This dataset contains information on the Surface Soil Moisture (SM) content derived from satellite observations in the microwave domain.

Dataset paper (public preprint)

A description of this dataset, including the methodology and validation results, is available at:

Preimesberger, W., Stradiotti, P., and Dorigo, W.: ESA CCI Soil Moisture GAPFILLED: An independent global gap-free satellite climate data record with uncertainty estimates, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-610, in review, 2025.

Abstract

ESA CCI Soil Moisture is a multi-satellite climate data record that consists of harmonized, daily observations coming from 19 satellites (as of v09.1) operating in the microwave domain. The wealth of satellite information, particularly over the last decade, facilitates the creation of a data record with the highest possible data consistency and coverage.
However, data gaps are still found in the record. This is particularly notable in earlier periods when a limited number of satellites were in operation, but can also arise from various retrieval issues, such as frozen soils, dense vegetation, and radio frequency interference (RFI). These data gaps present a challenge for many users, as they have the potential to obscure relevant events within a study area or are incompatible with (machine learning) software that often relies on gap-free inputs.
Since the requirement of a gap-free ESA CCI SM product was identified, various studies have demonstrated the suitability of different statistical methods to achieve this goal. A fundamental feature of such gap-filling method is to rely only on the original observational record, without need for ancillary variable or model-based information. Due to the intrinsic challenge, there was until present no global, long-term univariate gap-filled product available. In this version of the record, data gaps due to missing satellite overpasses and invalid measurements are filled using the Discrete Cosine Transform (DCT) Penalized Least Squares (PLS) algorithm (Garcia, 2010). A linear interpolation is applied over periods of (potentially) frozen soils with little to no variability in (frozen) soil moisture content. Uncertainty estimates are based on models calibrated in experiments to fill satellite-like gaps introduced to GLDAS Noah reanalysis soil moisture (Rodell et al., 2004), and consider the gap size and local vegetation conditions as parameters that affect the gapfilling performance.

Summary

• Gap-filled global estimates of volumetric surface soil moisture from 1991-2023 at 0.25° sampling
• Fields of application (partial): climate variability and change, land-atmosphere interactions, global biogeochemical cycles and ecology, hydrological and land surface modelling, drought applications, and meteorology
• Method: Modified version of DCT-PLS (Garcia, 2010) interpolation/smoothing algorithm, linear interpolation over periods of frozen soils. Uncertainty estimates are provided for all data points.
• More information: See Preimesberger et al. (2025) and https://doi.org/10.5281/zenodo.8320869" target="_blank" rel="noopener">ESA CCI SM Algorithm Theoretical Baseline Document [Chapter 7.2.9] (Dorigo et al., 2023)

Programmatic Download

You can use command line tools such as wget or curl to download (and extract) data for multiple years. The following command will download and extract the complete data set to the local directory ~/Download on Linux or macOS systems.

#!/bin/bash

# Set download directory
DOWNLOAD_DIR=~/Downloads

base_url="https://researchdata.tuwien.at/records/3fcxr-cde10/files"

# Loop through years 1991 to 2023 and download & extract data
for year in {1991..2023}; do
  echo "Downloading $year.zip..."
  wget -q -P "$DOWNLOAD_DIR" "$base_url/$year.zip"
  unzip -o "$DOWNLOAD_DIR/$year.zip" -d $DOWNLOAD_DIR
  rm "$DOWNLOAD_DIR/$year.zip"
done

Data details

The dataset provides global daily estimates for the 1991-2023 period at 0.25° (~25 km) horizontal grid resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD), month (MM) in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name has the following convention:

ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED_GAPFILLED-YYYYMMDD000000-fv09.1r1.nc

Data Variables

Each netCDF file contains 3 coordinate variables (WGS84 longitude, latitude and time stamp), as well as the following data variables:

• sm: (float) The Soil Moisture variable reflects estimates of daily average volumetric soil moisture content (m3/m3) in the soil surface layer (~0-5 cm) over a whole grid cell (0.25 degree).
• sm_uncertainty: (float) The Soil Moisture Uncertainty variable reflects the uncertainty (random error) of the original satellite observations and of the predictions used to fill observation data gaps.
• sm_anomaly: Soil moisture anomalies (reference period 1991-2020) derived from the gap-filled values (`sm`)
• sm_smoothed: Contains DCT-PLS predictions used to fill data gaps in the original soil moisture field. These values are also provided for cases where an observation was initially available (compare `gapmask`). In this case, they provided a smoothed version of the original data.
• gapmask: (0 | 1) Indicates grid cells where a satellite observation is available (1), and where the interpolated (smoothed) values are used instead (0) in the 'sm' field.
• frozenmask: (0 | 1) Indicates grid cells where ERA5 soil temperature is <0 °C. In this case, a linear interpolation over time is applied.

Additional information for each variable is given in the netCDF attributes.

Version Changelog

Changes in v9.1r1 (previous version was v09.1):

• This version uses a novel uncertainty estimation scheme as described in Preimesberger et al. (2025).

Software to open netCDF files

These data can be read by any software that supports Climate and Forecast (CF) conform metadata standards for netCDF files, such as:

• https://github.com/pydata/xarray" target="_blank" rel="noopener">Xarray (python)
• https://unidata.github.io/netcdf4-python/" target="_blank" rel="noopener">netCDF4 (python)
• https://github.com/TUW-GEO/esa_cci_sm">esa_cci_sm (python)
• Similar tools exists for other programming languages (Matlab, R, etc.)
• Software packages and GIS tools can open netCDF files, e.g. CDO, NCO, QGIS, ArCGIS
• You can also use the GUI software Panoply to view the contents of each file

References

• Preimesberger, W., Stradiotti, P., and Dorigo, W.: ESA CCI Soil Moisture GAPFILLED: An independent global gap-free satellite climate data record with uncertainty estimates, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-610, in review, 2025.
• Dorigo, W., Preimesberger, W., Stradiotti, P., Kidd, R., van der Schalie, R., van der Vliet, M., Rodriguez-Fernandez, N., Madelon, R., & Baghdadi, N. (2023). ESA Climate Change Initiative Plus - Soil Moisture Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version 08.1 (version 1.1). Zenodo. https://doi.org/10.5281/zenodo.8320869
• Garcia, D., 2010. Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics & Data Analysis, 54(4), pp.1167-1178. Available at: https://doi.org/10.1016/j.csda.2009.09.020
• Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data Assimilation System, Bulletin of the American Meteorological Society, 85, 381 – 394, https://doi.org/10.1175/BAMS-85-3-381, 2004.

Related Records

The following records are all part of the Soil Moisture Climate Data Records from satellites community

Geoscientists now live in a world with an exponential growth in digital data and methods.
Climate change studies usually describe computational methods informally. Climate scientists seek to
share their information, the justification of reproducible research has received increasing attention in
geosciences. To have it in an open-source format makes it easier to interchange not only with fellow
scientists but also a variety of sources including funders, publishers, and journalists. R is a open-source
computer language powerful and highly extensible that can promotes reproductive science techniques in a
easier way. R is highly accessible for non-computational scientists when coupled with packages like
‘raster', ‘netcdf', ´rgdal`and ‘rasterVis', R enables scientists to make sense of their data and to carry out
complex data analysis. In this paper we have assessed the power of R language for manipulating climate
data from a huge dataset: the Coupled Model Intercomparison Project Phase 5 (CMIP5). Moreover we
have proposed an example of best practices to handle model ensembles. This is the first study to our
knowledge to promote best practices for CMIP5 ensemble. The NetCDF data accessible to R via raster
package capabilities provides efficient access to the multi-model, with crucial applications in climate
change research. In recent years more than 100 peer-reviewed scientific publications have used the
CMIP5 data sets. We envision that in the near future (5-10 years), scientists will use radically new tools
to author papers and disseminate information about the process and products of their research.

This dataset contains sea surface temperature and salinity measurements taken on board the Ron Brown Ship during the ACE-Asia project, March-April 2001. This data is in netCDF format. However, the same dataset can also be ordered in an ASCII text format.

This data set contains NetCDF format files (version 1) of SMART-R1 radar data taken during the DYNAMO (Dynamics of the Madden-Julian Oscillation) project. The data are available as monthly TAR/GNU Zip files (approximately 1 - 3 GB/file). The data are provided by Texas A&M University.

The research is important for the Great Barrier Reef (GBR) water quality management. The data was collected for the quantification of the contribution of Trichodesmium to the nitrogen budget of the GBR. Linux operating system, C compiler and NETCDF library were used to build the modified EMS applications on AIMS HPC. The EMS version used is 1.2.1. The modified EMS was derived from the eReefs model (https://ereefs.org.au/ereefs) and the model descriptions are found in Baird et al. (2020). Methods for collecting the data include the following: Hydrodynamic model forcing available in https://research.csiro.au/ereefs/models/models-about/models-hydrodynamics/; Biogeochemical (BGC) model forcing (Simulated hydrodynamic model output, regional wave model data, 2019 catchment conditions of nutrient and sediment loads available in https://svnserv.csiro.au/svn/CEM/projects/eReefs/model/gbr4_bgc_hindcast/gbr4_H2p0_B3p2_Cb/); Initialisation file: GBR4 BGC 3p1 initialisation data. The 4km resolution grid of the EMS was run on AIMS HPC from 1/12/2010 to 30/11/2012 and the data was collected on 17/02/2022. Software-specific information needed to interpret the data are R Software version 3.5.1, GNU Compiler Collection (GCC) version 6.1.0, network Common Data Form (NetCDF-cxx) version 4.2.1, Open Message Passing Interface (OpenMPI-gcc) version 1.10.2 and NetCDF Operators (NCO) version 4.5.5. R scripts for post-processing simulated data are available in https://github.com/Chinenyeani1986/Trichodesmium-N-budget.

This dataset contains Carbon Monoxide measurements taken on board the Ron Brown Ship during the ACE-Asia project, March-April 2001. This data is in netCDF format. However, the same dataset can also be ordered in an ASCII text format.

The research is important for the Great Barrier Reef (GBR) water quality management. The data was collected for the parameterisation of the influence of tuft-shaped Trichodesmium colonies on the vertical movement of Trichodesmium in the GBR. Linux operating system, C compiler and NETCDF library were used to build the modified EMS applications on AIMS HPC. The EMS version used is 1.2.1. The modified EMS was derived from the eReefs model (https://ereefs.org.au/ereefs) and the model descriptions are found in Baird et al. (2020). Methods for collecting the data include the following:

Hydrodynamic model forcing available in https://research.csiro.au/ereefs/models/models-about/models-hydrodynamics/;

Biogeochemical (BGC) model forcing (Simulated hydrodynamic model output, regional wave model data, 2019 catchment conditions of nutrient and sediment loads available in https://svnserv.csiro.au/svn/CEM/projects/eReefs/model/gbr4_bgc_hindcast/gbr4_H2p0_B3p2_Cb/); Initialisation file: GBR4 BGC 3p1 initialisation data.

The 4km resolution grid of the EMS was run on AIMS HPC from 1/12/2010 to 30/11/2012 and the data was collected on 26/06/2023. Software-specific information needed to interpret the data are R Software version 3.5.1, GNU Compiler Collection (GCC) version 6.1.0, network Common Data Form (NetCDF-cxx) version 4.2.1, Open Message Passing Interface (OpenMPI-gcc) version 1.10.2 and NetCDF Operators (NCO) version 4.5.5. R scripts for post-processing simulated data are available in Chinenyeani1986/Trichodesmium-buoyancy (github.com)

This data set was acquired with an UH:Hawaii Mapping Research Group MR1 Sidescan Sonar during R/V Roger Revelle expedition DRFT04RR conducted in 2001 (Chief Scientist: Dr. Mark Kurz, Investigator: Dr. Daniel Fornari). These data files are of NetCDF Grid format and include Sidescan data that were processed after acquisition.

This dataset contains aqueous DMS measurements taken on board the Ron Brown Ship during the ACE-Asia project, March-April 2001. This data is in netCDF format. However, the same dataset can also be ordered in an ASCII text format.

Note: This dataset version has been superseded by a newer version. It is highly recommended that users access the current version. Users should only use this version for special cases, such as reproducing studies that used this version. The Intersatellite Calibrated Gridded Satellite Data from International Satellite Cloud Climatology Project (ISCCP) B1 data (or GridSat-B1) provides a uniform set of quality controlled observations for the infrared (IR) window channel at 11 microns for a 30 year record beginning in 1980. The ISCCP B1 data are quality controlled, calibrated, remapped and merged to provide nearly Global coverage of equal-angle uniform observations of IR brightness temperatures every 3 hours. Temporal normalization is performed via inter-satellite calibration against High Resolution Infrared Radiation Sounder (HIRS) channel 12 data during the ISCCP B1 period of record. For each 3-hour time segment (from 00 to 23 UTC), the IR channel from each satellite product is mapped to an equal-angle grid using nearest-neighbor sampling. Since the ISCCP B1 spatial resolution is approximately 8km, the resolution of the equal area grid is 0.07 degrees Latitude (approximately 8km at the Equator). The data span the Globe in Longitude and range from 70 degrees South to 70 degrees North Latitude. Satellites are merged by selecting the nadir-most observations for each grid point. Areas of satellite overlap are retained by storing data in layers. Channel primary layers (nadir-most observation) are written as 2-dimensional grids in the netCDF file, which facilitates processing of multiple files (e.g., aggregation of multiple times, etc.). Subsequent layers are written as either 2D grids or staggered arrays, which are 1-dimensional arrays that only record data when present. The fundamental Climate Data Record (CDR) is stored using netCDF and CF conventions to facilitate data usage with a wide range of processing software.

NetCDF model output of the entire state of the surface layer of the circum-Antarctic model, including ocean and sea ice physical variables, heat and freshwater (negative salt) fluxes between the ice shelves and the ocean, and simulated dFe dyes. Each of the 255 NetCDF (.nc) files contain two five day temporal averages of the model state for the seven years of the model simulation. The additional file \"so_grd.rtopo2.2.5km.nc.try16\" contains the model grid data (See \"Data Files\" section).

Abstract: These processed marine magnetic anomaly grids were derived from the processed ASCII magnetic anomaly data files and have been gridded at ~555 m resolution using a minimum-curvature algorithm, and exported as GMT grids. The magnetic surveys were conducted at the Hess Rise and Emperor Seamounts in the North Central Pacific Ocean during R/V Falkor cruise FK190726 (chief scientist Les Watling). The cruise was primarily a biogeographic study using ROV SuBastian and the surveys were designed for ROV dive site selection and multibeam gap filling when time allowed. The data set is divided into 10 sites that represent places where the magnetometer was deployed for a dedicated survey with nearly parallel lines or some other pattern. The Emperor Seamounts surveyed include Suiko (3 surveys), Yomei, Nintoku, Jingu, Annei, and Koko (from north to south, respectively). Two surveys were carried out on Hess Rise. The 11th and final file of the data set (file name containing 20190822) is a short test file acquired during the return transit and is not one of the project study sites. The data files are in GMT-compatible netCDF grid format and were generated as part of a project called Deep Coral Diversity at Emperor Seamount Chain 2019. Funding was provided by a grant from Schmidt Ocean Institute (Ship, ROV, gravimeter, magnetometer) to Les Watling, PI, and from NOAA OER grant number NA16OAR0110192 (Science support) to John R. Smith, PI.

This dataset is about: Physical oceanography from Seaglider mission CNCY201415008, links to files in NetCDF format. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.860867 for more information.

Abstract: This data set was acquired with an EdgeTech 2205 Sidescan Sonar on a REMUS 600 AUV during R/V Falkor expedition FK170825 conducted in 2017 (Chief Scientist: Dr. Kenneth Rubin, Investigator: Dr. Kenneth Rubin). These data files are of NetCDF Grid format and include processed composite Sidescan data from REMUS dives MS02 to MS08.

description: NetCDF files of PBL height (m), Shortwave Radiation, 10 m wind speed from WRF and Ozone from CMAQ. The data is the standard deviation of these variables for each hour of the 4 day simulation. Figure 4 is only one of the time periods: June 8, 2100 UTC. The NetCDF files have a time stamp (Times) that can be used to find this time in order to reproduce the Figure 4. Also included is a data dictionary that describes the domain and all other attributes of the model simulation. This dataset is not publicly accessible because: The file is 202Mb binary NetCDF file that is too large. It can be accessed through the following means: Archived on the US EPA HPC Sol computer system:/asm/grc/JGR_ENSEMBLE_ScienceHub/Figure4.tar.gz. Format: Tar.gz file that contains NetCDF files required to reproduce Figure 4. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).; abstract: NetCDF files of PBL height (m), Shortwave Radiation, 10 m wind speed from WRF and Ozone from CMAQ. The data is the standard deviation of these variables for each hour of the 4 day simulation. Figure 4 is only one of the time periods: June 8, 2100 UTC. The NetCDF files have a time stamp (Times) that can be used to find this time in order to reproduce the Figure 4. Also included is a data dictionary that describes the domain and all other attributes of the model simulation. This dataset is not publicly accessible because: The file is 202Mb binary NetCDF file that is too large. It can be accessed through the following means: Archived on the US EPA HPC Sol computer system:/asm/grc/JGR_ENSEMBLE_ScienceHub/Figure4.tar.gz. Format: Tar.gz file that contains NetCDF files required to reproduce Figure 4. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

Lake temperature is an important environmental metric for understanding habitat suitability for many freshwater species and is especially useful when temperatures are predicted throughout the water column (known as temperature profiles). In this data release, multiple modeling approaches were used to generate predictions of daily temperature profiles for thousands of lakes in the Midwest.

Predictions were generated using two modeling frameworks: a machine learning model (specifically an entity-aware long short-term memory or EA-LSTM model; Kratzert et al., 2019) and a process-based model (specifically the General Lake Model or GLM; Hipsey et al., 2019). Both the EA-LSTM and GLM frameworks were used to generate lake temperature predictions in the contemporary period (1979-04-12 to 2022-04-11 for EA-LSTM and 1980-01-01 to 2021-12-31 for GLM; times differ due to modeling spin-up/spin-down configurations) using the North American Land Data Assimilation System [NLDAS; Mitchell et al., 2004] as meteorological drivers. In addition, GLM was used to generate lake temperature predictions under future climate scenarios (covering 1981-2000, 2040-2059, and 2080-2099) using six dynamically downscaled Global Climate Models (GCM; Notaro et al., 2018) as meteorological drivers. Appropriate application of the six GCMs is dependent on the use-case and will be up to the user to determine. For an example of a similar analysis in the Midwest and Great Lakes region using 31 GCMs, see Byun and Hamlet, 2018.

The modeling frameworks and driver datasets have slightly different footprints and input data requirements. This means that some of the lakes do not meet the criteria to be included in all three modeling approaches, which results in different numbers of lakes in the output (noted in the file descriptions below). The input data requirements for lakes to be included in the EA-LSTM predictions are lake latitude, longitude, elevation, and surface area, plus NLDAS drivers at the lake's location. All 62,966 lakes included this data release met these requirements. The input data requirements for lakes to be included in the contemporary GLM NLDAS-driven predictions are lake location (within one of the following 11 states: North Dakota, South Dakota, Iowa, Michigan, Indiana, Illinois, Wisconsin, Minnesota, Missouri, Arkansas, and Ohio), latitude, longitude, maximum depth (though more detailed hypsography was used where available), surface area, and a clarity esitmate, plus NLDAS drivers at the lake's location. 12,688 lakes included this data release met these requirements. The input data requirements for lakes to be included in the future climate scenario GCM-driven predictions were the same as for the contemporary GLM predictions, except GCM drivers at the lake's location were required in place of NLDAS drivers. 11,715 lakes included this data release met these requirements.

This data release includes the following files:

1. lake_locations.zip: shapefiles with the centroid of each lake (62,966 lakes)
2. lake_metadata.csv: metadata for each lake with predictions available (62,966 lakes)
3. lake_id_crosswalk.csv: mapping between the identifications for lakes used in this data release to state and other organization systems
4. lake_hypsography.csv: lake-specific area-depth relationships (13,785 lakes)
5. lake_temperature_observations.zip: temperature observational data used in training and/or evaluation (8,760 lakes)
6. meteorological_inputs_GCM.zip: meteorological input data for future climate scenarios, zipped NetCDF files. One NetCDF file per climate model (see the "lake_metadata.csv" file for how to map the lakes to the cells in these NetCDF files).
7. meteorological_inputs_NLDAS_{GROUP}.zip: meteorological input data for the contemporary period organized into grids, groups of zipped CSV files (see the "lake_metadata.csv" file for how to map the lakes to these files).
8. lake_temp_preds_EALSTM_NLDAS_AR-MN.zip: daily lake temperature profiles for the contemporary period generated by the EA-LSTM model. The zip folder contains a NetCDF file for each of the following states: AR, IA, IL, IN, KS, KY, LA, MI, and MN. Includes data for 33,646 lakes across these 9 states.
9. lake_temp_preds_EALSTM_NLDAS_MO-WY.zip: daily lake temperature profiles for the contemporary period generated by the EA-LSTM model. The zip folder contains a NetCDF file for each of the following states: MO, MS, MT, ND, NE, OH, OK, SD, TN, TX, WI, and WY. Includes data for 29,320 lakes across these 12 states.
10. lake_temp_preds_GLM_NLDAS.zip: daily lake temperature profiles for the contemporary period generated by GLM. The zip folder contains a NetCDF file for each of the following states: AR, IA, IL, IN, MI, MN, MO, ND, OH, SD, and WI. Includes data for 12,688 lakes across these 11 states.
11. lake_temp_preds_GLM_GCM_{CLIMATE MODEL}.zip: daily lake temperature profiles for future climate scenarios generated by GLM, one zip file per climate model. Each zip file contains a NetCDF file for each of the following states: AR, IA, IL, IN, MI, MN, MO, ND, OH, SD, and WI. Includes data for 11,715 lakes across these 11 states.
12. lake_temp_metrics_GLM_NLDAS.feather: annual lake temperature metrics for the contemporary period derived from daily predictions generated by GLM (12,688 lakes)
13. lake_temp_metrics_GLM_GCM.feather: annual lake temperature metrics for future climate scenarios derived from daily predictions generated by GLM (11,715 lakes)
14. lake_temp_model_evaluation_metrics.csv: overall and seasonal evaluation metrics for each model + meteorological driver dataset
15. extract_output_from_netCDFs.R: an R script showing examples for how to pull lake temperature predictions and meteorological data from the NetCDF files
16. netCDF_extract_utils.R: an R script containing functions used in "extract_output_from_netCDFs.R"
17. lake_locations.png: a figure showing the centroids for all 62,966 lakes included in this data release

This work was completed with funding support from the Midwest Climate Adaptation Science Center (MW CASC) and as part of the USGS project on Predictive Understanding of Multiscale Processes (PUMP), an element of the Integrated Water Prediction Program, supported by the Water Availability and Use Science Program to advance multi-scale, integrated modeling capabilities to address water resource issues. Access to computing facilities was provided by USGS Advanced Research Computing, USGS Tallgrass Supercomputer (doi.org/10.5066/F7D798MJ).

Gridded bathymetry (40 m cell size) of the slope environment of Swains Island, American Samoa. Almost complete bottom coverage was achieved in depths between 7 and 4800 m. The multibeam data are from the Simrad EM300 system aboard the NOAA Ship Hi'ialakai, and the Reson 8101ER system aboard the R/V AHI and were collected from 10th - 13th February 2006.

This series is composed of five select physical marine parameters (water salinity and water temperature for surface and near bottom waters and sea ice) for two climate scenarios (RCP 45 and RCP 8.5) and three statistics (minimum, median and maximum) from an ensemble of five downscaled global climate models. The source data for this data series is global climate model outcomes from the Coupled Model Intercomparison Project 5 (CMIP5) published by the Intergovernmental Panel on Climate Change (Stocker et al 2013).

The source data were provided in NetCDF format for each of the downsampled climate models based on the five CMIP5 global climate models: MPI: MPI-ESM-LR, HAD: HadGEM2-ES, ECE: EC-EARTH, GFD: GFDL-ESM2M, IPS: IPSL-CM5A-MR. The data included monthly mean, maximum, minimum and standard deviation calculations and the physical variables provided with the climate scenario models included sea ice cover, water temperature, water salinity, sea level and current strength (as two vectors) as well as a range of derived biogeochemical variables (O2, PO4, NO3, NH4, Secci Depth and Phytoplankton).

These global atmospheric climate model data were subsequently downscaled from global to regional scale and incorporated into the high-resolution ocean–sea ice–atmosphere model RCA4–NEMO by the Swedish Meteorological and Hydrological Institute (Gröger et al 2019) thus providing a wide range of marine specific parameters. The Swedish Geological Survey used these data in the form of monthly mean averages to calculate change in multi-annual (30-year) climate averages from the beginning and end of the 21st century for the five select parameters as proxies for climate change pressures.

Each dataset uses only source data models based on an assumption of atmospheric climate gas concentrations in line with either the IPCCs representative concentration pathway RCP 4.5 or RCP 8.5. Changes were calculated as the difference between two multiannual (30 year) mean averages; one for a historical reference climate period (1976-2005) and one for an end of century projection (2070-2099). These data were extracted for each of the five downscaled CMIP5 models individually and then combined into ensemble summary statistics (ensemble minimum, median and maximum). In the Ensemble_Maximum/Median/Minimum_Rasters datasets, changes in mean (May-Sept) surface temperature and bottom temperature are given in Degrees Celsia (°C); changes in mean annual surface salinity and bottom salinity are given in Practical Salinity Units (PSU); changes in mean (October-April) sea ice are given in Percentage Points (pp).

In the Normalized_Rasters datasets, the changes are normalized using a linear stretch so that a cell value of zero represents no projected and a cell value of 100 represents a value equal to or above the mean change in Swedish national waters. The values representing 100 are: 4 °C for surface temperature; 3 °C for bottom temperature; -1.5 PSU for surface salinity; -2.0 PSU for bottom salinity; and -40 pp for sea ice. These were also the chosen reference values for determining, via expert review, the sensitivity of ecosystem components to changes in these parameters (for further information refer to the Symphony method).

Notes on interpretation. This dataset does not highlight inter-annual or inter-decadal climate variability (e.g. extreme events) or changes in biochemical parameters (e.g. O2, chlorophyll, secchi depth etc) resulting from change in surface temperature. Areas of no-data inshore were filled using extrapolating from nearby cells (using similar depths for benthic data) so data near the coast and particularly within archipelagos, bays and estuaries is not robust. Users should refer to the associated climemarine uncertainty map for this parameter. The uncertainty map shows the interquatile range from the climate ensemble and the area of no-data as 'interpolated values'. For any application which requires more temporally or spatially explicit information (e.g. at sub/national decision making) it is highly recommended that the user contact SMHI for access to the latest climate model source data (in NetCDF format) which contains much more detail and a far wider selection of parameters. For regional applications (e.g. at the scale of the Baltic Sea) - it should be noted that these data will likely require normalisation to regional rather than national values and that sensitivity scores used may differ.

ClimeMarine was selective in its choice of pressure parameters. SMHI have additional data available for other parameters such as O2, secchi depth and nutrients which could be included in future. This is complicated because many parameters are influenced by riverine discharge and therefore by decisions related to watershed management - disentanglement of impacts from climate vs river basin management becomes a complication. In a similar way, data on sealevel rise is also available which could be used to estimate impacts on the coast but likewise complicating factors such as isostatic uplift and coastal defence and management policies would need to be considered.

For simplicity and to reduce the amount of datasets to a manageable level for this assessment the source data were further limited and summarised in several ways:

Only the monthly mean averages of seawater temperature, salinity and sea ice (i.e. key physical parameters) were utilized.
For seawater salinity and temperature, the depth dimension (i.e. the water column) was summarised from 56 depth levels to just two: the surface and the deepest (bottom) waters.
Only two of the three climate periods were selected: a historical reference period: 1976-2005 (to represent the current status) and the projected end of century period: 2070-2099. Only two of the three available emission scenarios were selected detailing the consequence of intermediate and very high climate gas emissions : Representative Concentration Pathway (RCP) 4.5 and 8.5 (see SEDAC 2021).

Each dataset included in the series comes with extensive metadata.

The data processing followed the following steps:

Extraction of data for each parameter from NetCDF to TIFF Rasters for each model, emission scenario, depth level (using scripts in NCO, CDO and R). Calculation of climate ensemble statistics - Minimum, Mean, Median and Maximum (using Arcpy and Numpy)
Reprojection and resampling from the 2nm NEMO-RCO from Lat/Long WGS84 grid to the 250m ETRS89 LAEA Symphony grid (using Arcpy)
Extrapolation to fill no-data cells based on proximity and similar depths (using Arcpy script and the ArcGIS spatial analyst extension) Calculation of change for each parameter as the end of century multi-annual mean minus the reference multi-annual mean (using an Arcpy script)
Inversion of if negative (i.e. decreases) to positive (i.e. magnitude of change)
Normalisation as a linear stretch from 0 to 100 where zero equates to no change and 100 equates to the maximum pixel value in Swedish waters from the RCP 8.5 ensemble mean dataset with any values over this pixel value also set to 100 (Arcpy script)

NetCDF source data used in this analysis can be requested from the Swedish Meteorological and Hydrological Institute - kundtjanst@smhi.se

Processing scripts (R and arcpy) and interim raster data can be requested from the Geological Survey of Sweden - kundtjanst@sgu.se

This data set was acquired with a SeaBeam Instruments 2000 Multibeam Sonar during R/V Melville expedition BMRG08MV conducted in 1996 (Chief Scientist: Dr. Sherman Bloomer, Investigator: Dr. Sherman Bloomer). These data files are of NetCDF Grid format and include Bathymetry data that were processed after acquisition. Funding was provided by NSF award(s): OCE95-21023.

This data set was acquired with a Reson SeaBat 7125 Multibeam Sonar on AUV Sentry during R/V Atlantis expedition AT26-17 conducted in 2014 (Chief Scientist: Dr. Timothy Crone, Investigator: Dr. Timothy Crone). These data files are of NetCDF Grid format and include Bathymetry data that were processed after acquisition.