[1] We investigate the ionospheric effects of a solar energetic particle (SEP) event at Mars, specifically the 29 September 1989 event. We use its energy spectrum and a steady state ionospheric model to simulate vertical profiles of ion and electron densities. The ionospheric response to this large event would have been readily observable. It caused electron densities to exceed 10 4 cm À3 at 30-170 km, much larger than typically observed below 100 km. It also increased the ionosphere's total electron content by half of its subsolar value and would have caused strong attenuation of radio waves. The simulated attenuation is 462 dB at 5 MHz, which demonstrates that SEP events can cause sufficient attenuation (>13 dB) to explain the lack of surface reflections in some MARSIS topside radar sounder observations. We also develop a complementary generalized approach to the study of the ionospheric effects of SEP events. This approach predicts the threshold intensities at which a SEP event is likely to produce detectable changes in electron density profiles and radio wave attenuation measurements. An event one hundred times less intense than the 29 September 1989 event produces electron densities in excess of 3000 cm À3 at 80 km, which should be measurable by radio occultation observations, and causes sufficient attenuation to eliminate MARSIS surface reflections. However, although enhancements in total electron content have been observed during SEP events, predicted enhancements in low altitude electron density were not confirmed by observations.

We have used dust opacity values observed by the Thermal Emission Imaging System onboard Mars Odyssey to estimate the effect of dust aerosols in the D region of the Martian ionosphere. An ion‐dust aerosol model has been developed to calculate ion concentrations and conductivity at midlatitudes during a dust storm in the Southern Hemisphere. We report that the concentration of the water cluster ions H+(H2O)n, NO2−(H2O)n, and CO3−(H2O)n are reduced by 2 orders of magnitude in the presence of dust aerosols. This indicates that during a dust storm, when the optical depth changes considerably, a large hole in the ion concentrations may appear until this anomalous condition returns to the normal condition after a period of about a few days. During such dust storms, the total ion conductivity is reduced by an order of magnitude.

Abstract. About 70 % of the anthropogenic carbon dioxide (CO 2 ) is emitted from the megacities and urban areas of the world. In order to draw effective emission mitigation policies for combating future climate change as well as independently validating the emission inventories for constraining their large range of uncertainties, especially over major metropolitan areas of developing countries, there is an urgent need for greenhouse gas measurements over representative urban regions. India is a fast developing country, where fossil fuel emissions have increased dramatically in the last three decades and are predicted to continue to grow further by at least 6 % per year through to 2025. The CO 2 measurements over urban regions in India are lacking. To overcome this limitation, simultaneous measurements of CO 2 and carbon monoxide (CO) have been made at Ahmedabad, a major urban site in western India, using a state-of-the-art laser-based cavity ring down spectroscopy technique from November 2013 to May 2015. These measurements enable us to understand the diurnal and seasonal variations in atmospheric CO 2 with respect to its sources (both anthropogenic and biospheric) and biospheric sinks. The observed annual average concentrations of CO 2 and CO are 413.0 ± 13.7 and 0.50 ± 0.37 ppm respectively. Both CO 2 and CO show strong seasonality with lower concentrations (400.3 ± 6.8 and 0.19 ± 0.13 ppm) during the south-west monsoon and higher concentrations (419.6 ± 22.8 and 0.72 ± 0.68 ppm) during the autumn (SON) season. Strong diurnal variations are also observed for both the species. The common factors for the diurnal cycles of CO 2 and CO are vertical mixing and rush hour traffic, while the influence of biospheric fluxes is also seen in the CO 2 diurnal cycle. Using CO and CO 2 covariation, we differentiate the anthropogenic and biospheric components of CO 2 and found significant contributions of biospheric respiration and anthropogenic emissions in the late night (00:00-05:00 h, IST) and evening rush hours (18:00-22:00 h) respectively. We compute total yearly emissions of CO to be 69.2 ± 0.07 Gg for the study region using the observed CO : CO 2 correlation slope and bottom-up CO 2 emission inventory. This calculated emission of CO is 52 % larger than the estimated emission of CO by the emissions database for global atmospheric research (EDGAR) inventory. The observations of CO 2 have been compared with an atmospheric chemistry-transport model (ACTM), which incorporates various components of CO 2 fluxes. ACTM is able to capture the basic variabilities, but both diurnal and seasonal amplitudes are largely underestimated compared to the observations. We attribute this underestimation by the model to uncertainties in terrestrial biosphere fluxes and coarse model resolution. The fossil fuel signal from the model shows fairly good correlation with observed CO 2 variations, which supports the overall dominance of fossil fuel emissions over the biospheric fluxes in this urban region.

The results based on the analysis of satellite fire counts detected by the Along-Track Scanning Radiometer (ATSR) sensors over different regions of India during 1998-2009 have been presented. Generally, the activities of open biomass burning show large spatial and temporal variations in India. The highest and lowest values of monthly fire counts were detected during the periods of March-May and July-September, respectively over different regions of India. The activities of biomass burning in two central states of Madhya Pradesh and Maharashtra were the highest and together accounted for about 25-45% of total annual fire counts detected over India during the study period. However, in opposite phases, the rainfall and fire count data show strong seasonal variation. In addition to large regional and seasonal variations, the fire data also show significant year-to-year variation. The higher annual fire counts exceeding the mean of entire period by about 16% and 43% were detected during the two periods of 1998-2000 and 2007-2009, respectively. We have estimated normalized anomaly of annual fire count data which shows large positive departures from long-term mean for the years 1999, 2007, 2008 and 2009, while negative departures for the years 2002, 2003 and 2005. Consistently, the mixing ratio of carbon monoxide (CO) typical peaks during winter but extended to pre-monsoon season during extensive fire years. The annual data over the entire region of India show lesser positive trend of about 3% yr −1 .T h ei n t e r-a n n u a l variation of fire count over entire India follows the trend in the ENSO Precipitation Index (ESPI) but shows opposite trend to the multivariate ENSO Index (MEI).

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[1] We have developed a model to calculate the production rates and densities of positive and negative ions in the troposphere of Mars at evening. Using this model, the densities of 35 ions (A r +

In the lower troposphere, the CO mixing ratio showed strong seasonality, with higher levels (>300 ppbv) during the winter and premonsoon seasons associated with a stable anticyclonic circulation, while lower CO values (up to 100 ppbv) were observed in the monsoon season. In the planetary boundary layer (PBL), the seasonal distribution of CO shows the impact of both local meteorology and emissions. While the PBL CO is predominantly influenced by strong winds, bringing regional background air from marine and biomass burning regions, under calm conditions CO levels are elevated by local emissions. On the other hand, in the free troposphere, seasonal variation reflects the impact of long-range transport associated with the Intertropical Convergence Zone and biomass burning. The interannual variations were mainly due to transition from El Niño to La Niña conditions. The overall modified normalized mean biases (normalization based on the observed and model mean values) with respect to the observed CO profiles were lower for the MACC reanalysis than the MOZART and MRI-CCM2 models. The CO in the PBL region was consistently underestimated by MACC reanalysis during all the seasons, while MOZART and MRI-CCM2 show both positive and negative biases depending on the season.