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Hye-Mi Kim and Baek-Min Kim

Abstract

The relative contributions of atmospheric energy transport (via heat and moisture advection) and sea ice decline to recent Arctic warming were investigated using high-resolution reanalysis data up to 2017. During the Arctic winter, a variation of downward longwave radiation (DLR) is fundamental in modulating Arctic surface temperature. In the warm Arctic winter, DLR and precipitable water (PW) are increasing over the entire Arctic; however, the major drivers for such increases differ regionally. In areas such as the northern Greenland Sea, increasing DLR and PW are caused mainly by convergence of atmospheric energy transport from lower latitudes. In regions of maximum sea ice retreat (e.g., northern Barents–Kara Seas), continued sea ice melting from previous seasons drive the DLR and PW increases, consistent with the positive ice–insulation feedback. Distinct local feedbacks between open water and ice-retreat regions were further compared. In open water regions, a reduced ocean–atmosphere temperature gradient caused by atmospheric warming suppresses surface turbulent heat flux (THF) release from the ocean to the atmosphere; thus, surface warming cannot accelerate. Conversely, in ice-retreat regions, sea ice reduction allows the relatively warm ocean to interact with the colder atmosphere via surface THF release. This increases temperature and humidity in the lower troposphere consistent with the positive ice–insulation feedback. The implication of this study is that Arctic warming will slow as the open water fraction increases. Therefore, given sustained greenhouse warming, the roles of atmospheric heat and moisture transport from lower latitudes are likely to become increasingly critical in the future Arctic climate.

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Hye-Mi Kim and Michael A. Alexander

Abstract

The vertically integrated water vapor transport (IVT) over the Pacific–North American sector during three phases of ENSO in boreal winter (December–February) is investigated using IVT values calculated from the Climate Forecast System Reanalysis (CFSR) during 1979–2010. The shift of the location and sign of sea surface temperature (SST) anomalies in the tropical Pacific Ocean leads to different atmospheric responses and thereby changes the seasonal mean moisture transport into North America. During eastern Pacific El Niño (EPEN) events, large positive IVT anomalies extend northeastward from the subtropical Pacific into the northwestern United States following the anomalous cyclonic flow around a deeper Aleutian low, while a southward shift of the cyclonic circulation during central Pacific El Niño (CPEN) events induces the transport of moisture into the southwestern United States. In addition, moisture from the eastern tropical Pacific is transported from the deep tropical eastern Pacific into Mexico and the southwestern United States during CPEN. During La Niña (NINA), the seasonal mean IVT anomaly is opposite to that of two El Niño phases. Analyses of 6-hourly IVT anomalies indicate that there is strong moisture transport from the North Pacific into the northwestern and southwestern United States during EPEN and CPEN, respectively. The IVT is maximized on the southeastern side of a low located over the eastern North Pacific, where the low is weaker but located farther south and closer to shore during CPEN than during EPEN. Moisture enters the southwestern United States from the eastern tropical Pacific during NINA via anticyclonic circulation associated with a ridge over the southern United States.

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Hyemi Kim, Frédéric Vitart, and Duane E. Waliser

Abstract

There has been an accelerating interest in forecasting the weather and climate within the subseasonal time range. The Madden–Julian oscillation (MJO), an organized envelope of tropical convection, is recognized as one of the leading sources of subseasonal predictability. This review synthesizes the latest progress regarding the MJO predictability and prediction. During the past decade, the MJO prediction skill in dynamical prediction systems has exceeded the skill of empirical predictions. Such improvement has been mainly attributed to more observations and computer resources, advances in theoretical understanding, and improved numerical models aided in part by multinational efforts through field campaigns and multimodel experiments. The state-of-the-art dynamical forecasts have shown MJO prediction skill up to 5 weeks. Prediction skill can be extended by improving the ensemble generation approach tailored for MJO prediction and by averaging multiensembles or multimodels. MJO prediction skill can be influenced by the tropical mean state and low-frequency climate mode variations, as well as by the extratropical circulation. MJO prediction skill is proven to be sensitive to model physics, ocean–atmosphere coupling, and quality of initial conditions, while the impact of the model resolution seems to be marginal. Remaining challenges and recommendations on new research avenues to fully realize the predictability of the MJO are discussed.

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In-Sik Kang and Hye-Mi Kim

Abstract

The predictability of intraseasonal variation in the tropics is assessed in the present study by using various statistical and dynamical models with rigorous and fair measurements. For a fair comparison, the real-time multivariate Madden–Julian oscillation (MJO) (RMM) index, proposed by Wheeler and Hendon, is used as a predictand for all models. The statistical models include the models based on a multilinear regression, a wavelet analysis, and a singular spectrum analysis (SSA). The prediction limits (correlation skill of 0.5) of statistical models for RMM1 (RMM2) index are at days 16–17 (14–15) for the multiregression model, whereas they are at days 8–10 (9–12) for the wavelet- and SSA-based models. The poor predictability of the wavelet and SSA models is related to the tapering problem for a half-length of the time window before the initial condition.

To assess the dynamical predictability, long-term serial prediction experiments with a prediction interval of every 5 days are carried out with Seoul National University (SNU) AGCM and coupled general circulation model (CGCM) for 26 (1980–2005) boreal winters. The prediction limits of RMM1 and RMM2 occur at around 20 days for both AGCM and CGCM. These results demonstrate that the skills of dynamical models used in this study are better than those of the three statistical predictions. The dynamical and statistical predictions are combined using a multimodel ensemble method. The combination provides a superior skill to any of the statistical and dynamical predictions, with a prediction limit of 22–24 days. The dependencies of prediction skill on the initial phase and amplitude of the MJO are also investigated.

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Hye-Mi Kim, Peter J. Webster, and Judith A. Curry

Abstract

Tropical Pacific Ocean warming has been separated into two modes based on the spatial distribution of the maximum sea surface temperature (SST) anomaly: an east Pacific warming (EPW) and a central Pacific warming (CPW). When combined with east Pacific cooling (EPC), these three regimes are shown to have different impacts on tropical cyclone (TC) activity over the North Pacific by differential modulation of both local thermodynamic factors and large-scale circulation patterns. In EPW years, the genesis and the track density of TCs tend to be enhanced over the southeastern part and suppressed in the northwestern part of the western Pacific by strong westerly wind shear. The extension of the monsoon trough and the weak wind shear over the central Pacific increases the likelihood of TC activity to the east of the climatological mean TC genesis location. In CPW years, the TC activity is shifted to the west and is extended through the northwestern part of the western Pacific. The westward shifting of CPW-induced heating moves the anomalous westerly wind and monsoon trough through the northwestern part of the western Pacific and provides a more favorable condition for TC landfall. The CPW, on the other hand, produces a large suppression of TC activity in the eastern Pacific basin. In EPC years, all of the variables investigated show almost a mirror image of the EPW.

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Hye-Mi Kim, Edmund K. M. Chang, and Minghua Zhang

Abstract

This study attempts, for the first time, to predict the annual number of tropical cyclones (TCs) affecting New York State (NYS), as part of the effort of the New York State Resiliency Institute for Storms and Emergencies (RISE). A pure statistical prediction model and a statistical–dynamical hybrid prediction model have been developed based on the understanding of the physical mechanism between NYS TCs and associated large-scale climate variability. During the cold phase of El Niño–Southern Oscillation, significant circulation anomalies in the Atlantic Ocean provide favorable conditions for more recurving TCs into NYS. The pure statistical prediction model uses the sea surface temperature (SST) over the equatorial Pacific Ocean from the previous months. Cross validation shows that the correlation between the observed and predicted numbers of NYS TCs is 0.56 for the June 1979–2013 forecasts. Forecasts of the probability of one or more TCs impacting NYS have a Brier skill score of 0.35 compared to climatology. The statistical–dynamical hybrid prediction model uses Climate Forecast System, version 2, SST predictions, which are statistically downscaled to forecast the number of NYS TCs based on a stepwise regression model. Results indicate that the initial seasonal prediction for NYS TCs can be issued in February using the hybrid model, with an update in June using the pure statistical prediction model. Based on the statistical model, for 2014, the predicted number of TCs passing through NYS is 0.33 and the probability of one or more tropical cyclones crossing NYS is 30%, which are both below average and in agreement with the actual activity (0 NYS TCs).

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Hye-Mi Kim, Yoo-Geun Ham, and Adam A. Scaife

Abstract

The prediction skill and errors in surface temperature anomalies in initialized decadal hindcasts from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are assessed using six ocean–atmosphere coupled models initialized every year from 1961 to 2008. The initialized hindcasts show relatively high prediction skill over the regions where external forcing dominates, indicating that a large portion of the prediction skill is due to the long-term trend. After removing the linear trend, high prediction skill is shown near the centers of action of the dominant decadal climate oscillations, such as the Pacific decadal oscillation (PDO) and Atlantic multidecadal oscillation (AMO). Low prediction skill appears over the tropical and eastern North Pacific Ocean where the predicted anomaly patterns associated with the PDO are systematically different in model and observations. By statistically correcting those systematic errors using a stepwise pattern projection method (SPPM) based on the data in an independent training period, the prediction skill of sea surface temperature (SST) is greatly enhanced over the North Pacific Ocean. The SST prediction skill over the North Pacific Ocean after the SPPM error correction is as high as that over the North Atlantic Ocean. In addition, the prediction skill in a single model after correction exceeds the skill of the multimodel ensemble (MME) mean before correction, implying that the MME method is not as effective in addressing systematic errors as the SPPM correction.

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Jiabao Wang, Hye-Mi Kim, and Edmund K. M. Chang

Abstract

An interdecadal weakening in the North Atlantic storm track (NAST) and a poleward shift of the North Pacific storm track (NPST) are found during October–March for the period 1979–2015. A significant warming of surface air temperature (Ts) over northeastern North America and a La Niña–like change in the North Pacific under the background of Arctic amplification are found to be the contributors to the observed changes in the NAST and the NPST, respectively, via modulation of local baroclinicity. The interdecadal change in baroclinic energy conversion is consistent with changes in storm tracks with an energy loss from eddies to mean flow over the North Atlantic and an energy gain over the North Pacific. The analysis of simulations from the Community Earth System Model Large Ensemble project, although with some biases in storm-track and Ts simulations, supports the observed relationship between the NAST and Ts over northeastern North America, as well as the link between the NPST and El Niño–Southern Oscillation. The near-future projections of Ts and storm tracks are characterized by a warmer planet under the influence of increasing greenhouse gases and a significant weakening of both the NAST and the NPST. The potential role of the NAST in redistributing changes in Ts over the surrounding regions is also examined. The anomalous equatorward moisture flux associated with the weakening trend of the NAST would enhance the warming over its upstream region and hinder the warming over its downstream region via modulation of the downward infrared radiation.

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Changhyun Yoo, Sungsu Park, Daehyun Kim, Jin-Ho Yoon, and Hye-Mi Kim

Abstract

The Madden–Julian oscillation (MJO), the dominant mode of tropical intraseasonal variability, influences weather and climate in the extratropics through atmospheric teleconnection. In this study, two simulations using the Community Atmosphere Model version 5 (CAM5)—one with the default shallow and deep convection schemes and the other with the unified convection scheme (UNICON)—are employed to examine the impacts of cumulus parameterizations on the simulation of the boreal wintertime MJO teleconnection in the Northern Hemisphere. It is demonstrated that the UNICON substantially improves the MJO teleconnection. When the UNICON is employed, the simulated circulation anomalies associated with the MJO better resemble the observed counterpart, compared to the simulation with the default convection schemes. Quantitatively, the pattern correlation for the 300-hPa geopotential height anomalies between the simulations and observation increases from 0.07 for the default schemes to 0.54 for the UNICON. These circulation anomalies associated with the MJO further help to enhance the surface air temperature and precipitation anomalies over North America, although room for improvement is still evident. Initial value calculations suggest that the realistic MJO teleconnection with the UNICON is not due to the changes in the background wind, but rather primarily to the improved tropical convective heating associated with the MJO.

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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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