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1. The Response of Tropical Organized Convection to El Niño Warming

   https://doi.org/10.1029/2019JD031026  

   Sullivan, Sylvia C. ; Schiro, Kathleen A. ; Stubenrauch, Claudia ; Gentine, Pierre ( August 2019 , Journal of Geophysical Research: Atmospheres)

   Convective organization has a large impact on precipitation and feeds back on larger‐scale circulations in the tropics. The degree of this convective organization changes with modes of climate variability like the El Niño–Southern Oscillation (ENSO), but because organization is not represented in current climate models, a quantitative assessment of these shifts has not been possible. Here, we construct multidecade satellite climatologies of occurrence of tropical convective organization and its properties and assess changes with ENSO phase. The occurrence of organized deep convection becomes more concentrated, increasing threefold in the eastern and central Pacific during El Niño and decreasing twofold outside of these regions. Both horizontal extent of the cold cloud shield and convective depth increase in regions of positive sea surface temperature anomaly (SSTa); however, the regions of greatest convective deepening are those of large‐scale ascent, rather than those of warmest SSTa. Extent decreases with SSTa at a rate of about 20 km/K, while the SSTa dependence of depth is only about 0.2 K/K. We introduce two values to describe convective changes with ENSO more succinctly: (1) an information entropy metric to quantify the clustering of convective system occurrences and (2) a growth metric to quantify deepening relative to spreading overmore »the system lifetime. Finally, with collocated precipitation data, we see that rainfall attributable to convective organization jumps up to 5% with warming. Rain intensity and amount increase for a given system size during El Niño, but a given rain amount may actually fall with higher intensity during La Niña.

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2. Impact of solar geoengineering on temperatures over the Indonesian Maritime Continent

   https://doi.org/10.1002/joc.7391  

   Kuswanto, Heri ; Kravitz, Ben ; Miftahurrohmah, Brina ; Fauzi, Fatkhurokhman ; Sopahaluwaken, Ardhasena ; Moore, John ( September 2021 , International Journal of Climatology)

   Climate change has been projected to increase the intensity and magnitude of extreme temperature in Indonesia. Solar radiation management (SRM) has been proposed as a strategy to temporarily combat global warming, buying time for negative emissions. Although the global impacts of SRM have been extensively studied in recent years, regional impacts, especially in the tropics, have received much less attention. This article investigates the potential stratospheric sulphate aerosol injection (SAI) to modify mean and extreme temperature, as well as the relative humidity and wet bulb temperature (WBT) change over Indonesian Maritime Continent (IMC) based on simulations from three different earth system models. We applied a simple downscaling method and corrected the bias of model output to reproduce historical temperatures and relative humidity over IMC. We evaluated changes in geoengineering model intercomparison project (GeoMIP) experiment G4, an SAI experiment in 5 Tg of SO₂into the equatorial lower stratosphere between 2020 and 2069, concurrent with the RCP4.5 emissions scenario. G4 is able to significantly reduce the temperature means and extremes, and although differences in magnitude of response and spatial pattern occur, there is a generally consistent response. The spatial response of changes forced by RCP4.5 scenario and G4 are notably heterogeneousmore »in the archipelago, highlighting uncertainties that would be critical in assessing socio‐economic consequences of both doing, and not doing G4. In general, SAI has bigger impacts in reducing temperatures over land than oceans, and the southern monsoon region shows more variability. G4 is also effective at reducing the likelihood of WBT > 27°C events compared with RCP4.5 after some years of SAI deployment as well as during the post‐termination period of SAI. Regional downscaling may be an effective tool in obtaining policy‐relevant information about local effects of different future scenarios involving SAI.

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3. Statistical and machine learning methods applied to the prediction of different tropical rainfall types

   https://doi.org/10.1088/2515-7620/ac371f  

   Wang, Jiayi ; Wong, Raymond K. W. ; Jun, Mikyoung ; Schumacher, Courtney ; Saravanan, R. ; Sun, Chunmei ( November 2021 , Environmental Research Communications)

   Predicting rain from large-scale environmental variables remains a challenging problem for climate models and it is unclear how well numerical methods can predict the true characteristics of rainfall without smaller (storm) scale information. This study explores the ability of three statistical and machine learning methods to predict 3-hourly rain occurrence and intensity at 0.5° resolution over the tropical Pacific Ocean using rain observations the Global Precipitation Measurement (GPM) satellite radar and large-scale environmental profiles of temperature and moisture from the MERRA-2 reanalysis. We also separated the rain into different types (deep convective, stratiform, and shallow convective) because of their varying kinematic and thermodynamic structures that might respond to the large-scale environment in different ways. Our expectation was that the popular machine learning methods (i.e., the neural network and random forest) would outperform a standard statistical method (a generalized linear model) because of their more flexible structures, especially in predicting the highly skewed distribution of rain rates for each rain type. However, none of the methods obviously distinguish themselves from one another and each method still has issues with predicting rain too often and not fully capturing the high end of the rain rate distributions, both of which are commonmore »problems in climate models. One implication of this study is that machine learning tools must be carefully assessed and are not necessarily applicable to solving all big data problems. Another implication is that traditional climate model approaches are not sufficient to predict extreme rain events and that other avenues need to be pursued.

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4. A generalized Spatio-Temporal Threshold Clustering method for identification of extreme event patterns

   https://doi.org/10.5194/ascmo-7-35-2021  

   Kholodovsky, Vitaly ; Liang, Xin-Zhong ( January 2021 , Advances in Statistical Climatology, Meteorology and Oceanography)

   null (Ed.)

   Abstract. Extreme weather and climate events such as floods, droughts, and heat waves can cause extensive societal damages. While various statistical and climate models have been developed for the purpose of simulating extremes, a consistent definition of extreme events is still lacking. Furthermore, to better assess the performance of the climate models, a variety of spatial forecast verification measures have been developed. However, in most cases, the spatial verification measures that are widely used to compare mean states do not have sufficient theoretical justification to benchmark extreme events. In order to alleviate inconsistencies when defining extreme events within different scientific communities, we propose a new generalized Spatio-Temporal Threshold Clustering method for the identification of extreme event episodes, which uses machine learning techniques to couple existing pattern recognition indices with high or low threshold choices. The method consists of five main steps: (1) construction of essential field quantities; (2) dimension reduction; (3) spatial domain mapping; (4) time series clustering; and (5) threshold selection. We develop and apply this method using a gridded daily precipitation dataset derived from rain gauge stations over the contiguous United States. We observe changes in the distribution of conditional frequency of extreme precipitation from large-scale well-connected spatial patterns to smaller-scale more isolatedmore »rainfall clusters, possibly leading to more localized droughts and heat waves, especially during the summer months. The proposed method automates the threshold selection process through a clustering algorithm and can be directly applicable in conjunction with modeling and spatial forecast verification of extremes. Additionally, it allows for the identification of synoptic-scale spatial patterns that can be directly traced to the individual extreme episodes, and it offers users the flexibility to select an extreme threshold that is linked to the desired geometrical properties. The approach can be applied to broad scientific disciplines.« less
5. Assessment of climate change for extreme precipitation indices: A case study from the central United States

   https://doi.org/10.1002/joc.5858  

   Rahmani, Vahid ; Harrington, Jr., John ( September 2018 , International Journal of Climatology)

   Five extreme precipitation indicators were calculated on an annual basis for 1890 through 2013 and analysed to determine spatial patterns and temporal trends in the frequency and magnitude of observed extreme precipitation events in Kansas located in the central United States. Indicators were selected from the list of the World Meteorological Organization–Commission for Climatology (WMO–CCL) and the Research Programme on Climate Variability and Predictability (CLIVAR). The indicators included the number of days with precipitation greater than or equal to 10 mm/day (R10), maximum number of consecutive dry days (CDD; days with precipitation lower that 1 mm), maximum 5‐day precipitation total (R5D), and simple daily intensity index (SDII) which is the annual precipitation total divided by number of days with precipitation greater than or equal to 1 mm, and the fraction of annual total precipitation due to events exceeding the 95th percentile (R95T) based on the period 1961–1990. Positive trends in the results were found for a majority of stations for R10, R5D, SDII, and R95T. Consecutive dry days was the only index that had a negative trend at a majority of stations. Spatial pattern analysis indicates greater changes in frequencies and magnitudes for eastern Kansas. Results from this study highlightmore »observed climate shifts in precipitation patterns with a tendency towards greater and more frequent extreme precipitation in eastern Kansas and a tendency towards drier conditions in western Kansas. These hydroclimatic adjustments can produce costly impacts in areas that include flood management, hydraulic structures, water availability throughout the year, agricultural production, ecosystems, and human health.

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