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 21^st 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 21^st 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 21^st 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 https://github.com/pearseb/PISCESiso_Ncycle_analysis.  

 

Put δ¹⁵N_NO3 observations on model grid (process-d15Nno3_observations_on_model_grid.py):

• “RafterTuerena_watercolumn_d15N_no3.txt”

Model assessment (process-model_assessment.py):

• “ETOPO_spinup_d15Nno3.nc”
• “ETOPO_ORCA2.0_Basins_float.nc”
• “ETOPO_ORCA2.0.full_grid.nc”
• “RafterTuerena_watercolumn_d15N_no3_gridded.npz”

Time of emergence calculations (process-compute_toe.py):

• “ETOPO_picontrol_1y_no3_ez_utz_ltz.nc”
• “ETOPO_picontrol_1y_nst_ez_utz_ltz.nc”
• “ETOPO_picontrol_1y_d15n_no3_ez_utz_ltz.nc”
• “ETOPO_picontrol_1y_d15n_pom_ez_utz_ltz.nc”
• “ETOPO_picontrol_ndep_1y_no3_ez_utz_ltz.nc”
• “ETOPO_picontrol_ndep_1y_nst_ez_utz_ltz.nc”
• “ETOPO_picontrol_ndep_1y_d15n_no3_ez_utz_ltz.nc”
• “ETOPO_picontrol_ndep_1y_d15n_pom_ez_utz_ltz.nc”
• “ETOPO_future_1y_no3_ez_utz_ltz.nc”
• “ETOPO_future_1y_nst_ez_utz_ltz.nc”
• “ETOPO_future_1y_d15n_no3_ez_utz_ltz.nc”
• “ETOPO_future_1y_d15n_pom_ez_utz_ltz.nc”
• “ETOPO_future_ndep_1y_no3_ez_utz_ltz.nc”
• “ETOPO_future_ndep_1y_nst_ez_utz_ltz.nc”
• “ETOPO_future_ndep_1y_d15n_no3_ez_utz_ltz.nc”
• “ETOPO_future_ndep_1y_d15n_pom_ez_utz_ltz.nc”
• “ETOPO_picontrol_1y_temp_ez_utz_ltz.nc”
• “ETOPO_future_1y_temp_ez_utz_ltz.nc”
• “ETOPO_picontrol_1y_npp.nc”
• “ETOPO_picontrol_ndep_1y_npp.nc”
• “ETOPO_future_1y_npp.nc”
• “ETOPO_future_ndep_1y_npp.nc”
• “ETOPO_picontrol_1y_nfix.nc”
• “ETOPO_picontrol_ndep_1y_nfix.nc”
• “ETOPO_future_1y_nfix.nc”
• “ETOPO_future_ndep_1y_nfix.nc”

Figure 1 (fig-main1.py):

• “ncycle_changes.nc”
• “sources_and_sinks.nc”

Figure 2 (fig-main2.py):

• “figure2D_ndep_d15nno3_signal_usingPAR.nc”
• “figure2D_ndep_d15npom_signal_usingPAR.nc”
• “figure2D_cc_d15nno3_signal_usingPAR.nc”
• “figure2D_cc_d15npom_signal_usingPAR.nc”
• “figure2D_picdep_d15nno3_signal_usingPAR.nc”
• “figure2D_picdep_d15npom_signal_usingPAR.nc”
• “ETOPO_ToE_futndep_depthzones.nc”
• “ETOPO_ToE_fut_depthzones.nc”
• “ETOPO_ToE_picndep_depthzones.nc”
• “ToE_futndep_curves.txt”
• “ToE_fut_curves.txt”
• “ToE_picndep_curves.txt”

Figure 3 (fig-main3.py):

• “figure2D_cc_d15npom_signal_usingPAR.nc”
• “ETOPO_fluxanalysis_results.nc”
• “figure2D_cc_din_e15n.nc”

Figure 4 (fig-main4.py):

• “ETOPO_direct_indirect_effects.nc”

Supp Figure 1 (fig-supp1.py):

• “figure_d15Nmaps.nc”

Supp Figure 2 (process-model_assessment.py):

• Produced by process-model_assessment.py (see data above)

Supp Figure 3 (fig-supp3.py):

• “d15nstats.txt”

Supp Figure 4 (fig-supp4.py):

• “ndep_Tg_yr.nc”

Supp Figure 5 (fig-supp5.py):y

• “ncycle_changes_climatechangeonly.nc”

Supp Figure 6 (fig-supp6.py):

• “ncycle_changes_ndeponly.nc”

Supp Figure 7 (fig-supp7.py):

• “figure_depthzones.nc”

Supp Figure 8 (fig-supp8.py):

• “figure2D_ndep_d15nno3_signal_usingPAR.nc”
• “figure2D_ndep_d15npom_signal_usingPAR.nc”
• “figure2D_cc_d15nno3_signal_usingPAR.nc”
• “figure2D_cc_d15npom_signal_usingPAR.nc”
• “figure2D_picdep_d15nno3_signal_usingPAR.nc”
• “figure2D_picdep_d15npom_signal_usingPAR.nc”
• “BGCP_ETOPO_merged_alt.nc”
• “ETOPO_ToE_futndep_depthzones.nc”
• “ETOPO_ToE_fut_depthzones.nc”
• “ETOPO_ToE_picndep_depthzones.nc”
• “BGCP_ETOPO_merged_alt.nc”
• “ToE_fut_curves.txt”
• “ToE_futndep_curves.txt”
• “ToE_picndep_curves.txt”

Supp Figure 9 (fig-supp9.py):

• “figure2D_ndep_no3_utz.nc”

Supp Figures 10 and 11 (process-0D_model_phyto_frac.py):

• Produced by process-0D_model_phyto_frac.py and no data required.

Supp Figure 12 (process-compute_toe.py):

• Produced by process-compute_toe.py (see data above)

 

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