Canadian Regional Climate Model Research Articles

THE IMPACT OF EXTREME WEATHER EVENTS OF PROJECTED CANADIAN REGIONAL CLIMATE MODEL DATA ON FLEXIBLE PAVEMENT

This paper presents findings about the influence of projected data from the Canadian regional climate model on the performance of flexible pavement, building upon the results from previous work where the data was generated and published, in which a general trend of decreasing the design life was observed. Projected temperature is the most important extreme climate impact on flexible roads. Adopting a conservative approach demonstrated that two extreme events of Maximum Mean Annual Air Temperature (MMAAT) and Maximum Summer Average Air Temperature (MSAAT) resulted in significant reduction of 25 years road design life. The observed trend indicates a severity range of 7% to 15% in terms of design service life loss when considering events every year compared to every five years. The findings revealed a reduction in pavement design life by 34%, 50%, 73%, and 90% for historical, short, intermediate, and long-term life cycles in the city of Windsor, respectively.

Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble

Abstract. ​​​​​​​Severe floods with extreme return periods of 100 years and beyond have been observed in several large rivers in Bavaria in the last 3 decades. Flood protection structures are typically designed based on a 100-year event, relying on statistical extrapolations of relatively short observation time series while ignoring potential temporal non-stationarity. However, future precipitation projections indicate an increase in the frequency and intensity of extreme rainfall events, as well as a shift in seasonality. This study aims to examine the impact of climate change on the 100-year flood (HF100) events of 98 hydrometric gauges within hydrological Bavaria. A hydrological climate change impact (CCI) modeling chain consisting of a regional Single Model Initial-condition Large Ensemble (SMILE) and a single hydrological model was created. The 50 equally probable members of the Canadian Regional Climate Model version 5 large ensemble (CRCM5-LE) were used to drive the hydrological model WaSiM (Water balance Simulation Model) to create a hydro-SMILE. As a result, a database of 1500 model years (50 members × 30 years) per investigated time period was established for extreme value analysis (EVA) to illustrate the benefit of the hydro-SMILE approach for a robust estimation of HF100 based on annual maxima (AM) and to examine the CCI on the frequency and magnitude of HF100 in different discharge regimes under a strong-emission scenario (RCP8.5). The results demonstrate that the hydro-SMILE approach provides a clear advantage for a robust estimation of HF100 using the empirical probability of 1500 AM compared to its estimation using the generalized extreme value (GEV) distribution of 1000 samples of typically available time series sizes of 30, 100, and 200 years. Thereby, by applying the hydro-SMILE framework, the uncertainty from statistical estimation can be reduced. The study highlights the added value of using hydrological SMILEs to project future flood return levels. The CCI of HF100 varies for different flow regimes, with snowmelt-driven catchments experiencing severe increases in frequency and magnitude, leading to unseen extremes that impact the distribution. Pluvial regimes show a lower intensification or even decline. The dynamics of HF100 driving mechanisms depict a decline in snowmelt-driven events in favor of rainfall-driven events, an increase in events driven by convective rainfall, and almost no change in the ratio between single-driver and compound events towards the end of the century.

Rising risks of hydroclimatic swings: A large ensemble study of dry and wet spell transitions in North America

Hydroclimatic swings between dry and wet spells are increasing globally, raising concerns due to their severe impacts on society, agriculture, infrastructure systems, and water resource management. These extremes and their projected changes are traditionally assessed in isolation, which can underestimate associated risks and future resilience and adaptation plans. This study investigates climate change projections of such lagged compound dry and wet spells across North America using a single model initial-condition large ensemble (SMILE). Three dry-wet spell indices are merged into an integrated indicator to provide a comprehensive perspective on changing risks. We apply an ensemble pooling approach using the Canadian Regional Climate Model version 4 Large Ensemble (CanRCM4-LE) to enhance sample size for index estimation. Results suggest that hydroclimatic swings across North America are expected to become more frequent and intensified in a warmer climate. Trends of spatial fraction and aggregation during transitions between dry and wet spells indicate future water resource management challenges. Hot spots for intensified transitions with higher frequency, such as Northern Central America, and the southern part of Eastern and Western North America, including Mexico and the state of California, overlap with larger spatial extent and higher aggregation during transitions. The seasonal analysis of spatial characteristics indicates winter may become wetter and summer drier at higher warming levels, potentially intensifying transitions between dry and wet extremes. Clustered hydroclimatic swings may lead to severe environmental, hydrological, and socio-economic consequences, necessitating appropriate mitigation and adaptation measures.

Climate change impacts on regional fire weather in heterogeneous landscapes of central Europe

Abstract. Wildfires have reached an unprecedented scale in the Northern Hemisphere. The summers of 2022 and 2023 demonstrated the destructive power of wildfires, especially in North America and southern Europe. Global warming leads to changes in fire danger. Specifically, fire seasons are assumed to become more extreme and will extend to more temperate regions in northern latitudes in the future. However, the extent to which the seasonality and severity of fire danger in regions of central Europe will change in the future remains to be investigated. Multiple studies claim that natural variability and model uncertainty hide the trend of increasing fire danger in multi-model climate simulations for future potentially fire-prone areas. Such a trend might be isolated with single-model initial-condition large ensembles (SMILEs), which help scientists to distinguish the forced response from natural variability. So far, the SMILE framework has only been applied for fire danger estimation on a global scale. To date, only a few dynamically downscaled regional SMILEs exist, although they enhance the spatial representation of climatic patterns on a regional or local scale. In this study, we use a regional SMILE of the Canadian Regional Climate Model version 5 Large Ensemble (CRCM5-LE) over a region in central Europe under the RCP8.5 (Representative Concentration Pathway) scenario from 1980 to 2099 to analyze changes in fire danger in an area that is currently not fire prone. We use the Canadian Forest Fire Weather Index (FWI) as a fire danger indicator. The study area covers four heterogeneous landscapes, namely the Alps, the Alpine Foreland, the lowlands of the South German Escarpment, and the Eastern Mountain Ranges of the Bavarian Forest. We demonstrate that the CRCM5-LE is a dataset suitable for disentangling climate trends from natural variability in a multi-variate fire danger metric. Our results show the strongest increases in the median (50th) and extreme (90th) quantiles of the FWI in the northern parts (South German Escarpment and Eastern Mountain Ranges) of the study area in the summer months of July and August. There, high fire danger becomes the median condition by the end of the century, and levels of high fire danger occur earlier in the fire season. The southern parts (Alps and Alpine Foreland) are less strongly affected by changes in fire danger than the northern parts. However, these regions reach their time of emergence (TOE) in the early 2040s because of very low current fire danger. In the northern parts, the climate change trend exceeds natural variability only in the late 2040s. We find that today's 100-year FWI event will occur every 30 years by 2050 and every 10 years by the end of the century. Our results highlight the potential for severe future fire events in central Europe, which is currently not very fire prone, and demonstrate the need for fire management even in regions with a temperate climate.

Assessment of simulations of a polar low with the Canadian Regional Climate Model.

Polar lows (PLs), which are intense maritime polar mesoscale cyclones, are associated with severe weather conditions. Due to their small size and rapid development, PL forecasting remains a challenge. Convection-permitting models are adequate to forecast PLs since, compared to coarser models, they provide a better representation of convection as well as surface and near-surface processes. A PL that formed over the Norwegian Sea on 25 March 2019 was simulated using the convection-permitting Canadian Regional Climate Model version 6 (CRCM6/GEM4, using a grid mesh of 2.5 km) driven by the reanalysis ERA5. The objectives of this study were to quantify the impact of the initial conditions on the simulation of the PL, and to assess the skill of the CRCM6/GEM4 at reproducing the PL. The results show that the skill of the CRCM6/GEM4 at reproducing the PL strongly depends on the initial conditions. Although in all simulations the synoptic environment is favourable for PL development, with a strong low-level temperature gradient and an upper-level through, only the low-level atmospheric fields of three of the simulations lead to PL development through baroclinic instability. The two simulations that best captured the PL represent a PL deeper than the observed one, and they show higher temperature mean bias compared to the other simulations, indicating that the ocean surface fluxes may be too strong. In general, ERA5 has more skill than the simulations at reproducing the observed PL, but the CRCM6/GEM4 simulation with initialisation time closer to the genesis time of the PL reproduces quite well small scale features as low-level baroclinic instability during the PL development phase.

Uncertainty Quantification of Deep Learning–Based Statistical Downscaling of Climatic Parameters

Abstract This study investigated the uncertainty involved in statistical downscaling of hydroclimatic time series obtained by artificial neural networks (ANNs). Phase 6 of the Coupled Model Intercomparison Project (CMIP6) general circulation model (GCM) Canadian Earth System Model, version 5 (CanESM5), was used as large-scale predictor data for downscaling temperature and precipitation parameters. Two ANNs, feed forward and long short-term memory (LSTM), were utilized for statistical downscaling. To quantify the uncertainty of downscaling, prediction intervals (PIs) were estimated via the lower upper bound estimation (LUBE) method. To assess performance of proposed models in different climate regimes, data from the Tabriz and Rasht stations in Iran were employed. The calibrated models via historical GCM data were used for future projections via the high-forcing and fossil fuel–driven development scenario shared socioeconomic pathway (SSP) 5-8.5. Projections were compared with the Canadian Regional Climate Model 4 (Can-RCM4) projections via the same scenario. Results indicated that both LSTM-based point predictions and PIs are more accurate than the feedforward neural network (FFNN)-based predictions, with an average of 55% higher Nash–Sutcliffe efficiency (NSE) for point predictions and 25% lower coverage width criterion (CWC) for PIs. Projections suggested that Tabriz is going to experience a warmer climate with an increase in average temperature of 2° and 5°C for near and far futures, respectively, and a drier climate with a 20% decrease in precipitation until 2100. Future projections for the Rasht station, however, suggested a more uniform climate with less seasonal variability. Average precipitation will increase by up to 25% and 70% until near and far future periods, respectively. Ultimately, point predictions show that the average temperature in Rasht will increase by 1°C until the near future and then be a constant average temperature until the far future. Significance Statement The downscaling of hydroclimatic parameters is subject to uncertainty. The best way is to provide an area with the highest contingency of them as a prediction interval. The reduction width of such an interval leads to increased confidence in explaining and predicting these processes. We proposed and applied a deep learning–based machine learning method for both point prediction and prediction interval estimation of temperature and precipitation parameters for the future over two different climatic regions. The results show the superiority of such a machine learning–based prediction interval estimation for quantification of the downscaling uncertainty.

Hydrological models weighting for hydrological projections: The impacts on future peak flows

Reliable hydrological projections are of great importance for climate change impact studies, especially knowing that these analyses can allow identifying regional adaptation and mitigation strategies for the future. However, the literature has highlighted that hydrological models used under climate conditions that are contrasting to those used during their calibrations can lower their performance and reliability, an issue that lowers the confidence in hydrological projections and adds uncertainty to their analyses. More studies are needed to explore this issue and evaluate potential strategies that might improve hydrological models’ reliability in climate change impact studies. Thus, the present study evaluates the robustness of hydrological models under contrasting climatic conditions and investigates the use of weighting techniques to improve their combined performance and reliability. The robustness of five lumped hydrological models is analysed using a Differential Split-Sample Testing (DSST) that evaluates their performance under cold, warm, humid and dry historical contrasting conditions over 77 basins covering different hydroclimatic conditions (two domains, one located in Quebec, Canada, and one in Mexico). Additionally, four basins were selected from the study area to evaluate the robustness of a more complex semi-distributed and more physically-based hydrological model and compare its simulations against the simpler lumped hydrological models. Based on the resulting performance of each hydrological model, five different weighting methods were applied to evaluate the potential improvements in the multi-model ensemble performance and quantify their effects on hydrological projections, particularly on future peak flows. For each basin, these streamflow projections were produced using two regional climate simulations (one per studied domain) issued from the Canadian Regional Climate Model version 5 (CRCM5) under the Representative Concentration Pathway (RCP) 8.5 for the 1976–2005, 2041–2070 and 2070–2099 periods. The results showed that weighting hydrological models, even with the most simplistic methods, showed better performances over historical contrasting conditions than the best-performing lumped hydrological model. Between the different weighting methods, the Granger-Ramanathan type A showed the overall best performance among the different basins and climate conditions, particularly in peak streamflows. Over the CRCM5-driven peak flow projections, the weighting methods Granger-Ramanathan types A and B produced the largest impacts on the projected floods magnitudes and climate change signals. On the other hand, the additional tests using the semi-distributed and more physically-based hydrological model revealed that this model showed more robust simulations than the weighted lumped hydrological models on low flows over the four selected basins. Additionally, more robust high-flow simulations were observed over a small snow-dominated basin, suggesting a potential added value in adding more complex hydrological models to simulate conditions under a changed climate.

Future Physical and Biogeochemical Ocean Conditions under Climate Change along the British Columbia Continental Margin

ABSTRACT Climate change impacts coastal ecosystems through large scale changes in temperature, stratification, circulation and ocean acidification. Here, the potential response of the British Columbia continental margin to climate change is investigated using a regional ocean circulation-biogeochemical model to downscale climate change projections from the Canadian regional and global climate models (CanRCM4/CanESM2) under two Intergovernmental Panel on Climate Change emission scenarios (RCP 4.5 and RCP 8.5). Projections of future physical and biogeochemical conditions for the 2041–2070 period are compared to the recent past (1981–2010). We found an overall annual average warming of >1.6°C in sea surface temperature, increase in stratification in the upper layer, and decrease in surface pH of as much as 0.21. Increasing stratification and changing winds have a limited impact on nitrate availability, phytoplankton biomass and primary production, whilst ocean warming increases primary production by up to 30% in most of the model domain. Increased atmospheric CO2 contributes to acidification over the model domain with a decrease in pH and aragonite saturation (Ωarag) at all depths resulting in an increase of 20 to 32% of the volume of Ωarag ≤1 in the upper 100 m of the continental shelf depending on the climate scenario. Our projected results, therefore, show that future climate change may alter the amount of food available for higher trophic levels and the habitat of benthic species, since bottom waters on the shelf will be undersaturated with respect to aragonite for 2–3 months in mid-summer. Both climate change scenarios results in a similar pattern of changes but projected changes were stronger and more extensive under RCP 8.5 showing the benefit of mitigation efforts in reducing the effect of climate change on marine ecosystem stressors.

Precipitation Extremes and Their Links with Regional and Local Temperatures: A Case Study over the Ottawa River Basin, Canada

In the context of global warming, the Clausius–Clapeyron (CC) relationship has been widely used as an indicator of the evolution of the precipitation regime, including daily and sub-daily extremes. This study aims to verify the existence of links between precipitation extremes and 2 m air temperature for the Ottawa River Basin (ORB, Canada) over the period 1981–2010, applying an exponential relationship between the 99th percentile of precipitation and temperature characteristics. Three simulations of the Canadian Regional Climate Model version 5 (CRCM5), at three different resolutions (0.44°, 0.22°, and 0.11°), one simulation using the recent CRCM version 6 (CRCM6) at “convection-permitting” resolution (2.5 km), and two reanalysis products (ERA5 and ERA5-Land) were used to investigate the CC scaling hypothesis that precipitation increases at the same rate as the atmospheric moisture-holding capacity (i.e., 6.8%/°C). In general, daily precipitation follows a lower rate of change than the CC scaling with median values between 2 and 4%/°C for the ORB and with a level of statistical significance of 5%, while hourly precipitation increases faster with temperature, between 4 and 7%/°C. In the latter case, rates of change greater than the CC scaling were even up to 10.2%/°C for the simulation at 0.11°. A hook shape is observed in summer for CRCM5 simulations, near the 20–25 °C temperature threshold, where the 99th percentile of precipitation decreases with temperature, especially at higher resolution with the CRCM6 data. Beyond the threshold of 20 °C, it appears that the atmospheric moisture-holding capacity is not the only determining factor for generating precipitation extremes. Other factors need to be considered, such as the moisture availability at the time of the precipitation event, and the presence of dynamical mechanisms that increase, for example, upward vertical motion. As mentioned in previous studies, the applicability of the CC scaling should not be generalised in the study of precipitation extremes. The time and spatial scales and season are also dependent factors that must be taken into account. In fact, the evolution of precipitation extremes and temperature relationships should be identified and evaluated with very high spatial resolution simulations, knowing that local temperature and regional physiographic features play a major role in the occurrence and intensity of precipitation extremes. As precipitation extremes have important effects on the occurrence of floods with potential deleterious damages, further research needs to explore the sensitivity of projections to resolution with various air temperature and humidity thresholds, especially at the sub-daily scale, as these precipitation types seem to increase faster with temperature than with daily-scale values. This will help to develop decision-making and adaptation strategies based on improved physical knowledge or approaches and not on a single assumption based on CC scaling.

Changing Nature of High‐Impact Snowfall Events in Eastern North America

AbstractSnowstorms cause substantial disruption in the eastern United States and Canada each winter. While reductions in annual snowfall are projected over most of this region due to anthropogenic global warming, daily snowfall extremes that have the greatest impact may not decrease in the same manner. We examine changes to two extreme snowfall metrics: the 95th percentile of daily snowfall (SF95, cm) and the number of events during which 10% of the mean annual snowfall is exceeded during a single day (TC10, events yr−1). We explore changes to these metrics in two ensembles of the fifth‐generation Canadian Regional Climate Model, including four 0.22° (≈25 km) simulations driven by different coupled general circulation models as well as the higher‐resolution (0.11°, ≈12 km) ClimEx ensemble, driven by 50 members of a large initial‐condition ensemble of one global model. We find that while mean annual snowfall is projected to decrease over our domain, SF95 is projected to remain relatively constant, suggesting that the most extreme daily snowfalls currently observed are likely to occur even in a warmer future climate. The region of the largest TC10 values exhibits a northward shift, with a larger percentage of annual snowfall occurring during a few large events along the U.S.‐Canada border. These projected changes to the nature of snowfall events may have important socioeconomic consequences in this densely populated region of North America.

Analysis of the Development Mechanisms of a Polar Low over the Norwegian Sea Simulated with the Canadian Regional Climate Model

Polar lows (PLs) are maritime mesoscale cyclones associated with severe weather. They develop during marine cold air outbreaks near coastlines and the sea ice edge. Unfortunately, our knowledge about the mechanisms leading to PL development is still incomplete. This study aims to provide a detailed analysis of the development mechanisms of a PL that formed over the Norwegian Sea on 25 March 2019 using the output of a simulation with the sixth version of the Canadian Regional Climate Model (CRCM6/GEM4), a convection-permitting model. First, the life cycle of the PL is described and the vertical wind shear environment is analysed. Then, the horizontal wind divergence and the baroclinic conversion term are computed, and a surface pressure tendency equation is developed. In addition, the roles of atmospheric static stability, latent heat release, and surface heat and moisture fluxes are explored. The results show that the PL developed in a forward-shear environment and that moist baroclinic instability played a major role in its genesis and intensification. Baroclinic instability was initially only present at low levels of the atmosphere, but later extended upward until it reached the mid-troposphere. Whereas the latent heat of condensation and the surface heat fluxes also contributed to the development of the PL, convective available potential energy and barotropic conversion do not seem to have played a major role in its intensification. In conclusion, this study shows that a convection-permitting model simulation is a powerful tool to study the details of the structure of PLs, as well as their development mechanisms.

Projected changes of wind-driven rain and moisture load in wall assemblies across Canada

The changing climate is affecting many aspects of the built environment. In respect to the wall assemblies of buildings, any change in the moisture load to the wall would influence its long-term moisture performance. Given that Wind-Driven Rain (WDR) is the primary source of the moisture load to the wall and that this load may also change as a result of changes to the climate in the future, it is expected then, that such changes will subsequently affect the moisture performance of wall assemblies. In this study, changes in moisture load of a vinyl-clad and a brick veneer-clad wall assemblies, located in 11 selected Canadian cities, subjected to climate data for two future projected climate scenarios, 2.0 °C (2034–2064) and 3.5 °C (2062–2092), as generated from the Canadian Regional Climate Model (CanRCM4), were assessed. Climatic parameters such as, WDR and Driving Rain Wind Pressure (DRWP), as well as their Intensity-Duration-Frequency (IDF) curves were analyzed and produced to determine boundary conditions for watertightness testing. The two analyzed wall assemblies were thereafter subjected to such tests, and the results of which were used to establish a two-step approach to estimate the moisture loads in these two wall assembly types based on hourly WDR load and hourly DRWP. Moisture loads of each wall assembly type during future projected climate scenarios were compared to those of the historical baseline periods. Increase in moisture load to both wall assembly types were found for most of cities analyzed in this study.

Future change in amplitude and timing of high-flow events in a Canadian subarctic watershed

The Hudson Bay basin is a large contributor of freshwater input in the Arctic Ocean and is also an area affected by destructive spring floods. In this study, the hydrological model MESH (Modelisation Environmentale Communautaire - Surface and hydrology) was set up for the Groundhog River watershed situated in the Hudson Bay basin, to simulate the future evolution of streamflow and annual maximum streamflow. MESH was forced by meteorological data from ERA5 reanalyses in the historical period (1979–2018) and 12 models of the Coupled model intercomparison Project Phase 5 (CMIP5) downscaled with the Canadian Regional Climate model version 5 (CRCM5) in historical (1979–2005) and scenario period (2006–2098). The projections consistently indicate an earlier spring flow and a reduction in the amount of annual maximum streamflow by the end of the 21st century. Under the RCP8.5 scenario, the annual maximum streamflow occurring in the spring is expected to be advanced by 2 weeks and reduced on average from 852 m3/s (±265) in the historical period (1979–2018) to 717m3/s (±250) by the end of the 21st century (2059–2098). Because the seasonal projection of streamflow was not investigated in previous studies, this work is an important first step to assess the seasonal change of streamflow in the Hudson Bay region under climate change.

Inter-seasonal connection of typical European heatwave patterns to soil moisture

Although prolonged heat periods have become a recurring feature of European climate, little knowledge is available on dominant spatial patterns of heatwaves and their influence on moisture-related processes. Increased knowledge will help to improve heatwave and drought prediction and mitigation. This study uses hierarchical agglomerative clustering to derive nine dominating spatial heatwave patterns from a 50-member regional climate model (Canadian Regional Climate Model version 5, CRCM5-LE). The heatwave patterns correspond well with clusters derived from an observational data set (E-OBS) and with extreme historical heatwave events. Moreover, we analyse the occurrence of heatwaves in the identified spatial patterns regarding a soil moisture deficit present before and after the event. We show that negative soil moisture anomalies in the preceding winter/spring (JFMA) can serve as a predictor for heatwaves in South Europe. For North Europe, we find a negative correlation between the number of heatwave days in summer and autumn (OND) soil moisture content.

Representativeness of the Precipitation Observing Network for Monitoring Precipitation Change and Variability in Canada

ABSTRACT While there are thousands of precipitation stations in the Canadian climate archive, it has been challenging to estimate regional and national averages of precipitation for the purpose of monitoring climate change and variability, because of evolution of the monitoring network and generally sparse network density, in particular in the North. Changes in the observing network have resulted in segmentation and inhomogeneities in precipitation records. We used monthly precipitation from the Canadian Regional Climate Model (CanRCM4) large ensemble simulations as a proxy of observations with complete spatial and temporal coverage. By comparing results from the complete-coverage dataset with versions masked by observational coverage, we examined the representativeness of two long-term precipitation datasets for the estimation of annual mean precipitation at regional and national levels for the purpose of monitoring precipitation change and variability in Canada. We also analysed the implications of changes in the network and the possible added value of data processing, such as in-filling through spatially interpolating station records. We find that at the best coverage of approximately 450 precipitation stations in the Adjusted and/or Homogenized Canadian Climate Data dataset, station coverage is, in general, adequate for the purpose of monitoring long term precipitation trends. However, this capability is severely compromised if station density changes (reduces) with time or if there is a substantial number of missing values over time. The addition of station records in regions already better represented (i.e. regions with more population) does not provide significant improvement. In-filling over space through spatial interpolation does add value, provided that there is sufficient information in the station network. Our analysis demonstrates the importance of maintaining a consistent long-term network with sufficient station density for the purpose of monitoring climate change and variability.

On the Intercontinental Transferability of Regional Climate Model Response to Severe Forestation

The biogeophysical effects of severe forestation are quantified using a new ensemble of regional climate simulations over North America and Europe. Following the protocol outlined for the Land-Use and Climate Across Scales (LUCAS) intercomparison project, two sets of simulations are compared, FOREST and GRASS, which respectively represent worlds where all vegetation is replaced by trees and grasses. Three regional climate models were run over North America. One of them, the Canadian Regional Climate Model (CRCM5), was also run over Europe in an attempt to bridge results with the original LUCAS ensemble, which was confined to Europe. Overall, the CRCM5 response to forestation reveals strong inter-continental similarities, including a pronounced wintertime and springtime warming concentrated over snow-masking evergreen forests. Crucially, these northern evergreen needleleaf forests populate lower, hence sunnier, latitudes in North America than in Europe. Snow masking reduces albedo similarly over both continents, but stronger insolation amplifies the net shortwave radiation and hence warming simulated over North America. In the summertime, CRCM5 produces a mixed response to forestation, with warming over northern needleleaf forests and cooling over southern broadleaf forests. The partitioning of the turbulent heat fluxes plays a major role in determining this response, but it is not robust across models over North America. Implications for the inter-continental transferability of the original LUCAS results are discussed.

Quantifying the Impact of Precipitation-Type Algorithm Selection on the Representation of Freezing Rain in an Ensemble of Regional Climate Model Simulations

Abstract Given their potentially severe impacts, understanding how freezing rain events may change as the climate changes is of great importance to stakeholders including electrical utility companies and local governments. Identification of freezing rain in climate models requires the use of precipitation-type algorithms, and differences between algorithms may lead to differences in the types of precipitation identified for a given thermodynamic profile. We explore the uncertainty associated with algorithm selection by applying four algorithms (Cantin and Bachand, Baldwin, Ramer, and Bourgouin) offline to an ensemble of simulations of the fifth-generation Canadian Regional Climate Model (CRCM5) at 0.22° grid spacing. First, we examine results for the CRCM5 driven by ERA-Interim reanalysis to analyze how well the algorithms reproduce the recent climatology of freezing rain and how results vary depending on algorithm parameters and the characteristics of available model output. We find that while the Ramer and Baldwin algorithms tend to be better correlated with observations than Cantin and Bachand or Bourgouin, their results are highly sensitive to algorithm parameters and to the number of pressure levels used. We also apply the algorithms to four CRCM5 simulations driven by different global climate models (GCMs) and find that the uncertainty associated with algorithm selection is generally similar to or greater than that associated with choice of driving GCM for the recent past climate. Our results provide guidance for future studies on freezing rain in climate simulations and demonstrate the importance of accounting for uncertainty between algorithms when identifying precipitation type from climate model output. Significance Statement Freezing rain events and ice storms can have major consequences, including power outages and dangerous road conditions. It is therefore important to understand how climate change might affect the frequency and severity of these events. One source of uncertainty in climate studies of these events is related to the choice of algorithm used to detect freezing rain in model output. We compare the frequency of freezing rain identified using four different algorithms and find sometimes large differences depending on the algorithm chosen over some regions. Our findings highlight the importance of taking this source of uncertainty into account and will provide researchers with guidance as to which algorithms are best suited for climate studies of freezing rain.

Attributing heavy rainfall event in Berchtesgadener Land to recent climate change – Further rainfall intensification projected for the future

On 17 July 2021, storm “Bernd” hit the alpine region of Berchtesgadener Land inducing short-duration heavy rainfall, which triggered flash floods, debris flows, landslides, and flooding. Based on the observed precipitation data and an analysis of the runoff measurements, the driver of the event is defined as a 3-h rainfall. The 50-member single model initial-condition large ensemble of the Canadian Regional Climate Model version 5 under the emission scenario RCP 8.5 is employed to explore the occurrence probability of the event under historic, current, and future climate conditions. For the rainfall event, a return period of 21 years (17–25 years) is found for the current climate (2006-2035). Under historic climate (1970-1999) the event is estimated to be 3.1 (1.9–5.2) times less likely equalling a return period of 64 years (48–90 years). This is particularly critical as experience and observational data from the recent past have been crucial to the design of infrastructure and still influence current planning. For future climate conditions, the event probability is projected to increase resulting in return periods of 7.8 years (7.1–8.8 years; 2040-2069) and 4.9 years (4.6–5.4 years; 2070-2099), respectively. The future shifts in extreme precipitation must be urgently taken into account for appropriate adaptation measures.

A Multi‐Algorithm Analysis of Projected Changes to Freezing Rain Over North America in an Ensemble of Regional Climate Model Simulations

AbstractFreezing 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.

An Integrated Extreme Rainfall Modeling Tool (SDExtreme) for Climate Change Impacts and Adaptation

The estimation of the Intensity–Duration–Frequency (IDF) relation is often necessary for the planning and design of various hydraulic structures and design storms. It has been an increasingly greater challenge due to climate change conditions. This paper therefore proposes an integrated extreme rainfall modeling software package (SDExtreme) for constructing the IDF relations at a local site in the context of climate change. The proposed tool is based on a temporal downscaling method to describe the relationships between daily and sub-daily extreme precipitation using the scale-invariance General Extreme Value (GEV) distribution. In addition, SDExtreme provides a modified bootstrap technique to determine confidence intervals (CIs) of the estimated IDF curves for current and the future climate conditions. The feasibility and accuracy of SDExtreme were assessed using rainfall data available from the selected rain gauge stations in Quebec and Ontario provinces (Canada) and climate simulations under three different climate change scenarios provided by the Canadian Earth System Model (CanESM2) and the Canadian Regional Climate Model (CanRCM4).