scholarly journals Bias correction of temperature and precipitation over China for RCM simulations using the QM and QDM methods

2020 ◽  
Author(s):  
Yao Tong ◽  
Xuejie Gao ◽  
Zhenyu Han ◽  
Yaqi Xu ◽  
Ying Xu ◽  
...  
Keyword(s):  
Bias Correction ◽  
General Circulation ◽  
Climate Model ◽  
Regional Climate ◽  
General Circulation Models ◽  
Grid Spacing ◽  
Quantile Mapping ◽  
Temperature And Precipitation ◽  
June July August ◽  
June July

Abstract Two different bias correction methods, the quantile mapping (QM) and quantile delta mapping (QDM), are applied to simulated daily temperature and precipitation over China from a set of 21st century regional climate model (the ICTP RegCM4) projections. The RegCM4 is driven by five different general circulation models (GCMs) under the representative concentration pathway RCP4.5 at a grid spacing of 25 km using the CORDEX East Asia domain. The focus is on mean temperature and precipitation in December–January–February (DJF) and June–July–August (JJA). The impacts of the two methods on the present day biases and future change signals are investigated. Results show that both the QM and QDM methods are effective in removing the systematic model biases during the validation period. For the future changes, the QDM preserves the temperature change signals well, in both magnitude and spatial distribution, while the QM artificially modifies the change signal by decreasing the warming and modifying the patterns of change. For precipitation, both methods preserve the change signals well but they produce greater magnitude of the projected increase, especially the QDM. We also show that the effects of bias correction are variable- and season-dependent. Our results show that different bias correction methods can affect in different way the simulated change signals, and therefore care has to be taken in carrying out the bias correction process.

scholarly journals Bias correction can modify climate model-simulated precipitation changes without adverse affect on the ensemble mean

2013 ◽  
Vol 10 (9) ◽  
pp. 11585-11611
Author(s):  
E. P. Maurer ◽  
D. W. Pierce
Keyword(s):  
Bias Correction ◽  
General Circulation ◽  
Climate Model ◽  
General Circulation Models ◽  
Quantile Mapping ◽  
Mapping Bias ◽  
Simulated Precipitation ◽  
Before And After ◽  
Precipitation Changes ◽  
Precipitation Trends

Abstract. When applied to remove climate model biases in precipitation, quantile mapping can in some settings modify the simulated trends. This has important implications when the precipitation will be used to drive an impacts model that is sensitive to changes in precipitation. We use daily precipitation output from 12 general circulation models (GCMs) over the conterminous United States interpolated to a common 1° grid, and gridded observations aggregated to the same scale, to compare precipitation differences before and after quantile mapping bias correction. The change in seasonal mean (winter, DJF, and summer, JJA) precipitation between different 30-yr historical periods is compared to examine (1) the consensus among GCMs as to whether the bias correction tends to amplify or diminish their simulated precipitation trends, and (2) whether the modification of the change in precipitation tends to improve or degrade the correspondence to observed changes in precipitation for the same periods. In some cases, for a particular GCM, the trend modification can be as large as the original simulated change, though the areas where this occurs varies among GCMs so the ensemble median shows smaller trend modification. In specific locations and seasons the trend modification by quantile mapping improves correspondence with observed trends, and in others it degrades it. In the majority of the domain the ensemble median is for little effect on the correspondence of simulated precipitation trends with observed. This highlights the need to use an ensemble of GCMs rather than relying on a small number of models to estimate impacts.

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

2014 ◽  
Vol 18 (3) ◽  
pp. 915-925 ◽  
Author(s):  
E. P. Maurer ◽  
D. W. Pierce
Keyword(s):  
Bias Correction ◽  
General Circulation ◽  
Climate Model ◽  
General Circulation Models ◽  
Quantile Mapping ◽  
Mapping Bias ◽  
Simulated Precipitation ◽  
Before And After ◽  
Synthetic Precipitation ◽  
Precipitation Changes

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.

scholarly journals Present day bias and future change signal of temperature over China in a series of multi-GCM driven RCM simulations

2019 ◽  
Vol 54 (1-2) ◽  
pp. 1113-1130 ◽  
Author(s):  
Jia Wu ◽  
Xuejie Gao
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Air Temperature ◽  
General Circulation Models ◽  
Added Value ◽  
The Tibetan Plateau ◽  
Low Sensitivity ◽  
June July

Abstract Simulation of surface air temperature over China from a set of regional climate model (RCM) climate change experiments are analyzed with the focus on bias and change signal of the RCM and driving general circulation models (GCMs). The set consists of 4 simulations by the RCM of RegCM4 driven by 4 different GCMs for the period of 1979–2099 under the mid-range RCP4.5 (representative concentration pathway) scenario. Results show that for present day conditions, the RCM provides with more spatial details of the distribution and in general reduces the biases of GCM, in particular in DJF (December–January–February) and over areas with complex topography. Bias patterns show some correlation between the RCM and driving GCM in DJF but not in JJA (June–July–August). In JJA, the biases in RCM simulations show similar pattern and low sensitivity to the driving GCM, which can be attributed to the large effect of internal model physics in the season. For change signals, dominant forcings from the driving GCM are evident in the RCM simulations as shown by the magnitude, large scale spatial distribution, as well as interannual variation of the changes. The added value of RCM projection is characterized by the finer spatial detail in sub-regional (river basins) and local scale. In DJF, profound warming over the Tibetan Plateau is simulated by RCM but not GCMs. In general no clear relationships are found between the model bias and change signal, either for the driving GCMs or nested RCM.

scholarly journals Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods

2021 ◽  
Vol 18 (4) ◽  
pp. 1992
Author(s):  
Weijia Qian ◽  
Howard H. Chang
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Climate Science ◽  
Regional Climate Models ◽  
Health Impact ◽  
Quantile Mapping ◽  
Ed Visits

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.

scholarly journals Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R<sup>2</sup>D<sup>2</sup>) bias correction

2018 ◽  
Vol 22 (6) ◽  
pp. 3175-3196 ◽  
Author(s):  
Mathieu Vrac
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Grid Cell ◽  
Dependence Structure ◽  
Historical Period ◽  
Temporal Properties ◽  
Temperature And Precipitation ◽  
Climate Simulations ◽  
Site Dependence

Abstract. Climate simulations often suffer from statistical biases with respect to observations or reanalyses. It is therefore common to correct (or adjust) those simulations before using them as inputs into impact models. However, most bias correction (BC) methods are univariate and so do not account for the statistical dependences linking the different locations and/or physical variables of interest. In addition, they are often deterministic, and stochasticity is frequently needed to investigate climate uncertainty and to add constrained randomness to climate simulations that do not possess a realistic variability. This study presents a multivariate method of rank resampling for distributions and dependences (R2D2) bias correction allowing one to adjust not only the univariate distributions but also their inter-variable and inter-site dependence structures. Moreover, the proposed R2D2 method provides some stochasticity since it can generate as many multivariate corrected outputs as the number of statistical dimensions (i.e., number of grid cell  ×  number of climate variables) of the simulations to be corrected. It is based on an assumption of stability in time of the dependence structure – making it possible to deal with a high number of statistical dimensions – that lets the climate model drive the temporal properties and their changes in time. R2D2 is applied on temperature and precipitation reanalysis time series with respect to high-resolution reference data over the southeast of France (1506 grid cell). Bivariate, 1506-dimensional and 3012-dimensional versions of R2D2 are tested over a historical period and compared to a univariate BC. How the different BC methods behave in a climate change context is also illustrated with an application to regional climate simulations over the 2071–2100 period. The results indicate that the 1d-BC basically reproduces the climate model multivariate properties, 2d-R2D2 is only satisfying in the inter-variable context, 1506d-R2D2 strongly improves inter-site properties and 3012d-R2D2 is able to account for both. Applications of the proposed R2D2 method to various climate datasets are relevant for many impact studies. The perspectives of improvements are numerous, such as introducing stochasticity in the dependence itself, questioning its stability assumption, and accounting for temporal properties adjustment while including more physics in the adjustment procedures.

scholarly journals Panama’s Current Climate Replicability in a Non-Hydrostatic Regional Climate Model Nested in an Atmospheric General Circulation Model

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1543
Author(s):  
Reinhardt Pinzón ◽  
Noriko N. Ishizaki ◽  
Hidetaka Sasaki ◽  
Tosiyuki Nakaegawa
Keyword(s):  
Regional Climate Model ◽  
Air Temperature ◽  
General Circulation ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Air Temperature ◽  
Circulation Model ◽  
Horizontal Resolution ◽  
Temperature And Precipitation ◽  
Current Climate

To simulate the current climate, a 20-year integration of a non-hydrostatic regional climate model (NHRCM) with grid spacing of 5 and 2 km (NHRCM05 and NHRCM02, respectively) was nested within the AGCM. The three models did a similarly good job of simulating surface air temperature, and the spatial horizontal resolution did not affect these statistics. NHRCM02 did a good job of reproducing seasonal variations in surface air temperature. NHRCM05 overestimated annual mean precipitation in the western part of Panama and eastern part of the Pacific Ocean. NHRCM05 is responsible for this overestimation because it is not seen in MRI-AGCM. NHRCM02 simulated annual mean precipitation better than NHRCM05, probably due to a convection-permitting model without a convection scheme, such as the Kain and Fritsch scheme. Therefore, the finer horizontal resolution of NHRCM02 did a better job of replicating the current climatological mean geographical distributions and seasonal changes of surface air temperature and precipitation.

scholarly journals Alpine Younger Dryas glaciers as palaeo-precipitation gauges

2000 ◽  
Vol 31 ◽  
pp. 80-84 ◽  
Author(s):  
Hanns Kerschner ◽  
Georg Kaser ◽  
Rudolf Sailer
Keyword(s):  
General Circulation ◽  
Climate Model ◽  
Younger Dryas ◽  
General Circulation Models ◽  
Summer Temperature ◽  
Equilibrium Line Altitude ◽  
The West ◽  
Temperature And Precipitation ◽  
Data Points ◽  
The Alps

AbstractMoraines of the Younger Dryas ˚Egesen Stadial", which are widespread features in the Alps, are a valuable terrestrial data source for quantitative palaeoclimatic studies. The depression of the early Younger Dryas (Egesen-I) equilibrium-line altitude (ELA) shows a distinct spatial pattern. It was greatest (about –450 to –500 m vs present day) m areas exposed towards the west and northwest. In the central, more sheltered valleys it was on the order of –300 m or less. Summer temperature depression, which can be derived from the Younger Dryas timberline depression, was on the order of –3.5 K. The stochastic glacier-climate model of Ohmura and others (1992), which relates summer temperature and precipitation at the ELA, is used to infer precipitation change. Results are compared with those obtained from the glacial-meteorological approach of Kuhn (1981a). The two models produce highly similar results. During the early Younger Dryas, climate in the central valleys of the Alps seems to have been considerably drier than today In areas open to the west and northwest, precipitation seems to have been the same as today or even slightly higher. These results, which are based on a rather dense network of data points, agree well with results from permafrost-climate studies and the more qualitative information from palaeobotanical research. They also support the results from atmospheric general circulation models for the Younger Dryas in Europe, which point towards a more zonal type of circulation.

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

2017 ◽  
Vol 21 (6) ◽  
pp. 2649-2666 ◽  
Author(s):  
Matthew B. Switanek ◽  
Peter A. Troch ◽  
Christopher L. Castro ◽  
Armin Leuprecht ◽  
Hsin-I Chang ◽  
...  
Keyword(s):  
Error Correction ◽  
Bias Correction ◽  
Climate Model ◽  
Correction Method ◽  
Climate Change Signal ◽  
Quantile Mapping ◽  
Temperature And Precipitation ◽  
Distribution Mapping ◽  
Projected Changes ◽  
Time Invariant

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.

scholarly journals Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue

2013 ◽  
Vol 26 (6) ◽  
pp. 2137-2143 ◽  
Author(s):  
Douglas Maraun
Keyword(s):  
Regional Climate Model ◽  
Bias Correction ◽  
Climate Model ◽  
Daily Precipitation ◽  
Temporal Structure ◽  
Regional Climate ◽  
Quantile Mapping ◽  
Model Simulations ◽  
Scale Mismatch ◽  
Climate Model Simulations

Abstract Quantile mapping is routinely applied to correct biases of regional climate model simulations compared to observational data. If the observations are of similar resolution as the regional climate model, quantile mapping is a feasible approach. However, if the observations are of much higher resolution, quantile mapping also attempts to bridge this scale mismatch. Here, it is shown for daily precipitation that such quantile mapping–based downscaling is not feasible but introduces similar problems as inflation of perfect prognosis (“prog”) downscaling: the spatial and temporal structure of the corrected time series is misrepresented, the drizzle effect for area means is overcorrected, area-mean extremes are overestimated, and trends are affected. To overcome these problems, stochastic bias correction is required.

scholarly journals On the potential use of glacier and permafrost observations for verification of climate models

1997 ◽  
Vol 25 ◽  
pp. 400-406 ◽  
Author(s):  
Martin Beniston ◽  
Wilfried Haeberli ◽  
Martin Hoelzle ◽  
Alan Taylor
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Model Performance ◽  
Regional Climate Models ◽  
General Circulation Models ◽  
Model Verification ◽  
Dominant Feature

While the capability of global and regional climate models in reproducing current climate has significantly improved over the past few years, the confidence in model results for remote regions, or those where complex orography is a dominant feature, is still relatively low. This is, in part, linked to the lack of observational data for model verification and intercomparison purposes.Glacier and permafrost observations are directly related to past and present energy flux patterns at the Earth-atmosphere interface and could be used as a proxy for air temperature and precipitation, particularly of value in remote mountain regions and boreal and Arctic zones where instrumental climate records are sparse or non-existent. It is particularly important to verify climate-model performance in these regions, as this is where most general circulation models (GCMs) predict the greatest changes in air temperatures in a warmer global climate.Existing datasets from glacier and permafrost monitoring sites in remote and high altitudes are described in this paper; the data could be used in model-verification studies, as a means to improving model performance in these regions.

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