Predicting Indian monsoon rainfall from sea-surface temperature in the Indonesia–north Australia area

Nature ◽  
1983 ◽  
Vol 306 (5943) ◽  
pp. 576-577 ◽  
Author(s):  
N. Nicholls
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Monsoon Rainfall ◽  
Indian Monsoon ◽  
Sea Surface ◽  
Indian Monsoon Rainfall

scholarly journals Global sensitivity analysis of the Indian monsoon during the Pleistocene

2015 ◽  
Vol 11 (1) ◽  
pp. 45-61 ◽  
Author(s):  
P. A. Araya-Melo ◽  
M. Crucifix ◽  
N. Bounceur
Keyword(s):  
Sensitivity Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mixed Layer ◽  
Global Sensitivity Analysis ◽  
Indian Monsoon ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
Layer Depth ◽  
Global Sensitivity

Abstract. The sensitivity of the Indian monsoon to the full spectrum of climatic conditions experienced during the Pleistocene is estimated using the climate model HadCM3. The methodology follows a global sensitivity analysis based on the emulator approach of Oakley and O'Hagan (2004) implemented following a three-step strategy: (1) development of an experiment plan, designed to efficiently sample a five-dimensional input space spanning Pleistocene astronomical configurations (three parameters), CO2 concentration and a Northern Hemisphere glaciation index; (2) development, calibration and validation of an emulator of HadCM3 in order to estimate the response of the Indian monsoon over the full input space spanned by the experiment design; and (3) estimation and interpreting of sensitivity diagnostics, including sensitivity measures, in order to synthesise the relative importance of input factors on monsoon dynamics, estimate the phase of the monsoon intensity response with respect to that of insolation, and detect potential non-linear phenomena. By focusing on surface temperature, precipitation, mixed-layer depth and sea-surface temperature over the monsoon region during the summer season (June-July-August-September), we show that precession controls the response of four variables: continental temperature in phase with June to July insolation, high glaciation favouring a late-phase response, sea-surface temperature in phase with May insolation, continental precipitation in phase with July insolation, and mixed-layer depth in antiphase with the latter. CO2 variations control temperature variance with an amplitude similar to that of precession. The effect of glaciation is dominated by the albedo forcing, and its effect on precipitation competes with that of precession. Obliquity is a secondary effect, negligible on most variables except sea-surface temperature. It is also shown that orography forcing reduces the glacial cooling, and even has a positive effect on precipitation. As regards the general methodology, it is shown that the emulator provides a powerful approach, not only to express model sensitivity but also to estimate internal variability and detect anomalous simulations.

Investigating the factors affecting Monsoon precipitation under climate change

2020 ◽  
Author(s):  
Harry Mutton ◽  
Mat Collins ◽  
Hugo Lambert ◽  
Rob Chadwick
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Indian Monsoon ◽  
Physiological Effect ◽  
Sea Surface ◽  
Monsoon Circulation ◽  
Radiative Effect ◽  
Monsoon Precipitation ◽  
Surface Temperature Change ◽  
The Impact

<p>The Monsoons produce some of the largest levels of uncertainty in projected precipitation change across the globe, and addressing this uncertainty is a key issue that must be faced in order to allow correct adaptation policy to be put in place.</p><p> </p><p>A set of CMIP6 GCM experiments, that allow the full effect of CO<sub>2</sub> forcing to be decomposed into individual components, highlight the leading factors that produce changes in monsoon precipitation. The results reveal a high spatial variability in these factors, with changes in the Indian Monsoon dominated by the effect of sea surface temperatures and the direct radiative effect of increased CO<sub>2</sub>, and changes in the South American Monsoon governed by the plant physiological effect and the direct radiative effect of increased CO<sub>2</sub>. The processes behind these precipitation changes are also investigated by looking at variations in atmospheric circulation and surface temperature. Results of the patterned sea surface temperature experiment demonstrate a slow-down of the Indian Monsoon circulation possibly driven by an anomalously warm Indian Ocean.</p><p> </p><p>This analysis has been performed for all land monsoon regions, decomposing the full CO<sub>2</sub> forcing into; uniform and patterned sea surface temperature change, the plant physiological effect, the direct radiative effect and the impact of sea-ice melt. These results can help identify emergent constraints, as well as indicate which aspects of climate models need to be improved in order to reduce model uncertainty.</p>

Teleconnections between the sea surface temperature in the Bay of Bengal and monsoon rainfall in Bangladesh

2006 ◽  
Vol 53 (3) ◽  
pp. 188-197 ◽  
Author(s):  
Ahmed Salahuddin ◽  
Ronald H. Isaac ◽  
Scott Curtis ◽  
Jun Matsumoto
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Bay Of Bengal ◽  
Monsoon Rainfall ◽  
Sea Surface

scholarly journals Relationship between Indian summer monsoon rainfall and sea surface temperature anomalies over equatorial central and eastern Pacific

MAUSAM ◽  
2021 ◽  
Vol 49 (2) ◽  
pp. 229-234
Author(s):  
V. THAPLIYAL ◽  
M. RAJEEVAN ◽  
S. R. PATIL
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Monsoon Rainfall ◽  
Indian Summer Monsoon Rainfall ◽  
Monsoon Season ◽  
Summer Monsoon Rainfall ◽  
Sea Surface ◽  
Sst Anomalies

Sea surface temperature (SST) variations over the three key regions over equatorial Pacific, viz., Nino (1+2), Nino 3 and Nino 4 and their relationships with Indian summer monsoon rainfall have been examined in this study. On monthly scale, SST anomalies over the three key regions show an oscillatory type of lagged correlations with Indian monsoon rainfall, positive correlations almost one year before the monsoon season (CC's are of the order of 0.3) which gradually change to significant negative correlation peaking in September/October during/after the monsoon season. The variations on seasonal scale also exhibit the same pattern of monthly variations but more smooth in nature. Composites of similar monsoon years show that during deficient (excess) monsoon years SST anomalies over all the three regions have warmer (cooler) trend which starts about 6 months prior to monsoon season. Tendencies of SST anomalies from previous winter (DJF) to summer (MAM) seasons over Nino 3 and Nino 4 regions are better predictors than EI-Nino categories currently being used in IMD's operational LRF model. By using tendency of SST over EI- Nino -4 region, in place of the category of EI-Nino, the 16 parameter operational Power Regression Model of IMD has been modified. The new forecast model shows better reduction in the forecast error.

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