Find research datasets worth reusing

opencc-by-4.0Nov 2022View details →

Six hundred years of reconstructed atmospheric river activity on the US west coast

These are the reconstruction data from the article: Six hundred years of reconstructed atmospheric river activity along the US West Coast (under review at the Journal of Geophysical Research: Atmosphere). The earliest start year of the reconstruction are mentioned in the files (either 1916 or 1400) and the corresponding models are mentioned as PCR (Regression) or NN (Neural Network).

opencc-by-4.0Aug 2022View details →

Secondary Volcanic Atmospheres II: The Importance of Kinetics Results

Data used to produce plots for the paper "Growth and Evolution of Secondary Volcanic Atmospheres: II. The Importance of Kinetics", submitted to JGR: Planets.

dryad36/100

Isotopic evidence for increased carbon and nitrogen exchanges between peatland plants and their symbiotic microbes with rising atmospheric CO2 concentrations since 15000 cal. yr BP

Whether nitrogen (N) availability will limit plant growth and removal of atmospheric CO2 this century is controversial. Studies have suggested that N could progressively limit plant growth, as trees and soils accumulate N in slowly cycling biomass pools in response to increases in carbon sequestration. However, a question remains over the longer-term (decadal to century) feedbacks between climate, CO2 and plant N uptake. The symbiosis between plants and microbes can help plants with mycorrhizal N uptake or biological N2 fixation – the pathway through which N can be rapidly brought into ecosystems and thereby partially or completely alleviate N limitation on plant productivity. Here we present results for plant N isotope composition (δ15N) in a peat core that dates to 15000 cal. yr BP to ascertain ecosystem-level N cycling responses to rising atmospheric CO2 concentrations in the past. We found that an increase in atmospheric CO2 concentration happened with a decrease in δ15N values of both Sphagnum moss and Ericaceae over this time period when constrained for climatic factors. A modern experiment demonstrated that δ15N of Sphagnum mosses decreased with increasing N2 fixation rates. These findings suggested that N2 fixation in Sphagnum moss by symbiosis with cyanobacteria and N uptake in Ericaceae by symbiosis with mycorrhizal fungi both likely increased with rising atmospheric CO2 concentrations, highlighting a longer-term feedback mechanism whereby N constraints on terrestrial carbon storage can be overcome.

opencc-zeroDec 2022View details →

Correlated k coefficients for H2-He atmospheres; 196 spectral windows and 1460 pressure-temperature points

There are 108 correlated k-coefficients datasets, using the naming convention sonora_2020_fehxxxx_co_yyy.data.196.tar.gz (78 models) or sonora_2020_fehxxxx_co_yyy_noTiOVO.data.196.tar.gz (78 models), where xxxx is the metallicity in 10x dex relative to solar, and yyy is the 100x C/O ratio relative to solar, as a multiplication factor. For example a metallicity of +000 and a C/O ratio of 100 indicates solar abundances, feh+070 should be read as a metallicity of +0.7 dex, feh-100 as -1.0 dex, co_025 should be read as 0.25x C/O relative to solar, and co_200 as 2x C/O relative to solar. We use the Lodders et al. 2010 value for the solar C/O=0.458. The files with “noTiOVO” in the filename contain the correlated k-coefficients calculated without the opacity of TiO and VO. The rest of the molecular abundances and opacities are the same as the equilibrium chemistry values in the regular files. These files are useful for calculating models without any TiO- and VO-induced temperature inversion in the atmosphere. The correlated-k coefficients are calculated using pre-mixed opacities, with abundances given by equilibrium chemistry for each metallicity-C/O combination, as described in Marley et al. 2021. There are 13 Fe/H values: 0.0, 0.3, 0.5, 0.7, 1.0, 1.3, 1.5, 1.7, 2.0, -0.3, -0.5, -0.7, and -1.0; and 6 C/O values: 0.25, 0.5, 1.0, 1.5, 2.0 and 2.5. The k-coefficients are calculated for a grid of 1460 pressure-temperature points, from10^−6 to 3000 bar and from 75 to 4000 K, listed in the file 1460_layer_list, and can be read in using the IDL script read_k_coefficients.pro. The spectral windows are listed in the file 196_windows.txt (intervals defined as starting at lambda1 and ending at lambda2). NB Please note that for metallicities between 1.3 and 2.0 the value of max_windows has changed from 200 to 1000. The opacity sources included in the calculations are: C2H2, C2H4, C2H6, CH4, CO, CO2, CrH, Fe, FeH, H2, H3+, H2O, H2S, HCN, LiCl, LiF, LiH, MgH, N2, NH3, OCS, PH3, SiO, TiO, and VO, in addition to alkali metals (Li, Na, K, Rb, Cs). The corresponding high resolution opacities for these atoms and molecules can be found in the Zenodo repository 10.5281/zenodo.6600976. The references for the line lists used in these opacity calculations are listed in the file Opacity_references_2021.pdf. Please include these references, as well as the reference to this Zenodo repository when publishing your paper. Each dataset contains the following files: ascii_data: the correlated k coefficients file in ascii format. This can be read by the included IDL code. binary_data: the correlated k coefficients file in binary format full_abunds: the relative abundances for all the species from the chemistry files, on the 1460-point pressure-temperature grid sum_in_atoms: relative abundances for the alkali metals sum_in_layer: relative abundances for all molecules included in the correlated k-coefficients calculations Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.

Correlated k coefficients for H2-He atmospheres; 180 spectral windows and 1460 pressure-temperature points

There are 108 correlated k-coefficients datasets, using the naming convention sonora_2020_fehxxxx_co_yyy.data.196.tar.gz (78 models) or sonora_2020_fehxxxx_co_yyy_noTiOVO.data.196.tar.gz (78 models), where xxxx is the metallicity in 10x dex relative to solar, and yyy is the 100x C/O ratio relative to solar, as a multiplication factor. For example a metallicity of +000 and a C/O ratio of 100 indicates solar abundances, feh+070 should be read as a metallicity of +0.7 dex, feh-100 as -1.0 dex, co_025 should be read as 0.25x C/O relative to solar, and co_200 as 2x C/O relative to solar. We use the Lodders et al. 2010 value for the solar C/O=0.458. The files with “noTiOVO” in the filename contain the correlated k-coefficients calculated without the opacity of TiO and VO. The rest of the molecular abundances and opacities are the same as the equilibrium chemistry values in the regular files. These files are useful for calculating models without any TiO- and VO-induced temperature inversion in the atmosphere. The correlated-k coefficients are calculated using pre-mixed opacities, with abundances given by equilibrium chemistry for each metallicity-C/O combination, as described in Marley et al. 2021. There are 13 Fe/H values: 0.0, 0.3, 0.5, 0.7, 1.0, 1.3, 1.5, 1.7, 2.0, -0.3, -0.5, -0.7, and -1.0; and 6 C/O values: 0.25, 0.5, 1.0, 1.5, 2.0 and 2.5. The k-coefficients are calculated for a grid of 1460 pressure-temperature points, from10^−6 to 3000 bar and from 75 to 4000 K, listed in the file 1460_layer_list, and can be read in using the IDL script read_k_coefficients.pro. The spectral windows are listed in the file 180_windows.txt (intervals defined as starting at lambda1 and ending at lambda2). NB Please note that for metallicities between 1.3 and 2.0 the value of max_windows has changed from 200 to 1000. The opacity sources included in the calculations are: C2H2, C2H4, C2H6, CH4, CO, CO2, CrH, Fe, FeH, H2, H3+, H2O, H2S, HCN, LiCl, LiF, LiH, MgH, N2, NH3, OCS, PH3, SiO, TiO, and VO, in addition to alkali metals (Li, Na, K, Rb, Cs). The corresponding high resolution opacities for these atoms and molecules can be found in the Zenodo repository 10.5281/zenodo.6600976. The references for the line lists used in these opacity calculations are listed in the file Opacity_references_2021.pdf. Please include these references, as well as the reference to this Zenodo repository when publishing your paper. Each dataset contains the following files: ascii_data: the correlated k coefficients file in ascii format. This can be read by the included IDL code. binary_data: the correlated k coefficients file in binary format full_abunds: the relative abundances for all the species from the chemistry files, on the 1460-point pressure-temperature grid sum_in_atoms: relative abundances for the alkali metals sum_in_layer: relative abundances for all molecules included in the correlated k-coefficients calculations Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.

Correlated k coefficients for H2-He atmospheres; 11 spectral windows and 1460 pressure-temperature points

There are 108 correlated k-coefficients datasets, using the naming convention sonora_2020_fehxxxx_co_yyy.data.196.tar.gz (78 models) or sonora_2020_fehxxxx_co_yyy_noTiOVO.data.196.tar.gz (78 models), where xxxx is the metallicity in 10x dex relative to solar, and yyy is the 100x C/O ratio relative to solar, as a multiplication factor. For example a metallicity of +000 and a C/O ratio of 100 indicates solar abundances, feh+070 should be read as a metallicity of +0.7 dex, feh-100 as -1.0 dex, co_025 should be read as 0.25x C/O relative to solar, and co_200 as 2x C/O relative to solar. We use the Lodders et al. 2010 value for the solar C/O=0.458. The files with “noTiOVO” in the filename contain the correlated k-coefficients calculated without the opacity of TiO and VO. The rest of the molecular abundances and opacities are the same as the equilibrium chemistry values in the regular files. These files are useful for calculating models without any TiO- and VO-induced temperature inversion in the atmosphere. The correlated-k coefficients are calculated using pre-mixed opacities, with abundances given by equilibrium chemistry for each metallicity-C/O combination, as described in Marley et al. 2021. There are 13 Fe/H values: 0.0, 0.3, 0.5, 0.7, 1.0, 1.3, 1.5, 1.7, 2.0, -0.3, -0.5, -0.7, and -1.0; and 6 C/O values: 0.25, 0.5, 1.0, 1.5, 2.0 and 2.5. The k-coefficients are calculated for a grid of 1460 pressure-temperature points, from10^−6 to 3000 bar and from 75 to 4000 K, listed in the file 1460_layer_list, and can be read in using the IDL script read_k_coefficients.pro. The spectral windows are listed in the file 11_windows.txt (intervals defined as starting at lambda1 and ending at lambda2). NB Please note that for metallicities between 1.3 and 2.0 the value of max_windows has changed from 200 to 1000. The opacity sources included in the calculations are: C2H2, C2H4, C2H6, CH4, CO, CO2, CrH, Fe, FeH, H2, H3+, H2O, H2S, HCN, LiCl, LiF, LiH, MgH, N2, NH3, OCS, PH3, SiO, TiO, and VO, in addition to alkali metals (Li, Na, K, Rb, Cs). The corresponding high resolution opacities for these atoms and molecules can be found in the Zenodo repository 10.5281/zenodo.6600976. The references for the line lists used in these opacity calculations are listed in the file Opacity_references_2021.pdf. Please include these references, as well as the reference to this Zenodo repository when publishing your paper. Each dataset contains the following files: ascii_data: the correlated k coefficients file in ascii format. This can be read by the included IDL code. binary_data: the correlated k coefficients file in binary format full_abunds: the relative abundances for all the species from the chemistry files, on the 1460-point pressure-temperature grid sum_in_atoms: relative abundances for the alkali metals sum_in_layer: relative abundances for all molecules included in the correlated k-coefficients calculations Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.

Correlated k coefficients for H2-He atmospheres; 30 spectral windows and 1460 pressure-temperature points

There are 108 correlated k-coefficients datasets, using the naming convention sonora_2020_fehxxxx_co_yyy.data.196.tar.gz (78 models) or sonora_2020_fehxxxx_co_yyy_noTiOVO.data.196.tar.gz (78 models), where xxxx is the metallicity in 10x dex relative to solar, and yyy is the 100x C/O ratio relative to solar, as a multiplication factor. For example a metallicity of +000 and a C/O ratio of 100 indicates solar abundances, feh+070 should be read as a metallicity of +0.7 dex, feh-100 as -1.0 dex, co_025 should be read as 0.25x C/O relative to solar, and co_200 as 2x C/O relative to solar. We use the Lodders et al. 2010 value for the solar C/O=0.458. The files with “noTiOVO” in the filename contain the correlated k-coefficients calculated without the opacity of TiO and VO. The rest of the molecular abundances and opacities are the same as the equilibrium chemistry values in the regular files. These files are useful for calculating models without any TiO- and VO-induced temperature inversion in the atmosphere. The correlated-k coefficients are calculated using pre-mixed opacities, with abundances given by equilibrium chemistry for each metallicity-C/O combination, as described in Marley et al. 2021. There are 13 Fe/H values: 0.0, 0.3, 0.5, 0.7, 1.0, 1.3, 1.5, 1.7, 2.0, -0.3, -0.5, -0.7, and -1.0; and 6 C/O values: 0.25, 0.5, 1.0, 1.5, 2.0 and 2.5. The k-coefficients are calculated for a grid of 1460 pressure-temperature points, from10^−6 to 3000 bar and from 75 to 4000 K, listed in the file 1460_layer_list, and can be read in using the IDL script read_k_coefficients.pro. The spectral windows are listed in the file 30_windows.txt (intervals defined as starting at lambda1 and ending at lambda2). NB Please note that for metallicities between 1.3 and 2.0 the value of max_windows has changed from 200 to 1000. The opacity sources included in the calculations are: C2H2, C2H4, C2H6, CH4, CO, CO2, CrH, Fe, FeH, H2, H3+, H2O, H2S, HCN, LiCl, LiF, LiH, MgH, N2, NH3, OCS, PH3, SiO, TiO, and VO, in addition to alkali metals (Li, Na, K, Rb, Cs). The corresponding high resolution opacities for these atoms and molecules can be found in the Zenodo repository 10.5281/zenodo.6600976. The references for the line lists used in these opacity calculations are listed in the file Opacity_references_2021.pdf. Please include these references, as well as the reference to this Zenodo repository when publishing your paper. Each dataset contains the following files: ascii_data: the correlated k coefficients file in ascii format. This can be read by the included IDL code. binary_data: the correlated k coefficients file in binary format full_abunds: the relative abundances for all the species from the chemistry files, on the 1460-point pressure-temperature grid sum_in_atoms: relative abundances for the alkali metals sum_in_layer: relative abundances for all molecules included in the correlated k-coefficients calculations Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.

opencc-by-4.0Jan 2023View details →

Chemical Networks and Model Output and for "Evidence of Photochemistry in an Exoplanet Atmosphere"

The volume mixing ratio output of the key sulphur species computed by photochemical models for producing Fig. 1  Synthetic spectra in Fig. 2 The photochemical networks used in each model.

opencc-by-4.0Jan 2023View details →

Biomarker indices and concentrations and biomarker-based temperature estimates from the Iberian Margin core MD95-2042, composite atmospheric temperature record from Greenland, and stacks of delta 18Oice and atmospheric temperature records from three Antarctic sites

Core MD95-2042 alkenone and GDGT data: This dataset provides the following information for core MD95-2042: depth, age, summed OH-GDGT, iGDGT, and di-unsaturated and tri-unsaturated C37 alkenone concentrations, OH-GDGT-based, iGDGT-based, and alkenone-based paleothermometric indices, GDGT-2/GDGT-3 ratio, and biomarker-based sea surface temperature (SST) and 0‐ to 200‐m sea temperature (subT; gamma function probability distribution for target temperatures with a = 4.5 and b = 15) estimates. Sediment samples were taken every 5 cm from core MD95-2042 and homogenized before lipid extraction. The lipid extracts were splitted into two fractions: one for alkenone analysis by gas chromatography coupled to a flame ionization detector, and the other for GDGT analysis by high-performance liquid chromatography coupled to mass spectrometry. All GDGT analyses were done in duplicate. The 1σ analytical uncertainties from 37 replicate analyses of the core catcher sample from core MD95-2042 are 0.007 (0.4 °C) for RI-OH, 0.008 (0.2 °C) for RI-OH′, 0.003 (0.2 °C) for TEX86, 0.238 for GDGT-2/GDGT-3, and 0.010 (0.26 °C) for UK′37. RI-OH′-SST estimates are from the following global calibration: SST = (RI-OH′ + 0.029)/0.0422 (Fietz et al., 2020). RI-OH-SST estimates are from the following global calibration: SST = (RI-OH − 1.11)/0.018 (Lü et al., 2015). TEX86H-SST estimates are from the following regional paleocalibration: SST = 68.4 × TEX86H + 33.0 (Darfeuil et al., 2016). UK′37-SST estimates are from the following global calibration: SST = 29.876 × UK′37 − 1.334 (Conte et al., 2006). Bayesian calibrations were also used for TEX86-SST and TEX86-subT estimates (BAYSPAR; Tierney & Tingley, 2014, 2015) and for UK′37-SST estimates (BAYSPLINE; Tierney & Tingley, 2018). Alkenone data covering the 160–70 and 70–0 ka BP periods are from Davtian et al. (2021) and Darfeuil et al. (2016), respectively. GDGT data covering the 160–45 ka BP period are from Davtian et al. (2021). The age model of core MD95-2042 for the 160–43 and 43–0 ka BP periods was obtained by tuning to Chinese speleothems (Cheng et al., 2016) and by recalibrating existing 14C ages with the Marine20 calibration curve (Heaton et al., 2020), respectively. MIS, Marine Isotope Stage; GDGT, glycerol dialkyl glycerol tetraether; and N/A, not available. Greenland atmospheric temperature record: This dataset consists in a composite Greenland atmospheric temperature record, which was built with the following records: the GISP2 atmospheric temperature record by Kobashi et al. (2017) for the 10–0 ka BP period, the NGRIP atmospheric temperature record by Kindler et al. (2014) for the 120–10 ka BP period, and the NEEM atmospheric temperature record by NEEM community members (2013) for the 129–120 ka BP period. The NEEM temperature anomalies obtained by NEEM community members (2013) were shifted by –31 °C to obtain absolute air temperatures. The employed age model is the one of Davtian and Bard (2023) for Greenland and Antarctic ice-core records. Antarctic δ18Oice and atmospheric temperature stacks: This dataset consists in two stacks of three Antarctic records (EDC, EDML, and WD), one for δ18Oice and the other for atmospheric temperature: both stacks are provided with their stacking uncertainties. To build the Antarctic δ18Oice stack, the Antarctic δ18Oice records were resampled every 10 years before centering to zero means and normalization to unit standard deviations over the 140–0 ka BP period (68–0 ka BP for WD). To optimize the continuity between the portions with and without the WD ice core, the Antarctic δ18Oice records were centered to zero means over the 68–67 ka BP period. The resulting Antarctic δ18Oice records were then averaged and stacking uncertainties were calculated as the pooled standard deviation of the stacked Antarctic δ18Oice records divided by the square root of the number of stacked Antarctic δ18Oice records. The final Antarctic δ18Oice stack, expressed in &permil;, has the same standard deviation as the δ18Oice record from EDML over the 140–0 ka BP period, and has a zero mean over the 1–0 ka BP. The Antarctic atmospheric temperature stack was built like the Antarctic δ18Oice stack, except that the Antarctic δ18Oice records were corrected for seawater δ18Oice variations before conversion into atmospheric temperature. The employed age model is the one of Davtian and Bard (2023) for Greenland and Antarctic ice-core records.

opencc-by-4.0Feb 2023View details →

Data of Energy mechanism of atmospheric boundary layer development over the Tibetan Plateau

Processed data of Energy mechanism of atmospheric boundary layer development over the Tibetan Plateau

opencc-by-4.0Jan 2023View details →

RT Dataset -- Updated radiative transfer model for Titan in the near-infrared wavelength range: Validation against Huygens atmospheric and surface measurements and application to the Cassini/VIMS observations of the Dragonfly landing area

This dataset contains all Radiative Transfer (RT) results made for the paper. The data are stored in 5 zipped-folders names with the Cassini/VIMS cube flyby and id, or explicitly for Huygens/ULIS calibrated observations: <ul> <li>TB_C1481624349_1</li> <li>T40_C1578266417_1</li> <li>T38_C1575509158_1</li> <li>T40_C1578263500_1</li> <li>T40_C1578263152_1</li> <li>ULIS_observations</li> </ul> The TB_C1481624349_1 folder contains the Cassini/VIMS cube over HLS, the HLS end-member (End_member.txt), the surface albedo retrieved by Karkoschka et al. (2016) corrected for the photometry (HLS_Karkoschka_2016_spectrum.txt), and the inverted surface albedo (Surface_albedo.txt). In these folders, each VIMS pixel is stored in a .txt file with the following pattern: <CUBE_ID>_<PIXEL_SAMPLE>_<PIXEL_LINE> .txt It starts with a header describing the observation:  <ul> <li>CUBE_ID: the VIMS cube id (`C1234567890_1` format)</li> <li>SAMPLE: the pixel sample number.</li> <li>LINE: the pixel line number.</li> <li>LONG: the pixel longitude (in degree).</li> <li>LAT: the pixel latitude (in degree).</li> <li>INC: the surface incident angle (in degree).</li> <li>EMI: the surface emergent angle (in degree).</li> <li>PHASE: the surface phase angle (in degree).</li> </ul> For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), the header also contains the spatial sampling and the radiative transfer model outputs:  <ul> <li>Spatial sampling (km/pix).</li> <li>Fh: the haze scaling factor.</li> <li>Fm: the mist scaling factor.</li> <li>1-sigma (Fh): the 1-sigma uncertainty on Fh.</li> <li>1-sigma (Fm): the 1-sigma uncertainty on Fm.</li> <li>Reduced chi2: the reduced chi2. </li> </ul> Then contains the observed spectra: <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers).</li> <li>Column 2: the VIMS pixel I/F.</li> <li>Column 3: the VIMS pixel I/F 1-sigma uncertainty. </li> </ul> For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), 3 columns are added for:  <ul> <li>Column 4: the surface albedo.</li> <li>Column 5: the upper 1-sigma uncertainty on the surface albedo.</li> <li>Column 6 : the lower 1-sigma uncertainty on the surface albedo.</li> </ul> The ULIS folder contains the Huygens/ULIS calibrated observations (in I/F) and the simulations with 1-sigma uncertainties as a function of the altitude (in km): <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers), stopped at the end of the Huygens/ULIS wavelength range.</li> <li>Column 2: the ULIS I/F.</li> <li>Column 3 : the simulated I/F.</li> <li>Column 4: the lower 1-sigma uncertainty on the simulation.</li> <li>Column 5 : the upper 1-sigma uncertainty on the simulation.</li> </ul>

opencc-by-4.0Feb 2023View details →

Identification of the atmospheric water sources and pathways responsible for the East Asian summer monsoon rainfall

era_hydro_easm_jul2013_*.csv.gz: These three files are the raw output from the TRACMASS trajectory model. They stored positions of each trajectory at the start (ini file), during run (run file) and at the end (out file) during July 2013 and has been used for generating Figure 1. mask.nc: The basin definition in Figure 1 was plotted using this netcdf file. ep_traj_*.gz: The four zip files corresponds to four summer months (June, July, August, and September) and used to plot Figure 2. ep_traj_basins_*.gz: The four zip files corresponds to four summer months (June, July, August, and September) and used to plot Figure 3. quantification.gz: These files were used for quantification provided in Figure 4 and Figure 8. traj_pathways_*.gz: The two zip files (one for the South Indian Ocean, SIO and another for the Pacific Ocean, PAC) were used to generate Figure 5. traj_rt.gz:  The residence time of atmospheric waters in Figure 6 was created using the files in this zip. traj_age_*.gz: The two zip files (one for the South Indian Ocean, SIO and another for the Pacific Ocean, PAC) were used to generate Figure 7.

opencc-by-4.0Feb 2023View details →

GMSL experiments: atmosphere and ocean annual mean

The first is the pre-industrial control experiment (picontrol). It employs the Earth's orbital parameters of 1990 CE, and the atmospheric CO2 level 284.70 ppm and CH4 level 791.60 ppb. The second group (lig126 group) includes three simulations, lig126, lig126sl5m, lig126sl10m. The lig126 experiment, which only considers changes in the Earth's orbital configuration and greenhouse gas levels, uses the orbital parameters of 126 ka, the atmospheric CO2 level 274.99 ppm, and the atmospheric CH4 level 652.52 ppb. In addition to the orbital parameters and greenhouse gas level, lig126sl5m and lig126sl10m further consider the GMSL rise of 5 and 10 m, respectively. The third group (co2400 group) includes seven simulations, co2400, co2400sl0.625m, co2400sl1.25m, co2400sl2.5m, co2400sl5m, co2400sl10m and co2400sl20m. In co2400, the atmospheric CO2 level is 400 ppm, all other boundary conditions (including orbital parameters, CH4 level, bathymetry, and topography) are identical to the picontrol experiment. Compared to co2400, we consider GMSL rise of 0.625, 1.25, 2.5, 5, 10, and 20 m in other experiments in this group.

opencc-by-4.0Dec 2022View details →

Atmospheric oxygen as a tracer for fossil fuel carbon dioxide: a sensitivity study in the UK

Abstract. We investigate the use of oxygen (O2) and carbon dioxide (CO2) measurements for the estimation of the fossil fuel component of atmospheric CO2 in the UK. Atmospheric potential oxygen (APO) – a tracer that combines O2 and CO2, minimising the influence of terrestrial biosphere fluxes – is simulated at three sites in the UK, two of which have APO measurements. We present a set of model experiments that estimate the sensitivity of APO simulations to key inputs: fluxes from the ocean, fossil fuel flux magnitude and distribution, the APO baseline, and the ratio of O2 to CO2 fluxes from fossil fuel combustion and the terrestrial biosphere. To estimate the influence of uncertainties in ocean fluxes, we compared three ocean O2 flux estimates, from the NEMO – ERSEM and ECCO-Darwin ocean models, and the Jena Carboscope inversion. The sensitivity of APO to fossil fuel emission magnitudes and to terrestrial biosphere and fossil fuel exchange ratios was investigated through Monte Carlo sampling within literature uncertainty ranges, and by comparing different inventory estimates. Of the factors that could potentially compromise APO-derived fossil fuel CO2 estimates, we find that the ocean O2 flux estimate has the largest overall influence at the three sites in the UK. At times, this influence is comparable to the contribution to APO of simulated fossil fuel CO2. We find that simulations using different ocean fluxes differ from each other substantially, with no single estimate, or a simulation with zero ocean flux, providing a significantly closer fit to the observations. Furthermore, the uncertainty in the ocean contribution to APO could lead to uncertainty in defining an appropriate regional background from the data. Our findings suggest that the contribution of non-terrestrial sources need to be well accounted for, in order to reduce their potential influence on inferred fossil fuel CO2.

dryad36/100

Data for: Intercomparison of commercial analyzers for atmospheric ethane and methane observations

Methane (CH4) is a strong greenhouse gas that has become the focus of climate mitigation policies in recent years. Ethane / methane ratios can be used to identify and partition the different sources of methane, especially in areas with natural gas mixed with biogenic methane emissions, such as cities. We assessed the precision, accuracy, and selectivity of three commercially available laser-based analyzers that have been marketed as measuring instantaneous dry mole fractions of methane and ethane in ambient air. The Aerodyne SuperDUAL instrument performed best of the three instruments but it requires expertise to operate and space for the large footprint. The Aeris Mira Ultra LDS analyzer also performed well for the price point and small footprint but required characterization of the water vapor dependence of reported concentrations and careful setup for use. The Picarro G2210-i precisely measured methane but it did not detect the 10 ppbv increases in ambient ethane detected by the other two instruments when sampling a plume of incompletely combusted natural gas. For long-term tower deployments or those with large mobile laboratories, the Aerodyne SuperDUAL provides the best precision for methane and ethane. For smaller mobile platforms, the Aeris MIRA is a more compact analyzer, and with careful use, can quantify thermogenic methane sources to sufficient precision for short term deployments in urban or oil and gas areas. We weighed the advantages of each instrument, including size, power requirement, ease of use on mobile platforms, and expertise needed to operate the instrument, and we recommend the Aerodyne SuperDUAL or the Aeris MIRA Ultra LDS depending on the situation.

opencc-zeroMar 2023View details →

Model output data for 3D Climate modelling of LP 890-9 c with a modern Venus-like atmosphere

We make available the output data from 3D climate modelling of LP 890-9 c with a modern Venus-like atmosphere. The data here has been produced for the publication submitted to Monthly Notices of the Royal Astronomical Society: Letters under the title: «3D Global Climate Model of an Exo-Venus: a modern Venus-like Atmosphere for the Nearby Super-Earth LP 890-9 c». The data includes the temperature profiles, emission (thermal) phase curves and transmission spectra files calculated for JWST/NIRSpec Prism. We also make available larger versions of the synthetic observable figures. Proper credit should be given to the authors. For further information, please get in touch with the corresponding author (Diogo Quirino) at: dfquirino@fc.ul.pt

opencc-zeroMar 2023View details →

dryad36/100

Soil protist functional composition shifts with atmospheric nitrogen deposition in subtropical forests

1. Soil protist plays a key role in ecological functions through predation and parasitism. However, little is known about how nitrogen (N) deposition and seasonal variations influence soil protist function in forest soils. 2. Here, we assessed firstly the impacts of N deposition (control, 50 kg N ha-1 yr-1, 100 kg N ha-1 yr-1, and 150 kg N ha-1 yr-1) on the functional composition of the soil protist community in summer and winter, using amplicon sequencing of environmental DNA from a subtropical natural forest. 3. We found that soil protists were dominated by consumers (42.6–51.6%), followed by parasites (32.9–40.9%) and phototrophs (3.2–13.1%), implying a predominant role of consumers and potential top-down effects on the other trophic groups in subtropical forest soils. The functional composition of soil protists was greatly influenced by N deposition, but these responses were dependent on seasonal variations. The diversity of phototrophs was lower in summer than in winter. Instead, an opposite pattern was observed for consumers, resulting in a significantly higher protist diversity in summer than in winter, which indicates a greater sensitivity of soil protists to seasonal variations. Furthermore, low and high N deposition simplified the structural complexity of soil protist communities, suggesting a nonlinear response of the protist structural stability to N deposition. 4. Synthesis and applications. This study provides unprecedented evidence that season variation plays an important role in regulating responses of soil protist functional composition to N deposition, and highlights the nonlinear effects of rising N deposition levels on the soil food web.

Preliminary data of drifting snow mass flux from the lower SPC at MOSAiC (2020-01-26 to 2020-02-04) for the submitted paper "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model"

Preliminary data of lower SPC massflux from MOSAiC, for the time period 2020-01-26 -- 2020-02-04. 1-h averaged time series of mass flux (kg/m²/h) to compare with the ALPINE3D simulation results. Will soon be replaced with a DOI / Repositiry at the Arctic Data Centre from BAS.

META-DATA for IEA Wind Task 46. WP2: Atmospheric drivers of wind turbine blade leading edge erosion: Ancillary variables

Leading edge erosion (LEE) of wind turbine blades has been identified as a major factor in decreased wind turbine blade lifetimes and energy output over time. Accordingly, the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) created Task 46 to undertake cooperative research in the key topic of blade erosion. This report is a product of WorkPackage 2 Climatic conditions driving blade erosion. The objectives of the work summarized in this report are to: <ul> <li>Summarize efforts to elucidate critical atmospheric co-stressors that may accelerate leading edge erosion and hence for which meta-data regarding observations should be collated.</li> <li>Briefly describe and summarize additional data pertaining to those LEE co-stressors from sites that were the focus of analyses of hydrometeors in the report “Atmospheric drivers of wind turbine blade leading edge erosion: Hydrometeors” (Pryor et al. 2021)</li> </ul> Accompanying this report is a detailed spreadsheet that summarizes the meta-data regarding these co-stressor variables.

Self-consistent Models of Y Dwarf Atmospheres with Water Clouds and Disequilibrium Chemistry

This data set consists of 1d radiative-convective equilibrium models intended for Y Dwarfs or cool giant planets with negligible levels of external irradiation, computed using coolTLUSTY and a recently updated set of molecular absorption cross sections. Models span effective temperatures of 200 - 600 K, metallicities of [-0.5], [0], and [+0.5] dex, and surface gravities of log10(g / [cm/s^2]) = 3.5 - 5.0, and are computed for both cloudy and clear cases, in thermochemical equilibrium and with nonequilibrium carbon and nitrogen chemistry due to vertical mixing. Models extend from 0.5 microns to 300 microns with 30,000 frequency points evenly spaced in ln(frequency), which corresponds to an average resolving power of R ~ 4340. See README.txt for a description of file formats, naming conventions, and decompressed sizes. A more thorough summary of the assumptions made in computing these models and a walk-through of their properties can be found in Lacy & Burrows 2023 "Self-consistent Models of Y Dwarf Atmospheres with Water Clouds and Disequilibrium Chemistry" accepted for publication in the Astrophysical Journal and available on arXiv. If you make use of these models please cite that publication, along with this zenodo data set.