This dataset, produced through the Coordinated Ocean Wave Climate Project (COWCLIP) phase 2, represents the first coordinated multivariate ensemble of 21
Century global wind-wave climate projections ...available (henceforth COWCLIP2.0). COWCLIP2.0 comprises general and extreme statistics of significant wave height (H
), mean wave period (T
), and mean wave direction (θ
) 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.
Abstract
In this paper, a penalized maximal t test (PMT) is proposed for detecting undocumented mean shifts in climate data series. PMT takes the relative position of each candidate changepoint into ...account, to diminish the effect of unequal sample sizes on the power of detection. Monte Carlo simulation studies are conducted to evaluate the performance of PMT, in comparison with the most popularly used method, the standard normal homogeneity test (SNHT). An application of the two methods to atmospheric pressure series recorded at a Canadian site is also presented. It is shown that the false-alarm rate of PMT is very close to the specified level of significance and is evenly distributed across all candidate changepoints, whereas that of SNHT can be up to 10 times the specified level for points near the ends of series and much lower for the middle points. In comparison with SNHT, therefore, PMT has higher power for detecting all changepoints that are not too close to the ends of series and lower power for detecting changepoints that are near the ends of series. On average, however, PMT has significantly higher power of detection. The smaller the shift magnitude Δ is relative to the noise standard deviation σ, the greater is the improvement of PMT over SNHT. The improvement in hit rate can be as much as 14%–25% for detecting small shifts (Δ < σ) regardless of time series length and up to 5% for detecting medium shifts (Δ = σ–1.5σ) in time series of length N < 100. For all detectable shift sizes, the largest improvement is always obtained when N < 100, which is of great practical importance, because most annual climate data series are of length N < 100.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Wang comments on the study of Lund and Reeves (2002), entitled Detection of Undocumented Changepoints: A Revision of the Two-Phase Regression Model. He stresses that changes in trend slopes could be ...rooted in true climate change, and a trend-type changepoint could well be just a point between two phases of long quasi-periodic variation.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This study presents the development of a new dataset of homogenized temperature for use in trend analysis and monitoring climate change in Canada. This dataset contains daily data for 780 locations ...across the country: 508 locations with an active station (current observations) and long record (starting prior to 1990); 53 locations with an active station and short record (starting after 1990); and 219 locations with no current observations (station closed) but with more than 30 years of data. Daily observations from nearby sites were often merged into a single record to create a long time series. This new dataset includes observations taken at Reference Climate Stations and from the Canada Aviation Weather Services, which are used to extend past climate observations into recent times. First, the data were quality controlled. The daily minimum temperature was adjusted for the change in observing time at principal stations in 1961. Parallel daily data were used to detect non-climatic shifts when the observations from nearby sites were merged. Series of annual and seasonal mean temperatures were tested for homogeneity. Daily temperatures were adjusted using a quantile-matching procedure if needed. Two main causes of data inhomogeneity affecting the trends over the 1948-2018 and 1900-2018 periods were identified. First, the change in observing time in 1961 introduced a cold bias in the annual means of the daily minimum temperatures after 1961. Second, merging observations from airport stations with older records has often created an artificial decreasing shift in the unadjusted data because of the better exposure of the instruments at airport stations. This new homogenized dataset shows a slightly stronger warming than the unadjusted data: the trend in the annual mean temperature for Canada has changed from 1.69° to 1.74°C for 1948-2018, and the trend for southern Canada has changed from 1.32° to 1.62°C for 1900-2018 because of all the adjustments applied to daily temperature in this study.
This study presents and analyzes Environment Canada's Davis Strait Baffin Bay (EC-DSBB) Wind and Wave Reanalysis for the period 1979–2016 to characterize the historical changes in the surface wind ...speed and ocean surface waves. The trend analysis is carried out only for the months of May–December, when there is a significant ice-free sea area. The results show that 10-m wind speed (𝑊𝑠) has increased significantly inmost areas of the domain in September–December, with some significant decreases over the open water area in June and July. The 𝑊𝑠 increases are most extensive in September, with significant increases in both the mean and extremes. It is also shown that the mean wind direction (𝑊𝑑) has a distinctive seasonal variation, being mainly northward and northwestward in June–August, and predominantly southward and southeastward in May and September–December. The most notable changes in Wd
are seen in June. The results also show that significant wave height (𝐻𝑠) and wave power (𝑊𝑝) have significantly increased in September–December and decreased in June. For example, the September regional mean 𝐻𝑠 has increased at a rate of 0.4% yr⁻¹. In September–December, the local 𝑊𝑠 increases seem to be the main driver for the 𝐻𝑠 and 𝑊𝑝 increases, but the southeastward direction is favored by increasing fetch as sea ice retreats. In September and December, the positive trend in both 𝑊𝑠 and 𝐻𝑠 has intensified in the 2001–16. In June, however, the mean 𝑊𝑑 and the changes there in also play an important role in the 𝐻𝑠 changes, which are more affected by remotely generated waves.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The Arctic Ocean wave climate is undergoing a dramatic change due to the sea ice retreat. This study presents simulations of the Arctic regional wave climate corresponding to the surface winds and ...sea ice concentrations as simulated by five CMIP5 (Coupled Model Intercomparison Project Phase 5) climate models for the historical (1975–2005) and RCP8.5 scenario future (2081–2100) periods. The annual maximum significant wave height is projected to increase up to 6 m offshore and up to two to three times greater than the corresponding 1979–2005 value along some coastlines, as waves become more exposed to the fall storms there. The connection between the Atlantic Ocean and the Arctic wave climates is projected to strengthen due to increase of swell influence. Changes in the wave direction also seem to indicate a weakening of the Beaufort High illustrated by a counterclockwise rotation of the mean wave direction for extreme conditions in the Western Arctic. The projected changes in wave conditions lead to a general increase of the wave‐driven erosion and inundation potential along the Arctic coastlines. Potentially, hazardous extreme wave events are projected to become significantly more frequent and more intense. For example, in the Beaufort coastlines a once‐in‐20‐year event under the historical (1979–2005) climate is projected to occur, on average, once every 2–5 years during 2081–2100. This is a pressing issue, as it affects many Arctic coastal communities, as well as existing and emerging Arctic infrastructure and activities, with some of them having already suffered severe wave‐induced damage in the past years.
Plain Language Summary
The Arctic Ocean wave climate is drastically changing with remarkable sea ice retreat. This study presents simulations of historical and future wave climates for the Arctic Ocean. The results show that the largest waves will be significantly higher and longer by the end of the century as the ice‐free season lengthens and waves become more exposed to storms in autumn. Moreover, the Arctic wave climate was projected to be more influenced by ocean waves remotely generated in the North Atlantic, which will be able to propagate to higher latitudes. This could also lead to changes in the typical wave direction patterns in the Arctic. The more energetic waves projected for the future are likely to pose a hazard to the Arctic coastlines, as the extreme wave events that can cause erosion and inundation will be more frequent and intense. This is a pressing issue, as it affects many existing Arctic coastal communities and Arctic infrastructure and activities, some of which have already suffered severe damage in the past years.
Key Points
This study presents the first multimodel ensemble of projected extreme wave climate in the Arctic region
The annual maximum of the significant wave height is projected to increase up to 6 m offshore and up to two to three times along the coastlines
The more energetic future waves are likely to pose a hazard to the Arctic communities and infrastructure due to flooding and erosion
This study proposes an algorithm for blending multiple satellite precipitation estimates (SPEs) with in situ gauge precipitation measurements in Canada. Depending on the number of gauge stations in ...the target area, the algorithm employs gauge data alone or blends gauge data with the corresponding SPEs that have been corrected for biases using a novel bias removal procedure developed in this study. The performance of this algorithm is evaluated in terms of root‐mean‐square error (RMSE), frequency bias index, and Pierce skill score, using 10 year gauge data from southwestern Canada where there are enough valid gauge stations to be split into a training data set and an evaluation data set. Sensitivity of the algorithm to gauge density is assessed by using five training data sets representing sparse to moderate gauge densities. The results show that, in comparison with the SPEs and a kriging analysis of gauge data, the blended analysis has the smallest RMSE and is least biased and most skillful in all seasons, and that the lower the gauge density, the more superior the blended analysis is. When gauge density is low, kriging analysis of gauge data is worse than bias‐corrected SPEs. The unadjusted SPEs are the worst by all measures considered, which indicate a need for a proper correction of biases in the SPEs. The blending algorithm is promising for producing a more realistic gridded precipitation, especially for gauge sparse regions, such as northern Canada. A blended analysis of monthly precipitation is produced and compared with several existing precipitation analyses.
Key Points
Correction of biases in satellite precipitation estimates
Blending satellite precipitation estimates with gauge precipitation data
Evaluation of the blending algorithm using independent data sets
This article documents how Environment and Climate Change Canada's Adjusted Daily Rainfall and Snowfall (AdjDlyRS) dataset was developed. The adjustments include (i) conversion of ruler measurements ...of snowfall to its water equivalent using a previously developed snow water equivalent (SWE) ratio map for Canada; (ii) corrections for gauge-related issues including undercatch and evaporation caused by wind effects and gauge-specific wetting loss, as well as for trace precipitation amounts, using previously developed procedures for Canada. Various data flags (e.g., accumulation flags) were also treated. This dataset contains all Canadian stations reporting daily rainfall and snowfall for which we have metadata to implement the adjustments. The length of the data record varies from one station to another, starting as early as 1840. The results show that the original unadjusted total precipitation data in Environment and Climate Change Canada's digital archive underestimate the total precipitation in northeastern Canada by more than 25% and by about 10-15% in most of southern Canada. Such large underestimates make the original data unsuitable for water availability and/or balance studies or for numerical model validation, among many other applications. The use of the assumed 10:1 SWE ratio for the archived total precipitation data is the primary cause of the underestimate, which is most severe in northeastern Canada. The trace correction adds 5-20% to precipitation values in northern Canada but less than 5% in southern Canada. The gauge-related corrections do not show an organized spatial pattern but add 5-10% to the precipitation at 312 stations. Long runs (≥3 months) of miscoded missing values were also identified and corrected.
The latest version of the AdjDlyRS dataset is available from the Canadian Open Data Portal; currently it is version 2016, which contains 3346 stations and covers the period from station inception to February 2016. This dataset is suitable for producing gridded precipitation datasets, as well as other applications.
The d4PDF-WaveHs dataset represents the first single model initial-condition large ensemble of historical significant ocean wave height (H
) 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 H
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.
In this study, projections of seasonal means and extremes of ocean wave heights were made using projections of sea level pressure fields conducted with three global climate models for three ...forcing-scenarios. For each forcing-scenario, the three climate models' projections were combined to estimate the multi-model mean projection of climate change. The relative importance of the variability in the projected wave heights that is due to the forcing prescribed in a forcing-scenario was assessed on the basis of ensemble simulations conducted with the Canadian coupled climate model CGCM2. The uncertainties in the projections of wave heights that are due to differences among the climate models and/or among the forcing-scenarios were characterized. The results show that the multi-model mean projection of climate change has patterns similar to those derived from using the CGCM2 projections alone, but the magnitudes of changes are generally smaller in the boreal oceans but larger in the region nearby the Antarctic coastal zone. The forcing-induced variance (as simulated by CGCM2) was identified to be of substantial magnitude in some areas in all seasons. The uncertainty due to differences among the forcing-scenarios is much smaller than that due to differences among the climate models, although it was identified to be statistically significant in most areas of the oceans (this indicates that different forcing conditions do make notable differences in the wave height climate change projection). The sum of the model and forcing-scenario uncertainties is smaller in the JFM and AMJ seasons than in other seasons, and it is generally small in the mid-high latitudes and large in the tropics. In particular, some areas in the northern oceans were projected to have large changes by all the three climate models.
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