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The population sizes of tiger beetles are known to display fluctuations in response to several microclimatic and soil parameters. In this study, analytical models were developed using negative binomial regression to describe the effects of weather and soil parameters on the population count of selected tiger beetle species. The tiger beetle species Hypaetha biramosa was selected to represent the coastal habitats and Ifasina labioaenea represented crop cultivated habitats. Population sizes for each tiger beetle species were estimated by taking counts maximum of two times a month using visual encounter surveys. Three sites were selected for each species. At each site, weather and soil parameters were measured. Based on the statistical models, an increase in wind speed was associated significantly with the decline in the counts of H. biramosa in the study sites. Solar radiation and soil electric conductivity were associated with an increase in the count of H. biramosa. An increase in the soil salinity and air temperature was associated positively with the increase of the counts of I. labioaenea in the study sites. The understanding of the effects of these parameters can be used to design better monitoring and conservation plans for Sri Lankan tiger beetles.

Summarizing or averaging a sequential data set (i.e., a set of time series) can be comprehensively approached as a result of sophisticated computational tools. Averaging under Dynamic Time Warping (DTW) is one such tool that captures consensus patterns. DTW acts as a similarity measure between time series, and subsequently, an averaging method must be executed upon the behaviour of DTW. However, averaging under DTW somewhat neglects temporal aspect since it is on the search of similar appearances rather than stagnating on corresponding time-points. On the contrary, the mean series carrying point-wise averages provides only a weak consensus pattern as it may over-smooth important temporal variations. As a compromise, a pool of consensus series termed Ultimate Tamed Series (UTS) is studied here that adheres to temporal decomposition supported by the discrete Haar wavelet. We claim that UTS summarizes localized patterns, which would not be reachable via the series under DTW or the mean series. Neighbourhood of localization can be altered as a user can customize different levels of decomposition. In validation, comparisons are carried out with the series under DTW and the mean series via Euclidean distance and the distance resulted by DTW itself. Two sequential data sets are selected for this purpose from a standard repository.

Inverse modelling method named Maximum likelihood Ensemble Filter (MLEF) was used to estimate gridded surface CO fluxes using continuous, flask and Comprehensive Observation Network for TRace gases...

Vertical transport by moist sub-grid scale processes such as deep convection is a well-known source of uncertainty in CO2 source/sink inversion. However, a dynamical link between vertical transport, satellite based retrievals of column mole fractions of CO2, and source/sink inversion has not yet been established. By using the same offline transport model with meteorological fields from slightly different data assimilation systems, we examine sensitivity of frontal CO2 transport and retrieved fluxes to different parameterizations of sub-grid vertical transport. We find that frontal transport feeds off background vertical CO2 gradients, which are modulated by sub-grid vertical transport. The implication for source/sink estimation is two-fold. First, CO2 variations contained in moist poleward moving air masses are systematically different from variations in dry equatorward moving air. Moist poleward transport is hidden from orbital sensors on satellites, causing a sampling bias, which leads directly to small but systematic flux retrieval errors in northern mid-latitudes. Second, differences in the representation of moist sub-grid vertical transport in GEOS-4 and GEOS-5 meteorological fields cause differences in vertical gradients of CO2, which leads to systematic differences in moist poleward and dry equatorward CO2 transport and therefore the fraction of CO2 variations hidden in moist air from satellites. As a result, sampling biases are amplified and regional scale flux errors enhanced, most notably in Europe (0.43 ± 0.35 PgC yr−1). These results, cast from the perspective of moist frontal transport processes, support previous arguments that the vertical gradient of CO2 is a major source of uncertainty in source/sink inversion.

Synoptic variations of atmospheric CO2 produced by interactions between weather and surface fluxes are investigated mechanistically and quantitatively in midlatitude and tropical regions using continuous in-situ CO2 observations in North America, South America and Europe and forward chemical transport model simulations with the Parameterized Chemistry Transport Model. Frontal CO2 climatologies show consistently strong, characteristic frontal CO2 signals throughout the midlatitudes of North America and Europe. Transitions between synoptically identifiable CO2 air masses or transient spikes along the frontal boundary typically characterize these signals. One case study of a summer cold front shows CO2 gradients organizing with deformational flow along weather fronts, producing strong and spatially coherent variations. In order to differentiate physical and biological controls on synoptic variations in midlatitudes and a site in Amazonia, a boundary layer budget equation is constructed to break down boundary layer CO2 tendencies into components driven by advection, moist convection, and surface fluxes. This analysis suggests that, in midlatitudes, advection is dominant throughout the year and responsible for 60–70% of day-to-day variations on average, with moist convection contributing less than 5%. At a site in Amazonia, vertical mixing, in particular coupling between convective transport and surface CO2 flux, is most important, with advection responsible for 26% of variations, moist convection 32% and surface flux 42%. Transport model sensitivity experiments agree with budget analysis. These results imply the existence of a recharge-discharge mechanism in Amazonia important for controlling synoptic variations of boundary layer CO2, and that forward and inverse simulations should take care to represent moist convective transport. Due to the scarcity of tropical observations at the time of this study, results in Amazonia are not generalized for the tropics, and future work should extend analysis to additional tropical locations.

Ground-based data on crop production in the USA is provided through surveys conducted by the National Agricultural Statistics Service (NASS) and the Census of Agriculture (AgCensus). Statistics from these surveys are widely used in economic analyses, policy design, and for other purposes. However, missing data in the surveys presents limitations for research that requires comprehensive data for spatial analyses. We created comprehensive county-level databases for nine major crops of the USA for a 16-yr period, by filling the gaps in existing data reported by NASS and AgCensus. We used a combination of regression analyses with data reported by NASS and the AgCensus and linear mixed-effect models incorporating county-level environmental, management, and economic variables pertaining to different agroecozones. Predicted yield and crop area were very close to the data reported by NASS, within 10% relative error. The linear mixed-effect model approach gave the best results in filling 84% of the total gaps in yields and 83% of the gaps in crop areas of all the crops. Regression analyses with AgCensus data filled 16% of the gaps in yields and crop areas of the major crops reported by NASS.

Modeling storm occurrences has become a vital part of hurricane prediction. In this paper, a method for simulating event occurrences using a simulated annealing algorithm is described. The method is illustrated using annual counts of hurricanes and of tropical storms in the Atlantic Ocean and Gulf of Mexico. Simulations closely match distributional properties, including possible correlations, in the historical data. For hurricanes, traditionally used Poisson and negative binomial processes also predict univariate properties well, but for tropical storms parametric methods are less successful. The authors determined that simulated annealing replicates properties of both series. Simulated annealing can be designed so that simulations mimic historical distributional properties to whatever degree is desired, including occurrence of extreme events and temporal patterning.

The ability to reliably estimate CO2 fluxes from current in situ atmospheric CO2 measurements and future satellite CO2 measurements is dependent on transport model performance at synoptic and shorter timescales. The TransCom continuous experiment was designed to evaluate the performance of forward transport model simulations at hourly, daily, and synoptic timescales, and we focus on the latter two in this paper. Twenty-five transport models or model variants submitted hourly time series of nine predetermined tracers (seven for CO2) at 280 locations. We extracted synoptic-scale variability from daily averaged CO2 time series using a digital filter and analyzed the results by comparing them to atmospheric measurements at 35 locations. The correlations between modeled and observed synoptic CO2 variabilities were almost always largest with zero time lag and statistically significant for most models and most locations. Generally, the model results using diurnally varying land fluxes were closer to the observations compared to those obtained using monthly mean or daily average fluxes, and winter was often better simulated than summer. Model results at higher spatial resolution compared better with observations, mostly because these models were able to sample closer to the measurement site location. The amplitude and correlation of model-data variability is strongly model and season dependent. Overall similarity in modeled synoptic CO2 variability suggests that the first-order transport mechanisms are fairly well parameterized in the models, and no clear distinction was found between the meteorological analyses in capturing the synoptic-scale dynamics.

[1] A forward atmospheric transport modeling experiment has been coordinated by the TransCom group to investigate synoptic and diurnal variations in CO2. Model simulations were run for biospheric, fossil, and air-sea exchange of CO2 and for SF6 and radon for 2000-2003. Twenty-five models or model variants participated in the comparison. Hourly concentration time series were submitted for 280 sites along with vertical profiles, fluxes, and meteorological variables at 100 sites. The submitted results have been analyzed for diurnal variations and are compared with observed CO2 in 2002. Mean summer diurnal cycles vary widely in amplitude across models. The choice of sampling location and model level account for part of the spread suggesting that representation errors in these types of models are potentially large. Despite the model spread, most models simulate the relative variation in diurnal amplitude between sites reasonably well. The modeled diurnal amplitude only shows a weak relationship with vertical resolution across models; differences in near-surface transport simulation appear to play a major role. Examples are also presented where there is evidence that the models show useful skill in simulating seasonal and synoptic changes in diurnal amplitude.

Synoptic variations of CO2 mixing ratio produced by interactions between weather and surface fluxes are investigated mechanistically and quantitatively in midlatitude and tropical regions using continuous in-situ CO2 observations in North America, South America and Europe and forward chemical transport model simulations with the Parameterized Chemistry Transport Model. Frontal CO2 climatologies show consistently strong, characteristic frontal CO2 signals throughout the midlatitudes of North America and Europe. Transitions between synoptically identifiable CO2 air masses or transient spikes along the frontal boundary typically characterize these signals. One case study of a summer cold front shows that CO2 gradients organize with deformational flow along weather fronts producing strong and spatially coherent variations. A boundary layer budget equation is constructed in order to determine contributions to boundary layer CO2 tendencies by horizontal and vertical advection, moist convection, and biological and anthropogenic surface fluxes. Analysis of this equation suggests that, in midlatitudes, advection is responsible for 50–90% of the amplitude of frontal variations in the summer, depending on upstream influences, and 50% of all day-to-day variations throughout the year. Simulations testing sensitivity to local cloud and surface fluxes further suggest that horizontal advection is a major source of CO2 variability in midlatitudes. In the tropics, coupling between convective transport and surface CO2 flux is most important. Due to the scarcity of tropical observations available at the time of this study, future work should extend such mechanistic analysis to additional tropical locations.

Teak (Tectona grandis) is one of the highly demanded timber species in Sri Lanka. When buying teak logs, customers assess their quality by visual appraisal of surface characteristics such as visible defects. Hence, the buyers’ preference for logs with desired attributes is reflected by the price achieved in the market. In this study, we examined the influence of visually observable quality attributes of plantation grown teak logs on their market price. A set of 650 randomly selected teak logs were assessed for dimensions, shape defects, surface defects and end-defects. Length of the log, mid-circumference (under bark), diameter at top-end and butt-end, number of knots per meter, average diameter of the knots, bend fraction, percentage of heartwood, shape of the log, presence of buttress, position of the hollows, presence of felling damages, splits and heart rot were recorded using standard methods. Selling prices of logs were obtained from the records of log storing facilities. By using the multiple linear regressions, a price-quality model was developed to explain the impact of quality attributes on sellingprice. The regression model achieved a strong coefficient of determination (R2) of 0.87. Accordingly, the length, mid-circumference, number of knots per meter, bend fraction, hollow position, presence of buttresses and presence of heart rot were found to have a significant influence on the price. The relative price reduction due to presence of a specific log defect was further examined using the derived price-quality model. The presence of high number of knots had the highest detrimental impact on price, followed by the higher bend fraction, presence of hollows at top end and/or middle of the log, presence of heart rot and presence of buttresses respectively. These findings have implications in the improved management of teak plantations.