Prediction of the diurnal warming of sea surface temperature using an atmosphere‐ocean mixed layer coupled model (original) (raw)

A New Method to Produce Sea Surface Temperature Using Satellite Data Assimilation into an Atmosphere–Ocean Mixed Layer Coupled Model

Journal of Atmospheric and Oceanic Technology, 2013

A new method of producing sea surface temperature (SST) data for numerical weather prediction is suggested, which is obtained from the assimilation of satellite-derived SST into an atmosphere–ocean mixed layer coupled model. The Weather Research and Forecasting (WRF) Model and the Noh mixed layer model are used for the atmosphere and ocean mixed layer models, respectively. Data assimilation (DA) is carried out in two steps, based on the estimation from the covariance matching method that the daily mean SST of satellite data is more accurate than the model data, if the number of data in a grid per day is sufficiently large—that is, the daily mean SST bias correction in the first DA and the sequential SST anomaly correction in the second DA. For the second DA, the model restarts from the initial condition corrected by the first DA, and DA is applied every 30 min using the nudging method. The daily mean and the diurnal variation of satellite SST are assimilated to the bulk and skin SST...

Processes that influence sea surface temperature and ocean mixed layer depth variability in a coupled model

Journal of Geophysical Research, 2000

A 50-year coupled atmosphere-ocean model integration is used to study sea surface temperature (SST) and mixed layer depth (h), and the processes which influence them. The model consists of an atmospheric general circulation model coupled to an ocean mixed layer model in ice-free regions. The midlatitude SST variability is simulated fairly well, although the maximum variance is underestimated and located farther south than observed. The model is clearly deficient in the vicinity of the Gulf Stream and in the eastern tropical Pacific where advective processes are important. The model generally reproduces the observed structure of the mean h in both March and September but underestimates it in the North Atlantic during winter.

Diurnally Varying Wind Forcing and Upper Ocean Temperature: Implications for the Ocean Mixed Layer

Solar radiation varies on a diurnal cycle, and therefore so do all the climate variables that it forces, including sea surface temperature (SST), wind, and in turn mixed-layer depth and upper- oceanheatstorage. SatellitescatterometerdatafromtheQuikSCAT and ADEOS-2 tandem mission have been used to estimate the am- plitude and phasing of diurnal wind variations on a global basis. Statistically significant diurnal wind variations occur along coast- lines all over the world, where they are commonly thought of as the land/sea breeze. Open ocean winds also undergo substantial diurnal variability at latitudes equatorward of 30 latitude. The phasing of diurnal winds varies with distance from the shore. Up- per ocean temperatures measured from profiling Argo floats are compared with microwave SSTs from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) to estimate the amplitude and phasing of the diurnal cycle in up- per ocean temperature. Differences between ...

Seasonal Prediction of Sea Surface Temperature Anomalies Using a Suite of 13 Coupled Atmosphere–Ocean Models

Journal of Climate, 2006

Improved seasonal prediction of sea surface temperature (SST) anomalies over the global oceans is the theme of this paper. Using 13 state-of-the-art coupled global atmosphere-ocean models and 13 yr of seasonal forecasts, the performance of individual models, the ensemble mean, the bias-removed ensemble mean, and the Florida State University (FSU) superensemble are compared. A total of 23 400 seasonal forecasts based on 1-month lead times were available for this study. Evaluation metrics include both deterministic and probabilistic skill measures, such as verification of anomalies based on model and observed climatology, time series of specific climate indices, standard deterministic ensemble mean scores including anomaly correlations, root-mean-square (RMS) errors, and probabilistic skill measures such as equitable threat scores for seasonal SST forecasts. This study also illustrates the Niño-3.4 SST forecast skill for the equatorial Pacific Ocean and for the dipole index for the Indian Ocean. The relative skills of total SST fields and of the SST anomalies from the 13 coupled atmosphere-ocean models are presented. Comparisons of superensemble-based seasonal forecasts with recent studies on SST anomaly forecasts are also shown.

Response of the Joint Ocean-Atmosphere Model to the Seasonal Variation of the Solar Radiation

Monthly Weather Review, 1972

The effect of the seasonal variation of solar radiation is incorporated into the joint ocean-atmosphere model developed at the Geophysical Fluid Dynamics Laboratory of the National Oceanic and Atmospheric Administration, and the resulting system is integrated for the l>h-yr model time. The purpose of this study is to analyze the response of the joint air-sea model to seasonal changes in the solar zenith angle rather than to obtain a true equilibrium state. Comparisons are also made with results previously presented for the case of annual mean conditions.

Refinements to a prognostic scheme of skin sea surface temperature

Journal of Geophysical Research, 2010

Refinements to a prognostic scheme of skin sea surface temperature (SST) are proposed and tested. The refinements consist of two modifications of a Monin-Obukhov similarity function for stable conditions and mixing enhancement by the Langmuir circulation. The modified scheme is tested with the European Centre for Medium-Range Weather Forecasts model. The modified scheme shows better agreement of the diurnal SST amplitude with estimates from satellite observations. The scheme is also validated with moored buoy observations of the Arabian Sea Mixed Layer Dynamics Experiment. The off-line model with the modified scheme reproduces the observed diurnal SST variability well. Additionally, it is found that the parameterization of the effect of the Langmuir circulation enhances ocean mixing and reduces the diurnal variability of SST under wavy conditions.

Development of an atmosphere–ocean coupled operational forecast model for the Maritime Continent: Part 1 – Evaluation of ocean forecasts

2020

This article describes the development and ocean forecast evaluation of an atmosphere-ocean coupled prediction system for the Maritime Continent (MC) domain, which includes the eastern Indian and western Pacific Oceans. The coupled system comprises regional configurations of the atmospheric model MetUM and ocean model NEMO, at a uniform horizontal resolution of 4.5 km x 4.5 km, coupled using the OASIS3-MCT libraries. The coupled model is run as a pre-operational forecast system from 1 to 31 October 2019. Hindcast simulations performed for the period 1 January 2014 to 30 September 2019, using the stand-alone ocean configuration, provided the initial condition to the coupled ocean model. This paper details the evaluations of ocean-only model hindcast and 6-day coupled ocean forecast simulations. Direct comparison of sea surface temperature (SST) and sea surface height (SSH) with analysis as well as in situ observations are performed for the ocean-only hindcast evaluation. For the evaluation of coupled ocean model, comparisons of ocean forecast for different forecast lead times with SST analysis, and in situ observations of SSH, temperature and salinity have been performed. Overall, the model forecast deviation of SST, SSH, and subsurface temperature and salinity fields relative to observation is within acceptable error limits of operational forecast models. Typical runtimes of the daily forecast simulations are found to be suitable for the operational forecast applications. 1 Introduction Dynamical processes and flux exchanges between the Earth system components are better represented in coupled modelling systems rather than the single component models (e.g. Meehl, 1990). Hence, coupled models, particularly with dynamically interactive atmosphere, ocean, land surface and sea-ice models, are increasingly employed for climate research as well as operational forecast applications (e.g. Miller et al., 2017; Lewis et al., 2018, 2019a). The atmosphere and ocean are two major components of the Earth's climate system, and interactions between these two systems are key drivers of climate and weather. In the past, efforts toward the development of atmosphere-ocean coupled models were largely constrained by their high computational requirements, limited understanding of air-sea coupled processes and lower computational efficiency (Meehl,

Chapter 2 Review of Literature 2.1 Conceptualizing of Sea Surface Temperature (SST

The Sea Surface Temperature (SST) is a direct measure of the energy balance which drives the circulation and ultimately defines the climate. The energy transferred between the ocean and the atmosphere is to a large extent dependent on SST and functions of sea surface temperature such as the sensible heat flux, latent heat flux, and radiative flux at the sea surface. Sea surface temperature is an important physical property of the ocean to understand the features like current flows, precipitation, biological production, properties of surface air over the ocean, upwelling etc. SST is influenced by the parameters such as net incoming shortwave solar radiation, net long wave radiation, and the turbulent air-sea heat fluxes (the latent heat and sensible heat fluxes), wind stress curl, mixed layer depth,

An empirical stochastic model of sea-surface temperatures and surface winds over the Southern Ocean

2011

Abstract. This study employs NASA's recent satellite measurements of sea-surface temperatures (SSTs) and sea-level winds (SLWs) with missing data filled-in by Singular Spectrum Analysis (SSA), to construct empirical models that capture both intrinsic and SST-dependent aspects of SLW variability. The model construction methodology uses a number of algorithmic innovations that are essential in providing stable estimates of the model's propagator.