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1. Underestimated MJO Variability in CMIP6 Models

   https://doi.org/10.1029/2020GL092244  

   Le, Phong V. V.; Guilloteau, Clément; Mamalakis, Antonios; Foufoula‐Georgiou, Efi (June 2021, Geophysical Research Letters)

   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. 

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2. Projecting End-of-Century Human Exposure from Tornadoes and Severe Hailstorms in Eastern Colorado: Meteorological and Population Perspectives

   https://doi.org/10.1175/WCAS-D-19-0153.1  

   Childs, Samuel J.; Schumacher, Russ S.; Strader, Stephen M. (July 2020, Weather, Climate, and Society)

   Abstract Severe convective storms along the Front Range and eastern plains of Colorado frequently produce tornadoes and hail, leading to substantial damage and crop losses annually. Determination of future human exposure from these events must consider both changes in meteorological conditions and population dynamics. Projections of EF0 + tornadoes (on the enhanced Fujita scale) and severe [1.0+ in. (25.4+ mm)] hail reports out to the year 2100 are computed using convective parameter proxies generated from dynamically downscaled GFDL Climate Model, version 3 (GFDL CM3), output by the WRF Model for control and future climate scenarios. The proxies suggest that tornado and hail days in the region may increase by up to one tornado day and three hail days per year by 2100, with the greatest increases across northeastern Colorado. Using a spatially explicit Monte Carlo model, projected future frequency and spatial changes in tornadoes and hail are superimposed with population projections from the shared socioeconomic pathways (SSPs) to provide a range of possible scenarios for end-of-century human exposure to tornadoes and hailstorms. Changes in hazard frequency and spatial distribution may amplify human exposure up to 117% for tornadoes and 178% for hail in the region by 2100, although specific results are sensitive to uncertain combinations of future overlaps between hazard spatial distribution and population. Findings presented herein not only will provide the public, insurers, policy makers, land-use planners, and researchers with estimates of potential future tornado and hail impacts in the Front Range region, they also will allow the weather enterprise to better understand, prepare for, and communicate tornado and hail risk to eastern Colorado communities. 

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3. An Updated Severe Hail and Tornado Climatology for Eastern Colorado

   https://doi.org/10.1175/JAMC-D-19-0098.1  

   Childs, Samuel J.; Schumacher, Russ S. (October 2019, Journal of Applied Meteorology and Climatology)

   Abstract A localized tornado and severe hail climatology is updated and enhanced for eastern Colorado. This region is one of the most active severe weather areas in the United States because of its location immediately east of the Rocky Mountains, intrusions of Gulf of Mexico moisture into a dry climate, and various small-scale topographically forced features such as the “Denver Cyclone.” Since the 1950s, both annual tornado and severe (≥1.0 in.; 1 in. = 25.4 mm) hail reports and days have been increasing across the area, but several nonmeteorological factors distort the record. Of note is a large population bias in the severe hail data, with reports aligned along major roadways and in cities, and several field projects contributing to an absence of (E)F0 tornado reports [on the (enhanced) Fujita scale] in the 1980s. In the more consistently observed period since 1997, tornado reports and days show a slight decreasing trend while severe hail reports and days show an increasing trend, although large variability exists on the county level. Eastern Colorado tornadoes are predominantly weak, rarely above (E)F1 intensity, and with a maximum just east of the northern urban corridor. Severe hail has a maximum along the foothills and shows a trend toward a larger ratio of significant (≥2.0 in.; ≥50.8 mm) hail to severe hail reports over time. Both tornadoes and severe hail have trended toward shorter seasons since 1997, mostly attributable to an earlier end to the season. By assessing current and historical trends from a more localized perspective, small-scale climatological features and local societal impacts are exposed—features that national climatologies can miss. 

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4. Deep learning for bias correction of MJO prediction

   https://doi.org/10.1038/s41467-021-23406-3  

   Kim, H.; Ham, Y. G.; Joo, Y. S.; Son, S. W. (December 2021, Nature Communications)

   Abstract Producing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent. 

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5. Role of Dry Dynamics in the Maritime Continent Barrier Effect in the Madden–Julian Oscillation

   https://doi.org/10.1175/JAS-D-24-0072.1  

   Roundy, Paul_E (December 2024, Journal of the Atmospheric Sciences)

   Abstract Eastward-moving moist deep convection and atmospheric circulation signals associated with the tropical Madden–Julian oscillation (MJO) sometimes break down as they cross the Maritime Continent region, but other times, the signal propagates across the region maintaining amplitude or regaining it over the west Pacific basin. This paper assesses the hypothesis that upper-tropospheric zonal diffluence of the background wind over the Maritime Continent causes much of this Maritime Continent barrier effect and its variation over time, through two mechanisms: 1) by slowing down the MJO as stronger-than-average background upper-tropospheric zonal wind over the Indian Ocean advects the MJO circulation signal westward, slowing its eastward advance, and 2) through the zonal advection of the background wind by subseasonal zonal wind across a region of zonal diffluence of the background wind, which advects the background wind of the opposite sign to the MJO wind. Advection of the opposite-signed background wind counteracts the MJO wind and reduces its associated upper-tropospheric mass divergence, weakening the mechanisms of the upper-tropospheric Kelvin wave component of the MJO circulation. Composites of MJO-associated zonal wind and outgoing longwave radiation signals diminish as they cross the Maritime Continent region when the region’s background zonal winds are diffluent, and composites of data reconstructing the relevant advection terms reveal the direct action of the advection mechanisms. Significance StatementThe Madden–Julian oscillation (MJO) is the leading subseasonal variation of the tropical atmosphere. This project addresses how diffluence of the upper-tropospheric background zonal wind can break down MJO events through advection of and by the background wind. 

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