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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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Observations and ensemble statics of the TOPAZ4 reanalysis of 1991-2013

Jiping Xie

This dataset is collective static information outputted by the TOPAZ4 reanalysis in 1991-2013. They include the assimilated observations and the concerned optimization information using the EnKF. Also they are ones of the basic reference datasets for the paper of “Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991–2013” accepted by Ocean Science in January 2017. The file ensemble-static-TOPAZ4-part0.tar.gz contains files before 20000 (Julian day relative to 1/1/1950) and ensemble-static-TOPAZ4-part1.tar.gz contains files after 20000.

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Arctic Ocean and Sea-ice Reanalysis from TOPAZ4 (1991-2018)

Laurent Bertino

A data-assimilative reanalyses of ocean and sea ice in the Arctic are composed of 3D daily and monthly mean fields of temperature, salinity, sea surface height, zonal velocity, meridional velocity, sea ice concentration, sea ice thickness and sea ice velocity. It was generated from TOPAZ4 which is driven by the forcing of ERA-Interim reanalysis during the years of 1991-2018. The files named as TP4a_002-003_daily_yyyy.tgz in which the daily state using netCDF format covers the Arctic ocean (>58N) by the grids with a spatial resolution of 12.5x12.5 km and 40 levels on vertical (0, 2, 4, 6, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 250, 300, 350, 400, 450, 500, 600, 700, 800, 900, 1000, 1200, 1400, 1600, 1800, 2000, 2500, 3000, 3500, 4000). TOPAZ4 is a coupled ocean and sea ice data assimilation system for the North Atlantic and the Arctic that is based on the HYCOM ocean model and the ensemble Kalman filter data assimilation method using 100 dynamical members. The native ocean model uses 28 hybrid z-isopycnal layers, and the top layer has a minimum thickness of 3 m. The horizontal resolution in the model is 12–16 km, which is eddy permitting from the Equator to the Nordic Seas but is still far from being eddy resolving in the Arctic. The concerned daily outputs on the model native grids are also included in this dataset whose name takes the format of TP4DAILY_yyyy.tar.gz. These daily files are binary format with the suffix .a. The .b files are ACSII and list the related fields in the concerned .a files. The matlab script of loada.m is a function how to load one field from the input .a file, which needs the supports from the model grid information from reginal.

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NERSC Arctic Sea Ice Observing System

Tor I. Olaussen

Daily ice maps and statistics in the Arctic region since 1978 until present.

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Navn på prosjekter NERSC internal

Fram Strait/Ocean Acidification in the Arctic

Asuka Yamakawa

This dataset was collected as part of the Sailbuoy "Ocean Acidification Vehicle" (OAV) (Iskant seilas) project. A new sensor package developed by Aanderaa Data Instruments was used to measure temperature, conductivity, pH, partial pressure of carbon dioxide (pCO2), and dissolved oxygen (O2). Most of the sensors were housed in a bulb on the keel of the SailBuoy together with a UV-antifouling device. Data were recorded in the Fram Strait every 10 min. from 30 June 2016 to 18 July 2016, when the Sailbuoy was deployed and recovered during the UNDER-ICE 2016 field experiment.

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Navn på prosjekter {"Iskantseilas (RFF contract 248173)",""}