Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping

Thrasher, Bridget; Maurer, Edwin P.; Mckellar, C. J.; Duffy, Philip B.

doi:10.5194/hessd-9-5515-2012

Cited by 88 publications

(136 citation statements)

References 18 publications

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“…Maurer et al considered each month independently, so that for January a 15 yr period would have a CDF defined by 31 days × 15 yr = 465 points. One modification for this application is that, to avoid abrupt inconsistencies between months, we used a moving 31 day window centered on each day, producing a separate set of CDFs for each day of year (Dobler et al, 2012;Thrasher et al, 2012). This method employs a nonparametric quantile mapping; that is, there is no fitting of a theoretical probability distribution to the data in creating the CDFs.…”

Section: Methods and Datamentioning

confidence: 99%

Bias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble mean

Maurer

Pierce

2014

Hydrol. Earth Syst. Sci.

Self Cite

158

125

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Abstract. When applied to remove climate model biases in precipitation, quantile mapping can in some settings modify the simulated difference in mean precipitation between two eras. This has important implications when the precipitation is used to drive an impacts model that is sensitive to changes in precipitation. The tendency of quantile mapping to alter model-predicted changes is demonstrated using synthetic precipitation distributions and elucidated with a simple theoretical analysis, which shows that the alteration of model-predicted changes can be controlled by the ratio of model to observed variance. To further evaluate the effects of quantile mapping in a more realistic setting, we use daily precipitation output from 11 atmospheric general circulation models (AGCMs), forced by observed sea surface temperatures, over the conterminous United States to compare precipitation differences before and after quantile mapping bias correction. The effectiveness of the bias correction is not assessed, only its effect on precipitation differences. The change in seasonal mean (winter, DJF, and summer, JJA) precipitation between two historical periods is compared to examine whether the bias correction tends to amplify or diminish an AGCM's simulated precipitation change. In some cases the trend modification can be as large as the original simulated change, though the areas where this occurs varies among AGCMs so the ensemble median shows smaller trend modification. Results show that quantile mapping improves the correspondence with observed changes in some locations and degrades it in others. While not representative of a future where natural precipitation variability is much smaller than that due to external forcing, these results suggest that at least for the next several decades the influence of quantile mapping on seasonal precipitation trends does not systematically degrade projected differences.

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Section: Methods and Datamentioning

confidence: 99%

Bias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble mean

Maurer

Pierce

2014

Hydrol. Earth Syst. Sci.

Self Cite

158

125

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“…gov/nex-gddp/) [31]. The future climate estimates in this dataset were provided at daily time scale, which makes them easy for SWAT model application.…”

Section: Discharge and Sediment Yield Predictionmentioning

confidence: 99%

“…In this study, we only used the projected precipitation and minimum and maximum temperature downscaled at a high spatial resolution of 0.25° × 0.25° from the BNU-ESM model by the NASA Earth Exchange (NEX) Global Daily Downscaled Projections (GDDP) dataset (https://cds.nccs.nasa.gov/nex-gddp/) [31]. The future climate estimates in this dataset were provided at daily time scale, which makes them easy for SWAT model application.…”

Section: Discharge and Sediment Yield Predictionmentioning

confidence: 99%

Climate Change Impacts on Flow and Suspended Sediment Yield in Headwaters of High-Latitude Regions—A Case Study in China’s Far Northeast

Zhou

Xiao

et al. 2017

Water

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Climate change is expected to have stronger effects on water resources in higher latitude regions. Despite intensive research on possible hydrological responses in those regions to a warmer environment, our knowledge on erosion and sediment yield induced by the climate change in high-latitude headwaters is still limited. In this study, we estimated suspended sediment yields from 2021 to 2050 in a typical headwater area of far Northeast China to elucidate potential impacts of future climate change on surface runoff and erosion in higher latitude regions. We first parameterized the Soil and Water Assessment Tool (SWAT) using historical measurements to estimate runoff from the river basin. The model performed well in both the calibration (2006)(2007)(2008)(2009)(2010)(2011) and the validation (2012-2014) periods, with an R 2 of 0.85 and 0.88 and a Nash-Sutcliffe Efficiency (NSE) of 0.7 and 0.73, respectively. We also utilized historical measurements on sediment yields from the period 2006-2014 to develop a runoff-sediment yield rating curve, and the rating curve obtained an excellent goodness of fit (R 2 = 0.91, p < 0.001). We then applied the calibrated SWAT model to two climate change projections, also known as Representative Concentration Pathways (RCP4.5 and RCP8.5), for the period from 2021 to 2050 to obtain future runoff estimates. These runoff estimates were then used to predict future sediment yield by using the developed runoff-sediment yield rating curve. Our study found a significant increase of annual sediment yield (p < 0.05) for both climate change projections (RCP4.5 = 237%; RCP8.5 = 133%) in this, China's high-latitude region. The increases of sediment yield were prevalent in summer and autumn, varying from 102-299% between the two RCPs scenarios. Precipitation was the dominated factor that determined the variation of runoff and sediment yield. A warming climate could bring more snowmelt-induced spring runoff and longer rainy days in autumn, hence leading to higher erosion. These findings demonstrate that under the changing climate, soils in this high-latitude headwater area would be eroded twice to three times that of the baseline period , indicating a potential risk to the downstream water quality and reservoir management.

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“…Recently, bias correction techniques involving quantile mapping (Thrasher et al 2012), or quantile matching (Ho et al 2012), have become a popular way to adjust the entire probability distribution of temperature. Because of the mismatch in spatial (and sometimes temporal) scales involved in downscaling, the downscaled temperatures typically do not possess high enough variance.…”

Section: Statistical Modeling Of Temperature Extremesmentioning

confidence: 99%

North American extreme temperature events and related large scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

et al. 2015

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of: the physics of LSMP life cycles, comprehensive model assessment of LSMP-extreme temperature event linkages, and LSMP properties are needed. Generally, climate models capture observed properties of heat waves and cold air outbreaks with some fidelity. However they overestimate warm wave frequency and underestimate cold air outbreak frequency, and underestimate the collective influence of low-frequency modes on temperature extremes. Modeling studies have identified the impact of large-scale circulation anomalies and land-atmosphere interactions on changes in extreme temperatures. However, few studies have examined changes in LSMPs to more specifically understand the role of LSMPs on past and future extreme temperature changes. Even though LSMPs are resolvable by global and regional climate models, they are not necessarily well simulated. The paper concludes with unresolved issues and research questions.Abstract The objective of this paper is to review statistical methods, dynamics, modeling efforts, and trends related to temperature extremes, with a focus upon extreme events of short duration that affect parts of North America. These events are associated with large scale meteorological patterns (LSMPs). The statistics, dynamics, and modeling sections of this paper are written to be autonomous and so can be read separately. Methods to define extreme events statistics and to identify and connect LSMPs to extreme temperature events are presented. Recent advances in statistical techniques connect LSMPs to extreme temperatures through appropriately defined covariates that supplement more straightforward analyses. Various LSMPs, ranging from synoptic to planetary scale structures, are associated with extreme temperature events. Current knowledge about the synoptics and the dynamical mechanisms leading to the associated LSMPs is incomplete. Systematic studies

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Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping

Cited by 88 publications

References 18 publications

Bias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble mean

Bias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble mean

Climate Change Impacts on Flow and Suspended Sediment Yield in Headwaters of High-Latitude Regions—A Case Study in China’s Far Northeast

North American extreme temperature events and related large scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

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