Modelling marine emissions and atmospheric distributions of halocarbons and dimethyl sulfide: the influence of prescribed water concentration vs. prescribed emissions

Marine-produced short-lived trace gases such as dibromomethane (CH2Br2), bromoform (CHBr3), methyliodide (CH3I) and dimethyl sulfide (DMS) significantly impact tropospheric and stratospheric chemistry. Describing their marine emissions in atmospheric chemistry models as accurately as possible is necessary to quantify their impact on ozone depletion and Earth's radiative budget. So far, marine emissions of trace gases have mainly been prescribed from emission climatologies, thus lacking the interaction between the actual state of the atmosphere and the ocean. Here we present simulations with the chemistry climate model EMAC (ECHAM5/MESSy Atmospheric Chemistry) with online calculation of emissions based on surface water concentrations, in contrast to directly prescribed emissions. Considering the actual state of the model atmosphere results in a concentration gradient consistent with model real-time conditions at the ocean surface and in the atmosphere, which determine the direction and magnitude of the computed flux. This method has a number of conceptual and practical benefits, as the modelled emission can respond consistently to changes in sea surface temperature, surface wind speed, sea ice cover and especially atmospheric mixing ratio. This online calculation could enhance, dampen or even invert the fluxes (i.e. deposition instead of emissions) of very short-lived substances (VSLS). We show that differences between prescribing emissions and prescribing concentrations (−28 % for CH2Br2 to +11 % for CHBr3) result mainly from consideration of the actual, time-varying state of the atmosphere. The absolute magnitude of the differences depends mainly on the surface ocean saturation of each particular gas. Comparison to observations from aircraft, ships and ground stations reveals that computing the air–sea flux interactively leads in most of the cases to more accurate atmospheric mixing ratios in the model compared to the computation from prescribed emissions. Calculating emissions online also enables effective testing of different air–sea transfer velocity (k) parameterizations, which was performed here for eight different parameterizations. The testing of these different k values is of special interest for DMS, as recently published parameterizations derived by direct flux measurements using eddy covariance measurements suggest decreasing k values at high wind speeds or a linear relationship with wind speed. Implementing these parameterizations reduces discrepancies in modelled DMS atmospheric mixing ratios and observations by a factor of 1.5 compared to parameterizations with a quadratic or cubic relationship to wind spee

Enigmatic persistence of dissolved organic matter in the ocean

Marine dissolved organic matter (DOM) contains more carbon than the combined stocks of Earth’s biota. Organisms in the ocean continuously release a myriad of molecules that become food for microheterotrophs, but, for unknown reasons, a residual fraction persists as DOM for millennia. In this Perspective, we discuss and compare two concepts that could explain this persistence. The long-standing ‘intrinsic recalcitrance’ paradigm attributes DOM stability to inherent molecular properties. In the ‘emergent recalcitrance’ concept, DOM is continuously transformed by marine microheterotrophs, with recalcitrance emerging on an ecosystems level. Both concepts are consistent with observations in the modern ocean, but they imply very different responses of the DOM pool to climate-related changes. To better understand DOM persistence, we propose a new overarching research strategy — the ecology of molecules — that integrates the concepts of intrinsic and emergent recalcitrance with the ecological and environmental context

Application of Pulsed Field Gel Electrophoresis to Determine γ-ray-induced Double-strand Breaks in Yeast Chromosomal Molecules

The frequency of DNA double-strand breaks (dsb) was determined in yeast cells exposed to γ-rays under anoxic conditions. Genomic DNA of treated cells was separated by pulsed field gel electrophoresis, and two different approaches for the evaluation of the gels were employed: (1) The DNA mass distribution profile obtained by electrophoresis was compared to computed profiles, and the number of DSB per unit length was then derived in terms of a fitting procedure; (2) hybridization of selected chromosomes was performed, and a comparison of the hybridization signals in treated and untreated samples was then used to derive the frequency of dsb

Point-occurrence self-similarity in crackling-noise systems and in other complex systems

It has been recently found that a number of systems displaying crackling noise also show a remarkable behavior regarding the temporal occurrence of successive events versus their size: a scaling law for the probability distributions of waiting times as a function of a minimum size is fulfilled, signaling the existence on those systems of self-similarity in time-size. This property is also present in some non-crackling systems. Here, the uncommon character of the scaling law is illustrated with simple marked renewal processes, built by definition with no correlations. Whereas processes with a finite mean waiting time do not fulfill a scaling law in general and tend towards a Poisson process in the limit of very high sizes, processes without a finite mean tend to another class of distributions, characterized by double power-law waiting-time densities. This is somehow reminiscent of the generalized central limit theorem. A model with short-range correlations is not able to escape from the attraction of those limit distributions. A discussion on open problems in the modeling of these properties is provided.Comment: Submitted to J. Stat. Mech. for the proceedings of UPON 2008 (Lyon), topic: crackling nois

Role of the mesoamygdaloid dopamine projection in emotional learning

Amygdala dopamine is crucially involved in the acquisition of Pavlovian associations, as measured via conditioned approach to the location of the unconditioned stimulus (US). However, learning begins before skeletomotor output, so this study assessed whether amygdala dopamine is also involved in earlier 'emotional' learning. A variant of the conditioned reinforcement (CR) procedure was validated where training was restricted to curtail the development of selective conditioned approach to the US location, and effects of amygdala dopamine manipulations before training or later CR testing assessed. Experiment 1a presented a light paired (CS+ group) or unpaired (CS- group) with a US. There were 1, 2 or 10 sessions, 4 trials per session. Then, the US was removed, and two novel levers presented. One lever (CR+) presented the light, and lever pressing was recorded. Experiment 1b also included a tone stimulus. Experiment 2 applied intra-amygdala R(+) 7-OH-DPAT (10 nmol/1.0 A mu l/side) before two training sessions (Experiment 2a) or a CR session (Experiment 2b). For Experiments 1a and 1b, the CS+ group preferred the CR+ lever across all sessions. Conditioned alcove approach during 1 or 2 training sessions or associated CR tests was low and nonspecific. In Experiment 2a, R(+) 7-OH-DPAT before training greatly diminished lever pressing during a subsequent CR test, preferentially on the CR+ lever. For Experiment 2b, R(+) 7-OH-DPAT infusions before the CR test also reduced lever pressing. Manipulations of amygdala dopamine impact the earliest stage of learning in which emotional reactions may be most prevalent

Effects of long memory in the order submission process on the properties of recurrence intervals of large price fluctuations

Understanding the statistical properties of recurrence intervals of extreme events is crucial to risk assessment and management of complex systems. The probability distributions and correlations of recurrence intervals for many systems have been extensively investigated. However, the impacts of microscopic rules of a complex system on the macroscopic properties of its recurrence intervals are less studied. In this Letter, we adopt an order-driven stock market model to address this issue for stock returns. We find that the distributions of the scaled recurrence intervals of simulated returns have a power law scaling with stretched exponential cutoff and the intervals possess multifractal nature, which are consistent with empirical results. We further investigate the effects of long memory in the directions (or signs) and relative prices of the order flow on the characteristic quantities of these properties. It is found that the long memory in the order directions (Hurst index $H_s$) has a negligible effect on the interval distributions and the multifractal nature. In contrast, the power-law exponent of the interval distribution increases linearly with respect to the Hurst index $H_x$ of the relative prices, and the singularity width of the multifractal nature fluctuates around a constant value when $H_x<0.7$ and then increases with $H_x$. No evident effects of $H_s$ and $H_x$ are found on the long memory of the recurrence intervals. Our results indicate that the nontrivial properties of the recurrence intervals of returns are mainly caused by traders' behaviors of persistently placing new orders around the best bid and ask prices.Comment: 6 EPL pages including 6 figure