Ouranos

• Hydrological models’ response to RCM-simulated land use changes were evaluated. • Extreme deforestations shows higher flood peaks in snow-dominated catchments. • The choice of hydrological model structure (i.e., more sensitivity to snow melt/accumulation vs to evapotranspiration) impacts level of sensitivity to land use. The present study evaluates the hydrological response to severe land use changes using the distributed physically-based hydrological model HYDROTEL over 89 catchments in Eastern North America. Additionally, two catchments were selected to assess the impacts on past extreme flood events. Streamflow simulations were generated using three regional climate simulations from the Canadian Regional Climate Model version 5 (CRCM5) for the 1986–2015 period. The simulations were based on current land use (EVAL) and two extreme land use scenarios with only grass (GRASS) and only forest (FOREST) land use. The FOREST and GRASS simulations include the feedback of land-use to climate. The Multivariate Bias Correction (MBCn) algorithm was applied to correct biases in EVAL simulation, with the same coefficients used for GRASS and FOREST simulations to ensure a consistent climate baseline and maintain the spread of the simulations. HYDROTEL’s land-use inputs were adjusted to match those from the CRCM5 simulations. The results indicated significant changes in both the timing and magnitude of spring peak flows associated with snowmelt: the FOREST scenario showed earlier, reduced peak flows, while the GRASS scenario led to delayed, larger peaks. Additionally, the catchments were categorized by size-large (>1000 km 2 ), medium (500–1000 km 2 ), and small (<500 km 2 )-to evaluate size-related impacts. A model comparison was conducted between HYDROTEL and the physically-based distributed model WaSiM on two diverse catchments, revealing that HYDROTEL demonstrated higher sensitivity to snowmelt and accumulation processes (because of its multi-land use snow model structure), while WaSiM showed greater sensitivity to evapotranspiration (due to its Richards-equation-based soil module). Finally, it was found that the RCM-simulated land use-climate feedback is considerably more important than the hydrological model land-use change effect on the spring flood modification.

We create emulators to capture the effects of changes in weather on O 3 and NO X in eastern Canada. Climate change is projected to increase the US contribution to O 3 concentrations in eastern Canada more than the Canadian and Quebec contributions.

Teleconnections and the dynamics of extreme event co-occurrence across continents are relevant for understanding compounding risks and improving early warning systems. Pan-Atlantic compound extremes between climate extremes in North America and Europe have been receiving increasing attention lately, also due to the occurrence of recent events e.g., the February 2020 cold wave in North America that co-occurred with warm anomalies over Europe and wet and windy extremes in the UK, or the summer 2016 heat wave over Europe that was linked to upstream high-amplitude Rossby-wave packets originating from North America several days before the event. Previous studies furthermore suggest that wet extremes in Europe (e. g., 2021 floods in Germany) could be linked to particularly persistent atmospheric circulation and blocking patterns associated with upstream Rossby wave breaking over North America and the Atlantic.While recent research increasingly recognizes co-occurring extreme events over North America and Europe as interconnected phenomena—often mediated by Rossby wave breaking and atmospheric blocking—studies explicitly linking specific North American extremes (e.g., cold spells) to simultaneous European extremes (e.g., heavy precipitation) remain limited. This is particularly true when moving beyond individual historical cases to systematically analyze pan-Atlantic compound extremes using reanalysis data and climate model simulations. A key challenge lies in achieving high-resolution climate simulations that can accurately capture precipitation-related extremes, while also encompassing the broad spatial scale required to represent large-scale pan-Atlantic circulation patterns.To address these gaps, we take advantage of a large ensemble of high-resolution climate simulations over a North American and a European domain, which are nested in the atmospheric, westerly flow of a global earth system model. This dataset provides us a large data base of regional extremes in high-resolution of 0.11° while allowing us to study large-scale teleconnections of extreme events at both sides of the Atlantic. The regional nested climate simulations consist of 50 members of the Canadian Earth System Model version 2 (CanESM-2) as well as four members of the Max Planck Earth System Model (MPI-ESM), both dynamically downscaled with the Canadian Regional Climate Model version 5 (CRCM5) over the North-American and European CORDEX domain.Here, we present our research concept and preliminary results from analyzing pan-Atlantic compound extremes in reanalysis data, a reanalysis-driven CRCM5 simulation, and our ensemble of nested regional climate model simulations. This research aims to advance our understanding of the processes underlying co-occurring extreme events across the North Atlantic by identifying and investigating specific compound events in high resolution.

The application of a new approach to bias-correct Earth System Model (ESM) driving data for regional Climate Model (RCM) downscaling is presented. This approach employs a novel Empirical Runtime Bias Correction (ERBC) of the ESM, designed to self-consistently reduce climatological biases in the driving data. The impact of such ESM bias reduction on RCM downscaling is evaluated through an experimental protocol where a single ESM and its ERBC counterpart drive two different RCMs. A continental-scale analysis of these results in a North American regional domain over the historical period indicates that the impact of global model biases on RCM downscaling products can be mitigated significantly by employing ERBC driving data. A similar series of ESM downscaling simulations is conducted for future projections of climate change following the Coupled Model Intercomparison Project phase 6 SSP3-7.0 scenario of anthropogenic forcings. Unlike diagnostic bias corrections applied to model output, ERBCs have the potential to improve climate-change circulation responses, thereby reducing uncertainty. This reduction would be reflected in a narrower spread of responses within a multi-model ensemble of ESMs and RCMs. Since this study involves only one ESM, we investigate the necessary but not sufficient condition for uncertainty reduction: that ERBCs can alter climate change responses compared to their uncorrected counterparts. The results show that ERBCs induce statistically significant changes in the climate-change circulation responses in both the global and regional models.

A novel runtime empirical bias correction (EBC) has recently been developed and applied to enhance the Canadian Center for Climate Modelling and Analysis' (CCCma) global earth system model CanESM, demonstrating significant improvements in future climate projections, particularly under strong climate change scenarios. The application of EBC to CanESM provides enhanced driving data for dynamical downscaling through regional climate models (RCMs).This project aims to assess the impact of the improved EBC driving data on two RCMs, namely CanRCM5 (CCCma) and CRCM5 (Ouranos), in order to evaluate the systematic improvement of meteorological variables. Multiple 10-member ensembles are utilized to investigate the added value of employing EBC in driving the RCM simulations. The ensembles consist of three sets: the first set utilizes the original CanESM5 as driving data, the second set incorporates EBC on sea surface temperature (SST) and sea ice concentration (SIC) using the original CanESM5, and the third set employs bias-corrected atmosphere, SST, and SIC data. All three ensembles are compared against ERA5 data as a reference for the historical period.Results indicate a clear advantage of using EBC, particularly in cases where the initial bias is substantial. For instance, significant improvement in modeling key meteorological phenomena, notably the North American monsoon and the northeasters (extratropical cyclones). These improvements can be attributed not only to the refinement in addressing climatological biases in land and ocean data but also to an enhanced representation of cyclonic activities due to a better representation of overall circulation in our region. Ultimately, this research seeks to contribute to the scientific community by providing a methodology to mitigate uncertainties in downscaled projections of future climate change through the utilization of EBC.

Abstract The 2023 Atlantic hurricane season was above normal, producing 20 named storms, 7 hurricanes, 3 major hurricanes, and seasonal accumulated cyclone energy that exceeded the 1991–2020 average. Hurricane Idalia was the most damaging hurricane of the year, making landfall as a Category 3 hurricane in Florida, resulting in eight direct fatalities and 3.6 billion U.S. dollars in damage. The above-normal 2023 hurricane season occurred during a strong El Niño event. El Niño events tend to be associated with increased vertical wind shear across the Caribbean and tropical Atlantic, yet vertical wind shear during the peak hurricane season months of August–October was well below normal. The primary driver of the above-normal season was likely record warm tropical Atlantic sea surface temperatures (SSTs), which effectively counteracted some of the canonical impacts of El Niño. The extremely warm tropical Atlantic and Caribbean were associated with weaker-than-normal trade winds driven by an anomalously weak subtropical ridge, resulting in a positive wind–evaporation–SST feedback. We tested atmospheric circulation sensitivity to SSTs in both the tropical and subtropical Pacific and the Atlantic using the atmospheric component of the Community Earth System Model, version 2.3. We found that the extremely warm Atlantic was the primary driver of the reduced vertical wind shear relative to other moderate/strong El Niño events. The concentrated warmth in the eastern tropical Pacific in August–October may have contributed to increased levels of vertical wind shear than if the warming had been more evenly spread across the eastern and central tropical Pacific.

Abstract. Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. Consequently, the role of enhanced horizontal resolution in improved process representation in all components of the climate system continues to be of great interest. Recent simulations suggest both the possibility of significant changes in large-scale aspects of the ocean and atmospheric circulations and the regional responses to climate change, as well as improvements in representations of small-scale processes and extremes, when resolution is enhanced. The first phase of HighResMIP (HighResMIP1) was successful in producing a baseline multi-model assessment of global simulations with model grid spacings of 25–50 km in the atmosphere and 10–25 km in the ocean, a significant increase when compared to models with standard resolutions of order 1-degree typically used as part of the Coupled Model Intercomparison Project (CMIP) experiments. In addition to over 250 peer-reviewed manuscripts using the published HighResMIP1 datasets, the results were widely cited in the Intergovernmental Panel on Climate Change report and were the basis for a variety of derived datasets, including tracked cyclones (both tropical and extratropical), river discharge, storm surge, and others that were used for impact studies. There were also suggestions from the few ocean eddy-rich coupled simulations that aspects of climate variability and change might be significantly influenced by improved process representation in such models. The compromises that HighResMIP1 made should now be revisited, given the recent major advances in modelling and computing resources. Aspects that will be reconsidered include experimental design and simulation length, complexity, and resolution. In addition, larger ensemble sizes and a wider range of future scenarios would enhance the applicability of HighResMIP. Therefore, we propose an updated HighResMIP2 to improve and extend the previous work, to address new science questions, and to further advance our understanding of the role of horizontal resolution (and hence process representation) in state-of-the-art climate simulations. With further increases in high-performance computing resources and modelling advances along with the ability to take full advantage of these computational resources, an enhanced investigation of the drivers and consequences of variability and change in both large- and synoptic-scale weather and climate is now made possible. With the arrival of global cloud-resolving models (currently run for relatively short timescales), there is also an opportunity to improve links between such models and more traditional CMIP models, with HighResMIP providing a bridge to link understanding between these domains. HighResMIP also aims to link to other CMIP projects and international efforts such as the World Climate Research Program lighthouse activities and various Digital Twin initiatives, as well as having the potential to be used as training and validation data for the fast evolving Machine Learning climate models.

In this study, we propose and test a formulation for building causal physics-informed hybrid models over traditional physics-informed hybrid models (H-HBVo) and a convolutional neural network (1D-CNN) in groundwater level (GWL) modeling. Two types of models are built based on our formulation and named H-HBV and H-Lin, because they integrate two different hydrological models. The comparison is made in terms of performance and ability to learn cause-and-effect relationships between inputs and outputs. The novelty of this study lies in the CRC (Causal Relationship Constraints) conditions, derived from a mathematical development, that are imposed on the layers of the physics-informed hybrid model to force it to learn causal relationships. The results showed that the 1D-CNN algorithm performed slightly better than the hybrid algorithms, and that the H-Lin and H-HBV algorithms together achieved slightly better results than the traditional hybrid algorithm, H-HBVo. It is also obtained that the algorithms subjected to our formulations (H-Lin and H-HBV) are the ones with satisfactory causality properties and that the integration of domain knowledge is not sufficient to obtain causal and interpretable models within the framework of hybrid algorithms. Subsequently, an analysis of the effect of climate change is carried out for Quebec (Canada) using H-HBV models. The results show that the RCP and SSP scenarios give fairly similar results, i.e. an increase in GWL between 2011 and 2090 with median amplitudes varying between + 5.6 and + 7 cm. In some cases, the amplitude of increase can exceed between + 14 and + 23 cm, and even reach between + 35 and + 70 cm. The results of the climate change analysis are consistent with changes in vertical inflow and potential evapotranspiration, the input variables, for the different climate scenarios, confirming the robustness of our formulation.

Water is life. Water-related challenges, such as droughts, floods, water quality degradation, permafrost thaw and glacier melt, exacerbated by climate change, affect everyone. Yet, it is challenging to communicate science on complex and highly volatile topics such as water and climate change. Conceptualizing water-related environmental and social issues in novel ways, for example using art, with engagement between diverse audiences may lead to comprehensive solutions to these complex challenges. The Virtual Water Gallery (VWG) project, launched as part of the Global Water Futures (GWF) program in 2020 as a collaborative space merging science and art to address water challenges. Thirteen artists, representing diverse voices, teamed up with GWF scientists to explore specific challenges across Canada. The resulting artworks were exhibited on the VWG website (www.virtualwatergallery.ca) in 2021, with a first in-person exhibition in Canmore in 2022. Surveys were concurrently conducted to capture perspectives on climate change and water challenges, as well as on the role of art as a tool for engagement, from project participants, online and in-person gallery visitors. Join us as we share key findings and lessons learned on the SciArt collaborations and exhibition. Participant survey results highlight the participating artists and scientists&amp;#8217; experiences during the co-creation process. Visitor survey results help clarify the impact of art on people's understanding of climate change and its effects on water resources, alongside insights into behaviour changes (e.g., energy conservation, recycling, dietary choices) as a result of visiting the exhibition.