scholarly journals Moist Process Biases in Simulations of the Madden–Julian Oscillation Episodes Observed during the AMIE/DYNAMO Field Campaign

2016 ◽  
Vol 29 (3) ◽  
pp. 1091-1107 ◽  
Author(s):  
Samson M. Hagos ◽  
Zhe Feng ◽  
Casey D. Burleyson ◽  
Chun Zhao ◽  
Matus N. Martini ◽  
...  
Keyword(s):  
Statistical Model ◽  
Radiative Forcing ◽  
Surface Wind ◽  
Tropical Indian Ocean ◽  
Ocean Model ◽  
Madden Julian Oscillation ◽  
Model Errors ◽  
Field Campaign ◽  
Linear Statistical Model ◽  
Precipitation Bias

Abstract Two Madden–Julian oscillation (MJO) episodes observed during the 2011 Atmospheric Radiation Measurement Program MJO Investigation Experiment (AMIE)/DYNAMO field campaign are simulated using a regional model with various cumulus parameterizations, a regional cloud-permitting model, and a global variable-resolution model with a high-resolution region centered over the tropical Indian Ocean. Model biases in relationships relevant to existing instability theories of MJO are examined and their relative contributions to the overall model errors are quantified using a linear statistical model. The model simulations capture the observed approximately log-linear relationship between moisture saturation fraction and precipitation, but precipitation associated with the given saturation fraction is overestimated especially at low saturation fraction values. This bias is a major contributor to the excessive precipitation during the suppressed phase of MJO. After accounting for this bias using a linear statistical model, the spatial and temporal structures of the model-simulated MJO episodes are much improved, and what remains of the biases is strongly correlated with biases in saturation fraction. The excess precipitation bias during the suppressed phase of the MJO episodes is accompanied by excessive column-integrated radiative forcing and surface evaporation. A large portion of the bias in evaporation is related to biases in wind speed, which are correlated with those of precipitation. These findings suggest that the precipitation bias sustains itself at least partly by cloud radiative feedbacks and convection–surface wind interactions.

Coupled Ocean–Atmosphere Interactions between the Madden–Julian Oscillation and Synoptic-Scale Variability over the Warm Pool

2005 ◽  
Vol 18 (12) ◽  
pp. 2004-2020 ◽  
Author(s):  
Crispian P. Batstone ◽  
Adrian J. Matthews ◽  
David P. Stevens
Keyword(s):  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Surface Wind ◽  
Principal Component ◽  
Ocean Model ◽  
Dominant Mode ◽  
Madden Julian Oscillation ◽  
Wind Variability ◽  
Eastern Hemisphere

Abstract A principal component analysis of the combined fields of sea surface temperature (SST) and surface zonal and meridional wind reveals that the dominant mode of intraseasonal (30 to 70 day) covariability during northern winter in the tropical Eastern Hemisphere is that of the Madden–Julian oscillation (MJO). Regression calculations show that the submonthly (30-day high-pass filtered) surface wind variability is significantly modulated during the MJO. Regions of increased (decreased) submonthly surface wind variability propagate eastward, approximately in phase with the intraseasonal surface westerly (easterly) anomalies of the MJO. Because of the dependence of the surface latent heat flux on the magnitude of the total wind speed, this systematic modulation of the submonthly surface wind variability produces a significant component in the intraseasonal latent heat flux anomalies, which partially cancels the latent heat flux anomalies due to the slowly varying intraseasonal wind anomalies, particularly south of 10°S. A method is derived that demodulates the submonthly surface wind variability from the slowly varying intraseasonal wind anomalies. This method is applied to the wind forcing fields of a one-dimensional ocean model. The model response to this modified forcing produces larger intraseasonal SST anomalies than when the model is forced with the observed forcing over large areas of the southwest Pacific Ocean and southeast Indian Ocean during both phases of the MJO. This result has implications for accurate coupled modeling of the MJO. A similar calculation is applied to the surface shortwave flux, but intraseasonal modulation of submonthly surface shortwave flux variability does not appear to be important to the dynamics of the MJO.

scholarly journals The effect of coastal upwelling on the sea-breeze circulation at Cabo Frio, Brazil: a numerical experiment

1998 ◽  
Vol 16 (7) ◽  
pp. 866-871 ◽  
Author(s):  
S. H. Franchito ◽  
V. B. Rao ◽  
J. L. Stech ◽  
J. A. Lorenzzetti
Keyword(s):  
Coastal Upwelling ◽  
Surface Wind ◽  
Three Dimensional ◽  
Sea Breeze ◽  
Atmospheric Model ◽  
Ocean Model ◽  
Breeze Circulation ◽  
Mesoscale Meteorology ◽  
Forcing Function ◽  
Cabo Frio

Abstract. The effect of coastal upwelling on sea-breeze circulation in Cabo Frio (Brazil) and the feedback of sea-breeze on the upwelling signal in this region are investigated. In order to study the effect of coastal upwelling on sea-breeze a non-linear, three-dimensional, primitive equation atmospheric model is employed. The model considers only dry air and employs boundary layer formulation. The surface temperature is determined by a forcing function applied to the Earth's surface. In order to investigate the seasonal variations of the circulation, numerical experiments considering three-month means are conducted: January-February-March (JFM), April-May-June (AMJ), July-August-September (JAS) and October-November-December (OND). The model results show that the sea-breeze is most intense near the coast at all the seasons. The sea-breeze is stronger in OND and JFM, when the upwelling occurs, and weaker in AMJ and JAS, when there is no upwelling. Numerical simulations also show that when the upwelling occurs the sea-breeze develops and attains maximum intensity earlier than when it does not occur. Observations show a similar behavior. In order to verify the effect of the sea-breeze surface wind on the upwelling, a two-layer finite element ocean model is also implemented. The results of simulations using this model, forced by the wind generated in the sea-breeze model, show that the sea-breeze effectively enhances the upwelling signal.Key words. Meteorology and atmospheric dynamics (mesoscale meteorology; ocean-atmosphere interactions) · Oceanography (numerical modeling)

scholarly journals The Linear Response of ENSO to the Madden–Julian Oscillation

2005 ◽  
Vol 18 (13) ◽  
pp. 2441-2459 ◽  
Author(s):  
J. Zavala-Garay ◽  
C. Zhang ◽  
A. M. Moore ◽  
R. Kleeman
Keyword(s):  
Large Fraction ◽  
Surface Wind ◽  
Low Frequency ◽  
Coupled Model ◽  
Cumulative Effect ◽  
Enso Event ◽  
Kelvin Waves ◽  
Madden Julian Oscillation ◽  
Coupled Models ◽  
Stochastic Forcing

Abstract The possibility that the tropical Pacific coupled system linearly amplifies perturbations produced by the Madden–Julian oscillation (MJO) is explored. This requires an estimate of the low-frequency tail of the MJO. Using 23 yr of NCEP–NCAR reanalyses of surface wind and Reynolds SST, we show that the spatial structure that dominates the intraseasonal band (i.e., the MJO) also dominates the low-frequency band once the anomalies directly related to ENSO have been removed. This low-frequency contribution of the intraseasonal variability is not included in most ENSO coupled models used to date. Its effect in a coupled model of intermediate complexity has, therefore, been studied. It is found that this “MJO forcing” (τMJO) can explain a large fraction of the interannual variability in an asymptotically stable version of the model. This interaction is achieved via linear dynamics. That is, it is the cumulative effect of individual events that maintains ENSOs in this model. The largest coupled wind anomalies are initiated after a sequence of several downwelling Kelvin waves of the same sign have been forced by τMJO. The cumulative effect of the forced Kelvin waves is to persist the (small) SST anomalies in the eastern Pacific just enough for the coupled ocean–atmosphere dynamics to amplify the anomalies into a mature ENSO event. Even though τMJO explains just a small fraction of the energy contained in the stress not associated with ENSO, a large fraction of the modeled ENSO variability is excited by this forcing. The characteristics that make τMJO an optimal stochastic forcing for the model are discussed. The large zonal extent is an important factor that differentiates the MJO from other sources of stochastic forcing.

scholarly journals Improving subtropical boundary layer cloudiness in the 2011 NCEP GFS

2014 ◽  
Vol 7 (2) ◽  
pp. 2249-2291 ◽  
Author(s):  
J. K. Fletcher ◽  
C. S. Bretherton ◽  
H. Xiao ◽  
R. Sun ◽  
J. Han
Keyword(s):  
Boundary Layer ◽  
Radiative Forcing ◽  
Current Knowledge ◽  
Ocean Model ◽  
Climate Modelling ◽  
Shallow Convection ◽  
Shallow Cumulus ◽  
Single Column ◽  
Low Cloud ◽  
Environmental Prediction

Abstract. The current operational version of National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) shows significant low cloud bias. These biases also appear in the Coupled Forecast System (CFS), which is developed from the GFS. These low cloud biases degrade seasonal and longer climate forecasts, particularly of shortwave cloud radiative forcing, and affect predicted sea-surface temperature. Reducing this bias in the GFS will aid the development of future CFS versions and contributes to NCEP's goal of unified weather and climate modelling. Changes are made to the shallow convection and planetary boundary layer parametrisations to make them more consistent with current knowledge of these processes and to reduce the low cloud bias. These changes are tested in a single-column version of GFS and in global simulations with GFS coupled to a dynamical ocean model. In the single column model, we focus on changing parameters that set the following: the strength of shallow cumulus lateral entrainment, the conversion of updraught liquid water to precipitation and grid-scale condensate, shallow cumulus cloud top, and the effect of shallow convection in stratocumulus environments. Results show that these changes improve the single-column simulations when compared to large eddy simulations, in particular through decreasing the precipitation efficiency of boundary layer clouds. These changes, combined with a few other model improvements, also reduce boundary layer cloud and albedo biases in global coupled simulations.

scholarly journals Physical-biogeochemical regional ocean model uncertainties stemming from stochastic parameterizations and potential impact on data assimilation

2019 ◽  
Author(s):  
Vassilios D. Vervatis ◽  
Pierre De Mey-Frémaux ◽  
Nadia Ayoub ◽  
Sarantis Sofianos ◽  
Charles-Emmanuel Testut ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Spatial Scales ◽  
Ecosystem Model ◽  
Ocean Model ◽  
Open Ocean ◽  
Small Scale ◽  
Biogeochemical Model ◽  
Frequency Error ◽  
Model Errors ◽  
Potential Impact

Abstract. We generate ocean biogeochemical model ensembles including several kinds of stochastic parameterizations. The NEMO stochastic modules are complemented by integrating a subroutine to calculate variable anisotropic spatial scales, which are of particular importance in high-resolution coastal configurations. The domain covers the Bay of Biscay at 1/36° resolution, as a case study for open-ocean and coastal shelf dynamics. At first, we identify uncertainties from assumptions subject to erroneous atmospheric forcing, ocean model improper parameterizations and ecosystem state uncertainties. The error regimes are found to be mainly driven by the wind forcing, with the rest of the perturbed tendencies locally augmenting the ensemble spread. Biogeochemical uncertainties arise from inborn ecosystem model errors and from errors in the physical state. Model errors in physics are found to have larger impact on chlorophyll spread than those of the ecosystem. In a second step, the ensembles undergo verification with respect to observations, focusing on upper-ocean properties. We investigate the statistical consistency of prior model errors and observation estimates, in view of joint uncertainty vicinities, associated with both sources of information. OSTIA-SST L4 distribution appears to be compatible with ensembles perturbing physics, since vicinities overlap, enabling data assimilation. The most consistent configuration for SLA along-track L3 data is in the Abyssal plain, where the spread is increased due to mesoscale eddy decorrelation. The largest statistical SLA biases are observed in coastal regions, sometimes to the point that vicinities become disjoint. Missing error processes in relation to SLA hint at the presence of high-frequency error sources currently unaccounted for, potentially leading to ill-posed assimilation problems. Ecosystem model-data samples with respect to Ocean Colour L4 appear to be compatible with each other only at times, with data assimilation being marginally well-posed. In a third step, we illustrate the potential influence of those uncertainties on data assimilation impact exercise, by means of multivariate representers and EnKF-type incremental analysis for a few members. Corrections on physical properties are associated with large-scale biases between model and data, with diverse characteristics in the open-ocean and the shelves. The increments are often characteristic of the underlying mesoscale features, chlorophyll included due to the vertical velocity field. Small scale local corrections are visible over the shelves. Chlorophyll information seems to have a very measurable potential impact on physical variables.

scholarly journals A Study of the Interaction between Typhoon Francisco (2013) and a Cold-Core Eddy. Part I: Rapid Weakening

2020 ◽  
Vol 77 (1) ◽  
pp. 355-377 ◽  
Author(s):  
Zhanhong Ma
Keyword(s):  
Structural Changes ◽  
Surface Wind ◽  
Wrf Model ◽  
Ocean Model ◽  
Atmospheric Environment ◽  
Weather Research And Forecasting ◽  
Eye Size ◽  
Convective Bursts ◽  
Cold Core

Abstract Typhoon Francisco (2013) experienced unusually rapid weakening (RW) with its maximum surface wind decreasing by 45 kt (1 kt ≈ 0.51 m s−1) over 24 h as measured from the satellite-derived advanced Dvorak technique (ADT) dataset, which is more than twice the weakening rate defined as RW by DeMaria. The mechanisms leading to the extreme RW event of Francisco are examined based on observational analysis and simulations by coupling the Weather Research and Forecasting (WRF) Model, version 3.7, with the Stony Brook Parallel Ocean Model (sbPOM). The RW of Francisco took place in a relatively favorable atmospheric environment while passing over detrimental oceanic conditions, dominated by the presence of a cold-core eddy. The passages of two prior typhoons apparently intensified the cold-core eddy, contributing to a major role of eddy feedback on RW for Francisco. The structural changes in Francisco accompanying eddy interaction are characterized by a substantially enlarged eye size, which evolved from ~20 to ~100 km in diameter, as indicated from satellite images. Numerical simulations suggest that the eddy is prominent in weakening the intensity of Francisco during the storm–eddy interaction, with its role less significant but still comparable to that of the cold wake. Both the cooler water and stronger upward motion in the eddy lead to a larger sea surface temperature decrease induced by Francisco, which results in a nearly 50% decrease of surface enthalpy flux, suppressed convective bursts, and a 50% reduction in latent heat release. These results underscore the potential importance of open-ocean, cold-core eddies in contributing to the RW of tropical cyclones.

scholarly journals Vertical Structure and Radiative Forcing of Monsoon Clouds Over Kanpur During the 2016 INCOMPASS Field Campaign

2018 ◽  
Vol 123 (4) ◽  
pp. 2152-2174 ◽  
Author(s):  
Geet George ◽  
Chandan Sarangi ◽  
Sachchida Nand Tripathi ◽  
Tirthankar Chakraborty ◽  
Andrew Turner
Keyword(s):  
Radiative Forcing ◽  
Vertical Structure ◽  
Field Campaign

scholarly journals Impacts of atmospheric and oceanic initial conditions on boreal summer intraseasonal oscillation forecast in the BCC model

2020 ◽  
Vol 142 (1-2) ◽  
pp. 393-406
Author(s):  
Zhongkai Bo ◽  
Xiangwen Liu ◽  
Weizong Gu ◽  
Anning Huang ◽  
Yongjie Fang ◽  
...  
Keyword(s):  
Indian Ocean ◽  
North Pacific ◽  
Western North Pacific ◽  
Initial Conditions ◽  
Intraseasonal Oscillation ◽  
Tropical Indian Ocean ◽  
Boreal Summer ◽  
Model Errors ◽  
Maritime Continent ◽  
Boreal Summer Intraseasonal Oscillation

Abstract In this paper, we evaluate the capability of the Beijing Climate Center Climate System Model (BCC-CSM) in simulating and forecasting the boreal summer intraseasonal oscillation (BSISO), using its simulation and sub-seasonal to seasonal (S2S) hindcast results. Results show that the model can generally simulate the spatial structure of the BSISO, but give relatively weaker strength, shorter period, and faster transition of BSISO phases when compared with the observations. This partially limits the model’s capability in forecasting the BSISO, with a useful skill of only 9 days. Two sets of hindcast experiments with improved atmospheric and atmosphere/ocean initial conditions (referred to as EXP1 and EXP2, respectively) are conducted to improve the BSISO forecast. The BSISO forecast skill is increased by 2 days with the optimization of atmospheric initial conditions only (EXP1), and is further increased by 1 day with the optimization of both atmospheric and oceanic initial conditions (EXP2). These changes lead to a final skill of 12 days, which is comparable to the skills of most models participated in the S2S Prediction Project. In EXP1 and EXP2, the BSISO forecast skills are improved for most initial phases, especially phases 1 and 2, denoting a better description for BSISO propagation from the tropical Indian Ocean to the western North Pacific. However, the skill is considerably low and insensitive to initial conditions for initial phase 6 and target phase 3, corresponding to the BSISO convection’s active-to-break transition over the western North Pacific and BSISO convection’s break-to-active transition over the tropical Indian Ocean and Maritime Continent. This prediction barrier also exists in many forecast models of the S2S Prediction Project. Our hindcast experiments with different initial conditions indicate that the remarkable model errors over the Maritime Continent and subtropical western North Pacific may largely account for the prediction barrier.

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