Émilie Bresson

Climate services seek to inform decisions about long-term climate changes. Since the 1990s, the vast majority of climate projections have covered the period up to 2100, a horizon that is not so distant anymore. This leaves decision makers tasked with planning coastal development or building long-lived infrastructure with relatively sparse information to assess climate risks, evaluate the costs and benefits of adaptation options, and implement carbon emission mitigation actions. In a bid to better understand how climate services could extend further into the future, this paper reviews the literature to understand how and why post-2100 climate studies are conducted. It identifies three broad themes: understanding climate processes with great inertia, assessing climate risks and informing adaptation actions, and evaluating emission mitigation scenarios. Multiple papers reveal the complex interplay between climate feedbacks, and show how climate change post-2100 is not merely an extension of 21st century trends. The overwhelming focus on the current century downplays the long-term damages of current emissions and fails to convey the possibility that future climate conditions could exceed adaptation capacity. We reiterate numerous calls made in the literature to extend mainstream climate emission scenarios and climate model projections farther into the future, and provide the building blocks for robust climate products. Plain Language Summary Most climate products and services available today end in 2100. This is partly due to the fact that very few climate model projections extend past that date, so there is little data to work with to build robust, credible products. Such information is highly valuable to decision makers tasked with planning coastal development, large infrastructure projects or nature conservation that all have multi-century implications. This article looks at how and why researchers study climate change after 2100. Most papers investigate processes with great inertia, such as the Antarctic and Greenland ice sheets or ocean heat storage, along with their impacts, for example on sea-level rise or the ocean biosphere. Authors are motivated by a better understanding of risks, informing climate adaptation, and evaluating scenarios for carbon emissions reduction. Although the impacts of climate change are already felt worldwide, the bulk of costs and damages will occur after 2100. By focusing modeling efforts on 21st century climate change, we might very well underestimate the challenges ahead and fail to prepare adequately.

Freezing rain events have caused severe socioeconomic and ecosystem impacts. An understanding of how these events may evolve as the Earth warms is necessary to adequately adapt infrastructure to these changes. We present an analysis of projected changes to freezing rain events over North America relative to the 1980–2009 recent past climate for the periods during which +2, +3, and +4°C of global warming is attained. We diagnose freezing rain using four precipitation‐type algorithms (Cantin and Bachand, Bourgouin, Ramer, and Baldwin) applied to four simulations of the fifth‐generation Canadian Regional Climate Model (CRCM5) driven by four global climate models (GCMs). We find that the choice of driving GCM strongly influences the spatial pattern of projected change. The choice of algorithm has a comparatively smaller impact, and primarily affects the magnitude but not the sign of projected change. We identify several regions where all simulations and algorithms agree on the sign of change, with increases projected over portions of western Canada and decreases over the central, eastern, and southern United States. However, we also find large regions of disagreement on the sign of change depending on driving GCM and even ensemble member of the same GCM, highlighting the importance of examining freezing rain events in a multi‐member ensemble of simulations driven by multiple GCMs to sufficiently account for uncertainty in projections of these hazardous events.

Climate change (CC) has already and will continue to have a significant impact on the mining sector. A comprehensive and thorough analysis was performed to evaluate the risks and vulnerabilities resulting from CC during the exploration, operation, and reclamation phases of the mining life cycle. This analysis focused on six mining regions in Québec. Climate scenarios were produced for each region and, along with a literature review, used to assess the effects of CC on the mining sector. The results were presented to a panel of experts who highlighted the risks for each activity at each phase of the mining life cycle. A second group of experts evaluated the level of risk and the mining industry’s vulnerability for each risk identified by the first group. Six main risks due to CC were identified: activity schedule disruptions, loss or limitation of site accessibility, water management issues, instabilities and failures of storage facilities, operations infrastructure instabilities, and reduction of reclamation cover performance. The mining experts performed a Delphi-type survey for each mining activity to determine the risk level for each region, leading to the production of a risk matrix. Results indicate that CC is expected to particularly affect reclamation cover performance, and extreme climate events are projected to have significant impacts on operations. Vulnerability levels were assigned by a third group of experts based on the industry’s ability to adapt to these risks. Results showed a low vulnerability for exploration, low to high vulnerability for operations, and low to very high vulnerability for reclamation.

Cumulative CO₂ emissions are a robust predictor of mean temperature increase. However, many societal impacts are driven by exposure to extreme weather conditions. Here, we show that cumulative emissions can be robustly linked to regional changes of a heat exposure indicator, as well as the resulting socioeconomic impacts associated with labour productivity loss in vulnerable economic sectors. We estimate historical and future increases in heat exposure using simulations from eight Earth System Models. Both the global intensity and spatial pattern of heat exposure evolve linearly with cumulative emissions across scenarios (1% CO₂, RCP4.5 and RCP8.5). The pattern of heat exposure at a given level of global temperature increase is strongly affected by non-CO₂ forcing. Global non-CO₂ greenhouse gas emissions amplify heat exposure, while high local emissions of aerosols could moderate exposure. Considering CO₂ forcing only, we commit ourselves to an additional annual loss of labour productivity of about 2% of total GDP per unit of trillion tonne of carbon emitted. This loss doubles when adding non-CO₂ forcing of the RCP8.5 scenario. This represents an additional economic loss of about 4,400 G$ every year (i.e. 0.59 $/tCO₂), varying across countries with generally higher impact in lower-income countries.

Winds, waves and storm surges can inflict severe damage in coastal areas. In order to improve preparedness for such events, a better understanding of storm-induced coastal flooding episodes is necessary. To this end, this paper highlights the use of atmospheric downscaling techniques in order to improve wave and storm surge hindcasts. The downscaling techniques used here are based on existing European Centre for Medium-Range Weather Forecasts reanalyses (ERA-20C, ERA-40 and ERA-Interim). The results show that the 10 km resolution data forcing provided by a downscaled atmospheric model gives a better wave and surge hindcast compared to using data directly from the reanalysis. Furthermore, the analysis of the most extreme mid-latitude cyclones indicates that a four-dimensional blending approach improves the whole process, as it assimilates more small-scale processes in the initial conditions. Our approach has been successfully applied to ERA-20C (the 20th century reanalysis).

This article describes a reflectivity forward operator developed for the validation and assimilation of W‐band radar data into the regional AROME class of numerical weather prediction models. The forward operator is consistent with the AROME ICE3 one‐moment microphysical scheme and is devised for vertically pointing radars. A new neighbourhood validation method, the Most Resembling Column (MRC) method, is designed to disentangle spatial location model errors from errors in the forward operator. This novel method is used to validate the forward operator using data collected in diverse conditions by the airborne cloud radar RASTA (Radar Airborne System Tool for Atmosphere) during a 2 month period over a region of the Mediterranean. The MRC method is then applied to retrieve the optimal effective shapes (i.e. the mean axis ratios) of the predicted graupel, snow and pristine ice, by minimizing the standard deviation between observations and simulations. The optimal mean axis ratio is approximately 0.7 for snow and 0.8 for graupel. It is shown that treating snow and graupel particles as oblate spheroids with axis ratios close to their optimal values leads to good agreement between the observations and simulations of the ice levels. Conversely, there is a large bias if snow and graupel particles are considered to be either spherical or overly flattened. The results also indicate that pristine ice can be approximated by a sphere, but this conclusion should be taken cautiously since the amount of pristine ice particles is probably overestimated in the ICE3 microphysical scheme.

Winds, waves and storm surges can induce severe damages in coastal areas. The FP7 IncREO project aims to understand the impact of climate change on coastal areas and also to assess the predictability of such extreme events. Reproduce efficiently past events is the fisrt step to reach this purpose. This paper shows the use of atmospheric downscaling techniques in order to improve waves and storm surge hindcasts. Past storms which caused damages on European coastal areas are investigated using atmosphere, wave and storm surge numerical models and downscaling techniques are based on existing ECMWF reanalyses. The results show clearly that the 10 km resolution wind forcing provided by the downscaled atmospheric model gives better waves and surges hindcast against using wind from the reanalysis. Furthermore, the analysis of the most extreme mid-latitude cyclones indicates that a 4D blending approach improves the whole process as it includes small scale processes in the initial conditions.

Precipitation episodes in the form of freezing rain and ice pellets represent natural hazards affecting eastern Canada during the cold season. These types of precipitation mainly occur in the St. Lawrence River valley and the Atlantic provinces of Canada. This study aims to evaluate the ability of the fifth-generation Canadian Regional Climate Model (CRCM5), using a 0.11° horizontal grid mesh, to hindcast mixed precipitation when driven by reanalyses produced by the European Centre for Medium-range Weather Forecasts (ERA-Interim) for a 35-year period. In general, the CRCM5 simulation slightly overestimates the occurrence of freezing rain, but the geographical distribution is well reproduced. The duration of freezing rain events and accompanying surface winds in the Montréal region are reproduced by CRCM5. A case study is performed for an especially catastrophic freezing-rain event in January 1998; the model succeeds in simulating the intensity and duration of the episode, as well as the propitious meteorological environment. Overall, the model is also able to reproduce the climatology and a specific event of freezing rain and ice pellets.

The north‐western Mediterranean Sea is a key location where intense air‐sea exchanges occur in autumn and winter. The succession of strong mistral and tramontane situations, leading to significant evaporation and ocean heat loss, is well known as the controlling factor in the dense water formation (DWF) with deep convection episodes. During HyMeX‐SOP2 (1 February to 15 March 2013), several platforms sampled the area in order to document DWF and air‐sea exchanges. This study investigates the ability of the NEMO‐WMED36 ocean model (1/36°‐resolution), driven in surface by the hourly air‐sea fluxes from the AROME‐WMED forecasts (2.5 km resolution), to represent DWF during HyMeX‐SOP2 and focuses on the sensitivity to initial conditions. After a short evaluation of the atmospheric forcing, the high‐resolution oceanic simulations using three different data sets as initial and boundary conditions are compared to observations collected during the field campaign. It evidences that using regional model outputs may lead to unrealistic thermohaline characteristics for the intermediate and deep waters, which degrade the simulated new dense water formed. Using ocean analyses built from observations, permits to obtain more realistic characteristics of the Western Mediterranean dense water. However, a low stratification favors an overestimation of the convective area and of the DWF rate. The DWF chronology is also impacted. Nevertheless, in every run, SOP2 is characterized by the production of water denser than 29.11 kg m⁻³ with a peak during the strong mistral event of 23–25 February followed by a period of restratification, before a last event of bottom convection on 13–15 March.