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  • https://doi.org/10.1002/joc.70214Copy DOI Icon

Correcting Breaks in Temperature and Humidity Observations: Implications for Climate Variability Analysis in Austria

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Abstract

ABSTRACT A 2‐h shift in the timing of evening observations in 1971 resulted in a discontinuity in the estimation of daily means of air temperature and humidity across all climate stations in Austria. This study employs hourly observations to, for the first time, comprehensively quantify and correct this break. On average over all 134 long‐term stations, the observed break caused a non‐negligible, spurious cooling (−0.15 K) and drying (−2.1%) in the time series, with considerable variability among stations and seasons. The correction is achieved by eliminating the systematic bias in predicting true daily means from sub‐daily observations, utilising progressively refined, station‐specific multi‐linear regressions. This approach reduces the typical error in predicting true daily means by almost half, compared to the commonly used averaging formulae. Additionally, the regressions significantly decrease differences in the predictability of true daily means among stations, seasons and observation times. The final model is stratified by calendar month, station and diurnal temperature range class and uses two separate regressions to account for the differing evening observation times before and after 1971. It operates fully stand‐alone, requiring only the station's own subdaily time series as input. The method is applied to both air temperature and relative humidity, achieving comparable improvements in RMSE (up to 52% reduction for temperature and up to 57% reduction for humidity, depending on the averaging formulae) and bias elimination across all seasons. The importance of break correction is evident in basic applications: Without correction, the temperature and humidity trends for the period 1961–2022 are underestimated on average by −0.13 K and −1.5% respectively. Furthermore, employing break‐corrected series largely eliminates a systematic misestimation of climate indices and demonstrably improves temporal consistency in spatial climate monitoring. The simplicity and autonomy of this correction method make it easily applicable to similar issues in other regions. The break‐corrected series contribute to error reduction in widespread applications, a more reliable assessment of climate variations and a better understanding of underlying mechanisms.

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