scholarly journals Supplementary material to "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 ◽  
Author(s):  
Mathieu Vrac
Keyword(s):  
Bias Correction ◽  
High Dimensional ◽  
Bias Adjustment ◽  
Climate Simulations ◽  
Supplementary Material

Bias correction of extreme values of high resolution climate simulations for risk analysis.

2021 ◽  
Author(s):  
luis Augusto sanabria ◽  
Xuerong Qin ◽  
Jin Li ◽  
Robert Peter Cechet
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Extreme Values ◽  
The Body ◽  
Future Climate ◽  
Return Periods ◽  
Climate Conditions ◽  
Mitigation And Adaptation ◽  
Climate Simulations ◽  
The Impact

Abstract Most climatic models show that climate change affects natural perils' frequency and severity. Quantifying the impact of future climate conditions on natural hazard is essential for mitigation and adaptation planning. One crucial factor to consider when using climate simulations projections is the inherent systematic differences (bias) of the modelled data compared with observations. This bias can originate from the modelling process, the techniques used for downscaling of results, and the ensembles' intrinsic variability. Analysis of climate simulations has shown that the biases associated with these data types can be significant. Hence, it is often necessary to correct the bias before the data can be reliably used for further analysis. Natural perils are often associated with extreme climatic conditions. Analysing trends in the tail end of distributions are already complicated because noise is much more prominent than that in the mean climate. The bias of the simulations can introduce significant errors in practical applications. In this paper, we present a methodology for bias correction of climate simulated data. The technique corrects the bias in both the body and the tail of the distribution (extreme values). As an illustration, maps of the 50 and 100-year Return Period of climate simulated Forest Fire Danger Index (FFDI) in Australia are presented and compared against the corresponding observation-based maps. The results show that the algorithm can substantially improve the calculation of simulation-based Return Periods. Forthcoming work will focus on the impact of climate change on these Return Periods considering future climate conditions.

scholarly journals Cross-validation of bias-corrected climate simulations is misleading

2018 ◽  
Vol 22 (9) ◽  
pp. 4867-4873 ◽  
Author(s):  
Douglas Maraun ◽  
Martin Widmann
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Cross Validation ◽  
Climate Model ◽  
False Negative ◽  
Internal Variability ◽  
Validation Period ◽  
Climate Simulations ◽  
Free Running ◽  
The Difference

Abstract. We demonstrate both analytically and with a modelling example that cross-validation of free-running bias-corrected climate change simulations against observations is misleading. The underlying reasoning is as follows: a cross-validation can have in principle two outcomes. A negative (in the sense of not rejecting a null hypothesis), if the residual bias in the validation period after bias correction vanishes; and a positive, if the residual bias in the validation period after bias correction is large. It can be shown analytically that the residual bias depends solely on the difference between the simulated and observed change between calibration and validation periods. This change, however, depends mainly on the realizations of internal variability in the observations and climate model. As a consequence, the outcome of a cross-validation is also dominated by internal variability, and does not allow for any conclusion about the sensibility of a bias correction. In particular, a sensible bias correction may be rejected (false positive) and a non-sensible bias correction may be accepted (false negative). We therefore propose to avoid cross-validation when evaluating bias correction of free-running bias-corrected climate change simulations against observations. Instead, one should evaluate non-calibrated temporal, spatial and process-based aspects.

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.

Supplementary material to "COSMO-CLM Regional Climate Simulations in the CORDEX framework: a review"

2021 ◽  
Author(s):  
Silje Lund Sørland ◽  
Roman Brogli ◽  
Praveen Kumar Pothapakula ◽  
Emmanuele Russo ◽  
Jonas Van de Walle ◽  
...  
Keyword(s):  
Regional Climate ◽  
Climate Simulations ◽  
Supplementary Material

Performance evaluation of bias adjustment methods for wave climate projections in the Mediterranean Sea

2021 ◽  
Author(s):  
Andrea Lira Loarca ◽  
Giovanni Besio
Keyword(s):  
Bias Correction ◽  
Temporal Variability ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Wave Climate ◽  
Mapping Method ◽  
Climate Projections ◽  
Bias Adjustment ◽  
Distribution Mapping

&lt;p&gt;Global and regional climate models are the primary tools to investigate the climate system response to different scenarios and therefore allow to make future projections of different atmospheric variables which are used as input for wave generation models to assess future wave climate. Adequate projections of future wave climate are needed in order to analyze climate change impacts and hazards in coastal areas such as flooding and erosion with waves being the predominant factor with varied temporal variability.&amp;#160;&lt;/p&gt;&lt;p&gt;Bias adjustment methods are commonly used for climate impact variables dealing with systematic errors (biases) found in global and regional climate models.&amp;#160; While bias correction techniques are extended in the climate and hydrological impact modeling scientific communities, there is still a lack of consensus regarding their use in sea climate variables (Parker &amp; Hill, 2017; Lemos et al, 2020; Lira-Loarca et at, 2021)&lt;/p&gt;&lt;p&gt;In these work we assess the performance of different bias-adjustment methods such as the Empirical Gumbel Quantile Mapping (EGQM) method as a standard method which takes into the account the extreme values of the distribution takes, the Distribution Mapping method using Stationary Mixture Distributions (DM-stMix) allowing for a better representation of each variable in the mean regime and tails and the Distribution Mapping method using Non-Stationary Mixture Distributions (DM-nonstMix) as an improved methods which allows to take into account the temporal variability of wave climate according to different baseline periods such as monthly, seasonal, yearly and decadal. The performance of the different bias adjustment methods will be analyzed with particular interest on the futural temporal behavior of wave climate. The advantages and drawbacks of each bias adjustment method as well as their complexity will be discussed.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;em&gt;References:&lt;/em&gt;&lt;/p&gt;&lt;ul&gt;&lt;li&gt;Lemos, G., Menendez, M., Semedo, A., Camus, P., Hemer, M., Dobrynin, M., Miranda, P.M.A. (2020). On the need of bias correction methods for wave climate projections, Global and Planetary Change, 186, 103109.&lt;/li&gt; &lt;li&gt;&lt;span&gt;Lira-Loarca, A., Cobos, M., Besio, G., Baquerizo, A. (2021) Projected wave climate temporal variability due to climate change. Stoch Environ Res Risk Assess.&lt;/span&gt;&lt;/li&gt; &lt;li&gt;&lt;span&gt;Parker, K. &amp; Hill, D.F. (2017) Evaluation of bias correction methods for wave modeling output, Ocean Modelling 110, 52-65&lt;/span&gt;&lt;/li&gt; &lt;/ul&gt;&lt;p&gt;&lt;br&gt;&lt;br&gt;&lt;/p&gt;

scholarly journals High-dimensional genomic data bias correction and data integration using MANCIE

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Chongzhi Zang ◽  
Tao Wang ◽  
Ke Deng ◽  
Bo Li ◽  
Sheng’en Hu ◽  
...  
Keyword(s):  
Data Integration ◽  
Bias Correction ◽  
Genomic Data ◽  
High Dimensional

scholarly journals Skill assessment of sub-seasonal 2-m temperature forecasts for Helsinki, Finland

2021 ◽  
Author(s):  
Mika Rantanen ◽  
Matti Kämäräinen ◽  
Otto Hyvärinen ◽  
Andrea Vajda
Keyword(s):  
Bias Correction ◽  
Mapping Method ◽  
Early Summer ◽  
Skill Assessment ◽  
Added Value ◽  
Cold Bias ◽  
Seasonal Temperature ◽  
Seasonal Forecasts ◽  
Bias Adjustment ◽  
Extended Range

&lt;p&gt;Sub-seasonal to seasonal scale forecasts provide useful information for city authorities for operational planning, preparedness and maintenance costs optimization. In the EU H2020 E-SHAPE project the Finnish Meteorological Institute aims at developing an operational service providing user-oriented sub-seasonal and seasonal forecast products for the City of Helsinki tailored for winter maintenance activities. To be able to provide skilful sub-seasonal to seasonal forecasts products, bias adjustment and evaluation of the used weather parameters, i.e. temperature and snow is crucial.&amp;#160;&lt;/p&gt;&lt;p&gt;In this study, we focus on the skill assessment of sub-seasonal temperature forecasts in Helsinki, Finland, experimenting with various methods to adjust the bias from the raw temperature forecasts. Due to its coastal location, skilful forecasting of temperatures for Helsinki is challenging. The temperature gradient on the coastline is especially strong during spring when inland areas warm considerably faster than the coastline. Therefore, raw point forecasts for Helsinki suffer from cold bias during the March-July period.&lt;/p&gt;&lt;p&gt;We use the 2 m temperature extended-range reforecasts obtained from the ECMWF S2S database and apply two bias adjustment techniques: removing the mean bias and the quantile mapping method. Reforecasts for a 20-years period, 2000-2019 with 10 ensemble members, run twice a week for 46 days ahead were calibrated and evaluated. Two datasets are used as reference, observations from Helsinki Kaisaniemi weather station and gridded ERA5 reanalysis data. Thus, these combinations yield in total five sets of forecasts which are evaluated against the observations.&lt;/p&gt;&lt;p&gt;The results of the experiments and the potential added value of bias correction will be presented for discussion. Based on the preliminary results, especially the cold bias in spring and early summer can be improved with the bias-correction methods. The bias-adjusted extended-range temperature forecasts are used in the development of sub-seasonal winter forecast products tailored for the needs of city maintenance.&lt;/p&gt;

scholarly journals Improving pan-european hydrological simulation of extreme events through statistical bias correction of RCM-driven climate simulations

2011 ◽  
Vol 8 (2) ◽  
pp. 3883-3936 ◽  
Author(s):  
R. Rojas ◽  
L. Feyen ◽  
A. Dosio ◽  
D. Bavera
Keyword(s):  
Impact Assessment ◽  
Extreme Events ◽  
Bias Correction ◽  
Large Scale ◽  
Transfer Functions ◽  
Time Window ◽  
Control Period ◽  
Maximum Temperature ◽  
Data Set ◽  
Climate Simulations

Abstract. In this work we asses the benefits of removing bias in climate forcing data used for hydrological climate change impact assessment at pan-European scale, with emphasis on floods. Climate simulations from the HIRHAM5-ECHAM5 model driven by the SRES-A1B emission scenario are corrected for bias using a histogram equalization method. As predictand for the bias correction we employ gridded interpolated observations of precipitation, average, minimum, and maximum temperature from the E-OBS data set. Bias removal transfer functions are derived for the control period 1961–1990. These are subsequently used to correct the climate simulations for the control period, and, under the assumption of a stationary error model, for the future time window 2071–2100. Validation against E-OBS climatology in the control period shows that the correction method performs successfully in removing bias in average and extreme statistics relevant for flood simulation over the majority of the European domain in all seasons. This translates into considerably improved simulations with the hydrological model of observed average and extreme river discharges at a majority of 554 validation river stations across Europe. Probabilities of extreme events derived employing extreme value techniques are also more closely reproduced. Results indicate that projections of future flood hazard in Europe based on uncorrected climate simulations, both in terms of their magnitude and recurrence interval, are likely subject to large errors. Notwithstanding the inherent limitations of the large-scale approach used herein, this study strongly advocates the removal of bias in climate simulations prior to their use in hydrological impact assessment.

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