Evidence for added value of convection‐permitting models for studying changes in extreme precipitation

Meredith, Edmund P.; Maraun, Douglas; Семенов, В. А.; Park, Wonsun

doi:10.1002/2015jd024238

Cited by 46 publications

(40 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Changes in precipitation extremes are primarily influenced by changes in large-scale circulation over the tropics (Maredith et al 2015). Most of the reanalysis datasets reasonably reproduce the climatological circulation features.…”

Section: Climatological Features Over the Asian Domainmentioning

confidence: 87%

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

et al. 2018

View full text Add to dashboard Cite

In this study, a comparison in the precipitation extremes as exhibited by the seven reference datasets is made to ascertain whether the inferences based on these datasets agree or they differ. These seven datasets, roughly grouped in three categories i.e. rain-gauge based (APHRODITE, CPC-UNI), satellite-based (TRMM, GPCP1DD) and reanalysis based (ERA-Interim, MERRA, and JRA55), having a common data period 1998-2007 are considered. Focus is to examine precipitation extremes in the summer monsoon rainfall over South Asia, East Asia and Southeast Asia. Measures of extreme precipitation include the percentile thresholds, frequency of extreme precipitation events and other quantities. Results reveal that the differences in displaying extremes among the datasets are small over South Asia and East Asia but large differences among the datasets are displayed over the Southeast Asian region including the maritime continent. Furthermore, precipitation data appear to be more consistent over East Asia among the seven datasets. Decadal trends in extreme precipitation are consistent with known results over South and East Asia. No trends in extreme precipitation events are exhibited over Southeast Asia. Outputs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulation data are categorized as high, medium and lowresolution models. The regions displaying maximum intensity of extreme precipitation appear to be dependent on model resolution. High-resolution models simulate maximum intensity of extreme precipitation over the Indian sub-continent, medium-resolution models over northeast India and South China and the low-resolution models over Bangladesh, Myanmar and Thailand. In summary, there are differences in displaying extreme precipitation statistics among the seven datasets considered here and among the 29 CMIP5 model data outputs.

show abstract

Section: Climatological Features Over the Asian Domainmentioning

confidence: 87%

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Recent sensitivity studies of Regional Climate Models, for example, have suggested that they cannot adequately reproduce high intensity convective rainstorms and consequently might not be fit-forpurpose in predicting conditions for future flooding induced by such events (e.g. Kendon et al, 2014;Meredith et al, 2015).…”

mentioning

confidence: 99%

Epistemic uncertainties and natural hazard risk assessment. 2. What should constitute good practice?

Beven¹,

Aspinall²,

Bates³

et al. 2017

Preprint

View full text Add to dashboard Cite

Abstract. Part 1 of this paper has discussed the uncertainties arising from gaps in knowledge or limited understanding of the processes involved in different natural hazard areas. Such deficits may include uncertainties about frequencies, process representations, parameters, present and future boundary conditions, consequences and impacts, and the meaning of observations in evaluating simulation models. These are the epistemic uncertainties that can be difficult to constrain, 20 especially in terms of event or scenario probabilities, even as elicited probabilities rationalised on the basis of expert judgements. This paper reviews the issues raised by trying to quantify the effects of epistemic uncertainties. Such scientific uncertainties might have significant influence on decisions made, say, for risk management, so it is important to examine the sensitivity of such decisions to different feasible sets of assumptions, to communicate the meaning of associated uncertainty estimates and to provide an audit trail for the analysis. A conceptual framework for good practice in dealing with epistemic 25 uncertainties is outlined and implications of applying the principles to natural hazard science are discussed.

show abstract

“…The method generally performs better in JJA than in DJF and in DJF best in the Mediterranean region, with a mild winter climate, and worst for the continental winter climate in Midand eastern Europe or Scandinavia. Seasonal and regional differences depending on the underlying mechanism have al-ready been reported for resolution dependence of extreme precipitation in GCMs (Volosciuk et al, 2015) and RCMs (Prein et al, 2013;Meredith et al, 2015). Hence, for a good representation of precipitation extremes, the complexity of the model can be chosen at each step of the modeling cascade based on the underlying mechanism in order to use computational resources efficiently.…”

Section: Discussionmentioning

confidence: 99%

“…Our model is thus only applicable where changes are correctly simulated by the GCM/RCM. For instance, changes in the dynamics of local extreme convective events in summer that need even higher resolution up to convection-permitting simulations (e.g., Kendon et al, 2014;Chan et al, 2014;Meredith et al, 2015) will also not be represented after statistical postprocessing is applied. Bias correction and (dynamical and statistical) downscaling of precipitation is only applicable if the large-scale patterns and changes therein are simulated reasonably by the driving GCM (Eden et al, 2012;Hall, 2014).…”

Section: Discussionmentioning

confidence: 99%

A combined statistical bias correction and stochastic downscaling method for precipitation

Volosciuk¹,

Maraun²,

Vrac³

et al. 2017

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. Much of our knowledge about future changes in precipitation relies on global (GCMs) and/or regional climate models (RCMs) that have resolutions which are much coarser than typical spatial scales of precipitation, particularly extremes. The major problems with these projections are both climate model biases and the gap between gridbox and point scale. Wong et al. (2014) developed a model to jointly bias correct and downscale precipitation at daily scales. This approach, however, relied on pairwise correspondence between predictor and predictand for calibration, and, thus, on nudged simulations which are rarely available. Here we present an extension of this approach that separates the downscaling from the bias correction and in principle is applicable to free-running GCMs/RCMs. In a first step, we bias correct RCM-simulated precipitation against gridded observations at the same scale using a parametric quantile mapping (QM grid ) approach. In a second step, we bridge the scale gap: we predict local variance employing a regression-based model with coarse-scale precipitation as a predictor. The regression model is calibrated between gridded and point-scale (station) observations. For this concept we present one specific implementation, although the optimal model may differ for each studied location. To correct the whole distribution including extreme tails we apply a mixture distribution of a gamma distribution for the precipitation mass and a generalized Pareto distribution for the extreme tail in the first step. For the second step a vector generalized linear gamma model is employed. For evaluation we adopt the perfect predictor experimental setup of VALUE. We also compare our method to the classical QM as it is usually applied, i.e., between RCM and point scale (QM point ). Precipitation is in most cases improved by (parts of) our method across different European climates. The method generally performs better in summer than in winter and in winter best in the Mediterranean region, with a mild winter climate, and worst for continental winter climate in Mid-and eastern Europe or Scandinavia. While QM point performs similarly (better for continental winter) to our combined method in reducing the bias and representing heavy precipitation, it is not capable of correctly modeling point-scale spatial dependence of summer precipitation. A strength of this two-step method is that the best combination of bias correction and downscaling methods can be selected. This implies that the concept can be extended to a wide range of method combinations.

show abstract

Evidence for added value of convection‐permitting models for studying changes in extreme precipitation

Cited by 46 publications

References 55 publications

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

Epistemic uncertainties and natural hazard risk assessment. 2. What should constitute good practice?

A combined statistical bias correction and stochastic downscaling method for precipitation

Contact Info

Product

Resources

About