TY - JOUR

T1 - Uni- and multivariate bias adjustment methods in Nordic catchments

T2 - Complexity and performance in a changing climate br

AU - Tootoonchi, Faranak

AU - Haerter, Jan O.

AU - Todorovic, Andrijana

AU - Raty, Olle

AU - Grabs, Thomas

AU - Teutschbein, Claudia

PY - 2022/12/20

Y1 - 2022/12/20

N2 - For climate-change impact studies at the catchment scale, meteorological variables are typically extracted from ensem-ble simulations provided by global and regional climate models, which are then downscaled and bias-adjusted for eachstudy site. For bias adjustment, different statistical methods that re-scaleclimate model outputs have been suggested inthe scientific literature. They range from simple univariate methods that adjust each meteorological variable individ-ually, to more complex and more demanding multivariate methods that take existing relationships between meteoro-logical variables into consideration. Over the past decade, several attempts have been made to evaluate such methodsin various regions. There is, however, still no guidance for choosing appropriate bias adjustment methods for a study athand. In particular, the question whether the benefits of potentially improved adjustments outweigh the cost of in-creased complexity, remains unanswered.This paper presents a comprehensive evaluation of the performance of two commonly used univariate and two multi-variate bias adjustment methods in reproducing numerous univariate, multivariate and temporal features of precipita-tion and temperature series in different catchments in Sweden. The paper culminates in a discussion on trade-offs between the potential benefits (i.e., skills and added value) and disadvantages (complexity and computational de-mand) of each method to offer plausible, defensible and actionable insights from the standpoint of climate-change im-pact studies in high latitudes.We concluded that all selected bias adjustment methods generally improved the raw climate model simulations, but that not a single method consistently outperformed the other methods. There were, however, differences in the methods' performance for particular statistical features, indicating that other practical aspects such as computationaltime and heavy theoretical requirements should also be taken into consideration when choosing an appropriate biasadjustment method

AB - For climate-change impact studies at the catchment scale, meteorological variables are typically extracted from ensem-ble simulations provided by global and regional climate models, which are then downscaled and bias-adjusted for eachstudy site. For bias adjustment, different statistical methods that re-scaleclimate model outputs have been suggested inthe scientific literature. They range from simple univariate methods that adjust each meteorological variable individ-ually, to more complex and more demanding multivariate methods that take existing relationships between meteoro-logical variables into consideration. Over the past decade, several attempts have been made to evaluate such methodsin various regions. There is, however, still no guidance for choosing appropriate bias adjustment methods for a study athand. In particular, the question whether the benefits of potentially improved adjustments outweigh the cost of in-creased complexity, remains unanswered.This paper presents a comprehensive evaluation of the performance of two commonly used univariate and two multi-variate bias adjustment methods in reproducing numerous univariate, multivariate and temporal features of precipita-tion and temperature series in different catchments in Sweden. The paper culminates in a discussion on trade-offs between the potential benefits (i.e., skills and added value) and disadvantages (complexity and computational de-mand) of each method to offer plausible, defensible and actionable insights from the standpoint of climate-change im-pact studies in high latitudes.We concluded that all selected bias adjustment methods generally improved the raw climate model simulations, but that not a single method consistently outperformed the other methods. There were, however, differences in the methods' performance for particular statistical features, indicating that other practical aspects such as computationaltime and heavy theoretical requirements should also be taken into consideration when choosing an appropriate biasadjustment method

KW - Bias adjustment

KW - Bias correction

KW - Univariate and multivariate methods

KW - Precipitation and temperature

KW - Climate change

KW - Sweden

KW - SURFACE-TEMPERATURE

KW - CROSS-VALIDATION

KW - FUTURE CLIMATE

KW - PRECIPITATION

KW - SIMULATIONS

KW - MODEL

KW - IMPACT

KW - SCALE

KW - PROJECTIONS

KW - RISK

U2 - 10.1016/j.scitotenv.2022.158615

DO - 10.1016/j.scitotenv.2022.158615

M3 - Journal article

C2 - 36089026

VL - 853

JO - Science of the Total Environment

JF - Science of the Total Environment

SN - 0048-9697

M1 - 158615

ER -