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Microbially-explicit models may improve understanding and projections of carbon dynamics in response to future climate change, but their fidelity in simulating global-scale soil heterotrophic respiration (RH ), a stringent test for soil biogeochemical models, has never been evaluated. We used statistical global RH products, as well as 7,821 daily site-scale RH measurements, to evaluate the spatio-temporal performance of one first-order decay model (CASA-CNP) and two microbially-explicit biogeochemical models (CORPSE and MIMICS) that were forced by two different input datasets. CORPSE and MIMICS did not provide any measurable performance improvement; instead, the models were highly sensitive to the input data used to drive them. Spatial variability in RH fluxes was generally well simulated except in the northern middle latitudes (~50°N) and arid regions; models captured the seasonal variability of RH well, but showed more divergence in tropic and arctic regions. Our results demonstrate that the next generation of biogeochemical models shows promise, but also needs to be improved for realistic spatio-temporal variability of RH . Finally, we emphasize the importance of net primary production, soil moisture, and soil temperature inputs, and that jointly evaluating soil models for their spatial (global scale) and temporal (site scale) performance provides crucial benchmarks for improving biogeochemical models.

The pre-disturbance vegetation characteristics that predict carbon (C) cycling responses to disturbance are not well known. To address this gap, we initiated the Forest Resilience Threshold Experiment, a manipulative study in which more than 3600 trees were stem girdled to achieve replicated factorial combinations of four levels (control, 45, 65, and 85% gross defoliation) of disturbance severity and two disturbance types (targeting upper or lower canopy strata). Applying a standardized stability framework in which initial C cycling resistance to disturbance was calculated as the first-year natural log response ratio of disturbance and control treatments, we investigated to what extent pre-disturbance levels of species diversity, aboveground woody biomass, leaf area index, and canopy rugosity—a measure of structural complexity—predict the initial responses of subcanopy light-saturated leaf CO2 assimilation (Asat), aboveground wood NPP (ANPPw), and soil respiration (Rs) to phloem-disrupting disturbance. In the year following stem girdling, we found that above-ground C cycling processes, Asat and ANPPw, were highly resistant to increases in disturbance severity, while Rs resistance declined as severity increased. Disturbance type had no effect on first-year resistance. Pre-disturbance aboveground woody biomass, and canopy rugosity were positive predictors of ANPPw resistance and, conversely, negatively related to Rs resistance. Subcanopy Asat resistance was not related to pre-disturbance vegetation characteristics. Stability of C uptake processes along with Rs declines suggest the net C sink was sustained in the initial months following disturbance. We conclude that biomass and complexity are significant, but not universal, predictors of initial C cycling resistance to disturbance. Moreover, our findings highlight the utility of standardized stability measures when comparing functional responses to disturbance.

Forests dominate the global terrestrial carbon budget, but their ability to continue doing so in the face of a changing climate is uncertain. A key uncertainty is how forests will respond to (resistance) and recover from (resilience) rising levels of disturbance of varying intensities. This knowledge gap can optimally be addressed by integrating manipulative field experiments with ecophysiological modeling. We used the Ecosystem Demography-2.2 (ED-2.2) model to project carbon fluxes for a northern temperate deciduous forest subjected to a real-world disturbance severity manipulation experiment. ED-2.2 was run for 150 years, starting from near bare ground in 1900 (approximating the clear-cut conditions at the time), and subjected to three disturbance treatments under an ensemble of climate conditions. Both disturbance severity and climate strongly affected carbon fluxes such as gross primary production (GPP), and interacted with one another. We then calculated resistance and resilience, two dimensions of ecosystem stability. Modeled GPP exhibited a two-fold decrease in mean resistance across disturbance severities of 45%, 65%, and 85% mortality; conversely, resilience increased by a factor of two with increasing disturbance severity. This pattern held for net primary production and net ecosystem production, indicating a trade-off in which greater initial declines were followed by faster recovery. Notably, however, heterotrophic respiration responded more slowly to disturbance, and it's highly variable response was affected by different drivers. This work provides insight into how future conditions might affect the functional stability of mature forests in this region under ongoing climate change and changing disturbance regimes.

The concept of stability is central to the study and sustainability of vital ecosystem goods and services as disturbances increase globally. While ecosystem ecologists, including carbon (C) cycling scientists, have long‐considered multiple dimensions of disturbance response, our discipline lacks an agreed‐upon analytical framework for characterizing multidimensional stability. Here, we advocate for the broader adoption of a standardized and normalized multidimensional stability framework for analyzing disturbance response. This framework includes four dimensions of stability: the degree of initial change in C fluxes (i.e., resistance); rate (i.e., resilience) and variability (i.e., temporal stability) of return to pre‐disturbance C fluxes; and the extent of return to pre‐disturbance C fluxes (i.e., recovery). Using this framework, we highlight findings not readily seen from analysis of absolute fluxes, including trade‐offs between initial and long‐term C flux responses to disturbance; different overall stability profiles among fluxes; and, using a pilot dataset, similar relative stability of net primary production following fire and insect disturbances. We conclude that ecosystem ecologists’ embrace of a unifying multidimensional stability framework as a complement to approaches focused on absolute C fluxes could advance global change research by aiding in the novel interpretation, comprehensive synthesis, and improved forecasting of ecosystems’ response to an increasing array of disturbances.

Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state‐of‐the‐art knowledge in an internally self‐consistent modeling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain‐specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low‐emissions SSP1‐1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850–1900, using an observationally based historical warming estimate of 0.8°C between 1850–1900 and 1995–2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above preindustrial in the long‐term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections., Key Points Probabilistic global‐mean temperature projections often use reduced complexity climate models (RCMs) because of their low computational costWe evaluate how well RCMs’ probabilistic setups can synthesize knowledge from multiple research domains for policy relevant projectionsNo RCM is able to capture all forcing, warming, heat uptake and carbon cycle metrics we evaluate, however some come close across a range