scholarly journals Importance of the Vertical Resolution in Simulating SST Diurnal and Intraseasonal Variability in an Oceanic General Circulation Model

2017 ◽  
Vol 30 (11) ◽  
pp. 3963-3978 ◽  
Author(s):  
Xuyang Ge ◽  
Wanqiu Wang ◽  
Arun Kumar ◽  
Ying Zhang
Keyword(s):  
Temperature Gradient ◽  
General Circulation Model ◽  
General Circulation ◽  
Circulation Model ◽  
Vertical Resolution ◽  
Vertical Temperature Gradient ◽  
Vertical Temperature ◽  
Sst Variability ◽  
Oceanic General Circulation Model ◽  
High Vertical Resolution

Abstract In this paper, the influence of high vertical resolution near the surface in an oceanic general circulation model in simulating the observed sea surface temperature (SST) variability is investigated. In situ observations of vertical temperature profiles are first used to quantify temperature variability with depth near the ocean surface. The analysis shows that there is a sharp vertical temperature gradient within the top 10 m of the ocean. Both diurnal and intraseasonal variabilities of the ocean temperatures are largest near the surface and decrease with the ocean depth. Numerical experiments with an oceanic general circulation model are next carried out with 1- and 10-m vertical resolutions for the upper ocean to study the dependence of the simulated SST and vertical temperature structure on the vertical resolution. It is found that the simulated diurnal and intraseasonal variabilities, as well as the associated vertical temperature gradient near the surface, are strongly influenced by the oceanic vertical resolution, with the 1-m vertical resolution producing a stronger vertical temperature gradient and temporal variability than the 10-m vertical resolution. These results suggest that a realistic representation of SST variability with a high vertical resolution in the upper ocean is required for a coupled atmosphere–ocean model to correctly simulate the observed tropical intraseasonal oscillations, including the Madden–Julian oscillation and the boreal summer monsoon intraseasonal oscillation, which are strongly linked with the underlying SST.

scholarly journals Decadal SST Variability in the Southeast Indian Ocean and Its Impact on Regional Climate

2019 ◽  
Vol 32 (19) ◽  
pp. 6299-6318 ◽  
Author(s):  
Yuanlong Li ◽  
Weiqing Han ◽  
Lei Zhang ◽  
Fan Wang
Keyword(s):  
Heat Flux ◽  
Wind Speed ◽  
Indian Ocean ◽  
General Circulation Model ◽  
General Circulation ◽  
Circulation Model ◽  
Turbulent Heat ◽  
Central Pacific ◽  
Offshore Area ◽  
Sst Variability

Abstract The southeast Indian Ocean (SEIO) exhibits decadal variability in sea surface temperature (SST) with amplitudes of ~0.2–0.3 K and covaries with the central Pacific (r = −0.63 with Niño-4 index for 1975–2010). In this study, the generation mechanisms of decadal SST variability are explored using an ocean general circulation model (OGCM), and its impact on atmosphere is evaluated using an atmospheric general circulation model (AGCM). OGCM experiments reveal that Pacific forcing through the Indonesian Throughflow explains <20% of the total SST variability, and the contribution of local wind stress is also small. These wind-forced anomalies mainly occur near the Western Australian coast. The majority of SST variability is attributed to surface heat fluxes. The reduced upward turbulent heat flux (QT; latent plus sensible heat flux), owing to decreased wind speed and anomalous warm, moist air advection, is essential for the growth of warm SST anomalies (SSTAs). The warming causes reduction of low cloud cover that increases surface shortwave radiation (SWR) and further promotes the warming. However, the resultant high SST, along with the increased wind speed in the offshore area, enhances the upward QT and begins to cool the ocean. Warm SSTAs co-occur with cyclonic low-level wind anomalies in the SEIO and enhanced rainfall over Indonesia and northwest Australia. AGCM experiments suggest that although the tropical Pacific SST has strong effects on the SEIO region through atmospheric teleconnection, the cyclonic winds and increased rainfall are mainly caused by the SEIO warming through local air–sea interactions.

Modeling a 200-Yr Interruption of the Holocene Sapropel S1

2000 ◽  
Vol 53 (1) ◽  
pp. 98-104 ◽  
Author(s):  
Paul G. Myers ◽  
Eelco J. Rohling
Keyword(s):  
General Circulation Model ◽  
General Circulation ◽  
Deep Convection ◽  
Circulation Model ◽  
Intermediate Water ◽  
Gulf Of Lions ◽  
The Holocene ◽  
Circulation Mode ◽  
Oceanic General Circulation Model ◽  
Water Formation

AbstractAn oceanic general circulation model, previously used to simulate the conditions associated with the Holocene Sapropel S1, is used to simulate the effects of a climate deterioration (represented as a cooling event) on the sapropelic circulation mode. The enhanced cooling (2°–3°C) induces deep convection in the Adriatic and the Gulf of Lions and intermediate water formation in the Aegean, where in all cases there had previously been only stagnant unventilated waters. The depths of ventilation (to ∼1250 m) are in agreement with core data from this period. The short decadal timescales involved in modifying the sapropelic circulation suggest that such a climatic deterioration may be associated with the interruption of S1 between 7100 and 6900 14C yr B.P., which divided the sapropel into two subunits.

scholarly journals Predictions of Indian Ocean SST Indices with a Simple Statistical Model: A Null Hypothesis

2009 ◽  
Vol 22 (18) ◽  
pp. 4930-4938 ◽  
Author(s):  
Dietmar Dommenget ◽  
Malte Jansen
Keyword(s):  
Indian Ocean ◽  
Statistical Model ◽  
General Circulation Model ◽  
Null Hypothesis ◽  
General Circulation ◽  
Circulation Model ◽  
Tropical Indian Ocean ◽  
Model Studies ◽  
Sst Variability ◽  
The Indian Ocean

Abstract Several recent general circulation model studies discuss the predictability of the Indian Ocean dipole (IOD) mode, suggesting that it is predictable because of coupled ocean–atmosphere interactions in the Indian Ocean. However, it is not clear from these studies how much of the predictability is due to the response to El Niño. It is shown in this note that a simple statistical model that treats the Indian Ocean as a red noise process forced by tropical Pacific SST shows forecast skills comparable to those of recent general circulation model studies. The results also indicate that some of the eastern tropical Indian Ocean SST predictability in recent studies may indeed be beyond the skill of the simple model proposed in this note, indicating that dynamics in the Indian Ocean may have caused this improved predictability in this region. The model further indicates that the IOD index may be the least predictable index of Indian Ocean SST variability. The model is proposed as a null hypothesis for Indian Ocean SST predictions.

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