Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach.

The Society of Environmental Toxicology and Chemistry (SETAC) convened a Pellston workshop in 2022 to examine how information on climate change could be better incorporated into the ecological risk assessment (ERA) process for chemicals as well as other environmental stressors. A major impetus for this workshop is that climate change can affect components of ecological risks in multiple direct and indirect ways, including the use patterns and environmental exposure pathways of chemical stressors such as pesticides, the toxicity of chemicals in receiving environments, and the vulnerability of species of concern related to habitat quality and use. This article explores a modeling approach for integrating climate model projections into the assessment of near- and long-term ecological risks, developed in collaboration with climate scientists. State-of-the-art global climate modeling and downscaling techniques may enable climate projections at scales appropriate for the study area. It is, however, also important to realize the limitations of individual global climate models and make use of climate model ensembles represented by statistical properties. Here, we present a probabilistic modeling approach aiming to combine projected climatic variables as well as the associated uncertainties from climate model ensembles in conjunction with ERA pathways. We draw upon three examples of ERA that utilized Bayesian networks for this purpose and that also represent methodological advancements for better prediction of future risks to ecosystems. We envision that the modeling approach developed from this international collaboration will contribute to better assessment and management of risks from chemical stressors in a changing climate. Integr Environ Assess Manag 2024;20:367-383. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Incorporating climate projections in the environmental risk assessment of pesticides in aquatic ecosystems.

Global climate change will significantly impact the biodiversity of freshwater ecosystems, both directly and indirectly via the exacerbation of impacts from other stressors. Pesticides form a prime example of chemical stressors that are expected to synergize with climate change. Aquatic exposures to pesticides might change in magnitude due to increased runoff from agricultural fields, and in composition, as application patterns will change due to changes in pest pressures and crop types. Any prospective chemical risk assessment that aims to capture the influence of climate change should properly and comprehensively account for the variabilities and uncertainties that are inherent to projections of future climate. This is only feasible if they probabilistically propagate extensive ensembles of climate model projections. However, current prospective risk assessments typically make use of process-based models of chemical fate that do not typically allow for such high-throughput applications. Here, we describe a Bayesian network model that does. It incorporates a two-step univariate regression model based on a 30-day antecedent precipitation index, circumventing the need for computationally laborious mechanistic models. We show its feasibility and application potential in a case study with two pesticides in a Norwegian stream: the fungicide trifloxystrobin and herbicide clopyralid. Our analysis showed that variations in pesticide application rates as well as precipitation intensity lead to variations in in-stream exposures. When relating to aquatic risks, the influence of these processes is reduced and distributions of risk are dominated by effect-related parameters. Predicted risks for clopyralid were negligible, but the probability of unacceptable future environmental risks due to exposure to trifloxystrobin (i.e., a risk quotient >1) was 8%-12%. This percentage further increased to 30%-35% when a more conservative precautionary factor of 100 instead of 30 was used. Integr Environ Assess Manag 2024;20:384-400. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Exceptional warming over the Barents area.

In recent decades, surface air temperature (SAT) data from Global reanalyses points to maximum warming over the northern Barents area. However, a scarcity of observations hampers the confidence of reanalyses in this Arctic hotspot region. Here, we study the warming over the past 20-40 years based on new available SAT observations and a quality controlled comprehensive SAT dataset from the northern archipelagos in the Barents Sea. We identify a statistically significant record-high annual warming of up to 2.7 °C per decade, with a maximum in autumn of up to 4.0 °C per decade. Our results are compared with the most recent global and Arctic regional reanalysis data sets, as well as remote sensing data records of sea ice concentration (SIC), sea surface temperature (SST) and high-resolution ice charts. The warming pattern is primarily consistent with reductions in sea ice cover and confirms the general spatial and temporal patterns represented by reanalyses. However, our findings suggest even a stronger rate of warming and SIC-SAT relation than was known in this region until now.