scholarly journals Sensitivity of Sea Surface Temperature Simulation by an Ocean Model to the Resolution of the Meteorological Forcing

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
F. Chen ◽  
G. Shapiro ◽  
R. Thain
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Ocean Circulation ◽  
Circulation Model ◽  
Ocean Model ◽  
Sea Surface ◽  
Meteorological Forcing ◽  
Mean Values ◽  
Solar Radiation Flux ◽  
Shelf Sea

The effect on sea surface temperature (SST) predictions caused by varying atmospheric forcing within an ocean model is examined in the Celtic Sea, a typical shelf sea situated to the southwest of the British Isles. We use the 3D ocean circulation model POLCOMS, with 2 km resolution, 30 vertical layers, and two sets of meteorological forcing, at low (1.6°) and high (0.11°) horizontal resolutions. The model is validated against in situ and satellite observations. Comparisons made for the year 2008 show that increasing the resolution of the meteorological forcing does not necessarily lead to more accurate results in the modelled SST. The discrepancy between the low and high resolution cases was found to be the greatest in the summer, with the errors of the mean SST being 0.15°C and 1.15°C, respectively. Overall, the most accurate reproduction of SST throughout the year is obtained using the low resolution atmospheric data. We show that this is due not to the resolution of the forcing per se, but to the differences between the meteorological models in mean values of parameters such as cloud cover, which in turn reduce the solar radiation flux reaching the sea surface in the oceanographic model.

scholarly journals Controlling atmospheric forcing parameters of global ocean models: sequential assimilation of sea surface Mercator-Ocean reanalysis data

Ocean Science ◽  
2009 ◽  
Vol 5 (4) ◽  
pp. 403-419 ◽  
Author(s):  
C. Skandrani ◽  
J.-M. Brankart ◽  
N. Ferry ◽  
J. Verron ◽  
P. Brasseur ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Reanalysis Data ◽  
Ocean Model ◽  
Sea Surface ◽  
Global Ocean ◽  
Atmospheric Forcing ◽  
Ocean Reanalysis ◽  
Ocean Reanalysis Data

Abstract. In the context of stand alone ocean models, the atmospheric forcing is generally computed using atmospheric parameters that are derived from atmospheric reanalysis data and/or satellite products. With such a forcing, the sea surface temperature that is simulated by the ocean model is usually significantly less accurate than the synoptic maps that can be obtained from the satellite observations. This not only penalizes the realism of the ocean long-term simulations, but also the accuracy of the reanalyses or the usefulness of the short-term operational forecasts (which are key GODAE and MERSEA objectives). In order to improve the situation, partly resulting from inaccuracies in the atmospheric forcing parameters, the purpose of this paper is to investigate a way of further adjusting the state of the atmosphere (within appropriate error bars), so that an explicit ocean model can produce a sea surface temperature that better fits the available observations. This is done by performing idealized assimilation experiments in which Mercator-Ocean reanalysis data are considered as a reference simulation describing the true state of the ocean. Synthetic observation datasets for sea surface temperature and salinity are extracted from the reanalysis to be assimilated in a low resolution global ocean model. The results of these experiments show that it is possible to compute piecewise constant parameter corrections, with predefined amplitude limitations, so that long-term free model simulations become much closer to the reanalysis data, with misfit variance typically divided by a factor 3. These results are obtained by applying a Monte Carlo method to simulate the joint parameter/state prior probability distribution. A truncated Gaussian assumption is used to avoid the most extreme and non-physical parameter corrections. The general lesson of our experiments is indeed that a careful specification of the prior information on the parameters and on their associated uncertainties is a key element in the computation of realistic parameter estimates, especially if the system is affected by other potential sources of model errors.

scholarly journals Impact of high-resolution sea surface temperature representation on the forecast of small Mediterranean catchments' hydrological responses to heavy precipitation

2020 ◽  
Vol 24 (1) ◽  
pp. 269-291 ◽  
Author(s):  
Alfonso Senatore ◽  
Luca Furnari ◽  
Giuseppe Mendicino
Keyword(s):  
High Resolution ◽  
Boundary Conditions ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
General Circulation ◽  
Circulation Model ◽  
Sea Surface ◽  
Sources Of Uncertainty ◽  
Operational Forecasting ◽  
Forecasting System

Abstract. Operational meteo-hydrological forecasting chains are affected by many sources of uncertainty. In coastal areas characterized by complex topography, with several medium-to-small size catchments, quantitative precipitation forecast becomes even more challenging due to the interaction of intense air–sea exchanges with coastal orography. For such areas, which are quite common in the Mediterranean Basin, improved representation of sea surface temperature (SST) space–time patterns can be particularly important. The paper focuses on the relative impact of different resolutions of SST representation on regional operational forecasting chains (up to river discharge estimates) over coastal Mediterranean catchments, with respect to two other fundamental options while setting up the system, i.e. the choice of the forcing general circulation model (GCM) and the possible use of a three-dimensional variational assimilation (3D-Var) scheme. Two different kinds of severe hydro-meteorological events that affected the Calabria region (southern Italy) in 2015 are analysed using the WRF-Hydro atmosphere–hydrology modelling system in its uncoupled version. Both of the events are modelled using the 0.25∘ resolution global forecasting system (GFS) and the 16 km resolution integrated forecasting system (IFS) initial and lateral atmospheric boundary conditions, which are from the European Centre for Medium-Range Weather Forecasts (ECMWF), applying the WRF mesoscale model for the dynamical downscaling. For the IFS-driven forecasts, the effects of the 3D-Var scheme are also analysed. Finally, native initial and lower boundary SST data are replaced with data from the Medspiration project by Institut Français de Recherche pour L'Exploitation de la Mer (IFREMER)/Centre European Remote Sensing d'Archivage et de Traitement (CERSAT), which have a 24 h time resolution and a 2.2 km spatial resolution. Precipitation estimates are compared with both ground-based and radar data, as well as discharge estimates with stream gauging stations' data. Overall, the experiments highlight that the added value of high-resolution SST representation can be hidden by other more relevant sources of uncertainty, especially the choice of the general circulation model providing the boundary conditions. Nevertheless, in most cases, high-resolution SST fields show a non-negligible impact on the simulation of the atmospheric boundary layer processes, modifying flow dynamics and/or the amount of precipitated water; thus, this emphasizes the fact that uncertainty in SST representation should be duly taken into account in operational forecasting in coastal areas.

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.

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