Quantile Trends in Temperature Extremes in China

A number of recent studies have examined trends in extreme temperature indices using a linear regression model based on ordinary least-squares. In this study, quantile regression was, for the first time, applied to examine the trends not only in the mean but also in all parts of the distribution of several extreme temperature indices in China for the period 1960–2008. For China as a whole, the slopes in almost all the quantiles of the distribution showed a notable increase in the numbers of warm days and warm nights, and a significant decrease in the number of cool nights. These changes became much faster as the quantile increased. However, although the number of cool days exhibited a significant decrease in the mean trend estimated by classical linear regression, there was no obvious trend in the upper and lower quantiles. This finding suggests that examining the trends in different parts of the distribution of the time-series is of great importance. The spatial distribution of the trend in the 90 th quantile indicated that there was a pronounced increase in the numbers of warm days and warm nights, and a decrease in the number of cool nights for most of China, but especially in the northern and western parts of China, while there was no significant change for the number of cool days at almost all the stations.

The Interannual Variations of Summer Precipitation in the Northern Indian Ocean Associated with ENSO

Using rainfall data from the Global Precipitation Climatology Project(GPCP),NOAA extended reconstruction sea surface temperature(ERSST),and NCEP/NCAR reanalysis,this study investigates the interannual variation of summer rainfall southwest of the Indian Peninsula and the northeastern Bay of Bengal associated with ENSO.The composite study indicates a decreased summer rainfall southwest of the Indian Peninsula and an increase in the northeastern Bay of Bengal during the developing phase,but vice versa during the decay phase of El Ni o.Further regression analysis demonstrates that abnormal rainfall in the above two regions is controlled by different mechanisms.Southwest of the Indian Peninsula,the precipitation anomaly is related to local convection and water vapor flux in the decay phase of El Ni o.The anomalous cyclone circulation at the lower troposphere helps strengthen rainfall.In the northeastern Bay of Bengal,the anomalous rainfall depends on the strength of the Indian southwest summer monsoon(ISSM).A strong/weak ISSM in the developing/decay phase of El Ni o can bring more/less water vapor to strengthen/weaken the local summer precipitation.

Chemical Composition of Summertime PM_(2.5) and Its Relationship to Aerosol Optical Properties in Guangzhou,China

Urban aerosols have a large effect on the deterioration of air quality and the degradation of atmospheric visibility.Characterization of the chemical composition of PM 2.5 and in situ measurements of the optical properties of aerosols were conducted in July 2008 at an urban site in Guangzhou,Southern China.The mean PM 2.5 concentration for the entire period was 53.7±23.2 μg m 3.The mean PM 2.5 concentration (82.7±25.4 μg m 3) on hazy days was roughly two times higher than that on clear days (38.8±8.7 μg m 3).The total water-soluble ion species and the total average carbon accounted for 47.9%±4.3% and 35.2%±4.5%,respectively,of the major components of PM 2.5.The increase of secondary and carbonaceous aerosols,in particular ammonium sulfate,played an important role in the formation of haze pollution.The mean absorption and scattering coefficients and the single scattering albedo over the whole period were 53±20 M m 1,226±111 M m 1,and 0.80±0.04,respectively.PM 2.5 had a high linear correlation with the aerosol extinction coefficient,elemental carbon (EC) was correlated with aerosol absorption,and organic carbon (OC) and SO 4 2 were tightly linked to aerosol scattering.

A Comparison of Four Precipitation Distribution Models Used in Daily Stochastic Models

Stochastic weather generators are statistical models that produce random numbers that resemble the observed weather data on which they have been fitted; they are widely used in meteorological and hydrologi- cal simulations. For modeling daily precipitation in weather generators, first-order Markov chain-dependent exponential, gamma, mixed-exponential, and lognormal distributions can be used. To examine the perfor- mance of these four distributions for precipitation simulation, they were fitted to observed data collected at 10 stations in the watershed of Yishu River. The parameters of these models were estimated using a maximum-likelihood technique performed using genetic algorithms. Parameters for each calendar month and the Fourier series describing parameters for the whole year were estimated separately. Bayesian infor- mation criterion, simulated monthly mean, maximum daily value, and variance were tested and compared to evaluate the fitness and performance of these models. The results indicate that the lognormal and mixed-exponential distributions give smaller BICs, but their stochastic simulations have overestimation and underestimation respectively, while the gamma and exponential distributions give larger BICs, but their stochastic simulations produced monthly mean precipitation very well. When these distributions were fitted using Fourier series, they all underestimated the above statistics for the months of June, July and August.

Analogue-Dynamical Prediction of Monsoon Precipitation in Northeast China Based on Dynamic and Optimal Configuration of Multiple Predictors

Based on the National Climate Center(NCC)of China operational seasonal prediction model results for the period 1983-2009 and the US National Weather Service Climate Prediction Center merged analysis of precipitation in the same period,together with the 74 circulation indices of NCC Climate System Diagnostic Division and 40 climate indices of NOAA of US during 1951-2009,an analogue-dynamical technique for objective and quantitative prediction of monsoon precipitation in Northeast China is proposed and implemented.Useful information is extracted from the historical data to estimate the model forecast errors.Dominant predictors and the predictors that exhibit evolving analogues are identified through cross validating the anomaly correlation coefficients(ACC)among single predictors,meanwhile with reference of the results from the dynamic analogue bias correction using four analogue samples.Next,an optimal configuration of multiple predictors is set up and compared with historical optimal multi-predictor configurations and then dynamically adjusted.Finally,the model errors are evaluated and utilized to correct the NCC operational seasonal prediction model results,and the forecast of monsoon precipitation is obtained at last.The independent sample validation shows that this technique has effectively improved the monsoon precipitation prediction skill during 2005-2009.This study demonstrates that the analogue-dynamical approachis feasible in operational prediction of monsoon precipitation.

Sensitivity of simulated short-range high-temperature weather to land surface schemes by WRF

The simulations of a heat wave occurring in southern Yangtze-Huaihe valley and southern China during late July,2003 were conducted to examine the sensitivity of simulated surface air temperature(SAT) to different land surface schemes(LSSs) using the Weather Research and Forecasting Model(WRF) Version 2.2 in the short-range mode for 24-h integrations.Initial and boundary conditions employed a National Centers for Environmental Prediction(NCEP) analysis.The results showed that,overall,simulated high-temperature weather is sensitive to different LSSs.Large differences in simulated SAT intensity,threat score,and simulated error under different schemes are identified clearly.In addition,some systematic differences are also in-duced by the LSSs.In terms of threat score from the three LSSs,SLAB is the best,and RUC is better than NOAH.SLAB gives the lowest absolute error for area-averaged SAT,and tends to depict the western Pacific subtropical high with the east-ernmost position at low levels.The LSSs modify the simulated SAT,primarily via the transfer of sensible heat from the land surface to the atmosphere.The physical mechanism of the positive feedback between atmospheric circulation and the SAT is unimportant,with"negative"feedback over most of the simulated areas.This study emphasizes the importance of improving LSSs in SAT forecasting by numerical models.

A new index to describe the tropical Asian summer monsoon

We define a new monsoon index (MV) as the product of relative vorticity and equivalent potential temperature using the long-term NCEP/NCAR reanalysis data. The MV index provides new insights into the intraseasonal and interannual variabilities of the broad-scale tropical Asian summer monsoon (TASM), including the South Asian summer monsoon (SASM) and the South China Sea summer monsoon (SCSSM). On the intraseasonal timescale, the pentad-to-pentad MV index bears a close relationship to the broad-scale rainfall in the TASM regions. Among 29 summers from 1979 to 2007, in 23/27 summers the correlation coefficients are higher than 0.7 in the SASM/SCSSM region. However, in fewer than 9 summers, the correlations between the broad-scale rainfall and the existing circulation indices are higher than 0.7. On the interannual timescale, various existing SASM circulation indices are moderately or well correlated with all-India summer monsoon rainfall, whereas their correlations with broad-scale SASM rainfall are weak. In contrast, the summer mean MV index correlates well with the broad-scale SASM rainfall and all-India summer monsoon rainfall (correlation of 0.73 and 0.65, respectively). In the SCSSM region, the summer mean MV index also bears a close relationship to the SCSSM rainfall, although some discrepancies exist during certain years. The composite strong TASM shows a stronger low-tropospheric low pressure in association with the enhanced westerly winds and moisture transfer, stronger convection, and upper-tropospheric easterly winds, which indicate that the MV index can well capture the features of TASM.

Long-Term Trend of Temperature Derived by Statistical Downscaling Based on EOF Analysis

This study analyzes the ability of statistical downscaling models in simulating the long-term trend of temperature and associated causes at 48 stations in northern China in January and July 1961-2006.Thestatistical downscaling models are established through multiple stepwise regressions of predictor principal components(PCs).The predictors in this study include temperature at 850 hPa(T850),and the combination of geopotential height and temperature at 850 hPa(H850+T850).For the combined predictors,Empirical Orthogonal Function(EOF)analysis of the two combined fields is conducted.The modeling results fromHadCM3 and ECHAM5 under 20C3M and SERS A1B scenarios are applied to the statistical downscaling models to construct local present and future climate change scenarios for each station,during which the projected EOF analysis and the common EOF analysis are utilized to derive EOFs and PCs from the two general circulation models(GCMs).The results show that(1)the trend of temperature in July is associated with the first EOF pattern of the two combined fields,not with the EOF pattern of the regional warming;(2)although HadCM3 and ECHAM5 have simulated a false long-term trend of temperature,the statistical downscaling method is able to well reproduce a correct long-term trend of temperature in northern Chinadue to the successful simulation of the trend of main PCs of the GCM predictors;(3)when the two-field combination and the projected EOF analysis are used,temperature change scenarios have a similar season alvariation to the observed one;and(4)compared with the results of the common EOF analysis,those of the projected EOF analysis have been much more strongly determined by the observed large-scale atmospheric circulation patterns.

An Analysis of Interdecadal Variations of the Asian-African Summer Monsoon

The response of the Asian-African summer monsoon(AASM)to the fast global warming in the 1980s is studied based on several datasets,which span a long time period of nearly 100 yr,with two special periods 1980-1985 and 1990-1995 being focused on.Wavelet analyses are employed to explore the interdecadai variations of the AASM. It is found that after the mid-1980s,the global annual mean surface temperature rises more significantly and extensively over most parts of the African Continent,north of the Indian Ocean,and the Eurasian Continent excluding the Tibetan Plateau.Correspondingly,the global precipitation pattern alters with in- creased rainfall seen over the Sahel and North China in 1990-1995,though it is not recovered to the level of the rainy period before the mid-1960s. Changes of monsoonal circulations between the pre-and post-1980s periods display that,after the fast global warming of the 1980s,the African summer monsoon intensifies distinctly,the Indian summer mon- soon weakens a little bit,and the East Asian summer monsoon remains almost unchanged.The summer precipitation over the Asian-African Monsoon Belt(AAMB)does not change in phase coherently with the variations of the monsoonal circulations. Wavelet analyses of the land-sea thermal contrast and precipitation over North China and the Sahel indicate that interdecadai signals are dominant and in positive phases in the 1960s,leading to an overall enhanced interdecadal variation of the AASM,although the 1960s witnesses a global cooling.In the 1980s, however,in the context of a fast global warming,interdecadal signals are in opposite phases,and they coun- teract with each other,leading to a weakened interdecadal variation of the AASM. After the mid-1960s,the AASM weakened remarkably,whereas after the mid-1980s,the AASM as a whole did not strengthen uniformly and synchronously,because it is found that the interannual variations of the AASM in the 1980s are stronger than those in the 1960s,and they superimposed on the counteracting interdecadal signals,causing different regions of the AAMB behaving differently.Therefore,the response of the AASM to the accelerated global warming post the mid-1980s is not simply out-of-phase with that after the mid-1960s;it may involve more complicated multiscale physical elements.

Assessment of Central Asian heat extremes by statistical downscaling:Validation and future projection for 2015-2100

Increasing heatwaves and extreme temperatures have recently been observed across Central Asia(CA).Accurately assessing and projecting the changing climate extremes at the local(station)scale required for climate risk management are therefore highly important.However,global and regional climate models often fail to represent the statistical distributions of observed daily extreme variables and hence extremes in complex terrain.In this work,we developed a statistical downscaling(SD)model to project summer daily maximum temperature(Tmax)and heatwave indices for 65 meteorological stations in CA toward 2100.The SD model involves first-order autoregression and multiple linear regression using large-scale Tmax and circulation indices(Cis)as predictors,and the model is cross-validated against historical observations.The local Tmax and heatwave indices are then projected for 2015-2100 driven by the output of a global climate model(CNRM-CM6-1)under four Shared Socioeconomic Pathways(SSP126,SSP245,SSP370,and SSP585).The application of the SD model significantly improves forecasting of the probability distribution(10th/90th percentiles)of Tmax at stations,particularly across mountainous regions.The model also captures interannual variability and the long-term trend in Tmax,consistent with synoptic-scale inputs.SD projections demonstrate strong warming trends of summer Tmax in CA toward 2100 with rates between 0.35-0.64℃ per decade based on the SSP245 and SSP370 seenarios.Consequently,heatwave occurrence is projected to rise by 1.0-5.0 and 2.0-7.0 d per decade under the SSP245 and SSP370 scenarios,respectively,by 2100.Duration,intensity,and amplitude of heatwaves rise at greater rates under higher-emission scenarios,particularly in southeastern CA.The proposed SD model serves as a useful tool for assessing local climate extremes,which are needed for regional risk management and policymaking for adaption to climate change.

Molecular distribution and seasonal variation of hydrocarbons in PM_(2.5) from Beijing during 2006

正常(n) 链烷和多不的芳香的烃(哼) 在 2006 从北京在 PM2.5 被收集并且分析了使用一种热 desorption-GC/MS 技术。n 链烷的年度平均集中并且哼 282 是 ?????????????  ? ㈠ ?? 朠洯″ ???? ？？

Time-Dependent Solutions to the Fokker-Planck Equation of Maximum Reduced Air-Sea Coupling Climate Model

The time-dependent solution of reduced air-sea coupling stochastic-dynamic model is accurately obtainedby using the Fokker-Planck equation and the quantum mechanical method. The analysis of the time-dependent solution suggests that when the climate system is in the ground state, the behavior of the systemappears to be a Brownian movement, thus reasoning the foothold of Hasselmann’s stochastic climatic model;when the system is in the first excitation state, the motion of the system exhibits a form of time-decaying,or under certain condition a periodic oscillation with the main period being 2.3 yr. At last, the results areused to discuss the impact of the doubling of carbon dioxide on climate.