Phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating photosynthesis and other ecosystem processes, as well as competitive interactions and feedbacks to the climate system. We conducted an analysis to evaluate the representation of phenology, and the associated seasonality of ecosystem-scale CO 2 exchange, in 14 models participating in the North American Carbon Program Site Synthesis. Model predictions were evaluated using long-term measurements (emphasizing the period 2000-2006) from 10 forested sites within the AmeriFlux and Fluxnet-Canada networks. In deciduous forests, almost all models consistently predicted that the growing season started earlier, and ended later, than was actually observed; biases of 2 weeks or more were 566-584, doi: 10.1111/j.1365-2486.2011.02562.x This article is a U.S. government work, and is not subject to copyright in the United States.Global Change Biology (2012) 18,typical. For these sites, most models were also unable to explain more than a small fraction of the observed interannual variability in phenological transition dates. Finally, for deciduous forests, misrepresentation of the seasonal cycle resulted in over-prediction of gross ecosystem photosynthesis by +160 ± 145 g C m À2 yr À1 during the spring transition period and +75 ± 130 g C m À2 yr À1 during the autumn transition period (13% and 8% annual productivity, respectively) compensating for the tendency of most models to under-predict the magnitude of peak summertime photosynthetic rates. Models did a better job of predicting the seasonality of CO 2 exchange for evergreen forests. These results highlight the need for improved understanding of the environmental controls on vegetation phenology and incorporation of this knowledge into better phenological models. Existing models are unlikely to predict future responses of phenology to climate change accurately and therefore will misrepresent the seasonality and interannual variability of key biosphere-atmosphere feedbacks and interactions in coupled global climate models.

[1] Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical because errors in simulated GPP propagate through the model to introduce additional errors in simulated biomass and other fluxes. We evaluated simulated, daily average GPP from 26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States and Canada. None of the models in this study match estimated GPP within observed uncertainty. On average, models overestimate GPP in winter, spring, and fall, and underestimate GPP in summer. Models overpredicted GPP under dry conditions and for temperatures below 0 C. Improvements in simulated soil moisture and ecosystem response to drought or humidity stress will improve simulated GPP under dry conditions. Adding a low-temperature response to shut down GPP for temperatures below 0 C will reduce the positive bias in winter, spring, and fall and improve simulated phenology. The negative bias in summer and poor overall performance resulted from mismatches between simulated and observed light use efficiency (LUE). Improving simulated GPP requires better leaf-to-canopy scaling and better values of model parameters that control the maximum potential GPP, such as ɛ max (LUE), V cmax (unstressed Rubisco catalytic capacity) or J max (the maximum electron transport rate).

Northern hemisphere evergreen forests assimilate a significant fraction of global atmospheric CO2 but monitoring large-scale changes in gross primary production (GPP) in these systems is challenging. Recent advances in remote sensing allow the detection of solar-induced chlorophyll fluorescence (SIF) emission from vegetation, which has been empirically linked to GPP at large spatial scales. This is particularly important in evergreen forests, where traditional remote-sensing techniques and terrestrial biosphere models fail to reproduce the seasonality of GPP. Here, we examined the mechanistic relationship between SIF retrieved from a canopy spectrometer system and GPP at a winter-dormant conifer forest, which has little seasonal variation in canopy structure, needle chlorophyll content, and absorbed light. Both SIF and GPP track each other in a consistent, dynamic fashion in response to environmental conditions. SIF and GPP are well correlated (R2 = 0.62–0.92) with an invariant slope over hourly to weekly timescales. Large seasonal variations in SIF yield capture changes in photoprotective pigments and photosystem II operating efficiency associated with winter acclimation, highlighting its unique ability to precisely track the seasonality of photosynthesis. Our results underscore the potential of new satellite-based SIF products (TROPOMI, OCO-2) as proxies for the timing and magnitude of GPP in evergreen forests at an unprecedented spatiotemporal resolution.

Understanding of carbon exchange between terrestrial ecosystems and the atmosphere can be improved through direct observations and experiments, as well as through modeling activities. Terrestrial biosphere models (TBMs) have become an integral tool for extrapolating local observations and understanding to much larger terrestrial regions. Although models vary in their specific goals and approaches, their central role within carbon cycle science is to provide a better understanding of the mechanisms currently controlling carbon exchange. Recently, the North American Carbon Program (NACP) organized several interim-synthesis activities to evaluate and inter-compare models and observations at local to continental scales for the years 2000-2005. Here, we compare the results from the TBMs collected as part of the regional and continental interim-synthesis (RCIS) activities. The primary objective of this work is to synthesize and compare the 19 participating TBMs to assess current understanding of the terrestrial carbon cycle in North America. Thus, the RCIS focuses on model simulations available from analyses that have been completed by ongoing NACP projects and other recently published studies. The TBM flux estimates are compared and evaluated over different spatial (1 degrees X 1 degrees and spatially aggregated to different regions) and temporal (monthly and annually) scales. The range in model estimates of net ecosystem productivity (NEP) for North America is much narrower than estimates of productivity or respiration, with estimates of NEP varying between 0.7 and 2.2 PgC yr(-1), while gross primary productivity and heterotrophic respiration vary between 12.2 and 32.9 PgCyr(-1) and 5.6 and 13.2 PgC yr(-1), respectively. The range in estimates from the models appears to be driven by a combination of factors, including the representation of photosynthesis, the source and of environmental driver data and the temporal variability of those data, as well as whether nutrient limitation is considered in soil carbon decomposition. The disagreement in current estimates of carbon flux across North America, including whether North America is a net biospheric carbon source or sink, highlights the need for further analysis through the use of model runs following a common simulation protocol, in order to isolate the influences of model formulation, structure, and assumptions on flux estimates. (C) 2012 Elsevier B.V. All rights reserved

Interannual variability in biosphere-atmosphere exchange of CO 2 is driven by a diverse range of biotic and abiotic factors. Replicating this variability thus represents the 'acid test' for terrestrial biosphere models. Although such models are commonly used to project responses to both normal and anomalous variability in climate, they are rarely tested explicitly against inter-annual variability in observations. Here, using standardized data from the North American Carbon Program, we assess the performance of 16 terrestrial biosphere models and 3 remote sensing products against long-term measurements of biosphere-atmosphere CO 2 exchange made with eddy-covariance flux towers at 11 forested sites in North America. Instead of focusing on model-data agreement we take a systematic, variability-oriented, approach and show that although the models tend to reproduce the mean magnitude of the observed annual flux variability, they fail to reproduce the timing. Large biases in modeled annual means are evident for all models. Observed interannual variability is found to commonly be on the order of magnitude of the mean fluxes. None of the models consistently reproduce observed interannual variability within measurement uncertainty. Underrepresentation of variability in spring phenology, soil thaw and snowpack melting, and difficulties in reproducing the lagged response to extreme climatic events are identified as systematic errors, common to all models included in this study.

Abstract. Terrestrial biosphere models can help identify physical processes that control carbon dynamics, including land-atmosphere CO 2 fluxes, and have great potential to predict the terrestrial ecosystem response to changing climate. The skill of models that provide continental-scale carbon flux estimates, however, remains largely untested. This paper evaluates the performance of continental-scale flux estimates from 17 models against observations from 36 North American flux towers. Fluxes extracted from regional model simulations were compared with co-located flux tower observations at monthly and annual time increments. Site-level model simulations were used to help interpret sources of the mismatch between the regional simulations and site-based observations. On average, the regional model runs overestimated the annual gross primary productivity (5%) and total respiration (15%), and they significantly underestimated the annual net carbon uptake (64%) during the time period 2000-2005. Comparison with site-level simulations implicated choices specific to regional model simulations as contributors to the gross flux biases, but not the net carbon uptake bias. The models performed the best at simulating carbon exchange at deciduous broadleaf sites, likely because a number of models used prescribed phenology to simulate seasonal fluxes. The models did not perform as well for crop, grass, and evergreen sites. The regional models matched the observations most closely in terms of seasonal correlation and seasonal magnitude of variation, but they have very little skill at interannual correlation and minimal skill at interannual magnitude of variability. The comparison of site vs. regional-level model runs demonstrated that (1) the interannual correlation is higher for site-level model runs, but the skill remains low; and (2) the underestimation of year-to-year variability for all fluxes is an inherent weakness of the models. The best-performing regional models that did not use flux tower calibration were CLM-CN, CASA-GFEDv2, and SIB3.1. Two flux tower calibrated, empirical models, EC-MOD and MOD17þ, performed as well as the best process-based models. This suggests that (1) empirical, calibrated models can perform as well as complex, process-based models and (2) combining processbased model structure with relevant constraining data could significantly improve model performance.

Abstract. Land surface models are useful tools to quantify contemporary and future climate impact on terrestrial carbon cycle processes, provided they can be appropriately constrained and tested with observations. Stable carbon isotopes of CO2 offer the potential to improve model representation of the coupled carbon and water cycles because they are strongly influenced by stomatal function. Recently, a representation of stable carbon isotope discrimination was incorporated into the Community Land Model component of the Community Earth System Model. Here, we tested the model's capability to simulate whole-forest isotope discrimination in a subalpine conifer forest at Niwot Ridge, Colorado, USA. We distinguished between isotopic behavior in response to a decrease of δ13C within atmospheric CO2 (Suess effect) vs. photosynthetic discrimination (Δcanopy), by creating a site-customized atmospheric CO2 and δ13C of CO2 time series. We implemented a seasonally varying Vcmax model calibration that best matched site observations of net CO2 carbon exchange, latent heat exchange, and biomass. The model accurately simulated observed δ13C of needle and stem tissue, but underestimated the δ13C of bulk soil carbon by 1–2 ‰. The model overestimated the multiyear (2006–2012) average Δcanopy relative to prior data-based estimates by 2–4 ‰. The amplitude of the average seasonal cycle of Δcanopy (i.e., higher in spring/fall as compared to summer) was correctly modeled but only when using a revised, fully coupled An − gs (net assimilation rate, stomatal conductance) version of the model in contrast to the partially coupled An − gs version used in the default model. The model attributed most of the seasonal variation in discrimination to An, whereas interannual variation in simulated Δcanopy during the summer months was driven by stomatal response to vapor pressure deficit (VPD). The model simulated a 10 % increase in both photosynthetic discrimination and water-use efficiency (WUE) since 1850 which is counter to established relationships between discrimination and WUE. The isotope observations used here to constrain CLM suggest (1) the model overestimated stomatal conductance and (2) the default CLM approach to representing nitrogen limitation (partially coupled model) was not capable of reproducing observed trends in discrimination. These findings demonstrate that isotope observations can provide important information related to stomatal function driven by environmental stress from VPD and nitrogen limitation. Future versions of CLM that incorporate carbon isotope discrimination are likely to benefit from explicit inclusion of mesophyll conductance.

Terrestrial biosphere models can help identify physical processes that control carbon dynamics, including land-atmosphere CO 2 fluxes, and have the potential to project the terrestrial ecosystem response to changing climate. It is important to identify ecosystem processes most responsible for model predictive uncertainty and design improved model representation and observational system studies to reduce that uncertainty. Here we identified model parameters that contribute the most uncertainty to long-term (~100 years) projections of net ecosystem exchange, net primary production, and aboveground biomass within a mechanistic terrestrial biosphere model (Ecosystem Demography, version 2.1) ED2. An uncertainty analysis identified parameters that represent the quantum efficiency of light to photosynthetic conversion, leaf respiration and soil-plant water transfer as the highest contributors to model uncertainty regardless of time frame (annual, decadal, and centennial) and output (e.g., net ecosystem exchange, net primary production, aboveground biomass). Contrary to expectations, the contribution of successional processes related to reproduction, competition, and mortality did not increase as the time scale increased. These findings suggest that uncertainty in the parameters governing short-term ecosystem processes remains the most significant bottleneck to reducing predictive uncertainty. Key actions to reduce parameter uncertainty include more leaf-level trait measurements across multiple sites for quantum efficiency and leaf respiration rate. Further, the empirical representation of soil-plant water transfer should be replaced with a mechanistic, hydraulic representation of water flow, which can be constrained with direct measurements. This analysis focused on aboveground ecosystem processes. The impact of belowground carbon cycling, initial conditions, and meteorological forcing should be addressed in future studies.Plain Language Summary Terrestrial biosphere models (TBMs) are used in climate modeling to predict exchanges of carbon dioxide, water, and energy between the land and atmosphere of the Earth. TBMs are important because they can predict the rate at which plants and the soils absorb or emit carbon dioxide to the atmosphere. The amount of carbon dioxide in the atmosphere determines the rate of climate warming. Because TBMs are an approximation of the true land processes it is important to estimate the amount of uncertainty within the model projections. Here we conduct an uncertainty analysis that identifies two model components that contribute the most to model uncertainty: the efficiency at which light energy is converted to photosynthesis and the rate at which leaves emit carbon dioxide. We recommend that more leaf-level measurements be taken across multiple sites so that we better understand these two processes and reduce the uncertainty they add to model projections.Ecosystem observations help define appropriate ranges to model parameters that improve TBM performance (e.g., Dietze et al., 2013). Thes...