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Abstract Bias correction of climate model outputs has emerged as a standard procedure in most recent climate change impact studies. A crucial assumption of all bias correction approaches is that climate model biases are constant over time. The validity of this assumption has important implications for impact studies and needs to be verified to properly address uncertainty in future climate projections. Using 10 climate model simulations, this study specifically tests the bias stationarity of climate model outputs over Canada and the contiguous United States (U.S.) by comparing model outputs with corresponding observations over two 20 year historical periods (1961–1980 and 1981–2000). The results show that precipitation biases are clearly nonstationary over much of Canada and the contiguous U.S. and where they vary over much shorter time scales than those normally considered in climate change impact studies. In particular, the difference in biases over two very close periods of the recent past are, in fact, comparable to the climate change signal between future (2061–2080) and historical (1961–1980) periods for precipitation over large parts of Canada and the contiguous U.S., indicating that the uncertainty of future impacts may have been underestimated in most impact studies. In comparison, temperature bias can be considered to be approximately stationary for most of Canada and the contiguous U.S. when compared with the magnitude of the climate change signal. Given the reality that precipitation is usually considered to be more important than temperature for many impact studies, it is advisable that natural climate variability and climate model sensitivity be better emphasized in future impact studies. , Key Points Climate model biases are nonstationary over much of North America The difference in biases is comparable to the climate change signal The uncertainty of impacts may have been underestimated in most impact studies

Abstract Precipitation forcing is critical for hydrological modeling as it has a strong impact on the accuracy of simulated river flows. In general, precipitation data used in hydrological modeling are provided by weather stations. However, in regions with sparse weather station coverage, the spatial interpolation of the individual weather stations provides a rough approximation of the real precipitation fields. In such regions, precipitation from interpolated weather stations is generally considered unreliable for hydrological modeling. Precipitation estimates from reanalyses could represent an interesting alternative in regions where the weather station density is low. This article compares the performances of river flows simulated by a watershed model using precipitation and temperature estimates from reanalyses and gridded observations. The comparison was carried out based on the density of surface weather stations for 316 Canadian watersheds located in three climatic regions. Three state-of-the-art atmospheric reanalyses—ERA-Interim, CFSR, and MERRA—and one gridded observations database over Canada—Natural Resources Canada (NRCan)—were used. Results showed that the Nash–Sutcliffe values of simulated river flows using precipitation and temperature data from CFSR and NRCan were generally equivalent regardless of the weather station density. ERA-Interim and MERRA performed significantly better than NRCan for watersheds with weather station densities of less than 1 station per 1000 km2 in the mountainous region. Overall, these results indicate that for hydrological modeling in regions with high spatial variability of precipitation such as mountainous regions, reanalyses perform better than gridded observations when the weather station density is low.

Abstract Reanalyses have the potential to provide meteorological information in areas where few or no traditional observation records are available. The terrestrial branch of the water cycle of CFSR, MERRA, ERA-Interim, and NARR is examined over Quebec, Canada, for the 1979–2008 time period. Precipitation, evaporation, runoff, and water balance are studied using observed precipitation and streamflows, according to three spatial scales: 1) the entire province of Quebec, 2) five regions derived from a climate classification, and 3) 11 river basins. The results reveal that MERRA provides a relatively closed water balance, while a significant residual was found for the other three reanalyses. MERRA and ERA-Interim seem to provide the most reliable precipitation over the province. On the other hand, precipitation from CFSR and NARR do not appear to be particularly reliable, especially over southern Quebec, as they almost systematically showed the highest and the lowest values, respectively. Moreover, the partitioning of precipitation into evaporation and runoff from MERRA and NARR does not agree with what was expected, particularly over southern, central, and eastern Quebec. Despite the weaknesses identified, the ability of reanalyses to reproduce the terrestrial water cycle of the recent past (i.e., 1979–2008) remains globally satisfactory. Nonetheless, their potential to provide reliable information must be validated by comparing reanalyses directly with weather stations, especially in remote areas.

Abstract Postprocessing of climate model outputs is usually performed to remove biases prior to performing climate change impact studies. The evaluation of the performance of bias correction methods is routinely done by comparing postprocessed outputs to observed data. However, such an approach does not take into account the inherent uncertainty linked to natural climate variability and may end up recommending unnecessary complex postprocessing methods. This study evaluates the performance of bias correction methods using natural variability as a baseline. This baseline implies that any bias between model simulations and observations is only significant if it is larger than the natural climate variability. Four bias correction methods are evaluated with respect to reproducing a set of climatic and hydrological statistics. When using natural variability as a baseline, complex bias correction methods still outperform the simplest ones for precipitation and temperature time series, although the differences are much smaller than in all previous studies. However, after driving a hydrological model using the bias-corrected precipitation and temperature, all bias correction methods perform similarly with respect to reproducing 46 hydrological metrics over two watersheds in different climatic zones. The sophisticated distribution mapping correction methods show little advantage over the simplest scaling method. The main conclusion is that simple bias correction methods appear to be just as good as other more complex methods for hydrological climate change impact studies. While sophisticated methods may appear more theoretically sound, this additional complexity appears to be unjustified in hydrological impact studies when taking into account the uncertainty linked to natural climate variability.

Abstract This paper investigates the potential of reanalyses as proxies of observed surface precipitation and temperature to force hydrological models. Three global atmospheric reanalyses (ERA-Interim, CFSR, and MERRA), one regional reanalysis (NARR), and one global meteorological forcing dataset obtained by bias-correcting ERA-Interim [Water and Global Change (WATCH) Forcing Data ERA-Interim (WFDEI)] were compared to one gridded observation database over the contiguous United States. Results showed that all temperature datasets were similar to the gridded observation over most of the United States. On the other hand, precipitation from all three global reanalyses was biased, especially in summer and winter in the southeastern United States. The regional reanalysis precipitation was closer to observations since it indirectly assimilates surface precipitation. The WFDEI dataset was generally less biased than the reanalysis datasets. All datasets were then used to force a global conceptual hydrological model on 370 watersheds of the Model Parameter Estimation Experiment (MOPEX) database. River flows were computed for each watershed, and results showed that the flows simulated using NARR and gridded observations forcings were very similar to the observed flows. The simulated flows forced by the global reanalysis datasets were also similar to the observations, except in the humid continental and subtropical climatic regions, where precipitation seasonality biases degraded river flow simulations. The WFDEI dataset led to better river flows than reanalysis in the humid continental and subtropical climatic regions but was no better than reanalysis—and sometimes worse—in other climatic zones. Overall, the results indicate that global reanalyses have good potential to be used as proxies to observations to force hydrological models, especially in regions with few weather stations.

Abstract In spring 2011, an unprecedented flood hit the complex eastern United States (U.S.)–Canada transboundary Lake Champlain–Richelieu River (LCRR) Basin, destructing properties and inducing negative impacts on agriculture and fish habitats. The damages, covered by the Governments of Canada and the U.S., were estimated to C$90M. This natural disaster motivated the study of mitigation measures to prevent such disasters from reoccurring. When evaluating flood risks, long‐term evolving climate change should be taken into account to adopt mitigation measures that will remain relevant in the future. To assess the impacts of climate change on flood risks of the LCRR basin, three bias‐corrected multi‐resolution ensembles of climate projections for two greenhouse gas concentration scenarios were used to force a state‐of‐the‐art, high‐resolution, distributed hydrological model. The analysis of the hydrological simulations indicates that the 20‐year return period flood (corresponding to a medium flood) should decrease between 8% and 35% for the end of the 21st Century (2070–2099) time horizon and for the high‐emission scenario representative concentration pathway (RCP) 8.5. The reduction in flood risks is explained by a decrease in snow accumulation and an increase in evapotranspiration expected with the future warming of the region. Nevertheless, due to the large climate inter‐annual variability, short‐term flood probabilities should remain similar to those experienced in the recent past.