Underestimated MJO Variability in CMIP6 Models

Abstract

The Madden‐Julian Oscillation (MJO) is the leading mode of intraseasonal 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 intraseasonal 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.

The MJO From CMIP5 to CMIP6: Perspectives From Tracking MJO Precipitation

“…The amplitudes of the low‐level zonal winds and precipitation of the MJO are improved from CMIP5 to CMIP6 in six U.S. climate models (Orbe et al., 2020). However, the CMIP6 models still significantly underestimate the contribution of MJO to the total intraseasonal variabilities; for instance, the MJO‐related precipitation over the MC is still underestimated in CMIP6 (Le et al., 2021). Nevertheless, there is still a lack of understanding of to what extent the MJO simulation is improved from CMIP5 to CMIP6 models, especially from the perspectives of model development and identifying individual MJO events.…”

Projected future changes in equatorial wave spectrum in CMIP6

“…The Madden-Julian Oscillation (MJO) is the dominant mode of intraseasonal (1-3 months) variability in the tropical atmosphere (Le et al, 2021;Jiang et al, 2020;Ahn et al, 2017;Hung et al, 2013;Zhang, 2005;Madden and Julian, 1972). It is characterized by eastward-propagating, planetary-scale envelopes of convective cloud clusters that are tightly coupled with the large-scale wind field.…”

“…It is characterized by eastward-propagating, planetary-scale envelopes of convective cloud clusters that are tightly coupled with the large-scale wind field. Its large spatial extent and low frequency (zonal wavenumbers 1-3 and 30-90 days period) distinguishes it from convectively coupled equatorial waves (CCEWs) and other disturbances (Le et al, 2021;Ahn et al, 2020;Jiang et al, 2020;Ahn et al, 2017;Hung et al, 2013).…”

“…The MJO and CCEWs interact with a wide range of tropical weather and climate phenomena, including monsoonal systems, tropical cyclone activity, and the El Niño-Southern Oscillation (Le et al, 2021;Ahn et al, 2017;Hung et al, 2013). Furthermore, they also exhibit teleconnections to the extratropics, affecting regional hydroclimate, and influencing weather and climate phenomena in the mid-latitude and high-latitude regions (Le et al, 2021;Ahn et al, 2020;Schwartz and Garfinkel, 2020;Raghavendra et al, 2019;Ahn et al, 2017;Hung et al, 2013;Yoo et al, 2012).…”

Ocean mover’s distance: using optimal transport for analysing oceanographic data

“…The Wasserstein distance has been used in several other areas of geosciences. To list a few, it has been used to analyse particle distributions in the ocean [18]; for measuring error in temperature, precipitation, and sea ice projections [19]; for ocean data assimilation [20,21]; for analysing sea height images[22]; for ocean synthetic aperture radar (SAR) segmentation [23]; and for studying sea ice imagery [24]. However, [15] makes clear that the Wasserstein distance has not been thoroughly applied to the fundamental problem of model-to-data comparison and model-skill evaluation, particularly in the context of ocean biogeochemical models and the representation of marine ecosystem structure and function.…”