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Jieshun Zhu and Arun Kumar

Abstract

While previous studies suggested that salinity could feed back onto MJO variability via modulating upper ocean stratification and further on SST, there is no direct evidence yet proving (or disproving) the importance of this feedback in MJO evolution and its predictability. This study is an initial attempt to quantify the role of SSS feedback on MJO predictability, based on a “perfect model” framework with the CFSv2. Specifically, the SSS feedback is isolated by nudging model SSS to climatological states during forecasts. For comparison, two more experiments were done, one as a benchmark experiment by estimating MJO predictability in CFSv2 and another one for estimating the role of SST feedback. Analyses of these experiments indicate that SSS feedback exerts negligible influences on MJO predictability within the constraints of the model, in contrast to significant impacts from SST feedback. Further analysis showed that a lack of SSS influence in MJO predictability can be attributed to marginal changes in SST associated with the SSS nudging. However, there is a caveat to the conclusion about SSS feedback. Because the barrier layer (BL) acts as a “bridge” for possible SSS influences on SST over the tropical Indian and western Pacific oceans, its simulation in CFSv2 is further explored. Analyses indicate that, in spite of realistic simulations of the MJO and intraseasonal SSS variability in CFSv2, significant BL simulation biases are present in the tropical oceans, including too thin a climatological thickness, too small intraseasonal variations, and an unrealistic intraseasonal BL–SST relationship. Thus, our predictability experiments cannot reject the hypothesis that SSS does play a role in MJO predictability; it is possible that biases in CFSv2 influence its ability to capture such signals.

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Arun Kumar and Jieshun Zhu

Abstract

Seasonal prediction skill of SSTs from coupled models has considerable spatial variations. In the tropics, SST prediction skill in the tropical Pacific clearly exceeds prediction skill over the Atlantic and Indian Oceans. Such skill variations can be due to spatial variations in observing system used for forecast initializations or systematic errors in the seasonal prediction systems, or they could be a consequence of inherent properties of the coupled ocean–atmosphere system leaving a fingerprint on the spatial structure of SST predictability. Out of various alternatives, the spatial variability in SST prediction skill is argued to be a consequence of inherent characteristics of climate system. This inference is supported based on the following analyses. SST prediction skill is higher over the regions where coupled air–sea interactions (or Bjerknes feedback) are inferred to be stronger. Coupled air–sea interactions, and the longer time scales associated with them, imprint longer memory and thereby support higher SST prediction skill. The spatial variability of SST prediction skill is also consistent with differences in the ocean–atmosphere interaction regimes that distinguish between whether ocean drives the atmosphere or atmosphere drives the ocean. Regions of high SST prediction skill generally coincide with regions where ocean forces the atmosphere. Such regimes correspond to regions where oceanic variability is on longer time scales compared to regions where atmosphere forces the ocean. Such regional differences in the spatial characteristics of ocean–atmosphere interactions, in turn, also govern the spatial variations in SST skill, making spatial variations in skill an intrinsic property of the climate system and not an artifact of the observing system or model biases.

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Jieshun Zhu and Jagadish Shukla

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This study examines the role of the air–sea coupled process in the seasonal predictability of Asia–Pacific summer monsoon rainfall by comparing seasonal predictions from two carefully designed model experiments: tier 1 (fully coupled model) and tier 2 (AGCM with prescribed SSTs). In these experiments, an identical AGCM is used in both tier 1 and tier 2 predictions; the daily mean SSTs from tier 1 coupled predictions are prescribed as a boundary condition in tier 2 predictions. Both predictions start in April from 1982 to 2009, with four ensemble members for each case. The model used is the Climate Forecast System, version 2 (CFSv2), the current operational climate prediction model for seasonal-to-interannual prediction at the National Centers for Environmental Prediction (NCEP). Comparisons indicate that tier 2 predictions produce not only higher rainfall biases but also unrealistically high rainfall variations in the tropical western North Pacific (TWNP) and some coastal regions as well. While the prediction skill in terms of anomaly correlations does not present a significant difference between the two types of predictions, the root-mean-square errors (RMSEs) are clearly larger over the above-mentioned regions in the tier 2 prediction. The reduced RMSE skills in the tier 2 predictions are due to the lack of a coupling process in AGCM-alone simulations, which, particularly, results in an unrealistic SST–rainfall relationship over the TWNP region. It is suggested that for a prediction of summer monsoon rainfall over the Asia–Pacific region, it is necessary to use a coupled atmosphere–ocean (tier 1) prediction system.

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Jieshun Zhu and Jagadish Shukla

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This study presents a new method to estimate atmospheric weather noise from coupled models, which is based on initialized simulations with a CGCM. In this method, the weather noise is estimated by removing the signal part, as determined from the coupled ensemble mean simulations. The weather noise estimated from coupled models is compared with that estimated from uncoupled AGCM simulations. The model used in this study is CFSv2. The initialized simulations start from each April during 1982–2009 paired with four members and extend for 6 months. To make a clear comparison between weather noise in coupled and uncoupled simulations, a set of uncoupled AGCM (the atmospheric component of CFSv2) simulations are conducted, which are forced by the daily mean SSTs from the above initialized CGCM simulations. The comparison indicates that, over the Asia–Pacific monsoon region where the local air–sea coupling is important, the noise variances are generally reduced as a result of air–sea coupling, as are the total and signal variances. This result stands in contrast to the results of previous studies that suggested that the noise variance for coupled and uncoupled models is the same. It is shown that the previous conclusion is simply an artifact of the assumption applied in the AGCM-based approach (i.e., the signal is the same between coupled and uncoupled simulations). In addition, the variance difference also exhibits a clear seasonality, with a larger difference over the monsoon region appearing toward boreal summer. Another set of AGCM experiments forced by the same SST suggests that the CGCM-based method generally remains valid in estimating weather noise within 2 months of its initial start.

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Arun Kumar, Jieshun Zhu, and Wanqiu Wang

Abstract

In this paper, the question of potential predictability in meteorological variables associated with skillful prediction of the Madden–Julian oscillation (MJO) during boreal winter is analyzed. The analysis is motivated by the fact that dynamical prediction systems are now capable of predicting MJO up to 30 days or earlier (measured in terms of anomaly correlation for RMM indices). Translating recent gains in MJO prediction skill and relating them back to potential for predicting meteorological variables—for example, precipitation and surface temperature—is not straightforward because of a chain of steps that go into the computation and evaluation of RMM indices. This paper assesses potential predictability in meteorological variables that could be attributed to skillful prediction of the MJO. The analysis is based on the observational data alone and assesses the upper limit of MJO-associated predictability that could be achieved.

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Jieshun Zhu, Wanqiu Wang, and Arun Kumar

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The observed Madden–Julian oscillation (MJO) tends to propagate eastward across the Maritime Continent from the eastern equatorial Indian Ocean to the western Pacific. However, numerical simulations present different levels of fidelity in representing the propagation, especially for the tropical convection associated with the MJO. This study conducts a series of coupled simulations using the NCEP CFSv2 to explore the impacts of SST feedback and convection parameterization on the propagation simulations. First, two simulations differing in the model horizontal resolutions are conducted. The MJO propagation in these two simulations is found generally insensitive to the resolution change. Further, based on the CFSv2 with a lower resolution, two additional experiments are performed with model SSTs nudged to climatologies with different time scales representing different air–sea coupling strength. It is demonstrated that weakening the air–sea coupling strength significantly degrades the MJO propagation simulation, suggesting the critical role of SST feedback in maintaining MJO propagation. Last, the sensitivity to convection parameterization is explored by comparing two simulations with different convection parameterization schemes. Analyses of these simulations indicate that including air–sea coupling alone in a dynamical model does not result in realistic maintenance of the MJO eastward propagation without the development of favorable SST conditions in the western Pacific. In both observations and one simulation with realistic MJO propagations, the preconditioning of SSTs is strongly affected by surface latent heat fluxes that are modulated by surface wind anomalies in both zonal and meridional directions. The diagnostics highlight the critical contribution from meridional winds in wind speed variations, which has been neglected in most MJO studies.

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Jieshun Zhu, Bohua Huang, and Zhaohua Wu

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This study examines a mechanism of the interaction between the tropical Atlantic meridional and equatorial modes. To derive robust heat content (HC) variability, the ensemble-mean HC anomalies (HCA) of six state-of-the-art global ocean reanalyses for 1979–2007 are analyzed. Compared with previous studies, characteristic oceanic processes are distinguished through their dominant time scales. Using the ensemble empirical mode decomposition (EEMD) method, the HC fields are first decomposed into components with different time scales. The authors’ analysis shows that these components are associated with distinctive ocean dynamics. The high-frequency (first three) components can be characterized as the equatorial modes, whereas the low-frequency (the fifth and sixth) components are featured as the meridional modes. In between, the fourth component on the time scale of 3–4 yr demonstrates “mixed” characteristics of the meridional and equatorial modes because of an active transition from the predominant meridional to zonal structures on this time scale. Physically, this transition process is initiated by the discharge of the off-equatorial HCA, which is first accumulated as a part of the meridional mode, into the equatorial waveguide, which is triggered by the breakdown of the equilibrium between the cross-equatorial HC contrast and the overlying wind forcing, and results in a major heat transport through the equatorial waveguide into the southeastern tropical Atlantic. It is also shown that remote forcing from El Niño–Southern Oscillation (ENSO) exerts important influence on the transition from the equatorial to meridional mode and may partly dictate its time scale of 3–4 yr. Therefore, the authors’ results demonstrate another mechanism of the equatorial Atlantic response to the ENSO forcing.

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Jieshun Zhu, Arun Kumar, and Wanqiu Wang

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This study revisits MJO predictability based on the “perfect model” approach with a contemporary model. Experiments are performed to address the reasons for substantial uncertainties in current estimates of MJO predictability, with a focus on the influence of atmospheric convection parameterization. Specifically, two atmospheric convection schemes are applied for experiments with the NOAA Climate Forecast System, version 2 (CFSv2). MJO potential predictability and prediction skill are assessed, with MJO indices taken as the first two principal components of the combined fields of near-equatorially averaged 200-hPa zonal wind, 850-hPa zonal wind, and outgoing longwave radiation at the top of the atmosphere. Analyses indicate that the convection scheme alone can have substantial influence on the estimate of MJO predictability, with estimates differing by as much as 15 days. Further diagnostics suggest that the shorter predictability with one convection scheme is mainly caused by too weak of an MJO signal. The choice of atmospheric convection scheme also exerts effects on the phase dependency of MJO predictability, and the “Maritime Continent prediction barrier” is identified to be more evident with one convection scheme than with the other.

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Xiaofan Li, Zeng-Zhen Hu, Ping Liang, and Jieshun Zhu

Abstract

In this work, the roles of El Niño–Southern Oscillation (ENSO) in the variability and predictability of the Pacific–North American (PNA) pattern and precipitation in North America in winter are examined. It is noted that statistically about 29% of the variance of PNA is linearly linked to ENSO, while the remaining 71% of the variance of PNA might be explained by other processes, including atmospheric internal dynamics and sea surface temperature variations in the North Pacific. The ENSO impact is mainly meridional from the tropics to the mid–high latitudes, while a major fraction of the non-ENSO variability associated with PNA is confined in the zonal direction from the North Pacific to the North American continent. Such interferential connection on PNA as well as on North American climate variability may reflect a competition between local internal dynamical processes (unpredictable fraction) and remote forcing (predictable fraction). Model responses to observed sea surface temperature and model forecasts confirm that the remote forcing is mainly associated with ENSO and it is the major source of predictability of PNA and winter precipitation in North America.

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Jieshun Zhu, Arun Kumar, Hui Wang, and Bohua Huang

Abstract

In contrast to operational climate predictions based on sophisticated ocean data assimilation schemes at the National Centers for Environmental Predictions (NCEP), this study applied a simple ocean initialization scheme to the NCEP latest seasonal prediction model, the Climate Forecast System, version 2 (CFSv2). In the scheme, sea surface temperature (SST) was the only observed information applied to derive ocean initial states. The physical basis for the method is that, through air–sea coupling, SST is capable of reproducing some observed features of ocean evolutions by forcing the atmospheric winds. SST predictions based on the scheme are compared against hindcasts from the National (lately North American) Multimodel Ensemble (NMME) project.

It was found that due to substantial biases in the tropical eastern Pacific in the ocean initial conditions produced by SST assimilation, ENSO SST predictions were not as good as those with sophisticated initialization schemes (e.g., hindcasts in the NMME project). However, in other basins, SST predictions based on a simple ocean initialization procedure were not worse (sometimes even better) than those with sophisticated initialization schemes. These comparisons indicate that it was helpful that subsurface ocean information be assimilated to improve the tropical Pacific SST predictions, while SST-based ocean assimilation was an effective way to enhance SST prediction capability in other ocean basins. By examining multimodel ensembles with the simple scheme-based hindcasts either included or excluded in NMME, it is also suggested that including the hindcast would generally benefit multimodel ensemble forecasts. In addition, possible ways to further improve ENSO SST predictions with the simple initialization scheme are also discussed.

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