v2.0

This version contains updates to code for Uden et al. (2026) that correspond to the first round of comments from reviewers after submission to MDPI Atmosphere. Changes include:

1. Out-of-period performance tests in:

• Models/Lightning/Temporal_Transferability_Test.Rmd
• Models/Ignition_Efficiency/Temporal_Transferability_Test.Rmd

2. A shift in figure names, with figure 2 being inserted; Figure_2.Rmd - Figure_7.Rmd are now named Figure_3.Rmd - Figure_8.Rmd
3. Changes to Figure_3.Rmd and Figure_4.Rmd that incorporate output form files in (1)
4. Modifications to maps in Figure_7.Rmd (added bias maps with elevation)
5. Increased font size in Figure_1.Rmd

Initial release

This repository contains the accompanying code for Uden et al. (2026), A paleoclimate-compatible framework for modeling lightning-caused ignition probability in Alaska.

Abstract

Understanding the role of historical lightning-driven fire regimes in shaping terrestrial ecosystems and carbon cycles requires reconstructing fire from data beyond the instrumental record. Previous efforts have relied on paleo proxies, such as charcoal records, but these approaches are limited by their coarse spatial extent. Alternatively, process-based modelling offers a spatially continuous pathway for simulating lightning-caused fire regimes. However, existing lightning prediction models use upper-atmospheric variables, such as convective available potential energy (CAPE), that are not available in paleoclimate reconstructions, limiting their use beyond the instrumental period.
Here, we develop a probabilistic framework for simulating lightning-caused fire ignitions that (1) relies on variables available in paleo reconstructions (near-surface climate, fuel moisture, and land cover) and (2) decomposes lightning-caused fire occurrence into two components: lightning strike rate and lightning ignition efficiency. Both components were trained on modern observational data for Alaska during 2002-2011 and then combined in a Bernoulli model to estimate daily fire probability. Near-surface climate predictors captured spatial and temporal variability in lightning activity with performance comparable to CAPE-based models, and ignition efficiency models showed strong discrimination between fire-causing and non-fire-causing strikes. Despite overestimation under high-risk conditions, the Bernoulli model demonstrated strong discriminatory skill (ROC AUC=0.894), effectively ranking fire risk across space and time. By explicitly separating lightning occurrence from ignition efficiency and relying on variables available in paleo reconstructions, this approach provides a transferable framework for simulations of historical lightning-fire regimes.