PERSEUS libcbm vs GCBM cross-state intercomparison (CONUS) — with 50-year baseline + 48-state scenario sweep + cbm_conus framework

PERSEUS libcbm対GCBMクロスステート相互比較（CONUS）— 50年ベースライン + 48州シナリオスイープ + cbm_conusフレームワーク (AI 翻訳)

Weiskittel, Aaron R.

Zenodoデータセット2026-06-04#炭素会計Origin: US

DOI: 10.5281/zenodo.20541807

原典: https://zenodo.org/records/20541807

🤖 gxceed AI 要約

日本語

米国CONUSの48州を対象に、2つの森林炭素モデル（libcbmとGCBM）の相互比較とシナリオ分析を実施。歴史的撹乱下ではCONUS全体が炭素吸収源から排出源に転じ、皆伐削減が最も効率的な政策手段であることを示した。また、在庫層別化手法がモデル間不確実性を支配することを明らかにした。

English

This study conducts an intercomparison of two forest carbon models (libcbm and GCBM) across 48 CONUS states with a 50-year scenario sweep. Under historical disturbance, CONUS shifts from a carbon sink to a source; reduced clearcutting emerges as the most efficient policy lever. Inventory stratification, not model implementation, dominates cross-model 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

米国森林炭素モデルの比較研究であり、手法論は日本の森林炭素算定にも示唆を与えるが、直接的な日本関連性は低い。

In the global GX context

This paper advances forest carbon accounting methodology through model intercomparison and scenario analysis at a continental scale, providing insights for national inventory systems and climate mitigation strategies globally.

👥 読者別の含意

🔬研究者:Provides a benchmark for forest carbon model intercomparison and highlights the importance of inventory stratification over model choice.

🏢実務担当者:Offers evidence on the carbon impact of forest management scenarios, useful for corporate land-use carbon accounting and offset projects.

🏛政策担当者:Demonstrates the effectiveness of reduced clearcutting as a climate mitigation lever, informing land-use policy and national carbon budget planning.

📄 Abstract（原文）

v1.2.0 headline. A CONUS 48-state x 4-scenario sweep at the 50-year horizon under canonical B1.3 FIA EXPNS inventory. Under the historical disturbance schedule (HIST), CONUS shifts from a +4,870 TgC ecosystem sink (BAU no-disturbance counterfactual) to a -10,109 TgC source (15,000 TgC swing). Reduced harvest (RH, -30% probability) recovers +3,001 TgC vs HIST. Reduced clearcut (RH_CC, -50% clearcut, partial unchanged) recovers +5,042 TgC, the most efficient policy lever on a per-area basis. Climate-amplified wildfire (WARM_HIST, region-specific multipliers) adds another -998 TgC at CONUS scale. 36 of 48 states are sources under HIST vs 7 under BAU. The Pacific Northwest is the most extreme regional source under every scenario (-80 to -90 Mg/ha at 50 years). Scientific finding (v1.0). Two implementations of the CBM-CFS3 forest carbon model family, libcbm (Canadian Forest Service Python/C++ reimplementation) and GCBM (the moja FLINT spatially explicit engine), are compared across six US states under matched parameter conventions. The year-five carbon density gap is approximately +24%, with libcbm/GCBM ratios between 0.74 and 0.80 in WA, MN, IN, ME, OR, and a single warm-donor outlier in GA at 1.05. Replacing the uniform forest-type stratification used in legacy parity runs with a stratification anchored on FIA EXPNS expansion factors (the canonical FIA Total Area Estimator) adds +6,760 TgC to the CONUS year-five carbon stock at n=39 states, a +14% shift on 246.5 Mha. Inventory stratification, not engine implementation, dominates cross-model uncertainty for this generation of CBM-CFS3 carbon models. Regional DOM-pool fingerprint. Mean libcbm slow soil carbon under B1.3 FIA EXPNS is 184 Mg/ha in the PNW, 143 in the Atlantic Maritime NE, and 105 to 119 Mg/ha across the South, Lake States, and Mountain West. A Q10 mean annual temperature sweep on Oregon (MAT 4 to 13 C) shows the slow-soil overshoot is established during spinup rather than emerging from runtime Q10 scaling. Contents v1.2.0. 48-state libcbm year-five pool outputs (B1.3 FIA EXPNS); 48-state libcbm 50-year baseline trajectories (BAU); 192 50-year scenario trajectories (HIST, RH, RH_CC, WARM_HIST across 48 states; bundled as one tarball); CONUS, regional, and per-state scenario rollup CSVs; 6-state libcbm-vs-GCBM density gap matrices under both B1.1 v6 parity and B1.3 FIA EXPNS; per-pool and per-DOM-pool decompositions; F3 Q10 MAT sensitivity sweeps for Georgia and Oregon; seven publication figures (Fig 1 through 7 = CONUS scenario sweep panel); and the cbm_conus v0.2.1 source tarball. Pipeline. All libcbm outputs were produced with the GCBM2hpc pipeline on OSC Cardinal (allocation PUOM0008). Scenarios were generated with the cbm_conus tools/raster_to_sit_events.py in dry-run mode (literature-calibrated regional disturbance rates; production path reads HCS phase 5 + TREEMAP probability rasters). Six-state GCBM aggregates were produced spatially via moja FLINT containerized GCBM at 1-degree WGS84 tiles. The CONUS economic and ecosystem services framework that consumes the scenario sweep is at github.com/holoros/cbm_conus. Companion materials: methods note at holoros.github.io/perseus-forest-intelligence/methods/inventory-stratification/ and American Forests comparison dashboard at holoros.github.io/perseus-forest-intelligence/methods/af-comparison/ .

🔗 Provenance — このレコードを発見したソース

Zenodo https://zenodo.org/records/20541807first seen 2026-06-05 04:14:00 · last seen 2026-06-05 04:15:38

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