A stochastic model of temporal variations in monthly temperature, precipitation, snowfall, and resulting snowpack

We report a Monte Carlo representation of the long-term inter-annual variability of monthly snowfall on a detailed (1 km) grid of points throughout the southwest. An extension of the local climate model of the southwestern United States (Stamm and Craig 1992) provides spatially based estimates of mean and variance of monthly temperature and precipitation. The mean is the expected value from a canonical regression using independent variables that represent controls on climate in this area, including orography. Variance is computed as the standard error of the prediction and provides site-specific measures of (1) natural sources of variation and (2) errors due to limitations of the data and poor distribution of climate stations. Simulation of monthly temperature and precipitation over a sequence of years is achieved by drawing from a bivariate normal distribution. The conditional expectation of precipitation. given temperature in each month, is the basis of a numerical integration of the normal probability distribution of log precipitation below a threshold temperature (3°C) to determine snowfall as a percent of total precipitation. Snowfall predictions are tested at stations for which long-term records are available. At Donner Memorial State Park (elevation 1811 meters) a 34-year simulation - matching the length of instrumental record - is within 15 percent of observed for mean annual snowfall. We also compute resulting snowpack using a variation of the model of Martinec et al. (1983). This allows additional tests by examining spatial patterns of predicted snowfall and snowpack and their hydrologic implications.

A Periodic State Space Model to Monthly Long-term Temperature Data

This work presents a periodic state space model to model monthly temperature data. Additionally, some issues are discussed, as the parameter estimation or the Kalman filter recursions adapted to a periodic model. This framework is applied to monthly long-term temperature time series of Lisbon.

Plasmodium vivax malaria incidence over time and its association with temperature and rainfall in four counties of Yunnan Province, China

Background Transmission of Plasmodium vivax malaria is dependent on vector availability, biting rates and parasite development. In turn, each of these is influenced by climatic conditions. Correlations have previously been detected between seasonal rainfall, temperature and malaria incidence patterns in various settings. An understanding of seasonal patterns of malaria, and their weather drivers, can provide vital information for control and elimination activities. This research aimed to describe temporal patterns in malaria, rainfall and temperature, and to examine the relationships between these variables within four counties of Yunnan Province, China. Methods Plasmodium vivax malaria surveillance data (1991–2006), and average monthly temperature and rainfall were acquired. Seasonal trend decomposition was used to examine secular trends and seasonal patterns in malaria. Distributed lag non-linear models were used to estimate the weather drivers of malaria seasonality, including the lag periods between weather conditions and malaria incidence. Results There was a declining trend in malaria incidence in all four counties. Increasing temperature resulted in increased malaria risk in all four areas and increasing rainfall resulted in increased malaria risk in one area and decreased malaria risk in one area. The lag times for these associations varied between areas. Conclusions The differences detected between the four counties highlight the need for local understanding of seasonal patterns of malaria and its climatic drivers.

PREDICTING THE SPATIAL-DISTRIBUTION OF CLIMATE - TEMPERATURE IN GREAT-BRITAIN

1. The prediction and mapping of climate in areas between climate stations is of increasing importance in ecology.

2. Four categories of model, simple interpolation, thin plate splines, multiple linear regression and mixed spline-regression, were tested for their ability to predict the spatial distribution of temperature on the British mainland. The models were tested by external cross-verification.

3. The British distribution of mean daily temperature was predicted with the greatest accuracy by using a mixed model: a thin plate spline fitted to the surface of the country, after correction of the data by a selection from 16 independent topographical variables (such as altitude, distance from the sea, slope and topographic roughness), chosen by multiple regression from a digital terrain model (DTM) of the country.

4. The next most accurate method was a pure multiple regression model using the DTM. Both regression and thin plate spline models based on a few variables (latitude, longitude and altitude) only were comparatively unsatisfactory, but some rather simple methods of surface interpolation (such as bilinear interpolation after correction to sea level) gave moderately satisfactory results. Differences between the methods seemed to be dependent largely on their ability to model the effect of the sea on land temperatures.

5. Prediction of temperature by the best methods was greater than 95% accurate in all months of the year, as shown by the correlation between the predicted and actual values. The predicted temperatures were calculated at real altitudes, not subject to sea-level correction.

6. A minimum of just over 30 temperature recording stations would generate a satisfactory surface, provided the stations were well spaced.

7. Maps of mean daily temperature, using the best overall methods are provided; further important variables, such as continentality and length of growing season, were also mapped. Many of these are believed to be the first detailed representations at real altitude.

8. The interpolated monthly temperature surfaces are available on disk.

"Cool" vs. "warm" winter precipitation and its effect on streamflow in California

Precipitation is a difficult variable to understand and predict. In this study, monthly precipitation in California is divided into two classes according to the monthly temperature to better diagnose the atmospheric circulation that causes precipitation, and to illustrate how temperature compounds the precipitation to runoff process.

Aerosol-Radiation Feedback and PM10 Air Concentrations Over Poland

We have implemented the WRF-Chem model version 3.5 over Poland to quantify the direct and indirect feedback effects of aerosols on simulated meteorology and aerosol concentrations. Observations were compared with results from three simulations at high spatial resolutions of 5 × 5 km: (1) BASE—without any aerosol feedback effects; (2) DIR—with direct aerosol-radiative effects (3) INDIR—with direct and indirect aerosol-radiative effects. We study the overall effect during January 2011 as well as selected episodes of the highest differences in PM10 concentrations between the three simulations. For the DIR simulation, the decrease in monthly mean incoming solar radiation (SWDOWN) appears for the entire study area. It changes geographically, from about −8.0 to −2.0 W m−2, respectively for the southern and northern parts of the country. The highest changes do not correspond to the highest PM10 concentration. Due to the solar radiation changes, the surface mean monthly temperature (T2) decreases for 96 % of the area of Poland, but not more than 1.0 °C. Monthly mean PBLH changes by more than ±5 m for 53 % of the domain. Locally the differences in PBLH between the DIR and BASE are higher than ± 20 m. Due to the direct effect, for 84 % of the domain, the mean monthly PM10 concentrations increase by up to 1.9 µg m−3. For the INDIR simulation the spatial distribution of changes in incoming solar radiation as well as air temperature is similar to the DIR simulation. The decrease of SWDOWN is noticed for the entire domain and for 23 % of the domain is higher than −5.0 W m−2. The absolute differences of PBLH are slightly higher for INDIR than DIR but similarly distributed spatially. For daily episodes, the differences between the simulations are higher, both for meteorology and PM10 concentrations, and the pattern of changes is usually more complex. The results indicate the potential importance of the aerosol feedback effects on modelled meteorology and PM10 concentrations.

PALEOCLIMATIC CONTROLS ON STABLE OXYGEN AND CARBON ISOTOPES IN CALICHE OF THE ABO FORMATION (PERMIAN), SOUTH-CENTRAL NEW-MEXICO, USA

The stable oxygen and carbon isotopic composition of caliche in fluvial and supratidal rocks of the Abo Formation (Permian), south-central New Mexico, is controlled by palecoclimate and depositional environment. Fluvial caliche consists of low-Mg calcite nodules and vertically oriented tubules that display stage II texture. Micrite matrix support, brecciation, ooids/pisoliths, aveolar-septal texture, and peloids are common in the fluvial caliche and, along with red color and slickensides in the host shale, indicate pedogenesis in a well-oxidized vadose zone. In contrast, periodic waterlogging of the supratidal paleosols, probably due to high water table, is indicated by drab colors, carbonaceous flecks, horizontal rhizoliths, and the paucity of vadose textures in the stage II caliche nodules.Stable oxygen isotopes are similar in the fluvial and supratidal caliches and range from 21.6 to 30.5 parts per thousand (SMOW). The data exhibit a crude bimodality and delta-O-18 enrichment with a decrease in age (higher in the section). Consideration of these data in the context of delta-temperature relations suggests that 1) surface waters responsible for caliche formation increased in delta-O-18 (from roughly -8 to + 1 parts per thousand) over the 18 m.y. time interval that separated the lowest stratigraphic nodule horizon from the highest, 2) the increasing delta-O-18 values also reflect a warming trend (approximately 15-degrees to nearly 30-degrees-C) in the mean monthly temperature over this same time period, with perhaps an associated increase in Permian ocean temperatures, and 3) the significant variation in delta-O-18 from oldest to youngest caliche was probably enhanced by the amount effect, such that as the temperature increased, the amount of precipitation decreased, resulting in high delta-O-18 values.Caliches in the Abo are enriched in heavy carbon (-7.2 to -1.5 part per thousand PDB) compared to that of soil carbonate derived exclusively from C3 plants (-12 part per thousand PDB), and the supratidal caliches contain somewhat heavier carbon compared to the fluvial caliche. The delta-C-13 values for both environments increase with a decrease in caliche age. These results indicate that as the temperature increased and rainfall decreased with time, the level of C3 plant productivity apparently declined, allowing a greater influx of atmospheric CO2 into the soil. This can only occur when soil respiration rates are quite low or at very shallow depths (less than 10 cm), or both. Atmospheric CO2 seems to have invaded the supratidal soils to a somewhat greater extent than the fluvial soils.

Genotype×production environment interaction for weaning weight in Angus populations of Brazil and Uruguay

The objective of this study was to define production environments by grouping different environmental factors and, consequently, to assess genotype by production environment interactions on weaning weight (WW) in the Angus populations of Brazil and Uruguay. Climatic conditions were represented by monthly temperature means (°C), minimum and maximum temperatures in winter and summer respectively and accumulated rainfall (mm/year). Mode in month of birth and weaning, and calf weight (kg) and age (days) at weaning were used as indicators of management conditions of 33 and 161 herds in 13 and 34 regions in Uruguay and Brazil, respectively. Two approaches were developed: (a) a bi-character analysis of extreme sub-datasets within each environmental factor (bottom and top 33% of regions), (b) three different production environments (including farms from both countries) were defined in a cluster analysis using standardized environmental factors. To identify the variables that influenced the cluster formation, a discriminant analysis was previously carried out. Management (month, age and weight at weaning) and climatic factors (accumulated rainfalls and winter and summer temperatures) were the most important factors in the clustering of farms. Bi or trivariate analyses were performed to estimate heritability and genetic correlations for WW in extreme sub-datasets within environmental factor or between clusters, using MTDFREML software. Heritability estimates of WW in the first approach ranged from 0.27 to 0.54, and genetic correlations between top and bottom sub-datasets within environmental factors, from -0.29 to 0.70. In the cluster approach, heritabilities were 0.58±0.04 for cluster 1, 0.31±0.01 for Cluster 2 and 0.40±0.02 for Cluster 3. Genetic correlations were 0.27±0.08, 0.32±0.09 and 0.33±0.09, between clusters 1 and 2, 1 and 3, and 2 and 3, respectively. Both approaches suggest the existence of genotype x environment interaction for weaning weight in Angus breed of Brazil and Uruguay. © 2012 Elsevier B.V.

Abiotic factors shape temporal variation in the structure of an ant-plant network

Despite recognition of key biotic processes in shaping the structure of biological communities, few empirical studies have explored the influences of abiotic factors on the structural properties of mutualistic networks. We tested whether temperature and precipitation contribute to temporal variation in the nestedness of mutualistic ant-plant networks. While maintaining their nested structure, nestedness increased with mean monthly precipitation and, particularly, with monthly temperature. Moreover, some species changed their role in network structure, shifting from peripheral to core species within the nested network. We could summarize that abiotic factors affect plant species in the vegetation (e.g., phenology), meaning presence/absence of food sources, consequently an increase/decrease of associations with ants, and finally, these variations to fluctuations in nestedness. While biotic factors are certainly important, greater attention needs to be given to abiotic factors as underlying determinants of the structures of ecological networks.

Modeling the Potential Geographic Distribution of Black Pepper (Piper nigrum) in Asia Using GIS Tools

Known as the "king of spices", black pepper (Piper nigrum), a perennial crop of the tropics, is economically the most important and the most widely used spice crop in the world. To understand its suitable bioclimatic distribution, maximum entropy based on ecological niche modeling was used to model the bioclimatic niches of the species in its Asian range. Based on known occurrences, bioclimatic areas with higher probabilities are mainly located in the eastern and western coasts of the Indian Peninsula, the east of Sumatra Island, some areas in the Malay Archipelago, and the southeast coastal areas of China. Some undocumented places were also predicted as suitable areas. According to the jackknife procedure, the minimum temperature of the coldest month, the mean monthly temperature range, and the precipitation of the wettest month were identified as highly effective factors in the distribution of black pepper and could possibly account for the crop's distribution pattern. Such climatic requirements inhibited this species from dispersing and gaining a larger geographical range.

Amphibian biodiversity in Taita Hills, Kenya

Species richness varies greatly across geographical regions. Eastern Arc Mountains (EAM) of Kenya and Tanzania is one of the global biodiversity hotspots. Despite this, high species diversity the explanatory factors have remained largely unexplored. Herein, this study first investigated amphibian species richness patterns in the EAM and particularly the reasons for the low richness in Taita Hills. It tested the hypothesis that the low richness is due to past forest loss or other factors. The results demonstrated that the regional species richness pattern was influenced largely by mean annual rainfall and not forest area. Secondly, using the 26 currently recorded amphibians in the Taita Hills, it investigated the relationship between amphibian species composition along anthropogenic habitat disturbance and elevation gradients. It tested the hypothesis that sites with similar environmental characteristics (temperature, rainfall and elevation), in close proximity and with similar disturbance levels (habitat types) harbour similar species composition. It was found that amphibian species composition differed in terms of elevation and was explained by both temperature and rainfall. Therefore sites with similar environmental characteristics, disturbance levels and in close proximity geographically have similar amphibian composition. Thirdly, diagnostic characters, distribution, basic life history characteristics and conservation status of all currently known amphibians in the Taita Hills were provided. Finally, first long term life history and ecological characteristics of a brevicipitid frog (Callulina sp) was provided. The results showed that this frog abundance and distribution is influenced mainly by mean monthly temperature, breeds during the long dry season and exhibit parental care. Results of this study strongly recommend increasing indigenous forest cover in order to enhance the conservation of the endemic indigenous forest associated amphibians such as Callulina sp, Boulengerula taitana and Boulengerula niedeni.

Using water balance models to approximate the effects of climate change on spring catchment discharge : Mt. Hanang, Tanzania

This project addresses the potential impacts of changing climate on dry-season water storage and discharge from a small, mountain catchment in Tanzania. Villagers and water managers around the catchment have experienced worsening water scarcity and attribute it to increasing population and demand, but very little has been done to understand the physical characteristics and hydrological behavior of the spring catchment. The physical nature of the aquifer was characterized and water balance models were calibrated to discharge observations so as to be able to explore relative changes in aquifer storage resulting from climate changes. To characterize the shallow aquifer supplying water to the Jandu spring, water quality and geochemistry data were analyzed, discharge recession analysis was performed, and two water balance models were developed and tested. Jandu geochemistry suggests a shallow, meteorically-recharged aquifer system with short circulation times. Baseflow recession analysis showed that the catchment behavior could be represented by a linear storage model with an average recession constant of 0.151/month from 2004-2010. Two modified Thornthwaite-Mather Water Balance (TMWB) models were calibrated using historic rainfall and discharge data and shown to reproduce dry-season flows with Nash-Sutcliffe efficiencies between 0.86 and 0.91. The modified TMWB models were then used to examine the impacts of nineteen, perturbed climate scenarios to test the potential impacts of regional climate change on catchment storage during the dry season. Forcing the models with realistic scenarios for average monthly temperature, annual precipitation, and seasonal rainfall distribution demonstrated that even small climate changes might adversely impact aquifer storage conditions at the onset of the dry season. The scale of the change was dependent on the direction (increasing vs. decreasing) and magnitude of climate change (temperature and precipitation). This study demonstrates that small, mountain aquifer characterization is possible using simple water quality parameters, recession analysis can be integrated into modeling aquifer storage parameters, and water balance models can accurately reproduce dry-season discharges and might be useful tools to assess climate change impacts. However, uncertainty in current climate projections and lack of data for testing the predictive capabilities of the model beyond the present data set, make the forecasts of changes in discharge also uncertain. The hydrologic tools used herein offer promise for future research in understanding small, shallow, mountainous aquifers and could potentially be developed and used by water resource professionals to assess climatic influences on local hydrologic systems.

Campylobacter monitoring in German broiler flocks: an explorative time series analysis

Campylobacter, a major zoonotic pathogen, displays seasonality in poultry and in humans. In order to identify temporal patterns in the prevalence of thermophilic Campylobacter spp. in a voluntary monitoring programme in broiler flocks in Germany and in the reported human incidence, time series methods were used. The data originated between May 2004 and June 2007. By the use of seasonal decomposition, autocorrelation and cross-correlation functions, it could be shown that an annual seasonality is present. However, the peak month differs between sample submission, prevalence in broilers and human incidence. Strikingly, the peak in human campylobacterioses preceded the peak in broiler prevalence in Lower Saxony rather than occurring after it. Significant cross-correlations between monthly temperature and prevalence in broilers as well as between human incidence, monthly temperature, rainfall and wind-force were identified. The results highlight the necessity to quantify the transmission of Campylobacter from broiler to humans and to include climatic factors in order to gain further insight into the epidemiology of this zoonotic disease.