In this study, a cyclone detection/tracking algorithm was used to identify cyclones from two gridded 6-hourly mean sea level pressure datasets: the 40-yr ECMWF Re-Analysis (ERA-40) and the NCEP–NCAR ...reanalysis (NNR) for 1958–2001. The cyclone activity climatology and changes inferred from the two reanalyses are intercompared. The cyclone climatologies and trends are found to be in reasonably good agreement with each other over northern Europe and eastern North America, while ERA-40 shows systematically stronger cyclone activity over the boreal extratropical oceans than does NNR. However, significant differences between ERA-40 and NNR are seen over the austral extratropics. In particular, ERA-40 shows significantly greater strong-cyclone activity and less weak-cyclone activity over all oceanic areas south of 40°S in all seasons, while it shows significantly stronger cyclone activity over most areas of the austral subtropics in the warm seasons.
The most notable historical trends in cyclone activity are found to be associated with strong-cyclone activity. Over the boreal extratropics, both ERA-40 and NNR show a significant increasing trend in January–March (JFM) strong-cyclone activity over the high-latitude North Atlantic and over the midlatitude North Pacific, with a significant decreasing trend over the midlatitude North Atlantic and a small increasing trend over northern Europe. The JFM changes over the North Atlantic are associated with the mean position of the storm track shifting about 181 km northward. Importantly, there is no evidence of abrupt changes identified for the boreal extratropics, although previous studies have suggested that the upward trend found in the NNR data could be biased high. However, there exist a few abrupt changes over the austral extratropics, which appear to be attributable to the increasing availability of observations assimilated in the reanalyses. After diminishing the effects of these abrupt changes, strong-cyclone activity over the austral circumpolar oceanic region is identified to have an increasing trend in October–December (OND) and July–September (JAS), with a decreasing trend over the 40°–60°S zone in JAS.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This study develops a generalized extreme value (GEV) distribution analysis approach, namely, a GEV tree approach that allows for both stationary and nonstationary cases. This approach is applied to ...a century‐long homogenized daily temperature data set for Australia to assess changes in temperature extremes from 1910 to 2010. Changes in 20 year return values are estimated from the most suitable GEV distribution chosen from a GEV tree. Twenty year return values of extreme low minimum temperature are found to have warmed strongly over the century in most parts of the continent. There is also a tendency toward warming of extreme high maximum temperatures, but it is weaker than that for minimum temperatures, with the majority of stations not showing significant trends. The observed changes in extreme temperatures are broadly consistent with observed changes in mean temperatures and in the frequency of temperatures above the ninetieth and below the tenth percentile (i.e., extreme indices). The GEV tree analysis provides insight into behavior of extremes with re‐occurrence times of several years to decades that are of importance to engineering design/applications, while extreme indices represent moderately extreme events with re‐occurrence times of a year or shorter.
Key Points
Strong warming in Australian extreme low temperatures over the period 1910–2010
Warming in Australian extreme high temperatures over 1910–2010 relatively weak
Changes in extremes broadly consistent with changes in relevant seasonal means
This study assesses trends in seasonal extremes (90- and 99-percentiles) of Significant Wave Height (SWH) in the North Atlantic and the North Pacific, as simulated in a 40-yr global wave hindcast ...using the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis wind fields. For the last four decades, statistically significant changes in the seasonal extremes of SWH in the North Atlantic (NA) are detected only for the winter (January–March) season. These changes are found to be intimately connected with the North Atlantic oscillation (NAO). To be specific, significant increases of SWH in the northeast NA, matched by significant decreases in the subtropical NA, are found to be associated with an intensified Azores high and a deepened Icelandic low. This is consistent with the findings of previous studies based on different datasets. Changes in seasonal extremes of SWH in the North Pacific (NP) are found to be statistically significant for the winter and spring (April–June) seasons. Significant increases in the extremes of SWH in the central NP are found to be connected with a deeper and eastward extended Aleutian low. For both oceans, no significant trends of SWH are detected for the last century, though significant changes are found in the last four decades. However, multidecadal fluctuations are very noticeable, especially in the North Pacific.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The nonhomogeneous Poisson process is used to model extreme values of the 40-yr ECMWF Re-Analysis (ERA-40) significant wave height. The parameters of the model are expressed as functions of the ...seasonal mean sea level pressure anomaly and seasonal squared sea level pressure gradient index. Using projections of the sea level pressure under three different forcing scenarios by the Canadian coupled climate model, projections of the parameters of the nonhomogeneous Poisson process are made, trends in these projections are determined, return-value estimates of significant wave height up to the end of the twenty-first century are projected, and their uncertainties are assessed. The uncertainty of estimates associated with the nonhomogeneous Poisson process estimates is studied and compared with the homologous estimates obtained using a nonstationary generalized extreme value model.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Parallel daily temperatures at site pairs were used to derive adjustments required during homogenization process. The homogenization adjustments were obtained using three procedures (Seasonal Bias, ...Monthly Interpolation and Quantile Matching) and two approaches (parallel and neighbours observations). The results show that when the shift is large, both approaches reduce the error although the adjustments derived from parallel observations provide better results. The results also indicate that Quantile Matching adjustments can provide a better estimate of the adjustments to indices of extreme temperature.
ABSTRACT
Parallel daily temperature observations at site pairs over a 5‐year period at 88 locations across Canada were used to derive and validate adjustments required during homogenization process. The data was first ‘aligned’ for compatible observing times at 12 locations (other locations do not have this problem). Then the homogenization adjustments were obtained using three procedures (Seasonal Bias, Monthly Interpolation and Quantile Matching) and two approaches (using parallel and neighbours observations). The root mean squared error (RMSE) between the daily temperatures of site 1 and site 2, and the percentage of days within 0.5 °C (PD05) between site 1 and site 2 were used to assess the uncertainty in the mean and extreme values, respectively. The instruments were not necessarily collocated as the distance between the two observing sites varied from 0 to 30 km. The results confirm that it is necessary to apply adjustments for known issues first, such as a different observing time. They also show that when a shift between site 1 and site 2 (defined by the annual mean of the daily temperature differences) is small <0.25 standard deviation (SD), the adjustments do not reduce the error between site 1 and site 2. When the shift size is between 0.25 and 0.5 SD, the adjustments derived from parallel observations help to reduce the uncertainty. When the shift is large (>0.5 SD), both approaches reduce the error, although the adjustments derived from parallel observations provide better results as compared to those computed from neighbour observations. The results also indicate that Quantile Matching adjustments can provide a better estimate of the adjustments than the other methods evaluated to indices of extreme temperature computed from the adjusted daily values; however, highly correlated neighbours are needed when the adjustments are based on neighbours observations.
This study presents spatial models (i.e., thin-plate spatially continuous spline surfaces) of adjusted precipitation for Canada at daily, pentad (5 day), and monthly time scales from 1900 to 2015. ...The input data include manual observations from 3346 stations that were adjusted previously to correct for snow water equivalent (SWE) conversion and various gauge-related issues. In addition to the 42 331 models for daily total precipitation and 1392 monthly total precipitation models, 8395 pentad models were developed for the first time, depicting mean precipitation for 73 pentads annually. For much of Canada, mapped precipitation values from this study were higher than those from the corresponding unadjusted models (i.e., models fitted to the unadjusted data), reflecting predominantly the effects of the adjustments to the input data. Error estimates compared favorably to the corresponding unadjusted models. For example, root generalized cross-validation (GCV) estimate (a measure of predictive error) at the daily time scale was 3.6 mm on average for the 1960–2003 period as compared with 3.7 mm for the unadjusted models over the same period. There was a dry bias in the predictions relative to recorded values of between 1% and 6.7% of the average precipitations amounts for all time scales. Mean absolute predictive errors of the daily, pentad, and monthly models were 2.5 mm (52.7%), 0.9 mm (37.4%), and 11.2 mm (19.3%), respectively. In general, the model skill was closely tied to the density of the station network. The current adjusted models are available in grid form at ∼2–10-km resolutions.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This study has developed a methodology for blending in situ gauge precipitation measurements with satellite precipitation estimates in a region, which uses a Bayesian spatiotemporal model. A fast and ...simple procedure is proposed for implementing the proposed methodology, which consists of four steps that use kriging, expectation‐maximization, and Sampson‐Guttorp methods in turn. The evaluation study has confirmed that the use of the new method has helped to improve the quality of the prediction when the available gauge stations are very sparse. For example, for the training sets of size 20, 40, and 70 and the evaluation sets of size more than 850 located in southwestern Canada, the 10 year (1994–2003) root‐mean‐square errors of the proposed method are respectively 1.278, 1.236, and 1.168. Moreover, the proposed methodology can be easily extended to blend in situ gauge observations with satellite estimates for other types of climate data.
Key Points
Develop a methodology to blend the in situ observations with SPEs using a Bayesian model
A fast and simple procedure is given for implementing the blending methodology
It can be extended to blend gauge and satellite data for other climate data
This study uses the analysis of variance approaches to quantify the climate change signal and uncertainty in multimodel ensembles of statistical simulations of significant wave height (Hs), which are ...based on the CMIP5 historical, RCP4.5 and RCP8.5 forcing scenario simulations of sea level pressure. Here the signal of climate change refers to the temporal variations caused by the prescribed forcing. “Significant” means “significantly different from zero at 5% level.” In a four‐model ensemble of Hs simulations, the common signal—the signal that is simulated in all the four models—is found to strengthen over time. For the historical followed by RCP8.5 scenario, the common signal in annual mean Hs is found to be significant in 16.6% and 82.2% of the area by year 2005 and 2099, respectively. The global average of the variance proportion of the common signal increases from 0.75% in year 2005 to 12.0% by year 2099. The signal is strongest in the eastern tropical Pacific (ETP), featuring significant increases in both the annual mean and maximum of Hs in this region. The climate model uncertainty (i.e., intermodel variability) is significant nearly globally; its magnitude is comparable to or greater than that of the common signal in most areas, except in the ETP where the signal is much larger. In a 20‐model ensemble of Hs simulations for the period 2006–2099, the model uncertainty is found to be significant globally; it is about 10 times as large as the variability between the RCP4.5 and RCP8.5 scenarios.
Key Points:
Climate change signal has started to emerge in ocean surface wave heights
Climate change signal could emerge over 55% of the open ocean area by year 2050
Climate model uncertainty is still the dominant uncertainty in CMIP5 simulations
This article builds on the previous studies on storminess conditions in the northeast North Atlantic-European region. The period of surface pressure data analyzed is extended from 1881-1998 to ...1874-2007. The seasonality and regional differences of storminess conditions in this region are also explored in more detail. The results show that storminess conditions in this region have undergone substantial decadal or longer time scale fluctuations, with considerable seasonal and regional differences. The most notable differences are seen between winter and summer, and between the North Sea area and other parts of the region. In particular, winter storminess shows an unprecedented maximum in the early 1990s in the North Sea area and a steady upward trend in the northeastern part of the region, while it appears to have declined in the western part of the region. In summer, storminess appears to have declined in most parts of this region. In the transition seasons, the storminess trend is characterized by increases in the northern part of the region and decreases in the southeastern part, with increases in the north being larger in spring. In particular, the results also show that the earliest storminess maximum occurred in summer (around 1880), while the latest storminess maximum occurred in winter (in the early 1990s). Looking at the annual metrics alone (as in previous studies), one would conclude that the latest storminess maximum is at about the same level as the earliest storminess maximum, without realizing that this is comparing the highest winter storminess level with the highest summer storminess level in the period of record analyzed, while winter and summer storminess conditions have undergone very different long-term variability and trends. Also, storminess conditions in the NE Atlantic region are found to be significantly correlated with the simultaneous NAO index in all seasons but autumn. The higher the NAO index, the rougher the NE Atlantic storminess conditions, especially in winter and spring.
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