Probabilistic assessment of flood risks using trivariate copulas

In this paper, a copula-based methodology is presented for probabilistic assessment of flood risks and investigated the performance of trivariate copulas in modeling dependence structure of flood properties. The flood is a multi-attribute natural hazard and is characterized by mutually correlated flood properties peak flow, volume, and duration of flood hydrograph. For assessing flood risk, many studies have used bivariate analysis, but a more effective assessment can be possible considering all three mutually correlated flood properties simultaneously. This study adopts trivariate copulas for multivariate analysis of flood risks, and applied to a case study of flood flows of Delaware River basin at Port Jervis, NY, USA. On evaluation of various probability distributions for representation of flood variables, it is found that the flood peak flow and volumes can be best represented by Fréchet distribution, whereas flood duration by log-normal distribution. The joint distribution is modeled using four trivariate copulas, namely, three fully nested form of Archimedean copulas: Clayton, Gumbel–Hougaard, Frank copulas; and one elliptical copula: Student’s t copula. Based on distance-based performance measures, graphical tests, and tail-dependence measures, it is found that the Student’s t copula best representing the trivariate dependence structure of flood properties as compared to the other copulas. Similar results are found for bivariate copula modeling of flood variables pairs, where Student’s t copula performed better than the other copulas. The obtained copula-based joint distributions are used for multivariate analysis of flood risks, in terms of primary and secondary return periods. The resultant trivariate return periods are compared with univariate and bivariate return periods, and addressed the necessity of multivariate flood risk analysis. The study concludes that the trivariate copula-based methodology is a viable choice for effective risk assessment of floods.     

Analysis of streamflow characteristics over Northeastern Canada in a changing climate

An analysis of streamflow characteristics (i.e. mean annual and seasonal flows and extreme high and low flows) in current and future climates for 21 watersheds of north-east Canada covering mainly the province of Quebec is presented in this article. For the analysis, streamflows are derived from a 10-member ensemble of Canadian Regional Climate Model (CRCM) simulations, driven by the Canadian Global Climate Model simulations, of which five correspond to current 1970–1999 period, while the other five correspond to future 2041–2070 period. For developing projected changes of streamflow characteristics from current to future periods, two different approaches are used: one based on the concept of ensemble averaging while the other approach is based on merged samples of current and similarly future simulations following multiple comparison tests. Verification of the CRCM simulated streamflow characteristics for the 1970–1999 period suggests that the model simulated mean hydrographs and high flow characteristics compare well with those observed, while the model tends to underestimate low flow extremes. Results of projected changes to mean annual streamflow suggest statistically significant increases nearly all over the study domain, while those for seasonal streamflow show increases/decreases depending on the season. Two- and 5-year return levels of 15-day low flows are projected to increase significantly over most part of the study domain, though the changes are small in absolute terms. Based on the ensemble averaging approach, changes to 10- and 30-year return levels of high flows are not generally found significant. However, when a similar analysis is performed using longer samples, significant increases to high flow return levels are found mainly for northernmost watersheds. This study highlights the need for longer samples, particularly for extreme events in the development of robust projections.     

Climate projections over CORDEX Africa domain using the fifth-generation Canadian Regional Climate Model (CRCM5)

Following the CORDEX experimental protocol, climate simulations and climate-change projections for Africa were made with the new fifth-generation Canadian Regional Climate Model (CRCM5). The model was driven by two Global Climate Models (GCMs), one developed by the Max-Planck-Institut für Meteorologie and the other by the Canadian Centre for Climate Modelling and Analysis, for the period 1950–2100 under the RCP4.5 emission scenario. The performance of the CRCM5 simulations for current climate is discussed first and compared also with a reanalysis-driven CRCM5 simulation. It is shown that errors in lateral boundary conditions and sea-surface temperature from the GCMs have deleterious consequences on the skill of the CRCM5 at reproducing specific regional climate features such as the West African Monsoon and the annual cycle of precipitation. For other aspects of the African climate however the regional model is able to add value compared to the simulations of the driving GCMs. Climate-change projections for periods until the end of this century are also analysed. All models project a warming throughout the twenty-first century, although the details of the climate changes differ notably between model projections, especially for precipitation changes. It is shown that the climate changes projected by CRCM5 often differ noticeably from those of the driving GCMs.     

Evaluation of Total Precipitable Water from CRCM4 using the NVAP-MEaSUREs Dataset and ERA-Interim Reanalysis Data

The fourth-generation Canadian Regional Climate Model’s (CRCM4) precipitable water is evaluated and compared with observational data and ERA-Interim reanalysis data over five Canadian basins with simulations driven by ERA-Interim (two) and global climate models (two). Considering the 22 years of data available in the observations, we analyze precipitable water’s behaviour through its annual cycle, its daily distribution, and its annual daily maxima. For the simulations driven by reanalyses, differences in annual daily maximum values and their correlations with observations are examined. In general, the values for precipitable water simulated by CRCM4 are similar to those observed, and the model reproduces both the interannual and inter-basin variabilities. The simulation at 15 km resolution produces higher extreme values than simulations performed at 45 km resolution and higher than the observations taken at coarser resolution (1°), without much influence on the mean behaviour. Some underestimation is found with the simulation driven by the Canadian Centre for Climate Modelling and Analysis Model, version 3, a sign of a cold and dry bias, whereas the run driven by the European Centre Hamburg Model, version 5, is much closer to the observations, pointing to the importance of closely considering the regional–global model combination. Overall, CRCM4's ability to reproduce the major characteristics of observed precipitable water makes it a possible tool for providing precipitable water data that could serve as a basis for probable maximum precipitation and probable maximum flood studies at the basin scale.     

Reanalysis-driven climate simulation over CORDEX North America domain using the Canadian Regional Climate Model, version 5: model performance evaluation

The performance of reanalysis-driven Canadian Regional Climate Model, version 5 (CRCM5) in reproducing the present climate over the North American COordinated Regional climate Downscaling EXperiment domain for the 1989–2008 period has been assessed in comparison with several observation-based datasets. The model reproduces satisfactorily the near-surface temperature and precipitation characteristics over most part of North America. Coastal and mountainous zones remain problematic: a cold bias (2–6 °C) prevails over Rocky Mountains in summertime and all year-round over Mexico; winter precipitation in mountainous coastal regions is overestimated. The precipitation patterns related to the North American Monsoon are well reproduced, except on its northern limit. The spatial and temporal structure of the Great Plains Low-Level Jet is well reproduced by the model; however, the night-time precipitation maximum in the jet area is underestimated. The performance of CRCM5 was assessed against earlier CRCM versions and other RCMs. CRCM5 is shown to have been substantially improved compared to CRCM3 and CRCM4 in terms of seasonal mean statistics, and to be comparable to other modern RCMs.     

Probabilistic characterization of hydrological droughts

It is proposed to use the joint distribution of probabilities of minimum water discharges and the river runoff depth to characterize the severity and danger of durable low-flow periods. The copula theory is used to construct this distribution that enables to take account of differences in the types of one-dimensional probability distributions of initial variables and of the correlation between them. The studies were carried out for the vegetation periods using the data of the runoff depth measurements in Georghiu-Dej on the Don River in 1895–1980 and in Kirov on the Vyatka River in 1878–1980. The selection of copulas was based on comparing empirical and theoretical joint distributions of low-flow characteristics. It is demonstrated that the best results for the approximation of the joint probability distribution of low-flow characteristics are obtained from the Frank copula. Plotted are the dependences of low-flow frequency on the specified values of corresponding minimum water discharges and the river runoff depth during vegetation periods. Estimated are the probability and frequency of some most severe lowflow periods observed on the Don and Vyatka rivers.     

Climate and climate change in western canada as simulated by the Canadian regional climate model

Abstract

A þrst climate simulation performed with the novel Canadian Regional Climate Model (CRCM) is presented. The CRCM is based on fully elastic non‐hydrostatic þeld equations, which are solved with an efþcient semi‐implicit semi‐Lagrangian (SISL) marching algorithm, and on the parametrization package of subgrid‐scale physical effects of the second‐generation Canadian Global Climate Model (GCMII). Two 5‐year integrations of the CRCM nested with GCMII simulated data as lateral boundary conditions are made for conditions corresponding to current and doubled CO₂ scenarios. For these simulations the CRCM used a grid size of 45 km on a polar‐stereographic projection, 20 scaled‐height levels and a time step of 15 min; the nesting GCMII has a spectral truncation of T32, 10 hybrid‐pressure levels and a time step of 20 min. These simulations serve to document: (1) the suitability of the SISL numerical scheme for regional climate modelling, (2) the use of GCMII physics at much higher resolution than in the nesting model, (3) the ability of the CRCM to add realistic regional‐scale climate information to global model simulations, and (4) the climate of the CRCM compared to that of GCMII under two greenhouse gases (GHG) scenarios.     

Diagnostic analysis of future climate scenarios applied to urban flooding in the Chicago metropolitan area

Past heavy precipitation events in the Chicago metropolitan area have caused significant flood-related economic and environmental damages. A key component in flood management policies and actions is determining flood magnitudes for specified return periods. This is a particularly difficult task in areas with a complex and changing climate and land-use, such as the Chicago metropolitan area. The standard design storm methodology based on the NOAA Atlas 14 and ISWS Bulletin 70 has been used in the past to estimate flood hydrographs with variable return periods in this region. In a changing climate, however, these publications may not be accurate. This study presents and illustrates a methodology for diagnostic analysis of future climate scenarios in the framework of urban flooding, and assesses the corresponding uncertainties. First, the design storms are calculated using data downscaled by a regional climate model (RCM) at 30-km spacing for the present and 2050s under the IPCC A1Fi (high) and B1 (low) emissions scenarios. Next, the corresponding flood discharges at six watersheds in suburban Chicago are estimated using a hydrologic event model. The resulting scenarios in flood frequency were first assessed through a set of diagnostic tests for precipitation timing and frequency. The study did not reveal any significant changes in the 2050s in the average timing of heavy storms, but their regularity decreased. The average timing did not exhibit any significant spatial variability throughout the region. The precipitation frequency analysis revealed distinct differences between the northern and southeastern subregions of the Chicago metropolitan area. The quantiles in the northern subregion averaged for 2-year, 5-year, and 10-year return periods exhibited a 20% and 16% increase in daily precipitation for scenarios B1 and A1Fi, respectively. The southeastern subregion, however, exhibited a decrease of 12% for scenario B1 and a minor increase of 3% for scenario A1Fi. The hydrologic effects of changing precipitation on the flood quantiles were illustrated using six small watersheds in the region. The relative increases or decreases in precipitation translated into even larger relative increases or decreases in flood peaks, due to the nonlinear nature of the rainfall-runoff process. Simulations using multiple climate models, for longer periods, finer spatio-temporal resolution, and larger areal coverage could be used to more accurately account for numerous uncertainties in the precipitation and flood projections.     

On the application of copula theory for determination of probabilistic characteristics of springflood

Considered is the possibility of using copula theory for creating joint probability distributions of springflood peak discharges and flow volumes taking account of the relations between discharges and flow volumes. For approximation of marginal distributions, Gumbel distribution was used for peak discharges, and two-parameter gamma distribution, for flow volumes. Joint two-dimensional distribution was built as a marginal distribution function which was set as one of the three one-parameter Archimedean copulas using different ways of determining their parameters. The best results were obtained for Gumbel-Hougaard copula using the method of maximum likelihood to determine its parameters. Major flood risk estimates determined from one- and two-dimensional probability distributions of their characteristics were compared with each other. Demonstrated are the benefits of using two-dimensional probability distributions of flood characteristics as compared with one-dimensional distributions for probabilistic estimation of floods. The data on springflood peak discharges and flow volumes in the Belaya and Vyatka rivers were used for this study.     

Evaluation of regional climate model simulations versus gridded observed and regional reanalysis products using a combined weighting scheme

This study presents a combined weighting scheme which contains five attributes that reflect accuracy of climate data, i.e. short-term (daily), mid-term (annual), and long-term (decadal) timescales, as well as spatial pattern, and extreme values, as simulated from Regional Climate Models (RCMs) with respect to observed and regional reanalysis products. Southern areas of Quebec and Ontario provinces in Canada are used for the study area. Three series of simulation from two different versions of the Canadian RCM (CRCM4.1.1, and CRCM4.2.3) are employed over 23?years from 1979 to 2001, driven by both NCEP and ERA40 global reanalysis products. One series of regional reanalysis dataset (i.e. NARR) over North America is also used as reference for comparison and validation purpose, as well as gridded historical observed daily data of precipitation and temperatures, both series have been beforehand interpolated on the CRCM 45-km grid resolution. Monthly weighting factors are calculated and then combined into four seasons to reflect seasonal variability of climate data accuracy. In addition, this study generates weight averaged references (WARs) with different weighting factors and ensemble size as new reference climate data set. The simulation results indicate that the NARR is in general superior to the CRCM simulated precipitation values, but the CRCM4.1.1 provides the highest weighting factors during the winter season. For minimum and maximum temperature, both the CRCM4.1.1 and the NARR products provide the highest weighting factors, respectively. The NARR provides more accurate short- and mid-term climate data, but the two versions of the CRCM provide more precise long-term data, spatial pattern and extreme events. Or study confirms also that the global reanalysis data (i.e. NCEP vs. ERA40) used as boundary conditions in the CRCM runs has non-negligible effects on the accuracy of CRCM simulated precipitation and temperature values. In addition, this study demonstrates that the proposed weighting factors reflect well all five attributes and the performances of weighted averaged references are better than that of the best single model. This study also found that the improvement of WARs’ performance is due to the reliability (accuracy) of RCMs rather than the ensemble size.     

Evaluation of Precipitation Indices over North America from Various Configurations of Regional Climate Models

This study presents the evaluation of simulations from two new Canadian regional climate models (RCMs), CanRCM4 and CRCM5, with a focus on the models’ skill in simulating daily precipitation indices and the Standardized Precipitation Index (SPI). The evaluation was carried out over the past two decades using several sets of gridded observations that partially cover North America. The new Canadian RCMs were also compared with four reanalysis products and six other RCMs. The different configurations of the Canadian RCM simulations also permit evaluation of the impact of different spatial resolutions, atmospheric drivers, and nudging conditions. The results from the new Canadian models show some improvement in precipitation characteristics over the previous Canadian RCM (CRCM4), but these differ with the seasons. For winter, CanRCM4 and CRCM5 have better skill than most other models over all of North America. For the summer, CRCM5 0.44° performs best over the United States, while CRCM4 has the best skill over Canada. Good skill is exhibited by CanRCM4 and CRCM4 in simulating the 6-month SPI over the Prairies and the western US Corn Belt. In general, differences are small between runs with or without large-scale spectral nudging; differences are small when different boundary conditions are used.     

Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec,Canada

Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971–2000) and A2 (2041–2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate.     

Projections of climate change impacts on river flood conditions in Germany by combining three different RCMs with a regional eco-hydrological model

A general increase in precipitation has been observed in Germany in the last century, and potential changes in flood generation and intensity are now at the focus of interest. The aim of the paper is twofold: a) to project the future flood conditions in Germany accounting for various river regimes (from pluvial to nival-pluvial regimes) and under different climate scenarios (the high, A2, low, B1, and medium, A1B, emission scenarios) and b) to investigate sources of uncertainty generated by climate input data and regional climate models. Data of two dynamical Regional Climate Models (RCMs), REMO (REgional Model) and CCLM (Cosmo-Climate Local Model), and one statistical-empirical RCM, Wettreg (Wetterlagenbasierte Regionalisierungsmethode: weather-type based regionalization method), were applied to drive the eco-hydrological model SWIM (Soil and Water Integrated Model), which was previously validated for 15 gauges in Germany. At most of the gauges, the 95 and 99 percentiles of the simulated discharge using SWIM with observed climate data had a good agreement with the observed discharge for 1961–2000 (deviation within ±10 %). However, the simulated discharge had a bias when using RCM climate as input for the same period. Generalized Extreme Value (GEV) distributions were fitted to the annual maximum series of river runoff for each realization for the control and scenario periods, and the changes in flood generation over the whole simulation time were analyzed. The 50-year flood values estimated for two scenario periods (2021–2060, 2061–2100) were compared to the ones derived from the control period using the same climate models. The results driven by the statistical-empirical model show a declining trend in the flood level for most rivers, and under all climate scenarios. The simulations driven by dynamical models give various change directions depending on region, scenario and time period. The uncertainty in estimating high flows and, in particular, extreme floods remains high, due to differences in regional climate models, emission scenarios and multi-realizations generated by RCMs.     

Present climate and climate change over North America as simulated by the fifth-generation Canadian regional climate model

The fifth-generation Canadian Regional Climate Model (CRCM5) was used to dynamically downscale two Coupled Global Climate Model (CGCM) simulations of the transient climate change for the period 1950–2100, over North America, following the CORDEX protocol. The CRCM5 was driven by data from the CanESM2 and MPI-ESM-LR CGCM simulations, based on the historical (1850–2005) and future (2006–2100) RCP4.5 radiative forcing scenario. The results show that the CRCM5 simulations reproduce relatively well the current-climate North American regional climatic features, such as the temperature and precipitation multiannual means, annual cycles and temporal variability at daily scale. A cold bias was noted during the winter season over western and southern portions of the continent. CRCM5-simulated precipitation accumulations at daily temporal scale are much more realistic when compared with its driving CGCM simulations, especially in summer when small-scale driven convective precipitation has a large contribution over land. The CRCM5 climate projections imply a general warming over the continent in the 21st century, especially over the northern regions in winter. The winter warming is mostly contributed by the lower percentiles of daily temperatures, implying a reduction in the frequency and intensity of cold waves. A precipitation decrease is projected over Central America and an increase over the rest of the continent. For the average precipitation change in summer however there is little consensus between the simulations. Some of these differences can be attributed to the uncertainties in CGCM-projected changes in the position and strength of the Pacific Ocean subtropical high pressure.     

An assessment of Canadian prairie drought: past, present, and future

Within Canada, the Canadian Prairies are particularly drought-prone mainly due to their location in the lee of the western cordillera and distance from large moisture sources. Although previous studies examined the occurrence of Canadian Prairie droughts during instrumental, pre-instrumental and to a lesser extent, future periods, none have specifically focused on all time three scales. Using two different drought indicators, namely the Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI), this investigation assesses the variability of summer drought duration and intensity over a core region of the Prairies during (a) the pre-instrumental record extending back several centuries (inferred from tree rings), (b) the instrumental record (1901–2005), and (c) the twenty-first century using statistically downscaled climate variables from several Atmosphere–Ocean Global climate models with multiple emission scenarios. Results reveal that observed twentieth century droughts were relatively mild when compared to pre-settlement on the Prairies, but these periods are likely to return (and even worsen) in the future due to the anticipated warming during the course of the twenty-first century. However, future drought projections are distinctly different between the two indices. All PDSI-related model runs show greater drought frequency and severity mainly due to increasing temperatures. Conversely, the precipitation-based SPI indicates no significant changes to future summer drought frequency although there tends to be a higher persistence of multi-year droughts in central and southern portions of Canadian Prairies. These findings therefore stress the importance of considering anticipated warming trends when assessing future regional-scale drought, especially given the uncertainties and lack of consistency in future precipitation signals among climate models. This study can be considered an initial step toward quantifying and understanding Canadian Prairie drought occurrence and severity over several centuries as determined from paleo, instrumental, and climate model data sources.     

Application of the Canadian regional climate model to the Laurentian great lakes region: Implementation of a lake model

Abstract

This study reports on the implementation of an interactive mixed‐layer/thermodynamic‐ice lake model coupled with the Canadian Regional Climate Model (CRCM). For this application the CRCM, which uses a grid mesh of 45 km on a polar stereographic projection, 10 vertical levels, and a timestep of 15 min, is nested with the second generation Canadian General Circulation Model (GCM) simulated output. A numerical simulation of the climate of eastern North America, including the Laurentian Great Lakes, is then performed in order to evaluate the coupled model. The lakes are represented by a “mixed layer” model to simulate the evolution of the surface water temperature, and a thermodynamic ice model to simulate evolution of the ice cover. The mixed‐layer depth is allowed to vary spatially. Lake‐ice leads are parametrized as a function of ice thickness based on observations. Results from a 5‐year integration show that the coupled CRCM/lake model is capable of simulating the seasonal evolution of surface temperature and ice cover in the Great Lakes. When compared with lake climatology, the simulated mean surface water temperature agrees within 0.12°C on average. The seasonal evolution of the lake‐ice cover is realistic but the model tends to underestimate the monthly mean ice concentration on average. The simulated winter lake‐induced precipitation is also shown, and snow accumulation patterns on downwind shores of the lakes are found to be realistic when compared with observations.     

Coupling statistical and dynamical methods for spatial downscaling of precipitation

The resolution of General Circulation Models (GCMs) is too coarse for climate change impact studies at the catchment or site-specific scales. To overcome this problem, both dynamical and statistical downscaling methods have been developed. Each downscaling method has its advantages and drawbacks, which have been described in great detail in the literature. This paper evaluates the improvement in statistical downscaling (SD) predictive power when using predictors from a Regional Climate Model (RCM) over a GCM for downscaling site-specific precipitation. Our approach uses mixed downscaling, combining both dynamic and statistical methods. Precipitation, a critical element of hydrology studies that is also much more difficult to downscale than temperature, is the only variable evaluated in this study. The SD method selected here uses a stepwise linear regression approach for precipitation quantity and occurrence (similar to the well-known Statistical Downscaling Model (SDSM) and called SDSM-like herein). In addition, a discriminant analysis (DA) was tested to generate precipitation occurrence, and a weather typing approach was used to derive statistical relationships based on weather types, and not only on a seasonal basis as is usually done. The existing data record was separated into a calibration and validation periods. To compare the relative efficiency of the SD approaches, relationships were derived at the same sites using the same predictors at a 300km scale (the National Center for Environmental Prediction (NCEP) reanalysis) and at a 45km scale with data from the limited-area Canadian Regional Climate Model (CRCM) driven by NCEP data at its boundaries. Predictably, using CRCM variables as predictors rather than NCEP data resulted in a much-improved explained variance for precipitation, although it was always less than 50?% overall. For precipitation occurrence, the SDSM-like model slightly overestimated the frequencies of wet and dry periods, while these were well-replicated by the DA-based model. Both the SDSM-like and DA-based models reproduced the percentage of wet days, but the wet and dry statuses for each day were poorly downscaled by both approaches. Overall, precipitation occurrence downscaled by the DA-based model was much better than that predicted by the SDSM-like model. Despite the added complexity, the weather typing approach was not better at downscaling precipitation than approaches without classification. Overall, despite significant improvements in precipitation occurrence prediction by the DA scheme, and even going to finer scales predictors, the SD approach tested here still explained less than 50?% of the total precipitation variance. While going to even smaller scale predictors (10–15?km) might improve results even more, such smaller scales would basically transform the direct outputs of climate models into impact models, thus negating the need for statistical downscaling approaches.     

Searching for Added Value in Simulating Climate Extremes with a High-Resolution Regional Climate Model over Western Canada

We evaluate the capacity of a regional climate model to represent observed extreme temperature and precipitation events and also examine the impact of increased resolution, in an effort to identify added value in this respect. Two climate simulations of western Canada (WCan) were conducted with the Canadian Regional Climate Model (version 4) at 15 (CRCM15) and 45?km (CRCM45) horizontal resolution driven at the lateral boundaries by data from the European Centre for Medium-range Weather Forecasts (ECMWF) 40-year Reanalysis (ERA-40) for the period 1973–1995. The simulations were evaluated using the spline-interpolated dataset ANUSPLIN, a daily observational gridded surface temperature and precipitation product with a nominal resolution of approximately 10?km. We examine a range of climate extremes, comprising the 10th and 90th percentiles of daily maximum (TX) and minimum (TN) temperatures, the 90th percentile of daily precipitation (PR90), and the 27 core Climate Daily Extremes (CLIMDEX) indices.

Both simulations exhibit cold biases compared with observations over WCan, with the bias exacerbated at higher resolution, suggesting little added value for temperature overall. There are instances, however, of regional improvement in the spatial pattern of temperature extremes at the higher resolution of CRCM15 (e.g., the CLIMDEX index for the annual number of days when TX?>?25°C). The high-resolution simulations also reveal similarly localized features in precipitation (e.g., rain shadows) that are not resolved at the 45?km resolution. With regard to precipitation extremes, although both simulations generally display wet biases, CRCM15 features a reduced bias in PR90 in all seasons except winter. This improvement occurs despite the fact that spatial and interannual variability of PR90 in CRCM15 is significantly overestimated relative to both CRCM45 and ANUSPLIN. We posit that these characteristics are the result of demonstrable differences between corresponding topographical datasets used in the gridded observations and CRCM, the resulting errors propagated to physical variables tied to elevation and the beneficial effect of subsequent spatial averaging. Because topographical input is often discordant between simulations and gridded observations, it is argued that a limited form of spatial averaging may contribute added value beyond that which has already been noted in previous studies with respect to small-scale climate variability.     

利用二维极值分布模拟我国几个代表站的强降水概率特征

基于中国6个代表站5-9月的逐日降水资料,利用二维Gumbel-Logistic分布,研究了中国不同区域的过程降水量和日最大强降水雨量的联合概率特征。结果表明,各代表性台站的过程雨量和强降水雨量的联合分布均符合二维Gumbel分布。强降水雨量与过程降雨量联合分布所描述的极端事件是更小的小概率事件。相同强降水雨量条件下,过程雨量越大,重现期越长当强降水雨量增大时,同一过程雨量的重现期也延长。在同级强降水雨量出现的条件下,各地过程降雨量往往是愈往南方其条件概率愈大,而其出现的过程雨量也随之增大。这为研究强降水极端状况的全方位特征做出了新的试验．也曼加客观地揭示了极端气候事件的多方面概率特征．     

Projected Changes in Surface Air Temperature and Surface Wind in the Gulf of St. Lawrence

Abstract

The impacts of climate change on surface air temperature (SAT) and winds in the Gulf of St. Lawrence (GSL) are investigated by performing simulations from 1970 to 2099 with the Canadian Regional Climate Model (CRCM), driven by a five-member ensemble. Three members are from Canadian Global Climate Model (CGCM3) simulations following scenario A1B from the Intergovernmental Panel on Climate Change (IPCC); one member is from the Community Climate System Model, version 3 (CCSM3) simulation, also following the A1B scenario; and one member is from the CCSM4 (version 4) simulation following the Representative Concentration Pathway (RCP8.5) scenario. Compared with North America Regional Reanalysis (NARR) data, it is shown that CRCM can reproduce the observed SAT spatial patterns; for example, both CRCM simulations and NARR data show a warm SAT tongue along the eastern Gulf; CRCM simulations also capture the dominant northwesterly winds in January and the southwesterly winds in July. In terms of future climate scenarios, the spatial patterns of SAT show plausible seasonal variations. In January, the warming is 3°–3.5°C in the northern Gulf and 2.5°–3°C near Cabot Strait during 2040–2069, whereas the warming is more uniform during 2070–2099, with SAT increases of 4°–5°C. In summer, the warming gradually decreases from the western side of the GSL to the eastern side because of the different heat capacities between land and water. Moreover, the January winds increase by 0.2–0.4?m?s^?1 during 2040–2069, related to weakening stability in the atmospheric planetary boundary layer. However, during 2070–2099, the winds decrease by 0.2–0.4?m?s^?1 over the western Gulf, reflecting the northeastward shift in northwest Atlantic storm tracks. In July, enhanced baroclinicity along the east coast of North America dominates the wind changes, with increases of 0.2–0.4?m?s^?1. On average, the variance for the SAT changes is about 10% of the SAT increase, and the variance for projected wind changes is the same magnitude as the projected changes, suggesting uncertainty in the latter.