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opencc-by-4.0Sep 2022View details →

A global gross primary productivity product considering canopy nitrogen concentrations and multiple environmental factors from 2001 to 2018

The NI-LUE GPP with 0.05° spatial resolution and at 8 days interval from 2001 to 2018 was generated based on an improved light use efficiency (LUE) model that simultaneously considered temperature, water, atmospheric CO2 concentrations, radiation components, and nitrogen (N) index. In the model, a vegetation index capable of characterizing canopy N concentrations was selected to achieve dynamic mixmum LUE. In addition, global optimum temperature distributions mapped based on satellite-retrieved SIF were introduced to calculate the temperature stress factor. This dataset includes global GPP product and its uncertainty data. Period: 2001-2018 Spatial resolution: 0.05° Temporal resolution: 8 days Projection: geographic latitude/longitude Data format: Tiff Upper left coordinates: -180°E, 90°N Scale factor: 1000 Unit: gCm-2d-1

Figure 5 in Peculiarities of seasonal dynamics of net primary production and its microzooplankton grazing in the coastal waters of the Black Sea (Sevastopol region)

Figure 5. Seasonal dynamics of parameters: a – relative biomass of diatoms (1) and weighted average volume of phytoplankton cells (2), b – relative biomass of dinoflagellates (1) and coccolithophores (2), c – molar ratios N/P (1) and Si/N (2), d – net phytoplankton growth rate (1) and ratio g/µ (2) in station 2.

Figure 4 in Peculiarities of seasonal dynamics of net primary production and its microzooplankton grazing in the coastal waters of the Black Sea (Sevastopol region)

Figure 4. Seasonal dynamics of parameters: a – intensity of solar radiation (1) and water temperature (2), b – nitrates (1) and ammonium (2), c – silicates (1) and phosphates (2), c – net primary production (1) and chlorophyll a concentration (2) in station 2.

Figure 3 in Peculiarities of seasonal dynamics of net primary production and its microzooplankton grazing in the coastal waters of the Black Sea (Sevastopol region)

Figure 3. Seasonal dynamics of parameters: a – relative biomass of diatoms (1) and weighted average volume of phytoplankton cells (2), b – relative biomass of dinoflagellates (1) and coccolithophores (2), c – molar ratios N/P (1) and Si/N (2), d – net phytoplankton growth rate (1) and ratio g/µ (2) in station 1.

Figure 2 in Peculiarities of seasonal dynamics of net primary production and its microzooplankton grazing in the coastal waters of the Black Sea (Sevastopol region)

Figure 2. Seasonal dynamics of parameters: a – intensity of solar radiation (1) and water temperature (2), b – nitrates (1) and ammonium (2), c – silicates (1) and phosphates (2), c – net primary production (1) and chlorophyll a concentration (2) in station 1.

A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 1

This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPPluc), the harvested NPP (including losses, HANPPharv), and the NPP remaining in ecosystems after harvest (NPPeco) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication. The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPPharv to NPPeco results in the actual NPP available before harvest (NPPact=NPPeco+HANPPharv), and adding HANPPluc to NPPact results in the potential NPP available in the hypothetical absence of land use (NPPpot=NPPact+HANPPluc) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPPpot can be calculated by summing up HANPPharv and HANPPluc and dividing it by NPPpot. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. This Zenodo repository provides data on following land-use classes: unused productive wilderness areas (WILD-core); productive wilderness areas that are sporadically used at very low intensity (WILD-periphery); unused unproductive wilderness areas (WILD-nps); forestry areas, mainly coniferous (FO-con); forestry areas, mainly non-coniferous (FO-ncon); settlements, urban areas and infrastructure (BU-builtup)

Data for "Temporal and vertical variability in phytoplankton primary production and microbial community respiration in the North Pacific Subtropical Gyre"

This ALOHA_GOP&R.xslx data set provides measurements of biological rates conducted between April 2015 and July 2020 at different depths in the euphotic zone at or in the vicinity of Station ALOHA (22° 45' N, 158° W), the long-term sampling site of the Hawaii Ocean Time-series (HOT) program, within the North Pacific Subtropical Gyre.  The file ALOHA_GOP&R.xslx contains incubation-based measurements of gross oxygen production and community respiration that were measured in the same incubation bottles by applying the 18O-water method and tracking net changes in oxygen to argon ratios during dawn to dusk in situ incubations, following Ferrón et al. (2016). The samples were measured using membrane inlet mass spectrometry. Rates were measured at 6 depths within the euphotic zone: 5, 25, 45, 75, 100, 125 m, except in a few occasions in which there were no measurements made at 125 m. Data description <table> <tbody> <tr> <td> Variable </td> <td> Description </td> <td> Units </td> </tr> <tr> <td> Date  </td> <td> Date of sampling and start of incubation (UTC -10 hours) </td> <td>   </td> </tr> <tr> <td> Cruise ID </td> <td> Cruise identification </td> <td> # </td> </tr> <tr> <td> Latitude </td> <td> Latitude </td> <td> degrees N </td> </tr> <tr> <td> Longitude </td> <td> Longitude </td> <td> degrees E </td> </tr> <tr> <td>Stn ALOHA </td> <td>Whether the data are from Station ALOHA (yes/no)</td> <td> </td> </tr> <tr> <td>IncT</td> <td> Incubation time </td> <td> hours </td> </tr> <tr> <td> Depth </td> <td>Nominal depth of sampling and incubation </td> <td> meters </td> </tr> <tr> <td>GOP</td> <td>Gross oxygen production </td> <td>mmol O2 m-3 d-1</td> </tr> <tr> <td>CR</td> <td>Estimate of community respiration </td> <td>mmol O2 m-3 d-1</td> </tr> <tr> <td> Flag GOP </td> <td>Flag identification for gross oxygen production (good=1,questionable=2)</td> <td> # </td> </tr> <tr> <td> Flag CR </td> <td> Flag identification for community respiration (good=1,questionable=2) </td> <td> # </td> </tr> </tbody> </table>   The Light-dark_ALOHA_rates.xlsx file contains metabolic rates measured by the ligth-dark oxygen method between June 2005 and June 2007 at different depths in the euphotic zone at Station ALOHA (22° 45' N, 158° W). Rates of net community production, communnity respiration, and gross oxygen production were measured at 6 depths within the euphotic zone (5, 25, 45, 75, 100, 125 m)  in dawn to dawn incubations, following Williams et al. (2004).  Data description <table> <tbody> <tr> <td> Variable </td> <td> Description </td> <td> Units </td> </tr> <tr> <td> HOT </td> <td>HOT cruise number</td> <td> # </td> </tr> <tr> <td> Date </td> <td>Date of sampling and start of incubation (UTC -10)</td> <td>   </td> </tr> <tr> <td> Depth </td> <td>Nominal depth of sampling and incubation </td> <td> meters </td> </tr> <tr> <td>GOP</td> <td>Gross oxygen production, average of 8 replicates</td> <td>mmol O2 m-3 d-1</td> </tr> <tr> <td>GOP SE</td> <td>Gross oxygen production standard error </td> <td>mmol O2 m-3 d-1</td> </tr> <tr> <td>CR</td> <td> Dark community respiration, average of 8 replicates </td> <td> mmol O2 m-3 d-1 </td> </tr> <tr> <td> CR SE </td> <td>Dark community respiration standard error</td> <td> meters </td> </tr> <tr> <td>NCP</td> <td>Net community production,average of 8 replicates </td> <td>mmol O2 m-3 d-1</td> </tr> <tr> <td>NCP SE</td> <td>Net community production standard error </td> <td>mmol O2 m-3 d-1</td> </tr> </tbody> </table>

A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 6

This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPPluc), the harvested NPP (including losses, HANPPharv), and the NPP remaining in ecosystems after harvest (NPPeco) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication. The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPPharv to NPPeco results in the actual NPP available before harvest (NPPact=NPPeco+HANPPharv), and adding HANPPluc to NPPact results in the potential NPP available in the hypothetical absence of land use (NPPpot=NPPact+HANPPluc) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPPpot can be calculated by summing up HANPPharv and HANPPluc and dividing it by NPPpot. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. This Zenodo repository provides data on following land-use classes: cropland used for production of potatoes (CL-POTA); sweet potatoes and yams (CL-SWPY); and rest of crops (CL-REST)

A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 4

This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPPluc), the harvested NPP (including losses, HANPPharv), and the NPP remaining in ecosystems after harvest (NPPeco) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication. The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPPharv to NPPeco results in the actual NPP available before harvest (NPPact=NPPeco+HANPPharv), and adding HANPPluc to NPPact results in the potential NPP available in the hypothetical absence of land use (NPPpot=NPPact+HANPPluc) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPPpot can be calculated by summing up HANPPharv and HANPPluc and dividing it by NPPpot. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.  This Zenodo repository provides data on following land-use classes: cropland used for production of wheat (CL-WHEA); maize (CL-MAIZ); soybean (CL-SOYB)

A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 5

This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPPluc), the harvested NPP (including losses, HANPPharv), and the NPP remaining in ecosystems after harvest (NPPeco) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication. The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPPharv to NPPeco results in the actual NPP available before harvest (NPPact=NPPeco+HANPPharv), and adding HANPPluc to NPPact results in the potential NPP available in the hypothetical absence of land use (NPPpot=NPPact+HANPPluc) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPPpot can be calculated by summing up HANPPharv and HANPPluc and dividing it by NPPpot. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. This Zenodo repository provides data on following land-use classes: cropland used for production of millet (CL-MILL); barley (CL-BARL); sorghum (CL-SORG); rice (CL-RICE)

A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 7

This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPPluc), the harvested NPP (including losses, HANPPharv), and the NPP remaining in ecosystems after harvest (NPPeco) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication. The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPPharv to NPPeco results in the actual NPP available before harvest (NPPact=NPPeco+HANPPharv), and adding HANPPluc to NPPact results in the potential NPP available in the hypothetical absence of land use (NPPpot=NPPact+HANPPluc) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPPpot can be calculated by summing up HANPPharv and HANPPluc and dividing it by NPPpot. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. This Zenodo repository provides data on following land-use classes:  cropland used for production of cassava (CL-CASS); sugarcane (CL-SUGC); sugarbeet (CL-SUGB); cotton (CL-COTT); fruits and vegetables (CL-VEFR)

A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 8

This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPPluc), the harvested NPP (including losses, HANPPharv), and the NPP remaining in ecosystems after harvest (NPPeco) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication. The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPPharv to NPPeco results in the actual NPP available before harvest (NPPact=NPPeco+HANPPharv), and adding HANPPluc to NPPact results in the potential NPP available in the hypothetical absence of land use (NPPpot=NPPact+HANPPluc) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPPpot can be calculated by summing up HANPPharv and HANPPluc and dividing it by NPPpot. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. This Zenodo repository provides data on following land-use classes:  cropland used for production of beans (CL-BEAN); other pulses (CL-OPUL); groundnuts (CL-GROU); bananas and plantains (CL-BANP)

A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 9

This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPPluc), the harvested NPP (including losses, HANPPharv), and the NPP remaining in ecosystems after harvest (NPPeco) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication. The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPPharv to NPPeco results in the actual NPP available before harvest (NPPact=NPPeco+HANPPharv), and adding HANPPluc to NPPact results in the potential NPP available in the hypothetical absence of land use (NPPpot=NPPact+HANPPluc) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPPpot can be calculated by summing up HANPPharv and HANPPluc and dividing it by NPPpot. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. This Zenodo repository provides data on following land-use classes: cropland used for production of other oilcrops (CL-OOIL); coffee (CL-COFF); fodder crops (CL-FODD)

opencc-by-4.0Jul 2021View details →

A dataset of estimated net primary production of Japanese cedar plantations (ver.1)

# Abbreviations in this text NPP:        Net Primary Production, average of stand ages 36-40 GCM:        Global Climate Model HT:            Historical Trend, average of five years 1996-2000 FP2050:        Future Prediction, average of five years 2046-2050 FP2100:        Future Prediction, average of five years 2096-2100 (except for a GCM HadGEM2-ES of 2095-2099) # Description The data is prepared as four files in tab-delimited text format or ten netcdf files in a zip file. The annual NPP of cedar plantations under current and future climates are calculated in 196928 meshes in Japan. The climate scenarios used are owned and distributed by the third party (National Agriculture and Food Research Organization, Japan). For the methodology on modeling, parameterization and nation-wide calculation, please check the paper below. # Reference Toriyama J, Hashimoto S, Osone Y, Yamashita N, Tsurita T, Shimizu T, Saitoh TM, Sawano S, Lehtonen A, Ishizuka S (2021) Estimating spatial variation in the effects of climate change on the net primary production of Japanese cedar plantations based on modeled carbon dynamics. PLoS ONE 16(2): e0247165. https://doi.org/10.1371/journal.pone.0247165 # List of variables in text files # site.txt (6.8 MB) mesh:        Number of third-mesh order in the Japanese grid square system lat:        Latitude (degree) of center point of third-mesh order long:        Longitude (degree) of center point of third-mesh order block:        Block number in Toriyama et al. (2021), 1, 2 and 3 for SW, CT and NW, respectively pref:        Prefecture number in the Japanese administrative system # npp_2000.txt (8.7 MB) mesh:                Number of third-mesh order in the Japanese grid square system npp_2000_cgcm:        Annual NPP (kgC m-2 year-1) in HT, MRI-CGCM3 npp_2000_csiro:        Annual NPP (kgC m-2 year-1) in HT, CSIRO-Mk3-6-0 npp_2000_gfdl:        Annual NPP (kgC m-2 year-1) in HT, GFDL-CM3 npp_2000_hadgem:    Annual NPP (kgC m-2 year-1) in HT, HadGEM2-ES npp_2000_miroc:        Annual NPP (kgC m-2 year-1) in HT, MIROC5 # npp_2050.txt (15.4 MB) mesh:                    Number of third-mesh order in the Japanese grid square system npp_rcp26_2050_cgcm:    Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, MRI-CGCM3 npp_rcp26_2050_csiro:    Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, CSIRO-Mk3-6-0 npp_rcp26_2050_gfdl:    Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, GFDL-CM3 npp_rcp26_2050_hadgem:    Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, HadGEM2-ES npp_rcp26_2050_miroc:    Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, MIROC5 npp_rcp85_2050_cgcm:    Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, MRI-CGCM3 npp_rcp85_2050_csiro:    Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, CSIRO-Mk3-6-0 npp_rcp85_2050_gfdl:    Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, GFDL-CM3 npp_rcp85_2050_hadgem:    Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, HadGEM2-ES npp_rcp85_2050_miroc:    Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, MIROC5 # npp_2100.txt (15.4 MB) mesh:                    Number of third-mesh order in the Japanese grid square system npp_rcp26_2100_cgcm:    Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, MRI-CGCM3 npp_rcp26_2100_csiro:    Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, CSIRO-Mk3-6-0 npp_rcp26_2100_gfdl:    Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, GFDL-CM3 npp_rcp26_2100_hadgem:    Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, HadGEM2-ES npp_rcp26_2100_miroc:    Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, MIROC5 npp_rcp85_2100_cgcm:    Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, MRI-CGCM3 npp_rcp85_2100_csiro:    Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, CSIRO-Mk3-6-0 npp_rcp85_2100_gfdl:    Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, GFDL-CM3 npp_rcp85_2100_hadgem:    Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, HadGEM2-ES npp_rcp85_2100_miroc:    Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, MIROC5 # npp_netcdf.zip (594 MB) The 10 files in netcdf format are compiled in a zip file for NPP data of different RCP scenarios and GCMs. Please check the content of each file by following command. ncdump -h filename # summary_map.zip (5.8 MB) The 20 files in png format are compiled in a zip file for maps of NPP and its change. The maps were created using the Generic Mapping Tools version 5 (http://gmt.soest.hawaii.edu/). The average values of five GCMs are used for mapping. # Acknowledgement This dataset was funded by the Agriculture, Forestry and Fisheries Research Council in Japan, under the project “Research on adaptation to climate change for agriculture, forestry and fisheries”.

opencc-by-4.0Sep 2021View details →

Model output for "Impact of intensifying nitrogen limitation of ocean net primary production is fingerprinted by nitrogen isotopes"

Description. The data included in this repository is output of simulations performed with the NEMO-PISCESv2 global ocean-biogeochemical model. Simulations involved forcing the NEMO-PISCESv2 with global warming associated with historical and future emissions, as well as the historical and future trends in atmospheric nitrogen deposition. Future climate change was according to the Representative Concentration Pathway 8.5 scenario (Dufresne et al., 2013; Riahi et al., 2011), which sees rapid warming during the 21st century. Historical and future atmospheric nitrogen deposition fields were created via linear interpolation of fields produced by Hauglustaine et al. (2014) at years 1850, 2000, 2030, 2050 and 2100. To represent the amplification of deposition since 1950 (Galloway 2014), 60 % of the increase between 1850 and 2000 occurred from 1950 onwards. In this study, we quantified the effect anthropogenic climate change and anthropogenic increases in atmospheric nitrogen deposition on the marine nitrogen cycle. The response of the marine nitrogen cycle to these combined stressors is highly uncertain, and we therefore employed this complex model with a strong representation of nitrogen cycling in an attempt to constrain the global behaviour of this important cycle. In addition, through the addition of nitrogen isotopes to the ocean-biogeochemical model, we also explored and described how the isotopes responded to these anthropogenic forcings, and if the isotopes uniquely fingerprinted the response for potential monitoring/detection purposes. Our abstract reads: “The open ocean nitrogen cycle is being altered by increases in anthropogenic atmospheric nitrogen deposition and climate change. How the nitrogen cycle responds will determine long-term trends in net primary production (NPP) in the nitrogen-limited low latitude ocean, but is poorly constrained by uncertainty in how the source-sink balance will evolve. Here we show that intensifying nitrogen limitation of phytoplankton, associated with near-term reductions in NPP, causes detectable declines in nitrogen isotopes (δ15N) and constitutes the primary perturbation of the 21st century nitrogen cycle. Model experiments show that ~75% of the low latitude twilight zone develops anomalously low δ15N by 2060, predominantly due to the effects of climate change that alter ocean circulation, with implications for the nitrogen sources-sink balance. Our results highlight that δ15N changes in the low latitude twilight zone may provide a useful constraint on emerging changes to nitrogen limitation and NPP over the 21st century.”   Coordinates Spatial resolution is global (90°S-90°N, 180°W-180°E, surface ocean to 5000 metres depth) and temporal resolution runs from years 1801 to 2100.   Citation. Buchanan PJ, Aumont O, Bopp L, Mahaffey C, and Tagliabue A (2021): An isotopic fingerprint of increasingly nitrogen-limited phytoplankton in a changing oceanic nitrogen cycle. Nature Communications.   Files provided. The data files provided are those that are required to create the figures for this study and/or perform key analyses (i.e. the time of emergence calculations). In the following, each figure or analysis has an associated python script and we list the data files needed to run that script. Python scripts can be found the lead authors GitHub at <a href="https://github.com/pearseb/PISCESiso_Ncycle_analysis">https://github.com/pearseb/PISCESiso_Ncycle_analysis</a>.     Put δ15NNO3 observations on model grid (process-d15Nno3_observations_on_model_grid.py): <ul> <li>“RafterTuerena_watercolumn_d15N_no3.txt”</li> </ul> Model assessment (process-model_assessment.py): <ul> <li>“ETOPO_spinup_d15Nno3.nc”</li> <li>“ETOPO_ORCA2.0_Basins_float.nc”</li> <li>“ETOPO_ORCA2.0.full_grid.nc”</li> <li>“RafterTuerena_watercolumn_d15N_no3_gridded.npz”</li> </ul> Time of emergence calculations (process-compute_toe.py): <ul> <li>“ETOPO_picontrol_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_temp_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_temp_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_npp.nc”</li> <li>“ETOPO_picontrol_ndep_1y_npp.nc”</li> <li>“ETOPO_future_1y_npp.nc”</li> <li>“ETOPO_future_ndep_1y_npp.nc”</li> <li>“ETOPO_picontrol_1y_nfix.nc”</li> <li>“ETOPO_picontrol_ndep_1y_nfix.nc”</li> <li>“ETOPO_future_1y_nfix.nc”</li> <li>“ETOPO_future_ndep_1y_nfix.nc”</li> </ul> Figure 1 (fig-main1.py): <ul> <li>“ncycle_changes.nc”</li> <li>“sources_and_sinks.nc”</li> </ul> Figure 2 (fig-main2.py): <ul> <li>“figure2D_ndep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_ndep_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15npom_signal_usingPAR.nc”</li> <li>“ETOPO_ToE_futndep_depthzones.nc”</li> <li>“ETOPO_ToE_fut_depthzones.nc”</li> <li>“ETOPO_ToE_picndep_depthzones.nc”</li> <li>“ToE_futndep_curves.txt”</li> <li>“ToE_fut_curves.txt”</li> <li>“ToE_picndep_curves.txt”</li> </ul> Figure 3 (fig-main3.py): <ul> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“ETOPO_fluxanalysis_results.nc”</li> <li>“figure2D_cc_din_e15n.nc”</li> </ul> Figure 4 (fig-main4.py): <ul> <li>“ETOPO_direct_indirect_effects.nc”</li> </ul> Supp Figure 1 (fig-supp1.py): <ul> <li>“figure_d15Nmaps.nc”</li> </ul> Supp Figure 2 (process-model_assessment.py): <ul> <li>Produced by process-model_assessment.py (see data above)</li> </ul> Supp Figure 3 (fig-supp3.py): <ul> <li>“d15nstats.txt”</li> </ul> Supp Figure 4 (fig-supp4.py): <ul> <li>“ndep_Tg_yr.nc”</li> </ul> Supp Figure 5 (fig-supp5.py):y <ul> <li>“ncycle_changes_climatechangeonly.nc”</li> </ul> Supp Figure 6 (fig-supp6.py): <ul> <li>“ncycle_changes_ndeponly.nc”</li> </ul> Supp Figure 7 (fig-supp7.py): <ul> <li>“figure_depthzones.nc”</li> </ul> Supp Figure 8 (fig-supp8.py): <ul> <li>“figure2D_ndep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_ndep_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15npom_signal_usingPAR.nc”</li> <li>“BGCP_ETOPO_merged_alt.nc”</li> <li>“ETOPO_ToE_futndep_depthzones.nc”</li> <li>“ETOPO_ToE_fut_depthzones.nc”</li> <li>“ETOPO_ToE_picndep_depthzones.nc”</li> <li>“BGCP_ETOPO_merged_alt.nc”</li> <li>“ToE_fut_curves.txt”</li> <li>“ToE_futndep_curves.txt”</li> <li>“ToE_picndep_curves.txt”</li> </ul> Supp Figure 9 (fig-supp9.py): <ul> <li>“figure2D_ndep_no3_utz.nc”</li> </ul> Supp Figures 10 and 11 (process-0D_model_phyto_frac.py): <ul> <li>Produced by process-0D_model_phyto_frac.py and no data required.</li> </ul> Supp Figure 12 (process-compute_toe.py): <ul> <li>Produced by process-compute_toe.py (see data above)</li> </ul>   References. Dufresne, J. L., Foujols, M. A., Denvil, S., Caubel, A., Marti, O., Aumont, O., et al. (2013). Climate change projections using the IPSL-CM5 Earth System Model: From CMIP3 to CMIP5. Climate Dynamics (Vol. 40). https://doi.org/10.1007/s00382-012-1636-1 Galloway, J. N. (2014). The Global Nitrogen Cycle. In Treatise on Geochemistry (2nd ed., Vol. 10, pp. 475–498). Elsevier. https://doi.org/10.1016/B978-0-08-095975-7.00812-3 Hauglustaine, D. A., Balkanski, Y., & Schulz, M. (2014). A global model simulation of present and future nitrate aerosols and their direct radiative forcing of climate. Atmospheric Chemistry and Physics, 14(20), 11031–11063. https://doi.org/10.5194/acp-14-11031-2014 Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., et al. (2011). RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Climatic Change, 109(1–2), 33–57. https://doi.org/10.1007/s10584-011-0149-y

opencc-by-4.0Nov 2022View details →

PML-V2 China staple crop (maize, wheat, rice) evapotranspiration, gross primary production and yield over 2003-2018

This dataset provides the yearly evapotranspiration (ET), gross primary production (GPP) and yield of the three staple crops (i.e., maize, wheat and rice) of China from 2003 to 2018. ET and GPP are estimated by the Penman-Monteith-Leuning version 2 model (PML-V2 model), and crop yield is the product of GPP and harvest index. The units of ET, GPP and yield are mm year-1, g C m-2 year-1 and kg ha-1, respectively. The PML-V2 model is calibrated and validated against the observed ET and GPP at EC sites for each crop type. The PML_V2 model uses CMFD meteorological drive and MODIS leaf area index (LAI), reflectivity (Albedo), emissivity (Emissivity) as inputs, and finally obtains PML_V2 crop evapotranspiration, gross primary production datasets. The data format is tiff, the spatiotemporal resolution is yearly and 0.0125°, and the time span is 2003-2018. The file name is "crop_variable_year.tif". For example, a file named Maize_ETsum_2003.tif corresponds to the total ET of maize in 2003.

opencc-by-4.0Nov 2022View details →

Data and code for "Hotspots and drivers of compound marine heatwave and low net primary production extremes"

This repository provides the code and data for the study "Hotspots and drivers of compound marine heatwave and low net primary production extremes". All processed data required to produce the figures in this study are provided. However, not all raw data are provided, because of too large file sizes. For more information, please contact natacha.legrix@unibe.ch.

opencc-by-4.0Feb 2023View details →

8-day, 500-m Gross Primary Production for Europe from 2001 to 2016(YEAR_doy: 2004337 to 2010073)

FGM GPP from YEAR_DOY of 2004337 to 20010073