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Metadata Report for BODC Series Reference Number 1760499

Metadata Summary

Data Description

Data Category CTD or STD cast
Instrument Type
Sea-Bird SBE 911plus CTD  CTD; water temperature sensor; salinity sensor
WET Labs {Sea-Bird WETLabs} C-Star transmissometer  transmissometers
WET Labs {Sea-Bird WETLabs} ECO FL fluorometer  fluorometers
Biospherical QCP-2300 underwater PAR sensor  radiometers
Sea-Bird SBE 3plus (SBE 3P) temperature sensor  water temperature sensor
Sea-Bird SBE 4C conductivity sensor  salinity sensor
Paroscientific Digiquartz depth sensors  water pressure sensors
Instrument Mounting research vessel
Originating Country United Kingdom
Originator Dr Rosie Chance
Originating Organization University of York Department of Chemistry
Processing Status banked
Online delivery of data Download available - Ocean Data View (ODV) format
Project(s) ACCACIA
Arctic Research Programme

Data Identifiers

Originator's Identifier Lance_CTD5
BODC Series Reference 1760499

Time Co-ordinates(UT)

Start Time (yyyy-mm-dd hh:mm) 2013-03-21 08:22
End Time (yyyy-mm-dd hh:mm) -
Nominal Cycle Interval 1.0 decibars

Spatial Co-ordinates

Latitude 78.00400 N ( 78° 0.2' N )
Longitude 8.99933 E ( 8° 60.0' E )
Positional Uncertainty 0.0 to 0.01 n.miles
Minimum Sensor or Sampling Depth 1.0 m
Maximum Sensor or Sampling Depth 149.0 m
Minimum Sensor or Sampling Height 1057.81 m
Maximum Sensor or Sampling Height 1205.81 m
Sea Floor Depth 1206.81 m
Sea Floor Depth Source GEBCO1401
Sensor or Sampling Distribution Variable common depth - All sensors are grouped effectively at the same depth, but this depth varies significantly during the series
Sensor or Sampling Depth Datum Instantaneous - Depth measured below water line or instantaneous water body surface
Sea Floor Depth Datum Chart reference - Depth extracted from available chart


BODC CODERankUnitsTitle
ACYCAA011DimensionlessSequence number
CDOMZZ011Parts per billionConcentration of coloured dissolved organic matter {CDOM Gelbstoff} per unit volume of the water body [dissolved plus reactive particulate phase] by fluorometry
CNDCST011Siemens per metreElectrical conductivity of the water body by CTD
CPHLPR011Milligrams per cubic metreConcentration of chlorophyll-a {chl-a CAS 479-61-8} per unit volume of the water body [particulate >unknown phase] by in-situ chlorophyll fluorometer
IRRDPP011MicroEinsteins per square metre per secondDownwelling 2-pi scalar irradiance as photons of electromagnetic radiation (PAR wavelengths) in the water body by 2-pi scalar radiometer
POPTZZ011PercentTransmittance (unspecified wavelength) per unspecified length of the water body by transmissometer
POTMCV011Degrees CelsiusPotential temperature of the water body by computation using UNESCO 1983 algorithm
PRESPR011DecibarsPressure (spatial coordinate) exerted by the water body by profiling pressure sensor and correction to read zero at sea level
PSALST011DimensionlessPractical salinity of the water body by CTD and computation using UNESCO 1983 algorithm
SIGTPR011Kilograms per cubic metreSigma-theta of the water body by CTD and computation from salinity and potential temperature using UNESCO algorithm
TEMPST011Degrees CelsiusTemperature of the water body by CTD or STD

Definition of Rank

  • Rank 1 is a one-dimensional parameter
  • Rank 2 is a two-dimensional parameter
  • Rank 0 is a one-dimensional parameter describing the second dimension of a two-dimensional parameter (e.g. bin depths for moored ADCP data)

Problem Reports

No Problem Report Found in the Database

Data Quality report RV Lance ACCACIA

CPHLPR01 is completely flagged for all casts, this seems to indicate that there is a problem with the calibration applied to the data.

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

Instrument description for RV Lance ACCACIA CTD frame

CTD Unit and Auxiliary Sensors

The CTD unit comprised a Sea-Bird Electronics (SBE) 9 plus underwater unit, an SBE 11 plus deck unit, a NPI 12-way frame and 12 10 L Niskin Water Samplers; all of which were mounted on a stainless steel 12-way CTD frame. The table below identifies the sensors and relevant details associated with them.

Sensor unit Model Serial number Full specification Calibration dates Comments
CTD underwater unit SBE 9 plus   SBE 9 plus    
CTD deck unit SBE 11 plus   SBE 11 plus    
Carousel NPI 12-way   Caroussel    
Pressure sensor Digiquartz 0972 Digiquartz 05/01/2010  
Temperature sensor SBE3 2400 SBE3 20/11/2012  
Temperature sensor SBE3 4052 SBE3 15/11/2012  
Conductivity sensor SBE4C 2063 SBE4C 21/11/2012  
Conductivity sensor SBE4C 2056 SBE4C 21/11/2012  
Irradiance sensor Biospherical QCP 70257 Biospherical QCP PAR sensor 08/01/2010  
Transmissometer   CST-1306DR C-Star 08/01/2010 25 cm pathlength
Fluorometer Wetlabs ECO Chlorophyll FLRTD-1547 Fluorometer 08/01/2010  
Fluorometer Wetlabs ECO CDOM FLCDRTD-1930 Fluorometer 08/01/2010  

Sea-Bird Electronics SBE 911 and SBE 917 series CTD profilers

The SBE 911 and SBE 917 series of conductivity-temperature-depth (CTD) units are used to collect hydrographic profiles, including temperature, conductivity and pressure as standard. Each profiler consists of an underwater unit and deck unit or SEARAM. Auxiliary sensors, such as fluorometers, dissolved oxygen sensors and transmissometers, and carousel water samplers are commonly added to the underwater unit.

Underwater unit

The CTD underwater unit (SBE 9 or SBE 9 plus) comprises a protective cage (usually with a carousel water sampler), including a main pressure housing containing power supplies, acquisition electronics, telemetry circuitry, and a suite of modular sensors. The original SBE 9 incorporated Sea-Bird's standard modular SBE 3 temperature sensor and SBE 4 conductivity sensor, and a Paroscientific Digiquartz pressure sensor. The conductivity cell was connected to a pump-fed plastic tubing circuit that could include auxiliary sensors. Each SBE 9 unit was custom built to individual specification. The SBE 9 was replaced in 1997 by an off-the-shelf version, termed the SBE 9 plus, that incorporated the SBE 3 plus (or SBE 3P) temperature sensor, SBE 4C conductivity sensor and a Paroscientific Digiquartz pressure sensor. Sensors could be connected to a pump-fed plastic tubing circuit or stand-alone.

Temperature, conductivity and pressure sensors

The conductivity, temperature, and pressure sensors supplied with Sea-Bird CTD systems have outputs in the form of variable frequencies, which are measured using high-speed parallel counters. The resulting count totals are converted to numeric representations of the original frequencies, which bear a direct relationship to temperature, conductivity or pressure. Sampling frequencies for these sensors are typically set at 24 Hz.

The temperature sensing element is a glass-coated thermistor bead, pressure-protected inside a stainless steel tube, while the conductivity sensing element is a cylindrical, flow-through, borosilicate glass cell with three internal platinum electrodes. Thermistor resistance or conductivity cell resistance, respectively, is the controlling element in an optimized Wien Bridge oscillator circuit, which produces a frequency output that can be converted to a temperature or conductivity reading. These sensors are available with depth ratings of 6800 m (aluminium housing) or 10500 m (titanium housing). The Paroscientific Digiquartz pressure sensor comprises a quartz crystal resonator that responds to pressure-induced stress, and temperature is measured for thermal compensation of the calculated pressure.

Additional sensors

Optional sensors for dissolved oxygen, pH, light transmission, fluorescence and others do not require the very high levels of resolution needed in the primary CTD channels, nor do these sensors generally offer variable frequency outputs. Accordingly, signals from the auxiliary sensors are acquired using a conventional voltage-input multiplexed A/D converter (optional). Some Sea-Bird CTDs use a strain gauge pressure sensor (Senso-Metrics) in which case their pressure output data is in the same form as that from the auxiliary sensors as described above.

Deck unit or SEARAM

Each underwater unit is connected to a power supply and data logging system: the SBE 11 (or SBE 11 plus) deck unit allows real-time interfacing between the deck and the underwater unit via a conductive wire, while the submersible SBE 17 (or SBE 17 plus) SEARAM plugs directly into the underwater unit and data are downloaded on recovery of the CTD. The combination of SBE 9 and SBE 17 or SBE 11 are termed SBE 917 or SBE 911, respectively, while the combinations of SBE 9 plus and SBE 17 plus or SBE 11 plus are termed SBE 917 plus or SBE 911 plus.


Specifications for the SBE 9 plus underwater unit are listed below:

Parameter Range Initial accuracy Resolution at 24 Hz Response time
Temperature -5 to 35°C 0.001°C 0.0002°C 0.065 sec
Conductivity 0 to 7 S m-1 0.0003 S m-1 0.00004 S m-1 0.065 sec (pumped)
Pressure 0 to full scale (1400, 2000, 4200, 6800 or 10500 m) 0.015% of full scale 0.001% of full scale 0.015 sec

Further details can be found in the manufacturer's specification sheet.

WETLabs ECO-FL Fluorometer

The Environmental Characterization Optics series of single channel fluorometers are designed to measure concentrations of natural and synthetic substances in water, and are therefore useful for biological monitoring and dye trace studies. Selected excitation and emission filters allow detection of the following substances: chlorophyll-a, coloured dissolved organic matter (CDOM), uranine (fluorescein), rhodamine, phycoerythrin and phycocyanin.

The ECO-FL can operate continuously or periodically and has two different types of connectors to output the data (analogue and RS-232 serial output). The potted optics block results in long term stability of the instrument and the optional anti-biofouling technology delivers truly long term field measurements.

In addition to the standard model, five variants are available, and the differences between these and the basic ECO-FL are listed below:

  • FL(RT): similar to the FL but operates continuously when power is supplied
  • FL(RT)D: similar model to the (RT) but has a depth rating of 6000 m
  • FLB: includes internal batteries for autonomous operation and periodic sampling
  • FLS: similar to FLB but has an integrated anti-fouling bio-wiper
  • FLSB: similar to the FLS, but includes internal batteries for autonomous operation


Temperature range 0 to 30°C
Depth rating

600 m (standard)

6000 m (deep)

Linearity 99 % R2
Wavelength (excitation/emission) 470/695 nm
Sensitivity 0.01 µg L-1
Typical range 0.01 to 125 µg L-1
Wavelength (excitation/emission) 370/460 nm
Sensitivity 0.01 ppb
Typical range 0.09 to 500 ppb
Wavelength (excitation/emission) 470/530 nm
Sensitivity 0.07 ppb
Typical range 0.12 to 230 ppb
Wavelength (excitation/emission) 540/570 nm
Sensitivity 0.01 ppb
Typical range 0.01 to 230 ppb
Wavelength (excitation/emission) 540/570 nm
Sensitivity 0.01 ppb
Typical range 0.01 to 230 ppb
Wavelength (excitation/emission) 630/680 nm
Sensitivity 0.15 ppt
Typical range 0.15 to 400 ppt

Further details can be found in the manufacturer's specification sheet.

Biospherical Instruments Log Quantum Cosine Irradiance Sensor QCP-2300 & QCP-2350

The QCP-2300 is a submersible cosine-collector radiometer designed to measure irradiance over Photosynthetically Active Radiation (PAR) wavelengths. It features a constant (better than ±10%) quantum response from 400 to 700 nm with the response being sharply attenuated above 700 nm and below 400 nm.

The sensor is a blue-enhanced high stability silicon photovoltaic detector with dielectric and absorbing glass filter assembly. The output is a DC voltage typically between 0 and 5 VDC that is proportional to the log of the incident irradiance.

The QCP-2300 is specifically designed for integration with 12-bit CTD systems and dataloggers requiring a limited-range of signal input.


Wavelength 400 to 700 nm
PAR Spectral Response better than ± 10% over 400-700 nm
Cosine Directional Response ± 5% 0 to 65°; ± 10% 0 to 85°
Noise level < 1 mV
Temperature Range -2 to 35 °C
Depth Range (standard) 1000 m

Further details can be found in the manufacturer's manual.


WETLabs C-Star transmissometer

This instrument is designed to measure beam transmittance by submersion or with an optional flow tube for pumped applications. It can be used in profiles, moorings or as part of an underway system.

Two models are available, a 25 cm pathlength, which can be built in aluminum or co-polymer, and a 10 cm pathlength with a plastic housing. Both have an analog output, but a digital model is also available.

This instrument has been updated to provide a high resolution RS232 data output, while maintaining the same design and characteristics.


Pathlength 10 or 25 cm
Wavelength 370, 470, 530 or 660 nm

~ 20 nm for wavelengths of 470, 530 and 660 nm

~ 10 to 12 nm for a wavelength of 370 nm

Temperature error 0.02 % full scale °C-1
Temperature range 0 to 30°C
Rated depth

600 m (plastic housing)

6000 m (aluminum housing)

Further details are available in the manufacturer's specification sheet or user guide.

BODC Processing

The data were processed by the data originator and sent to BODC as 16 cnv files. The files were analysed and found to contain the up and downcasts and to not be binned to regular intervals. In order to overcome these issues, the following procedures were applied:

    1- Section: Extract the downcast from the originator's files
    2- BinAverage: Average data to regular intervals of 1 dbar

These procedures were done using SeaBird Seasave Version 7.23.2. Once completed, the files were then used for the remaining procedures.


The data from the originator's file were reformatted into BODC's internal format using standard procedures. For the parameters which had primary and secondary sensors, data from both channels were transferred but after screening, given that both channels exhibited a similar quality, the secondary channels were dropped. All secondary channels are available upon request.

The following table shows the final parameters and how they were mapped to BODC parameter codes:

Originator's Variable Originator's Units Description BODC Parameter Code BODC Units Comments
prDM dbar Pressure (spatial co-ordinate) exerted by the water body by profiling pressure sensor and corrected to read zero at sea level PRESPR01 dbar  
t090c °C Temperature of the water body by CTD or STD TEMPST01 °C  
t190c °C Temperature of the water body by CTD or STD (second sensor) TEMPST02 °C Parameter dropped following screening as there was no difference between the quality of the data from the first and second temperature sensors
c0uS/cm µs cm-1 Electrical conductivity of the water body by CTD CNDCST01 S m-1 /10000
c1uS/cm µs cm-1 Electrical conductivity of the water body by CTD (sensor 2) CNDCST02 S m-1


Parameter dropped following screening as there was no difference between the quality of the data from the first and second temperature sensors

xmiss % Transmittance (unspecified wavelength) per unspecified length of the water body by transmissometer POPTZZ01 %  
flECO-AFL1 ppb Concentration of coloured dissolved organic matter {cdom gelbstoff} per unit volume of the water body [dissolved plus reactive particulate phase] by fluorometry CDOMZZ01 ppb  
flECO-AFL mg m-3 Concentration of chlorophyll-a {chl-a cas 479-61-8} per unit volume of the water body [particulate >unknown phase] by in-situ chlorophyll fluorometer CPHLPR01 mg m-3  
par µE m-2 s-1 Downwelling 2-pi scalar irradiance as photons (PAR wavelengths) in the water body by 2-pi scalar radiometer IRRDPP01 µE m-2 s-1  
sal00 PSU Practical salinity of the water body by CTD and computation using UNESCO 1983 algorithm PSALST01    
sal11 PSU Practical salinity of the water body by CTD (second sensor) and computation using UNESCO 1983 algorithm PSALST02   Parameter dropped following screening as there was no difference between the quality of the data from the first and second temperature sensors.
- - Potential temperature of the water body by computation using UNESCO 1983 algorithm POTMCV01 °C Derived from TEMPST01, PSALST01 and PRESPR01
- - Sigma-theta of the water body by CTD and computation from salinity and potential temperature using UNESCO algorithm SIGTPR01 kg m-3 Derived from TEMPST01, PSALST01 and PRESPR01


The reformatted data were visualised using the in-house EDSERPLO software. Suspect data were marked by adding an appropriate quality control flag, and missing data by setting the data to an appropriate value and applying the quality control flag.

Originator's data processing

Sampling strategy

A total of 16 shallow CTD casts were made during cruise Lance ACCACIA as part of the Aerosol-Cloud Coupling And Climate Interactions in the Arctic project. The cruise sailed from TromsØ on 15 March 2013 and docked in TromsØ on 31 March 2015, collecting data in the Greenland Sea. One CTD cast was made each day collecting water samples between the surface and 200 metres depth.

Data processing

The data were processed by the originator using Seabird SBE data processing software (Version 7.21b) before submission to BODC. Sensors were maintained and calibrated by the Norweigan Polar Institute (NPI). Oxygen data were also collected, however the calibration was overdue therefore the data are not considered reliable.

For each CTD cast the following raw data files were generated:

  • (a record of bottle firing locations)
  • ctdX.hdr (header file)
  • ctdX.hex (raw data file)
  • ctdX.con (configuration file)

where X is the cast number of the CTD data series.

The following procedures were applied:

  • 1. DatCNV was used to read in the raw CTD data file (.hex) which contained the data in engineering units and apply calibrations as appropriate through the instrument configurations (.con) file
  • 2. Filter was run on the pressure channel to smooth out the high frequency data
  • 3. CellTM was run using alpha = 0.03 and 1/beta = 7, to correct for conductivity errors induced by the transfer of heat from the conductivity cell to the seawater
  • 4. LoopEdit was run to mark scans with badflags when scan fails pressure reversal or minimum velocity tests and to eliminate surface soak data
  • 5. Derive was run to create the variables Salinity, Salinity 2
  • 6. BinAverage was run to average the data to 2Hz bins (0.5 seconds)

Field calibrations

No attempt was made to calibrate the CTD sensors against independent samples.

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.


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.


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.


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.


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 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