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Abstract Following a nuclear war, destruction would extend well beyond the blast zones due to the onset of a nuclear winter that can devastate the biosphere, including agriculture. Understanding the damage magnitude and preparing for the folly of its occurrence are critical given current geopolitical tensions. We developed and applied a framework to simulate global crop production under a nuclear winter using the Cycles agroecosystem model, incorporating ultraviolet (UV)-B radiation effects on plant growth and adaptive selection of crop maturity types (shorter cycle the lower the temperature). Using maize (Zea maizeL.) as a sentinel crop, we found that annual maize production could decline from 7% after a small-scale regional nuclear war with 5 Tg soot injection, to 80% after a global nuclear war with 150 Tg soot injection, with recovery taking from 7 to 12 years. UV-B damage would peak 6–8 years post-war and can further decrease annual maize production by 7%. Over the recovery period, adaptive selection of maize maturity types to track changing temperatures could increase production by 10% compared to a no-adaptation strategy. Seed availability may become a critical adaptation bottleneck; this and prior studies might underestimate food production declines. We propose that adaptation must include the development of Agricultural Resilience Kits consisting of region- and climate-specific seed and technology packages designed to buffer against uncertainty while supply chains recover. These kits would be congenial with the transient conditions during the recovery period, and would also be applicable to other catastrophes affecting food production. 

Abstract We present a Python package geared toward the intuitive analysis and visualization of paleoclimate timeseries,Pyleoclim. The code is open‐source, object‐oriented, and built upon the standard scientific Python stack, allowing users to take advantage of a large collection of existing and emerging techniques. We describe the code's philosophy, structure, and base functionalities and apply it to three paleoclimate problems: (a) orbital‐scale climate variability in a deep‐sea core, illustrating spectral, wavelet, and coherency analysis in the presence of age uncertainties; (b) correlating a high‐resolution speleothem to a climate field, illustrating correlation analysis in the presence of various statistical pitfalls (including age uncertainties); (c) model‐data confrontations in the frequency domain, illustrating the characterization of scaling behavior. We show how the package may be used for transparent and reproducible analysis of paleoclimate and paleoceanographic datasets, supporting Findable, Accessible, Interoperable, and Reusable software and an open science ethos. The package is supported by an extensive documentation and a growing library of tutorials shared publicly as videos and cloud‐executable Jupyter notebooks, to encourage adoption by new users.