PERSEUS libcbm vs GCBM cross-state intercomparison (CONUS) — with 50-year baseline trajectory + cbm_conus framework code
PERSEUS libcbm vs GCBM 全米州間比較 (CONUS) — 50年ベースライン軌道 + cbm_conusフレームワークコード (AI 翻訳)
Weiskittel, Aaron R.
🤖 gxceed AI 要約
日本語
本研究は、森林炭素モデルCBM-CFS3の2つの実装(libcbmとGCBM)を米国6州で比較し、5年目の炭素密度差が約24%であることを示した。さらに、FIA EXPNS拡大係数に基づく層化により、CONUSの5年目炭素ストックが14%増加した。v1.1.0では48州の50年ベースライン軌道を提供し、太平洋北西部を除くほとんどの州が炭素吸収源であることを明らかにした。
English
This study compares two implementations of the CBM-CFS3 forest carbon model (libcbm and GCBM) across six US states, finding a ~24% carbon density gap at year five. Stratification based on FIA EXPNS expansion factors increases CONUS year-five carbon stock by 14%. The v1.1.0 provides 50-year baseline trajectories for 48 states, showing most US states are carbon sinks except the Pacific Northwest.
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
日本では森林炭素吸収量の算定に独自モデルが用いられているが、本手法(層化による不確実性低減やオープンソースパイプライン)は、日本版森林炭素モデル開発や検証に参考となる。特にSSBJ対応での森林関連Scope 1算定に示唆を与える可能性がある。
In the global GX context
This paper advances forest carbon accounting methodology by demonstrating that inventory stratification, not model implementation, dominates uncertainty. The open-source pipeline (cbm_conus) and 50-year baseline provide a replicable framework for national-scale forest carbon tracking, relevant to global carbon accounting standards and REDD+ MRV.
👥 読者別の含意
🔬研究者:森林炭素モデルの比較と不確実性の要因を明示し、モデル開発者にとって重要な知見を提供する。
🏢実務担当者:オープンソースのパイプラインとベースラインは、企業の森林炭素プロジェクトの算定・検証に活用可能。
🏛政策担当者:森林炭素吸収源の長期軌道と地域差を示し、気候政策における森林管理の優先順位付けに有用。
📄 Abstract(原文)
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 Washington, Minnesota, Indiana, Maine, and Oregon, and a single warm-donor outlier in Georgia 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. We conclude that inventory stratification, not engine implementation, dominates cross-model uncertainty for this generation of CBM-CFS3 carbon models. New in v1.1.0: 50-year baseline trajectory at n=48 CONUS states. Under canonical B1.3 FIA EXPNS inventory, libcbm projects a +4,870 TgC net CONUS gain over 50 years (177.2 to 191.3 Mg/ha mean). Forty one of forty eight states are sinks. The Pacific Northwest is the only net regional source (-6.5 Mg/ha mean, driven by California at -17.1 Mg/ha), while the Lake States is the largest gaining region (+42.4 Mg/ha mean). The PNW source signature is consistent with the regional dead organic matter pool fingerprint: cool moist climates that carry the highest libcbm baseline slow soil stocks fail to keep accumulating over the horizon. Regional DOM-pool fingerprint (v1.0). Mean libcbm slow soil carbon under B1.3 FIA EXPNS is 184 Mg/ha in the Pacific Northwest, 143 in the Atlantic Maritime Northeast, 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: libcbm/GCBM stays at 1.35 even at MAT 13 C. Contents. 48-state libcbm year-five pool outputs under canonical B1.3 FIA EXPNS inventory; new in v1.1.0: 48-state libcbm 50-year baseline trajectories under B1.3 FIA EXPNS plus regional and CONUS rollups; 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; six publication figures supporting the methods finding (Fig 1 through Fig 6); and the cbm_conus v0.2.1 source tarball with the reproduction pipeline. Pipeline. All libcbm outputs were produced with the GCBM2hpc pipeline (github.com/holoros/GCBM2hpc) on the Ohio Supercomputer Center Cardinal cluster (allocation PUOM0008). 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 50-year baseline is at github.com/holoros/cbm_conus (v0.2.1 archived here). Companion methods note: holoros.github.io/perseus-forest-intelligence/methods/inventory-stratification/
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20519700first seen 2026-06-03 04:22:58 · last seen 2026-06-04 04:25:58
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