Atmospheric boundary-layer structure observed during a haze event due to forest-fire smoke

During a haze event in Baltimore, U.S.A. from July 6 to 8, 2002, smoke from forest fires in the Québec region (Canada), degraded air quality and impacted upon local climate, decreasing solar radiation and air temperature. The smoke particles in and above the atmospheric boundary layer (ABL) served as a tracer and provided a unique opportunity to investigate the ABL structure, especially entrainment. Elastic backscatter lidar measurements taken during the haze event distinctly reveal the downward sweeps (or wisps) of smoke-laden air from the free atmosphere into the ABL. Visualisations of mechanisms such as dry convection, the entrainment process, detrainment, coherent entrainment structures, and mixing inside the ABL, are presented. Thermals overshooting at the ABL top are shown to create disturbances in the form of gravity waves in the free atmosphere aloft, as evidenced by a corresponding ripple structure at the bottom of the smoke layer. Lidar data, aerosol ground-based measurements and supporting meteorological data are used to link free atmosphere, mixed-layer and ground-level aerosols. During the peak period of the haze event (July 7, 2002), the correlation between time series of elastic backscatter lidar data within the mixed layer and the scattering coefficient from a nephelometer at ground level was found to be high (R=0.96 for z =324 m, and R=0.89 for z=504 m). Ground-level aerosol concentration was at a maximum about 2 h after the smoke layer intersected with the growing ABL, confirming that the wisps do not initially reach the ground.     

Detection and quantification of local anthropogenic and regional climatic transient signals in temperature logs from Czechia and Slovenia

The paper reports on detection and quantification of the impact of local anthropogenic structures and regional climatic changes on subsurface temperature field. The analyzed temperature records were obtained by temperature monitoring in a borehole in Prague-Spo?ilov (Czechia) and by repeated logging of a borehole in ?empeter (Slovenia). The observed data were compared with temperatures yielded by mathematical 3D time-variable geothermal models of the boreholes’ sites with the aim to decompose the observed transient component of the subsurface temperature into the part affected by construction of new buildings and other anthropogenic structures in surroundings of the boreholes and into the part affected by the ground surface temperature warming due to the surface air temperature rise. A direct human impact on the subsurface temperature warming was proved and contributions of individual anthropogenic structures to this change were evaluated. In the case of Spo?ilov, where the mean annual warming rate reached 0.034°C per year at the depth of 38.3?m during the period 1993–2008, it turned out that about half of the observed warming can be attributed to the air (ground) surface temperature change and half to the human activity on the surface in the immediate vicinity of the borehole. The situation is similar in ?empeter, where the effect of the recently built surface anthropogenic structures is detectable down to the depth of 80?m and the share of the anthropogenic signal on the non-stationary component of the observed subsurface temperature amounts to 30% at the depth of 50?m.     

Sea water surface energy balance in the Arctic fjord (Hornsund,SW Spitsbergen) in May–November 2014

Maize is grown by millions of smallholder farmers in South Asia (SA) under diverse environments. The crop is grown in different seasons in a year with varying exposure to weather extremes, including high temperatures at critical growth stages which are expected to increase with climate change. This study assesses the impact of current and future heat stress on maize and the benefit of heat-tolerant varieties in SA. Annual mean maximum temperatures may increase by 1.4–1.8 °C in 2030 and 2.1–2.6 °C in 2050, with large monthly, seasonal, and spatial variations across SA. The extent of heat stressed areas in SA could increase by up to 12 % in 2030 and 21 % in 2050 relative to the baseline. The impact of heat stress and the benefit from heat-tolerant varieties vary with the level of temperature increase and planting season. At a regional scale, climate change would reduce rainfed maize yield by an average of 3.3–6.4 % in 2030 and 5.2–12.2 % in 2050 and irrigated yield by 3–8 % in 2030 and 5–14 % in 2050 if current varieties were grown under the future climate. Under projected climate, heat-tolerant varieties could minimize yield loss (relative to current maize varieties) by up to 36 and 93 % in 2030 and 33 and 86 % in 2050 under rainfed and irrigated conditions, respectively. Heat-tolerant maize varieties, therefore, have the potential to shield maize farmers from severe yield loss due to heat stress and help them adapt to climate change impacts.     

Evaluation of current statistical approaches for predictive geomorphological mapping

Predictive models are increasingly used in geomorphology, but systematic evaluations of novel statistical techniques are still limited. The aim of this study was to compare the accuracy of generalized linear models (GLM), generalized additive models (GAM), classification tree analysis (CTA), neural networks (ANN) and multiple adaptive regression splines (MARS) in predictive geomorphological modelling. Five different distribution models both for non-sorted and sorted patterned ground were constructed on the basis of four terrain parameters and four soil variables. To evaluate the models, the original data set of 9997 squares of 1 ha in size was randomly divided into model training (70%, n=6998) and model evaluation sets (30%, n=2999).In general, active sorted patterned ground is clearly defined in upper fell areas with high slope angle and till soils. Active non-sorted patterned ground is more common in valleys with higher soil moisture and fine-scale concave topography. The predictive performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC) and the Kappa value. The relatively high discrimination capacity of all models, AUC=0.85–0.88 and Kappa=0.49–0.56, implies that the model's predictions provide an acceptable index of sorted and non-sorted patterned ground occurrence. The best performance for model calibration data for both data sets was achieved by the CTA. However, when the predictive mapping ability was explored through the evaluation data set, the model accuracies of CTA decreased clearly compared to the other modelling techniques. For model evaluation data MARS performed marginally best.Our results show that the digital elevation model and soil data can be used to predict relatively robustly the activity of patterned ground in fine scale in a subarctic landscape. This indicates that predictive geomorphological modelling has the advantage of providing relevant and useful information on earth surface processes over extensive areas, such data being unavailable through more conventional survey methods.     

Palaeoclimatic studies in South Shetland Islands,Antarctica, based on numerous stratigraphic variables in lake sediments

The hitherto longest found lake sediment sequence on Byers Peninsula, Livingston Island, South Shetland Islands, was analysed with respect to lithology, chronology, diatoms, Pediastrum, pollen and spores, mosses, mineralogy, and sediment chemistry. During the ca. 5000 year long development the sediments were influenced by frequent tephra fall-outs. This volcanic impact played a major role in the lake's history during two periods, 4700–4600 and 2800–2500 BP, but was of importance during the lake's entire history with considerable influence on many of the palaeoenvironmentally significant indicators. The large and complex data set was analysed and zonated with different types of multivariate analysis. This resulted in a subdivision of the sequence into 8 time periods and 21 variables. Redundancy analysis (RDA) of this data set, both without and with the tephra periods, and with 4–6 of the variables as explanatory environmental variables, reveal that climatic/environmental signals are detectable. The palaeoclimatic picture that emerged out of the tephra noise suggests that the first 100 years were characterized by mild, humid conditions. This was followed by a less mild and humid climate until ca. 4000 BP when a gradual warming seems to have started, coupled with increased humidity. These mild and humid conditions seem to have reached an optimum slightly after 3000 BP. At ca. 2500 BP a distinct climatic deterioration occurred with colder and drier conditions and long seasons with ice cover. This arid, cold phase probably reached its optimum conditions at ca. 1500 BP, when slightly warmer conditions might have prevailed for a while. Except for the modern sample with rather mild climate, the last 1400 years seem to have been fairly arid and cold, and the effects of the frequent volcanic activity during this period is only vaguely seen in the records.     

采点和去趋势方法对天山西部云杉上树线树轮宽度年表相关性及其气候信号的影响

针对新疆天山西部伊犁、博州三大山体北坡云杉上树线树木年轮资料,采用区域曲线、负指数曲线、样条函数三种不同生长去趋势方法,研制树轮宽度年表,分析不同采点和树轮去趋势方法对树轮宽度年表在不同频域的相关性及气候信号的影响发现：①采点间的距离和是否位于同一气候区是影响云杉上树线树轮宽度年表间相关性的首要因子。三个采点间的树轮宽度变化,在高频变化方面最为相似,低频变化存在着较大差异,表明它们在揭示和重建大范围的高频气候变化方面可能具有较大的应用潜力。②三种不同去趋势方法对天山西部山脉北坡上树线的云杉树轮宽度年表的研制具有一定影响,负指数和样条函数去趋势方法研制的树轮年表较为相似,而区域曲线去趋势方法研制的树轮宽度年表中则可保留较多的轮宽低频变化信息。③降水是天山西部云杉上树线的树轮宽度生长的主要限制因子,且树轮宽度生长对降水的响应具有显著的滞后性。④在天山西部的云杉上树线采点,使用区域曲线去趋势方法有利于在树轮宽度年表中保留较多的与水分有关的气候信息。     

Rapid ground deformation corresponding to a mining-induced seismic event followed by a massive collapse

Natural Hazards - On 17 April 2015, the Wujek/?l?sk underground coal mine in Poland was struck by a strong induced tremor of magnitude M4.0. The event was followed by a massive rock burst...     

Quality and timing of crowd-based water level class observations

Crowd-based hydrological observations can supplement existing monitoring networks and allow data collection in regions where otherwise no data would be available. In the citizen science project CrowdWater, repeated water level observations using a virtual staff gauge approach result in time series of water level classes (WL-classes). To investigate the quality of these observations, we compared the WL-class data with “real” (i.e., measured) water levels from the same stream at a nearby gauging station. We did this for nine locations where citizen scientists reported multiple observations using a smartphone app and at 12 locations where signposts were set up to ask citizens to record observations on a paper form that could be left in a letterbox. The results indicate that the quality of the data collected with the app was better than for the forms. A possible explanation is that for each app location, a single person submitted the vast majority of the observations, whereas at the locations of the forms almost every observation was made by a different person. On average, there were more contributions between May and September than during the other months. Observations were submitted for a range of flow conditions, with a higher fraction of high flow observations for the locations were data were collected with the app. Overall, the results are encouraging for citizen science approaches in hydrology and demonstrate that the smartphone application and the virtual staff gauge are a promising approach for crowd-based water level class observations.