A collection of scientific analyses, metrics, and visualizations for robust validation of ice sheet models is presented using the Land Ice Verification and Validation toolkit (LIVVkit), version 2.1, and the LIVVkit Extensions repository (LEX), version 0.1. This software collection targets stand-alone ice sheet or coupled Earth system models, and handles datasets and analyses that require high-performance computing and storage. LIVVkit aims to enable efficient and fully reproducible workflows for postprocessing, analysis, and visualization of observational and model-derived datasets in a shareable format, whereby all data, methodologies, and output are distributed to users for evaluation. Extending from the initial LIVVkit software framework, we demonstrate Greenland ice sheet simulation validation metrics using the coupled Community Earth System Model (CESM) as well as an idealized stand-alone high-resolution Community Ice Sheet Model, version 2 (CISM2), coupled to the Albany/FELIX velocity solver (CISM-Albany or CISM-A). As one example of the capability, LIVVkit analyzes the degree to which models capture the surface mass balance (SMB) and identifies potential sources of bias, using recently available in situ and remotely sensed data as comparison. Related fields within atmosphere and land surface models, e.g., surface temperature, radiation, and cloud cover, are also diagnosed. Applied to the CESM1.0, LIVVkit identifies a positive SMB bias that is focused largely around Greenland's southwest region that is due to insufficient ablation.

Epidemic propagation on complex networks has been widely investigated, mostly with invariant parameters. However, the process of epidemic propagation is not always constant. Epidemics can be affected by various perturbations and may bounce back to its original state, which is considered resilient. Here, we study the resilience of epidemics on networks, by introducing a different infection rate λ₂ during SIS (susceptible-infected-susceptible) epidemic propagation to model perturbations (control state), whereas the infection rate is λ₁ in the rest of time. Noticing that when λ₁ is below λ_c, there is no resilience in the SIS model. Through simulations and theoretical analysis, we find that even for λ₂ < λ_c, epidemics eventually could bounce back if the control duration is below a threshold. This critical control time for epidemic resilience, i.e., cd_max, seems to be predicted by the diameter (d) of the underlying network, with the quantitative relation cd_max ~ d^α. Our findings can help to design a better mitigation strategy for epidemics.