The Reliability of Trends in Convective Parameters from Single Reanalysis Datasets

This dataset contains global trend analysis data for atmospheric convective parameters, provided as supplementary material for the manuscript "The Reliability of Trends in Convective Parameters from Single Reanalysis Datasets". It includes calculated trends and statistical significance metrics derived from three major global reanalysis datasets: ERA5, MERRA2, and JRA3Q. The provided NetCDF files have been subsetted to focus on the key variables, thresholds, and modified statistical significance tests discussed in the paper. The files contain trend slopes (e.g., _slope) and modified p-values (e.g., _pval_mod) computed using the Hamed-Rao modified Mann-Kendall test to account for autocorrelation. Original, unadjusted significance tests have been excluded to focus on the methodology presented in the manuscript. The base convective and thermodynamic parameters analyzed include: CAPE / Convective Available Potential Energy: CAPES06, cape_ml500 CIN / Convective Inhibition: cin_ml500 Shear: S01 (0-1 km), S06 (0-6 km) Thermodynamics: T_ml500 (Temperature), Q_ml500 (Specific Humidity), LAPSE24 (Lapse Rate) Precipitation/Convective Precipitation: CP The data is sliced along a threshold dimension to evaluate trends in extreme or threshold-specific convective environments. The included thresholds are: CAPE>0 CAPE>1000 S06>0 S06>10 CAPE>1000&S06>10 (representing severe convective environments) In addition to the base parameters, this repository contains three files detailing trends in threshold exceedances: ERA5_Exceedances_Trends_Paper.nc MERRA2_Exceedances_Trends_Paper.nc JRA3Q_Exceedances_Trends_Paper.nc These files capture the frequency and trends of concurrent extremes (e.g., specific thresholds of convective precipitation coupled with severe CAPE and Deep Layer Shear environments, such as cp5_cape1000_s06_10). Like the base parameter files, these are subsetted to contain only the modified Hamed-Rao significance metrics. The data is formatted as standard CF-compliant NetCDF files. They can be easily opened and manipulated using Python (xarray, netCDF4), R (ncdf4), or command-line tools like CDO (Climate Data Operators) and NCO (NetCDF Operators).