ALBERT

Description: Understanding of the macroeconomic effects of climate change is developing rapidly, but the implications for past and future inflation remain less well understood. Here we exploit a global dataset of monthly consumer price indices to identify the causal impacts of changes in climate on inflation, and to assess their implications under future warming. Flexibly accounting for heterogenous impacts across seasons and baseline climatic and socio-economic conditions, we find that increased average temperatures cause non-linear upwards inflationary pressures which persist over 12 months in both higher- and lower-income countries. Projections from state-of- the-art climate models show that in the absence of historically un-precedented adaptation, future warming will cause global increases in annual food and headline inflation of 0.92-3.23 and 0.32-1.18 percentage-points per year respectively, under 2035 projected climate (uncertainty range across emission scenarios, climate models and empirical specifications), as well as altering the seasonal dynamics of inflation. Moreover, we estimate that the 2022 summer heat extreme increased food inflation in Europe by 0.67 (0.43-0.93) percentage-points and that future warming projected for 2035 would amplify the impacts of such extremes by 50%. These results suggest that climate change poses risks to price stability by having an upward impact on inflation, altering its seasonality and amplifying the impacts caused by extremes.

Description: Climate impacts on economic productivity indicate that climate change may threaten price stability. Here we apply fixed-effects regressions to over 27,000 observations of monthly consumer price indices worldwide to quantify the impacts of climate conditions on inflation. Higher temperatures increase food and headline inflation persistently over 12 months in both higher- and lower-income countries. Effects vary across seasons and regions depending on climatic norms, with further impacts from daily temperature variability and extreme precipitation. Evaluating these results under temperature increases projected for 2035 implies upwards pressures on food and headline inflation of 0.92-3.23 and 0.32-1.18 percentage-points per-year respectively on average globally (uncertainty range across emission scenarios, climate models and empirical specifications). Pressures are largest at low latitudes and show strong seasonality at high latitudes, peaking in summer. Finally, the 2022 extreme summer heat increased food inflation in Europe by 0.43-0.93 percentage-points which warming projected for 2035 would amplify by 30-50%.

Description: Seit 2012 beschäftigt sich das Forschungsprojekt ClimPol (Climate Change and Air Pollution)am Institute for Advanced Sustainability Studies (IASS) in Potsdam mit dem Zusammenhängenzwischen Klimawandel und Luftverschmutzung. Mit unseren wissenschaftlichen Untersuchungenwollen wir diese Zusammenhänge besser verstehen. Wir engagieren uns aber auchan der Schnittstelle von Wissenschaft und Politik, indem wir koordinierte und effektiveMaßnahmen für Klimaschutz und bessere Luftqualität prüfen.

Description: Programs to plant millions of trees in cities around the world aim at the reduction of summer temperatures,increase of carbon storage, storm water control, and recreational space, as well as at poverty alleviation. Theseurban greening programs, however, do not take into account how closely human and natural systems are coupledin urban areas. Compared with the surroundings of cities, elevated temperatures together with high anthropogenicemissions of air and water pollutants are quite typical in urban systems. Urban and sub-urban vegetation respondto changes in meteorology and air quality and can react to pollutants. Neglecting this coupling may lead tounforeseen negative effects on air quality resulting from urban greening programs. The potential of emissions ofvolatile organic compounds (VOC) from vegetation combined with anthropogenic emissions of air pollutants toproduce ozone has long been recognized. This ozone formation potential increases under rising temperatures.Here we investigate how emissions of VOC from urban vegetation affect corresponding ground-level ozoneand PM10 concentrations in summer and especially during heat wave periods. We use the Weather Researchand Forecasting Model with coupled atmospheric chemistry (WRF-CHEM) to quantify these feedbacks inthe Berlin-Brandenburg region, Germany during the two summers of 2006 (heat wave) and 2014 (referenceperiod). VOC emissions from vegetation are calculated by MEGAN 2.0 coupled online with WRF-CHEM. Ourpreliminary results indicate that the contribution of VOCs from vegetation to ozone formation may increase bymore than twofold during heat wave periods. We highlight the importance of the vegetation for urban areas in thecontext of a changing climate and discuss potential tradeoffs of urban greening programs.

Description: Lately, black carbon (BC) has received significant attention due to its climate-warming properties and adverse health effects. Nevertheless, long-term observations in urban areas are scarce, most likely because BC monitoring is not required by environmental legislation. This, however, handicaps the evaluation of air quality models which can be used to assess the effectiveness of policy measures which aim to reduce BC concentrations. Here, we present a new dataset of atmospheric BC measurements from Germany constructed from over six million measurements at over 170 stations. Data covering the period between 1994 and 2014 were collected from twelve German Federal States and the Federal Environment Agency, quality checked and harmonized into a database with comprehensive metadata. The final data in original time resolution are available for download (https://doi.org/10.1594/PANGAEA.881173). Though assembled in a consistent way, the dataset is characterized by differences in (a) measurement methodologies for determining evolved carbon and optical absorption, (b) covered time periods, and (c) temporal resolutions that ranged from half hourly to measurements every 6th day. Usage and interpretation of this dataset thus requires a careful consideration of these differences. Our analysis focuses on 2009, the year with the largest data coverage with one single methodology, as well as on the relative changes in long-term trends over ten years. For 2009, we find that BC concentrations at traffic sites were at least twice as high as at urban background, industrial and rural sites. Weekly cycles are most prominent at traffic stations, however, the presence of differences in concentrations during the week and on weekends at other station types suggests that traffic plays an important role throughout the full network. Generally higher concentrations and weaker weekly cycles during the winter months point towards the influence of other sources such as domestic heating. Regarding the long-term trends, advanced statistical techniques allow us to account for instrumentation changes and to separate seasonal and long-term changes in our dataset. Analysis shows a downward trend in BC at nearly all locations and in all conditions, with a high level of confidence for the period of 2005–2014. In depth analysis indicates that background BC is decreasing slowly, while the occurrences of high concentrations are decreasing more rapidly. In summary, legislation – both in Europe and locally – to reduce particulate emissions and indirectly BC appear to be working, based on this analysis. Adverse human health and climate impacts are likely to be diminished because of the improvements in air quality.

Description: Air pollution is the number one environmental cause of premature deaths in Europe. Despite extensive regulations, air pollution remains a challenge, especially in urban areas. For studying summertime air quality in the Berlin–Brandenburg region of Germany, the Weather Research and Forecasting Model with Chemistry (WRF-Chem) is set up and evaluated against meteorological and air quality observations from monitoring stations as well as from a field campaign conducted in 2014. The objective is to assess which resolution and level of detail in the input data is needed for simulating urban background air pollutant concentrations and their spatial distribution in the Berlin–Brandenburg area. The model setup includes three nested domains with horizontal resolutions of 15, 3 and 1 km and anthropogenic emissions from the TNO-MACC III inventory. We use RADM2 chemistry and the MADE/SORGAM aerosol scheme. Three sensitivity simulations are conducted updating input parameters to the single-layer urban canopy model based on structural data for Berlin, specifying land use classes on a sub-grid scale (mosaic option) and downscaling the original emissions to a resolution of ca. 1 km × 1 km for Berlin based on proxy data including traffic density and population density. The results show that the model simulates meteorology well, though urban 2 m temperature and urban wind speeds are biased high and nighttime mixing layer height is biased low in the base run with the settings described above. We show that the simulation of urban meteorology can be improved when specifying the input parameters to the urban model, and to a lesser extent when using the mosaic option. On average, ozone is simulated reasonably well, but maximum daily 8 h mean concentrations are underestimated, which is consistent with the results from previous modelling studies using the RADM2 chemical mechanism. Particulate matter is underestimated, which is partly due to an underestimation of secondary organic aerosols. NOx (NO + NO2) concentrations are simulated reasonably well on average, but nighttime concentrations are overestimated due to the model's underestimation of the mixing layer height, and urban daytime concentrations are underestimated. The daytime underestimation is improved when using downscaled, and thus locally higher emissions, suggesting that part of this bias is due to deficiencies in the emission input data and their resolution. The results further demonstrate that a horizontal resolution of 3 km improves the results and spatial representativeness of the model compared to a horizontal resolution of 15 km. With the input data (land use classes, emissions) at the level of detail of the base run of this study, we find that a horizontal resolution of 1 km does not improve the results compared to a resolution of 3 km. However, our results suggest that a 1 km horizontal model resolution could enable a detailed simulation of local pollution patterns in the Berlin–Brandenburg region if the urban land use classes, together with the respective input parameters to the urban canopy model, are specified with a higher level of detail and if urban emissions of higher spatial resolution are used.

Description: With NO2 limit values being frequently exceeded in European cities, complying with the European air quality regulations still poses a problem for many cities. Traffic is typically a major source of NOx emissions in urban areas. High-resolution chemistry transport modelling can help to assess the impact of high urban NOx emissions on air quality inside and outside of urban areas. However, many modelling studies report an underestimation of modelled NOx and NO2 compared with observations. Part of this model bias has been attributed to an underestimation of NOx emissions, particularly in urban areas. This is consistent with recent measurement studies quantifying underestimations of urban NOx emissions by current emission inventories, identifying the largest discrepancies when the contribution of traffic NOx emissions is high. This study applies a high-resolution chemistry transport model in combination with ambient measurements in order to assess the potential underestimation of traffic NOx emissions in a frequently used emission inventory. The emission inventory is based on officially reported values and the Berlin–Brandenburg area in Germany is used as a case study. The WRF-Chem model is used at a 3 km  ×  3 km horizontal resolution, simulating the whole year of 2014. The emission data are downscaled from an original resolution of ca. 7 km  ×  7 km to a resolution of 1 km  ×  1 km. An in-depth model evaluation including spectral decomposition of observed and modelled time series and error apportionment suggests that an underestimation in traffic emissions is likely one of the main causes of the bias in modelled NO2 concentrations in the urban background, where NO2 concentrations are underestimated by ca. 8 µg m−3 (−30 %) on average over the whole year. Furthermore, a diurnal cycle of the bias in modelled NO2 suggests that a more realistic treatment of the diurnal cycle of traffic emissions might be needed. Model problems in simulating the correct mixing in the urban planetary boundary layer probably play an important role in contributing to the model bias, particularly in summer. Also taking into account this and other possible sources of model bias, a correction factor for traffic NOx emissions of ca. 3 is estimated for weekday daytime traffic emissions in the core urban area, which corresponds to an overall underestimation of traffic NOx emissions in the core urban area of ca. 50 %. Sensitivity simulations for the months of January and July using the calculated correction factor show that the weekday model bias can be improved from −8.8 µg m−3 (−26 %) to −5.4 µg m−3 (−16 %) in January on average in the urban background, and −10.3 µg m−3 (−46 %) to −7.6 µg m−3 (−34 %) in July. In addition, the negative bias of weekday NO2 concentrations downwind of the city in the rural and suburban background can be reduced from −3.4 µg m−3 (−12 %) to −1.2 µg m−3 (−4 %) in January and from −3.0 µg m−3 (−22 %) to −1.9 µg m−3 (−14 %) in July. The results and their consistency with findings from other studies suggest that more research is needed in order to more accurately understand the spatial and temporal variability in real-world NOx emissions from traffic, and apply this understanding to the inventories used in high-resolution chemical transport models.

Description: Exceedances of the concentration limit value for ambient nitrogen dioxide (NO2) at roadside sites are an issue in many cities throughout Europe. This is linked to the emissions of light duty diesel vehicles which have on-road emissions that are far greater than the regulatory standards. These exceedances have substantial implications for human health and economic loss. This study explores the possible gains in ambient air quality if light duty diesel vehicles were able to meet the regulatory standards (including both emissions standards from Europe and the United States). We use two independent methods: a measurement-based and a model-based method. The city of Berlin is used as a case study. The measurement-based method used data from 16 monitoring stations throughout the city of Berlin to estimate annual average reductions in roadside NO2 of 9.0 to 23 µg m-3 and in urban background NO2 concentrations of 1.2 to 2.7 µg m-3. These ranges account for differences in fleet composition assumptions, and the stringency of the regulatory standard. The model simulations showed reductions in urban background NO2 of 2.0 µg m-3, and at the scale of the greater Berlin area of 1.6 to 2.0 µg m-3 depending on the setup of the simulation and resolution of the model. Similar results were found for other European cities. The similarities in results using the measurement- and model-based methods support our ability to draw robust conclusions that are not dependent on the assumptions behind either methodology. The results show the significant potential for NO2 reductions if regulatory standards for light duty diesel vehicles were to be met under real-world operating conditions. Such reductions could help improve air quality by reducing NO2 exceedances in urban areas, but also have broader implications for improvements in human health and other benefits.