The ability of CMIP5 models to simulate North Atlantic extratropical cyclones

The ability of the climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) to simulate North Atlantic extratropical cyclones in winter [December–February (DJF)] and summer [June–August (JJA)] is investigated in detail. Cyclones are identified as maxima in T42 vorticity at 850 hPa and their propagation is tracked using an objective feature-tracking algorithm. By comparing the historical CMIP5 simulations (1976–2005) and the ECMWF Interim Re-Analysis (ERA-Interim; 1979–2008), the authors find that systematic biases affect the number and intensity of North Atlantic cyclones in CMIP5 models. In DJF, the North Atlantic storm track tends to be either too zonal or displaced southward, thus leading to too few and weak cyclones over the Norwegian Sea and too many cyclones in central Europe. In JJA, the position of the North Atlantic storm track is generally well captured but some CMIP5 models underestimate the total number of cyclones. The dynamical intensity of cyclones, as measured by either T42 vorticity at 850 hPa or mean sea level pressure, is too weak in both DJF and JJA. The intensity bias has a hemispheric character, and it cannot be simply attributed to the representation of the North Atlantic large- scale atmospheric state. Despite these biases, the representation of Northern Hemisphere (NH) storm tracks has improved since CMIP3 and some CMIP5 models are able of representing well both the number and the intensity of North Atlantic cyclones. In particular, some of the higher-atmospheric-resolution models tend to have a better representation of the tilt of the North Atlantic storm track and of the intensity of cyclones in DJF.

Evaluating adjusted forcing and model spread for historical and future scenarios in the CMIP5 generation of climate models

We utilize energy budget diagnostics from the Coupled Model Intercomparison Project phase 5 (CMIP5) to evaluate the models' climate forcing since preindustrial times employing an established regression technique. The climate forcing evaluated this way, termed the adjusted forcing (AF), includes a rapid adjustment term associated with cloud changes and other tropospheric and land-surface changes. We estimate a 2010 total anthropogenic and natural AF from CMIP5 models of 1.9 ± 0.9 W m−2 (5–95% range). The projected AF of the Representative Concentration Pathway simulations are lower than their expected radiative forcing (RF) in 2095 but agree well with efficacy weighted forcings from integrated assessment models. The smaller AF, compared to RF, is likely due to cloud adjustment. Multimodel time series of temperature change and AF from 1850 to 2100 have large intermodel spreads throughout the period. The intermodel spread of temperature change is principally driven by forcing differences in the present day and climate feedback differences in 2095, although forcing differences are still important for model spread at 2095. We find no significant relationship between the equilibrium climate sensitivity (ECS) of a model and its 2003 AF, in contrast to that found in older models where higher ECS models generally had less forcing. Given the large present-day model spread, there is no indication of any tendency by modelling groups to adjust their aerosol forcing in order to produce observed trends. Instead, some CMIP5 models have a relatively large positive forcing and overestimate the observed temperature change.

Causes of plasma flow bursts and dayside auroral transients: An evaluation of two models Invoking reconnection pulses and changes in the Y component of the magnetosheath field

Longitudinal flow bursts observed by the European Incoherent Scatter (EISCAT) radar, in association with dayside auroral transients observed from Svalbard, have been interpreted as resulting from pulses of enhanced reconnection at the dayside magnetopause. However, an alternative model has recently been proposed for a steady rate of magnetopause reconnection, in which the bursts of longitudinal flow are due to increases in the field line curvature force, associated with the By component of the magnetosheath field. We here evaluate these two models, using observations on January 20, 1990, by EISCAT and a 630-nm all-sky camera at Ny Ålesund. For both models, we predict the behavior of both the dayside flows and the 630-nm emissions on newly opened field lines. It is shown that the signatures of steady reconnection and magnetosheath By changes could possibly resemble the observed 630-nm auroral events, but only for certain locations of the observing site, relative to the ionospheric projection of the reconnection X line: however, in such cases, the flow bursts would be seen between the 630-nm transients and not within them. On the other hand, the model of reconnection rate pulses predicts that the flows will be enhanced within each 630-nm transient auroral event. The observations on January 20, 1990, are shown to be consistent with the model of enhanced reconnection rate pulses over a background level and inconsistent with the effects of periodic enhancements of the magnitude of the magnetosheath By component. We estimate that the reconnection rate within the pulses would have to be at least an order of magnitude larger than the background level between the pulses.

Soils of contrasting pH affect the decomposition of buried mammalian (Ovis aries) skeletal muscle tissue

Little is known about the effect of edaphic conditions on the decomposition of buried mammalian tissues. To address this, we set up a replicated incubation study with three fresh soils of contrasting pH: a Podsol (acidic), a Cambisol (neutral), and a Rendzina (alkaline), in which skeletal muscle tissue (SMT) of known mass was allowed to decompose. Our results clearly demonstrated that soil type had a considerable effect on the decomposition of SMT buried in soil. Differences in the rate of decomposition were up to three times greater in the Podsol compared with the Rendzina. The rate of microbial respiration was correlated to the rate of soft tissue loss, which suggests that the decomposition of SMT is dependent on the microbial community present in the soil. Decompositional by-products caused the pH of the immediate soil environment to change, becoming more alkaline at first, before acidifying. Our results demonstrate the need for greater consideration of soil type in future taphonomic studies.

Temperature affects microbial decomposition of cadavers (Rattus rattus) in contrasting soils

The ecology of soils associated with dead mammals (i.e. cadavers) is poorly understood. Although temperature and soil type are well known to influence the decomposition of other organic resource patches, the effect of these variables on the degradation of cadavers in soil has received little experimental investigation. To address this, cadavers of juvenile rats (Rattus rattus) were buried in one of three contrasting soils (Sodosol, Rudosol, and Vertosol) from tropical savanna ecosystems in Queensland, Australia and incubated at 29 °C, 22 °C, or 15 °C in a laboratory setting. Cadavers and soils were destructively sampled at intervals of 7 days over an incubation period of 28 days. Measurements of decomposition included cadaver mass loss, carbon dioxide–carbon (CO2–C) evolution, microbial biomass carbon (MBC), protease activity, phosphodiesterase activity, and soil pH, which were all significantly positively affected by cadaver burial. A temperature effect was observed where peaks or differences in decomposition that at occurred at higher temperature would occur at later sample periods at lower temperature. Soil type also had an important effect on some measured parameters. These findings have important implications for a largely unexplored area of soil ecology and nutrient cycling, which are significant for forensic science, cemetery planning and livestock carcass disposal.

Microbial decomposition of skeletal muscle tissue (Ovis aries) in a sandy loam soil at different temperatures

A laboratory experiment was conducted to determine the effect of temperature (2, 12, 22 °C) on the rate of aerobic decomposition of skeletal muscle tissue (Ovis aries) in a sandy loam soil incubated for a period of 42 days. Measurements of decomposition processes included skeletal muscle tissue mass loss, carbon dioxide (CO2) evolution, microbial biomass, soil pH, skeletal muscle tissue carbon (C) and nitrogen (N) content and the calculation of metabolic quotient (qCO2). Incubation temperature and skeletal muscle tissue quality had a significant effect on all of the measured process rates with 2 °C usually much lower than 12 and 22 °C. Cumulative CO2 evolution at 2, 12 and 22 °C equaled 252, 619 and 905 mg CO2, respectively. A significant correlation (P<0.001) was detected between cumulative CO2 evolution and tissue mass loss at all temperatures. Q10s for mass loss and CO2 evolution, which ranged from 1.19 to 3.95, were higher for the lower temperature range (Q10(2– 12 °C)>Q10(12–22 °C)) in the Ovis samples and lower for the low temperature range (Q10(2–12 °C)decomposition was most efficient at 2 °C. These phenomena may be due to lower microbial catabolic requirements at lower temperature.

Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures

The level of agreement between climate model simulations and observed surface temperature change is a topic of scientific and policy concern. While the Earth system continues to accumulate energy due to anthropogenic and other radiative forcings, estimates of recent surface temperature evolution fall at the lower end of climate model projections. Global mean temperatures from climate model simulations are typically calculated using surface air temperatures, while the corresponding observations are based on a blend of air and sea surface temperatures. This work quantifies a systematic bias in model-observation comparisons arising from differential warming rates between sea surface temperatures and surface air temperatures over oceans. A further bias arises from the treatment of temperatures in regions where the sea ice boundary has changed. Applying the methodology of the HadCRUT4 record to climate model temperature fields accounts for 38% of the discrepancy in trend between models and observations over the period 1975–2014.

Assessment and improvement of biotransfer models to cow’s milk and beef used in exposure assessment tools for organic pollutants

The aim of this study was to assess and improve the accuracy of biotransfer models for the organic pollutants (PCBs, PCDD/Fs, PBDEs, PFCAs, and pesticides) into cow’s milk and beef used in human exposure assessment. Metabolic rate in cattle is known as a key parameter for this biotransfer, however few experimental data and no simulation methods are currently available. In this research, metabolic rate was estimated using existing QSAR biodegradation models of microorganisms (BioWIN) and fish (EPI-HL and IFS-HL). This simulated metabolic rate was then incorporated into the mechanistic cattle biotransfer models (RAIDAR, ACC-HUMAN, OMEGA, and CKow). The goodness of fit tests showed that RAIDAR, ACC-HUMAN, OMEGA model performances were significantly improved using either of the QSARs when comparing the new model outputs to observed data. The CKow model is the only one that separates the processes in the gut and liver. This model showed the lowest residual error of all the models tested when the BioWIN model was used to represent the ruminant metabolic process in the gut and the two fish QSARs were used to represent the metabolic process in the liver. Our testing included EUSES and CalTOX which are KOW-regression models that are widely used in regulatory assessment. New regressions based on the simulated rate of the two metabolic processes are also proposed as an alternative to KOW-regression models for a screening risk assessment. The modified CKow model is more physiologically realistic, but has equivalent usability to existing KOW-regression models for estimating cattle biotransfer of organic pollutants.

A constrained recursive least squares algorithm for adaptive combination of multiple models

In this paper, we develop a novel constrained recursive least squares algorithm for adaptively combining a set of given multiple models. With data available in an online fashion, the linear combination coefficients of submodels are adapted via the proposed algorithm.We propose to minimize the mean square error with a forgetting factor, and apply the sum to one constraint to the combination parameters. Moreover an l1-norm constraint to the combination parameters is also applied with the aim to achieve sparsity of multiple models so that only a subset of models may be selected into the final model. Then a weighted l2-norm is applied as an approximation to the l1-norm term. As such at each time step, a closed solution of the model combination parameters is available. The contribution of this paper is to derive the proposed constrained recursive least squares algorithm that is computational efficient by exploiting matrix theory. The effectiveness of the approach has been demonstrated using both simulated and real time series examples.

HIERARCHICAL DECOMPOSITION OF MULTICLASS PROBLEMS

Several popular Machine Learning techniques are originally designed for the solution of two-class problems. However, several classification problems have more than two classes. One approach to deal with multiclass problems using binary classifiers is to decompose the multiclass problem into multiple binary sub-problems disposed in a binary tree. This approach requires a binary partition of the classes for each node of the tree, which defines the tree structure. This paper presents two algorithms to determine the tree structure taking into account information collected from the used dataset. This approach allows the tree structure to be determined automatically for any multiclass dataset.

Many-body energy decomposition of hydrogen-bonded glycine clusters in gas-phase

A detailed analysis of the many-body contribution to the interaction energies of the gas-phase hydrogen-bonded glycine clusters, (Gly)(N), N = 1-4 is presented. The energetics of the hydrogen-bonded dimer, trimer and tetramer complexes have been analyzed using density-functional theory. The magnitude of the two-through four-body energy terms have been calculated and compared. The relaxation energy and the two-body energy terms are the principal contributors to the total binding energy. Four-body contribution is negligible. However, the three-body contribution is found to be sizable and the formation of the cyclic glycine trimer presents geometric strains that make it less favorable. (C) 2010 Elsevier B.V. All rights reserved.

Decomposition of spectral density in individual eigenvalue contributions

The eigenvalue densities of two random matrix ensembles, the Wigner Gaussian matrices and the Wishart covariant matrices, are decomposed in the contributions of each individual eigenvalue distribution. It is shown that the fluctuations of all eigenvalues, for medium matrix sizes, are described with a good precision by nearly normal distributions.

Evaluation of petrogenetic models for intermediate and silicic plutonic rocks from the Sierra de Valle Fertil-La Huerta, Argentina: Petrologic constraints on the origin of igneous rocks in the Ordovician Famatinian-Puna paleoarc

The whole Valle Fertil-La Huerta section appears as a calc-alkaline plutonic suite typical of a destructive plate margin. New Sr and Nd isotopic whole-rock data and published whole-rock geochemistry suggest that the less-evolved intermediate (dioritic) rocks can be derived by magmatic differentiation, mainly by hornblende + plagioclase +/- Fe-Ti oxide fractional crystallization, from mafic (gabbroic) igneous precursors. Closed-system differentiation, however, cannot produce the typical intermediate (tonalitic) and silicic (granodioritic) plutonic rocks, which requires a preponderant contribution of crustal components. Intermediate and silicic plutonic rocks from Valle Fertil-La Huerta section have formed in a plate subduction setting where the thermal and material input of mantle-derived magmas promoted fusion of fertile metasedimentary rocks and favored mixing of gabbroic or dioritic magmas with crustal granitic melts. Magma mixing is observable in the field and evident in variations of chemical elemental parameters and isotopic ratios, revealing that hybridization coupled with fractionation of magmas took place in the crust. Consideration of the whole-rock geochemical and isotopic data in the context of the Famatinian-Puna magmatic belt as a whole demonstrates that the petrologic model postulated for the Sierra Valle Fertil-La Huerta section has the potential to explain the generation of plutonic and volcanic rocks across the Early Ordovician paleoarc from central and northwestern Argentina. As the petrologic model does not require the intervention of old Precambrian continental crust, the nature of the basement on which thick accretionary turbiditic sequences were deposited remains a puzzling aspect. Discussion in this paper provides insights into the nature of magmatic source rocks and mechanisms of magma generation in Cordilleran-type volcano-plutonic arcs of destructive plate margins. (C) 2010 Elsevier Ltd. All rights reserved.

Some restriction tests in a new class of regression models for proportions

The main purpose of this work is to study the behaviour of Skovgaard`s [Skovgaard, I.M., 2001. Likelihood asymptotics. Scandinavian journal of Statistics 28, 3-32] adjusted likelihood ratio statistic in testing simple hypothesis in a new class of regression models proposed here. The proposed class of regression models considers Dirichlet distributed observations, and the parameters that index the Dirichlet distributions are related to covariates and unknown regression coefficients. This class is useful for modelling data consisting of multivariate positive observations summing to one and generalizes the beta regression model described in Vasconcellos and Cribari-Neto [Vasconcellos, K.L.P., Cribari-Neto, F., 2005. Improved maximum likelihood estimation in a new class of beta regression models. Brazilian journal of Probability and Statistics 19,13-31]. We show that, for our model, Skovgaard`s adjusted likelihood ratio statistics have a simple compact form that can be easily implemented in standard statistical software. The adjusted statistic is approximately chi-squared distributed with a high degree of accuracy. Some numerical simulations show that the modified test is more reliable in finite samples than the usual likelihood ratio procedure. An empirical application is also presented and discussed. (C) 2009 Elsevier B.V. All rights reserved.