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Grandma of 12 breaks Guinness World Record for completing 1,575 pushups in an hourA late-game rally derailed by a missed field goal and Cowboys stun Commanders 34-26The Chicago Cubs, in an ongoing effort to strategically reconstruct their offensive lineup, have signaled openness to potentially trading outfielder Seiya Suzuki. Suzuki, respected for being one of the top bats in the league following a top-20 offensive season , finds himself at the center of trade speculation despite his full no-trade clause. Joel Wolfe, Suzuki’s agent, communicated a surprising willingness from his client to consider waiving his no-trade clause under certain scenarios. "Seiya has an open mind," Wolfe disclosed to reporters, shedding light on recent conversations with Cubs president of baseball operations Jed Hoyer. This openness stems from an understanding that, while a trade is not actively sought, circumstances might align making a move beneficial for all parties involved. This revelation springs from Cody Bellinger’s recent decision to opt into his 2025 player option, creating a repeat scenario where Craig Counsell juggled outfield positions. Despite Suzuki’s disinterest in continuing as a designated hitter—a role he assumed in 59 of his 131 starts last season—his exceptional .365 weighted on-base average and .848 OPS reflect his immense value at the plate. However, his defensive game, marred slightly by routine fly ball misreads, underlines his preference for outfield play. In fact, Bellinger's return likely forces Suzuki into the DH role on a more consistent basis with Pete Crow-Armstrong playing Gold Glove-level defense in center field. It's clear the Cubs are approaching a crucial junction. With Suzuki's contractual commitment standing at two years and $36 million, the organization is balancing the desire to retain his bat and the potential benefits of leveraging his current trade value. The situation complicates further when considering Cody Bellinger, whose defensive prowess makes him another valuable asset for the Cubs to possibly retain or trade. There is no clear answer, but a lot of what-ifs to reach an answer. The strategic calculus for the Cubs involves not only player statistics but also future team composition and the cultivation of new talent like Owen Caissie and Kevin Alcántara. Trading Suzuki might disrupt the Cubs' efforts to nurture a pipeline of Japanese talent, yet it remains a contemplative direction should the right deal emerge. This article first appeared on On Tap Sports Net and was syndicated with permission.
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( MENAFN - The Conversation) Extreme temperature and rainfall events are increasing around the world, including Australia. What makes them extreme is their rarity and severity compared to the typical climate. A region's“climate” is defined by a 30-year average of mainly rainfall and temperature. Increasingly, these climate definitions have become less appropriate – we need to look at events over shorter time periods to gain a more accurate picture. We can see this in the recent worldwide proliferation of extreme flooding and prolonged heatwaves . Using southern Australia as a prime example, our newly published research in Academia Environmental Sciences and Sustainability shows that machine learning techniques can help identify key climate drivers, supporting a redefinition of climate in a warming world. In Australia, eastern coastal regions of Queensland and New South Wales continue to receive record downpours and flash floods , interspersed by dry periods of a few months to a few years. In stark contrast, southern coastal regions are drying and facing more extreme heatwaves. With already parched vegetation and catastrophic fire dangers, this region is experiencing drought conditions due to decreased cool season rainfall and increased temperatures. Notably, flash droughts and flash floods have adversely affected both agricultural crop yields and grazing pasture quality. Flash droughts greatly reduce moisture for germination. Flash floods ruin crops close to harvest time. The problem with these“flash” events is just how difficult they are to forecast. To make more accurate seasonal and annual predictions for rainfall and temperatures, we need to update our climate models. But how do we know which climate drivers need to be included? To keep track of typical climate conditions and provide context for weather and climate forecasts, the World Meteorological Organization uses a set of data products known as climatological standard normals . They define climate as averages of monthly, seasonal and annual weather-related variables such as temperature and rainfall, over consecutive 30-year periods. Climate normals can be used to assess how typical of the current climate a particular event was in a given location. It's how we arrive at temperature anomalies. For example, to tell whether a year was relatively“hot” or“cool”, we look at the anomaly – the difference between the average temperature for the calendar year in question, compared to the climate normal. But extreme variations are now occurring in periods of ten years or even shorter. Consequently, multiple increases and decreases can cancel each other out over a 30-year period. This would hide the large changes in statistics of weather variables within that period. For example, large rainfall changes in average monthly, seasonal and annual amounts can be hidden within 30-year averages. Global warming often amplifies or diminishes the impacts of multiple climate driver phases within approximately ten-year periods . When averaged over 30 consecutive years, some information is lost. Over the past decade or so, machine learning (where computers learn from past data to make inferences about the future) has become a powerful tool for detecting potential links between global warming and extreme weather events. This is referred to as attribution . Machine learning techniques are simple to code and are well-suited to the highly repetitive task of searching through numerous combinations of observational data for possible triggers of severe weather events. In our new study , machine learning helped us untangle the dominant climate drivers responsible for recent flash flood rainfall on the east coast of Australia, and a lack of rainfall on the southern coast. Along the southern coast, the cool season from May to October is typically produced by mid-latitude westerly winds. In recent years these winds were farther away from the Australian continents, resulting in the recent drought of 2017–19 and flash drought of 2023–24 . In contrast, after the 2020–22 La Niña, the east coast continues to experience wetter conditions. These come from generally higher than average sea-surface temperatures off the east coast and Pacific Ocean, due to the presence of onshore winds. Machine learning identified the dominant drivers of the scenario above: the El Niño-Southern Oscillation , the Southern Annular Mode , the Indian Ocean Dipole , and both local and global sea surface temperatures. A key finding was the prominence of global warming as an attribute, both individually and in combination with other climate drivers. Climate drivers and their combinations can change with increasing global warming over shorter periods that contain extremes of climate. Hence, the use of 30-year periods as climate normals becomes less useful. Climate models often disagree on the climate drivers likely to be relevant to extreme events. A key feature of machine learning is the ability to deal with multi-source data by identifying regional attributes . We can combine possible climate-driver predictors with high-resolution climate model predictions, especially after the climate model data are downsized to cover specific regions of concern. This can help with extreme event forecasting at a local scale. Scientists are continuously developing new methods for applying machine learning to weather and climate prediction. The scientific consensus is that global warming has dramatically increased the frequency of extreme rainfall and temperature events. However, the impacts are not uniform across the world, or even across Australia. Some regions have been more affected than others. Currently there is no single alternative definition to the traditional 30-year climate normal, given the variable impacts across the planet. Each region will need to determine its own relevant climate time period definition – and machine learning tools can help. MENAFN27112024000199003603ID1108934309 Legal Disclaimer: MENAFN provides the information “as is” without warranty of any kind. We do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the provider above.