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The Nansen Legacy cruise Q1 was part of the seasonal investigation of the northern Barents Sea and adjacent Arctic Basin. The cruise was conducted in 2-24 March 2021 onboard R/V Kronprins Haakon, and focused on studying the physical, chemical and biological conditions along the Nansen Legacy main transect in open waters and within the sea ice. While in sea ice we conducted ten regional scale sea ice helicopter-borne surveys of ice conditions along the Nansen Legacy transect using a helicopter-borne electromagnetic instrument (HEM) EM-bird. This dataset presents processed EM-bird data on total snow and sea-ice thickness along the flight tracks.
This is a contribution to the Research Council of Norway project “Nansen Legacy” (https://arvenetternansen.com/), WP RF-1 “Physical drivers”.
Quality
See the attached docuement “AeN_Q1_202103_HEM_icethickness_metadata_v1.0.pdf” for details on the data acqusition, processing and structure.
Output from model runs to examine potential for snow ice formation in the Arctic Ocean over the period 1980-2016
Quality
Output from model runs to examine the potential for snow ice formation in the Arctic Ocean over the period 1980-2016. We used a 1-D, high resolution thermodynamic ice and snow model HIGHTSI [Launiainen and Cheng, 1998], to simulate sea ice thickness, snow-ice thickness and snow depth in the Arctic Ocean. HIGHTSI is designed to resolve the evolution of snow and ice thickness, and temperature profiles. It has been widely used in process studies and validated extensively against observations.
We implemented HIGHTSI in a Lagrangian framework to examine Arctic snow-ice distributions. Ice motion vectors are derived by satellite products, and are provided from the National Snow and Ice Data Center (NSIDC) [Tschudi et al., 2016]. Based on the motion vectors we performed Lagrangian tracking of ice parcels over the Arctic Ocean and its marginal seas from 1980 to 2016. This resulted in a daily sea ice motion product of 25 km spatial resolution. Throughout this period ice parcels disappear and new parcels are being generated. At any given time, the Arctic simulation domain can hold a total of 60000 individual ice parcels. At each time step the MicroMet meteorological preprocessor [Liston and Elder, 2006] was used to extract the atmospheric forcing based on the position of each ice parcel. Ice concentration data from Cavalieri et al. [1996] were used to initialize an ice parcel. We considered ice parcels initialized when ice concentration exceeded a 15% concentration threshold.
We used atmospheric data from reanalyses to force HIGHTSI, including 10 m wind speed, 2 m air temperature and relative humidity, and total precipitation, while MicroMet provided the solid precipitation, downwelling shortwave and longwave radiation. We used ERA-I and MERRA-2 atmospheric reanalyses [Dee et al., 2011; Gelaro et al., 2017] in order to examine the snow-ice sensitivity to the magnitude of precipitation over sea ice. These reanalyses have shown relatively good agreement for air temperature and timing of precipitation events [Merkouriadi et al., 2017b], although there is a warm bias in both products during the lowest temperatures in winter [Graham et al., 2019]. But especially, they exhibit significant differences in the magnitude of precipitation [Chaudhuri et al., 2014; Merkouriadi et al., 2017b; Boisvert et al., 2018] with ERA-I producing relatively low and MERRA-2 producing relatively high precipitation amounts [Merkouriadi et al., 2017b; Boisvert et al., 2018].
HIGHTSI simulations began each year on 1 August (1980-2016), and run through one full year at a time, using a 3-hour time step. Based on the ice motion and concentration information, existing ice parcels on 1 August were considered SYI/MYI. On 1 August we assumed that there is no snow on SYI/MYI. We performed model experiments with 4 different initial thicknesses for the existing SYI/MYI parcels on 1 August (h0=0.5, 1, 1.5 and 2 m). Thus, we conducted 8 experiments in total, 4 with ERA-I and 4 with MERRA-2 forcing. Initial ice thickness of 2 m was likely more common in 1980’s and 1990’s, whereas thicknesses of 1.5 m and less is becoming more typical in recent years [Kwok and Untersteiner, 2011]. We acknowledge that a uniform initial SYI/MYI thickness over the entire ice-covered Arctic Ocean is not realistic. However, our purpose is to examine the inter-decadal sensitivity of snow-ice formation to the regional patterns and trends of weather conditions and sea ice motion. For the same reason we chose a constant, low ocean heat flux (Fw = 1 W m-2). In a similar study we carried out north of Svalbard, in a region where ocean heat flux is of greatest importance due to the proximity to the North Atlantic, we concluded that the choice of ocean heat flux did not significantly affect the results [Merkouriadi et al., 2017b]. These simplifications allow us to examine the sensitivity of snow-ice formation to a limited number of factors, keeping in mind our level ice assumption.
The outputs of the HIGHTSI model experiments for each ice parcel at each time-step are: snow-ice layer thickness, thermal ice thickness (i.e. total ice thickness minus snow-ice thickness) and snow depth. After we conducted the simulations, the model output was gridded to the 25x25 km Equal-Area Scalable Earth Grid (EASE-Grid), provided by NSIDC. At each time step, the parcels’ location was used to calculate the overlap between the parcel and the EASE grid cell. The overlap is calculated as fractional area of the EASE grid cell. The fractional area was then multiplied by the sea ice concentration of the parcel, and the result was used to weigh the parcels’ contribution to each EASE grid cell. This procedure of area- and concentration-weighted averages within the EASE grid cells, conserves the examined parameters. In order to look separately into FYI and SYI/MYI, existing parcels on 1 August were considered to be SYI/MYI. New parcels that appear after 1 August each year were considered to be FYI.
This data set includes the results of total 8 model experiments: 4 with ERA-I and 4 with MERRA-2 atmospheric reanalysis forcing. The 4 experiments from each reanalysis correspond to different initial SYI/MYI thickness (h0 = 0.5, 1, 1.5 and 2 m). In each experiment 3 variables are produced by the HIGHTSI model: snow-ice thickness (sice), thermal ice thickness (tice) and snow depth (snod). The data format is GrADS (.gdat). The time step is 3-hours, for the period 1980-2016. The initial date and time of the data set is 1 August 1980, 00:00. Each result has 3 dimensions: the ice parcels (total number = 70000), the variable value in meters [m], and time (total number of steps = 105192).
The model outputs (sice, tice and snod) for each model experiment (ERA_0.5, ERA_1.0, ERA_1.5, ERA_2.0, MERRA_0.5, MERRA_1.0, MERRA_1.5 and MERRA_2.0,) are in the folder output/’name of model experiment’. The process scripts (Fortran files) that put the parcel data on the EASE-grid and separate between FYI and SYI/MYI, are provided in the folder ‘EASE-grid_process’. The ice parcel tracks and concentration data from NSIDC are in the folder ‘parcel_tracks’. Finally, a python script for reading Grads files is provided: ‘import_grads.py’.
References
- Boisvert, L. N., M. A. Webster, A. A. Petty, T. Markus, D. H. Bromwich, and R. I. Cullather (2018), Intercomparison of precipitation estimates over the Arctic ocean and its peripheral seas from reanalyses, J. Clim., 31(20), 8441–8462, doi:10.1175/JCLI-D-18-0125.1.
- Cavalieri, D. J., C. L. Parkinson, P. Gloersen, and H. J. Zwally. 1996, updated yearly. Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/8GQ8LZQVL0VL. - Chaudhuri, A. H., R. M. Ponte, and A. T. Nguyen (2014), A comparison of atmospheric reanalysis products for the Arctic Ocean and implications for uncertainties in air-sea fluxes, J. Clim., 27(14), 5411–5421, doi:10.1175/JCLI-D-13-00424.1.
- Dee, D. P. et al. (2011), The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. R. Meteorol. Soc., 137(656), 553–597, doi:10.1002/qj.828.
- Gelaro, R. et al. (2017), The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), J. Clim., 30(14), 5419–5454, doi:10.1175/JCLI-D-16-0758.1.
- Graham, R. M., L. Cohen, N. Ritzhaupt, B. Segger, R. G. Graversen, A. Rinke, V. P. Walden, M. A. Granskog, and S. R. Hudson (2019), Evaluation of six atmospheric reanalyses over Arctic sea ice from winter to early summer, J. Clim., 32(14), 4121–4143, doi:10.1175/JCLI-D-18-0643.1.
- Kwok, R., and N. Untersteiner (2011), The thinning of Arctic ice, in AIP Conference Proceedings, vol. 1401, pp. 220–231. Launiainen, J., and B. Cheng (1998), Modelling of ice thermodynamics in natural water bodies, Cold Reg. Sci. Technol., 27(3), 153–178, doi:10.1016/S0165-232X(98)00009-3.
- Liston, G. E., and K. Elder (2006), A meteorological distribution system for high-resolution terrestrial modeling (MicroMet), J. Hydrometeorology, 7, 217–234.
- Merkouriadi, I., B. Cheng, R. M. Graham, A. Rösel, and M. A. Granskog (2017b), Critical Role of Snow on Sea Ice Growth in the Atlantic Sector of the Arctic Ocean, Geophys. Res. Lett., 44(20), 10,479–10,485, doi:10.1002/2017GL075494.
- Tschudi, M., C. Fowler, J. Maslanik, J. S. Stewart, and W. Meier. (2016), Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, Version 3. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi:10.5067/O57VAIT2AYYY.