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Start Over Author "Keenlyside N." Publisher american meteorological society
10 results on '"Keenlyside N."'

1. Propagation of Thermohaline Anomalies and Their Predictive Potential along the Atlantic Water Pathway

Author

Langehaug, H. R., primary, Ortega, P., additional, Counillon, F., additional, Matei, D., additional, Maroon, E., additional, Keenlyside, N., additional, Mignot, J., additional, Wang, Y., additional, Swingedouw, D., additional, Bethke, I., additional, Yang, S., additional, Danabasoglu, G., additional, Bellucci, A., additional, Ruggieri, P., additional, Nicolì, D., additional, and Årthun, M., additional

Published
2022
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2. Development of a European multimodel ensemble system for seasonal-to-interannual prediction (demeter)

Author

Palmer, T. N., Alessandri, A., Andersen, U., Cantelaube, P., Davey, M., Delecluse, P., Deque, M., Diez, E., Doblas-Reyes, F.J., Feddersen, H., Graham, R., Gualdi, S., Gueremy, J.-F., Hagedorn, R., Hoshen, M., Keenlyside, N., Latif, M., Lazar, A., Maisonnave, E., Marletto, V., Morse, A.P., Orfila, B., Rogel, P., Terres, J.-M., and Thomson, M.C.

Subjects
Meteorology -- Research, Business, Earth sciences
Abstract

A multi-model ensemble-based system for seasonal-to-interannual prediction has been developed in a joint European project known as DEMETER (Development of a European Multimodel Ensemble Prediction System for Seasonal to Inter-annual Prediction). The DEMETER system comprises seven global atmosphere-ocean coupled models, each running from an ensemble of initial conditions. Comprehensive hindcast evaluation demonstrates the enhanced reliability and skill of the multimodel ensemble over a more conventional single-model ensemble approach. In addition, innovative examples of the application of seasonal ensemble forecasts in malaria and crop yield prediction are discussed. The strategy followed in DEMETER deals with important problems such as communication across disciplines, downscaling of climate simulations, and use of probabilistic forecast information in the applications sector, illustrating the economic value of seasonal-to-interannual prediction for society as a whole.

Published
2004

3. Decadal Climate Prediction: An Update from the Trenches

Author

Meehl, G., Goddard, L., Kirtman, B., Branstator, G., Danabasoglu, G., Hawkins, E., Kumar, A., Rosati, T., Smith, D., Sutton, R., Boer, G., Burgman, R., Carson, C., Corti, S., Karspeck, A., Keenlyside, N., Kimoto, M., Matei, D., https://orcid.org/0000-0002-3735-8802, Mignot, J., Msadek, R., Navarra, A., Pohlmann, H., Rienecker, M., Schneider, E., Tebaldi, C., Teng, H., van Oldenborgh, G., Vecchi, G., Yeager, S., National Center for Atmospheric Research [Boulder] ( NCAR ), International Research Institute for Climate and Society ( IRI ), Earth Institute at Columbia University, Columbia University [New York]-Columbia University [New York], CERFACS [Toulouse], Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ), Department of Neurological Sciences, University of Milan, Institut Català de Ciències del Clima, Institució Catalana de Recerca i Estudis Avançats ( ICREA ), Department of Meteorology [Reading], University of Reading ( UOR ), Laboratoire Interdisciplinaire Carnot de Bourgogne ( LICB ), Université de Bourgogne ( UB ) -Centre National de la Recherche Scientifique ( CNRS ), Max Planck Institute for Meteorology ( MPI-M ), Processus de la variabilité climatique tropicale et impacts ( PARVATI ), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques ( LOCEAN ), Muséum National d'Histoire Naturelle ( MNHN ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Muséum National d'Histoire Naturelle ( MNHN ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ), NOAA Geophysical Fluid Dynamics Laboratory ( GFDL ), National Oceanic and Atmospheric Administration ( NOAA ), Dipartimento di Matematica e Informatica [Perugia] ( DMI ), Università degli Studi di Perugia ( UNIPG ), NASA Goddard Space Flight Center ( GSFC ), Espaces et Sociétés ( ESO ), Université de Caen Normandie ( UNICAEN ), Normandie Université ( NU ) -Normandie Université ( NU ) -Le Mans Université ( UM ) -Université d'Angers ( UA ) -AGROCAMPUS OUEST-Université de Rennes 2 ( UR2 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut de Géographie et d'Aménagement ( IGARUN ), Université de Nantes ( UN ) -Université de Nantes ( UN ) -Centre National de la Recherche Scientifique ( CNRS ), Royal Netherlands Meteorological Institute ( KNMI ), National Center for Atmospheric Research [Boulder] (NCAR), International Research Institute for Climate and Society (IRI), Canadian Centre for Climate Modelling and Analysis (CCCma), Environment and Climate Change Canada, Florida International University [Miami] (FIU), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institució Catalana de Recerca i Estudis Avançats (ICREA), University of Reading (UOR), The University of Tokyo (UTokyo), Laboratoire Interdisciplinaire Carnot de Bourgogne (LICB), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS), Max Planck Institute for Meteorology (MPI-M), Max-Planck-Gesellschaft, Processus de la variabilité climatique tropicale et impacts (PARVATI), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), NOAA Geophysical Fluid Dynamics Laboratory (GFDL), National Oceanic and Atmospheric Administration (NOAA), Dipartimento di Matematica e Informatica [Perugia] (DMI), Università degli Studi di Perugia (UNIPG), NASA Goddard Space Flight Center (GSFC), Espaces et Sociétés (ESO), Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université d'Angers (UA)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Le Mans Université (UM), Met Office Hadley Centre for Climate Change (MOHC), United Kingdom Met Office [Exeter], National Centre for Atmospheric Science, University of Reading, Reading, United Kingdom, Royal Netherlands Meteorological Institute (KNMI), Meehl GA, Goddard L, Boer G, Burgman R, Branstator G, Cassou C, Corti S, Danabasoglu G, Doblas-Reyes F, Hawkins E, Karspeck A, Kimoto M, Kumar A, Matei D, Mignot J, Msadek R, Navarra A, Pohlmann H, Rienecker M, Rosati T, Schneider E, Smith D, Sutton R, Teng HY, van Oldenborgh GJ, Vecchi G, and Yeager S

Subjects
Atmospheric Science, 010504 meteorology & atmospheric sciences, Atmosphere, Climate system, Initialization, [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], Forcing (mathematics), Hiatus, 010502 geochemistry & geophysics, 01 natural sciences, [ PHYS.PHYS.PHYS-GEO-PH ] Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], Climatic changes--Simulation methods, Climatic changes--Forecasting, 13. Climate action, Climatology, Environmental science, decadal predictions, climate, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences
Abstract

This paper provides an update on research in the relatively new and fast-moving field of decadal climate prediction, and addresses the use of decadal climate predictions not only for potential users of such information but also for improving our understanding of processes in the climate system. External forcing influences the predictions throughout, but their contributions to predictive skill become dominant after most of the improved skill from initialization with observations vanishes after about 6–9 years. Recent multimodel results suggest that there is relatively more decadal predictive skill in the North Atlantic, western Pacific, and Indian Oceans than in other regions of the world oceans. Aspects of decadal variability of SSTs, like the mid-1970s shift in the Pacific, the mid-1990s shift in the northern North Atlantic and western Pacific, and the early-2000s hiatus, are better represented in initialized hindcasts compared to uninitialized simulations. There is evidence of higher skill in initialized multimodel ensemble decadal hindcasts than in single model results, with multimodel initialized predictions for near-term climate showing somewhat less global warming than uninitialized simulations. Some decadal hindcasts have shown statistically reliable predictions of surface temperature over various land and ocean regions for lead times of up to 6–9 years, but this needs to be investigated in a wider set of models. As in the early days of El Niño–Southern Oscillation (ENSO) prediction, improvements to models will reduce the need for bias adjustment, and increase the reliability, and thus usefulness, of decadal climate predictions in the future.

Published
2014

9. Mitigating Climate Biases in the Midlatitude North Atlantic by Increasing Model Resolution: SST Gradients and Their Relation to Blocking and the Jet

Author

Panos J. Athanasiadis, Fumiaki Ogawa, Nour-Eddine Omrani, Noel Keenlyside, Reinhard Schiemann, Alexander J. Baker, Pier Luigi Vidale, Alessio Bellucci, Paolo Ruggieri, Rein Haarsma, Malcolm Roberts, Chris Roberts, Lenka Novak, Silvio Gualdi, Athanasiadis P.J., Ogawa F., Omrani N.-E., Keenlyside N., Schiemann R., Baker A.J., Vidale P.L., Bellucci A., Ruggieri P., Haarsma R., Roberts M., Roberts C., Novak L., and Gualdi S.

Subjects
Blocking, Atmosphere-ocean interaction, North Atlantic Ocean, Atmospheric Science, Model error, Surface fluxes, Sea surface temperature
Abstract

Starting to resolve the oceanic mesoscale in climate models is a step change in model fidelity. This study examines how certain obstinate biases in the midlatitude North Atlantic respond to increasing resolution (from 1° to 0.25° in the ocean) and how such biases in sea surface temperature (SST) affect the atmosphere. Using a multimodel ensemble of historical climate simulations run at different horizontal resolutions, it is shown that a severe cold SST bias in the central North Atlantic, common to many ocean models, is significantly reduced with increasing resolution. The associated bias in the time-mean meridional SST gradient is shown to relate to a positive bias in low-level baroclinicity, while the cold SST bias causes biases also in static stability and diabatic heating in the interior of the atmosphere. The changes in baroclinicity and diabatic heating brought by increasing resolution lead to improvements in European blocking and eddy-driven jet variability. Across the multimodel ensemble a clear relationship is found between the climatological meridional SST gradients in the broader Gulf Stream Extension area and two aspects of the atmospheric circulation: the frequency of high-latitude blocking and the southern-jet regime. This relationship is thought to reflect the two-way interaction (with a positive feedback) between the respective oceanic and atmospheric anomalies. These North Atlantic SST anomalies are shown to be important in forcing significant responses in the midlatitude atmospheric circulation, including jet variability and the storm track. Further increases in oceanic and atmospheric resolution are expected to lead to additional improvements in the representation of Euro-Atlantic climate.

Published
2022

10. Decadal Prediction

Author

Doug Smith, David J. Karoly, Keith W. Dixon, Ben P. Kirtman, Masahide Kimoto, Ed Hawkins, Timothy N. Stockdale, Gerald A. Meehl, Arthur M. Greene, Noel Keenlyside, Gokhan Danabasoglu, Lisa Goddard, James M. Murphy, Gabriele C. Hegerl, Detlef Stammer, Antonio Navarra, Roger S. Pulwarty, Ronald J. Stouffer, Marco Giorgetta, George J. Boer, Meehl GA, Goddard L, Murphy J, Stouffer RJ, Boer G, Danabasoglu G, Dixon K, Giorgetta MA, Greene AM, Hawkins E, Hegerl G, Karoly D, Keenlyside N, Kimoto M, Kirtman B, Navarra A, Pulwarty R, Smith D, Stammer D, and Stockdale T

Subjects
Atmospheric Science, 010504 meteorology & atmospheric sciences, Meteorology, 0207 environmental engineering, Climate commitment, Forecast skill, Climate change, SEA-LEVEL RISE, 02 engineering and technology, 01 natural sciences, MULTIDECADAL VARIABILITY, SURFACE-TEMPERATURE TRENDS, GLOBAL CLIMATE MODEL, IPCC Fifth Assessment Report, GENERAL-CIRCULATION MODEL, INTERDECADAL VARIATIONS, Predictability, 020701 environmental engineering, decadal predictions, MULTIMODEL-ENSEMBLE, 0105 earth and related environmental sciences, THERMOHALINE CIRCULATION, Transient climate simulation, NORTH-ATLANTIC, 13. Climate action, Climatology, Greenhouse gas, Environmental science, Climate model, SOLAR-CYCLE VARIABILITY
Abstract

A new field of study, "decadal prediction," is emerging in climate science. Decadal prediction lies between seasonal/interannual forecasting and longer-term climate change projections, and focuses on time-evolving regional climate conditions over the next 10-30 yr. Numerous assessments of climate information user needs have identified this time scale as being important to infrastructure planners, water resource managers, and many others. It is central to the information portfolio required to adapt effectively to and through climatic changes. At least three factors influence time-evolving regional climate at the decadal time scale: 1) climate change commitment (further warming as the coupled climate system comes into adjustment with increases of greenhouse gases that have already occurred), 2) external forcing, particularly from future increases of greenhouse gases and recovery of the ozone hole, and 3) internally generated variability. Some decadal prediction skill has been demonstrated to arise from the first two of these factors, and there is evidence that initialized coupled climate models can capture mechanisms of internally generated decadal climate variations, thus increasing predictive skill globally and particularly regionally. Several methods have been proposed for initializing global coupled climate models for decadal predictions, all of which involve global time-evolving threedimensional ocean data, including temperature and salinity. An experimental framework to address decadal predictability/prediction is described in this paper and has been incorporated into the coordinated Coupled Model Intercomparison Model, phase 5 (CMIP5) experiments, some of which will be assessed for the IPCC Fifth Assessment Report (AR5). These experiments will likely guide work in this emerging field over the next 5 yr.

Published
2009

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