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865 results for “Net primary production”
Data from: Opposing responses of temporal stability of aboveground and belowground net primary productivity to water and nitrogen enrichment in a temperate grassland
<p><span>Changes in water and nitrogen availability, as important elements of global environmental change, are known to affect the temporal stability of aboveground net primary productivity (ANPP). However, evidences for their effects on the temporal stability of belowground net primary productivity (BNPP), and whether such effects are consistent between belowground and aboveground, are rather scarce. Here, we investigated the responses of temporal stability of both ANPP and BNPP to water and nitrogen addition based on a 9-year manipulative experiment in a temperate grassland in northern China. The results showed that the temporal stability of ANPP increased with water addition but decreased with nitrogen addition. By contrast, the temporal stability of BNPP decreased with water addition but increased with nitrogen enrichment. The temporal stability of ANPP was mainly determined by the soil moisture and inorganic nitrogen, which modulated species asynchrony, as well as by the stability of dominant species. On the other hand, the temporal stability of BNPP was mainly driven by the soil moisture and inorganic nitrogen that modulated ANPP of grasses, and by the direct effect of soil water availability. Our study provides the first evidence on the opposite responses of aboveground and belowground grassland temporal stability to increased water and nitrogen availability, highlighting the importance of considering both aboveground and belowground components of ecosystems for a more comprehensive understanding of their dynamics.</span></p>
Code and data for 'Human modification of land cover alters net primary productivity, species richness and their relationship' manuscript
<p>The data and scripts in this database are analyses for a research paper in Global Ecology and Biogeography in 2023: Human modification of land cover alters net primary productivity, species richness and their relationship. Please refer to the README file and the paper for details about the usage of the data and methodology.</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 2
<p>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 (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) 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.</p> <p>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 HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) 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 NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. </p> <p>This Zenodo repository provides data on following land-use classes: grazing land characterized by open wooded lands (GL-owl)</p>
Dataset for 'Global declines in net primary production underestimated by climate models'
<p>The dataset provided here is to be used in conjuction with the JuPyTer notebook provided here: https://github.com/tjryankeogh/global_npp_trends/tree/main</p> <p> </p> <p>Download the file and uncompress in a root directory where there is a folder 'FIGURES'. When running the notebook make sure to change this root directory when importing packages.</p> <p> </p>
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
<p>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 (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) 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.</p> <p>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 HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) 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 NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>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)</p> <p> </p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 6
<p>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 (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) 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.</p> <p>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 HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) 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 NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>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)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 4
<p>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 (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) 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.</p> <p>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 HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) 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 NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. </p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of wheat (CL-WHEA); maize (CL-MAIZ); soybean (CL-SOYB)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 5
<p>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 (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) 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.</p> <p>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 HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) 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 NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>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)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 7
<p>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 (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) 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.</p> <p>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 HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) 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 NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>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)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 8
<p>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 (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) 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.</p> <p>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 HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) 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 NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>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)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 9
<p>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 (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) 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.</p> <p>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 HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) 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 NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>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)</p>
A dataset of estimated net primary production of Japanese cedar plantations (ver.1)
<p># Abbreviations in this text<br> NPP: Net Primary Production, average of stand ages 36-40<br> GCM: Global Climate Model<br> HT: Historical Trend, average of five years 1996-2000<br> FP2050: Future Prediction, average of five years 2046-2050<br> FP2100: Future Prediction, average of five years 2096-2100 (except for a GCM HadGEM2-ES of 2095-2099)</p> <p># Description<br> 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.</p> <p># Reference<br> 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</p> <p># List of variables in text files</p> <p># site.txt (6.8 MB)<br> mesh: Number of third-mesh order in the Japanese grid square system<br> lat: Latitude (degree) of center point of third-mesh order<br> long: Longitude (degree) of center point of third-mesh order<br> block: Block number in Toriyama et al. (2021), 1, 2 and 3 for SW, CT and NW, respectively<br> pref: Prefecture number in the Japanese administrative system</p> <p># npp_2000.txt (8.7 MB)<br> mesh: Number of third-mesh order in the Japanese grid square system<br> npp_2000_cgcm: Annual NPP (kgC m-2 year-1) in HT, MRI-CGCM3<br> npp_2000_csiro: Annual NPP (kgC m-2 year-1) in HT, CSIRO-Mk3-6-0<br> npp_2000_gfdl: Annual NPP (kgC m-2 year-1) in HT, GFDL-CM3<br> npp_2000_hadgem: Annual NPP (kgC m-2 year-1) in HT, HadGEM2-ES<br> npp_2000_miroc: Annual NPP (kgC m-2 year-1) in HT, MIROC5</p> <p># npp_2050.txt (15.4 MB)<br> mesh: Number of third-mesh order in the Japanese grid square system<br> npp_rcp26_2050_cgcm: Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, MRI-CGCM3<br> npp_rcp26_2050_csiro: Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, CSIRO-Mk3-6-0<br> npp_rcp26_2050_gfdl: Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, GFDL-CM3<br> npp_rcp26_2050_hadgem: Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, HadGEM2-ES<br> npp_rcp26_2050_miroc: Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, MIROC5<br> npp_rcp85_2050_cgcm: Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, MRI-CGCM3<br> npp_rcp85_2050_csiro: Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, CSIRO-Mk3-6-0<br> npp_rcp85_2050_gfdl: Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, GFDL-CM3<br> npp_rcp85_2050_hadgem: Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, HadGEM2-ES<br> npp_rcp85_2050_miroc: Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, MIROC5</p> <p># npp_2100.txt (15.4 MB)<br> mesh: Number of third-mesh order in the Japanese grid square system<br> npp_rcp26_2100_cgcm: Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, MRI-CGCM3<br> npp_rcp26_2100_csiro: Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, CSIRO-Mk3-6-0<br> npp_rcp26_2100_gfdl: Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, GFDL-CM3<br> npp_rcp26_2100_hadgem: Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, HadGEM2-ES<br> npp_rcp26_2100_miroc: Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, MIROC5<br> npp_rcp85_2100_cgcm: Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, MRI-CGCM3<br> npp_rcp85_2100_csiro: Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, CSIRO-Mk3-6-0<br> npp_rcp85_2100_gfdl: Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, GFDL-CM3<br> npp_rcp85_2100_hadgem: Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, HadGEM2-ES<br> npp_rcp85_2100_miroc: Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, MIROC5</p> <p># npp_netcdf.zip (594 MB)<br> The 10 files in netcdf format are compiled in a zip file for NPP data of different RCP scenarios and GCMs.<br> Please check the content of each file by following command.<br> ncdump -h filename</p> <p># summary_map.zip (5.8 MB)<br> 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.</p> <p># Acknowledgement<br> 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”.</p>
Model output for "Impact of intensifying nitrogen limitation of ocean net primary production is fingerprinted by nitrogen isotopes"
<p><strong>Description.</strong></p> <p>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 21<sup>st</sup> 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.</p> <p>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.</p> <p>Our abstract reads:</p> <p>“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 (δ<sup>15</sup>N) and constitutes the primary perturbation of the 21<sup>st</sup> century nitrogen cycle. Model experiments show that ~75% of the low latitude twilight zone develops anomalously low δ<sup>15</sup>N 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 δ<sup>15</sup>N changes in the low latitude twilight zone may provide a useful constraint on emerging changes to nitrogen limitation and NPP over the 21<sup>st</sup> century.”</p> <p> </p> <p><strong>Coordinates</strong></p> <p>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.</p> <p> </p> <p><strong>Citation.</strong></p> <p>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.</p> <p> </p> <p><strong>Files provided.</strong></p> <p>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.</p> <p>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>. </p> <p> </p> <p>Put δ<sup>15</sup>N<sub>NO3</sub> observations on model grid (<em>process-d15Nno3_observations_on_model_grid.py</em>):</p> <ul> <li>“RafterTuerena_watercolumn_d15N_no3.txt”</li> </ul> <p>Model assessment (<em>process-model_assessment.py</em>):</p> <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> <p>Time of emergence calculations (<em>process-compute_toe.py</em>):</p> <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> <p>Figure 1 (<em>fig-main1.py</em>):</p> <ul> <li>“ncycle_changes.nc”</li> <li>“sources_and_sinks.nc”</li> </ul> <p>Figure 2 (<em>fig-main2.py</em>):</p> <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> <p>Figure 3 (<em>fig-main3.py</em>):</p> <ul> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“ETOPO_fluxanalysis_results.nc”</li> <li>“figure2D_cc_din_e15n.nc”</li> </ul> <p>Figure 4 (<em>fig-main4.py</em>):</p> <ul> <li>“ETOPO_direct_indirect_effects.nc”</li> </ul> <p>Supp Figure 1 (<em>fig-supp1.py</em>):</p> <ul> <li>“figure_d15Nmaps.nc”</li> </ul> <p>Supp Figure 2 (<em>process-model_assessment.py</em>):</p> <ul> <li>Produced by <em>process-model_assessment.py </em>(see data above)</li> </ul> <p>Supp Figure 3 (<em>fig-supp3.py</em>):</p> <ul> <li>“d15nstats.txt”</li> </ul> <p>Supp Figure 4 (<em>fig-supp4.py</em>):</p> <ul> <li>“ndep_Tg_yr.nc”</li> </ul> <p>Supp Figure 5 (<em>fig-supp5.py</em>):y</p> <ul> <li>“ncycle_changes_climatechangeonly.nc”</li> </ul> <p>Supp Figure 6 (<em>fig-supp6.py</em>):</p> <ul> <li>“ncycle_changes_ndeponly.nc”</li> </ul> <p>Supp Figure 7 (<em>fig-supp7.py</em>):</p> <ul> <li>“figure_depthzones.nc”</li> </ul> <p>Supp Figure 8 (<em>fig-supp8.py</em>):</p> <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> <p>Supp Figure 9 (<em>fig-supp9.py</em>):</p> <ul> <li>“figure2D_ndep_no3_utz.nc”</li> </ul> <p>Supp Figures 10 and 11 (<em>process-0D_model_phyto_frac.py</em>):</p> <ul> <li>Produced by <em>process-0D_model_phyto_frac.py</em> and no data required.</li> </ul> <p>Supp Figure 12 (<em>process-compute_toe.py</em>):</p> <ul> <li>Produced by <em>process-compute_toe.py </em>(see data above)</li> </ul> <p> </p> <p><strong>References.</strong></p> <p>Dufresne, J. L., Foujols, M. A., Denvil, S., Caubel, A., Marti, O., Aumont, O., et al. (2013). <em>Climate change projections using the IPSL-CM5 Earth System Model: From CMIP3 to CMIP5</em>. <em>Climate Dynamics</em> (Vol. 40). https://doi.org/10.1007/s00382-012-1636-1</p> <p>Galloway, J. N. (2014). The Global Nitrogen Cycle. In <em>Treatise on Geochemistry</em> (2nd ed., Vol. 10, pp. 475–498). Elsevier. https://doi.org/10.1016/B978-0-08-095975-7.00812-3</p> <p>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. <em>Atmospheric Chemistry and Physics</em>, <em>14</em>(20), 11031–11063. https://doi.org/10.5194/acp-14-11031-2014</p> <p>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. <em>Climatic Change</em>, <em>109</em>(1–2), 33–57. https://doi.org/10.1007/s10584-011-0149-y</p>
Data and code for "Hotspots and drivers of compound marine heatwave and low net primary production extremes"
<p>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. </p>
Data from: Opposing responses of temporal stability of aboveground and belowground net primary productivity to water and nitrogen enrichment in a temperate grassland
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Latitudinal gradient, MEND experiment, and BioGen experiment relating species richness and net primary productivity (NPP)
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