Description: Abstract. A realistic simulation of physical and dynamical processes in the Arctic atmosphere and its feedbacks with the surface conditions is still a challenge for state-of-the-art Arctic climate models. This is of critical importance because studies of, for example, transport of pollutants from middle latitudes into the Arctic rely on the skill of the model in correctly representing atmospheric circulation including the key mechanisms and pathways of pollutant transport. In this work the performance of the Weather Research and Forecast model (WRF) with two land surface model schemes (Noah and NoahMP) and two reanalysis data sets for creation of lateral boundary conditions (ERA-interim and ASR) is evaluated focusing on meteorological surface properties and atmospheric dynamics. This includes the position and displacement of the polar dome and other features characterizing atmospheric circulation associated to sea ice maxima/minima extent within the Eurasian Arctic. The model simulations analyzed are carried out at 15-km horizontal resolution over a period of five years (2008 to 2012). The WRF model simulations are evaluated against surface meteorological data from automated weather stations and vertical profiles from radiosondes. Results show that the model is able to reproduce the main features of the atmospheric dynamics and vertical structure of the Arctic atmosphere reasonably well. The influence of the choice of the reanalyses used as initial and lateral boundary condition and of the LSM on the model results is complex and no combination is found to be clearly superior in all variables analyzed. The model results show that a more sophisticated formulation of land surface processes does not necessarily lead to significant improvements in the model results. This suggests that other factors such as the decline of the Arctic sea ice, stratosphere-troposphere interactions, atmosphere-ocean interaction, and boundary layer processes are also highly important and can have a significant influence on the model results. The “best” configuration for simulating Arctic meteorology and processes most relevant for pollutant transport (ASR + NoahMP) is then used in a simulation with WRF including aerosols and chemistry (WRF-Chem) to simulate black carbon (BC) concentrations in and around the Arctic and to assess the role of the modeled atmospheric circulation in the simulated BC concentrations inside the Arctic domain. Results from simulations with chemistry are evaluated against aerosol optical depth from several Aeronet stations and BC concentrations and particle number concentrations from several stations from the EBAS database. The results with WRF-Chem show a strong dependency of the simulated BC concentration on the modeled meteorology and the transport of the pollutants around our domain. The results also show that biases in the modeled BC concentrations can also be related to the emission data. Significant improvements of the models and of our understanding of the impact of anthropogenic BC emissions on the Arctic strongly depends on the availability of suitable, long-term observational data of concentrations of BC and particulate matter, vertical profiles of temperature and humidity and wind.

Description: The discovery of atmospheric micro(nano)plastic transport and ocean–atmosphere exchange points to a highly complex marine plastic cycle, with negative implications for human and ecosystem health. Yet, observations are currently limited. In this Perspective, we quantify the processes and fluxes of the marine-atmospheric micro(nano)plastic cycle, with the aim of highlighting the remaining unknowns in atmospheric micro(nano)plastic transport. Between 0.013 and 25 million metric tons per year of micro(nano)plastics are potentially being transported within the marine atmosphere and deposited in the oceans. However, the high uncertainty in these marine-atmospheric fluxes is related to data limitations and a lack of study intercomparability. To address the uncertainties and remaining knowledge gaps in the marine-atmospheric micro(nano)plastic cycle, we propose a future global marine-atmospheric micro(nano)plastic observation strategy, incorporating novel sampling methods and the creation of a comparable, harmonized and global data set. Together with long-term observations and intensive investigations, this strategy will help to define the trends in marine-atmospheric pollution and any responses to future policy and management actions.

Description: The discovery of atmospheric micro(nano)plastic transport and ocean–atmosphere exchange points to a highly complex marine plastic cycle, with negative implications for human and ecosystem health. Yet, observations are currently limited. In this Perspective, we quantify the processes and fluxes of the marine-atmospheric micro(nano)plastic cycle, with the aim of highlighting the remaining unknowns in atmospheric micro(nano)plastic transport. Between 0.013 and 25 million metric tons per year of micro(nano)plastics are potentially being transported within the marine atmosphere and deposited in the oceans. However, the high uncertainty in these marine-atmospheric fluxes is related to data limitations and a lack of study intercomparability. To address the uncertainties and remaining knowledge gaps in the marine-atmospheric micro(nano)plastic cycle, we propose a future global marine-atmospheric micro(nano)plastic observation strategy, incorporating novel sampling methods and the creation of a comparable, harmonized and global data set. Together with long-term observations and intensive investigations, this strategy will help to define the trends in marine-atmospheric pollution and any responses to future policy and management actions.

Description: The contamination of shellfish harvesting areas by fecal bacteria in the Annapolis Basin of Nova Scotia, Canada, is a recurring problem which has consequences for industry, government, and local communities. This study contributes to the development of an integrated water quality forecasting system to improve the efficiency and effectiveness of industry management. The proposed integrated forecasting framework is composed of a database containing contamination sources, hydrodynamics of the Annapolis Basin, Escherichia coli (E. coli) loadings and watershed hydrology scenarios, coupled with environmental conditions of the region (e.g., temperature, precipitation, evaporation, and ultraviolet light). For integration into this framework, this study presents a viable methodology for assessing the contribution of fecal bacteria originating from a watershed. The proposed methodology investigated the application of high resolution remote sensing, coupled with the commercially available product, MIKE 11, to monitor watershed land use and its impact on water quality. Remote sensing proved to be an extremely useful tool in the identification of sources of fecal bacteria contamination, as well as the detection of land use change over time. Validation of the MIKE 11 model produced very good agreement (R2 = 0.88, E = 0.85) between predicted and observed river flows, while model calibration of E. coli concentrations showed fair agreement (R2 = 0.51 and E = 0.38) between predicted and observed values. A proper evaluation of the MIKE 11 model was constrained due to limited water sampling. However, the model was very effective in predicting times of high contamination for use in the integrated forecasting framework, especially during substantial precipitation events.