global ocean dimethylsulfide (DMS) photolysis model

# Data and code from: Global ocean dimethylsulfide photolysis rates quantified with a spectrally and vertically resolved model --- DOI: 10.1002/LOL2.10342 Martí Galí Institut de Ciències del Mar, CSIC, Passeig Marítim de la Barceloneta 37-49, 08003 Barcelona, Catalonia, Spain Corresponding author: mgali@icm.csic.es Emmanuel Devred Fisheries and Oceans Canada, Bedford Institute of Oceanography, Dartmouth, NS B2Y 4A2, Canada Gonzalo L. Pérez Instituto INIBIOMA (CRUB Comahue, CONICET), Quintral 1250, 8400 S.C. de Bariloche, Rio Negro, Argentina David J. Kieber Department of Chemistry, State University of New York, College of Environmental Science and Forestry, 1 Forestry Drive, Syracuse, New York 13210, United States Rafel Simó Institut de Ciències del Mar, CSIC, Passeig Marítim de la Barceloneta 37-49, 08003 Barcelona, Catalonia, Spain Keywords: DMS; UV radiation; photolysis; remote sensing; photochemical model; CDOM; photosensitizer; ocean. Abstract: Photochemical reactions initiated by ultraviolet (UV) radiation remove the climate-active gas dimethylsulfide (DMS) from the ocean’s surface layer. Here we quantified DMS photolysis using a satellite-based model that accounts for spectral irradiance attenuation in the water column, its absorption by chromophoric dissolved organic matter (CDOM), and the apparent quantum yields (AQYs) with which absorbed photons degrade DMS. Models with two alternative parameterizations for AQY estimate global DMS photolysis at between 17 and 20 Tg S yr-1, equivalent to 13–15 Tg C yr-1, of which ~73% occurs in the Southern hemisphere. This asymmetry results mostly from the high AQYs found south of 40°S, which more than counteract the prevailing low irradiance and deep mixing in that region. Simplified schemes currently used in biogeochemical models, whereby photolysis follows the vertical attenuation of visible radiation, overestimate DMS photolysis by around 150% globally. We propose relevant corrections and simple adjustments to those models. Preliminary note: This repository contains the code, data and figures corresponding to the accepted paper (June 2023). The calculations and figures presented in the article can be reproduced using this repository and Matlab 2010b or later. ## Description of the data and file structure The repository contains this README file, five main folders, and three additional folders were free Matlab packages may be OPTIONALLY placed. initial_datasets: contains all the datasets needed to reproduce calculations and figures of the main paper and SM section in *.mat format. These include a compilation of in-situ photolysis data (aqy_model_data_in_situ_Gali2016.mat), bio-optical variables (ag443, adg443, CHL, Ed, Kd), other geophysical fields (gebco bathymetry, MLD, SST, no3 and po4 climatologies) and a list for matching 5-degree and 1-degree grid pixels (select_valid5degpixels24-Nov-2015.mat). Glob_figModel: contains the script Fig_model.m used to produce Fig. 1, S1, S2 and S3 Modeled_kphoto_KdEdCDOM: contains the main scripts and functions of the global photolysis model, as well as those needed for the uncertainty assessment, and the corresponding output *.mat files. Glob_fig_photoMaps: contains the script Fig_glob_photo.m used to produce Fig. 2, pluts some *.mat files produced by scripts in Modeled_kphoto_KdEdCDOM and used for plotting. Glob_fig_anomalyMaps: contains the script Fig_glob_anomalyMaps.m used to produce Fig. 3. m_map: contains the scripts of the m_map Matlab package. PLACEHOLDER_BrewerMap: suggested path where to download the ColorBrewer Matlab package (https://www.mathworks.com/matlabcentral/fileexchange/45208-colorbrewer-attractive-and-distinctive-colormaps). PLACEHOLDER_freezeColors_v23_cbfreeze: suggested path where to download the freezeColors Matlab package (https://www.mathworks.com/matlabcentral/fileexchange/7943-freezecolors-unfreezecolors) The content and usage of all folders is further described in the Code/Software section. The code is self-explanatory and contains information on the units of the input data and their usage in the calculations. ## Sharing/Access information The in situ data compilation on which part of this article is based was described in: Galí, M. et al. (2016). CDOM sources and photobleaching control quantum yields for oceanic DMS photolysis. Environmental Science & Technology, 50(24), 13361-13370. A compact version of that dataset, containing only the essential variables in the current article, is provided in the file initial_datasets/aqy_model_data_in_situ_Gali2016.mat Old versions of the code and figures that were modified during the revision process are available on Zenodo: Version 1.0: https://zenodo.org/record/7204133 Version 2.0: https://zenodo.org/record/7890912 ## Code/Software Note: Additional Matlab packages required to run the scripts in their current form are m_map, freezeColors and BrewerMap. - The m_map package is publicly available and provided in the m_map directory. - The other two packages can be freely downloaded, and are not provided here to avoid licensing incompatibilities. Two folders named PLACEHOLDER_freezeColors_v23_cbfreeze and PLACEHOLDER_BrewerMap are created to suggest a possible location for these packages, but other locations will be fine as long as they are added to the Matlab search path. - These packages affect only the data visualization. The BrewerMap color scales can be replaced by native Matlab ones or by other packages. The freezeColors package is only used to allow the use of different color maps in one figure and can therefore be omitted, too. - m_map: https://www.eoas.ubc.ca/~rich/map.html. (version used in my code downloaded November 2014) - BrewerMap: https://www.mathworks.com/matlabcentral/fileexchange/45208-colorbrewer-attractive-and-distinctive-colormaps. (version used in my code downloaded September 2016) - freezeColors: https://www.mathworks.com/matlabcentral/fileexchange/7943-freezecolors-unfreezecolors. (version 2.3 used in my code downloaded on June 2022) ### Getting started: - Unzip photolysis_dms_gali.zip - Edit 'basedir' in all scripts that contains this variable to match your path - If necessary, download the required packages and place them in the desired path ### Input datasets: Provided in ${basedir}/initial_datasets/ aqy_model_data_in_situ_Gali2016.mat contains the following variables [units]: latp: latitude [degrees N] lonp: longitude [degrees E] month studyID: code of individual datasets included in the compilation according to Galí et al. 2016 ES&T. aqy330remerged: Apparent Quantum Yield for DMS photolysis at 330 nm, datasets D1+D2+D3* [s^-1 (mol photons m^-3 s^-1)^-1] = [m^3 (mol photons)^-1] aqy330 merged: Apparent Quantum Yield for DMS photolysis at 330 nm, datasets D1+D2* [s^-1 (mol photons m^-3 s^-1)^-1] = [m^3 (mol photons)^-1] acdom330: absorption coefficient of chromophoric dissolved organic matter [m^-1] no3: nitrate concentration [µmol L^-1] = [mmol m^-3] sst: sea surface temperature [degrees C] * The datasets D1, D2 and D3 are defined in Galí et al. 2016 ES&T. Other data files include in this folder and used in subsequent calculations are: ag443*.mat: global monthly climatology of satellite-retrieved (SeaWiFS sensor) absorption coefficient of chromophoric detrital matter [m^-1] adg443suwi_halfdegree.mat: global climatologies for the winter and summer semester of satellite-retrieved (SeaWiFS sensor) absorption coefficient of chromophoric detrital matter [m^-1], used only to make Fig. 1c. CHL19972009.mat: global climatology of satellite-retrieved (SeaWiFS sensor) chlorophyll a concentration [µg L^-1] = [mg m^-3] DMSmoclim_L11.mat: global sea-surface gridded DMS climatology (Lana et al. 2011) DMSmoclim_Rev3_NoIceMask.mat: global sea-surface gridded DMS climatology (Hulswar et al. 2022) Ed_l_moclim_*_25-Nov-2015.mat global 5-degree monthly climatology of spectral irradiance just below the sea-surface [µmol photons m^-2 s^-1 nm^-1] gebco_08_05degr.mat: global ocean bathymetry GEBCO2008 [m] ICECmoclim_1deg.mat: global climatology of sea ice concentration [-] Kd_mo*.mat: global climatology of spectral diffuse attenuation coefficient of downwelling irradiance based on the SeaUV algorithm [m^-1] Kd490_S_moclim_1deg.mat: global climatology of spectral diffuse attenuation coefficient of 490 nm irradiance [m^-1] MLDclim_MIMOC_1deg.mat: global mixed layer depth MIMOC climatology of Schmidtko et al. 2013 [m] select_valid5degpixels24-Nov-2015.mat: list that links 1x1 degree pixels to their parent 5x5 degree macropixels where Ed_l_moclim_*_25-Nov-2015.mat was computed SSTmoclim_1deg.mat: global ocean sea surface temperature climatology [degrees C] WOA_no3_po4.mat: global nitrate and phosphate WOA2009 climatologies [µmol L^-1] = [mmol m^-3] ### Global photolysis calculations: For models CDOM_NO3 and CDOM_SST run ${basedir}/Modeled_kphoto_KdEdCDOM/model_kphoto0moins_EdRT_CDOMsat_highres.m For models K0_FIXED and K0_SCALED run ${basedir}/Modeled_kphoto_KdEdCDOM/model_kphoto0moins_EdRT_RUDE_highres.m (NOTE: instead of “fixed” I used “constant” in the code. It is just a nomenclature issue, treat them as synonyms) For global integration of previously computed photolysis fields, run ${basedir}/Modeled_kphoto_KdEdCDOM/plot_global_photolysis_highres.m Edit 'vaqymod' (and if running also with Gaussian perturbation, 'perturb') to integrate fields for the various runs and produce *.mat files that will be used in subsequent scripts. ### Figure 1, S1, S2 and S3: Run ${basedir}/Glob_figModel/Fig_model.m ### Figure 2: Run ${basedir}/Glob_fig_photoMaps/Fig_glob_photo.m In its current form requires BrewerMap, m_map and freezeColors_v23_cbfreeze ### Figure 3: Run ${basedir}/Glob_fig_anomalyMapsFig_glob_anomaly

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