Abstract. Nitrogen oxide biogenic emissions from soils are driven by soil and environmental parameters. The relationship between these parameters and NO fluxes is highly non linear. A new algorithm, based on a neural network calculation, is used to reproduce the NO biogenic emissions linked to precipitations in the Sahel on the 6 August 2006 during the AMMA campaign. This algorithm has been coupled in the surface scheme of a coupled chemistry dynamics model (MesoNH Chemistry) to estimate the impact of the NO emissions on NO x and O 3 formation in the lower troposphere for this particular episode. Four different simulations on the same domain and at the same period are compared: one with anthropogenic emissions only, one with soil NO emissions from a static inventory, at low time and space resolution, one with NO emissions from neural network, and one with NO from neural network plus lightning NO x . The influence of NO x from lightning is limited to the upper troposphere. The NO emission from soils calculated with neural network responds to changes in soil moisture giving enhanced emissions over the wetted soil, as observed by aircraft measurements after the passing of a convective system. The subsequent enhancement of NO x and ozone is limited to the lowest layers of the atmosphere in modelling, whereas measurements show higher concentrations above 1000 m. The neural network algorithm, applied in the Sahel region for one particular day of the wet season, allows an immediate response of fluxes to environmental parameters, unlike static emission inventories. Stewart et al. (2008) is a companion paper to this one which looks at NO x and ozone concentrations in the boundary layer as measured on a research aircraft, examinesCorrespondence to: C. Delon (claire.delon@aero.obs-mip.fr) how they vary with respect to the soil moisture, as indicated by surface temperature anomalies, and deduces NO x fluxes. In this current paper the model-derived results are compared to the observations and calculated fluxes presented by Stewart et al. (2008).

In this paper we report the results of the first laboratory study on the relationship between the initial growth of the short wind waves and the simultaneous development of the wind-induced drift current. The phenomenon of the first visible ripples appearing “suddenly” on the water surface and forming V-shaped streaks aligned with the wind is explained. We show that the laminar–turbulent transition of the near surface water flow causes an explosive growth of the initial wind-generated ripples. The grown ripples become visible and thus mark the surface of the well-localized V-shaped turbulent zones forming the streaks.

Soils are considered as an important source for NO emissions, but the uncertainty in quantifying these emissions worldwide remains large due to the lack of field experiments and high variability in time and space of environmental parameters influencing NO emissions. In this study, the development of a relationship for NO flux emission from soil with pertinent environmental parameters is proposed. An Artificial Neural Network (ANN) is used to find the best non-linear regression between NO fluxes and seven environmental variables, introduced step by step: soil surface temperature, surface water filled pore space, soil temperature at depth (20-30 cm), fertilisation rate, sand percentage in the soil, pH and wind speed. The network performance is evaluated each time a new variable is introduced in the network, i.e. each variable is justified and evaluated in improving the network performance. A resulting equation linking NO flux from soil and the seven variables is proposed, and shows to perform well with measurements (R 2 = 0.71), whereas other regression models give a poor correlation coefficient between calculation and measurements (R 2 ≤ 0.12 for known algorithms used at regional or global scales). ANN algorithm is shown to be a good alternative between biogeochemical and large-scale models, for future application at regional scale.

Abstract.We use ozone and carbon monoxide measurements from the Tropospheric Emission Spectrometer (TES), model estimates of Ozone, CO, and ozone pre-cursors from the Real-time Air Quality Modeling System (RAQMS), and data from the NASA DC8 aircraft to characterize the source and dynamical evolution of ozone and CO in Asian wildfire plumes during the spring ARCTAS campaign 2008. On the 19 April, NASA DC8 O 3 and aerosol Differential Absorption Lidar (DIAL) observed two biomass burning plumes originating from North-Western Asia (Kazakhstan) and SouthEastern Asia (Thailand) that advected eastward over the Pacific reaching North America in 10 to 12 days. Using both TES observations and RAQMS chemical analyses, we track the wildfire plumes from their source to the ARCTAS DC8 platform. In addition to photochemical production due to ozone pre-cursors, we find that exchange between the stratosphere and the troposphere is a major factor influencing O 3 concentrations for both plumes. For example, the Kazakhstan and Siberian plumes at 55 degrees North is a region of significant springtime stratospheric/tropospheric exchange. Stratospheric air influences the Thailand plume after it is lofted to high altitudes via the Himalayas. Using comparisons of the model to the aircraft and satellite measurements, we estimate that the Kazakhstan plume is responsible for increases of O 3 and CO mixing ratios by approximately 6.4 ppbv and 38 ppbv in the lower troposphere (height of 2 to 6 km), and the Thailand plume is responsible for increases of O 3 and CO mixing ratios of approximately 11 ppbv and 71 ppbv in the upper troposphere (height of 8 to 12 km) respectively. However, there are significant sources of uncertainty in these estimates that point to the need for future improvements in both model and satellite observations. For example, it is challenging to characterize the fraction of air parcels from the stratosphere versus those from the fire because of the low sensitivity of the TES CO estimates used to mark stratospheric air versus air parcels affected by the smoke plume. Model transport uncertainties, such as too much dispersion, results in a broad plume structure from the Kazakhstan fires that is approximately 2 km lower than the plume observed by aircraft. Consequently, the model and TES data do not capture the photochemical production of ozone in the Kazakhstan plume that is apparent in the aircraft in situ data. However, ozone and CO distributions from TES and RAQMS model estimates of the Thailand plume are within the uncertainties of the TES data. Therefore, the RAQMS model is better able to characterize the emissions from this fire, the mixing of ozone from the stratosphere to the plume, and the photochemical production and transport of ozone and ozone pre-cursors as the plume moves across the Pacific.