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Sources of Uncertainty in the Tropical Pacific Warming Pattern under Global Warming Projected by Coupled Ocean-Atmosphere Models de Jun Ying

Sources of Uncertainty in the Tropical Pacific Warming Pattern under Global Warming Projected by Coupled Ocean-Atmosphere Models: Springer Theses

Autor Jun Ying
en Limba Engleză Paperback – 19 sep 2020
This book discusses the sources of uncertainty in future model projections of the tropical Pacific SST warming pattern under global warming. It mainly focuses on cloud radiation feedback and ocean dynamical effect, which reveal to be the two greatest sources of uncertainty in the tropical Pacific SST warming pattern. Moreover, the book presents a correction for model projections of the tropical Pacific SST warming pattern based on the concept of “observational constraints”; the corrected projection exhibits a more El Niño-like warming pattern.

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Specificații

ISBN-13: 9789813298460
ISBN-10: 9813298464
Pagini: 81
Ilustrații: XII, 81 p.
Dimensiuni: 155 x 235 mm
Greutate: 0.15 kg
Ediția:1st ed. 2020
Editura: Springer Nature Singapore
Colecția Springer
Seria Springer Theses

Locul publicării:Singapore, Singapore

Cuprins

Introduction.- Evaluating the formation mechanisms of the tropical Pacific SST warming pattern in CMIP5 multi-model ensemble.- Influence of cloud radiation feedback on the uncertainty in projecting tropical Pacific SST warming pattern.- Effects of large-scale ocean dynamics on the uncertainty in projecting tropical Pacific SST warming pattern.- Correcting the multi-model ensemble tropical Pacific SST warming pattern.- Summary and prospects.

Notă biografică



Area of work: Tropical ocean-atmosphere interaction; Climate change; El Niño–Southern Oscillation.
Honors:
The excellent doctoral dissertation of institute of atmospheric physics, Chinese academy of science in 2016;
The excellent doctoral dissertation of Chinese academy of science in 2017.
Publications:
(1) Ying Jun and Ping Huang, Cloud-radiation feedback as a leading source of uncertainty in the tropical Pacific SST warming pattern in CMIP5 models. Journal of Climate, 2016, 29: 3867-3881.
(2) Ying Jun, Ping Huang, and Ronghui Huang, Evaluating the formation mechanisms of the equatorial Pacific SST warming pattern in CMIP5 models. Advances in Atmospheric Sciences, 2016, 33(4): 433-441.
(3) Ying Jun and Ping Huang, The large-scale ocean dynamical effect on uncertainty in the tropical Pacific SST warming pattern in CMIP5 models. Journal of Climate, 2016, 29: 8051-8065.
(4) Ying Jun, Ping Huang, Tao Lian and Hongjian Tan, Understanding the effect of an excessive cold tongue bias on projecting the tropical Pacific SST warming pattern in CMIP5 models. Climate Dynamics, 2019, 52,1805-1818.
(5) Ying, Jun., P. Huang, T. Lian, and D. Chen, Intermodel Uncertainty in the Change of ENSO’s Amplitude under Global Warming: Role of the Response of Atmospheric Circulation to SST Anomalies. Journal of Climate, 2019, 32, 369-383.
(6) Huang Ping and Jun Ying, A multimodel ensemble pattern regression method to correct the tropical Pacific SST change patterns under global warming. Journal of Climate, 2016, 28: 4706-4723.

Textul de pe ultima copertă

This book discusses the sources of uncertainty in future model projections of the tropical Pacific SST warming pattern under global warming. It mainly focuses on cloud radiation feedback and ocean dynamical effect, which reveal to be the two greatest sources of uncertainty in the tropical Pacific SST warming pattern. Moreover, the book presents a correction for model projections of the tropical Pacific SST warming pattern based on the concept of “observational constraints”; the corrected projection exhibits a more El Niño-like warming pattern.

Caracteristici

Offers readers a comprehensive understanding of the various formation mechanisms of the tropical Pacific SST warming pattern Proposes two major sources of uncertainty in model projections of the tropical Pacific SST warming pattern under global warming Provides an improved projection of the tropical Pacific SST warming pattern by eliminating certain climatological model biases