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異なるモデル、類似の結果:気候変動下のカリフォルニアにおける森林炭素損失の予測

Different models, similar outcomes: projected loss of forest carbon in California under climate change (原題)

Charles J. Maxwell, Benjamin M. Sleeter, Matthew D. Hurteau, A. L. Westerling, W. Jonthan Baldwin, Shane Romsos, David Saah

Carbon Balance and Management📚 査読済 / ジャーナル2026-10-03#気候科学Origin: US
DOI: 10.1186/s13021-026-00519-0
原典: https://doi.org/10.1186/s13021-026-00519-0

🤖 gxceed AI 要約

日本語

構造の異なる2つの景観植生モデル(LUCASとLANDIS-II)を同一の気候シナリオと初期条件で比較し、カリフォルニア森林の炭素動態を予測した。両モデルは今後40年で純炭素損失(LUCAS 6%・145TgC、LANDIS-II 4%・94TgC)を予測し、森林が炭素吸収源から排出源へ転換する方向性で一致した。土壌炭素や火災後の回復経路には構造的不確実性が残る。

English

Two structurally distinct landscape models (LUCAS and LANDIS-II) were run under shared climate scenarios and current management to project California forest carbon. Both converged on net carbon loss over four decades (6%/145 Tg C and 4%/94 Tg C), suggesting forests may shift from sink to source. Soil carbon and post-fire recovery diverged, highlighting structural uncertainty.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

カリフォルニア州の気候政策における森林炭素吸収源の信頼性評価に直結する研究。日本でも森林吸収源は地球温暖化対策計画・GX推進の炭素吸収量計上で重要であり、モデル間比較による不確実性評価の手法は日本の森林吸収量報告の頑健性検討に示唆を与える。

In the global GX context

Speaks to how jurisdictions count on land-sector sinks in climate policy; relevant to ISSB/GHG Protocol land-sector and natural-climate-solution accounting where sink-to-source transitions create disclosure risk. Model-convergence as an uncertainty-communication device is transferable to national inventory and TCFD nature-related assessments.

👥 読者別の含意

🔬研究者:構造の異なるモデルを同一入力で比較し方向性の一致から確信度を高める手法は、炭素収支予測の不確実性評価の参考になる。

🏢実務担当者:森林・土地利用に依存する企業は、自然由来吸収源が排出源化し得るリスクをTCFD・自然関連開示で考慮する材料になる。

🏛政策担当者:森林吸収源を政策目標に算入する際、モデル構造由来の不確実性を織り込んだ保守的評価の必要性を示す。

📄 Abstract(原文)

Abstract Changing climatic conditions and disturbance regimes are reshaping ecosystems globally, with profound implications for forest carbon dynamics. Because California’s forests represent a substantial carbon sink, understanding how climate-driven disturbances will alter their capacity to store and sequester carbon is critical for meeting the state’s emissions reduction and adaptation goals. Here we compared two structurally distinct landscape vegetation models, a state and transition model Land Use and Carbon Scenario Simulator (LUCAS) and mechanistic Second-generation Landscape Disturbance and Succession model (LANDIS-II), to evaluate projections of forest carbon dynamics under shared climate scenarios and a continuation of current management practices. Because model structure can substantially influence projected outcomes, comparing structurally distinct models under shared inputs is essential for characterizing uncertainty and informing confidence in projected trends. Both models were run with same initial conditions and utilized the same climate projections but were allowed to use their own respective wildfire model extensions. The model outputs compared were aboveground biomass, net ecosystem exchange, total carbon, and cumulative area burned. Despite structural differences, both models projected net carbon losses over the next four decades, with LUCAS estimating a 6% decline (145 Tg C) and LANDIS‑II a 4% decline (94 Tg C). Fire activity was projected to keep pace or increase depending on climate projection, with a future burned area equivalent to the past 30 years occurring within the next 15 to 25 years. While model outputs converged on broad trends, differences in soil carbon pools and post-fire recovery trajectories highlight structural uncertainties that influenced these projections. These findings suggest that California’s forests are at risk of transitioning from carbon sinks to sources, with significant implications for climate policy. By demonstrating that models built on fundamentally different structural assumptions converge on the same directional trend of forest carbon loss, this comparison provides greater confidence in the projected sink-to-source transition than any single model can, while quantifying the structural sources of uncertainty that will shape the reliability of future carbon projections used in state climate policy.

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