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695 results for “model output”
Multi-proxy agreement on Atlantic circulation dynamics since the last ice age: Model output data
<p>This dataset contains model output for the simulations presented in: <em>"Multi-proxy agreement on Atlantic circulation dynamics since the last ice age"</em>.</p> <p> </p>
Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output
<p>We produce a dataset that uses pattern scaling, a common method of emulating climate models. Our dataset is built on the Pangeo CMIP6 archive, which has the advantage that we don't need to actually download the climate model output. Here we demonstrate the utility of our dataset, called Pangeo-Enabled ESM Pattern Scaling (PEEPS). The dataset, which is encapsulated in a Jupyter notebook (and replicated in a Python file), is flexible and can be extended to multiple scenarios and multiple variables, as long as they are in the Pangeo-accessible archive.</p>
Model output from the Bern3D model of 600 kyr weathering step-change experiments
<p>Model output data from the Bern3D model that were used for the manuscript "Carbon cycle responses to changes in weathering and the long-term fat of stable carbon isotopes".</p> <p>Information on the different data files is included in the README.</p>
Model output for "Enrichment of ammonium in the future ocean threatens diatom productivity"
<p>Each netcdf file (.nc) contains model output from simulations performed with the<br> NEMO-PISCES global ocean-biogeochemistry model. These simulations were<br> forced by physical output from the IPSL-CM5A Earth System Model, which <br> performed both the natural (no anthropogenic activities) and RCP8.5 scenarios.</p> <p>Variables in spin-up "ptrc" files are:</p> <p> name title I J K L<br> PHY (Nano)Phytoplankton Concentrati 1:360 1:180 1:31 1:12<br> PHY2 Diatoms Concentration 1:360 1:180 1:31 1:12<br> O2 Oxygen Concentration 1:360 1:180 1:31 1:12<br> PREO2 Abiotic Oxygen Concentration 1:360 1:180 1:31 1:12<br> FER Dissolved Iron Concentration 1:360 1:180 1:31 1:12<br> NO3 Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2 Nitrite Concentration 1:360 1:180 1:31 1:12<br> NH4 Ammonium Concentration 1:360 1:180 1:31 1:12<br> NO3_15 15N Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2_15 15N Nitrite Concentration 1:360 1:180 1:31 1:12<br> NH4_15 15N Ammonium Concentration 1:360 1:180 1:31 1:12<br> O2_18 18O Dissolved Oxygen Concentrat 1:360 1:180 1:31 1:12<br> NO3_18 18O Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2_18 18O Nitrite Concentration 1:360 1:180 1:31 1:12</p> <p> </p> <p>Variables in scenario "ptrc" files are:</p> <p> name title I J K L<br> PHY (Nano)Phytoplankton Concentrati 1:360 1:180 1:31 1:12<br> PHY2 Diatoms Concentration 1:360 1:180 1:31 1:12<br> ZOO (Micro)Zooplankton Concentratio 1:360 1:180 1:31 1:12<br> ZOO2 Mesozooplankton Concentration 1:360 1:180 1:31 1:12<br> O2 Oxygen Concentration 1:360 1:180 1:31 1:12<br> PREO2 Abiotic Oxygen Concentration 1:360 1:180 1:31 1:12<br> FER Dissolved Iron Concentration 1:360 1:180 1:31 1:12<br> NO3 Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2 Nitrite Concentration 1:360 1:180 1:31 1:12<br> NH4 Ammonium Concentration 1:360 1:180 1:31 1:12<br> DOC Dissolved organic Concentration 1:360 1:180 1:31 1:12<br> POC Small organic carbon Concentrat 1:360 1:180 1:31 1:12<br> GOC Big organic carbon Concentratio 1:360 1:180 1:31 1:12<br> NO3_15 15N Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2_15 15N Nitrite Concentration 1:360 1:180 1:31 1:12<br> NH4_15 15N Ammonium Concentration 1:360 1:180 1:31 1:12<br> PHY_15 15N Nanophytoplankton Concentra 1:360 1:180 1:31 1:12<br> PHY2_15 15N Diatoms Concentration 1:360 1:180 1:31 1:12<br> DOC_15 15N Dissolved organic Concentra 1:360 1:180 1:31 1:12<br> POC_15 15N Small particulate Concentra 1:360 1:180 1:31 1:12<br> GOC_15 15N Large particulate Concentra 1:360 1:180 1:31 1:12<br> ZOO_15 15N Microzooplankton Concentrat 1:360 1:180 1:31 1:12<br> ZOO2_15 15N Mesozooplankton Concentrati 1:360 1:180 1:31 1:12<br> O2_18 18O Dissolved Oxygen Concentrat 1:360 1:180 1:31 1:12<br> NO3_18 18O Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2_18 18O Nitrite Concentration 1:360 1:180 1:31 1:12</p> <p>Variables in the scenario "diad" files are:</p> <p> name title I J K L<br> PH PH 1:360 1:180 1:31 1:12<br> HEUP Euphotic layer depth 1:360 1:180 ... 1:12<br> PAR Photosynthetically Available Ra 1:360 1:180 1:31 1:12<br> PARDM Daily mean PAR 1:360 1:180 1:31 1:12<br> PPPHYN Primary production of nanophyto 1:360 1:180 1:31 1:12<br> PPPHYD Primary production of diatoms 1:360 1:180 1:31 1:12<br> PPNEWN New Primary production of nanop 1:360 1:180 1:31 1:12<br> PPNEWD New Primary production of diato 1:360 1:180 1:31 1:12<br> PPNO2N NO2 Primary production of nanop 1:360 1:180 1:31 1:12<br> PPNO2D NO2 Primary production of diato 1:360 1:180 1:31 1:12<br> NITRNH4 Ammonia-oxidation rate (NH4-->N 1:360 1:180 1:31 1:12<br> NITRNO2 Nitrite-oxidation rate (NO2-->N 1:360 1:180 1:31 1:12<br> MUAOA Growth rate of ammonia oxidiser 1:360 1:180 1:31 1:12<br> MUAOAMAX Max potential ammonia oxidation 1:360 1:180 1:31 1:12<br> LAOANH4 Substrate limitation of NH4 oxi 1:360 1:180 1:31 1:12<br> LAOAFER Iron limitation of NH4 oxidatio 1:360 1:180 1:31 1:12<br> LAOAPAR Light limitation of NH4 oxidati 1:360 1:180 1:31 1:12<br> LAOAPH pH limitation of NH4 oxidation 1:360 1:180 1:31 1:12<br> LNOBNO2 Substrate limitation of NO2 oxi 1:360 1:180 1:31 1:12<br> LNOBFER Iron limitation of NO2 oxidatio 1:360 1:180 1:31 1:12<br> LNOBPAR Light limitation of NO2 oxidati 1:360 1:180 1:31 1:12<br> NFIX Nitrogen fixation 1:360 1:180 1:31 1:12<br> RIVER_NO3<br> Nitrate added by rivers 1:360 1:180 ... 1:12<br> NDEP_NO3 Nitrate added by deposition 1:360 1:180 ... 1:12<br> REMIN Oxic remineralization of OM (DO 1:360 1:180 1:31 1:12<br> EXCR1 Excretion by microzooplankton 1:360 1:180 1:31 1:12<br> EXCR2 Excretion by mesozooplankton 1:360 1:180 1:31 1:12<br> DENITNO3 Denitrification rate (NO3-->NO2 1:360 1:180 1:31 1:12<br> DENITNO2 Denitrification rate (NO2-->N2) 1:360 1:180 1:31 1:12<br> ANAMMOX Anaerobic oxidation of NH4 (NH4 1:360 1:180 1:31 1:12<br> ALTREM Alternative anaerobic remin (DO 1:360 1:180 1:31 1:12<br> SDEN3D Sed denitrification of OM (NO3- 1:360 1:180 1:31 1:12<br> SREM3D Sed remineralisation of OM (DOC 1:360 1:180 1:31 1:12<br> <br> Files:</p> <ul> <li> ETOPO_nitr_kaoafer00_1m_ptrc.nc</li> <li> ETOPO_nitr_kaoafer00_1m_diad.nc</li> <li> ETOPO_nitr_kaoafer00_2ndpicontrol_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_2ndpicontrol_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_acid_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_acid_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_warm_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_warm_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_circ_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_circ_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_full_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_full_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_picontrolalt_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_picontrolalt_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_acidalt_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_acidalt_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_fullalt_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_fullalt_1m_diad_2081-2100_ave.nc</li> </ul> <p> </p> <p>Naming convention:<br> "ETOPO" - refers to being on a regular 1x1 degree horizontal grid<br> "nitri" - refers to the developed PISCES model with explicit two-step nitrification<br> "kaoafer00" - refers to no iron limitation of AOA <br> "1m" - refers to the timestep resolution, here 1 month. Thus, all data presented here is monthly averaged values.<br> "2ndpicontrol" - refers to preindustrial control run<br> "acid" - refers to the control run + ocean acidification<br> "warm" - refers to the control run + warming<br> "circ" - refers to the control run + circulation change<br> "full" - refers to the control run + ocean acidification + warming + circulation change<br> "picontrolalt" - refers to preindustrial control run (alternative pH parameterisation)<br> "acidalt" - refers to the control run + ocean acidification (alternative pH parameterisation)<br> "fullalt" - refers to the control run + ocean acidification + warming + circulation change (alternative pH parameterisation)<br> <br> Contact: Pearse.Buchanan@liverpool.ac.uk or pbuchanan@carnegiescience.edu</p> <p> </p>
Outputs of current speed and sea otter abundance models in Glacier Bay, Alaska
<p>Sea otters are apex predators that can exert considerable influence over the nearshore communities they occupy. Since facing near extinction in the early 1900s, sea otters are making a remarkable recovery in Southeast Alaska, particularly in Glacier Bay, the largest protected tidewater glacier fjord in the world. The expansion of sea otters across Glacier Bay offers both a challenge to monitoring and stewardship and an unprecedented opportunity to study the top-down effect of a novel apex predator across a diverse and productive ecosystem. Our goal was to integrate monitoring data across trophic levels, space, and time to quantify and map the predator-prey interaction between sea otters and butter clams <em>(Saxidomus</em> <em>gigantea</em>), one of the dominant large bivalves in Glacier Bay and a favored prey of sea otters. To do so, we developed a modeling framework to account for both bottom-up and top-down drivers of butter clam abundance and dynamics. For the bottom-up driver, we used the root-mean-square current speed (m/s) predicted by a tidal circulation model of Glacier Bay developed by Drew <em>et al. </em>(2013). For top-down sea otter dynamics, we used the posterior mean sea otter abundance estimates from Lu <em>et al. </em>(2019). This repository contains the current speed raster (100m x 100m resolution) produced by Drew <em>et al. </em>(2013) and the files and model output from Lu <em>et al.</em> (2019) necessary to generate a time series of rasters (400m x 400m resolution raster brick with 26 layers for the years 1993-2018) of estimated posterior mean sea otter abundance. These data layers are used in Leach <em>et al. </em>(2023) to model butter clam dynamics at sampling sites across Glacier Bay.</p>
Model inputs and outputs for: Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
<p>Files used in initial submission of manuscript to <em>Geoscientific Model Development</em>. Files with names beginning sdates and gdds were used as model inputs for some experiments. File with name beginning hdates was used in postprocessing. ZIP archives are model experimental outputs.</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)</p>
ICON-Coast model output for a study on increasing CO2 uptake of the coastal ocean
<p>Primary output of the ocean-biogeochemistry model ICON-Coast that has been used to create the figures in the publication Mathis et al. (2024) on the increasing CO2 uptake of the coastal ocean.<br>https://www.nature.com/articles/s41558-024-01956-w</p>
Input and Output simulation data of the THOR GCM for the paper Dynamical and radiative effects resulting from the deep non-hydrostatic vs deep quasi-hydrostatic equations in the global circulation model THOR with an added non-grey radiative transfer scheme
<p>The input and ouput simulation data of the THOR GCM for Dynamical and radiative effects resulting from the deep non-hydrostatic vs deep quasi-hydrostatic equations in the global circulation model THOR with an added non-grey radiative transfer scheme</p> <p>Global circulation models (GCMs) play an important role in contemporary investigations of exoplanet atmospheres. Different GCMs evolve various sets of dynamical equations which can result in obtaining different atmospheric properties between models. In this study, we investigate the effect of different dynamical equation sets on the atmospheres of hot Jupiter exoplanets. We compare GCM simulations using the quasi-primitive dynamical equations (QHD) and the deep Navier-Stokes equations (NHD) in the GCM THOR. We utilise a two-stream non-grey "picket-fence" scheme to increase the realism of the radiative transfer scheme. We perform GCM simulations covering a wide parameter range grid of system parameters in the population of exoplanets. Our results show significant differences between simulations with the NHD and QHD equation sets at lower gravity, higher rotation rates or at higher irradiation temperatures. The parameter exploration shows the relevance of choosing dynamical equation sets dependent on system and planetary properties.Climate states of hot Jupiters seemed to be more diverse than previously thought. There are exceptions to prograde superrotation. Overall, our study shows the evolution of different climate states which arise just due to different selection of Navier-Stokes equations and approximations. We show the shortcomings of approximations in GCMs made for Earth, but used for non Earth-like planets.</p>
CROVER model output data for paper "Future Warming Decreases the Benefits of Irrigation in Global Food Production"
<p>This data is output data simulated by the CROVER crop-river coupled model for the scientific paper entitled: “Future Warming Decreases the Benefits of Irrigation in Global Food Production”. Simulations are based on 120-year simulations (1981-2100) using 20 climate projections (four Representative Concentration Pathways (RCPs) (2.6, 4.5, 6.0, and 8.5) × five General Circulation Models (GCMs) (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M)). The dataset includes crop yield of the modeled five crops (maize, rice, soybean, spring wheat, and winter wheat) each for irrigated and rainfed with a spatial resolution of 1.125 degree.</p> <p>The file name consists of a series of identifiers, separated by underscore, according to the following pattern.</p> <p>crover_<gcm>_<rcp>_yield_<crop>_<irrigated|rainfed>.grd</p> <p>crover_<gcm>_<rcp>_yield_<crop>_<irrigated|rainfed>.ctl</p> <p>Format: grads binary (global, 1.125 degree)</p> <p>These files were compressed as a zip file for each GCM and RCP.</p> <p> </p>
Model output from Snow Ensemble Uncertainty Project (SEUP) as used in Seasonal Snow Predictability Derived from Early-Season Snow in North America
<p>The files provided here are the output from the median peak snow water equivalent (peak_SWE.mat), 1 December snow water equivalent (Dec1_SWE.mat), and 1 January snow water equivalent (Jan1_SWE.mat) model simulations for the Noah-MP run with MERRA-2 forcing, as used in Lundquist et al. (2023) and described in Kim et al. (2021). We also include the model grid's latitude, longitude and elevation data (SEUPlatlon.mat), and example code for plotting the data (Plotmodeldata.m) as in the Lundquist et al. (2023) paper. </p> <p>Kim, R. S., Kumar, S., Vuyovich, C., Houser, P., Lundquist, J., Mudryk, L., et al. (2021). Snow Ensemble Uncertainty Project (SEUP): Quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling. <em>The Cryosphere, 15</em>(2), 771-791.</p> <p>Lundquist, J. D., R. S. Kim, M. Durand, and L. R. Prugh, 2023, Seasonal Peak Snow Predictability Derived from Early-Season Snow in North America, Geophysical Research Letters, (submitted 2023)</p>
Output of the Land Surface Model ORCHIDEE over river catchments in Europe, run with GSWP3 and synthetic forcings where the precipitation is modified
<p># Description of the data file</p> <p>This dataset contains the main outputs used for the results of the article: "Budyko framework based analysis of the effect of climate change on watershed evaporation efficiency and its impact on discharge over Europe", by Julie Collignan, Jan Polcher, Sophie Bastin, Pere Quintana-Segui, accepted by the journal *Water Resources Research*.</p> <p>This study uses the outputs of a land surface model (LSM), forced with differentan atmospheric datasets from 1901 to 2010. The atmospheric dataset are based on GSWP3 (Hyungjun, K. (2017), doi: 10.20783/DIAS.501) and was modified to create synthetic forcings with different precipitation characteristics (annual average, intra-annual distribution). The LSM was run with all synthetic forcings, and the outputs (precipitation, evapotranspiration, potential evapotranspiration, discharge) were integrated at the level of each river basin which are sampled by gauging stationss. These outputs are used to fit a parametric equation of the Budyko framework to decompose the partial trends in discharge and the relative weight of the different climatic components.</p> <p># Details of the variables and attribute of the file</p> <p>This study was led over 2196 river basins over Europe. </p> <p>For each catchment used in the study, it gathers:<br> - name of the station at the outlet *name*<br> - name of the river associated *rivers*<br> - lat/lon of the station *Localisation*<br> - upstream area of the catchment *upstream*<br> - Observed discharge (data not used in the article) *DisObs*<br> These data come from three different sources:<br> * *Global Runoff Data Centre (GRDC)*, https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html ;<br> * *Ministere de lenergie* (February 2021), https://www.hydro.eaufrance.fr/" ;<br> * *Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico*, 2020</p> <p>Each catchment was projected on the grid of the LSM ORCHIDEE (*IPSL, https://orchidee.ipsl.fr/*) during the construction of its river routing system. More details are given in the associated article.</p> <p>The results for four different run of the LSM are included in this dataset. This dataset gathers for each run:<br> * Precipitation *P*<br> * Evapotranspiration *E*<br> * Potential evapotranspiration *PET*<br> * Discharge *DisMod*</p> <p>The different synthetic forcings are:<br> - the reference forcing with the un-modified atmospheric dataset: *the Global Soil Wetness Project Phase 3 (GSWP3)*, Hyungjun, K. (2017), doi: 10.20783/DIAS.501<br> - *f2000*: A forcing where all 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year. Therefore, all components of $P$ (average and intra-annual variations) are set constant.<br> - *cstmean*: A forcing for which we keep the relative intra-annual distribution of $P$ of each year, but where the average $P$ of each year is set constant. The 3h values of $P$ are scaled so the hydrological year average is set to the one of the year 2000 (September 1999 to September 2000).<br> - *cstintravar*: A forcing for which we keep the annual average of $P$ for each year, but where the relative intra-annual distribution of $P$ is set constant. The 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year and then scaled over each hydrological year so the yearly average is set to the one of the corresponding years in the reference forcing.<br> \end{itemize}</p> <p>## ncdump -h Filename.nc</p> <p>```<br> dimensions:<br> basins = 2196 ;<br> years = UNLIMITED ; // (110 currently)<br> loc = 2 ;<br> lenstr = 57 ;<br> variables:<br> short years(years) ;<br> years:long_name = "Years" ;<br> years:units = "year" ;<br> char names(basins, lenstr) ;<br> names:long_name = "Name of the station at the catchment output" ;<br> char rivers(basins, lenstr) ;<br> rivers:long_name = "Name of the river where the station is positioned" ;<br> float upstream(basins) ;<br> upstream:long_name = "Upstream area of the catchment" ;<br> upstream:units = "km^2" ;<br> float Localisation(basins, loc) ;<br> Localisation:long_name = "Position of each catchment: (Lon, Lat)" ;<br> Localisation:units = "degrees_east, degrees_north" ;<br> float DisObs(years, basins) ;<br> DisObs:long_name = "Discharge observation at the outlet of the catchment" ;<br> DisObs:units = "m3/s" ;<br> float E_ref(years, basins) ;<br> E_ref:long_name = "Modeled average annual evaporation with forcing ref" ;<br> E_ref:units = "m3/s" ;<br> float P_ref(years, basins) ;<br> P_ref:long_name = "Average annual precipitation for forcing ref" ;<br> P_ref:units = "m3/s" ;<br> float PET_ref(years, basins) ;<br> PET_ref:long_name = "Modeled average annual potential evaporation with forcing ref" ;<br> PET_ref:units = "m3/s" ;<br> float DisMod_ref(years, basins) ;<br> DisMod_ref:long_name = "Modeled Discharge with forcing ref" ;<br> DisMod_ref:units = "m3/s" ;<br> float E_f2000(years, basins) ;<br> E_f2000:long_name = "Modeled average annual evaporation with forcing f2000" ;<br> E_f2000:units = "m3/s" ;<br> float P_f2000(years, basins) ;<br> P_f2000:long_name = "Average annual precipitation for forcing f2000" ;<br> P_f2000:units = "m3/s" ;<br> float PET_f2000(years, basins) ;<br> PET_f2000:long_name = "Modeled average annual potential evaporation with forcing f2000" ;<br> PET_f2000:units = "m3/s" ;<br> float DisMod_f2000(years, basins) ;<br> DisMod_f2000:long_name = "Modeled Discharge with forcing f2000" ;<br> DisMod_f2000:units = "m3/s" ;<br> float E_cstmean(years, basins) ;<br> E_cstmean:long_name = "Modeled average annual evaporation with forcing cstmean" ;<br> E_cstmean:units = "m3/s" ;<br> float P_cstmean(years, basins) ;<br> P_cstmean:long_name = "Average annual precipitation for forcing cstmean" ;<br> P_cstmean:units = "m3/s" ;<br> float PET_cstmean(years, basins) ;<br> PET_cstmean:long_name = "Modeled average annual potential evaporation with forcing cstmean" ;<br> PET_cstmean:units = "m3/s" ;<br> float DisMod_cstmean(years, basins) ;<br> DisMod_cstmean:long_name = "Modeled Discharge with forcing cstmean" ;<br> DisMod_cstmean:units = "m3/s" ;<br> float E_cstintravar(years, basins) ;<br> E_cstintravar:long_name = "Modeled average annual evaporation with forcing cstintravar" ;<br> E_cstintravar:units = "m3/s" ;<br> float P_cstintravar(years, basins) ;<br> P_cstintravar:long_name = "Average annual precipitation for forcing cstintravar" ;<br> P_cstintravar:units = "m3/s" ;<br> float PET_cstintravar(years, basins) ;<br> PET_cstintravar:long_name = "Modeled average annual potential evaporation with forcing cstintravar" ;<br> PET_cstintravar:units = "m3/s" ;<br> float DisMod_cstintravar(years, basins) ;<br> DisMod_cstintravar:long_name = "Modeled Discharge with forcing cstintravar" ;<br> DisMod_cstintravar:units = "m3/s" ;</p> <p>// global attributes:<br> :author = "Julie Collignan, julie.collignan@lmd.ipsl.fr" ;<br> :model = "ORCHIDEE, IPSL, https://orchidee.ipsl.fr/" ;<br> :source_stations1 = "Global Runoff Data Centre (GRDC), https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html" ;<br> :source_stations2 = "Ministere de lenergie (February 2021), https://www.hydro.eaufrance.fr/" ;<br> :source_stations3 = "Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico, 2020" ;<br> :atmospheric_dataset = "GSWP3, Hyungjun, K. (2017), doi: 10.20783/DIAS.501" ;<br> :date = "28/07/2023";<br> }<br> ```</p>
Output raster datasets from Apalachicola Regional Restoration Initiative Open Pine Ecological Condition Model (2023)
<p>Output raster datasets from the 2023 Ecological Condition Model (ECM) for open pine ecosystems in the Apalachicola Regional Restoration Initiative (ARRI) area of the eastern Florida Panhandle. Our goal was to develop an ECM that would span all lands in the Apalachicola Regional Restoration Initiative (ARRI) area. As such, we used only datasets that were available throughout this region and did not rely on any corporate data layers from specific landowners. Furthermore, we sought to assess ecological condition at a high enough resolution to inform management decisions down to the level of individual forest stands. By taking this approach, we hoped to create ecological condition scores that could be used to inform restoration activities across all lands, and which could be updated through time to measure progress and to gauge the effectiveness of management activities.</p> <p>Output raster datasets include ecological condition for canopy, midstory and groundcover/shrub layers as well as overall ecological condition. Each raster contains ranked scores of estimated ecological condition: 1- Excellent, 2- Good, 3-Fair, and 4-Poor. </p> <p>NOTE- These outputs were created using tools stored in this repository: <a href="https://doi.org/10.5281/zenodo.8236853">https://doi.org/10.5281/zenodo.8236853</a> as well as several raster input layers stored in this repository: https://doi.org/10.5281/zenodo.8234220. </p> <p> </p> <p> </p> <p> </p>
Output raster datasets from an application of a fine resolution spatially explicit forest water yield model in Florida's panhandle
<p>These raster datasets are the output results for a spatial water yield model applied to an 11 county area in the state of Florida panhandle. The water yield model is adapted from Acharya, et al. 2022 and the spatial modelling process is detailed in this datasets associated publication. All data are in the WGS 1984 UTM Zone 16N coordinate system and have 10m horizontal spatial resolution. </p> <p>The output raster datasets contained here are water yield estimate informed with 2018 pine basal area, binary depth to water table, and average aridity index input rasters. These rasters have 10m spatial resolution, the raster extent covers 11 counties in the panhandle of Florida, the units are in centimeters of water yield per year. The water yield outputs consist of ten rasters representing the current water yield using the mean aridity raster, the water yield expected from the three pine tree thinning scenarios: 7m/hectare ba, 11 m/hectare, and 18 m/hectare, taken from the mean aridity index. Then rasters representing the water yield expected from the three thinning scenarios under maximum, and minimum aridity indexes.</p> <p> </p> <p>These ten outputs are listed here:</p> <p>"wy_current_mean" Based on 2018 BA conditions; Mean ARID</p> <p>"wy_18_mean" BA reduced to 18m2ha-1; Mean ARID</p> <p>"wy_11_mean" BA reduced to 11m2ha-1; Mean ARID</p> <p>"wy_7_mean" BA reduced to 7m2ha-1; Mean ARID</p> <p>"wy_ 18_max" BA reduced to 18m2ha-1; Maximum ARID</p> <p>"wy_ 11_max" BA reduced to 11m2ha-1; Maximum ARID</p> <p>"wy_7_max" BA reduced to 7m2ha-1; Maximum ARID</p> <p>"wy_ 18_min" BA reduced to 18m2ha-1; Minimum ARID</p> <p>"wy_ 11_min" BA reduced to 11m2ha-1; Minimum ARID</p> <p>"wy_ 7_min" BA reduced to 7m2ha-1; Minimum ARID</p> <p> </p> <p>Water yield was estimated for 2018 using the following datasets to inform the model in the Current Water Yield Calculation tool:</p> <ul> <li>Leaf area index modeled from a 2018 pine species basal area raster,</li> <li>Depth to water table data provided by Florida Geological Survey and reclassified as a binary raster,</li> <li>Average aridity index raster generated with precipitation data from PRISM Climate Group and MODIS PET data.</li> </ul> <p>For detailed information on how the above inputs were developed, please see the associated publication:</p> <p>Vernon, J., St. Peter, J., Crandall, C., Awowale, O.E., Medley, P., Drake, J., & Ibeanusi, V. (2023). Spatial application of southern pine water yield for prioritizing forest management activities. ISPRS International Journal of Geo-Information, 12(2), 34. <a href="https://doi.org/10.3390/ijgi12020034">https://doi.org/10.3390/ijgi12020034</a> </p>
Model_Input_Files_and_Simulation_Output
<p>This provides the BioRT-HBV model input and output for the paper submission "As above, so below: the growing importance of water and carbon processes beneath soils in a warmer and drier climate".<br><br>Coal_Creek_Original: Input and Output files associated with the model calibrated to average DOC years; used for making Figures 3, 4, 5, 6, 7, and 8.<br><br>Coal_Creek_High: Input and Output files associated with the model calibrated to high DOC years; used for making Figure 4.</p> <p>Rxn_Implementation_Results: Output files associated with the model simulations that have Resp-SZ only, and Resp-SZ & Resp-DZ; used for making Figure 5.<br><br>Input files and output for the various numerical experiments, used in figure 9: Coal_Creek_DryandWet, Coal_Creek_DryYears, Num_Exp_HBVResults_and_precipchem_files, Num_Exp_output<br><br>HBV-BioRT-new_ver(1).zip : files for the BioRT-HBV model</p>
Standard Error Estimates from ARRI ensemble GAM model outputs
<p><strong>Summary (Purpose)</strong></p> <p>Basal area per acre (BAA) standard error estimate (SEE) for all trees, pine trees, and non-pine trees across three diameter at breast height (DBH) size classes, 2- to 10-inch, 10- to 14-inch, and 14+ inch. Models were informed by relative density rasters from 2018 Light Detection and Ranging (lidar) point clouds.</p> <p><strong>Description</strong></p> <p>The GAM_SEE_rasters are modelled basal area per acre standard error estimate single band rasters. The units for the rasters’ are square foot per acre. Rasters are divided into three tree species groups: 'All' trees, 'Pine' trees (defined as trees of the genus<em> Pinus)</em>, and 'No-Pine' trees, and three size classes: LT 10 for trees with DBH between 2- and 10-inches, 10-14 for trees with DBH between 10- and 14- inch DBH, and GT 14 for trees with DBH greater than 14-inches.</p> <p>Ensemble generalized additive models (GAM) of estimated tree basal area per DBH class were created from Restore field plots and relative density canopy cover rasters, or RDCC (St. Peter, et al., 2023). This ensemble GAM model was created using the R script detailed in Hogland, 2021. The parameters used were 0.75 for the percent of data used to train the model (selected using random sampling with replacement), 50 models, and using gaussian family. The estimated BAA for each of the 50 ensemble GAM models for each cell were averaged (mean) to produce the GAM estimate, additionally the variability between these estimates was used to create the standard error estimate (SEE) for each cell. </p> <p> </p> <p>The 246 Restore field plots used to train the GAM model of basal area include measurements of all trees within four non-overlapping 9m radius circular subplots within a 36m square plot. Tree diameter at breast height (DBH), species, count, and condition measurements were recorded. Measurements were summarized to the plot and DBH (square inches) was converted to basal area per acre (square feet per acre) using the formula 0.005454 * DBH^2. Restore field plots were measured in the Spring of 2018. The RDCC metrics are 5m resolution multiband rasters produced by applying a custom r software function that uses the r software’s ‘lidR’ package to produce forest metrics summarized from Light Imaging Detection and Ranging (LiDAR) point clouds. ARSA LiDAR is a combination of three collections, Block 2 and 3 were collected in early 2018 and has a NPS of 0.7-m using a Riegl VQ-1560i lidar system. Leon county LiDAR data has a nominal pulse spacing (NPS) of 0.35-m and was acquired between February 05, 2018 and April 25, 2018 using the Leica ALS80 HP SN8137 and SN8235 lidar systems. Choctawhatchee data was acquired in early 2017, using the Riegl LMS-Q1560 lidar system and has a NPS of 0.7-m. Rasters were generated in their vendor provided spatial projection before being reprojected to UTM Zone 16.</p> <p> </p> <p>The 5m resolution RDCC bands were summarized to 40x40m to correspond with the area of our plots and used as predictor variables in the models of all trees basal area. Each 5m pixel value represents the estimated standard error of the basal area per acre estimate as if it was the center of a 40x40m (8x8 cells) plot surrounding that pixel. </p> <p> </p> <p>Reference:</p> <p>Hogland, J. (2021). Ensemble Generalized Additive Models (EGAM). Retrieved from Jupyter Notebook: <a href="https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing">https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing</a></p> <p>St. Peter, Joseph, Drake, Jason, Medley, Paul, & Ibeanusi, Victor. (2023). Relative Density Canopy Cover Outputs for Leon Lidar data in the Florida Panhandle 2018 [Data set]. In Remote Sensing (Vol. 13, Number 23, p. 4763). Zenodo. https://doi.org/10.5281/zenodo.8222114</p> <p><strong>Credits</strong></p> <p>This dataset was built by Joseph St. Peter of FAMU’s Center for Spatial Ecology and Restoration using 2018 LiDAR data funded by Leon County, Northwest Florida Water Management District, US Geological Survey and the USDA Forest Service and processed using the r package lidR. Restore plots were funded by the Gulf Coast Ecosystem Restoration Council (RESTORE Council) through an interagency agreement with the USDA Forest Service (17-IA-11083150-001) for the Apalachicola Tate’s Hell Strategy 1 project.</p> <p><strong>Use Limitations</strong></p> <p>This spatial data is based on various data collection and processing techniques as well as on modeling or interpretation. While this data uses the most current and complete information available at the time of production, spatial data and derivative products may vary in accuracy. Spatial data are often developed from sources of differing accuracy which may be accurate only at certain scales. This data has been quality checked but may contain spurious errors or be incomplete or inappropriate for certain uses. Spatial data products used for purposes other than those for which they were created, may yield inaccurate or misleading results. The USDA Forest Service, Florida A&M University, and the Center for Spatial Ecology & Restoration (CSER) reserves the right to correct, update, modify, remove or replace GIS products at any time and without notification. This data may not be distributed without written permission from the Center for Spatial Ecology & Restoration (CSER) at Florida A&M University, and/or the USDA Forest Service.</p>
Input and output data from simulations of 2D valves and 3D inflow-outflow model using particle methods
<p>Input and output data of open-source softwares for computational fluid dynamics simulation involving fluid-structure interaction.</p> <p> </p> <p><strong>Data from two studies</strong></p> <ol> <li>Verifications of the weakly-compressible smoothed particle hydrodynamics (WCSPH) method, open-source code <a href="https://www.sphinxsys.org">SPHinXsys</a>, when applied to the flow of idealized 2D valve models.</li> <li>Validations of inflow-outflow model in moving particle semi-implicit (MPS) method, open-source code <a href="https://github.com/rubensamarojr/polymps/tree/inOutflow">PolyMPS</a>.</li> </ol> <p> </p> <p><strong>Folders and Files</strong></p> <p><strong>valve-2D.zip </strong>is the folder with data from the idealized models of vertical and curved 2D valves:</p> <ul> <li>Vertical valves with parameters provided in <a href="https://doi.org/10.1016/j.jcp.2010.08.005">Gil et al., 2010</a></li> <li>Curved valves with parameters provided in <a href="http://doi.org/10.1007/s00466-013-0890-3">Wick, 2014</a></li> <li>source files (.cpp): input data (physical and numerical parameters) for SPHinXsys</li> <li>text files: SPHinXsys (.dat) and Reference (.tsv) results</li> <li>python files (.py): Generates the graphics</li> </ul> <p> </p> <p><strong>inflow-outflow-3D.zip </strong>is the folder with data from the inflow-outflow model in MPS:</p> <ul> <li>Fluid physical properties of water <ul> <li><span>\(\rho=1000kg/m^3 , \,\, \nu=10^{-6}m/s^{-2}\)</span></li> </ul> </li> <li>Pipes of length <span>\(L=0.15m\)</span>: <ul> <li>circular section of diameter <span>\(D=0.1m\)</span>.</li> <li>square section of sides <span>\(S=0.1m\)</span>.</li> </ul> </li> <li>Constante pressure variation (<span>\(\Delta P = 30 \,\, or \,\, 50 \,\, Pa\)</span>) between inflow and outflow: <ul> <li><span>\(\frac{\partial p}{\partial x} = - \frac{\Delta P}{L}, \\ \Delta P = P_{outflow} - P_{inflow}\)</span></li> </ul> </li> </ul> <ul> <li>Sinusoidal pressure variation (<span>\(\Delta P =700Pa \,\, , \,\, T = 2.0s\)</span>) between inflow and outflow <ul> <li><span>\(\frac{\partial p}{\partial x} = - \frac{\Delta P}{L} \sin \omega t \, \\ \omega = \frac{2\pi}{T} \\ Delta P = P_{outflow} - P_{inflow}\)</span></li> </ul> </li> <li>input data (.json, .grid, .stl): physical properties, numerical parameters and geometries for PolyMPS can be found at <a href="https://github.com/rubensamarojr/polymps/tree/inOutflow/input">https://github.com/rubensamarojr/polymps/tree/inOutflow/input</a></li> <li>text files (.txt): PolyMPS and OpenFOAM results</li> <li>python files (.py): Generates the graphics</li> </ul> <p> </p> <p><strong>References</strong></p> <p><a href="https://doi.org/10.1016/j.jcp.2010.08.005">A. J. Gil. The Immersed Structural Potential Method for haemodynamic applications. J. Comput. Phys., 229 (2010), pp. 8613-8641</a></p> <p><a href="https://doi.org/10.1007/s00466-013-0890-3">T. Wick. Flapping and contact FSI computations with the fluid–solid interface-tracking/interface-capturing technique and mesh adaptivity. Comput Mech 53, 29–43 (2014)</a></p> <p><a href="https://doi.org/10.1016/j.cma.2014.10.040">D. Kamensky, et al. An immersogeometric variational framework for fluid–structure interaction: Application to bioprosthetic heart valves Comput. Methods Appl. Mech. Engrg., 284 (2015), pp. 1005-1053</a></p> <p><a href="https://doi.org/10.1016/j.cma.2015.12.023">C. Kadapa et al. A fictitious domain/distributed Lagrange multiplier based fluid–structure interaction scheme with hierarchical B-Spline grids. Comput. Methods Appl. Mech. Engrg., 301 (2016), pp. 1-27</a></p> <p><a href="https://doi.org/10.1016/j.jcp.2015.10.015">Jie Liu. A second-order changing-connectivity ALE scheme and its application to FSI with large convection of fluids and near contact of structures. J. Comput. Phys., 304 (2016), pp. 308-423</a></p>
Model output from "The chance of freezing – a conceptional study to parameterize temperature-dependent freezing by including randomness of ice-nucleating particle concentrations"
<p>Model output from "The chance of freezing – a conceptional study to parameterize temperature-dependent freezing by including randomness of ice-nucleating particle concentrations", accepted for publication in Atmospheric Chemistry and Physics, 2023, same authors.<br> The simulations were done using MIMICA version4 (Savre at el., 2014) and the data includes all model output presented in the publication.</p>
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model - model outputs
<p>This dataset contains the model outputs used in the figures in the paper entitled "Implementing the iCORAL (version 1.0) coral reef CaCO<sub>3</sub> production module in the iLOVECLIM climate model" submitted to GMD. For the description of the model and simulations we refer to this article.</p> <p>Provided files:</p> <ul> <li>Surface values of temperature (temp), salinity (salt), phosphate (opo4) and aragonite saturation state (omega) for:</li> </ul> <p>The modern period (mean of 2000-2010): <strong>temp_modern.nc</strong>, <strong>salt_modern.nc</strong>, <strong>opo4_modern.nc</strong>, <strong>omega_modern.nc</strong></p> <p>The pre-industrial (PI, mean of last 100 years of the simulation): <strong>temp_PI.nc</strong>, <strong>salt_PI.nc</strong>, <strong>opo4_PI.nc</strong>, <strong>omega_PI.nc</strong></p> <ul> <li>Coral location for:</li> </ul> <p>Imin=50 μE/m2/s: <strong>coral_location_Imin50.nc</strong></p> <p>Imin=300 μE/m2/s: <strong>coral_location_Imin300.nc</strong></p> <p>The values indicate:</p> <p>4 = presence of corals in the model simulation (coral area less or equal to 5% of the grid cell area) but not in observations</p> <p>3 = presence of corals in both model and observational data</p> <p>2 = presence of corals in observational data but not in the model simulation</p> <p>1 = presence of corals in the model simulation (coral area more than 5% of the grid cell area) but not in observations</p> <ul> <li>Global coral reef area (10<sup>3</sup> km<sup>2</sup>) and <em>I<sub>min</sub></em> (the minimum light intensity necessary for reef growth, µE m<sup>-2</sup> s<sup>-1</sup>): <strong>Total_area_vs_Imin.txt</strong></li> <li>Global coral reef carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and <em>I<sub>min</sub></em> (the minimum light intensity necessary for reef growth, µE m<sup>-2</sup> s<sup>-1</sup>): <strong>Total_prod_vs_Imin.txt</strong></li> <li>Global coral reef carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and <em>I<sub>k</sub></em> (the saturating light intensity, µE m<sup>-2</sup> s<sup>-1</sup>): <strong>Total_prod_vs_Ik.txt</strong></li> <li>Global coral reef carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and <em>g<sub>max</sub></em> (the maximum production growth): <strong>Total_prod_vs_gmax.txt</strong></li> <li>Global carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and global coral reef area (10<sup>3</sup> km<sup>2</sup>):<strong> Total_prod_vs_total_area.txt</strong></li> <li>Root mean square error (RMSE, kg CaCO<sub>3</sub> m<sup>-2</sup> yr<sup>-1</sup>) between the simulations and the observational data of regional production (Perry et al., 2018) and global production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>): <strong>Total_production_vs_rmse_Perry.txt</strong></li> <li>Root mean square error (RMSE, kg CaCO<sub>3</sub> m<sup>-2</sup> yr<sup>-1</sup>) between the simulations and the observational data of regional production (Perry et al., 2018) and coral reef area (10<sup>3</sup> km<sup>2</sup>):<strong> Total_area_vs_rmse_Perry.txt</strong></li> </ul>
Aotearoa New Zealand RSQSim model outputs for tsunami modelling
<p>RSQSim model outputs to support the publication: "<strong>A Novel Method to Determine Probabilistic Tsunami Hazard using a Physics-based Synthetic Earthquake Catalog: a New Zealand Case Study</strong>" by Hughes et al. (2023; JGR, submitted). In many respects, these model outputs represent an updated version of the New Zealand RSQSim catalogue published by Shaw et al. (2022; <a href="http://doi.org/10.5281/zenodo.5534462">doi.org/10.5281/zenodo.5534462</a>).</p> <p>This archive contains:</p> <ol> <li>The outputs of an updated RSQSim synthetic earthquake catalogue for Aotearoa-New Zealand.</li> <li>A ~30kyr subset of the main catalogue, trimmed to a minimum M<sub>W</sub> of 7.0.</li> <li>Summary figures comparing the statistical properties of the synthetic catalogue to observations of New Zealand seismicity.</li> <li>Python scripts for visualizing the synthetic earthquake catalogue, and calculating seafloor displacements from synthetic earthquakes that can be used as tsunami model inputs.</li> </ol> <p>For more information, please refer to the publications cited above and/or the more detailed README files within the archive.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.