A 1D microphysical cloud model for Earth,and Earth-like exoplanets: Liquid water and water ice clouds in the convective troposphere

One significant difference between the atmospheres of stars and exoplanets is the presence of condensed particles (clouds or hazes) in the atmosphere of the latter. In current 1D models clouds and hazes are treated in an approximate way by raising the surface albedo, or adopting measured Earth cloud properties. The former method introduces errors to the modeled spectra of the exoplanet, as clouds shield the lower atmosphere and thus modify the spectral features. The latter method works only for an exact Earth-analog, but it is challenging to extend to other planets.The main goal of this paper is to develop a self-consistent microphysical cloud model for 1D atmospheric codes, which can reproduce some observed properties of Earth, such as the average albedo, surface temperature, and global energy budget. The cloud model is designed to be computationally efficient, simple to implement, and applicable for a wide range of atmospheric parameters for planets in the habitable zone.We use a 1D, cloud-free, radiative–convective, and photochemical equilibrium code originally developed by Kasting, Pavlov, Segura, and collaborators as basis for our cloudy atmosphere model. The cloud model is based on models used by the meteorology community for Earth’s clouds. The free parameters of the model are the relative humidity and number density of condensation nuclei, and the precipitation efficiency. In a 1D model, the cloud coverage cannot be self-consistently determined, thus we treat it as a free parameter.We apply this model to Earth (aerosol number density 100 cm^?3, relative humidity 77%, liquid cloud fraction 40%, and ice cloud fraction 25%) and find that a precipitation efficiency of 0.8 is needed to reproduce the albedo, average surface temperature and global energy budget of Earth. We perform simulations to determine how the albedo and the climate of a planet is influenced by the free parameters of the cloud model. We find that the planetary climate is most sensitive to changes in the liquid water cloud fraction and precipitation efficiency.The advantage of our cloud model is that the cloud height and the droplet sizes are self-consistently calculated, both of which influence the climate and albedo of exoplanets.     

Use of Systematic, Palaeoflood and Historical Data for the Improvement of Flood Risk Estimation. Review of Scientific Methods

The catastrophic floods recently occurring in Europe warn of the critical need forhydrologic data on floods over long-time scales. Palaeoflood techniques provideinformation on hydrologic variability and extreme floods over long-time intervals(100 to 10,000 yr) and may be used in combination with historical flood data (last1,000 yr) and the gauge record (last 30–50 yr). In this paper, advantages anduncertainties related to the reconstruction of palaeofloods in different geomorphologicalsettings and historical floods using different documentary sources are described.Systematic and non-systematic data can be combined in the flood frequency analysisusing different methods for the adjustment of distribution functions. Technical toolsintegrating multidisciplinary approaches (geologic, historical, hydraulic and statistical)on extreme flood risk assessment are discussed. A discussion on the potential theoreticalbases for solving the problem of dealing with non-systematic and non-stationary data ispresented. This methodology is being developed using new methodological approachesapplied to European countries as a part of a European Commission funded project (SPHERE).     

Predicting evaporation from mountain streams

Evaporation can be an important control on stream temperature, particularly in summer when it acts to limit daily maximum stream temperature. Evaporation from streams is usually modelled with the use of a wind function that includes empirically derived coefficients. A small number of studies derived wind functions for individual streams; the fitted parameters varied substantially among sites. In this study, stream evaporation and above-stream meteorological conditions (at 0.5 and 1.5 m above the water surface) were measured at nine mountain streams in southwestern British Columbia, Canada, covering a range of stream widths, temperatures, and riparian vegetation. Evaporation was measured on 20 site-days in total, at approximately hourly intervals, using nine floating evaporation pans distributed across the channels. The wind function was fit using mixed-effects models to account for among-stream variability in the parameters. The fixed-effects parameters were tested using leave-one-site-out cross-validation. The model based on 0.5 m measurements provided improved model performance compared to that based on 1.5 m values, with RMSE of 0.0162 and 0.0187 mm h⁻¹, respectively, relative to a mean evaporation rate of 0.06 mm h⁻¹. Inclusion of atmospheric stability and canopy openness as predictors improved model performance when using the 1.5 m meteorological measurements, with minimal improvement when based on 0.5 m measurements. Of the wind functions reported in the literature, two performed reasonably while five others exhibited substantial bias.     

Bathonian (Middle Jurassic) crinoids of the Hidas Valley (Mecsek Mountains, S Hungary) and their biogeographic significance

The Bathonian crinoid fauna that occurs in red nodular limestone and argillaceous limestones from the Hidas Valley, Mecsek Mts (southern Hungary) consists of three isocrinid and six cyrtocrinid species. Isocrinids are represented by Balanocrinus inornatus (d’Orbigny), B. berchteni Hess and Pugin and Balanocrinus sp. Cyrtocrinids are represented by Phyllocrinus stellaris Zar?czny, P. birkenmajeri G?uchowski, P. malbosianus d’Orbigny, Apsidocrinus sp., Lonchocrinus sp., and the new species Psalidocrinus hidasinus sp. nov. This last species is the earliest occurrence of the genus Psalidocrinus previously known from the Early Tithonian to Valanginian. This is the first crinoid fauna described from the Middle Jurassic (Bathonian) of Hungary. The co-occurrence of isocrinids and cyrtocrinids indicates an environment subject to weak currents. The stratigraphical and geographical distribution of the identified cyrtocrinid genera and species suggests a Tethyan origin and subsequent migration to the northern Tethyan shelf.     

Conditional statistical inverse modeling in groundwater flow by multigrid methods

Due to the notorious lack of data, stochastic simulation and conditioning of distributed parameter fields is generally acknowledged as a major task in order to produce realistic prognoses for groundwater flow phenomena, thus honouring the maximum of information available. In this paper, a new conditioning approach is presented which treats the distributed parameters directly without projection onto lower dimensional spaces and preserves certain desired statistical properties by explicitly stating them as constraints for the conditioning optimization problem. Typically, the conditioning task must be performed very often and the conditioning optimization problems are highly dimensional. Therefore, a second main focus of the paper is on the presentation of efficient multigrid methods for the solution of the conditioning problems. Numerical results are given for a practical application problem. This revised version was published online in July 2006 with corrections to the Cover Date.     

Succession in Gulf of Mexico Cold Seep Vestimentiferan Aggregations: The Importance of Spatial Variability

Abstract. The slow rate of change in hydrocarbon seep communities on the upper ­Louisiana slope prevents the use of direct observation in studying successional trends. We used a chronosequence consisting of three presumed stages – juvenile, adult and senescent – to test a previous model which proposed that sulfide availability and vestimentiferan growth and physiological health decline over the lifespan of a vestimenti­feran aggregation. We replicated the chronosequence at two sites to simultaneously ­explore the influence of spatial heterogeneity on the characteristics of these communities. We determined environmental sulfide concentrations and vestimentiferan growth and condition in at least two vestimentiferan aggregations representative of each stage at each of these two sites. Hydrogen sulfide concentrations were highly variable both above and below the sediment's surface, and sulfide was present in high concentrations to sediment depths of 70  cm. Vestimentiferan growth and condition varied significantly on multiple spatial scales from sites separated by tens of kilometers, to aggregations separated by tens to hundreds of meters within a site, to individual vestimentiferans ­separated by tens of centimeters within an aggregation. The striking variability in both environmental sulfide and vestimentiferan growth and condition within individual ­aggregations suggests a crucial role for microhabitat variability in the persistence of vestimentiferan aggregations at these sites. Few significant successional trends in ­environmental sulfide or vestimentiferan growth and condition were found over the three stages tested.     

Notes on the robustness of the kriging system

The robustness of the kriging system with respect to uncertainty of the theoretical variogram is investigated. Inequalities for possible changes of the kriging estimator and the estimation variance are derived. Results of a numerical study show that changes of kriging weights can be predicted partly with the help of the maximal kriging weight.     

Modelling snowpack bulk density using snow depth,cumulative degree-days,and climatological predictor variables

Snowpack water equivalent (SWE) is a key variable for water resource management in snow-dominated catchments. While it is not feasible to quantify SWE at the catchment scale using either field surveys or remotely sensed data, technologies such as airborne LiDAR (light detection and ranging) support the mapping of snow depth at scales relevant to operational water management. To convert snow depth to water equivalent, models have been developed to predict SWE or snowpack density based on snow depth and additional predictor variables. This study builds upon previous models that relate snowpack density to snow depth by including additional predictor variables to account for (1) long-term climatologies that describe the prevailing conditions influencing regional snowpack properties, and (2) the effect of intra- and inter-year variability in meteorological conditions on densification through a cumulative degree-day index derived from North American Regional Reanalysis products. A non-linear model was fit to 114 506 snow survey measurements spanning 41 years from 1166 snow courses across western North America. Under spatial cross-validation, the predicted densities had a root-mean-square error of 47.1 kg m⁻³, a mean bias of −0.039 kg m⁻³, and a Nash-Sutcliffe Efficiency of 0.70. The model developed in this study had similar overall performance compared to a similar regression-based model reported in the literature, but had reduced seasonal biases. When applied to predict SWE from simulated depths with random errors consistent with those obtained from LiDAR or Structure-from-Motion, 50% of the SWE estimates for April and May fell within −45 to 49 mm of the observed SWE, representing prediction errors of −15% to 20%.