Abstract

This is the second part of a study investigating the 1991 severe precipitation event over the Yangtze–Huai River valley (YHRV) in China using both observations and regional model simulations. While Part I reported on the Mei-yu front and its association with large-scale circulation, this study documents the biases associated with the treatment of the lateral boundary in the regional model. Two aspects of the biases were studied: the driving field, which provides large-scale boundary forcing, and the coupling scheme, which specifies how the forcing is adopted by the model. The former bias is defined as model uncertainty because it is not related to the model itself, while the latter bias (as well as those biases attributed to other sources) is referred to as model error. These two aspects were examined by analyzing the regional model simulations of the 1991 summer severe precipitation event over YHRV using different driving fields (ECMWF–TOGA objective analysis, ECMWF reanalysis, and NCEP–NCAR reanalysis) and coupling scheme (distribution function of the nudging coefficient and width of the buffer zone). Spectral analysis was also used to study the frequency distribution of the bias.

The analyses suggest that the 200-hPa winds, 500-hPa geopotential height, and 850-hPa winds and water vapor mixing ratio, which have dominant influences on Mei-yu evolution, are sensitive to large-scale boundary forcing. In particular the 500-hPa geopotential height, and 850-hPa water vapor mixing ratio near the Tibetan Plateau and over the western Pacific Oceans are highly dependent on the driving field. On the other hand, the water vapor in the lower troposphere, wind at all levels, and precipitation pattern are much more affected by the treatment of nudging in the coupling scheme. It is interesting to find that the two commonly used coupling schemes, the lateral boundary coupling and the spectral coupling, provide similar large-scale information to the simulation domain when the former scheme used a wider buffer zone and stronger nudging coefficient. Systematical model errors, existing in the north of the simulation domain, are caused by the overprediction of low-level inversion stratiform clouds.

The analyses further indicate that the model mesoscale signal is not significantly influenced by the different treatments of the nudging procedure. However, it is also shown that the model performance, especially the monthly mean precipitation and its spatial pattern, is substantially improved with the increase of buffer zone width and nudging coefficient.

Abstract

The summer Mei-yu event over eastern China, which is strongly influenced by large-scale circulation, is an important aspect of East Asian climate; for example, the Mei-yu frequently brings heavy precipitation to the Yangtze–Huai River valley (YHRV). Both observations and a regional model were used to study the Mei-yu front and its relation to large-scale circulation during the summer of 1991 when severe floods occurred over YHRV. This study has two parts: the first part, presented here, analyzes the association between heavy Mei-yu precipitation and relevant large-scale circulation, while the second part, documented by W. Gong and W.-C. Wang, examines the model biases associated with the treatment of lateral boundary conditions (the objective analyses and coupling schemes) used as the driving fields for the regional model.

Observations indicate that the Mei-yu season in 1991 spans 18 May–14 July, making it the longest Mei-yu period during the last 40 yr. The heavy precipitation over YHRV is found to be intimately related to the western Pacific subtropical high, upper-tropospheric westerly jet at midlatitudes, and lower-tropospheric southwest wind and moisture flux. The regional model simulates reasonably well the regional mean surface air temperature and precipitation, in particular the precipitation evolution and its association with the large-scale circulation throughout the Mei-yu season. However, the model simulates smaller precipitation intensity, which is due partly to the colder and drier model atmosphere resulting from excessive low-level clouds and the simplified land surface process scheme used in the present study.

Abstract

Net primary productivity (NPP) is an important component of the carbon cycle and a key indicator of ecosystem performance. The aim of this study is to construct a more accurate regional vegetation NPP estimation model and explore the relationship between NPP and climatic factors (air temperature, rainfall, sunshine hours, relative humidity, air pressure, global radiation, and surface net radiation). As a key variable in NPP modeling, photosynthetically active radiation (PAR) was obtained by finding a linear relationship between PAR and horizontal direct radiation, scattered radiation, and net radiation with high accuracy. The fraction of absorbed photosynthetically active radiation (FPAR) was estimated by enhanced vegetation index (EVI) instead of the widely used normalized difference vegetation index (NDVI). Stress factors of temperature/humidity for different types of vegetation were also considered in the simulation of light use efficiencies (LUE). The authors used EVI datasets of Moderate Resolution Imaging Spectroradiometer (MODIS) from 2001 to 2011 and geographic information techniques to reveal NPP variations in Wuhan. Time lagged serial correlation analysis was employed to study the delayed and continuous effects of climatic factors on NPP. The results showed that the authors’ improved model can simulate vegetation NPP in Wuhan effectively, and it may be adopted or used in other regions of the world that need to be further tested. The results indicated that air temperature and air pressure contributed significantly to the interannual changes of plant NPP while rainfall and global radiation were major climatic factors influencing seasonal NPP variations. A significant positive 32-day lagged correlation was observed between seasonal variation of NPP and rainfall (P < 0.01); the influence of changing climate on NPP lasted for 64 days. The impact of air pressure, global radiation, and net radiation on NPP persisted for 48 days, while the effects of sunshine hours and air temperature on NPP only lasted for 16 and 32 days, respectively.

Abstract

Measurements of air temperature and precipitation at 35 stations in Hubei Province, China, during 1962–2011 are used to investigate the regional climate change. There is an increasing trend for observed air temperature (0.23°C decade⁻¹), which is slightly higher than that from multiple model simulations/predictions [phase 5 of CMIP (CMIP5) datasets] (0.16°C decade⁻¹). The observed precipitation increases at the rate of 11.4 mm decade⁻¹, while the CMIP5 results indicate a much lower decreasing trend (0.8 mm decade⁻¹) in this region. To examine the ecological responses to the climate changes in Hubei Province, annual gross primary productivity (GPP) and net primary productivity (NPP) products during 2000–10 and leaf area index (LAI) products during 1981–2011 are also analyzed. It is discovered that GPP, NPP, and LAI increase at the rate of 1.8 TgC yr⁻¹ yr⁻¹, 1.1 TgC yr⁻¹ yr⁻¹, and 0.14 m² m⁻² decade⁻¹, respectively. A linear model is further used to conduct the correlation analyses between climatic parameters (i.e., air temperature and precipitation) and ecological indicators (i.e., GPP, NPP, and LAI). The results indicate that the air temperature has a significant positive correlation with LAI (R ² = 0.311) and GPP (R ² = 0.189); precipitation is positively correlated with NPP (R ² = 0.209). Thus, it is concluded that the air temperature exerts a stronger effect on the ecosystem than precipitation in Hubei Province over the past decades.

Abstract

Observations indicate that the East Asian summer monsoon (EASM) exhibits distinctive characteristics of large cloud amounts with associated heavy and persistent rainfall, although short breaks for clear sky usually occur. Consequently, the effects of cloud–radiation interactions can play an important role in the general circulation of the atmosphere and, thus, the evolution of the EASM. In this note, as a first step toward studying the topic, the 5-yr (January 1985–December 1989) Earth Radiation Budget Experiment (ERBE) dataset is used to show the spatial and temporal patterns of both shortwave (SW) and longwave (LW) cloud radiative forcing (CRF) at the top of the atmosphere over east China, and to compare the observed features with Atmospheric Model Intercomparison Project-II (AMIP-II) simulations with the University at Albany, State University of New York (SUNYA) Community Climate Model 3 (CCM3) and the ECHAM4 general circulation models.

The observations indicate that the net CRF provides a cooling effect to the atmosphere–surface climate system, dominated by the SW CRF cooling (albedo effect) with partial compensation from the LW CRF warming (greenhouse effect). The SW CRF shows a strong seasonal cycle, and its peak magnitude is particularly large, ∼110 W m⁻², for south China and the Yangtze–Huai River valley (YHRV) during May and June, while the LW CRF is about 50 W m⁻² for the same months with a weak dependence on the latitudes and seasons. These characteristics are in sharp contrast to the Northern Hemispheric zonal means of the same latitude bands and seasons, thus implying a unique role for cloud–radiation interaction in east China. Both model simulations show similar observed characteristics, although biases exist. For example, in May, the ECHAM4 underestimates the SW CRF while the SUNYA CCM3 simulates a significantly larger value, both attributed to the respective biases in the simulated total cloud cover. Model-to-observation comparisons of the association between total cloud cover and SW CRF, and between high cloud cover and LW CRF, are also presented and their differences are discussed. Finally, the SUNYA CCM3 biases in the CRF and its relevance to the model cloud biases are discussed in the context of model cold and dry biases in climate simulations.

Abstract

In this paper the model outputs from the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) are used to examine the climatology and interannual variability of the East Asian winter monsoon (EAWM). The multimodel ensemble (MME) is able to reproduce reasonably well the circulation features of the EAWM. The simulated surface air temperature still suffers from a cold bias over East Asia, but this bias is reduced compared with CMIP phase 3 models. The intermodel spread is relatively small for the large-scale circulations, but is large for the lower-tropospheric meridional wind and precipitation along the East Asian coast. The interannual variability of the EAWM-related circulations can be captured by most of the models. A general bias is that the simulated variability is slightly weaker than in the observations. Based on a selected dynamic EAWM index, the patterns of the EAWM-related anomalies are well reproduced in MME although the simulated anomalies are slightly weaker than the observations. One general bias is that the northeasterly anomalies over East Asia cannot be captured to the south of 30°N. This bias may arise both from the inadequacies of the EAWM index and from the ability of models to capture the EAWM-related tropical–extratropical interactions. The ENSO–EAWM relationship is then evaluated and about half of the models can successfully capture the observed ENSO–EAWM relationship, including the significant negative correlation between Niño-3.4 and EAWM indices and the anomalous anticyclone (or cyclone) over the northwestern Pacific. The success of these models is attributed to the reasonable simulation of both ENSO’s spatial structure and its strength of interannual variability.

Abstract

Seasonal predictions of precipitation and surface air temperature from the Climate Forecast System, version 2 (CFSv2), are evaluated against gridded daily observations from 1982 to 2007 over 17 hydroclimatic regions in China. The seasonal predictive skill is quantified with skill scores including correlation coefficient, RMSE, and mean bias for spatially averaged seasonal precipitation and temperature forecasts for each region. The evaluation focuses on identifying regions and seasons where significant skill exists, thus potentially contributing to skill in hydrological prediction. The authors find that the predictive skill of CFSv2 precipitation and temperature forecasts has a stronger dependence on seasons and regions than on lead times. Both temperature and precipitation forecasts show higher skill from late summer [July–September (JAS)] to late autumn [October–December (OND)] and from winter [December–February (DJF)] to spring [March–May (MAM)]. The skill of CFSv2 precipitation forecasts is low during summer [June–August (JJA)] and winter (DJF) over all of China because of low potential predictability of the East Asian summer monsoon and the East Asian winter monsoon for China. As expected, temperature predictive skill is much higher than precipitation predictive skill in all regions. As observed precipitation shows significant correlation with the Oceanic Niño index over western, southwestern, and central China, the authors found that CFSv2 precipitation forecasts generally show similar correlation pattern, suggesting that CFSv2 precipitation forecasts can capture ENSO signals. This evaluation suggests that using CFSv2 forecasts for seasonal hydrological prediction over China is promising and challenging.

Abstract

This study aims to better understand the ENSO impacts on climate anomalies over East Asia in early winter (November–December) and late winter (January–February). In particular, the possible mechanisms during early winter are investigated. The results show that ENSO is associated with a Rossby wave train emanating from the tropical Indian Ocean toward East Asia (denoted as tIO-EA) in early winter. This tIO-EA wave train in El Niño (La Niña) is closely related to a weakening (strengthening) of the East Asian trough, and thereby a weakened (strengthened) East Asian winter monsoon and warm (cold) temperature anomalies over northeastern China and Japan. By using partial regression analysis and numerical experiments, we identify that the formation of tIO-EA wave train is closely related to precipitation anomalies in the tropical eastern Indian Ocean and western Pacific (denoted as eIO/wP). In addition, the ENSO-induced North Atlantic anomalies may also contribute to formation of the tIO-EA wave train in conjunction with the eIO/wP precipitation. The response of eIO/wP precipitation to ENSO is stronger in early winter than in late winter. This can be attributed to the stronger anomalous Walker circulation over the Indian Ocean, which in turn is caused by higher climatological SST and stronger mean precipitation state in the Indian Ocean during early winter.