Multi-year weather data in the remote semi-arid to arid mountain region of the Saharan flank of the Atlas Mountains

In 2001, a weather and climate monitoring network was established along the temperature and aridity gradient between the sub-humid Moroccan High Atlas Mountains and the former end lake of the Middle Drâa in a pre-Saharan environment. The highest Automated Weather Stations (AWS) was installed just below the M'Goun summit at 3850 m, the lowest station Lac Iriki was at 450 m. This network of 13 AWS stations was funded and maintained by the German IMPETUS (BMBF Grant 01LW06001A, North Rhine-Westphalia Grant 313-21200200) project and since 2011 five stations were further maintained by the GERMAN DFG Fennec project (FI 786/3-1), this way some stations of the AWS network provided data for almost 12 years from 2001-2012. Standard meteorological variables such as temperature, humidity, and wind were measured at an altitude of 2 m above ground. Other meteorological variables comprise precipitation, station pressure, solar irradiance, soil temperature at different depths and for high mountain station snow water equivalent. The stations produced data summaries for 5-minute-precipitation-data, 10- or 15-minute-data and a daily summary of all other variables. This network is a unique resource of multi-year weather data in the remote semi-arid to arid mountain region of the Saharan flank of the Atlas Mountains. The network is described in Schulz et al. (2010) and its further continuation until 2012 is briefly discussed in Redl et al. (2015, doi:10.1175/MWR-D-15-0223.1) and Redl et al. (2016, doi:10.1002/2015JD024443).

Temperature variation and emergency hospital admissions for stroke in Brisbane, Australia, 1996-2005

Stroke is a leading cause of disability and death. This study evaluated the association between temperature variation and emergency admissions for stroke in Brisbane, Australia. Daily emergency admissions for stroke, meteorologic and air pollution data were obtained for the period of January 1996 to December 2005. The relative risk of emergency admissions for stroke was estimated with a generalized estimating equations (GEE) model. For primary intracerebral hemorrhage (PIH) emergency admissions, the average daily PIH for the group aged < 65 increased by 15% (95% Confidence Interval (CI): 5, 26%) and 12% (95% CI: 2, 22%) for a 1°C increase in daily maximum temperature and minimum temperature in summer, respectively, after controlling for potential confounding effects of humidity and air pollutants. For ischemic stroke (IS) emergency admissions, the average daily IS for the group aged ≥ 65 decreased by 3% (95% CI: -6, 0%) for a 1°C increase in daily maximum temperature in winter after adjustment for confounding factors. Temperature variation was significantly associated with emergency admissions for stroke, and its impact varied with different type of stroke. Health authorities should pay greater attention to possible increasing emergency care for strokes when temperature changes, in both summer and winter.

The impact of temperature on mortality in Tianjin, China : a case-crossover design with a distributed lag non-linear model

Background There has been increasing interest in assessing the impacts of temperature on mortality. However, few studies have used a case–crossover design to examine non-linear and distributed lag effects of temperature on mortality. Additionally, little evidence is available on the temperature-mortality relationship in China, or what temperature measure is the best predictor of mortality. Objectives To use a distributed lag non-linear model (DLNM) as a part of case–crossover design. To examine the non-linear and distributed lag effects of temperature on mortality in Tianjin, China. To explore which temperature measure is the best predictor of mortality; Methods: The DLNM was applied to a case¬−crossover design to assess the non-linear and delayed effects of temperatures (maximum, mean and minimum) on deaths (non-accidental, cardiopulmonary, cardiovascular and respiratory). Results A U-shaped relationship was consistently found between temperature and mortality. Cold effects (significantly increased mortality associated with low temperatures) were delayed by 3 days, and persisted for 10 days. Hot effects (significantly increased mortality associated with high temperatures) were acute and lasted for three days, and were followed by mortality displacement for non-accidental, cardiopulmonary, and cardiovascular deaths. Mean temperature was a better predictor of mortality (based on model fit) than maximum or minimum temperature. Conclusions In Tianjin, extreme cold and hot temperatures increased the risk of mortality. Results suggest that the effects of cold last longer than the effects of heat. It is possible to combine the case−crossover design with DLNMs. This allows the case−crossover design to flexibly estimate the non-linear and delayed effects of temperature (or air pollution) whilst controlling for season.

Estimating the effects of ambient temperature on mortality : methodological challenges and proposed solutions

The health impacts of exposure to ambient temperature have been drawing increasing attention from the environmental health research community, government, society, industries, and the public. Case-crossover and time series models are most commonly used to examine the effects of ambient temperature on mortality. However, some key methodological issues remain to be addressed. For example, few studies have used spatiotemporal models to assess the effects of spatial temperatures on mortality. Few studies have used a case-crossover design to examine the delayed (distributed lag) and non-linear relationship between temperature and mortality. Also, little evidence is available on the effects of temperature changes on mortality, and on differences in heat-related mortality over time. This thesis aimed to address the following research questions: 1. How to combine case-crossover design and distributed lag non-linear models? 2. Is there any significant difference in effect estimates between time series and spatiotemporal models? 3. How to assess the effects of temperature changes between neighbouring days on mortality? 4. Is there any change in temperature effects on mortality over time? To combine the case-crossover design and distributed lag non-linear model, datasets including deaths, and weather conditions (minimum temperature, mean temperature, maximum temperature, and relative humidity), and air pollution were acquired from Tianjin China, for the years 2005 to 2007. I demonstrated how to combine the case-crossover design with a distributed lag non-linear model. This allows the case-crossover design to estimate the non-linear and delayed effects of temperature whilst controlling for seasonality. There was consistent U-shaped relationship between temperature and mortality. Cold effects were delayed by 3 days, and persisted for 10 days. Hot effects were acute and lasted for three days, and were followed by mortality displacement for non-accidental, cardiopulmonary, and cardiovascular deaths. Mean temperature was a better predictor of mortality (based on model fit) than maximum or minimum temperature. It is still unclear whether spatiotemporal models using spatial temperature exposure produce better estimates of mortality risk compared with time series models that use a single site’s temperature or averaged temperature from a network of sites. Daily mortality data were obtained from 163 locations across Brisbane city, Australia from 2000 to 2004. Ordinary kriging was used to interpolate spatial temperatures across the city based on 19 monitoring sites. A spatiotemporal model was used to examine the impact of spatial temperature on mortality. A time series model was used to assess the effects of single site’s temperature, and averaged temperature from 3 monitoring sites on mortality. Squared Pearson scaled residuals were used to check the model fit. The results of this study show that even though spatiotemporal models gave a better model fit than time series models, spatiotemporal and time series models gave similar effect estimates. Time series analyses using temperature recorded from a single monitoring site or average temperature of multiple sites were equally good at estimating the association between temperature and mortality as compared with a spatiotemporal model. A time series Poisson regression model was used to estimate the association between temperature change and mortality in summer in Brisbane, Australia during 1996–2004 and Los Angeles, United States during 1987–2000. Temperature change was calculated by the current day's mean temperature minus the previous day's mean. In Brisbane, a drop of more than 3 �C in temperature between days was associated with relative risks (RRs) of 1.16 (95% confidence interval (CI): 1.02, 1.31) for non-external mortality (NEM), 1.19 (95% CI: 1.00, 1.41) for NEM in females, and 1.44 (95% CI: 1.10, 1.89) for NEM aged 65.74 years. An increase of more than 3 �C was associated with RRs of 1.35 (95% CI: 1.03, 1.77) for cardiovascular mortality and 1.67 (95% CI: 1.15, 2.43) for people aged < 65 years. In Los Angeles, only a drop of more than 3 �C was significantly associated with RRs of 1.13 (95% CI: 1.05, 1.22) for total NEM, 1.25 (95% CI: 1.13, 1.39) for cardiovascular mortality, and 1.25 (95% CI: 1.14, 1.39) for people aged . 75 years. In both cities, there were joint effects of temperature change and mean temperature on NEM. A change in temperature of more than 3 �C, whether positive or negative, has an adverse impact on mortality even after controlling for mean temperature. I examined the variation in the effects of high temperatures on elderly mortality (age . 75 years) by year, city and region for 83 large US cities between 1987 and 2000. High temperature days were defined as two or more consecutive days with temperatures above the 90th percentile for each city during each warm season (May 1 to September 30). The mortality risk for high temperatures was decomposed into: a "main effect" due to high temperatures using a distributed lag non-linear function, and an "added effect" due to consecutive high temperature days. I pooled yearly effects across regions and overall effects at both regional and national levels. The effects of high temperature (both main and added effects) on elderly mortality varied greatly by year, city and region. The years with higher heat-related mortality were often followed by those with relatively lower mortality. Understanding this variability in the effects of high temperatures is important for the development of heat-warning systems. In conclusion, this thesis makes contribution in several aspects. Case-crossover design was combined with distribute lag non-linear model to assess the effects of temperature on mortality in Tianjin. This makes the case-crossover design flexibly estimate the non-linear and delayed effects of temperature. Both extreme cold and high temperatures increased the risk of mortality in Tianjin. Time series model using single site’s temperature or averaged temperature from some sites can be used to examine the effects of temperature on mortality. Temperature change (no matter significant temperature drop or great temperature increase) increases the risk of mortality. The high temperature effect on mortality is highly variable from year to year.

Multi-site downscaling of maximum and minimum daily temperature using support vector machine

Climate change impact assessment studies involve downscaling large-scale atmospheric predictor variables (LSAPVs) simulated by general circulation models (GCMs) to site-scale meteorological variables. This article presents a least-square support vector machine (LS-SVM)-based methodology for multi-site downscaling of maximum and minimum daily temperature series. The methodology involves (1) delineation of sites in the study area into clusters based on correlation structure of predictands, (2) downscaling LSAPVs to monthly time series of predictands at a representative site identified in each of the clusters, (3) translation of the downscaled information in each cluster from the representative site to that at other sites using LS-SVM inter-site regression relationships, and (4) disaggregation of the information at each site from monthly to daily time scale using k-nearest neighbour disaggregation methodology. Effectiveness of the methodology is demonstrated by application to data pertaining to four sites in the catchment of Beas river basin, India. Simulations of Canadian coupled global climate model (CGCM3.1/T63) for four IPCC SRES scenarios namely A1B, A2, B1 and COMMIT were downscaled to future projections of the predictands in the study area. Comparison of results with those based on recently proposed multivariate multiple linear regression (MMLR) based downscaling method and multi-site multivariate statistical downscaling (MMSD) method indicate that the proposed method is promising and it can be considered as a feasible choice in statistical downscaling studies. The performance of the method in downscaling daily minimum temperature was found to be better when compared with that in downscaling daily maximum temperature. Results indicate an increase in annual average maximum and minimum temperatures at all the sites for A1B, A2 and B1 scenarios. The projected increment is high for A2 scenario, and it is followed by that for A1B, B1 and COMMIT scenarios. Projections, in general, indicated an increase in mean monthly maximum and minimum temperatures during January to February and October to December.

Understanding the rapid summer warming and changes in temperature extremes since the mid-1990s over Western Europe

Analysis of observations indicates that there was a rapid increase in summer (June-August, JJA) mean surface air temperature (SAT) since the mid-1990s over Western Europe. Accompanying this rapid warming are significant increases in summer mean daily maximum temperature, daily minimum temperature, annual hottest day temperature and warmest night temperature, and an increase in frequency of summer days and tropical nights, while the change in the diurnal temperature range (DTR) is small. This study focuses on understanding causes of the rapid summer warming and associated temperature extreme changes. A set of experiments using the atmospheric component of the state-of-the-art HadGEM3 global climate model have been carried out to quantify relative roles of changes in sea surface temperature (SST)/sea ice extent (SIE), anthropogenic greenhouse gases (GHGs), and anthropogenic aerosols (AAer). Results indicate that the model forced by changes in all forcings reproduces many of the observed changes since the mid-1990s over Western Europe. Changes in SST/SIE explain 62.2% ± 13.0% of the area averaged seasonal mean warming signal over Western Europe, with the remaining 37.8% ± 13.6% of the warming explained by the direct impact of changes in GHGs and AAer. Results further indicate that the direct impact of the reduction of AAer precursor emissions over Europe, mainly through aerosol-radiation interaction with additional contributions from aerosol-cloud interaction and coupled atmosphere-land surface feedbacks, is a key factor for increases in annual hottest day temperature and in frequency of summer days. It explains 45.5% ± 17.6% and 40.9% ± 18.4% of area averaged signals for these temperature extremes. The direct impact of the reduction of AAer precursor emissions over Europe acts to increase DTR locally, but the change in DTR is countered by the direct impact of GHGs forcing. In the next few decades, greenhouse gas concentrations will continue to rise and AAer precursor emissions over Europe and North America will continue to decline. Our results suggest that the changes in summer seasonal mean SAT and temperature extremes over Western Europe since the mid-1990s are most likely to be sustained or amplified in the near term, unless other factors intervene.

Evaluation of a PVT Air Collector

Hybrid Photovoltaic Thermal (PVT) collectors are an emerging technology that combines PV and solar thermal systems in a single solar collector producing heat and electricity simultaneously. The focus of this thesis work is to evaluate the performance of unglazed open loop PVT air system integrated on a garage roof in Borlänge. As it is thought to have a significant potential for preheating ventilation of the building and improving the PV modules electrical efficiency. The performance evaluation is important to optimize the cooling strategy of the collector in order to enhance its electrical efficiency and maximize the production of thermal energy. The evaluation process involves monitoring the electrical and thermal energies for a certain period of time and investigating the cooling effect on the performance through controlling the air mass flow provided by a variable speed fan connected to the collector by an air distribution duct. The distribution duct transfers the heated outlet air from the collector to inside the building. The PVT air collector consists of 34 Solibro CIGS type PV modules (115 Wp for each module) which are roof integrated and have replaced the traditional roof material. The collector is oriented toward the south-west with a tilt of 29 ᵒ. The collector consists of 17 parallel air ducts formed between the PV modules and the insulated roof surface. Each air duct has a depth of 0.05 m, length of 2.38 m and width of 2.38 m. The air ducts are connected to each other through holes. The monitoring system is based on using T-type thermocouples to measure the relevant temperatures, air sensor to measure the air mass flow. These parameters are needed to calculate the thermal energy. The monitoring system contains also voltage dividers to measure the PV modules voltage and shunt resistance to measure the PV current, and AC energy meters which are needed to calculate the produced electrical energy. All signals recorded from the thermocouples, voltage dividers and shunt resistances are connected to data loggers. The strategy of cooling in this work was based on switching the fan on, only when the difference between the air duct temperature (under the middle of top of PV column) and the room temperature becomes higher than 5 °C. This strategy was effective in term of avoiding high electrical consumption by the fan, and it is recommended for further development. The temperature difference of 5 °C is the minimum value to compensate the heat losses in the collecting duct and distribution duct. The PVT air collector has an area of (Ac=32 m2), and air mass flow of 0.002 kg/s m2. The nominal output power of the collector is 4 kWppv (34 CIGS modules with 115 Wppvfor each module). The collector produces thermal output energy of 6.88 kWth/day (0.21 kWth/m2 day) and an electrical output energy of 13.46 kWhel/day (0.42 kWhel/m2 day) with cooling case. The PVT air collector has a daily thermal energy yield of 1.72 kWhth/kWppv, and a daily PV electrical energy yield of 3.36 kWhel /kWppv. The fan energy requirement in this case was 0.18 kWh/day which is very small compared to the electrical energy generated by the PV collector. The obtained thermal efficiency was 8 % which is small compared to the results reported in literature for PVT air collectors. The small thermal efficiency was due to small operating air mass flow. Therefore, the study suggests increasing the air mass flow by a factor of 25. The electrical efficiency was fluctuating around 14 %, which is higher than the theoretical efficiency of the PV modules, and this discrepancy was due to the poor method of recording the solar irradiance in the location. Due to shading effect, it was better to use more than one pyranometer.

Regulation of the abundance and turnover of copepod species by temperature, turbidity and habitat type in a large river basin

Freshwater copepods were sampled in the La Plata River basin to identify the processes that affect beta diversity and to determine the main factors influencing their geographical distribution and patterns of endemism. Beta diversity patterns exhibited strong dissimilarity between locations; the turnover process was predominant and indicated a replacement of species along the basin. Redundancy analysis indicated the presence of two large sets of species separated geographically by a boundary zone, with several associated variables. Northern species were associated with water transparency and temperature, mean air temperature, mean air temperature during winter and minimum air temperature of coldest month, indicating that these species are not tolerant to low temperatures and are abundant in reservoirs that are common in the upper stretch of the Paraná River basin. Southern species were related with amplitude of air temperature, turbidity, total phosphorus and total suspended matter, indicating that these species are polythermic and have adapted to live in river stretches. From 20 environmental variables analyzed in our study, partial least squares analysis indicated four variables with increased retention of effects on copepod abundance: air temperature, minimum temperature of coldest month, turbidity and transparency. Because almost all of the species found in this study occurred across a wide range of habitat types, the cause of the separation between river and reservoir species could be considered to be more anthropogenic than natural, and it primarily affected species abundance. For certain members of the northern group of copepod species, distribution was dependent on high temperatures, whereas the distribution of the southern group indicated that the species were polythermic.

Temperature-dependent phenology of Plutella xylostella (Lepidoptera: Plutellidae)

Temperature-dependent population growth of diamondback moth (DBM) Plutella xylostella (L.), a prolific insect pest of crucifer vegetables, was studied under six constant temperatures in the laboratory. The objective of the study was to predict the impacts of temperature changes on the population of DBM at high-resolution scales along altitudinal gradients and under climate change scenarios. Non-linear functions were fitted on the data for modeling the development, mortality, longevity and oviposition of the pest. The best-fitted functions for each life stage were compiled for estimating the life table parameters of the species by stochastic simulations. To quantify the impacts on the pest, three indices (establishment, generation and activity) were computed using the estimates of life table parameters and temperature data obtained at local scale (current scenario 2013) and downscaled climate change data (future scenario 2055) from the AFRICLIM database. To measure and represent the impacts of temperature change along the altitude on the pest; the indices were mapped along the altitudinal gradients of Kilimanjaro and Taita Hills, in Tanzania and Kenya, respectively. Potential impact of the changes between climate scenarios 2013 and 2055 was assessed. The data files included in this database were utilized for the above analysis to develop temperature dependent phenology of Plutella xylostella to assess current and future distribution along eastern African Afromontanes.