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# Output from impact experiment for estimating the patter effect of SST trend
#                                                S.Yukimoto 2025/6/5
#
# Description of the Experiment
As an example of how feedback patterns affect SST trends, we conducted AGCM experiments 
with MRI-ESM2.0 (Yukimoto et al., 2019), one of the CMIP6 models. In the control run, 
we prescribed the observed 2010–2023 SST climatology. In the forced run, we added the 
annual‐mean SST anomaly pattern scaled to 1 K of global warming to that climatology.
Both runs used the 2010–2023 mean sea‐ice climatology, preindustrial GHGs, constant 
aerosol emissions, and other forcings. Each integration spanned 31 years, and we 
computed feedback strength λΔSST from the difference between the final 30-year means.

# Experiment Name
Control run : sstClim_control
Forced run  : sstClim_trend

# Experiment Period
1850-1880 (31 years)

# Variables
tas   : Surface (2-m) Air Temperature [K]
net   : Net TOA Radiative Flux for Total-sky  [W m-2 K-1]
swt   : Shortwave TOA Radiative Flux for Total-sky  [W m-2 K-1]
lwt   : Longwave TOA Radiative Flux for Total-sky  [W m-2 K-1]
cnet  : Net TOA Cloud Radiative Effect  [W m-2 K-1]
cswt  : Shortwave TOA Cloud Radiative Effect  [W m-2 K-1]
clwt  : Longwave TOA Cloud Radiative Effect  [W m-2 K-1]
netcs : Net TOA Radiative Flux for Clear-sky  [W m-2 K-1]
swcs  : Shortwave TOA Radiative Flux for Clear-sky  [W m-2 K-1]
lwcs  : Longwave TOA Radiative Flux for Clear-sky  [W m-2 K-1]
# all fluxes are positive downward

# File Format
NetCDF-4
horizontal grid : lon=320 x lat=160