The Super-large Ensemble Experiments of CAS FGOALS-g3

A super-large ensemble simulation dataset with 110 members has been produced by the fully coupled model FGOALS-g3 developed by researchers at the Institute of Atmospheric Physics,Chinese Academy of Sciences.This is the first dataset of large ensemble simulations with a climate system model developed by a Chinese modeling center.The simulation has the largest realizations up to now worldwide in terms of single-model initial-condition large ensembles.Each member includes a historical experiment(1850-2014)and an experiment(2015-99)under the very high greenhouse gas emissions Shared Socioeconomic Pathway scenario(SSP5-8.5).The dataset includes monthly and daily temperature,precipitation,and other variables,requiring storage of 275 TB.Additionally,the surface air temperature(SAT)and land precipitation simulated by the FGOALS-g3 super-large ensemble have been validated and projected.The ensemble can capture the response of SAT and land precipitation to external forcings well,and the internal variabilities can be quantified.The availability of more than 100 realizations will help researchers to study rare events and improve the understanding of the impact of internal variability on forced climate changes.

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

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 Ni?o-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.

Sea surface salinity-derived indexes for distinguishing two types of El Niño events in the tropical Pacific

In this study,sea surface salinity(SSS)indexes are derived from reanalysis and observational datasets to distinguish the two types of(Central Pacific(CP)and Eastern Pacific(EP))El Niño events in the tropical Pacific.Based on the SSS anomalous spatial and temporal pointwise correlations with sea surface temperature(SST)indexes of two types of El Niño events,the key areas with SSS variations for EP and CP El Niño events are identified.For EP El Niño events,the key areas are located over an arcuate area centered at(0°,130°E)and in the central equatorial Pacific covering(5°S–5°N,175°W–158°W).For CP El Niño events,the key areas are located in the northeastern western Pacific covering(2°N,142°E–170°E)and in the southeastern Pacific covering(20°S–10°S,135°W–95°W).The key areas for EP and CP El Niño events in this study are not located near the dateline in the equatorial Pacific and differ from those obtained from the regression or composite methods.Accordingly,these key areas are used to construct SSS indexes,termed as the CP/EP El Niño SSS index(CSI/ESI),to distinguish EP and CP El Niño events independently.The SSS indexes are verified by different datasets over varying time periods and they can be adequately used to identify the two types of El Niño events and serve as another useful tool for monitoring ENSO.These analyses offer novel insight into how to represent the diversity of El Niño events.