CLIMATE CHANGE EFFECTS ON URBAN LEVEL: CITIZEN HEALTH AND BUILDING ENERGY DEMAND

A primary sink of air pollutants is dry deposition. Dry deposition estimates differ across the models used to simulate atmospheric chemistry. Here, we introduce an effort to examine dry deposition schemes from atmospheric chemistry models. We provide our approach’s rationale, document the schemes, and describe datasets used to drive and evaluate the schemes. We also launch the analysis of results by evaluating against observations and identifying the processes leading to model–model differences.

This technical note presents the research protocols for phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4). This initiative has three goals: (i) to define the state of wet and dry deposition in regional models, (ii) to evaluate how dry deposition influences air concentration and flux predictions, and (iii) to identify the causes for prediction differences. The evaluation compares LULC-specific dry deposition and effective conductances and fluxes.

Atmospheric aerosols modify the radiative budget of the Earth, and it is therefore mandatory to have an accurate representation of their optical properties for understanding their climatic role. This work therefore evaluates the skill in the representation of optical properties by different remote-sensing sensors and regional online coupled chemistry–climate models over Europe.

The influence on modeled max., mean and min. temperature over Europe of including aerosol–radiation–cloud interactions has been assessed for two case studies in 2010. Data were taken from an ensemble of online regional chemistry–climate models from EuMetChem COST Action. The results indicate that including these interactions clearly improves the spatiotemporal variability in the temperature signal simulated by the models, with implications for reducing the uncertainty in climate projections.

Four ensemble methods are applied to two annual AQMEII datasets and their performance is compared for O₃, NO₂ and PM₁₀. The goal of the study is to quantify to what extent we can extract predictable signals from an ensemble with superior skill at each station over the single models and the ensemble mean. The promotion of the right amount of accuracy and diversity within the ensemble results in an average additional skill of up to 31 % compared to using the full ensemble in an unconditional way.