Assimilation systems absorb both satellite measurements and Argo observations.This assimilation is essential to diagnose and evaluate the contribution from each type of data to the reconstructed analysis,allowing for ...Assimilation systems absorb both satellite measurements and Argo observations.This assimilation is essential to diagnose and evaluate the contribution from each type of data to the reconstructed analysis,allowing for better configuration of assimilation parameters.To achieve this,two comparative reconstruction schemes were designed under the optimal interpolation framework.Using a static scheme,an in situ-only field of ocean temperature was derived by correcting climatology with only Argo profiles.Through a dynamic scheme,a synthetic field was first derived from only satellite sea surface height and sea surface temperature measurements through vertical projection,and then a combined field was reconstructed by correcting the synthetic field with in situ profiles.For both schemes,a diagnostic iterative method was performed to optimize the background and observation error covariance statics.The root mean square difference(RMSD)of the in situ-only field,synthetic field and combined field were analyzed toward assimilated observations and independent observations,respectively.The rationale behind the distribution of RMSD was discussed using the following diagnostics:(1)The synthetic field has a smaller RMSD within the global mixed layer and extratropical deep waters,as in the Northwest Pacific Ocean;this is controlled by the explained variance of the vertical surface-underwater regression that reflects the ocean upper mixing and interior baroclinicity.(2)The in situ-only field has a smaller RMSD in the tropical upper layer and at midlatitudes;this is determined by the actual noise-to-signal ratio of ocean temperature.(3)The satellite observations make a more significant contribution to the analysis toward independent observations in the extratropics;this is determined by both the geographical feature of the synthetic field RMSD(smaller at depth in the extratropics)and that of the covariance correlation scales(smaller in the extratropics).展开更多
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...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.展开更多
基金The National Natural Science Foundation of China under contract Nos 41706021 and 41976188。
文摘Assimilation systems absorb both satellite measurements and Argo observations.This assimilation is essential to diagnose and evaluate the contribution from each type of data to the reconstructed analysis,allowing for better configuration of assimilation parameters.To achieve this,two comparative reconstruction schemes were designed under the optimal interpolation framework.Using a static scheme,an in situ-only field of ocean temperature was derived by correcting climatology with only Argo profiles.Through a dynamic scheme,a synthetic field was first derived from only satellite sea surface height and sea surface temperature measurements through vertical projection,and then a combined field was reconstructed by correcting the synthetic field with in situ profiles.For both schemes,a diagnostic iterative method was performed to optimize the background and observation error covariance statics.The root mean square difference(RMSD)of the in situ-only field,synthetic field and combined field were analyzed toward assimilated observations and independent observations,respectively.The rationale behind the distribution of RMSD was discussed using the following diagnostics:(1)The synthetic field has a smaller RMSD within the global mixed layer and extratropical deep waters,as in the Northwest Pacific Ocean;this is controlled by the explained variance of the vertical surface-underwater regression that reflects the ocean upper mixing and interior baroclinicity.(2)The in situ-only field has a smaller RMSD in the tropical upper layer and at midlatitudes;this is determined by the actual noise-to-signal ratio of ocean temperature.(3)The satellite observations make a more significant contribution to the analysis toward independent observations in the extratropics;this is determined by both the geographical feature of the synthetic field RMSD(smaller at depth in the extratropics)and that of the covariance correlation scales(smaller in the extratropics).
基金supported by the National Key Research and Development Program(Grant No.2020YFA0608902)the National Natural Sciences Foundation of China(Grant Nos.41976026,41931183,41706021&41976188)。
文摘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.
