Overview: This repository contains output from the NASA Eulerian Snow On Sea Ice Model (NESOSIM; Petty et al., 2018; available at https://zenodo.org/doi/10.5281/zenodo.4448355 ) calibrated to snow depth and density observations using a Markov chain Monte Carlo (MCMC) approach (Cabaj et al. 2023; available at https://zenodo.org/doi/10.5281/zenodo.7644947 ) with ECMWF ERA5 (Hersbach et al., 2020), NASA GMAO MERRA-2 (Gelaro et al., 2017), and JMA JRA-55 (Kobayashi et al., 2015) reanalysis snowfall inputs, for the 1980-2020 time period. Reanalysis snowfall input used with NESOSIM is scaled to scaling factors calculated from CloudSat-derived monthly snowfall climatologies, interpolated over the NESOSIM model domain (Cabaj et al. 2020, 2023). The MERRA-2 and JRA-55 reanalysis inputs and CloudSat scaling factors are included in this repository. Other forcing data used as inputs to NESOSIM to generate this output are provided in https://zenodo.org/records/7051062 . The NESOSIM-MCMC-Average product is a snow-on-sea-ice product constructed from the average of MCMC-calibrated NESOSIM outputs when the model is run with CloudSat-scaled snowfall from ERA5, MERRA-2, and JRA55. This product also includes an estimate of uncertainty due to model parameter uncertainties (derived from the MCMC calibration process run for each reanalysis product) and due to differences between reanalysis snowfall products providing snowfall input to NESOSIM. Snow depth and bulk snow density, with associated uncertainty estimates, are provided. Repository Structure MERRA2.zip and JRA55.zip: Contain MERRA-2 and JRA-55 (respectively) reanalysis snowfall data regridded for use as forcing input to NESOSIM, from 1980-2020. This data is stored as daily binary NumPy files (the default NESOSIM input format) and does not have CloudSat scaling applied. Corresponding ERA5 snowfall for NESOSIM input is available at https://zenodo.org/records/7051062. CloudSat_Scaling_Factors.zip: Contains netCDF files with monthly scaling factors generated from the monthly climatology of the CloudSat 2C-SNOW-PROFILE product, version P1_R05 (Wood et al., 2013, 2014; scaling method cf. Cabaj et al., 2020) to be applied to ERA5, MERRA-2, and JRA-55 snowfall in NESOSIM. To be placed in the anc_data folder when running the model. NESOSIM_MCMC-*.zip: NESOSIM output data from 1980-2020 for the model when MCMC-calibrated (following the approach in Cabaj et al., 2023) with snowfall input from ERA5, MERRA-2, and JRA-55, respectively. The NESOSIM-MCMC-Average product (calculated as the average of the outputs) is also included. Within each zip file, model output is included in the 'Output' subdirectory, and snow depth and density uncertainties estimated from the ensemble-propagated spread of the posterior MCMC distributions (cf. Cabaj et al., 2023) are included in the 'Uncertainty' subdirectory. For the multi-product average, uncertainties are calculated as the combined standard deviation of the three separate output ensembles, and are provided in separate files for snow depth and snow density. All output is stored in netCDF format. References: Cabaj, A., P. J. Kushner, C. G. Fletcher, S. Howell, A. Petty (2020), Constraining reanalysis snowfall over the Arctic Ocean using CloudSat observations, Geophysical Research Letters, 47, doi:10.1029/2019GL086426. Cabaj, A., P. J. Kushner, A. A. Petty (2023), Automated calibration of a snow-on-sea-ice model. Earth and Space Science, 10, doi:10.1029/2022EA002655. Gelaro, R. et al. (2017), The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Journal of Climate, 30, 5419–5454, doi:10.1175/JCLI-D-16-0758.1. Hersbach, H. et al. (2020), The ERA5 Global Reanalysis, Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, doi:10.1002/qj.3803. Kobayashi, S. et al. (2015), The JRA-55 Reanalysis: General Specifications and Basic Characteristics, Journal of the Meteorological Society of Japan, 93, 5–48, doi:10.2151/jmsj.2015-001. Petty, A. A., M. Webster, L. N. Boisvert, T. Markus (2018), The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: Initial model development and analysis, Geosci. Model Dev., doi: 10.5194/gmd-11-4577-2018. Wood, N. B., T. S. L'Ecuyer, A. J. Heymsfield, G. L. Stephens, D. R. Hudak, P. Rodrigues (2014), Estimating snow microphysical properties using collocated multisensor observations. J. Geophys. Res. Atmos., 119, 8941-8961, doi:10.1002/2013JD021303. Wood, N. B., T. S. L'Ecuyer, F. L. Bliven, and G. L. Stephens (2013), Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity, Atmos. Meas. Tech., 6, 3635-3648, doi:10.5194/amt-6-3635-2013.