Synthesis, structure, and electrochemical properties of a family of 2-(arylazo)phenolate complexes of ruthenium with unusual C-C coupling and N=Ncleavage

Reaction of 2-(4'-R-phenylazo)-4-methylphenols (R = OCH3, CH3, H, Cl, and NO2) with [Ru(dmso)(4)Cl-2] affords a family of five ruthenium(III) complexes, containing a 2-(arylazo)phenolate ligand forming a six-membered chelate ring and a tetradentate ligand formed from two 2-(arylazo) phenols via an unusual C-C coupling linki.ng the two ortho carbons of the phenyl rings in the arylazo fragment. A similar reaction with 2-(2'-methylphenylazo)-4-methylphenol with [Ru(dmso)(4)Cl-2] has afforded a similar complex, in which one 2-(2'-methylphenylazo)-4-methylphenolate ligand is coordinated forming a six-membered chelate ring, and the other two ligands have undergone the C-C coupling reaction, and the coupled species is coordinated as a tetradentate ligand forming a five-membered N,O-chelate ring, a nine-membered N,N-chelate ring, and another five-membered chelate ring. Reaction of 2-(2',6'-dimethylphenylazo)-4-methylphenol with [Ru(dmso)(4)Cl-2] has afforded a complex in which two 2-(2',6'-dimethylphenylazo)-4-methylphenols are coordinated as bidentate N,O-donors forming five- and six-membered chelate rings, while the third one has undergone cleavage across the N=N bond, and the phenolate fragment, thus generated, remains coordinated to the metal center in the iminosemiquinonate form. Structures of four selected complexes have been determined by X-ray crystallography. The first six complexes are one-electron paramagnetic and show rhombic ESR spectra. The last complex is diamagnetic and shows characteristic H-1 NMR signals. All the complexes show intense charge-transfer transitions in the visible region and a Ru(III)-Ru(IV) oxidation on the positive side of SCE and a Ru(III)-Ru(II) reduction on the negative side.

Emulating AOGCM results using simple climate models

Three simple climate models (SCMs) are calibrated using simulations from atmosphere ocean general circulation models (AOGCMs). In addition to using two conventional SCMs, results from a third simpler model developed specifically for this study are obtained. An easy to implement and comprehensive iterative procedure is applied that optimises the SCM emulation of global-mean surface temperature and total ocean heat content, and, if available in the SCM, of surface temperature over land, over the ocean and in both hemispheres, and of the global-mean ocean temperature profile. The method gives best-fit estimates as well as uncertainty intervals for the different SCM parameters. For the calibration, AOGCM simulations with two different types of forcing scenarios are used: pulse forcing simulations performed with 2 AOGCMs and gradually changing forcing simulations from 15 AOGCMs obtained within the framework of the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. The method is found to work well. For all possible combinations of SCMs and AOGCMs the emulation of AOGCM results could be improved. The obtained SCM parameters depend both on the AOGCM data and the type of forcing scenario. SCMs with a poor representation of the atmosphere thermal inertia are better able to emulate AOGCM results from gradually changing forcing than from pulse forcing simulations. Correct simultaneous emulation of both atmospheric temperatures and the ocean temperature profile by the SCMs strongly depends on the representation of the temperature gradient between the atmosphere and the mixed layer. Introducing climate sensitivities that are dependent on the forcing mechanism in the SCMs allows the emulation of AOGCM responses to carbon dioxide and solar insolation forcings equally well. Also, some SCM parameters are found to be very insensitive to the fitting, and the reduction of their uncertainty through the fitting procedure is only marginal, while other parameters change considerably. The very simple SCM is found to reproduce the AOGCM results as well as the other two comparably more sophisticated SCMs.

Structural and magnetic studies of Schiff base complexes of nickel(ii) nitrite: change in crystalline state, ligand rearrangement and a very rare μ-nitrito-1κO:2κN:3κO′ bridging mode

Four new nickel(II) complexes, [Ni2L2(NO2)2]·CH2Cl2·C2H5OH, 2H2O (1), [Ni2L2(DMF)2(m-NO2)]ClO4·DMF (2a), [Ni2L2(DMF)2(m-NO2)]ClO4 (2b) and [Ni3L¢2(m3-NO2)2(CH2Cl2)]n·1.5H2O (3) where HL = 2-[(3-amino-propylimino)-methyl]-phenol, H2L¢ = 2-({3-[(2-hydroxy-benzylidene)-amino]-propylimino}-methyl)-phenol and DMF = N,N-dimethylformamide, have been synthesized starting with the precursor complex [NiL2]·2H2O, nickel(II) perchlorate and sodium nitrite and characterized structurally and magnetically. The structural analyses reveal that in all the complexes, NiII ions possess a distorted octahedral geometry. Complex 1 is a dinuclear di-m2-phenoxo bridged species in which nitrite ion acts as chelating co-ligand. Complexes 2a and 2b also consist of dinuclear entities, but in these two compounds a cis-(m-nitrito-1kO:2kN) bridge is present in addition to the di-m2-phenoxo bridge. The molecular structures of 2a and 2b are equivalent; they differ only in that 2a contains an additional solvated DMF molecule. Complex 3 is formed by ligand rearrangement and is a one-dimensional polymer in which double phenoxo as well as m-nitrito-1kO:2kN bridged trinuclear units are linked through a very rare m3-nitrito-1kO:2kN:3kO¢ bridge. Analysis of variable-temperature magnetic susceptibility data indicates that there is a global weak antiferromagnetic interaction between the nickel(II) ions in four complexes, with exchange parameters J of -5.26, -11.45, -10.66 and -5.99 cm-1 for 1, 2a, 2b and 3, respectively

An assessment of a three-beam Doppler lidar wind profiling method for use in urban areas

Currently there are few observations of the urban wind field at heights other than rooftop level. Remote sensing instruments such as Doppler lidars provide wind speed data at many heights, which would be useful in determining wind loadings of tall buildings, and predicting local air quality. Studies comparing remote sensing with traditional anemometers carried out in flat, homogeneous terrain often use scan patterns which take several minutes. In an urban context the flow changes quickly in space and time, so faster scans are required to ensure little change in the flow over the scan period. We compare 3993 h of wind speed data collected using a three-beam Doppler lidar wind profiling method with data from a sonic anemometer (190 m). Both instruments are located in central London, UK; a highly built-up area. Based on wind profile measurements every 2 min, the uncertainty in the hourly mean wind speed due to the sampling frequency is 0.05–0.11 m s−1. The lidar tended to overestimate the wind speed by ≈0.5 m s−1 for wind speeds below 20 m s−1. Accuracy may be improved by increasing the scanning frequency of the lidar. This method is considered suitable for use in urban areas.

A survey of simultaneous observations of the high-latitude ionosphere and interplanetary magnetic field with EISCAT and AMPTE-UKS

This paper surveys the results of simultaneous observations by the EISCAT incoherent scatter radar and the AMPTE-UKS satellite, made during three periods in September and October 1984, when AMPTE-UKS was in the solar wind on the dayside of the Earth and the UK-POLAR EISCAT experiment was measuring ionospheric parameters at invariant latitudes 70.8–75.0°. A total of 42 h of EISCAT convection velocity data, with 2.5 min resolution, were obtained, together with 28 h of simultaneous 5 s resolution AMPTE-UKS observations of the solar wind and interplanetary magnetic field (IMF). The general features of the AMPTE-UKS data are described in Section 2 and those of the EISCAT data are described in Sections 3 and 4. The main subjects discussed are the form of the plasma convection patterns and their dependence on all three components of the IMF (Section 5), the ionospheric response to abrupt changes in the IMF (Section 6), in particular a sharp ‘southward turning’ of the IMF on 27 October 1984, and a crossing of an IMF sector boundary. Section 7 describes ‘short lived rapid flow burst’, which are believed to be related to flux transfer events at the magnetopause.

Radiative forcing and climate change

Aviation causes climate change as a result of its emissions of CO2, oxides of nitrogen, aerosols, and water vapor. One simple method of quantifying the climate impact of past emissions is radiative forcing. The radiative forcing due to changes in CO2 is best characterized, but there are formidable difficulties in estimating the non-CO2 forcings – this is particularly the case for possible aviation-induced changes in cloudiness (AIC). The most recent comprehensive assessment gave a best estimate of the 2005 total radiative forcing due to aviation of about 55–78 mW m−2 depending on whether AIC was included or not, with an uncertainty of at least a factor of 2. The aviation CO2 radiative forcing represents about 1.6% of the total CO2 forcing from all human activities. It is estimated that, including the non-CO2 effects, aviation contributes between 1.3 and 14% of the total radiative forcing due to all human activities. Alternative methods for comparing the future impact of present-day aviation emissions are presented – the perception of the relative importance of the non-CO2 emissions, relative to CO2, depends considerably on the chosen method and the parameters chosen within those methods.