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2016 Vol. 33, No. 2

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The Impact of Cut-off Lows on Ozone in the Upper Troposphere and Lower Stratosphere over Changchun from Ozonesonde Observations
Yushan SONG, Daren LÜ, Qian LI, Jianchun BIAN, Xue WU, Dan LI
2016, 33(2): 135-150. doi: 10.1007/s00376-015-5054-2
In situ measurements of the vertical structure of ozone were made in Changchun (43.53°N, 125.13°E), China, by the Institute of Atmosphere Physics, in the summers of 2010-13. Analysis of the 89 validated ozone profiles shows the variation of ozone concentration in the upper troposphere and lower stratosphere (UTLS) caused by cut-off lows (COLs) over Changchun. During the COL events, an increase of the ozone concentration and a lower height of the tropopause are observed. Backward simulations with a trajectory model show that the ozone-rich airmass brought by the COL is from Siberia. A case study proves that stratosphere-troposphere exchange (STE) occurs in the COL. The ozone-rich air mass transported from the stratosphere to the troposphere first becomes unstable, then loses its high ozone concentration. This process usually happens during the decay stage of COLs. In order to understand the influence of COLs on the ozone in the UTLS, statistical analysis of the ozone profiles within COLs, and other profiles, are employed. The results indicate that the ozone concentrations of the in-COL profiles are significantly higher than those of the other profiles between 4 km around the tropopause. The COLs induce an increase in UTLS column ozone by 32% on average. Meanwhile, the COLs depress the lapse-rate tropopause (LRT)/dynamical tropopause height by 1.4/1.7 km and cause the atmosphere above the tropopause to be less stable. The influence of COLs is durable because the increased ozone concentration lasts at least one day after the COL has passed over Changchun. Furthermore, the relative coefficient between LRT height and lower stratosphere (LS) column ozone is -0.62, which implies a positive correlation between COL strength and LS ozone concentration.
Influence of Soil Moisture in Eastern China on the East Asian Summer Monsoon
Zhiyan ZUO, Renhe ZHANG
2016, 33(2): 151-163. doi: 10.1007/s00376-015-5024-8
The sensitivity of the East Asian summer monsoon to soil moisture anomalies over China was investigated based on ensembles of seasonal simulations (March-September) using the NCEP GCM coupled with the Simplified Simple Biosphere Model (NCEP GCM/SSiB). After a control experiment with free-running soil moisture, two ensembles were performed in which the soil moisture over the vast region from the lower and middle reaches of the Yangtze River valley to North China (YRNC) was double and half that in the control, with the maximum less than the field capacity. The simulation results showed significant sensitivity of the East Asian summer monsoon to wet soil in YRNC. The wetter soil was associated with increased surface latent heat flux and reduced surface sensible heat flux. In turn, these changes resulted in a wetter and colder local land surface and reduced land-sea temperature gradients, corresponding to a weakened East Asian monsoon circulation in an anomalous anticyclone over southeastern China, and a strengthened East Asian trough southward over Northeast China. Consequently, less precipitation appeared over southeastern China and North China and more rainfall over Northeast China. The weakened monsoon circulation and strengthened East Asian trough was accompanied by the convergence of abnormal northerly and southerly flow over the Yangtze River valley, resulting in more rainfall in this region. In the drier soil experiments, less precipitation appeared over YRNC. The East Asian monsoon circulation seems to show little sensitivity to dry soil anomalies in NCEP GCM/SSiB.
Trends of Regional Precipitation and Their Control Mechanisms during 1979-2013
Run LIU, Shaw Chen LIU, Chein-Jung SHIU, Jun LI, Yuanhang ZHANG
2016, 33(2): 164-174. doi: 10.1007/s00376-015-5117-4
Trends in precipitation are critical to water resources. Considerable uncertainty remains concerning the trends of regional precipitation in response to global warming and their controlling mechanisms. Here, we use an interannual difference method to derive trends of regional precipitation from GPCP (Global Precipitation Climatology Project) data and MERRA (Modern-Era Retrospective Analysis for Research and Applications) reanalysis in the near-global domain of 60°S-60°N during a major global warming period of 1979-2013. We find that trends of regional annual precipitation are primarily driven by changes in the top 30% heavy precipitation events, which in turn are controlled by changes in precipitable water in response to global warming, i.e., by thermodynamic processes. Significant drying trends are found in most parts of the U.S. and eastern Canada, the Middle East, and eastern South America, while significant increases in precipitation occur in northern Australia, southern Africa, western India and western China. In addition, as the climate warms there are extensive enhancements and expansions of the three major tropical precipitation centers-the Maritime Continent, Central America, and tropical Africa-leading to the observed widening of Hadley cells and a significant strengthening of the global hydrological cycle.
Temporal Statistical Downscaling of Precipitation and Temperature Forecasts Using a Stochastic Weather Generator
Yongku KIM, Balaji RAJAGOPALAN, GyuWon LEE
2016, 33(2): 175-183. doi: 10.1007/s00376-015-5115-6
Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society (IRI). In conjunction with the GLM (generalized linear modeling) weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts (the IRI format only specifies probabilities for three categories: below normal, near normal, and above normal) into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.
A Microscale Model for Air Pollutant Dispersion Simulation in Urban Areas: Presentation of the Model and Performance over a Single Building
Ning ZHANG, Yunsong DU, Shiguang MIAO
2016, 33(2): 184-192. doi: 10.1007/s00376-015-5152-1
A microscale air pollutant dispersion model system is developed for emergency response purposes. The model includes a diagnostic wind field model to simulate the wind field and a random-walk air pollutant dispersion model to simulate the pollutant concentration through consideration of the influence of urban buildings. Numerical experiments are designed to evaluate the model's performance, using CEDVAL (Compilation of Experimental Data for Validation of Microscale Dispersion Models) wind tunnel experiment data, including wind fields and air pollutant dispersion around a single building. The results show that the wind model can reproduce the vortexes triggered by urban buildings and the dispersion model simulates the pollutant concentration around buildings well. Typically, the simulation errors come from the determination of the key zones around a building or building cluster. This model has the potential for multiple applications; for example, the prediction of air pollutant dispersion and the evaluation of environmental impacts in emergency situations; urban planning scenarios; and the assessment of microscale air quality in urban areas.
Implementation of a One-Dimensional Enthalpy Sea-Ice Model in a Simple Pycnocline Prediction Model for Sea-Ice Data Assimilation Studies
Xinrong WU, Shaoqing ZHANG, Zhengyu LIU
2016, 33(2): 193-207. doi: 10.1007/s00376-015-5099-2
To further explore enthalpy-based sea-ice assimilation, a one-dimensional (1D) enthalpy sea-ice model is implemented into a simple pycnocline prediction model. The 1D enthalpy sea-ice model includes the physical processes such as brine expulsion, flushing, and salt diffusion. After being coupled with the atmosphere and ocean components, the enthalpy sea-ice model can be integrated stably and serves as an important modulator of model variability. Results from a twin experiment show that the sea-ice data assimilation in the enthalpy space can produce smaller root-mean-square errors of model variables than the traditional scheme that assimilates the observations of ice concentration, especially for slow-varying states. This study provides some insights into the improvement of sea-ice data assimilation in a coupled general circulation model.
Evaluation of the Tropical Variability from the Beijing Climate Center's Real-Time Operational Global Ocean Data Assimilation System
Wei ZHOU, Mengyan CHEN, Wei ZHUANG, Fanghua XU, Fei ZHENG, Tongwen WU, Xin WANG
2016, 33(2): 208-220. doi: 10.1007/s00376-015-4282-9
The second-generation Global Ocean Data Assimilation System of the Beijing Climate Center (BCC_GODAS2.0) has been run daily in a pre-operational mode. It spans the period 1990 to the present day. The goal of this paper is to introduce the main components and to evaluate BCC_GODAS2.0 for the user community. BCC_GODAS2.0 consists of an observational data preprocess, ocean data quality control system, a three-dimensional variational (3DVAR) data assimilation, and global ocean circulation model [Modular Ocean Model 4 (MOM4)]. MOM4 is driven by six-hourly fluxes from the National Centers for Environmental Prediction. Satellite altimetry data, SST, and in-situ temperature and salinity data are assimilated in real time. The monthly results from the BCC_GODAS2.0 reanalysis are compared and assessed with observations for 1990-2011. The climatology of the mixed layer depth of BCC_GODAS2.0 is generally in agreement with that of World Ocean Atlas 2001. The modeled sea level variations in the tropical Pacific are consistent with observations from satellite altimetry on interannual to decadal time scales. Performances in predicting variations in the SST using BCC_GODAS2.0 are evaluated. The standard deviation of the SST in BCC_GODAS2.0 agrees well with observations in the tropical Pacific. BCC_GODAS2.0 is able to capture the main features of El Niño Modoki I and Modoki II, which have different impacts on rainfall in southern China. In addition, the relationships between the Indian Ocean and the two types of El Niño Modoki are also reproduced.
Application of an Artificial Neural Network for a Direct Estimation of Atmospheric Instability from a Next-Generation Imager
Su Jeong LEE, Myoung-Hwan AHN, Yeonjin LEE
2016, 33(2): 221-232. doi: 10.1007/s00376-015-5084-9
Atmospheric instability information derived from satellites plays an important role in short-term weather forecasting, especially the forecasting of severe convective storms. For the next generation of weather satellites for Korea's multi-purpose geostationary satellite program, a new imaging instrument has been developed. Although this imaging instrument is not designed to perform full sounding missions and its capability is limited, its multi-spectral infrared channels provide information on vertical sounding. To take full advantage of the observation data from the much improved spatiotemporal resolution of the imager, the feasibility of an artificial neural network approach for the derivation of the atmospheric instability is investigated. The multi-layer perceptron model with a feed-forward and back-propagation training algorithm shows quite a sensitive response to the selection of the training dataset and model architecture. Through an extensive performance test with a carefully selected training dataset of 7197 independent profiles, the model architectures are selected to be 12, 5000, and 0.3 for the number of hidden nodes, number of epochs, and learning rate, respectively. The selected model gives a mean absolute error, RMSE, and correlation coefficient of 330 J kg-1, 420 J kg-1, and 0.9, respectively. The feasibility is further demonstrated via application of the model to real observation data from a similar instrument that has comparable observation channels with the planned imager.
The Impact of Deformation on Vortex Development in a Baroclinic Moist Atmosphere
Na LI, Lingkun RAN, Shouting GAO
2016, 33(2): 233-246. doi: 10.1007/s00376-015-5082-y
A mathematical relation between deformation and vertical vorticity tendency is built by introducing the frontogenesis function and the complete vertical vorticity equation, which is derived by virtue of moist potential vorticity. From the mathematical relation, it is shown that properly configured atmospheric conditions can make deformation exert a positive contribution to vortex development at rates comparable to other favorable factors. The effect of deformation on vortex development is not only related to the deformation itself, but also depends on the current thermodynamic and dynamic structures of the atmosphere, such as the convective stability, moist baroclinicity and vertical wind shear (or horizontal vorticity). A diagnostic study of a heavy-rainfall case that occurred during 20-22 July 2012 shows that deformation has the most remarkable effect on the increase in vertical vorticity during the rapid development stage of the low vortex during its whole life cycle. This feature is mainly due to the existence of an approximate neutral layer (about 700 hPa) in the atmosphere where the convective stability tends to be zero. The neutral layer makes the effect of deformation on the vertical vorticity increase significantly during the vortex development stage, and thus drives the vertical vorticity to increase.
Dynamic Responses of Atmospheric Carbon Dioxide Concentration to Global Temperature Changes between 1850 and 2010
Weile WANG, Ramakrishna NEMANI
2016, 33(2): 247-258. doi: 10.1007/s00376-015-5090-y
Changes in Earth’s temperature have significant impacts on the global carbon cycle that vary at different time scales, yet to quantify such impacts with a simple scheme is traditionally deemed difficult. Here, we show that, by incorporating a temperature sensitivity parameter (1.64 ppm yr-1 °C-1) into a simple linear carbon-cycle model, we can accurately characterize the dynamic responses of atmospheric carbon dioxide (CO2) concentration to anthropogenic carbon emissions and global temperature changes between 1850 and 2010 (r2>0.96 and the root-mean-square error <1 ppm for the period from 1960 onward). Analytical analysis also indicates that the multiplication of the parameter with the response time of the atmospheric carbon reservoir (∼12 year) approximates the long-term temperature sensitivity of global atmospheric CO2 concentration (∼15 ppm °C-1), generally consistent with previous estimates based on reconstructed CO2 and climate records over the Little Ice Age. Our results suggest that recent increases in global surface temperatures, which accelerate the release of carbon from the surface reservoirs into the atmosphere, have partially offset surface carbon uptakes enhanced by the elevated atmospheric CO2 concentration and slowed the net rate of atmospheric CO2 sequestration by global land and oceans by ∼30% since the 1960s. The linear modeling framework outlined in this paper thus provides a useful tool to diagnose the observed atmospheric CO2 dynamics and monitor their future changes.
A Solely Radiance-based Spectral Angular Distribution Model and Its Application in Deriving Clear-Sky Spectral Fluxes over Tropical Oceans
Lei SONG, Yinan WANG
2016, 33(2): 259-268. doi: 10.1007/s00376-015-5040-8
The radiation budget at the top of the atmosphere plays a critical role in climate research. Compared to the broadband flux, the spectrally resolved outgoing longwave radiation or flux (OLR), with rich atmospheric information in different bands, has obvious advantages in the evaluation of GCMs. Unlike methods that need auxiliary measurements and information, here we take atmospheric infrared sounder (AIRS) observations as an example to build a self-consistent algorithm by an angular distribution model (ADM), based solely on radiance observations, to estimate clear-sky spectrally resolved fluxes over tropical oceans. As the key step for such an ADM, scene type estimations are obtained from radiance and brightness temperature in selected AIRS channels. Then, broadband OLR as well as synthetic spectral fluxes are derived by the spectral ADM and validated using both synthetic spectra and CERES (Clouds and the Earth's Radiant Energy System) observations. In most situations, the mean OLR differences between the spectral ADM products and the CERES observations are within 2 W m-2, which is less than 1% of the typical mean clear-sky OLR over tropical oceans. The whole algorithm described in this study can be easily extended to other similar hyperspectral radiance measurements.