# Yunzhe Liu: Assessment of emergent spatial variability of community-integrated grazing dynamics in CMIP6 models

**URL:** <https://forum.access-hive.org.au/t/yunzhe-liu-assessment-of-emergent-spatial-variability-of-community-integrated-grazing-dynamics-in-cmip6-models/7245>\
**Category:** COSIMA/Cryosphere Workshop 2026 Lightning Talks\
**Created:** [5 November 2026 22:10 UTC](https://forum.access-hive.org.au/t/yunzhe-liu-assessment-of-emergent-spatial-variability-of-community-integrated-grazing-dynamics-in-cmip6-models/7245 "2026-11-05T22:10:49Z")\
**Posts on this page:** 1\
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**Author:** ![lachlanswhyborn](https://sea2.discourse-cdn.com/flex020/user_avatar/forum.access-hive.org.au/lachlanswhyborn/32/1837_2.png) [@lachlanswhyborn](https://forum.access-hive.org.au/u/lachlanswhyborn)\
**Post date:** [5 November 2026 22:10 UTC](https://forum.access-hive.org.au/t/yunzhe-liu-assessment-of-emergent-spatial-variability-of-community-integrated-grazing-dynamics-in-cmip6-models/7245/1 "2026-11-05T22:10:49Z")

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Yunzhe Liu: Assessment of emergent spatial variability of community-integrated grazing dynamics in CMIP6 models

Zooplankton grazing is the primary top-down control on phytoplankton biomass and influences primary production. Grazing dynamics are a key characteristic of zooplankton grazing, describing how zooplankton-specific grazing rates respond to phytoplankton carbon concentration. Evaluating and constraining grazing dynamics is therefore essential for improving the representation of top-down control on phytoplankton in ocean biogeochemical models. However, it is difficult to evaluate and compare community-integrated grazing dynamics across models, which incorporate a varying degree of zooplankton functional groups, trophic interactions, and mechanistic complexity in their grazing formulations. To address this, we developed a new diagnostic, the Apparent Community Functional Response (ACFR). The ACFR quantifies emergent community-integrated grazing dynamics and can be described by three empirical ‘apparent parameters’. We quantified the ACFR and its apparent parameters for 11 CMIP6 models and evaluated them against three observation-informed estimates of analogous parameters. We found that incorporating multiple phytoplankton groups alone, without explicit competition among multiple zooplankton groups, is insufficient to reproduce realistic, broad-scale patterns in ACFR. Spatial variability in grazing dynamics improves in models with multiple phytoplankton and zooplankton functional groups. Temperature-dependent grazing further improves agreement with observation-informed estimates, particularly in COBALTv2 and PISCESv2, which exhibit the highest skill. The ACFR framework provides a standardized method for evaluating model performance in reproducing community-level grazing dynamics across models with increasingly diverse food webs and grazing formulations.