Occurrence,durée et intensité des précipitations simulées par deux modèles régionaux canadiens du climat sur la région du Maghreb

[Translated by the editorial staff] Simulating the precipitation regime of Northern Africa is challenging for regional climate models, particularly because of the strong spatial and temporal variability of rain events in the region. In this study we evaluate simulations conducted with two recent versions of regional climate models (RCM) developed in Canada: the CRCM5 and CanRCM4. Both are also used in the COordinated Regional Climate Downscaling EXperiment (CORDEX)-Africa. The assessment is based on the occurrence, duration, and intensity indices of daily precipitation in Maghreb during the fall and spring seasons from 1998 to 2008. We also examine the links between the North-Atlantic Oscillation (NAO) index, weather systems, and the precipitation regime over the region. During the rainy season (September to February), the CRCM5 reproduces the frequency and intensity of extreme precipitation adequately, as well as the occurrence of days with rain, while the CanRCM4 underestimates precipitation extremes. The study of links between weather systems and the precipitation regime shows that, along the Atlantic coast, precipitation (occurrence, intensity, and wet sequences) increases significantly with storm frequency in the fall. In winter, these links grow stronger going east, from the Atlantic coast to the Mediterranean coast. The negative phases of the NAO index are statistically associated with the increase in rain intensity, extremes, and accumulation along the Atlantic coast in the fall. However, the link weakens in winter over these regions and strengthens along the Mediterranean coast as the precipitation frequency rises during negative phases of the NAO. Both RCMs generally reproduce the links between the NAO and the precipitation regime well, regardless of location.     

Southern Quebec (Canada) summer-season heat spells over the 1941–2000 period: an assessment of observed changes

Summary Summer-season (May–September) daily maximum temperature (T _max) and daily minimum temperature (T _min) observations and three types of heat spells obtained from these temperature observations at seven weather stations located in southern Quebec (Canada) for the 60-year period from 1941 to 2000 are studied to assess temporal changes in their characteristics (i.e. frequency of occurrence, seasonal hot days and extremal durations of heat spells). Type-A and Type-B heat spells are obtained respectively from T _max and T _min observations and Type-C heat spells from simultaneous joint observations of T _max and T _min using suitable thresholds and spells of duration ≥1-day and ≥3-day. The results of this investigation show that the majority of the selected percentiles (i.e. 5P, 10P, 25P, 50P, 75P, 80P, 90P, 92P, 95P, and 98P) of T _max observations show a negative time-trend with statistically significant decreases (at 10% level) in some of the higher percentiles and in the maximal values at four out of seven stations. Almost all of the selected percentiles (same as for the T _max) and the maximal and minimal values of T _min observations show a positive trend, with statistically significant increases for all seven stations. Examination of frequencies of occurrence of heat spells, seasonal hot days and annual extremes of heat spell durations indicate that many of these characteristics of heat spells have undergone statistically significant changes over time at some of the stations for Type-A and Type-B heat spells as compared to Type-C heat spells. The Type-C heat spells are generally small in number and are found to be relatively temporally stable. More severe Type-C heat spells, i.e. the ones having T _max and T _min values simultaneously above very high thresholds and with duration ≥3-day have been rarely observed in southern Quebec.     

Comparison of transfer functions in statistical downscaling models for daily temperature and precipitation over Canada

This study compares three linear models and one non-linear model, specifically multiple linear regression (MLR) with ordinary least squares (OLS) estimates, robust regression, ridge regression, and artificial neural networks (ANNs), to identify an appropriate transfer function in statistical downscaling (SD) models for the daily maximum and minimum temperatures (T_max and T_min) and daily precipitation occurrence and amounts (P_occ and P_amount). This comparison was made over twenty-five observation sites located in five different Canadian provinces (British Columbia, Saskatchewan, Manitoba, Ontario, and Québec). Reanalysis data were employed as atmospheric predictor variables of SD models. Predictors of linear transfer functions and ANN were selected by linear correlations coefficient and mutual information, respectively. For each downscaled case, annual and monthly models were developed and analysed. The monthly MLR, annual ANN, annual ANN, and annual MLR yielded the best performance for T_max, T_min, P_occ and P_amont according to the modified Akaike information criterion (AICu). A monthly MLR is recommended for the transfer functions of the four predictands because it can provide a better performance for the T_max and as good performance as the annual MLR for the T_min, P_occ, and P_amount. Furthermore, a monthly MLR can provide a slightly better performance than an annual MLR for extreme events. An annual MLR approach is also equivalently recommended for the transfer functions of the four predictands because it showed as good a performance as monthly MLR in spite of its mathematical simplicity. Robust and ridge regressions are not recommended because the data used in this study are not greatly affected by outlier data and multicollinearity problems. An annual ANN is recommended only for the T_min, based on the best performance among the models in terms of both the RMSE and AICu.     

Modelling the sea ice-ocean seasonal cycle in Hudson Bay, Foxe Basin and Hudson Strait, Canada

The seasonal cycle of water masses and sea ice in the Hudson Bay marine system is examined using a three-dimensional coastal ice-ocean model, with 10 km horizontal resolution and realistic tidal, atmospheric, hydrologic and oceanic forcing. The model includes a level 2.5 turbulent kinetic energy equation, multi-category elastic-viscous-plastic sea-ice rheology, and two layer sea ice with a single snow layer. Results from a two-year long model simulation between August 1996 and July 1998 are analyzed and compared with various observations. The results demonstrate a consistent seasonal cycle in atmosphere-ocean exchanges and the formation and circulation of water masses and sea ice. The model reproduces the summer and winter surface mixed layers, the general cyclonic circulation including the strong coastal current in eastern Hudson Bay, and the inflow of oceanic waters into Hudson Bay. The maximum sea-ice growth rates are found in western Foxe Basin, and in a relatively large and persistent polynya in northwestern Hudson Bay. Sea-ice advection and ridging are more important than local thermodynamic growth in the regions of maximum sea-ice cover concentration and thickness that are found in eastern Foxe Basin and southern Hudson Bay. The estimate of freshwater transport to the Labrador Sea confirms a broad maximum during wintertime that is associated with the previous summers freshwater moving through Hudson Strait from southern Hudson Bay. Tidally driven mixing is shown to have a strong effect on the modeled ice-ocean circulation.     

Hydrothermal alteration associated with rift zones at Fangataufa atoll (French Polynesia)

 The Mururoa and Fangataufa atoll basement consists of superimposed submarine and subaerial lava flows which have been intruded by late volcanics. The intrusions have developed large hydrothermal alteration haloes throughout the basaltic wall rock. The cuttings of the Natice-1 and Mitre-1 holes, drilled into the submarine volcanic pile at Fangataufa atoll, show a vertical zonation of clay minerals ranging from 270 to 850 m depth. The newly formed clay minerals occurring from top to bottom of the altered pile are: dioctahedral aluminous smectites, saponite, an intimate assemblage of saponite with two random chlorite/saponite mixed layers and an intimate assemblage of one random chlorite/saponite mixed-layer with one ordered chlorite/saponite mixed layer and one chlorite below 816 m depth. These clay mineral assemblages indicate a general increase in the chloritic component with depth. They are associated throughout the pile with secondary carbonates and quartz. The ∂¹⁸O and ∂¹³C of calcite and ∂¹⁸O of clay minerals, on the one hand, and the intimate mixtures of trioctahedral species, on the other, suggest a general cooling with the evolution of a paleogeothermal gradient from approximately 300  °C/km during the crystallization of chlorite to 150  °C/km for the late calcite precipitation. Received: 2 October 1995 / Accepted: 14 January 1997     

Evaluation of Linear and Non-Linear Downscaling Methods in Terms of Daily Variability and Climate Indices: Surface Temperature in Southern Ontario and Quebec,Canada

We downscaled atmospheric reanalysis data using linear regression and Bayesian neural network (BNN) ensembles to obtain daily maximum and minimum temperatures at ten weather stations in southern Quebec and Ontario, Canada. Performance of the linear and non-linear downscaling models was evaluated using four different sets of predictors, not only in terms of their ability to reproduce the magnitude of day-to-day variability (i.e., “weather,” mean absolute error between the daily values of the predictand(s) and the downscaled data) but also in terms of their ability to reproduce longer time scale variability (i.e., “climate,” indices of agreement between the predictand's observed annual climate indices and the corresponding downscaled values). The climate indices used were the 90th percentile of the daily maximum temperature, 10th percentile of the daily minimum temperature, number of frost days, heat wave duration, growing season length, and intra-annual temperature range.

Our results show that the non-linear models usually outperform their linear counterparts in the magnitude of daily variability and, to a greater extent, in annual climate variability. In particular, the best model simulating weather and climate was a BNN ensemble using stepwise selection from 20 reanalysis predictors, followed by a BNN ensemble using the three leading principal components from the aforementioned predictors. Finally, we showed that, on average, the first three indices presented higher skills than the growing season length, number of frost days, and the heat wave duration.     

Inconsistent linear trends in Senegalese rainfall indices from 1950 to 2007

Abstract

Daily precipitation data from 31 Senegalese stations spanning the period from 1950 to 2007 were used to examine the inter-annual variations of seven rainfall indices: the annual mean precipitation (MEAN); the annual standard deviation of daily precipitation (STD); the frequency of wet days (Prcp1); the maximum number of consecutive dry days (CDD); the maximum 3-day rainfall total (R3D); the wet day precipitation intensity (SDII); and the 90th percentile of rain-day precipitation (Prec90p). The indices were spatially averaged over three agro-climatic regions in Senegal. Trends in the time series of the averaged indices were assessed using both visual examination and a modified version of the Mann-Kendall (MM-K) test. Initially negative significant trends in all seven indices suggest gradually drier conditions over the three agro-climatic regions between 1950 and 1980. In contrast, no significant trends, or even positive significant trends, were observed from the mid-1980s to 2007. The MM-K test was applied to all available data (1950–2007) and the period from 1971 to 2000. While several indices were found to have significant trends towards drier conditions for the 1950–2007 period, only PRCP1 showed a positive significant trend for the 1971–2000 period. The MM-K test did not detect a significant trend for the other indices. It was found that the rainfall deficit and therefore drought is no longer intensifying, and that the region may even become wetter. However, the period covered by the observations is still too short to resolve the question of whether there is now a trend towards wetter conditions.

Editor Z.W. Kundzewicz; Associate editor K. Hamed     

Multisite statistical downscaling model for daily precipitation combined by multivariate multiple linear regression and stochastic weather generator

This study provides a multi-site hybrid statistical downscaling procedure combining regression-based and stochastic weather generation approaches for multisite simulation of daily precipitation. In the hybrid model, the multivariate multiple linear regression (MMLR) is employed for simultaneous downscaling of deterministic series of daily precipitation occurrence and amount using large-scale reanalysis predictors over nine different observed stations in southern Québec (Canada). The multivariate normal distribution, the first-order Markov chain model, and the probability distribution mapping technique are employed for reproducing temporal variability and spatial dependency on the multisite observations of precipitation series. The regression-based MMLR model explained 16?%?~?22?% of total variance in daily precipitation occurrence series and 13?%?~?25?% of total variance in daily precipitation amount series of the nine observation sites. Moreover, it constantly over-represented the spatial dependency of daily precipitation occurrence and amount. In generating daily precipitation, the hybrid model showed good temporal reproduction ability for number of wet days, cross-site correlation, and probabilities of consecutive wet days, and maximum 3-days precipitation total amount for all observation sites. However, the reproducing ability of the hybrid model for spatio-temporal variations can be improved, i.e. to further increase the explained variance of the observed precipitation series, as for example by using regional-scale predictors in the MMLR model. However, in all downscaling precipitation results, the hybrid model benefits from the stochastic weather generator procedure with respect to the single use of deterministic component in the MMLR model.     

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.