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Sea-ice induced Southern Ocean subsurface warming and surface cooling in a warming climate: ROMS model data
<p><strong>Abstract:</strong></p> <p>This data set contains model output data from the regional ocean modelling system (ROMS) that was set up for the Southern Ocean (south of 24 °S; Haumann, 2016) to analyze the effects of changing surface freshwater and momentum fluxes on Southern Ocean water-mass changes over the period 1980 to 2011. All data is provided in NetCDF format. The data set contains 3 sets 40-year long model spin-up, control, and 3 perturbation simulations each. The sets differ by their model mean state to assess the effect of surface salinity biases on the results. One set of simulations are reference simulations where salinity is restored to the observed surface salinity during model spin-up, and the other two sets are simulations in which the restoring surface salinity has been altered by plus and minus 0.1 PSU, respectively. The control and perturbation simulations are 40-year extensions of the respective model spin-up simulations. In the perturbation simulations either the surface freshwater fluxes or momentum fluxes are instantaneously perturbed after the spin-up simulation to reflect the observation-derived changes in these surface fluxes and then held constant (at the perturbed level) for 40 years. They consist of sea-ice freshwater flux, glacial meltwater flux, and atmosphere-ocean momentum flux changes. Details on the model setup, forcing, and simulations can be obtained from the related research article by Haumann et al. (2020; https://doi.org/10.1029/2019AV000132).</p> <p><br> <strong>Contacts:</strong></p> <p>F. Alexander Haumann, ORCID: 0000-0002-8218-977X. Email: alexander.haumann@gmail.com</p> <p><br> <strong>License:</strong></p> <p>When using this data users must cite the original research article by Haumann et al. (2020) published in AGU Advances (https://doi.org/10.1029/2019AV000132). This model output data set is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p> <p><br> <strong>Citations:</strong></p> <p>Haumann, F. A., Gruber, N., Münnich, M. (2020): Sea-ice Induced Southern Ocean Subsurface Warming and Surface Cooling in a Warming Climate. AGU Advances, 1, e2019AV000132. https://doi.org/10.1029/2019AV000132</p> <p>Haumann, F. A., Gruber, N., Münnich, M. (2020): Sea-ice induced Southern Ocean subsurface warming and surface cooling in a warming climate: ROMS model data. (Version 1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3709154</p> <p><br> <strong>Data processing:</strong></p> <p>The model data provided here has been processed for analysis and, due to storage constraints, only variables and averages used in the related research article (Haumann et al., 2020) are published here. Years refer to model years since initiation, i.e. the start of the spin-up simulation, and reach a maximum of 80 at the end of the control and perturbation experiments. Averages are either annual means over an indicated period ('yearly'), overall means over an indicated period ('mean'), or monthly climatologies over an indicated period ('clim'). 'Yearly' and 'Clim' values are provided either for surface fields or vertically integrated fields ('surf'). 'Mean' values are provided on the native vertical model grid as 3D fields. 'Yearly' zonal mean ('zonalmean') values have been first interpolated to from the native vertical model grid to a regular vertical z-level grid and then zonally averaged. All data has been stored in NetCDF format and compressed using level 1 deflation (https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression). Full monthly mean model output from these simulations and the forcing fields can be obtained from the corresponding author upon request (alexander.haumann@gmail.com).</p> <p><br> <strong>Standards:</strong></p> <p>Data files have been formatted as Network Common Data Form (NetCDF; https://www.unidata.ucar.edu/software/netcdf/) in Hierarchical Data Format, version 5 (https://portal.hdfgroup.org/display/knowledge/HDF5+Documentation)</p> <p><br> <strong>Contents:</strong></p> <p><em>Data</em><br> - ROMS_SO_d025_grd.nc, ROMS grid file (Note: one row of ghost points on either side of the grid), compressed netcdf-file<br> - ROMS_SO_d025_ref_spinup, ROMS output from reference spin-up simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_ref_spinup_clim.31-40.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_ref_spinup_mean.31-40.nc, 3D averaged fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_ref_spinup_yearly.31-40.surf.nc, 2D surface or vertically integrated annual mean fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_ref_spinup_yearly.zlev.zonalmean.31-40.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 31 to 40, netcdf-file<br> - ROMS_SO_d025_minus01_spinup, ROMS output from minus 0.1 PSU sensitivity spin-up simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_minus01_spinup_clim.31-40.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_minus01_spinup_mean.31-40.nc, 3D averaged fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_minus01_spinup_yearly.31-40.surf.nc, 2D surface or vertically integrated annual mean fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_minus01_spinup_yearly.zlev.zonalmean.31-40.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 31 to 40, netcdf-file<br> - ROMS_SO_d025_plus01_spinup, ROMS output from plus 0.1 PSU sensitivity spin-up simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_plus01_spinup_clim.31-40.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_plus01_spinup_mean.31-40.nc, 3D averaged fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_plus01_spinup_yearly.31-40.surf.nc, 2D surface or vertically integrated annual mean fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_plus01_spinup_yearly.zlev.zonalmean.31-40.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 31 to 40, netcdf-file<br> - ROMS_SO_d025_ref_ctrl, ROMS output from reference control simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_ref_ctrl_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_ctrl_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_ctrl_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_ctrl_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_ctrl_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_ctrl_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_minus01_ctrl, ROMS output from minus 0.1 PSU sensitivity control simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_minus01_ctrl_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_ctrl_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_ctrl_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_ctrl_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_ctrl_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_ctrl_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_plus01_ctrl, ROMS output from plus 0.1 PSU sensitivity control simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_plus01_ctrl_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_ctrl_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_ctrl_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_ctrl_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_ctrl_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_ctrl_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_ref_seaice, ROMS output from reference sea-ice freshwater flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_ref_seaice_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_seaice_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_seaice_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_seaice_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_seaice_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_seaice_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_minus01_seaice, ROMS output from minus 0.1 PSU sensitivity sea-ice freshwater flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_minus01_seaice_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_seaice_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_seaice_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_seaice_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_seaice_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_seaice_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_plus01_seaice, ROMS output from plus 0.1 PSU sensitivity sea-ice freshwater flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_plus01_seaice_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_seaice_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_seaice_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_seaice_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_seaice_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_seaice_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_ref_glacial, ROMS output from reference glacial meltwater flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_ref_glacial_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_glacial_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_glacial_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_glacial_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_glacial_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_glacial_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_minus01_glacial, ROMS output from minus 0.1 PSU sensitivity glacial meltwater flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_minus01_glacial_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_glacial_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_glacial_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_glacial_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_glacial_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_glacial_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_plus01_glacial, ROMS output from plus 0.1 PSU sensitivity glacial meltwater flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_plus01_glacial_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_glacial_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_glacial_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_glacial_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_glacial_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_glacial_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_ref_momentum, ROMS output from reference atmopshere-ocean momentum flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_ref_momentum_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_momentum_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_momentum_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_momentum_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_momentum_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_momentum_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_minus01_momentum, ROMS output from minus 0.1 PSU sensitivity atmopshere-ocean momentum flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_minus01_momentum_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_momentum_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_momentum_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_momentum_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_momentum_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_momentum_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_plus01_momentum, ROMS output from plus 0.1 PSU sensitivity atmopshere-ocean momentum flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_plus01_momentum_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_momentum_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_momentum_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_momentum_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_momentum_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_momentum_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> <br> <em>File headers</em><br> - ROMS_SO_d025_grd:<br> angle [radians]: angle between xi axis and east<br> f [second-1]: Coriolis parameter at RHO-points<br> h [meter]: Final bathymetry at RHO-points<br> hraw [meter]: Working bathymetry at RHO-points<br> lat_rho [degree_north]: latitude of RHO-points<br> lat_psi [degree_north]: latitude of PSI-points<br> lat_u [degree_north]: latitude of U-points<br> lat_v [degree_north]: latitude of V-points<br> lon_rho [degree_east]: longitude of RHO-points<br> lon_psi [degree_east]: longitude of PSI-points<br> lon_u [degree_east]: longitude of U-points<br> lon_v [degree_east]: longitude of V-points<br> mask_rho [-]: mask on RHO-points<br> mask_u [-]: mask on U-points<br> mask_v [-]: mask on V-points<br> pm [meter-1]: curvilinear coordinate metric in XI<br> pn [meter-1]: curvilinear coordinate metric in ETA<br> spherical [char]: Grid type logical switch<br> - ROMS_SO_d025_*_clim.*.surf.nc, ROMS_SO_d025_*_yearly.*.surf.nc:<br> time [days since 0000-01-01 00:00:00]: time<br> lon [degree_east]: longitude<br> lat [degree_north]: latitude<br> zeta [meter]: averaged free-surface elevation<br> temp [degree_celsius]: averaged potential temperature<br> salt [psu]: averaged salinity<br> rho [kilogram meter-3]: averaged density anomaly from model reference density of 1027 kilogram meter-3<br> mld [meter]: averaged mixed layer depth<br> n2 [seconds-2]: averaged buoyancy frequency (upper 100 m)<br> n2temp [seconds-2]: averaged buoyancy frequency due to temperature (upper 100 m)<br> n2salt [seconds-2]: averaged buoyancy frequency due to salinity (upper 100 m)<br> heat_100 [10^18 J]: averaged ocean heat content (upper 100 m)<br> heat_100_2000 [10^18 J]: averaged ocean heat content (between 100 m and 2000 m)" ;<br> - ROMS_SO_d025_*_mean.*.nc:<br> time [days since 0000-01-01 00:00:00]: time<br> lon [degree_east]: longitude<br> lat [degree_north]: latitude<br> zeta [meter]: averaged free-surface elevation<br> temp [degree_celsius]: averaged potential temperature<br> salt [psu]: averaged salinity<br> rho [kilogram meter-3]: averaged density anomaly from model reference density of 1027 kilogram meter-3<br> - ROMS_SO_d025_*_yearly.zlev.zonalmean.*.nc:<br> time [days since 0000-01-01 00:00:00]: time<br> lat [degree_north]: latitude of RHO-points<br> lat_v [degree_north]: latitude of V-points<br> depth [meter]: depth of levels<br> temp [degree_celsius]: averaged potential temperature<br> salt [psu]: averaged salinity<br> rho [kilogram meter-3]: averaged density anomaly from model reference density of 1027 kilogram meter-3<br> moc [10^6 meter^3 second^-1]: averaged meridional overturning circulation on V-points<br> Missing values: missing values are filled as '-9.99e+20'.</p> <p><em>Metadata</em><br> - README.txt, metadata, text format</p> <p><br> <strong>References:</strong></p> <p>Haumann, F. A. (2016): Southern Ocean response to recent changes in surface freshwater fluxes. Doctoral Thesis. ETH Zurich. doi:10.3929/ethz-b-000166276.</p> <p><br> <strong>Acknowledgments:</strong></p> <p>This work was supported by ETH Research Grant CH2-01 11-1 and by the SNSF grant numbers P2EZP2_175162 and P400P2_186681, as well as NSF’s SOCCOM Project under NSF Award No. PLR-1425989.</p>
Short-Term Effects on Vegetation in Soil Warming Plus Nitrogen Addition Experiment at Harvard Forest 2006
The purpose of this experiment is to examine the interactive affects of warming and N additions on plant diversity. The experiment consists of four treatments (control, heated +N, heated -N, +N only) with six replicates per treatment in a completely randomized design. Average soil temperature in the heated plots will be elevated 5 deg C above ambient by the use of buried heating cables placed at 10 cm depth in the soil and spaced 20 cm apart. The heating cables will be controlled by a data logger that monitors thermistors (5 per plot) every 10 min. Plots will automatically turn on and off to maintain a 5 deg C temperature difference between the heated and control plots. The N addition plots (heated +N, +N only) will be fertilized following the protocol of the Chronic Nitrogen Addition Study. An aqueous solution of NH4NO3 will be applied at a rate equivalent to the low N plots at the chronic N study (5 g m-2 yr-1). Fertilizer will be applied in equal monthly doses during the growing season (Apr-Oct). The control plots and unfertilized, heated plots (heated -N) will receive water only.
Effects of Warming on Soil Microbial Oxidative Activity at Harvard Forest 2013
As Earth’s climate warms, the massive stores of carbon found in soil are predicted to become depleted, and leave behind a smaller carbon pool that is less accessible to microbes. At a long-term forest soil-warming experiment in central Massachusetts, soil respiration and bacterial diversity have increased, while fungal biomass and microbially-accessible soil carbon have decreased. Here, we evaluate how warming has affected the microbial community’s capability to degrade chemically-complex soil carbon using lignin-amended BioSep beads. We profiled the bacterial and fungal communities using PCR-based methods and completed extracellular enzyme assays as a proxy for potential community function. We found that lignin-amended beads selected for a distinct community containing bacterial taxa closely related to known lignin degraders, as well as members of many genera not previously noted as capable of degrading lignin. Warming tended to drive bacterial community structure more strongly in the lignin beads, while the effect on the fungal community was limited to unamended beads. Of those bacterial operational taxonomic units (OTUs) enriched by the warming treatment, many were enriched uniquely on lignin-amended beads. These taxa may be contributing to enhanced soil respiration under warming despite reduced readily available C availability. In aggregate, these results suggest that there is genetic potential for chemically complex soil carbon degradation that may lead to extended elevated soil respiration with long-term warming.
Soil Carbon Dioxide and Oxygen at the Soil Warming Plus Nitrogen Experiment at Harvard Forest since 2018
This dataset includes soil air CO2 and O2 concentrations measured from the organic/mineral horizon interface and 10, 30, and 50 cm depths of the mineral soil at SWaN using permanently installed stainless steel gas wells. Measurements were made 4-8 times throughout the year in 2018, 2019, and 2020.
Effects of Long-Term Soil Warming on Ecosystem Function at Harvard Forest 2019
Across biomes, soil biodiversity promotes ecosystem functions. However, whether this relationship will be maintained within ecosystems under climate change is uncertain. Here, using two long-term soil warming experiments, we investigated how warming affects the relationship between ecosystem functions and bacterial diversity across seasons, soil horizons, and warming duration. Soils were sampled from these warming experiments located at the Harvard Forest Long-Term Ecological Research (LTER) site, where soils had been heated +5°C above ambient for 13 or 28 years at the time of sampling. We assessed seven measurements representative of different ecosystem functions and nutrient pools. We also surveyed bacterial community diversity. We found that ecosystem function was significantly affected by season, with autumn samples having a higher intercept than summer samples in our model, suggesting a higher overall baseline of ecosystem function in the fall. The effect of warming on bacterial diversity was similarly affected by season, where warming in the summer was associated with decreased bacterial evenness in the organic horizon. Despite the decreased bacterial evenness in the warmed plots, we found that the relationship between ecosystem function and bacterial diversity was unaffected by warming or warming duration. Our findings highlight that season is a consistent driver of ecosystem function as well as a modulator of climate change effects on bacterial community evenness.
Effects of Long-Term Soil Warming on Microbial Yield, Acquisition, and Stress Traits at Harvard Forest 2014
Soil microbial traits drive ecosystem functions. This relationship can explain why microbial functional diversity is typically positively correlated with ecosystem function. However, microbial adaptation to climate change related warming stress can shift microbial traits with direct implications for carbon cycling in the soil. Here, we investigated how long-term warming affects the relationship between microbial trait diversity and ecosystem function. Soils were sampled after 24 years of +5\degree C warming alongside unheated control soils from the Harvard Forest Long-Term Ecological Research site. Ecosystem function was estimated from six different enzyme activities and microbial biomass. This data was coupled with metatranscriptomics sequencing, where reads were assigned to yield, acquisition, or stress trait categories. We found that in organic horizon soils, warming decreased the richness of acquisition-related traits. In the mineral soils, we observed that heated soils exhibited a negative relationship with the richness of acquisition related traits. These results suggest that the microbial communities exposed to long-term warming is shifting away from a resource acquisition life history strategy.
Water quality, temperature, ash-free dry mass, photosynthetic activate radiation (PAR), and zooplankton data from a warming and DOC subsidy experiment, 2020 - 2021.
This dataset includes chlorophyll-a concentrations, periphyton biomass estimates, water quality measurements, and qualitative observations from a large-scale mesocosm experiment conducted in the Green Lakes Watershed, Colorado. The experiment was designed to test how earlier lake ice-off and increased dissolved organic material (DOM), associated with terrestrial plant encroachment in alpine watersheds, interactively influence aquatic food webs. In fall 2019, twenty 2600L “megacosms” were established at Sandy Corner (3300 m ASL; 40.042289, -105.584006), left to fill with snowmelt, and maintained throughout the 2020 open water season. The experiment followed a 2 × 2 randomized block design manipulating ice-off timing (via black vs. beige tank coloration) and DOM inputs (presence/absence of willow leaf packs), with five replicates per treatment. All tanks were seeded with sediments and zooplankton from both alpine and montane lakes (Green Lake 1 and Green Lake 4), and instrumented with thermistors recording surface and hypolimnion temperature every two hours year-round. Periphyton growth was monitored using clay tiles, sampled across five time points. Chlorophyll-a concentrations were extracted from filtered water samples and analyzed spectrophotometrically. Periphyton biomass was estimated via ash-free dry mass (AFDM) determinations, based on the mass lost on combustion of material scraped from tiles. Water quality was measured 1–2 times weekly using a YSI ProPlus multiprobe and Li-Cor quantum sensor, and snow/ice cover was qualitatively assessed monthly during winter.
Forest expansion for different warming scenarios simulated for 2010 to 3000 CE with LAVESI for Siberia
<p>Simulations with the spatially explicit and individual-based Siberian forest model LAVESI (Kruse et al., 2016, 2018, 2019) were set-up for transect in four focus regions covering the East Siberian treeline and tundra area (details in Kruse & Herzschuh, submitted). The model was updated to include climate forcing data for 300-800 km long and 20 m wide transects necessary for simulating the forest development between the northern taiga forests and the coast of the Arctic Ocean. Forced with climate forecasts driven by relative concentration pathway (RCP) scenarios 2.6, 4.5 and 8.5 and one with half the warming of RCP 2.6 named 2.6*. These were extended until 3000 AD either following the cooling of the scenarios after peak-warming, or with an arbitrary cooling back to levels of the 20th century.</p> <p>During the simulations, three key variables were extracted in 10-year steps for 2000-3000 AD: single-tree line, treeline, and, forest line, which are defined as the northernmost position of stands with >1 stem (tree > 1.3 m tall) per ha, the northernmost position of a forest cover not falling below 1 stem per ha, and, the northernmost position of a forest cover not falling below 100 stems ha per ha (see for a graphical representation Fig. 2 in Kruse et al., 2019). The determined treeline at year 2000 was used as baseline expansion and subtracted from each following years’ values.</p> <p>Furthermore, the tundra area was estimated for each of the four regions as the area between the treeline and the Arctic Ocean, based on interpolating the treeline position at the four transects over the complete modern treeline (Walker et al., 2005).</p> <ol> <li>Content of Table 1 "Kruse_and_Herzschuh_2022_Forest_expansion_in_Siberia_2010_to_3000_CE.csv": <ul> <li>Column 1: Scenario: RCP scenario used</li> <li>Column 2: Region: One of the four regions, from east-to-west Taimyr Peninsula, Buor Khaya Peninsula, Kolyma River Basin, Chukotka</li> <li>Column 3: Year: Year in CE of the simulation in 10 year steps</li> <li>Column 4: Forest line in m</li> <li>Column 5: Treeline in m</li> <li>Column 6: Single-tree line in m</li> </ul> </li> <li>Content of Table 2 "Kruse_and_Herzschuh_2022_Tundra_area_in_Siberia_2010_to_3000_CE.csv": <ul> <li>Column 1: Scenario: RCP scenario used</li> <li>Column 2: Year: Year in CE of the simulation in 10 year steps</li> <li>Column 3: Tundra area at region Taimyr Peninsula in km²</li> <li>Column 4: Tundra area at region Buor Khaya Peninsula in km²</li> <li>Column 5: Tundra area at region Kolyma River Basin in km²</li> <li>Column 6: Tundra area at region Chukotka in km²</li> </ul> </li> <li>The zip-file "Kruse_and_Herzschuh_2022_Forest_expansion_maps_in_Siberia_2010_to_3000_CE.zip" contains shape files with the tundra area in 10 year steps starting in 2000 until 3000 CE <ul> <li>projection: Albers azimuthal equidistant projection centered at Longitude of 100 °E (PROJ4 string: "+proj=aea +lat_1=50 +lat_2=70 +lat_0=56 +lon_0=100 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs")</li> </ul> </li> </ol> <p>This study was supported by the Initiative and Networking Fund of the Helmholtz Association and by the ERC consolidator grant Glacial Legacy of Ulrike Herzschuh (grant no. 772852).</p>
Forage Resources in Warming and Removal Plots, Almont, CO, 2019
This is data collected to explore the impacts of warming and dominant species removal on the quantity and quality of plants for cattle foraging. The data were collected from the Colorado low elevation site (Almont) of the Warming and Removal in Mountains experiment which examines the direct and indirect impacts of climate change on plant and soil communities. Treatments include a control, warming (+1.5C), removal of the dominant species (Wyethia Amplexicalus), and both warming and dominant species removal. The dataset includes data that were collected in 2019 as well as historical data from the site. From 2019 we have in situ air temperature contained in and soil temperature data and an assessment of plant cover from every plot. We then have a compiled set of plant traits for each of the nine most common species including the leaf nitrogen, crude protein content, and forage quality class which are used for analysis on forage quality. The dataset also includes the annual plant cover data collected at peak season from 2013 to 2021 which was compared to daily temperature and precipitation data from the same date range collected by the National Oceanic and Atmospheric Administration. All reported figures and statistics published can be created from this data package.
Modeling the effect of explicit vs implicit representation of grazing on ecosystem carbon and nitrogen cycling in response to elevated carbon dioxide and warming in arctic tussock tundra, Alaska - Dataset A
We use a simple model of coupled carbon and nitrogen cycles in terrestrial ecosystems to examine how explicitly representing grazers versus having grazer effects implicitly aggregated in with other biogeochemical processes in the model alters predicted responses to elevated carbon dioxide and warming. The aggregated approach can affect model predictions because grazer-mediated processes can respond differently to changes in climate from the processes with which they are typically aggregated. We use small-mammal grazers in arctic tundra as an example and find that the typical three-to-four-year cycling frequency is too fast for the effects of cycle peaks and troughs to be fully manifested in the ecosystem biogeochemistry. We conclude that implicitly aggregating the effects of small-mammal grazers with other processes results in an underestimation of ecosystem response to climate change relative to estimations in which the grazer effects are explicitly represented. The magnitude of this underestimation increases with grazer density. We therefore recommend that grazing effects be incorporated explicitly when applying models of ecosystem response to global change.
Modeling the effect of explicit vs implicit representation of grazing on ecosystem carbon and nitrogen cycling in response to elevated carbon dioxide and warming in arctic tussock tundra, Alaska - Dataset B
We use a simple model of coupled carbon and nitrogen cycles in terrestrial ecosystems to examine how explicitly representing grazers versus having grazer effects implicitly aggregated in with other biogeochemical processes in the model alters predicted responses to elevated carbon dioxide and warming. The aggregated approach can affect model predictions because grazer-mediated processes can respond differently to changes in climate from the processes with which they are typically aggregated. We use small-mammal grazers in arctic tundra as an example and find that the typical three-to-four-year cycling frequency is too fast for the effects of cycle peaks and troughs to be fully manifested in the ecosystem biogeochemistry. We conclude that implicitly aggregating the effects of small-mammal grazers with other processes results in an underestimation of ecosystem response to climate change relative to estimations in which the grazer effects are explicitly represented. The magnitude of this underestimation increases with grazer density. We therefore recommend that grazing effects be incorporated explicitly when applying models of ecosystem response to global change.
Phenology of flowers and leaves following experimental warming in the initiation and maturation years for 7 understory boreal plants at the Bonanza Creek Long Term Ecological Research (BNZ LTER) site in Interior Alaska: 2017-2019
This dataset contains the results of experimental warming of flower and leaf buds for 7 understory boreal plants: Rhododendron groenlandicum, Rosa acicularis, Rubus chamaemorus, Shepherdia canadensis, Viburnum edule, Vaccinium uliginosum, and Vaccinium vitis-idaea. Plants in two cohorts were subjected to one of four treatments: warming in the initiation year (the year prior to flowering or leaf-out) only, warming in the maturation year (the year of flowering or leaf-out) only, warming in both years, or no warming (controls). The timing of flowering (both cohorts) and leaf-out (usually one cohort) was monitored. We also tracked developmental stages of the flower bud primordia using repeated desctructive sampling followed scanning electron microscopy throughout the initiation years. Environmental data associated with the plots, including air temperature throughout the summer, soil temperature and depth of ground thaw in late May, and canopy cover, are also reported.
Phenological Responses of Equisetum arvense to Early-season Soil and Air Warming in Reseach Plots on the University of Alaska Fairbanks North Campus - May through September 2023
This dataset contains the phenological stages of Equisetum arvense (horsetails) subjected to snow removal in spring (which led to ground warming) and to air warming using open topped chambers in a two by two factorial experiment. The main dataset contains the phenological phases of plants in plots of subjected to the four treatments between 1 May and 29 September 2023, and height data. Supplementary files contain temperature data for dataloggers placed in the soil and in the air and file information.
warmXtrophic: plant community responses to the individual and interactive effects of climate warming and herbivory across multiple years at Kellogg Biological Station Long-Term Ecological Research Sites (KBS LTER), Michigan, USA, and University of Michigan Biological Station (UMBS), Michigan, USA.
Climate change has both direct and indirect effects on ecological communities. Whereas most climate change ecology experiments manipulate abiotic drivers to measure direct effects of climate on species or communities, fewer quantify the indirect effects through biotic interactions, especially over multiple sites and years. In this factorial experiment we manipulate temperature through open-top chambers, and the level of insect herbivory through insecticide. At two early successional field sites separated by 3 degrees of latitude and 3°C of mean annual temperature (University of Michigan Biological Station, Pellston, MI and Kellogg Biological Station, Hickory Corners, MI), 6 replicate 1-m2 plots per treatment were installed in May 2015. 12 plots per site are at ambient temperature, 12 are warmed with year-round non-UV filtering polycarbonate and wood frame construction OTCs for tall-stature plants (Welshofer et al. 2018 MEE). Insecticide reduces insect herbivory in half the plots (Welshofer et al. 2018 Oecologia). Over the course of the experiment, OTCs warmed the plant communities by 1.9°C-3.0°C on average over the growing season. Each year, through 2021, plant traits and community responses were measured at the species level: plant phenology (green-up, flowering, flowering duration, seed set); plant percent cover (aerial % cover of the 1m2 plot); plant traits (specific leaf area, C and N content), herbivory damage to leaves, and plant species biomass (only in 2021). Further methodological details are found within each response variable metadata. This experiment is ongoing and further data package updates are planned. L0 data is available upon request. R scripts can be found here: https://github.com/SpaCE-Lab-MSU/warmXtrophic. The biotic and abiotic community context and relative strengths of direct vs. indirect effects may yield ecological surprises under climate change unless addressed together. Large-scale experiments like this one can improve our ability to unde
Data for: Climate warming and drought effects on volatile organic compound emissions from Solidago altissima
Volatile organic compounds (VOCs) were collected from Solidago altissima in drought and warming treatments in the KBS-LTER Rain Exclusion Experiment (REX). This sampling took place in July 2022 when the plants had been experiencing warming (via open-top chambers) for 20 months, and drought (via rainout shelters) for 3 weeks. The data presented here are the final data files used for analysis, and contain VOC abundance values per plant across the four climate treatments: ambient, warmed, drought, and warmed + drought. Code is available at: https://github.com/dobsonk2/REX_VOCs (https://doi.org/10.5281/zenodo.15169943)
Warming-El Nino-Nitrogen Deposition Experiment (WENNDEx): Net Primary Production Quadrat Data at the Sevilleta National Wildlife Refuge, New Mexico
Humans are creating significant global environmental change, including shifts in climate, increased nitrogen (N) deposition, and the facilitation of species invasions. A multi-factorial field experiment is being performed in an arid grassland within the Sevilleta National Wildlife Refuge (NWR) to simulate increased nighttime temperature, higher N deposition, and heightened El Niño frequency (which increases winter precipitation by an average of 50%). The purpose of the experiment is to better understand the potential effects of environmental change on grassland community composition and the growth of introduced creosote seeds and seedlings. The focus is on the response of three dominant species, all of which are near their range margins and thus may be particularly susceptible to environmental change. It is hypothesized that warmer summer temperatures and increased evaporation will favor growth of black grama (Bouteloua eriopoda), a desert grass, but that increased winter precipitation and/or available nitrogen will favor the growth of blue grama (Bouteloua gracilis), a shortgrass prairie species. Furthermore, it is thought that the growth and survival of introduced creosote (Larrea tridentata) seeds and seedlings will be promoted by heightened winter precipitation, N addition, and warmer nighttime temperatures. Treatment effects on limiting resources (soil moisture, nitrogen mineralization), species growth (photosynthetic rates, creosote shoot elongation), species abundance, and net primary production (NPP) are all being measured to determine the interactive effects of key global change drivers on arid grassland plant community dynamics. To measure above-ground NPP (i.e., the change in plant biomass, represented by stems, flowers, fruit and foliage, over time), the vegetation variables in this dataset, including species composition and the cover and height of individuals, are sampled twice yearly (spring and fall) at permanent 1m x 1m plots. The data from these pl
Warming-El Nino-Nitrogen Deposition Experiment (WENNDEx): Seasonal Biomass and Seasonal and Annual NPP at the Sevilleta National Wildlife Refuge, New Mexico
Begun in winter 2006, this long-term study at the Sevilleta LTER examines how heightened winter precipitation, N addition, and warmer nighttime temperatures affect above-ground biomass production (ANPP) in a mixed desert-grassland. Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. While measures of both below- and above-ground biomass are important in estimating total NPP, this study focuses on above-ground net primary production (ANPP). Above-ground net primary production is the change in plant biomass, including loss to death and decomposition, over a given period of time. Volumetric measurements are made using vegetation data from permanent plots (SEV176, "Warming-El Nino-Nitrogen Deposition Experiment (WENNDEx): Net Primary Production Quadrat Data") and regressions correlating species biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data."
Hydrological regime in a model High Arctic catchment (Bratteggdalen, Svalbard) under warming and precipitation rise
<p><span>Climate change is impacting water flow worldwide and is particularly important for High Arctic basins. Thawing permafrost and melting of glaciers, as well as higher air temperatures and precipitation, affect hydrological regimes and retention in polar basins. However, knowledge is limited as regards long-term changes in discharge from catchments in the High Arctic. Our aim was to evaluate the impact of local conditions on hydrological regime in glacial-fluvio-lacustrine model system in the High Arctic. We used mainly hydrological and meteorological data from 9 summer seasons (June-September) between 2005 and 2019 extracted from the entire database (16 seasons in 1972-2019). Wide range of statistical methods was applied including bootstrapping, random forest and multiple regression, to determine the coupling between hydrometeorological parameters (air and water temperature, discharge, sunshine duration, precipitation). The hydrological regime exhibits a distinct seasonal pattern with a pronounced, snowmelt-derived peak (maximum discharge) in the early part of the season (June-July) affected by precipitation. In the late part of the season (August-September), low-intermediate discharge is primarily governed by air temperatures and, only secondarily by precipitation. The hydrometeorological coupling in August-September is stronger that in June-July. The statistically significant increase in air temperature (0.45°C per decade) in August-September during 1979-2018 makes this part of the season important in terms of long-term changes in the permafrost-underlain catchment. Thawing of the permafrost active layer thaw is clearly reflected by air–temperature-dependent low-to-intermediate discharge.</span></p> <p><span>Database consists of following data obtained from long-term discharge analyses: daily discharge data at the gauging station from 1983-2019 (1983-2019</span><span>_Brattegg_River_Discharge_v1.csv</span><span>), daily water stage data from 1972-1983 (1972-1983 </span><span>_ Brattegg_River_Water_Stage_v1.csv</span><span>), daily water level at gauging station and outflow from Bratteggbreen from 2017 (</span><span>2017_Brattegg_River_water_stage_gauging_station_Bratteggbreen_v1.csv</span><span>).</span></p> <p><span>This study is a contribution to the National Science Centre projects: 2021/43/D/ST10/00687 (SONATA17 funding scheme, ŁS), 2020/39/I/ST10/02129 (OPUS-LAP funding scheme, MB), 2017/27/B/ST10/01269 (OPUS funding scheme, KM), and SONATA 2015/19/D/ST10/02869 (SONATA funding scheme, MK). For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission. ŁS was also supported from the Bekker Programme (award no. BPN/BEK/2021/1/00431) at the Polish National Agency for Scientific Exchange. The study was carried out by DI, EL as part of scientific activity of the Centre for Polar Studies (University of Silesia in Katowice) with the use of research and logistic equipment (monitoring and measuring equipment, sensors, multiple AWS, GNSS receivers, snowmobiles and other supporting equipment) of the Polar Laboratory of the University of Silesia in Katowice. MW and HM acknowledge the </span><span>statutory fund of University of Wrocław for suport during fieldwork in 2005-2010.</span></p> <p> </p> <p> </p>
Belowground nitrogen cycling in a montane grassland exposed to elevated CO2, warming and drought
<p>#### Data description<br> Data from a multi-factorial global change experiment (elevated CO<sub>2</sub>, warming and drought) in a montane grassland experiment in Austria (ClimGrass). Variables presented are soil nitrogen cycling rates measured using isotope pool dilutions.</p> <p>Companion paper will be linked following manuscript publication.</p> <p>#### Metadata<br> climgrass_soil_N_cycling.csv data description</p> <p>Year: 2017<br> Harvest: three harvests (May 30, July 25, October 3)<br> Season: numerical column for harvest number<br> Plot: location of plot within the ClimGrass experiment<br> Treatment: eight treatment levels<br> c0t0 (ambient CO<sub>2</sub>, ambient temperature)<br> c0t1 (ambient CO<sub>2</sub>, + 1.5°C)<br> c0t2 (ambient CO<sub>2</sub>, + 3°C)<br> c1t1 (+150 ppm, +1.5°C)<br> c2t0 (+300 ppm, ambient temperature)<br> c2t2 (+300 ppm, +3°C)<br> c0t0-d (ambient CO<sub>2</sub>, ambient temperature, extended drought)<br> c2t2-d (+300 ppm, +3°C, extended drought)<br> CO2_ppm: three values of carbon dioxide enrichment treatment (+0, +150, or +300 ppm)<br> Temp_C: three values of elevated temperature treatment (+0, +1.5, +3°C)<br> Drought: two levels (control, drought)<br> Prot_depoly: gross protein depolymerization rates (micrograms nitrogen per grams dry soil per day = µg N g-1 d-1)<br> FAA_uptake: gross free amino acid uptake rates (µg N g-1 d-1)<br> MRT_FAA_hrs: mean residence time of free amino acids (hours)<br> FAA: free amino acids (µg N g-1)<br> Mineralization: gross mineralization rates (µg N g-1 d-1)<br> Nitrification: gross nitrification rates (µg N g-1 d-1)</p> <p>#### References<br> Additional information on the experimental design can be found in the following paper:<br> Piepho, H.-P., Herndl, M., Pötsch, E.M., Bahn, M., 2017. Designing an experiment with quantitative treatment factors to study the effects of climate change. Journal of Agronomy and Crop Science 203, 584–592. doi:https://doi.org/10.1111/jac.12225</p> <p>More information on the isotope pool dilution method used can be found in the following paper:<br> Wanek, W., Mooshammer, M., Blöchl, A., Hanreich, A., Richter, A., 2010. Determination of gross rates of amino acid production and immobilization in decomposing leaf litter by a novel 15 N isotope pool dilution technique. Soil Biology and Biochemistry 42, 1293–1302. doi:10.1016/j.soilbio.2010.04.001</p>
Rising CO2 and warming reduce global canopy demand for nitrogen
<ul> <li>Nitrogen (N) limitation has been considered as a constraint on terrestrial carbon uptake in response to rising CO<sub>2</sub>and climate change. By extension, it has been suggested that declining carboxylation capacity (<em>V</em><sub>cmax</sub>) and leaf N content in enhanced-CO<sub>2</sub>­ experiments and satellite records signify increasing N limitation of primary production.</li> <li>We predicted <em>V</em><sub>cmax </sub>using the coordination hypothesis, and estimated changes in leaf-level photosynthetic N for 1982–2016 assuming proportionality with leaf-level <em>V</em><sub>cmax</sub> at 25˚C. Whole-canopy photosynthetic N waas derived using satellite-based leaf area index (LAI) data and an empirical extinction coefficient for <em>V</em><sub>cmax</sub>, and converted to annual N demand using estimated leaf turnover times.</li> <li>The predicted spatial pattern of <em>V</em><sub>cmax </sub>shares key features with an independent reconstruction from remotely-sensed leaf chlorophyll content. Predicted leaf photosynthetic N declined by 0.28 %/year, while observed leaf (total) N declined by 0.2–0.25 %/year. Predicted global canopy N (and N demand) declined from 1997 onwards, despite increasing LAI.</li> <li>Leaf-level responses to rising CO<sub>2</sub>, and to a lesser extent temperature, may have reduced the canopy requirement for N by more than rising LAI has increased it. This finding provides an alternative explanation for declining leaf N that does not depend on increasing N limitation.</li> </ul>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.