FCO2 Grazing Flux Issue In NorESMhub CTSM: A Deep Dive

Introduction to the Grazing Flux Issue

The grazing flux issue within the FCO2 (Flux Coupling of Ocean-Atmosphere) calculations in the NorESMhub CTSM (Community Terrestrial Systems Model) has recently come to light, sparking significant discussion and investigation. This article delves into the specifics of this issue, its potential causes, and the steps being taken to resolve it. Understanding the nuances of this problem is crucial for climate modelers and researchers who rely on accurate carbon cycle simulations. The core of the issue, as initially observed by @kjetilaas, lies in the divergence between FCO2 and the calculated Net Biome Production (NBP) flux when grazing is active. However, this discrepancy vanishes when grazing is turned off, suggesting a direct link between grazing processes and the inaccurate FCO2 calculations. This observation is not just a minor modeling quirk; it potentially signifies a misrepresentation of carbon dynamics within the ecosystem, which could have cascading effects on broader climate projections. Therefore, a meticulous examination of the underlying mechanisms and model formulations is essential.

Visual Evidence of the Discrepancy

To illustrate the problem, graphical representations were provided, clearly showing the divergence in FCO2 flux when grazing is enabled versus when it is disabled. These visuals serve as a compelling starting point for understanding the scope and nature of the issue. The first image demonstrates the stark contrast in FCO2 behavior under different grazing scenarios, immediately highlighting the anomaly. The second image further elaborates on this, providing a more detailed view of the fluxes and their interactions. These visual aids are invaluable for both diagnosing the problem and communicating its significance to the wider scientific community. By visually presenting the data, it becomes easier to identify patterns, anomalies, and potential correlations that might otherwise be obscured in numerical outputs alone. This approach not only aids in the immediate investigation but also sets a precedent for transparent and accessible scientific communication.

Locating the Calculations: EDPhysiologyMod.F90 and FatesHistoryInterfaceMod.F90

The investigation led to pinpointing the locations where the relevant variables are calculated within the Fates model. The interface variable calculation resides in EDPhysiologyMod.F90, while the history variable calculation is found in FatesHistoryInterfaceMod.F90. Both calculations utilize cohort%leaf_herbivory, a metric that is exclusively computed within the EDPhysiology module. To fully grasp the discrepancy, it's imperative to dissect the formulas used in both locations. The interface calculation uses the following formula:

bc_out%grazing_closs_to_atm_si = bc_out%grazing_closs_to_atm_si + leaf_herbivory * (1._r8 - herbivory_element_use_efficiency) * currentCohort%n * ha_per_m2 * days_per_sec

On the other hand, the history calculation employs this formula:

hio_grazing_si(io_si) = hio_grazing_si(io_si) + leaf_herbivory * n_perm2 / days_per_year / sec_per_day

These formulas, while seemingly similar, exhibit subtle yet crucial differences that contribute to the observed divergence. Understanding these differences requires a close examination of each component and how they interact within the broader model framework. The goal is to reconcile these calculations to ensure consistency in how grazing fluxes are represented across different model components.

Simplifying the Equations: The Role of herbivory_element_use_efficiency

Further analysis revealed that the parameter herbivory_element_use_efficiency is set to 0 in the parameter file. This simplification allows for a more streamlined comparison of the two equations. By eliminating this term, the equations become:

History:

leaf_herbivory  * currentCohort%n * ha_per_m2 * days_per_sec

Interface:

leaf_herbivory * n_perm2 / days_per_year / sec_per_day

This simplification underscores the core discrepancy between the two calculations, focusing attention on the remaining terms. It becomes apparent that the disparity primarily stems from differences in how time is accounted for in the two calculations. This realization is a critical step in narrowing down the source of the error and formulating a targeted solution.

Identifying the Time Unit Discrepancy

A critical step in the analysis involved recognizing that currentCohort%n * ha_per_m2 is equivalent to n_perm2 (where cohort%n represents individuals per hectare, and n_perm2 represents individuals per square meter). This equivalence allows for the cancellation of these terms, further isolating the time unit differences. The remaining time units are:

History:

days_per_sec = 1.0/86400

Interface:

days_per_year / sec_per_day = 365/86400

These values, derived from FatesConstantsMod.F90, reveal that the interface calculation appears to be 365 times larger than the history calculation. This significant numerical discrepancy strongly suggests a misrepresentation or miscalculation of temporal scaling within the model. However, this finding doesn't fully align with the initial observation that the grazing flux seemed insufficient in the interface. This apparent contradiction highlights the complexity of the issue and the need for further investigation to reconcile these findings.

Addressing the Discrepancy and Future Steps

While the time unit analysis provides valuable insight, the initial observation suggested that the grazing flux in the interface was too small, not too large. This discrepancy necessitates a more thorough examination of the calculations and potentially other factors influencing the grazing flux. To address these concerns, the next logical step is to unify the calculation methods for these variables, ensuring consistency across the model. The proposed approach involves creating a dedicated branch to test the impact of these changes on FCO2 fluxes, specifically in a 4x5 configuration. This controlled experiment will allow for a direct assessment of the effects of the modifications, providing empirical evidence to support or refute the proposed solutions. Such systematic testing is essential for ensuring the accuracy and reliability of climate models.

Implications and Broader Context

The grazing flux issue, while seemingly specific, has broader implications for climate modeling. Accurate representation of carbon cycling processes, including herbivory, is crucial for reliable climate projections. Miscalculations in these processes can lead to significant errors in simulated carbon fluxes, affecting the overall accuracy of the model. Therefore, resolving this issue is not merely a matter of technical correction; it is a necessary step towards improving the predictive capabilities of climate models. Furthermore, this investigation underscores the importance of interdisciplinary collaboration in climate research. By bringing together expertise in terrestrial ecology, biogeochemistry, and computational modeling, researchers can more effectively address complex challenges such as this. This collaborative approach fosters a deeper understanding of the intricate interactions within the Earth system and facilitates the development of more robust and reliable climate models.

Conclusion: Towards Accurate Climate Models

The investigation into the grazing flux discrepancy in NorESMhub CTSM exemplifies the meticulous and iterative nature of climate model development. Identifying the time unit discrepancy is a significant step forward, but further work is needed to reconcile the apparent contradiction with the initial observations. By unifying the calculation methods and conducting controlled experiments, researchers aim to refine the model and improve the accuracy of FCO2 flux simulations. This ongoing effort highlights the commitment to creating reliable and robust climate models that can inform our understanding of the Earth system and guide climate action. Addressing these challenges not only enhances the credibility of climate models but also contributes to a more comprehensive understanding of the complex interplay between ecological processes and the global carbon cycle. The journey towards accurate climate models is a continuous process of discovery, refinement, and collaboration, and this investigation serves as a testament to that endeavor.

For more information on climate modeling and related research, visit trusted websites such as the National Center for Atmospheric Research (NCAR).