Using a 19-year altimetric dataset, the mean properties and spatiotemporal variations of eddies in the Kuroshio recirculation region are examined. A total of 2 001 cyclonic tracks and 1 847 anticyclonic tracks were identifi ed using a geometry-based eddy detection method. The mean radius was 57 km for cyclonic eddies and was 61 km for anticyclonic eddies, respectively, and the mean lifetime was about 10 weeks for both type eddies. There were asymmetric spatial distributions for eddy generation and eddy termination, which were domain-dependent. Mean eddy generation rates were 2.0 per week for cyclonic eddies and were 1.9 per week for anticyclonic eddies. Both type eddies tended to deform during their lifetime and had different propagation characteristics, which mainly propagated westward and southwestward with velocities 4.0–9.9 cm/s, in the Kuroshio recirculation region. Further discussion illustrates that the eddy westward speed maybe infl uenced by the combined effect of vertical shear of horizontal currents and nonlinearity of eddy. To better understand the evolution of eddy tracks, a total of 134 long-lived tracks(lifetime ≥20 weeks) were examined. Comparison between short-span eddies(lifetime ≥4 weeks and <20 weeks) and long-lived eddies is also conducted and the result shows that the short-span and long-lived eddies have similar time evolution. Finally, eddy seasonal variations and interannual changes are discussed. Correlation analysis shows that eddy activity is sensitive to the wind stress curl and meridional gradient of sea surface temperature on interannual timescales. Besides, the strength and orientation of background fl ows also have impacts on the eddy genesis.
Targeted observation is an observation strategy by which the concerned phenomenon is observed. In geoscienee, targeted ob- servation is mainly related to the forecasts of weather events or predictions of climate events. This paper will first review the history of targeted observation, and then introduce the main methods used in targeted observation. The discussion on the theo- retical basis of targeted observation includes its advantages and limitations. After presenting the current situation of domestic and international targeted observations in atmospheric and oceanic sciences, the methods used for targeted observation, and their effect evaluation and testing are mainly discussed here. Finally, the author presents his suggestion about the prospect of further development in the field, and how to extend the method of targeted observation to deal with numerical model errors.
Model errors offset by constant and time-variant optimal forcing vector approaches （termed COF and OFV, respectively） are analyzed within the framework of E1 Nifio simulations. Applying the COF and OFV approaches to the well-known Zebiak-Cane model, we re-simulate the 1997 and 2004 E1 Nifio events, both of which were poorly degraded by a certain amount of model error when the initial anomalies were generated by coupling the observed wind forcing to an ocean com- ponent. It is found that the Zebiak-Cane model with the COF approach roughly reproduced the 1997 E1 Nifio, but the 2004 E1 Nifio simulated by this approach defied an ENSO classification, i.e., it was hardly distinguishable as CP-E1 Nifio or EP-E1 Nifio. In hoth E1 Nifio simulations, substituting the COF with the OFV improved the fit between the simulations and obser- vations because the OFV better manages the time-variant errors in the model. Furthermore, the OFV approach effectively corrected the modeled E1 Nifio events even when the observational data （and hence the computational time） were reduced. Such a cost-effective offset of model errors suggests a role for the OFV approach in complicated CGCMs.
This paper reviews progress in the application of conditional nonlinear optimal perturbation to targeted observation studies of the atmosphere and ocean in recent years, with a focus on the E1 Nifio-Southern Oscillation （ENSO）, Kuroshio path variations, and blocking events. Through studying the optimal precursor （OPR） and optimally growing initial error （OGE） of the occurrence of the above events, the similarity and localization features of OPR and OGE spatial structures have been found for each event. Ideal hindcasting experiments have shown that, if initial errors are reduced in the areas with the largest amplitude for the OPR and OGE for ENSO and Kuroshio path variations, the forecast skill of the model for these events is significantly improved. Due to the similarity between patterns of the OPR and OGE, additional observations implemented in the same sensitive region would help to not only capture the precursors, but also reduce the initial errors in the predictions, greatly increasing the forecast abilities. The similarity and localization of the spatial structures of the OPR and OGE during the onset of blocking events have also been investigated, but their application to targeted observation requires further study.
Conditional nonlinear optimal perturbation(CNOP) is an extension of the linear singular vector technique in the nonlinear regime.It represents the initial perturbation that is subjected to a given physical constraint,and results in the largest nonlinear evolution at the prediction time.CNOP-type errors play an important role in the predictability of weather and climate.Generally,when calculating CNOP in a complicated numerical model,we need the gradient of the objective function with respect to the initial perturbations to provide the descent direction for searching the phase space.The adjoint technique is widely used to calculate the gradient of the objective function.However,it is difficult and cumbersome to construct the adjoint model of a complicated numerical model,which imposes a limitation on the application of CNOP.Based on previous research,this study proposes a new ensemble projection algorithm based on singular vector decomposition(SVD).The new algorithm avoids the localization procedure of previous ensemble projection algorithms,and overcomes the uncertainty caused by choosing the localization radius empirically.The new algorithm is applied to calculate the CNOP in an intermediate forecasting model.The results show that the CNOP obtained by the new ensemble-based algorithm can effectively approximate that calculated by the adjoint algorithm,and retains the general spatial characteristics of the latter.Hence,the new SVD-based ensemble projection algorithm proposed in this study is an effective method of approximating the CNOP.
With the Zebiak-Cane model, the present study investigates the role of model errors represented by the nonlinear forcing singular vector （NFSV） in the ＂spring predictability barrier＂ （SPB） phenomenon in ENSO prediction. The NFSV-related model errors are found to have the largest negative effect on the uncertainties of El Nino prediction and they can be classified into two types： the first is featured with a zonal dipolar pattern of SST anomalies （SSTA）, with the western poles centered in the equatorial central western Pacific exhibiting positive anomalies and the eastern poles in the equatorial eastern Pacific exhibiting negative anomalies; and the second is characterized by a pattern almost opposite to the first type. The first type of error tends to have the worst effects on El Nifio growth-phase predictions, whereas the latter often yields the largest negative effects on decaying-phase predictions. The evolution of prediction errors caused by NFSV- related errors exhibits prominent seasonality, with the fastest error growth in spring and/or summer; hence, these errors result in a significant SPB related to El Nifio events. The linear counterpart of NFSVs, the （linear） forcing singular vector （FSV）, induces a less significant SPB because it contains smaller prediction errors. Random errors cannot generate an SPB for El Nifio events. These results show that the occurrence of an SPB is related to the spatial patterns of tendency errors. The NFSV tendency errors cause the most significant SPB for El Nifio events. In addition, NFSVs often concentrate these large value errors in a few areas within the equatorial eastern and central-western Pacific, which likely represent those areas sensitive to El Nifio predictions associated with model errors. Meanwhile, these areas are also exactly consistent with the sensitive areas related to initial errors determined by previous studies. This implies that additional observations in the sensitive areas would not only improve the accuracy of the initial
