A Note on the Role of Meridional Wind Stress Anomalies and Heat Flux in ENSO Simulations

Four comparative experiments and some supplementary experiments were conducted to examine the role of meridional wind stress anomalies and heat flux variability in ENSO simulations by using a high-resolution Ocean General Circulation Model （OGCM）. The results indicate that changes in the direction and magnitude of meridional wind stress anomalies have little influence on ENSO simulations until meridional wind stress anomalies are unrealistically enlarged by a factor of 5.0. However, evidence of an impact on ENSO simulations due to heat flux variability was found. The simulated Nino-3 index without the effect of heat flux anomalies tended to be around 1.0° lower than the observed, as well as the control run, during the peak months of ENSO events.     

Improved reliability of ENSO hindcasts with multi-ocean analyses ensemble initialization

Currently, ensemble seasonal forecasts using a single model with multiple perturbed initial conditions generally suffer from an “overconfidence” problem, i.e., the ensemble evolves such that the spread among members is small, compared to the magnitude of the mean error. This has motivated the use of a multi-model ensemble (MME), a technique that aims at sampling the structural uncertainty in the forecasting system. Here we investigate how the structural uncertainty in the ocean initial conditions impacts the reliability in seasonal forecasts, by using a new ensemble generation method to be referred to as the multiple-ocean analysis ensemble (MAE) initialization. In the MAE method, multiple ocean analyses are used to build an ensemble of ocean initial states, thus sampling structural uncertainties in oceanic initial conditions (OIC) originating from errors in the ocean model, the forcing flux, and the measurements, especially in areas and times of insufficient observations, as well as from the dependence on data assimilation methods. The merit of MAE initialization is demonstrated by the improved El Niño and the Southern Oscillation (ENSO) forecasting reliability. In particular, compared with the atmospheric perturbation or lagged ensemble approaches, the MAE initialization more effectively enhances ensemble dispersion in ENSO forecasting. A quantitative probabilistic measure of reliability also indicates that the MAE method performs better in forecasting all three (warm, neutral and cold) categories of ENSO events. In addition to improving seasonal forecasts, the MAE strategy may be used to identify the characteristics of the current structural uncertainty and as guidance for improving the observational network and assimilation strategy. Moreover, although the MAE method is not expected to totally correct the overconfidence of seasonal forecasts, our results demonstrate that OIC uncertainty is one of the major sources of forecast overconfidence, and suggest that the MAE is an essential component of an MME system.     

Eddy-permitting simulations of the sub-polar North Atlantic: impact of the model bias on water mass properties and circulation

Some previous studies demonstrated that model bias has a strong impact on the quality of long-term prognostic model simulations of the sub-polar North Atlantic Ocean. Relatively strong bias of water mass characteristics is observed in both eddy-permitting and eddy-resolving simulations, suggesting that an increase of model resolution does not reduce significantly the model bias. This study is an attempt to quantify the impact of model bias on the simulated water mass and circulation characteristics in an eddy-permitting model of the sub-polar ocean. This is done through comparison of eddy-permitting prognostic model simulations with the results from two other runs in which the bias is constrained by using spectral nudging. In the first run, the temperature and salinity are nudged towards climatology in the whole column. In the second run, the spectral nudging is applied in the surface 30 m layer and at depths below 560 m only. The biases of the model characteristics of the unconstrained run are similar to those reported in previous eddy-permitting and eddy-resolving studies. The salinity in the surface and intermediate waters of the Labrador Sea waters increases with respect to the climatology, which reduces the stability of the water column. The deep convection in the unconstrained run is artificially intensified and the transport in the sub-polar gyre stronger than in the observations. In particular, the transport of relatively salty and warm Irminger waters into the Labrador Sea is unrealistically high. While the water mass temperature and salinity in the run with spectral nudging in the whole column are closest to the observations, the depth of the winter convection is underestimated in the model. The water mass characteristics and water transport in the run with spectral nudging in the surface and deep layers only are close to observations and at the same time represent well the deep convection in terms of its intensity and position. The source of the bias in the prognostic model run is discussed.     

一个改进的混合型海气耦合模式：ENSO模拟

通过在中国科学院大气物理研究所热带太平洋环流模式与一个统计大气模式所建立的混合型海气耦合模式中引入次表层上卷海温非局地参数化方案, 对比分析了次表层上卷海温对耦合模式模拟结果的影响, 表明在引入次表层上卷海温非局地参数化方案前耦合模式模拟的SSTA最大变率中心位于日界线附近赤道南北狭窄范围内, 而在赤道东太平洋及南美沿岸一带变率过低, 周期呈准2年振荡。改进后, 耦合模式模拟结果的分布不论在东西方向亦或南北方向与观测更为相近, 振荡周期为4年左右, 而且还能模拟出观测中ENSO振荡的季节依赖性特征。进一步分析改进的耦合模式中海气耦合特征, 表明 “延迟振子” 理论、 “西太平洋振子” 理论、 “充电-放电振子” 理论及 “平流-反射” 理论所揭示的一些规律在该模式中都能被不同程度地描述出来, 这说明在实际的ENSO循环过程中, 可能有多种机制在同时起作用。     

Predicting US summer precipitation using NCEP Climate Forecast System version 2 initialized by multiple ocean analyses

This study examines the prediction skill of the contiguous United States (CONUS) precipitation in summer, as well as its potential sources using a set of ensemble hindcasts conducted with the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 and initialized from four independent ocean analyses. The multiple ocean ensemble mean (MOCN_ESMEAN) hindcasts start from each April for 26 summers (1982–2007), with each oceanic state paired with four atmosphere-land states. A subset of hindcasts from the NCEP CFS Reanalysis and Reforecast (CFSRR) project for the same period, from the same initial month and with the same total ensemble size, is also analyzed. Compared with CFSRR, MOCN_ESMEAN is distinguished by its oceanic ensemble spread that introduces potentially larger perturbations and better spatial representation of the oceanic uncertainty. The prediction skill of the CONUS precipitation in summer shows a similar spatial pattern in both MOCN_ESMEAN and CFSRR, but the results suggested that initialization from multiple ocean analyses may bring more robust signals and additional skills to the seasonal prediction for both sea surface temperature and precipitation. Among the predictable areas for precipitation, the northwestern CONUS (NWUS) is the most robust. A further analysis shows that the enhanced summer precipitation prediction skill in NWUS is mainly associated with the El Niño/Southern Oscillation, with possible influence also from the Pacific Decadal Oscillation. Through this work, we argue that a large ensemble is necessary for precipitation forecast in mid-latitudes, such as the CONUS, and taking into account of the oceanic initial state uncertainty is an efficient way to build such an ensemble.     

Prediction skill of monthly SST in the North Atlantic Ocean in NCEP Climate Forecast System version 2

This work evaluates the skill of retrospective predictions of the second version of the NCEP Climate Forecast System (CFSv2) for the North Atlantic sea surface temperature (SST) and investigates the influence of El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) on the prediction skill over this region. It is shown that the CFSv2 prediction skill with 0–8 month lead displays a “tripole”-like pattern with areas of higher skills in the high latitude and tropical North Atlantic, surrounding the area of lower skills in the mid-latitude western North Atlantic. This “tripole”-like prediction skill pattern is mainly due to the persistency of SST anomalies (SSTAs), which is related to the influence of ENSO and NAO over the North Atlantic. The influences of ENSO and NAO, and their seasonality, result in the prediction skill in the tropical North Atlantic the highest in spring and the lowest in summer. In CFSv2, the ENSO influence over the North Atlantic is overestimated but the impact of NAO over the North Atlantic is not well simulated. However, compared with CFSv1, the overall skills of CFSv2 are slightly higher over the whole North Atlantic, particularly in the high latitudes and the northwest North Atlantic. The model prediction skill beyond the persistency initially presents in the mid-latitudes of the North Atlantic and extends to the low latitudes with time. That might suggest that the model captures the associated air-sea interaction in the North Atlantic. The CFSv2 prediction is less skillful than that of SSTA persistency in the high latitudes, implying that over this region the persistency is even better than CFSv2 predictions. Also, both persistent and CFSv2 predictions have relatively low skills along the Gulf Stream.     

Improving prediction of two ENSO types using a multi-model ensemble based on stepwise pattern projection model

Climate Dynamics - This study focuses on improving prediction of the two types of ENSO by combining multi-model ensemble (MME) with a statistical error correction method that is based on a stepwise...