Ole j benedictow biography of alberta

Roeland Van Malderen, Zhou Zang, Kai-Lan Chang, Robin Björklund, Owen R. Cooper, Jane Liu, Eliane Maillard Barras, Corinne Vigouroux, Irina Petropavlovskikh, Thierry Leblanc, Valérie Thouret, Pawel Wolff, Peter Effertz, Audrey Gaudel, David W. Tarasick, Herman G. J. Smit, Anne M. Thompson, Ryan M. Stauffer, Debra E. Kollonige, Deniz Poyraz, Gérard Ancellet, Marie-Renée De Backer, Matthias M. Frey, James W. Hannigan, José L. Hernandez, Bryan J. Johnson, Nicholas Jones, Rigel Kivi, Emmanuel Mahieu, Isamu Morino, Glen McConville, Katrin Müller, Isao Murata, Justus Notholt, Ankie Piters, Maxime Prignon, Richard Querel, Vincenzo Rizi, Dan Smale, Wolfgang Steinbrecht, Kimberly Strong, and Ralf Sussmann

EGUsphere, https://doi.org/10.5194/egusphere-2024-3745,https://doi.org/10.5194/egusphere-2024-3745, 2025

This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Published in final edited form as: Atmos Chem Phys. 2021 Oct 20;21(20):1–15663. doi: 10.5194/acp-21-15663-2021

Abstract

We present in this technical note the research protocol for phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4). This research initiative is divided into two activities, collectively having three goals: (i) to define the current state of the science with respect to representations of wet and especially dry deposition in regional models, (ii) to quantify the extent to which different dry deposition parameterizations influence retrospective air pollutant concentration and flux predictions, and (iii) to identify, through the use of a common set of detailed diagnostics, sensitivity simulations, model evaluation, and reduction of input uncertainty, the specific causes for the current range of these predictions. Activity 1 is dedicated to the diagnostic evaluation of wet and dry deposition processes in regional air quality models (described in this paper), and Activity 2 to the evaluation of dry deposition point models against ozone flux measurements at multiple towers with multiyear observations (to be described in future submissions as part of the special issue on AQMEII4). The scope of this paper is to present the scientific protocols for Activity 1, as well as to summarize the technical information associated with the different dry deposition approaches used by the participating research groups of AQMEII4. In addition to describing all common aspects and data used for this multi-model evaluation activity, most importantly, we present the strategy devised to allow a common process-level comparison of dry deposition obtained from models using sometimes very different dry deposition schemes. The strategy is based on adding detailed diagnostics to the algorithms used in the dry deposition modules of existing regional air quality models, in particular archiving diagnost

1.2 Objectives of this report

This inaugural edition of the State of Wildfires report aims to stimulate development of tools for understanding and predicting extreme fires and to deliver actionable information to policy and practice stakeholders and wider society. In this edition, we do the following:

1. regionally identify extreme individual wildfires or extreme wildfire seasons of the period March 2003–February 2024 and place them in the context of recent trends;

2. shortlist a selective number of extremes (extreme individual wildfires or extreme wildfire seasons) with notable impacts on society or the environment, which we term the “focal events” in this report;

3. diagnose the contributions of fuel dryness, fuel load, ignitions, and suppression to the occurrence of each focal event;

4. assess the capacity of operational predictive systems to predict each focal event;

5. attribute each focal event to anthropogenic factors including climate change and land use;

6. provide an outlook on the probability of extreme events in the coming fire season (that commenced on March 2024); and

7. project future changes in the probability of each focal event under future climate scenarios.

Key methodologies used to achieve the above objectives are summarised as follows. To address objectives 1 and 2, we build a comprehensive dataset of fire metrics including BA, fire counts, fire C emissions, and individual fire properties (size and rate of growth) for consistent world regions, and we quantitatively identify anomalies in these metrics during the past fire season (Giglio et al., 2018; van der Werf et al., 2017; Andela et al., 2019). To address objective 3 and 4, we leverage seasonal to sub-seasonal forecasts of fire weather from the European Centre for Medium-Range Weather Forecasts (ECMWF) and additionally employ two state-of-the-art fire models, “Controls on Fire” (ConFire) and “Probability of Fire” (PoF) (Kelley et al., 2019; McNorton et al