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The UQAM Heatwave ERA5 Archive and Temperatures (U-HEAT) catalog is a global dataset of temperature and heatwave data spanning 1940 to 2022. The temperature data features the maximum daily 2-m temperature, the 90th percentile of the maximum daily 2-m temperature, and an indication as to whether a given location (grid point) is experiencing a heatwave or not on a given day. The heatwave data includes metrics such as the duration, the cumulated intensity and the maximum intensity of heatwaves occuring in the study period as well as their location (grid point) and start date. Both the temperature and the heatwave metrics data were calculated from the ERA5 data produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). More information on the catalog can be found in the documentation and the README files. Le catalogue UQAM Heatwave ERA5 Archive and Temperatures (U-HEAT) est un jeu de données global de température et de vague de chaleur pour la période entre 1940 et 2022. Les données de température comprennent le maximum quotidien de la température à 2m, le 90e percentile du maximum quotidien de la température à 2m et une indication permettant de savoir si un lieu donné (point de grille) subit ou non une vague de chaleur pour un jour donné. Les données de vague de chaleur incluent des métriques comme la durée, l'intensité cumulée et l'intensité maximale de vagues de chaleur qui se sont produites durant la période d'étude en plus de leur emplacement (point de grille) et leur date de début. Les données de température et de vague de chaleur ont été calculées à partir du jeu de données ERA5 produit par le European Centre for Medium-Range Weather Forecasts (ECMWF). Le fichier de documentation et le fichier README peuvent être consultés pour obtenir plus d'information à propos du catalogue.

Abstract Several observational precipitation products that provide high temporal (≤3 h) and spatial (≤0.25°) resolution gridded estimates are available, although no single product can be assumed worldwide to be closest to the (unknown) “reality.” Here, we propose and apply a methodology to quantify the uncertainty of a set of precipitation products and to identify, at individual grid points, the products that are likely wrong (i.e., outliers). The methodology is applied over eastern North America for the 2015–2019 period for eight high‐resolution gridded precipitation products: CMORPH, ERA5, GSMaP, IMERG, MSWEP, PERSIANN, STAGE IV and TMPA. Four difference metrics are used to quantify discrepancies in different aspects of the precipitation time series, such as the total accumulation, two characteristics of the intensity‐frequency distribution, and the timing of precipitating events. Large regional and seasonal variations in the observational uncertainty are found across the ensemble. The observational uncertainty is higher in Canada than in the United States, reflecting large differences in the density of precipitation gauge measurements. In northern midlatitudes, the uncertainty is highest in winter, demonstrating the difficulties of satellite retrieval algorithms in identifying precipitation in snow‐covered areas. In southern midlatitudes, the uncertainty is highest in summer, probably due to the more discontinuous nature of precipitation. While the best product cannot be identified due to the lack of an absolute reference, our study is able to identify products that are likely wrong and that should be excluded depending on the specific application.

This catalogue includes seasonal mean precipitation fields from simulations and reanalysis used for the calculation of the Spatial Spin-Up Distance (SSUD). A total of seven simulation were conducted using the convection-permitting configuration (2.5-km grid spacing) of version 6 of the Canadian Regional Climate Model (CRCM6/GEM5; hereafter denoted as GEM2.5 for simplicity). The CRCM6/GEM5 version used here is based on version 5.0.2 of the Global Environmental Multiscale model (GEM5). GEM2.5 simulations were driven directly by the ERA5 reanalysis or by 12-km (GEM12) simulations also performed using the CRCM6/GEM5 model (which was in turn driven by the ERA5 reanalysis). The catalogue comprises seasonal mean precipitation fields from all seven GEM2.5 simulations, from the ERA5 reanalysis and from two GEM12 intermediate simulations (GEM12_SUN and GEM12_P3). Seasonal means were calculated using the common convention: December, January and February (DJF) for winter; March, April and May (MAM) for spring; June, July and August (JJA) for summer; and September, October and November (SON) for fall. Precipitations fields are given in mm/h.

Abstract While the ERA5 reanalysis is commonly utilized in climate studies on extratropical cyclones (ETCs), only a few studies have quantified its ability in the representation of ETCs over land. To address this gap, this study evaluates ERA5's skill in representing the ETC‐associated 10‐m wind speed and the precipitation in central and eastern North America during 2005–2019. Hourly data collected from ~3000 stations, amounting to around 420 million reports stored in the Integrated Surface Database, is used as reference. For the spatial‐averaged ETC properties, ERA5 shows a good skill for wind speed with normalized mean bias (NMB) of −0.7% and normalized root‐mean‐square error (NRMSE) of 14.3%, despite a tendency to overestimate low winds and underestimate high winds. The ERA5 skill is worse for precipitation than for wind speed with NMB of −10.4% and NRMSE of 56.5% and a strong tendency to underestimate high values. For both variables, the best and worst performance is found in DJF and JJA, respectively. Negative biases are often identified over regions with stronger precipitation/wind speeds, and a systematic underestimation of wind speed is found over the Rockies with complex topography. Compared to the averaged ETCs, ERA5's performance deteriorates for the top 5% extreme ETCs with a stronger tendency to underestimate both wind speed and precipitation (NMB of −10.2% and −22.6%, respectively). Furthermore, ERA5's skill is worse for local extreme values within ETCs than for spatial averages. Our results highlight some important limitations of the ERA5 reanalysis products for studies looking at the possible impacts of ETCs.

Abstract We quantify the skill of Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 models to represent daily temperature extremes. We find CMIP models systematically exaggerate the magnitude of daily temperature anomalies for both cold and hot extremes. We assess the contribution to a daily temperature extreme from four terms: the long‐term mean annual cycle, the diurnal cycle, synoptic variability, and seasonal variability for both cold and hot extremes. These four terms are combined, and the overall performance of individual climate models assessed. This identifies those models that can simulate temperature extremes well and simulate them well for the right reasons. The new error metric shows that increases in horizontal resolution usually lead to a better performance particularly for the coarser resolution models. The CMIP6 improvements relative to CMIP5 are systematic across most land regions and are only partially explained by the increase in horizontal resolution, and other differences must therefore help explain the higher CMIP6 skill. , Key Points CMIP5 and CMIP6 models exaggerate the magnitude of daily temperature anomalies for hot days and cold nights extremes Higher‐resolution models improve the simulation of temperature extremes largely due to better simulation of synoptic scales CMIP6 outperforms the simulation of temperature extremes compared to CMIP5 beyond the benefits given by the higher resolution

The NA-ISD2ERA is a station-based gridded dataset of hourly 10-m wind speed, surface total precipitation, sea-level pressure, and 2-m air and dew point temperature observations interpolated on the regular 0.25° latitude-longitude ERA5 grid over North America for the 1990-2021 period. Station observations are from the Integrated Surface Database (ISD) developed by the National Centers for Environmental Information (NCEI) of the National Oceanic and Atmospheric Administration (NOAA) (Smith et al. 2011). It includes over 35,000 weather stations around the world of hourly to sub-hourly in situ observations for numerous variables such as wind speed, precipitation, sea-level pressure, air and dew point temperature. The NCEI ISD dataset is available at https://www.ncei.noaa.gov. ERA5 is the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (Hersbach et al., 2020). Quality checks implemented in ISD are used to select reliable observations. For each ERA5 grid cell and at each hour, the data are interpolated by taking the nearest available ISD observation to the grid cell center that is located within the targeted grid cell.

The NAEC catalogue comprises information on extratropical cyclone (ETC) tracks in North America (20–80 N and 180-0W) from January 1979 to December 2020. The source data used to produce this dataset is obtained from the ECMWF ERA5 reanalysis at 1-hour spatial resolution and 0.25x0.25 degree spatial resolution. In addition to the location, time, and intensity, this dataset also includes ETC-associated impact variables such as the near-surface wind speed, wind gust, and precipitation, averaged using different radii around the ETC center. Both absolute and relative (to the local climatology) measures are provided. This catalogue provides useful information for the assessment of ETC-induced impacts over North America.

Abstract. A fundamental issue associated with the dynamical downscaling technique using limited-area models is related to the presence of a “spatial spin-up” belt close to the lateral boundaries where small-scale features are only partially developed. Here, we introduce a method to identify the distance from the border that is affected by the spatial spin-up (i.e., the spatial spin-up distance) of the precipitation field in convection-permitting model (CPM) simulations. Using a domain over eastern North America, this new method is applied to several simulations that differ on the nesting approach (single or double nesting) and the 3-D variables used to drive the CPM simulation. Our findings highlight three key points. Firstly, when using a single nesting approach, the spin-up distance from lateral boundaries can extend up to 300 km (around 120 CPM grid points), varying across seasons, boundaries and driving variables. Secondly, the greatest spin-up distances occur in winter at the western and southern boundaries, likely due to strong atmospheric inflow during these seasons. Thirdly, employing a double nesting approach with a comprehensive set of microphysical variables to drive CPM simulations offers clear advantages. The computational gains from reducing spatial spin-up outweigh the costs associated with the more demanding intermediate simulation of the double nesting. These results have practical implications for optimizing CPM simulation configurations, encompassing domain selection and driving strategies.

Abstract This study investigates the seasonality of near‐surface wind speeds associated with extratropical cyclones (ETCs) over northeastern North America using a global reanalysis data set during 1979–2020. As opposed to most studies that emphasize winter storms, ETCs during the fall exhibit significantly stronger 10‐m winds over this region due to the slightly stronger continental cyclones and significantly weaker low‐level stability during that time of the year. Also, ETCs favor inland lakes and Hudson Bay during the low‐ice‐content fall season, leading to lower surface roughness. Combining these results, we derive simple linear regressions to predict the 10‐m wind speed given three variables: 850‐hPa wind speed, low‐level Richardson number, and surface roughness length. This formula captures the observed seasonality and serves as a valuable tool for cyclone near‐surface wind risk assessment. , Plain Language Summary Extratropical cyclones can bring powerful winds that can cause severe damage to infrastructure. We find that cyclones with severe winds are the most frequent in the fall season over continental northeastern North America. Three reasons are found responsible: stronger continental cyclones, weaker low‐level atmospheric stability, and the lower surface roughness over lakes and Hudson Bay, where cyclones frequently occur in fall. A simple formula that can effectively assess the near‐surface wind speeds associated with cyclones is derived based on these results. , Key Points Extratropical‐cyclone‐associated 10‐m wind speeds are the strongest in the fall season over northeastern North America Besides stronger continental cyclones and 850‐hPa winds, weaker low‐level stability in fall favors stronger 10‐m wind speeds in this region Linear regression using 850‐hPa wind, Richardson number, and surface roughness well predicts cyclones' 10‐m wind speeds and seasonality

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.

The Australian Alps are the highest mountain range in Australia, which are important for biodiversity, energy generation and winter tourism. Significant increases in temperature in the past decades has had a huge impact on biodiversity and ecosystem in this region. In this study, observed temperature is used to assess how temperature changed over the Australian Alps and surrounding areas. We also use outputs from two generations of NARCliM (NSW and Australian Regional Climate Modelling) to investigate spatial and temporal variation of future changes in temperature and its extremes. The results show temperature increases faster for the Australian Alps than the surrounding areas, with clear spatial and temporal variation. The changes in temperature and its extremes are found to be strongly correlated with changes in albedo, which suggests faster warming in cool season might be dominated by decrease in albedo resulting from future changes in natural snowfall and snowpack. The warming induced reduction in future snow cover in the Australian Alps will have a significant impact on this region.

Abstract Like many western boundary currents, the East Australian Current (EAC) extension is projected to get stronger and warmer in the future. The CMIP5 multimodel mean (MMM) projection suggests up to 5°C of warming under an RCP85 scenario by 2100. Previous studies employed Sverdrup balance to associate a trend in basin wide zonally integrated wind stress curl (resulting from the multidecadal poleward intensification in the westerly winds over the Southern Ocean) with enhanced transport in the EAC extension. Possible regional drivers are yet to be considered. Here we introduce the NEMO‐OASIS‐WRF coupled regional climate model as a framework to improve our understanding of CMIP5 projections. We analyze a hierarchy of simulations in which the regional atmosphere and ocean circulations are allowed to freely evolve subject to boundary conditions that represent present‐day and CMIP5 RCP8.5 climate change anomalies. Evaluation of the historical simulation shows an EAC extension that is stronger than similar ocean‐only models and observations. This bias is not explained by a linear response to differences in wind stress. The climate change simulations show that regional atmospheric CMIP5 MMM anomalies drive 73% of the projected 12 Sv increase in EAC extension transport whereas the remote ocean boundary conditions and regional radiative forcing (greenhouse gases within the domain) play a smaller role. The importance of regional changes in wind stress curl in driving the enhanced EAC extension is consistent with linear theory where the NEMO‐OASIS‐WRF response is closer to linear transport estimates compared to the CMIP5 MMM. , Plain Language Summary In recent decades, enhanced warming, severe marine heatwaves, and increased transport by the East Australian Current have led to the invasion of nonnative species and the destruction of kelp forests east of Tasmania. The East Australian Current extension is projected to get stronger and warmer in the future. We seek to better understand coupled climate model projections for the Tasman Sea. This is difficult because there is large model diversity and considerable uncertainty as to how and where future changes will occur. In addition, little is known about the possible importance of regional versus large‐scale changes in surface time‐mean winds in driving future circulation changes. Here we use a single limited‐domain ocean‐atmosphere coupled model that takes the average model projections as its inputs and finds that changes in the regional wind stress are most important for the enhanced projected East Australian Current extension. We also find that these projected changes are consistent with simple linear theory and the simulated regional changes in wind stress. , Key Points NEMO‐OASIS‐WRF coupled regional climate model is evaluated and introduced as a new tool for analyzing Tasman Sea climate projections NEMO‐OASIS‐WRF projections suggest that local atmospheric changes drive 73% of the projected 12 Sv increase in EAC extension transport The importance of regional changes in wind stress curl driving the enhanced EAC extension is consistent with linear theory

Abstract The strength and variability of the Southern Ocean carbon sink is a significant source of uncertainty in the global carbon budget. One barrier to reconciling observations and models is understanding how synoptic weather patterns modulate air-sea carbon exchange. Here, we identify and track storms using atmospheric sea level pressure fields from reanalysis data to assess the role that storms play in driving air-sea CO 2 exchange. We examine the main drivers of CO 2 fluxes under storm forcing and quantify their contribution to Southern Ocean annual air-sea CO 2 fluxes. Our analysis relies on a forced ocean-ice simulation from the Community Earth System Model, as well as CO 2 fluxes estimated from Biogeochemical Argo floats. We find that extratropical storms in the Southern Hemisphere induce CO 2 outgassing, driven by CO 2 disequilibrium. However, this effect is an order of magnitude larger in observations compared to the model and caused by different reasons. Despite large uncertainties in CO 2 fluxes and storm statistics, observations suggest a pivotal role of storms in driving Southern Ocean air-sea CO 2 outgassing that remains to be well represented in climate models, and needs to be further investigated in observations.

Abstract Compound events (CEs) are weather and climate events that result from multiple hazards or drivers with the potential to cause severe socio-economic impacts. Compared with isolated hazards, the multiple hazards/drivers associated with CEs can lead to higher economic losses and death tolls. Here, we provide the first analysis of multiple multivariate CEs potentially causing high-impact floods, droughts, and fires. Using observations and reanalysis data during 1980–2014, we analyse 27 hazard pairs and provide the first spatial estimates of their occurrences on the global scale. We identify hotspots of multivariate CEs including many socio-economically important regions such as North America, Russia and western Europe. We analyse the relative importance of different multivariate CEs in six continental regions to highlight CEs posing the highest risk. Our results provide initial guidance to assess the regional risk of CE events and an observationally-based dataset to aid evaluation of climate models for simulating multivariate CEs.

Storms are the most significant meteorological phenomena that affect the formation of coasts and human livelihood along them. Thus, risks related to coastal storms, such as flooding, loss of land, shipping, and other offshore activity, have had a significant influence on coastal societies and their economies. In the early 21st century, anthropogenic climate change will affect the locations and intensities of coastal storminess, impacting society. Storms are studied not only by natural scientists but also by social scientists. The former deal with the climatologies, dynamics, and mechanisms of storms but also with the identification of different types of storms, such as extratropical baroclinic storms, explosive cyclones, tropical storms, polar lows, medicanes, Vb-cyclones, and Australian east coast storms. Their significance is often through their physical impacts, in particular ocean waves and storm surges, which were and are associated with massive losses of lives, sometimes up to several hundred thousand people, and wealth. The perceptions of what storms constitute were different in different cultural contexts and times. In earlier days, higher forces were responsible for such storms, which they used to transfer messages to humans, physically based ideas have been forming since the 16th century. Another significant historical development was societies preparing to reduce their vulnerability to storms and to implement practices of insurance and risk management.