
| custom ranges | 0-0.05 to 200 ppm 0-0.1 to 400 mg m-3 |
| Zero noise | 0.25 ppb RMS (60 s averaging time) |
| Lower detectable limit | 0.50 ppb |
| Zero drift (24 hours) | <1.0 ppb |
| Span drift | <1% full scale per month |
| Response time | 20 s (10 s lag time) |
| Precision | 1.0 ppb |
| Linearity | ±1% full scale |
| Sample flow rate | 1-3 l min-1 |
Further information can be found in the manufacturer data sheet.
Gill Instruments Windsonic Anemometer
The Gill Windsonic is a 2-axis ultrasonic wind sensor that monitors wind speed and direction using four transducers. The time taken for an ultrasonic pulse to travel from the North to the South transducers is measured and compared with the time for a pulse to travel from South to North. Travel times between the East and West transducers are similarly compared. The wind speed and direction are calculated from the differences in the times of flight along each axis. This calculation is independent of environmental factors such as temperature.
Specifications
| Ultrasonic output rate | 0.25, 0.5, 1, 2 or 4 Hz |
| Operating Temperature | -35 to 70°C |
| Operating Humidity | < 5 to 100% RH |
| Anemometer start up time | < 5 s |
| Wind speed | |
| Range | 0 to 60 m s-1 |
| Accuracy | ± 2% at 2 m s-1 |
| Resolution | 0.01 m s-1 |
| Response time | 0.25 s |
| Threshold | 0.01 m s-1 |
| Wind direction | |
| Range | 0 to 359° |
| Accuracy | ± 3° at 12 m s-1 |
| Resolution | 1° |
| Response time | 0.25 s |
Further details can be found in the manufacturer's specification sheet.
Kongsberg Seatex Seapath 300
A combined GPS receiver and inertial measurement unit for use in hydroacoustic positioning and seabed mapping systems. The system comprises 2 built-in GPS receivers and a MRU 5. The inertial unit consists of linear accelometers and MEMS type angular rate gyros. The system can achieve 0.02° RMS roll and pitch accuracy, 2 cm heave accuracy (delayed-signal), 20 cm positional accuracy (with RTK corrections) and <200 Hz data output rate. It is a member of the Kongsberg Seatex Seapath family of position, heading, time and attitude sensors.
Further specifications can be found in the manufacturer's data sheet.
SeaBird MicroTSG Thermosalinograph SBE 45
The SBE45 MicroTSG is an externally powered instrument designed for shipboard measurement of temperature and conductivity of pumped near-surface water samples. The instrument can also compute salinity and sound velocity internally.
The MicroTSG comprises a platinum-electrode glass conductivity cell and a stable, pressure-protected thermistor temperature sensor. It also contains an RS-232 port for appending the output of a remote temperature sensor, allowing for direct measurement of sea surface temperature.
The instrument can operate in Polled, Autonomous and Serial Line Sync sampling modes:
- Polled sampling: the instrument takes one sample on command
- Autonomous sampling: the instrument samples at preprogrammed intervals and does not enter quiescence (sleep) state between samples
- Serial Line Sync: a pulse on the serial line causes the instrument to wake up, sample and re-enter quiescent state automatically
Specifications
| Conductivity | Temperature | Salinity | |
|---|---|---|---|
| Range | 0 to 7 Sm-1 | -5 to 35°C | |
| Initial accuracy | 0.0003 Sm-1 | 0.002°C | 0.005 (typical) |
| Resolution | 0.00001 Sm-1 | 0.0001°C | 0.0002 (typical) |
| Typical stability (per month) | 0.0003 Sm-1 | 0.0002°C | 0.003 (typical) |
Further details can be found in the manufacturer's specification sheet.
ACCACIA RV Lance ACCACIA Underway Navigation Document
Content of data series
| Parameter | Units | Parameter code | Comments |
|---|---|---|---|
| Latitude | Degrees (+ve N) | ALATGP01 | - |
| Longitude | Degrees (+ve E) | ALONGP01 | - |
| Ship's heading | degrees | HEADCM01 | Raw data |
| Ship's eastward velocity | cm s-1 | APEWGP01 | Derived by BODC |
| Ship's northward velocity | cm s-1 | APNSGP01 | Derived by BODC |
| Distance run | km | DSRNCV01 | Derived by BODC |
Instrumentation
The following scientific navigational and bathymetry systems were fitted.
| Manufacturer | Model | Main role |
| Kongsberg | Seapath 300 | GPS and gyrocompass |
| Simrad | Doppler NL | velocity logger |
Data Processing Procedures
Originator's Data Processing
A number of navigation parameters (position, ship's speed over ground, course made good, heading) were obtained from the different instruments installed on board the RV Lance. The data were obtained as 10 minute averages from the data downloaded from the ship's data log.
Files delivered to BODC
| Filename | Content description | Format | Interval | Start date/time (UTC) | End date/time (UTC) |
| BODC_ACCACIA1_salinity_met_O3_data.xls | Navigation, Meteorology and Surface Hydrography data | .xls | 10 minutes | 15/03/2013 00:00:00 hours | 31/03/2013 23:50:00 hours |
BODC Data Processing
The data were banked at BODC following standard data banking procedures, including checking navigation channels for improbable values, working out speed over ground, and screening the data for anomalous values.
The originator's variables were mapped to appropriate BODC parameter codes as follows:
BODC_ACCACIA1_salinity_met_O3_data.xls
| Originator's variable | Originator's units | Description | BODC Code | BODC Units | Unit conversion |
| lat | decimal degrees | Latitude north (WGS84) by unspecified GPS system | ALATGP01 | decimal degrees | - |
| lon | decimal degrees | Longitude east (WGS84) by unspecified GPS system | ALONGP01 | decimal degrees | - |
| boat speed | knots | Speed (over ground) of measurement platform | APSAZZ01 | m s-1 | *0.51444 |
| boat course | degrees | Direction of motion (over ground) of measurement platform {course made good} | APDAZZ01 | degrees | - |
| heading | degrees | Orientation (horizontal relative to true north) of measurement device {heading} | HEADCM01 | degrees | - |
All the reformatted data were visualised using the in-house EDSERPLO software. Suspect data were marked by adding an appropriate quality control flag, missing data by both setting the data to an appropriate value and setting the quality control flag.
Ship's speed and direction (APSAZZ01 and APDAZZ01) were not included in the final file, however they are available upon request.
Position
A check was run on the position data and no gaps or speed fails were identified.
Ship's velocities
Ship's eastward and northward velocities were calculated from the main latitude and longitude channels.
Distance Run
Distance run was calculated from the main latitude and longitude channels, starting from the beginning of the file.
Project Information
ACCACIA- Aerosol Cloud Coupling and Climate Interactions in the Arctic
The ACCACIA project is a £2.05 million component of the Natural Environment Research Council (NERC) Arctic Research programme (ARP) running from 2012 to 2016. The aim of ACCACIA is to collect data using both airplanes and ships based in the Svalbard archipelago near the margin of permanent Arctic sea ice cover and study fluxes of solar and infrared radiation above and below the clouds, the vertical structure of the low-level atmosphere and how aerosol concentration levels change with the seasons and with the extent of sea-ice cover. The results from this research will inform not only cutting-edge modelling of the global climate system and predictions for future climatic change, but also more immediate weather forecasts for mid-to-high-latitude locations such as the UK.
Background
Over most of the globe low clouds act to cool the surface since they reflect sunlight; over the arctic the highly reflective ice surface reduces the significance of cloud reflectivity, and the absorption of infrared radiation by cloud water droplets becomes the dominant effect - this acts to trap heat below cloud, warming the surface. Although climate models generally show a strong greenhouse warming effect in the Arctic, they also disagree with each other more in the Arctic than anywhere else, producing a wider range of possible future climate conditions. The models also tend not to be able to reproduce current Arctic climate conditions very accurately. This large uncertainty in models of the Arctic climate results primarily from poor representation of physical processes within the models, and some unique and particularly challenging conditions. The largest single source of uncertainty is the representation of clouds. The models use simple representations of cloud properties that were developed from observations in mid latitude or tropical cloud systems - very different conditions from those that exist in the Arctic.
The main activities of this project are to make airborne in situ measurements of cloud microphysical properties, the vertical structure of the boundary layer and aerosol properties, and the fluxes of solar and infra red radiation above, below, and within cloud. It will also measure the production rates and properties of aerosol at the surface and their variability with season and extent of sea ice cover. These measurements will be used, along with a range of numerical models of aerosol and cloud processes, and atmospheric dynamics to evaluate the interactions between sea ice extent, aerosol production and cloud properties. New and improved descriptions of these processes suitable for use within climate models will be developed, tested, and implemented within the MetOffice climate model HadGEM. The ability of the current MetOffice models to reproduce the observed Arctic cloud and boundary layer properties will be tested and the impact of the new parameterization schemes evaluated. Finally we will undertake a series of climate simulations to examine how future climate will evolve, and the feedbacks between warming of the Arctic, melting of sea ice, production of aerosol, and the properties of clouds evaluated.
Participants
Dr Ian Brooks is the Lead Investigator and Professor Lucy Carpenter and Dr Amélie Kirchgaessner are Co-Investigators. The project is made up of collaborations with universities and research organisations including:
- British Antarctic Survey
- University of East Anglia
- University of Leeds
- University of Manchester
- University of York
Fieldwork and data collection
The ACCACIA project involves the collection of data in the Svalbard archipelago near the margin of permanent sea ice cover using research vessels as well as the BAS MASIN and FAAM BAe146 aircrafts. The project consisted of two cruises, as detailed below:
| Cruise identifier | Research ship | Cruise dates |
|---|---|---|
| Lance ACCACIA | RV Lance | 15 March 2013 - 31 March 2013 |
| JR20130713 (JR288) | RRS James Clark Ross | 13 July 2013 - 16 August 2013 |
The aims of the research cruises are to make surface based in-situ measurements of marine aerosol composition and its properties, and aerosol precursor gases including DMS, VOCs and halocarbons. Trace gases were measured in air and water, and high volume aerosol samples were collected for off-line characterisation of organic composition. Ambient aerosol measurements and bubble tank experiments were conducted to characterise aerosol physical and chemical properties using a suite of instrumentation by the Manchester group. Together with black carbon/soot optical measurements and CCN measurements made as function of particle size and super-saturation, these will be used as input in cloud microphysical models to investigate their influence on aerosol-cloud feedback sensitivity whereas bubble tank results will be used to develop a primary multicomponent sea-spray aerosol flux parameterisation.
Arctic Research Programme
The Arctic Research Programme (ARP) is a £15m, five year (2010-2015) research programme funded by the Natural Environment Research Council (NERC). The aim of the programme is to improve our capability to predict changes in the Arctic, particularly over timescales of months to decades, including regional impacts and the potential for feedbacks on the global Earth System.
Background
The Arctic is a region of higher than average climate change and is predicted to remain so, the main evidences of this rapid climate change are the loss of summer sea ice, the thawing of permafrost (perennially frozen earth), melting of land ice, including ice sheets and glaciers, and the changing physical environment of Arctic ecosystems.
The Arctic represents a critical region for global environmental change and one where the UK has significant strategic interests. Understanding the drivers and feedbacks of this change, and predicting its scale and rate on timescales from months to decades, represents a major and urgent global scientific challenge of great societal importance.
The Arctic Programme will focus on four linked scientific objectives:
- Understanding and attributing the current rapid changes in the Arctic
- Quantifying processes leading to Arctic methane and carbon dioxide release
- Reducing uncertainty in Arctic climate and associated regional biogeochemistry predictions
- Assessing the likely risks of submarine hazards associated with rapid Arctic climate change
Deliverables from this programme will include:
- New or improved models for process studies
- Improved parameterisation of Arctic processes
- Improved capabilities for predicting changes in the Arctic
- Interpretation of current Arctic climate change and its implications for policy makers and Arctic communities
To achieve these objectives and deliverables, the Arctic Research Programme will aim to harness and co-ordinate UK scientific expertise and facilities in these areas, and link these to other international efforts. Fieldwork is expected to be highly interdisciplinary, potentially involving campaigns on land and ice stations, from ships, aircraft and satellites. Work on understanding longer term change in the Arctic will involve sediment cores and sampling on land. The use of a range of numerical models leading to improved predictability will be a vital element of this programme. Process-level understanding developed through observation-based work will be used to improve model components, and these will be used to test the impact of the processes on large-scale predictions. Clearly, integrated and innovative research across all science areas will be needed to achieve the programme's objectives.
Further details can be found on the ARP website.
Participants
34 different partner institutions are involved with the ARP. These are:
- University of Aberdeen
- Bangor University
- British Antarctic Survey
- British Geological Survey
- Centre for Ecology and Hydrology
- University of Cambridge
- University of Dundee
- Durham University
- University of East Anglia
- University of Leeds
- Loughborough University
- University of Manchester
- National Centre for Atmospheric Science
- National Oceanography Centre
- University of Nottingham
- Office of Naval Research
- University of Oxford
- University of Portsmouth
- University of Reading
- Royal Holloway, University of London
- University of Sheffield
- University of Southampton
- University of Stirling
- University of Sussex
- University College London
- University of Ulster
- University of York
- Natural Environment Research Council
- Department for Energy and Climate Change
- Department for Environment, Food and Rural Affairs
- Foreign and Commonwealth Office
- Met Office
- Royal Navy
- Living with Environmental Change
Research details
Overall 15 projects have been funded through ARP:
- ACCACIA- Aerosol Cloud Coupling and Climate Interactions in the Arctic
- APPOSITE- Arctic Predictability and Prediction on Seasonal to Inter-annual Timescales
- CYCLOPS- Carbon Cycling Linkages to Permafrost Systems
- HYDRA- Hydrological Controls on Carbon Cycling and Greenhouse Gas Budgets
- LAC- Lakes and the Arctic Carbon Cycle
- Landslide-Tsunami
- MAMM- Methane and other Greenhouse Gases in the Arctic: Measurements, Process Studies and Modelling
- SEATS- Submarine Estimates of Arctic Turbulence Spectra
- TEA-COSI- The Environment of the Arctic: Climate, Ocean and Sea-Ice
- Arctic methane hydrates and climate change
- Canadian Archipelago Oceanography
- Effects of a warming climate on the key organic carbon cycle processes in the Eurasian Arctic
- Is the Arctic methane budget changing?
- MIZ- The role to atmospheric, ice and oceanic interactions in the marginal ice zone
- MIZ-WAVE- Wave-ice interaction and the marginal ice zone
Data Activity or Cruise Information
Cruise
| Cruise Name | ACCACIA |
| Departure Date | 2013-03-15 |
| Arrival Date | 2013-03-31 |
| Principal Scientist(s) | James Lee (University of York Department of Chemistry) |
| Ship | Lance |
Complete Cruise Metadata Report is available here
Fixed Station Information
No Fixed Station Information held for the Series
BODC Quality Control Flags
The following single character qualifying flags may be associated with one or more individual parameters with a data cycle:
| Flag | Description |
|---|---|
| Blank | Unqualified |
| < | Below detection limit |
| > | In excess of quoted value |
| A | Taxonomic flag for affinis (aff.) |
| B | Beginning of CTD Down/Up Cast |
| C | Taxonomic flag for confer (cf.) |
| D | Thermometric depth |
| E | End of CTD Down/Up Cast |
| G | Non-taxonomic biological characteristic uncertainty |
| H | Extrapolated value |
| I | Taxonomic flag for single species (sp.) |
| K | Improbable value - unknown quality control source |
| L | Improbable value - originator's quality control |
| M | Improbable value - BODC quality control |
| N | Null value |
| O | Improbable value - user quality control |
| P | Trace/calm |
| Q | Indeterminate |
| R | Replacement value |
| S | Estimated value |
| T | Interpolated value |
| U | Uncalibrated |
| W | Control value |
| X | Excessive difference |
SeaDataNet Quality Control Flags
The following single character qualifying flags may be associated with one or more individual parameters with a data cycle:
| Flag | Description |
|---|---|
| 0 | no quality control |
| 1 | good value |
| 2 | probably good value |
| 3 | probably bad value |
| 4 | bad value |
| 5 | changed value |
| 6 | value below detection |
| 7 | value in excess |
| 8 | interpolated value |
| 9 | missing value |
| A | value phenomenon uncertain |
| B | nominal value |
| Q | value below limit of quantification |
