Investigation of the summer 2018 European ozone air pollution episodes using novel satellite data and modelling

In the summer of 2018, Europe experienced an intense heat wave which coincided with several persistent large-scale ozone (O3) pollution episodes. Novel satellite data of lower tropospheric column O3 from the Global Ozone Monitoring Experiment-2 (GOME-2) and Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp satellite showed substantial enhancements in 2018 relative to other years since 2012. Surface observations also showed ozone enhancements across large regions of continental Europe in summer 2018 compared to 2017. Enhancements to surface temperature and the O3 precursor gases carbon monoxide and methanol in 2018 were co-retrieved from MetOp observations by the same scheme. This analysis was supported by the TOMCAT chemistry transport model (CTM) to investigate processes driving the observed O3 enhancements. Through several targeted sensitivity experiments we show that meteorological processes, and emissions to a secondary order, were important for controlling the elevated O3 concentrations at the surface. However, mid-tropospheric (~500 hPa) O3 enhancements were dominated by meteorological processes. We find that contributions from stratospheric O3 intrusions ranged between 15&ndash;40 %. Analysis of back trajectories indicates that the import of O3-enriched air masses into Europe originated over the North Atlantic substantially increasing O3 in the 500 hPa layer during summer 2018.</p

Inundation prediction in tropical wetlands from JULES-CaMa-Flood global land surface simulations

Wetlands play a key role in hydrological and biogeochemical cycles and provide multiple ecosystem services to society. However, reliable data on the extent of global inundated areas and the magnitude of their contribution to local hydrological dynamics remain surprisingly uncertain. Global hydrological models and land surface models (LSMs) include only the most major inundation sources and mechanisms; therefore, quantifying the uncertainties in available data sources remains a challenge. We address these problems by taking a leading global data product on inundation extents (Global Inundation Extent from Multi-Satellites, GIEMS) and matching against predictions from a global hydrodynamic model (Catchment-based Macro-scale Floodplain – CaMa-Flood) driven by runoff data generated by a land surface model (Joint UK Land and Environment Simulator, JULES). The ability of the model to reproduce patterns and dynamics shown by the observational product is assessed in a number of case studies across the tropics, which show that it performs well in large wetland regions, with a good match between corresponding seasonal cycles. At a finer spatial scale, we found that water inputs (e.g. groundwater inflow to wetland) became underestimated in comparison to water outputs (e.g. infiltration and evaporation from wetland) in some wetlands (e.g. Sudd, Tonlé Sap), and the opposite occurred in others (e.g. Okavango) in our model predictions. We also found evidence for an underestimation of low levels of inundation in our satellite-based inundation data (approx. 10 % of total inundation may not be recorded). Additionally, some wetlands display a clear spatial displacement between observed and simulated inundation as a result of overestimation or underestimation of overbank flooding upstream. This study provides timely information on inherent biases in inundation prediction and observation that can contribute to our current ability to make critical predictions of inundation events at both regional and global levels

Carbon budget for 1.5 and 2oC targets lowered by natural wetland and permafrost feedbacks

Methane emissions from natural wetlands and carbon release from permafrost thaw have a positive feedback on climate, yet are not represented in most state-of-the-art climate models. Furthermore, a fraction of the thawed permafrost carbon is released as methane, enhancing the combined feedback strength. We present simulations with an intermediate complexity climate model which follow prescribed global warming pathways to stabilisation at 1.5°C or 2.0°C above pre-industrial levels by the year 2100, and that incorporates a state-of-the-art global land surface model with updated descriptions of wetland and permafrost carbon release. We demonstrate that the climate feedbacks from those two processes are substantial. Specifically, permissible anthropogenic fossil fuel CO2 emission budgets are reduced by 17-23% (47-56 GtC) for stabilisation at 1.5°C, and 9-13% (52-57 GtC) for 2.0°C stabilisation. In our simulations these feedback processes respond faster at temperatures below 1.5°C, and the differences between the 1.5°C and 2°C targets are disproportionately small. This key finding is due to our interest in transient emission pathways to the year 2100 and does not consider the longer term implications of these feedback processes. We conclude that natural feedback processes from wetlands and permafrost must be considered in assessments of transient emission pathways to limit global warming

Research into land atmosphere interactions supports the Sustainable Development agenda

Greenhouse gas emissions and land use change - from deforestation, forest degradation and agricultural intensification - are contributing to climate change and biodiversity loss. Important landbased strategies such as planting trees or growing bioenergy crops (with carbon capture and storage) are needed to achieve the goals of the Paris Climate Agreement and to enhance biodiversity. The integrated Land Ecosystems Atmospheric Processes Study (iLEAPS) is an international knowledge-exchange and capacity-building network, specialising in ecosystems and their role in controlling the exchange of water, energy and chemical compounds between the land surface and the atmosphere. We outline priority directions for land-atmosphere interaction research and its contribution to the sustainable development agenda

Modeled microbial dynamics explain the apparent temperature sensitivity of wetland methane emissions

Methane emissions from natural wetlands tend to increase with temperature and therefore may lead to a positive feedback under future climate change. However, their temperature response includes confounding factors and appears to differ on different time scales. Observed methane emissions depend strongly on temperature on a seasonal basis, but if the annual mean emissions are compared between sites, there is only a small temperature effect. We hypothesize that microbial dynamics are a major driver of the seasonal cycle and that they can explain this apparent discrepancy. We introduce a relatively simple model of methanogenic growth and dormancy into a wetland methane scheme that is used in an Earth system model. We show that this addition is sufficient to reproduce the observed seasonal dynamics of methane emissions in fully saturated wetland sites, at the same time as reproducing the annual mean emissions. We find that a more complex scheme used in recent Earth system models does not add predictive power. The sites used span a range of climatic conditions, with the majority in high latitudes. The difference in apparent temperature sensitivity seasonally versus spatially cannot be recreated by the non‐microbial schemes tested. We therefore conclude that microbial dynamics are a strong candidate to be driving the seasonal cycle of wetland methane emissions. We quantify longer‐term temperature sensitivity using this scheme and show that it gives approximately a 12% increase in emissions per degree of warming globally. This is in addition to any hydrological changes, which could also impact future methane emissions

Enhancements to the UK Photochemical Trajectory Model for simulation of secondary inorganic aerosol

Particulate matter remains a challenging pollutant for air pollution control in the UK and across much of Europe. Particulate matter is a complex mixture of which secondary inorganic compounds (sulphates, nitrates) are a major component. This paper is concerned with taking a basic version of the UK Photochemical Trajectory Model and enhancing a number of features in the model in order to better represent boundary layer processes and to improve the description of secondary inorganic aerosol formation. The enhancements include an improved treatment of the boundary layer, deposition processes (both wet and dry), attenuation of photolysis rates by cloud cover, and inclusion of the aerosol thermodynamic model ISORROPIA II to account both for chemistry within the aerosol and between the particles and gas phase. Emissions inventories have been updated and are adjusted according to season, day of the week and hour of the day. Stack emissions from high level sources are now adjusted according to the height of the boundary layer and a scheme for generating marine aerosol has been included. The skill of the improved model has been evaluated through predictions of the concentrations of particulate chloride, nitrate and sulphate and the results show increased accuracy and lower mean bias. There is a much higher proportion of the values lying within a factor of 2 of the observed values compared to the basic model and Normalised Mean Bias has reduced by at least 89% for nitrate and sulphate. Similarly, the Index of Agreement between calculated and measured values has improved by ∼10%. Considering the contribution of each enhancement to the improvement in the performance metrics, the most significant enhancement was the replacement of the parameterisation of the boundary layer height, relative humidity and temperature by HYSPLIT values calculated for each trajectory. The second most significant enhancement was the parameterisation of the photolysis rates by values calculated by an off line database accounting for the dependence of photolysis rates on zenith angle, cloud cover, land surface type and column ozone. The inclusion of initial conditions which were dependent on the starting point of the trajectory and the modulation of stack emissions made the most significant improvement to sulphate. Furthermore, in order to assess the model's response to abatement scenarios, 30% abatements of either NH3, NOx or SO2 showed a reduction in the sum of chloride, nitrate and sulphate of between 3.1% and 8.5% (with a corresponding estimated reduction of 1.6–3.7% reduction in PM10). The largest reduction in this contribution is due to the abatement of NOx

Predictions of U.K. regulated power station contributions to regional air pollution and deposition: a model comparison exercise

Contributions of the emissions from a U.K. regulated fossil-fuel power station to regional air pollution and deposition are estimated using four air quality modeling systems for the year 2003. The modeling systems vary in complexity and emphasis in the way they treat atmospheric and chemical processes, and include the Community Multiscale Air Quality (CMAQ) modeling system in its versions 4.6 and 4.7, a nested modeling system that combines long- and short-range impacts (referred to as TRACK-ADMS [Trajectory Model with Atmospheric Chemical Kinetics–Atmospheric Dispersion Modelling System]), and the Fine Resolution Atmospheric Multi-pollutant Exchange (FRAME) model. An evaluation of the baseline calculations against U.K. monitoring network data is performed. The CMAQ modeling system version 4.6 data set is selected as the reference data set for the model footprint comparison. The annual mean air concentration and total deposition footprints are summarized for each modeling system. The footprints of the power station emissions can account for a significant fraction of the local impacts for some species (e.g., more than 50% for SO2 air concentration and non-sea-salt sulfur deposition close to the source) for 2003. The spatial correlation and the coefficient of variation of the root mean square error (CVRMSE) are calculated between each model footprint and that calculated by the CMAQ modeling system version 4.6. The correlation coefficient quantifies model agreement in terms of spatial patterns, and the CVRMSE measures the magnitude of the difference between model footprints. Possible reasons for the differences between model results are discussed. Finally, implications and recommendations for the regulatory assessment of the impact of major industrial sources using regional air quality modeling systems are discussed in the light of results from this case study

A.: Predictions of UK Regulated Power Station Contributions to Regional Air Pollution and Deposition: A Model Comparison Exercise

ABSTRACT 16 Contributions of the emissions from a UK regulated fossil-fuel power station to regional air 17 pollution and deposition are estimated using four air quality modeling systems for the year 2003. 18 The modeling systems vary in complexity and emphasis in the way they treat atmospheric and 19 chemical processes, and include the Community Multiscale Air Quality (CMAQ) modeling 20 system in its versions 4.6 and 4.7, a nested modeling system that combines long-and short-range 21 impacts (referred to as TRACK-ADMS), and the Fine Resolution Atmospheric Multi-pollutant 22 Exchange (FRAME) model. An evaluation of the baseline calculations against UK monitoring 23 network data is performed. The CMAQ modeling system version 4.6 dataset is selected as the 24 reference dataset for the model footprint comparison. The annual mean air concentration and 25 total deposition footprints are summarized for each modeling system. The footprints of the power 26 station emissions can account for a significant fraction of the local impacts for some species (e.g. 27 more than 50% for SO 2 air concentration and non-sea-salt sulfur deposition close to the source