North American Carbon Program Research Articles

Using climate-driven leaf phenology and growth to improve predictions of gross primary productivity in North American forests.

Forest ecosystems are an important sink for terrestrial carbon sequestration. Hence, accurate modeling of the intra- and interannual variability of forest photosynthetic productivity remains a key objective in global biology. Applying climate-driven leaf phenology and growth in models may improve predictions of the forest gross primary productivity (GPP). We used a dynamic non-structural carbohydrates (NSC) model (FORCCHN2) that couples leaf development and phenology to investigate the relationships among photosynthesis and environmental factors. FORCCHN2 simulates spring and autumn phenological events from heat and chilling, respectively. Leaf area index data from satellites along with climate data estimated localized phenological parameters. NSC limitation, immediate temperature, accumulated heat, and growth potential comprised a daily leaf-growth model. Functionally, leaf growth was decoupled from photosynthesis. Leaf biomass determined overall photosynthetic production. We compared this model with outputs of the other six terrestrial biospheric models and with observations from the North American Carbon Program Site Interim Synthesis in 18 forest sites. This model improved the predicted performance of yearly GPP with a 57%-210% increase in correlation (median) and up to a 102% reduction in biases (median), compared to three prognostic models and three prescribed models. At the North America continental scale, the model predicted the average annual GPP of 7.38Pg C/year from forest ecosystems during 1985-2016. The results showed an increasing trend of GPP in North America (1.0Pg C/decade). The inclusion of climate-driven phenology and growth has a significant potential for improving dynamic vegetation models, and promotes a further understanding of the complex relationship between environment and photosynthesis.

Development of Landsat-based annual US forest disturbance history maps (1986–2010) in support of the North American Carbon Program (NACP)

In Phase III of the North American Forest Dynamics (NAFD) study an automatic workflow has been developed for evaluating forest disturbance history using Landsat observations. It has four major components: an automated approach for image selection and preprocessing, the vegetation change tracker (VCT) forest disturbance analysis, postprocessing, and validation. This approach has been applied to the conterminous US (CONUS) to produce a comprehensive analysis of US forest disturbance history using the NASA Earth Exchange (NEX) cloud computing system. The resultant NAFD-NEX product includes 25 annual forest disturbance maps for 1986–2010 and two time-integrated maps to provide spatial-temporal synoptic view of disturbances over this time period. These maps were derived based on 24,000+ scenes selected from 350,000+ available Landsat images at 30-m resolution, and were validated using a visual assessment of Landsat time-series images in combination with high-resolution and other ancillary data sources over samples selected using a probability based sampling method. The validation revealed no major biases in the NAFD-NEX maps for disturbance events that resulted in at least 20% canopy cover loss. The average user's and producer's accuracies for the disturbance class were 53.6% and 53.3%, respectively, with the individual year's user's accuracy varying from 42.8% to 73.6% and producer's accuracy from 39.0% to 84.8% over the 25-year period. The NAFD-NEX disturbance maps are available from a web portal of the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL-DAAC) at https://doi.org/10.3334/ORNLDAAC/1290.

Intercomparison and evaluation of spring phenology products using National Phenology Network and AmeriFlux observations in the contiguous United States

Many remote sensing based spring phenology products have been developed to monitor and study vegetation phenology at regional and global scales. It is important to understand how these products perform relative to each other and to ground observations. In this study, we extracted spring green-up onset dates (GUD) over the contiguous United States (CONUS) from six major land surface phenology (LSP) products: (1) Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Dynamics Phenology (MCD12Q2); (2) Vegetation Index and Phenology Multi-sensor Phenology (VIPPHENEVI2); (3) Global Long-Term Climate Modeling Grid Land Surface Phenology (CMGLSP); (4 and 5) North American Carbon Program (NACP) Phenology (MOD09Q1PEVI and MOD15PHN); and (6) USGS/EROS advanced very high resolution radiometer (AVHRR) phenology (AVHRRP). We characterized and compared the GUD data in these LSP products, and evaluated their accuracy using ground-based phenology observations [i.e., human observations of first leaf and sensor readings of gross primary productivity (GPP)] from the USA National Phenology Network (USA-NPN) and AmeriFlux. The results revealed the consistencies and discrepancies of GUD estimates among LSP products. Intercomparison of the six products indicated that the root mean square error (RMSE) of these products range from 17.8days to 31.5days, whereas AVHRRP GUD has the lowest correlation and largest RMSE (∼30days) relative to other products. When compared to ground observations, GUD estimates in six LSP products generally have RMSE values of ∼20days and significant correlations (p<0.001). For the products (MCD12Q2, AVHRRP, MOD09Q1PEVI, and MOD15PHN) available for comparisons in the short-term period (from 2001–2007), AVHRRP GUD presented relatively weaker correlations and a lower index of agreement (IOA), however, MCD12Q2 GUD showed overall slightly better consistencies with ground observations. In the two long-term products (CMGLSP and VIPPHENEVI2 from 1982–2013), CMGLSP exhibited stronger correlations, lower RMSE, and higher IOA with ground observations of the first leaf dates than VIPPHENEVI2 did. To our knowledge, our study provides the first comprehensive evaluation of phenology products using two independent ground-based datasets.

Evaluating land cover influences on model uncertainties—A case study of cropland carbon dynamics in the Mid-Continent Intensive Campaign region

Quantifying spatial and temporal patterns of carbon sources and sinks and their uncertainties across agriculture-dominated areas remains challenging for understanding regional carbon cycles. Characteristics of local land cover inputs could impact the regional carbon estimates but the effect has not been fully evaluated in the past. Within the North American Carbon Program Mid-Continent Intensive (MCI) Campaign, three models were developed to estimate carbon fluxes on croplands: an inventory-based model, the Environmental Policy Integrated Climate (EPIC) model, and the General Ensemble biogeochemical Modeling System (GEMS) model. They all provided estimates of three major carbon fluxes on cropland: net primary production (NPP), net ecosystem production (NEP), and soil organic carbon (SOC) change. Using data mining and spatial statistics, we studied the spatial distribution of the carbon fluxes uncertainties and the relationships between the uncertainties and the land cover characteristics. Results indicated that uncertainties for all three carbon fluxes were not randomly distributed, but instead formed multiple clusters within the MCI region. We investigated the impacts of three land cover characteristics on the fluxes uncertainties: cropland percentage, cropland richness and cropland diversity. The results indicated that cropland percentage significantly influenced the uncertainties of NPP and NEP, but not on the uncertainties of SOC change. Greater uncertainties of NPP and NEP were found in counties with small cropland percentage than the counties with large cropland percentage. Cropland species richness and diversity also showed negative correlations with the model uncertainties. Our study demonstrated that the land cover characteristics contributed to the uncertainties of regional carbon fluxes estimates. The approaches we used in this study can be applied to other ecosystem models to identify the areas with high uncertainties and where models can be improved to reduce overall uncertainties for regional carbon flux estimates.

An analysis of global terrestrial carbon, water and energy dynamics using the carbon–nitrogen coupled CLASS-CTEMN+ model

A carbon–nitrogen (C–N) coupled terrestrial ecosystem model, CLASS-CTEMN+, was used to simulate global terrestrial carbon, water, and energy cycles at 0.5-degree spatial resolution from 1901 to 2010. Forcing and initializing data sets from the North American Carbon Program (NACP)—Multi-Scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) were used. Two versions of the model (the C–N coupled and the C-only version) were compared. The C–N (C) version of the model simulated global annual total gross ecosystem productivity (GEP) of 122.7 (128.2) PgCyr−1, ecosystem respiration (Re) of 119.1 (122.4) PgCyr−1, net ecosystem productivity (NEP) of 3.64 (5.82) PgCyr−1, net primary productivity (NPP) of 62.7 (67.4) PgCyr−1, latent heat (LE) of 146.2 (167.4) ZJyr−1, sensible heat (H) of 194.0 (162) ZJyr−1, soil organic carbon (SOC) of 1230 (1328) PgC, and total vegetation biomass (Tvg) of 608 (629) PgC, over the 1980–2010 period were comparable to scaled observations and modelling studies in the literature. Our results suggest that altough inclusion of N cycling has not had a large effect on the simulated total global terrestrial ecosystem C budget over this period; however the temporal evolution and spatial distribution of the simulated C budget revealed important influences of the N cycle. Analysis of long-term annual variations in response to evolving climate, CO2 concentration levels, and N deposition over the 1901–2010 period showed largely similar increasing trends in global GEP, Re, NEP and NPP until about 1970, after which the rate of increase was suppressed in the C–N coupled version of the model relative to the C-only version. Spatial plots showed that GEP and Re were reduced in the C–N coupled version of the model over the temperate and boreal regions and in high latitudes, where N limitation is well documented. The strongest response in NEP was in the boreal forests of North America, Northern Europe and Northwestern Asia, which showed decreases in response to the inclusion of a N cycle. Commensurate with the reductions in GEP and NEP, leaf area index (LAI) and total vegetation biomass (Tvg) also showed decreases in the boreal region, while SOC showed wider decreases in temperate, boreal and northern regions.This study helps to identify and diagnose the impact of N controls on the simulated spatiotemporal patterns of terrestrial C sinks and sources, and to evaluate and quantify the variability of simulated N cycle effects on different plant functional types (PFTs). These results have important implications for future climate change scenarios because the response to increasing CO2, N deposition, and climate patterns shows diverging model behavior as compared with the C-only version, when a N cycle is incorporated in the model.

Carbon and energy fluxes in cropland ecosystems: a model-data comparison

Croplands are highly productive ecosystems that contribute to land–atmosphere exchange of carbon, energy, and water during their short growing seasons. We evaluated and compared net ecosystem exchange (NEE), latent heat flux (LE), and sensible heat flux (H) simulated by a suite of ecosystem models at five agricultural eddy covariance flux tower sites in the central United States as part of the North American Carbon Program Site Synthesis project. Most of the models overestimated H and underestimated LE during the growing season, leading to overall higher Bowen ratios compared to the observations. Most models systematically under predicted NEE, especially at rain-fed sites. Certain crop-specific models that were developed considering the high productivity and associated physiological changes in specific crops better predicted the NEE and LE at both rain-fed and irrigated sites. Models with specific parameterization for different crops better simulated the inter-annual variability of NEE for maize-soybean rotation compared to those models with a single generic crop type. Stratification according to basic model formulation and phenological methodology did not explain significant variation in model performance across these sites and crops. The under prediction of NEE and LE and over prediction of H by most of the models suggests that models developed and parameterized for natural ecosystems cannot accurately predict the more robust physiology of highly bred and intensively managed crop ecosystems. When coupled in Earth System Models, it is likely that the excessive physiological stress simulated in many land surface component models leads to overestimation of temperature and atmospheric boundary layer depth, and underestimation of humidity and CO2 seasonal uptake over agricultural regions.

Social network and content analysis of the North American Carbon Program as a scientific community of practice

The North American Carbon Program (NACP) was formed to further the scientific understanding of sources, sinks, and stocks of carbon in Earth's environment. Carbon cycle science integrates multidisciplinary research, providing decision-support information for managing climate and carbon-related change across multiple sectors of society. This investigation uses the conceptual framework of communities of practice (CoP) to explore the role that the NACP has played in connecting researchers into a carbon cycle knowledge network, and in enabling them to conduct physical science that includes ideas from social science. A CoP describes the communities formed when people consistently engage in shared communication and activities toward a common passion or learning goal. We apply the CoP model by using keyword analysis of abstracts from scientific publications to analyze the research outputs of the NACP in terms of its knowledge domain. We also construct a co-authorship network from the publications of core NACP members, describe the structure and social pathways within the community. Results of the content analysis indicate that the NACP community of practice has substantially expanded its research on human and social impacts on the carbon cycle, contributing to a better understanding of how human and physical processes interact with one another. Results of the co-authorship social network analysis demonstrate that the NACP has formed a tightly connected community with many social pathways through which knowledge may flow, and that it has also expanded its network of institutions involved in carbon cycle research over the past seven years.

Integrating Carbon Cycle Research into Decision-Making Processes

North American Carbon Program Principal Investigators Meeting; Washington, D.C., 26–29 January 2015

LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial random effects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.

Mapping Biophysical Parameters for Land Surface Modeling over the Continental US Using MODIS and Landsat

In terms of the space cities occupy, urbanization appears as a minor land transformation. However, it permanently modifies land’s ecological functions, altering its carbon, energy, and water fluxes. It is therefore necessary to develop a land cover characterization at fine spatial and temporal scales to capture urbanization’s effects on surface fluxes. We develop a series of biophysical vegetation parameters such as the fraction of photosynthetically active radiation, leaf area index, vegetation greenness fraction, and roughness length over the continental US using MODIS and Landsat products for 2001. A 13-class land cover map was developed at a climate modeling grid (CMG) merging the 500 m MODIS land cover and the 30 m impervious surface area from the National Land Cover Database. The landscape subgrid heterogeneity was preserved using fractions of each class from the 500 m and 30 m into the CMG. Biophysical parameters were computed using the 8-day composite Normalized Difference Vegetation Index produced by the North American Carbon Program. In addition to urban impact assessments, this dataset is useful for the computation of surface fluxes in land, vegetation, and urban models and is expected to be widely used in different land cover and land use change applications.

Methods used to parameterize the spatially-explicit components of a state-and-transition simulation model

Spatially-explicit state-and-transition simulation models of land use and land cover (LULC) increase our ability to assess regional landscape characteristics and associated carbon dynamics across multiple scenarios. By characterizing appropriate spatial attributes such as forest age and land-use distribution, a state-and-transition model can more effectively simulate the pattern and spread of LULC changes. This manuscript describes the methods and input parameters of the Land Use and Carbon Scenario Simulator (LUCAS), a customized state-and-transition simulation model utilized to assess the relative impacts of LULC on carbon stocks for the conterminous U.S. The methods and input parameters are spatially explicit and describe initial conditions (strata, state classes and forest age), spatial multipliers, and carbon stock density. Initial conditions were derived from harmonization of multi-temporal data characterizing changes in land use as well as land cover. Harmonization combines numerous national-level datasets through a cell-based data fusion process to generate maps of primary LULC categories. Forest age was parameterized using data from the North American Carbon Program and spatially-explicit maps showing the locations of past disturbances (i.e. wildfire and harvest). Spatial multipliers were developed to spatially constrain the location of future LULC transitions. Based on distance-decay theory, maps were generated to guide the placement of changes related to forest harvest, agricultural intensification/extensification, and urbanization. We analyze the spatially-explicit input parameters with a sensitivity analysis, by showing how LUCAS responds to variations in the model input. This manuscript uses Mediterranean California as a regional subset to highlight local to regional aspects of land change, which demonstrates the utility of LUCAS at many scales and applications.

The North American Carbon Program Multi-scale Synthesis and Terrestrial Model Intercomparison Project – Part 2: Environmental driver data

Abstract. Ecosystems are important and dynamic components of the global carbon cycle, and terrestrial biospheric models (TBMs) are crucial tools in further understanding of how terrestrial carbon is stored and exchanged with the atmosphere across a variety of spatial and temporal scales. Improving TBM skills, and quantifying and reducing their estimation uncertainties, pose significant challenges. The Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) is a formal multi-scale and multi-model intercomparison effort set up to tackle these challenges. The MsTMIP protocol prescribes standardized environmental driver data that are shared among model teams to facilitate model–model and model–observation comparisons. This paper describes the global and North American environmental driver data sets prepared for the MsTMIP activity to both support their use in MsTMIP and make these data, along with the processes used in selecting/processing these data, accessible to a broader audience. Based on project needs and lessons learned from past model intercomparison activities, we compiled climate, atmospheric CO2 concentrations, nitrogen deposition, land use and land cover change (LULCC), C3 / C4 grasses fractions, major crops, phenology and soil data into a standard format for global (0.5° × 0.5° resolution) and regional (North American: 0.25° × 0.25° resolution) simulations. In order to meet the needs of MsTMIP, improvements were made to several of the original environmental data sets, by improving the quality, and/or changing their spatial and temporal coverage, and resolution. The resulting standardized model driver data sets are being used by over 20 different models participating in MsTMIP. The data are archived at the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC, http://daac.ornl.gov) to provide long-term data management and distribution.

Ozone vegetation damage effects on gross primary productivity in the United States

Abstract. We apply an off-line process-based vegetation model (the Yale Interactive Terrestrial Biosphere model) to assess the impacts of ozone (O3) vegetation damage on gross primary productivity (GPP) in the United States during the past decade (1998–2007). The model's GPP simulation is evaluated at 40 sites of the North American Carbon Program (NACP) synthesis. The ecosystem-scale model version reproduces interannual variability and seasonality of GPP at most sites, especially in croplands. Inclusion of the O3 damage impact decreases biases of simulated GPP at most of the NACP sites. The simulation with the O3 damage effect reproduces 64% of the observed variance in summer GPP and 42% on the annual average. Based on a regional gridded simulation over the US, summertime average O3-free GPP is 6.1 g C m−2 day−1 (9.5 g C m−2 day−1 in the east of 95° W and 3.9 g C m−2 day−1 in the west). O3 damage decreases GPP by 4–8% on average in the eastern US and leads to significant decreases of 11–17% in east coast hot spots. Sensitivity simulations show that a 25% decrease in surface O3 concentration halves the average GPP damage to only 2–4%, suggesting the substantial co-benefits to ecosystem health that may be achieved via O3 air pollution control.

Model‐data comparison of MCI field campaign atmospheric CO2 mole fractions

AbstractAtmospheric transport model errors are a major contributor to uncertainty in CO2 inverse flux estimates. Our study compares CO2 mole fraction observations from the North American Carbon Program Mid‐Continental Intensive (MCI) field campaign and modeled mole fractions from two atmospheric transport models: the global Transport Model 5 from NOAA's CarbonTracker system and the mesoscale Weather Research and Forecasting model. Both models are coupled to identical CO2 fluxes and lateral boundary conditions from CarbonTracker (CT2009 release). Statistical analyses were performed for two periods of 2007 using observed daily daytime average mole fractions of CO2 to test the ability of these models to reproduce the observations and to infer possible causes of the discrepancies. TM5‐CT2009 overestimates midsummer planetary boundary layer CO2 for sites in the U.S. corn belt by 10 ppm. Weather Research and Forecasting (WRF)‐CT2009 estimates diverge from the observations with similar magnitudes, but the signs of the differences vary from site to site. The modeled mole fractions are highly correlated with the observed seasonal cycle (r ≥ 0.7) but less correlated in the growing season, where weather‐related changes in CO2 dominate the observed variability. Spatial correlations in residuals from TM5‐CT2009 are higher than WRF‐CT2009 perhaps due to TM5's coarse horizontal resolution and shallow vertical mixing. Vertical mixing appears to have influenced CO2 residuals from both models. TM5‐CT2009 has relatively weak vertical mixing near the surface limiting the connection between local CO2 surface fluxes and boundary layer. WRF‐CT2009 has stronger vertical mixing that may increase the connections between local surface fluxes and the boundary layer.

Carbon cycle uncertainty in the Alaskan Arctic

Abstract. Climate change is leading to a disproportionately large warming in the high northern latitudes, but the magnitude and sign of the future carbon balance of the Arctic are highly uncertain. Using 40 terrestrial biosphere models for the Alaskan Arctic from four recent model intercomparison projects – NACP (North American Carbon Program) site and regional syntheses, TRENDY (Trends in net land atmosphere carbon exchanges), and WETCHIMP (Wetland and Wetland CH4 Inter-comparison of Models Project) – we provide a baseline of terrestrial carbon cycle uncertainty, defined as the multi-model standard deviation (σ) for each quantity that follows. Mean annual absolute uncertainty was largest for soil carbon (14.0 ± 9.2 kg C m−2), then gross primary production (GPP) (0.22 ± 0.50 kg C m−2 yr−1), ecosystem respiration (Re) (0.23 ± 0.38 kg C m−2 yr−1), net primary production (NPP) (0.14 ± 0.33 kg C m−2 yr−1), autotrophic respiration (Ra) (0.09 ± 0.20 kg C m−2 yr−1), heterotrophic respiration (Rh) (0.14 ± 0.20 kg C m−2 yr−1), net ecosystem exchange (NEE) (−0.01 ± 0.19 kg C m−2 yr−1), and CH4 flux (2.52 ± 4.02 g CH4 m−2 yr−1). There were no consistent spatial patterns in the larger Alaskan Arctic and boreal regional carbon stocks and fluxes, with some models showing NEE for Alaska as a strong carbon sink, others as a strong carbon source, while still others as carbon neutral. Finally, AmeriFlux data are used at two sites in the Alaskan Arctic to evaluate the regional patterns; observed seasonal NEE was captured within multi-model uncertainty. This assessment of carbon cycle uncertainties may be used as a baseline for the improvement of experimental and modeling activities, as well as a reference for future trajectories in carbon cycling with climate change in the Alaskan Arctic and larger boreal region.

Characterizing the diurnal patterns of errors in the prediction of evapotranspiration by several land‐surface models: An NACP analysis

AbstractLand‐surface models use different formulations of stomatal conductance and plant hydraulics, and it is unclear which type of model best matches the observed surface‐atmosphere water flux. We use the North American Carbon Program data set of latent heat flux (LE) measurements from 25 sites and predictions from 9 models to evaluate models' ability to resolve subdaily dynamics of transpiration. Despite overall good forecast at the seasonal scale, the models have difficulty resolving the dynamics of intradaily hysteresis. The majority of models tend to underestimate LE in the prenoon hours and overestimate in the evening. We hypothesize that this is a result of unresolved afternoon stomatal closure due to hydrodynamic stresses. Although no model or stomata parameterization was consistently best or worst in terms of ability to predict LE, errors in model‐simulated LE were consistently largest and most variable when soil moisture was moderate and vapor pressure deficit was moderate to limiting. Nearly all models demonstrate a tendency to underestimate the degree of maximum hysteresis which, across all sites studied, is most pronounced during moisture‐limited conditions. These diurnal error patterns are consistent with models' diminished ability to accurately simulate the natural hysteresis of transpiration. We propose that the lack of representation of plant hydrodynamics is, in part, responsible for these error patterns.

Impact of large‐scale climate extremes on biospheric carbon fluxes: An intercomparison based on MsTMIP data

AbstractUnderstanding the role of climate extremes and their impact on the carbon (C) cycle is increasingly a focus of Earth system science. Climate extremes such as droughts, heat waves, or heavy precipitation events can cause substantial changes in terrestrial C fluxes. On the other hand, extreme changes in C fluxes are often, but not always, driven by extreme climate conditions. Here we present an analysis of how extremes in temperature and precipitation, and extreme changes in terrestrial C fluxes are related to each other in 10 state‐of‐the‐art terrestrial carbon models, all driven by the same climate forcing. We use model outputs from the North American Carbon Program Multi‐scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). A global‐scale analysis shows that both droughts and heat waves translate into anomalous net releases of CO2 from the land surface via different mechanisms: Droughts largely decrease gross primary production (GPP) and to a lower extent total respiration (TR), while heat waves slightly decrease GPP but increase TR. Cold and wet periods have a smaller opposite effect. Analyzing extremes in C fluxes reveals that extreme changes in GPP and TR are often caused by strong shifts in water availability, but for extremes in TR shifts in temperature are also important. Extremes in net CO2 exchange are equally strongly driven by deviations in temperature and precipitation. Models mostly agree on the sign of the C flux response to climate extremes, but model spread is large. In tropical forests, C cycle extremes are driven by water availability, whereas in boreal forests temperature plays a more important role. Models are particularly uncertain about the C flux response to extreme heat in boreal forests.

CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;, CO, and CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt; measurements from tall towers in the NOAA Earth System Research Laboratory's Global Greenhouse Gas Reference Network: instrumentation, uncertainty analysis, and recommendations for future high-accuracy greenhouse gas monitoring efforts

Abstract. A reliable and precise in situ CO2 and CO analysis system has been developed and deployed at eight sites in the NOAA Earth System Research Laboratory's (ESRL) Global Greenhouse Gas Reference Network. The network uses very tall (&gt; 300 m) television and radio transmitter towers that provide a convenient platform for mid-boundary-layer trace-gas sampling. Each analyzer has three sample inlets for profile sampling, and a complete vertical profile is obtained every 15 min. The instrument suite at one site has been augmented with a cavity ring-down spectrometer for measuring CO2 and CH4. The long-term stability of the systems in the field is typically better than 0.1 ppm for CO2, 6 ppb for CO, and 0.5 ppb for CH4, as determined from repeated standard gas measurements. The instrumentation is fully automated and includes sensors for measuring a variety of status parameters, such as temperatures, pressures, and flow rates, that are inputs for automated alerts and quality control algorithms. Detailed and time-dependent uncertainty estimates have been constructed for all of the gases, and the uncertainty framework could be readily adapted to other species or analysis systems. The design emphasizes use of off-the-shelf parts and modularity to facilitate network operations and ease of maintenance. The systems report high-quality data with &gt; 93% uptime. Recurrent problems and limitations of the current system are discussed along with general recommendations for high-accuracy trace-gas monitoring. The network is a key component of the North American Carbon Program and a useful model for future research-grade operational greenhouse gas monitoring efforts.

The North American Carbon Program Multi-Scale Synthesis and Terrestrial Model Intercomparison Project – Part 1: Overview and experimental design

Abstract. Terrestrial biosphere models (TBMs) have become an integral tool for extrapolating local observations and understanding of land–atmosphere carbon exchange to larger regions. The North American Carbon Program (NACP) Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP) is a formal model intercomparison and evaluation effort focused on improving the diagnosis and attribution of carbon exchange at regional and global scales. MsTMIP builds upon current and past synthesis activities, and has a unique framework designed to isolate, interpret, and inform understanding of how model structural differences impact estimates of carbon uptake and release. Here we provide an overview of the MsTMIP effort and describe how the MsTMIP experimental design enables the assessment and quantification of TBM structural uncertainty. Model structure refers to the types of processes considered (e.g., nutrient cycling, disturbance, lateral transport of carbon), and how these processes are represented (e.g., photosynthetic formulation, temperature sensitivity, respiration) in the models. By prescribing a common experimental protocol with standard spin-up procedures and driver data sets, we isolate any biases and variability in TBM estimates of regional and global carbon budgets resulting from differences in the models themselves (i.e., model structure) and model-specific parameter values. An initial intercomparison of model structural differences is represented using hierarchical cluster diagrams (a.k.a. dendrograms), which highlight similarities and differences in how models account for carbon cycle, vegetation, energy, and nitrogen cycle dynamics. We show that, despite the standardized protocol used to derive initial conditions, models show a high degree of variation for GPP, total living biomass, and total soil carbon, underscoring the influence of differences in model structure and parameterization on model estimates.

Evaluating the agreement between measurements and models of net ecosystem exchange at different times and timescales using wavelet coherence: an example using data from the North American Carbon Program Site-Level Interim Synthesis

Abstract. Earth system processes exhibit complex patterns across time, as do the models that seek to replicate these processes. Model output may or may not be significantly related to observations at different times and on different frequencies. Conventional model diagnostics provide an aggregate view of model–data agreement, but usually do not identify the time and frequency patterns of model–data disagreement, leaving unclear the steps required to improve model response to environmental drivers that vary on characteristic frequencies. Wavelet coherence can quantify the times and timescales at which two time series, for example time series of models and measurements, are significantly different. We applied wavelet coherence to interpret the predictions of 20 ecosystem models from the North American Carbon Program (NACP) Site-Level Interim Synthesis when confronted with eddy-covariance-measured net ecosystem exchange (NEE) from 10 ecosystems with multiple years of available data. Models were grouped into classes with similar approaches for incorporating phenology, the calculation of NEE, the inclusion of foliar nitrogen (N), and the use of model–data fusion. Models with prescribed, rather than prognostic, phenology often fit NEE observations better on annual to interannual timescales in grassland, wetland and agricultural ecosystems. Models that calculated NEE as net primary productivity (NPP) minus heterotrophic respiration (HR) rather than gross ecosystem productivity (GPP) minus ecosystem respiration (ER) fit better on annual timescales in grassland and wetland ecosystems, but models that calculated NEE as GPP minus ER were superior on monthly to seasonal timescales in two coniferous forests. Models that incorporated foliar nitrogen (N) data were successful at capturing NEE variability on interannual (multiple year) timescales at Howland Forest, Maine. The model that employed a model–data fusion approach often, but not always, resulted in improved fit to data, suggesting that improving model parameterization is important but not the only step for improving model performance. Combined with previous findings, our results suggest that the mechanisms driving daily and annual NEE variability tend to be correctly simulated, but the magnitude of these fluxes is often erroneous, suggesting that model parameterization must be improved. Few NACP models correctly predicted fluxes on seasonal and interannual timescales where spectral energy in NEE observations tends to be low, but where phenological events, multi-year oscillations in climatological drivers, and ecosystem succession are known to be important for determining ecosystem function. Mechanistic improvements to models must be made to replicate observed NEE variability on seasonal and interannual timescales.