Srdjan Dobricic

Methane (CH ₄ ) and carbon monoxide (CO) are gases with important climate impacts as direct and indirect greenhouse gases, respectively. Methane has a warming potential 28 times that of carbon dioxide on a 100‐year timescale, and carbon monoxide is a precursor to ozone in the troposphere. Modeling trace gas concentrations in the Arctic atmosphere can be challenging due to Arctic conditions and sensitivity to long‐range transport, and comparing model outputs to remote sensing measurements is essential for ensuring that models are performing well. Ground‐based Arctic measurements are spatially sparse, so it is important to make use of all such available data sets. In this study, we assess eight atmospheric models, comparing their simulations of atmospheric CO and CH ₄ column‐averaged dry‐air mole fractions for 2014 and 2015 with ground‐based retrievals of these species at three Arctic stations in the Total Carbon Column Observing Network (TCCON). The multi‐model mean had mean biases (± one standard deviation of the mean) of −5.4% ± 8% at Eureka, Canada, −6.5% ± 8% at Ny‐Ålesund, Norway, and −11% ± 7% at Sodankylä, Finland for CO, and mean biases of −0.25% ± 0.5% at Eureka, −0.90% ± 0.5% at Ny‐Ålesund, and −1.0% ± 0.5% at Sodankylä for CH ₄ . Individual model mean biases range from −33% to +35% for CO and −2.5% to +1.9% for CH ₄ . These results indicate that models could benefit from improvements targeting simulations of Arctic CO.

In this study, we present a statistical forecasting framework and assess its efficacy using a range of established machine learning algorithms for predicting Particulate Matter (PM) concentrations in the Arctic, specifically in Pallas (FI), Reykjavik (IS), and Tromso (NO). Our framework leverages historical ground measurements and 24 h predictions from nine models by the Copernicus Atmosphere Monitoring Service (CAMS) to provide PM₁₀ predictions for the following 24 h. Furthermore, we compare the performance of various memory cells based on artificial neural networks (ANN), including recurrent neural networks (RNNs), gated recurrent units (GRUs), long short-term memory networks (LSTMs), echo state networks (ESNs), and windowed multilayer perceptrons (MLPs). Regardless of the type of memory cell chosen, our results consistently show that the proposed framework outperforms the CAMS models in terms of mean squared error (MSE), with average improvements ranging from 25% to 40%. Furthermore, we examine the impact of outliers on the overall performance of the model.

Cyanobacteria blooms are a worldwide concern for water bodies and may be promoted by eutrophication and climate change. The prediction of cyanobacterial blooms and identification of the main triggering factors are of paramount importance for water management. In this study, we analyzed a comprehensive dataset including ten-years measurements collected at Lake Varese, an eutrophic lake in Northern Italy. Microscopic analysis of the water samples was performed to characterize the community distribution and dynamics along the years. We observed that cyanobacteria represented a significant fraction of the phytoplankton community, up to 60% as biovolume, and a shift in the phytoplankton community distribution towards cyanobacteria dominance onwards 2010 was detected. The relationships between cyanobacteria biovolume, nutrients, and environmental parameters were investigated through simple and multiple linear regressions. We found that 14-days average air temperature together with total phosphorus may only partly explain the cyanobacteria biovolume variance at Lake Varese. However, weather forecasts can be used to predict an algal outbreak two weeks in advance and, eventually, to adopt management actions. The prediction of cyanobacteria algal blooms remains challenging and more frequent samplings, combined with the microscopy analysis and the metagenomics technique, would allow a more conclusive analysis.

Heat waves in the Arctic may strongly impact environment and local communities. Recently several indices have been proposed for monitoring environmental changes in the Arctic, but heat waves have not been addressed. By applying a structured approach for evaluating occurrences of periods with exceptionally high temperatures, this study demonstrates that in the last decades there was an increase of heat wave occurrences over the terrestrial Arctic. The increase is mainly over the Canadian Arctic Archipelago and Greenland that are surrounded by ocean undergoing a sea-ice melting trend, while the Eurasian Arctic shows no significant change in heat wave occurrence. Since 2002 the probability of experiencing heat waves in the Arctic has been similar or even higher than in the middle and low latitudes and heat waves have already started to increasingly threaten local vegetation, ecology, human health and economy.

Past reductions of anthropogenic aerosol concentrations in Europe and North America could have amplified Arctic warming. In the future the impact of air pollution policies may differ, because the major anthropogenic sources of atmospheric aerosols are increasingly located in Asia. In this study numerical experiments indicate that, while reduced carbon dioxide (CO2) emissions weaken Arctic warming, direct radiative forcing effects by reductions of anthropogenic aerosol concentrations, additional to those obtained by lower CO2 emissions, can either amplify or diminish it. Interactions between regionally modified radiation in Asia and internal climate variability may differently initiate and sustain atmospheric planetary waves propagating into the Arctic. In a non-linear manner planetary waves may redistribute atmospheric and oceanic meridional heat fluxes at the high latitudes and either amplify or diminish Arctic warming in 2050. Lower CO2 concentrations may apparently contribute to reduce the interactions between the Arctic system and the lower latitudes, thus reducing the influence of strong air quality measures in Asia on the Arctic amplification of global warming. While past and present air pollution policies could have amplified Arctic warming, in the future the effects from atmospheric pollution reductions are less certain, depending on the future CO2 concentrations, and requiring improved simulations of changing aerosol concentrations and their interactions with clouds in Asia and the Arctic.