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In this study, we detect and track extratropical cyclones using 6-hourly mean sea level pressure data taken from the Twentieth Century Reanalysis version 3 (20CRv3) over the period 1951–2015 and compare them with those in the Interim and fifth generation of ECMWF reanalyses over the period 1979–2018. Three indices were employed to characterize cyclone activity, including cyclone count, cyclone intensity, and a cyclone activity index (CAI) that combines the count and intensity. The results show that the cyclone indices in the three datasets have comparable annual climatologies and seasonal evolution over the northern extratropical land and ocean in recent decades. Based on the cyclone indices over the period 1951–2010 in 80 ensemble members of 20CRv3, cyclone count and intensity are negatively correlated in winter and tend to be positively and weakly correlated in summer. The interannual CAI variability is dominated by the cyclone count variability. Regional mean cyclone activity can be well represented using the ensemble average cyclone index. We then examined the linkage of the cyclone activity in 20CRv3 and observed cold and warm extremes over Eurasia and North America over the period 1951–2010. In winter, the principal components of interannual cold and warm extreme anomalies are more correlated with the regional mean cyclone count index over Eurasia, while they are more correlated with the cyclone intensity index over North America. The temperature anomalies associated with the regional and ensemble mean cyclone count index explain about 10% (20%) of interannual cold (warm) extreme variances averaged over Eurasia. The temperature anomalies associated with the mean cyclone intensity explain about 10% of interannual cold and warm extreme variances over North America. Large-scale atmospheric circulation anomalies in association with cyclone activity and the induced temperature advection drive temperature anomalies over Eurasia and North America. In summer, circulation and thermal advection anomalies associated with cyclone activity are weak over the two continents. Hence, that season’s relationship between cyclone activity and extreme temperature variability is weak.

This dataset, produced through the Coordinated Ocean Wave Climate Project (COWCLIP) phase 2, represents the first coordinated multivariate ensemble of 21st Century global wind-wave climate projections available (henceforth COWCLIP2.0). COWCLIP2.0 comprises general and extreme statistics of significant wave height (HS), mean wave period (Tm), and mean wave direction (θm) computed over time-slices 1979-2004 and 2081-2100, at different frequency resolutions (monthly, seasonally and annually). The full ensemble comprising 155 global wave climate simulations is obtained from ten CMIP5-based state-of-the-art wave climate studies and provides data derived from alternative wind-wave downscaling methods, and different climate-model forcing and future emissions scenarios. The data has been produced, and processed, under a specific framework for consistency and quality, and follows CMIP5 Data Reference Syntax, Directory structures, and Metadata requirements. Technical comparison of model skill against 26 years of global satellite measurements of significant wave height has been undertaken at global and regional scales. This new dataset provides support for future broad scale coastal hazard and vulnerability assessments and climate adaptation studies in many offshore and coastal engineering applications.

Understanding climate-driven impacts on the multivariate global wind-wave climate is paramount to effective offshore/coastal climate adaptation planning. However, the use of single-method ensembles and variations arising from different methodologies has resulted in unquantified uncertainty amongst existing global wave climate projections. Here, assessing the first coherent, community-driven, multi-method ensemble of global wave climate projections, we demonstrate widespread ocean regions with robust changes in annual mean significant wave height and mean wave period of 5–15% and shifts in mean wave direction of 5–15°, under a high-emission scenario. Approximately 50% of the world’s coastline is at risk from wave climate change, with ~40% revealing robust changes in at least two variables. Furthermore, we find that uncertainty in current projections is dominated by climate model-driven uncertainty, and that single-method modelling studies are unable to capture up to ~50% of the total associated uncertainty.

Weather and climate extremes profoundly affect society and the environment, resulting in the loss of life, property, and habitat. The extremes discussed herein are causally related: extratropical storms account for the majority of extreme winds during the cold season, and extreme waves are largely driven by extreme winds. For assessment purposes, extremes are defined based on meteorological principles rather than physical destructiveness. Nevertheless, each of these extremes can result in substantial societal impacts. Estimates of extratropical storm activity primarily derive from two sources: atmospheric reanalyses and pressure-based indices. Reanalysis products have the advantage of uniform space and time fields on which to locate pressure minima or vorticity maxima, facilitating the identification of storm tracks. In contrast, pressure-based indices have the advantage of being directly computed from in situ observations, which often extend further back in time than most reanalyses.

This study first developed a comprehensive semiautomatic data homogenization procedure to produce gap-infilled and homogenized monthly precipitation data series for 425 long-term/critical stations in Canada, which were then used to assess Canadian historical precipitation trends. Data gaps in the 425 series were infilled by advanced spatial interpolation of a much larger dataset. The homogenization procedure repeatedly used multiple homogeneity tests without and with reference series to identify changepoints/inhomogeneities, the results from which were finalized by manual analysis using metadata and visual inspection of the multiphase regression fits. As a result, 298 out of the 425 data series were found to be inhomogeneous. These series were homogenized using quantile matching adjustments. The homogenized dataset shows better spatial consistency of trends than does the raw dataset. The improved gridding and regional mean trend estimation methods also provide more realistic trend estimates. With these improvements, Canadian historical precipitation trends were found to be dominantly positive and significant, except in central-south Canada where the trends are generally insignificant and small with mixed directions. For annual precipitation, the largest increases are seen in southeastern Canada and along the Pacific coast; however, the largest relative increases (in percent of the 1961–90 mean) are seen in northern Canada. The largest trend difference between northern and southern Canada is seen in winter, in which significant increases in the north were matched with significant decreases in the south. Significance Statement: This study aims to produce a homogenized long-term monthly precipitation dataset for Canada, which is then used to assess Canadian historical precipitation trends. The work is important because it developed a comprehensive algorithm for homogenization of precipitation data, and the results provide better representation of precipitation climate and more robust estimates of precipitation trends. It also identified the causes for large biases in the published estimates of precipitation trends over Canada. [ABSTRACT FROM AUTHOR]

The d4PDF-WaveHs dataset represents the first single model initial-condition large ensemble of historical significant ocean wave height (Hs) at a global scale. It was produced using an advanced statistical model with predictors derived from Japan's d4PDF ensemble of historical simulations of sea level pressure. d4PDF-WaveHs provides 100 realizations of Hs for the period 1951–2010 (hence 6,000 years of data) on a 1° × 1° lat.-long. grid. Technical comparison of model skill against modern reanalysis and other historical wave datasets was undertaken at global and regional scales. d4PDF-WaveHs provides unique data to understand better the poorly known role of internal climate variability in ocean wave climate, which can be used to estimate better trend signals. It also provides a better sampling of extreme events. Overall, this is crucial to properly assess wave-driven impacts, such as extreme sea levels on low-lying populated coastal areas. This dataset may be of interest to a variety of researchers, engineers and stakeholders in the fields of climate science, oceanography, coastal management, offshore engineering, and energy resource development. [ABSTRACT FROM AUTHOR]