How well do we understand the Earth's radiation budget and the role of clouds? Selected results of the GEWEX radiation flux assessment

Multi-year average radiative flux maps of three satellite data-sets (CERES, ISSCP and GEWEX-SRB) are compared to each other and to typical values by global modeling (median values of results of 20 climate models of the 4th IPCC Assessment). Diversity assessments address radiative flux products and at the top of the atmosphere (TOA) and the surface, with particular attention to impacts by clouds. Involving both data from surface and TOA special attention is given to the vertical radiation flux divergence and on the infrared Greenhouse effect, which are rarely shown in literature. © 2013 AIP Publishing LLC

The Spaceborne Global Climate Observing Center (SGCOC): Executive summary

Conceptual planning of the Spaceborne portion of the Global Climate Observing Systems (SGCOS) is reviewed. Fundamentals of the SGCOS are summarized

An assessment of radiation budget data provided by ISCCP and GEWEX-SRB

The projects ISCCP and GEWEX-SRB compute global data sets of radiation budget components at the top of the atmosphere and at the surface. Time series range from July 1983 to June 2001, and to October 1995, respectively. Comparing monthly averages over broader zones we find that the SRB underestimates the incident radiation at TOA by more than 2–5 Wm−2 over the tropics and up to 40 Wm−2 over polar regions. The ISCCP infrared radiation fluxes near the surface and at TOA, in particular over both polar zones, are higher than those of the SRB. Clouds in the ISCCP appear optically less effective than in the SRB. Interannual and month-to-month variations are observed indicating serious errors in ancillary data. Complete reprocessing is recommended. End products need validation within this large domain in space and time with correlated radiation budget measurements at TOA and at ground

How accurate did GCMs compute the insolation at TOA for AMIP-2?

Monthly averages of solar radiation reaching the Top of the Atmosphere (TOA) as simulated by 20 General Circulation Models (GCMs) during the period 1985–1988 are compared. They were part of submissions to AMIP-2 (Atmospheric Model Intercomparison Project). Monthly averages of ISCCP-FD (International Satellite Cloud Climatology Project – Flux Data) are considered as reference. Considerable discrepancies are found: Most models reproduce the prescribed Total Solar Irradiance (TSI) value within ±0.7 Wm−2. Monthly zonal averages disagree between ±2 to ±7 Wm−2, depending on latitude and season. The largest model diversity occurs near polar regions. Some models display a zonally symmetric insolation, while others and ISCCP show longitudinal deviations of the order of ±1 Wm−2. With such differences in meridional gradients impacts in multi-annual simulations cannot be excluded. Sensitivity studies are recommended

Comparison of Mouse Ly5a and Ly5b Leucocyte Common Antigen Alleles

The family of leucocyte common antigen (LCA) transmembrane glycoproteins is expressed in most hematopoietic cells. Molecular isoforms of the LCA molecule are generated by alternative splicing of a single gene encoded on the murine chromosome 1. Three LCA alleles with different antigenic reactivities have been identified in inbred mouse strains. To investigate the divergence between alleles, cDNA clones to the SJA (Ly5a) LCA gene have been isolated and sequenced. A comparison of this information to the Ly5b allele sequence identifies 12 allele-specific nucleotide changes. These base substitutions correspond to five amino-acid changes within the extracellular domain of the LCA molecule. These amino-acid differences are clustered in a region that also contains the greatest divergence between mouse and rat LCA sequences. Thus, these two mouse LCA alleles exhibit a pattern of sequence conservation that mimics that found over a much broader scale of evolution. Analysis of antigenicity profiles for each of the allelic sequence changes reveals three molecular domains of altered antigenicity that could account for observed serological differences between the two alleles. Sequence information from the 5' end of the Ly5a LCA gene, generated using polymerase chain-reaction techniques on genomic DNA, reveals eight additional nucleotide differences between the Ly5a and Ly5b alleles

Comparison of radiative energy flows in observational datasets and climate modeling

This study examines radiative flux distributions and local spread of values from three major observational datasets (CERES, ISCCP, and SRB) and compares them with results from climate modeling (CMIP3). Examinations of the spread and differences also differentiate among contributions from cloudy and clear-sky conditions. The spread among observational datasets is in large part caused by noncloud ancillary data. Average differences of at least 10 W m-2 each for clear-sky downward solar, upward solar, and upward infrared fluxes at the surface demonstrate via spatial difference patterns major differences in assumptions for atmospheric aerosol, solar surface albedo and surface temperature, and/or emittance in observational datasets. At the top of the atmosphere (TOA), observational datasets are less influenced by the ancillary data errors than at the surface. Comparisons of spatial radiative flux distributions at the TOA between observations and climate modeling indicate large deficiencies in the strength and distribution of model-simulated cloud radiative effects. Differences are largest for lower-altitude clouds over low-latitude oceans. Global modeling simulates stronger cloud radiative effects (CRE) by +30 W m-2 over trade wind cumulus regions, yet smaller CRE by about -30 W m-2 over (smaller in area) stratocumulus regions. At the surface, climate modeling simulates on average about 15 W m-2 smaller radiative net flux imbalances, as if climate modeling underestimates latent heat release (and precipitation). Relative to observational datasets, simulated surface net fluxes are particularly lower over oceanic trade wind regions (where global modeling tends to overestimate the radiative impact of clouds). Still, with the uncertainty in noncloud ancillary data, observational data do not establish a reliable reference. © 2016 American Meteorological Society

Experimental Fracture Model versus Osteotomy Model in Metacarpal Bone Plate Fixation

Introduction. Osteotomy or fracture models can be used to evaluate mechanical properties of fixation techniques of the hand skeleton in vitro. Although many studies make use of osteotomy models, fracture models simulate the clinical situation more realistically. This study investigates monocortical and bicortical plate fixation on metacarpal bones considering both aforementioned models to decide which method is best suited to test fixation techniques. Methods. Porcine metacarpal bones (n = 40) were randomized into 4 groups. In groups I and II bones were fractured with a modified 3-point bending test. The intact bones represented a further control group to which the other groups after fixation were compared. In groups III and IV a standard osteotomy was carried out. Bones were fixated with plates monocortically (group I, III) and bicortically (group II, IV) and tested for failure. Results. Bones fractured at a mean maximum load of 482.8 N ± 104.8 N with a relative standard deviation (RSD) of 21.7%, mean stiffness was 122.3 ± 35 N/mm. In the fracture model, there was a significant difference (P = 0.01) for maximum load of monocortically and bicortically fixed bones in contrast to the osteotomy model (P = 0.9). Discussion. In the fracture model, because one can use the same bone for both measurements in the intact state and the bone-plate construct states, the impact of inter-individual differences is reduced. In contrast to the osteotomy model there are differences between monocortical and bicortical fixations in the fracture model. Thus simulation of the in vivo situation is better and seems to be suitable for the evaluation of mechanical properties of fixation techniques on metacarpals

Measuring degree-degree association in networks

The Pearson correlation coefficient is commonly used for quantifying the global level of degree-degree association in complex networks. Here, we use a probabilistic representation of the underlying network structure for assessing the applicability of different association measures to heavy-tailed degree distributions. Theoretical arguments together with our numerical study indicate that Pearson's coefficient often depends on the size of networks with equal association structure, impeding a systematic comparison of real-world networks. In contrast, Kendall-Gibbons' $\tau_{b}$ is a considerably more robust measure of the degree-degree association

Statistical modeling of ground motion relations for seismic hazard analysis

We introduce a new approach for ground motion relations (GMR) in the probabilistic seismic hazard analysis (PSHA), being influenced by the extreme value theory of mathematical statistics. Therein, we understand a GMR as a random function. We derive mathematically the principle of area-equivalence; wherein two alternative GMRs have an equivalent influence on the hazard if these GMRs have equivalent area functions. This includes local biases. An interpretation of the difference between these GMRs (an actual and a modeled one) as a random component leads to a general overestimation of residual variance and hazard. Beside this, we discuss important aspects of classical approaches and discover discrepancies with the state of the art of stochastics and statistics (model selection and significance, test of distribution assumptions, extreme value statistics). We criticize especially the assumption of logarithmic normally distributed residuals of maxima like the peak ground acceleration (PGA). The natural distribution of its individual random component (equivalent to exp(epsilon_0) of Joyner and Boore 1993) is the generalized extreme value. We show by numerical researches that the actual distribution can be hidden and a wrong distribution assumption can influence the PSHA negatively as the negligence of area equivalence does. Finally, we suggest an estimation concept for GMRs of PSHA with a regression-free variance estimation of the individual random component. We demonstrate the advantages of event-specific GMRs by analyzing data sets from the PEER strong motion database and estimate event-specific GMRs. Therein, the majority of the best models base on an anisotropic point source approach. The residual variance of logarithmized PGA is significantly smaller than in previous models. We validate the estimations for the event with the largest sample by empirical area functions. etc