The ecological forecast limit revisited: Potential, actual and relative system predictability

Carsten F. Dormann; Ewan Pinnington; Florian Pappenberger; Jakob Albrecht; Marieke Wesselkamp; William J. Castillo

REVIEW 4 major objections 5 minor 15 references

The ecological forecast limit revisited: Potential, actual and relative system predictability

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper claims that forecast limits can be computed for practically any ecological forecast using just a verification reference, a scoring function, and a predictive error tolerance, and unifies potential, absolute, and relative limits…

desk verdict Useful synthesis and case studies, but the defining equations for forecast limits are wrong as written and need repair before this can be adopted. read the letter →

arxiv 2412.00753 v2 pith:ELPZSKHB submitted 2024-12-01 stat.AP physics.data-anq-bio.PEstat.ME

Marieke Wesselkamp , Jakob Albrecht , Ewan Pinnington , William J. Castillo , Florian Pappenberger , Carsten F. Dormann This is my paper · ORCID

classification stat.APphysics.data-anq-bio.PEstat.ME MSC 62P1262M20

keywords ecologicalforecastingforecastlimitpredictabilityverificationscoringfunctionsCRPSbenchmarkmodelsMAE

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that every ecological forecast can be assigned a forecast limit, the moment in time beyond which the forecast's predictive error is no longer acceptable, and that this limit is computable from three ingredients: a verification reference, a scoring function, and a predictive error tolerance. It distinguishes three kinds of limits: the potential limit using simulated verification in a perfect-model setting, the absolute limit using an ad-hoc error tolerance, and the relative limit using a benchmark or null model such as climatology. The authors test the framework on three case studies, a stochastic Ricker population model, the iLand forest ecosystem model, and the aiLand machine-learning land-surface emulator, and conclude that forecast limits are defined for practically any ecological forecast. A sympathetic reader would care because forecast limits convert vague claims about model skill into an interpretable statement of how far into the future a prediction can be trusted, which is what decision-makers need.

What carries the argument

The load-bearing machinery is the inequality $S(\varepsilon_t) \leq \varrho$ combined with the crossing-time identity $h = \mathrm{argmin}_t\,(S(\varepsilon_t) - \varrho)$. Here $S$ is any monotonic scoring function of the predictive error $\varepsilon_t = \hat{Y}_t - Y_t$, and $\varrho$ is the scoring tolerance, which can be a reference model's score, a skill-score threshold, or an ad-hoc error bound. For the relative limit the machinery becomes the skill-score ratio $1 - S(\varepsilon_t)/S(\varepsilon_t^R)$ with a benchmark $R$ such as climatology, and for the potential limit both verification and reference are drawn from the forecast model's own ensemble, so the framework needs no observations at all. The same pair $(S, \varrho)$ carries all three case studies: MAE for the iLand point forecast, CRPS for the Ricker and aiLand ensembles, and either a neighbour yield-class or the climatological distribution as the tolerance.

What would settle it

Take a synthetic forecast system with known ground truth, compute its forecast limit using the true state as verification, then recompute it using a degraded, noisy observation of that state; if the two limits differ substantially, the framework's reliance on verification fidelity is the source of error and the claim that limits are defined for any forecast fails for realistic observations.

Watch

Extended reading notes

Core claim

The paper's central claim is that the forecast limit, the lead time at which predictive error grows beyond acceptable tolerance, can be defined for essentially any ecological forecast, and that all existing definitions collapse into one formal scheme. The scheme requires exactly three ingredients: a verification reference Y_t, which may be observations or model simulations; a scoring function S that measures the discrepancy between forecast and verification, with the authors using mean absolute error for point forecasts and the continuous ranked probability score for ensembles; and a scoring tolerance ϱ that sets the acceptable error, either as a benchmark model or as an ad-hoc threshold. Depending on the choice of verification and tolerance, the same equation yields the relative forecast limit, where the forecast is no better than a reference model such as climatology; the potential forecast limit, a perfect-model upper bound with both verification and reference simulated from the model; and the absolute forecast limit, where the error exceeds an ad-hoc standard. The authors demonstrate all three in case studies and conclude that within this framework forecast limits are defined for practically any ecological forecast.

Load-bearing premise

The framework treats the verification reference as a faithful stand-in for the true ecosystem state, ignoring observation error; if that reference is noisy or itself model-derived, the computed forecast limit can describe the reference rather than the system.

Editorial extensions

If this is right

Forecast limits give ecological forecasts an interpretable trust horizon instead of a raw skill score at arbitrary lead times.
The relative forecast limit ranks models by how long they stay ahead of a benchmark such as climatology, making model comparison a statement about lead time.
The potential forecast limit estimates the upper bound of system predictability from within the model, requiring no observations at all.
The absolute forecast limit couples forecast evaluation to user-defined error tolerances, so adequacy becomes decision-specific.
The same three-requirement recipe works for population, ecosystem, and Earth-system models, so forecast limits become comparable across ecological scales.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the framework is correct, forecast limits could be reported as routinely as skill scores in ecological forecasting, giving every issued forecast an explicit expiry date in lead time.
A large gap between a model's potential and relative forecast limits would diagnose that model structure or observation error, not intrinsic system unpredictability, is the binding constraint, telling modellers where improved data or process representation would buy the most lead time.
The absolute limit's dependence on ad-hoc tolerances implies the same forecast can be adequate for one user and inadequate for another, so formalising the tolerance choice would turn forecast evaluation into a stakeholder-driven quantity.
Applying the framework to hindcasts with different verification types, direct observations versus model reconstructions, would quantify how much the reported limit is an artefact of the verification, testing the paper's reliance on verification fidelity.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Two things to know. First, this is a genuinely useful synthesis: it gives ecological forecasters a clear vocabulary (potential, absolute, relative forecast limits), recipe boxes, and three worked examples with code and data links. The aiLand relative-limit case uses independent station observations, which is the cleanest of the three. Second, the formal definitions of the forecast limit are wrong as written. Eq. 6 defines h as argmin(S−ϱ), which before the crossing selects the earliest lead time rather than the crossing. Eq. 7's step function, which fires only at exact equality, will never fire for continuous scores. In §2.3 the skill-ratio direction is inverted: S(εt)/S(εR_t)>1 means the forecast is worse than the reference, not better, and the claim that 1−S/S_R is monotonic by definition is contradicted by their own CRPSS curves. These are load-bearing because the paper says the framework defines forecast limits for practically any ecological forecast, but the three-type distinction and the case studies survive the bad equations—the fix is to replace argmin with something like min{t : S(εt)>ϱ}.

What is genuinely new: the three-way distinction and the decision tree are a reasonable repackaging of existing forecast-horizon and skill-horizon concepts from Petchey et al. and Buizza and Leutbecher. That is not a flaw for a synthesis; the novelty is modest but real in packaging and terminology. The case studies demonstrate the framework concretely, and the limitations section is unusually honest, especially on observation error and the iLand yield-table reconstruction. The iLand absolute limit is partly a consistency check against yield tables, as the authors concede, not an independent verification.

Who this is for: applied ecologists and forecasting practitioners who want a checklist for reporting how far ahead their models are useful. It deserves a serious referee because the framework is standardizable and the errors are repairable. I would not cite the current version in my own work. If they fix the equations and make the yield-table tolerance framing explicit, I'd likely cite it.

Referee Report

4 major / 5 minor

Summary. The paper proposes a unified framework for defining empirical forecast limits in ecological forecasting, distinguishing potential, absolute, and relative forecast limits. The framework rests on three ingredients: a verification reference Y_t, a scoring function S, and a predictive-error tolerance ϱ. The authors formalize the forecast limit as the first time the score crosses the tolerance, provide recipes and a decision tree, and demonstrate the three limit types with a stochastic Ricker population model, the iLand forest model, and the aiLand land-surface emulator. The central claim is that, within this framework, forecast limits can be defined for practically any ecological forecast.

Significance. The taxonomic distinction among potential, absolute, and relative forecast limits, and the attempt to unify existing practices under common formal definitions, is a useful contribution to ecological forecasting. The paper is commendably concrete: it provides a decision tree, worked case studies at three ecological scales, and code/data availability statements, and it candidly acknowledges several limitations, including model-conditionality of limits and observation error. However, the central formalization in Sections 2.2 and 2.3 is internally inconsistent as written: the defining equations do not return the first crossing of the tolerance, and one sign argument is inverted. Because the equations are the paper's principal claimed contribution, they need to be corrected before the framework can be accepted.

major comments (4)

[§2.2, Eq. (6)–(7)] Equation (6), h = argmin_t (S(ε_t) − ϱ), does not define the first time the acceptable condition S(ε_t) ≤ ϱ fails. Before the crossing, S(ε_t) − ϱ is negative, so the argmin over t will typically be the lead time with the smallest score, not the crossing time. Equation (7), with γ = 1 only when S(ε_t) − ϱ = 0, cannot represent the transition from acceptable to unacceptable unless the score exactly equals the tolerance at the boundary. The correct definition would be h = min{t ∈ T_i : S(ε_t) > ϱ}, with a corresponding step function that switches from 0 to 1 at that time. Because all three limit types inherit this definition, this error is load-bearing.
[§2.3, Eqs. (9)–(10)] The relative-skill formulation contains a sign error and an invalid argmin. For an error score, S(ε_t)/S(ε_R_t) > 1 means the forecast model is worse than the reference, yet the text states that the more the ratio exceeds 1, the better the forecast performance. Equation (10), h_r = argmin_t (1 − S(ε_t)/S(ε_R_t)), selects the time where the skill expression is most negative, i.e. where the forecast model is worst relative to the reference, not the first time skill drops to zero. The intended object is the first lead time at which S(ε_t) > S(ε_R_t), equivalently the first time the skill score drops below zero.
[§2.3, monotonicity claim] The statement 'By definition, the expression in the brackets of 10 is monotonic' is not correct for the quantities used in this paper, and it is contradicted by the paper's own results. The CRPSS time series shown in Figures 5B, 6B, 7B, and 8B oscillate around zero and cross the threshold more than once; for example, the surface-layer soil temperature series in Figures 5B and 7B repeatedly leaves and re-enters the skilful region. The 'first crossing' of a non-monotonic curve is still definable, so the framework does not require monotonicity, but the claim as written should be removed or replaced by a statement about the first crossing.
[§3.2 and §4.6, iLand verification] The iLand case study demonstrates the absolute forecast limit, but both the verification Y_t and the tolerance ϱ are derived from the same yield-table model: Y_t is reconstructed from yield tables, and the tolerance is the neighbouring yield class k ± 1. Consequently, the computed absolute limit partly measures the distance to an alternative yield-table trajectory rather than the distance to the observed ecosystem state. The manuscript acknowledges this in Sections 4.2 and 4.6, and the acknowledgment is appreciated, but the issue should also be stated where the framework's generality is claimed, because the trustworthiness of the verification is a precondition for the central claim that forecast limits are defined for any ecological forecast.

minor comments (5)

[§2.2, Eq. (12)] Equation (12) defines S_AE_t = ϱ − |ε_t| and says the forecast limit is reached when AE_t < 0, which is consistent with the tolerance crossing, but the preceding text says 'the forecast limit is reached when AEt < 0' while Eq. (5) frames acceptability as S(ε_t) ≤ ϱ; the use of strict versus non-strict inequalities should be harmonized throughout.
[§2.3, Eq. (8)] Equation (8), S(ε_t) ≤ ϱ ≤ S(ε_t^R), is stated as a chain of inequalities, but ϱ is introduced as a scalar threshold; if the reference score is below the threshold, the chain cannot hold. The intended meaning is presumably that the reference defines the threshold at each lead time, i.e. ϱ(t) = S(ε_t^R).
[§3.3, results text] In the second paragraph of Section 3.3, 'CPRPSS' is a typo for 'CRPSS'.
[Figure 7 caption] The caption begins 'The resulting heat maps in Figures 7 and 7 A reveal...' and later refers to 'Figures 7 and 7 A'; the duplicated reference should be corrected to point once to the figure.
[Appendix B, Table 3] In the station list, the soil type for LaGrandCombe is given as 'LaGrandCombe', which appears to be a placeholder or typo; the soil type should be completed or omitted.

Circularity Check

1 steps flagged · score 2.0 of 10

No central circularity; one acknowledged yield-table self-reference in the iLand case study keeps the finding in the mild range.

other [Section 3.2 (iLand setup) and Section 4.2 (iLand discussion); Appendix B.4 Algorithm]
"However, with only three inventory measurements per stand over a 30-year span, we relied on reconstructed observations from yield tables as the most viable alternative, which, strictly speaking, are also model-based (see description in the Appendix, Case Study 2); thus, this analysis cannot be used to draw definitive conclusions about iLand, but it demonstrates what we defined as absolute forecast limit."

The absolute forecast limit is computed from Eq. 2 using a verification Y_t reconstructed from regional yield tables, while the tolerance ϱ(t,k) is the neighboring yield class k±1 from the same tables. The first-crossing computation therefore measures how long iLand's simulated dominant height stays within the spread of adjacent yield-table curves, i.e. consistency with the same yield-table system used to build the verification, rather than an independent ecosystem predictability estimate. The authors explicitly concede the verification is model-based and that the analysis is a demonstration. Because the central framework does not depend on this case study, this is a minor, contained circularity rather than a load-bearing one.

full rationale

The central derivation is self-contained: the forecast limit is defined as the first lead time at which a scoring function exceeds a tolerance (Eqs. 5-6), and the three limit types are obtained by choosing whether the verification is observed or simulated and whether the tolerance is a reference model or an ad-hoc value. No parameter is fitted to the reported limits and then renamed as a prediction; the potential Ricker limit is model-intrinsic by construction and the paper states that forecast limits are conditional on M (Section 4.6). The self-citations to Wesselkamp et al. (2025) describe the aiLand emulator used in one case study but do not supply the formal framework, so they are not load-bearing. The only mild self-referential element is the iLand case: verification Y_t is reconstructed from regional yield tables and the tolerance is the neighboring yield class from the same tables, so the absolute limit partly measures consistency with that yield-table system. The authors explicitly acknowledge this, so it is a contained limitation rather than a hidden circularity in the central claim. The mathematical issues with Eqs. 6 and 10 (argmin selecting minimum score rather than first crossing, and the direction statement in Sec. 2.3) are correctness defects, not circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The framework itself introduces no fitted constants and no new physical entities. The free parameters are choices of tolerance and uncertainty magnitudes that directly shape the reported limits. The main axioms are domain assumptions about spread-error correlation, observation-error neglect, and model fidelity in perfect-model experiments. One ad-hoc assumption, monotonicity of skill scores, is contradicted by the paper's own figures.

free parameters (3)

iLand tolerance rho = Neighboring yield classes k +/- 1
Chosen by hand as a conservative ad-hoc standard for the absolute forecast limit in forest productivity; forecast limits in Figure 4 depend strongly on this choice, since a tolerance of two neighboring classes extends limits to the full horizon.
Ricker Monte Carlo perturbation CVs = Growth rate and carrying capacity CV = 0.03, initial condition CV = 0.001
The spread of the forecast ensemble, and therefore the potential forecast limit, is set by these chosen noise levels; no sensitivity analysis is reported.
aiLand Monte Carlo perturbation settings = Initial state CV = 0.05, dropout rate 18%
The relative forecast limits for soil temperature and moisture depend on these chosen uncertainty propagation settings; the paper notes the network was not trained for probabilistic forecasting.

assumptions (4)

domain assumption Predictability is inversely related to forecast uncertainty, so wider forecast distributions imply larger predictive error.
Stated in Section 1 and Section 4.1 citing Hopson 2014; the relative and potential limits inherit this assumption, which is not always valid.
domain assumption The verification reference Y_t is the best approximation to the true state and observation error can be neglected.
Equation 2 defines predictive error against Y_t; Section 4.6 concedes the framework neglects observation error and that real verification data are often mixtures of models and observations.
ad hoc to paper The relative skill score 1 - S_M/S_R is monotonic in lead time, so the first crossing defines the forecast limit uniquely.
Equation 10 and the surrounding text assert monotonicity by definition, but case-study CRPSS curves in Figures 5 to 8 are non-monotonic; this is an unproven simplifying assumption.
domain assumption For potential forecasts, the model M perfectly describes the process, so a simulated trajectory can stand in for observations.
Section 3.1 uses a perfect-model setting and Section 4.6 notes potential limits are conditional on M, so the upper-bound interpretation depends on model fidelity.

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Cite this review

Pith. "Pith review of The ecological forecast limit revisited: Potential, actual and relative system predictability." pith.science (2026). https://pith.science/paper/ELPZSKHB

@misc{pith2026241200753,
  author       = {Pith},
  title        = {Pith review of: The ecological forecast limit revisited: Potential, actual and relative system predictability},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ELPZSKHB}},
  note         = {Machine review of arXiv:2412.00753}
}

read the original abstract

Ecological forecasts are model-based statements about currently unknown ecosystem states in time or space. For a model forecast to be useful to inform decision makers, model validation and verification determine adequateness. The measure of forecast goodness that can be translated into a limit up to which a forecast is acceptable is known as the 'forecast limit'. While verification in weather forecasting follows strict criteria with established metrics and forecast limits, assessments of ecological forecasting models still remain experiment-specific, and forecast limits are rarely reported. As such, users of ecological forecasts remain uninformed of how far into the future statements can be trusted. In this work, we synthesise existing approaches to define empirical forecast limits in a unified framework for assessing ecological predictability and offer recipes for their computation. We distinguish the model's potential and absolute forecast limit, and show how a benchmark model can help determine its relative forecast limit. The approaches are demonstrated with three case studies from population, ecosystem, and Earth system research. We found that forecast limits can be computed with three requirements: A verification reference, a scoring function, and a predictive error tolerance. Within our framework, forecast limits are defined for practically any ecological forecast and support research on ecological predictability analysis.

Figures

Figures reproduced from arXiv: 2412.00753 by the authors.

**Figure 2.** Decision tree for forecast limits: Depending on different choices of verification, scoring reference, [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗

**Figure 3.** The forecast limit in a case study with the stochastic Ricker model. [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗

Figures from the paper (10 more)

**Figure 4.** Figure 4: Absolute forecast limits for the dominant height of five tree species, forecasted with iLand, starting at a [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]

**Figure 5.** Figure 5: Soil temperature [K]. (A) Upper panel: Single, 6-hourly ensemble forecast over two weeks for one station (Condom-en-Armagnac) from 1) the aiLand (gray), propagating initial and model-structural uncertainty, and 2) the station climatological distribution (blue). aiLand …

**Figure 6.** Figure 6: Soil moisture [m3m−3 ]. (A) Upper panel: Single, 6-hourly ensemble forecast over two weeks for one station (Condom-en-Armagnac) from 1), the aiLand (gray), propagating initial and model-structural uncertainty and 2) the station climatological distribution (blue). aiLan…

**Figure 7.** Figure 7: Soil temperature [K]. A. Skill (CRPSS) over a forecast horizon of two weeks (312 hours) from varying initialisation times: Approx. nine weeks at 6-hourly resolution, starting on February 1st, 2022, 00:00:00. Light pattern indicates skilful forecasts, while dark pattern…

**Figure 8.** Figure 8: Soil moisture [m3m−3 ]. A. Skill (CRPSS) over a forecast horizon of eight weeks (1248 hours) from varying initialisation times: Approx. nine weeks at 6-hourly resolution, starting on February 1st, 2022, 00:00:00. Light pattern indicates skilful forecasts, while dark pa…

**Figure 9.** Figure 9: Correlation of observed and simulated dominant heights at age 100. [PITH_FULL_IMAGE:figures/full_fig_p029_9.png]

**Figure 10.** Figure 10: A. Forecast of aiLand and of numerical ecLand for soil temperature measurements across stations at the surface and two subsurface layers over a medium-range test period in February 2022. B. Actual forecast limits of aiLand and ecLand with a tolerance of 1.5 K toward t…

**Figure 11.** Figure 11: Seasonal-range soil moisture forecasts (A) actual (B) and relative (C) forecast limits, aggregated over eight [PITH_FULL_IMAGE:figures/full_fig_p032_11.png]

**Figure 12.** Figure 12: The actual (ha) and relative (hr) empirical forecast limits computed for ecLand and aiLand at three different soil layers. Variability in the box plots refers to variability over network stations. The median limits (orange) indicate an increasing predictability toward…

**Figure 13.** Figure 13: The actual (ha) and relative (hr) empirical forecast limits computed for ecLand and aiLand at three different soil layers. Variability in the box plots refers to variability over network stations. The median limits (orange) indicate an increasing predictability toward…

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Works this paper leans on

15 extracted references · 13 canonical work pages

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After this time period, the information from the observations seems to vanish and aiLand approaches the physical model again. Discussion We demonstrate actual and relative forecast limits (bha, bhr) of an ensemble of ecLand emulators, aiLand, on the medium-range, testing suitability for weather forecasting. With an scoring tolerance of 1.5 Kelvin we compu...

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B.4 Algorithm for determining the forecast limit Algorithm 1 Generalized process to receive ˆh(g) for all tree species, where g is the observed yield class of a stand. 1: for each species in tree species do 2: Get forecast and observations for species 3: ˆY ← dominant_height_f...

2021
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Management interventions were implemented using the agent-based forest management model ABE (Rammer and Seidl, 2015), which is fully integrated into the iLand simulation framework

and we used the default model parameters from the iLand 1.0 example landscape. Management interventions were implemented using the agent-based forest management model ABE (Rammer and Seidl, 2015), which is fully integrated into the iLand simulation framework. A detailed descri...

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and downscaled from 5x5 km to 100x100 m, following Thom et al. (2022). The method is based on the relationship between climatic variables and elevation. A daily laps rate 27 THE ECOLOGICAL FORECAST LIMIT REVISITED for each climate variable is calculated and then used for downs...

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B. Actual forecast limits of aiLand and ecLand with a tolerance of 1.5 K toward the predictive MAE on the station measurements. C. Relative forecast limit based on the MAE-SS of aiLand toward ecLand. Gray shaded regions indicate areas where the MAE of aiLand is smaller than th...

2022
[2025]

which we use to demonstrate both absolute and relative forecast limits (Buizza and Leutbecher, 2015). aiLand refers to three models: A feed forward neural network, a long short-term memory neural network and an extreme gradient boosting algorithm (Wesselkamp 29 THE ECOLOGICAL ...

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

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B., White, E

Adler, P. B., White, E. P., and Cortez, M. H. (2020). Matching the forecast horizon with the relevant spatial and temporal processes and data sources. Ecography, 43(11):1729–1739. Albrich, K., Rammer, W., Thom, D., and Seidl, R. (2018). Trade-offs between temporal stability and level of forest ecosystem services provisioning under climate change. Ecologic...

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Jolliffe, I

ed edition. Jolliffe, I. T. and Stephenson, D. B., editors (2003). Forecast verification: a practitioner’s guide in atmospheric science. J. Wiley, Chichester, West Sussex, England ; Hoboken, NJ. Judd, K., Reynolds, C. A., Rosmond, T. E., and Smith, L. A. (2008). The geometry of model error. Journal of the Atmospheric Sciences, 65(6):1749–1772. Jørgensen, ...

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(2019b) and Dorigo et al

For more information, see Zhang et al. (2019b) and Dorigo et al. (2021). The station data were matched with the according grid cell of the physiographic and climate fields, which were used to force the models during the period. Station data were further resampled to the 6-hourly resolution of the forcing data for evaluation and standardised by z-scoring w...

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Getz, W. M., Marshall, C. R., Carlson, C. J., Giuggioli, L., Ryan, S. J., Romañach, S. S., Boettiger, C., Chamberlain, S. D., Larsen, L., D’Odorico, P., and O’Sullivan, D. (2018). Making ecological models adequate. Ecology Letters, 21(2):153–166. Gneiting, T. and Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation.Journal of t...

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Bergwald

were used, which are within the Freiburger Stadtwald (only the “Bergwald” inventory plots were available). Each species measured in a plot is assigned with a SI100 using yield tables, which provide a SI100 estimate based on dominant height and age. Based on the SI100 the dominant height growth of each individual tree is reconstructed from yield tables ove...

work page 1951

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=0 "=1 "=0

and 20-30 cm (subsurface layer 2). aiLand is exemplarily initialised with observations on February 1st 2022, constituting initial conditions Y0, and is then integrated at a 6-hourly temporal resolution over τ = 1, · · ·56 lead times, representing the medium-range. For long-range forecasts τ = 1, · · ·1200 and soil water volume results, see Appendix. Actua...

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After this time period, the information from the observations seems to vanish and aiLand approaches the physical model again. Discussion We demonstrate actual and relative forecast limits (bha, bhr) of an ensemble of ecLand emulators, aiLand, on the medium-range, testing suitability for weather forecasting. With an scoring tolerance of 1.5 Kelvin we compu...

work page 2025

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