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  • 1
    Online Resource
    Online Resource
    Satellite Altimetry over Oceans and Land Surfaces. (2017)
    Milton :Taylor & Francis Group,
    Details
    Keywords: Satellite geodesy-Technique. ; Electronic books.
    Description / Table of Contents: Satellite remote sensing, in particular by radar altimetry, is a crucial technique for observations of the ocean surface and of many aspects of land surfaces, and of paramount importance for climate and environmental studies. It provides a state-of-the-art overview of the satellite altimetry techniques and related missions.
    Type of Medium: Online Resource
    Pages: 1 online resource (645 pages)
    Edition: 1st ed.
    ISBN: 9781498743464
    Series Statement: Earth Observation of Global Changes Series
    URL: https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=5117883
    DDC: 551.48
    Language: English
    Note: Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Editors -- Contributors -- Chapter 1: Satellite Radar AltimetryPrinciple, Accuracy, and Precision -- 1.1 Introduction -- 1.1.1 Satellite Altimetry Measurement Principle -- 1.1.2 Satellite Radar Altimetry Historical Perspective -- 1.1.2.1 Satellite Altimetry Missions -- 1.1.2.2 Geographical Perspective and International Cooperation -- 1.1.2.3 Altimetry Products: History of Continuous Progress -- 1.1.3 Altimetry System Requirements -- 1.2 Radar Instrument -- 1.2.1 Radar Altimeter Instrument Principles -- 1.2.2 Observation Geometry -- 1.2.3 Radar Operation -- 1.2.4 Transmitted Waveform -- 1.2.5 Instrument Architecture -- 1.2.6 Instrument Example: Poseidon-3 of Jason-2 Mission -- 1.2.6.1 Poseidon-3 Architecture -- 1.2.6.2 Poseidon-3 Main Characteristics -- 1.2.7 Key Instrument Performance -- 1.2.8 Echo Formation -- 1.3 Echo characterization and processing -- 1.3.1 Speckle Noise -- 1.3.2 Analytical and Numerical Models -- 1.3.3 Estimation Strategies -- 1.3.4 New Altimeters -- 1.3.5 Non-Ocean Surfaces -- 1.4 Precise Orbit Determination -- 1.4.1 Orbit Determination Technique -- 1.4.1.1 Performance Requirements -- 1.4.1.2 Radial Error Properties -- 1.4.2 Orbit Determination Measurement Systems -- 1.4.3 Satellite Trajectory Modeling and Parameterization -- 1.4.4 Major Modeling Evolution since the Beginning of the 1990s -- 1.4.5 Long-Term Orbit Error and Stability Budget -- 1.4.6 Foreseen Modeling Improvement -- 1.5 Geophysical Corrections -- 1.5.1 Sea State Bias Correction -- 1.5.1.1 Origins of the Sea State Effects and Correction -- 1.5.1.2 Theoretical Solutions -- 1.5.1.3 Empirical Solutions. , 1.5.2 Atmospheric Propagation Effect Corrections -- 1.5.2.1 Ionospheric Correction -- 1.5.2.2 Dry Tropospheric Correction -- 1.5.2.3 The Wet Tropospheric Correction -- 1.6 Altimetry Product Auxiliary Information: Reference Surfaces, Tides, and High-Frequency Signal -- 1.6.1 Reference Surfaces -- 1.6.2 Tides, High-Frequency Signals -- 1.6.2.1 The Tide Correction -- 1.6.2.2 The High-Frequency Correction -- 1.6.2.3 S1 and S2 Atmospheric and Ocean Signals -- 1.7 Altimetry Time and Space Sampling: Orbit Selection and Virtual Constellation Approach -- 1.7.1 Sampling Properties of a Single Altimeter Orbit -- 1.7.2 Orbit Sub-Cycles and Sampling Properties -- 1.7.3 Altimeter Virtual Constellation and Phasing -- 1.8 Altimetry error budget -- 1.8.1 Error Budget for Mesoscale Oceanography -- 1.8.2 Error Budget for Mean Sea Level Trend Monitoring -- 1.8.3 Error Budget for Sub-Mesoscale -- References -- Chapter 2: Wide-Swath AltimetryA Review -- 2.1 Introduction -- 2.2 Ocean and Hydrology Sampling Requirements -- 2.3 Approaches to Wide-Swath Altimetry -- 2.3.1 From Nadir Altimetry to Wide-Swath Altimetry: Three-Dimensional Geolocation -- 2.3.2 Wide-Swath Altimetry Using Waveform Tracking -- 2.3.3 Wide-Swath Altimetry Using Radar Interferometry -- 2.4 The Interferometric Error Budget -- 2.4.1 Roll Errors -- 2.4.2 Phase Errors -- 2.4.3 Range Errors -- 2.4.4 Baseline Errors -- 2.4.5 Finite Azimuth Footprint Biases -- 2.4.6 Radial Velocity Errors -- 2.4.7 Calibration Methods -- 2.5 Wide-Swath Altimetry Phenomenology -- 2.5.1 Water Brightness -- 2.5.2 Wave Effects -- 2.5.2.1 The "Surfboard Effect" -- 2.5.2.2 Temporal Correlation Effects -- 2.5.2.3 Wave Bunching -- 2.5.2.4 The EM Bias. , 2.5.3 Layover and Vegetation Effects -- 2.6 Wide-Swath Altimetry Mission Design -- 2.7 Summary and Prospects -- References -- Acknowledgments -- Chapter 3: In Situ Observations Needed to Complement, Validate, and Interpret Satellite Altimetry -- 3.1 Introduction -- 3.2 Sea Surface Heights Obtained from Tide Gauge/GNSS Networks -- 3.2.1 Sea Level Measurements before the Altimeter Era -- 3.2.2 Tide Gauge and Altimeter Data Complementarity -- 3.2.3 Tide Gauges Used for Altimeter Calibration -- 3.2.4 Tide Gauge and Altimeter Data in Combination in Studies of Long-Term Sea Level Change -- 3.2.5 GNSS Equipment at Tide Gauges -- 3.2.6 New Developments in Tide Gauges and Data Availability -- 3.2.7 Tide Gauges and Altimetry in the Future -- 3.3 Upper-Ocean (0 to 2000 decibars) Steric Variability: The XBT and Argo Networks -- 3.3.1 The Relationship of SSH Variability with Subsurface T and S-Steric Height -- 3.3.2 A Brief History of Systematic Ocean Sampling by the XBT and Argo Networks -- 3.3.3 Ocean Heat Content and Steric Sea Level -- 3.3.4 The Global Pattern of SSH and Upper-Ocean Steric Height -- 3.3.5 Geostrophic Ocean Circulation -- 3.3.6 Horizontal Scales of Variability in the Ocean: The Challenge of Resolution -- 3.4 Deep-Ocean (greater than 2000 m) Steric Variability: Repeat Hydrography and Deep Argo -- 3.4.1 Ventilating the Deep Ocean: Deep Water Production and the Global MOC -- 3.4.2 Monitoring Deep Steric Variability through Repeat Hydrography -- 3.4.3 The Deep Ocean Contribution to Steric Sea Level -- 3.4.4 Future of Deep Observing: Deep Argo -- 3.6 Dynamic Topography and Surface Velocity -- 3.6.1 Eulerian Velocity Measurements -- 3.6.2 Lagrangian Velocity Measurements -- 3.6.3 Geostrophic Currents and Mean Dynamic Topography. , 3.6.4 Ageostrophic Motions -- 3.7 The Technology Revolution and the Future of Ocean Observations -- References -- Acknowledgments -- Chapter 4: Auxiliary Space-Based Systems for Interpreting Satellite Altimetry -- 4.1 Introduction -- 4.2 Measurements: Mean Geoid and Sea Surface -- 4.2.1 Parameterizing Gravity and the Geoid -- 4.2.2 GRACE and GOCE -- 4.2.3 Surface Gravity Data and Combination Geoids -- 4.2.4 Mean Sea Surface Models -- 4.3 Measurements: Time-Variable Gravity -- 4.4 Applications: Dynamic Ocean Topography -- 4.4.1 Importance of Consistency between Geoid and MSS -- 4.4.2 Improvements in MDT with GRACE and GOCE Geoids -- 4.4.3 Toward a Higher Spatial Resolution MDT -- 4.5 Applications: Global and Regional Ocean Mass Variations -- 4.6 Conclusions and Future Prospects -- References -- Chapter 5: A 25-Year Satellite Altimetry-Based Global Mean Sea Level Record -- 5.1 Introduction -- 5.2 The Altimeter Mean Sea Level Record -- 5.2.1 Computing Global and Regional Mean Sea Level Time Series -- 5.2.2 Altimeter Missions -- 5.2.3 Altimeter Corrections -- 5.2.4 Intermission Biases -- 5.2.5 Averaging Process -- 5.2.6 Validation of the GMSL Record with Tide Gauge Measurements -- 5.2.7 Mean Sea Level Variation and Uncertainties -- 5.2.7.1 Global Scale Uncertainty -- 5.2.7.2 Regional Scales -- 5.3 Interpreting the Altimeter GMSL Record -- 5.3.1 Steric Sea Level Contribution -- 5.3.2 The Cryosphere Contributions to GMSL -- 5.3.3 The Land Water Storage Contributions to GMSL -- 5.3.3.1 Interannual Variations -- 5.3.3.2 Long-Term Variations -- 5.4 Closing the Sea Level Budget and Uncertainties -- 5.4.1 Glacial Isostatic Adjustment -- 5.4.2 Ocean Mass/Barystatic Sea Level from GRACE. , 5.4.3 Closure and Missing Components -- 5.5 How Altimetry Informs Us About the Future -- References -- Chapter 6: Monitoring and Interpreting Mid-Latitude Oceans by Satellite Altimetry -- 6.1 Introduction: Role of Mid-Latitude Oceans -- 6.2 Western Boundary Currents -- 6.3 Meridional Circulation and Interbasin Exchanges -- 6.4 Climate Change -- 6.5 Summary and Future Research -- References -- Acknowledgments -- Chapter 7: Monitoring and Interpreting the Tropical Oceans by Satellite Altimetry -- 7.1 Introduction -- 7.2 Tropical Atlantic Ocean -- 7.2.1 Intraseasonal and Eddy Activities -- 7.2.1.1 Eddy Structures -- 7.2.1.2 Tropical Instability Waves -- 7.2.2 The Seasonal Cycle -- 7.2.3 Equatorial Waves -- 7.2.4 Interannual Variability -- 7.3 Tropical Indo-Pacific Ocean -- 7.3.1 Tropical Pacific -- 7.3.1.1 Intraseasonal Variability -- 7.3.1.2 Seasonal Variability -- 7.3.1.3 Interannual and Decadal Variability -- 7.3.2 Tropical Indian Ocean -- 7.3.2.1 Intraseasonal Variability -- 7.3.2.2 Seasonal Cycle -- 7.3.2.3 Interannual Variability -- 7.3.2.4 Decadal and Multidecadal Changes -- 7.3.3 Indo-Pacific Linkage and Indonesian Throughflow -- 7.4 Summary -- References -- Acknowledgments -- Chapter 8: The High Latitude Seas and Arctic Ocean -- 8.1 Introduction -- 8.1.1 Satellite Altimetry in the High Latitude and Arctic Ocean -- 8.2 Mapping the Sea Ice Thickness in the Arctic Ocean -- 8.3 Sea Level Change -- 8.3.1 The Seasonal Cycle -- 8.3.2 Secular and Long-Term Sea Level Changes -- 8.3.3 Arctic Sea Level Budget -- 8.3.4 The Polar Gap and Accuracy Estimates -- 8.4 Mean Dynamic Topography -- 8.5 Ocean Circulation and Volume Transport -- 8.5.1 Surface Circulation -- 8.5.2 Volume Transport. , 8.6 Summary and Outlook.
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  • 2
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    Quality assessment of spaceborne sea surface salinity observations over the northern North Atlantic (2015)
    AGU (American Geophysical Union) | Wiley
    In:  Journal of Geophysical Research-Oceans, 120 (1). pp. 94-112.
    Details
    Publication Date: 2015-09-28
    Description: Spaceborne sea surface salinity (SSS) measurements provided by the European Space Agency's (ESA) “Soil Moisture and Ocean Salinity” (SMOS) and the National Aeronautical Space Agency's (NASA) “Aquarius/SAC-D” missions, covering the period from May 2012 to April 2013, are compared against in situ salinity measurements obtained in the northern North Atlantic between 20°N and 80°N. In cold water, SMOS SSS fields show a temperature-dependent negative SSS bias of up to −2 g/kg for temperatures 〈5°C. Removing this bias significantly reduces the differences to independent ship-based thermosalinograph data but potentially corrects simultaneously also other effects not related to temperature, such as land contamination or radio frequency interference (RFI). The resulting time-mean bias, averaged over the study area, amounts to 0.1 g/kg. A respective correction applied previously by the Jet Propulsion Laboratory to the Aquarius data is shown here to have successfully removed an SST-related bias in our study area. For both missions, resulting spatial structures of SSS variability agree very well with those available from an eddy-resolving numerical simulation and from Argo data and, additionally they also show substantial salinity changes on monthly and seasonal time scales. Some fraction of the root-mean-square difference between in situ, and SMOS and Aquarius data (approximately 0.9 g/kg) can be attributed to short time scale ocean processes, notably at the Greenland shelf, and could represent associated sampling errors there.
    Type: Article , PeerReviewed
    Format: text
    DOI: 10.1002/2014JC010067
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 3
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    Initialization and ensemble generation for decadal climate predictions: A comparison of different methods (2019)
    AGU (American Geophysical Union) | Wiley
    In:  Journal of Advances in Modeling Earth Systems, 11 . pp. 149-172.
    Details
    Publication Date: 2022-01-31
    Description: Five initialization and ensemble generation methods are investigated with respect to their impact on the prediction skill of the German decadal prediction system "Mittelfristige Klimaprognose" (MiKlip). Among the tested methods, three tackle aspects of model‐consistent initialization using the ensemble Kalman filter (EnKF), the filtered anomaly initialization (FAI) and the initialization method by partially coupled spin‐up (MODINI). The remaining two methods alter the ensemble generation: the ensemble dispersion filter (EDF) corrects each ensemble member with the ensemble mean during model integration. And the bred vectors (BV) perturb the climate state using the fastest growing modes. The new methods are compared against the latest MiKlip system in the low‐resolution configuration (Preop‐LR), which uses lagging the climate state by a few days for ensemble generation and nudging toward ocean and atmosphere reanalyses for initialization. Results show that the tested methods provide an added value for the prediction skill as compared to Preop‐LR in that they improve prediction skill over the eastern and central Pacific and different regions in the North Atlantic Ocean. In this respect, the EnKF and FAI show the most distinct improvements over Preop‐LR for surface temperatures and upper ocean heat content, followed by the BV, the EDF and MODINI. However, no single method exists that is superior to the others with respect to all metrics considered. In particular, all methods affect the Atlantic Meridional Overturning Circulation in different ways, both with respect to the basin‐wide long‐term mean and variability, and with respect to the temporal evolution at the 26° N latitude.
    Type: Article , PeerReviewed
    Format: text
    Format: text
    DOI: 10.1029/2018MS001439
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  • 4
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    Ten new insights in climate science 2021 – a horizon scan (2021)
    Cambridge Univ. Press
    Details
    Publication Date: 2024-02-07
    Type: Article , PeerReviewed
    Format: text
    Format: text
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  • 5
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    Sea ice assimilation into a coupled ocean–sea ice model using its adjoint (2017)
    COPERNICUS GESELLSCHAFT MBH
    In:  EPIC3Cryosphere, COPERNICUS GESELLSCHAFT MBH, 11, pp. 2265-2281, ISSN: 1994-0416
    Details
    Publication Date: 2017-11-06
    Description: Satellite sea ice concentrations (SICs), together with several ocean parameters, are assimilated into a regional Arctic coupled ocean–sea ice model covering the period of 2000–2008 using the adjoint method. There is substantial improvement in the representation of the SIC spatial distribution, in particular with respect to the position of the ice edge and to the concentrations in the central parts of the Arctic Ocean during summer months. Seasonal cycles of total Arctic sea ice area show an overall improvement. During summer months, values of sea ice extent (SIE) integrated over the model domain become underestimated compared to observations, but absolute differences of mean SIE to the data are reduced in nearly all months and years. Along with the SICs, the sea ice thickness fields also become closer to observations, providing added value by the assimilation. Very sparse ocean data in the Arctic, corresponding to a very small contribution to the cost function, prevent sizable improvements of assimilated ocean variables, with the exception of the sea surface temperature.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
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  • 6
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    Putting it all together: Adding value to the global ocean and climate observing systems with complete self-consistent ocean state and parameter estimates. (2019)
    Frontiers Media
    Details
    Publication Date: 2022-10-26
    Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Heimbach, P., Fukumori, I., Hills, C. N., Ponte, R. M., Stammer, D., Wunsch, C., Campin, J., Cornuelle, B., Fenty, I., Forget, G., Koehl, A., Mazloff, M., Menemenlis, D., Nguyen, A. T., Piecuch, C., Trossman, D., Verdy, A., Wang, O., & Zhang, H. Putting it all together: Adding value to the global ocean and climate observing systems with complete self-consistent ocean state and parameter estimates. Frontiers in Marine Science, 6 (2019):55, doi:10.3389/fmars.2019.00055.
    Description: In 1999, the consortium on Estimating the Circulation and Climate of the Ocean (ECCO) set out to synthesize the hydrographic data collected by the World Ocean Circulation Experiment (WOCE) and the satellite sea surface height measurements into a complete and coherent description of the ocean, afforded by an ocean general circulation model. Twenty years later, the versatility of ECCO's estimation framework enables the production of global and regional ocean and sea-ice state estimates, that incorporate not only the initial suite of data and its successors, but nearly all data streams available today. New observations include measurements from Argo floats, marine mammal-based hydrography, satellite retrievals of ocean bottom pressure and sea surface salinity, as well as ice-tethered profiled data in polar regions. The framework also produces improved estimates of uncertain inputs, including initial conditions, surface atmospheric state variables, and mixing parameters. The freely available state estimates and related efforts are property-conserving, allowing closed budget calculations that are a requisite to detect, quantify, and understand the evolution of climate-relevant signals, as mandated by the Coupled Model Intercomparison Project Phase 6 (CMIP6) protocol. The solutions can be reproduced by users through provision of the underlying modeling and assimilation machinery. Regional efforts have spun off that offer increased spatial resolution to better resolve relevant processes. Emerging foci of ECCO are on a global sea level changes, in particular contributions from polar ice sheets, and the increased use of biogeochemical and ecosystem data to constrain global cycles of carbon, nitrogen and oxygen. Challenges in the coming decade include provision of uncertainties, informing observing system design, globally increased resolution, and moving toward a coupled Earth system estimation with consistent momentum, heat and freshwater fluxes between the ocean, atmosphere, cryosphere and land.
    Description: Major support for ECCO is provided by NASA's Physical Oceanography program via a contract to JPL/Caltech, with additional support through NASA's Modeling, Analysis and Prediction program, the Cryosphere Science program, and the Computational Modeling and Cyberinfrastructure program. Supplemental funding was obtained throughout the years via standard grants to individual team members from NSF, NOAA, and ONR.
    Keywords: ECCO ; Global ocean inverse modeling ; Optimal state and parameter estimation ; Adjoint method ; Ocean observations ; Coupled Earth system data assimilation ; Ocean reanalysis ; Global ocean circulation
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 7
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    Measuring global ocean heat content to estimate the Earth energy Imbalance (2019)
    Frontiers Media
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    Publication Date: 2022-10-26
    Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Meyssignac, B., Boyer, T., Zhao, Z., Hakuba, M. Z., Landerer, F. W., Stammer, D., Koehl, A., Kato, S., L'Ecuyer, T., Ablain, M., Abraham, J. P., Blazquez, A., Cazenave, A., Church, J. A., Cowley, R., Cheng, L., Domingues, C. M., Giglio, D., Gouretski, V., Ishii, M., Johnson, G. C., Killick, R. E., Legler, D., Llovel, W., Lyman, J., Palmer, M. D., Piotrowicz, S., Purkey, S. G., Roemmich, D., Roca, R., Savita, A., von Schuckmann, K., Speich, S., Stephens, G., Wang, G., Wijffels, S. E., & Zilberman, N. Measuring global ocean heat content to estimate the Earth energy Imbalance. Frontiers in Marine Science, 6, (2019): 432, doi: 10.3389/fmars.2019.00432.
    Description: The energy radiated by the Earth toward space does not compensate the incoming radiation from the Sun leading to a small positive energy imbalance at the top of the atmosphere (0.4–1 Wm–2). This imbalance is coined Earth’s Energy Imbalance (EEI). It is mostly caused by anthropogenic greenhouse gas emissions and is driving the current warming of the planet. Precise monitoring of EEI is critical to assess the current status of climate change and the future evolution of climate. But the monitoring of EEI is challenging as EEI is two orders of magnitude smaller than the radiation fluxes in and out of the Earth system. Over 93% of the excess energy that is gained by the Earth in response to the positive EEI accumulates into the ocean in the form of heat. This accumulation of heat can be tracked with the ocean observing system such that today, the monitoring of Ocean Heat Content (OHC) and its long-term change provide the most efficient approach to estimate EEI. In this community paper we review the current four state-of-the-art methods to estimate global OHC changes and evaluate their relevance to derive EEI estimates on different time scales. These four methods make use of: (1) direct observations of in situ temperature; (2) satellite-based measurements of the ocean surface net heat fluxes; (3) satellite-based estimates of the thermal expansion of the ocean and (4) ocean reanalyses that assimilate observations from both satellite and in situ instruments. For each method we review the potential and the uncertainty of the method to estimate global OHC changes. We also analyze gaps in the current capability of each method and identify ways of progress for the future to fulfill the requirements of EEI monitoring. Achieving the observation of EEI with sufficient accuracy will depend on merging the remote sensing techniques with in situ measurements of key variables as an integral part of the Ocean Observing System.
    Description: GJ was supported by the NOAA Research. MP and RK were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra. JC was partially supported by the Centre for Southern Hemisphere Oceans Research, a joint research centre between QNLM and CSIRO. CD and AS were funded by the Australian Research Council (FT130101532 and DP160103130) and its Centre of Excellence for Climate Extremes (CLEX). IQuOD team members (TB, RC, LC, CD, VG, MI, MP, and SW) were supported by the Scientific Committee on Oceanic Research (SCOR) Working Group 148, funded by the National SCOR Committees and a grant to SCOR from the U.S. National Science Foundation (Grant OCE-1546580), as well as the Intergovernmental Oceanographic Commission of UNESCO/International Oceanographic Data and Information Exchange (IOC/IODE) IQuOD Steering Group. ZZ was supported by the National Aeronautics and Space Administration (NNX17AH14G). LC was supported by the National Key Research and Development Program of China (2017YFA0603200 and 2016YFC1401800).
    Keywords: Ocean heat content ; Sea level ; Ocean mass ; Ocean surface fluxes ; ARGO ; Altimetry ; GRACE ; Earth Energy Imbalance
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 8
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    Towards comprehensive observing and modeling systems for monitoring and predicting regional to coastal sea level (2019)
    Frontiers Media
    Details
    Publication Date: 2022-10-26
    Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ponte, R. M., Carson, M., Cirano, M., Domingues, C. M., Jevrejeva, S., Marcos, M., Mitchum, G., van de Wal, R. S. W., Woodworth, P. L., Ablain, M., Ardhuin, F., Ballu, V., Becker, M., Benveniste, J., Birol, F., Bradshaw, E., Cazenave, A., De Mey-Fremaux, P., Durand, F., Ezer, T., Fu, L., Fukumori, I., Gordon, K., Gravelle, M., Griffies, S. M., Han, W., Hibbert, A., Hughes, C. W., Idier, D., Kourafalou, V. H., Little, C. M., Matthews, A., Melet, A., Merrifield, M., Meyssignac, B., Minobe, S., Penduff, T., Picot, N., Piecuch, C., Ray, R. D., Rickards, L., Santamaria-Gomez, A., Stammer, D., Staneva, J., Testut, L., Thompson, K., Thompson, P., Vignudelli, S., Williams, J., Williams, S. D. P., Woppelmann, G., Zanna, L., & Zhang, X. Towards comprehensive observing and modeling systems for monitoring and predicting regional to coastal sea level. Frontiers in Marine Science, 6, (2019): 437, doi:10.3389/fmars.2019.00437.
    Description: A major challenge for managing impacts and implementing effective mitigation measures and adaptation strategies for coastal zones affected by future sea level (SL) rise is our limited capacity to predict SL change at the coast on relevant spatial and temporal scales. Predicting coastal SL requires the ability to monitor and simulate a multitude of physical processes affecting SL, from local effects of wind waves and river runoff to remote influences of the large-scale ocean circulation on the coast. Here we assess our current understanding of the causes of coastal SL variability on monthly to multi-decadal timescales, including geodetic, oceanographic and atmospheric aspects of the problem, and review available observing systems informing on coastal SL. We also review the ability of existing models and data assimilation systems to estimate coastal SL variations and of atmosphere-ocean global coupled models and related regional downscaling efforts to project future SL changes. We discuss (1) observational gaps and uncertainties, and priorities for the development of an optimal and integrated coastal SL observing system, (2) strategies for advancing model capabilities in forecasting short-term processes and projecting long-term changes affecting coastal SL, and (3) possible future developments of sea level services enabling better connection of scientists and user communities and facilitating assessment and decision making for adaptation to future coastal SL change.
    Description: RP was funded by NASA grant NNH16CT00C. CD was supported by the Australian Research Council (FT130101532 and DP 160103130), the Scientific Committee on Oceanic Research (SCOR) Working Group 148, funded by national SCOR committees and a grant to SCOR from the U.S. National Science Foundation (Grant OCE-1546580), and the Intergovernmental Oceanographic Commission of UNESCO/International Oceanographic Data and Information Exchange (IOC/IODE) IQuOD Steering Group. SJ was supported by the Natural Environmental Research Council under Grant Agreement No. NE/P01517/1 and by the EPSRC NEWTON Fund Sustainable Deltas Programme, Grant Number EP/R024537/1. RvdW received funding from NWO, Grant 866.13.001. WH was supported by NASA (NNX17AI63G and NNX17AH25G). CL was supported by NASA Grant NNH16CT01C. This work is a contribution to the PIRATE project funded by CNES (to TP). PT was supported by the NOAA Research Global Ocean Monitoring and Observing Program through its sponsorship of UHSLC (NA16NMF4320058). JS was supported by EU contract 730030 (call H2020-EO-2016, “CEASELESS”). JW was supported by EU Horizon 2020 Grant 633211, Atlantos.
    Keywords: Coastal sea level ; Sea-level trends ; Coastal ocean modeling ; Coastal impacts ; Coastal adaptation ; Observational gaps ; Integrated observing system
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 9
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    Ocean climate observing requirements in support of climate research and climate information (2019)
    Frontiers Media
    Details
    Publication Date: 2022-10-26
    Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Stammer, D., Bracco, A., AchutaRao, K., Beal, L., Bindoff, N. L., Braconnot, P., Cai, W., Chen, D., Collins, M., Danabasoglu, G., Dewitte, B., Farneti, R., Fox-Kemper, B., Fyfe, J., Griffies, S. M., Jayne, S. R., Lazar, A., Lengaigne, M., Lin, X., Marsland, S., Minobe, S., Monteiro, P. M. S., Robinson, W., Roxy, M. K., Rykaczewski, R. R., Speich, S., Smith, I. J., Solomon, A., Storto, A., Takahashi, K., Toniazzo, T., & Vialard, J. Ocean climate observing requirements in support of climate research and climate information. Frontiers in Marine Science, 6, (2019): 444, doi:10.3389/fmars.2019.00444.
    Description: Natural variability and change of the Earth’s climate have significant global societal impacts. With its large heat and carbon capacity and relatively slow dynamics, the ocean plays an integral role in climate, and provides an important source of predictability at seasonal and longer timescales. In addition, the ocean provides the slowly evolving lower boundary to the atmosphere, driving, and modifying atmospheric weather. Understanding and monitoring ocean climate variability and change, to constrain and initialize models as well as identify model biases for improved climate hindcasting and prediction, requires a scale-sensitive, and long-term observing system. A climate observing system has requirements that significantly differ from, and sometimes are orthogonal to, those of other applications. In general terms, they can be summarized by the simultaneous need for both large spatial and long temporal coverage, and by the accuracy and stability required for detecting the local climate signals. This paper reviews the requirements of a climate observing system in terms of space and time scales, and revisits the question of which parameters such a system should encompass to meet future strategic goals of the World Climate Research Program (WCRP), with emphasis on ocean and sea-ice covered areas. It considers global as well as regional aspects that should be accounted for in designing observing systems in individual basins. Furthermore, the paper discusses which data-driven products are required to meet WCRP research and modeling needs, and ways to obtain them through data synthesis and assimilation approaches. Finally, it addresses the need for scientific capacity building and international collaboration in support of the collection of high-quality measurements over the large spatial scales and long time-scales required for climate research, bridging the scientific rational to the required resources for implementation.
    Description: This work was partly supported by the DFG funded excellence center CliSAP of the Universituat Hamburg (DS). AB was supported by the National Science Foundation through award NSF-1658174 and by the NOAA through award NA16OAR4310173. SM was supported by the Earth Systems and Climate Change Hub of the Australian Government’s National Environmental Science Program.
    Keywords: Ocean observing system ; Ocean climate ; Earth observations ; In situ measurements ; Satellite observations ; Ocean modeling ; Climate information
    Repository Name: Woods Hole Open Access Server
    Type: Article
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