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A Signal Processing Approach to Correct Systematic Bias in Trend and Variability in Climate Model Simulations
Author(s) -
Kusumastuti Cilcia,
Jiang Ze,
Mehrotra Rajeshwar,
Sharma Ashish
Publication year - 2021
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2021gl092953
Subject(s) - gcm transcription factors , climate change , climatology , wavelet , environmental science , climate model , signal (programming language) , series (stratigraphy) , general circulation model , wavelet transform , econometrics , computer science , geology , mathematics , paleontology , oceanography , artificial intelligence , programming language
Bias correction of General Circulation Model (GCM) is now an essential part of climate change studies. However, the climate change trend has been overlooked in majority of bias correction approaches. Here, a novel signal processing‐based approach for correcting systematic biases in the time‐varying trend of GCM simulations is proposed. The approach corrects for systematic deviations in spectral attributes of raw GCM simulations using discrete wavelet transforms. The order one and two moments of the underlying trend represented by the lowest frequency of wavelet component are corrected to ensure continuity in the corrected time series from the current to the future simulation period. The approach is applied to correct two data sets that exhibit opposite time‐varying trends representing the global mean sea level (GMSL) and the Arctic sea‐ice extent. Results indicate that bias in trend is corrected, while continuity in time and observed variability at all frequencies in current climate simulations are maintained.