Mercury (Hg) emissions pose a global problem that requires global cooperation for a solution. However, neither emissions nor regulations are uniform world-wide, and hence the impacts of regulations are also likely to vary regionally. We report here an approach to model the effectiveness of regulations at different scales (local, regional, global) in reducing Hg deposition and fish Hg concentrations in the Laurentian Great Lakes (GL) region. The potential effects of global change on deposition are also modeled. We focus on one of the most vulnerable communities within the region, an Indigenous tribe in Michigan's Upper Peninsula (UP) with a high fish consumption rate. For the GL region, elements of global change (climate, biomass burning, land use) are projected to have modest impacts (<5% change from the year 2000) on Hg deposition. For this region, our estimate of the effects of elimination of anthropogenic emissions is a 70% decrease in deposition, while our minimal regulation scenario increases emissions by 35%. Existing policies have the potential to reduce deposition by 20% with most of the reduction attributable to U.S. policies. Local policies within the Great Lakes region show little effect, and global policy as embedded in the Minamata Convention is projected to decrease deposition by approximately 2.8%. Even within the GL region, effects of policy are not uniform; areas close to emission sources (Illinois, Indiana, Ohio, Pennsylvania) experience larger decreases in deposition than other areas including Michigan's UP. The UP landscape is highly sensitive to Hg deposition, with nearly 80% of lakes estimated to be impaired. Sensitivity to mercury is caused primarily by the region's abundant wetlands. None of the modeled policy scenarios are projected to reduce fish Hg concentrations to the target that would be safe for the local tribe. Regions like Michigan's UP that are highly sensitive to mercury deposition and that will see little reduction in deposition due to regulations require more aggressive policies to reduce emissions to achieve recovery. We highlight scientific uncertainties that continue to limit our ability to accurately predict fish Hg changes over time.

Extreme precipitation events pose a significant threat to public safety, natural and managed resources, and the functioning of society. Changes in such high-impact, low-probability events have profound implications for decision-making, preparation and costs of mitigation and adaptation efforts. Understanding how extreme precipitation events will change in the future and enabling consistent and robust projections is therefore important for the public and policymakers as we prepare for consequences of climate change.

Projection of extreme precipitation events, however, particularly at the local scale, presents a critical challenge: the climate model-based simulations of precipitation that we currently rely on for such projections—general circulation models (GCMs)—are not very realistic, mainly due to the models’ coarse spatial resolution. This coarse resolution precludes adequate representation of highly influential, small-scale features such as moisture convection and topography. Regional circulation models (RCMs) provide much higher resolution and better representation of such features, and are thus often perceived as an optimum approach to producing more accurate heavy precipitation statistics than GCMs. However, they are much more computationally intensive, time-consuming and expensive to run.

In a previous paper, the researchers developed an algorithm that detects the occurrence of heavy precipitation events based on climate models’ well-resolved, large-scale atmospheric circulation conditions associated with those events—rather than relying on these models’ simulated precipitation. The algorithm’s results corresponded with observations with much greater precision than the model-simulated precipitation.

In this paper, the researchers show that the performance of the new algorithm in detecting heavy precipitation event is not dependent on the model resolution and even better than that of precipitation simulated from RCMs. The algorithm thus presents a robust and economic way to assess extreme precipitation frequency across a broad range of GCMs and multiple climate change scenarios with minimal computational requirements.   

This article provides a proof of concept for using a biogeochemical/ecosystem/optical model with a radiative transfer component as a laboratory to explore aspects of ocean colour. We focus here on the satellite ocean colour chlorophyll a (Chl a) product provided by the often-used blue/green reflectance ratio algorithm. The model produces output that can be compared directly to the real-world ocean colour remotely sensed reflectance. This model output can then be used to produce an ocean colour satellite-like Chl a product using an algorithm linking the blue versus green reflectance similar to that used for the real world. Given that the model includes complete knowledge of the (model) water constituents, optics and reflectance, we can explore uncertainties and their causes in this proxy for Chl a (called derived Chl ain this paper). We compare the derived Chl a to the actual model Chl a field. In the model we find that the mean absolute bias due to the algorithm is 22 % between derived and actual Chl a. The real-world algorithm is found using concurrent in situ measurement of Chl a and radiometry. We ask whether increased in situ measurements to train the algorithm would improve the algorithm, and find a mixed result. There is a global overall improvement, but at the expense of some regions, especially in lower latitudes where the biases increase. Not surprisingly, we find that region-specific algorithms provide a significant improvement, at least in the annual mean. However, in the model, we find that no matter how the algorithm coefficients are found there can be a temporal mismatch between the derived Chl a and the actual Chl a. These mismatches stem from temporal decoupling between Chl a and other optically important water constituents (such as coloured dissolved organic matter and detrital matter). The degree of decoupling differs regionally and over time. For example, in many highly seasonal regions, the timing of initiation and peak of the spring bloom in the derived Chl a lags the actual Chl a by days and sometimes weeks. These results indicate that care should also be taken when studying phenology through satellite-derived products of Chl a. This study also reemphasizes that ocean-colour-derived Chl a is not the same as the real in situ Chl a. In fact the model derived Chl a compares better to real-world satellite-derived Chl a than the model actual Chl a. Modellers should keep this is mind when evaluating model output with ocean colour Chl a and in particular when assimilating this product. Our goal is to illustrate the use of a numerical laboratory that (a) helps users of ocean colour, particularly modellers, gain further understanding of the products they use and (b) helps the ocean colour community to explore other ocean colour products, their biases and uncertainties, as well as to aid in future algorithm development.