We have identified a subpopulation of stem cells within adult human BM, isolated at the single-cell level, that self-renew without loss of multipotency for more than 140 population doublings and exhibit the capacity for differentiation into cells of all 3 germ layers. Based on surface marker expression, these clonally expanded human BM-derived multipotent stem cells (hBMSCs) do not appear to belong to any previously described BM-derived stem cell population. Intramyocardial transplantation of hBMSCs after myocardial infarction resulted in robust engraftment of transplanted cells, which exhibited colocalization with markers of cardiomyocyte (CMC), EC, and smooth muscle cell (SMC) identity, consistent with differentiation of hBMSCs into multiple lineages in vivo. Furthermore, upregulation of paracrine factors including angiogenic cytokines and antiapoptotic factors, and proliferation of host ECs and CMCs, were observed in the hBMSC-transplanted hearts. Coculture of hBMSCs with CMCs, ECs, or SMCs revealed that phenotypic changes of hBMSCs result from both differentiation and fusion. Collectively, the favorable effect of hBMSC transplantation after myocardial infarction appears to be due to augmentation of proliferation and preservation of host myocardial tissues as well as differentiation of hBMSCs for tissue regeneration and repair. To our knowledge, this is the first demonstration that a specific population of multipotent human BM-derived stem cells can induce both therapeutic neovascularization and endogenous and exogenous cardiomyogenesis.

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Future changes in the East Asian summer monsoon (EASM) are estimated from historical and Representative Concentration Pathway 6.0 (RCP6) experiments of the fifth phase of the Coupled Model Intercomparison Project (CMIP5). The historical runs show that, like the CMIP3 models, the CMIP5 models produce slightly smaller precipitation. A moisture budget analysis illustrates that this precipitation deficit is due to an underestimation in evaporation and ensuing moisture flux convergence. Of the two components of the moisture flux convergence (i.e., moisture convergence and horizontal moist advection), moisture convergence associated with mass convergence is underestimated to a greater degree. Precipitation is anticipated to increase by 10%–15% toward the end of the twenty-first century over the major monsoonal front region. A statistically significant increase is predicted to occur mostly over the Baiu region and to the north and northeast of the Korean Peninsula. This increase is attributed to an increase in evaporation and moist flux convergence (with enhanced moisture convergence contributing the most) induced by the northwestward strengthening of the North Pacific subtropical high (NPSH), a characteristic feature of the future EASM that occurred in CMIP5 simulations. Along the northern and northwestern flank of the strengthened NPSH, intensified southerly or southwesterly winds lead to the increase in moist convergence, enhancing precipitation over these areas. However, future precipitation over the East China Sea is projected to decrease. In the EASM domain, a local mechanism prevails, with increased moisture and moisture convergence leading to a greater increase in moist static energy in the lower troposphere than in the upper troposphere, reducing tropospheric stability.

In this paper, the prediction skills of five ensemble methods for temperature and precipitation are discussed by considering 20 yr of simulation results (from 1989 to 2008) for four regional climate models (RCMs) driven by NCEP-Department of Energy and ECMWF Interim Re-Analysis (ERA-Interim) boundary conditions. The simulation domain is the Coordinated Regional Downscaling Experiment (CORDEX) for East Asia, and the number of grid points is 197 3 233 with a 50-km horizontal resolution. Three new performance-based ensemble averaging (PEA) methods are developed in this study using 1) bias, root-mean-square errors (RMSEs) and absolute correlation (PEA_BRC), RMSE and absolute correlation (PEA_RAC), and RMSE and original correlation (PEA_ROC). The other two ensemble methods are equal-weighted averaging (EWA) and multivariate linear regression (Mul_Reg). To derive the weighting coefficients and cross validate the prediction skills of the five ensemble methods, the authors considered 15-yr and 5-yr data, respectively, from the 20-yr simulation data. Among the five ensemble methods, the Mul_Reg (EWA) method shows the best (worst) skill during the training period. The PEA_RAC and PEA_ROC methods show skills that are similar to those of Mul_Reg during the training period. However, the skills and stabilities of Mul_Reg were drastically reduced when this method was applied to the prediction period. But, the skills and stabilities of PEA_RAC were only slightly reduced in this case. As a result, PEA_RAC shows the best skill, irrespective of the seasons and variables, during the prediction period. This result confirms that the new ensemble method developed in this study, PEA_RAC, can be used for the prediction of regional climate.

In this study, the severe flood case over East Asia during the 1998 summer was simulated using a regional climate model (SNURCM) with 60 km horizontal resolution (EX60), and the model performance in reproducing the extreme climate events was evaluated. An experiment with higher horizontal resolution of 20 km (EX20) was also performed in order to assess the impact of increased resolution on precipitation simulation of the severe flood.The model reproduced the severe precipitation events occurring in central China in June. In EX60, the temporal and spatial variations of the abnormal Meiyu monsoon fronts, which were well observed were also simulated reasonably except in southern China. The area-averaged daily precipitation and surface air temperatures were underestimated, but their temporal evolutions were in good agreement with observation. In the higher resolution experiment (EX20), simulated downward solar radiation, latent heat flux and convective rain were increased in the major severe rain area over the Yangtze River Basin. The increased precipitation in EX20, which was attributed mainly to the increase of convective rain, resulted in the enhanced precipitation intensity, but only slightly affected total precipitation amounts. The improvement in the higher horizontal resolution simulation appeared in precipitation resulting, in particular, from increased convective activity due to increased latent heat flux at the surface. Nevertheless, the model had significant precipitation bias in some areas with disagreement between the simulated precipitation patterns and distribution, and the observations. The model also had surface air temperature bias resulting from cold biases of the land surface model. With horizontal resolution increased to 20 km, the convective and non-convective precipitation was increased for the late afternoon and early evening time, increasing the total precipitation slightly.

[1] This study examines simulated typhoon sensitivities to spectral nudging (SN) to investigate the effects on values added by regional climate models, which are not properly resolved by low-resolution global models. SN is suitably modified to mitigate its negative effects while maintaining the positive effects, and the effects of the modified SN are investigated through seasonal simulations. In the sensitivity experiments to nudging intervals of SN, the tracks of simulated typhoons are improved as the SN effect increases; however, the intensities of the simulated typhoons decrease due to the suppression of the typhoon developing process by SN. To avoid such suppression, SN is applied at intermittent intervals only when the deviation between the large-scale driving forcing and the model solution is large. In seasonal simulations, intermittent SN is applied for only 7% of the total time steps; however, this results in not only maintaining the large-scale features of monsoon circulation and precipitation corresponding to observations but also improving the intensification of mesoscale features by reducing the suppression.

To improve the initial conditions of tropical cyclone (TC) forecast models, a dynamical initialization (DI) scheme using cycle runs is developed and implemented into a real-time forecast system for northwest Pacific TCs based on the Weather Research and Forecasting (WRF) Model. In this scheme, cycle runs with a 6-h window before the initial forecast time are repeatedly conducted to spin up the axisymmetric component of the TC vortex until the model TC intensity is comparable to the observed. This is followed by a 72-h forecast using the Global Forecast System (GFS) prediction as lateral boundary conditions. In the DI scheme, the spectral nudging technique is employed during each cycle run to reduce bias in the large-scale environmental field, and the relocation method is applied after the last cycle run to reduce the initial position error. To demonstrate the effectiveness of the proposed DI scheme, 69 forecast experiments with and without the DI are conducted for 13 TCs over the northwest Pacific in 2010 and 2011. The DI shows positive effects on both track and intensity forecasts of TCs, although its overall skill depends strongly on the performance of the GFS forecasts. Compared to the forecasts without the DI, on average, forecasts with the DI reduce the position and intensity errors by 10% and 30%, respectively. The results demonstrate that the proposed DI scheme improves the initial TC vortex structure and intensity and provides warm physics spinup, producing initial states consistent with the forecast model, thus achieving improved track and intensity forecasts.

Forecasts of maximum and minimum air temperatures are essential to mitigate the damage of extreme weather events such as heat waves and tropical nights. The Numerical Weather Prediction (NWP) model has been widely used for forecasting air temperature, but generally it has a systematic bias due to its coarse grid resolution and lack of parametrizations. This study used random forest (RF), support vector regression (SVR), artificial neural network (ANN) and a multi-model ensemble (MME) to correct the Local Data Assimilation and Prediction System (LDAPS; a local NWP model over Korea) model outputs of next-day maximum and minimum air temperatures (T maxtþ1 and T mintþ1 ) in Seoul, South Korea. A total of 14 LDAPS model forecast data, the daily maximum and minimum air temperatures of in-situ observations, and five auxiliary data were used as input variables. The results showed that the LDAPS model had an R 2 of 0.69, a bias of −0.85°C and an RMSE of 2.08°C for T maxtþ1 forecast, whereas the proposed models resulted in the improvement with R 2 from 0.75 to 0.78, bias from −0.16 to −0.07°C and RMSE from 1.55 to 1.66°C by hindcast validation. For forecasting T mintþ1 , the LDAPS model had an R 2 of 0.77, a bias of 0.51°C and an RMSE of 1.43°C by hindcast, while the bias correction models showed R 2 values ranging from 0.86 to 0.87, biases from −0.03 to 0.03°C, and RMSEs from 0.98 to 1.02°C. The MME model had better generalization performance than the three single machine learning models by hindcast validation and leave-one-station-out cross-validation.