Observed particle sizes and fluxes of Aeolian sediment in the near surface layer during sand-dust storms in the Taklamakan Desert

Monitoring, modeling and predicting the formation and movement of dust storms across the global deserts has drawn great attention in recent decades. Nevertheless, the scarcity of real-time observations of the wind-driven emission, transport and deposition of dusts has severely impeded progress in this area. In this study, we report an observational analysis of sand-dust storm samples collected at seven vertical levels from an 80-m-high flux tower located in the hinterland of the great Taklamakan Desert for ten sand-dust storm events that occurred during 2008–2010. We analyzed the vertical distribution of sandstorm particle grain sizes and horizontal sand-dust sediment fluxes from the near surface up to 80 m high in this extremely harsh but highly representative environment. The results showed that the average sandstorm grain size was in the range of 70 to 85 μm. With the natural presence of sand dunes and valleys, the horizontal dust flux appeared to increase with height within the lower surface layer, but was almost invariant above 32 m. The average flux values varied within the range of 8 to 14 kg m^?2 and the vertical distribution was dominated by the wind speed in the boundary layer. The dominant dust particle size was PM₁₀₀ and below, which on average accounted for 60–80 % of the samples collected, with 0.9–2.5 % for PM_0–2.5, 3.5–7.0 % for PM_0–10, 5.0–14.0 % for PM_0–20 and 20.0–40.0 % for PM_0–50. The observations suggested that on average the sand-dust vertical flux potential is about 0.29 kg m^?2 from the top of the 80 m tower to the upper planetary boundary layer and free atmosphere through the transport of particles smaller than PM₂₀. Some of our results differed from previous measurements from other desert surfaces and laboratory wind-dust experiments, and therefore provide valuable observations to support further improvement of modeling of sandstorms across different natural environmental conditions.     

Evaluation of NCEP TIGGE short-range forecast for Indian summer monsoon intraseasonal oscillation

Potential evapotranspiration (PET) is one of the most critical parameters in the research on agro-ecological systems. The computational methods for the estimation of PET vary in data demands from very simple (empirically based), requiring only information based on air temperatures, to complex ones (more physically based) that require data on radiation, relative humidity, wind speed, etc. The current research is focused on three study areas in Greece that face different climatic conditions due to their location. Twelve PET formulae were used, analyzed and inter-compared in terms of their sensitivity regarding their input coefficients for the Ardas River basin in north-eastern Greece, Sperchios River basin in Central Greece and Geropotamos River basin in South Greece. The aim was to compare all the methods and conclude to which empirical PET method(s) better represent the PET results in each area and thus should be adopted and used each time and which factors influence the results in each case. The results indicated that for the areas that face Mediterranean climatic conditions, the most appropriate method for the estimation of PET was the temperature-based, Hamon’s second version (PET_Ham2). Furthermore, the PET_Ham2 was able to estimate PET almost similarly to the average results of the 12 equations. For the Ardas River basin, the results indicated that both PET_Ham2 and PET_Ham1 can be used to estimate PET satisfactorily. Moreover, the temperature-based equations have proven to produce better results, followed by the radiation-based equations. Finally, PET_ASCE, which is the most commonly used PET equation, can also be applied occasionally in order to provide satisfactory results.     

Warm spells in Northern Europe in relation to atmospheric circulation

This study describes warm spells in Northern Europe and determines the synoptic situations that cause their occurrence. In this article, a relatively warm day was defined as a day when the maximum temperature exceeded the 95th annual percentile, and a warm spell (WS) was considered to be a sequence of at least five relatively warm days. In the analysed multiannual period and within the investigated area, 24 (Kallax) to 53 (Oslo) WSs were observed. The occurrence of WSs was mainly connected with positive anomalies of sea level pressure and a 500-hPa isobaric surface, displaying the presence of high-pressure systems. This occurrence was also accompanied by positive T850 anomalies.     

Long-term trend and variability of precipitation in Chhattisgarh State,India

Spatial and temporal precipitation variability in Chhattisgarh State in India was examined by using monthly precipitation data for 102 years (1901–2002) from 16 stations. The homogeneity of precipitation data was evaluated by the double-mass curve approach and the presence of serial correlation by lag-1 autocorrelation coefficient. Linear regression analysis, the conventional Mann–Kendall (MK) test, and Spearman’s rho were employed to identify trends and Sen’s slope to estimate the slope of trend line. The coefficient of variation (CV) was used to analyze precipitation variability. Spatial interpolation was done by a Kriging process using ArcGIS 9.3. Results of both parametric and non-parametric tests and trend tests showed that at 5 % significance level, annual precipitation exhibited a decreasing trend at all stations except Bilaspur and Dantewada. For both annual and monsoon precipitation, Sen’s test showed a decreasing trend for all stations, except Bilaspur and Dantewada. The highest percentage of variability was observed in winter precipitation (88.75 %) and minimum percentage variability in annual series (14.01 %) over the 102-year periods.     

Variances in the projections,resulting from CLIMEX,Boosted Regression Trees and Random Forests techniques

The aim of this study was to have a comparative investigation and evaluation of the capabilities of correlative and mechanistic modeling processes, applied to the projection of future distributions of date palm in novel environments and to establish a method of minimizing uncertainty in the projections of differing techniques. The location of this study on a global scale is in Middle Eastern Countries. We compared the mechanistic model CLIMEX (CL) with the correlative models MaxEnt (MX), Boosted Regression Trees (BRT), and Random Forests (RF) to project current and future distributions of date palm (Phoenix dactylifera L.). The Global Climate Model (GCM), the CSIRO-Mk3.0 (CS) using the A2 emissions scenario, was selected for making projections. Both indigenous and alien distribution data of the species were utilized in the modeling process. The common areas predicted by MX, BRT, RF, and CL from the CS GCM were extracted and compared to ascertain projection uncertainty levels of each individual technique. The common areas identified by all four modeling techniques were used to produce a map indicating suitable and unsuitable areas for date palm cultivation for Middle Eastern countries, for the present and the year 2100. The four different modeling approaches predict fairly different distributions. Projections from CL were more conservative than from MX. The BRT and RF were the most conservative methods in terms of projections for the current time. The combination of the final CL and MX projections for the present and 2100 provide higher certainty concerning those areas that will become highly suitable for future date palm cultivation. According to the four models, cold, hot, and wet stress, with differences on a regional basis, appears to be the major restrictions on future date palm distribution. The results demonstrate variances in the projections, resulting from different techniques. The assessment and interpretation of model projections requires reservations, especially in correlative models such as MX, BRT, and RF. Intersections between different techniques may decrease uncertainty in future distribution projections. However, readers should not miss the fact that the uncertainties are mostly because the future GHG emission scenarios are unknowable with sufficient precision. Suggestions towards methodology and processing for improving projections are included.     

A new approach to identify the sensitivity and importance of physical parameters combination within numerical models using the Lund–Potsdam–Jena (LPJ) model as an example

Daily global solar irradiation (R _s) is one of the main inputs in environmental modeling. Because of the lack of its measuring facilities, high-quality and long-term data are limited. In this research, R _s values were estimated based on measured sunshine duration and cloud cover of our synoptic meteorological stations in central and southern Iran during 2008, 2009, and 2011. Clear sky solar irradiation was estimated from linear regression using extraterrestrial solar irradiation as the independent variable with normalized root mean square error (NRMSE) of 4.69 %. Daily R _s was calibrated using measured sunshine duration and cloud cover data under different sky conditions during 2008 and 2009. The 2011 data were used for model validation. According to the results, in the presence of clouds, the R _s model using sunshine duration data was more accurate when compared with the model using cloud cover data (NRMSE = 11. 69 %). In both models, with increasing sky cloudiness, the accuracy decreased. In the study region, more than 92 % of sunshine durations were clear or partly cloudy, which received close to 95 % of total solar irradiation. Hence, it was possible to estimate solar irradiation with a good accuracy in most days with the measurements of sunshine duration.     

Summer temperature variability across four urban neighborhoods in Knoxville,Tennessee, USA

The urban heat island (UHI) is a well-documented effect of urbanization on local climate, identified by higher temperatures compared to surrounding areas, especially at night and during the warm season. The details of a UHI are city-specific, and microclimates may even exist within a given city. Thus, investigating the spatiotemporal variability of a city’s UHI is an ongoing and critical research need. We deploy ten weather stations across Knoxville, Tennessee, to analyze the city’s UHI and its differential impacts across urban neighborhoods: two each in four neighborhoods, one in more dense tree cover and one in less dense tree cover, and one each in downtown Knoxville and Ijams Nature Center that serve as control locations. Three months of temperature data (beginning 2 July 2014) are analyzed using paired-sample t tests and a three-way analysis of variance. Major findings include the following: (1) Within a given neighborhood, tree cover helps negate daytime heat (resulting in up to 1.19 ^°C lower maximum temperature), but does not have as large of an influence on minimum temperature; (2) largest temperature differences between neighborhoods occur during the day (0.38–1.16 ^°C difference), but larger differences between neighborhoods and the downtown control occur at night (1.04–1.88 ^°C difference); (3) presiding weather (i.e., air mass type) has a significant, consistent impact on the temperature in a given city, and lacks the differential impacts found at a larger-scale in previous studies; (4) distance from city center does not impact temperature as much as land use factors. This is a preliminary step towards informing local planning with a scientific understanding of how mitigation strategies may help minimize the UHI and reduce the effects of extreme weather on public health and well-being.     

The impact of different cooling strategies on urban air temperatures: the cases of Campinas,Brazil and Mendoza,Argentina

This paper describes different ways of reducing urban air temperature and their results in two cities: Campinas, Brazil—a warm temperate climate with a dry winter and hot summer (Cwa), and Mendoza, Argentina—a desert climate with cold steppe (BWk). A high-resolution microclimate modeling system—ENVI-met 3.1—was used to evaluate the thermal performance of an urban canyon in each city. A total of 18 scenarios were simulated including changes in the surface albedo, vegetation percentage, and the H/W aspect ratio of the urban canyons. These results revealed the same trend in behavior for each of the combinations of strategies evaluated in both cities. Nevertheless, these strategies produce a greater temperature reduction in the warm temperate climate (Cwa). Increasing the vegetation percentage reduces air temperatures and mean radiant temperatures in all scenarios. In addition, there is a greater decrease of urban temperature with the vegetation increase when the H/W aspect ratio is lower. Also, applying low albedo on vertical surfaces and high albedo on horizontal surfaces is successful in reducing air temperatures without raising the mean radiant temperature. The best combination of strategies—60 % of vegetation, low albedos on walls and high albedos on pavements and roofs, and 1.5 H/W—could reduce air temperatures up to 6.4 °C in Campinas and 3.5 °C in Mendoza.     

阿克苏初秋一次暴雨过程诊断分析

本文利用常规地面与高空观测资料、自动站逐时资料、NECP1°×1°再分析资料,对阿克苏地区2015年9月7日局地暴雨进行分析。结果表明,造成此次暴雨的主要原因有(1)中尺度分析暴雨发生前阿克苏处于高能高湿不稳定区域;(2)08时至20时探空图中湿层、风向以及不稳定能量的突变预示了强对流天气的发生;(3)暴雨过程的大部分水汽是通过低层偏东气流输送,水汽的来源主要为孟加拉湾,其次北疆短波槽及蒙古槽也提供了少量水汽(4)锋区加强、垂直风切变增大、水平风速辐合提供了热力及动力条件,垂直速度伸展高度影响了对流云厚度及高度。(5)地面风速辐合触发对流,加之较好的地面热力条件以及一定的地形增益暴雨强度。(6)地面辐合线及偏东风输送位置影响暴雨落区。     

三峡库区气温变化高精度区域气候模式模拟与预估

基于三峡库区1961—2005年气温逐日格点数据,评估由BCC_CSM1.1模式驱动的RegCM4区域气候模式、MPI-ESM-LR模式驱动的CCLM区域气候模式对三峡库区平均气温、极端高温的模拟能力,选用与观测值更为接近的区域气候模式模拟结果,预估三峡库区在RCP4.5温室气体排放情景下2016—2035年气温变化。结果表明:RegCM4和CCLM模式均能模拟出三峡库区多年平均气温、高温日数和高温强度的季节变化和空间分布形态,但均在库区东北部模拟的年平均气温偏低、高温日数偏少、高温强度偏小。同时,模式均能较好地反映出三峡库区年平均气温、年高温日数的年际变化,但对高温强度的年际变化模拟较差。总体而言,CCLM模式对三峡库区气温的模拟效果好于RegCM4。RCP4.5情景下,三峡库区2016—2035年平均气温、高温日数比当代(1986—2005年)分别增加0.6℃和5d,高温强度变化不明显。