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PV-gradient tropopause time series General description These datasets contain time series of the PV-gradient tropopause (PVG tropopause) introduced by A. Kunz (2011, doi:10.1029/2010JD014343) and calculated by K. Turhal (2024, paper " Variability and Trends in the PVG Tropopause", preprint in EGUsphere: https://doi.org/10.5194/egusphere-2024-471). Data and methods The PVG tropopause has been computed by means of the Eddy Tracking Toolkit (developed by J. Clemens and K. Turhal, to be published): from four reanalyses: ERA5, ERA-Interim, MERRA-2 and JRA-55 for the time range 1980/01/01 -- 2017/12/31 in time steps of the according reanalyses, i.e. four times daily at 00h, 06h, 12h and 18h on each isentropic level, with potential temperatures (theta) ranging from 320 K to 380 K, in steps of 5 K for ERA5 and 10 K for the other reanalyses. Contents Datasets are provided for each year and isentropic level in NetCDF4 format, every file consisting of two groups for the northern and southern hemisphere. Each group contains the following variables, with time as dimension: time in seconds since 2000/01/01 00:00 UTC u_lim: Zonal wind speed at the PVG tropopause vh_lim: Horizontal wind speed at the PVG tropopause q_lim: Maximum of Q = vh * Grad PV eqlat_lim: Location of the PVG tropopause in equivalent latitudes latmean_lim: Location of the PVG tropopause in latitudes pv_lim: PV value at the PVG tropopause In this upload, the PVG tropopause time series are included as *.zip files: ERA5 dataset: "pvg-tp_era5_ts.zip" ERA-Interim dataset: "pvg-tp_eraint_ts.zip" MERRA-2 dataset: "pvg-tp_merra2_ts.zip" JRA-55 dataset: "pvg-tp_jra55_ts.zip" Plots of time series for each reanalysis of the variables eqlat_lim, latmean_lim and pv_lim: "pvg_tropopause_timeseries_plots.zip". How to use The variables in these netCDF files are grouped by hemisphere. To read in the data, specify the group first ("NorthernHemisphere" or "SouthernHemisphere") and then the variable name (see list above). In Python, this can be done as follows: import netCDF4 as nc file="" d = nc.Dataset(file) # read in a variable. Syntax: d["group name"]["variable name"][:]. For example: latmean_lim = d["NorthernHemisphere"]["latmean_lim"][:] # test print print(f"First value of latmean_lim in NH: {latmean_lim[0]}") If you would like to read in all variables in both hemispheres, you can loop e.g. as follows: import netCDF4 as nc file = "" d = nc.Dataset(file) # iterate through both hemispheres for hem in ["NorthernHemisphere", "SouthernHemisphere"]: # select the group to each hemisphere in the netCDF file g = d.groups[hem] # iterate through variables in each hemisphere. "v" is the name of each variable in the group. for v in g.variables: # read in the data for variable 'v' in hemisphere 'hem' as an array var = g[v][:] # just a test print, optional print(f"First value of {v} in {hem.replace('Hem', ' Hem')} is {var[0]}") Funding This project has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – TRR 301 – Project-ID 428312742, TPChange: The Tropopause Region in a Changing Atmosphere (https://tpchange.de/).

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.MRI.MRI-ESM2-0.ssp534-over' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MRI-ESM2.0 climate model, released in 2017, includes the following components: aerosol: MASINGAR mk2r4 (TL95; 192 x 96 longitude/latitude; 80 levels; top level 0.01 hPa), atmos: MRI-AGCM3.5 (TL159; 320 x 160 longitude/latitude; 80 levels; top level 0.01 hPa), atmosChem: MRI-CCM2.1 (T42; 128 x 64 longitude/latitude; 80 levels; top level 0.01 hPa), land: HAL 1.0, ocean: MRI.COM4.4 (tripolar primarily 0.5 deg latitude/1 deg longitude with meridional refinement down to 0.3 deg within 10 degrees north and south of the equator; 360 x 364 longitude/latitude; 61 levels; top grid cell 0-2 m), ocnBgchem: MRI.COM4.4, seaIce: MRI.COM4.4. The model was run by the Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan (MRI) in native nominal resolutions: aerosol: 250 km, atmos: 100 km, atmosChem: 250 km, land: 100 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

Datasets associated with Agostini, S., Houlbreque, F., Biscéré, T., Harvey, B. P., Heitzman, J. M., Takimoto, R., et al. (2020). Greater mitochondrial energy production provides resistance to ocean acidification in ‘winning’ hermatypic corals. Front. Mar. Sci. 7. doi:10.3389/fmars.2020.600836.

The aim of this study was to quantify direct and indirect relationships between soil microbial community properties (potential basal respiration, microbial biomass) and abiotic factors (soil, climate) in three major land-cover types. Location: Europe Time period: 2018 Major taxa studied: Microbial community (fungi and bacteria) We collected 881 soil samples from across Europe in the framework of the Land Use/Land Cover Area Frame Survey (LUCAS). We measured potential soil basal respiration at 20ºC and microbial biomass (substrate-induced respiration) using an O2-microcompensation apparatus. Climate and soil data were obtained from previous LUCAS surveys and online databases. Structural equation modeling (SEM) was used to quantify relationships between variables, and equations extracted from SEMs were used to create predictive maps. Fatty acid methyl esters were measured in a subset of samples to distinguish fungal from bacterial biomass. Soil microbial properties in croplands were more heavily affected by climate variables than those in forests. Potential soil basal respiration and microbial biomass were correlated in forests but decoupled in grasslands and croplands, where microbial biomass depended on soil carbon. Forests had a higher ratio of fungi to bacteria than grasslands or croplands. Soil microbial communities in grasslands and croplands are likely carbon-limited in comparison with those in forests, and forests have a higher dominance of fungi indicating differences in microbial community composition. Notably, the often already-degraded soils of croplands could be more vulnerable to climate change than more natural soils. The provided maps show potentially vulnerable areas that should be explicitly accounted for in coming management plans to protect soil carbon and slow the increasing vulnerability of European soils to climate change. [Methods] Soil samples were collected during the 2018 LUCAS soil sampling campaign. Soil chemical and physical properties were measured at the Joint Research Centre in Ispra, Italy (Orgiazzi et al., 2018). Soil microbial respiration and biomass, as well as water content and water holding capacity, were measured in the Eisenhauer lab of the German Centre for Integrative Biodiversity Research. Fungi/Bacteria was measured by fatty acid analysis by Felipe Bastida at CEBAS CSIC. Climate and geographical data were harvested from various databases, which are listed in Appendix 1 (data sources) of the associated paper. For more details on the soil sampling and physical and chemical properties, see: Orgiazzi, A., Ballabio, C., Panagos, P., Jones, A., & Fernández-Ugalde, O. (2018). LUCAS Soil, the largest expandable soil dataset for Europe: a review. European Journal of Soil Science, 69(1), 140-153. https://doi.org/10.1111/ejss.12499 For more details on the measurements of soil microbial respiration and biomass, fatty acids, and water holding capacity, see the supplementary methods of the associated paper (Appendix 2). [Usage Notes] Fatty acid analysis was performed for a subset of 267 samples. Water holding capacity and associated measurements of basal respiration was analyzed in a subset of 100 samples. The samples that were not in these subsets have NA values for the columns associated with these measurements. In order to protect the precise locations of the LUCAS sampling sites, latitude and longitude values could not be given. The approximate location of each sampling site is instead described by the NUTS3 region. If you wish to replicate the structural equation modeling described in the paper, for which latitude is required, please get in touch. A description of each column is available in the associated metadata file. Deutsche Forschungsgemeinschaft, Award: FZT 118-202548816. European Research Council, Award: 694368. European Commission. Directorate-General for the Environment. Direction Générale Opérationnelle Agriculture, Ressources Naturelles et Environnement du Service Public de Wallonie. Eurostat. Peer reviewed

Structural, optoelectronic, photovoltaic, and supplementary characterization data for “Benzylammonium-Mediated Formamidinium Lead Iodide Perovskite Phase Stabilization for Photovoltaics”, DOI:10.1002/adfm.202101163. Figure_2_XRD.zip: Data described in Figure 2 (XRD patterns) as Origin (.opj) software file. Figure_3_NMR_data.zip: Data described in Figure 3 (NMR spectra) in the file structure of the TopSpin software, which is available from Bruker. Figure_4_spectra.zip: Data described in Figure 4 (UV-vis absorption, PL and IPCE spectra) as Origin (.opj) software files. Figure_5_PV.zip: Data described in Figure 5 (photovoltaic characterization) as Origin (.opj) software files. Figure_6_spectra.zip: Data described in Figure 6 (PLQY and TRPL) as Origin (.opj) and *.csv files. Figure_7_stability.zip: Data described in Figure 7 (stability analysis) as Origin (.opj) software files. Figure_SI.zip: Data described in the Supporting Information Figures S1, S2, S3, S5, and S6 (XRD data, reciprocal space maps, radial profiles of q-maps, UV-vis absorption spectra, PL spectra, and additional photovoltaic characterization) as Origin (.opj), text (.txt), and image (.tiff) files.

This repository contains additional data for the publication "Experimental Data of Supercritical Carbon Dioxide (sCO2) Compressor at Various Fluid States". The data repository contains the entire compressor geometry, including CAD models, and input files suitable for mean-line and grid generation programs. Thus, the experimental results presented and discussed in the paper are exploitable by the scientific community and pave the road for validated analysis and design tools in the context of the sCO2-Joule cycle.

General implementation of air conditioning in buildings and facilities is such that in order to keep room temperature and humidity within predetermined range, heat pump is used to lower the air temperature to achieve desired humidity and then the air is heated to the specified temperature. From energy-saving perspective, this process is inefficient. To overcome such inefficiency, separation of latent heat and sensible heat has been proposed. This is a two-step process in which temperature and humidity are adjusted separately in two steps by combining desiccant dehumidifier and heat pump. Theoretically, the system is known to be effective in reducing energy consumption, but in practical application, this technology can be further improved. For this research, we have chosen liquid desiccant dehumidifier as the desiccant can be regenerated at low temperature. For heat pump system, we have chosen R718 centrifugal as it is suited for increasing efficiency for such combination of desiccant and heat pump. By improving each element of the system and seeking optimization of operating conditions, we aim to develop a high efficiency air-conditioning system. The result is reported here.

Dataset Description. This dataset was the primary input for the publication: Magesa, B.A., Mohan, G., Melts, I., Matsuda, H., Pu, J., Fukushi, K. (2023) Interactions between Farmers’ Adaptation Strategies to Climate Change and Sustainable Development Goals in Tanzania, East Africa. Sustainability, 15(6), 4911. https://doi.org/10.3390/su15064911. The primary dataset, provided in SPSS format (.sav), is structured into cases and variables. Cases correspond to individual survey respondents, while variables represent the responses to survey questions. The data were collected from March to April 2022 across selected villages in Mwanga and Same Districts, Tanzania, East Africa. This comprehensive dataset was combined from different sources: • 200 household surveys; • 36 key informant interviews (KII); • 4 focus group discussions (FGD) The dataset includes detailed demographic information, agricultural practices, climate change adaptation strategies, and their impact on Sustainable Development Goals (SDGs), particularly focusing on indicators related to poverty reduction and food security. Quantitative analyses using network theory were conducted to explore the interactions between adaptation strategies and SDGs. Ethical Considerations. The study adhered to the principles of the Declaration of Helsinki and received approval from the Institutional Review Board (or Ethics Committee) of the United Nations University Institute for the Advanced Study of Sustainability on 23 September 2021. Funding. This research was funded by the Japan Society for the Promotion of Science (KAKENHI grant number 17KT0073), the Estonian Research Council and the European Regional Development Fund (Mobilitas+ grant number MOBTP122) and the European Union’s Horizon 2020 research and innovation programme (grant number 862480).