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  • 1
    Online Resource
    Online Resource
    American Meteorological Society ; 2006
    In:  Journal of Climate Vol. 19, No. 13 ( 2006-07-01), p. 3112-3132
    In: Journal of Climate, American Meteorological Society, Vol. 19, No. 13 ( 2006-07-01), p. 3112-3132
    Abstract: An analysis of several multidecadal simulations of the present (1971–90) and future (2041–60) climate from the Canadian Regional Climate Model (CRCM) is presented. The effects on the CRCM climate of model domain size, internal variability of the general circulation model (GCM) used to provide boundary conditions, and modifications to the physical parameterizations used in the CRCM are investigated. The influence of boundary conditions is further investigated by comparing the GCM-driven simulations of the current climate with simulations performed using boundary conditions from meteorological reanalyses. The present climate of the model in these different configurations is assessed by comparing the seasonal averages and interannual variability of precipitation and surface air temperature with an observed climatology. Generally, small differences are found between the two simulations on different domains, though both domains are quite large as compared with previously reported results. Simulations driven by GCM output show a significant warm bias for wintertime surface air temperatures over northern regions. This warm bias is much reduced in the GCM-driven simulation when an updated set of physical parameterizations is used in the CRCM. The warm bias is also reduced for simulations with the standard set of physical parameterizations when the CRCM is driven with reanalysis data. However, use of the modified physics package for reanalysis-driven simulations results in surface air temperatures that are colder than the observations. Summertime precipitation in the model is much larger than observed, a bias that is present in both the GCM-driven and reanalysis-driven simulations. The bias in summertime precipitation is reduced for both types of driving data when the updated set of physical parameterizations is used. Model projections of climate change between the present and future periods are also presented and the sensitivity of these projections to many of the above-mentioned modifications is assessed. Changes in surface air temperature are predicted to be largest over northern regions in winter, with smaller changes over more southerly regions and in the summer season. Changes in seasonal average precipitation are projected to be in the range of ±10% of present-day amounts for most regions and seasons. The CRCM projections of surface air temperature changes are strongly affected by the internal variability of the driving GCM over high northern latitudes and to changes in the physical parameterizations over many regions for the summer season.
    Type of Medium: Online Resource
    ISSN: 1520-0442 , 0894-8755
    RVK:
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2006
    detail.hit.zdb_id: 246750-1
    detail.hit.zdb_id: 2021723-7
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  • 2
    In: Advances in Geosciences, Copernicus GmbH, Vol. 21 ( 2009-08-11), p. 63-71
    Abstract: Abstract. Little quantitative knowledge is as yet available about the role of hydrological model complexity for climate change impact assessment. This study investigates and compares the varieties of different model response of three hydrological models (PROMET, Hydrotel, HSAMI), each representing a different model complexity in terms of process description, parameter space and spatial and temporal scale. The study is performed in the Ammer watershed, a 709 km2 catchment in the Bavarian alpine forelands, Germany. All models are driven and validated by a 30-year time-series (1971–2000) of observation data. It is expressed by objective functions, that all models, HSAMI and Hydrotel due to calibration, perform almost equally well for runoff simulation over the validation period. Some systematic deviances in the hydrographs and the spatial patterns of hydrologic variables are however quite distinct and thus further discussed. Virtual future climate (2071–2100) is generated by the Canadian Regional Climate Model (vers 3.7.1), driven by the Coupled Global Climate Model (vers. 2) based on an A2 emission scenario (IPCC 2007). The hydrological model performance is evaluated by flow indicators, such as flood frequency, annual 7-day and 30-day low flow and maximum seasonal flows. The modified climatic boundary conditions cause dramatic deviances in hydrologic model response. HSAMI shows tremendous overestimation of evapotranspiration, while Hydrotel and PROMET behave in comparable range. Still, their significant differences, like spatially explicit patterns of summerly water shortage or spring flood intensity, highlight the necessity to extend and quantify the uncertainty discussion in climate change impact analysis towards the remarkable effect of hydrological model complexity. It is obvious that for specific application purposes, water resources managers need to be made aware of this effect and have to take its implications into account for decision making. The paper concludes with an outlook and a proposal for future research necessities.
    Type of Medium: Online Resource
    ISSN: 1680-7359
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2009
    detail.hit.zdb_id: 2625759-2
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  • 3
    In: Hydrology and Earth System Sciences, Copernicus GmbH, Vol. 17, No. 3 ( 2013-03-19), p. 1189-1204
    Abstract: Abstract. In climate change impact research, the assessment of future river runoff as well as the catchment-scale water balance is impeded by different sources of modeling uncertainty. Some research has already been done in order to quantify the uncertainty of climate projections originating from the climate models and the downscaling techniques, as well as from the internal variability evaluated from climate model member ensembles. Yet, the use of hydrological models adds another layer of uncertainty. Within the QBic3 project (Québec–Bavarian International Collaboration on Climate Change), the relative contributions to the overall uncertainty from the whole model chain (from global climate models to water management models) are investigated using an ensemble of multiple climate and hydrological models. Although there are many options to downscale global climate projections to the regional scale, recent impact studies tend to use regional climate models (RCMs). One reason for that is that the physical coherence between atmospheric and land-surface variables is preserved. The coherence between temperature and precipitation is of particular interest in hydrology. However, the regional climate model outputs often are biased compared to the observed climatology of a given region. Therefore, biases in those outputs are often corrected to facilitate the reproduction of historic runoff conditions when used in hydrological models, even if those corrections alter the relationship between temperature and precipitation. So, as bias correction may affect the consistency between RCM output variables, the use of correction techniques and even the use of (biased) climate model data itself is sometimes disputed among scientists. For these reasons, the effect of bias correction on simulated runoff regimes and the relative change in selected runoff indicators is explored. If it affects the conclusion of climate change analysis in hydrology, we should consider it as a source of uncertainty. If not, the application of bias correction methods is either unnecessary to obtain the change signal in hydro-climatic projections, or safe to use for the production of present and future river runoff scenarios as it does not alter the change signal. The results of the present paper highlight the analysis of daily runoff simulated with four different hydrological models in two natural-flow catchments, driven by different regional climate models for a reference and a future period. As expected, bias correction of climate model outputs is important for the reproduction of the runoff regime of the past, regardless of the hydrological model used. Then again, its impact on the relative change of flow indicators between reference and future periods is weak for most indicators, with the exception of the timing of the spring flood peak. Still, our results indicate that the impact of bias correction on runoff indicators increases with bias in the climate simulations.
    Type of Medium: Online Resource
    ISSN: 1607-7938
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2013
    detail.hit.zdb_id: 2100610-6
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  • 4
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2003
    In:  Climate Dynamics Vol. 21, No. 5-6 ( 2003-11-1), p. 405-421
    In: Climate Dynamics, Springer Science and Business Media LLC, Vol. 21, No. 5-6 ( 2003-11-1), p. 405-421
    Type of Medium: Online Resource
    ISSN: 0930-7575 , 1432-0894
    Language: Unknown
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2003
    detail.hit.zdb_id: 382992-3
    detail.hit.zdb_id: 1471747-5
    SSG: 16,13
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  • 5
    In: Climatic Change, Springer Science and Business Media LLC, Vol. 120, No. 4 ( 2013-10), p. 965-975
    Type of Medium: Online Resource
    ISSN: 0165-0009 , 1573-1480
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2013
    detail.hit.zdb_id: 751086-X
    detail.hit.zdb_id: 1477652-2
    SSG: 14
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  • 6
    Online Resource
    Online Resource
    Wiley ; 2013
    In:  International Journal of Climatology Vol. 33, No. 3 ( 2013-03-15), p. 758-769
    In: International Journal of Climatology, Wiley, Vol. 33, No. 3 ( 2013-03-15), p. 758-769
    Abstract: Paleoclimate reconstructions are generally validated on recent periods. To obtain a set of instrumental records at the regional scale, a time series of monthly mean temperatures in Northeastern Canada were interpolated for the 1961–2000 period. Records were provided by 202 meteorological stations. Temperatures derived from a Canadian regional climate model (Climate Model CRCM 4.2.3 from the AMNO domain produced at ∼50 km resolution) were added as secondary information to take into account local heterogeneity and temporal dependencies. Geostatistical interpolation of the measured temperature was calculated using CRCM modelled data as a covariable and then compared to an ordinary kriging performed on a time series of mean temperature anomalies. Spherical distances between locations were calculated taking into account the curvature of the Earth with monthly semivariances being modelled using Cauchy variograms. Mean absolute error values (1.5 ± 1.2 °C) were calculated for the whole period using cross‐validation procedures. Errors were found to have the same order of magnitude in the central part of the study area where few recorded temperatures were available. Monthly mean temperature grids are publicly available through the Institut National de la Recherche Scientifique ( http://url.in.rs/3P ). Copyright © 2012 Royal Meteorological Society
    Type of Medium: Online Resource
    ISSN: 0899-8418 , 1097-0088
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2013
    detail.hit.zdb_id: 1491204-1
    SSG: 14
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  • 7
    In: Hydrology and Earth System Sciences, Copernicus GmbH, Vol. 17, No. 2 ( 2013-02-08), p. 565-578
    Abstract: Abstract. Over the recent years, several research efforts investigated the impact of climate change on water resources for different regions of the world. The projection of future river flows is affected by different sources of uncertainty in the hydro-climatic modelling chain. One of the aims of the QBic3 project (Québec-Bavarian International Collaboration on Climate Change) is to assess the contribution to uncertainty of hydrological models by using an ensemble of hydrological models presenting a diversity of structural complexity (i.e., lumped, semi distributed and distributed models). The study investigates two humid, mid-latitude catchments with natural flow conditions; one located in Southern Québec (Canada) and one in Southern Bavaria (Germany). Daily flow is simulated with four different hydrological models, forced by outputs from regional climate models driven by global climate models over a reference (1971–2000) and a future (2041–2070) period. The results show that, for our hydrological model ensemble, the choice of model strongly affects the climate change response of selected hydrological indicators, especially those related to low flows. Indicators related to high flows seem less sensitive on the choice of the hydrological model.
    Type of Medium: Online Resource
    ISSN: 1607-7938
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2013
    detail.hit.zdb_id: 2100610-6
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  • 8
    Online Resource
    Online Resource
    American Geophysical Union (AGU) ; 2007
    In:  Geophysical Research Letters Vol. 34, No. 20 ( 2007-10-31)
    In: Geophysical Research Letters, American Geophysical Union (AGU), Vol. 34, No. 20 ( 2007-10-31)
    Type of Medium: Online Resource
    ISSN: 0094-8276
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2007
    detail.hit.zdb_id: 2021599-X
    detail.hit.zdb_id: 7403-2
    SSG: 16,13
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  • 9
    Online Resource
    Online Resource
    American Meteorological Society ; 2012
    In:  Journal of Hydrometeorology Vol. 13, No. 1 ( 2012-02-01), p. 324-337
    In: Journal of Hydrometeorology, American Meteorological Society, Vol. 13, No. 1 ( 2012-02-01), p. 324-337
    Abstract: Precipitation has a high spatial variability, and thus some modeling applications require high-resolution data ( & lt;10 km). Unfortunately, in some cases, such as meteorological forecasts and future regional climate projections, only spatial averages over large areas are available. While some attention has been given to the disaggregation of mean areal precipitation estimates, the computation of a disaggregated field with a realistic spatial structure remains a difficult task. This paper describes the development of a statistical disaggregation model based on Gibbs sampling. The model disaggregates 45.6-km-resolution rainfall fields to grids with pixel sizes ranging from 3.8 to 22.8 km. The model is conceptually simple, as the algorithm is straightforward to compute with only a few parameters to estimate. The rainfall depth at each grid pixel is related to the depths of the neighboring pixels, while the spatial variability is related to the convective available potential energy (CAPE) field. The model is developed using daily rainfall data over a 40 000-km2 area located in the southeastern United States. Four-kilometer-resolution rainfall estimates obtained from NCEP’s stage IV analysis were used to estimate the model parameters (2002–04) and as a reference to validate the disaggregated fields (2005/06). Results show that the model accurately simulates rainfall depths and the spatial structure of the observed field. Because the model has low computational requirements, an ensemble of disaggregated data series can be generated.
    Type of Medium: Online Resource
    ISSN: 1525-755X , 1525-7541
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2012
    detail.hit.zdb_id: 2042176-X
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  • 10
    Online Resource
    Online Resource
    Informa UK Limited ; 2008
    In:  Atmosphere-Ocean Vol. 46, No. 3 ( 2008-09), p. 377-390
    In: Atmosphere-Ocean, Informa UK Limited, Vol. 46, No. 3 ( 2008-09), p. 377-390
    Type of Medium: Online Resource
    ISSN: 0705-5900 , 1480-9214
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2008
    detail.hit.zdb_id: 443534-5
    detail.hit.zdb_id: 2025886-0
    SSG: 16,13
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