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   https://doi.org/10.1029/2021GL096596  

   Mann, Michael E.; Steinman, Byron A.; Brouillette, Daniel J.; Fernandez, Alejandro; Miller, Sonya K. (January 2022, Geophysical Research Letters)

   Abstract We use an ensemble of simulations of a coupled model (NCAR Community Earth System Model) driven by natural radiative forcing estimates over the pre‐industrial past millennium to test the efficacy of methods designed to remove forced variability from proxy‐based climate reconstructions and estimate residual internal variability (e.g., a putative “Atlantic Multidecadal Oscillation”). Within the framework of these experiments, the forced component of surface temperature change can be estimated accurately from the ensemble mean, and the internal variability of each of the independent realizations can be accurately assessed by subtracting off that estimate. We show in this case, where the true internal variability is known, that regression‐based methods of removing the forced component from proxy reconstructions will, in the presence of uncertainties in the underlying natural radiative forcing, fail to yield accurate estimates thereof, incorrectly attributing unresolved forced features (and multidecadal spectral peaks associated with them) to internal variability. 

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2. Variability in Oceanic Particle Size Distributions and Estimation of Size Class Contributions Using a Non‐parametric Approach

   https://doi.org/10.1029/2021JC017946  

   Reynolds, Rick A.; Stramski, Dariusz (December 2021, Journal of Geophysical Research: Oceans)
3. Assessing variability of optimum air temperature for photosynthesis across site-years, sites and biomes and their effects on photosynthesis estimation

   https://doi.org/10.1016/j.agrformet.2020.108277  

   Chang, Qing; Xiao, Xiangming; Doughty, Russell; Wu, Xiaocui; Jiao, Wenzhe; Qin, Yuanwei (March 2021, Agricultural and Forest Meteorology)

   null (Ed.)
4. Deep Modeling of Quasar Variability

   https://doi.org/10.3847/1538-4357/abb9a9  

   Tachibana, Yutaro; Graham, Matthew J.; Kawai, Nobuyuki; Djorgovski, S. G.; Drake, Andrew J.; Mahabal, Ashish A.; Stern, Daniel (November 2020, The Astrophysical Journal)

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5. An Empirical Study of Real-World Variability Bugs Detected by Variability-Oblivious Tools

   https://doi.org/10.1145/3338906.3338967  

   Mordahl, Austin; Oh, Jeho; Koc, Ugur; Wei, Shiyi; Gazzillo, Paul (August 2019, ESEC/FSE 2019: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering)

   Many critical software systems developed in C utilize compile-time configurability. The many possible configurations of this software make bug detection through static analysis difficult. While variability-aware static analyses have been developed, there remains a gap between those and state-of-the-art static bug detection tools. In order to collect data on how such tools may perform and to develop real-world benchmarks, we present a way to leverage configuration sampling, off-the-shelf “variability-oblivious” bug detectors, and automatic feature identification techniques to simulate a variability-aware analysis. We instantiate our approach using four popular static analysis tools on three highly configurable, real-world C projects, obtaining 36,061 warnings, 80% of which are variability warnings. We analyze the warnings we collect from these experiments, finding that most results are variability warnings of a variety of kinds such as NULL dereference. We then manually investigate these warnings to produce a benchmark of 77 confirmed true bugs (52 of which are variability bugs) useful for future development of variability-aware analyses. 

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