Atmospheric excitation of length of day inferred from 21st century climate projections (CMIP6 multi-model ensemble results)

Results of the study "Atmospheric excitation of length of day inferred from 21st century climate projections"

This dataset contains the results of a study presented in the scientific article "Atmospheric excitation of length of day inferred from 21st century climate projections", which is published in Journal of Geophysical Research: Atmospheres.

Context and methodology

• The study investigates trends in atmospheric angular momentum in historical and 21st century simulations using a CMIP6 multi-model ensemble.
• The dataset serves as a public resource in the sense of open research; it provides all results of the above-mentioned study. 
• The dataset has been created using standard functions of MATLAB R2023b.

Technical details

• The zip file contains five MATLAB files Boehm_Salstein_aamwind_lod.mat, Boehm_Salstein_aampressure_lod.mat, Boehm_Salstein_aamtotal_lod.mat, Boehm_Salstein_surface_temperature.mat, Boehm_Salstein_zonalmean_zonalwind.mat, Boehm_Salstein_aam2D_preslat_ring.mat that host five MATLAB structure arrays described in the following. Each structure has six fields for each simulation type: historical, ssp119, ssp126, ssp245, ssp370, ssp585; some also have a field times with the epochs of the time series or amplitudes given inside the fields. The simulation type fields have one line per model used in the study (11+1 for the multi-model mean). For each model different sets of parameters or results are present.
  
  • aamwind_lod, aampressure_lod, aamtotal_lod: atmospheric angular momentum (AAM) in equivalent length of day (LOD) units (ms). Provided are ensemble means, arrays of ensemble member AAM results, base values for calculating anomalies with respect to 1976-2014, smoothed anomalies, centennial trends including standard deviations and p-values, mean annual amplitudes, and time dependent annual amplitudes.
  • surftemp: time series of global surface temperature in degree Celsius, provided are ensemble means and ensemble member results and the same for annual averages.
  • zmzonalwind: trends in zonal mean zonal wind speed (m s^-1 cy^-1) as a function of latitude and pressure. Latitude and pressure levels are provided as well as the trends interpolated to the coarsest grid and the intermodel standard deviation and model agreement.
  • aam2D_preslat: same parameters as for zmzonalwind but in terms of angular momentum per pressure/latitude ring (kg m^2 s^-1 cy^-1).
• The data can be accessed with MATLAB, but there are also Python functions to load MATLAB structure arrays (not tested in this case).