Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. northeast

Ahmed, K. F.; Wang, Guiling; Silander, John A.; Wilson, Arnold; Allen, Jenica M.; Horton, Radley M.; Anyah, Richard

doi:10.1016/j.gloplacha.2012.11.003

Cited by 210 publications

(118 citation statements)

References 37 publications

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“…For example, Abatzoglou and Brown (2012), Stoner et al (2013), Ahmed et al (2013) each applied quantile mapping to daily climate model data that had been interpolated to a high-resolution observational grid. However, Maraun (2013) and Gutmann et al (2014) demonstrated that this approach-applying a univariate bias correction algorithm to interpolated climate data at individual grid points-can lead to fields with unrealistic spatial structure, especially if the variable being downscaled operates on spatial scales that are substantially finer than the climate model grid.…”

Section: Spatial Precipitation Examplementioning

confidence: 99%

“…In all cases, the first half of the record is used for calibration and the second half is used as for out-of-sample verification; all reported statistics are from the verification period. Results from MBCn are compared with those from univariate QDM (e.g., as in Abatzoglou andBrown 2012, Stoner et al 2013;Ahmed et al 2013), as well as MBCp, and MBCr. For the three multivariate algorithms, all 25 grid points are corrected simultaneously, whereas QDM is applied to each grid point in turn.…”

Section: Spatial Precipitation Examplementioning

confidence: 99%

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Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

2017

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Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI distribution and spatiotemporal autocorrelation of precipitation fields.

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Section: Spatial Precipitation Examplementioning

confidence: 99%

Section: Spatial Precipitation Examplementioning

confidence: 99%

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

2017

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“…GCM or RCM outputs are generally biased (Ahmed et al, 2013;Teutschbein and Seibert, 2012;Mehrotra and Sharma, 2012) and there is a need to correct these outputs before they are used for regional impact studies. The RCM outputs used in this study are based on the work done by Gao et al (2013).…”

Section: Simulated Meteorological Variables From the Rcmmentioning

confidence: 99%

Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China

Fang

Jian

Chen

et al. 2015

Hydrol. Earth Syst. Sci.

397

174

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Abstract. Water resources are essential to the ecosystem and social economy in the desert and oasis of the arid Tarim River basin, northwestern China, and expected to be vulnerable to climate change. It has been demonstrated that regional climate models (RCMs) provide more reliable results for a regional impact study of climate change (e.g., on water resources) than general circulation models (GCMs). However, due to their considerable bias it is still necessary to apply bias correction before they are used for water resources research. In this paper, after a sensitivity analysis on input meteorological variables based on the Sobol' method, we compared five precipitation correction methods and three temperature correction methods in downscaling RCM simulations applied over the Kaidu River basin, one of the headwaters of the Tarim River basin. Precipitation correction methods applied include linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), distribution mapping (DM) and quantile mapping (QM), while temperature correction methods are LS, variance scaling (VARI) and DM. The corrected precipitation and temperature were compared to the observed meteorological data, prior to being used as meteorological inputs of a distributed hydrologic model to study their impacts on streamflow. The results show (1) streamflows are sensitive to precipitation, temperature and solar radiation but not to relative humidity and wind speed; (2) raw RCM simulations are heavily biased from observed meteorological data, and its use for streamflow simulations results in large biases from observed streamflow, and all bias correction methods effectively improved these simulations; (3) for precipitation, PT and QM methods performed equally best in correcting the frequency-based indices (e.g., standard deviation, percentile values) while the LOCI method performed best in terms of the time-series-based indices (e.g., Nash-Sutcliffe coefficient, R 2 ); (4) for temperature, all correction methods performed equally well in correcting raw temperature; and (5) for simulated streamflow, precipitation correction methods have more significant influence than temperature correction methods and the performances of streamflow simulations are consistent with those of corrected precipitation; i.e., the PT and QM methods performed equally best in correcting flow duration curve and peak flow while the LOCI method performed best in terms of the time-series-based indices. The case study is for an arid area in China based on a specific RCM and hydrologic model, but the methodology and some results can be applied to other areas and models.

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“…This regression model corrects systematic local effects (e.g., whether a rain gauge is positioned on the lee or windward side of a mountain). It also adds random (unexplained) small-scale variability, in contrast to approaches of combined methods that employ spatial interpolation for downscaling (Wood and Maurer, 2002;Wood et al, 2004;Payne et al, 2004) or rescale the grid-scale precipitation with a factor to match the observations (Ahmed et al, 2013). We calibrate the probabilistic regression model Figure 1.…”

Section: General Conceptmentioning

confidence: 99%

A combined statistical bias correction and stochastic downscaling method for precipitation

Volosciuk¹,

Maraun²,

Vrac³

et al. 2017

Hydrol. Earth Syst. Sci.

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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.

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Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. northeast

Cited by 210 publications

References 37 publications

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China

A combined statistical bias correction and stochastic downscaling method for precipitation

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