The ECOlogical Model for Applied Geophysics (ECOMAG) and the HYdrological Predictions for the Environment (HYPE) process-based hydrological models were set up to assess possible impacts of climate change on the hydrological regime of two pan-Arctic great drainage basins of the Lena and the Mackenzie Rivers. We firstly assessed the reliability of the hydrological models to reproduce the historical streamflow series and analyzed the hydrological projections driven by the climate change scenarios. The impacts were assessed for three 30-year periods (early-(2006-2035), mid-(2036-2065), and end-century (2070-2099)) using an ensemble of five global climate models (GCMs) and four Representative Concentration Pathway (RCP) scenarios. Results show, particularly, that the basins react with a multi-year delay to changes in RCP2.6, so-called Bmitigation^scenario, and consequently to the potential mitigation measures. Then, we assessed the hydrological projections' variability, which is caused by the GCM's and RCP's uncertainties, and found that the variability rises with the time horizon of the projection, and generally, the projection variability is larger for the Mackenzie than for the Lena. We finally compared the mean annual runoff anomalies projected under the GCM-based data for the twenty-first century with the corresponding Climatic Change anomalies projected under a modified observed climatology using the delta-change method in the Lena basin. We found that the compared projections are closely correlated for the earlycentury period. Thus, for the Lena basin, the modified observed climatology can be used as driving force for hydrological model-based projections and considered as an alternative to the GCM-based scenarios.

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Abstract. An approach is proposed to assess hydrological simulation uncertainty originating from internal atmospheric variability. The latter is one of three major factors contributing to uncertainty of simulated climate change projections (along with so-called "forcing" and "climate model" uncertainties). Importantly, the role of internal atmospheric variability is most visible over spatio-temporal scales of water management in large river basins. Internal atmospheric variability is represented by large ensemble simulations (45 members) with the ECHAM5 atmospheric general circulation model. Ensemble simulations are performed using identical prescribed lower boundary conditions (observed sea surface temperature, SST, and sea ice concentration, SIC, for 1979-2012) and constant external forcing parameters but different initial conditions of the atmosphere. The ensemble of bias-corrected ECHAM5 outputs and ensemble averaged ECHAM5 output are used as a distributed input for the ECOMAG and SWAP hydrological models. The corresponding ensembles of runoff hydrographs are calculated for two large rivers of the Arctic basin: the Lena and Northern Dvina rivers. A number of runoff statistics including the mean and the standard deviation of annual, monthly and daily runoff, as well as annual runoff trend, are assessed. Uncertainties of runoff statistics caused by internal atmospheric variability are estimated. It is found that uncertainty of the mean and the standard deviation of runoff has a significant seasonal dependence on the maximum during the periods of spring-summer snowmelt and summer-autumn rainfall floods. Noticeable nonlinearity of the hydrological models' results in the ensemble ECHAM5 output is found most strongly expressed for the Northern Dvina River basin. It is shown that the averaging over ensemble members effectively filters the stochastic term related to internal atmospheric variability. Simulated discharge trends are close to normally distributed around the ensemble mean value, which fits well to empirical estimates and, for the Lena River, indicates that a considerable portion of the observed trend can be externally driven.