Local impact analysis of climate change on precipitation extremes: are high-resolution climate models needed for realistic simulations?

Tabari, Hossein; Troch, Rozemien De; Giot, Olivier; Hamdi, Rafiq; Termonia, Piet; Saeed, Sajjad; Brisson, Erwan; Lipzig, Nicole Van; Willems, Patrick

doi:10.5194/hess-20-3843-2016

Cited by 56 publications

(48 citation statements)

References 55 publications

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“…Although the importance of the model spatial resolution for the simulation of summer extreme precipitation has also been emphasized in previous studies (Tabari et al 2016, Karmacharya et al 2017, Gadian et al 2018, Tabari and Willems 2018, a clear pattern is not found in this study. Nevertheless, SDS is mostly less than one except for winter in north and west Europe (figures 6 and S12(b)-(e)).…”

Section: Resultscontrasting

confidence: 56%

“…The projections are usually performed by means of global climate models (GCMs). GCM simulations are, however, typically at course spatial (hundreds of kilometers) and temporal (daily) resolutions which poses some limitations to accurately capture more details such as land topography or orographic effects and mesoscale processes (Tabari et al 2016). Therefore, dynamical downscaling has been implemented using regional climate models (RCMs), which provide an improved representation of the physical processes in the atmosphere and the topographic variability which cannot be captured by the coarse resolution of GCMs (Dosio andFischer 2018, Soares andCardoso 2018).…”

Section: Introductionmentioning

confidence: 99%

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Regionalization of anthropogenically forced changes in 3 hourly extreme precipitation over Europe

Hosseinzadehtalaei

Tabari

Willems

2019

Environ. Res. Lett.

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Future extreme precipitation events are expected to be influenced by climate change; however, the robustness of this anthropogenically forced response in respect to projection uncertainty especially for sub-daily extremes is not fully understood. We analyze the impact of anthropogenic climate change on 3 hourly extreme precipitation with return periods ranging between 5 and 50 years over Europe using the RCA4 model ensemble simulations at 0.11°resolution. The robustness of the signals is examined based on a regionalized signal-to-noise (S2N) technique by taking the spatial pooling into account and the efficacy of the regionalization is tested by a sensitivity analysis. The results show an increasing signal in 3 hourly extreme precipitation over Europe for all seasons except summer for which a bipolar pattern (increase in the north and decrease in the south) is discerned. For the businessas-usual scenario RCP8.5, the regionalized winter 3 hourly extreme precipitation signals over 9 × 9 model grid cells are statistically significant in roughly 72%, 65%, 59% and 48% of the European area for 5, 15, 25 and 50 year return periods respectively, while 16%-21% of the area will experience significant changes in summer. The S2N values for 3 hourly extreme precipitation changes rise after the spatial pooling by about a factor of 1.4-1.7 for all seasons except summer when they decline by about a factor of 0.78. The results of sensitivity analysis reveal that the regionalization influence is sensitive-in order of decreasing importance-to season, precipitation time scale, precipitation intensity, emission scenario and model spatial resolution. The precipitation time scale is particularly important seasonally in summer and regionally in south Europe when/where short-duration convective precipitation is dominant.

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Section: Resultscontrasting

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Regionalization of anthropogenically forced changes in 3 hourly extreme precipitation over Europe

Hosseinzadehtalaei

Tabari

Willems

2019

Environ. Res. Lett.

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“…The underlying assumption of stationarity is generally not expected to be valid at subhourly to subdaily precipitation durations under projected 21st century warming (Cheng & AghaKouchak, ; Lenderink & Van Meijgaard, ), but the reliability of stationarity assumptions in multiday duration prediction is less clear (Nissen & Ulbrich, ). Alternatives that develop IDF information from global or regional climate models may not be practical for mountain areas given the difficulties of downscaling precipitation in complex terrain (Tabari et al, ). Together these challenges call for simultaneous efforts to test and improve standard IDF techniques in snow‐dominated mountains, as done in this study, combined with improved atmospheric and snowpack energy budget modeling efforts that better consider the effects of changing climate on extreme snowmelt and precipitation processes.…”

Section: Discussionmentioning

confidence: 99%

Potential for Changing Extreme Snowmelt and Rainfall Events in the Mountains of the Western United States

Harpold

Kohler

2017

JGR Atmospheres

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The intensity of mountain precipitation is often modified by snow accumulation and melt, yet rainfall‐based observations are widely used in planning and design. Comparisons of extreme rainfall versus snowmelt intensities are needed because they have different predictability and hazard implications. Regional warming is expected to intensify not only rainfall and snowfall but also slow snowmelt, which could further challenge intensity duration frequency (IDF) techniques. We use observations from 379 mountain sites across the western U.S. to estimate the 10 and 100 year intensity at 1, 2, and 30 day durations for historical snowmelt (SM), precipitation occurring during snow cover (SCP), and precipitation during the snow‐free period (SFP). At 1 day durations, 100 year SCP was greater than SM and SFP at 40% of sites, while SM was larger than SCP and SFP at 39% of sites. At 30 day durations, SM was greater than SCP and SFP at 95% of sites. The continental sites are generally insensitive to increased water input intensity from SCP occurring as rainfall. In contrast, the maritime mountains are relatively insensitive to changes in SM but have the potential for increased water input intensity from greater SCP occurring as rainfall. Standard precipitation intensity data sets accurately estimated the 100 year, 1 day SCP and SM but underestimated SM at 78 continental sites where SM was greater than SCP and SFP. These results confirm that snow processes modify IDF estimates and highlight regional sensitivity to increased winter rainfall and slower snowmelt that may necessitate local adaptation strategies.

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“…Finally, our work provides a theoretically consistent methodology that can be applied to disaggregate actual rainfall (or model outputs) at fine time scales, which can be used in several fields that have been significant catalysts for the development of recent hydrological research. In fact, a wide range of studies concerning, e.g., climate-related issues, resilience of urban areas to hydrological extremes, and online prediction/warning systems for urban hydrology require accurate characterization of rainfall inputs at fine time scales Lombardo et al, 2009;Fletcher et al, 2013;Tabari et al, 2016;McCabe et al, 2017]. Hence, complete rainfall disaggregation methods with solid theoretical basis together with reliable data series are crucial to meet these needs.…”

Section: 1002/2017wr020529mentioning

confidence: 99%

A theoretically consistent stochastic cascade for temporal disaggregation of intermittent rainfall

Lombardo

Volpi

Koutsoyiannis

et al. 2017

Water Resources Research

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Generating fine-scale time series of intermittent rainfall that are fully consistent with any given coarse-scale totals is a key and open issue in many hydrological problems. We propose a stationary disaggregation method that simulates rainfall time series with given dependence structure, wet/dry probability, and marginal distribution at a target finer (lower-level) time scale, preserving full consistency with variables at a parent coarser (higher-level) time scale. We account for the intermittent character of rainfall at fine time scales by merging a discrete stochastic representation of intermittency and a continuous one of rainfall depths. This approach yields a unique and parsimonious mathematical framework providing general analytical formulations of mean, variance, and autocorrelation function (ACF) for a mixed-type stochastic process in terms of mean, variance, and ACFs of both continuous and discrete components, respectively. To achieve the full consistency between variables at finer and coarser time scales in terms of marginal distribution and coarse-scale totals, the generated lower-level series are adjusted according to a procedure that does not affect the stochastic structure implied by the original model. To assess model performance, we study rainfall process as intermittent with both independent and dependent occurrences, where dependence is quantified by the probability that two consecutive time intervals are dry. In either case, we provide analytical formulations of main statistics of our mixed-type disaggregation model and show their clear accordance with Monte Carlo simulations. An application to rainfall time series from real world is shown as a proof of concept. Plain Language Summary Rainfall is the main input to most hydrological systems. A wide range of studies concerning floods, water resources and water quality require characterization of rainfall inputs at fine time scales. This may be possible using empirical observations, but there is often a need to extend available data in terms of temporal resolution satisfying some additive property (i.e. that the sum of the values of consecutive variables within a period be equal to the corresponding coarse-scale amount). Hence, rainfall disaggregation models are required. Although there is substantial experience in stochastic disaggregation of rainfall to fine time scales, most modeling schemes existing in the literature are ad hoc techniques rather than consistent generalized methods. This is mainly due to the skewed distributions and the intermittent nature of the rainfall process at fine time scales, which are severe obstacles for the application of a theoretically consistent scheme to rainfall disaggregation. We propose a consistent disaggregation model that first generates lognormal time series of rainfall depths based on a random cascade structure. Then, such time series are multiplied by binary sequences (i.e., rainfall occurrences) to obtain intermittent rainfall time series with known summary statistics.

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Local impact analysis of climate change on precipitation extremes: are high-resolution climate models needed for realistic simulations?

Cited by 56 publications

References 55 publications

Regionalization of anthropogenically forced changes in 3 hourly extreme precipitation over Europe

Regionalization of anthropogenically forced changes in 3 hourly extreme precipitation over Europe

Potential for Changing Extreme Snowmelt and Rainfall Events in the Mountains of the Western United States

A theoretically consistent stochastic cascade for temporal disaggregation of intermittent rainfall

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