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Observation and model data for locations Fendt (DE-Fen), Rottenbuch (DE-RbW) and Graswang (DE-Gwg), in conjunction with selected journal publications. These data have primarily been used for investigation of surface carbon fluxes (Net Ecosystem Exchange, Gross Primary Productivity), seasonal climatic trends and land management. The sites are part of TERENO, a network of observatories in Germany. The TERENO Data Portal should provide other and more up-to-date information. The time period includes the ScaleX intensive observation campaigns that took place in 2015 and 2016. The data format is NetCDF4. A Jupyter notebook is available (see Related identifiers, GitLab) with technical notes and examples.

Archetypes are increasingly used as a methodological approach to understand recurrent patterns in variables and processes that shape the sustainability of social-ecological systems. The rapid growth and diversification of archetype analyses has generated variations, inconsistencies, and confusion about the meanings, potential, and limitations of archetypes. Based on a systematic review, a survey, and a workshop series, we provide a consolidated perspective on the core features and diverse meanings of archetype analysis in sustainability research, the motivations behind it, and its policy relevance. We identify three core features of archetype analysis: Recurrent patterns, multiple models, and intermediate abstraction. Two gradients help to apprehend the variety of meanings of archetype analysis that sustainability researchers have developed: (1) understanding archetypes as building blocks or as case typologies and (2) using archetypes for pattern recognition, diagnosis, or scenario development. We demonstrate how archetype analysis has been used to synthesize results from case studies, bridge the gap between global narratives and local realities, foster methodological interplay, and transfer knowledge about sustainability strategies across cases. We also critically examine the potential and limitations of archetype analysis in supporting evidence-based policy making through context-sensitive generalizations with case-level empirical validity. Finally, we identify future priorities, with a view to leveraging the full potential of archetype analysis for supporting sustainable development.

This dataset is associated with the following publication: 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 cycle for 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 for trend 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 of averaged in situ pressures. Code that generated and used this data is available on github: https://github.com/CarolineHaslebacher/Astroclimate-future-project

Thermal vacuum deposition is a stimulating technique for upscaling perovskite solar cells. The operational complexity of multi-source vapor deposition (MSVD) in achieving stoichiometric perovskite layers may impede its implementation. The single-source vapor deposition (SSVD) method has recently emerged as an alternative for MSVD due to its inherent functionality. Here for the first time, we demonstrate the successful SSVD approach in fabricating pure CsFAPbI3 and FAPbI3 thin films from pre-synthesized perovskite powders. A series of structural, optoelectronic, and morphological investigations were carried out to optimize the phase purity and shelf-life of the deposited thin films. Furthermore, we probed the perovskite layer compatibility with various underlayers; we noted the organic underlayer displayed superiority over the metal oxide layer, suggesting the suitability of p-i-n devices over the n-i-p.