JRA55-do is a surface dataset for driving ocean-sea ice models and used in phase 2 of OMIP (OMIP-2). JRA55-do corrects the atmospheric reanalysis product JRA-55 (Kobayashi et al., 2015) using satellite and other atmospheric reanalysis products. The merits of JRA55-do are the high horizontal resolution (~55 km) and temporal interval (3 h). An assessment by Tsujino et al. (2020) implies that JRA55-do can suitably replace the current CORE/OMIP-1 dataset. This reanalysis of atmospheric variables is provided by the Japanese Meteorological Agency starting in the year 1958 and will be used to drive the coupled NEMO-ERSEM model in the hindcast configuration.

CMIP6 Forcing Datasets (input4MIPs). These data include all datasets published for 'input4MIPs.CMIP6.OMIP.MRI.MRI-JRA55-do-1-5-0' with the full Data Reference Syntax following the template 'activity_id.mip_era.target_mip.institution_id.source_id.realm.frequency.variable_id.grid_label'.

The MRI JRA55-do 1.5.0: Atmospheric state generated for OMIP based on the JRA-55 reanalysis (Based on JRA-55 reanalysis (1958-01 to 2020-07)) climate model, released in 2020, includes the following components: . The model was run by the Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan (MRI) in native nominal resolutions: none.

Project: The forcing datasets (and boundary conditions) needed for CMIP6 experiments are being prepared by a number of different experts. Initially many of these datasets may only be available from those experts, but over time as part of the 'input4MIPs' activity most of them will be archived by PCMDI and served by the Earth System Grid Federation (https://esgf-node.llnl.gov/search/input4mips/ ). More information is available in the living document: http://goo.gl/r8up31 .

This data set includes modeled significant wave height and ustar driven by the JRA55-do atmospheric reanalysis product (Tsujino et al. (2018) 'JRA-55 based surface dataset for driving ocean-sea-ice models (JRA55-do)'). The fields are originally created for an application for predicting sea-state dependent air-sea gas transfer velocities, and therefore have been remapped to the pCO2 spatial grid of Landschutzer et al. (2016) for the same time frame ('Decadal variations and trends of the global ocean carbon sink'). The wave height is simulated using NOAA's WAVEWATCH-III surface model on the native JRA55-do grid with the JRA55-do wind input and model simulated monthly sea-ice data. The ustar fields assume neutral stability and use the COARE3.5 algorithm (Edson et al., 2013) to diagnose the stress. For more information on the wave simulation or to inquire about accessing wave data on the native grid and/or for the extended JRA55 time period please contact the author.

Important Notice: Update of JRA-55 data will terminate at the end of January 2024. Please use Japanese Reanalysis for Three Quarters of a Century (JRA-3Q) [https://rda.ucar.edu/datasets/d640000/] at that time. The Japan Meteorological Agency (JMA) conducted JRA-55, the second Japanese global atmospheric reanalysis project. It covers 55 years, extending back to 1958, coinciding with the establishment of the global radiosonde observing system. Compared to its predecessor, JRA-25, JRA-55 is based on a new data assimilation and prediction system (DA) that improves many deficiencies found in the first Japanese reanalysis. These improvements have come about by implementing higher spatial resolution (TL319L60), a new radiation scheme, four-dimensional variational data assimilation (4D-Var) with Variational Bias Correction (VarBC) for satellite radiances, and introduction of greenhouse gases with time varying concentrations. The entire JRA-55 production was completed in 2013, and thereafter will be continued on a real time basis. Specific early results of quality assessment of JRA-55 indicate that a large temperature bias in the lower stratosphere has been significantly reduced compared to JRA-25 through a combination of the new radiation scheme and application of VarBC (which also reduces unrealistic temperature variations). In addition, a dry land surface anomaly in the Amazon basin has been mitigated, and overall forecast scores are much improved over JRA-25. Most of the observational data employed in JRA-55 are those used in JRA-25. Additionally, newly reprocessed METEOSAT and GMS data were supplied by EUMETSAT and MSC/JMA respectively. Snow depth data over the United States, Russia and Mongolia were supplied by UCAR, RIHMI and IMH respectively. The Data Support Section (DSS) at NCAR has processed the 1.25 degree version of JRA-55 with the RDA (Research Data Archive) archiving and metadata system. The model resolution data has also been acquired, archived and...

DOI VIKING20X-JRA-short (Biastoch et al., 2021) is part of a series of VIKING20X simulations under JRA55-do atmospheric forcing. It is based on a restart from a pre-spun experiment in 1980 (VIKING20X-CORE; see Biastoch et al., 2021) and integrated for the period 1980 to 2019 in the framework of the RACE–Synthesis: Regional Atlantic Circulation and Global Change (https://race-synthese.de). The applied atmospheric forcing JRA55-do (Tsujino et al., 2020, http://doi.org/10.5194/gmd-13-3643-2020) builds on the Japanese reanalysis product JRA-55 with improvements through the implementation of satellite and several other reanalysis products. Details of the Configuration: - eddy-rich 1/20° nest using the two-way nesting technique Adaptive Grid Refinement In Fortran (AGRIF; http://doi.org/10.1016/j.cageo.2007.01.009) that covers the Atlantic Ocean from 33.5° S to ∼65° N embedded in a 1/4° resolution global grid - initialization from a pre-spun experiment in 1980 - time step refinement factor 3 between host and nest - momentum advection scheme in vector form with Hollingsworth correction, conserving both energy and enstrophy - tracer advection as two-step flux corrected transport, total variance dissipation scheme - linearized filtered free surface - weak sea surface salinity restoring towards Levitus WOA98 (piston velocity 12.2 m/yr), suppressed under sea-ice - thermodynamic, dynamic sea-ice model LIM2 (https://doi.org/10.1029/97JC00480) with viscous-plastic rheology - turbulent kinetic energy scheme - bi-Laplacian lateral viscosity - non-linear bottom friction Note of advise on re-using the provided simulation output data We recommend to only use the high-resolution simulation output data from the 1/20 degree nested region for any analysis (nest file names "1_VIKING20X.L46-KKG36107B_*.nc"). The simulation was designed to improve the understanding of specific key processes in the Atlantic Ocean and their effect on the large scale and inter-hemispheric circulation in the Atlantic. The simulation results can only be interpreted in this context and are not necessarily applicable to other arbitrary research questions on ocean and climate dynamics. In particular, the output fields of the global simulation at coarser resolution (host file names "VIKING20X.L46-KKG36107B_*.nc") must be understood as an interactive boundary condition to the focus region of the nest and are here provided for completeness only. For questions in this regard we recommend to contact datamanagement@geomar.de and generally encourage potential users to reach out to us for clarification. A detailed description of the configuration and experiments is given in Biastoch et al. (2021): Biastoch, A., F. U. Schwarzkopf, K. Getzlaff, S. Rühs, T. Martin, M. Scheinert, T. Schulzki, P. Handmann, R. Hummels, and C. W. Böning, 2021, Regional Imprints of Changes in the Atlantic Meridional Overturning Circulation in the Eddy-rich Ocean Model VIKING20X, Ocean. Sci., 17, 1177–1211, http://doi.org/10.5194/os-17-1177-2021

The dataset is a 37 year control run of a NEMO-based 1/12 degree grid spacing model of the Southern Ocean as part of the ORCHESTRA LTS-M project. It uses the NEMO "extended" grid, although ice cavities are closed. The model was run on Archer, the national HPC platform. The dataset covers the full length of the model run (excluding a three year spinup period) and includes regular (5 day mean) output of the model state, as well as more frequent (1 day mean) output of surface variables and fluxes and 1 month mean of more extensive transport diagnostics. Forced by the GFDL (Geophysical Fluid Dynamics Laboratory) CORE2 (corrected normal year forcing version 2.0) normal year forcing. With some additional forcing as supplied by the UK Met Office (freshwater runoff, tidal friction, geothermal heating) and additional freshwater runoff to suppress polynya formation. Initialised from January of a climatology of ECCOv4r2 (Estimating the Circulation and Climate of the Ocean) in nominal year 1948.

Output files from an ICTP-MOM5 simulation driven by JRA55-do surface dataset following the OMIP-2 protocol (Griffies et al., 2016: https://doi.org/10.5194/gmd-9-3231-2016 ; Tsujino et al., 2020: https://doi.org/10.5194/gmd-13-3643-2020).

Datasets include monthly data for the sixth cycle from January 1958 until December 2021.

This is part 1 of 2.

Part 2 of this experiment is here: 10.5281/zenodo.14624388

The Japan Meteorological Agency (JMA) conducted JRA-55, the second Japanese global atmospheric reanalysis project. It covers 55 years, extending back to 1958, coinciding with the establishment of the global radiosonde observing system. Compared to its predecessor, JRA-25, JRA-55 is based on a new data assimilation and prediction system (DA) that improves many deficiencies found in the first Japanese reanalysis. These improvements have come about by implementing higher spatial resolution (TL319L60), a new radiation scheme, four-dimensional variational data assimilation (4D-Var) with Variational Bias Correction (VarBC) for satellite radiances, and introduction of greenhouse gases with time varying concentrations.

The entire JRA-55 production was completed in 2013, and thereafter will be continued on a real time basis. Specific early results of quality assessment of JRA-55 indicate that a large temperature bias in the lower stratosphere has been significantly reduced compared to JRA-25 through a combination of the new radiation scheme and application of VarBC (which also reduces unrealistic temperature variations). In addition, a dry land surface anomaly in the Amazon basin has been mitigated, and overall forecast scores are much improved over JRA-25.

Most of the observational data employed in JRA-55 are those used in JRA-25. Additionally, newly reprocessed METEOSAT and GMS data were supplied by EUMETSAT and MSC/JMA respectively. Snow depth data over the United States, Russia and Mongolia were supplied by UCAR, RIHMI and IMH respectively. The Data Support Section (DSS) at NCAR has processed the 1.25 degree version of JRA-55 with the RDA (Research Data Archive) archiving and metadata system. The model resolution data has also been acquired, archived and processed as well, including transformation of the TL319L60 grid to a regular latitude-longitude Gaussian grid (320 latitudes by 640 longitudes, nominally 0.5625 degree). All RDA JRA-55 data is available for internet download, including complete subsetting and data format conversion services.

Datasets for the Geoscientific Model Development publication: "Impact of horizontal resolution on global ocean-sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2)"

Abstract: This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea-ice simulations that are obtained following the OMIP-2 protocol (Griffies et al., 2016) and integrated for one cycle (1958-2018) of the JRA55-do atmospheric state and runoff dataset (Tsujino et al., 2018). Our goal is to assess the robustness of climate-relevant improvements in ocean simulations (mean and variability) associated with moving from coarse (~1º) to eddy-resolving (~0.1º) horizontal resolutions. The models are diverse in their numerics and parameterizations, but each low-resolution and high-resolution pair of models is matched so as to isolate, to the 20 extent possible, the effects of horizontal resolution. A variety of observational datasets are used to assess the fidelity of simulated temperature and salinity, sea surface height, kinetic energy, heat and volume transports, and sea ice distribution. This paper provides a crucial benchmark for future studies comparing and improving different schemes in any of the models used in this study or similar ones. The biases in the low-resolution simulations are familiar and their gross features – position, strength, and variability of western boundary currents, equatorial currents, and Antarctic Circumpolar Current – are 25 significantly improved in the high-resolution models. However, despite the fact that the high-resolution models “resolve’’ most of these features, the improvements in temperature or salinity are inconsistent among the different model families and some regions show increased bias over their low-resolution counterparts. Greatly enhanced horizontal resolution does not deliver unambiguous bias improvement in all regions for all models.

This dataset represents a high-resolution (1/12° in the horizontal) regional ocean simulation of the North Atlantic, designed to explore the impact of atmospheric winds magnitude and variability on subsynoptic (<2 days) and synoptic (2-10 days) timescales on the ocean circulation in the region. The MITgcm (Massachusetts Institute of Technology General Circulation Model) ocean general circulation model was used, forced by repeated year forcing conditions from JRA55-do, corresponding to 1 May 2003 to 1 May 2004, and lateral boundary conditions from Arctic STate Estimate (ASTE) dataset (Nguyen et al., 2021), all these experiments lasted for 15 years. In total, this dataset contains three sensitivity experiments: (1) WIND_LF_SYNOP - where the variability of surface forcing winds filtered out on periods shorter than 2 days; (2) WIND_LF - where the synoptic plus higher frequency variability of surface forcing winds were filtered out; and (3) WIND_LF_SCALED - experiment where the synoptic and higher frequency variability were filtered out; however, the magnitude of the wind speed was scaled so the total wind energy input was the same as in the control run forced by reanalysis winds. Full 3D U/V/T/S fields and 2D fields containing information about mixed layer depth, sea ice, sea surface height, heat and freshwater surface fluxes are provided every 5 days. The outputs were generated by the University of Oxford under the Natural Environment Research Council (NERC) project SNAP-DRAGON: Subpolar North Atlantic Processes - Dynamics and pRedictability of vAriability in Gyre and OverturNing (grant reference NE/T013494/1).

Gross Primary Production of Antarctic Landfast Sea Ice: A Model-Based Estimate

These are the input and outputs containing gross primary production of Antarctic landfast sea ice (fast ice) data used in the paper " Gross Primary Production of Antarctic Landfast Sea Ice: A Model-Based Estimate" by Wongpan et al.

There are required inputs and processed outputs from the 1-dimensional Louvain-la-Neuve Sea Ice Model (LIM1D). The model was configured to model Antarctic fast ice, assumed to form in situ with its spatial distribution prescribed from the recent satellite-derived fast ice product of Fraser et al. (2020), with an initial thickness of 0.05 m (Wongpan et al., 2021), and evolving with a 1–h time step. LIM1D represents the ice column as ten layers with equal thickness plus one additional snow layer. Four categories of physical processes are implemented: sea-ice growth and melt, thermal diffusion, brine dynamics, and radiative transfer. Photosynthesis was limited by light and macro-nutrient availability, temperature, and brine salinity. A full description of LIM1D is given in Vancoppenolle et al. (2010), Moreau et al. (2015) and Vancoppenolle and Tedesco (2016). We followed initialization and parameterizations as described in Lim et al. (2019) suggesting that ice algae (represented as diatoms) have higher silicate half-saturation constants (KSi) than pelagic diatom species. The model can be downloaded from http://forge.ipsl.jussieu.fr/lim1d revision #3.26.

The Japanese 55-year atmospheric reanalysis product for driving ocean-sea ice models (JRA55-do; Tsujino et al., 2018) was used as surface forcing, selected because of its high resolution and development for forcing ocean and sea-ice models of the Ocean Model Intercomparison Project phase 2 (OMIP-2; Tsujino et al., 2020). To avoid truncation of extremely low air temperatures applied in JRA55-do around the Antarctic coast as a function of time and latitude(Large and Yeager, 2004; Tsujino et al., 2018), JRA55-do temperatures were replaced with data from the fifth-generation European Centre for Medium-Range Weather Forecasts re-analysis (ERA5; Hersbach et al., 2018). A simulation (JRATEMP, Table 1) was also performed using the JRA55-do temperatures, despite their unrealistic truncation described above.

For 2005-2006, fast-ice pixels at a native resolution of 1 km from the satellite-based dataset of Fraser et al. (2020) were distributed into 1690 grid cells, matching JRA55-do’s 0.5625° grid. The details for each run are

CONTROL = RUN006 For all runs, each grid cell is divided into nine equal-area snow depth categories. For each category, snowfall is multiplied by a log-normally distributed, category-specific factor (0.102, 0.272, 0.427, 0.532, 0.721, 0.952, 1.310, 1.740, 3.310), in order to approach a log-normal snow depth distribution (see Table 1 in Saenz and Arrigo, 2014). Finally, primary production in the grid cell is the average of the nine equal-area productivities calculated with different snow depths. This experiment is considered the most realistic approach and is named CONTROL;

OHF = RUN004 This run used an oceanic heat flux of 30 W m–2 during summer which was derived from observations at Davis Station (Swadling, 1998).

KSI = RUN005 An experiment to test the effect of modified silicate half-saturation constants (KSI) from KSi = 50 μM in the CONTROL experiment (after Lim et al., 2019) to KSi = 3.9 μM in 176 the KSI experiment (after Sarthou et al., 2005).

OHF_KSI = RUN007 Combining OHF and KSI changed as above.

JRASNOW = RUN009 This run includes a prescribed subgrid scale snow thickness distribution, as CONTROL but using snow input directly from JRA55-do.

JRATEMP = RUN010 This run uses the JRA55-do temperatures

NOSUB = RUN006 without sub-grid-scale snow Spatially-uniform snow cover increasing linearly throughout the year at a rate of 0.29 m y–1 (as with CONTROL), but without subgrid-scale snow thickness distribution.

The folder tree is

├── INPUT │ ├── RUN004 │ ├── RUN005 │ ├── RUN006 │ ├── RUN007 │ ├── RUN009 │ └── RUN010 └── OUTPUT ├── RUN004 ├── RUN005 ├── RUN006 ├── RUN007 ├── RUN009 └── RUN010

References

Fraser, A. D., Massom, R. A., Ohshima, K. I., Willmes, S., Kappes, P. J., Cartwright, J., & Porter-Smith, R. (2020). High-resolution mapping of circum-Antarctic landfast sea ice distribution, 2000–2018. Earth System Science Data, 12(4), 2987-2999. http://doi.org/10.5194/essd-12-2987-2020 Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., et al. (2018). ERA5 hourly data on single levels from 1979 to present. Copernicus climate change service (c3s) climate data store (cds). Retrieved from https://doi.org/10.24381/cds.adbb2d47 Large, W. G., and Yeager, S. G. (2004). Diurnal to decadal global forcing for ocean and sea-ice models: The data sets and flux climatologies. In: National Center for Atmospheric Research Boulder. Lim, S. M., Moreau, S., Van...

These files contain the gx1 JRA55do forcing data by year for CICE.

The latest information about CICE forcing data and files can be found at the GitHub Resource Index: https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index under the "Input Data" link. This is a dataset remapped from the original JRA55do dataset (Tsujino et al. 2018; https://doi.org/10.1016/j.ocemod.2018.07.002; version 1.5.0.1).

These data are provided by the CICE Consortium (https://github.com/CICE-Consortium).

This dataset represents a high-resolution (1/12° in the horizontal) regional ocean simulation of the North Atlantic, designed to serve as a control run for further sensitivity tests exploring how various changes in surface and lateral boundary conditions impact the ocean circulation in the region. The MITgcm (Massachusetts Institute of Technology General Circulation Model) ocean general circulation model was used, forced by repeated year forcing conditions from JRA55-do, corresponding to 1 May 2003 to 1 May 2004, and lateral boundary conditions from Arctic STate Estimate (ASTE) dataset (Nguyen et al., 2021). This dataset contains information from 15 years of experiments, and does not include 50 years of model spinup. Full 3D U/V/T/S fields and 2D fields containing information about mixed layer depth, sea ice, sea surface height, heat and freshwater surface fluxes are provided every 5 days. The outputs were generated by the University of Oxford under the Natural Environment Research Council (NERC) project SNAP-DRAGON: Subpolar North Atlantic Processes - Dynamics and pRedictability of vAriability in Gyre and OverturNing (grant reference NE/T013494/1).

In the Arctic, short-lived summer snowstorms can provide snow cover that can increase surface reflectivity and heat capacity. Despite their potential importance, little research has been done to understand the impact of summer snowstorms on basin-scale Arctic sea ice cover. Our observational analysis shows that a summer snowstorm event is accompanied by cyclonic ice drift, increases in surface albedo and surface air cooling that can persist for up to ~2 weeks, dampening sea ice loss. Specifically, multiple snowstorm events in a summer, on average, results in net increase in sea ice extent of ~0.2×106 km2 by early September. Experiments with a sophisticated ice-ocean model framework indicate that the initial expansion of sea ice extent is driven by cyclonic wind-driven ice drifts driving sea ice southwards and increasing albedo around the summer ice edge, however the thermal effects from the associated snowfall and atmospheric conditions result in a stronger overall impact on basin-avera...

This dataset contains the numerical model input files that were used to produce the model simulation presented in "Evaluation of a coupled ocean and sea-ice model (MOM6-NEP10k) over the Bering Sea and its sensitivity to turbulence decay scales", submitted to Geoscientific Model Development.

The Model boundary conditions, initial conditions, river forcing and grid file can be accessed from https://zenodo.org/records/13936479. Also attached here.

When using these files to run a model, most files should be placed inside a directory named INPUT/ that resides in the working directory where the model is being run. The following files should be at the top level in the working directory: data_table, diag_table, field_table, input.nml.

For large files, a subset in time is provided for the first year of the simulation. This dataset does not include the JRA55 atmospheric data, which can be downloaded directly from https://climate.mri-jma.go.jp/pub/ocean/JRA55-do/

Portions of the initial and boundary condition data were generated using E.U. Copernicus Marine Service Information:https://doi.org/10.48670/moi-00021. Refer to the manuscript for references to other data sources.

The annual mean of potential temperature at 250 m depth in 1995 simulated by an ocean model. The model is an ocean general circulation model named COCO. The initial condition of temperature and salinity is an observation-based data (ProjD; Ishii and Kimoto, 2009; Ishii et al., 2017). The sea surface boundary condition is applied based on the JRA55-do data set (Tsujino et al., 2018). The simulation is conducted from 1980, and temperature and salinity are nudged toward the ProjD for only the first 10 years (1980-1990). We have the result for the following two horizontal resolution models.

Files:

t_hires.nc: The high-resolution model. The horizontal resolution spacially varies (2-3 km in the Barents Sea Opening and Fram Strait, and 3-10 km in the Nordic and Barents Seas). The map of the horizontal resolution of the model is shown in Kawasaki and Hasumi (2016). t_lowres.nc: The low-resolution model. The horizontal resolution is about 1 degree. The tripolar coordinate is utilized. This topography is also employed in the MIROC6 (one of the CMIP6 models; Tatebe et al., 2019), whose ocean component is the COCO.

References:

Ishii, M., and M. Kimoto, 2009: Reevaluation of historical ocean heat content variations with time-varying XBT and MBT depth bias corrections. J. Oceanography, 65, 287-299. https://doi.org/10.1007/s10872-009-0027-7 Ishii, M., Y. Fukuda, S. Hirahara, S. Yasui, T. Suzuki, and K. Sato, 2017: Accuracy of Global Upper Ocean Heat Content Estimation Expected from Present Observational Data Sets. SOLA, 13, 163-167. https://doi.org/10.2151/sola.2017-030 Tsujino, H., et al. (30 authors), 2018: JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do). Ocean Modelling, 130, 79-139. https://doi.org/10.1016/j.ocemod.2018.07.002 Kawasaki, T., and H. Hasumi, 2016: The inflow of Atlantic water at the Fram Strait and its interannual variability. J. Geophys. Res. Oceans, 121, 502-519. https://doi.org/10.1002/2015JC011375 Tatebe, H., et al. (24 authors), 2019: Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6, Geosci. Model Dev., 12, 2727–2765. https://doi.org/10.5194/gmd-12-2727-2019

This is a 3 km resolution regional model (ROMS, Regional Ocean Modeling System) output of the Gulf of Mexico sea surface height [cm] from 1993-01-01 to 2007-10-13. The regional model domain includes the Gulf of Mexico and part of the Caribbean Sea and the Gulf Stream and run in an ocean-only uncoupled mode. The model has been initialized and forced at the lateral open boundaries with fields from the uncoupled global 10-km CESM (Community Earth System Model) FOSI (Forced Ocean Sea-Ice experiments) experiment. Surface atmospheric forcing for both the global CESM FOSI and the regional ROMS downscaling simulations is from the JRA55-do reanalysis dataset. The purpose of this downscaling simulation is to check the improvements in the Gulf of Mexico sea level and circulation brought in by a high-resolution regional downscaling method and to tune it if needed. Only the sea surface height field (as a 10-day average) is made available now; other fields such as temperature and currents will be added later.

This dataset provides the atmospheric forcing fields for conducting the projection experiment for the Ice Algae Model Intercomparison Project phase 2 (IAMIP2), which is a new model intercomparison effort focused on algae that are attached to sea ice.

It is based on the output of CMIP6 EC-Earth3, experiment scenario ssp585 and the r1i1p1f1 ensemble member (EC-EARTH Consortium, 2019). This forcing dataset consists of near-surface atmospheric variables simulated by EC-Earth3 under the CMIP6 SSP585 scenario over 2015-2100. The dataset is interpolated to the JRA55-do v1.4 grid (Tsujino, H. and al., 2018), to make it easy for using it in ocean and sea-ice models.

This dataset is a global product of 0.5-degree resolution near-surface atmospheric variables over the period 2015-2100. The variables included in the dataset are:

tas - surface air temperature

vas - surface northward wind

uas - surface eastward wind

huss - surface specific humidity

licalvf - land ice calving flux

psl - sea level pressure

rsds - surface downwelling shortwave radiation flux

rlds -surface downwelling longwave radiation flux

friver - water flux into sea water from rivers

pr - total precipitation flux

prra - rainfall flux

prsn - snowfall flux

The code to interpolate JRA55-do v1.4 is available from github and published on zenodo (Hayashida, H., 2021).

In particular:

• cdo -remapnn was used for the river runoff into sea water (friver) variable and cdo -remapcon for all others.

• rainfall flux (prra), which is not available from EC-Earth3, was derived from snowfall flux (prsn) and total precipitation (pr) summing rainfall and snowfall flux.

• the land ice calving flux (licalvf) is not available from EC-Earth3 so the original JRA55-do v1.4.0 values were prescribed for it.

This dataset was created by Dr Hakase Hayashida as part of the Centre of Excellence for Climate Extremes (CLEX) Climate variability and teleconnections research program.

Results of an eddy-rich ocean, sea-ice, carbon cycle model, with a nominal resolution of 1/10 degree, simulation covering the period 1980-2021 and focusing on changes in total, natural and anthropogenic CO2 fluxes in the Southern Ocean. The data includes: - natural and total CO2 fluxes averaged over years 1982-2021 - natural & total CO2 fluxes, and surface natural DIC for a composite of positive phases of the SAM and negative phases of the SAM - natural and total DIC distributions averaged over years 1980-1982 and 2017-2021

Seven simulations
three are conventional ECCO runs with climatology river runoff
LLC90-Fekete (aka ECCO-V4R4)
LLC270-Fekete (aka ECCO-LLC270)
CS510-Stammer (aka ECCO2)
four are same as above, except for JRA55-DO daily river runoff
LLC90-JRA55DO
LLC270-JRA55DO
LLC540-JRA55DO
CS510-JRA55DO

PART 1 MODEL SET-UP

============
LLC90-Fekete
============
https://ecco.jpl.nasa.gov/drive/files/Version4/Release4/doc/v4r4_reproduction_howto.pdf

==============
LLC90-JRA55DO
==============
similar to LLC90-Fekete except for JRA55DO runoff
#define CHECK_SALINITY_FOR_NEGATIVE_VALUES @CPP_OPTIONS.h
river runoff forcing from "LLC90-JRA55DO.tar", pickup from "LLC90-JRA55DO-PICKUP.tar"

(each "JRA55DO.tar" contains 2015-2017 daily river runoff and "data.exf" for specification of JRA55DO river runoff; each "PICKUP.tar" contains pickup files for starting from 2015/01/01 and "data" for specification of initial conditions and model parameters)

=============
LLC270-Fekete
=============
https://github.com/MITgcm-contrib/ecco_darwin/blob/master/v04/llc270_JAMES_paper/readme/readme_physics.txt

==============
LLC270-JRA55DO
==============
https://github.com/MITgcm-contrib/ecco_darwin/blob/master/v05/llc270_jra55do/readme.txt
river runoff temperature NOT used here:
#undef ALLOW_RUNOFTEMP @EXF_OPTIONS.h
delete "runoftempfile" @data.exf
river runoff forcing from "LLC270-JRA55DO.tar", pickup from "LLC270-JRA55DO-PICKUP.tar"

==============
LLC540-JRA55DO
==============
https://github.com/MITgcm-contrib/llc_hires/blob/master/llc_540/readme
https://github.com/MITgcm-contrib/llc_hires/blob/master/llc_540/readme#L16
river runoff forcing from "LLC540-JRA55DO.tar", pickup from "LLC540-JRA55DO-PICKUP.tar"

=============
CS510-Stammer
=============
http://wwwcvs.mitgcm.org/viewvc/MITgcm/MITgcm_contrib/high_res_cube/cube92/README.cs510?revision=1.11&view=co

=============
CS510-JRA55DO
=============
similar to CS510-Stammer except for JRA55DO runoff
#define CHECK_SALINITY_FOR_NEGATIVE_VALUES @CPP_OPTIONS.h
#define USE_NO_INTERP_RUNOFF @EXF_OPTIONS.h
river runoff forcing from "CS510-JRA55DO.tar", pickup from "CS510-JRA55DO-PICKUP.tar"

PART 2 MODEL OUTPUT
LLC###_${river}_SSS.mat
where ### for 90, 270, or 540
and ${river} for eight major rivers in the world.
each "mat" file contains the daily mean SSS over 2015-2017 near the corresponding river.