Åpne datasett

Totalt antall datasett: 49

UAK - Active and Passive Acoustic data - 26 Jun 2020

Espen Storheim

Ambient sound was measured in Storfjorden, Svalbard at frequencies from 30 to 24000 Hz at a depth of around 20 meters. The measurements were carried out in open sea at depths of about 100 metres or more. The measurements were carried out using a drifting small boat, where the engine was turned off. Thereafter, a second small boat was used for transmitting pings at 11 kHz towards the first small boat at different depths and ranges. The experiment was carried out in June 2020 by Master students under supervision, as part of a research school in the Barents Sea with K/V Svalbard, organized under the UAK project lead by the Nansen Environmental and Remote Sensing Centre.

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Navn på prosjekter Useful Arctic Knowledge (UAK)

CTD data collected north of Svalbard, during the UAK 2021 Cruise

Hanne Sagen

CTD data collected north of Svalbard, during the UAK 2021 Cruise with KV Svalbard.

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Navn på prosjekter Useful Arctic Knowledge (UAK), INTAROS

XBT data collected north of Svalbard, during the UAK 2021 Cruise

Hanne Sagen

XBT data collected north of Svalbard, during the UAK 2021 Cruise with KV Svalbard.

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Navn på prosjekter Useful Arctic Knowledge (UAK), INTAROS

XBT stations collected north of Svalbard 2021

Stein Sandven, Hanne Sagen

XBT data collected north of Svalbard, during the 2021 dry run with Le Commandant Charcot to the North Pole. Data were collected and processed the data in the H2020 project INTAROS (GA no. 727890), using XBT provided by CAATEX (project no. 280531), and made freely available by NERSC under projects INTAROS (GA No. 727890) and Norwegian Marine Data Centre (project no. 208849). Users must display the following citation in any publication or product using this dataset. “Storheim, Espen, Sagen, Hanne, Monsen, Frode, Olaussen, Tor I. (2022). XBT station collected north of Svalbard 2021." Dataset will be published in the Norwegian Marine Data Centre in fall 2022.

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Navn på prosjekter INTAROS

GODAE Arctic Class4 metrics

Laurent Bertino

Validation results of 7 international Ocean forecasting systems in the period 2018-2021 in the Arctic.

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Supermodel ocean connected

Francois Counillon

Supermodel experiments. The data sets contains two experiments, 1 with the unconnected model run from 1980-2021 and another one with connected earth system models via their SST as described in the article. The supermodel has been trained based on the biased of SST. The ocean surface and atmospheric surface are provided

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Arctic sea ice simulations from TOPAZ4 assimilating SMOS-Ice

Jiping Xie

This dataset contains the TOPAZ simulations for the two assimilation runs in the two time period of 2014. Details can be found in the paper: doi:10.5194/tc-2016-112.

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Navn på prosjekter ESA (contracts : 4000101476/10/NL/CT & 4000112022/14/I-AM) and NOTUR II (grant number nn2993k)

Extreme wind and five-day precipitation dataset based on Euro-CORDEX regional climate models.

Stephen Outten

This dataset contains the return levels and parameters of the GEV and GPD extreme value distributions fitted to maximum surface daily wind speed and 5-day precipitation for every grid point in 52 of the EURO-CORDEX simulations. This data was produced as the primary objective part of the DEWPAD project, and internal project at the Bjerknes Centre for Climate Research, Bergen, Noway. The underlying model data used was from the high resolution (0.11 degree) EURO-CORDEX simulations which downscaled the CMIP5 climate simulations and the fields analysed were the maximum surface daily wind speed (sfcWindmax) and precipitation flux (pr). The precipitation was converted into a 5-day precipitation using a moving average. Four 30-year time slices were selected, as per the previous work of Outten and Sobolowski 2021, one at the end of the Historical simulations and three in the future simulations under RCP 8.5. These time slices were named, and covered the periods of 1976-2005 (Historical), 2011-2040 (Near Future), 2041-2070 (Mid-Future), and 2071-2100 (Far Future). For each of the 30-year time slices, the annual maxima and peaks-over-threshold approaches of Extreme Value Theory (EVT) were applied to both the extreme wind and 5-day precipitation. This resulted in a Generalised Extreme Value distribution (GEV) and a Generalised Pareto Distribution (GPD) being fitted to the annual maxima and exceedances respectively. Return levels for nine different return periods were calculated using both of the fitted distributions, specifically for the 2, 5, 10, 20, 30, 50, 70, 100, and 200-year return periods. For the peaks-over-threshold approach, the threshold was taken as the minimum of the annual maxima for each grid point separately. This method does not guarantee convergence in all locations and care must be taken to evaluate the quality of the return levels and fitted distributions when using the GPD results. This method was used due to the lack of a more reliable method to automate threshold selection, which is usually done through visual inspection of plots of Sample Mean Excess and parameter stabilities. The dataset is divided into extreme winds (sfcWindmax) and 5-day precipitation, with each further sub-divided into the four periods: Historical, Near Future, Mid-Future, and Far Future. For each of these, there are 52 NetCDF files, one for each model of EURO-CORDEX analysed. These files each contain the following variables: X = Number of grid points in the x direction, [x=412] Y = Number of grid points in the y direction, [y=424] Return levels = Return levels, values of the return periods for which return levels are assessed [return levels=9] Parameters = Names of the three parameters for the distributions, [parameters=3] Lat = Latitude, degrees north, [x,y] Lon = Longitude, degrees east, [x,y] Threshold = Threshold used in GPD in each grid point, m/s or mm accordingly, [x,y] Count of annual maxima = Number of annual maxima in each grid point, [x,y] Count of exceedances = Number of exceedances in each grid point, [x,y] Exceedance lambda = Parameter of Poisson distribution used in calculating return levels from GPD at each grid point, [x,y] GEV parameters = Parameters of the GEV distribution fitted at each grid point, [parameters, x, y] GEV return levels = Return levels based on the GEV distribution calculated at each grid point, [return levels, x, y] GPD parameters = Parameters of the GPD distribution fitted at each grid point, [parameters, x, y] GPD return levels = Return levels based on the GPD distribution calculated at each grid point, [return levels, x, y] The count of annual maxima should in theory be 30 at every grid point, since there are 30 years in each period analysed, however, in some locations in some models there were less than 30 annual maxima for the precipitation because those locations had no day with precipitation over 1mm in the entire year. These were primarily in the Sahara desert. The Exceedances lambda is equivalent to the average number of exceedances that occurred in each year. For more detailed information please contact the dataset creators.

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NorCPM ensemble hindcasts

Julien Brajard

We use a reanalysis and hindcast dataset from NorCPM, which combines the Norwegian Earth system model (Bentsen et al. 2013) and an ensemble Kalman filter (Evensen 2003). This system version is comparable to the one providing operational forecast, namely, it assimilates sea-surface temperature and hydrographic profiles (temperature and salinity) using 60 members and strongly coupled data assimilation between the ocean and sea ice component (Bethke et al. 2021) - meaning that the ocean data correct also the sea ice component. We perform anomaly assimilation, meaning that the climatological monthly mean of the observations and the model are removed before comparing the two. The monthly climatology is constructed from the 60-member historical ensemble run (without assimilation) over the period 1982--2010. For the hydrographic profiles, it is constructed from EN4 objective analysis (Good et al. 2013). Only the ocean and sea ice are directly updated by the data assimilation. The other components of the model (atmosphere, land) are adjusting dynamically through the coupling in between the monthly assimilation steps. The initial ensemble at the start of the reanalysis in 1980 is constructed by selecting 60 random initial conditions from a stable pre-industrial simulation and integrating the ensemble from 1850 to 1980 using historical forcings from the Coupled Model Intercomparison Project version 5 (Taylor et al. 2012). The seasonal hindcasts start on the 15th January, April, July, and October each year from 1985--2010, i.e., in total 104 hindcasts (26 years with four hindcasts per year). Each hindcast runs 60 realizations (ensemble members) for 13 months, initialized from the corresponding member in the reanalysis. Data is organized with a separate folder for each hindcast start date. There is a file for each member.

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