land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolutions.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on pressure levels from 1940 to present".

# ERA-NUTS (1980-2018)

This dataset contains a set of time-series of meteorological variables based on Copernicus Climate Change Service (C3S) ERA5 reanalysis. The data files can be downloaded from here while notebooks and other files can be found on the associated Github repository.

This data has been generated with the aim of providing hourly time-series of the meteorological variables commonly used for power system modelling and, more in general, studies on energy systems.

An example of the analysis that can be performed with ERA-NUTS is shown in this video.

Important: this dataset is still a work-in-progress, we will add more analysis and variables in the near-future. If you spot an error or something strange in the data please tell us sending an email or opening an Issue in the associated Github repository.

## Data
The time-series have hourly/daily/monthly frequency and are aggregated following the NUTS 2016 classification. NUTS (Nomenclature of Territorial Units for Statistics) is a European Union standard for referencing the subdivisions of countries (member states, candidate countries and EFTA countries).

This dataset contains NUTS0/1/2 time-series for the following variables obtained from the ERA5 reanalysis data (in brackets the name of the variable on the Copernicus Data Store and its unit measure):

- t2m: 2-meter temperature (`2m_temperature`, Celsius degrees)
- ssrd: Surface solar radiation (`surface_solar_radiation_downwards`, Watt per square meter)
- ssrdc: Surface solar radiation clear-sky (`surface_solar_radiation_downward_clear_sky`, Watt per square meter)
- ro: Runoff (`runoff`, millimeters)

There are also a set of derived variables:
- ws10: Wind speed at 10 meters (derived by `10m_u_component_of_wind` and `10m_v_component_of_wind`, meters per second)
- ws100: Wind speed at 100 meters (derived by `100m_u_component_of_wind` and `100m_v_component_of_wind`, meters per second)
- CS: Clear-Sky index (the ratio between the solar radiation and the solar radiation clear-sky)
- HDD/CDD: Heating/Cooling Degree days (derived by 2-meter temperature the EUROSTAT definition.

For each variable we have 350 599 hourly samples (from 01-01-1980 00:00:00 to 31-12-2019 23:00:00) for 34/115/309 regions (NUTS 0/1/2).

The data is provided in two formats:

- NetCDF version 4 (all the variables hourly and CDD/HDD daily). NOTE: the variables are stored as `int16` type using a `scale_factor` of 0.01 to minimise the size of the files.
- Comma Separated Value ("single index" format for all the variables and the time frequencies and "stacked" only for daily and monthly)

All the CSV files are stored in a zipped file for each variable.

## Methodology

The time-series have been generated using the following workflow:

1. The NetCDF files are downloaded from the Copernicus Data Store from the ERA5 hourly data on single levels from 1979 to present dataset
2. The data is read in R with the climate4r packages and aggregated using the function `/get_ts_from_shp` from panas. All the variables are aggregated at the NUTS boundaries using the average except for the runoff, which consists of the sum of all the grid points within the regional/national borders.
3. The derived variables (wind speed, CDD/HDD, clear-sky) are computed and all the CSV files are generated using R
4. The NetCDF are created using `xarray` in Python 3.7.

NOTE: air temperature, solar radiation, runoff and wind speed hourly data have been rounded with two decimal digits.

## Example notebooks

In the folder `notebooks` on the associated Github repository there are two Jupyter notebooks which shows how to deal effectively with the NetCDF data in `xarray` and how to visualise them in several ways by using matplotlib or the enlopy package.

There are currently two notebooks:

- exploring-ERA-NUTS: it shows how to open the NetCDF files (with Dask), how to manipulate and visualise them.
- ERA-NUTS-explore-with-widget: explorer interactively the datasets with [jupyter]() and ipywidgets.

The notebook `exploring-ERA-NUTS` is also available rendered as HTML.

## Additional files

In the folder `additional files`on the associated Github repository there is a map showing the spatial resolution of the ERA5 reanalysis and a CSV file specifying the number of grid points with respect to each NUTS0/1/2 region.

## License

This dataset is released under CC-BY-4.0 license.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 monthly mean data on single levels from 1940 to present".

This dataset provides daily gridded data of sea ice edge and sea ice type derived from brightness temperatures measured by satellite passive microwave radiometers. Sea ice is an important component of our climate system and a sensitive indicator of climate change. Its presence or its retreat has a strong impact on air-sea interactions, the Earth’s energy budget as well as marine ecosystems. It is recognized by the Global Climate Observing System as an Essential Climate Variable. Sea ice edge and type are some of the parameters used to characterise sea ice. Other parameters include sea ice concentration and sea ice thickness, also available in the Climate Data Store. Sea ice edge and type are defined as follows:

Sea ice edge classifies the sea surface into open water, open ice, and closed ice depending on the amount of sea ice present in each grid cell. This variable is provided for both the Northern and Southern Hemispheres. Note that a sea ice concentration threshold of 30% is used to distinguish between open water and open ice, which differs from the 15% threshold commonly used for other sea ice products such as sea ice extent. Sea ice type classifies ice-covered areas into two categories based on the age of the sea ice: multiyear ice versus seasonal first-year ice. This variable is currently only available for the Northern Hemisphere and limited to the extended boreal winter months (October through April). Sea ice type classification during summer is difficult due to the effect of melting at the ice surface which disturbs the passive microwave signature.

Both sea ice products are based on measurements from the series of Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), and Special Sensor Microwave Imager/Sounder (SSMIS) sensors and share the same algorithm baseline. However, sea ice edge makes use of two lower frequencies near 19 GHz and 37 GHz and a higher frequency near 90 GHz whereas sea ice type only uses the two lower frequencies. This dataset combines Climate Data Records (CDRs), which are intended to have sufficient length, consistency, and continuity to assess climate variability and change, and Interim Climate Data Records (ICDRs), which provide regular temporal extensions to the CDRs and where consistency with the CDRs is expected but not extensively checked. For this dataset, both the CDR and ICDR parts of each product were generated using the same software and algorithms. The CDRs of sea ice edge and type currently extend from 25 October 1978 to 31 December 2020 whereas the corresponding ICDRs extend from January 2021 to present (with a 16-day latency behind real time). All data from the current release of the datasets (version 3.0) are Level-4 products, in which data gaps are filled by temporal and spatial interpolation. For product limitations and known issues, please consult the Product User Guide. This dataset is produced on behalf of Copernicus Climate Change Service (C3S), with heritage from the operational products generated by EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF).

ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. ERA5-Land provides a consistent view of the water and energy cycles at surface level during several decades. It contains a detailed record from 1950 onwards, with a temporal resolution of 1 hour. The native spatial resolution of the ERA5-Land reanalysis dataset is 9km on a reduced Gaussian grid (TCo1279). The data in the CDS has been regridded to a regular lat-lon grid of 0.1x0.1 degrees. The data presented here is a post-processed subset of the full ERA5-Land dataset. Monthly-mean averages have been pre-calculated to facilitate many applications requiring easy and fast access to the data, when sub-monthly fields are not required.

This dataset contains values calculated using existing data produced by Environment and Climate Change Canada (ECCC). Freezing degree-days (FDD) correspond to the negative difference between the average daily temperature and the freezing point of seawater (Tf = -1.8°C). For example, if for one day the average temperature is -21.8°C, it raises the annual FDD value by 20.0 FDD. When the daily averaged temperature is above Tf, the FDD value is negative. FDDs are summed starting on September 1st each year. When the cumulative number of FDDs (CFDD) becomes negative, it is reset to zero. The start of winter corresponds to the first time the CFDD is and remains above 0. In this data set, the daily temperature averaged over the entire marine domain of the Gulf of St. Lawrence is used. The data comes from surface temperature forecasts (T2m) from ECCC's High Resolution Deterministic Prediction System (HRDPS). cdm_data_type=Other comment=Data from 2024-9-01 to 2025-01-22 are transiently not from the HRDPS model but from the Copernicus ERA5 model. HRDPS data will replace ERA5 data when historical HRDPS data become available.

  Data prior to September 1, 2024 are temporarily calculated over a different period from November 1 to September 1 of each year. These data will soon be updated with the new period (i.e. September 1 to August 31)

contributor_institution=(a) Université du Québec à Rimouski, (b) Université du Québec à Rimouski, (c) Service Hydrographique et Océanographique de la Marine, (d) Fisheries and Oceans Canada, (e) St. Lawrence Global Observatory contributor_name=(a) Dany Dumont, (b) Sébastien Dugas, (c) Eliott Bismuth, (d) Peter Galbraith, (e) Antoine Biehler contributor_role=(a) Metadata Custodian, Author, (b) Coauthor, Contributor, (c) Coauthor, Contributor, (d) Contributor, (e) Metadata Custodian, Contributor, Editor Conventions=COARDS, CF-1.12, ACDD-1.3, NCCSV-1.2 data_source_01=Environment and Climate Change Canada - HRDPS model - https://eccc-msc.github.io/open-data/msc-data/nwp_hrdps/readme_hrdps_en/ data_source_02=From 2024-09-01 to 2025-01-22 only Copernicus Climate Change Service, Climate Data Store, (2023) - ERA5 hourly data on single levels from 1940 to present - https://doi.org/10.24381/cds.adbb2d47 dataset_status=OnGoing defaultGraphQuery=&time>=max(time)-1year&.bgColor=0xffffffff DOI=A VERIFIER geospatial_lat_max=52.2 geospatial_lat_min=45.1 geospatial_lat_units=degrees_north geospatial_lon_max=-55.2 geospatial_lon_min=-71.3 geospatial_lon_units=degrees_east grid_mapping_epsg_code=EPSG:4326 grid_mapping_epsg_code_url=https://epsg.io/4326 grid_mapping_geographic_crs_name=WGS 84 grid_mapping_inverse_flattening=298.2572236 grid_mapping_name=latitude_longitude grid_mapping_prime_meridian_name=Greenwich grid_mapping_semi_major_axis=6378137 infoUrl=https://ogsl.ca/cartesglacesstlaurent/ institution=Institut des sciences de la mer de Rimouski keywords_fr=glace de mer, température de l'air, changement climatique keywords_vocabulary=NASA Global Change Master Directory (GCMD) Science Keywords and homemade keywords licenseUrl=https://creativecommons.org/licenses/by/4.0/ marine_region=Gulf of St. Lawrence marine_region_identifier=http://marineregions.org/mrgid/4290 publisherID=https://ror.org/03wfagk22 sourceUrl=(local files) standard_name_nerc_vocabulary=The NERC Vocabulary Server (NVS) standard_name_other_vocabulary=dwc: Darwin Core List of Terms (v 2023-09) standard_name_vocabulary=CF Standard Name Table v86 summary_fr=Ce jeu de données contient des valeurs calculées à partir de données existantes produites par Environnement et changement climatique Canada (ECCC). Un degré-jour de gel (DJG) correspond à la différence négative entre la température moyenne journalière et le point de congélation de l'eau de mer (Tf = -1.8°C). Si pour un jour la température moyenne est de -21.8°C, par exemple, il élève la valeur DJG annuelle de 20.0 DJG. Les jours où la température moyenne est supérieure Tf, la valeur de DJG diminue. Les DJG sont calculés à partir du 1er septembre. Lorsque le nombre cumulé de DJG (DJGC) devient négatif, il est remis à zéro. Le début de l’hiver correspond au moment où les DJG commencent à augmenter de manière persistante, donc au premier moment où DJGC est plus grand que 0. Dans ce jeu de données, on utilise la température journalière moyennée sur l'ensemble du domaine marin du golfe du Saint-Laurent. Les données sont issues des prévisions de température de surface (T2m) du système de prévision déterministe à haute résolution (HRDPS) d'ECCC. time_coverage_end=2025-05-01 time_coverage_start=1996-11-01

ERA5-Land ist ein Reanalysedatensatz, der einen konsistenten Überblick über die Entwicklung von Landvariablen über mehrere Jahrzehnte hinweg bei einer höheren Auflösung als ERA5 bietet. ERA5-Land wurde durch die Wiederholung der Landkomponente der ERA5-Klimareanalyse des ECMWF erstellt. Bei der Reanalyse werden Modelldaten mit Beobachtungen aus der ganzen Welt kombiniert…

Determining concentrations of cloud condensation nuclei (CCN) is one of the first steps in the chain in analysis of cloud droplet formation, the direct microphysical link between aerosols and cloud droplets, a process key for aerosol-cloud interactions (ACI). However, due to sparse coverage of in-situ measurements and difficulties associated with retrievals from satellites, a global exploration of their magnitude, source, temporal and spatial distribution cannot be easily obtained. Thus, a better representation of CCN is one of the goals for quantifying ACI processes and achieving uncertainty reduced estimates of their associated radiative forcing. Here, we introduce a new CCN dataset which is derived based on aerosol mass mixing ratios from the latest Copernicus Atmosphere Monitoring Service (CAMS) reanalysis (RA: EAC4) in a diagnostic model that uses CAMSRA aerosol properties and a simplified kappa-Köhler framework suitable for global models. The emitted aerosols in CAMS are not only based on input from emission inventories using aerosol observations, they also have a strong tie to satellite-retrieved aerosol optical depth (AOD) as this is assimilated as a constraining factor in the reanalysis. Furthermore, the reanalysis interpolates for cases of poor or missing retrievals and thus allows for a full spatio-temporal quantification of CCN. Therefore, the CCN retrieved from the CAMS aerosol reanalysis succeed the sole use of AOD as a proxy for global CCN. This CCN dataset features CCN concentrations of global coverage for various supersaturations and aerosol species covering the years from 2003 to 2021 with daily frequency and a spatial resolution of 0.75×0.75 degree and 60 vertical levels. Apart from the CAMSRA data, which is available every 3 hours, CCN are currently only computed once a day at 00:00 UTC. The data comprises 3-D fields of total CCN computed for six different supersaturations (s: 0.1, 0.2, 0.4, 0.6, 0.8 and 1 %) and 3-D CCN fields containing aerosol species CCN from sulfate (SO4), hydrophilic black carbon (BCh) and organic matter (OMh) and three size bins of sea salt aerosol (SS) computed for two supersaturations (s: 0.02 % and 0.8 %) comprising additional aerosol information in the lower and upper supersaturation range, respectively. The current choice of data frequency, resolution and variable dependencies such as supersaturation is made regarding general interest and suitability as well as file size, data storage and computational costs. This dataset offers the opportunity to be used for evaluation of general circulation and earth system models as well as in studies of aerosol-cloud interactions.

The file name of the data sets is composed as follows.

project: QUAERERE (Quantifying aerosol-cloud-climate effects by regime) experiment: CCNCAMS (Cloud condensation nuclei derived from the CAMS reanalysis) version: v1 dataset: Total_CCN (total cloud condensation nuclei) and Aerosol_species_CCN (aerosol species cloud condensation nuclei) year: 2003 to 2021 mon: 1 to 12

Acknowledgement: This dataset was generated using Copernicus Atmosphere Monitoring Service information [2003-2021]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. The source data is downloaded from the Copernicus Atmosphere Monitoring Service (CAMS) Atmosphere Data Store (ADS) (https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4?tab=overview)

The data are vertical profiles of mineral dust aerosols from IASI (Metop-A and Metop-C) obtained with the Mineral Aerosol Profiling from Infrared Radiances (MAPIR) version 4.1 algorithm. The data available here is the level 2 data (at satellite pixel resolution). Users should read the data explanation and description available as resource file, and contact the data provider with any question. This data is available here: https://iasi.aeronomie.be/index.php/data-products/download-the-data-3/requestors Level 3 (gridded daily and monthly AM, PM and total) total column AOD and mean altitude are available through the Climate Data Store (https://cds.climate.copernicus.eu). Search for "dust", select "Aerosol properties gridded data from 1995 to present derived from satellite observations", in the download tab, select the sensor IASI on METOPA or METOPC and the MAPIR algorithm. Algorithm description and validation: https://doi.org/10.5194/amt-12-3673-2019 Size: 75Mb per day

The dataset provides global annual glacier mass changes distributed on a global regular grid at 0.5° resolution (latitude, longitude) based on the Fluctuations of Glaciers (FoG) database of the World Glacier Monitoring Service (WGMS). Glaciers play a fundamental role in the Earth’s water cycles. They are one of the most important freshwater resources for societies and ecosystems and the recent increase in ice melt contributes directly to the rise of ocean levels. Due to this they have been declared as an Essential Climate Variable (ECV) by GCOS, the Global Climate Observing System. Within the Copernicus Services, the global gridded annual glacier mass change dataset provides information on changing glacier resources by combining glacier change observations from the Fluctuations of Glaciers (FoG) database that is brokered from World Glacier Monitoring Service (WGMS). Inspired by previous methodological frameworks, a new approach was developed to combine the glacier mass balance and elevation change observations, providing a new and unique product of annual glacier mass change and related uncertainties for every hydrological year since 1975/76 distributed on a 0.5° global regular grid. The present dataset bridges the gap regarding the spatio-temporal coverage of glacier change observations, providing for the first time in the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) an annually resolved glacier mass change product using the glacier elevation change sample as calibration. This goal has become feasible at the global scale thanks to a new globally near-complete (96% of the world glaciers) dataset of glacier elevation change observations ingested by the FoG database. To develop the distributed glacier change product, the use of glacier outlines from the C3S Glacier Area product version 2 are used. A glacier is considered to belong to a grid-point when its geometric centroid lies within the grid point. The centroid is obtained from the glacier outlines from the C3S Glacier Area product version 2. The glacier changes in Gt correspond to the total mass of water lost/gained over the glacier surface during a given year. Note that to propagate to mm/cm/m of water column on the grid cell, the grid cell area needs to be considered. Note that hydrological year vary on the Southern Hemisphere (October to September next year) and Northern Hemispheres (April to March next year). The annual distributed glacier change dataset cannot resolve for this seasonal difference and is important for the user to account for them when using the datasets. This issue can only be resolved with a monthly distributed glacier change product. This dataset has been produced by researchers at the WGMS on behalf of Copernicus Climate Change Service.

Code and data for Section 2 of the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines

This repository contains the code and data needed to produce the trajectories, projections, and observations for the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines.

The report can be found on https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html

An interactive tool to study the observations, trajectories, and scenarios can be accessed from https://sealevel.nasa.gov/task-force-scenario-tool

Frequently-asked questions: https://sealevel.nasa.gov/faq/16/

Authors

• William V. Sweet, NOAA National Ocean Service
• Benjamin D. Hamlington, NASA Jet Propulsion Laboratory
• Robert E. Kopp, Rutgers University
• Christopher P. Weaver, U.S. Environmental Protection Agency
• Patrick L. Barnard, U.S. Geological Survey
• Michael Craghan, U.S. Environmental Protection Agency
• Gregory Dusek, NOAA National Ocean Service
• Thomas Frederikse, NASA Jet Propulsion Laboratory
• Gregory Garner, Rutgers University
• Ayesha S. Genz, University of Hawai‘i at Mānoa, Cooperative Institute for Marine and Atmospheric Research
• John P. Krasting, NOAA Geophysical Fluid Dynamics Laboratory
• Eric Larour, NASA Jet Propulsion Laboratory
• Doug Marcy, NOAA National Ocean Service
• John J. Marra, NOAA National Centers for Environmental Information
• Jayantha Obeysekera, Florida International University
• Mark Osler, NOAA National Ocean Service
• Matthew Pendleton, Lynker
• Daniel Roman, NOAA National Ocean Service
• Lauren Schmied, FEMA Risk Management Directorate
• William C. Veatch, U.S. Army Corps of Engineers
• Kathleen D. White, U.S. Department of Defense
• Casey Zuzak, FEMA Risk Management Directorate

Contents

This data and code set contains the following directories:

Results

The Results folder contains the resulting projections, trajectories and observations from the report.

• TR_global_projections.nc: GMSL projections, trajectory, and observations
• TR_regional_projections.nc: Regional observations, projections and trajectories
• TR_local_projections.nc: Local observations, projections and trajectories
• TR_gridded_projections.nc: Gridded projections

These files are in the NetCDF forrmat. To read the NetCDF files, many free software packages are available, including ncview and Panoply. Free NetCDF packages are available to directly import the data into Julia and Python code.

Code

The Code folder contains all the computer code used to read and analyze the observations and the projections, and to generate the trajectories.

To run this code, you need Julia. The code requires the Julia packages CSV, Interpolations, JSON, LoopVectorization, MAT, NCDatasets, NetCDF, Plots, XLSX, LinearAlgebra, and Statistics. They can be installed by pressing ] at the Julia REPL and typing:

add CSV Interpolations JSON LoopVectorization MAT NCDatasets NetCDF Plots XLSX LinearAlgebra Statistics

This program also requires Hector. Hector needs to be installed or compiled. In the file Hector.jl update the path to the Hector executable on lines 30 and 104.

Run Run_TR.jl in the REPL or run julia Run_TR.jl from the command line to run the projections. The projections are then written to the .\Data directory.

The folder contains the following files:

• Run_TR.jl: This is the main routine that (eventually) calls all the functions to compute the projections.
• ConvertNCA5ToGrid.jl: Converts the original NCA5 projections to a set of netCDF files that's used throughout this code
• ProcessObservations.jl: Reads and processes the tide-gauge and altimetry observations
• GlobalProjections.jl: Reads and processes the GMSL observations and projections, and computes the trajectory
• RegionalProjections.jl: Reads and processes the regional projections and computes the trajectories
• LocalProjections.jl: Reads and processes the local projections at the tide-gauge locations and computes the trajectories
• GriddedProjections.jl: Reads the gridded NCA5 projections and add a GMSL baseline correction for the 2005 vs 2000 baseline
• SaveFigureData.jl: Reads the results and writes text files for GMT
• Hector.jl: Wrapper for Hector, used to compute trends and uncertainties.
• Masks.jl: Defines the region masks for each region.

Data

The Data directory contains the input data sets used during the computations. Please appropriately cite the input data if you use it. It contains the following:

Directories:

• ClimIdx: Map with climate indices (NAO, PDO, MEI) used to remove internal variability. All the indices come from NOAA Physical Sciences Laboratory (PSL) and NOAA Climate Prediction Centre (CPC)
• NCA5_projections Contains the NCA5 projections for each scenario (Low, IntLow, Int, IntHigh, and High). For each scenario, the GMSL projections, projections at tide-gauge locations and on a 1-degree grid are provided.

Files:

• basin_codes.nc: Map with basin codes. from Eric Leuliette/NOAA. Data provided by the NOAA Laboratory for Satellite Altimetry.
• CDS_monthly_1993_2020.nc: Monthly-mean sea level (1993-2020) from gridded altimetry. Obtained from Copernicus Climate Data Store. This dataset contains modified Copernicus Climate Change Service information [2020]
• enso_correction.mat: GMSL correction for ENSO/PDO from Hamlington, B. D., Frederikse, T., Nerem, R. S., Fasullo, J. T., & Adhikari, S. (2020). Investigating the Acceleration of Regional Sea‐level Rise During the Satellite Altimeter Era. Geophysical Research Letters. https://doi.org/10.1029/2019GL086528
• filelist_psmsl.txt: List with PSMSL file names and PSMSL IDs. Obtained from the Permanent Service for Mean Sea Level (PSMSL), 2021, Retrieved 29 Nov 2021. Simon J. Holgate, Andrew Matthews, Philip L. Woodworth, Lesley J. Rickards, Mark E. Tamisiea, Elizabeth Bradshaw, Peter R. Foden, Kathleen M. Gordon, Svetlana Jevrejeva, and Jeff Pugh (2013) New Data Systems and Products at the Permanent Service for Mean Sea Level. Journal of Coastal Research: Volume 29, Issue 3: pp. 493 – 504. https://doi.org/:10.2112/JCOASTRES-D-12-00175.1.
• GEBCO_bathymetry_05.nc: Bathymetry map of the global oceans from the General Bathymetric Chart of the Oceans (GEBCO). Source: GEBCO Compilation Group (2021) GEBCO 2021 Grid (doi:10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f) The source data have been re-gridded onto a 0.5 degree grid.
• GIA_Caron_stats_05.nc: Glacial Isostatic Adjustment estimates from Caron, L., Ivins, E. R., Larour, E., Adhikari, S., Nilsson, J., & Blewitt, G. (2018). GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science. Geophysical Research Letters, 45(5), 2203–2212. https://doi.org/10.1002/2017GL076644. The source data have been re-gridded onto a 0.5 degree grid.
• global_timeseries_measures.nc: Time series of estimated 20th-century GMSL and its components, based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. https://doi.org/10.1038/s41586-020-2591-3
• GMSL_ensembles.nc: Ensemble GMSL reconstruction from tide-gauges based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a