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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

ABSTRACT High‐resolution reanalyses offer the potential to improve our understanding of midlatitude cyclones, particularly smaller‐scale systems and those with complex structures. However, previous studies have demonstrated large variations in the frequency and characteristics of Australian midlatitude cyclones between reanalyses when using their native resolution. In this paper we use satellite observations of winds and rainfall in order to evaluate the ability of the ERA‐Interim, JRA55, MERRA and CFSR reanalyses to reproduce Australian east coast cyclones. The MERRA reanalysis produces a large number of erroneous small‐scale lows without cyclonic wind patterns using a simple pressure‐difference‐based cyclone identification and tracking method. Consequently, we recommend the ERA‐Interim reanalysis when using such methods, or applying more complex tracking methods that are able to compensate for these issues.

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 Increased temperature will result in longer, more frequent, and more intense heat waves. Changes in temperature variability have been deemed necessary to account for future heat wave characteristics. However, this has been quantified only in Europe and North America, while the rest of the globe remains unexplored. Using late century global climate projections, we show that annual mean temperature increases is the key factor defining heat wave changes in most regions. We find that commonly studied areas are an exception rather than the standard and the mean climate change signal generally outweighs any influence from variability changes. More importantly, differences in warming across seasons are responsible for most of the heat wave changes and their consideration relegates the contribution of variability to a marginal role. This reveals that accurately capturing mean seasonal changes is crucial to estimate future heat waves and reframes our interpretation of future temperature extremes. , Key Points The influence of projected temperature variability changes on future heat waves varies across the globe Future heat waves are primarily controlled by annual mean changes, except in Europe and North America Mean seasonal warming is responsible for over 95% of projected heat wave changes in most region

Abstract The climate of the eastern seaboard of Australia is strongly influenced by the passage of low pressure systems over the adjacent Tasman Sea due to their associated precipitation and their potential to develop into extreme weather events. The aim of this study is to quantify differences in the climatology of east coast lows derived from the use of six global reanalyses. The methodology is explicitly designed to identify differences between reanalyses arising from differences in their horizontal resolution and their structure (type of forecast model, assimilation scheme, and the kind and number of observations assimilated). As a basis for comparison, reanalysis climatologies are compared with an observation-based climatology. Results show that reanalyses, specially high-resolution products, lead to very similar climatologies of the frequency, intensity, duration, and size of east coast lows when using spatially smoothed (about 300-km horizontal grid meshes) mean sea level pressure fields as input data. Moreover, at these coarse horizontal scales, monthly, interannual, and spatial variabilities appear to be very similar across the various reanalyses with a generally stronger agreement between winter events compared with summer ones. Results also show that, when looking at cyclones using reanalysis data at their native resolution (approaching 50-km grid spacing for the most recent products), uncertainties related to the frequency, intensity, and size of lows are very large and it is not clear which reanalysis, if any, gives a better description of cyclones. Further work is needed in order to evaluate the usefulness of the finescale information in modern reanalyses and to better understand the sources of their differences.