An ensemble of bias-adjusted CMIP6 climate simulations based on a high-resolution North American reanalysis.

ESPO-G6-R2 v1.0 is a set of statistically downscaled and bias-adjusted climate simulations based on the Coupled Model Intercomparison Project 6 (CMIP6) models. The dataset is composed of daily timeseries of three variables: daily maximum temperature, daily minimum temperature and daily precipitation. Data are available from 1950 to 2100 over North America. The simulation ensemble is comprised of 14 models driven by two emissions scenarios (SSP2-4.5 and SSP3-7.0). In this paper, we describe the workflow used for the bias-adjustment, which relies on the detrended quantile mapping method and the Regional Deterministic Reforecast System (RDRS) v2.1 reference dataset. Using the framework defined in the VALUE project, we show the improvements made by the bias-adjustment on marginal, temporal and multivariate aspects of the data. We also verify that the bias-adjusted climate data have similar climate change signal to the original climate model simulations. Finally, we provide guidance to users on how to use this dataset.

Drought timing and local climate determine the sensitivity of eastern temperate forests to drought.

Projected changes in temperature and drought regime are likely to reduce carbon (C) storage in forests, thereby amplifying rates of climate change. While such reductions are often presumed to be greatest in semi-arid forests that experience widespread tree mortality, the consequences of drought may also be important in temperate mesic forests of Eastern North America (ENA) if tree growth is significantly curtailed by drought. Investigations of the environmental conditions that determine drought sensitivity are critically needed to accurately predict ecosystem feedbacks to climate change. We matched site factors with the growth responses to drought of 10,753 trees across mesic forests of ENA, representing 24 species and 346 stands, to determine the broad-scale drivers of drought sensitivity for the dominant trees in ENA. Here we show that two factors-the timing of drought, and the atmospheric demand for water (i.e., local potential evapotranspiration; PET)-are stronger drivers of drought sensitivity than soil and stand characteristics. Drought-induced reductions in tree growth were greatest when the droughts occurred during early-season peaks in radial growth, especially for trees growing in the warmest, driest regions (i.e., highest PET). Further, mean species trait values (rooting depth and ψ50 ) were poor predictors of drought sensitivity, as intraspecific variation in sensitivity was equal to or greater than interspecific variation in 17 of 24 species. From a general circulation model ensemble, we find that future increases in early-season PET may exacerbate these effects, and potentially offset gains in C uptake and storage in ENA owing to other global change factors.

Comparison of spatiotemporal prediction models of daily exposure of individuals to ambient nitrogen dioxide and ozone in Montreal, Canada.

BACKGROUND: In previous studies investigating the short-term health effects of ambient air pollution the exposure metric that is often used is the daily average across monitors, thus assuming that all individuals have the same daily exposure. Studies that incorporate space-time exposures of individuals are essential to further our understanding of the short-term health effects of ambient air pollution. OBJECTIVES: As part of a longitudinal cohort study of the acute effects of air pollution that incorporated subject-specific information and medical histories of subjects throughout the follow-up, the purpose of this study was to develop and compare different prediction models using data from fixed-site monitors and other monitoring campaigns to estimate daily, spatially-resolved concentrations of ozone (O3) and nitrogen dioxide (NO2) of participants' residences in Montreal, 1991-2002. METHODS: We used the following methods to predict spatially-resolved daily concentrations of O3 and NO2 for each geographic region in Montreal (defined by three-character postal code areas): (1) assigning concentrations from the nearest monitor; (2) spatial interpolation using inverse-distance weighting; (3) back-extrapolation from a land-use regression model from a dense monitoring survey, and; (4) a combination of a land-use and Bayesian maximum entropy model. We used a variety of indices of agreement to compare estimates of exposure assigned from the different methods, notably scatterplots of pairwise predictions, distribution of differences and computation of the absolute agreement intraclass correlation (ICC). For each pairwise prediction, we also produced maps of the ICCs by these regions indicating the spatial variability in the degree of agreement. RESULTS: We found some substantial differences in agreement across pairs of methods in daily mean predicted concentrations of O3 and NO2. On a given day and postal code area the difference in the concentration assigned could be as high as 131ppb for O3 and 108ppb for NO2. For both pollutants, better agreement was found between predictions from the nearest monitor and the inverse-distance weighting interpolation methods, with ICCs of 0.89 (95% confidence interval (CI): 0.89, 0.89) for O3 and 0.81 (95%CI: 0.80, 0.81) for NO2, respectively. For this pair of methods the maximum difference on a given day and postal code area was 36ppb for O3 and 74ppb for NO2. The back-extrapolation method showed a higher degree of disagreement with the nearest monitor approach, inverse-distance weighting interpolation, and the Bayesian maximum entropy model, which were strongly constrained by the sparse monitoring network. The maps showed that the patterns of agreement differed across the postal code areas and the variability depended on the pair of methods compared and the pollutants. For O3, but not NO2, postal areas showing greater disagreement were mostly located near the city centre and along highways, especially in maps involving the back-extrapolation method. CONCLUSIONS: In view of the substantial differences in daily concentrations of O3 and NO2 predicted by the different methods, we suggest that analyses of the health effects from air pollution should make use of multiple exposure assessment methods. Although we cannot make any recommendations as to which is the most valid method, models that make use of higher spatially resolved data, such as from dense exposure surveys or from high spatial resolution satellite data, likely provide the most valid estimates.

An Objective Approach to Select Climate Scenarios when Projecting Species Distribution under Climate Change.

An impressive number of new climate change scenarios have recently become available to assess the ecological impacts of climate change. Among these impacts, shifts in species range analyzed with species distribution models are the most widely studied. Whereas it is widely recognized that the uncertainty in future climatic conditions must be taken into account in impact studies, many assessments of species range shifts still rely on just a few climate change scenarios, often selected arbitrarily. We describe a method to select objectively a subset of climate change scenarios among a large ensemble of available ones. Our k-means clustering approach reduces the number of climate change scenarios needed to project species distributions, while retaining the coverage of uncertainty in future climate conditions. We first show, for three biologically-relevant climatic variables, that a reduced number of six climate change scenarios generates average climatic conditions very close to those obtained from a set of 27 scenarios available before reduction. A case study on potential gains and losses of habitat by three northeastern American tree species shows that potential future species distributions projected from the selected six climate change scenarios are very similar to those obtained from the full set of 27, although with some spatial discrepancies at the edges of species distributions. In contrast, projections based on just a few climate models vary strongly according to the initial choice of climate models. We give clear guidance on how to reduce the number of climate change scenarios while retaining the central tendencies and coverage of uncertainty in future climatic conditions. This should be particularly useful during future climate change impact studies as more than twice as many climate models were reported in the fifth assessment report of IPCC compared to the previous one.

Aboveground carbon in Quebec forests: stock quantification at the provincial scale and assessment of temperature, precipitation and edaphic properties effects on the potential stand-level stocking.

Biological carbon sequestration by forest ecosystems plays an important role in the net balance of greenhouse gases, acting as a carbon sink for anthropogenic CO2 emissions. Nevertheless, relatively little is known about the abiotic environmental factors (including climate) that control carbon storage in temperate and boreal forests and consequently, about their potential response to climate changes. From a set of more than 94,000 forest inventory plots and a large set of spatial data on forest attributes interpreted from aerial photographs, we constructed a fine-resolution map (∼375 m) of the current carbon stock in aboveground live biomass in the 435,000 km(2) of managed forests in Quebec, Canada. Our analysis resulted in an area-weighted average aboveground carbon stock for productive forestland of 37.6 Mg ha(-1), which is lower than commonly reported values for similar environment. Models capable of predicting the influence of mean annual temperature, annual precipitation, and soil physical environment on maximum stand-level aboveground carbon stock (MSAC) were developed. These models were then used to project the future MSAC in response to climate change. Our results indicate that the MSAC was significantly related to both mean annual temperature and precipitation, or to the interaction of these variables, and suggest that Quebec's managed forests MSAC may increase by 20% by 2041-2070 in response to climate change. Along with changes in climate, the natural disturbance regime and forest management practices will nevertheless largely drive future carbon stock at the landscape scale. Overall, our results allow accurate accounting of carbon stock in aboveground live tree biomass of Quebec's forests, and provide a better understanding of possible feedbacks between climate change and carbon storage in temperate and boreal forests.

Impacts of Climate Change on the Timing of the Production Season of Maple Syrup in Eastern Canada.

Maple syrup production is an important economic activity in north-eastern North-America. The beginning and length of the production season is linked to daily variation in temperature. There are increasing concerns about the potential impact of climatic change on this industry. Here, we used weekly data of syrup yield for the 1999-2011 period from 121 maple stands in 11 regions of Québec (Canada) to predict how the period of production may be impacted by climate warming. The date at which the production begins is highly variable between years with an average range of 36 days among the regions. However, the average start date for a given region, which ranged from Julian day 65 to 83, was highly predictable (r2 = 0.88) using the average temperature from January to April (TJ-A). A logistic model predicting the weekly presence or absence of production was also developed. Using the inputs of 77 future climate scenarios issued from global models, projections of future production timing were made based on average TJ-A and on the logistic model. The projections of both approaches were in very good agreement and suggest that the sap season will be displaced to occur 15-19 days earlier on average in the 2080-2100 period. The data also show that the displacement in time will not be accompanied by a greater between years variability in the beginning of the season. However, in the southern part of Québec, very short periods of syrup production due to unfavourable conditions in the spring will occur more frequently in the future although their absolute frequencies will remain low.

Climate change and habitat fragmentation drive the occurrence of Borrelia burgdorferi, the agent of Lyme disease, at the northeastern limit of its distribution.

Lyme borreliosis is rapidly emerging in Canada, and climate change is likely a key driver of the northern spread of the disease in North America. We used field and modeling approaches to predict the risk of occurrence of Borrelia burgdorferi, the bacteria causing Lyme disease in North America. We combined climatic and landscape variables to model the current and future (2050) potential distribution of the black-legged tick and the white-footed mouse at the northeastern range limit of Lyme disease and estimated a risk index for B. burgdorferi from these distributions. The risk index was mostly constrained by the distribution of the white-footed mouse, driven by winter climatic conditions. The next factor contributing to the risk index was the distribution of the black-legged tick, estimated from the temperature. Landscape variables such as forest habitat and connectivity contributed little to the risk index. We predict a further northern expansion of B. burgdorferi of approximately 250-500 km by 2050 - a rate of 3.5-11 km per year - and identify areas of rapid rise in the risk of occurrence of B. burgdorferi. Our results will improve understanding of the spread of Lyme disease and inform management strategies at the most northern limit of its distribution.

Poleward expansion of the white-footed mouse (Peromyscus leucopus) under climate change: implications for the spread of lyme disease.

The white-footed mouse (Peromyscus leucopus) is an important reservoir host for Borrelia burgdorferi, the pathogen responsible for Lyme disease, and its distribution is expanding northward. We used an Ecological Niche Factor Analysis to identify the climatic factors associated with the distribution shift of the white-footed mouse over the last 30 years at the northern edge of its range, and modeled its current and potential future (2050) distributions using the platform BIOMOD. A mild and shorter winter is favouring the northern expansion of the white-footed mouse in Québec. With more favorable winter conditions projected by 2050, the distribution range of the white-footed mouse is expected to expand further northward by 3° latitude. We also show that today in southern Québec, the occurrence of B. burgdorferi is associated with high probability of presence of the white-footed mouse. Changes in the distribution of the white-footed mouse will likely alter the geographical range of B. burgdorferi and impact the public health in northern regions that have yet to be exposed to Lyme disease.