Multi-site multivariate downscaling of global climate model outputs: an integrated framework combining quantile mapping, stochastic weather generator and Empirical Copula approaches

2018 ◽  
Vol 52 (9-10) ◽  
pp. 5775-5799 ◽  
Author(s):  
Xin Li ◽  
Vladan Babovic
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Weather Generator ◽  
Quantile Mapping ◽  
Stochastic Weather Generator ◽  
Integrated Framework ◽  
Empirical Copula

scholarly journals Does Quantile Mapping of Simulated Precipitation Correct for Biases in Transition Probabilities and Spell Lengths?

2016 ◽  
Vol 29 (5) ◽  
pp. 1605-1615 ◽  
Author(s):  
Jan Rajczak ◽  
Sven Kotlarski ◽  
Christoph Schär
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Transition Probabilities ◽  
Weather Generator ◽  
Mapping Technique ◽  
Model Output ◽  
Quantile Mapping ◽  
Stochastic Weather Generator ◽  
Simulated Precipitation ◽  
Climate Model Output

Abstract Climate impact studies constitute the basis for the formulation of adaptation strategies. Usually such assessments apply statistically postprocessed output of climate model projections to force impact models. Increasingly, time series with daily resolution are used, which require high consistency, for instance with respect to transition probabilities (TPs) between wet and dry days and spell durations. However, both climate models and commonly applied statistical tools have considerable uncertainties and drawbacks. This paper compares the ability of 1) raw regional climate model (RCM) output, 2) bias-corrected RCM output, and 3) a conventional weather generator (WG) that has been calibrated to match observed TPs to simulate the sequence of dry, wet, and very wet days at a set of long-term weather stations across Switzerland. The study finds systematic biases in TPs and spell lengths for raw RCM output, but a substantial improvement after bias correction using the deterministic quantile mapping technique. For the region considered, bias-corrected climate model output agrees well with observations in terms of TPs as well as dry and wet spell durations. For the majority of cases (models and stations) bias-corrected climate model output is similar in skill to a simple Markov chain stochastic weather generator. There is strong evidence that bias-corrected climate model simulations capture the atmospheric event sequence more realistically than a simple WG.

scholarly journals Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?

2015 ◽  
Vol 28 (17) ◽  
pp. 6938-6959 ◽  
Author(s):  
Alex J. Cannon ◽  
Stephen R. Sobie ◽  
Trevor Q. Murdock
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Coupled Model ◽  
Precipitation Extremes ◽  
Extreme Value Analysis ◽  
Quantile Mapping ◽  
Value Analysis

Abstract Quantile mapping bias correction algorithms are commonly used to correct systematic distributional biases in precipitation outputs from climate models. Although they are effective at removing historical biases relative to observations, it has been found that quantile mapping can artificially corrupt future model-projected trends. Previous studies on the modification of precipitation trends by quantile mapping have focused on mean quantities, with less attention paid to extremes. This article investigates the extent to which quantile mapping algorithms modify global climate model (GCM) trends in mean precipitation and precipitation extremes indices. First, a bias correction algorithm, quantile delta mapping (QDM), that explicitly preserves relative changes in precipitation quantiles is presented. QDM is compared on synthetic data with detrended quantile mapping (DQM), which is designed to preserve trends in the mean, and with standard quantile mapping (QM). Next, methods are applied to phase 5 of the Coupled Model Intercomparison Project (CMIP5) daily precipitation projections over Canada. Performance is assessed based on precipitation extremes indices and results from a generalized extreme value analysis applied to annual precipitation maxima. QM can inflate the magnitude of relative trends in precipitation extremes with respect to the raw GCM, often substantially, as compared to DQM and especially QDM. The degree of corruption in the GCM trends by QM is particularly large for changes in long period return values. By the 2080s, relative changes in excess of +500% with respect to historical conditions are noted at some locations for 20-yr return values, with maximum changes by DQM and QDM nearing +240% and +140%, respectively, whereas raw GCM changes are never projected to exceed +120%.

scholarly journals Impacts of climate and catastrophic forest changes on streamflow and water balance in a mountainous headwater stream in Southern Alberta

2013 ◽  
Vol 17 (12) ◽  
pp. 4941-4956 ◽  
Author(s):  
V. Mahat ◽  
A. Anderson
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Similar Amount ◽  
Climate Change Scenarios ◽  
Climate Data ◽  
Stochastic Weather Generator ◽  
Precipitation Decrease ◽  
Southern Alberta

Abstract. Rivers in Southern Alberta are vulnerable to climate change because much of the river water originates as snow in the eastern slopes of the Rocky Mountains. Changes in likelihood of forest disturbance (wildfire, insects, logging, etc.) may also have impacts that are compounded by climate change. This study evaluates the impacts of climate and forest changes on streamflow in the upper parts of the Oldman River in Southern Alberta using a conceptual hydrological model, HBV-EC (Hydrologiska Byråns attenbalansavdelning, Environment Canada), in combination with a stochastic weather generator (LARS-WG) driven by GCM (global climate model) output climate data. Three climate change scenarios (A1B, A2 and B1) are selected to cover the range of possible future climate conditions (2020s, 2050s, and 2080s). The GCM projected less than a 10% increase in precipitation in winter and a similar amount of precipitation decrease in summer. These changes in projected precipitation resulted in up to a 200% (9.3 mm) increase in winter streamflow in February and up to a 63% (31.2 mm) decrease in summer flow in June. Flow also decreased in July and August, when irrigation is important; these reduced river flows during this season could impact agriculture production. The amplification in the streamflow is mostly driven by the projected increase in temperature that is predicted to melt winter snow earlier, resulting in lower water availability during the summer. Uncertainty analysis was completed using a guided GLUE (generalized likelihood uncertainty estimation) approach to obtain the best 100 parameter sets and associated ranges of streamflows. The impacts of uncertainty in streamflows were higher in spring and summer than in winter and fall. Forest change compounded the climate change impact by increasing the winter flow; however, it did not reduce the summer flow.

scholarly journals Bias in downscaled rainfall characteristics

2019 ◽  
Author(s):  
Nicholas J. Potter ◽  
Francis H. S. Chiew ◽  
Stephen P. Charles ◽  
Guobin Fu ◽  
Hongxing Zheng ◽  
...  
Keyword(s):  
Water Resource ◽  
Bias Correction ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Transition Probabilities ◽  
Global Climate ◽  
Global Climate Model ◽  
Hydrological Modelling ◽  
Quantile Mapping ◽  
Rainfall Characteristics

Abstract. Dynamical downscaling of future projections of global climate model outputs can potentially provide useful information about plausible and possible changes to water resource availability, for which there is increasing demand for regional water resource planning processes. By explicitly modelling climate processes within and across global climate model gridcells for a region, dynamical downscaling can provide higher resolution hydroclimate projections, as well as independent (from historical timeseries) and physically plausible future rainfall timeseries for hydrological modelling applications. However, since rainfall is not typically constrained to observations by these methods, there is often a need for bias correction before use in hydrological modelling. Many bias correction methods (such as scaling, empirical and distributional mapping) have been proposed in the literature, but methods that treat daily amounts only (and not sequencing) can result in residual biases in certain rainfall characteristics, which flow through to biases and problems with subsequently modelled runoff. We apply quantile-quantile mapping to rainfall dynamically downscaled by NARCliM in the State of Victoria, Australia and examine the effect of this on: (i) biases both before and after bias correction in different rainfall metrics; (ii) change signals in metrics in comparison to the bias; and (iii) the effect of bias correction on wet-wet and dry-dry transition probabilities. After bias correction, persistence of wet states is under-correlated (i.e. more random than observations), and this results in a significant bias (underestimation) of runoff using hydrological models calibrated on historical data. A novel representation of quantile-quantile mapping is developed based on lag-one transition probabilities of dry and wet states, and we use this to explain residual biases in transition probabilities. This demonstrates that any quantile mapping bias correction methods are unable to correct the underestimation of autocorrelation of rainfall sequencing, which suggests that new methods are needed to properly bias correct dynamical downscaling rainfall outputs.

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

2012 ◽  
Vol 16 (9) ◽  
pp. 3309-3314 ◽  
Author(s):  
B. Thrasher ◽  
E. P. Maurer ◽  
C. McKellar ◽  
P. B. Duffy
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Strong Relationship ◽  
Technical Note ◽  
Daily Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Quantile Mapping

Abstract. When applying a quantile mapping-based bias correction to daily temperature extremes simulated by a global climate model (GCM), the transformed values of maximum and minimum temperatures are changed, and the diurnal temperature range (DTR) can become physically unrealistic. While causes are not thoroughly explored, there is a strong relationship between GCM biases in snow albedo feedback during snowmelt and bias correction resulting in unrealistic DTR values. We propose a technique to bias correct DTR, based on comparing observations and GCM historic simulations, and combine that with either bias correcting daily maximum temperatures and calculating daily minimum temperatures or vice versa. By basing the bias correction on a base period of 1961–1980 and validating it during a test period of 1981–1999, we show that bias correcting DTR and maximum daily temperature can produce more accurate estimations of daily temperature extremes while avoiding the pathological cases of unrealistic DTR values.

scholarly journals Impacts of climate and forest changes on streamflow and water balance in a mountainous headwater stream in Southern Alberta

2013 ◽  
Vol 10 (7) ◽  
pp. 8503-8536 ◽  
Author(s):  
V. Mahat ◽  
A. Anderson
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Similar Amount ◽  
Climate Change Scenarios ◽  
Climate Data ◽  
Stochastic Weather Generator ◽  
Precipitation Decrease ◽  
Southern Alberta

Abstract. Rivers in Southern Alberta are vulnerable to climate change because much of the river water originates as snow in the eastern slopes of the Rocky Mountains. Changes in likelihood of forest disturbance (wildfire, insects, logging, etc.) may also have impacts that are compounded by climate change. This study evaluates the impacts of climate and forest changes on streamflow in the upper parts of the Oldman River in Southern Alberta using a conceptual hydrological model, HBV-EC in combination with a stochastic weather generator (LARS-WG) driven by GCM (Global Climate Model) output climate data. Three climate change scenarios (A1B, A2 and B1) are selected to cover the range of possible future climate conditions (2020s, 2050s, and 2080s). GCM projected less than a 10% increase in precipitation in winter and a similar amount of precipitation decrease in summer. These changes in projected precipitation resulted in up to a 200% (9.3 mm) increase in winter streamflow in February and up to a 63% (31.2 mm) decrease in summer flow in June. This amplification is mostly driven by the projected increase in temperature that is predicted to melt winter snow earlier, possibly resulting in lower water availability in the snowmelt dominated regions during the summer. Uncertainty analysis was completed using a guided GLUE (generalized likelihood uncertainty estimation) approach to obtain the best 100 parameter sets and associated ranges of streamflows. The impacts of uncertainty were higher in spring and summer flows than in winter and fall flows. Forest change compounded the climate change impact by increasing winter flow; however, it did not reduce the summer flow.

scholarly journals Bias in dynamically downscaled rainfall characteristics for hydroclimatic projections

2020 ◽  
Vol 24 (6) ◽  
pp. 2963-2979 ◽  
Author(s):  
Nicholas J. Potter ◽  
Francis H. S. Chiew ◽  
Stephen P. Charles ◽  
Guobin Fu ◽  
Hongxing Zheng ◽  
...  
Keyword(s):  
Time Series ◽  
Water Resource ◽  
Bias Correction ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Transition Probabilities ◽  
Global Climate ◽  
Global Climate Model ◽  
Quantile Mapping ◽  
Rainfall Characteristics

Abstract. Dynamical downscaling of future projections of global climate model outputs can provide useful information about plausible and possible changes to water resource availability, for which there is increasing demand in regional water resource planning processes. By explicitly modelling climate processes within and across global climate model grid cells for a region, dynamical downscaling can provide higher-resolution hydroclimate projections and independent (from historical time series), physically plausible future rainfall time series for hydrological modelling applications. However, since rainfall is not typically constrained to observations by these methods, there is often a need for bias correction before use in hydrological modelling. Many bias-correction methods (such as scaling, empirical and distributional mapping) have been proposed in the literature, but methods that treat daily amounts only (and not sequencing) can result in residual biases in certain rainfall characteristics, which flow through to biases and problems with subsequently modelled runoff. We apply quantile–quantile mapping to rainfall dynamically downscaled by the NSW and ACT Regional Climate Modelling (NARCliM) Project in the state of Victoria, Australia, and examine the effect of this on (i) biases both before and after bias correction in different rainfall metrics, (ii) change signals in metrics in comparison to the bias and (iii) the effect of bias correction on wet–wet and dry–dry transition probabilities. After bias correction, persistence of wet states is under-correlated (i.e. more random than observations), and this results in a significant bias (underestimation) of runoff using hydrological models calibrated on historical data. A novel representation of quantile–quantile mapping is developed based on lag-one transition probabilities of dry and wet states, and we use this to explain residual biases in transition probabilities. Representing quantile–quantile mapping in this way demonstrates that any quantile mapping bias-correction method is unable to correct the underestimation of autocorrelation of rainfall sequencing, which suggests that new methods are needed to properly bias-correct dynamical downscaling rainfall outputs.

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

2012 ◽  
Vol 9 (4) ◽  
pp. 5515-5529 ◽  
Author(s):  
B. L. Thrasher ◽  
E. P. Maurer ◽  
C. McKellar ◽  
P. B. Duffy
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Strong Relationship ◽  
Technical Note ◽  
Daily Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Quantile Mapping

Abstract. When applying a quantile-mapping based bias correction to daily temperature extremes simulated by a global climate model (GCM), the transformed values of maximum and minimum temperatures are changed, and the diurnal temperature range (DTR) can become physically unrealistic. While causes are not thoroughly explored, there is a strong relationship between GCM biases in snow albedo feedback during snowmelt and bias correction resulting in unrealistic DTR values. We propose a technique to bias correct DTR, based on comparing observations and GCM historic simulations, and combine that with either bias correcting daily maximum temperatures and calculating daily minimum temperatures or vice versa. By basing the bias correction on a base period of 1961–1980 and validating it during a test period of 1981–1999, we show that bias correcting DTR and maximum daily temperature can produce more accurate estimations of daily temperature extremes while avoiding the pathological cases of unrealistic DTR values.

High bit rate ACTS experiments for performing global science - Keck Telescope and global climate model

1996 ◽  
Author(s):  
Larry Bergman ◽  
J. Gary ◽  
Burt Edelson ◽  
Neil Helm ◽  
Judith Cohen ◽  
...  
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Bit Rate ◽  
Global Science ◽  
High Bit Rate
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