Influence of the modes of climate variability in the Tropical Pacific and Atlantic on accumulated rainfall and reservoir water volumes in the Northeast Brazil

2021 ◽  
Author(s):  
Rafaela Gomes ◽  
Kellen Carla Lima
Keyword(s):  
Climate Variability ◽  
Tropical Pacific ◽  
Northeast Brazil ◽  
Reservoir Water ◽  
Accumulated Rainfall ◽  
Water Volumes ◽  
Modes Of Climate Variability ◽  
The Tropical Pacific

Interactions of the Indian Ocean climate with other tropical oceans

2020 ◽  
Author(s):  
Matthieu Lengaigne ◽  
Keyword(s):  
Indian Ocean ◽  
Climate Variability ◽  
Future Climate ◽  
Future Climate Change ◽  
The Pacific ◽  
Ocean Atmosphere ◽  
The Indian Ocean ◽  
The Tropics ◽  
Modes Of Climate Variability ◽  
The Tropical Pacific

<p>Ocean-atmosphere interactions in the tropics have a profound influence on the climate system. El Niño–Southern Oscillation (ENSO), which is spawned in the tropical Pacific, is the most prominent and well-known year-to-year variation on Earth. Its reach is global, and its impacts on society and the environment are legion. Because ENSO is so strong, it can excite other modes of climate variability in the Indian Ocean by altering the general circulation of the atmosphere. However, ocean-atmosphere interactions internal to the Indian Ocean are capable of generating distinct modes of climate variability as well. Whether the Indian Ocean can feedback onto Atlantic and Pacific climate has been an on-going matter of debate. We are now beginning to realize that the tropics, as a whole, are a tightly inter-connected system, with strong feedbacks from the Indian and Atlantic Oceans onto the Pacific. These two-way interactions affect the character of ENSO and Pacific decadal variability and shed new light on the recent hiatus in global warming.</p><p>Here we review advances in our understanding of pantropical interbasins climate interactions with the Indian Ocean and their implications for both climate prediction and future climate projections. ENSO events force changes in the Indian Ocean than can feed back onto the Pacific. Along with reduced summer monsoon rainfall over the Indian subcontinent, a developing El Niño can trigger a positive Indian Ocean Dipole (IOD) in fall and an Indian Ocean Basinwide (IOB) warming in winter and spring. Both IOD and IOB can feed back onto ENSO. For example, a positive IOD can favor the onset of El Niño, and an El Niño–forced IOB can accelerate the demise of an El Niño and its transition to La Niña. These tropical interbasin linkages however vary on decadal time scales. Warming during a positive phase of Atlantic Multidecadal Variability over the past two decades has strengthened the Atlantic forcing of the Indo-Pacific, leading to an unprecedented intensification of the Pacific trade winds, cooling of the tropical Pacific, and warming of the Indian Ocean. These interactions forced from the tropical Atlantic were largely responsible for the recent hiatus in global surface warming.</p><p>Climate modeling studies to address these issues are unfortunately compromised by pronounced systematic errors in the tropics that severely suppress interactions with the Indian and Pacific Oceans. As a result, there could be considerable uncertainty in future projections of Indo-Pacific climate variability and the background conditions in which it is embedded. Projections based on the current generation of climate models suggest that Indo-Pacific mean-state changes will involve slower warming in the eastern than in the western Indian Ocean. Given the presumed strength of the Atlantic influence on the pantropics, projections of future climate change could be substantially different if systematic model errors in the Atlantic were corrected. There is hence tremendous potential for improving seasonal to decadal climate predictions and for improving projections of future climate change in the tropics though advances in our understanding of the dynamics that govern interbasin linkages.</p>

scholarly journals Decadal climate variability in the tropical Pacific: Characteristics, causes, predictability, and prospects

Science ◽  
2021 ◽  
Vol 374 (6563) ◽  
Author(s):  
Scott Power ◽  
Matthieu Lengaigne ◽  
Antonietta Capotondi ◽  
Myriam Khodri ◽  
Jérôme Vialard ◽  
...  
Keyword(s):  
Climate Variability ◽  
Tropical Pacific ◽  
Decadal Climate Variability ◽  
Decadal Climate ◽  
The Tropical Pacific

scholarly journals On the structure and evolution of ENSO-related climate variability in the tropical Pacific: Lessons from TOGA

1998 ◽  
Vol 103 (C7) ◽  
pp. 14241-14259 ◽  
Author(s):  
J. M. Wallace ◽  
E. M. Rasmusson ◽  
T. P. Mitchell ◽  
V. E. Kousky ◽  
E. S. Sarachik ◽  
...  
Keyword(s):  
Climate Variability ◽  
Tropical Pacific ◽  
The Tropical Pacific

scholarly journals Climate Variability in a Coupled GCM. Part I: The Tropical Pacific

1993 ◽  
Vol 6 (1) ◽  
pp. 5-21 ◽  
Author(s):  
M. Latif ◽  
A. Sterl ◽  
E. Maier-Reimer ◽  
M. M. Junge
Keyword(s):  
Climate Variability ◽  
Tropical Pacific ◽  
Coupled Gcm ◽  
The Tropical Pacific

scholarly journals Reconstructing the Past Wind Stresses over the Tropical Pacific Ocean from 1875 to 1947

2009 ◽  
Vol 48 (6) ◽  
pp. 1181-1198 ◽  
Author(s):  
Ziwang Deng ◽  
Youmin Tang
Keyword(s):  
Pacific Ocean ◽  
Climate Variability ◽  
Regression Model ◽  
Wind Stress ◽  
Tropical Pacific ◽  
Tropical Pacific Ocean ◽  
The Past ◽  
Past Climate Variability ◽  
The Tropical Pacific

Abstract An important step in understanding the climate system is simulating and studying the past climate variability, using oceanic models, atmospheric models, or both. Toward this goal, long-term wind stress data, as the forcing of oceanic or climate models, are often required. In this study, the possibility of reconstructing the past winds of the tropical Pacific Ocean using historical sea surface temperature (SST) and sea level pressure (SLP) datasets was explored. Four statistical models, based on principal component (PC) regression and singular vector decomposition (SVD), were developed for reconstructing monthly pseudo wind stress over the tropical Pacific for the period 1875–1947. The high-frequency noise was removed from the raw data prior to the reconstruction. These models are SST-based PC regression (model 1), SLP-based PC regression (model 2), SST-based SVD (model 3), and SLP-based SVD (model 4). The results show that reconstructed wind stresses from all models can account for more than one-half of the total variances. In general, the SLP is better than SST as a predictor and the SVD method is superior to the PC regression. Forced by these reconstructed wind stresses, an oceanic general circulation model can simulate realistic interannual variability of the tropical Pacific SST. However, the wind stress reconstructed by SST-based models leads to better simulation skill in comparison with that from SLP-based models. Last, a long-term wind stress dataset was constructed for the period from 1875 to 1947 by the SST-based SVD model, which provides a useful tool for studying the past climate variability over the tropical Pacific, especially for El Niño–Southern Oscillation.

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