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Jia-Zhen Wang and Chunzai Wang

Abstract

Super El Niño has been a research focus since the first event occurred. On the basis of observations and models, we propose that a super El Niño emerges if El Niño is an early-onset type coincident with the distribution of an Atlantic Niña (AN) in summer and a positive Indian Ocean dipole (IOD) in autumn, conditions referred to as the Indo-Atlantic Booster (IAB). The underlying physical mechanisms refer to three-ocean interactions with seasonality. Early onset endows super El Niño with adequate strength in summer to excite wind-driven responses over the Indian and Atlantic Oceans, which further facilitate IAB formation by coupling with the seasonal cycle. In return, IAB alternately produces additional zonal winds U over the Pacific Ocean, augmenting super El Niño via the Bjerknes feedback. Adding AN and IOD indices into the regression model of U leads to a better performance than the single Niño-3.4 model, with a rise in the total explained variances by 10%–20% and a reduction in the misestimations of super El Niños by 50%. Extended analyses using Coupled Model Intercomparison Project models further confirm the sufficiency and necessity of early onset and IAB on super El Niño formation. Approximately 70% of super El Niños are early-onset types accompanied by IAB and 60% of early-onset El Niños with IAB finally grow into extreme events. These results highlight the super El Niño as an outcome of pantropical interactions, so including both the Indian and Atlantic Oceans and their teleconnections with the Pacific Ocean will greatly improve super El Niño prediction.

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Bingyi Wu, Jia Wang, and John E. Walsh

Abstract

This paper identified an atmospheric circulation anomaly–dipole structure anomaly in the Arctic atmosphere and its relationship with winter sea ice motion, based on the International Arctic Buoy Program (IABP) dataset (1979–98) and datasets from the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) for the period 1960–2002. The dipole anomaly corresponds to the second-leading mode of EOF of monthly mean sea level pressure (SLP) north of 70°N during the winter season (October–March) and accounts for 13% of the variance. One of its two anomalous centers is stably occupied between the Kara Sea and Laptev Sea; the other is situated from the Canadian Archipelago through Greenland extending southeastward to the Nordic seas. The dipole anomaly differs from one described in other papers that can be attributed to an eastward shift of the center of action of the North Atlantic Oscillation. The finding shows that the dipole anomaly also differs from the “Barents Oscillation” revealed in a study by Skeie. Since the dipole anomaly shows a strong meridionality, it becomes an important mechanism to drive both anomalous sea ice exports out of the Arctic Basin and cold air outbreaks into the Barents Sea, the Nordic seas, and northern Europe.

When the dipole anomaly remains in its positive phase, that is, negative SLP anomalies appear between the Kara Sea and the Laptev Sea with concurrent positive SLP over from the Canadian Archipelago extending southeastward to Greenland, there are large-scale changes in the intensity and character of sea ice transport in the Arctic basin. The significant changes include a weakening of the Beaufort gyre, an increase in sea ice export out of the Arctic basin through Fram Strait and the northern Barents Sea, and enhanced sea ice import from the Laptev Sea and the East Siberian Sea into the Arctic basin. Consequently, more sea ice appears in the Greenland and the Barents Seas during the positive phase of the dipole anomaly. During the negative phase of the dipole anomaly, SLP anomalies show an opposite scenario in the Arctic Ocean and its marginal seas when compared to the positive phase, with the center of negative SLP anomalies over the Nordic seas. Correspondingly, sea ice exports decrease from the Arctic basin flowing into the Nordic seas and the northern Barents Sea because of the strengthened Beaufort gyre.

The finding indicates that influences of the dipole anomaly on winter sea ice motion are greater than that of the winter AO, particularly in the central Arctic basin and northward to Fram Strait, implying that effects of the dipole anomaly on sea ice export out of the Arctic basin become robust. The dipole anomaly is closely related to atmosphere–ice–ocean interactions that influence the Barents Sea sector.

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XiaoJing Jia, Hai Lin, June-Yi Lee, and Bin Wang

Abstract

Multimodel ensemble (MME) seasonal forecasts are analyzed to evaluate numerical model performance in predicting the leading forced atmospheric circulation pattern over the extratropical Northern Hemisphere (NH). Results show that the time evolution of the leading tropical Pacific sea surface temperature (SST)-coupled atmospheric pattern (MCA1), which is obtained by applying a maximum covariance analysis (MCA) between 500-hPa geopotential height (Z 500) in the extratropical NH and SST in the tropical Pacific Ocean, can be predicted with a significant skill in March–May (MAM), June–August (JJA), and December–February (DJF) one month ahead. However, most models perform poorly in capturing the time variation of MCA1 in September–November (SON) with 1 August initial condition. Two possible reasons for the models’ low skill in SON are identified. First, the models have the most pronounced errors in the mean state of SST and precipitation along the central equatorial Pacific. Because of the link between the divergent circulation forced by tropical heating and the midlatitude atmospheric circulation, errors in the mean state of tropical SST and precipitation may lead to a degradation of midlatitude forecast skill. Second, examination of the potential predictability of the atmosphere, estimated by the ratio of the total variance to the variance of the model forecasts due to internal dynamics, shows that the atmospheric potential predictability over the North Pacific–North American (NPNA) region is the lowest in SON compared to the other three seasons. The low ratio in SON is due to a low variance associated with external forcing and a high variance related to atmospheric internal processes over this area.

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MENGMENG LU, SONG YANG, JUNBIN WANG, YUTING WU, and XIAOLONG JIA

Abstract

The thermal effect of entire Tibetan Plateau (TP) tends to strengthen the South Asian summer monsoon (SASM); however, how does this monsoon component respond to the thermal conditions of different TP domains? How do the thermal condition of entire TP influences other monsoons including the East Asian summer monsoon (EASM) and the Southeast Asian summer monsoon (SEASM)? These questions are addressed by conducting an experiment with the CESM, which is forced by reducing the surface albedo over the plateau by half, from a TP-averaged 0.20 to 0.10, from May to September, and similar experiments for different TP domains. Both observation and model results show that the entire-TP heating intensifies the large-scale Asian monsoon, the SASM, and the EASM, but surprisingly weakens the SEASM. It is also surprising that the TP heating exerts a stronger effect on the EASM than on the SASM. The southern TP (south of 35°N) does not show the strongest impact on the SASM compared to other TP domains and it exerts a weakest impact on the EASM, which is most strongly influenced by the thermal effect of eastern (east of 90°E) and northern TP. The western TP weakens the SEASM as the other domains, while it strengthens other monsoon components. The thermal condition of southern and eastern TP are accompanied by signals of tropical atmospheric response at relatively broader spatial scales, while that of northern TP more apparently leads to a significant wave train extending eastward from the TP to western Eurasia over the higher latitudes.

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Jia Wang, Xuezhi Bai, Haoguo Hu, Anne Clites, Marie Colton, and Brent Lofgren

Abstract

In this study, temporal and spatial variability of ice cover in the Great Lakes are investigated using historical satellite measurements from 1973 to 2010. The seasonal cycle of ice cover was constructed for all the lakes, including Lake St. Clair. A unique feature found in the seasonal cycle is that the standard deviations (i.e., variability) of ice cover are larger than the climatological means for each lake. This indicates that Great Lakes ice cover experiences large variability in response to predominant natural climate forcing and has poor predictability. Spectral analysis shows that lake ice has both quasi-decadal and interannual periodicities of ~8 and ~4 yr. There was a significant downward trend in ice coverage from 1973 to the present for all of the lakes, with Lake Ontario having the largest, and Lakes Erie and St. Clair having the smallest. The translated total loss in lake ice over the entire 38-yr record varies from 37% in Lake St. Clair (least) to 88% in Lake Ontario (most). The total loss for overall Great Lakes ice coverage is 71%, while Lake Superior places second with a 79% loss. An empirical orthogonal function analysis indicates that a major response of ice cover to atmospheric forcing is in phase in all six lakes, accounting for 80.8% of the total variance. The second mode shows an out-of-phase spatial variability between the upper and lower lakes, accounting for 10.7% of the total variance. The regression of the first EOF-mode time series to sea level pressure, surface air temperature, and surface wind shows that lake ice mainly responds to the combined Arctic Oscillation and El Niño–Southern Oscillation patterns.

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Shi-Xin Wang, Hong-Chao Zuo, Fen Sun, Li-Yang Wu, Yixing Yin, and Jing-Jia Luo

Abstract

Dynamics of the East Asian spring rainband are investigated with a reanalysis dataset and station observations. Here, it is revealed that the rainband is anchored by external forcings. The midtropospheric jet core stays quasi-stationary around Japan. It has two branches in its entry region, which originate from the south and north flanks of the Tibetan Plateau and then run northeastward and southeastward, respectively. The southern branch advects warm air from the Tibetan–Hengduan Plateau northeastward, forming a rainband over southern China through causing adiabatic ascent motion and triggering diabatic feedback. The rainband is much stronger in spring than in autumn due to the stronger diabatic heating over the Tibetan–Hengduan Plateau, a more southward-displaced midtropospheric jet, and the resulting stronger warm advection over southern China. The northern jet branch forms a zonally elongated cold advection belt, which reaches a maximum around northern China, and then weakens and extends eastward to east of Japan. The westerly jet also steers strong disturbance activities roughly collocated with the cold advection belt via baroclinic instability. The high disturbance activities belt causes large cumulative warm advection (CWA) through drastically increasing extremely warm advection days on its eastern and south flanks, where weak cold advection prevails. CWA is more essential for monthly/seasonally rainfall than conventionally used time-average temperature advection because it is shown that strengthened warm advection can increase rainfall through positive diabatic feedback, while cold advection cannot cause negative rainfall. Thus, the rainband is collocated with the large CWA belt instead of the warm advection south of it. This rainband is jointed to the rainband over southern China, forming the long southwest–northeast-oriented East Asian spring rainband. Increasing moisture slightly displaces the rainband southeastward.

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Joshua-Xiouhua Fu, Wanqiu Wang, Yuejian Zhu, Hong-Li Ren, Xiaolong Jia, and Toshiaki Shinoda

Abstract

Six sets of hindcasts conducted with the NCEP GFS have been used to study the SST-feedback processes and assess the relative contributions of atmospheric internal dynamics and SST feedback on the October and November MJO events observed during the DYNAMO IOP (Oct- and Nov-MJO). The hindcasts are carried out with three variants of the Arakawa–Shubert cumulus scheme under TMI and climatological SST conditions. The positive intraseasonal SST anomaly along with its convergent Laplacian produces systematic surface disturbances, which include enhanced surface convergence, evaporation, and equivalent potential temperature no matter which cumulus scheme is used. Whether these surface disturbances can grow into a robust response of MJO convection depends on the characteristics of the cumulus schemes used. If the cumulus scheme is able to amplify the SST-initiated surface disturbances through a strong upward–downward feedback, the model is able to produce a robust MJO convection response to the underlying SST anomaly; otherwise, the model will not produce any significant SST feedback. A new method has been developed to quantify the “potential” and “practical” contributions of the atmospheric internal dynamics and SST feedback on the MJOs. The present results suggest that, potentially, the SST feedback could have larger contributions than the atmospheric internal dynamics. Practically, the contributions to the Oct- and Nov-MJO events are, respectively, dominated by atmospheric internal dynamics and SST feedback. Averaged over the entire period, the contributions from the atmospheric internal dynamics and SST feedback are about half and half.

Open access
Meng-Pai Hung, Jia-Lin Lin, Wanqiu Wang, Daehyun Kim, Toshiaki Shinoda, and Scott J. Weaver

Abstract

This study evaluates the simulation of the Madden–Julian oscillation (MJO) and convectively coupled equatorial waves (CCEWs) in 20 models from the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and compares the results with the simulation of CMIP phase 3 (CMIP3) models in the IPCC Fourth Assessment Report (AR4). The results show that the CMIP5 models exhibit an overall improvement over the CMIP3 models in the simulation of tropical intraseasonal variability, especially the MJO and several CCEWs. The CMIP5 models generally produce larger total intraseasonal (2–128 day) variance of precipitation than the CMIP3 models, as well as larger variances of Kelvin, equatorial Rossby (ER), and eastward inertio-gravity (EIG) waves. Nearly all models have signals of the CCEWs, with Kelvin and mixed Rossby–gravity (MRG) and EIG waves being especially prominent. The phase speeds, as scaled to equivalent depths, are close to the observed value in 10 of the 20 models, suggesting that these models produce sufficient reduction in their effective static stability by diabatic heating. The CMIP5 models generally produce larger MJO variance than the CMIP3 models, as well as a more realistic ratio between the variance of the eastward MJO and that of its westward counterpart. About one-third of the CMIP5 models generate the spectral peak of MJO precipitation between 30 and 70 days; however, the model MJO period tends to be longer than observations as part of an overreddened spectrum, which in turn is associated with too strong persistence of equatorial precipitation. Only one of the 20 models is able to simulate a realistic eastward propagation of the MJO.

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Jia Wang, James Kessler, Xuezhi Bai, Anne Clites, Brent Lofgren, Alexandre Assuncao, John Bratton, Philip Chu, and George Leshkevich

Abstract

In this study, decadal variability of ice cover in the Great Lakes is investigated using historical airborne and satellite measurements from 1963 to 2017. It was found that Great Lakes ice cover has 1) a linear relationship with the Atlantic multidecadal oscillation (AMO), similar to the relationship of lake ice cover with the North Atlantic Oscillation (NAO), but with stronger impact than NAO; 2) a quadratic relationship with the Pacific decadal oscillation (PDO), which is similar to the relationship of lake ice cover to Niño-3.4, but with opposite curvature; and 3) decadal variability with a positive (warming) trend in AMO contributes to the decreasing trend in lake ice cover. Composite analyses show that during the positive (negative) phase of AMO, the Great Lakes experience a warm (cold) anomaly in surface air temperature (SAT) and lake surface temperature (LST), leading to less (more) ice cover. During the positive (negative) phase of PDO, the Great Lakes experience a cold (warm) anomaly in SAT and LST, leading to more (less) ice cover. Based on these statistical relationships, the original multiple variable regression model established using the indices of NAO and Niño-3.4 only was improved by adding both AMO and PDO, as well as their interference (interacting or competing) mechanism. With the AMO and PDO added, the correlation between the model and observation increases to 0.69, compared to 0.48 using NAO and Niño-3.4 only. When November lake surface temperature was further added to the regression model, the prediction skill of the coming winter ice cover increased even more.

Open access
Rongqing Han, Hui Wang, Zeng-Zhen Hu, Arun Kumar, Weijing Li, Lindsey N. Long, Jae-Kyung E. Schemm, Peitao Peng, Wanqiu Wang, Dong Si, Xiaolong Jia, Ming Zhao, Gabriel A. Vecchi, Timothy E. LaRow, Young-Kwon Lim, Siegfried D. Schubert, Suzana J. Camargo, Naomi Henderson, Jeffrey A. Jonas, and Kevin J. E. Walsh

Abstract

An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Niño–Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large-scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs, and the multimodel ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation.

Most GCMs can simulate the interannual variability of WNP TCs well, with stronger TC activities during two types of El Niño—namely, eastern Pacific (EP) and central Pacific (CP) El Niño—and weaker activity during La Niña. However, none of the models capture the differences in TC activity between EP and CP El Niño as are shown in observations. The inability of models to distinguish the differences in TC activities between the two types of El Niño events may be due to the bias of the models in response to the shift of tropical heating associated with CP El Niño.

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