Development of statistical seasonal prediction models of Arctic Sea Ice concentration using CERES absorbed solar radiation

Statistical seasonal prediction models for the Arctic sea ice concentration (SIC) were developed for the late summer (August-October) when the downward trend is dramatic. The absorbed solar radiation (ASR) at the top of the atmosphere in June has a significant seasonal leading role on the SIC. Based on the lagged ASR-SIC relationship, two simple statistical models were established: the Markovian stochastic and the linear regression models. Crossvalidated hindcasts of SIC from 1979 to 2014 by the two models were compared with each other and observation. The hindcasts showed general agreement between the models as they share a common predictor, ASR in June and the observed SIC was well reproduced, especially over the relatively thin-ice regions (of one- or multi-year sea ice). The robust predictability confirms the functional role of ASR in the prediction of SIC. In particular, the SIC prediction in October was quite promising probably due to the pronounced icealbedo feedback. The temporal correlation coefficients between the predicted SIC and the observed SIC were 0.79 and 0.82 by the Markovian and regression models, respectively. Small differences were observed between the two models; the regression model performed slightly better in August and September in terms of temporal correlation coefficients. Meanwhile, the prediction skills of the Markovian model in October were higher in the north of Chukchi, the East Siberian, and the Laptev Seas. A strong non-linear relationship between ASR in June and SIC in October in these areas would have increased the predictability of the Markovian model.     

High-resolution climate simulation of the last glacial maximum

The climate of the last glacial maximum (LGM) is simulated with a high-resolution atmospheric general circulation model, the NCAR CCM3 at spectral truncation of T170, corresponding to a grid cell size of roughly 75 km. The purpose of the study is to assess whether there are significant benefits from the higher resolution simulation compared to the lower resolution simulation associated with the role of topography. The LGM simulations were forced with modified CLIMAP sea ice distribution and sea surface temperatures (SST) reduced by 1°C, ice sheet topography, reduced CO₂, and 21,000 BP orbital parameters. The high-resolution model captures modern climate reasonably well, in particular the distribution of heavy precipitation in the tropical Pacific. For the ice age case, surface temperature simulated by the high-resolution model agrees better with those of proxy estimates than does the low-resolution model. Despite the fact that tropical SSTs were only 2.1°C less than the control run, there are many lowland tropical land areas 4–6°C colder than present. Comparison of T170 model results with the best constrained proxy temperature estimates (noble gas concentrations in groundwater) now yield no significant differences between model and observations. There are also significant upland temperature changes in the best resolved tropical mountain belt (the Andes). We provisionally attribute this result in part as resulting from decreased lateral mixing between ocean and land in a model with more model grid cells. A longstanding model-data discrepancy therefore appears to be resolved without invoking any unusual model physics. The response of the Asian summer monsoon can also be more clearly linked to local geography in the high-resolution model than in the low-resolution model; this distinction should enable more confident validation of climate proxy data with the high-resolution model. Elsewhere, an inferred salinity increase in the subtropical North Atlantic may have significant implications for ocean circulation changes during the LGM. A large part of the Amazon and Congo Basins are simulated to be substantially drier in the ice age—consistent with many (but not all) paleo data. These results suggest that there are considerable benefits derived from high-resolution model regarding regional climate responses, and that observationalists can now compare their results with models that resolve geography at a resolution comparable to that which the proxy data represent.     

Impact of the thermal state of the tropical western Pacific on onset date and process of the South China Sea summer monsoon

Since the early or late onset of the South China Sea summer monsoon （SCSM） has a large impact on summer monsoon rainfall in East Asia, the mechanism and process of early or late onset of the SCSM are an worthy issue to study. In this paper, the results analyzed by using the observed data show that the onset date and process of the SCSM are closely associated with the thermal state of the tropical western Pacific in spring. When the tropical western Pacific is in a warming state in spring, the western Pacific subtropical high shifts eastward, and twin cyclones are early caused over the Bay of Bengal and Sumatra before the SCSM onset. In this case, the cyclonic circulation located over the Bay of Bengal can be early intensified and become into a strong trough. Thus, the westerly flow and convective activity can be intensified over Sumatra, the Indo-China Peninsula and the South China Sea （SCS） in mid-May. This leads to early onset of the SCSM. In contrast, when the tropical western Pacific is in a cooling state, the western Pacific subtropical high anomalously shifts westward, the twin cyclones located over the equatorial eastern Indian Ocean and Sumatra are weakened, and the twin anomaly anticyclones appear over these regions from late April to mid-May. Thus, the westerly flow and convective activity cannot be early intensified over the Indo-China Peninsula and the SCS. Only when the western Pacific subtropical high moves eastward, the weak trough located over the Bay of Bengal can be intensified and become into a strong trough, the strong southwesterly wind and convective activity can be intensified over the Indo-China Peninsula and the SCS in late May. Thus, this leads to late onset of the SCSM. Moreover, in this paper, the influencing mechanism of the thermal state of the tropical western Pacific on the SCSM onset is discussed further from the Walker circulation anomalies in the different thermal states of the tropical western Pacific.     

Feedbacks in the Land-Surface and Mixed-Layer Energy Budgets

A mixed-layer model of the surface energy budget and the planetary boundary layer (PBL) is developed, based on the prognostic equations for soil temperature, mixed layer potential temperature and specific humidity and the growth and abrupt collapse of the PBL. Detailed parameterizations of the longwave radiative fluxes are included. The feedbacks in the uncoupled (i.e. surface energy budget with non-responding PBL) and coupled land surface and atmospheric mixed-layer energy budgets are examined. A simplified, time continuous, version of the model, in which the specific humidity budget is the balance of evapotranspiration and dry-air entrainment, and the PBL height is given by the lifted condensation level, is shown to be in good agreement with the complete model. By forcing the simplified model with daily mean rather than periodic solar radiation, an equilibrium model state is achieved where the fluxes are in close agreement with the daily mean fluxes corresponding to the periodic forcing. The model also agrees favorably with measurements from the FIFE field experiment. Feedbacks are examined using the equilibrium model state. The uncoupled and coupled model sensitivities with respect to the minimal stomatal resistance and the atmospheric specific humidity not only differ in magnitude, but in sign as well. This results puts into question the extent to which uncoupled land-surface models that are forced with atmospheric variables may be used in sensitivity studies.     

关于黄海深部构造的地球物理认识

利用黄海海区重磁观测数据,以最新的地震层析成像和浅层反射地震探测结果作为约束,并利用小波分解、纹理特征图像处理等手段,对研究区进行了地球物理场特征分析、岩石物性总结、断裂信息提取和磁性基底埋深反演计算,同时对南黄海海域三条剖面进行了广义逆重磁数据拟合反演和地震P波速度成像.根据地球物理数据的各种处理结果,提出在南黄海西部存在一条串珠状地球物理线性构造带,并将这条NNW向断续延伸的构造带称之为南黄海西缘断裂带.该断裂带延伸长,断裂两侧前新生代地层差异较大,有可能是造成陆区和南黄海南部盆地区前新生代油气远景差异的原因之一.文中得到南黄海磁性基底埋藏分布具有“中间浅四周深”的分布特征,与地震层析成像结果相吻合.磁性基底的局部起伏和局部构造的边界断层共同控制了前新生代残留盆地的格架和残余厚度分布,反映出“区域控制局部,深层约束浅层”的规律.     

Long term evolution of the magnetized Kelvin Helmholtz instability with resistivity

MHD simulation study is performed to investigate magnetic reconnection induced by the Kelvin Helmholtz instability in the parallel configuration of the uniform magnetic field geometry as well as the sheared field geometry. Highly distorted magnetic field lines due to Kelvin Helmholtz instability become reconnected and flattened so that they resume the straight field line structure in the final stage. When the initial magnetic field is sheared, magnetic islands formed as a result of magnetic reconnection are transported toward the weak field region but they soon disappear since these islands are of small scales and suffer strong diffusions. Morphological change in the long term evolution is most dramatic in the small range of magnetic field intensity in both the uniform field case and the sheared field case, which is not too strong to stabilize vortex growing early on or too weak to have negligible effect on the instability. Energy conversion and the momentum transport are also most effective in this small range.     

Comparison of Statistical Post-Processing Methods for Probabilistic Wind Speed Forecasting

In this study, the statistical post-processing methods that include bias-corrected and probabilistic forecasts of wind speed measured in PyeongChang, which is scheduled to host the 2018 Winter Olympics, are compared and analyzed to provide more accurate weather information. The six post-processing methods used in this study are as follows: mean bias-corrected forecast, mean and variance bias-corrected forecast, decaying averaging forecast, mean absolute bias-corrected forecast, and the alternative implementations of ensemble model output statistics (EMOS) and Bayesian model averaging (BMA) models, which are EMOS and BMA exchangeable models by assuming exchangeable ensemble members and simplified version of EMOS and BMA models. Observations for wind speed were obtained from the 26 stations in PyeongChang and 51 ensemble member forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF Directorate, 2012) that were obtained between 1 May 2013 and 18 March 2016. Prior to applying the post-processing methods, reliability analysis was conducted by using rank histograms to identify the statistical consistency of ensemble forecast and corresponding observations. Based on the results of our study, we found that the prediction skills of probabilistic forecasts of EMOS and BMA models were superior to the biascorrected forecasts in terms of deterministic prediction, whereas in probabilistic prediction, BMA models showed better prediction skill than EMOS. Even though the simplified version of BMA model exhibited best prediction skill among the mentioned six methods, the results showed that the differences of prediction skills between the versions of EMOS and BMA were negligible.     

Effect of Data Assimilation Parameters on The Optimized Surface CO<Subscript>2</Subscript> Flux in Asia

In this study, CarbonTracker, an inverse modeling system based on the ensemble Kalman filter, was used to evaluate the effects of data assimilation parameters (assimilation window length and ensemble size) on the estimation of surface CO₂ fluxes in Asia. Several experiments with different parameters were conducted, and the results were verified using CO₂ concentration observations. The assimilation window lengths tested were 3, 5, 7, and 10 weeks, and the ensemble sizes were 100, 150, and 300. Therefore, a total of 12 experiments using combinations of these parameters were conducted. The experimental period was from January 2006 to December 2009. Differences between the optimized surface CO₂ fluxes of the experiments were largest in the Eurasian Boreal (EB) area, followed by Eurasian Temperate (ET) and Tropical Asia (TA), and were larger in boreal summer than in boreal winter. The effect of ensemble size on the optimized biosphere flux is larger than the effect of the assimilation window length in Asia, but the importance of them varies in specific regions in Asia. The optimized biosphere flux was more sensitive to the assimilation window length in EB, whereas it was sensitive to the ensemble size as well as the assimilation window length in ET. The larger the ensemble size and the shorter the assimilation window length, the larger the uncertainty (i.e., spread of ensemble) of optimized surface CO₂ fluxes. The 10-week assimilation window and 300 ensemble size were the optimal configuration for CarbonTracker in the Asian region based on several verifications using CO₂ concentration measurements.     

Development of the Expert Seasonal Prediction System: an Application for the Seasonal Outlook in Korea

An Expert Seasonal Prediction System for operational Seasonal Outlook (ESPreSSO) is developed based on the APEC Climate Center (APCC) Multi-Model Ensemble (MME) dynamical prediction and expert-guided statistical downscaling techniques. Dynamical models have improved to provide meaningful seasonal prediction, and their prediction skills are further improved by various ensemble and downscaling techniques. However, experienced scientists and forecasters make subjective correction for the operational seasonal outlook due to limited prediction skills and biases of dynamical models. Here, a hybrid seasonal prediction system that grafts experts’ knowledge and understanding onto dynamical MME prediction is developed to guide operational seasonal outlook in Korea. The basis dynamical prediction is based on the APCC MME, which are statistically mapped onto the station-based observations by experienced experts. Their subjective selection undergoes objective screening and quality control to generate final seasonal outlook products after physical ensemble averaging. The prediction system is constructed based on 23-year training period of 1983–2005, and its performance and stability are assessed for the independent 11-year prediction period of 2006–2016. The results show that the ESPreSSO has reliable and stable prediction skill suitable for operational use.