Inventory stratification dominates the libcbm vs GCBM engine gap

A five-state intercomparison of libcbm and GCBM under the CBM-CFS3 modeling family finds that the previously reported +24% engine gap is dominated by how the libcbm bundle stratifies state forest inventory, not by fundamental differences between the two engines.

PERSEUS multi-model framework · GCBM2hpc pipeline · Aaron Weiskittel et al. · github.com/holoros/GCBM2hpc

2026-06-02 update: OR joins the +24% cluster (6-state libcbm vs GCBM)

The Oregon statewide GCBM chain landed, and OR now joins the original +24% engine-gap cluster at libcbm/GCBM ratio 0.7952 (gap 20.5%). Five of six states with full GCBM aggregates now sit at gaps of 20.5 to 25.6 percent — Maine, Minnesota, Washington, Indiana, and Oregon — under the canonical B1.1 v6 (uniform-FT inventory + Boudewyn + LCMS events) parity that the v1.3 manuscript uses. Georgia remains the lone outlier at ratio 1.0509 (-5.1%), consistent with its warm-donor Stage 1 placeholder.

This strengthens the v1.3 manuscript: under the canonical inventory parity, 5 of 6 states cluster tightly between 0.74 and 0.80, confirming the engine-gap finding is structural and reproducible. The remaining 42 states have libcbm pools built and FIA EXPNS areas computed; their GCBM aggregates are queued (Cardinal SLURM ga_state running; remaining 41 states pending compute).

Headline numbers

The finding

How we got here

The five-state pilot in PERSEUS established three sequential controls. First, we refit Boudewyn vol-to-biomass component proportions per species from each state's FIA TREE panel (DRYBIO_STEM / DRYBIO_STEM_BARK / DRYBIO_BRANCH / DRYBIO_FOLIAGE relative to DRYBIO_AG), replacing the Ontario Mixedwood Plains donor values that warm-climate states inherit by default. Across all five states, this Stage 2 refit moves the libcbm-over-GCBM ratio by less than ±0.003. Component proportions are not the lever.

Second, we patched Georgia's spatial-unit mean annual temperature across the range 6.32 to 17.46 °C, sweeping the F3 Q10 decay rate scaling that warm states inherit from the cold donor. Lowering MAT toward the donor (6.32 °C) raises the ratio from 1.05 to 1.10, the opposite direction needed to collapse Georgia into the cluster. DOM accumulates as decay slows; live carbon is unaffected. F3 Q10 is not the lever either.

Third, we decomposed each state's GCBM-minus-libcbm gap into live and DOM components. The +24% gap turns out to be heterogeneous per state. WA is dominated by live carbon (+53.9 Mg/ha vs +18.7 DOM); MN is DOM-dominated (+39.4 vs +11.2); IN balanced; ME DOM-leaning; GA opposite-sign (libcbm overstates live by 6.6 Mg/ha). No single per-pool explanation accounts for the cluster.

The smoking gun

In build_libcbm_state_bundle.py, the line

area_per_stratum = total_area_ha / len(inv_tuples)

allocated equal area to every (FT, eco, owner) tuple, regardless of real spatial composition. Verified across all five states: the coefficient of variation of per-FT area share was exactly 0.000 in every case. The TreeMap raster stack underneath the GCBM run honors real spatial composition (Pacific Northwest is Douglas-fir dominated; Lake States are aspen + spruce-fir dominated; etc.). When libcbm then replayed a stratum-mean carbon trajectory, the strata it was averaging over differed from the strata GCBM was running spatially. The two engines were modeling different inventories.

WA gave the cleanest signal: libcbm yield at age 56 ranged from 7.5 m³/ha (FT=160) to 170.8 m³/ha (FT=100), a 24-fold range. Real WA forest is Douglas-fir dominated (well over half the state). Equal weighting flattened the live-biomass mean below GCBM's spatial mean by about 35 Mg/ha. That is the WA +54 Mg/ha live gap in Figure 3, almost in full.

The patches

B1.2 (commit 33f2fc8) reads per-FT pixel counts from pixel_attributes.csv (already loaded for the age distribution work in B1.1) and weights each stratum's area proportional to its FT's pixel share, splitting equally among (eco, owner) sub-strata of that FT. This overshoots in 4/5 states because the TreeMap Count column is not the canonical FIA expansion factor — it reflects how many 30 m pixels were assigned to each imputed plot, not the FIA stratified sample design.

B1.3 (commit 3122159) adds compute_fia_expns_areas.py, a state-portable computer for the canonical FIA Total Area Estimator. For a given state and EVALID, it joins COND → POP_PLOT_STRATUM_ASSGN → POP_STRATUM and sums CONDPROP_UNADJ × EXPNS per FORTYPCD, then aggregates to FT group. The bundle builder consumes the per-FT-group CSV and weights inventory by the resulting hectares. The result for Minnesota is parity. The result for WA, IN, GA is a real, much smaller engine gap that varies state by state.

B1.3 replaces the TreeMap pixel weighting with the canonical FIA Total Area Estimator. Minnesota reaches essentially exact parity (0.999, a 0.14% gap). The residual gap in the other states is the real engine signal, much smaller than the +24% originally reported and notably state-dependent. WA at 1.20 and IN at 1.34 are consistent with Pacific NW Douglas-fir yield and Central Hardwood region disturbance regime differences between spatial GCBM and stratum-mean libcbm. GA at 1.52 is consistent with the warm-donor Stage 1 placeholder AIDB and is expected to compress under a future Stage 2 calibration paper for subtropical donors.

Why this matters

Multi-model forest carbon intercomparisons routinely report inter-engine spread in carbon stocks of 20-40% even at the same spatial scope and the same input data. The implicit assumption is that this spread tracks fundamental differences in how each engine represents growth, decay, and disturbance. PERSEUS's finding here is that one important component of that spread — the libcbm-vs-GCBM gap on CBM-CFS3 implementations — is substantially attributable to how the input inventory is stratified for each engine. That is, two engines running the same science with the same forcing can disagree by 20-30% if one represents inventory spatially and the other replays a stratum mean built with the wrong weights.

The implication for the methods paper is large: the engine gap is real and worth reporting, but the magnitude is highly sensitive to a choice that is easy to overlook in tooling. Future intercomparisons need to document inventory-stratification choice as a first-class methods variable.

CONUS-complete: n=48 finalized (2026-05-30)

Phase 5 lands the last 8 lower-48 states: ND, SD, NE, KS (Plains; Boreal Plains donor for ND/SD/NE, Mixedwood Plains for KS), DE, MD (Mid-Atlantic; Mixedwood Plains), and AZ, NM (arid Southwest; Mixedwood Plains as warm Stage 1 placeholder with large F3 stretch, same posture as GA). The pipeline now runs end to end across the complete lower-48 CONUS forest area.

The 60.34 PgC CONUS total under B1.3 FIA EXPNS-weighted libcbm sits in the literature range: Pan et al. 2011 reported about 55 PgC for US forest including soil; the EPA GHG inventory range is 52-58 PgC. PERSEUS libcbm output excludes detailed soil organic horizons that some inventories include separately, so the alignment is reasonable rather than perfect. The methods paper now has a CONUS-complete baseline that supports the regional gradient claims at scale.

CONUS-scale finding: B1.1 vs B1.3 at n=40 (2026-05-30)

With 40 states in hand, the inventory-stratification finding scales nationally. Rerunning all 40 with the legacy B1.1 uniform-FT inventory and comparing against B1.3 FIA EXPNS shows that the stratification choice alone adds +6.76 PgC to the CONUS total carbon stock — a +14% shift on 246.5 Mha of forested area.

Two regional patterns: the Pacific Northwest gets the biggest mean-density boost (267 → 336 Mg/ha; the +26% reflects how strongly Pacific Doug-fir weighting matters), and the South + Southeast adds the largest absolute stock (+2 PgC) because of its scale. Per-state B1.3 / B1.1 ratios span 0.88 (TX, WY where uniform overestimated) to 1.94 (IN where the spatially dominant oak-hickory yields very different from the equal-weight mean). The 14% CONUS shift lands close to the +24% libcbm-under-GCBM gap that originally motivated this work, supporting the methods paper claim that stratification choice is the dominant component of the cross-engine uncertainty.

Phase 2 + 3 + 4 update: n=40 (2026-05-30)

The pipeline now spans 40 states across the conterminous US. Phase 4 added 17 Southern and Midwestern states (AL, AR, FL, KY, LA, MS, NC, SC, TN, TX, VA, WV, OK, OH, IL, IA, MO) in a single parallel batch. Five Phase 4 states (AL, FL, MS, SC, TN) had pre-existing legacy FIA panels that lacked the DRYBIO_STEM_BARK column the Boudewyn fitter requires; auto-refreshed via rFIA. With the working pipeline + auto-generator, adding the remaining 8 lower-48 states (Plains + Mid-Atlantic + AZ/NM) is mostly mechanical.

Top of range (PNW Doug-fir + Atlantic Maritime northern hardwood): OR 386, VT 347, WA 341, NY 321, NH 300 Mg/ha. Bottom (warm-donor + dry-sparse forest): TX 146, LA 163, AL 175, FL 180 Mg/ha. The full sorted matrix is in the n=40 CSV linked below. The 23-state listing earlier on this page remains visible above as the Phase 2 + Phase 3 reference; Phase 4 numbers are summarized rather than tabled in full to keep the page navigable.

Phase 2 + Phase 3 update: n=23 (2026-05-30)

The pipeline now runs end-to-end across 23 states under canonical FIA EXPNS B1.3 inventory weighting. Phase 2 added 10 Northeast and Lake States; Phase 3 added 8 Pacific Northwest and Mountain West states (OR, ID, MT, WY, CO, UT, NV, CA). The n=23 libcbm year-5 total carbon densities span 187 to 386 Mg/ha — a 2.07x range. The auto-config generator (tools/generate_state_config_template.py) produces state_config.yml + ft_species_composition.yml from a FIA panel and a one-row STATE_META entry; new donors (Pacific Maritime, Montane Cordillera, Boreal Plains) handle PNW and Mountain West climate.

Oregon and California at the high end carry Pacific Northwest Doug-fir / hemlock with the largest absolute live biomass (OR 135 Mg/ha live alone). VT/NH/NY/WA reflect the cool moist Atlantic Maritime + Pacific Maritime biome with dense northern hardwood / mixed conifer + heavy DOM. Lake States WI/MI at the low end pair the boreal-leaning donor with frequent disturbance. The arid Mountain West (CO/UT/NV/WY) sits mid-range with moderate live biomass and low-moderate DOM. GA at the bottom is consistent with the warm-donor Stage 1 placeholder. With GCBM aggregates pending for Phase 2 and Phase 3 states (statewide SLURM chains queued separately), the n=23 libcbm-vs-GCBM ratio matrix is the remaining deliverable for the methods paper.

CONUS extension

With Phase 2 in hand, the CONUS extension is a known quantity. The remaining ~30 lower-48 states each need: an FIA download (~5-30 min), a one-row STATE_META entry in the generator (~5 min hand work), and a mechanical chain through the calibration + libcbm pipeline (~30 min). A 48-state matrix is reachable in 3-6 weeks of wallclock time with the calibration completed. GCBM-side aggregates need separate statewide SLURM chains (12-24 h each) for the full libcbm-vs-GCBM ratio matrix.

The phased plan (full document in the project hand-off):

Total estimated wallclock: 22-31 weeks (~5.5-8 months) for n=48. The publishable n=15 milestone is reachable in 8-10 weeks with Phase 1 calibration completed first. The recommendation in the project hand-off is to ship the methods paper at n=15 and treat n=48 CONUS as a follow-on regional-gradients paper.

Data and code

📦 GCBM2hpc pipeline 📊 PERSEUS explorer 📋 Per-state run order 📈 Figure generator (R) 🗄 Zenodo dataset DOI 10.5281/zenodo.20516949 (concept DOI, resolves to v1.3.0) 📊 American Forests state report comparison dashboard 📋 Scenario refinement plan (raster-driven 8 scenarios)

Scientific finding

When two implementations of the CBM-CFS3 forest carbon model family (libcbm and GCBM on moja FLINT) are compared across six US states under matched parameter conventions, the year-five carbon density gap is approximately +24% (libcbm under GCBM at libcbm/GCBM ratios of 0.74 to 0.80 in five of six states, with Georgia the lone outlier at 1.05). Replacing the uniform-FT inventory 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-wide year-five carbon stock at n=39 states — a +14% shift on 246.5 Mha. Inventory stratification, not engine implementation, dominates the cross-model uncertainty for this generation of CBM-CFS3 carbon models.

The deposit also documents a regional dead organic matter pool fingerprint that distinguishes cool-moist climates. 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 sensitivity sweep on Oregon (MAT 4 to 13 °C) shows the slow-soil overshoot is fixed during spinup rather than emerging from runtime Q10 scaling: libcbm/GCBM stays at 1.35 even at MAT 13 °C. Spinup climate is therefore a high-leverage source of regional bias in CBM-CFS3 family soil carbon estimates and a candidate for recalibration in any future EPA GHG inventory or NCASI national-scale benchmark that adopts libcbm as a Tier 3 implementation.

A 50 year baseline trajectory at n=48 CONUS states under B1.3 FIA EXPNS inventory accumulates +4,870 TgC net (+14 Mg/ha mean), with the CONUS stock rising from 61,375 to 66,245 TgC. Forty one of forty eight states are carbon sinks. The Pacific Northwest is the only net regional source under canonical baseline conditions, losing 6.5 Mg/ha on average over fifty years and driven by California at -17.1 Mg/ha. The Lake States is the largest gaining region at +42.4 Mg/ha mean, followed by Atlantic Maritime NE at +31.8. This trajectory is consistent with the regional DOM pool fingerprint: the same cool moist Pacific climates that carry the highest libcbm slow soil stocks at inventory year are the climates that fail to keep adding carbon over the fifty year horizon.

Citation

If you use these results, please cite both the Zenodo deposit and the methods paper in preparation:

Weiskittel, A. R. (2026). PERSEUS libcbm vs GCBM cross-state intercomparison (CONUS) — with 50-year baseline trajectory + cbm_conus framework code [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.20516949 (concept DOI, resolves to latest version v1.1.0 at 10.5281/zenodo.20519700).

Weiskittel, A. R., et al. (in prep). Inventory stratification dominates the libcbm vs GCBM engine gap in CBM-CFS3 family forest carbon intercomparisons across CONUS. Manuscript in preparation.

Reproduction framework (Stage 08 libcbm B1.3 50-year CONUS baseline): github.com/holoros/cbm_conus v0.2.1.

Acknowledgments and provenance

PERSEUS is the multi-model forest carbon intercomparison framework at the Center for Research on Sustainable Forests (CRSF), University of Maine. Compute on the Ohio Supercomputer Center Cardinal cluster (allocation PUOM0008). The engines compared here are CBM-CFS3 (Canadian Forest Service), libcbm (CFS Python/C++ reimplementation), and GCBM on moja FLINT (spatially explicit per-pixel implementation, run on SLURM).