noaa-model-23471 Simulations of ocean physical and biogeochemical fields under different biological functioning in CSIRO Mk3L 1.2 v1.0 Buchanan, P.J.; Matear, R.J.; Chase, Z.; Phipps, S.J.; Bindoff, N.L. Simulations of ocean physical and biogeochemical fields under different biological functioning in CSIRO Mk3L 1.2 v1.0 2018-02-27 NCDC-Paleoclimatology ONLINE Files Pending https://www.ncdc.noaa.gov/paleo/study/23471 Investigator P.J. Buchanan Investigator R.J. Matear Investigator Z. Chase Investigator S.J. Phipps Investigator N.L. Bindoff earth science paleoclimate paleoclimatic modeling latitude,null,null,degree north,null,paleoclimatic modeling,null,null,N,null earth science paleoclimate paleoclimatic modeling dissolved inorganic carbon,sea water,null,mole per cubic meter,null,paleoclimatic modeling,null,null,N,null earth science paleoclimate paleoclimatic modeling organic matter,sea water,null,mole per square meter per second,null,paleoclimatic modeling,null,null,N,sinking flux of particulate organic matter as carbon earth science paleoclimate paleoclimatic modeling sea ice cover,null,null,percent,null,paleoclimatic modeling,null,null,N,null earth science paleoclimate paleoclimatic modeling iron,sea water,null,mole per square meter per second,null,paleoclimatic modeling,null,null,N,tendency of iron due to deposition+runoff+sediment dissolution earth science paleoclimate paleoclimatic modeling wind speed,null,null,meter per second,null,paleoclimatic modeling,null,null,N,near-surface earth science paleoclimate paleoclimatic modeling nitrogen,sea water,null,mole per square meter per second,null,paleoclimatic modeling,null,null,N,tendency of elemental nitrogen due to denitrification and sedimentation earth science paleoclimate paleoclimatic modeling nitrogen,sea water,null,mole per square meter per second,null,paleoclimatic modeling,null,null,N,tendency of elemental nitrogen due to fixation earth science paleoclimate paleoclimatic modeling dissolved oxygen,sea water,null,mole per cubic meter,null,paleoclimatic modeling,null,null,N,null earth science paleoclimate paleoclimatic modeling salinity,sea water,null,gram per kilogram,null,paleoclimatic modeling,null,null,N,null earth science paleoclimate paleoclimatic modeling nitrate,sea water,null,mole per cubic meter,null,paleoclimatic modeling,null,null,N,null earth science paleoclimate paleoclimatic modeling carbon dioxide,sea water,null,pascal,null,paleoclimatic modeling,null,null,N,surface partial pressure earth science paleoclimate paleoclimatic modeling alkalinity,sea water,null,mole per cubic meter,null,paleoclimatic modeling,null,null,N,null earth science paleoclimate paleoclimatic modeling potential temperature,sea water,null,kelvin,null,paleoclimatic modeling,null,null,N,null earth science paleoclimate paleoclimatic modeling aragonite,sea water,null,mole per square meter per second,null,paleoclimatic modeling,null,null,N,sinking flux expressed as carbon earth science paleoclimate paleoclimatic modeling surface net downward shortwave radiation,null,null,watt per square meter,null,paleoclimatic modeling,null,null,N,null earth science paleoclimate paleoclimatic modeling longitude,null,null,degree east,null,paleoclimatic modeling,null,null,N,null earth science paleoclimate paleoclimatic modeling dissolved iron,sea water,null,mole per cubic meter,null,paleoclimatic modeling,null,null,N,null earth science paleoclimate paleoclimatic modeling nitrogen,sea water,null,mole per square meter per second,null,paleoclimatic modeling,null,null,N,tendency of elemental nitrogen due to deposition+fixation+runoff earth science paleoclimate paleoclimatic modeling dissolved oxygen,sea water,null,mole per cubic meter,null,paleoclimatic modeling,null,null,N,due to physical processes earth science paleoclimate paleoclimatic modeling dissolved phosphate,sea water,null,mole per cubic meter,null,paleoclimatic modeling,null,null,N,null earth science paleoclimate model ocean model geoscientificInformation Complete -90 90 -180 180 Geographic Region Global Global>LATITUDE >LONGITUDE None Please cite original publication, online resource, dataset and publication DOIs (where available), and date accessed when using downloaded data. If there is no publication information, please cite investigator, title, online resource, and date accessed. The appearance of external links associated with a dataset does not constitute endorsement by the Department of Commerce/National Oceanic and Atmospheric Administration of external Web sites or the information, products or services contained therein. For other than authorized activities, the Department of Commerce/NOAA does not exercise any editorial control over the information you may find at these locations. These links are provided consistent with the stated purpose of this Department of Commerce/NOAA Web site. English DOC/NOAA/NESDIS/NCEI National Centers for Environmental Information, NESDIS, NOAA, U.S. Department of Commerce https://www.ncdc.noaa.gov/data-access/paleoclimatology-data DATA Center Contact Bruce Bauer bruce.a.bauer@noaa.gov paleo@noaa.gov 303-497-6280 303-497-6513

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Boulder CO 80305-3328 USA online ASCII Buchanan P.J., R.J. Matear, Z. Chase, S.J. Phipps, and N.L. Bindoff 2018 Global Biogeochemical Cycles 10.1002/2017GB005753 http://onlinelibrary.wiley.com/doi/10.1002/2017GB005753/full

The biogeochemistry of the ocean exerts a strong influence on the climate by modulating atmospheric greenhouse gases. In turn, ocean biogeochemistry depends on numerous physical and biological processes that change over space and time. Accurately simulating these processes is fundamental for accurately simulating the ocean's role within the climate. However, our simulation of these processes is often simplistic, despite a growing understanding of underlying biological dynamics. Here we explore how new parameterisations of biological processes affect simulated biogeochemical properties in a global ocean model. We combine 6 different physical realisations with 6 different biogeochemical parameterisations (36 unique ocean states). The biogeochemical parameterisations, all previously published, aim to more accurately represent the response of ocean biology to changing physical conditions. We make three major findings. First, oxygen, carbon, alkalinity and phosphate fields are more sensitive to changes in the ocean's physical state. Only nitrate is more sensitive to changes in biological processes, and we suggest that assessment protocols for ocean biogeochemical models formally include the marine nitrogen cycle to constrain biological processes. Second, we show that dynamic variations in the production, remineralisation and stoichiometry of organic matter in response to changing environmental conditions benefits the simulation of ocean biogeochemistry. Third, dynamic biological functioning reduces the sensitivity of biogeochemical properties to physical change. Carbon and nitrogen inventories were 50 % and 20 % less sensitive to physical changes, respectively, in simulations that incorporated dynamic biological functioning. These results highlight the importance of a dynamic biology for ocean properties and climate. STUDY NOTES: Simulations of ocean physical and biogeochemical fields under different biological functioning in CSIRO Mk3L-1.2 v1.0 The data included in this repository includes both the physical and biogeochemical fields that were generated by 36 simulations over 10,000 years with an Ocean General Circulation Model (GCM): CSIRO Mk3L v1.2. The 36 simulations represent a factorial experimental set-up, where 6 different parameterisations of ocean biology were broadcast across six different physical states. The aim of these experiments was to test if new insights into how the cycling of organic matter in the ocean functions can offer some benefit to climate models. But to be sure that a particular process is important to global ocean biogeochemistry (i.e carbon storage), we needed to make these tests across a range of different circulations and physical states. All physical states were of Pre-Industrial (PI; 1850 CE) climate. They were generated by forcing the Ocean GCM with the boundary conditions produced by piControl runs of CSIRO Mk3L v1.2, GFDL-ESM2G, IPSL-CM5A-MR, HadGEM2-CC, MPI-ESM-MR and MRI-CGCM3. These boundary conditions were sea surface temperature, salinity and the meridional and zonal components of surface wind stresses, and the final 10 years of these runs were averaged and regridded onto the CSIRO Mk3L v1.2 grid space. Therefore, six unique physical ocean states were generated. Six unique biological states were also generated by implementing different biological parameterisations within the biogeochemical model code. These included variable nutrient dependence for phytoplankton growth (Smith et al., 2009), variable remineralisation profiles with depth that were dependent on temperature (Marsay et al., 2015) or community composition (Weber et al., 2016), and a variable stoichiometry of organic matter based on nutrient concentrations (Galbratih and Martiny, 2015). These four different and already published parameterisations were implemented into the biogeochemical code individually, and once in combination (excluding the temperature-dependent remineralisation), to create five new biogeochemical models. Hence, six unique biological states were created, including the basic, unaltered biogeochemical model. Climatologies of sea surface temperature, sea surface salinity, and x and y vectors of sea surface wind stresses were produced by both the PI and LGM coupled experiments and were used to force the ocean general circulation model. Additional climatologies of sea ice fractional cover, sea surface wind speeds, net incident short-wave radiation, and the aeolian deposition of iron and reactive nitrogen were important for forcing the biogeochemical model. These climatologies are made available here. Also available are the three-dimensional global annual averages of oceanic properties for all 36 simulations at their steady-state solutions. These include temperature, salinity, oxygen, apparent oxygen utilisation, dissolved inorganic carbon, alkalinity, phosphate, nitrate and iron concentrations. A full description of the CSIRO Mk3L v1.2 can be found in both: Phipps, S. J., Rotstayn, L. D., Gordon, H. B., Roberts, J. L., Hirst, A. C., and Budd, W. F. The CSIRO Mk3L climate system model version 1.0 - Part 1: Description and evaluation, Geosci. Model Dev., 4, 483-509, doi:10.5194/gmd-4-483-2011, 2011. Phipps, S. J., Rotstayn, L. D., Gordon, H. B., Roberts, J. L., Hirst, A. C., and Budd, W. F. The CSIRO Mk3L climate system model version 1.0 - Part 2: Response to external forcings, Geosci. Model Dev., 5, 649-682, doi:10.5194/gmd-5-649-2012, 2012. Descriptions of the biogeochemical ocean model that was used can be found in the appendices of: Matear, R. J. and Lenton, A. Quantifying the impact of ocean acidification on our future climate, Biogeosciences, 11, 3965-3983, doi:10.5194/bg-11-3965-2014, 2014 Buchanan, P. J., Matear, R. J., Chase, Z., Phipps, S. J., and Bindoff, N. J. (2017) The importance of biological heterogeneity for simulating and stabilising ocean biogeochemistry. Global Biogeochemical Cycles. ARCCSS CMS wiki: http://climate-cms.unsw.wikispaces.net/ARCCSS+published+datasets File organisation: /g/data1/ua8/ARCCSS_Data/Mk3L-BioPump/v1-0 contains the CF - ACDD compliant netcdf output filenames: <GCM>-<biological-state>.nc where GCM - conditions generated by ithe specified climate system model used to force the CSIRO Mk3L ocean general circulation model Biological-state are: Base - Biological state Base, where all biological functioning was static OUK - Biological state OUK, where Optimal Uptake Kinetics replaced Michaelis Menten kinetics for controlling nutrient limitation RemT - Biological state RemT, temperature-dependent remineralisation scheme RemP - Biological state RemP, phytoplankton-dependent remineralisation scheme Vele - Biological state Vele, variable elemental ratios of organic matter (stoichiometry) COM - Biological state COM, where full dynamic biological functioning was enabled in the biogeochemical model (OUK + RemP + Vele) OGCM - physical variables outputted by the ocean general circulation model Contact: pearse.buchanan@utas.edu.au for any question on the dataset content and provenance paola.petrelli@utas.edu.au for questions or issues with file accessibility Citation: Buchanan, Pearse, 2017: Idealised simulations assessing the response of ocean oxygen to physical and biological factors v1.0. NCI National Research Data Collection , doi:10.4225/41/ Buchanan, P.J., Matear, R.J., Chase, Z., Phipps, S.J., and Bindoff, N.L. (2018) Dynamic biological functioning important for simulating and stabilising ocean biogeochemistry. Global Biogeochemical Cycles. Provided Keywords: ocean biogeochemistry, paleoclimatology, Pleistocene, CO2, biological pump

GET DATA http://dx.doi.org/10.4225/41/5a1b6aa448c32 DOI Landing Page; Study Description and Link to CSIRO Mk3L 1.2 v1.0 BioPump on THREDDS Server GET DATA https://www1.ncdc.noaa.gov/pub/data/paleo/gcmoutput/buchanan2018/buchanan2018-biopump.txt NOAA Template File; CSIRO Mk3L 1.2 v1.0 BioPump Study Metadata GET DATA https://www1.ncdc.noaa.gov/pub/data/paleo/gcmoutput/buchanan2018/ Data Folder; CSIRO Mk3L 1.2 v1.0 BioPump Description and subdirectory of netCDF Data USA/NOAA DIF Version 9.8.4 2018-12-11 2018-12-11