These Land-Ocean-Coastline-Ice (LOCI) files provide land classification masks derived from the Boston University MOD12Q1 V004 MODIS/Terra 1 km Land Cover Product (Friedl et al. 2002). The masks are available in various EASE-Grid 2.0 azimuthal and global projections, at various spatial resolutions ranging from 3 km to 100 km.The masks are in flat binary, 1 byte files stored by row. Quick-look browse images of the masks are also available in PNG (.png) format.

The SMOS F/T Service generates daily information on the Northern hemisphere soil state from SMOS daily gridded level 3 brightness temperatures (data provided by Centre Aval de Traitement des Données SMOS - CATDS). The Service uses two ancillary datasets: ECMWF 2m air temperature data and NSIDC IMS snow cover data. The data are provided in The Equal-Area Scalable Earth Grid (EASE2-Grid) at 25 km x 25 km resolution. The product start date is July 1st 2010. Detailed information on the algorithm can be found in the Algorithm Theoretical Baseline Document. The latest version is available from the Service web page: http://nsdc.fmi.fi/data/data_smos Temporal coverage July 2010 - present Temporal resolution: Daily Spatial coverage: 0 - 85 N Spatial resolution 25 km x 25 km Projection Polar-stereographic Grid EASE-2 Data dimension 720 x 720 (columns x rows) Estimated soil state within a pixel in three levels as follows: 0 – thaw 1 – partially frozen 2 – frozen 255 – no data value

The SMOS Level 3 Freeze and Thaw (F/T) product provides daily information on the soil state in the Northern Hemisphere based on SMOS observations and associated ancillary data. Daily products, in NetCDF format, are generated by the Finnish Meteorological Institute (FMI) and are available from 2010 onwards. The processing algorithm makes use of gridded Level 3 brightness temperatures provided by CATDS (https://www.catds.fr). The data is provided in the Equal-Area Scalable Earth Grid (EASE2-Grid), at 25 km x 25 km resolution. For an optimal exploitation of this dataset, please refer to the Resources section below to access Product Specifications, read-me-first notes, etc.

The SMOS-CryoSat merged Sea Ice Thickness Level 4 product, in NetCDF format, is based on estimates from both the MIRAS and the SIRAL instruments, with a significant reduction in the relative uncertainty for the thickness of the thin ice. A weekly averaged product is generated every day by the Alfred Wegener Institut (AWI), by merging the weekly AWI CryoSat-2 sea ice product and the daily SMOS sea ice thickness retrieval. All grids are projected onto the 25 km EASE2 Grid, based on a polar aspect spherical Lambert azimuthal equal-area projection. The grid dimension is 5400 x 5400 km, equal to a 432 x 432 grid centered on the geographic Pole. Coverage is limited to the October-April (winter) period for the Northern Hemisphere, due to the melting season, from year 2010 onwards.

This dataset includes the data needed to reproduce the results and figures of the manuscript “Projections and Physical Drivers of Extreme Precipitation in Greenland & Baffin Bay”, submitted to Journal of Geophysical Research: Atmospheres. Two types of files are included:

A. CSV files of summaries for nine subregions: 6 Greenland watersheds and 3 Canadian islands. Greenland Subregions include Central West (‘CW’), Northeast (‘NE’), North (‘NO’), Northwest (‘NW”), Southeast (‘SE’), and Southwest (‘SW’). Canadian Subregions: Baffin Island (‘baffin’), Devon Island (‘devon’), and Ellesmere Island (‘ellesmere’)

B. NetCDF files with gridded fields of atmospheric parameters.The projection for all netCDF files is the 25-km EASE2 grid with a central latitude of 90°N and a central longitude of 0° (i.e., EPSG 6391). All data are provided on a 720 by 720 grid, which encompasses the entire Northern Hemisphere. However, precipitation data is set to NaN beyond the study region of (55°N-90°N, 130°W-25°E).

Input datasets for these derived products include the fifth-generation atmospheric reanalysis from the ECMWF (ERA5; Hersbach et al., 2020) and a simulation of the second-generation Community Earth System Model (CESM2.2; Danabasoglu et al., 2020) run on a variable-resolution “ARCTIC” grid (Herrington et al., 2022). The historical and future simulations of this dataset are referred to as “VR-CESM HIST” and “VR-CESM FUT”. The version of CESM2.2 run on the default CESM grid is referred to as “CESM2.2” and only includes an historical (HIST) simulation.

Note that the native grid for these datasets is different from the grid used for derived data products. A single spatial grid is used here to facilitate the combination / overlay of the data contained within the dataset. Cyclone detection is performed using version 13.2 of the CEOS/NSIDC Extratropical Cyclone Tracking algorithm (Crawford et al., 2021). Atmospheric rivers are detected using the algorithm from TempestExtremes v2.1 (Ullrich et al., 2021).

This dataset presents monthly gridded sea ice and ocean parameters for the Arctic derived from the European Space Agency's satellite CryoSat-2. Parameters include sea ice freeboard, sea ice thickness, sea ice surface roughness, mean sea surface height, sea level anomaly, and geostrophic circulation. Data are provided as monthly grids with a resolution of 25 km, mapped onto the NSIDC EASE2-Grid, covering the Arctic region north of 50 degrees latitude, for all winter months (Oct-Apr) between 2010 and 2018. CryoSat-2 Level 1b Baseline C observed waveforms have been retracked using a numerical model for the SAR altimeter backscattered echo from snow-covered sea ice presented in Landy et al. (2019), which offers a sophisticated physically-based treatment of the effect of ice surface roughness on retracked ice and ocean elevations. Methods for optimizing echo model fits to observed CryoSat-2 waveforms, retracking waveforms, classifying returns, deriving sea ice freeboard, and converting to thickness are detailed in Landy et al. (In Review). This dataset contains derived sea ice thicknesses from two processing chains, the first using the conventional snow depth and density climatology from Warren et al. (1999) and the second using reanalysis and model-based snow data from SnowModel (Stroeve et al., In Review). Sea surface height and ocean topography grids were derived from only those CryoSat-2 samples classified as leads. Both the random and systematic uncertainties relevant for each parameter have been carefully estimated and are provided in the data files. NetCDF files contain detailed descriptions of each derived parameter. Funding was provided by ESA Living Planet Fellowship Arctic-SummIT grant ESA/4000125582/18/I-NS and NERC Project PRE-MELT grant NE/T000546/1.

This record contains data related to article "Constructing Satellite-based Ocean-surface Stress and Ekman Circulation in the Arctic and Antarctic Oceans". It offers a high-resolution, daily analysis of ocean-surface stress and Ekman circulation over the Arctic and Southern Ocean, derived from multiplatform satellite observations.

All data are projected onto a 25 km EASE2 grid with daily resolution. The dataset (netcdf) contains the following variables:

- zonal components of ocean-surface stress (TAUx, N/m2)

- meridional components of ocean-surface stress (TAUy, N/m2)

- magnitude of ocean-surface stress (TAU, N/m2)

- uncertainty estimates for TAUx (N/m2)

- uncertainty estimates for TAUy (N/m2)

- Ekman Pumping Rate (m/s)

- Land mask

- Longitude

- Latitude

L.Yu acknowledges the support of the NASA Vector Wind Science Team program for this research.

I) SUMMARY This soil moisture and vegetation optical depth product is called the Multi-Temporal Dual Channel Algorithm (MT-DCA). It retrieves surface soil moisture and vegetation optical depth (directly related to total water volume in the vegetation canopy) from SMAP level 1C brightness temperature observations using a robust estimation technique. It is an in-house MIT algorithm and is not an official SMAP product. The data are freely available on 9km and 36km grids from April 2015 to July 2021 in daily time steps. No co-authorship is required for use of this data in publications. However, to properly acknowledge the dataset when publishing any research using the MT-DCA, we ask data users to (1) cite the DOI as an in-text citation and/or in the data acknowledgements in any publication and (2) reference Konings et al. (2017) when referring to the MT-DCA in the text. Feel free to send us an email at afeld24@mit.edu to let us know how you are using the data. The version 5 update is a re-implementation of the MT-DCA using the updated SMAP L1C brightness temperatures. It extends the data through July 2021. II) CONTACT For questions, please email Andrew Feldman at afeld24@mit.edu. III) ALGORITHM DESCRIPTION The algorithmic approach uses both horizontally and vertically polarized brightness temperatures to retrieve soil moisture and VOD simultaneously. The key innovation of the MT-DCA is that it recognizes that classical dual-channel algorithms are under-determined: brightness temperature observations are correlated and cannot retrieve two unknowns (soil moisture and VOD) (as illustrated in Konings et al, RSE 2016). This creates amplifying errors in retrievals from snapshot dual-channel algorithms. The MT-DCA uses a viable assumption that VOD changes more slowly than soil moisture between overpasses, and uses information from multiple SMAP overpasses to stabilize the retrieval. It is considered a regularization approach similar to the Sobolev Norm regularization. Specifically, this approach is applied to each temporally adjacent pair of overpasses (for SMAP, two overpasses approximately 2-3-days apart), which includes four brightness temperature measurements. For each overpass pair, the soil moisture at both overpasses is retrieved, along with a constant VOD for both overpasses. This leads to two retrievals of each of soil moisture and VOD at any given overpass time: one where the parameters are retrieved using additional TB information from the overpass before and one from the overpass after. Both retrievals of VOD and soil moisture values at each overpass are averaged. Ultimately, VOD is not held constant, but rather is slowed in time between overpasses. A second key innovation of the MT-DCA is that, because the retrievals are no longer under determined, it is also possible to retrieve a constant single scattering albedo for each pixel. The single scattering albedo is estimated through model selection of the value of the parameter that minimizes the sum of all overpass cost functions. The retrieved albedo is also included in the files here. VOD is reported at nadir. The single scattering albedo is assumed constant over the full record of SMAP data, as is currently accepted practice across approaches with SMAP, SMOS, and AMSR. There is a high amount of computational power required to retrieve an albedo over more than three years of SMAP data. Therefore, an adjustment was made: the single scattering albedo was retrieved over the third year of SMAP data (April 1st, 2017 to March 31st, 2018). This constant value was then applied to the other years without requiring the albedo optimization loop. Tests across many individual pixels revealed that albedo in the third year does not differ greatly from albedo over all years and the other individual years. The algorithm is described in more detail in Konings et al. (2017). The algorithm is based on principles explained in more detail in Konings et al. (2016), which describes the original algorithm development using Aquarius observations. See also the related Konings et al. (2015) publication for quantitative justification for the approach. While the dataset has not been officially validated, the MT-DCA soil moisture retrievals show in-situ comparison statistics similarly to the official baseline SMAP soil moisture product (SMAP soil moisture retrieval in-situ assessment can be found in Chan et al. (2016)). Finally, the MT-DCA vegetation optical depth retrievals are not validated due to only sparsely available ground information related to vegetation water content. Nevertheless, information about error propagation into the MT-DCA soil moisture and VOD retrievals as well as VOD error reductions using the MT-DCA regularization technique can be found in Feldman et al. (2021). Chan, S.K., Bindlish, R., O’Neill, P.E., Njoku, E., Jackson, T., Colliander, A., Chen, F., Burgin, M., Dunbar, S., Piepmeier, J., Yueh, S., Entekhabi, D., Cosh, M.H., Caldwell, T., Walker, J., Wu, X., Berg, A., Rowlandson, T., Pacheco, A., McNairn, H., Thibeault, M., Martinez-Fernandez, J., Gonzalez-Zamora, A., Seyfried, M., Bosch, D., Starks, P., Goodrich, D., Prueger, J., Palecki, M., Small, E.E., Zreda, M., Calvet, J.C., Crow, W.T., Kerr, Y., 2016. Assessment of the SMAP Passive Soil Moisture Product. IEEE Trans. Geosci. Remote Sens. 54, 4994–5007. https://doi.org/10.1109/TGRS.2016.2561938 Feldman, A.F., D. Chaparro, and D. Entekhabi (2021). Error propagation in microwave soil moisture and vegetation optical depth retrievals. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. In Press. Konings, A.G., M. Piles, N. Das, and D. Entekhabi (2017). L-band vegetation optical depth and effective scattering albedo estimation from SMAP. Remote Sensing of Environment, 198:460-470. https://doi.org/10.1016/j.rse.2017.06.037 Konings, A.G., M. Piles, K. Rötzer, K.A. McColl, S. Chan, and D. Entekhabi (2016). Vegetation optical depth and scattering albedo retrieval using time-series of dual-polarized L-band radiometer observations. Remote Sensing of Environment. 172, 178-189. https://doi.org/10.1016/j.rse.2015.11.009 Konings, A.G., K.A. McColl, M. Piles and D. Entekhabi (2015): How many parameters can be maximally estimated from a set of measurements? IEEE Geoscience and Remote Sensing Letters, 12(5), 1081-1085. https://doi.org/10.1109/LGRS.2014.2381641 IV) QUALITY CONTROL Several conditions can create uncertainty in the MT-DCA retrievals including surface water bodies (lakes, rivers, coastal areas, etc.), radio frequency interference (RFI), highly sloped surfaces (mountainous regions), dense vegetation, frozen ground, and others. The MT-DCA removes time periods of frozen ground and removes pixels with water body fractions of greater than 0.5. SMAP L1C brightness temperatures are adjusted considering RFI and surface water body information. Nevertheless, the MT-DCA retrievals are purposefully not substantially quality controlled to increase the range of science applications of the data. Therefore, the retrievals are subject to uncertainty in regions where and times when these aforementioned issues occur. We suggest the data user familiarize themselves with quality flags in the SMAP algorithm theoretical basis document in https://nsidc.org/data/SPL3SMP_E. Conservative quality control can be applied using SMAP quality flag information directly applicable to the dataset here. These quality flags can be downloaded from the SMAP official product files at https://nsidc.org/data/SPL3SMP_E. V) DATA FORMATTING AND FILE NAMES Data are provided in zipped folders in both netcdf4 (.nc) and matfile (.mat) formats. Each zipped folder contains soil moisture, vegetation optical depth, single scattering albedo, latitude, longitude, and time vector information. Note that as an update in Version 5, the zipped folders for 9km .mat files are separated into soil moisture and vegetation optical depth to reduce zip folder size. The other zipped folders still have all variables within them. These variables are provided at a 9km resolution as well as upscaled to 36km. For both .nc and .mat files, the 9km data are provided in 3-month periods with a naming convention of ‘YYYYMM_YYYYMM’ where YYYY is the 4-digit year, and MM is the 2-digit month. The first YYYYMM string represents the first month and the second YYYYMM string is the final month of the period. The 36km data are provided in 12-month periods with the same naming conventions in the file names. Retrievals are obtained from enhanced-resolution brightness temperatures from SMAP that are gridded at 9km. As such, they are on a 9km EASE2-grid. These retrievals are upscaled to 36km and gridded on a 36km EASE2 grid. Additional information and geolocation tools are available at https://nsidc.org/data/ease/ease_grid2.html. Information specific to folders with .nc and .mat formats is given below: a) NETCDF Files (.nc): The folders with netcdf files contain files with the convention MTDCA_YYYYMM_YYYYMM_Xkm_VX.nc where VX is the version number, Xkm is the grid scale, and YYYYMM strings are the first and last months of the range of data saved in the file. Soil moisture, vegetation optical depth, latitude, longitude, and time index information are provided in these files. A map of single scattering albedo for the full time series is saved in a separate file as MTDCA_OMEGA_Xkm_VX.nc along with latitude and longitude information. b) MATFILES (.mat): The folders with matfiles contain individual files for: Soil moisture: MTDCA_VX_SM_YYYYXX_YYYYXX_Xkm.mat Vegetation Optical Depth: MTDCA_VX_TAU_YYYYXX_YYYYXX_Xkm.mat Single Scattering Albedo: MTDCA_VX_OMEGA_Xkm.mat Latitude/Longitude: SMAPCenterCoordinatesXKM.mat A datevector variable in each soil moisture and vegetation optical depth file contains information on the year, month, and day corresponding to the timestep of each variable.

Sea Level Anomaly measured by Altimetry and derived variables cdm_data_type=Grid comment=Sea Level Anomaly measured by Altimetry and derived variables contact=matthis.auger@locean.ipsl.fr Conventions=CF-1.10, COARDS, ACDD-1.3 Easternmost_Easting=349.0 geospatial_lat_max=349.0 geospatial_lat_min=0.0 geospatial_lat_resolution=1.0 geospatial_lat_units=degrees_north geospatial_lon_max=349.0 geospatial_lon_min=0.0 geospatial_lon_resolution=1.0 geospatial_lon_units=degrees_east Grid=Subset of Southern Hemisphere 25km EASE2 Grid history=Altimetry measurements infoUrl=http://aviso.altimetry.fr institution=CLS,CNES keywords_vocabulary=GCMD Science Keywords Northernmost_Northing=349.0 platform=AL_C2_S3A processing_level=L4 references=http://aviso.altimetry.fr source=Altimetry measurements sourceUrl=(local files) Southernmost_Northing=0.0 standard_name_vocabulary=CF Standard Name Table v70 time_coverage_duration=P2312.0D time_coverage_end=2019-07-31T00:00:00Z time_coverage_resolution=P1.0D time_coverage_start=2013-04-01T00:00:00Z Westernmost_Easting=0.0

This product is a synthesis product of brightness temperatures for L-Band frequency. It includes all brightness temperatures acquired that very day by the SMOS satellite operating in full pol mode. Values correspond to those at the top of the atmosphere level, transformed to the ground polarisation reference frame, binned and averaged into fixed incidence angle classes. This product is available on each of the EASE2 grid projections (cylindrical and polar).