Christian Pagé

Climate change impacts on tree ranges: model intercomparison facilitates understanding and quantification of uncertainty

Model-based projections of shifts in tree species range due to climate change are becoming an important decision support tool for forest management. However, poorly evaluated sources of uncertainty require more scrutiny before relying heavily on models for decision-making. We evaluated uncertainty arising from differences in model formulations of tree response to climate change based on a rigorous intercomparison of projections of tree distributions in France. We compared eight models ranging from niche-based to process-based models. On average, models project large range contractions of temperate tree species in lowlands due to climate change. There was substantial disagreement between models for temperate broadleaf deciduous tree species, but differences in the capacity of models to account for rising CO(2) impacts explained much of the disagreement. There was good quantitative agreement among models concerning the range contractions for Scots pine. For the dominant Mediterranean tree species, Holm oak, all models foresee substantial range expansion.

Projected 21st century snowfall changes over the French Alps and related uncertainties

Snowfall changes in mountain areas in response to anthropogenic forcing could have widespread hydrological, ecological and economic impacts. In this paper, the robustness of snowfall changes over the French Alps projected during the 21st century and the associated uncertainties are studied. In particular, the role of temperature changes on snowfall changes is investigated. Those issues are tackled through the analysis of the results of a very large ensemble of high-resolution regional climate projections, obtained either through dynamical or statistical downscaling. We find that, at the beginning and at the end of the cold season extending from November to March (included), temperature change is an important source of spread in snowfall changes. However, no link is found between temperature and snowfall changes in January and February. At the beginning and at the end of the cold season, the rate of change in snowfall per Kelvin does not depend much on the bias correction step, the period or the greenhouse gas scenario but mostly on the downscaling method and the climate models, the latter uncertainty source being dominant.

Towards a dedicated impact portal to bridge the gap between the impact and climate communities : Lessons from use cases

Future climate evolution is of primary importance for the societal, economical, political orientations and decision-making. It explains the increasing use of climate projections as input for quantitative impact studies, assessing vulnerability and defining adaptation strategies in different sectors. Here we analyse 17 national and representative use cases so as to identify the diversity of the demand for climate information depending on user profiles as well as the best practices, methods and tools that are needed to answer the different requests. A particular emphasis is put on the workflow that allows to translate climate data into suitable impact data, the way to deal with the different sources of uncertainty and to provide a suited product to users. We identified three complementary tools to close the gap between climate scientists and user needs: an efficient interface between users and providers; an optimized methodology to handle user requests and a portal to facilitate access to data and elaborated products. We detail in the paper how these three tools can limit the intervention of experts, educate users, and lead to the production of useful information. This work provides the basis on which the ENES (European Network for Earth System Modelling) Portal Interface for the Climate Impact Communities is built.

A statistical method to estimate PM2.5 concentrations from meteorology and its application to the effect of climate change

A statistical algorithm was developed to estimate PM2.5 concentrations over Europe based on a weather-type representation of the meteorology. We used modeled PM2.5 concentrations as pseudoobservations, because of a lack of PM2.5 speciated measurements over Europe, and included four meteorological variables. This algorithm was evaluated on the learning period (2000–2008) to test its ability to reproduce the pseudoobserved data set and then applied for two climatological scenarios (RCP4.5 and RCP8.5) and one historical (1975–2004) and two future periods (2020–2049 and 2070–2099). In Italy, Poland, and northern, eastern, and southeastern Europe, all future scenarios lead to decreases in PM2.5, whereas in the Balkans, Benelux, the UK, and northern France, they lead to increases in PM2.5. Considering each season separately shows stronger responses, which may vary for a given region and scenario. Decomposing the changes in PM2.5 concentrations as the sum of intertype and intratype changes, and a residual term shows that (1) the residual term is negligible; (2) intertype changes affect more the regions along the Atlantic Ocean; and (3) in most other regions, intertype and intratype changes are often on the same order of magnitude. The relationship between the atmospheric circulation and weather types evolves and therefore modifies the mean of meteorological variables and PM2.5 concentrations. This algorithm offers a novel approach to investigate the effect of climate change on air quality and can be applied to other pollutants, regions, and meteorological models. Furthermore, this approach can be applied using actual speciated PM2.5 observations, if a sufficiently dense monitoring network were available.