Intercomparisons of methods for extracting the internal climate variability from the observed records over the Indo‐Pacific sector

• Published in: International journal of climatology 2023-01, Vol.43 (1), p.57-75
• Main Authors: Miyaji, Yuma, Hasegawa, Takuya, Tanimoto, Youichi
• Format: Article
• Language: English
• Subjects: Anomalies; Climate; Climate models; Climate variability; cross‐basin interaction; decadal; Empirical analysis; external forcing; Indian Ocean; Interdecadal Pacific Oscillation; internal variability; Mean; Orthogonal functions; Pacific; Radiative forcing; Records; Sea level; Sea level pressure; Sea surface; Sea surface temperature; Sea surface temperature anomalies; Simulation; Spatial analysis; statistical methods; Surface temperature; Variability
• ISSN: 0899-8418; 1097-0088
• DOI: 10.1002/joc.7729

A careful isolation of the externally forced component (EFC) and the internal climate variability (ICV) embedded in the observed records as well as the climate simulations is critical to investigate an actual response to the external radiative forcing and/or background dynamics in the ICV. Employing three different methods, we evaluate the EFCs contained in the observed sea surface temperature (SST) and sea level pressure (SLP) fields in the Indo‐Pacific region. After removing these EFCs, we obtain the respective ICVs as the remaining anomalies. The remaining SST and SLP anomalies are then evaluated on decadal time scales in a combined empirical orthogonal function (EOF) analysis of different spatial portions: the tropical Pacific, the Indian Ocean and the whole Indo‐Pacific region. After making statistical intercomparisons of the spatial patterns and associated time series of the EOF analyses, we found that the EFCs of the individual grid point values (GPVs) were appropriately estimated by regressing onto the multi‐model ensemble global mean surface temperature (GMSTMME) and were less well approximated by the conventional linear trend and the multi‐model ensemble mean of the simulated GPVs. The regressed SST anomalies of the individual historical simulations onto the GMSTMME were much larger than the observed anomalies, illustrating that the ICV‐to‐EFC variance ratio is a performance‐improvement indicator of climate models. Long‐term observed records such as sea surface temperature contain the internal climate variability (ICV) and the externally forced component (EFC). While most of previous studies remove a linear trend from the observed record (upper panel) and treat the remaining signal as the ICV, this detrending treatment is expected to generate artificial ICV (bottom panel). We found that the ICV was most appropriately estimated by utilizing the multi‐model ensemble global mean surface temperature as a representative index of the EFC.