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El Ni?o and El Ni?o Modoki variability based on a new ocean reanalysis |
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| Authors: | Dongxiao Wang Yinghao Qin Xianjun Xiao Zuqiang Zhang Xiangyu Wu |
| Institution: | 1. State Key Laboratory of Tropical Oceanography (LTO), SCSIO, CAS, Guangzhou, 510301, China 2. Graduate School of the Chinese Academy of Sciences, Beijing, 100049, China 3. National Climate Center, Beijing, 100081, China 4. National Marine Environmental Forecasting Center, Beijing, 100081, China |
| Abstract: | A new ocean reanalysis, covering the period from 1990 to 2009, is evaluated against observational sea surface temperature (SST) and sea surface height (SSH) data in reproducing the temporal characteristics of El Ni?o and El Ni?o Modoki. The new reanalysis assimilates the available SST, temperature–salinity profile, and satellite altimetry data sets into a global ocean model forced with surface boundary conditions from the National Centers for Environmental Prediction atmospheric reanalysis 2. Using the Ni?o 3 index and the improved El Ni?o Modoki index, to distinguish between El Ni?o and El Ni?o Modoki signals, our results show that the two time series in the new reanalysis are in agreement with those obtained from observations during the study period. A composite analysis method is used to demonstrate the temporal evolution of these two types of El Ni?o. The new reanalysis has the advantage of representing the strength and location of El Ni?o events better than the control run, with an increase in the spatial correlation, but El Ni?o variability in the reanalysis is weak in the eastern Pacific, particularly off the coast of South America. As for the El Ni?o Modoki events, the initiation, development, and termination of the warm SST anomalies all occur in the central Pacific. All main features associated with the warm SST anomaly pattern of El Ni?o Modoki are well represented in the reanalysis. Furthermore, using this new ocean reanalysis, we select two strong cases to investigate possible mechanisms that may lead to the different warm SST anomaly patterns. |
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