首页|Evaluating CMIP6 models in simulating the North Pacific decadal variability in sea surface salinity

Evaluating CMIP6 models in simulating the North Pacific decadal variability in sea surface salinity

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The North Pacific sea surface salinity(SSS)decadal variability(NPSDV)and its potential forcing were evaluated from 25 coupled models of the Coupled Model Intercomparison Project phase 6(CMIP6)considering the prospects for decadal climate predictions.The results indicated that the CMIP6 models generally reproduced the spatial patterns of NPSDV The large standard deviation of the SSS anomaly over the strong current regions,such as the Kuroshio-Oyashio Extension(KOE),North Pacific Current(NPC),California Current System(CCS),and Alaskan Coastal Current(ACC),is reflected in the two leading modes of NPSDV:a dipole with out-of-phase loadings in the KOE-NPC versus CCS-ACC and a monopole with positive loading over the KOE-NPC.The order of modes is sensitive to individual models that exhibit discrepancies,especially in temporal phases and power spectra.An autoregressive model of order-1 was used to reconstruct the NPSDV with several forcing terms.The generally weaker influence of forcings in an autoregressive model of order-1 is partly related to the overestimated response time of NPSDV relative to forcings.Most NPSDV variances originate from the persistence of SSS anomalies,but the dominant forcing factors are diverse among models.The model diversity for the NPSDV simulation mainly arises from the influence of the tropical El Niño-Southern Oscillation through teleconnection on the North Pacific Oscillation or Aleutian Low with timescale dependence.Conversely,models that can reproduce the NPSDV well are not dependent on those with larger impacts from the North Pacific oceanic processes.

North PacificSea surface salinityDecadal variabilityCMIP6Leading mode

Jian CHEN、Hailong LIU、Pengfei LIN、Wenrong BAI、Chengzu BAI、Lu WANG

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Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China

State Key Laboratory of Geo-Information Engineering,Xi'an 710054,China

Beijing Municipal Climate Center,Beijing Meteorological Bureau,Beijing 100089,China

Space System Department Equipment Department,Beijing 100096,China

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CMIP6 model data,ORAS4NCEP-NCAR datasetNational Key Research and Development ProgramNational Natural Sciences Foundation of ChinaNational Natural Sciences Foundation of ChinaNational Natural Sciences Foundation of China

2020YFA0608902419760264193118341706021& 41976188

2024

10.1007/s11430-023-1373-8

中国科学:地球科学(英文版)
中国科学院

中国科学:地球科学(英文版)

影响因子：1.002
ISSN：1674-7313
年,卷(期)：2024.67(8)