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1. On the Estimation of Internal Climate Variability During the Preindustrial Past Millennium

   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. Natural variability can increase human walking metabolic costs and its implications to simulation-based metabolic estimation

   https://doi.org/10.1098/rsif.2025.0602  

   Alwan, Aya; Srinivasan, Manoj (November 2025, Journal of The Royal Society Interface)

   Human walking contains variability due to small intrinsic perturbations arising from sensory or motor noise, or to promote motor learning. We hypothesize that such stride-to-stride variability may increase the metabolic cost of walking over and above a perfectly periodic motion, and that neglecting such variability in simulations may mis-estimate the metabolic cost. Here, we quantify the metabolic estimation errors accrued by neglecting the stride-to-stride variability using human data and a musculoskeletal model by comparing the cost of multiple strides of walking and the cost of a perfectly periodic stride with averaged kinematics and kinetics. We find that using an averaged stride underestimates the cost by approximately 2.5%, whereas using a random stride may mis-estimate the cost positively or negatively by up to 15%, ignoring the contribution of measurement errors to the observed stride-to-stride variability. As a further illustration of the cost increase in a simpler dynamical context, we use a feedback-controlled inverted pendulum walking model to show that increasing the sensory or motor noise increases the overall metabolic cost, as well as the variability of stride-to-stride metabolic costs, as seen with the musculoskeletal simulations. Our work establishes the importance of accounting for stride-to-stride variability when estimating metabolic costs from motion. 

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4. 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)

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5. 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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