Authigenic sericite record of a fossil geothermal system: the Offenburg trough,central Black Forest,Germany

A fossil geothermal area is hosted by the Carboniferous, Permian and Bunter sandstones of the Offenburg intramontane trough in the central Black Forest. The hydrothermal alteration is identified on the basis of newly formed sericites, which appear as pseudomorphs after feldspar and filling of pore spaces. According to K–Ar dating of sericite, serititization occurred about 145 Ma ago (Jurassic). On the basis of ¹⁸O analyses of sericite, sericite composition and vitrinite reflectance, the hydrothermal fluids had temperatures of 150–210 °C. Because their electrolyte content was low, these fluids are assumed to have derived from meteoric water. A second pulse of electrolyte-rich hydrothermal fluids resulted in quartz overgrowths. Fluid mobilization seems to be linked to the disintegration of Pangaea and to reactivated fault systems extending from the crystalline basement into the intramontane sediments.     

应用通量方差法估算戈壁绿洲下垫面湍流通量的研究

利用“绿洲系统能量与水分循环过程观测试验” 2005年绿洲、戈壁点的观测资料, 分析与讨论了温度、水汽的归一化标准差随稳定度变化的通量方差关系, 应用通量方差法对感热、 潜热通量进行了计算, 并同涡动相关系统的观测结果进行了比较。不稳定条件下, 戈壁点温度归一化标准差随稳定度变化的通量方差关系优于下垫面非均匀性更强的绿洲点, 绿洲点水汽的归一化标准差随稳定度变化的通量方差关系较温度量表现得更好。对同一站点, 归一化温度标准差的通量方差关系并不总是优于水汽的通量方差关系, 其取决于该站点的温度以及水汽的源汇分布情况; 通量方差法对两个站点的感热、 潜热通量均有较好的再现, 但戈壁点感热通量的计算效果优于非均匀性更强的绿洲点。应用通量方差法对潜热通量计算时若采用直接观测的感热通量, 则潜热通量的计算效果具有一定程度的改善。     

The exceptionally wet year of 2014 over Greece: a statistical and synoptical-atmospheric analysis over the region of Thessaloniki

The highest rainfall totals (912.2 mm) and the largest number of raindays (133 days), since 1958, were recorded in Thessaloniki during the year of 2014. Extreme precipitation heights were also observed on a seasonal, monthly and daily basis. The examined year presented the highest daily rainfall intensity, the maximum daily precipitation and the largest number of heavy precipitation days (greater than 10 mm), and it also exceeded the previous amounts of precipitation of very wet (95th percentile) and extremely wet (99th percentile) days. According to the automatic circulation type classification scheme that was used, it was found that during this exceptionally wet year, the frequency of occurrence of cyclonic types at the near surface geopotential level increases, while the same types decreased at a higher atmospheric level (500 hPa). The prevailing type was type C which is located at the centre of the study area (Greece), but several other cyclonic types changed during this year not only their frequency but also their percentage of rainfall as well as their daily precipitation intensity. It should be highlighted that these findings differentiated on the seasonal-scale analysis. Moreover, out of the three teleconnection patterns that were examined (Scandinavian Pattern, Eastern Mediterranean Teleconnection Pattern and North Sea-Caspian Pattern), the Scandinavian one (SCAND) was detected during the most of the months of 2014 meaning that it was highly associated with intense precipitation over Greece.     

Coupling statistical and dynamical methods for spatial downscaling of precipitation

The resolution of General Circulation Models (GCMs) is too coarse for climate change impact studies at the catchment or site-specific scales. To overcome this problem, both dynamical and statistical downscaling methods have been developed. Each downscaling method has its advantages and drawbacks, which have been described in great detail in the literature. This paper evaluates the improvement in statistical downscaling (SD) predictive power when using predictors from a Regional Climate Model (RCM) over a GCM for downscaling site-specific precipitation. Our approach uses mixed downscaling, combining both dynamic and statistical methods. Precipitation, a critical element of hydrology studies that is also much more difficult to downscale than temperature, is the only variable evaluated in this study. The SD method selected here uses a stepwise linear regression approach for precipitation quantity and occurrence (similar to the well-known Statistical Downscaling Model (SDSM) and called SDSM-like herein). In addition, a discriminant analysis (DA) was tested to generate precipitation occurrence, and a weather typing approach was used to derive statistical relationships based on weather types, and not only on a seasonal basis as is usually done. The existing data record was separated into a calibration and validation periods. To compare the relative efficiency of the SD approaches, relationships were derived at the same sites using the same predictors at a 300km scale (the National Center for Environmental Prediction (NCEP) reanalysis) and at a 45km scale with data from the limited-area Canadian Regional Climate Model (CRCM) driven by NCEP data at its boundaries. Predictably, using CRCM variables as predictors rather than NCEP data resulted in a much-improved explained variance for precipitation, although it was always less than 50?% overall. For precipitation occurrence, the SDSM-like model slightly overestimated the frequencies of wet and dry periods, while these were well-replicated by the DA-based model. Both the SDSM-like and DA-based models reproduced the percentage of wet days, but the wet and dry statuses for each day were poorly downscaled by both approaches. Overall, precipitation occurrence downscaled by the DA-based model was much better than that predicted by the SDSM-like model. Despite the added complexity, the weather typing approach was not better at downscaling precipitation than approaches without classification. Overall, despite significant improvements in precipitation occurrence prediction by the DA scheme, and even going to finer scales predictors, the SD approach tested here still explained less than 50?% of the total precipitation variance. While going to even smaller scale predictors (10–15?km) might improve results even more, such smaller scales would basically transform the direct outputs of climate models into impact models, thus negating the need for statistical downscaling approaches.     

Impact of single-point GPS integrated water vapor estimates on short-range WRF model forecasts over southern India

Specifying physically consistent and accurate initial conditions is one of the major challenges of numerical weather prediction (NWP) models. In this study, ground-based global positioning system (GPS) integrated water vapor (IWV) measurements available from the International Global Navigation Satellite Systems (GNSS) Service (IGS) station in Bangalore, India, are used to assess the impact of GPS data on NWP model forecasts over southern India. Two experiments are performed with and without assimilation of GPS-retrieved IWV observations during the Indian winter monsoon period (November–December, 2012) using a four-dimensional variational (4D-Var) data assimilation method. Assimilation of GPS data improved the model IWV analysis as well as the subsequent forecasts. There is a positive impact of ～10 % over Bangalore and nearby regions. The Weather Research and Forecasting (WRF) model-predicted 24-h surface temperature forecasts have also improved when compared with observations. Small but significant improvements were found in the rainfall forecasts compared to control experiments.     

Evaluation of global climate model on performances of precipitation simulation and prediction in the Huaihe River basin

Using climate models with high performance to predict the future climate changes can increase the reliability of results. In this paper, six kinds of global climate models that selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under Representative Concentration Path (RCP) 4.5 scenarios were compared to the measured data during baseline period (1960–2000) and evaluate the simulation performance on precipitation. Since the results of single climate models are often biased and highly uncertain, we examine the back propagation (BP) neural network and arithmetic mean method in assembling the precipitation of multi models. The delta method was used to calibrate the result of single model and multimodel ensembles by arithmetic mean method (MME-AM) during the validation period (2001–2010) and the predicting period (2011–2100). We then use the single models and multimodel ensembles to predict the future precipitation process and spatial distribution. The result shows that BNU-ESM model has the highest simulation effect among all the single models. The multimodel assembled by BP neural network (MME-BP) has a good simulation performance on the annual average precipitation process and the deterministic coefficient during the validation period is 0.814. The simulation capability on spatial distribution of precipitation is: calibrated MME-AM > MME-BP > calibrated BNU-ESM. The future precipitation predicted by all models tends to increase as the time period increases. The order of average increase amplitude of each season is: winter > spring > summer > autumn. These findings can provide useful information for decision makers to make climate-related disaster mitigation plans.     

Climate Change Impacts for the Conterminous USA: An Integrated Assessment Summary

This special issue of Climatic Change describes an effort to improve methodology for integrated assessment of impacts and consequences of climatic change. Highlights of the seven foregoing Parts (papers) that constitute this special issue are summarized here. The methodology developed involves construction of scenarios of climate change that are used to drive individual sectoral models for simulating impacts on crop production, irrigation demand, water supply and change in productivity and geography of unmanaged ecosystems. Economic impacts of the changes predicted by integrating the results of the several sectoral simulation models are calculated through an agricultural land-use model. While these analyses were conducted for the conterminous United States alone, their global implications are also considered in this summary as is the need for further improvements in integrated assessment methodology.     

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.     

An Observational Study of the Mesoscale Mistral Dynamics

We investigate the mesoscale dynamics of the mistral through the wind profiler observations of the MAP (autumn 1999) and ESCOMPTE (summer 2001) field campaigns. We show that the mistral wind field can dramatically change on a time scale less than 3 hours. Transitions from a deep to a shallow mistral are often observed at any season when the lower layers are stable. The variability, mainly attributed in summer to the mistral/land–sea breeze interactions on a 10-km scale, is highlighted by observations from the wind profiler network set up during ESCOMPTE. The interpretations of the dynamical mistral structure are performed through comparisons with existing basic theories. The linear theory of R. B. Smith [Advances in Geophysics, Vol. 31, 1989, Academic Press, 1–41] and the shallow water theory [Schär, C. and Smith, R. B.: 1993a, J. Atmos. Sci. 50, 1373–1400] give some complementary explanations for the deep-to-shallow transition especially for the MAP mistral event. The wave breaking process induces a low-level jet (LLJ) downstream of the Alps that degenerates into a mountain wake, which in turn provokes the cessation of the mistral downstream of the Alps. Both theories indicate that the flow splits around the Alps and results in a persistent LLJ at the exit of the Rhône valley. The LLJ is strengthened by the channelling effect of the Rhône valley that is more efficient for north-easterly than northerly upstream winds despite the north–south valley axis. Summer moderate and weak mistral episodes are influenced by land–sea breezes and convection over land that induce a very complex interaction that cannot be accurately described by the previous theories.     

Comparative analysis of different underlying surfaces using a high-resolution assimilation dataset in semi-arid areas in China

To study the land surface and atmospheric meteorological characteristics of non-uniform underlying surfaces in the semi-arid area of Northeast China, we use a “High-Resolution Assimilation Dataset of the water-energy cycle in China (HRADC)”. The grid points of three different underlying surfaces were selected, and their meteorological elements were averaged for each type (i.e., mixed forest, grassland, and cropland). For 2009, we compared and analyzed the different components of leaf area index (LAI), soil temperature and moisture, surface albedo, precipitation, and surface energy for various underlying surfaces in Northeast China. The results indicated that the LAI of mixed forest and cropland during the summer is greater than 5 m² m^?2 and below 2.5 m² m^?2 for grassland; in the winter and spring seasons, the Green Vegetation Fraction (GVF) is below 30%. The soil temperature and moisture both vary greatly. Throughout the year, the mixed forest is dominated by latent heat evaporation; in grasslands and croplands, the sensible heat flux and the latent heat flux are approximately equal, and the GVF contributed more to latent heat flux than sensible heat flux in the summer. This study compares meteorological characteristics between three different underlying surfaces of the semi-arid area of Northeast China and makes up for the insufficiency of purely using observations for the study. This research is important for understanding the water-energy cycle and transport in the semi-arid area.