General comments:

General Comments：

The manuscript introduces ForEdgeClim v1.0, a timely and computationally innovative 3D process-based model designed to simulate the complex microclimates of fragmented forest landscapes. The authors should be commended for their ambitious integration of high-resolution Terrestrial Laser Scanning (TLS) data into a voxel-based energy balance framework, which addresses a long-standing limitation in traditional 1D microclimate modeling. The open-source nature of the code and the successful demonstration of lateral thermal gradients provide a valuable foundation for the forest ecology community.

However, while the structural framework is impressive, the physical representation of certain dynamic processes requires further refinement. A primary concern is the current model’s reliance on several static assumptions—most notably regarding vegetation phenology, radiative transfer anisotropy, and the absence of advective heat transport. For a model aiming at "high-resolution" 3D simulation, the gap between structural complexity (via TLS) and process simplification remains significant. Major revisions are suggested to either incorporate these dynamic factors or, at a minimum, provide a rigorous discussion of how these limitations constrain the model’s applicability across different seasons and environments.

Specific Comments：

Point 1: The Paradox of Structural Simplification

The decision to omit the Clumping Index and LAD as independent parameters is a critical weakness. While the authors may frame this as a "trade-off" between computational efficiency and data availability, this logic is inherently flawed for a model utilizing TLS data. TLS is precisely the tool meant to resolve fine-scale clustering and structural orientation. By treating voxels as homogeneous turbid media, the model misses the "gap fraction" dynamics essential for edge effects, where lateral light penetration is governed by canopy gaps rather than bulk density. This simplification leads to a systematic underestimation of radiation transmission to the sub-canopy and biases the resulting thermal equilibrium.

Point 2: Incorporating Seasonal Phenology into Optical Properties

For a "process-based" model applied to deciduous forests, the assumption of static leaf optical properties (e.g., constant reflectance in the 400–700 nm range) may limit accuracy during transitional periods. In reality, leaf spectral signatures and Leaf Area Density (LAD) shift significantly from summer greenness to winter senescence. While I recognize that on-site spectral measurements may not always be available, a high-resolution 3D model should ideally account for this temporal resolution. At a minimum, the authors should explicitly discuss this limitation and its potential impact on energy balance during spring and autumn. If empirical leaf-off/leaf-on scaling is not feasible in v1.0, this should be identified as a critical area for future development.

Point 3: Physical Consistency of the 3D Radiative Transfer Scheme

The application of the two-stream approximation in a 3D voxelized framework at forest edges introduces a fundamental physical inconsistency. The classical two-stream theory (as presented in Eqs. 2-5) was originally derived for 1D horizontally homogeneous media, assuming an isotropic diffuse radiation field. However, at a forest edge, the radiative environment is characterized by extreme anisotropy due to the lateral influx of direct and diffuse solar radiation.

In Section 2.1.1 and Figure 1, the model handles lateral fluxes (Φx and Φy) by extending the vertical two-stream equations. While this approach simplifies the computation, it may not fully capture the directional nature of photon interception at the edge, which is governed by the specific geometry of canopy gaps and the solar position. Although the authors describe this as a "3D process-based" model, the current use of two-stream approximations for lateral fluxes effectively treats the complex 3D light field as a series of coupled 1D problems. This simplification could potentially limit the model's ability to accurately resolve the extinction coefficient and the resulting thermal gradients in highly heterogeneous transition zones.

Given that the model already operates on a 3D voxel grid, more advanced schemes such as the Discrete Ordinates (DO) method or Spherical Harmonics expansion would be more robust for discretizing the angular domain and capturing the non-isotropic nature of the edge radiation field. At a minimum, I recommend that the authors provide a more detailed justification or a sensitivity analysis to demonstrate the validity of the isotropic assumption, particularly at the immediate forest edge where the thermal gradient is most pronounced.

Summary Recommendation:

The model shows great promise, but the transition from a "static structural model" to a "dynamic process model" requires a more rigorous treatment of vegetation phenology and fluid dynamics.

General comments

This manuscript presents a new model for a 3D process-based microclimate model that incorporates both vertical and lateral energy fluxes to simulate forest microclimates. The authors provide a detailed description of the model structure, governing equations, and parameterization, together with a calibration procedure and a demonstration case study along a core-to-edge transect in a temperate forest in Belgium. The aim of the model is to provide a tool to better study microhabitats in forest edge-to-core transition zones.

The paper addresses a very timely and relevant scientific modelling question within the scope of GMD, namely the role of microclimate in governing ecological processes, and presents a novel modelling tool suitable for addressing important research questions in this area and in the broader scope of EGU. The work represents a substantial advancement in the field of microclimate modelling, as it brings in a whole new dimension/set of processes, and opens up potential for new research in ecology and climate science.

The methods and assumptions appear valid and are clearly outlined. The manuscript provides a clear and very detailed description of the governing equations, parameterizations, calibration and validation procedures, and overall model structure, illustrated by a schematic figure. In addition, the extensive supplementary material and the reference to the code repository on GitHub further support reproducibility and transparency. The code itself is well written and documented, meeting a high code quality standard. This level of detail should allow other researchers to reproduce and build upon the work. The assumptions and limitations of the approach are discussed in detail and are generally supported by previously published studies. The presented results are sufficient to support the interpretations and conclusions presented in the manuscript. The figures are generally well chosen and support the main messages of the paper.

The overall presentation is well structured and clear, and I enjoyed reading the manuscript. The title clearly reflects the contents of the paper, and the abstract provides a concise and complete summary of the study. The language is generally fluent and precise, although in a few places sentences appear a bit unclear or inconsistent , potentially due to the use of LLM for tidying the final version. Mathematical formulation, symbols, abbreviations, and units are generally defined and used appropriately. The number and quality of references are appropriate and the cited literature is recent. The supplementary material is extensive and of good quality, and appropriately complements the methods presented in the main text. I recommend minor revisions.

Specific comments

1. Time step / temporal representation

It was not entirely clear to me what temporal resolution the model operates at. From the introduction, my understanding is that the model assumes an equilibrium state; however, the manuscript also mentions a time interval of one hour. It would be helpful if the authors could clarify how time is represented in the model. Following that, I would be interested how would the model perform over multiple time steps potentially integrated with other ecological models, like a forest gap model.

2. Temperature only

Right now, the model only outputs temperature, which is common in this field. But since microclimates usually involve more than just temperature, it could be interesting to extend it to other variables too, like relative humidity or wind speed. Could you comment on the potential for the model to do so?