scholarly journals Role of surface fluxes in ocean general circulation models using satellite sea surface temperature: Validation of and sensitivity to the forcing frequency of the Mediterranean thermohaline circulation

2002 ◽  
Vol 107 (C8) ◽  
Author(s):  
Vincenzo Artale
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
General Circulation ◽  
Thermohaline Circulation ◽  
General Circulation Models ◽  
Surface Fluxes ◽  
Sea Surface ◽  
Ocean General Circulation Models ◽  
Ocean General Circulation

scholarly journals Assessing bias corrections of oceanic surface conditions for atmospheric models

2019 ◽  
Vol 12 (1) ◽  
pp. 321-342 ◽  
Author(s):  
Julien Beaumet ◽  
Gerhard Krinner ◽  
Michel Déqué ◽  
Rein Haarsma ◽  
Laurent Li
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
General Circulation ◽  
General Circulation Models ◽  
Substantial Improvement ◽  
Sea Surface ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Analogue Method

Abstract. Future sea surface temperature and sea-ice concentration from coupled ocean–atmosphere general circulation models such as those from the CMIP5 experiment are often used as boundary forcings for the downscaling of future climate experiments. Yet, these models show some considerable biases when compared to the observations over present climate. In this paper, existing methods such as an absolute anomaly method and a quantile–quantile method for sea surface temperature (SST) as well as a look-up table and a relative anomaly method for sea-ice concentration (SIC) are presented. For SIC, we also propose a new analogue method. Each method is objectively evaluated with a perfect model test using CMIP5 model experiments and some real-case applications using observations. We find that with respect to other previously existing methods, the analogue method is a substantial improvement for the bias correction of future SIC. Consistency between the constructed SST and SIC fields is an important constraint to consider, as is consistency between the prescribed sea-ice concentration and thickness; we show that the latter can be ensured by using a simple parameterisation of sea-ice thickness as a function of instantaneous and annual minimum SIC.

CONTINUUM CLIMATE VARIABILITY: LONG-TERM MEMORY, SCALING, AND 1/F-NOISE

2009 ◽  
Vol 23 (28n29) ◽  
pp. 5403-5416 ◽  
Author(s):  
KLAUS FRAEDRICH ◽  
RICHARD BLENDER ◽  
XIUHUA ZHU
Keyword(s):  
General Circulation ◽  
General Circulation Models ◽  
Sea Surface ◽  
Long Term Memory ◽  
Sea Surface Temperatures ◽  
Term Memory ◽  
Ocean General Circulation Models ◽  
Ocean General Circulation ◽  
One Year

Continuum temperature variability represents the response of the Earth's climate to deterministic external forcing. Scaling regimes are observed which range from hours to millennia with low frequency fluctuations characterizing long-term memory. The presence of 1/f power spectra in weather and climate is noteworthy: (i) In the tropical atmosphere 1/f scaling ranging from hours to weeks is found for several variables; it emerges as superposition of uncorrelated pulses with individual 1/f spectra. (ii) The daily discharge of the Yangtze shows 1/f within one week to one year, although the precipitation spectrum is white. (iii) Beyond one year mid-latitude sea surface temperatures reveal 1/f scaling in large parts of the global ocean. The spectra can be simulated by complex atmosphere-ocean general circulation models and understood as a two layer heat diffusion process forced by an uncorrelated stochastic atmospheric. Long-term memory on time scales up to millennia are the global sea surface temperatures and the Greenland ice core records (GISP2, GRIP) with δ18 O temperature proxy data during the Holocene. Complex atmosphere ocean general circulation models reproduce this behavior quantitatively up to millennia without solar variability, interacting land-ice and vegetation components.

Global monthly sea surface temperature and sea ice reconstruction for historical simulations

2020 ◽  
Author(s):  
Eric Samakinwa ◽  
Stefan Brönnimann
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
General Circulation ◽  
Large Scale ◽  
Southern Oscillation ◽  
General Circulation Models ◽  
Sea Surface ◽  
Annual Variability ◽  
Instrumental Records

<p>Variability in Sea Surface Temperature (SST) is one of the prime sources of intra-annual variability, and also an important boundary condition for Atmospheric General Circulation Models (AGCMs). In many AGCM simulations, SST and Sea Ice Concentration (SIC) are prescribed. While SSTs are specified according to observations available in recent period of instrumental records (1850 – present), SIC depends on climatological averages with less variability prior to the inception of satellite measurements. This limits our understanding of large-scale climate variations in the past.</p><p>In this study, we augment multi-proxy reconstructed annual mean temperature of Neukom et al. (2019) with intra-annual variability from HadISST (v2.0), for 850 years (1000 – 1849). Intra-seasonal variability, such as the phase-locking of El-Nino Southern Oscillation, Indian Ocean Dipole and Tropical Atlantic SST indices to annual-cycle, are utilized. The intra-annual component of HadISST and SST indices estimated from the multi-proxy reconstructed annual mean, are used to develop grid-based multivariate linear regression models using the Frisch-Waugh-Lovell theorem, in a monthly stratified approach. Furthermore, we introduce a scaling technique to ensure homogeneous mean and variance, similar to that of the target. SST observations obtained from ship measurements by ICOADS before 1850, will be integrated in an off-line data assimilation approach.</p><p>Similarly, we reconstruct SIC via analogue resampling of HadISST SIC (1941 – 2000), for both hemispheres. We pool our analogues in four seasons, comprising of 3 months each, such that for each month within a season, there are 180 possible analogues. The best analogues are selected based on correlation coefficients between reconstructed SST and its target.</p>

scholarly journals Retrospective Forecasts of Interannual Sea Surface Temperature Anomalies from 1982 to Present Using a Directly Coupled Atmosphere–Ocean General Circulation Model

2005 ◽  
Vol 133 (10) ◽  
pp. 2972-2995 ◽  
Author(s):  
David G. DeWitt
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
General Circulation Model ◽  
General Circulation ◽  
Coupled Model ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Sea Surface ◽  
Initial Condition ◽  
Ocean General Circulation

Abstract A large number of ensemble hindcasts (or retrospective forecasts) of tropical Pacific sea surface temperature (SST) have been made with a coupled atmosphere–ocean general circulation model (CGCM) that does not employ flux correction in order to evaluate the potential skill of the model as a seasonal forecasting tool. Oceanic initial conditions are provided by an ocean data assimilation system. Ensembles of seven forecasts of 6-month length are made starting each month in the 1982 to 2002 period. Skill of the coupled model is evaluated from both a deterministic and a probabilistic perspective. The skill metrics are calculated using both the bulk method, which includes all initial condition months together, and as a function of initial condition month. The latter method allows a more objective evaluation of how the model has performed in the context in which forecasts are actually made and applied. The deterministic metrics used are the anomaly correlation and the root-mean-square error. The coupled model deterministic skill metrics are compared with those from persistence and damped persistence reference forecasts. Despite the fact that the coupled model has a large cold bias in the central and eastern equatorial Pacific this coupled model is shown to have forecast skill that is competitive with other state-of-the-art forecasting techniques. Potential skill from probabilistic forecasts made using the coupled model ensemble members are evaluated using the relative operating characteristics method. This analysis indicates that for most initial condition months this coupled model has more skill at forecasting cold events than warm or neutral events in the central Pacific. In common with other forecasting systems, the coupled model forecast skill is found to be lowest for forecasts passing through the Northern Hemisphere (NH) spring. Diagnostics of this so-called spring predictability barrier in the context of this coupled model indicate that two factors likely contribute to this predictability barrier. First, the coupled model shows a too-weak coupling of the surface and subsurface temperature anomalies during NH spring. Second, the coupled-model-simulated signal-to-noise ratio for SST anomalies is much lower during NH spring than at other times of the year, indicating that the model’s potential predictability is low at this time.

scholarly journals Assessing bias-corrections of oceanic surface conditions for atmospheric models

2017 ◽  
Author(s):  
Julien Beaumet ◽  
Gerhard Krinner ◽  
Michel Déqué ◽  
Rein Haarsma ◽  
Laurent Li
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
General Circulation ◽  
General Circulation Models ◽  
Substantial Improvement ◽  
Sea Surface ◽  
Analog Method ◽  
Sea Ice Concentration ◽  
Ice Concentration

Abstract. Future sea–surface temperature and sea–ice concentration from coupled ocean–atmosphere general circulation models such as those from the CMIP5 experiment are often used as boundary forcing for the downscaling of future climate experiment. Yet, these models show some considerable biases when compared to the observations over present climate. In this paper, existing methods such as an absolute anomaly and a quantile–quantile method for sea surface temperature (SST) as well as a look-up table and a relative anomaly method for sea–ice concentration (SIC) are presented. For SIC, we also propose a new analog method. Each method is objectively evaluated with a perfect model test using CMIP5 model experiment and some real-case applications using observations. With respect to other previously existing methods for SIC, the analog method is a substantial improvement for the bias correction of future sea–ice concentrations.

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