Huopo Chen

Haze Days in North China and the associated atmospheric circulations based on daily visibility data from 1960 to 2012

Haze is a severe hazard that greatly influences traffic and daily life with great economic losses and threats to human health. To enhance understanding of the haze occurrences, this study examined the haze variations over North China and their associated atmospheric circulations for the period of 1960–2012 using daily visibility data. Results indicate that the haze events over this region primarily occur in boreal winter of year and mainly in the morning of day. The results of the analysis of the long-term variations indicate that the annual haze days were relatively few in the 1960s but increased steeply in the 1970s and have remained stable to the present. Some differences are obvious among seasons. A stably increasing trend is discernable in summer and autumn, relatively low in the 1960s and the 1990s–2000s and relatively high in the 1970s–1980s in spring and winter. Haze variations in urban regions are quite similar to haze variations in rural regions but with more haze days in urban regions because of the high aerosol emissions. Further analyses indicate that the occurrences of severe haze events in boreal winter generally correlate with the weakened northerly winds and the development of inversion anomalies in the lower troposphere, the weakened East Asian trough in the midtroposphere, and the northward East Asian jet in the high troposphere. All of these factors provide a favorable atmospheric background for the maintenance and development of haze events in this region.

Spatial-temporal features of intense snowfall events in China and their possible change

[1] The statistical spatial‐temporal features of the intense snowfall event (ISE) in China are investigated over the period of 1962–2000. The results indicate that eastern China, northern Xinjiang, the eastern Tibetan plateau, and northeastern China are four key regions for the ISE, with more frequency and strong variability. Annual cycle analysis shows the ISE exhibits a unimodal distribution with maximum frequency at winter months for eastern China, a bimodal distribution with maximum frequency at early winter and spring months for northern Xinjiang and northeastern China, and a bimodal distribution with maximum frequency at autumn and spring months for the eastern Tibetan plateau. Linear trend analysis indicates that in the last 39 years, the ISE exhibits a decreasing trend for eastern China and an increasing trend for northern Xinjiang and the eastern Tibetan plateau. The linear trend of the ISE is weak over northeastern China. Based on the simulations of the most recent and comprehensive climate models in the 20th century run, the performance of the current climate models in simulating the Chinese ISE is investigated. The results indicate that, of the 20 models, there are four models that can reasonably reproduce the spatial‐temporal features of the Chinese ISE. Based on these four models’ simulation for the 21st century under A1B and A2 scenarios, the future variability of the Chinese ISE is projected. It is found that global warming will cause the ISE frequency over southern China to decrease, while the ISE over northern China will initially increase and then decrease.

Changes in Drought Characteristics over China Using the Standardized Precipitation Evapotranspiration Index

The standardized precipitation evapotranspiration index (SPEI) is computed and compared in China using reference evapotranspiration calculated using the Thornthwaite (TH) approach and the Penman‐Monteith (PM) equation. The analysis reveals that SPEI_PM outperforms the SPEI_TH with regard to drought monitoring during the period 1961‐2012 over China, especially in arid regions of China. Furthermore, the SPEI_PM also performs better with regard to observed variations in soil moisture and streamflow in China. Thus, changes in drought characteristics over China are detected on the basis of variations in the SPEI_PM. The results indicate that droughts over China exhibit pronounced decadal variations over the past 50yr, with more frequent and severe droughts occurring before the 1980s and in the 2000s compared with the 1980s and 1990s. Since the late 1990s, droughts have become more frequent and severe across China, especially in some regions of northern China. Concurrently, consecutive drought events have also increased across China. This suggests that dry conditions in China have been enhanced in recent years. Further analyses illustrate that the temperature and precipitation anomalies exhibit different roles in detecting droughts across China, which is primarily due to the magnitude of their variations and different climate variability. Considering temperature and precipitation perturbations, droughts exhibit relatively larger responses to temperature fluctuations in northern China and relatively larger responses to precipitation anomalies in southern China.

Improving the Prediction of the East Asian Summer Monsoon: New Approaches

AbstractEast Asian summer monsoon (EASM) prediction is difficult because of the summer monsoon’s weak and unstable linkage with El Nino–Southern Oscillation (ENSO) interdecadal variability and its complicated association with high-latitude processes. Two statistical prediction schemes were developed to include the interannual increment approach to improve the seasonal prediction of the EASM’s strength. The schemes were applied to three models [i.e., the Centre National de Recherches Meteorologiques (CNRM), the Met Office (UKMO), and the European Centre for Medium-Range Weather Forecasts (ECMWF)] and the Multimodel Ensemble (MME) from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) results for 1961–2001. The inability of the three dynamical models to reproduce the weakened East Asian monsoon at the end of the 1970s leads to low prediction ability for the interannual variability of the EASM. Therefore, the interannual increment prediction approach wa...

A Statistical Downscaling Model for Forecasting Summer Rainfall in China from DEMETER Hindcast Datasets

AbstractA new statistical downscaling (SD) scheme is proposed to predict summertime multisite rainfall measurements in China. The potential predictors are multiple large-scale variables from operational dynamical model output. A key step in this SD scheme is finding optimal predictors that have the closest and most stable relationship with rainfall at a given station. By doing so, the most robust signals from the large-scale circulation can be statistically projected onto local rainfall, which can significantly improve forecast skill in predicting the summer rainfall at the stations. This downscaling prediction is performed separately for each simulation with a leave-one-out cross-validation approach and an independent sample validation framework. The prediction skill scores exhibited at temporal correlation, anomaly correlation coefficient, and root-mean-square error consistently demonstrate that dynamical model prediction skill is significantly improved under the SD scheme, especially in the multimodel ...

A statistical downscaling scheme to improve global precipitation forecasting

Based on hindcasts obtained from the “Development of a European Multimodel Ensemble system for seasonal to inTERannual prediction” (DEMETER) project, this study proposes a statistical downscaling (SD) scheme suitable for global precipitation forecasting. The key idea of this SD scheme is to select the optimal predictors that are best forecast by coupled general circulation models (CGCMs) and that have the most stable relationships with observed precipitation. Developing the prediction model and further making predictions using these predictors can extract useful information from the CGCMs. Cross-validation and independent sample tests indicate that this SD scheme can significantly improve the prediction capability of CGCMs during the boreal summer (June–August), even over polar regions. The predicted and observed precipitations are significantly correlated, and the root-mean-square-error of the SD scheme-predicted precipitation is largely decreased compared with the raw CGCM predictions. An inter-model comparison shows that the multi-model ensemble provides the best prediction performance. This study suggests that combining a multi-model ensemble with the SD scheme can improve the prediction skill for precipitation globally, which is valuable for current operational precipitation prediction.

Decadal Features of Heavy Rainfall Events in Eastern China

Based on daily precipitation data, the spatial-temporal features of heavy rainfall events (HREs) during 1960–2009 are investigated. The results indicate that the HREs experienced strong decadal variability in the past 50 years, and the decadal features varied across regions. More HRE days are observed in the 1960s, 1980s, and 1990s over Northeast China (NEC); in the 1960s, 1970s, and 1990s over North China (NC); in the early 1960s, 1980s, and 2000s over the Huaihe River basin (HR); in the 1970s–1990s over the mid-lower reaches of the Yangtze River valley (YR); and in the 1970s and 1990s over South China (SC). These decadal changes of HRE days in eastern China are closely associated with the decadal variations of water content and stratification stability of the local atmosphere. The intensity of HREs in each sub-region is also characterized by strong decadal variability. The HRE intensity and frequency co-vary on the long-term trend, and show consistent variability over NEC, NC, and YR, but inconsistent variability over SC and HR. Further analysis of the relationships between the annual rainfall and HRE frequency as well as intensity indicates that the HRE frequency is the major contributor to the total rainfall variability in eastern China, while the HRE intensity shows only relative weak contribution.

Assessing model performance of climate extremes in China: an intercomparison between CMIP5 and CMIP3

In this study, we present a brief analysis of the performances of global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) in simulating climate extreme events in China and compare the results with those of the previous model generation (CMIP3). The primary focus of this analysis is the climate mean and variability of each extreme index. Results show that the CMIP5 models are generally able to capture the mean climate extremes and trends compared with a new gridded observational dataset. The model spread for some extreme indices is reduced in CMIP5 when compared with CMIP3. Furthermore, the models generally show higher skills in simulating the temperature-based indices than the precipitation-based indices in terms of means and linear trends. Results from six reanalyses further reveal large uncertainties for these indices and it is difficult to say which reanalysis is better for comparison with the simulations of all indices. Based on the relative errors of the climatology, the model evaluation varies considerably from one index to another. However, some models appear to perform substantially better than the others when the average of all indices is considered for each model, and the median ensembles outperform the individual models in terms of all the extreme indices and their means. Additionally, a relationship is observed between the improved simulation of the climate mean and the improved performance of its variability, although this improvement is limited to particular models.

Contribution of human influence to increased daily precipitation extremes over China

This study provides an estimate of the human influence on increases in daily precipitation extremes over China using data sets from multiple coupled climate models participating in the Coupled Model Intercomparison Project Phase 5. The effects of human forcings can be detected in the observed changes of daily precipitation extremes, but the effects of external natural forcings as well as the aerosols are not detected using the optimal fingerprint methods. Estimation showed that human influence has increased daily precipitation extremes by approximately 13% (1% to 25% for 90% confidence interval) on average over China in recent decades. With further warming, human influences on precipitation extremes would be amplified. For a temperature increase of 1.5°C with respect to the preindustrial time, the occurrence probability of severe extremes is doubled, and approximately 51% of these events occurring over China are attributable to human influences. This fraction increases with temperature. Furthermore, the contributions of human influences are much stronger for the high-percentile extremes, and the highest sensitivity of the changes in daily precipitation extremes due to human influences is observed in the region of the Tibetan Plateau in the southwest of China.

Effects of anthropogenic activity emerging as intensified extreme precipitation over China

This study aims to provide an assessment of the effects of anthropogenic (ANT) forcings and other external factors on observed increases in extreme precipitation over China from 1961 to 2005. Extreme precipitation is represented by the annual maximum one-day precipitation (RX1D) and the annual maximum five-day consecutive precipitation (RX5D), and these variables are investigated using observations and simulations from the Coupled Model Intercomparison Project Phase 5. The analyses mainly focus on the probability-based index (PI), which is derived from RX1D and RX5D by fitting generalized extreme value distributions. The results indicate that the simulations that include the ANT forcings provide the best representation of the spatial and temporal characteristics of extreme precipitation over China. We use the optimal fingerprint method to obtain the univariate and multivariate fingerprints of the responses to external forcings. The results show that only the ANT forcings are detectable at a 90% confidence level, both individually and when natural forcings are considered simultaneously. The impact of the forcing associated with greenhouse gases (GHG) is also detectable in RX1D, but its effects cannot be separated from those of combinations of forcings that exclude the GHG forcings in the two-signal analyses. Besides, the estimated changes of PI, extreme precipitation, and events with a 20-year return period under nonstationary climate states are potentially attributable to ANT or GHG forcings, and the relationships between extreme precipitation and temperature from ANT forcings show agreement with observations.