Optimal error growth of South Asian monsoon forecast associated with the uncertainties in the sea surface temperature

2015 ◽  
Vol 46 (5-6) ◽  
pp. 1953-1975 ◽  
Author(s):  
Siraj Ul Islam ◽  
Youmin Tang ◽  
Peter L. Jackson
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
South Asian ◽  
Asian Monsoon ◽  
Sea Surface ◽  
South Asian Monsoon ◽  
Error Growth ◽  
Optimal Error

Disentangling sea-surface temperature and anthropogenic aerosol influences on recent trends in South Asian monsoon rainfall

2018 ◽  
Vol 52 (3-4) ◽  
pp. 2287-2302 ◽  
Author(s):  
Nitin Patil ◽  
Chandra Venkataraman ◽  
Kaushik Muduchuru ◽  
Subimal Ghosh ◽  
Arpita Mondal
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
South Asian ◽  
Asian Monsoon ◽  
Monsoon Rainfall ◽  
Sea Surface ◽  
South Asian Monsoon ◽  
Anthropogenic Aerosol ◽  
Recent Trends

scholarly journals Optimal error analysis of MJO prediction associated with uncertainties in sea surface temperature over Indian Ocean

2020 ◽  
Vol 54 (9-10) ◽  
pp. 4331-4350
Author(s):  
Xiaojing Li ◽  
Youmin Tang ◽  
Lei Zhou ◽  
Zhixiong Yao ◽  
Zheqi Shen ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Error Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Optimal Error

scholarly journals Assessment of Better Prediction of Seasonal Rainfall by Climate Predictability Tool Using Global Sea Surface Temperature in Bangladesh

2019 ◽  
pp. 1-13 ◽  
Author(s):  
Md. Zakaria Hossain ◽  
Md. Abul Kalam Azad ◽  
Samarendra Karmakar ◽  
Md. Nazrul Islam Mondal ◽  
Mohan Das ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
South Asian ◽  
Rainy Season ◽  
Regional Cooperation ◽  
Sea Surface ◽  
Seasonal Rainfall ◽  
Research Centre ◽  
Positive Deviation ◽  
Climate Predictability

This study was conducted to determine better prediction result of seasonal rainfall. To evaluate the better prediction of seasonal rainfall of rainy season (15 June-15 August) by Climate Predictability Tools (CPT) in the context of using sea surface temperature (SST) of starting month of rainy season compare to using SST of one month before the rainy season. The study was carried out at the South Asian Association for Regional Cooperation Meteorological Research Centre, Dhaka; Bangladesh between January and December, 2010. A correlation between rainfall at Rangpur, Dhaka, Barisal and Sylhet and global SST of different areas of the world was studied by using the both data of 1975- 2008 years with the help of the CPT to find more positive correlated SST with observed rainfall and use as predictor for giving the prediction of the year 2009. The statistical method applied using CPT which is canonical correlation analysis. Using SST of one month before rainy season as predictor, the positive deviation of predicted rainfall from observed rainfall was 1.34 mm/day at Sylhet and 0.9 mm/day at Dhaka. The negative deviation of mean rainfall was 1.16 mm/day at Rangpur and 1.10 mm/day at Barisal. Again, using of starting one month SST of rainy season as predictor, positive deviation of predicted rainfall from observed rainfall was 4.03 mm/day at Sylhet. The positive deviation of daily mean rainfall was found 6.58 mm/day at Dhaka and 6.23 mm/day over southern Bangladesh. The study reveals that SST of one month before rainy season was better predictor than SST of starting month of rainy season.

Summer monsoon–induced upwelling dominated coastal sea surface temperature variations in the northern South China Sea over the last two millennia

The Holocene ◽  
2019 ◽  
Vol 29 (4) ◽  
pp. 691-698 ◽  
Author(s):  
Wing-Man Lee ◽  
Kit-Chi Poon ◽  
Deming Kong ◽  
Roderick J Sewell ◽  
Yongqiang Zong ◽  
...  
Keyword(s):  
South China Sea ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Summer Monsoon ◽  
South China ◽  
Asian Monsoon ◽  
Sea Surface ◽  
China Sea ◽  
Coastal Sea ◽  
Monsoon System

The South China Sea (SCS), situated to the north of the Indo-Pacific Warm Pool (IPWP), is under the strong influence of the Asian monsoon system. However, coastal sea surface temperature (SST) records from the SCS, which are of vital importance to exploring ocean-atmosphere-land interactions behind the Asian monsoon system, remain scarce. Here, we use a sediment core collected at the coast of northern SCS to investigate alkenone-SST variations over the past two millennia. On multi-centennial timescale, SST changes in our record exhibit an opposite pattern to that of Northern Hemisphere temperature and solar irradiance, for example, relatively cool SST during the Medieval Warm Period (MWP) and warm conditions during the Little Ice Age (LIA). Together with alkenone content and existing records, we suggest that the regional SST changes result from a strengthening (weakening) of wind-driven coastal upwelling, associated with variability of the Asian summer monsoon intensity during the MWP (LIA).

Pacific Sea Surface Temperature Forcing Dominates Orbital Forcing of the Early Holocene Monsoon

2001 ◽  
Vol 55 (1) ◽  
pp. 25-32 ◽  
Author(s):  
Andrew Basil George Bush
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
General Circulation ◽  
Asian Monsoon ◽  
Sea Surface ◽  
Early Holocene ◽  
Monsoon Circulation ◽  
Primary Role ◽  
Orbital Forcing ◽  
Orbital Parameters

AbstractOrbital forcing is known to play a primary role in regulating the strength of the south Asian monsoon circulation. In this study, a comparison is made between orbital forcing and Pacific sea surface temperature (SST) forcing of the monsoon through a sequence of atmospheric general circulation model experiments configured for 6,000 and 9,000 yr B.P. Early–mid Holocene orbital parameters are shown to increase continental seasonality as well as the meridional mean, the zonal mean, and the summer monsoon circulations. Winds in the southeast Asian monsoon are weakened by warm Pacific SST to such an extent that the increase in strength caused by early Holocene orbital parameters is offset. These results imply that SSTs are potentially as important as orbital parameters in governing the monsoon and that more data—particularly from the equatorial Pacific—are crucial to deciphering Holocene climate.

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