Bern3D model output data from idealized co2 increase-decrease simulations to investigate reversibility in the Earth system

• Main Authors: Jeltsch-Thömmes, Aurich, Joos, Fortunat
• Format: Dataset
• Language: unknown
• Published: 2023
• Subjects: K5; Sea ice
• Online Access: https://zenodo.org/record/8224373; https://doi.org/10.5281/zenodo.8224373

The data described below is output from the Bern3D intermediate complexity model and idealized CO2 increase-decrease simulations to investigate reversibilty and hysteresis for different maximum co2 forcings. The data are provided as .csv and .nc files The first row in the .csv files contains the header, which describes the variable. The naming convention is as follows: c#k#_VARIABLE c# indicates the maximum co2 as times pre-industrial (c2 to c5) k# indicates the equilibrium climate sensitivity of the respective simulation in degrees C (k2 to k5) and VARIABLE indicates the value of the respective variable, which are: co2: change in atmospheric co2 concentration in [ppm] amoc: change in maximum of the Atlantic meridional overturning circulation in [Sv] ohc: change in ocean heat content in [10^24 J] seaice: sea-ice area remaining as fraction of the pre-industrial cover Om_arag: fraction of water with Omega_arag > 3 in the upper 175 m o2_thermo: change in thermocline (200-600 m) oxygen concentration in [mmol m^-3] for each variable a separate file exists where the variable and co2 are provided. Spatial data to create the maps of hysteresis on a grid-cell basis are provided for the two scenarios as .nc files. The naming is as follows: c#k#_hyst_o2thermo.nc where c# corresponds again to maximum co2 as times pre-industrial and k# to the equilibrium climate sensitivity. The .nc files contain the coordinate (latitude, longitude) centers (lat_t, lon_t) and edges (lat_u, lon_u) as well as the hysteresis area (hystA_o2thermo) in [mmol m^-3]. The files can be readily importet in python, for example, by: import pandas as pd import xarray as xr # for the .csv files df = pd.read_csv('path+filename', sep=',', header=0, index_col=None) # for the .nc files ds = xr.open_dataset('path+filename') For additional information or in case of questions please contact: Aurich Jeltsch-Thömmes [e-mail]