Accompanying data to "Could an extremely cold central European winter such as 1963 happen again despite climate change?"

Accompanying data to "Could an extremely cold central European winter such as 1963 happen again despite climate change?"

16.07.2024 This repository contains data that underlies the following publication:

Sippel, S., Barnes, C., Cadiou, C., Fischer, E., Kew, S., Kretschmer, M., Philip, S., Shepherd, T. G., Singh, J., Vautard, R., and Yiou, P.: Could an extremely cold central European winter such as 1963 happen again despite climate change? Weather and Climate Dynamics (accepted), 2024. Preprint: https://doi.org/10.5194/egusphere-2023-2523.

This repository is a data collection, which contains simulated extremely cold Central European winter storylines. Climate model simulations use the technique of climate model boosting, and statistical generation using stochastic weather generators (SWG) empirical importance sampling. The repository contains the following data files:

(1) Climate model ensemble boosting for extremely cold winter storylines. 

Zip file BSSP370cmip6.0000013.zip: Contains all 750 files of the first-order boosting. First order boosting is based on ensemble member 21 in the CESM2-ETH ensemble, and with restart dates between 01.12 and 15.12.2022 (SSP3-70 scenario), with 50 members for each starting date. Example file: BSSP370cmip6.0000013.2022-12-06.ens023.cam.h1.2022-12-07-00000.nc

The boosting files follow a naming convention: 

BSSP370cmip6 all files based on CMIP6 SSP3-70 forcing.

2022-12-06 starting date of the respective ensemble member.

0000013 Ensemble member of CESM2-ETH that was used for boosting (i.e. member 13 of CESM2-ETH).

ens023 Ensemble member of the boosted ensemble (i.e. member 23 with starting date 06.12.2022).

The second-order boosting was branched off from first-order boosting file BSSP370cmip6.0000013.2022-12-06.ens023.cam.h1.2022-12-07-00000.nc.

Zip file BSSP370cmip6.0230013.zip: Contains all 750 files of the first set of second-order boosting simulations. All these simulations are based on first-order boosting file  BSSP370cmip6.0000013.2022-12-06.ens023.cam.h1.2022-12-07-00000.nc. That is, the first-order boosting file started from ensemble member 13 of CESM2-ETH, starting date 06.12.2022 and ensemble member 23 of the first-order boosted ensemble. The second-order boosting file shown in Figs. 5-6 is the file BSSP370cmip6.0230013.2023-01-08.ens047.cam.h1.2023-01-09-00000.nc. That is, ensemble member 47 in second-order boosting ensemble from starting date 08.01.2023. 

Zip file BSSP370cmip6.0480013.zip: Contains all 750 files of the second set of second-order boosting simulations. All these simulations are based on first-order boosting file  BSSP370cmip6.0000013.2022-12-15.ens048.cam.h1.2022-12-16-00000.nc. That is, the first-order boosting file started from ensemble member 13 of CESM2-ETH, starting date 15.12.2022 and ensemble member 48 of the first-order boosted ensemble. The second-order boosting file shown in Figs. 5-6 is the file BSSP370cmip6.0480013.2023-01-08.ens032.cam.h1.2023-01-09-00000.nc. That is, ensemble member 32 in second-order boosting ensemble from starting date 08.01.2023. 

(2) CESM2 maps of extremely cold winters (to generate Fig. 5)

* Zip file cesm2_maps.zip. Contains the following entries, all for DJF average anomalies (relative to the ensemble average climatology):

- tas_ssp370_r2i1p1.2005-2035_anom.nc

- tas_ssp370_r12i1p1.2005-2035_anom.nc

Two members (r2i1p1 in 2008, r12i1p1 in 2007) from the CESM2-ETH ensemble, which produce very cold winters. Variables tas (surface air temperature), Z500 (geopotential height at 500 hPa), FSDS (surface downwelling shortwave radiation), and FSNS (surface net shortwave radiation) are available (FSDS and FSNS to calculate albedo). 

- tas_ssp370_0230013.2023-01-08.ens047_anom.nc

- tas_ssp370_0480013.2023-01-08.ens032_anom.nc

The two extremely cold boosted winters as described above, concatenated with their parent files from boosting. 

(3) Storylines of extremely cold winters generated via Stochastic weather generator (SWG) empirical importance sampling

SWG-empirical-importance-sampling.zip Storylines of extremely cold winters generated via Stochastic weather generator (SWG) empirical importance sampling (Yiou and Jézéquel, 2020, https://doi.org/10.5194/gmd-13-763-2020). The available maps are seasonal average anomalies resampled from ERA5 (to generate Fig. 5):

Surface air temperature: t2m_WEGE_germany_1963_1972-2021_DJFmean.nc

Albedo: fal_WEGE_germany_1963_1972-2021_DJFmean.nc

z500: z500_WEGE_germany_1963_1972-2021_DJFmean.nc