DOCSLIB.ORG

GSFC Earth-Sun Exploration Division

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
GSFC Earth-Sun Exploration Division National Aeronautics and Space Administration Goddard Space Flight Center’s Earth Sciences Division Activities, Challenges, and Strategic Plan Our Mission “To Improve Life on Earth and to Enable Space Exploration through the Use of Space-Based Observations” February 2009 www.nasa.gov Table of Contents Preface ................................................................................................................................... v Part I: The Earth Sciences Division 1. The NASA Vision and Mission ................................................................................................ 3 2. GSFC Support of National Needs for Earth System Science .............................................. 4 3. The Earth Sciences Division Mission and Goals .................................................................. 7 4. Inside the Earth Sciences Division ........................................................................................ 9 4.1 What the Earth Sciences Division Does .............................................................................. 9 4.2 Development and Management of Long-Term Data Sets ................................................ 15 4.3 Winning New Business and Supporting National Needs .................................................. 16 4.4 Supporting Mission Planning for HQ ................................................................................. 17 4.5 Partnerships with the Academic Community and Government Laboratories ................... 17 4.6 Partnerships with Operational Agencies ........................................................................... 18 4.7 Education and Public Outreach ......................................................................................... 18 4.8 Toward a More Diversified Workforce ............................................................................... 19 5. How the Earth Sciences Division Operates ........................................................................ 21 5.1 Organizational and Administrative Structure ..................................................................... 21 5.2 Crosscutting Themes ........................................................................................................ 22 5.3 The Workforce Composition .............................................................................................. 23 5.4 The Funding Sources for the Division ............................................................................... 25 6. Challenges and Opportunities .............................................................................................. 28 6.1 The Funding Process and Full Cost Accounting ............................................................... 28 6.2 Retention of Skills and New Hires ..................................................................................... 28 6.3 Some Measures of Performance ....................................................................................... 30 6.4 The Opportunity to Apply Terrestrial Knowledge to Other Planets ................................... 30 i Part II: Our Scientific Foci and Strategic Plan 7. Science/Research Areas ....................................................................................................... 35 7.1 Atmospheric Composition ................................................................................................. 35 7.1.1 Atmospheric Chemistry ........................................................................................... 35 7.1.1.1 Observations of Chemical Constituents ......................................................... 36 7.1.1.2 Chemical Modeling ............................................................................................ 39 7.1.2 Atmospheric Aerosols .............................................................................................. 42 7.2 Hydrospheric Processes ................................................................................................... 45 7.2.1 Oceanography ......................................................................................................... 45 7.2.1.1 Physical Oceanography .................................................................................... 46 7.2.1.2 Biological Oceanography .................................................................................. 47 7.2.2 Polar Climate Change ............................................................................................. 49 7.2.3 Terrestrial Water Cycle ............................................................................................ 52 7.2.4 Atmospheric Water Cycle ........................................................................................ 55 7.3 Carbon Cycle and Ecosystems ......................................................................................... 59 7.3.1 Terrestrial Carbon Cycle .......................................................................................... 62 7.3.2 Terrestrial Ecosystems and Land Cover ................................................................. 63 7.3.3 Biological Oceanography ......................................................................................... 64 7.4 Climate and Weather Prediction ........................................................................................ 64 7.4.1 Climate Modeling and Analysis ................................................................................ 66 7.4.2 Weather and Short-Term Climate ............................................................................ 68 7.4.2.1 Assimilation ......................................................................................................... 69 7.4.2.2 Weather Prediction ............................................................................................ 71 7.4.2.3 Subseasonal-to-Decadal Climate Variability and Prediction ....................... 72 7.4.2.4 Prediction of Constituents – Chemical Weather Prediction ......................... 73 7.5 Managing and Analyzing Data .......................................................................................... 73 7.5.1 Data Management Focus Areas .............................................................................. 74 7.5.2 High Performance Computing ................................................................................. 78 7.5.3 Advanced Software Development ........................................................................... 79 8. Instruments, Technology, and New Missions ..................................................................... 80 8.1 Instruments ........................................................................................................................ 80 8.2 Improving the Instrument and Mission Development Process .......................................... 82 8.3 New Platforms and Vantage Points ................................................................................... 82 8.3.1 Unmanned Aircraft Systems .................................................................................... 82 8.3.2 Geostationary Orbit.................................................................................................. 83 8.3.3 Venture Class Concepts .......................................................................................... 84 9. Applications ........................................................................................................................... 85 10. Education and Public Outreach ........................................................................................... 87 ii Part III: Appendices Appendix A. Missions and Application Activities ................................................................................. 91 Appendix B. Education and Public Outreach Activities ....................................................................... 96 Appendix C. Hiring Strategies .............................................................................................................. 109 Appendix D. Project Scientists and Deputy Project Scientists ......................................................... 116 Appendix E. Field Campaigns and Workshops .................................................................................. 117 Appendix F. Professional Activities, Honors, and Awards ................................................................ 121 Appendix G. The Organizational Structure of the Division’s Laboratories and Offices .................. 129 Appendix H. Acronyms ......................................................................................................................... 132 iii iv Preface Welcome to the report and strategic plan of the Earth Sciences Division (ESD) at the Goddard Space Flight Center (GSFC). This Division, Code 610, is one of the four Divisions of the Sciences and Exploration Directorate. The others are the Astrophysics Science Division, the Heliophysics Science Division, and the Solar System Exploration Division. The writing of this report takes place at a particularly interesting and hopeful time for Earth system science. The National Research Council (NRC) report entitled Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond, 2007, and the Intergovernmental Panel on Climate Change (IPCC) report, 2007, have called the attention of the general public as well as the politicians and decision makers to the potential impact of global change on society. Wildfires, floods,
Load more

Recommended publications
  • Whither the Weather Analysis and Forecasting Process?
    520 WEATHER AND FORECASTING VOLUME 18 FORECASTER'S FORUM Whither the Weather Analysis and Forecasting Process? LANCE F. B OSART Department of Earth and Atmospheric Sciences, The University at Albany, State University of New York, Albany, New York 5 December 2002 and 19 December 2002 ABSTRACT An argument is made that if human forecasters are to continue to maintain a skill advantage over steadily improving model and guidance forecasts, then ways have to be found to prevent the deterioration of forecaster skills through disuse. The argument is extended to suggest that the absence of real-time, high quality mesoscale surface analyses is a signi®cant roadblock to forecaster ability to detect, track, diagnose, and predict important mesoscale circulation features associated with a rich variety of weather of interest to the general public. 1. Introduction h day-1 QPF scores made by selected NCEP models, By any objective or subjective measure, weather fore- including the Nested Grid Model (NGM; Hoke et al. casting skill has improved signi®cantly over the last 40 1989), the Eta Model (Black 1994), and the Aviation years. By way of illustration, Fig. 1 shows the annual Model [AVN, now called the Global Forecast System threat score for 24-h quantitative precipitation forecast (GFS); Kanamitsu et al. (1991); Kalnay et al. (1998)]. (QPF) amounts of 1.00 in. (2.5 cm) or more over the The key point to be made from Fig. 2 is that HPC contiguous United States for 1961±2001 as produced by forecasters have been able to sustain approximately a forecasters at the Hydrometeorological Prediction Cen- 0.05 threat score advantage over the NCEP numerical ter (HPC) of the National Centers for Environmental models during this 12-yr period (and longer, not shown) Prediction (NCEP).
    [Show full text]
  • The Operational CMC–MRB Global Environmental Multiscale (GEM)
    VOLUME 126 MONTHLY WEATHER REVIEW JUNE 1998 The Operational CMC±MRB Global Environmental Multiscale (GEM) Model. Part I: Design Considerations and Formulation JEAN COÃ TEÂ AND SYLVIE GRAVEL Meteorological Research Branch, Atmospheric Environment Service, Dorval, Quebec, Canada ANDREÂ MEÂ THOT AND ALAIN PATOINE Canadian Meteorological Centre, Atmospheric Environment Service, Dorval, Quebec, Canada MICHEL ROCH AND ANDREW STANIFORTH Meteorological Research Branch, Atmospheric Environment Service, Dorval, Quebec, Canada (Manuscript received 31 March 1997, in ®nal form 3 October 1997) ABSTRACT An integrated forecasting and data assimilation system has been and is continuing to be developed by the Meteorological Research Branch (MRB) in partnership with the Canadian Meteorological Centre (CMC) of Environment Canada. Part I of this two-part paper motivates the development of the new system, summarizes various considerations taken into its design, and describes its main characteristics. 1. Introduction time and space scales that are commensurate with those An integrated atmospheric environmental forecasting associated with the phenomena of interest, and this im- and simulation system, described herein, has been and poses serious practical constraints and compromises on is continuing to be developed by the Meteorological their formulation. Research Branch (MRB) in partnership with the Ca- Emphasis is placed in this two-part paper on the con- nadian Meteorological Centre (CMC) of Environment cepts underlying the long-term developmental strategy,
    [Show full text]
  • MP - Mechanistic Habitat Modeling with Multi-Model Climate Ensembles - Final - HMJ5.Docx
    MP - Mechanistic Habitat Modeling with Multi-Model Climate Ensembles - Final - HMJ5.docx Mechanistic Habitat Modeling with Multi-Model Climate Ensembles A practical guide to preparing and using Global Climate Model output for mechanistically modeling habitat, as exemplified by the modeling of the fundamental niche of a pagophilic Pinniped species through 2100. Masters project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University, May 2013. Author: Hunter Jones Adviser: Dr. David Johnston Abstract Projections of future Sea Ice Concentration (SIC) were prepared using a 13- member ensemble of climate model output from the Coupled Model Inter- comparison Project (CMIP5). Three climate change scenarios (RCP 2.6, RCP 6.0, RCP 8.5), corresponding to low, moderate, and high climate change possibilities, were used to generate these projections for known Harp Seal whelping locations. The projections were splined and statistically downscaled via the CCAFS Delta method using satellite-derived observations from the National Sea Ice Data Center (NSIDC) to prepare a spatial representation of sea ice decline through the year 2100. Multi-Model Ensemble projections of the mean sea ice concentration anomaly for Harp Seal whelping locations under the moderate and high climate change scenarios (RCP 6.0 and RCP 8.5) show a decline of 10% to 40% by 2100 from a modern baseline climatology (average of SIC, 1988 - 2005) while sea ice concentrations under the low climate change scenario remain fairly stable. Projected year-over-year sea ice concentration variability decreases with time through 2100, but uncertainty in the prediction (model spread) increases.
    [Show full text]
  • Downscaling Geopotential Height Using Lapse Rate
    KMITL Sci. Tech. J. Vol. 12 No. 2 Jul.-Dec. 2012 Downscaling Geopotential Height Using Lapse Rate Chiranya Surawut1 and Dusadee Sukawat2* 1Earth System Science, King Mongkut ’s University of Technology Thonburi, Bangkok, Thailand 2*Department of Mathematics, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand Abstract In order to utilize the output from a global climate model, relevant information for the area of interest must be extracted. This is called "downscaling". In this paper, the derivation of a local geopotential height in terms of lapse rate is presented. The main assumptions are hydrostatic balance, perfect gas, constant gravity, and constant lapse rate. Two sets of data are required for this method, simulation outputs from the Education Global Climate Model (EdGCM) and the observed data at the points of interest (Chiangmai, Bangkok, Ubon Ratchathani, Phuket and Songkla). The results show that downscaling of the geopotential heights by using lapse rate dynamic equation are closer to the observed data than the geopotential heights from EdGCM. Keywords: Downscaling, Geopotential height, Lapse rate 1. Introduction Climate change data are simulation outputs from global climate models (GCMs). Outputs of GCMs are coarse resolution, so it must be downscaled for use in regional applications by downscaling [1]. The starting point for downscaling is a larger scale atmospheric or coupled oceanic atmospheric model run from a GCM [2]. There are two major kinds of downscaling, statistical and dynamical methods. Statistical downscaling methods use historical data and archived forecasts to produce downscaled information from large scale forecasts. Dynamical downscaling methods involve dynamical models of the atmosphere nested within the grids of the large scale forecast models [1].
    [Show full text]
  • Bias Removal and Model Consensus Forecasts of Maximum and Minimum Temperatures Using the Graphical Forecast Editor 1. Introducti
    Bias Removal and Model Consensus Forecasts of Maximum and Minimum Temperatures Using the Graphical Forecast Editor Jeffrey T. Davis National Weather Service Office Tucson, Arizona 1. Introduction Operational Numerical Weather Prediction (NWP) models have inherent biases that need to be removed either objectively or subjectively before used in official National Weather Service (NWS) forecasts. The recent shift of the NWS to the Interactive Forecast Preparation System (IFPS) to create and distribute digital weather forecasts has led to more of a dependency on the direct use of NWP models. This dependency on raw model output is mainly due to the gridded format needed to initialize digital weather forecasts. The primary tool for creating these digital forecasts is the Graphical Forecast Editor (GFE) (Lefebvre, 1995). The GFE has two design features that account for the objective and subjective adjustments of the native NWP model grid. The front-end feature is referred to as “Smart Initialization” which is used to objectively derive sensible weather elements and downscale the coarser resolution models to a higher 2.5 km or 5 km grid spacing. The second feature is interactive and accounts for both subjective and objective adjustments through the use of simple graphical editing tools and “Smart Tools”. The baseline GFE design lacks the capability to objectively remove model biases in the initialization process. This is not to say that the GFE should have this capability, but high resolution, bias-corrected model grids are not currently included in the overall IFPS design. As a result, forecasters are tasked with subjectively correcting for these model biases using the GFE tools.
    [Show full text]
  • Envisioning Climate Change Using a Global Climate Model
    Envisioning Climate Change Using a Global Climate Model Betsy Youngman, Mark Chandler, Linda Sohl, Mark Hafen, Tamara Ledley, Steve Ackerman, and Steve Kluge The Earth Exploration Toolbook is a collection in the National Science Digital Library (http://nsdl.org) and the Digital Library for Earth System Education (http://www.dlese.org). The National Science Foundation provides funding for the development of the collection under NSF Award #0226199. Permission to reuse and disseminate Earth Exploration Toolbook material for educational, non-commercial purposes is offered as long as attribution is retained, as per the terms of Creative Commons License Attribution - NonCommercial-ShareAlike 1.0 (http://creativecommons.org/licenses/by-nc-sa/1.0/). * * * * * The EdGCM software suite was developed under the auspices of the EdGCM Project of Columbia University and NASA’s Goddard Institute for Space Studies. © 2003-2010 Columbia University. All rights reserved. The EdGCM Project acknowledges the support of the National Science Foundation, Division of Atmospheric Science—Paleoclimate Program (NSF Award #0231400), and by NASA’s Climate Programs (NASA Award #NNG04GP65G). Contents 1. Envisioning Climate Change Using a Global Climate Model 1 2. Teaching Notes 3 3. Case Study 15 4. Step-by-Step Instructions 19 Part 1 - Download Software and Data 22 Part 2 - Climate Models 27 Part 3 - Generate a Time Series Plot of Temperature 31 Part 4 - Generate Temperature Maps Using EVA 37 Part 5 - Generate Snow and Ice Coverage Maps 43 Part 6 - Explore Climate Change
    [Show full text]
  • The State-Of-The-Art in Short-Term Prediction of Wind Power. A
    Project funded by the European Commission under the 5th (EC) RTD Framework Programme (1998 - 2002) within the thematic programme "Energy, Environment and Sustainable Development" Project ANEMOS “Development of a Next Generation Wind Resource Forecasting System Contract No.: ENK5-CT-2002-00665 for the Large-Scale Integration of Onshore and Offshore Wind Farms” The State-Of-The-Art in Short-Term Prediction of Wind Power A Literature Overview Version 1.1 AUTHOR: Gregor Giebel AFFILIATION: Risø National Laboratory ADDRESS: P.O. Box 49, DK-4000 Roskilde TEL.: +45 4677 5095 EMAIL: [email protected] FURTHER AUTHORS: Richard Brownsword, RAL; George Kariniotakis, ARMINES REVIEWER: ANEMOS WP-1 members APPROVER: Gregor Giebel Document Information DOCUMENT TYPE Deliverable report D1.1 DOCUMENT NAME: ANEMOS_D1.1_StateOfTheArt_v1.1.pdf REVISION: 5 REV. DATE: 2003.08.12 CLASSIFICATION: Public STATUS: Approved Abstract: Based on an appropriate questionnaire (WP1.1) and some other works already in progress, this report details the state-of-the-art in short term prediction of wind power, mostly summarising nearly all existing literature on the topic. ANEMOS ANEMOS_D1.1_StateOfTheArt_v1.1.pdf, 2003.08.12. Contents 1. Introduction......................................................................................................... 3 1.1 The typical model chain................................................................................... 3 1.2 Typical results ................................................................................................
    [Show full text]
  • An Application to Ocean Modelling
    * Manuscript Click here to download Manuscript: paper_rev_final.tex Web-enabled Configuration and Control of Legacy Codes: an Application to Ocean Modelling ∗ C. Evangelinos a, , P.F.J. Lermusiaux b,S.K.Geigera, R.C. Chang a,N.M.Patrikalakisa aDepartment of Mechanical Engineering, MIT, Cambridge, MA 02139, USA bDivision of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02139, USA Abstract For modern interdisciplinary ocean prediction and assimilation systems, a sig- nificant part of the complexity facing users is the very large number of possible setups and parameters, both at build-time and at run-time, especially for the core physical, biological and acoustical ocean predictive models. The configuration of these modeling systems for both local as well as remote execution can be a daunt- ing and error-prone task in the absence of a Graphical User Interface (GUI) and of software that automatically controls the adequacy and compatibility of options and parameters. We propose to encapsulate the configurability and requirements of ocean prediction codes using an eXtensible Markup Language (XML) based descrip- tion, thereby creating new computer-readable manuals for the executable binaries. These manuals allow us to generate a GUI, check for correctness of compilation and input parameters, and finally drive execution of the prediction system components, all in an automated and transparent manner. This web-enabled configuration and automated control software has been developed (it is currently in “beta” form) and exemplified for components of the interdisciplinary Harvard Ocean Prediction Sys- tem (HOPS) and for the uncertainty prediction components of the Error Subspace Statistical Estimation (ESSE) system.
    [Show full text]
  • Downloaded 09/28/21 03:43 PM UTC 2944 MONTHLY WEATHER REVIEW VOLUME 125
    NOVEMBER 1997 FOX-RABINOVITZ ET AL. 2943 A Finite-Difference GCM Dynamical Core with a Variable-Resolution Stretched Grid MICHAEL S. FOX-RABINOVITZ AND GEORGIY L. STENCHIKOV University of Maryland at College Park, College Park, Maryland MAX J. SUAREZ Goddard Space Flight Center, Greenbelt, Maryland LAWRENCE L. TAKACS General Sciences Corporation, Laurel, Maryland (Manuscript received 30 December 1996, in ®nal form 2 April 1997) A ®nite-difference atmospheric model dynamics,ABSTRACT or dynamical core using variable resolution, or stretched grids, is developed and used for regional±global medium-term and long-term integrations. The goal of the study is to verify whether using a variable-resolution dynamical core allows us to represent adequately the regional scales over the area of interest (and its vicinity). In other words, it is shown that a signi®cant downscaling is taking place over the area of interest, due to better-resolved regional ®elds and boundary forcings. It is true not only for short-term integrations, but also for medium-term and, most im- portantly, long-term integrations. Numerical experiments are performed with a stretched grid version of the dynamical core of the Goddard Earth Observing System (GEOS) general circulation model (GCM). The dynamical core includes the discrete (®nite-difference) model dynamics and a Newtonian-type rhs zonal forcing, which is symmetric for both hemispheres about the equator. A ¯exible, portable global stretched grid design allows one to allocate the area of interest with uniform ®ne-horizontal (latitude by longitude) resolution over any part of the globe, such as the U.S. territory used in these experiments.
    [Show full text]
  • The NOAA National Operational Model Archive and Distribution
    THE NOAA OPERATIONAL MODEL ARCHIVE AND DISTRIBUTION SYSTEM (NOMADS) G. K. RUTLEDGE† National Climatic Data Center 151 Patton Ave, Asheville, NC 28801, USA E-mail: [email protected] J. ALPERT National Centers for Environmental Prediction Camp Springs, MD 20736, USA E-mail: [email protected] R. J. STOUFFER Geophysical Fluid Dynamics Laboratory Princeton, NJ 08542, USA E-mail: [email protected] B. LAWRENCE British Atmospheric Data Centre Chilton, Didcot, OX11 0QX, U.K. E-mail: [email protected] Abstract An international collaborative project to address a growing need for remote access to real-time and retrospective high volume numerical weather prediction and global climate model data sets (Atmosphere-Ocean General Circulation Models) is described. This paper describes the framework, the goals, benefits, and collaborators of the NOAA Operational Model Archive and Distribution System (NOMADS) pilot project. The National Climatic Data Center (NCDC) initiated NOMADS, along with the National Centers for Environmental Prediction (NCEP) and the Geophysical Fluid Dynamics Laboratory (GFDL). A description of operational and research data access needs is provided as outlined in the U.S. Weather Research Program (USWRP) Implementation Plan for Research in Quantitative Precipitation Forecasting and Data Assimilation to “redeem practical value of research findings and facilitate their transfer into operations.” † Work partially supported by grant NES-444E (ESDIM) of the National Oceanic and Atmospheric Administration (NOAA). 1 2 1.0 Introduction To address a growing need for real-time and retrospective General Circulation Model (GCM) and Numerical Weather Prediction (NWP) data, the National Climatic Data Center (NCDC) along with the National Centers for Environmental Prediction (NCEP) and the Geophysical Fluid Dynamics Laboratory (GFDL) have initiated the collaborative NOAA Operational Model Archive and Distribution System (NOMADS) (Rutledge, et.
    [Show full text]
  • Performance Assessment of ERA5 Wave Data in a Swell Dominated Region
    Journal of Marine Science and Engineering Article Performance Assessment of ERA5 Wave Data in a Swell Dominated Region Maria Francesca Bruno * , Matteo Gianluca Molfetta , Vincenzo Totaro and Michele Mossa Department of Civil, Environmental, Building Engineering and Chemistry, Polytechnic University of Bari -Via E. Orabona, 4, 70125 Bari, Italy; [email protected] (M.G.M.); [email protected] (V.T.); [email protected] (M.M.) * Correspondence: [email protected]; Tel.: +39-080-4605-207 Received: 18 February 2020; Accepted: 15 March 2020; Published: 19 March 2020 Abstract: The present paper deals with a performance assessment of the ERA5 wave dataset in an ocean basin where local wind waves superimpose on swell waves. The evaluation framework relies on observed wave data collected during a coastal experimental campaign carried out offshore of the southern Oman coast in the Western Arabian Sea. The applied procedure requires a detailed investigation on the observed waves, and aims at classifying wave regimes: observed wave spectra have been split using a 2D partition scheme and wave characteristics have been evaluated for each wave component. Once the wave climate was defined, a detailed wave model assessment was performed. The results revealed that during the analyzed time span the ERA5 wave model overestimates the swell wave heights, whereas the wind waves’ height prediction is highly influenced by the wave developing conditions. The collected field dataset is also useful for a discussion on spectral wave characteristics during monsoon and post-monsoon season in the examined region; the recorded wave data do not suffice yet to adequately describe wave fields generated by the interaction of monsoon and local winds.
    [Show full text]
  • Ocean Model, Interconnections Within the Climate Model
    Ocean model, Interconnections within the climate model Vladimir Djurdjevic and Bora Rajkovic EXPERT WORKSHOP “SEE RESEARCH FRAMEWORK IN REGIONAL CLIMATE MODELING FOR 2012-2017” Belgrade, Serbia, April 11-13, 2011 Interaction between atmosphere and ocean plays important role for many processes in both medium. Interaction happens on all possible spatial and time scales. spatial scales: meters - ocean surface ocean waves planetary - ENSO, thermohaline circulation time scales: daily - hurricanes, sea-land breeze decade to century - global ocean circulation, The Atlantic Meridional Overturning Circulation (source: Large 2008) From Kuhlbrodt et al. (source:(2007) Jungclaus) Exchanges between atmosphere and ocean: momentum exchange formation of ocean currents heat exchange, turbulent and radiation fluxes mixed layer mass exchange fresh water flux gases and chemical exchange CO2 uptake, deposition of minerals in atmospheric aerosols (source: http://www.oceanclimate.se/research_atmosphere_ocean_interaction.htm) Approaches for ocean modeling and coupling Q-flux, mixed layer model or slab ocean models •model without ocean dynamics •each grid cell in the ocean is given a temperature and a depth for the mixed layer •energy convergence (divergence) at each grid cell is calculated based on the heat storage capacity of the surface ocean and the vertical energy fluxes at the air/sea interface •allows the ocean to adjust to the surface air temperature advantages: simple for implementation suitable for different climate experiments CPU efficient disadvantages: no ocean dynamics training run - fluxes are derived from specified SST control runs where the specified SSTs are from climatology (source: http://forums.edgcm.columbia.edu/showthread.php?p=315) Approaches for ocean modeling and coupling K Profile Parameterization (KPP) mixed-layer ocean model Example from seasonal forecast in ECMWF (Vitart, 2008): •mixed layer model is based on the KPP scheme (Large et al., 1994).
    [Show full text]