Multivariate bias adjustment of high-dimensional climate simulations: The Rank Resampling for Distributions and Dependences (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;D&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;) Bias Correction

Vrac, Mathieu

doi:10.5194/hess-2017-747

Cited by 15 publications

(20 citation statements)

References 28 publications

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“…The quantile–quantile correction methodology has three main limitations to be considered (Boé et al ., ): the temporal autocorrelation properties of the series are not corrected (e.g., wet spells in the RCM may still exist after the correction); second, each variable is corrected independently, whereas for instance, bias in precipitation might not be independent of bias in temperature; finally, climate model outputs have a strong spatial autocorrelation which may be biased. Recent studies deal with the characterization of the above limitations (Maraun et al ., ), while other authors address the need for methods adjusting not only the marginal distributions of the climate simulations but also their multivariate dependence structures (e.g., Ivanov et al ., ; Vrac, ).…”

Section: Database and Methodsmentioning

confidence: 99%

A quantile–quantile adjustment of the EURO‐CORDEX projections for temperatures and precipitation

Cardell

Romero

Amengual

et al. 2019

Intl Journal of Climatology

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Projections of climate change impacts over Europe are derived using a new quantile–quantile adjustment method. E‐OBS high‐resolution gridded data sets of daily observed precipitation and 2‐m surface minimum and maximum temperatures have been used as the current climate baseline. For projections, the same meteorological variables have been obtained from a set of regional climate models (RCMs) integrated in the EURO‐CORDEX project, and by considering the RCP4.5 and RCP8.5 future emissions scenarios. To enhance the reliability of RCM data at local scale, new developments of a previous quantile–quantile adjustment have been applied to the simulated regional scenarios. This method focuses not only on the bulk spectrum of the cumulative distribution functions but also on its tails. Results show an overall improvement in reproducing the present climate baseline when using calibrated series instead of raw RCM outputs. Next, we have used these locally adjusted series to quantify the climate change signal through a number of annual and seasonal indicators. A significant increase of the minimum and maximum temperatures in all seasons is projected over Europe, being more marked in the Mediterranean for summer and autumn. Prospects on future seasonal and annual changes in precipitation are more diverse, showing an overall decrease in southern Europe and the Mediterranean, while precipitation is expected to increase towards the north of the continent. With these sources of information at hand, including and accounting for the identification of the most vulnerable geographical areas, policy makers and stakeholders can respond more effectively to the future challenges imposed by climate change.

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Section: Database and Methodsmentioning

confidence: 99%

A quantile–quantile adjustment of the EURO‐CORDEX projections for temperatures and precipitation

Cardell

Romero

Amengual

et al. 2019

Intl Journal of Climatology

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“…These findings exemplify the need for multivariate bias adjustment methods, which can adjust climate model biases in the dependencies between multiple drivers of hazards (Francois et al, 2020;Vrac, 2018). Climate model output should be a reliable input for the bias adjustment methods, e.g., models should provide a plausible representation of large-scale atmospheric circulation (Maraun et al, 2016;.…”

Section: Discussionmentioning

confidence: 90%

Towards a compound event-oriented climate model evaluation: A decomposition of the underlying biases in multivariate fire and heat stress hazards

Herrera¹,

Bevacqua²,

Ribeiro³

et al. 2020

Preprint

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Abstract. Climate models' outputs are affected by biases that need to be detected and adjusted to model climate impacts. Many climate hazards and climate-related impacts are associated with the interaction between multiple drivers, i.e. by compound events. So far climate model biases are typically assessed based on the hazard of interest, and it is unclear how much a potential bias in the dependence of the hazard drivers contributes to the overall bias and how the biases in the drivers interact. Here, based on copula theory, we develop a multivariate bias assessment framework, which allows for disentangling the biases in hazard indicators in terms of the underlying univariate drivers and their statistical dependence. Based on this framework, we dissect biases in fire and heat stress hazards in a suite of global climate models by considering two simplified hazard indicators, the wet-bulb globe temperature (WBGT) and the Chandler Burning Index (CBI). Both indices solely rely on temperature and relative humidity. The spatial pattern of the hazards indicators is well represented by climate models. However, substantial biases exist in the representation of extreme conditions, especially in the CBI (spatial average of absolute bias: 21 °C) due to the biases driven by relative humidity (20 °C). Biases in WBGT (1.1 °C) are small compared to the biases driven by temperature (1.9 °C) and relative humidity (1.4 °C), as the two biases compensate each other. In many regions, also biases related to the statistical dependence (0.85 °C) are important for WBGT, which indicates that well-designed physically-based multivariate bias adjustment should be considered for hazards and impacts that depend on multiple drivers. The proposed compound event-oriented evaluation of climate model biases is easily applicable to other hazard types. Furthermore, it can contribute to improved present and future risk assessments through increasing our understanding of the biases’ sources in the simulation of climate impacts.

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“…The representation of the climate in ERAI and models has uncertainties and errors, especially in atmospheric dynamics (Shepherd (2014)) and surface temperature in models (Jones et al (2013)). Bias correction methods could lead to more realistic seasonal weather regimes but could imply other issues such as modifications of spatial and temporal structures (and trends) that could possibly generate physical inconsistencies (Vrac (2018), François (2020).…”

Section: Limitations and Perspectivesmentioning

confidence: 99%

Seasonal weather regimes in the North Atlantic region: towards new seasonality?

Breton

Vrac

Yiou

et al. 2020

Preprint

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Abstract. European climate variability is shaped by atmospheric dynamics and local physical processes over the North Atlantic region. As both have strong seasonal features, a better insight of their future seasonality is essential to anticipate changes in weather conditions for human and natural systems. We explore the weather seasonality of the North Atlantic over 1979–2017 and 1979–2100 by using seasonal weather regimes (SWRs) defined by clustering year-round daily fields of geopotential height at 500 hPa (Z500) from the ERA-Interim reanalysis and 12 climate models of the Coupled Model Intercomparison Project fifth phase (CMIP5). The spatial and temporal variability of SWR structures is investigated, as well as associated patterns of surface air temperature. Although the climate models have biases, they reproduce structures and evolutions of SWRs similar to the reanalysis over 1979–2017: decreasing frequency of winter conditions (starting later and ending earlier in the year) and increasing frequency of summer conditions (starting earlier and ending later). These changes are stronger over 1979–2100 than over 1979–2017, associated with a large increase of North Atlantic seasonal mean Z500 and temperature. When using more SWRs (i.e. more freedom in the definition of seasonality), the changes over 1979–2100 correspond to a long-term swap between SWRs, resulting in similar structures (seasonal cycle and weather patterns) with respect to the evolution of the seasonal cycle of Z500 and temperature. To understand whether the evolution of the SWRs is linked to uniform Z500 increase (i.e. uniform warming), or to changes in Z500 spatial patterns (i.e. changes in circulation patterns), we remove the calendar trend in the Z500 regional average to define SWRs based on detrended data (d-SWRs). The temporal properties of d-SWRs appear almost constant, whereas their spatial patterns change. This indicates that the calendar Z500 regional trend drives the evolution of the SWRs and that the changing spatial patterns in d-SWRs account for the heterogeneity of this trend. Our study suggests that historical winter conditions will continue to decrease in the future while historical summer conditions continue to increase. It also suggests that according to an increasing seasonal cycle, the seasonality of weather conditions would not change in a major way.

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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

Cited by 15 publications

References 28 publications

A quantile–quantile adjustment of the EURO‐CORDEX projections for temperatures and precipitation

A quantile–quantile adjustment of the EURO‐CORDEX projections for temperatures and precipitation

Towards a compound event-oriented climate model evaluation: A decomposition of the underlying biases in multivariate fire and heat stress hazards

Seasonal weather regimes in the North Atlantic region: towards new seasonality?

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