Model agreement and trend analysis data associated to the publication: "Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050" (Q6709585)

This dataset is associated with the followingpublication: Haslebacher, C., Demory, M.-E., Demory, B.-O., Sarazin, M., and Vidale, P. L., Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050. Projected increase in temperature and humidity leads to poorer astronomical observing conditions, Astronomy and Astrophysics, vol. 665, 2022. doi:10.1051/0004-6361/202142493. In the folder model_agreement, there are pickle files from which a python dictionary can be extracted with: with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile) Pickle files ending with _d_obs_ERA5.pkl contain in situ data and ERA5 data. Pickle files ending with d_model.pkl contain PRIMAVERA model data. A few explanations: - ds_sel: contains monthly timeseries of selected intersecting data - ds_taylor: contains data used for the Taylor diagram(Figs. 4-10) - ds_mean_month: contains seasonal cyclefor plotting (Figs. 4-10) -ds_mean_year: contains yearly timeseries for plotting (Figs. 4-10) The subfolder median_nc_u_v_t contains NETCDF files with the median and interquartile range of the wind speed in u and v direction, the temperature and geopotential height. This was used for Figs. G1-G8 and to calculate the refractive index structure constant Cn2. The subfolder skill_score_classification contains csv files with the sorted skill score classifications. The column headers are: model_name, skill score, correlation coefficient, standard deviation, centred root mean square error. The folder trend_analysis contains for each variable csv files of ERA5 and PRIMAVERA monthly time series used fortrend analysis, pdf files of analysis summaries, csv files of Bayesian analysis results and png files of longitude-latitude maps of trends (analysed with linear regression). Additionally, there is a csv file ofaveraged in situ pressures. Code that generated and used this datais available on github:https://github.com/CarolineHaslebacher/Astroclimate-future-project