Abstract:

The 19 days research cruise VIMOA was conducted around Disko Bay in Vaigat, west Greenland, in the summer of 2019. Measurements were made from the R/V Tulu 2019-07-29 through 2019-08-17. Wind conditions and air-sea fluxes were collected from the ship-based meteorological tower associated with different environmental scenarios: floating iceberg cluster area, river water plume front zone, delta coastal area, etc. In the meantime, surface water temperature and salinity were obtained near the icebergs, also, hydrodynamic properties of surface waves were recorded, even luckily during an extremely high wind event. This dataset contains wind speed, air temperature, relative humidity, temperature, conductivity, wave statistics and spectra, and raw images of an iceberg-rich region obtained with a polarimetric imaging system. Some pre-cruise test data from the atmospheric and wave measurement systems is also included for comparison.

Suggested Citation:

Haus, Brian, and Hanjing Dai. 2021. Air-sea interaction observations on iceberg marine environment in Vaigat, West Greenland, collected aboard the R/V Tulu from 2019-07-29 to 2019-08-17. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/J7VVSD76

Purpose:

The Arctic is rapidly changing in snow, ice, water, permafrost, etc. that the nature of its physical environment in the last decades. The surface air temperature has warmed at twice the global average over the past 50 years while snow cover extent (SCE) and snow cover duration (SCD) are observed to decrease. Land ice losses are intensified from increased surface melting and runoff, iceberg calving, ice-ocean melting, and evaporation/sublimation which dominates the contribution of accelerated freshwater fluxes and high iceberg discharge calving from the glaciers into the seas. Meanwhile, sea-ice extent and thickness are continuing to decline for more open water beyond the expected autumn period. The coastal retreat processes also speed up because of sea-ice cover declining and increasing seawater temperature with its complex positive and negative feedback. Such these active changes related to global warming trigger a new economic, societal, and environmental Arctic. Oil and gas exploitation, transarctic shipping, cruise tourism boost the transformation of economic structure from single fishing -the lifeline and fragile industry- to a diverse and modern service-oriented economy. Along with urbanization processes, Arctic cities growing and rural exodus, great changes in livelihoods and lifestyles of the inhabitants have taken place. A large portion of Arctic indigenous populations work in public administration and service, transportation, and wholesale. The adoption of “western” consumption mode -financial loan, non-cash payment, hypermarket shopping and etc.- results in traditional methods to acquire food and other commodities dwindling away to nothing. Sufficient transportation links provide opportunities in terms of mobility, advanced education as apprenticeships in companies or at the school, and career promotion. Quietly, the image of the Global Arctic takes shape by linking to global trade networks, resource supply chains, and information and communication systems. Inevitably, new challenges have come out along with more frequent physical and human disturbance events. All routine operations of offshore oil and gas exploitation and of ship traffic increase the risk of accidental oil spills in the Arctic marine environment. Cleaning up a spill in cold, remote open water infested with icebergs is logistically difficult. Therefore, the removal of oil mainly relies on natural weathering processes. However, recent studies in Western Greenland have shown that the most toxic hydrocarbons were not biodegraded. Further, the mixing enhances the dispersion of oil droplets in the water column contaminating the Arctic ecosystem eventually. The Disko Bay region in Vaigat, West Greenland, exemplifies the multifaceted problem of balancing economic development, resource management, and environment changes. Hence, the Vaigat Iceberg - Microbial Oil degradation and Archaeological heritage investigation (VIMOA) aimed to obtain first-hand observations to investigate this new state.

Data Parameters and Units:

The dataset is organized into four different directories. Run05_iceberg_survey_1 contains the raw imagery from the polarimeter camera, organized into three subdirectories by camera number/angle of polarization; containing tif files named by date and time stamp. 'data108SDcard copy' and 'data109SDcard copy' contain measurements from the Spotter buoy, including the original files, a parser script written in python, and aggregated measurement values of different variables: time [Unix epoch format], including latitude [degree, minutes x 1e5], longitude [degree, minutes x 1e5], 3D movement (Sx, Sy, Sz), GPS, and wave statistics (significant wave height [m], mean period [s], peak period [s], mean direction [degrees], peak direction [degrees], mean spreading, peak spreading. Spectra include Sxx (spectra of zonal displacement, [m^2/Hz]), Syy (spectra of meridional displacement, [m^2/Hz]), Szz (spectra of vertical displacement, [m^2/Hz]), Qxz (quad-spectrum of zonal and vertical displacement, [m^2/Hz]), Qyz (quad-spectrum f zonal and vertical displacement, [m^2/Hz]), first and second-order coefficients for sine and cosine terms. Parameters are all year [2019], month [7 or 8], day, hour, min, sec, milisec, dof (degrees of freedom used to calculate spectra), 129 frequencies (0 to the Nyquist frequency, [1/s]). The python parser script details the variables, units, and methodology. CTD data: timestamp [YYYY-MM_DD HH:MM:SS]], record number [enum], depth [mm], temperature [degrees C], conductivity [uS/cm]; Compass data: timestamp [YYYY-MM_DD HH:MM:SS]], record number [enum], heading [degrees], deviation (correction to true, [degrees]), deviation direction [compass direction], variation [degrees], variation direction [compass direction], heading number, pitch [degrees], roll [degrees] GPS data: timestamp [YYYY-MM_DD HH:MM:SS]], record number [enum], latitude [degrees], latitude [minutes], longitude [degrees], longitude [minutes], speed [knots], speed [m/s], course [degrees], magnetic variation [unitless] fix quality [unitless], number of satellites [enum], altitude [m], pps pms], dt since gprmc [s], gps_ready [unitless], max_clock_change [ms], nmbr_clock_chnage [samples] Wind speed: timestamp [YYYY-MM_DD HH:MM:SS]], record number [enum], U1x (zonal component, anemometer 1, [m/s]), U1y (meridional component, anemomenter 1, [m/s]), U1z (vertical component, anemomenter 1, [m/s]), temperature (anemometer 1, [degrees C]), sonic disable flag 1 [unitless], checksum flag 1 [unitless] , U2x (zonal component, anemometer 2, [m/s]), U2y (meridional component, anemomenter 2, [m/s]), U2z (vertical component, anemomenter 2, [m/s]), temperature (anemometer 2, [degrees C]), sonic disable flag 2 [unitless], checksum flag 2 [unitless] Rotations: timestamp [YYYY-MM_DD HH:MM:SS]], record number [enum], yaw [degrees], pitch [degrees], roll [degrees], XMag [gauss], YMag [gauss], XMag [gauss], Xaccel [m/s^2], Yaccel [m/s^2], Zaccel [m/s^2], Xrot [rad/s], Yrot [rad/s], Zrot [rad/s] Mean wind speed: timestamp [YYYY-MM_DD HH:MM:SS], record number [enum], U1x_Std (x-component standard deviation, anemometer 1, [m/s]), U1y_Std (x-component standard deviation, anemometer 1, [m/s]) , U1z_Std (x-component standard deviation, anemometer 1, [m/s]) , Ts1_Std (temperature standard deviation, anemometer 1, [m/s]), U1x_Avg (x-component mean, anemometer 1, [m/s]), U1y_Avg (y-component mean, anemometer 1, [m/s]), U1z_Avg(z-component mean, anemometer 1, [m/s]), Ts1_Avg (temperature mean, anemometer 1, [m/s]), U2x_Std (x-component standard deviation, anemometer 2, [m/s]), U2y_Std (y-component standard deviation, anemometer 2, [m/s]), U2z_Std (z-component standard deviation, anemometer 2, [m/s]), Ts2_Std (x-component standard deviation, anemometer 2, [m/s]) , U2x_Avg (x-component mean, anemometer 2, [m/s]), U2y_Avg (y-component mean, anemometer 2, [m/s]), U2z_Avg (z-component mean, anemometer 2, [m/s]), Ts2_Avg (temperature mean, anemometer 2, [m/s]), batt_volt_M in [V].

Methods:

A meteorological tower was installed at the bow of R/V Tulu as a mobile platform. 3D wind speed and momentum flux were obtained using ultrasonic anemometers, while corrected estimates were calculated by subtracting ship motion. Air temperature and relative humidity were recorded right next to the wind sensor. Also, surface water temperature and salinity were collected using mini CTD float. The surface wave spectrum and significant wave height were measured by Spoondrift buoys. The mounting heights of the instruments ranged from 5.90 - 6.64 m, detailed in the included cruise information spreadsheet.

Instruments:

RM Young 81000 ultrasonic anemometer, Licor LI-200R, Garmin 6X-HVS GPS, Honeywell HMR-3000 compass, VectoVectorNav VN100T inertial navigation system, Campbell HC2S3-L333-PT temperature and relative humidity probe, FluxData MultiCam FD-1665P-M polarimetric camera, Campbell CR3000 data logger.

Individual Files: