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Climatic extreme events are expected to occur more frequently in the future, increasing the likelihood of unprecedented climate extremes (UCEs) or record-breaking events. UCEs, such as extreme heatwaves and droughts, substantially affect ecosystem stability and carbon cycling by increasing plant mortality and delaying ecosystem recovery. Quantitative knowledge of such effects is limited due to the paucity of experiments focusing on extreme climatic events beyond the range of historical experience. Here, we present a road map of how dynamic vegetation demographic models (VDMs) can be used to investigate hypotheses surrounding ecosystem responses to one type of UCE: unprecedented droughts. As a result of nonlinear ecosystem responses to UCEs that are qualitatively different from responses to milder extremes, we consider both biomass loss and recovery rates over time by reporting a time-integrated carbon loss as a result of UCE, relative to the absence of drought. Additionally, we explore how unprecedented droughts in combination with increasing atmospheric CO2 and/or temperature may affect ecosystem stability and carbon cycling. We explored these questions using simulations of pre-drought and post-drought conditions at well-studied forest sites using well-tested models (ED2 and LPJ-GUESS). The severity and patterns of biomass losses differed substantially between models. For example, biomass loss could be sensitive to either drought duration or drought intensity depending on the model approach. This is due to the models having different, but also plausible, representations of processes and interactions, highlighting the complicated variability of UCE impacts that still need to be narrowed down in models. Elevated atmospheric CO2 concentrations (eCO2) alone did not completely buffer the ecosystems from carbon losses during UCEs in the majority of our simulations. Our findings highlight the consequences of differences in process formulations and uncertainties in models, most notably related to availability in plant carbohydrate storage and the diversity of plant hydraulic schemes, in projecting potential ecosystem responses to UCEs. We provide a summary of the current state and role of many model processes that give way to different underlying hypotheses of plant responses to UCEs, reflecting knowledge gaps which in future studies could be tested with targeted field experiments and an iterative modeling-experimental conceptual framework.

Climatic extreme events are expected to occur more frequently in the future, increasing the likelihood of unprecedented climate extremes (UCEs), or record-breaking events. UCEs, such as extreme heatwaves and droughts, substantially affect ecosystem stability and carbon cycling by increasing plant mortality and delaying ecosystem recovery. Quantitative knowledge of such effects is limited due to the paucity of experiments focusing on extreme climatic events beyond the range of historical experience. Here, we use two dynamic vegetation demographic models (VDMs), ED2 and LPJ-GUESS, to investigate the hypothesis that ecosystem responses to UCEs (e.g., unprecedented droughts) differ qualitatively from ecosystem responses to milder extremes, as a result of non-linear ecosystem responses. Additionally, we explore how unprecedented droughts in combination with increasing atmospheric CO2 and/or temperature may affect ecosystem stability and carbon cycling. We explored these questions using simulations of pre-drought and post-drought conditions at well-studied forest sites in Australia and Costa Rica. Both models produced nonlinear responses to UCEs. Due to the two models having different but plausible representations of processes and interactions, they diverge in sensitivity of biomass loss due to drought duration or intensity, and differ between each site. Biomass losses are most sensitive to drought duration in ED2, but to drought intensity in LPJ-GUESS. Elevated atmospheric CO2 concentrations (eCO2) alone did not completely buffer the ecosystems from carbon losses during UCEs in the majority of our simulations. Our findings highlight contrasting differences in process formulations and uncertainties in models, notably related to availability in plant carbohydrate storage and the diversity of plant hydraulic schemes, in projecting potential ecosystem responses to UCEs. The different hypotheses of plant responses to UCEs existing in models reflect knowledge gaps, which should be tested with targeted field experiments. This iterative modeling-experimental framework would help improve predictions of terrestrial ecosystem responses and climate feedbacks. [ABSTRACT FROM AUTHOR]

Secondary organic aerosol particles (SOA) are important climate forcers, especially in otherwise clean environments such as the boreal forest. There are, however, major uncertainties in the mechanisms behind the formation of SOA, and in order to predict the growth and abundance of SOA at different conditions, process-based understanding is needed. In this study, the processes behind new particle formation (NPF) events and subsequent growth of these particles in the northern Europe sub-Arctic forest region are explored with the one-dimensional column trajectory model ADCHEM. The results from the model are compared with particle number size distribution measurements from Pallas Atmosphere-Ecosystem Supersite in Northern Finland. The model was able to reproduce the observed growth of the newly formed particles if a small fraction of the emitted monoterpenes that are oxidized by O3 and OH undergo autoxidation and form highly oxidized multifunctional organic molecules (HOMs) with low or extremely low volatility. The modeled particles originating from the NPF events (diameter < 100 nm) are composed predominantly of HOMs. While the model seems to capture the growth of the newly formed particles between 1.5 and ~ 20 nm in diameter, it underestimated the particle growth between ~ 20 and 80 nm in diameter. Due to the high fraction of HOMs in the particle phase, the oxygen-to-carbon (O : C) atomic ratio of the SOA was nearly 1. This unusually high O : C and the discrepancy between the modeled and observed particle growth might be explained by the fact that the model did not consider any particle-phase reactions involving semi-volatile organic compounds with relatively low O : C. According to the model the phase state of the SOA (assumed either liquid or amorphous solid) had an insignificant effect on the evolution of the particle number size distribution during the NPF events. The results were sensitive to the method used to estimate the vapor pressures of the HOMs. If the HOMs were assumed to be extremely low volatile organic compounds (ELVOCs) or non-volatile the modeled particle growth was substantially higher than when the vapor pressures of the HOMs were estimated based on continuum solvent model calculations using quantum chemical data. Overall, the model was able to capture the main features of the observed formation and growth rates during the studied NPF-events if the HOM mechanism was included. [ABSTRACT FROM AUTHOR]

The response of terrestrial ecosystems to rising atmospheric CO2 concentration (Ca), particularly under nutrient-limited conditions, is a major uncertainty in Earth System models. The Eucalyptus Free-Air CO2 Enrichment (EucFACE) experiment, recently established in a nutrient- and water-limited woodland presents a unique opportunity to address this uncertainty, but can best do so if key model uncertainties have been identified in advance. We applied seven vegetation models, which have previously been comprehensively assessed against earlier forest FACE experiments, to simulate a priori possible outcomes from EucFACE. Our goals were to provide quantitative projections against which to evaluate data as they are collected, and to identify key measurements that should be made in the experiment to allow discrimination among alternative model assumptions in a postexperiment model intercomparison. Simulated responses of annual net primary productivity (NPP) to elevated Ca ranged from 0.5 to 25% across models. The simulated reduction of NPP during a low-rainfall year also varied widely, from 24 to 70%. Key processes where assumptions caused disagreement among models included nutrient limitations to growth; feedbacks to nutrient uptake; autotrophic respiration; and the impact of low soil moisture availability on plant processes. Knowledge of the causes of variation among models is now guiding data collection in the experiment, with the expectation that the experimental data can optimally inform future model improvements. [ABSTRACT FROM AUTHOR]