Data for "Increasing Frequency and Persistence of the Summertime Greenland High Regime Not Captured by a Seasonal Prediction Model Very Large Ensemble"

These data support the work presented in Lee & Polvani (2025) "Increasing Frequency and Persistence of the Summertime Greenland High Regime Not Captured by a Seasonal Prediction Model Very Large Ensemble", published in Geophysical Research Letters: https://doi.org/10.1029/2025GL119421 This work is based on the year-round weather regime classification introduced by Lee et al. (2023) and Lee & Messori (2024), with slight modifications as outlined in Lee & Polvani (2025). era5_1981_2024_year_round_north_america_weather_regimes_running_mean.nc The ERA5 daily weather regime classification for 1981–2024 and associated parameters. The weather regime index (WRI) is also supplied here following Lee & Messori (2024). Dimensions: latitude = 61 longitude = 151 time = 16060 eof_number = 12 regime = 5 trend_params = 2 doy = 365 Variables: latitude(latitude) longitude(longitude) time(time) (units = "days since 1970-01-01") z_anoms_detrend(time, latitude, longitude); units = "gpm" ; long_name = "Detrended Z500 anomalies 1981-2024 ERA5 climate" z_norm(time, latitude, longitude) ; units = "std" ; long_name = "Z500 anomalies normalized by seasonal cycle of area-averaged standard deviation" z_climo(doy, latitude, longitude) ; units = "gpm" ; long_name = "Z500 climatology 1981-2024 ERA5" trend(doy, trend_params) ; units = "m per day since 1981-01-01" ; long_name = "60d smoothed linear trend slope & intercept,1981-2024" norm_factor(doy) ; long_name = "Variance normalization factor" pc(time, eof_number) ; units = "raw pc" ; long_name = "pc time series" eof(eof_number, latitude, longitude) ; long_name = "raw EOFs" eigs(eof_number) ; long_name = "eigenvalues" var_frac(eof_number) ; long_name = "variance fraction" regime(time) ; long_name = "Daily regime attribution" WRI(regime, time) ; units = "std" ; long_name = "Weather regime index" WRI_mean(regime) ; long_name = "WRI mean" WRI_std(regime) ; long_name = "WRI standard deviation" cluster_mean_norm(regime, latitude, longitude) ; units = "std" ; long_name = "Cluster-mean normalized Z500 anomalies" double cluster_mean(regime, latitude, longitude) ; units = "gpm" ; long_name = "Cluster-mean Z500 anomalies" double cluster_centroids(regime, eof_number) ; long_name = "Cluster centroids in PC space" double dist_to_regime_centroid(time) ; long_name = "Distance to centroid of assigned regime" seas5_may_1981_2024_jja_year_round_north_america_weather_regimes_monte_carlo_10000_member.nc The weather regime classification for 1 June–31 August using a Monte Carlo/random sampling of ECMWF's SEAS5 hindcasts & forecasts from 1981–2024. Variables are defined similarly to the ERA5 dataset above. Dimensions: lead_time = 92 year = 44 ens_member = 10000 cluster = 5 pc = 12 trend_parameter = 2 Variables: year(year) lead_time(lead_time) (units = "days since 1 May") ens_member(ens_member) regime(ens_member, year, lead_time) # Daily regime attribution wri(ens_member, year, lead_time, cluster) # Weather regime index pcs(ens_member, year, lead_time, pc) # PCs obtained from projecting onto ERA5 EOFs norm_factor(ens_member, lead_time) # variance normalisation factor trend(ens_member, lead_time, trend_parameter) # Z500 trend in each ensemble member (slope + intercept) member_id(ens_member, year) # this corresponds to the true ensemble member selected in each random sampling