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Jiabao Wang, Hyemi Kim, Daehyun Kim, Stephanie A. Henderson, Cristiana Stan, and Eric D. Maloney

Abstract

We propose a set of MJO teleconnection diagnostics that enables an objective evaluation of model simulations, a fair model-to-model comparison, and a consistent tracking of model improvement. Various skill metrics are derived from teleconnection diagnostics including five performance-based metrics that characterize the pattern, amplitude, east–west position, persistence, and consistency of MJO teleconnections and additional two process-oriented metrics that are designed to characterize the location and intensity of the anomalous Rossby wave source (RWS). The proposed teleconnection skill metrics are used to compare the characteristics of boreal winter MJO teleconnections (500-hPa geopotential height anomaly) over the Pacific–North America (PNA) region in 29 global climate models (GCMs). The results show that current GCMs generally produce MJO teleconnections that are stronger, more persistent, and extend too far to the east when compared to those observed in reanalysis. In general, models simulate more realistic teleconnection patterns when the MJO is in phases 2–3 or phases 7–8, which are characterized by a dipole convection pattern over the Indian Ocean and western to central Pacific. The higher model skill for phases 2, 7, and 8 may be due to these phases producing more consistent teleconnection patterns between individual MJO events than other phases, although the consistency is lower in most models than observed. Models that simulate realistic RWS patterns better reproduce MJO teleconnection patterns.

Open access
Hye-Mi Kim, Myong-In Lee, Peter J. Webster, Dongmin Kim, and Jin Ho Yoo

Abstract

The relationship between El Niño–Southern Oscillation (ENSO) and tropical storm (TS) activity over the western North Pacific Ocean is examined for the period from 1981 to 2010. In El Niño years, TS genesis locations are generally shifted to the southeast relative to normal years and the passages of TSs tend to recurve to the northeast. TSs of greater duration and more intensity during an El Niño summer induce an increase of the accumulated tropical cyclone kinetic energy (ACE). Based on the strong relationship between the TS properties and ENSO, a probabilistic prediction for seasonal ACE is investigated using a hybrid dynamical–statistical model. A statistical relationship is developed between the observed ACE and large-scale variables taken from the ECMWF seasonal forecast system 4 hindcasts. The ACE correlates positively with the SST anomaly over the central to eastern Pacific and negatively with the vertical wind shear near the date line. The vertical wind shear anomalies over the central and western Pacific are selected as predictors based on sensitivity tests of ACE predictive skill. The hybrid model performs quite well in forecasting seasonal ACE with a correlation coefficient between the observed and predicted ACE at 0.80 over the 30-yr period. A relative operating characteristic analysis also indicates that the ensembles have significant probabilistic skill for both the above-normal and below-normal categories. By comparing the ACE prediction over the period from 2003 to 2011, the hybrid model appears more skillful than the forecast from the Tropical Storm Risk consortium.

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Jiabao Wang, Hyemi Kim, Daehyun Kim, Stephanie A. Henderson, Cristiana Stan, and Eric D. Maloney

Abstract

In an assessment of 29 global climate models (GCMs), of this study identified biases in boreal winter MJO teleconnections in anomalous 500-hPa geopotential height over the Pacific–North America (PNA) region that are common to many models: an eastward shift, a longer persistence, and a larger amplitude. In Part II, we explore the relationships of the teleconnection metrics developed in with several existing and newly developed MJO and basic state (the mean subtropical westerly jet) metrics. The MJO and basic state diagnostics indicate that the MJO is generally weaker and less coherent and propagates faster in models compared to observations. The mean subtropical jet also exhibits notable biases such as too strong amplitude, excessive eastward extension, or southward shift. The following relationships are found to be robust among the models: 1) models with a faster MJO propagation tend to produce weaker teleconnections; 2) models with a less coherent eastward MJO propagation tend to simulate more persistent MJO teleconnections; 3) models with a stronger westerly jet produce stronger and eastward shifted MJO teleconnections; 4) models with an eastward extended jet produce an eastward shift in MJO teleconnections; and 5) models with a southward shifted jet produce stronger MJO teleconnections. The results are supported by linear baroclinic model experiments. Our results suggest that the larger amplitude and eastward shift biases in GCM MJO teleconnections can be attributed to the biases in the westerly jet, and that the longer persistence bias is likely due to the lack of coherent eastward MJO propagation.

Free access
Hye-Mi Kim, Peter J. Webster, Violeta E. Toma, and Daehyun Kim

Abstract

The authors examine the predictability and prediction skill of the Madden–Julian oscillation (MJO) of two ocean–atmosphere coupled forecast systems of ECMWF [Variable Resolution Ensemble Prediction System (VarEPS)] and NCEP [Climate Forecast System, version 2 (CFSv2)]. The VarEPS hindcasts possess five ensemble members for the period 1993–2009 and the CFSv2 hindcasts possess three ensemble members for the period 2000–09. Predictability and prediction skill are estimated by the bivariate correlation coefficient between the observed and predicted Wheeler–Hendon real-time multivariate MJO index (RMM). MJO predictability is beyond 32 days lead time in both hindcasts, while the prediction skill is about 27 days in VarEPS and 21 days in CFSv2 as measured by the bivariate correlation exceeding 0.5. Both predictability and prediction skill of MJO are enhanced by averaging ensembles. Results show clearly that forecasts initialized with (or targeting) strong MJOs possess greater prediction skill compared to those initialized with (or targeting) weak or nonexistent MJOs. The predictability is insensitive to the initial MJO phase (or forecast target phase), although the prediction skill varies with MJO phases.

A few common model issues are identified. In both hindcasts, the MJO propagation speed is slower and the MJO amplitude is weaker than observed. Also, both ensemble forecast systems are underdispersive, meaning that the growth rate of ensemble error is greater than the growth rate of the ensemble spread by lead time.

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Cheng Zheng, Edmund Kar-Man Chang, Hyemi Kim, Minghua Zhang, and Wanqiu Wang

Abstract

The prediction of wintertime extratropical cyclone activity (ECA) on subseasonal time scales by models participating in the Subseasonal Experiment (SubX) and the Seasonal to Subseasonal Prediction (S2S) is assessed. Consistent with a previous study that investigated the S2S models, the SubX models have skillful predictions of ECA over regions from central North Pacific across North America to western North Atlantic, as well as East Asia and northern and southern part of eastern North Atlantic at 3–4 weeks lead time. SubX provides daily mean data, while S2S provides instantaneous data at 0000 UTC each day. This leads to different variance of ECA. Different S2S and SubX models have different reforecast initialization times and reforecast time periods. These factors can all lead to differences in prediction skill. To fairly compare the prediction skill between different models, we develop a novel way to evaluate the prediction of individual model across the two ensembles by comparing every model to the Climate Forecast System, version 2 (CFSv2), as CFSv2 has 6-hourly output and forecasts initialized every day. Among the S2S and SubX models, the European Centre for Medium-Range Weather Forecasts model exhibits the best prediction skill, followed by CFSv2. Our results also suggest that while the prediction skill is sensitive to forecast lead time, including forecasts up to 4 days old into the ensemble may still be useful for weeks 3–4 predictions of ECA.

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Hye-Mi Kim, Carlos D. Hoyos, Peter J. Webster, and In-Sik Kang

Abstract

The influence of sea surface temperature (SST) on the simulation and predictability of the Madden–Julian oscillation (MJO) is examined using the Seoul National University atmospheric general circulation model (SNU AGCM). Forecast skill was examined using serial climate simulations spanning eight different winter seasons with 30-day forecasts commencing every 5 days, giving a total of 184 thirty-day simulations. The serial runs were repeated using prescribing observed SST with monthly, weekly, and daily temporal resolutions. The mean SST was the same for all cases so that differences between experiments result from the different temporal resolutions of the SST boundary forcing.

It is shown that high temporal SST frequency acts to improve 1) the MJO activity of 200-hPa velocity potential field over the entire Asian monsoon region at all lead times; 2) the percentage of filtered variance of the two leading EOF modes that explain the eastward propagation of MJO; 3) the power of the wavenumber 1 eastward propagating mode; and 4) the forecast skill of MJO, maintaining it for longer periods. However, the MJO phase relationship between MJO convection and SST, as is often the case with many atmosphere-only models, although well simulated at the beginning of forecast period becomes distorted rapidly as the forecast lead time increases, even with the daily SST forcing case. Comparison of AGCM simulations with coupled GCM (CGCM) integrations shows that ocean–atmosphere coupling improves considerably the phase relationship between SST and convection. The CGCM results reinforce that the MJO is a coupled phenomenon and suggest strongly the need of the ocean–atmosphere coupled processes to extend predictability.

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Jung Choi, Seok-Woo Son, Yoo-Geun Ham, June-Yi Lee, and Hye-Mi Kim

Abstract

This study explores the seasonal-to-interannual near-surface air temperature (TAS) prediction skills of state-of-the-art climate models that were involved in phase 5 of the Coupled Model Intercomparison Project (CMIP5) decadal hindcast/forecast experiments. The experiments are initialized in either November or January of each year and integrated for up to 10 years, providing a good opportunity for filling the gap between seasonal and decadal climate predictions. The long-lead multimodel ensemble (MME) prediction is evaluated for 1981–2007 in terms of the anomaly correlation coefficient (ACC) and mean-squared skill score (MSSS), which combines ACC and conditional bias, with respect to observations and reanalysis data, paying particular attention to the seasonal dependency of the global-mean and equatorial Pacific TAS predictions. The MME shows statistically significant ACCs and MSSSs for the annual global-mean TAS for up to two years, mainly because of long-term global warming trends. When the long-term trends are removed, the prediction skill is reduced. The prediction skills are generally lower in boreal winters than in other seasons regardless of lead times. This lack of winter prediction skill is attributed to the failure of capturing the long-term trend and interannual variability of TAS over high-latitude continents in the Northern Hemisphere. In contrast to global-mean TAS, regional TAS over the equatorial Pacific is predicted well in winter. This is mainly due to a successful prediction of the El Niño–Southern Oscillation (ENSO). In most models, the wintertime ENSO index is reasonably well predicted for at least one year in advance. The sensitivity of the prediction skill to the initialized month and method is also discussed.

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Cheng Zheng, Edmund Kar-Man Chang, Hye-Mi Kim, Minghua Zhang, and Wanqiu Wang

Abstract

In this study, the intraseasonal variations in storm-track activity, surface air temperature, and precipitation over North America associated with the Madden–Julian oscillation (MJO) in boreal winter (November–April) are investigated. A lag composite strategy that considers different MJO phases and different lag days is developed. The results highlight regions over which the MJO has significant impacts on surface weather on intraseasonal time scales. A north–south shift of storm-track activity associated with the MJO is found over North America. The shift is consistent with the MJO-related surface air temperature anomaly over the eastern United States. In many regions over the western, central, and southeastern United States, the MJO-related precipitation signal is also consistent with nearby storm-track activity. An MJO-related north–south shift of precipitation is also found near the west coast of North America, with the precipitation over California being consistent with the MJO-related storm-track activity over the eastern Pacific. MJO-related temperature and storm-track anomalies are also found near Alaska. Further analyses of streamfunction anomalies and wave activity flux show clear signatures of Rossby wave trains excited by convection anomalies related to MJO phases 3 and 8. These wave trains propagate across the Pacific and North America, bringing an anticyclonic (cyclonic) anomaly to the eastern part of North America, shifting the westerly jet to the north (south), thereby modulating the surface air temperature and storm-track activity over the continent. Rossby waves associated with phases 2 and 6 are also found to impact the U.S. West Coast.

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Ping Liu, Qin Zhang, Chidong Zhang, Yuejian Zhu, Marat Khairoutdinov, Hye-Mi Kim, Courtney Schumacher, and Minghua Zhang

Abstract

This study investigates why OLR plays a small role in the Real-time Multivariate (Madden–Julian oscillation) MJO (RMM) index and how to improve it. The RMM index consists of the first two leading principal components (PCs) of a covariance matrix, which is constructed by combined daily anomalies of OLR and zonal winds at 850 (U850) and 200 hPa (U200) in the tropics after being normalized with their globally averaged standard deviations of 15.3 W m−2, 1.8 m s−1, and 4.9 m s−1, respectively. This covariance matrix is reasoned mathematically close to a correlation matrix. Both matrices substantially suppress the overall contribution of OLR and make the index more dynamical and nearly transparent to the convective initiation of the MJO. A covariance matrix that does not use normalized anomalies leads to the other extreme where OLR plays a dominant role while U850 and U200 are minor. Numerous tests indicate that a simple scaling of the anomalies (i.e., 2 W m−2, 1 m s−1, and 1 m s−1) can better balance the roles of OLR and winds. The revised PCs substantially enhance OLR over the eastern Indian and western Pacific Oceans and change it less notably in other locations, while they reduce U850 and U200 only slightly. Comparisons with the original RMM in spatial structure, power spectra, and standard deviation demonstrate improvements of the revised RMM index.

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Jadwiga H. Richter, Kathy Pegion, Lantao Sun, Hyemi Kim, Julie M. Caron, Anne Glanville, Emerson LaJoie, Stephen Yeager, Who M. Kim, Ahmed Tawfik, and Dan Collins

Abstract

There is a growing demand for understanding sources of predictability on subseasonal to seasonal (S2S) time scales. Predictability at subseasonal time scales is believed to come from processes varying slower than the atmosphere such as soil moisture, snowpack, sea ice, and ocean heat content. The stratosphere as well as tropospheric modes of variability can also provide predictability at subseasonal time scales. However, the contributions of the above sources to S2S predictability are not well quantified. Here we evaluate the subseasonal prediction skill of the Community Earth System Model, version 1 (CESM1), in the default version of the model as well as a version with the improved representation of stratospheric variability to assess the role of an improved stratosphere on prediction skill. We demonstrate that the subseasonal skill of CESM1 for surface temperature and precipitation is comparable to that of operational models. We find that a better-resolved stratosphere improves stratospheric but not surface prediction skill for weeks 3–4.

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