Towards a climatology of stratospheric bromine monoxide from SCIAMACHY limb observations

International audienceRetrievals of stratospheric bromine monoxide (BrO) profiles from two years of limb measurements from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instrument onboard ENVISAT are analysed and a global climatology of stratospheric BrO is prepared. A comparison of the SCIAMACHY BrO retrievals with a set of four balloon-borne BrO profiles shows mean relative differences in the altitude range from 18 to 30 km between ?42%. The SCIAMACHY BrO observations provide for the first time a picture of the seasonal variation of stratospheric BrO on a global scale. At mid-latitudes of both hemispheres BrO shows a strong seasonal cycle with a maximum in winter and a minimum in summer. The seasonal variation of BrO is closely correlated with changes in nitrogen dioxide (NO2), confirming our present understanding of gas phase bromine chemistry. Using the SCIAMACHY BrO observations together with the calculated bromine partitioning from a photochemical model constrained by the SCIAMACHY NO2 observations, the total stratospheric bromine loading is estimated to be 18.5±4 pptv. This indicates a contribution of about 3.5±4 pptv from short lived bromine species in addition to methyl bromide and the halons

Measurements of O3</sub>, NO₂ and BrO at the Kaashidhoo Climate Observatory (KCO) during the INDOEX (INDian Ocean EXperiment) Campaign using ground based DOAS (Differential Optical Absorption Spectroscopy) and satellite based GOME (Global Ozone Monitoring Experiment) data

International audienceThe INDian Ocean EXperiment (INDOEX) was an international, multi-platform field campaign to measure long-range transport of air masses from South and South-East-(SE) Asia towards the Indian Ocean. During the dry monsoon season between January and March 1999, local measurements were carried out from ground based platforms and were compared with satellite based data. The objective of this study was to characterise stratospheric and tropospheric trace gas amounts in the equatorial region, and to investigate the impact of air pollution at this remote site. For the characterisation of the chemical composition of the outflow from the S-SE-Asian region, we performed ground based dual-axis-DOAS (Differential Optical Absorption Spectroscopy) measurements at the KCO (Kaashidhoo Climate Observatory) in the Maldives (5.0° N, 73.5° E). The ground based dual-axis-DOAS measurements were conducted using two different observation modes (off-axis and zenith-sky). This technique allows the separation of the tropospheric and stratospheric columns for different trace gases like O3 and NO2. These dual-axis DOAS data were compared with O3-sonde measurements performed at KCO and satellite based GOME (Global Ozone Measuring Experiment) data during the intensive measuring phase of the INDOEX campaign in February and March 1999. From GOME observations, tropospheric and stratospheric columns for O3 and NO2 were retrieved. In addition, the analysis of the O3-sonde measurements allowed the determination of the tropospheric O3 amount. The comparison shows that the results of all three measurement systems agree within their error limits. During the INDOEX campaign, background conditions were observed most of the time, but in a single case an increase of tropospheric NO2 during a short pollution event was observed and the impact on the vertical columns was calculated. In the GOME measurements, evidence was found for large tropospheric contributions to the BrO budget, probably located in the free troposphere and present throughout the year. The latter has been investigated by the comparison of satellite pixels influenced by high and low cloud conditions based on GOME data which allows the determination of the detection limit of tropospheric BrO columns

Multivariate Granger Causality and Generalized Variance

Granger causality analysis is a popular method for inference on directed interactions in complex systems of many variables. A shortcoming of the standard framework for Granger causality is that it only allows for examination of interactions between single (univariate) variables within a system, perhaps conditioned on other variables. However, interactions do not necessarily take place between single variables, but may occur among groups, or "ensembles", of variables. In this study we establish a principled framework for Granger causality in the context of causal interactions among two or more multivariate sets of variables. Building on Geweke's seminal 1982 work, we offer new justifications for one particular form of multivariate Granger causality based on the generalized variances of residual errors. Taken together, our results support a comprehensive and theoretically consistent extension of Granger causality to the multivariate case. Treated individually, they highlight several specific advantages of the generalized variance measure, which we illustrate using applications in neuroscience as an example. We further show how the measure can be used to define "partial" Granger causality in the multivariate context and we also motivate reformulations of "causal density" and "Granger autonomy". Our results are directly applicable to experimental data and promise to reveal new types of functional relations in complex systems, neural and otherwise.Comment: added 1 reference, minor change to discussion, typos corrected; 28 pages, 3 figures, 1 table, LaTe

Spectral Analysis of Multi-dimensional Self-similar Markov Processes

In this paper we consider a discrete scale invariant (DSI) process $\{X(t), t\in {\bf R^+}\}$ with scale $l>1$. We consider to have some fix number of observations in every scale, say $T$, and to get our samples at discrete points $\alpha^k, k\in {\bf W}$ where $\alpha$ is obtained by the equality $l=\alpha^T$ and ${\bf W}=\{0, 1,...\}$. So we provide a discrete time scale invariant (DT-SI) process $X(\cdot)$ with parameter space $\{\alpha^k, k\in {\bf W}\}$. We find the spectral representation of the covariance function of such DT-SI process. By providing harmonic like representation of multi-dimensional self-similar processes, spectral density function of them are presented. We assume that the process $\{X(t), t\in {\bf R^+}\}$ is also Markov in the wide sense and provide a discrete time scale invariant Markov (DT-SIM) process with the above scheme of sampling. We present an example of DT-SIM process, simple Brownian motion, by the above sampling scheme and verify our results. Finally we find the spectral density matrix of such DT-SIM process and show that its associated $T$-dimensional self-similar Markov process is fully specified by $\{R_{j}^H(1),R_{j}^H(0),j=0, 1,..., T-1\}$ where $R_j^H(\tau)$ is the covariance function of $j$th and $(j+\tau)$th observations of the process.Comment: 16 page

Error budget analysis of SCIAMACHY limb ozone profile retrievals using the SCIATRAN model

A comprehensive error characterization of SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY) limb ozone profiles has been established based upon SCIATRAN transfer model simulations. The study was carried out in order to evaluate the possible impact of parameter uncertainties, e.g. in albedo, stratospheric aerosol optical extinction, temperature, pressure, pointing, and ozone absorption cross section on the limb ozone retrieval. Together with the a posteriori covariance matrix available from the retrieval, total random and systematic errors are defined for SCIAMACHY ozone profiles. Main error sources are the pointing errors, errors in the knowledge of stratospheric aerosol parameters, and cloud interference. Systematic errors are of the order of 7%, while the random error amounts to 10–15% for most of the stratosphere. These numbers can be used for the interpretation of instrument intercomparison and validation of the SCIAMACHY V 2.5 limb ozone profiles in a rigorous manner

Adaptive density estimation for stationary processes

We propose an algorithm to estimate the common density $s$ of a stationary process $X_1,...,X_n$. We suppose that the process is either $\beta$ or $\tau$-mixing. We provide a model selection procedure based on a generalization of Mallows' $C_p$ and we prove oracle inequalities for the selected estimator under a few prior assumptions on the collection of models and on the mixing coefficients. We prove that our estimator is adaptive over a class of Besov spaces, namely, we prove that it achieves the same rates of convergence as in the i.i.d framework

On the Hiatus in the Acceleration of Tropical Upwelling Since the Beginning of the 21st Century

Chemistry-climate models predict an acceleration of the upwelling branch of the Brewer-Dobson circulation as a consequence of increasing global surface temperatures, resulting from elevated levels of atmospheric greenhouse gases. The observed decrease of ozone in the tropical lower stratosphere during the last decades of the 20th century is consistent with the anticipated acceleration of upwelling. However, more recent satellite observations of ozone reveal that this decrease has unexpectedly stopped in the first decade of the 21st century, challenging the implicit assumption of a continuous acceleration of tropical upwelling. In this study we use three decades of chemistry transport-model simulations (1980-2013) to investigate this phenomenon and resolve this apparent contradiction. Our model reproduces the observed tropical lower stratosphere ozone record, showing a significant decrease in the early period followed by a statistically robust trend-change after 2002. We demonstrate that this trend-change is correlated with corresponding changes in the vertical transport and conclude that a hiatus in the acceleration of tropical upwelling occurred during the last decade