De nouveaux algorithmes de tri par transpositions

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

PPv0: A Primary Productivity Algorithm Implementation from surface chlorophyll-a data above 45 degrees North (release PPv0.36.0.0)

Introduction Implementation at Takuvik of the algorithm of (Belanger, Babin et al. 2013) to compute daily primary productivities and other physical and biological products above 45° North. Data and Methods Data The primary production model used three kinds of data. The three kinds of data were sea ice concentrations, atmospheric products and water-leaving reflectances (Rrs). Two data sets were used for the sea ice concentrations. The title of the first data set is Sea Ice Concentrations from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I)-Special Sensor Microwave Imager/Sounder (SSMIS) Passive Microwave Data (Cavalieri 1996, updated yearly) (v1.1). This data set comes from the National Snow and Ice Data Center (NSIDC) (ftp://sidads.colorado.edu/pub/DATASETS/nsidc0051_gsfc_nasateam_seaice). This data set was used for years 1998 through 2015. From 1998 to 2007, the platform DMSP-F13 and the instrument SSM/I were used. From 2008 to 2015, the platform DMSP-F17 and the instrument SSMIS were used. The temporal resolution is daily. The spatial resolution is 25 x 25 km. The title of the second sea ice concentration data set is Near-Real-Time DMSP SSM/I-SSMIS Daily Polar Gridded Sea Ice Concentrations (Maslanik 1999, updated daily). This data set comes from the NSIDC (ftp://sidads.colorado.edu/pub/DATASETS/nsidc0081_nrt_nasateam_seaice/). This data set was used for year 2016. The platform DMSP-F17 and the instrument SSMIS were used. The temporal resolution is daily. The spatial resolution is 25 x 25 km. The atmospheric products data were the total ozone concentration O3, cloud fraction CF over the pixel and cloud optical thickness τCL. Two data sets were used and compared. The title of the first data set is Flux Data-Surface Radiative Fluxes (FD-SRF). The product total column ozone (tlo3__) was used for the parameter O3. The product mean cloud fraction (cf_m__) was used for the parameter CF. The product Mean cloud optical thickness (tau_m_) was used for the parameter τCL. This data comes from the International Satellite Cloud Climatology Project (ISCCP). This data set was used for years 1998 through 2009. The three products were derived from satellite data (mainly Advanced Very High Resolution Radiometer (AVHRR); (Schweiger, Lindsay et al. 1999)) following the method developed by (Zhang, Rossow et al. 2004). The temporal resolution is three hours. The spatial resolution is a 280 x 280 km equal-area grid. The title of the second atmospheric products data set is Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua Aerosol Cloud Water Vapor Ozone Daily L3 Global 1Deg CMG (MYD08_D3). The product Total Ozone Mean (TO3M) was used for the parameter O3. The product Cloud Fraction Day Mean (CFDM) was used for the parameter CF. The product Cloud Optical Thickness Combined Mean (COTCM) was used for the product τCL. This data set comes from the National Aeronautics and Space Administration (NASA) Level 1 and Atmosphere Archive and Distribution System (LAADS) (v5.1). This data set was used for years 2002 through 2016. The platform Aqua and the instrument MODIS were used. The temporal resolution is daily. The spatial resolution is 1 x 1 degree. Two data sets were used and compared for the Rrs. The first data set is Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Level 3 daily Rrs at 412, 443, 490, 510, 555 and 670 nm. This data set comes from the NASA's Goddard Space Flight Center (GSFC) (reprocessing 2014.0). The url is https://oceandata.sci.gsfc.nasa.gov/SeaWiFS/L3BIN. This data set was used for years 1998 through 2008. The platform is SeaStar Spacecraft and the instrument is SeaWiFS. The temporal resolution is daily. The spatial resolution is a 9.28 x 9.28 km equal-area grid. The second Rrs data set is MODIS/Aqua Level 3 daily Rrs at 412, 443, 488, 531, 555 and 667 nm. This data set comes from the NASA's Goddard Space Flight Center (GSFC) (reprocessing 2014.0). The url is https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/L3BIN. This data set was used for years 2002 through 2016. The platform is Aqua and the instrument is MODIS. Temporal resolution is daily. Spatial resolution is a 4.64 x 4.64 km equal-area grid. Methods Irradiance model The incident spectral downwelling irradiance just below the water surface Ed(λ, 0-, t); in mol photon.m-2.h-1 was computed from the three atmospheric parameters O3, CF and τCL and the solar zenith angle (θs) using the Santa Barbara DISORT Atmospheric Radiative Transfer model (SBDART, (Ricchiazzi, Yang et al. 1998)). Primary productivity model The primary productivity model is from (Belanger, Babin et al. 2013). The Rrs were the inputs of a quasi-analytical algorithm (QAA) (Lee, Carder et al. 2002) to compute the total absorption (a(λ)); in m-1) and the backscattering (bb(λ); in m-1) coefficients. These spectral inherent optical properties (IOPs) were used with θs as the inputs of (Lee, Darecki et al. 2005) and (Lee, Du et al. 2005) to compute the spectral diffuse attenuation coefficient Kd(λ). The spectral downwelling irradiance at different depths (Ed(λ, z, t) was estimated from Ed(λ, 0-, t) and Kd(λ). The spectral scalar irradiance (E0(λ, z, t)) is Ed(λ, z, t) divided by the mean cosine of the zenith angle of incidence of radiation (Morel 1991). This mean cosine was estimated by (a+bb)/Kd (Sathyendranath, Platt et al. 1989). a(490), bb(490) and Kd(490) were used for this estimation. The Rrs were also the inputs of the algorithm GSM (Garver and Siegel 1997) and (Maritorena, Siegel et al. 2002) to compute the chlorophyll-a concentration (CHL; in mgCHL.m-3). CHL was used in the relationship by (Matsuoka, Huot et al. 2007) to compute the spectral phytoplankton absorption coefficient (aph(λ); in m-1). The photosynthetically usable radiation (PUR(z, t); in mol photon.m-2.s-1) was computed (Morel 1978): $PUR(z, t)=\int_{\lambda=400}^{\lambda=700\mathrm{nm}} E^{0}(\lambda, z, t) \frac{a_{ph}(\lambda)}{a_{ph}(443)} \mathrm{d}\lambda$ The saturation irradiance (Ek; in mol photon.m-2.s-1) was estimated from the mean daily PUR (Arrigo and Sullivan 1994), (Arrigo 1994) and (Arrigo, Worthen et al. 1998). The light-saturated CHL-normalized carbon fixation rate (; in mgC.(mgCHL)-1.h-1) was set at 2 mgC.(mgCHL)-1.h-1 following in situ measurements in Arctic (Harrison and Platt 1986) and (Rey 1991). As shown by the model of (Platt, Gallegos et al. 1980), all parameters needed to compute the daily primary production rates (PP; in mgC.m-2.d-1) were now available: $PP=CHL \bullet P^{B}_{m} \int_{t=0}^{24h} \int_{z=0.1\%}^{100\%} 1 - e^{\frac{-PUR(z, t)}{E_k}} \mathrm{d}z\mathrm{d}t$ Bibliography Arrigo, K. R. (1994). "Impact of ozone depletion on phytoplankton growth in the southern-ocean - large-scale spatial and temporal variability." Marine Ecology Progress Series 114(1-2): 1-12. Arrigo, K. R. and C. W. Sullivan (1994). "A high resolution bio-optical model of microalgal growth: tests using sea-ice algal community time-series data." Limnology and Oceanography 39(3): 609-631. Arrigo, K. R., D. Worthen, A. Schnell and M. P. Lizotte (1998). "Primary production in Southern Ocean waters." Journal of Geophysical Research-Oceans 103(C8): 15587-15600. Belanger, S., M. Babin and J. E. Tremblay (2013). "Increasing cloudiness in Arctic damps the increase in phytoplankton primary production due to sea ice receding." Biogeosciences 10(6): 4087-4101. Cavalieri, D. J., C. L. Parkinson, P. Gloersen, and H. J. Zwally. (1996, updated yearly). Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1. Subset used: 1998 to 2015. NASA National Snow and Ice Data Center Distributed Active Archive Center. Boulder, Colorado USA. Garver, S. A. and D. A. Siegel (1997). "Inherent optical property inversion of ocean color spectra and its biogeochemical interpretation .1. Time series from the Sargasso Sea." Journal of Geophysical Research-Oceans 102(C8): 18607-18625. Harrison, W. G. and T. Platt (1986). "Photosynthesis-irradiance relationships in polar and temperate phytoplankton populations." Polar Biology 5(3): 153-164. Lee, Z. P., K. L. Carder and R. A. Arnone (2002). "Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters." Applied Optics 41(27): 5755-5772. Lee, Z. P., M. Darecki, K. L. Carder, C. O. Davis, D. Stramski and W. J. Rhea (2005). "Diffuse attenuation coefficient of downwelling irradiance: An evaluation of remote sensing methods." Journal of Geophysical Research-Oceans 110(C2): 9. Lee, Z. P., K. P. Du and R. Arnone (2005). "A model for the diffuse attenuation coefficient of downwelling irradiance." Journal of Geophysical Research-Oceans 110(C2): 10. Maritorena, S., D. A. Siegel and A. R. Peterson (2002). "Optimization of a semianalytical ocean color model for global-scale applications." Applied Optics 41(15): 2705-2714. Maslanik, J., J. Stroeve. (1999, updated daily). Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations, Version 1. Subset used: 2016. NASA National Snow and Ice Data Center Distributed Active Archive Center. Boulder, Colorado USA. Matsuoka, A., Y. Huot, K. Shimada, S. I. Saitoh and M. Babin (2007). "Bio-optical characteristics of the western Arctic Ocean: implications for ocean color algorithms." Canadian Journal of Remote Sensing 33(6): 503-518. Morel, A. (1978). "Available, usable, and stored radiant energy in relation to marine photosynthesis." Deep-Sea Research 25(8): 673-688. Morel, A. (1991). "Light and marine photosynthesis - a spectral model with geochemical and climatological implications." Progress in Oceanography 26(3): 263-306. Platt, T., C. L. Gallegos and W. G. Harrison (1980). "Photoinhibition of photosynthesis in natural assemblages of marine-phytoplankton." Journal of Marine Research 38(4): 687-701. Rey, F. (1991). "Photosynthesis-irradiance relationships in natural phytoplankton populations of the barents sea." Polar Research 10(1): 105-116. Ricchiazzi, P., S. R. Yang, C. Gautier and D. Sowle (1998). "SBDART: A research and teaching software tool for plane-parallell radiative transfer in the Earth's atmosphere." Bulletin of the American Meteorological Society 79(10): 2101-2114. Sathyendranath, S., T. Platt, C. M. Caverhill, R. E. Warnock and M. R. Lewis (1989). "Remote-sensing of oceanic primary production - computations using a spectral model." Deep-Sea Research Part a-Oceanographic Research Papers 36(3): 431-453. Schweiger, A. J., R. W. Lindsay, J. R. Key and J. A. Francis (1999). "Arctic clouds in multiyear satellite data sets." Geophysical Research Letters 26(13): 1845-1848. Zhang, Y. C., W. B. Rossow, A. A. Lacis, V. Oinas and M. I. Mishchenko (2004). "Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data." Journal of Geophysical Research-Atmospheres 109(D19): 27

Exploring controls on the timing of the phytoplankton bloom in western Baffin Bay, Canadian Arctic

International audienceIn the Arctic Ocean the peak of the phytoplankton bloom occurs around the period of sea ice break-up. Climate change is likely to impact the bloom phenology and its crucial contribution to the production dynamics of Arctic marine ecosystems. Here we explore and quantify controls on the timing of the spring bloom using a one-dimensional biogeochemical/ecosystem model configured for coastal western Baffin Bay. The model reproduces the observations made on the phenology and the assemblage of the phytoplankton community from an ice camp in the region. Using sensitivity experiments, we found that two essential controls on the timing of the spring bloom were the biomass of phytoplankton before bloom initiation and the light under sea ice before sea ice break-up. The level of nitrate before bloom initiation was less important. The bloom peak was delayed up to 20 days if the overwintering phytoplankton biomass was too low. This result highlights the importance of phytoplankton survival mechanisms during polar winter to the pelagic ecosystem of the Arctic Ocean and the spring bloom dynamics

Exploring controls on the timing of the phytoplankton bloom in western Baffin Bay, Canadian Arctic

International audienceIn the Arctic Ocean the peak of the phytoplankton bloom occurs around the period of sea ice break-up. Climate change is likely to impact the bloom phenology and its crucial contribution to the production dynamics of Arctic marine ecosystems. Here we explore and quantify controls on the timing of the spring bloom using a one-dimensional biogeochemical/ecosystem model configured for coastal western Baffin Bay. The model reproduces the observations made on the phenology and the assemblage of the phytoplankton community from an ice camp in the region. Using sensitivity experiments, we found that two essential controls on the timing of the spring bloom were the biomass of phytoplankton before bloom initiation and the light under sea ice before sea ice break-up. The level of nitrate before bloom initiation was less important. The bloom peak was delayed up to 20 days if the overwintering phytoplankton biomass was too low. This result highlights the importance of phytoplankton survival mechanisms during polar winter to the pelagic ecosystem of the Arctic Ocean and the spring bloom dynamics

Exploring controls on the timing of the phytoplankton bloom in western Baffin Bay, Canadian Arctic

International audienceIn the Arctic Ocean the peak of the phytoplankton bloom occurs around the period of sea ice break-up. Climate change is likely to impact the bloom phenology and its crucial contribution to the production dynamics of Arctic marine ecosystems. Here we explore and quantify controls on the timing of the spring bloom using a one-dimensional biogeochemical/ecosystem model configured for coastal western Baffin Bay. The model reproduces the observations made on the phenology and the assemblage of the phytoplankton community from an ice camp in the region. Using sensitivity experiments, we found that two essential controls on the timing of the spring bloom were the biomass of phytoplankton before bloom initiation and the light under sea ice before sea ice break-up. The level of nitrate before bloom initiation was less important. The bloom peak was delayed up to 20 days if the overwintering phytoplankton biomass was too low. This result highlights the importance of phytoplankton survival mechanisms during polar winter to the pelagic ecosystem of the Arctic Ocean and the spring bloom dynamics

An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models

We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll-a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed-layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite-derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low-productivity seasons as well as in sea ice-covered/deep-water regions. Depth-resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption-based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll-a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic-relevant parameters.Upon publication, the model input data provided to the participants will be available for academic purposes through the NASA SeaWiFS Bio-optical Archive and Storage System (http://seabass.gsfc.nasa.gov/), including integrated NPP, surface chlorophyll, SST, PAR, MLD, Rrs, aph, a443, bbp443, Zeu_lee, and sea ice concentration.This is the publisher’s final pdf. The article is copyrighted by the authors and published by John Wiley & Sons, Inc on behalf of American Geophysical Union. It can be found at: http://agupubs.onlinelibrary.wiley.com/agu/jgr/journal/10.1002/%28ISSN%292169-9291/Keywords: net primary productivity, ocean color model, model skill assessment, subsurface chlorophyll-a maximum, remote sensing, Arctic Ocea

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use