MODELING STUDY OF SEVERE PERSISTENT DROUGHT EVENTS OVER EASTERN CHINA DURING THE LAST MILLENNIUM

We use modeled data from the Community Earth System Model Last Millennium Ensemble (CESM-LME)project and paleo-proxy records to examine the occurrence of severe event of persistent drought over Eastern China during the last millennium and diagnose the mechanisms. Modeled data are CESM 1.1 simulations for the CESM-LME project include an ensemble of 13 simulations with all forcings. To assess the influence of solar forcing and volcanic forcing, smaller ensembles with each forcing separately including an ensemble of 4 simulations with solar forcing and an ensemble of 5 simulations with volcanic forcing are also used. The paleo-proxy records include a data set of the dryness-wetness index (DWI)over Eastern China (25°~41°N, 105°~122°E,) during the last 530 years which derived from Chinese historical documents and a data set of regional DWI over Eastern China (east of approximately 25°~40°N, 105°E)during the last 1500 years. Here, severe events of persistent drought in the model are defined as periods with a rainy season (May-September)precipitation anomaly of at least widespread (over Eastern China, 25°~40°N, 105°~122°E), persistent (less than zero for at least 10 consecutive years)and great precipitation anomaly (severe drought). To consider the intensity, we selected the periods of negative values of 10-year running mean of precipitation for Eastern China exceeding 1.31 times the standard deviation for at least 5 consecutive years. -1.31Δ was used here as a threshold to classify the severity of severe drought, which corresponds closely to the drought classification scheme of 10% occurrence. The same approach is also applied to identify the severe decadal drought in the proxy data. In this drought classification, the model was simulated seven droughts over the study area during the last millennium such as those that occurred during the periods of 883~910, 951~977, 1253~1305, 1327~1346, 1471~1488, 1587~1610 and 1688~1699. To assess the possible linkage between the simulated severe decadal droughts and sea surface temperatures (SSTs)in the Pacific Ocean during the last millennium, we examine the simulated Niño3 index. It is calculated with SSTs in the box 150°~90°W, 5°S~5°N. The model results suggest the El Niño-Southern Oscillation (ENSO)plays an important role in precipitation variability, a temporally consistent relationship between the droughts and negative phases of ENSO could be found in the modeled data. The precipitation during the periods 1253~1305 exist coherent evolution among the individual all-forcing simulations is simulated leading to large fluctuations in the ensemble average. Similar to the results in all forcing simulations, the ensemble response in volcanic forcing simulations is outstandingly coherent during the early periods of the drought 1253~1305 and the latter periods of the drought 1587~1610. These analyses indicate that large volcanic eruptions may cause the droughts of 1253~1305 and play a important role in 1587~1610 events. Simulated precipitation evolves rather incoherently among the individual all-forcing simulations, solar forcing simulations and volcanic forcing simulations during the droughts occurred 883~910, 951~977. It indicated simulated precipitation during these periods describes an internal mode of climate variability which is rather resilient to external natural perturbations. Thus, 883~910, 951~977 drought events may be related to internal variability such as ENSO. However, it remains an open question whether the 1327~1346, 1471~1488 and 1632~1645 droughts, as identified by the model, can be or not be considered as driven by solar forcing. Droughts simulated in CESM disagree with the proxy record in East China except the drought occurred in 1471~1488, suggesting that model East Asian summer monsoon representation cannot be solely responsible. Possible explanations include issues with the uncertainties in the reconstructions, including forcing and precipitation and the models of chaotic components of internal variability.