An Air–Sea Coupled Skeleton Model for the Madden–Julian Oscillation*

2013 ◽  
Vol 70 (10) ◽  
pp. 3147-3156 ◽  
Author(s):  
Fei Liu ◽  
Bin Wang
Keyword(s):  
Boundary Layer ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Western Hemisphere ◽  
Sea Surface ◽  
Longitudinal Variation ◽  
Madden Julian Oscillation ◽  
Skeleton Model ◽  
Planetary Scale ◽  
The Mean

Abstract This work is an extension and improvement of the minimal Madden–Julian oscillation (MJO) “skeleton” model developed by Majda and Stechmann, which can capture some important features of the MJO—slow eastward propagation, quadrupole-vortex structure, and independence of frequency on wavelength—but is unable to produce unstable growth and selection of eastward-propagating planetary waves. With the addition of planetary boundary layer frictional moisture convergence, these deficiencies can be remedied. The frictional boundary layer “selects” the planetary-scale eastward propagation as the most unstable mode, but the dynamics remains confined to atmospheric processes only. Here the authors study the role of air–sea interaction by implementing an oceanic mixed-layer (ML) model of Wang and Xie into the MJO skeleton model. In this new air–sea coupled skeleton model, the features of the original skeleton model remain; additionally, the air–sea interaction under mean westerly winds is shown to produce a strong instability that selectively destabilizes the eastward-propagating planetary-scale waves. Although the cloud–shortwave radiation–sea surface temperature (CRS) feedback destabilizes both eastward and westward modes, the air–sea feedback associated with the evaporation and oceanic entrainment favors planetary-scale eastward modes. Over the Western Hemisphere where easterly background winds prevail, the evaporation and entrainment feedbacks yield damped modes, indicating that longitudinal variation of the mean surface winds plays an important role in regulation of the MJO intensity in addition to the longitudinal variation of the mean sea surface temperature or mean moist static stability. This theoretical analysis suggests that accurate simulation of the climatological mean state is critical for capturing the realistic air–sea interaction and thus the MJO.

scholarly journals Impact of the sea surface temperature forcing on hindcasts of Madden-Julian Oscillation events using the ECMWF model

2012 ◽  
Vol 9 (4) ◽  
pp. 2535-2559
Author(s):  
E. de Boisséson ◽  
M. A. Balmaseda ◽  
F. Vitart ◽  
K. Mogensen
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Phase Relationship ◽  
Sea Surface ◽  
Madden Julian Oscillation ◽  
Weather Forecasts ◽  
Skill Scores ◽  
The Mean ◽  
Ecmwf Model ◽  
The Impact

Abstract. This paper explores the sensitivity of the prediction of Madden Julian Oscillation (MJO) events to different aspects of the sea surface temperature (SST) in the European Centre for Medium-range Weather Forecasts (ECMWF) model. The impact of temporal resolution of SST on the MJO is first evaluated via a set of monthly hindcast experiments. The experiments are conducted with an atmosphere forced by persisted SST anomalies, monthly and weekly SSTs. Skill scores are clearly degraded when weekly SSTs are replaced by monthly values or persisted anomalies. The new high resolution OSTIA SST daily reanalysis would in principle allow to establish the impact of daily versus weekly SST values on the MJO prediction. It is found however that OSTIA SSTs provide lower skill scores than the weekly product. Further experiments show that this loss of skill cannot be attributed to either the mean state or the daily frequency of OSTIA SSTs. Additional diagnostics show that the phase relationship between OSTIA SSTs and tropical convection is not optimal with repspect to observations. Such result suggests that capturing the correct SST-convection phase relationship is a major challenge for the MJO predictions.

Modeling the Atmospheric Boundary Layer Wind Response to Mesoscale Sea Surface Temperature Perturbations

2014 ◽  
Vol 142 (11) ◽  
pp. 4284-4307 ◽  
Author(s):  
Natalie Perlin ◽  
Simon P. de Szoeke ◽  
Dudley B. Chelton ◽  
Roger M. Samelson ◽  
Eric D. Skyllingstad ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Coupling Coefficient ◽  
Eddy Viscosity ◽  
Sea Surface ◽  
Coupling Coefficients ◽  
Wind Response ◽  
Speed Response

Abstract The wind speed response to mesoscale SST variability is investigated over the Agulhas Return Current region of the Southern Ocean using the Weather Research and Forecasting (WRF) Model and the U.S. Navy Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) atmospheric model. The SST-induced wind response is assessed from eight simulations with different subgrid-scale vertical mixing parameterizations, validated using Quick Scatterometer (QuikSCAT) winds and satellite-based sea surface temperature (SST) observations on 0.25° grids. The satellite data produce a coupling coefficient of sU = 0.42 m s−1 °C−1 for wind to mesoscale SST perturbations. The eight model configurations produce coupling coefficients varying from 0.31 to 0.56 m s−1 °C−1. Most closely matching QuikSCAT are a WRF simulation with the Grenier–Bretherton–McCaa (GBM) boundary layer mixing scheme (sU = 0.40 m s−1 °C−1), and a COAMPS simulation with a form of Mellor–Yamada parameterization (sU = 0.38 m s−1 °C−1). Model rankings based on coupling coefficients for wind stress, or for curl and divergence of vector winds and wind stress, are similar to that based on sU. In all simulations, the atmospheric potential temperature response to local SST variations decreases gradually with height throughout the boundary layer (0–1.5 km). In contrast, the wind speed response to local SST perturbations decreases rapidly with height to near zero at 150–300 m. The simulated wind speed coupling coefficient is found to correlate well with the height-averaged turbulent eddy viscosity coefficient. The details of the vertical structure of the eddy viscosity depend on both the absolute magnitude of local SST perturbations, and the orientation of the surface wind to the SST gradient.

scholarly journals Satellite-based time-series of sea-surface temperature since 1981 for climate applications

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Christopher J. Merchant ◽  
Owen Embury ◽  
Claire E. Bulgin ◽  
Thomas Block ◽  
Gary K. Corlett ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Ocean Model ◽  
Sea Surface ◽  
Climate Data ◽  
Satellite Measurements ◽  
Data Record ◽  
The Mean ◽  
Climate Data Record

Abstract A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 1012 satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km2 and 45 km2. The mean density of good-quality observations is 13 km−2 yr−1. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr−1 of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics.

What Controls the Mean East–West Sea Surface Temperature Gradient in the Equatorial Pacific: The Role of Cloud Albedo

2014 ◽  
Vol 27 (7) ◽  
pp. 2757-2778 ◽  
Author(s):  
N. J. Burls ◽  
A. V. Fedorov
Keyword(s):  
Temperature Gradient ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Walker Circulation ◽  
Equatorial Pacific ◽  
Sea Surface ◽  
Sea Surface Temperature Gradient ◽  
The Mean ◽  
East West ◽  
Surface Temperature Gradient

Abstract The mean east–west sea surface temperature gradient along the equator is a key feature of tropical climate. Tightly coupled to the atmospheric Walker circulation and the oceanic east–west thermocline tilt, it effectively defines tropical climate conditions. In the Pacific, its presence permits the El Niño–Southern Oscillation phenomenon. What determines this temperature gradient within the fully coupled ocean–atmosphere system is therefore a central question in climate dynamics, critical for understanding past and future climates. Using a comprehensive coupled model [Community Earth System Model (CESM)], the authors demonstrate how the meridional gradient in cloud albedo between the tropics and midlatitudes (Δα) sets the mean east–west sea surface temperature gradient in the equatorial Pacific. To change Δα in the numerical experiments, the authors change the optical properties of clouds by modifying the atmospheric water path, but only in the shortwave radiation scheme of the model. When Δα is varied from approximately −0.15 to 0.1, the east–west SST contrast in the equatorial Pacific reduces from 7.5°C to less than 1°C and the Walker circulation nearly collapses. These experiments reveal a near-linear dependence between Δα and the zonal temperature gradient, which generally agrees with results from the Coupled Model Intercomparison Project phase 5 (CMIP5) preindustrial control simulations. The authors explain the close relation between the two variables using an energy balance model incorporating the essential dynamics of the warm pool, cold tongue, and Walker circulation complex.

Diversity of the Madden-Julian Oscillation

2020 ◽  
Author(s):  
Guosen Chen ◽  
Bin Wang ◽  
Fei Liu
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Dominant Mode ◽  
Sea Surface ◽  
Extreme Weather Events ◽  
Madden Julian Oscillation ◽  
Kelvin Wave ◽  
Tight Coupling ◽  
Zonal Scale ◽  
Wave Response

<p>Madden-Julian Oscillation (MJO) is the dominant mode of atmospheric intraseasonal variability and the cornerstone for subseasonal prediction of extreme weather events. Climate modeling and prediction of MJO remain a big challenge, partially due to lack of understanding the MJO diversity. Here, we delineate observed MJO diversity by cluster analysis of propagation patterns of MJO events, which reveals four archetypes: standing, jumping, slow eastward propagation, and fast eastward propagation. Each type of MJO exhibits distinctive east-west asymmetric circulation and thermodynamic structures. Tight coupling between the Kelvin wave response and major convection is unique for the propagating events (slow and fast propagations), while the strength and length of Kelvin wave response distinguish slow and fast propagations. The Pacific sea surface temperature anomalies can affect MJO diversity by modifying the Kelvin wave response and its coupling to MJO convection. An El Niño state tends to increase the zonal scale of Kelvin wave response, to amplify it, and to enhance its coupling to the convection, while a La Niña state tends to decrease the zonal scale of Kelvin wave response, to suppress it, and to weaken its coupling to the major convection. This effect of background sea surface temperature on the MJO diversity has been verified by using a theoretical model. The results shed light on the mechanisms responsible for MJO diversity and provide potential precursors for foreseeing MJO propagation.</p>

scholarly journals Mean Biases, Variability, and Trends in Air–Sea Fluxes and Sea Surface Temperature in the CCSM4

2012 ◽  
Vol 25 (22) ◽  
pp. 7781-7801 ◽  
Author(s):  
Susan C. Bates ◽  
Baylor Fox-Kemper ◽  
Steven R. Jayne ◽  
William G. Large ◽  
Samantha Stevenson ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Time Scales ◽  
Hydrological Cycle ◽  
Sea Surface ◽  
Specific Humidity ◽  
Shortwave Radiation ◽  
Climate System Model ◽  
Frequency Bands

Abstract Air–sea fluxes from the Community Climate System Model version 4 (CCSM4) are compared with the Coordinated Ocean-Ice Reference Experiment (CORE) dataset to assess present-day mean biases, variability errors, and late twentieth-century trend differences. CCSM4 is improved over the previous version, CCSM3, in both air–sea heat and freshwater fluxes in some regions; however, a large increase in net shortwave radiation into the ocean may contribute to an enhanced hydrological cycle. The authors provide a new baseline for assessment of flux variance at annual and interannual frequency bands in future model versions and contribute a new metric for assessing the coupling between the atmospheric and oceanic planetary boundary layer (PBL) schemes of any climate model. Maps of the ratio of CCSM4 variance to CORE reveal that variance on annual time scales has larger error than on interannual time scales and that different processes cause errors in mean, annual, and interannual frequency bands. Air temperature and specific humidity in the CCSM4 atmospheric boundary layer (ABL) follow the sea surface conditions much more closely than is found in CORE. Sensible and latent heat fluxes are less of a negative feedback to sea surface temperature warming in the CCSM4 than in the CORE data with the model’s PBL allowing for more heating of the ocean’s surface.

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