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  • 1
    Online Resource
    Online Resource
    Synoptic airflow and UK daily precipitation extremes : Development and validation of a vector generalised linear model
    Springer Science and Business Media LLC ; 2010
    In:  Extremes Vol. 13, No. 2 ( 2010-6), p. 133-153
    Details
    In: Extremes, Springer Science and Business Media LLC, Vol. 13, No. 2 ( 2010-6), p. 133-153
    Type of Medium: Online Resource
    ISSN: 1386-1999 , 1572-915X
    URL: Article
    DOI: 10.1007/s10687-010-0102-x
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2010
    detail.hit.zdb_id: 2013246-3
    SSG: 11
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  • 2
    Online Resource
    Online Resource
    Cosmic rays, carbon dioxide, and climate
    American Geophysical Union (AGU) ; 2004
    In:  Eos, Transactions American Geophysical Union Vol. 85, No. 4 ( 2004-01-27), p. 38-41
    Details
    In: Eos, Transactions American Geophysical Union, American Geophysical Union (AGU), Vol. 85, No. 4 ( 2004-01-27), p. 38-41
    Abstract: Several recent papers have applied correlation analysis to climate‐related time series in the hope of finding evidence for causal relationships. For a critical discussion of correlations between solar variability, cosmic rays, and cloud cover, see Laut [2003]. A prominent new example is a paper by Shaviv and Veizer [2003], which claims that fluctuations in cosmic ray flux reaching the Earth can explain 66% of the temperature variance over the past 520 m.y.,and that the sensitivity of climate to a doubling of CO 2 is less than previously estimated.
    Type of Medium: Online Resource
    ISSN: 0096-3941 , 2324-9250
    URL: Article
    DOI: 10.1029/2004EO040002
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2004
    detail.hit.zdb_id: 24845-9
    detail.hit.zdb_id: 2118760-5
    detail.hit.zdb_id: 240154-X
    SSG: 16,13
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  • 3
    Online Resource
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    Validation of spatial variability in downscaling results from the VALUE perfect predictor experiment
    Wiley ; 2019
    In:  International Journal of Climatology Vol. 39, No. 9 ( 2019-07), p. 3819-3845
    Details
    In: International Journal of Climatology, Wiley, Vol. 39, No. 9 ( 2019-07), p. 3819-3845
    Abstract: The spatial dependence of meteorological variables is crucial for many impacts, for example, droughts, floods, river flows, energy demand, and crop yield. There is thus a need to understand how well it is represented in downscaling (DS) products. Within the COST Action VALUE, we have conducted a comprehensive analysis of spatial variability in the output of over 40 different DS methods in a perfect predictor setup. The DS output is evaluated against daily precipitation and temperature observations for the period 1979–2008 at 86 sites across Europe and 53 sites across Germany. We have analysed the dependency of correlations of daily temperature and precipitation series at station pairs on the distance between the stations. For the European data set, we have also investigated the complexity of the downscaled data by calculating the number of independent spatial degrees of freedom. For daily precipitation at the German network, we have additionally evaluated the dependency of the joint exceedance of the wet day threshold and of the local 90th percentile on the distance between the stations. Finally, we have investigated regional patterns of European monthly precipitation obtained from rotated principal component analysis. We analysed Perfect Prog (PP) methods, which are based on statistical relationships derived from observations, as well as Model Output Statistics (MOS) approaches, which attempt to correct simulated variables. In summary, we found that most PP DS methods, with the exception of multisite analog methods and a method that explicitly models spatial dependence yield unrealistic spatial characteristics. Regional climate model‐based MOS methods showed good performance with respect to correlation lengths and the joint occurrence of wet days, but a substantial overestimation of the joint occurrence of heavy precipitation events. These findings apply to the spatial scales that are resolved by our observation network, and similar studies with higher resolutions, which are relevant for small hydrological catchment, are desirable.
    Type of Medium: Online Resource
    ISSN: 0899-8418 , 1097-0088
    URL: Issue
    URL: Article
    DOI: 10.1002/joc.2019.39.issue-9
    DOI: 10.1002/joc.6024
    RVK:
    RA 1000
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 1491204-1
    SSG: 14
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  • 4
    Online Resource
    Online Resource
    Stochastic downscaling of gridded precipitation to spatially coherent subgrid precipitation fields using a transformed Gaussian model
    Wiley ; 2022
    In:  International Journal of Climatology Vol. 42, No. 12 ( 2022-10), p. 6126-6147
    Details
    In: International Journal of Climatology, Wiley, Vol. 42, No. 12 ( 2022-10), p. 6126-6147
    Abstract: Climate impact models often require unbiased point‐scale observations, but climate models typically provide biased simulations at the grid scale. While standard bias adjustment methods have shown to generally perform well at adjusting climate model biases, they cannot overcome the gap between grid‐box and point scale. To overcome this limitation, combined bias adjustment and stochastic downscaling methods have been developed. These methods, however, are single‐site methods and cannot represent spatial dependence. Here we propose a multisite stochastic downscaling method that can be applied to bias‐adjusted climate model output for generating spatially coherent time series of daily precipitation at multiple stations, conditional on the driving climate model. The method is based on a transformed truncated multivariate Gaussian model and can also be used to downscale to a full field at finer‐grid resolution. An evaluation for stations across selected catchments in Austria demonstrates the good performance of the stochastic model at representing marginal, temporal and spatial aspects of daily precipitation, including extreme events.
    Type of Medium: Online Resource
    ISSN: 0899-8418 , 1097-0088
    URL: Issue
    URL: Article
    DOI: 10.1002/joc.v42.12
    DOI: 10.1002/joc.7581
    RVK:
    RA 1000
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 1491204-1
    SSG: 14
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  • 5
    Online Resource
    Online Resource
    Stochastic Model Output Statistics for Bias Correcting and Downscaling Precipitation Including Extremes
    American Meteorological Society ; 2014
    In:  Journal of Climate Vol. 27, No. 18 ( 2014-09-15), p. 6940-6959
    Details
    In: Journal of Climate, American Meteorological Society, Vol. 27, No. 18 ( 2014-09-15), p. 6940-6959
    Abstract: Precipitation is highly variable in space and time; hence, rain gauge time series generally exhibit additional random small-scale variability compared to area averages. Therefore, differences between daily precipitation statistics simulated by climate models and gauge observations are generally not only caused by model biases, but also by the corresponding scale gap. Classical bias correction methods, in general, cannot bridge this gap; they do not account for small-scale random variability and may produce artifacts. Here, stochastic model output statistics is proposed as a bias correction framework to explicitly account for random small-scale variability. Daily precipitation simulated by a regional climate model (RCM) is employed to predict the probability distribution of local precipitation. The pairwise correspondence between predictor and predictand required for calibration is ensured by driving the RCM with perfect boundary conditions. Wet day probabilities are described by a logistic regression, and precipitation intensities are described by a mixture model consisting of a gamma distribution for moderate precipitation and a generalized Pareto distribution for extremes. The dependence of the model parameters on simulated precipitation is modeled by a vector generalized linear model. The proposed model effectively corrects systematic biases and correctly represents local-scale random variability for most gauges. Additionally, a simplified model is considered that disregards the separate tail model. This computationally efficient model proves to be a feasible alternative for precipitation up to moderately extreme intensities. The approach sets a new framework for bias correction that combines the advantages of weather generators and RCMs.
    Type of Medium: Online Resource
    ISSN: 0894-8755 , 1520-0442
    URL: Article
    DOI: 10.1175/JCLI-D-13-00604.1
    RVK:
    UA 4548
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2014
    detail.hit.zdb_id: 246750-1
    detail.hit.zdb_id: 2021723-7
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  • 6
    Online Resource
    Online Resource
    Extreme Precipitation in an Atmosphere General Circulation Model: Impact of Horizontal and Vertical Model Resolutions
    American Meteorological Society ; 2015
    In:  Journal of Climate Vol. 28, No. 3 ( 2015-02-01), p. 1184-1205
    Details
    In: Journal of Climate, American Meteorological Society, Vol. 28, No. 3 ( 2015-02-01), p. 1184-1205
    Abstract: To investigate the influence of atmospheric model resolution on the representation of daily precipitation extremes, ensemble simulations with the atmospheric general circulation model ECHAM5 at different horizontal (from T213 to T31 spectral truncation) and vertical (from L31 to L19) resolutions and forced with observed sea surface temperatures and sea ice concentrations have been carried out for January 1982–September 2010. All results have been compared with the highest resolution, which has been validated against observations. Resolution affects both the representation of physical processes and the averaging of precipitation across grid boxes. The latter, in particular, smooths out localized extreme events. These effects have been disentangled by averaging precipitation simulated at the highest resolution to the corresponding coarser grid. Extremes are represented by seasonal maxima, modeled by the generalized extreme value distribution. Effects of averaging and representation of physical processes vary with region and season. In the tropical summer hemisphere, extreme precipitation is reduced by up to 30% due to the averaging effect, and a further 65% owing to a coarser representation of physical processes. Toward middle to high latitudes, the latter effect reduces to 20%; in the winter hemisphere it vanishes toward the poles. A strong drop is found between T106 and T63 in the convection-dominated tropics. At the lowest resolution, Northern Hemisphere winter precipitation extremes, mainly caused by large-scale weather systems, are in general represented reasonably well. Coarser vertical resolution causes an equatorward shift of maximum extreme precipitation in the tropics. The impact of vertical resolution on mean precipitation is less pronounced; for horizontal resolution it is negligible.
    Type of Medium: Online Resource
    ISSN: 0894-8755 , 1520-0442
    URL: Article
    DOI: 10.1175/JCLI-D-14-00337.1
    RVK:
    UA 4548
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2015
    detail.hit.zdb_id: 246750-1
    detail.hit.zdb_id: 2021723-7
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  • 7
    Online Resource
    Online Resource
    Increased spatial extent and likelihood of compound long-duration dry and hot events in China, 1961–2014
    Copernicus GmbH ; 2023
    In:  Natural Hazards and Earth System Sciences Vol. 23, No. 2 ( 2023-02-15), p. 693-709
    Details
    In: Natural Hazards and Earth System Sciences, Copernicus GmbH, Vol. 23, No. 2 ( 2023-02-15), p. 693-709
    Abstract: Abstract. Compound dry and hot events can cause aggregated damage compared with isolated hazards. Although increasing attention has been paid to compound dry and hot events, the persistence of such hazards is rarely investigated. Moreover, little attention has been paid to the simultaneous evolution process of such hazards in space and time. Based on observations during 1961–2014, the spatiotemporal characteristics of compound long-duration dry and hot (LDDH) events in China during the summer season are investigated on both a grid basis and a 3D event basis. Grid-scale LDDH events mainly occur in eastern China, especially over northeastern areas. Most regions have experienced a pronounced increase in the likelihood of LDDH events, which is dominated by increasing temperatures. From a 3D perspective, 146 spatiotemporal LDDH (SLDDH) events are detected and grouped into 9 spatial patterns. Over time, there is a significant increase in the frequency and spatial extent of SLDDH events. Consistent with the grid-scale LDDH events, hotspots of SLDDH events mainly occur in northern China, such as the Northeast China, North China and Qinghai clusters, which are accompanied by a high occurrence frequency and large affected areas greater than 300 000 km2.
    Type of Medium: Online Resource
    ISSN: 1684-9981
    URL: Article
    DOI: 10.5194/nhess-23-693-2023
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2023
    detail.hit.zdb_id: 2069216-X
    detail.hit.zdb_id: 2064587-9
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  • 8
    Online Resource
    Online Resource
    Cross-validation of bias-corrected climate simulations is misleading
    Copernicus GmbH ; 2018
    In:  Hydrology and Earth System Sciences Vol. 22, No. 9 ( 2018-09-18), p. 4867-4873
    Details
    In: Hydrology and Earth System Sciences, Copernicus GmbH, Vol. 22, No. 9 ( 2018-09-18), p. 4867-4873
    Abstract: Abstract. We demonstrate both analytically and with a modelling example that cross-validation of free-running bias-corrected climate change simulations against observations is misleading. The underlying reasoning is as follows: a cross-validation can have in principle two outcomes. A negative (in the sense of not rejecting a null hypothesis), if the residual bias in the validation period after bias correction vanishes; and a positive, if the residual bias in the validation period after bias correction is large. It can be shown analytically that the residual bias depends solely on the difference between the simulated and observed change between calibration and validation periods. This change, however, depends mainly on the realizations of internal variability in the observations and climate model. As a consequence, the outcome of a cross-validation is also dominated by internal variability, and does not allow for any conclusion about the sensibility of a bias correction. In particular, a sensible bias correction may be rejected (false positive) and a non-sensible bias correction may be accepted (false negative). We therefore propose to avoid cross-validation when evaluating bias correction of free-running bias-corrected climate change simulations against observations. Instead, one should evaluate non-calibrated temporal, spatial and process-based aspects.
    Type of Medium: Online Resource
    ISSN: 1607-7938
    URL: Article
    DOI: 10.5194/hess-22-4867-2018
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2018
    detail.hit.zdb_id: 2100610-6
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  • 9
    Online Resource
    Online Resource
    VALUE : A framework to validate downscaling approaches for climate change studies
    American Geophysical Union (AGU) ; 2015
    In:  Earth's Future Vol. 3, No. 1 ( 2015-01), p. 1-14
    Details
    In: Earth's Future, American Geophysical Union (AGU), Vol. 3, No. 1 ( 2015-01), p. 1-14
    Abstract: VALUE has developed a framework to validate and compare downscaling methods The experiments comprise different observed and pseudo‐reality reference data The framework is the basis for a comprehensive downscaling comparison study
    Type of Medium: Online Resource
    ISSN: 2328-4277 , 2328-4277
    URL: Issue
    URL: Article
    DOI: 10.1002/eft2.2015.3.issue-1
    DOI: 10.1002/2014EF000259
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2015
    detail.hit.zdb_id: 2746403-9
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  • 10
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    Comparison of GCM‐ and RCM‐simulated precipitation following stochastic postprocessing
    American Geophysical Union (AGU) ; 2014
    In:  Journal of Geophysical Research: Atmospheres Vol. 119, No. 19 ( 2014-10-16)
    Details
    In: Journal of Geophysical Research: Atmospheres, American Geophysical Union (AGU), Vol. 119, No. 19 ( 2014-10-16)
    Abstract: Comparison between bias‐corrected RCMs and bias‐corrected GCMs The first application of stochastic MOS for GCM precipitation It is challenging to demonstrate the value added by RCMs in this setup
    Type of Medium: Online Resource
    ISSN: 2169-897X , 2169-8996
    URL: Issue
    URL: Article
    DOI: 10.1002/jgrd.v119.19
    DOI: 10.1002/2014JD021732
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2014
    detail.hit.zdb_id: 710256-2
    detail.hit.zdb_id: 2016800-7
    detail.hit.zdb_id: 2969341-X
    SSG: 16,13
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