Corresponding to the pronounced amplitude asymmetry for the central Pacific (CP) and eastern Pacific (EP) types of El Niño, an asymmetry in the strength of the Bjerknes positive feedback is found between these two types of El Niño, which is manifested as a weaker relationship between the zonal wind anomaly and the zonal gradient of sea surface temperature (SST) anomaly in the CP El Niño. The strength asymmetry mainly comes from a weaker sensitivity of the zonal gradient of sea level pressure (SLP) anomaly to that of diabatic heating anomaly during CP El Niño. This weaker sensitivity is caused by (1) a large cancelation induced by the negative SST-cloud thermodynamic feedback to the positive dynamical feedback for CP El Niño, (2) an off-equator shift of the maximum SLP anomalies during CP El Niño, and (3) a suppression of the mean low-level convergence when CP El Niño events occur more often.
[1] In this paper, electron densities during 25-28 September 2000 observed by the Millstone Hill incoherent scatter radar (ISR) are assimilated into a one-dimensional midlatitude ionospheric theoretical model by using an ensemble Kalman filter (EnKF) technique. It is found that (1) the derived vertical correlation coefficients of electron density show obvious altitude dependence. These variations are consistent with those from ISR observations. (2) The EnKF technique has a better performance than the 3DVAR technique especially in the data-gap regions, which indicates that the EnKF technique can extend the influences of observations from data-rich regions to data-gap regions more effectively. (3) Both the altitude and local time variations of the root mean square error (RMSE) of electron densities for the ensemble spread and ensemble mean from observation behave similarly. It is shown that the spread of the ensemble members can represent the deviations of ensemble mean from observations. (4) To achieve a better prediction performance, the external driving forces should also be adjusted simultaneously to the real weather conditions. For example, the performance of prediction can be improved by adjusting neutral meridional wind using equivalent wind method. (5) In the EnKF, there are often erroneous correlations over large distance because of the sampling error. This problem may be avoided by using a relative larger ensemble size.
Based on an intermediate coupled model (ICM), a probabilistic ensemble prediction system (EPS) has been developed. The ensemble Kalman filter (EnKF) data assimilation approach is used for generating the initial ensemble conditions, and a linear, first-order Markov-Chain SST anomaly error model is embedded into the EPS to provide model-error perturbations. In this study, we perform ENSO retrospective forecasts over the 120 year period 1886-2005 using the EPS with 100 ensemble members and with initial conditions obtained by only assimilating historic SST anomaly observations. By examining the retrospective ensemble forecasts and available observations, the verification results show that the skill of the ensemble mean of the EPS is greater than that of a single deterministic forecast using the same ICM, with a distinct improvement of both the correlation and root mean square (RMS) error between the ensemble-mean hindcast and the deterministic scheme over the 12-month prediction period. The RMS error of the ensemble mean is almost 0.2 • C smaller than that of the deterministic forecast at a lead time of 12 months. The probabilistic skill of the EPS is also high with the predicted ensemble following the SST observations well, and the areas under the relative operating characteristic (ROC) curves for three different ENSO states (warm events, cold events, and neutral events) are all above 0.55 out to 12 months lead time.However, both deterministic and probabilistic prediction skills of the EPS show an interdecadal variation. For the deterministic skill, there is high skill in the late 19th century and in the middle-late 20th century (which includes some artificial skill due to the model training period), and low skill during the period from 1906 to 1961. For probabilistic skill, for the three different ENSO states, there is still a similar interdecadal variation of ENSO probabilistic predictability during the period 1886-2005. There is high skill in the late 19th century from 1886 to 1905, and a decline to a minimum of skill around 1910-50s, beyond which skill rebounds and increases with time until the 2000s.
During 2010–11, a La Niña condition prevailed in the tropical Pacific. An intermediate coupled model (ICM) is used to demonstrate a real-time forecast of sea surface temperature (SST) evolution during the event. One of the ICM's unique features is an empirical parameterization of the temperature of subsurface water entrained into the mixed layer (Te). This model provided a good prediction, particularly of the "double dip" evolution of SST in 2011 that followed the La Niña event peak in October 2010. Thermocline feedback, explicitly represented by the relationship between Te and sea level in the ICM, is a crucial factor affecting the second cooling in 2011. Large negative Te anomalies were observed to persist in the central equatorial domain during 2010–11, inducing a cold SST anomaly to the east during July–August 2011 and leading to the development of a La Niña condition thereafter.
[1] The ensemble Kalman filter (EnKF) depends on a set of ensemble forecasts to calculate the background error covariances. Without model error perturbations and the inflation of forecast ensembles, the spread of the ensemble forecasts can collapse rapidly. There are several ways to generate model perturbations, i.e., perturbations in model parameters/parameterizations, perturbations in the forcing fields of the model and adding some error terms to the right-hand side of the model equations. In this paper, we focus on the ''adding model error terms'' approach, which utilizes a first-order Markov chain model. This approach is suitable to those unforced models, such as the coupled atmosphere-ocean models. However, for a multivariate model, the balance between different model variables could be an important issue in building its model-error model. In this paper, we focus on building a balanced error model for an intermediate coupled model for El Niño-Southern Oscillation (ENSO) predictions. A simple approach to build such a model-error model is proposed on the basis of the multivariate empirical orthogonal functions method. EnKF data assimilation experiments with different configurations of multivariate model error treatments (no model errors, unbalanced and balanced model errors) are performed using realistic sea surface temperature (SST) and sea level (SL) observations. Results show that it is necessary to develop balanced, multivariate modelerror models in order to successfully assimilate both SST and SL observations. The hindcasts initialized from these different assimilation experiment results also demonstrate that the balanced model errors can yield more balanced initial conditions that lead to improved predictions of ENSO events.
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