DOCSLIB.ORG

Data Assimilative Hindcast for the Gulf of Mexico

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Data Assimilative Hindcast for the Gulf of Mexico OCS Study BOEM 2015-035 Data Assimilative Hindcast for the Gulf of Mexico US Department of the Interior Bureau of Ocean Energy Management Headquarters, Sterling, VA OCS Study BOEM 2015-035 Data Assimilative Hindcast for the Gulf of Mexico Eric P. Chassignet Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL Ashwanth Srinivasan Tendral LLC, Miami, FL Prepared under BOEM Award M12AC00019 US Department of the Interior Bureau of Ocean Energy Management Headquarters i DISCLAIMER Study concept, oversight, and funding were provided by the US Department of the Interior, Bureau of Ocean Energy Management, Environmental Studies Program, Washington, DC, under Contract Number M12AC00019. This report has been technically reviewed by BOEM and it has been approved for publication. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the US Government, nor does mention of trade names or commercial products constitute endorsement or recommendation for use. REPORT AVAILABILITY To download a PDF file of this Environmental Studies Program report, go to the US Department of the Interior, Bureau of Ocean Energy Management, Environmental Studies Program Information System website and search on OCS Study BOEM 2015-035. This report can be viewed at select Federal Depository Libraries. It can also be obtained from the National Technical Information Service; the contact information is below. US Department of Commerce National Technical Information Service 5301 Shawnee Rd. Springfield, VA 22312 Phone: (703) 605-6000, 1(800)553-6847 Fax: (703) 605-6900 Website: http://www.ntis.gov/ CITATION Chassignet E.P., and A. Srinivasan, 2015. Data Assimilative Hindcast for the Gulf of Mexico. US Dept. of the Interior, Bureau of Ocean Energy Management, Headquarters, Sterling, VA. OCS Study BOEM 2015-035. 46 pp. Cover Page Image Information: A sample snapshot of the surface temperature (oC) from the hindcast for January 2, 2003. ACKNOWLEDGMENT This study was funded by the US Department of the Interior, Bureau of Ocean Energy Management under the cooperative agreement M12AC00019. Zhen Li is the Project Officer of this study. ii Data Assimilative Hindcast for the Gulf of Mexico Contents Contents iii List of Figures iv List of Tables iv List of Acronyms v Executive Summary vi 1. Introduction 1 2. Methodology 2 2.1 The Ocean Model and its Configuration for the Gulf of Mexico. 2 2.2 Observations for Providing Data Constraints 2 2.3 The Ocean Data Assimilation System 3 3. Initial Conditions for the Hindcast 5 4. Qualitative Evaluation of the 2003-2012 Hindcast 6 5. Quantitative Comparisons 9 5.1 Comparisons with the Along Track Altimeter Data 9 5.2 Comparison with SST Data 11 5.3 Comparison with In-Situ Profiles 12 5.4 Comparison with Surface Drifting Buoys 15 5.5 Verification with Oil Spill Trajectories 17 6. Discussion and Conclusions 17 7. Project Deliverables 18 References 20 Appendix A. Optimizing the Atmospheric Forcing 22 A-1. Radiation Flux Corrections for Thermal Forcing 22 A-2. Wind Speed Correction Based on Linear Regression to QuickSCAT Winds 23 Appendix B. Observation Error Specification and processing 25 Appendix C: Tendral-Statistical Interpolation system (T-SIS) implementation details for HYCOM 27 Appendix D: System Documentation 29 D-1. Overview 29 D-2. Contents of the Package 29 D-3. Compiling the package 29 D-3.1 Required Compilers and Libraries 29 iii D-3.2 Compiling the HYCOM ocean model 30 D-3.3 Compiling the T-SIS Assimilation Codes 31 D-4. Setting up the 2010 Hindcast 31 D-5. Reference Model Inputs Parameters 33 D-5.1 HYCOM Reference blkdat.input 33 D-5.2 T-SIS Reference Namelist 36 List of Figures Figure 1. The multiscale assimilation methodology. 5 Figure 2.Initial model state on January 1, 2003 derived from the Global HYCOM Reanalysis. 6 Figure 3. Loop current state on July 9, 2003 (sea surface height). 7 Figure 4. Loop current state on July 9, 2003 (ocean color and ocean current speed). 8 Figure 5. Time series of rms errors between model hindcast and assimilated sla observations. 10 Figure 6. Altimetry and model based estimates of the mean and variance of the sea surface elevation. 11 Figure 7. Sea surface tempreature (°C ) for july 9, 2003. 12 Figure 8.Conductivity temperature depth (CTD) stations from the wod 2009 for the 2003-2012 time frame. 13 Figure 9. Mean bias and rmsd profiles for temperature and salinity. 14 Figure 10. Location of the P3 airborne expendable bathythermograph (AXBT) observations collected during the DwH incident. 14 Figure 11. Temperature bias (°C ) and RMSD at AXBTS’ location. 15 Figure 12. Location of velocity estimates from the drifters. 16 Figure 13. Velocity RMSD and observed standard deviation; model drifter velocity correlation. 16 Figure 14. Location of the particles one month after release at the wellhead. 18 Figure 15. Scatter plot between monthly mean QSCAT winds and the NARR winds. 24 Figure 16. Schematic of the statistical interpolation algorithm used in the T-SIS assimilation scheme. 28 List of Tables Table 1. Overview of the available observations in the Gulf of Mexico 3 Table 2. Timing of separation/reattachment and shedding events as reproduced by the hindcast and CCAR altimetry 8 Table 3. Total number of profiles available in the World Ocean Database for the 2003-2012 hindcast 13 Table 4. Summary of the assumed standard deviations of instruments errors. 25 iv List of Acronyms AOML Atlantic Oceanographic and Meteorological Laboratory AVHRR Advanced Very High Resolution Radiometer CCAR Colorado Center for Astrodynamics Research CTD Conductivity, Temperature, and Depth DwH Deepwater Horizon ECMWF European Center For Medium Range Weather Forecasting ERM Exact Repeat Mission ERS European Remote Sensing GDEM Global Digital Elevation Map GHRSST Group for High Resolution Sea Surface Temperature GoM Gulf of Mexico GFO Geosat Follow-on HYCOM HYbrid Coordinate Ocean Model MODIS Moderate Resolution Imaging Spectroradiometer NARR North American Regional Reanalysis NCEP National Centers For Environmental Prediction NAVOCEANO Naval Oceanographic Office NOAA National Oceanic & Atmospheric Administration NODC National Oceanographic Data Center OSTM Ocean Surface Topography Mission QuickSCAT Quick Scatterometer RMS Root Mean Square RMSD Root Mean Square Deviation SLA Sea Level Anomaly SSH Sea-surface height SST Sea Surface Temperature T/P Topex/Poseidon T-SIS Tendral–Statistical Interpolation System T/S Temperature/Salinity v Executive Summary Accurate estimates of ocean currents are critical for oil spill risk analysis. As the government agency responsible for oil spill risk analysis, the Bureau of Ocean Energy Management (BOEM) requires estimates of ocean currents for oil spill risk analysis in the Gulf of Mexico. The cooperative agreement, M12AC00019, was initiated to produce a validated 2003-2012 Gulf of Mexico ocean state hindcast. Toward this end, we have used the latest version of the HYbrid Coordinate Ocean Model (HYCOM) and an ensemble based data assimilative framework to estimate the ocean state variables necessary for oil spill risk analysis. Along-track sea level anomalies, gridded sea surface temperature, in-situ and climatological profiles of salinity and temperature were assimilated to provide data constraints to the ocean model. An assessment of the hindcast shows robust model skill in reproducing the various features of the circulation in the Gulf of Mexico. As expected for a model assimilating sea level anomalies, there is a good agreement between the hindcast and independent estimates of Loop Current behavior inferred from altimetry and ocean color both in terms of temporal and spatial characteristics. Furthermore, the assimilation of a limited number of in-situ and climatological profiles of temperature and salinity is effective in controlling bias and reduces errors in the vertical density structure in the model. Comparison with both assimilated and unassimilated in-situ observations indicate that the upper ocean temperature and salinity errors are within 1.5°C and 0.3 psu for the hindcast time period. Comparisons with independent drifter derived velocity data indicate significant correlation (>0.5) between the drifter data and model velocities. Simplified oil spill simulations of the Deep Water Horizon event captures the main features of surface plume indirectly verifying the underlying model circulation fields. Overall, the hindcast dataset is consistent with what is observed during the 2003-2012 time frame and is appropriate for a range of applications including oil spill risk analysis. This report details the methodology and data used for the hindcast and includes an assessment of the 2003-2012 hindcast. The complete model hindcast dataset, model code, documentation, and a test case for the year 2010 is provided to BOEM along with this report. vi 1. Introduction The Gulf of Mexico (GoM) region accounts for 30% of the total oil production in the US. At present, there are more than 3,500 oil platforms generating more than 1.7 million barrels of oil per day. Oil production in the GoM is expected to continue at these levels into the next decade and thus carries the risk of potential oil spills into the future. Oil spill risk analysis is critical for evaluating the potential environmental impacts associated with the oil and gas development in the Gulf and underpins all preparations for spill response. In these analyses, ocean currents and their statistics are used for simulating trajectories to infer potential pathways and rates of contamination in case of a spill. A necessary requirement for a robust and scientifically rigorous risk assessment is an accurate knowledge of the statistics of powerful, complex and highly variable ocean currents. One way of obtaining such estimates is by means of data assimilative hindcasts. Such an hindcast blends observations with a dynamical circulation model to provide regular three dimensional estimates of ocean currents taking into account the errors in both observations and models.
Load more

Recommended publications
  • World Ocean Thermocline Weakening and Isothermal Layer Warming
    applied sciences Article World Ocean Thermocline Weakening and Isothermal Layer Warming Peter C. Chu * and Chenwu Fan Naval Ocean Analysis and Prediction Laboratory, Department of Oceanography, Naval Postgraduate School, Monterey, CA 93943, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-831-656-3688 Received: 30 September 2020; Accepted: 13 November 2020; Published: 19 November 2020 Abstract: This paper identifies world thermocline weakening and provides an improved estimate of upper ocean warming through replacement of the upper layer with the fixed depth range by the isothermal layer, because the upper ocean isothermal layer (as a whole) exchanges heat with the atmosphere and the deep layer. Thermocline gradient, heat flux across the air–ocean interface, and horizontal heat advection determine the heat stored in the isothermal layer. Among the three processes, the effect of the thermocline gradient clearly shows up when we use the isothermal layer heat content, but it is otherwise when we use the heat content with the fixed depth ranges such as 0–300 m, 0–400 m, 0–700 m, 0–750 m, and 0–2000 m. A strong thermocline gradient exhibits the downward heat transfer from the isothermal layer (non-polar regions), makes the isothermal layer thin, and causes less heat to be stored in it. On the other hand, a weak thermocline gradient makes the isothermal layer thick, and causes more heat to be stored in it. In addition, the uncertainty in estimating upper ocean heat content and warming trends using uncertain fixed depth ranges (0–300 m, 0–400 m, 0–700 m, 0–750 m, or 0–2000 m) will be eliminated by using the isothermal layer.
    [Show full text]
  • Downloaded from the Coriolis Global Data Center in France (Ftp://Ftp.Ifremer.Fr)
    remote sensing Article Impact of Enhanced Wave-Induced Mixing on the Ocean Upper Mixed Layer during Typhoon Nepartak in a Regional Model of the Northwest Pacific Ocean Chengcheng Yu 1 , Yongzeng Yang 2,3,4, Xunqiang Yin 2,3,4,*, Meng Sun 2,3,4 and Yongfang Shi 2,3,4 1 Ocean College, Zhejiang University, Zhoushan 316000, China; [email protected] 2 First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China; yangyz@fio.org.cn (Y.Y.); sunm@fio.org.cn (M.S.); shiyf@fio.org.cn (Y.S.) 3 Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology, Qingdao 266071, China 4 Key Laboratory of Marine Science and Numerical Modeling (MASNUM), Ministry of Natural Resources, Qingdao 266061, China * Correspondence: yinxq@fio.org.cn Received: 30 July 2020; Accepted: 27 August 2020; Published: 30 August 2020 Abstract: To investigate the effect of wave-induced mixing on the upper ocean structure, especially under typhoon conditions, an ocean-wave coupled model is used in this study. Two physical processes, wave-induced turbulence mixing and wave transport flux residue, are introduced. We select tropical cyclone (TC) Nepartak in the Northwest Pacific ocean as a TC example. The results show that during the TC period, the wave-induced turbulence mixing effectively increases the cooling area and cooling amplitude of the sea surface temperature (SST). The wave transport flux residue plays a positive role in reproducing the distribution of the SST cooling area. From the intercomparisons among experiments, it is also found that the wave-induced turbulence mixing has an important effect on the formation of mixed layer depth (MLD).
    [Show full text]
  • Influences of the Ocean Surface Mixed Layer and Thermohaline Stratification on Arctic Sea Ice in the Central Canada Basin J
    JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115, C10018, doi:10.1029/2009JC005660, 2010 Influences of the ocean surface mixed layer and thermohaline stratification on Arctic Sea ice in the central Canada Basin J. M. Toole,1 M.‐L. Timmermans,2 D. K. Perovich,3 R. A. Krishfield,1 A. Proshutinsky,1 and J. A. Richter‐Menge3 Received 22 July 2009; revised 19 April 2010; accepted 1 June 2010; published 8 October 2010. [1] Variations in the Arctic central Canada Basin mixed layer properties are documented based on a subset of nearly 6500 temperature and salinity profiles acquired by Ice‐ Tethered Profilers during the period summer 2004 to summer 2009 and analyzed in conjunction with sea ice observations from ice mass balance buoys and atmosphere‐ocean heat flux estimates. The July–August mean mixed layer depth based on the Ice‐Tethered Profiler data averaged 16 m (an overestimate due to the Ice‐Tethered Profiler sampling characteristics and present analysis procedures), while the average winter mixed layer depth was only 24 m, with individual observations rarely exceeding 40 m. Guidance interpreting the observations is provided by a 1‐D ocean mixed layer model. The analysis focuses attention on the very strong density stratification at the base of the mixed layer in the Canada Basin that greatly impedes surface layer deepening and thus limits the flux of deep ocean heat to the surface that could influence sea ice growth/ decay. The observations additionally suggest that efficient lateral mixed layer restratification processes are active in the Arctic, also impeding mixed layer deepening. Citation: Toole, J.
    [Show full text]
  • Coastal Upwelling Revisited: Ekman, Bakun, and Improved 10.1029/2018JC014187 Upwelling Indices for the U.S
    Journal of Geophysical Research: Oceans RESEARCH ARTICLE Coastal Upwelling Revisited: Ekman, Bakun, and Improved 10.1029/2018JC014187 Upwelling Indices for the U.S. West Coast Key Points: Michael G. Jacox1,2 , Christopher A. Edwards3 , Elliott L. Hazen1 , and Steven J. Bograd1 • New upwelling indices are presented – for the U.S. West Coast (31 47°N) to 1NOAA Southwest Fisheries Science Center, Monterey, CA, USA, 2NOAA Earth System Research Laboratory, Boulder, CO, address shortcomings in historical 3 indices USA, University of California, Santa Cruz, CA, USA • The Coastal Upwelling Transport Index (CUTI) estimates vertical volume transport (i.e., Abstract Coastal upwelling is responsible for thriving marine ecosystems and fisheries that are upwelling/downwelling) disproportionately productive relative to their surface area, particularly in the world’s major eastern • The Biologically Effective Upwelling ’ Transport Index (BEUTI) estimates boundary upwelling systems. Along oceanic eastern boundaries, equatorward wind stress and the Earth s vertical nitrate flux rotation combine to drive a near-surface layer of water offshore, a process called Ekman transport. Similarly, positive wind stress curl drives divergence in the surface Ekman layer and consequently upwelling from Supporting Information: below, a process known as Ekman suction. In both cases, displaced water is replaced by upwelling of relatively • Supporting Information S1 nutrient-rich water from below, which stimulates the growth of microscopic phytoplankton that form the base of the marine food web. Ekman theory is foundational and underlies the calculation of upwelling indices Correspondence to: such as the “Bakun Index” that are ubiquitous in eastern boundary upwelling system studies. While generally M. G. Jacox, fi [email protected] valuable rst-order descriptions, these indices and their underlying theory provide an incomplete picture of coastal upwelling.
    [Show full text]
  • Ocean 423 Vertical Circulation 1 When We Are Thinking About How The
    Ocean 423 Vertical circulation 1 When we are thinking about how the density, temperature and salinity structure is set in the ocean, there are different processes at work depending on where in the water column we are looking. If we are considering the thermocline, then we notice first that north-south distribution of the surface density/temperature/ salinity across the subtropical gyre can be mapped into their structure in the vertical. This suggests that vertical structure originates at the surface. To figure this out, we can look any one particular isopycnal layer, and see that at some point north and south it intersects the sea surface. Poleward of that point, the layer below the mixed layer is a slightly denser layer. Following stratigraphy, we call the point where an isopycnal layer intersects the sea-surface its outcrop. isopycnal outcrop w < 0 (downwelling), v < 0 z w(0)=we z=0 y z=-H(y) vG mixed layer, winter L.I. V. P. permanent thermocline = const Let’s imagine a water parcel starting somewhere at the surface in the northern half of the subtropical gyre. We ignore the Ekman layer, except for the Ekman pumping it produces, which we specify as a boundary condition on vertical velocity at the ocean surface. The geostrophic velocity is southward everywhere (including the mixed layer) in this part of the ocean because of the downward Ekman pumping. Imagine water parcels flowing southward in the mixed layer Ocean 423 Vertical circulation 2 (and no heating or cooling). A water parcel, when it encounters lighter water to the south will tend to follow its own isopycnal down out of the mixed layer.
    [Show full text]
  • Implementation of an Ocean Mixed Layer Model in IFS
    622 Implementation of an ocean mixed layer model in IFS Y. Takaya12, F. Vitart1, G. Balsamo1, M. Balmaseda1, M. Leutbecher1, F. Molteni1 Research Department 1European Centre for Medium-Range Weather Forecasts, Reading, UK 2Japan Meteorological Agency, Tokyo, Japan March 2010 Series: ECMWF Technical Memoranda A full list of ECMWF Publications can be found on our web site under: http://www.ecmwf.int/publications/ Contact: [email protected] c Copyright 2010 European Centre for Medium-Range Weather Forecasts Shinfield Park, Reading, RG2 9AX, England Literary and scientific copyrights belong to ECMWF and are reserved in all countries. This publication is not to be reprinted or translated in whole or in part without the written permission of the Director. Appropriate non-commercial use will normally be granted under the condition that reference is made to ECMWF. The information within this publication is given in good faith and considered to be true, but ECMWF accepts no liability for error, omission and for loss or damage arising from its use. Implementation of an ocean mixed layer model Abstract An ocean mixed layer model has been implemented in the ECMWF Integrated Forecasting System (IFS) in order to have a better representation of the atmosphere-upper ocean coupled processes in the ECMWF medium-range forecasts. The ocean mixed layer model uses a non-local K profile parameterization (KPP; Large et al. 1994). This model is a one-dimensional column model with high vertical resolution near the surface to simulate the diurnal thermal variations in the upper ocean. Its simplified dynamics makes it cheaper than a full ocean general circulation model and its implementation in IFS allows the atmosphere- ocean coupled model to run much more efficiently than using a coupler.
    [Show full text]
  • Characterizing Transport Between the Surface Mixed Layer and the Ocean Interior with a Forward and Adjoint Global Ocean Transport Model
    APRIL 2005 P R I M E A U 545 Characterizing Transport between the Surface Mixed Layer and the Ocean Interior with a Forward and Adjoint Global Ocean Transport Model FRANÇOIS PRIMEAU Department of Earth System Science, University of California, Irvine, Irvine, California (Manuscript received 1 July 2003, in final form 7 September 2004) ABSTRACT The theory of first-passage time distribution functions and its extension to last-passage time distribution functions are applied to the problem of tracking the movement of water masses to and from the surface mixed layer in a global ocean general circulation model. The first-passage time distribution function is used to determine in a probabilistic sense when and where a fluid element will make its first contact with the surface as a function of its position in the ocean interior. The last-passage time distribution is used to determine when and where a fluid element made its last contact with the surface. A computationally efficient method is presented for recursively computing the first few moments of the first- and last-passage time distributions by directly inverting the forward and adjoint transport operator. This approach allows integrated transport information to be obtained directly from the differential form of the transport operator without the need to perform lengthy multitracer time integration of the transport equations. The method, which relies on the stationarity of the transport operator, is applied to the time-averaged transport operator obtained from a three-dimensional global ocean simulation performed with an OGCM. With this approach, the author (i) computes surface maps showing the fraction of the total ocean volume per unit area that ventilates at each point on the surface of the ocean, (ii) partitions interior water masses based on their formation region at the surface, and (iii) computes the three-dimensional spatial distribution of the mean and standard deviation of the age distribution of water.
    [Show full text]
  • Lecture 4: OCEANS (Outline)
    LectureLecture 44 :: OCEANSOCEANS (Outline)(Outline) Basic Structures and Dynamics Ekman transport Geostrophic currents Surface Ocean Circulation Subtropicl gyre Boundary current Deep Ocean Circulation Thermohaline conveyor belt ESS200A Prof. Jin -Yi Yu BasicBasic OceanOcean StructuresStructures Warm up by sunlight! Upper Ocean (~100 m) Shallow, warm upper layer where light is abundant and where most marine life can be found. Deep Ocean Cold, dark, deep ocean where plenty supplies of nutrients and carbon exist. ESS200A No sunlight! Prof. Jin -Yi Yu BasicBasic OceanOcean CurrentCurrent SystemsSystems Upper Ocean surface circulation Deep Ocean deep ocean circulation ESS200A (from “Is The Temperature Rising?”) Prof. Jin -Yi Yu TheThe StateState ofof OceansOceans Temperature warm on the upper ocean, cold in the deeper ocean. Salinity variations determined by evaporation, precipitation, sea-ice formation and melt, and river runoff. Density small in the upper ocean, large in the deeper ocean. ESS200A Prof. Jin -Yi Yu PotentialPotential TemperatureTemperature Potential temperature is very close to temperature in the ocean. The average temperature of the world ocean is about 3.6°C. ESS200A (from Global Physical Climatology ) Prof. Jin -Yi Yu SalinitySalinity E < P Sea-ice formation and melting E > P Salinity is the mass of dissolved salts in a kilogram of seawater. Unit: ‰ (part per thousand; per mil). The average salinity of the world ocean is 34.7‰. Four major factors that affect salinity: evaporation, precipitation, inflow of river water, and sea-ice formation and melting. (from Global Physical Climatology ) ESS200A Prof. Jin -Yi Yu Low density due to absorption of solar energy near the surface. DensityDensity Seawater is almost incompressible, so the density of seawater is always very close to 1000 kg/m 3.
    [Show full text]
  • NOAA Atlas NESDIS 81 WORLD OCEAN ATLAS 2018 Volume 1
    NOAA Atlas NESDIS 81 WORLD OCEAN ATLAS 2018 Volume 1: Temperature Silver Spring, MD July 2019 U.S. DEPARTMENT OF COMMERCE National Oceanic and Atmospheric Administration National Environmental Satellite, Data, and Information Service National Centers for Environmental Information NOAA National Centers for Environmental Information Additional copies of this publication, as well as information about NCEI data holdings and services, are available upon request directly from NCEI. NOAA/NESDIS National Centers for Environmental Information SSMC3, 4th floor 1315 East-West Highway Silver Spring, MD 20910-3282 Telephone: (301) 713-3277 E-mail: [email protected] WEB: http://www.nodc.noaa.gov/ For updates on the data, documentation, and additional information about the WOA18 please refer to: http://www.nodc.noaa.gov/OC5/indprod.html This document should be cited as: Locarnini, R.A., A.V. Mishonov, O.K. Baranova, T.P. Boyer, M.M. Zweng, H.E. Garcia, J.R. Reagan, D. Seidov, K.W. Weathers, C.R. Paver, and I.V. Smolyar (2019). World Ocean Atlas 2018, Volume 1: Temperature. A. Mishonov, Technical Editor. NOAA Atlas NESDIS 81, 52pp. This document is available on line at http://www.nodc.noaa.gov/OC5/indprod.html . NOAA Atlas NESDIS 81 WORLD OCEAN ATLAS 2018 Volume 1: Temperature Ricardo A. Locarnini, Alexey V. Mishonov, Olga K. Baranova, Timothy P. Boyer, Melissa M. Zweng, Hernan E. Garcia, James R. Reagan, Dan Seidov, Katharine W. Weathers, Christopher R. Paver, Igor V. Smolyar Technical Editor: Alexey Mishonov Ocean Climate Laboratory National Centers for Environmental Information Silver Spring, Maryland July 2019 U.S. DEPARTMENT OF COMMERCE Wilbur L.
    [Show full text]
  • Oxygen, Apparent Oxygen Utilization, and Dissolved Oxygen Saturation
    NOAA Atlas NESDIS 83 WORLD OCEAN ATLAS 2018 Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Dissolved Oxygen Saturation Silver Spring, MD July 2019 U.S. DEPARTMENT OF COMMERCE National Oceanic and Atmospheric Administration National Environmental Satellite, Data, and Information Service National Centers for Environmental Information NOAA National Centers for Environmental Information Additional copies of this publication, as well as information about NCEI data holdings and services, are available upon request directly from NCEI. NOAA/NESDIS National Centers for Environmental Information SSMC3, 4th floor 1315 East-West Highway Silver Spring, MD 20910-3282 Telephone: (301) 713-3277 E-mail: [email protected] WEB: http://www.nodc.noaa.gov/ For updates on the data, documentation, and additional information about the WOA18 please refer to: http://www.nodc.noaa.gov/OC5/indprod.html This document should be cited as: Garcia H. E., K.W. Weathers, C.R. Paver, I. Smolyar, T.P. Boyer, R.A. Locarnini, M.M. Zweng, A.V. Mishonov, O.K. Baranova, D. Seidov, and J.R. Reagan (2019). World Ocean Atlas 2018, Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Dissolved Oxygen Saturation. A. Mishonov Technical Editor. NOAA Atlas NESDIS 83, 38pp. This document is available on-line at https://www.nodc.noaa.gov/OC5/woa18/pubwoa18.html NOAA Atlas NESDIS 83 WORLD OCEAN ATLAS 2018 Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Dissolved Oxygen Saturation Hernan E. Garcia, Katharine W. Weathers, Chris R. Paver, Igor Smolyar, Timothy P. Boyer, Ricardo A. Locarnini, Melissa M. Zweng, Alexey V. Mishonov, Olga K. Baranova, Dan Seidov, James R.
    [Show full text]
  • Three-Dimensional Model-Observation Intercomparison
    Dynamics of Atmospheres and Oceans 76 (2016) 283–305 Contents lists available at ScienceDirect Dynamics of Atmospheres and Oceans journal homepage: www.elsevier.com/locate/dynatmoce Three-dimensional model-observation comparison in the Loop Current region a,∗ a b K.C. Rosburg , K.A. Donohue , E.P. Chassignet a Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, USA b Center for Ocean-Atmosphere Prediction Studies, Florida State University, Tallahassee, FL, USA a r t i c l e i n f o a b s t r a c t Article history: Accurate high-resolution ocean models are required for hurricane and oil spill pathway pre- Received 9 October 2015 dictions, and to enhance the dynamical understanding of circulation dynamics. Output from Received in revised form 4 May 2016 ◦ the 1/25 data-assimilating Gulf of Mexico HYbrid Coordinate Ocean Model (HYCOM31.0) Accepted 4 May 2016 is compared to daily full water column observations from a moored array, with a focus on Available online 6 May 2016 Loop Current path variability and upper-deep layer coupling during eddy separation. Array- mean correlation was 0.93 for sea surface height, and 0.93, 0.63, and 0.75 in the thermocline Keywords: for temperature, zonal, and meridional velocity, respectively. Peaks in modeled eddy kinetic Evaluation energy were consistent with observations during Loop Current eddy separation, but with Modelling modeled deep eddy kinetic energy at half the observed amplitude. Modeled and observed Ocean currents LC meander phase speeds agreed within 8% and 2% of each other within the 100 – 40 and 40 Mesoscale eddies Baroclinic instability – 20 day bands, respectively.
    [Show full text]
  • Arabian Sea Mixed Layer Deepening During the Monsoon: Observations and Dynamics
    Arabian Sea Mixed Layer Deepening during the Monsoon: Observations and Dynamics by Albert S. Fischer S.B., Physics (1994) Massachusetts Institute of Technology Submitted to the Massachusetts Institute of Technology / Woods Hole Oceanographic Institution Joint Program in Physical Oceanography in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY and the WOODS HOLE OCEANOGRAPHIC INSTITUTION September 1997 © Albert S. Fischer 1997. All rights reserved. The author hereby grants MIT and WHOI permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part. Signature of Author ........./...................................................... Joint Program in Physical Oceanography Massachusetts Institute of Technology Woods Hole Oceanographic Institution 18 June 1997 C ertified by ............=.... ..... .. ..................................................................................... Robert A. Weller Senior Scientist Thesis Supervisor A ccepted by ....................... ......... ... ...... Accepted -............... ........................ ........... by.............Paola Malanotte-Rizzoli Chairman, Joint Committee for Physical Oceanography A , i Massachusetts Institute of Technology a IA ' ,-TIM1"" Woods Hole Oceanographic Institution oil~~~E Arabian Sea Mixed Layer deepening during the Monsoon: Observations and Dynamics by Albert S. Fischer Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution Abstract The unusual twice-yearly cycle of mixed layer deepening and cooling driven by the mon- soon is analyzed using a recently collected (1994-95) dataset of concurrent local air-sea fluxes and upper ocean dynamics from the Arabian Sea. The winter northeast monsoon has moderate wind forcing and a strongly destabilizing surface buoyancy flux, driven by large radiative and latent heat losses at the sea surface.
    [Show full text]