The regional impact of urban emissions on air quality in Europe: the role of the urban canopy effects

Abstract. Urban areas are hot spots of intense emissions, and they influence air quality not only locally but on a regional or even global scale. The impact of urban emissions over different scales depends on the dilution and chemical transformation of the urban plumes which are governed by the local- and regional-scale meteorological conditions. These are influenced by the presence of urbanized land surface via the so-called urban canopy meteorological forcing (UCMF). In this study, we investigate for selected central European cities (Berlin, Budapest, Munich, Prague, Vienna and Warsaw) how the urban emission impact (UEI) is modulated by the UCMF for present-day climate conditions (2015–2016) using two regional climate models, the regional climate models RegCM and Weather Research and Forecasting model coupled with Chemistry (WRF-Chem; its meteorological part), and two chemistry transport models, Comprehensive Air Quality Model with Extensions (CAMx) coupled to either RegCM and WRF and the “chemical” component of WRF-Chem. The UCMF was calculated by replacing the urbanized surface by a rural one, while the UEI was estimated by removing all anthropogenic emissions from the selected cities. We analyzed the urban-emission-induced changes in near-surface concentrations of NO2, O3 and PM2.5. We found increases in NO2 and PM2.5 concentrations over cities by 4–6 ppbv and 4–6 µg m−3, respectively, meaning that about 40 %–60 % and 20 %–40 % of urban concentrations of NO2 and PM2.5 are caused by local emissions, and the rest is the result of emissions from the surrounding rural areas. We showed that if UCMF is included, the UEI of these pollutants is about 40 %–60 % smaller, or in other words, the urban emission impact is overestimated if urban canopy effects are not taken into account. In case of ozone, models due to UEI usually predict decreases of around −2 to −4 ppbv (about 10 %–20 %), which is again smaller if UCMF is considered (by about 60 %). We further showed that the impact on extreme (95th percentile) air pollution is much stronger, and the modulation of UEI is also larger for such situations. Finally, we evaluated the contribution of the urbanization-induced modifications of vertical eddy diffusion to the modulation of UEI and found that it alone is able to explain the modeled decrease in the urban emission impact if the effects of UCMF are considered. In summary, our results showed that the meteorological changes resulting from urbanization have to be included in regional model studies if they intend to quantify the regional footprint of urban emissions. Ignoring these meteorological changes can lead to the strong overestimation of UEI.

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The regional impact of urban emissions on air quality in Europe: the role of the urban canopy effects

10.5194/acp-2021-355 ◽

2021 ◽

Author(s):

Peter Huszar ◽

Jan Karlický ◽

Jana Marková ◽

Tereza Nováková ◽

Marina Liaskoni ◽

...

Keyword(s):

Air Quality ◽

Rural Areas ◽

Land Surface ◽

Urban Areas ◽

Regional Climate ◽

Regional Scale ◽

Urban Canopy ◽

Near Surface ◽

Urban Emissions ◽

The Impact

Abstract. Urban areas are hot-spots of intense emissions and they influence air-quality not only locally but on regional or even global scales. The impact of urban emissions over different scales depends on the dilution and chemical transformation of the urban plumes which are governed by the local and regional scale meteorological conditions. These are influenced by the presence of urbanized land-surface via the so called urban canopy meteorological forcing (UCMF). In this study, we investigate for selected central European cities (Berlin, Budapest, Munich, Prague, Vienna and Warsaw), how the urban emission impact (UEI) is modulated by the UCMF for present day climate conditions (2015–2016) using three regional climate-chemistry models: the regional climate models RegCM and WRF-Chem (its meteorological part), the chemistry transport model CAMx coupled to either RegCM and WRF and the “chemical” component of WRF-Chem. The UCMF was calculated by replacing the urbanized surface by rural one while the UEI was estimated by removing all anthropogenic emissions from the selected cities. We analyzed the urban emissions induced changes of near surface concentrations of NO2, O3 and PM2.5. We found increases of NO2 and PM2.5 concentrations over cities by 4–6 ppbv, and 4–6 μgm−3, respectively meaning that about 40–60 % and 20–40 % of urban concentrations of NO2 and PM2.5 are caused by local emissions and the rest is the result of emissions from surrounding rural areas. We showed that if UCMF is included, the UEI of these pollutants is about 40–60 % smaller, or in other words, the urban emission impact is overestimated if urban canopy effects are not taken into account. In case of ozone, models due to UEI usually predict decreases around −2 to −4 ppbv (about 10–20 %), which is again smaller if UCMF is considered (by about 60 %). We further showed that the impact on extreme (95th percentile) air-pollution is much stronger, as well as the modulation of UEI is larger for such situations. Finally, we evaluated the contribution of the urbanization induced modifications of vertical eddy-diffusion to the modulation of UEI, and found that it alone is able to explain the modelled decrease of the urban emission impact if the effects of UCMF are considered. In summary, our results showed that the meteorological changes resulting from urbanization have to be included in regional model studies if they intend to quantify the regional fingerprint of urban emissions. Ignoring these meteorological changes can lead to strong overestimation of UEI.

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Hydrological simulations in the Rhine basin

Water Science & Technology ◽

10.2166/wst.2005.0094 ◽

2005 ◽

Vol 51 (5) ◽

pp. 1-4

Author(s):

B. van den Hurk ◽

J. Beersma ◽

G. Lenderink

Keyword(s):

Land Surface ◽

Climate Models ◽

Global Climate ◽

Regional Climate ◽

Regional Climate Models ◽

Global Climate Models ◽

Rhine River ◽

Rhine Basin ◽

The Impact ◽

Selection Of

Simulations with regional climate models (RCMs), carried out for the Rhine basin, have been analyzed in the context of implications of the possible future discharge of the Rhine river. In a first analysis, the runoff generated by the RCMs is compared to observations, in order to detect the way the RCMs treat anomalies in precipitation in their land surface component. A second analysis is devoted to the frequency distribution of area averaged precipitation, and the impact of selection of various driving global climate models.

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Impact of urban emission on local and regional air-quality: investigating the role of the urban canopy meteorological forcing

10.5194/egusphere-egu21-1992 ◽

2021 ◽

Author(s):

Peter Huszar ◽

Jan Karlicky ◽

Jana Markova ◽

Tereza Novakova ◽

Marina Liaskoni ◽

...

Keyword(s):

Rural Areas ◽

Urban Areas ◽

Climate Models ◽

Regional Scale ◽

Urban Canopy ◽

Meteorological Forcing ◽

Island Effect ◽

Regional Air Quality ◽

Urban Emissions ◽

The Impact

Urban canopies impact the meteorological conditions in the planetary boundary layer (PBL) and above in many ways: apart from urban heat island effect, the urban breeze circulation can form. Further, the enhanced drag causes intensification of the turbulent diffusion leading to elevated PBL height and this drag, at the same time causes lower windspeeds. These changes together act as a 'meteorological forcing' for the chemical processes involing transport, diffusion and chemical transformation of urban pollutants in the urban canopy and over larger scales, therefor we use the term urban canopy meteorological forcing (UCMF). Using regional scale coupled chemistry-climate models over central Europe (involving models RegCM, CAMx and WRF-Chem),&#160; we investigate here how the UCMF influences the urban emissions and their dispersion into regional scales. The analysis covers key pollutants as O3, NO2 and PM2.5 and the 2015-2016 period. While urban emissions contribute by about 60-80% to the total NO2 and PM2.5 concentrations in cities, for ozone, they cause decrease in the urban cores and slight increase over sourrounding rural areas. More importantly, we found that if UCMF is considered, the impacts on all three pollutants are reduced, by about 20-30%. This is caused by the fact that vertical turbulence is greatly enhanced in urban areas leading to reduced fingerprint of the urban emissions (the case of NO2 and PM2.5) while in case of O3, reduction of the NO2 impact means smaller first-order titraltion therefor higher ozone concentrations - i.e. the ozone fingerprint of urban emissions is smaller. Our analysis showed that for evaluating the impact of urban emissions over regional scales, the meterological effects caused by the urban canopy have to be considered in modeling studies.

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Impacts of Green Vegetation Fraction Derivation Methods on Regional Climate Simulations

Atmosphere ◽

10.3390/atmos10050281 ◽

2019 ◽

Vol 10 (5) ◽

pp. 281 ◽

Cited By ~ 1

Author(s):

Jose Manuel Jiménez-Gutiérrez ◽

Francisco Valero ◽

Sonia Jerez ◽

Juan Pedro Montávez

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Spatial Average ◽

Area Index ◽

Near Surface ◽

Vegetation Fraction ◽

Selection Of

The representation of vegetation in land surface models (LSM) is crucial for modeling atmospheric processes in regional climate models (RCMs). Vegetation is characterized by the green fractional vegetation cover (FVC) and/or the leaf area index (LAI) that are obtained from nearest difference vegetation index (NDVI) data. Most regional climate models use a constant FVC for each month and grid cell. In this work, three FVC datasets have been constructed using three methods: ZENG, WETZEL and GUTMAN. These datasets have been implemented in a RCM to explore, through sensitivity experiments over the Iberian Peninsula (IP), the effects of the differences among the FVC data-sets on the near surface temperature (T2m). Firstly, we noted that the selection of the NDVI database is of crucial importance, because there are important bias in mean and variability among them. The comparison between the three methods extracted from the same NDVI database, the global inventory modeling and mapping studies (GIMMS), reveals important differences reaching up to 12% in spatial average and and 35% locally. Such differences depend on the FVC magnitude and type of biome. The methods that use the frequency distribution of NDVI (ZENG and GUTMAN) are more similar, and the differences mainly depends on the land type. The comparison of the RCM experiments exhibits a not negligible effect of the FVC uncertainty on the monthly T2m values. Differences of 30% in FVC can produce bias of 1 ∘ C in monthly T2m, although they depend on the time of the year. Therefore, the selection of a certain FVC dataset will introduce bias in T2m and will affect the annual cycle. On the other hand, fixing a FVC database, the use of synchronized FVC instead of climatological values produces differences up to 1 ∘ C, that will modify the T2m interannual variability.

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Enhanced Detectability of Added Value in Limited-Area Model Results Separated into Different Spatial Scales

Monthly Weather Review ◽

10.1175/mwr3183.1 ◽

2006 ◽

Vol 134 (8) ◽

pp. 2180-2190 ◽

Cited By ~ 74

Author(s):

Frauke Feser

Keyword(s):

Large Scale ◽

Climate Models ◽

Spatial Scales ◽

Regional Climate ◽

Regional Scale ◽

Regional Climate Models ◽

Added Value ◽

Limited Area ◽

Near Surface ◽

Pattern Correlation

Abstract Regional climate models (RCMs) are a widely used tool to describe regional-scale climate variability and change. However, the added value provided by such models is not well explored so far, and claims have been made that RCMs have little utility. Here, it is demonstrated that RCMs are indeed returning significant added value. Employing appropriate spatial filters, the scale-dependent skill of a state-of-the-art RCM (with and without nudging of large scales) is examined by comparing its skill with that of the global reanalyses driving the RCM. This skill is measured by pattern correlation coefficients of the global reanalyses or the RCM simulation and, as a reference, of an operational regional weather analysis. For the spatially smooth variable air pressure the RCM improves this aspect of the simulation for the medium scales if the RCM is driven with large-scale constraints, but not for the large scales. For the regionally more structured quantity near-surface temperature the added value is more obvious. The simulation of medium-scale 2-m temperature anomaly fields amounts to an increase of the mean pattern correlation coefficient up to 30%.

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The impact of urban land-surface on extreme air pollution over central Europe

10.5194/acp-2020-399 ◽

2020 ◽

Author(s):

Peter Huszar ◽

Jan Karlický ◽

Jana Ďoubalová ◽

Tereza Nováková ◽

Kateřina Šindelářová ◽

...

Keyword(s):

Air Pollution ◽

Land Surface ◽

Climate Models ◽

Extreme Values ◽

Transport Model ◽

Regional Climate ◽

Regional Scale ◽

Urban Land ◽

Climate Conditions ◽

The Impact

Abstract. This paper deals with the urban land-surface impact (i.e. the urban canopy meteorological forcing; UCMF) on extreme air pollution for selected central European cities for present-day climate conditions (2015–2016) using three regional climate-chemistry models: the regional climate models RegCM and WRF-Chem (its meteorological part), the chemistry transport model CAMx coupled to either RegCM and WRF and the chemical component of WRF-Chem. Most of the studies focused on the change of average conditions or only on a selected winter and summer air pollution episode. Here we extend these studies by focusing on long term extreme air pollution levels by looking at not only the change of average values but also their high (and low) percentile values and we combine the analysis with investigating selected high pollution episodes too. As extreme air pollution is often linked to extreme values of meteorological variables (e.g. low planetary boundary layer height, low winds, high temperatures), the extreme meteorological modifications will be analyzed too. The validation of model results shows reasonable model performance for regional scale temperature and precipitation. Ozone is overestimated by about 10–20 μg m−3, on the other hand, extreme summertime ozone values are underestimated by all models. Modeled nitrogen dioxide (NO2) concentrations are well correlated with observations, but results are marked with a systematic underestimation up to 20 μg m−3. PM2.5 (particles with diameter

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Regional Climate Model Inter-Comparison for Antarctica within a Data Science Framework

10.5194/egusphere-egu21-4892 ◽

2021 ◽

Author(s):

Jeremy Carter ◽

Amber Leeson ◽

Andrew Orr ◽

Christoph Kittel ◽

Melchior van Wessem

Keyword(s):

Regional Climate Model ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Future Climate Change ◽

Near Surface ◽

Virtual Lab ◽

Model Variability ◽

The Antarctic

Understanding the surface climatology of the Antarctic ice sheet is essential if we are to adequately predict its response to future climate change. This includes both primary impacts such as increased ice melting and secondary impacts such as ice shelf collapse events. Given its size, and inhospitable environment, weather stations on Antarctica are sparse. Thus, we rely on regional climate models to 1) develop our understanding of how the climate of Antarctica varies in both time and space and 2) provide data to use as context for remote sensing studies and forcing for dynamical process models. Given that there are a number of different regional climate models available that explicitly simulate Antarctic climate, understanding inter- and intra model variability is important.Here, inter- and intra-model variability in Antarctic-wide regional climate model output is assessed for: snowfall; rainfall; snowmelt and near-surface air temperature within a cloud-based virtual lab framework. State-of-the-art regional climate model runs from the Antarctic-CORDEX project using the RACMO, MAR and MetUM models are used, together with the ERA5 and ERA-Interim reanalyses products. Multiple simulations using the same model and domain boundary but run at either different spatial resolutions or with different driving data are used. Traditional analysis techniques are exploited and the question of potential added value from more modern and involved methods such as the use of Gaussian Processes is investigated. The advantages of using a virtual lab in a cloud based environment for increasing transparency and reproducibility, are demonstrated, with a view to ultimately make the code and methods used widely available for other research groups.

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Sensitivity of biogenic volatile organic compounds to land surface parameterizations and vegetation distributions in California

Geoscientific Model Development ◽

10.5194/gmd-9-1959-2016 ◽

2016 ◽

Vol 9 (5) ◽

pp. 1959-1976 ◽

Cited By ~ 14

Author(s):

Chun Zhao ◽

Maoyi Huang ◽

Jerome D. Fast ◽

Larry K. Berg ◽

Yun Qian ◽

...

Keyword(s):

Air Quality ◽

Volatile Organic Compounds ◽

Organic Compounds ◽

Land Surface ◽

Aerosol Formation ◽

Biogenic Volatile Organic Compounds ◽

Vegetation Map ◽

Near Surface ◽

Volatile Organic ◽

The Impact

Abstract. Current climate models still have large uncertainties in estimating biogenic trace gases, which can significantly affect atmospheric chemistry and secondary aerosol formation that ultimately influences air quality and aerosol radiative forcing. These uncertainties result from many factors, including uncertainties in land surface processes and specification of vegetation types, both of which can affect the simulated near-surface fluxes of biogenic volatile organic compounds (BVOCs). In this study, the latest version of Model of Emissions of Gases and Aerosols from Nature (MEGAN v2.1) is coupled within the land surface scheme CLM4 (Community Land Model version 4.0) in the Weather Research and Forecasting model with chemistry (WRF-Chem). In this implementation, MEGAN v2.1 shares a consistent vegetation map with CLM4 for estimating BVOC emissions. This is unlike MEGAN v2.0 in the public version of WRF-Chem that uses a stand-alone vegetation map that differs from what is used by land surface schemes. This improved modeling framework is used to investigate the impact of two land surface schemes, CLM4 and Noah, on BVOCs and examine the sensitivity of BVOCs to vegetation distributions in California. The measurements collected during the Carbonaceous Aerosol and Radiative Effects Study (CARES) and the California Nexus of Air Quality and Climate Experiment (CalNex) conducted in June of 2010 provided an opportunity to evaluate the simulated BVOCs. Sensitivity experiments show that land surface schemes do influence the simulated BVOCs, but the impact is much smaller than that of vegetation distributions. This study indicates that more effort is needed to obtain the most appropriate and accurate land cover data sets for climate and air quality models in terms of simulating BVOCs, oxidant chemistry and, consequently, secondary organic aerosol formation.

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Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

10.21203/rs.3.rs-725819/v1 ◽

2021 ◽

Author(s):

Antoine Doury ◽

Samuel Somot ◽

Sébastien Gadat ◽

Aurélien Ribes ◽

Lola Corre

Keyword(s):

Climate Change ◽

Network Architecture ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Future Climate ◽

Near Surface ◽

The Way

Abstract Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

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Evaluating the effect of climate change on snow load on structures

IABSE Symposium, Guimarães 2019: Towards a Resilient Built Environment Risk and Asset Management ◽

10.2749/guimaraes.2019.0666 ◽

2019 ◽

Cited By ~ 1

Author(s):

Pietro Croce ◽

Paolo Formichi ◽

Filippo Landi ◽

Francesca Marsili

Keyword(s):

Climate Change ◽

Climate Models ◽

Regional Climate ◽

Climate Extremes ◽

Regional Climate Models ◽

Extreme Weather Events ◽

Uncertainty Range ◽

Greenhouse Gasses ◽

Weather Events ◽

The Impact

As consequence of global warming extreme weather events might become more frequent and severe across the globe. The evaluation of the impact of climate change on extremes is then a crucial issue for the resilience of infrastructures and buildings and is a key challenge for adaptation planning. In this paper, a suitable procedure for the estimation of future trends of climatic actions is presented starting from the output of regional climate models and taking into account the uncertainty in the model itself. In particular, the influence of climate change on ground snow loads is discussed in detail and the typical uncertainty range is determined applying an innovative algorithm for weather generation. Considering different greenhouse gasses emission scenarios, some results are presented for the Italian Mediterranean region proving the ability of the method to define factors of change for climate extremes also allowing a sound estimate of the uncertainty range associated with different models.

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