Link to the data : Environmental predictors - OBS
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- You are using these data for marine SDM (Species Distribution Models) or marine SDM-related applications.
- You properly cite the data in any publication.
Dataset Name: OBS-SDM: Environmental predictors from observation for Species Distribution Models
Authors: Corentin Clerc (ETHZ)
Dataset description : Observation-based monthly climatologies of environmental predictors were interpolated on a common grid (i.e., 1 x 1 degree resolution, global scale, gap-filled). We consider monthly climatologies encompassing the 1900-2020 period and integrated over surface or epipelagic layers. These climatologies correspond to a set of features derived from in-situ measurements and satellite observations that are commonly used to characterize the physical (e.g., sea surface temperature), chemical (e.g., pH) and biological (e.g., chlorophyll-a concentration) conditions of water masses as well as their circulation and turbulence patterns (e.g., Eddy Kinetic Energy; EKE). All these features have been shown to have a major influence on the biology and spatial distribution of marine organisms, including plankton.
I) AVISO : https://www.aviso.altimetry.fr/en/my-aviso-plus/my-products.html
Variable : FSLE
Initial format : multimission altimetry-derived gridded backward-in-time Finite Size Lyapunov Exponents and Orientations of associated eigenvectors. These products have been computed in collaboration between CLS, LOcean, CTOH and Cnes. DOI: 10.24400/527896/a01-2022.002 gridded product, provided in delayed time and routine production of 20-day latency. As a snapshot, each map represents the sea state for a given day. One spatial resolution is available: 1/25°x1/25° on a cartesian grid.
Treatment applied : regridded to a 1 x 1 degree resolution grid, averaged into a monthly climatology (1993-2021)
Variable : EKE
Initial format : Link to raw data (needs registration): https://www.aviso.altimetry.fr/en/data/index.php?id=1526&L=1 Climatological Eddy Kinetic Energy 1993 to 2021/01. Delayed Time Level-4 monthly climatology of Eddy Kinetic Energy from sea surface height above Mean Sea Surface products from multi-satellite observations over Global Ocean. From January 1993 to the last extension of the Delayed-time products, the long delayed-time dataset allows to compute statistical means of seven variales over different periods of time. Data are created from daily Ssalto/Duacs and CMEMS products. Monthly averaged corresponds to the weekly maps of delayed-time data averaging month by month from January 1993. We obtain one file and one map per month since January 1993. Coverage : global (1/4°x1/4°, cartesian grid)
Treatment applied : regridded to a 1 x 1 degree resolution grid
II) CMEMS : https://data.marine.copernicus.eu/product/OCEANCOLOUR_GLO_BGC_L4_MY_009_104/description
Variables : CHL, DIATO, DINO, GREEN, HAPTO, MICRO, NANO, PICO, PROCHLO, PROKAR Mass concentration of chlorophyll a in sea water (CHL) Mass concentration of diatoms expressed as chlorophyll in sea water (DIATOM) Mass concentration of picophytoplankton expressed as chlorophyll in sea water (PICO) Mass concentration of dinophytes expressed as chlorophyll in sea water (DINO) Mass concentration of haptophytes expressed as chlorophyll in sea water (HAPTO) Mass concentration of greenalgae and prochlorophytes expressed as chlorophyll in sea water (GREEN) Mass concentration of prokaryotes expressed as chlorophyll in sea water (PROKAR) Mass concentration of prochlorococcus expressed as chlorophyll in sea water (PROCHLO) Mass concentration of microphytoplankton expressed as chlorophyll in sea water (MICRO) Mass concentration of nanophytoplankton expressed as chlorophyll in sea water (NANO)
Initial format : 4 × 4 km, 1 Sep 1997 to 10 Jun 2024, DailyMonthly, Level 4 Gohin, F., Druon, J. N., Lampert, L. (2002). A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International journal of remote sensing, 23(8), 1639-1661 + Hu, C., Lee, Z., Franz, B. (2012). Chlorophyll an algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference. Journal of Geophysical Research, 117(C1). doi: 10.1029/2011jc007395 + Xi H, Losa S N, Mangin A, Garnesson P, Bretagnon M, Demaria J, Soppa M A, Hembise Fanton d Andon O, Bracher A (2021) Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi-sensor ocean color and sea surface temperature satellite products, JGR, in review.
Treatment applied : regridded to a 1 x 1 degree resolution grid, averaged into a monthly climatology (1998-2020), and gap-filled (with yearly mean of the month with data at the location)
Variable : POC
Initial format : Author: Urs Hofmann Elizondo (urs.hofmann@usys.ethz.ch) I downloaded the surface POC as calculated by the GlobColour processor (https://hermes.acri.fr). The data products found in GlobColour are based on the merging of the sensors SeaWiFS, MODIS, MERIS, VIIRS-SNPP&JPSS1, OLCI-S3A&S3B. The data cover 01/09/1997 to 31/01/2021 for the ocean surface at a 4km X 4km resolution. The download was done using the script “Get_GlobColourPOC.sh”. Next, the individual files were merged into one, the multi-year monthly climatology was calculated, and the data were re-mapped to a 1°X1° resolution. These steps were done with the script “PrepareGlobColourPOC.py”. Associated reference according to the GlobColour product user guide: Stramski. D., R.A. Reynolds, M. Babin, S. Kaczmarek, M.R. Lewis, R. Rottgers, A. Sciandra, M. Stramska, M.S. Twardowski, B.A. Franz, and H. Claustre (2008). Relationships between the surface concentration of particulate organic carbon and optical properties in the eastern South Pacific and eastern Atlantic Oceans, Biogeosci., 5, 171-201. Monthly climatology of POC from monthly data between 1997 and 2020
Treatment applied : regridded to a 1 x 1 degree resolution grid, and gap-filled (with yearly min of the month with data at the location)
III) GMIS : https://data.jrc.ec.europa.eu/collection/gmis
Variables : A_ADG, A_CHLA, M_CHLA, S_ADG, S_CHLA, S_PP, V_APH, V_PAR, A_APH, A_K490, M_K490, S_APH, S_K490, T_SST, V_CHLA, A_BBP, A_PAR, P_SST, S_BBP, S_PAR, V_ADG, V_K490
Description : OCEAN COLOUR SENSORS A_: MODIS-AQUA T_: MODIS-TERRA S_: SEAWIFS M_: MERIS P_: PATHFINDER V_: VIIRS DEFINITION OF DATASETS' ACRONYMS CHLA_: Chlorophyll Concentration K490_: Diffuse Attenuation Coefficient PAR_: Photosynthetically Available Radiation APH_: Absorption Coefficient of Phytoplankton at 443nm ADG_: Absorption Coefficient of detritus/CDOM at 443nm BBP_: Particulate backscatter Coefficient at 443 nm ZEU_: Surface Productive Layer PP_: Primary Production SST_: Sea Surface Temperature
Initial format : The Global Marine Information System has been developed to provide the Users community with an appropriate set of bio-physical information, of importance to conduct water quality assessment and resource monitoring in the coastal and marine waters. The bulk of environmental analysis in GMIS relies on Earth Observation data, and the provision of continuous, detailed and accurate information on relevant marine biophysical parameters as derived from optical, and infrared satellite sensors. The datasets are at global scale and available at a spacial resolution of 9 and 4km.
Treatment applied : regridded to a 1 x 1 degree resolution grid, un-logged transformed, and gap-filled (with yearly min of the month with data at the location)
IV) ETHZ SODA : https://gitlab.ethz.ch/oceansoda
Variables : co2,co3,dic,hco3,ice,omega_ar,omega_ca,ph,revelle_factor,sfco2,spco2,talk Mole Concentration of CO2 in Sea Water Carbonate Ion Concentration in Sea Water Mole Concentration of Dissolved Inorganic Carbon in Sea Water Bicarbonate Ion Concentration in Sea Water Sea Ice Concentration Aragonite Saturation State Calcite Saturation State Surface Ocean pH on the Total Scale Revelle Factor Surface dfCO2 Predicted with a Two-Step Cluster-Regression Approach Surface Ocean pCO2 Total Alkalinity Estimated with an Ensemble of SVR Models (scikit-learn)
Initial format: see Gregor, L. and Gruber, N.: OceanSODA-ETHZ: A global gridded data set of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-13-777-2021, 2020.
Treatment applied : regridded to a 1 x 1 degree resolution grid and yarly averaged into a monthly climatology.
V) WOA 2018 : https://www.ncei.noaa.gov/access/world-ocean-atlas-2018/
Variables : t Temperature (°C) s Salinity (unitless) M Mixed Layer Depth (m) o Dissolved Oxygen (µmol/kg) O Percent Oxygen Saturation (%) A Apparent Oxygen Utilization (µmol/kg) i Silicate (µmol/kg) p Phosphate (µmol/kg) n Nitrate (µmol/kg) NSTAR (N-16P) SiStar (Si-N) Initial format : see link above
Treatment applied : integrated over depth layers (0-200 m, 0-10m and 200-300 m)
VI) Poc export : https://darchive.mblwhoilibrary.org/entities/publication/8ae49c0f-fb64-56b5-8724-741b3ba9cace
Variables : POCflux : Global reconstructions of particle carbon export flux from the seasonal euphotic zone (mgC / m2 / d) Clements et al 2023)
Initial format : see link above
Treatment applied : [FILL]
VII) Predictors previously compiled by Giacomo Poli
Wind speed (m/s, from Hersbach et al. (2020)) Northward wind speed (m/s, from Hersbach et al. (2020)) Eastward wind speed (m/s, from Hersbach et al. (2020)) Wind direction (rad, from Hersbach et al. (2020)) Water depth (m, from Amante and Eakins (2009)) Fishing hours (hours, from Kroodsma et al. (2018), temporal coverage: 2016) Distance to closest coast (m, from Bathymetry: Amante and Eakins (2009)) Distance to closest largest rivers (m, from Bathymetry: Amante and Eakins (2009), rivers: Fekete et al. (2002)) Distance to closest largest city (m, from Bathymetry: Amante and Eakins (2009), cities: opendatasoft) Sea surface salinity (psu, from Olmedo et al. (2017), temporal coverage: 2010-2012) Mixed layer depth (m, from Carton et al. (2018)) Dissolved inorganic carbon (μmol/kg, from Gregor and Gruber (2021), temporal coverage: 1982-2020) Sea level anomaly (m, from AVISO (2016), temporal coverage: 1993-2019) Geostrophic currents velocity (m/s, from AVISO (2016), temporal coverage: 1993-2019) Northward geostrophic currents velocity (m/s, from AVISO (2016), temporal coverage: 1993-2019) Eastward geostrophic currents velocity (m/s, from AVISO (2016), temporal coverage: 1993-2019) Wind divergence (1/s, from Hersbach et al. (2020)) Relative vorticity (1/s, from AVISO (2016), temporal coverage: 1993-2019) Divergence (1/s, from AVISO (2016), temporal coverage: 1993-2019) Currents direction (rad, from AVISO (2016), temporal coverage: 1993-2019)
Treatment applied : Formated to the same format as the other predictors
— Corentin Clerc 2024/10/22 17:33