Characterizing Climate Fluctuations over Wide-Scale Ranges
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TL;DR: Researchers analyze atmospheric variability across scales, observing structures from small to large, embedded within larger weather systems, highlighting the complexity of climate fluctuations over wide-scale ranges.
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Abstract: In this image of a supercell storm, the enormous atmospheric variability across scales can be seen with the naked eye. Near the center bottom of the cloud we can see structures far smaller than the size of the “cell” which is itself only partially visible and embedded in an even larger-scale atmospheric weather system. Structures of all intermediate scales can also be seen. Credit: iStock.com/petesphotography By S. Lovejoy, M. Crucifix
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The 2021 “Complex Systems” Nobel Prize: The Climate, With and Without Geocomplexity
TL;DR: One half of the 2021's Nobel Physics prize was awarded to statistical physicist Giorgio Parisi and the other half to geophysics in 75 years as discussed by the authors for climate scientists Syukoro Manabe and Klaus Hasselmann, the former for pioneering General Circulation Models and the latter for proposing a statistical model explaining the climate as a slowly varying state driven by random weather noise.
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A cheap data assimilation approach for expensive numerical simulations
TL;DR: Using a very cheap Data Assimilation (DA) method, an alternative approach to classical DA is shown for numerical climate models which produce a large amount of "big data", which might help to reduce the bias of numerical models based on available observations within the model domain.
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A Cheap Data Assimilation Approach for Expensive Numerical Simulations
Bijan Fallah
TL;DR: A cheap data assimilation method is proposed for expensive numerical climate models, addressing sensitivity to initial conditions and reducing bias through assimilation of scattered observational data, particularly for time-averaged and long-term simulations.