Dataset Information

Underestimated MJO variability in CMIP6 models.

ABSTRACT: The Madden-Julian Oscillation (MJO) is the leading mode of intra-seasonal climate variability, having profound impacts on a wide range of weather and climate phenomena. Here, we use a wavelet-based spectral Principal Component Analysis (wsPCA) to evaluate the skill of 20 state-of-the-art CMIP6 models in capturing the magnitude and dynamics of the MJO. By construction, wsPCA has the ability to focus on desired frequencies and capture each propagative physical mode with one principal component (PC). We show that the MJO contribution to the total intra-seasonal climate variability is substantially underestimated in most CMIP6 models. The joint distribution of the modulus and angular frequency of the wavelet PC series associated with MJO is used to rank models relatively to the observations through the Wasserstein distance. Using Hovmöller phase-longitude diagrams, we also show that precipitation variability associated with MJO is underestimated in most CMIP6 models for the Amazonia, Southwest Africa, and Maritime Continent.

SUBMITTER: Le PVV

PROVIDER: S-EPMC8312700 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Underestimated MJO variability in CMIP6 models.

Le Phong V V PVV Guilloteau Clément C Mamalakis Antonios A Foufoula-Georgiou Efi E

Geophysical research letters 20210504 12

The Madden-Julian Oscillation (MJO) is the leading mode of intra-seasonal climate variability, having profound impacts on a wide range of weather and climate phenomena. Here, we use a wavelet-based spectral Principal Component Analysis (wsPCA) to evaluate the skill of 20 state-of-the-art CMIP6 models in capturing the magnitude and dynamics of the MJO. By construction, wsPCA has the ability to focus on desired frequencies and capture each propagative physical mode with one principal component (PC ...[more]

PMID: 34321701

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Project description:The frequency and intensity of extreme thermal stress conditions during summer are expected to increase due to climate change. This study examines sixteen models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) that have been bias-adjusted using the quantile delta mapping method. These models provide Universal Thermal Climate Index (UTCI) for summer seasons between 1979 and 2010, which are regridded to a similar spatial grid as ERA5-HEAT (available at 0.25° × 0.25° spatial resolution) using bilinear interpolation. The evaluation compares the summertime climatology and trends of the CMIP6 multi-model ensemble (MME) mean UTCI with ERA5 data, focusing on a regional hotspot in northwest India (NWI). The Pattern Correlation Coefficient (between CMIP6 models and ERA5) values exceeding 0.9 were employed to derive the MME mean of UTCI, which was subsequently used to analyze the climatology and trends of UTCI in the CMIP6 models.The spatial climatological mean of CMIP6 MME UTCI demonstrates significant thermal stress over the NWI region, similar to ERA5. Both ERA5 and CMIP6 MME UTCI show a rising trend in thermal stress conditions over NWI. The temporal variation analysis reveals that NWI experiences higher thermal stress during the summer compared to the rest of India. The number of thermal stress days is also increasing in NWI and major Indian cities according to ERA5 and CMIP6 MME. Future climate projections under different scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) indicate an increasing trend in thermal discomfort conditions throughout the twenty-first century. The projected rates of increase are approximately 0.09 °C per decade, 0.26 °C per decade, and 0.56 °C per decade, respectively. Assessing the near (2022-2059) and far (2060-2100) future, all three scenarios suggest a rise in intense heat stress days (UTCI > 38 °C) in NWI. Notably, the CMIP6 models predict that NWI could reach deadly levels of heat stress under the high-emission (SSP5-8.5) scenario. The findings underscore the urgency of addressing climate change and its potential impacts on human well-being and socio-economic sectors.

Dataset Information

Underestimated MJO variability in CMIP6 models.

Publications

Underestimated MJO variability in CMIP6 models.

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