Jurnal

Examining the Impact of Bias Correction Configurations on a Multivariate Meteorological Index: A Case Study of Heat Index Analysis in the Northeast United States

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ABSTRACT: Bias correction (BC) is commonly used to refine dynamical climate model results for use in applied research. However, uncertainties linked to the BC of multivariate meteorological indices, crucial for many applied research applications, are often underappreciated. Focusing on daily maximum values of the multivariate heat index (HI, a function of temperature and relative humidity), this study investigates uncertainties across four BC workflow configurations available to users with varying expertise levels and data availability. Univariate quantile delta mapping (QDM) and multivariate BC (MBC) were used to bias correct nine global climate models trained on 30 years of data from 13 Northeast U.S. weather stations. Two workflows use QDM to directly BC HI variables. Two workflows adopt different componentwise approaches to BC temperature and humidity, computing HI from those bias-corrected outputs. Workflow accuracy is assessed by comparing extreme HI day occurrences against reference station data. The best results are produced by the QDM-direct workflow, when hourly historical station data provide accurate daily maximum HI for training. Conversely, the common componentwise QDM (QDMc) workflow that independently adjusts temperature and humidity yields errors up to 147%, due to lost intervariable dependence. The MBCN-dimensional (MBCn) componentwise approach, which jointly adjusts temperature and humidity, better preserves correlations than QDMc, yielding more reliable results. These findings underscore that BC methodological choices, including the order of operations, training data accuracy, and whether variable dependence is preserved, markedly impact accuracy. This offers practical guidance for researchers and practition ers quantifying climate risks using bias corrected climate model data.

KEYWORDS: Bias; Statistical techniques; Postprocessing

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

551.602 85 ROS e

Penerbit

Boston, USA : American Meteorological Society., 2026

Deskripsi Fisik

Page(s): 269-284; Illus

Bahasa

English

ISBN/ISSN

1558-8432

Klasifikasi

551.602 85

Tipe Isi

text

Tipe Media

unspecified

Tipe Pembawa

volume

Edisi

Volume 65 (2026): Issue 2 (Feb 2026)

Subjek

Climatology – Statistical methods – Bias correctio
Heat index – Northeastern States – Case studies
Meteorology – Computer data processing
Multivariate analysis

Info Detail Spesifik

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Pernyataan Tanggungjawab

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Versi lain/terkait

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