The Key Role of Heavy Precipitation Events in Climate Model Disagreements of Future Annual Precipitation Changes in California

Pierce, David W.; Cayan, Daniel R.; Das, Tapash; Maurer, Edwin P.; Miller, Norman L.; Bao, Yan; Kanamitsu, Masao; Yoshimura, Kei; Snyder, Mark A.; Sloan, L. C.; Franco, Guido; Tyree, Mary

doi:10.1175/jcli-d-12-00766.1

Cited by 98 publications

(70 citation statements)

References 43 publications

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“…So the projections from dynamical downscaling of rainfall increase over all of eastern Australia in summer and autumn rainfal l beyond that from GCMs are very plausible, and may be driven by an increase in rainfall associated with thunderstorms and ECLs. Changes to heavy rainfall contributed strongly to differences in projection for the west coast of the United States (Pierce et al 2013), a location with many similarities to the Australian eastern seaboard. The results presented here suggest that further investigation of changes to the rainfall distribution and synoptic climatology in GCMs compared to downscaling is worthwhile, particularly for spring but also for summer and autumn.…”

Section: Discussionmentioning

confidence: 99%

“…Consequently, downscaling can potentially add significant value to projections of heavy rainfall, and therefore potentially improve projections of mean rainfall since heavy rainfall events can make a significant contribution to total rainfall amounts. Model disagreement in the projection of heavy rainfalls was the largest cause of difference in regional mean rainfall projections from downscaling in the mountainous western seaboard of California (Pierce et al 2013) and there could be a similar situation in eastern Australia.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of various climate change projections of eastern Australian rainfall

Grose¹,

Bhend²,

Argüeso³

et al. 2015

AMOJ

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The Australian eastern seaboard is a distinct climate entity from the interior of the continent, with different climatic influences on each side of the Great Dividing Range. Therefore, it is plausible that downscaling of global climate models could reveal meaningful regional detail, or 'added value', in the climate change signal of mean rainfall change in eastern Australia under future scenarios. However, because downscaling is typically done using a limited set of global climate models and downscaling methods, the results from a downscaling study may not represent the range of uncertainty in plausible projected change for a region suggested by the ensemble of host global climate models. A complete and unbiased representation of the plausible changes in the climate is essential in producing climate projections useful for future planning. As part of this aim it is important to quantify any differences in the change signal between global climate models and downscaling, and understand the cause of these differences in terms of plausible added regional detail in the climate change signal, the impact of sub-sampling global climate models and the effect of the downscaling models themselves. Here we examine rainfall projections in eastern Australia under a high emissions scenario by late in the century from ensembles of global climate models, two dynamical downscaling models and one statistical downscaling model. We find no cases where all three downscaling methods show the same clear regional spatial detail in the change signal that is distinct from the host models. However, some downscaled projections suggest that the eastern seaboard could see little change in spring rainfall, in contrast to the substantial rainfall decrease inland. The change signal in the downscaled outputs is broadly similar at the large scale in the various model outputs, with a few notable exceptions. For example, the model median from dynamical downscaling projects a rainfall increase over the entirety of eastern Australia in autumn that is greater than the global models. Also, there are some instances where a downscaling method produces changes outside the range of host models over eastern Australia as a whole, thus expanding the projected range of uncertainty. Results are particularly uncertain for summer, where no two downscaling studies clearly agree. There are also some confounding factors from the model configuration used in downscaling, where the particular zones used for statistical models and the model components used in dynamical models have an influence on results and produce additional uncertainty.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of various climate change projections of eastern Australian rainfall

Grose¹,

Bhend²,

Argüeso³

et al. 2015

AMOJ

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“…Independent of downscaling, bias correction with QM is well known to alter the raw modeled climate change signal (Hagemann et al, 2011;Themeßl et al, 2012;Brekke et al, 2013;Maurer et al, 2013;Pierce et al, 2013;Maurer and Pierce, 2014). This alteration of the raw modeled climate change signal can be attributed to the stationarity assumption, which implies that the error correction values established in a calibration period can be applied to any time period within or outside the calibration time period.…”

Section: Stationarity and Quantile Mappingmentioning

confidence: 99%

Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes

Switanek

Troch

Castro

et al. 2017

Hydrol. Earth Syst. Sci.

170

130

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Abstract. Commonly used bias correction methods such as quantile mapping (QM) assume the function of error correction values between modeled and observed distributions are stationary or time invariant. This article finds that this function of the error correction values cannot be assumed to be stationary. As a result, QM lacks justification to inflate/deflate various moments of the climate change signal. Previous adaptations of QM, most notably quantile delta mapping (QDM), have been developed that do not rely on this assumption of stationarity. Here, we outline a methodology called scaled distribution mapping (SDM), which is conceptually similar to QDM, but more explicitly accounts for the frequency of rain days and the likelihood of individual events. The SDM method is found to outperform QM, QDM, and detrended QM in its ability to better preserve raw climate model projected changes to meteorological variables such as temperature and precipitation.

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“…The impact of ARs on extreme rainfall has been well established on the west coast from the observational record (Dettinger 2013;Dettinger et al 2011;Weller et al 2012Weller et al , 2013. Differences between the simulations of extreme rainfall in models may be associated with the high uncertainty in the sign and magnitude of rainfall projections (Pierce et al 2013). However, regardless of the driving model's projections, it is important to also assess the ability of regional climate models to reproduce the extreme rainfall events that are associated with ARs.…”

mentioning

confidence: 99%