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52 results for “trend models.”
Model Simulations of The Effects of Shifts in High-frequency Weather Variability (No Long-term Weather Trend) Control Carbon Loss from Land to the Atmosphere, Toolik Lake, Alaska, 2022-2122
Climate change is increasing extreme weather events, but effects on high-frequency weather variability and the resultant impacts on ecosystem function are poorly understood. We assessed ecosystem responses of arctic tundra to changes in day-to-day weather variability using a biogeochemical model and stochastic simulations of daily temperature, precipitation, and light. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates. Some responses of processes (e.g., respiration) were inconsistent with expectations, indicating that whole-ecosystem interactions and feedbacks moderate or even reverse responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere, and less variability led to higher carbon sequestration on land. The magnitude of response to weather variability was similar to that predicted from climate mean trend effects. This dataset consists of the MEL parameter file, driver files and output files for simulations without a long term weather trend.
Model Simulations of The Effects of Shifts in High-frequency Weather Variability (With a Long-term Trend) on Carbon Loss from Land to the Atmosphere, Toolik Lake, Alaska, 2022-2122
Climate change is increasing extreme weather events, but effects on high-frequency weather variability and the resultant impacts on ecosystem function are poorly understood. We assessed ecosystem responses of arctic tundra to changes in day-to-day weather variability using a biogeochemical model and stochastic simulations of daily temperature, precipitation, and light. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates. Some responses of processes (e.g., respiration) were inconsistent with expectations, indicating that whole-ecosystem interactions and feedbacks moderate or even reverse responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere, and less variability led to higher carbon sequestration on land. The magnitude of response to weather variability was similar to that predicted from climate mean trend effects. This dataset consists of the MEL parameter file, driver files and output files for simulations with a long-term weather trend.
Supplementary data for the article: Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges
<p>This repository provides the supplementary data to the paper titled <a href="https://doi.org/10.1016/j.resconrec.2024.107572" target="_blank" rel="noopener"><em>"Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges"</em></a>, published 2024 in <em>Resources, Conservation and Recycling</em>.</p> <h4><strong>Contents</strong></h4> <p>The repository is split in 3 parts and comprises the following files (more details are provided in the <em>README.md</em>):</p> <p><strong>A_Database of reviewed studies:</strong></p> <ul> <li>contains the detailed review data, meant for readers to use as an overview file to gather studies relevant to them. It also includes an overview of all data sources that the reviewed studies used.</li> </ul> <p><strong>B_Scientific supplement to paper:</strong></p> <ul> <li>Contains all data relevant to the related publication Harpprecht et al. (2024), such as studies screened , FAIR data analysis, or analyzed impact trends.</li> </ul> <p><strong>C_Data for figures in paper:</strong></p> <ul> <li>This file contains all the data for Figures 3, 4 and 5 in tabular form, representing impact trends, scenario variables, scenario modelling approaches and data sources used.</li> </ul> <h4><strong>Summary</strong></h4> <p>These files allow to reproduce the results of our study. In this work, we systematically reviewed studies which assessed future environmental impacts of metal supply chains. Our review yielded 40 publications covering 15 metals: copper, iron, aluminium, nickel, zinc, lead, cobalt, lithium, gold, manganese, neodymium, dysprosium, praseodymium, terbium, and titanium. We evaluated their results regarding future impact trends, and their methods, i.e., modelling approaches, scenario variables, and data sources of scenario variables. We identified 15 scenario variables. The most common variables are background electricity mix, ore grade, recycling shares, demand, and energy efficiency. We identified 229 unique data sources for the reviewed scenario variables.</p> <h4><strong>Related publication</strong></h4> <p>More details on the data and its interpretation as well as the scientific context are provided in the publication itself:</p> <p><a href="https://doi.org/10.1016/j.resconrec.2024.107572" target="_blank" rel="noopener">Harpprecht, C., Miranda Xicotencatl, B., van Nielen, S., van der Meide, M., Li, C. , Li, Z., Tukker, A., Steubing, B. (2024). <em>Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges.</em> Resources, Conservation and Recycling.</a></p> <h4><strong>Funding </strong></h4> <p>Carina Harpprecht received funding from the Energy Program of the German Aerospace Center in 2022. Zhijie Li received funding from the European Institute of Innovation and Technology (EIT) under the project Valomag (Project No. 14049).</p> <h4><strong>License</strong></h4> <p>CC-BY 4.0 license for DLR (German Aerospace Center)</p>
Modelled isoscape data for: "Oceanographic and biogeochemical drivers cause divergent trends in the nitrogen isoscape in a changing Arctic Ocean"
<p>The data included in this repository includes the biogeochemical model output of nitrogen isotope fields. These data were generated by simulations with the NEMOv4.0 Ocean General Circulation Model, SI3 sea ice model, and Pelagic Interactions Scheme for Carbon and Ecosystem Studies version 2 (PISCESv2) biogeochemical model. Nitrogen isotopes were integrated within PISCESv2 for the purpoes of this study.</p> <p>All data here are in longitude, latitude and time cordinates. No depth coordinate is provided as all values are averaged over the upper 100 metres of the model.</p> <p> </p> <p>The file names mean the following:<br> </p> <p>ETOPO - refers to how the curvilinear, native grid of the model was re-gridded to a regular 360x180 longitude-latitude grid uisng the etopo60 coordinate system.</p> <p>JRA55 - these are the reanalysis-driven simulations, for which we used the Japanese Atmospheric Reanalysis (JRA55do).</p> <p>future - these are the emissions-driven simulations (historical from 1850-2005, then according to Representative Concentration Pathway 8.5 from 2006-2100.)</p> <p>picontrol - these are parallel to the emissions-driven simulations but do not include the increase in emissions.</p> <p>ndep - refers to if the historical increase in anthropogenic nitrogen deposition was included in the simulation</p> <p>d15Nno3 - isotopic composition of nitrate averaged over the upper 100 metres</p> <p>d15Npom - isotopic composition of particulate organic matter averaged over the upper 100 metres</p> <p>predictors - the average values of salinity, N* and particulate organic matter over the upper 100 metres</p> <p>annualave - annual averages, so that the data are inter-annual</p> <p>1970-1990ave_months - average monthy values over the period 1970-1990.</p>
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"
<p>This dataset is associated with the following publication:</p> <p>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”, <em>Astronomy and Astrophysics</em>, vol. 665, 2022. doi:10.1051/0004-6361/202142493.</p> <p>In the folder 'model_agreement', there are pickle files from which a python dictionary can be extracted with:</p> <pre><code>with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile)</code></pre> <p>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:<br> - 'ds_sel': contains monthly timeseries of selected intersecting data<br> - 'ds_taylor': contains data used for the Taylor diagram (Figs. 4-10)<br> - 'ds_mean_month': contains seasonal cycle for plotting (Figs. 4-10)<br> - 'ds_mean_year': contains yearly timeseries for plotting (Figs. 4-10) </p> <p>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.</p> <p>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.</p> <p>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.</p> <p>Code that generated and used this data is available on github: <a href="https://github.com/CarolineHaslebacher/Astroclimate-future-project">https://github.com/CarolineHaslebacher/Astroclimate-future-project</a> </p> <p> </p>
Decreasing trends of ammonia emissions over Europe seen from remote sensing and inverse modelling
<p>The set consists of 5 files that constitute the main calculations of ammonia emissions over Europe for the years 2013-2020. <br> The detailed description of variables follows:</p> <p>1) PriorEmission.nc<br> - Pall: tensor of the size 240 x 200 x 12 x 8 (lat x lon x months x years) with ammonia prior emissions used in the study [ng/m2/s]</p> <p>2) PosteriorEmission.nc<br> - Xall: tensor of the size 240 x 200 x 12 x 8 (lat x lon x months x years) with ammonia posterior emissions [ng/m2/s]</p> <p>3) UncertaintyEmission.nc<br> - Uall: tensor of the size 240 x 200 x 12 x 8 (lat x lon x months x years) with uncertainty of posterior emissions [ng/m2/s]</p> <p>4) stations_vmodVSobs.mat <br> - st_list: list of stations identifiers, 53 stations in total<br> - st_coord: stations coordinates [lot,lat]<br> - st_OBSdays: matrix of the size 53 x (366*8) with observations in daily resolution [ug/m3]<br> - st_ind_obs: logical matrix of the size 53 x (366*8) with indicators when each station provides observation (1) and when not (0)<br> - st_prior_vmod_days: matrix of the size 53 x (366*8) with calculated concentrations using model with prior emission [ug/m3]- st_post_vmod_days: matrix of the size 53 x (366*8) with calculated concentrations using model with posterior emission [ug/m3]<br> - st_prior1_vmod_days: same as st_prior_vmod_days for EC6G4 prior<br> - st_prior2_vmod_days: same as st_prior_vmod_days for EGG prior<br> - st_prior3_vmod_days: same as st_prior_vmod_days for NE prior<br> - st_prior4_vmod_days: same as st_prior_vmod_days for VD prior</p>
BTO Garden BirdWatch: Weekly butterfly abundance data for modelling trends in UK gardens
<p>Dataset used to estimate annual abundance indices and trends for UK butterflies in gardens, covering the period 2007 to 2020.</p> <p>Data have been collected as part of the British Trust for Ornithology (BTO) Garden BirdWatch (GBW) survey. GBW is a structured, citizen science monitoring programme whereby volunteers record weekly abundances of various bird, invertebrate, mammal, reptile and amphibian species in (predominantly suburban and rural) gardens. See <a href="http://www.bto.org/gbw">www.bto.org/gbw</a> for further information about the survey. </p> <p>This dataset has been pre-filtered to meet criteria for inclusion in the modelling of butterfly species trends, as described by <a href="https://doi.org/10.1111/icad.12645">Plummer et al 2023</a>. Please refer to the 'readme' file for further details.</p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p>
Observed Indian Ocean Warming Trend Applied to the IPSL-CM6A-LR model
<p>This experiment applies the observed warming trend of the tropical Indian Ocean (0.15 deg C per decade; Hu and Fedorov, 2019) to the tropical Indian Ocean region in the IPSL-CM6A-LR pre-industrial control run. The methods of nudging are defined in Ferster et al. (2021), where here we apply an observed warming rate rather than a constant (as in Ferster et al., 2021).</p> <p>This experiment relates to the submitted article entitled <em>Variations in tropical Indian Ocean SST drive multi-decadal AMOC variability in models and observations</em>.</p> <p>*The datasets are created from the IPSL-CM6A-LR output files</p> <p>**This version serves as a preliminary version of the dataset for publication purposes, additional output is available.</p>
data sets of "Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland"
<p>data sets from "Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland"</p> <p>Monthly means ozone profiles data sets of MCH homogenized Dobson D051 and of Brewer B040 used in the article entitled: "Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland" by Eliane Maillard Barras, Alexander Haefele, René Stübi, Achille Jouberton, Herbert Schill, Irina Petropavlovskikh, Koji Miyagawa, Martin Stanek, and Lucien Froidevaux.</p> <p><a href="https://doi.org/10.5194/acp-2022-344">https://doi.org/10.5194/acp-2022-344</a></p>
Text-fig. 5. Macroevolutionary trends related to the IC model in the first three teeth of the six families of extinct sloths, as well as specimens of the "basal Megatherioidea", Pseudoglyptodon, and Bradypus. Dashed line (- -) shows the regression including all data; solid line shows the regression after the exclusion of Octodontotherium (shown in the plot as a filled triangle). in Unexpected Inhibitory Cascade In The Molariforms Of Sloths (Folivora, Xenarthra): A Case Study In Xenarthrans Honouring Gerhard Storch'S Open-Mindedness
Text-fig. 5. Macroevolutionary trends related to the IC model in the first three teeth of the six families of extinct sloths, as well as specimens of the "basal Megatherioidea", Pseudoglyptodon, and Bradypus. Dashed line (- -) shows the regression including all data; solid line shows the regression after the exclusion of Octodontotherium (shown in the plot as a filled triangle).
Text-fig. 4. Macroevolutionary trends related to the IC model in the last three teeth of the six families of extinct sloths, as well as specimens of the "basal Megatherioidea", Pseudoglyptodon, and Bradypus. Dash-dot line (-.-) shows the regression including all data; solid line shows the regression after the exclusion of Octodontotherium (shown in the plot as a filled triangle). in Unexpected Inhibitory Cascade In The Molariforms Of Sloths (Folivora, Xenarthra): A Case Study In Xenarthrans Honouring Gerhard Storch'S Open-Mindedness
Text-fig. 4. Macroevolutionary trends related to the IC model in the last three teeth of the six families of extinct sloths, as well as specimens of the "basal Megatherioidea", Pseudoglyptodon, and Bradypus. Dash-dot line (-.-) shows the regression including all data; solid line shows the regression after the exclusion of Octodontotherium (shown in the plot as a filled triangle).
Proccessed data for Trend Validation of Metabolic Models Against Measurements Using Indirect Calorimetry
<p>A cleaned data set used to validate metabolism models in a muscuskeletal modeling software.<br> The dataset contains 240 rows and 18 columns. </p> <p>Labels:</p> <ul> <li>AnyMet = Metabolic output by the modelling software. Calculated as the mean energy cost per repetition [J] .</li> <li>VynMet = Metabolic output by the indirect calorimetry system (Vyntus CPX). Calculated as the mean energy cost per repetition [J].</li> <li>rest_energy = total energy cost during rest [J]. Measured with Indirect caliometry</li> <li>rest_time = total time of rest [min]</li> <li>Work = Energy cost times the displacement per rep [J].</li> <li>watt = Work divided by total duration of a repetition [J/s]</li> <li>extension time = duration of the extension part of the movement [s]</li> <li>flexion time = duration of the flexion part of the movement [s]</li> <li>bw = bodyweight [kg]</li> <li>height [m]</li> <li>CV = coefficient of variation for the measured rest_energy. </li> <li>model = model type used for AnyMet. </li> <li>Subject </li> <li>Contraction = Contraction type performed</li> <li>intensity = Intensity to overcome created by the dynamometer. </li> <li>mech_watt_kg = mechcanical watt, watt divided by bodyweight</li> <li>any_met_watt_kg = watt pr kg: (AnyMet / bw) / (extension time + flexion time)</li> <li>vyn_met_watt_kg = watt pr kg: (VynMet / bw) / (extension time + flexion time)<br> <br> There is also a zip file containing the raw data from the dynanometer and the Vyntus PGE system.</li> </ul>
MESA histories for "Characterizing Observed Extra Mixing Trends in Red Giants using the Reduced Density Ratio from Thermohaline Models"
<p>This repository provides MESA history files for each of the stellar models in the publication "Characterizing Observed Extra Mixing Trends in Red Giants using the Reduced Density Ratio from Thermohaline Models". The MESA version used was stable release version 21.12.21. Runs are organized into tarballs according to the thermohaline mixing prescription used:</p> <ul> <li>BGS13 = Brown, Garaud, Stellmach 2013</li> <li>Kipp1e-1 = Kippenhahn with alpha_th = 0.1</li> <li>Kipp2 = Kippenhahn with alpha_th = 2</li> <li>Kipp7e2 = Kippenhahn with alpha_th = 700</li> </ul> <p>and are additionally grouped according to the stellar mass (M = 0.9, 1.1, 1.3, 1.5, 1.7 in units of Msol). Within each tarball is a number of run directories which contain a LOGS/history.data file from the MESA run. The subdirectories in the tarball contain runs at various metallicities; conversion between Z (MESA input) and [Fe/H] (paper reported value) are found in Table 2 of the manuscript.<br> <br> Inlists and information for recreating these MESA simulations can be found online at the paper's github repository: <a href="https://github.com/afraser3/Empirical-Magnetic-Thermohaline">https://github.com/afraser3/Empirical-Magnetic-Thermohaline</a> (a copy of the code from this Github repository is located in this Zenodo repository in: Empirical-Magnetic-Thermohaline-main.zip)</p>
Processed data of grasshoppers, butterflies and moths for analyses of species trend models for the regional WWF Living Planet Index for Belgium
<p>This archive contains pre-processed datasets used for the analysis of species occupancy models, the results of which were used in the calculation of multi-species indices as part of the regional WWF Living Planet Index for Belgium.</p> <p>The datasets are csv files (comma separated and . as decimal mark). </p> <p>For each species group (moths, butterflies and grasshoppers), the following files are available:</p> <ul> <li>a species list (files with 'species' in the name)</li> <li>an observations list (files with 'observations' in the name - for butterfly or moth species with two distinct flight periods, also a file with the data for the second generation is available)</li> </ul> <p>For one species, <em>Fabriciana adippe</em>, separate files are available with corrected data.</p> <p>The species list files contain the following variables:</p> <ul> <li>species_id (unique species id)</li> <li>scientific_name (accepted scientific name according to the GBIF taxonomic backbone)</li> <li>species_name_NL (Dutch species name)</li> <li>species_name_FR (French species name)</li> <li>season_start (n-th day of the year that marks the beginning of the first -and possibly only- generation)</li> <li>season_end (n-th day of the year that marks the end of the first -and possibly only- generation)</li> </ul> <p>The observations files contain the following variables:</p> <ul> <li>species_id (a unique identifier)</li> <li>year (year of observation)</li> <li>month (month of observation)</li> <li>day (day of observation)</li> <li>julian_day (n-th day of the year)</li> <li>site_id (unique identifier for the 1 km x 1km EEA 1 km x 1 km reference grid square <a href="https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2">https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2</a>)</li> <li>source (name of data provider)</li> <li>count (max number of sightings for the species for that day and site</li> </ul>
Climate trends and behavior of a model Amazonian terrestrial insectivore, Black-faced Antthrush, indicate adjustment to hot and dry conditions
<p>Rainforest loss threatens terrestrial insectivorous birds throughout the world's tropics. Recent evidence suggests these birds are declining in undisturbed Amazonian rainforest, possibly due to climate change. Here, we first asked whether Amazonian terrestrial insectivorous birds were exposed to increasingly extreme ambient conditions using 38 years of climate data. We found long-term trends in temperature and precipitation at our study site, especially in the dry season, which was ~1.3 °C hotter and 21% drier in 2019 than in 1981. Second, to test whether birds actively avoided hot and dry conditions, we used field sensors to identify periodic intervals of ambient extremes and prospective microclimate refugia within undisturbed rainforest from 2017–2019. Simultaneously, we examined how tagged Black-faced Antthrushes (Formicarius analis) used this space. We collected >1.3 million field measurements quantifying ambient conditions in the forest understory, including along elevation gradients. For 11 birds, we obtained GPS data to test whether birds adjusted their cover usage using variation in GPS fix success (<em>n</em> = 2,724) as a proxy and elevation using successful locations (<em>n</em> = 640) across seasonal and daily cycles. For four additional birds, we collected >180,000 light and temperature readings to assess exposure. Field measurements in the modern landscape revealed that temperature was higher in the dry season and highest on plateaus. Thus, low-lying areas were relatively buffered, providing microclimate refugia during hot afternoons in the dry season. At those times, birds apparently entered cover and shifted downslope. Because climate change intensifies the hot, dry conditions that antthrushes seemingly avoid, our results are consistent with the hypothesis that climate change decreases habitat quality for this species. If other terrestrial insectivores are similarly sensitive, climate-induced changes to otherwise intact rainforest may be related to their recent declines.</p>
data sets from "Updated trends of the stratospheric ozone vertical distribution in the 60S–60N latitude range based on the LOTUS regression model"
<p>Monthly means data sets from satellite, ground-based and model records used in the article entitled: "Updated trends of the stratospheric ozone vertical distribution in the 60 S–60 N latitude range based on the LOTUS regression model"</p> <p> </p>
data sets from "Updated trends of the stratospheric ozone vertical distribution in the 60S–60N latitude range based on the LOTUS regression model"
<p>Monthly means data sets from satellite, ground-based and model records used in the article entitled: "Updated trends of the stratospheric ozone vertical distribution in the 60 S–60 N latitude range based on the LOTUS regression model".</p> <p>Information about and the most recent versions of each dataset can be found at their individual source locations:</p> <p>Merged satellite datasets</p> <ol> <li>SBUV MOD – https://acd-ext.gsfc.nasa.gov/Data_services/merged/index.html (NASA GSFC, USA)</li> <li>SBUV COH: https://ftp.cpc.ncep.noaa.gov/SBUV_CDR/ (NOAA, USA).</li> <li>GOZCARDS: https://www.earthdata.nasa.gov/esds/competitive-programs/measures/gozcards (JPL, NASA, USA)</li> <li>SWOOSH: https://csl.noaa.gov/groups/csl8/swoosh/ (NOAA, USA).</li> <li>SAGE-CCI-OMPS and MEGRIDOP datasets are available through https://climate.esa.int/en/projects/ozone/data/ and ftp://cci_web@ftp-ae.oma.be/esacci (ESA Climate Office). They are provided by FMI, Finland</li> <li>SAGE-SCIAMACHY-OMPS: data record is available upon registration via the following link: http://www.iup.uni-bremen.de/DataRequest/ (U. Bremen, Germany).</li> <li>SAGE-OSIRIS-OMPS: downloading instructions can be found at https://research-groups.usask.ca/osiris/data-products.php#OSIRISLevel3andMergedDataProducts (U. Saskatchewan, Canada).</li> </ol> <p>Ground-based records:</p> <ol> <li>Umkehr – https://gml.noaa.gov/aftp/data/ozwv/Dobson/AC4/Umkehr/Monthly/ (NOAA, USA)</li> <li>ozonesondes – https://hegiftom.meteo.be/datasets/ozonesondes (HEGIFTOM). Measurements at the various stations are provided by the following institutions: <ul> <li>Hohenpeissenberg: DWD, Germany</li> <li>Payerne:MeteoSwiss, Switzerland</li> <li>OHP, CNRS, France</li> <li>Hilo, NOAA, USA</li> <li>Lauder, NIWA, New Zealand</li> </ul> </li> <li>lidar: <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> . Measurement at the various stations are provided by the following institutions: <ul> <li>Hohenpeissenberg: DWD, Germany</li> <li>OHP: CNRS, France</li> <li>MLO: JPL, NASA, USA</li> <li>Lauder: NIWA, New Zealand</li> </ul> </li> <li>FTIR spectrometers – <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> Three sites only provided quality checked measurements relevant for the article. For other ozone FTIR measurements, data in <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> must be used. Measurement used in the article are provided by the following institutions: <ul> <li>Zugspitze: KIT, Germany</li> <li>Jungfraujoch: ULiège, GIRPAS team, Belgium</li> <li>Lauder: NIWA, New Zealand</li> </ul> </li> <li>Microwave spectrometers: <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> Measurement at the various stations are provided by the following institutions: <ul> <li>Payerne: MeteoSwiss, Switzerland</li> <li>Mauna Loa: NRL, USA</li> <li>Lauder: NRL, USA</li> </ul> </li> </ol> <p>Chemistry Climate Model (CCM) CCMI simulations are avilable at https://blogs.reading.ac.uk/ccmi</p>
Effects of Reanalysis Forcing Fields on Ozone Trends and Age of Air from a Chemical Transport Model
<p>This dataset is based on the global off-line 3-D chenmical transport model (TOMCAT/SLIMCAT) forced with ECMWF reanalyses (ERA-Interim and ERA5) to compare the performance of the stratospheric ozone simulations. Each field is separately saved as NETCDF file. Each field is show on geographic coordinates, which can be longitude, latitude, vertical hybrid-pressure level (for zonal mean fields, such as ozone, temperature and age-of-air).</p> <p>The dimensions in each field are:</p> <p>lat --> latitude</p> <p>lon --> longitude</p> <p>lev --> hydrid pressure level</p> <p>time --> months of the simulation</p> <p>The output of the total column ozone from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figures 1-4 and Figure S2 in the supplement are in files:</p> <p>toz_A_ERAI.nc</p> <p>toz_B_ERA5.nc</p> <p>The output of the stratospheric column ozone (SCO) in Figure S1 in the supplement are in the file (levels1-3 are SWOOSH, B_ERA5 and A_ERAI SCO data, respectively):</p> <p>sco_SWOOSH_A_ERAI_B_ERA5.nc</p> <p>The output of zonal mean ozone profiles from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figures 5-7, 9 and Figures S3-4 are in files:</p> <p>O3_mm_A_ERAI.nc</p> <p>O3_mm_B_ERA5.nc</p> <p>The output of zonal mean temperature from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figure 8 are in files:</p> <p>te_mm_A_ERAI.nc</p> <p>te_mm_B_ERA5.nc</p> <p>The output of zonal mean age-of-air from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figures 10-12 are in files:</p> <p>Age_mm_A_ERAI.nc</p> <p>Age_mm_B_ERA5.nc</p> <p>The output of the zonal mean ozone, temperature and age-of-air from the ERA5.1 reanalysis corrected simulations during the period from 2000 to 2006 in all Figures above using ERA5 are in files:</p> <p>ERA5_1_O3_2000_18.nc</p> <p>ERA5_1_te_2000_18.nc</p> <p>ERA5_1_Age_2000_18.nc</p> <p> </p>
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</p>
Data from: Socio-ecological drivers of long-term ecosystem carbon stock trend: An assessment with the LUCCA model of the French case
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