Climate prediction of summer extreme precipitation frequency in the Yangtze River valley based on sea surface temperature in the southern Indian Ocean and ice concentration in the Beaufort Sea

2019 ◽  
Vol 40 (9) ◽  
pp. 4117-4130
Author(s):  
Baoqiang Tian ◽  
Ke Fan
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Extreme Precipitation ◽  
Yangtze River ◽  
Yangtze River Valley ◽  
Sea Surface ◽  
The Yangtze River ◽  
Precipitation Frequency ◽  
Ice Concentration

scholarly journals Interannual Variability of Sea Surface Temperature off Java and Sumatra in a Global GCM*

2008 ◽  
Vol 21 (11) ◽  
pp. 2451-2465 ◽  
Author(s):  
Yan Du ◽  
Tangdong Qu ◽  
Gary Meyers
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mixed Layer ◽  
Heat Budget ◽  
Sea Surface ◽  
Surface Mixed Layer ◽  
Horizontal Advection ◽  
Cold Anomaly ◽  
Sst Anomalies

Abstract Using results from the Simple Ocean Data Assimilation (SODA), this study assesses the mixed layer heat budget to identify the mechanisms that control the interannual variation of sea surface temperature (SST) off Java and Sumatra. The analysis indicates that during the positive Indian Ocean Dipole (IOD) years, cold SST anomalies are phase locked with the season cycle. They may exceed −3°C near the coast of Sumatra and extend as far westward as 80°E along the equator. The depth of the thermocline has a prominent influence on the generation and maintenance of SST anomalies. In the normal years, cooling by upwelling–entrainment is largely counterbalanced by warming due to horizontal advection. In the cooling episode of IOD events, coastal upwelling–entrainment is enhanced, and as a result of mixed layer shoaling, the barrier layer no longer exists, so that the effect of upwelling–entrainment can easily reach the surface mixed layer. Horizontal advection spreads the cold anomaly to the interior tropical Indian Ocean. Near the coast of Java, the northern branch of an anomalous anticyclonic circulation spreads the cold anomaly to the west near the equator. Both the anomalous advection and the enhanced, wind-driven upwelling generate the cold SST anomaly of the positive IOD. At the end of the cooling episode, the enhanced surface thermal forcing overbalances the cooling effect by upwelling/entrainment, and leads to a warming in SST off Java and Sumatra.

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.

scholarly journals Basin-Scale Prediction of Sea Surface Temperature with Artificial Neural Networks

2018 ◽  
Vol 35 (7) ◽  
pp. 1441-1455 ◽  
Author(s):  
Kalpesh Patil ◽  
M. C. Deo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Prediction Skill ◽  
Basin Scale ◽  
Ann Models ◽  
Artificial Neural

AbstractThe prediction of sea surface temperature (SST) on the basis of artificial neural networks (ANNs) can be viewed as complementary to numerical SST predictions, and it has fairly sustained in the recent past. However, one of its limitations is that such ANNs are site specific and do not provide simultaneous spatial information similar to the numerical schemes. In this work we have addressed this issue by presenting basin-scale SST predictions based on the operation of a very large number of individual ANNs simultaneously. The study area belongs to the basin of the tropical Indian Ocean (TIO) having coordinates of 30°N–30°S, 30°–120°E. The network training and testing are done on the basis of HadISST data of the past 140 yr. Monthly SST anomalies are predicted at 3813 nodes in the basin and over nine time steps into the future with more than 20 million ANN models. The network testing indicated that the prediction skill of ANNs is attractive up to certain lead times depending on the subbasin. The ANN models performed well over both the western Indian Ocean (WIO) and eastern Indian Ocean (EIO) regions up to 5 and 4 months lead time, respectively, as judged by the error statistics of the correlation coefficient and the normalized root-mean-square error. The prediction skill of the ANN models for the TIO region is found to be better than the physics-based coupled atmosphere–ocean models. It is also observed that the ANNs are capable of providing an advanced warning of the Indian Ocean dipole as well as abnormal basin warming.

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