Jieshun Zhu

Ensemble ENSO hindcasts initialized from multiple ocean analyses

n n In this study, the impact of ocean initial conditions (OIC) on the prediction skill in the tropical Pacific Ocean is examined. Four sets of OIC are used to initialize the 12-month hindcasts of the tropical climate from 1979 to 2007, using the Climate Forecast System, version 2 (CFSv2), the current operational climate prediction model at the National Centers for Environmental Predictions (NCEP). These OICs are chosen from four ocean analyses produced by the NCEP and the European Center for Medium Range Weather Forecasts (ECMWF). For each hindcast starting from a given OIC, four ensemble members are generated with different atmosphere and land initial states. The predictive skill in the tropical Pacific Ocean is assessed based on the ensemble mean hindcasts from each individual as well as multiple oceanic analyses. To reduce the climate drift from various oceanic analyses, an anomaly initialization strategy is used for all hindcasts. The results indicate that there exists a substantial spread in the sea surface temperature (SST) prediction skill with different ocean analyses. Specifically, the ENSO prediction skill in terms of the anomaly correlation of Nino-3.4 index can differ by as much as 0.1-0.2 at lead times longer than 2 months. The ensemble mean of the predictions initialized from all four ocean analyses gives prediction skill equivalent to the best one derived from the individual ocean analysis. It is suggested that more accurate OIC can improve the ENSO prediction skill and an ensemble ocean initialization has the potential of enhancing the skill at the present stage.

An ensemble estimation of the variability of upper-ocean heat content over the tropical Atlantic Ocean with multi-ocean reanalysis products

Current ocean reanalysis systems contain considerable uncertainty in estimating the subsurface oceanic state, especially in the tropical Atlantic Ocean. Given this level of uncertainty, it is important to develop useful strategies to identify realistic low-frequency signals optimally from these analyses. In this paper, we present an “ensemble” method to estimate the variability of upper-ocean heat content (HC) in the tropical Atlantic based on multiple-ocean reanalysis products. Six state-of-the-art global ocean reanalaysis products, all of which are widely used in the climate research community, are examined in terms of their HC variability from 1979 to 2007. The conventional empirical orthogonal function (EOF) analysis of the HC anomalies from each individual analysis indicates that their leading modes show significant qualitative differences among analyses, especially for the first modes, although some common characteristics are discernable. Then, the simple arithmetic average (or ensemble mean) is applied to produce an ensemble dataset, i.e., the EM analysis. The leading EOF modes of the EM analysis show quantitatively consistent spatial–temporal patterns with those derived from an alternative EOF technique that maximizes signal-to-noise ratio of the six analyses, which suggests that the ensemble mean generates HC fields with the noise reduced to an acceptable level. The quality of the EM analysis is further validated against AVISO altimetry sea level anomaly (SLA) data and PIRATA mooring station data. A regression analysis with the AVISO SLA data proved that the leading modes in the EM analysis are realistic. It also demonstrated that some reanalysis products might contain higher level of intrinsic noise than others. A quantitative correlation analysis indicates that the HC fields are more realistic in the EM analysis than in individual products, especially over the equatorial regions, with signals contributed from all ensemble members. A direct comparison with the HC anomalies derived from in situ temperature measurements showed that the EM analysis generally gets realistic HC variability at the five chosen PIRATA mooring stations. Overall, these results demonstrate that the EM analysis is a promising alternative for studying physical processes and possibly for initializing climate predictions.

Salinity anomaly as a trigger for ENSO events

According to the classical theories of ENSO, subsurface anomalies in ocean thermal structure are precursors for ENSO events and their initial specification is essential for skillful ENSO forecast. Although ocean salinity in the tropical Pacific (particularly in the western Pacific warm pool) can vary in response to El Niño events, its effect on ENSO evolution and forecasts of ENSO has been less explored. Here we present evidence that, in addition to the passive response, salinity variability may also play an active role in ENSO evolution, and thus important in forecasting El Niño events. By comparing two forecast experiments in which the interannually variability of salinity in the ocean initial states is either included or excluded, the salinity variability is shown to be essential to correctly forecast the 2007/08 La Niña starting from April 2007. With realistic salinity initial states, the tendency to decay of the subsurface cold condition during the spring and early summer 2007 was interrupted by positive salinity anomalies in the upper central Pacific, which working together with the Bjerknes positive feedback, contributed to the development of the La Niña event. Our study suggests that ENSO forecasts will benefit from more accurate salinity observations with large-scale spatial coverage.

The role of off-equatorial surface temperature anomalies in the 2014 El Nino prediction

The 2014 El Niño, anticipated to be a strong event in early 2014, turned out to be fairly weak. In early 2014, the tropical Pacific exhibited persistent negative SST anomalies in the southeastern Pacific and positive SST anomalies in north, following the pattern of the Southern Pacific Meridional Mode. In this study, we explored the role of the off-equatorial SST anomalies in the 2014 prediction. Our experiments show that 40% of the amplitude error at the peak phase could be attributed to the lack of prediction of negative SST anomalies in the southeastern Pacific. However, the impact of this model error is partially compensated by the absence of the positive SST anomalies in the tropical western North Pacific in the model. The model response to the amplitude of negative southeastern Pacific SST anomalies is nonlinear in terms of equatorial warming, because the enhanced meridional pressure gradient forces very strong meridional winds without accelerating the zonal wind component near the equator. Our study suggests that reliable forecasts of ENSO strongly rely on correctly modeling the meridional SST gradient, as well as its delicate feedback with the zonal (ENSO) mode.

ENSO Prediction in Project Minerva: Sensitivity to Atmospheric Horizontal Resolution and Ensemble Size

AbstractThis study examines El Nino–Southern Oscillation (ENSO) prediction in Project Minerva, a recent collaboration between the Center for Ocean–Land–Atmosphere Studies (COLA) and the European Centre for Medium-Range Weather Forecasts (ECMWF). The focus is primarily on the impact of the atmospheric horizontal resolution on ENSO prediction, but the effect from different ensemble sizes is also discussed. Particularly, three sets of 7-month hindcasts performed with ECMWF prediction system are compared, starting from 1 May (1 November) during 1982–2011 (1982–2010): spectral T319 atmospheric resolution with 15 ensembles, spectral T639 with 15 ensembles, and spectral T319 with 51 ensembles. The analysis herein shows that simply increasing either ensemble size from 15 to 51 or atmospheric horizontal resolution from T319 to T639 does not necessarily lead to major improvement in the ENSO prediction skill with current climate models. For deterministic prediction skill metrics, the three sets of predictions do not...

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.

The relationship between thermocline depth and SST anomalies in the eastern equatorial Pacific: Seasonality and decadal variations

Even though the vital role of thermocline fluctuation in El Nino–Southern Oscillation (ENSO) cycle has been established previously, the direct relationship between the thermocline depth and sea surface temperature (SST) anomalies in the equatorial Pacific is yet to be fully understood, especially its seasonality. Thermocline depth anomalies have been found to lead SST anomalies in time with a longitude-dependent delay, but our study also suggests that the relationship shows considerable seasonal dependency and is strongest (weakest) during the boreal spring (summer). Over the eastern equatorial Pacific where there is least delay (compared to that in the western and central Pacific), the connection between thermocline and SST is the weakest during the boreal spring. This feature may be one of causes for ENSO spring persistence barrier. Furthermore, the thermocline-SST connections exhibit significant decadal variations, which are remarkably consistent with the decadal changes in the persistence barrier of SST anomalies over the eastern Pacific. It is also found that the decadal shift in the timing of the thermocline-SST connection barrier is caused by the changes in the seasonal cycle of tropical trade winds and thermocline depths.

Climate drift of AMOC, North Atlantic salinity and arctic sea ice in CFSv2 decadal predictions

Abstract There are potential advantages to extending operational seasonal forecast models to predict decadal variability but major efforts are required to assess the model fidelity for this task. In this study, we examine the North Atlantic climate simulated by the NCEP Climate Forecast System, version 2 (CFSv2), using a set of ensemble decadal hindcasts and several 30-year simulations initialized from realistic ocean–atmosphere states. It is found that a substantial climate drift occurs in the first few years of the CFSv2 hindcasts, which represents a major systematic bias and may seriously affect the model’s fidelity for decadal prediction. In particular, it is noted that a major reduction of the upper ocean salinity in the northern North Atlantic weakens the Atlantic meridional overturning circulation (AMOC) significantly. This freshening is likely caused by the excessive freshwater transport from the Arctic Ocean and weakened subtropical water transport by the North Atlantic Current. A potential source of the excessive freshwater is the quick melting of sea ice, which also causes unrealistically thin ice cover in the Arctic Ocean. Our sensitivity experiments with adjusted sea ice albedo parameters produce a sustainable ice cover with realistic thickness distribution. It also leads to a moderate increase of the AMOC strength. This study suggests that a realistic freshwater balance, including a proper sea ice feedback, is crucial for simulating the North Atlantic climate and its variability.

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

Abstract 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.

Prediction Skill of North Pacific Variability in NCEP Climate Forecast System Version 2: Impact of ENSO and Beyond

AbstractThis work examines the impact of El Nino–Southern Oscillation (ENSO) on the prediction skill of North Pacific variability (NPV) in retrospective predictions of the NCEP Climate Forecast System, version 2. It is noted that the phase relationship between ENSO and NPV at initial conditions (ICs) affects the prediction skill of NPV. For average lead times of 0–6 months, the prediction skills of sea surface temperature anomalies (SSTAs) in NPV (defined as the NPV index) increase from 0.42 to 0.63 from the cases of an out-of-phase relation between the Nino-3.4 and NPV indices in ICs to the cases of an in-phase relation. It is suggested that when ENSO and NPV are in phase in ICs, ENSO plays a constructive role in the NPV development and enhances its signals. Nevertheless, when ENSO and NPV are out of phase, some pronounced positive NPV events are still predictable. In these cases, the North Pacific is dominated by strong positive SSTAs, which may overcome the opposing influence from the tropical Pacific ...