| Summary: | This data set is model output derived from numerical simulationsusing MIROC6-AGCM(Onuma et al., in review). The contents areas below. - python : Python scripts for the visualization - figure : PNG files created by the Python scripts - data : model output data for the Python scripts. The data directory includes the below contents. -- LMIP : Output data simulated using MATSIRO6 with GSWP3 reanalysis data (LMIP experiment) are included. Monthly time-series data and regional averages for 1901-2010 derived from LMIP experiment (netCDF and csv files, respectively). -- AMIP : Output data simulated using MIROC6 with prescribed sea surface temperature and sea ice concentration (AMIP experiment) are included. Monthly time-series data and regional averages for 1901-2010 derived from AMIP ensemble experiments (netCDF and csv files, respectively). -- AMIP+SWE : Output data simulated using MIROC6 with prescribed sea surface temperature, sea ice concentration and snow water equivalent (AMIP+SWE experiment) are included. Monthly time-series data and regional averages for 1901-2010 derived from AMIP+SWE ensemble experiments (netCDF and csv files, respectively). Difference in a surface energy budget between AMIP+SWE and AMIP is also included (feedback_*.csv). -- AS-A : Statistical results analyzed from output data in AMIP+SWE and AMIP experiments (csv files). R_a (R_b) means the correlation coefficients of the outputs in AMIP+SWE (AMIP) for inter-annual change from 1901 to 2010 with those in LMIP. P means p-value. RMSD_a (RMSD_b) means the root mean square difference of the outputs in AMIP+SWE (AMIP) for inter-annual change from 1901 to 2010 with those in LMIP. Tr_a (Tr_b, Tr_ref) means the inter-annual trends from 1901 to 2010 in AMIP+SWE (AMIP, LMIP). -- else : CMIP6 amip multi-models output, GlobSnow3 observation data and mask files for the regional averages. CMIP6 and GlobSnow3 data have been processed for analysis. *Regarding AMIP and AMIP+SWE data, the uploaded data are limited to ensemble mean data and run01 data due to ...
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