Top PDF Ship emitted NO<sub>2</sub> in the Indian Ocean: comparison of model results with satellite data

Ship emitted NO<sub>2</sub> in the Indian Ocean: comparison of model results with satellite data

Ship emitted NO<sub>2</sub> in the Indian Ocean: comparison of model results with satellite data

is negligible (Fig. 1). The SCD can be converted to a vertical column density (VCD) by division with an Air Mass Fac- tor (AMF). The AMF corrects for the different sensitivity of measurements to absorption in different altitudes, which is determined by the relative penetration depth and depends on the magnitude of the surface spectral reflectance and multiple scattering within the atmosphere. This is of particular impor- tance for absorbers located close to the surface. The resulting tropospheric excess column is denoted as TEC. This method belongs to a family of retrieval approaches called residual techniques. The analysis used here is described in Richter et al. (2004). The main assumptions made for the airmass factors are a surface albedo of 4%, a well mixed boundary layer of 700 m and a cloud threshold of 20%. More detailed descriptions of the retrieval method can be found in Richter and Burrows (2002) and in Burrows et al. (1999). Uncer- tainty is introduced into the satellite columns in the different steps of the analysis: the fitting of the slant columns, the correction of the stratospheric contribution, the assumptions made for the airmass factor and contamination by residual clouds. An additional source of variability is the sparse cov- erage in particular of SCIAMACHY measurements which can lead to sampling artifacts as only few measurements con- tribute to the monthly average. A detailed discussion of the error budget of satellite measurements is given in Boersma et al. (2004). However, for this specific application, the rel- ative importance of the different error contributions differs from that presented for the global data set.
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Ship emitted NO<sub>2</sub> in the Indian Ocean: comparison of model results with satellite data

Ship emitted NO<sub>2</sub> in the Indian Ocean: comparison of model results with satellite data

istry general circulation model ECHAM5/MESSy1 (January 2000 to October 2005). The data set from SCIAMACHY yields the first monthly analysis of ship induced NO 2 enhancements in the Indian Ocean. For both data and model consistently the tropo- spheric excess method was used to obtain mean NO 2 columns over the shipping lane from India to Indonesia, and over two ship free regions, the Bay of Bengal and the cen-

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Constraints on ship NO<sub>x</sub> emissions in Europe using GEOS-Chem and OMI satellite NO<sub>2</sub> observations

Constraints on ship NO<sub>x</sub> emissions in Europe using GEOS-Chem and OMI satellite NO<sub>2</sub> observations

cue System (AMVER) and International Comprehensive Ocean-Atmosphere Data Set (ICOADS). Comparison with annual OMI NO 2 observations (Fig. 6b) for the eastern part of Mediterranean Sea shows that the AMVER-ICOADS inventory (Fig. 6c) simu- lates the ship track closer to the observed tracks. The location of the EMEP emissions is misplaced by up to 150 km (too close to Crete, Fig. 6a). However, as the EMEP in-

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A two-channel, Thermal Dissociation Cavity-Ringdown Spectrometer for the detection of ambient NO<sub>2</sub>, RO<sub>2</sub>NO<sub>2</sub> and RONO<sub>2</sub>

A two-channel, Thermal Dissociation Cavity-Ringdown Spectrometer for the detection of ambient NO<sub>2</sub>, RO<sub>2</sub>NO<sub>2</sub> and RONO<sub>2</sub>

ity of 0.999965 (Advanced Thin Films). The mirrors are mounted in a self-made mirror holder system, the ∼ 70 cm distance between them being rigidly fixed using three hol- low carbon-fibre rods (o.d. 15 mm) per cavity. This combination of mirror reflectivity and separation results in ring down times under typical conditions (830 mbar air) of about τ 0 = 38 µs when no absorber is present, or optical path lengths of > 10 km. A purge

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Estimation of NO<sub><b>x</b></sub> emissions from Delhi using car MAX-DOAS observations and comparison with OMI satellite data

Estimation of NO<sub><b>x</b></sub> emissions from Delhi using car MAX-DOAS observations and comparison with OMI satellite data

In order to investigate the effect of limited spatial sampling in the presence of strong gradients in more detail, a second comparison was made including only observations, for which the car MAX-DOAS observations cover large fractions of the OMI pixels (at least 50 % in east-west direction (defined by the most eastern and most western MAX- DOAS observation within the OMI pixel) like e.g. shown in Fig. 9). Only five mea-

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Ozone and NO<sub><i>x</i></sub> chemistry in the eastern US: evaluation of CMAQ/CB05 with satellite (OMI) data

Ozone and NO<sub><i>x</i></sub> chemistry in the eastern US: evaluation of CMAQ/CB05 with satellite (OMI) data

For this analysis we use the Community Multiscale Air Qual- ity (CMAQ) modeling system version 4.7.1 (https://www. cmascenter.org/cmaq/). This model has been used exten- sively by states that are members of the Ozone Transport Commission (OTC) in preparation for the 2015 ozone SIP call. For this analysis, CMAQ simulations were performed for the Eastern US with a 12 km×12 km horizontal resolution and a 34 layer (σ coordinate) vertical grid from the surface to ∼ 20 km with hourly output. CMAQ does not include strato- spheric processes so the upper layers of the model atmo- sphere should not be used for research purposes. The analysis presented here is limited to altitudes below the tropopause. Simulated meteorology is driven by output from the Weather Research Forecasting (WRF v3.1.1) model for year 2007 and processed for use in CMAQ by the Meteorological Chemistry Interface Processor (MCIP).
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A satellite data driven biophysical modeling approach for estimating northern peatland and tundra CO<sub>2</sub> and CH<sub>4</sub> fluxes

A satellite data driven biophysical modeling approach for estimating northern peatland and tundra CO<sub>2</sub> and CH<sub>4</sub> fluxes

trum, including blue and green reflectance, in addition to NDVI has helped to alleviate this problem in remote sensing applications (Marushchak et al., 2013) and should be considered in subsequent studies. Incorporating phenological constraints into the TCF LUE model may also be necessary to better characterize early season changes in GPP, especially for plant communities such as E. vaginatum that are sensitive to active layer

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On the improvement of NO<sub>2</sub> satellite retrievals – aerosol impact on the airmass factors

On the improvement of NO<sub>2</sub> satellite retrievals – aerosol impact on the airmass factors

by 10% from 425 to 450 nm. This variation is relatively small and will largely cancel if it is linear with wavelength but might be relevant in some cases. For different solar zenith angles (SZA), the general trend shows that the AMF increases for higher sun, but for specific cases, this tendency can also be reverted. In some circumstances (not presented here), when considering fine aerosol, a decrease occurs with high sun, and

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Assimilation of surface NO<sub>2</sub> and O<sub>3</sub> observations into the SILAM chemistry transport model

Assimilation of surface NO<sub>2</sub> and O<sub>3</sub> observations into the SILAM chemistry transport model

Kukkonen, J., Olsson, T., Schultz, D. M., Baklanov, A., Klein, T., Miranda, A. I., Monteiro, A., Hirtl, M., Tarvainen, V., Boy, M., Peuch, V.-H., Poupkou, A., Kioutsioukis, I., Finardi, S., Sofiev, M., Sokhi, R., Lehtinen, K. E. J., Karatzas, K., San José, R., Astitha, M., Kal- los, G., Schaap, M., Reimer, E., Jakobs, H., and Eben, K.: A review of operational, regional-

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Direct detection of OH formation in the reactions of HO<sub>2</sub> with CH<sub>3</sub> C(O)O<sub>2</sub> and other substituted peroxy radicals

Direct detection of OH formation in the reactions of HO<sub>2</sub> with CH<sub>3</sub> C(O)O<sub>2</sub> and other substituted peroxy radicals

mendation is based on the results of just one experimental study (Bridier et al., 1993), and is consequently quoted with an uncertainty of approximately a factor of 2. An im- proved reproduction of the data was achieved (e.g. in Fig. 4) when values close to the lower error limit for k 2 of 5×10 −12 cm 3 molecule −1 s −1 were used. However, given that OH is produced via (R1) which follows the break-up (R28) of CH 3 C(O)CH 2 O and rapid

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Geoengineering by stratospheric SO<sub>2</sub> injection: results from the Met Office HadGEM2 climate model and comparison with the Goddard Institute for Space Studies ModelE

Geoengineering by stratospheric SO<sub>2</sub> injection: results from the Met Office HadGEM2 climate model and comparison with the Goddard Institute for Space Studies ModelE

When geoengineering is terminated the sulphate aerosol burden returns to its unperturbed state after about 5 years in HadGEM2 and global mean temperature increases at an av- erage rate of 0.77 K decade −1 , returning to the A1B value after about 15 years. This rate of warming is more than twice that in A1B (0.34 K decade −1 over years 20–60). The be- haviour of ModelE is somewhat different, warming strongly at 1.01 K decade −1 for the first 7 years or so, after which the rate of warming reduces to approximately 0.27 K decade −1 as it slowly approaches A1B temperatures. These rates com- pare with a mean warming of 0.16 K decade −1 in A1B over the whole period. The results from HadGEM2 shown in Fig. 3a suggest that a given amount of warming under the A1B scenario may be delayed by some 30–35 years by the SO 2 injection rates considered here.
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Measurements of N<sub>2</sub>O<sub>5</sub>, NO<sub>2</sub>, and O<sub>3</sub> east of the San Francisco Bay

Measurements of N<sub>2</sub>O<sub>5</sub>, NO<sub>2</sub>, and O<sub>3</sub> east of the San Francisco Bay

similar glass window 13 cm from the center. Vertical slit geometric baffles were used to reduce laser scatter and to exclude ambient light from the cell. The laser power exiting the cell was monitored by a photodiode. The electric field vector of the laser light was aligned parallel to the detection axis in order to minimize the collection of Rayleigh, Raman, and other polarized scattered laser light. NO 3 fluorescence was collected by a

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A two-channel thermal dissociation cavity ring-down spectrometer for  the detection of ambient NO<sub>2</sub>, RO<sub>2</sub>NO<sub>2</sub> and RONO<sub>2</sub>

A two-channel thermal dissociation cavity ring-down spectrometer for the detection of ambient NO<sub>2</sub>, RO<sub>2</sub>NO<sub>2</sub> and RONO<sub>2</sub>

been reviewed in detail (Berden et al., 2000; Brown, 2003). CRDS is based on direct absorption spectroscopy in which the absorption path length is enhanced by a high finesse cav- ity formed by a set of two highly reflective mirrors. Most applications use pulsed or intensity-modulated continuous- wave (CW) lasers as a light source with direct coupling into the cavity via one (front) mirror. The present experiment uses a square-wave, on/off-modulated CW laser. During the “laser on” phase the light intensity in the cavity builds up to a level determined by mirror reflectivity and transmission. The light leaking out through the back mirror during “laser off” mode is analysed to derive an exponential decay constant, which is reduced in the presence of an absorbing or scattering gas. This provides an absolute measurement of optical extinction, as given in Eq. (1).
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Validation of TANSO-FTS/GOSAT XCO<sub>2</sub> and XCH<sub>4</sub> glint mode retrievals  using TCCON data from near-ocean sites

Validation of TANSO-FTS/GOSAT XCO<sub>2</sub> and XCH<sub>4</sub> glint mode retrievals using TCCON data from near-ocean sites

NIES v02.21, SRFP v2.3.5, SRPR v2.3.5 and ACOS v3.5 al- gorithms were validated with the FTIR measurements from five TCCON stations and nearby GOSAT land data. As the GOSAT land data have already been validated with TCCON measurements in previous studies, we mainly focused on the differences between ocean data and nearby land data. Due to the low data density of sun glint mode retrievals, all the GOSAT footprints located within ±5 ◦ latitude and ±15 ◦ lon- gitude around each TCCON site were selected. The a priori profile of TCCON is used as the common profile to elimi- nate the differences between GOSAT and FTIR data due to the use of different a priori profiles in their retrievals. An altitude-correction method is applied to eliminate the bias due to altitude differences between the FTIR station loca- tion and the GOSAT footprints, but only in the comparisons made at Izaña; it is particularly important when comparing the XCH 4 data.
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Corrigendum to ''The relative roles of CO<sub>2</sub> and palaeogeography in determining late Miocene climate: results from a terrestrial model-data comparison'' published in Clim. Past, 8, 1257&ndash;1285, 2012

Corrigendum to ''The relative roles of CO<sub>2</sub> and palaeogeography in determining late Miocene climate: results from a terrestrial model-data comparison'' published in Clim. Past, 8, 1257&ndash;1285, 2012

In the original manuscript, Figs. 7–16 included fonts which were not correctly embedded in the file. As such, unless cer- tain propriety software (ArcGIS) is installed on the viewing platform, the figures will appear corrupted. In this Corrigen- dum, Figs. 7–16 and their captions are reproduced with the fonts correctly embedded.

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Five years of CO, HCN, C<sub>2</sub>H<sub>6</sub>, C<sub>2</sub>H<sub>2</sub>, CH<sub>3</sub>OH, HCOOH and H<sub>2</sub>CO total columns measured in the Canadian high Arctic

Five years of CO, HCN, C<sub>2</sub>H<sub>6</sub>, C<sub>2</sub>H<sub>2</sub>, CH<sub>3</sub>OH, HCOOH and H<sub>2</sub>CO total columns measured in the Canadian high Arctic

In contrast to these measurements, the ground-based FTIR technique can provide total columns, with good sensitiv- ity in the lower troposphere (compared to satellite measure- ments), as well as long-term spectral acquisition, in clear- sky conditions, thereby enabling an assessment of the tem- poral variabilities of the target species in the high Arctic (compared to campaign-based measurements). We focused on these species because there remain numerous gaps in the available observational data sets, especially at high latitudes. Furthermore, the transport and the degradation mechanisms of non-methane hydrocarbons (NMHCs) are poorly under- stood and should be better quantified in order to increase our ability to predict trace gas concentrations and variability in models (Stavrakou et al., 2009).
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Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO<sub>2</sub> data assimilation

Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO<sub>2</sub> data assimilation

For experiments AR–CR, the posterior uncertainty esti- mates for all the three approaches are higher compared to experiments A–C, as expected due to the higher prescribed modeldata mismatch error. Similarly to experiments A– C, the 4D-VAR uncertainty estimates for individual loca- tions/times are too variable relative to BIM (Fig. 5). Aver- aged over time and space, the 4D-VAR uncertainty estimates underestimate the BIM uncertainty estimates by approxi- mately 25 % (Fig. 5ar–cr). Thus, even though the 4D-VAR uncertainty estimates for experiments AR–CR are higher than the corresponding uncertainty estimates for experiments A–C, they fail to capture the full magnitude of the BIM un- certainty estimates. This makes intuitive sense due to the indirect approach adopted for generating the 4D-VAR un- certainty estimates. Conversely, as the observational net- work becomes sparser and more heterogeneous, the EnSRF slightly overestimates the BIM average uncertainties by 3 % (HM; Fig. 5br) and 5 % (HT; Fig. 5cr), while it underesti- mates the uncertainty by only 1 % for the reference network (Fig. 5ar). The EnSRF uncertainty estimates for individual locations/times are more closely distributed around the BIM estimates (Fig. 5). The better performance of EnSRF in terms of the uncertainty estimation can be directly related to the en- semble spread. Relative to experiments A–C, when the pre- scribed modeldata mismatch error is high in experiments AR–CR, the initial ensemble spread is reduced by a lower amount as observations are now being given less weight and hence have lower impact on the ensemble spread. Con- sequently, the ensemble members maintain a large spread throughout the analysis and results in large posterior uncer- tainty estimates that are more realistic relative to 4D-VAR.
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Influence of the substrate on the overall sensor impedance of planar H<sub>2</sub> sensors involving TiO<sub>2</sub>–SnO<sub>2</sub> interfaces

Influence of the substrate on the overall sensor impedance of planar H<sub>2</sub> sensors involving TiO<sub>2</sub>–SnO<sub>2</sub> interfaces

Received: 9 September 2014 – Revised: 19 December 2014 – Accepted: 20 January 2015 – Published: 23 February 2015 Abstract. To date, very little has been written about the influence of the substrate layer on the overall sensor impedance of single- and multilayer planar sensors (e.g., metal-oxide sensors). However, the substrate is an elementary part of the sensor element. Through the selection of a substrate, the sensor performance can be manipulated. The current contribution reports on the substrate influence in multilayer metal-oxide chemical sensors. Measurements of the impedance are used to discuss the sensor performance with quartz substrates, (laboratory) glass substrates and substrates covered by silicon-dioxide insulating layers. Numerical experiments based on previous measurement results show that inexpensive glass substrates contribute up to 97 % to the overall sensor responses. With an isolating layer of 200 nm SiO 2 , the glass substrate contribution is reduced to about
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Sea–air CO<sub>2</sub> fluxes in the Southern Ocean for the period 1990&ndash;2009

Sea–air CO<sub>2</sub> fluxes in the Southern Ocean for the period 1990&ndash;2009

the fluxes estimated from other regions or in the modelled transport of those fluxes to the high latitude southern hemi- sphere could lead to biases in the seasonal cycle. Secondly, some of the inversions that solve for smaller ocean regions (e.g. C13 CCAM law) show very different seasonality be- tween basins, suggesting some caution should be applied to those results. Possibly, the greater flexibility in those inver- sions to fit the atmospheric data makes them vulnerable to any data quality or representativeness issues as well as trans- port model errors. When inversions are solved for fewer re- gions, the inversion compromises the fit across sites effec- tively ignoring any poorly calibrated data. Conversely, in- versions that solve for only a few regions could be missing important spatial variability in the seasonal cycle, which can lead to biases in the inverse estimates (Kaminski et al., 2001). The representation of the seasonal cycle varies widely across the ocean biogeochemical models (Fig. 7). If the sum- mer warming is too strong in the upper ocean, the solubility response can dominate over the biological productivity lead- ing to a peak in the sea–air flux that is several months out of phase with observations (Fig. 8a). The net primary productiv- ity from the models (not shown) has a similar magnitude over summer. This suggests that changes in the seasonal temper- ature and mixed layer depth are likely to be more important than the differences between biological models in causing the varied responses in the ocean models. Interestingly, capturing the seasonal cycle of the Southern Ocean is not a prerequisite to reproducing the annual mean sea–air flux calculated from observations. However, the inability of the models to simu- late the observation-based seasonality in the sea–air CO 2 flux
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The Economics of Sub-optimal Policies for Traffic Congestion

The Economics of Sub-optimal Policies for Traffic Congestion

However, such policies are not the ones economic theory would prescribe. The theory of congestion pricing has a long tradition in economics. Pioneered by the early work of Pigou [1932] and Knight [1924], it prescribes a tax - often called a pigouvian tax - to road usage. This tax would solve the problem of excess usage, bringing the traffic system to a situation of optimal allocation. In more general terms, it is simply a matter of pricing a negative externality: when a driver decides to drive on a congested road, her decision increases the travel time of other users. The social cost of road usage is greater than the private cost, and a pigouvian tax would equalize social and private cost, maximizing social welfare.
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