Abstract. Most forests of the world are recovering from a past disturbance. It is well known that forest disturbances profoundly affect carbon stocks and fluxes in forest ecosystems, yet it has been a great challenge to assess disturbance impacts in estimates of forest carbon budgets. Net sequestration or loss of CO 2 by forests after disturbance follows a predictable pattern with forest recovery. Forest age, which is related to time since disturbance, is a useful surrogate variable for analyses of the impact of disturbance on forest carbon. In this study, we compiled the first continental forest age map of North America by combining forest inventory data, historical fire data, optical satellite data and the dataset from NASA's Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) project. A companion map of the standard deviations for age estimates was developed for quantifying uncertainty. We discuss the significance of the disturbance legacy from the past, as represented by current forest age structure in different regions of the US and Canada, by analyzing the causes of disturbances from land management and nature over centuries and at various scales. We also show how such information can be used with inventory data for analyzing carbon management opportunities. By combining geographic information about forest age with estimated C dynamics by forest type, it is possible to conduct a simple but powerful analysis of the net CO 2 uptake by forests, and the potential for increasing (or decreasing) this rate as a result of direct human intervention in the disturbance/age status. Finally, we describe how the forest age data can be used in large-scale carbon modeling, both for land-based biogeochemistry models and atmosphere-based inversion models, in order to improve the spatial accuracy of carbon cycle simulations.

Abstract. Most forests of the world are recovering from a past disturbance. It is well known that forest disturbances profoundly affect carbon stock and fluxes in forest ecosystems, yet it has been a great challenge to assess disturbance impacts in estimates of forest carbon budgets. Net sequestration or loss of CO2 by forests after disturbance follows a predictable pattern with forest recovery. Forest age, which is related to time since disturbance, is the most available surrogate variable for various forest carbon analyses that concern the impact of disturbance. In this study, we compiled the first continental forest age map of North America by combining forest inventory data, historical fire data, optical satellite data and the dataset from NASA's LEDAPS project. Mexico and interior Alaska are excluded from this initial map due to unavailability of all required data sets, but work is underway to develop some different methodology for these areas. We discuss the significance of disturbance legacy from the past, as represented by current forest age structure in different regions of the US and Canada, tracking back disturbances caused by human and nature over centuries and at various scales. We also show how such information can be used with inventory data for analyzing carbon management opportunities, and other modeling applications. By combining geographic information about forest age with estimated C dynamics by forest type, it is possible to conduct a simple but powerful analysis of the net CO2 uptake by forests, and the potential for increasing (or decreasing) this rate as a result of direct human intervention in the disturbance/age status. The forest age map may also help address the recent concern that the terrestrial C sink from forest regrowth in North America may saturate in the next few decades. Finally, we describe how the forest age data can be used in large-scale carbon modeling, both for land-based biogeochemistry models and atmosphere-based inversion models, in order to improve the spatial accuracy of carbon cycle simulations.

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Leaf chlorophyll is central to the exchange of carbon, water and energy between the biosphere and the atmosphere, and to the functioning of terrestrial ecosystems. This paper presents the first spatially continuous view of terrestrial leaf chlorophyll content (ChlLeaf) across a global scale. Weekly maps of ChlLeaf were produced from ENIVSAT MERIS full resolution (300 m) satellite data with a two-stage physically-based radiative transfer modelling approach. Firstly, leaf-level reflectance was derived from top-of-canopy satellite reflectance observations using 4-Scale and SAIL canopy radiative transfer models 3 for woody and non-woody vegetation, respectively. Secondly, the modelled leaf-level reflectance was used in the PROSPECT leaf-level radiative transfer model to derive ChlLeaf. The ChlLeaf retrieval algorithm was validated with measured ChlLeaf data from sample measurements at field locations, and covering six plant functional types (PFTs). Modelled results show strong relationships with field measurements, particularly for deciduous broadleaf forests (R 2 = 0.67; RMSE = 9.25 µg cm -2 ; p<0.001), croplands (R 2 = 0.41; RMSE = 13.18 µg cm -2 ; p<0.001) and evergreen needleleaf forests (R 2 = 0.47; RMSE = 10.63 µg cm -2 ; p<0.001). When the modelled results from all PFTs were considered together, the overall relationship with measured ChlLeaf remained good (R 2 = 0.47, RMSE = 10.79 µg cm -2 ; p<0.001).This result was an improvement on the relationship between measured ChlLeaf and a commonly used chlorophyll-sensitive spectral vegetation index; the MERIS Terrestrial Chlorophyll Index (MTCI; R 2 = 0.27, p<0.001). The global maps show large temporal and spatial variability in ChlLeaf, with evergreen broadleaf forests presenting the highest leaf chlorophyll values with global annual median of 54.4 µg cm -2 . Distinct seasonal ChlLeaf phenologies are also visible, particularly in deciduous plant forms, associated with budburst and crop growth, and leaf senescence. It is anticipated that this global ChlLeaf product will make an important step towards the explicit consideration of leaf-level biochemistry in terrestrial water, energy and carbon cycle modelling.

[1] Net primary productivity (NPP) is a key flux in the terrestrial ecosystem carbon balance, as it summarizes the autotrophic input into the system. Forest NPP varies predictably with stand age, and quantitative information on the NPP-age relationship for different regions and forest types is therefore fundamentally important for forest carbon cycle modeling. We used four terms to calculate NPP: annual accumulation of live biomass, annual mortality of aboveground and belowground biomass, foliage turnover to soil, and fine root turnover in soil. For U.S. forests the first two terms can be reliably estimated from the Forest Inventory and Analysis (FIA) data. Although the last two terms make up more than 50% of total NPP, direct estimates of these fluxes are highly uncertain due to limited availability of empirical relationships between aboveground biomass and foliage or fine root biomass. To resolve this problem, we developed a new approach using maps of leaf area index (LAI) and forest age at 1 km resolution to derive LAI-age relationships for 18 major forest type groups in the USA. These relationships were then used to derive foliage turnover estimates using species-specific trait data for leaf specific area and longevity. These turnover estimates were also used to derive the fine root turnover based on reliable relationships between fine root and foliage turnover. This combination of FIA data, remote sensing, and plant trait information allows for the first empirical and reliable NPP-age relationships for different forest types in the USA. The relationships show a general temporal pattern of rapid increase in NPP in the young ages of forest type groups, peak growth in the middle ages, and slow decline in the mature ages. The predicted patterns are influenced by climate conditions and can be affected by forest management. These relationships were further generalized to three major forest biomes for use by continentalscale carbon cycle models in conjunction with remotely sensed land cover types.

[1] Recent climate variability (increasing temperature, droughts) and atmospheric composition changes (nitrogen deposition, rising CO 2 concentration) along with harvesting, wildfires, and insect infestations have had significant effects on U.S. forest carbon (C) uptake. In this study, we attribute C changes in the conterminous U.S. forests to disturbance and non-disturbance factors with the help of forest inventory data, a continental stand age map, and an updated Integrated Terrestrial Ecosystem Carbon Cycle model (InTEC). We grouped factors into disturbances (harvesting, fire, insect infestation) and non-disturbances (CO 2 concentration, N deposition, and climate variability) and estimated their subsequent impacts on forest regrowth patterns. Results showed that on average, the C sink in the conterminous U.S. forests from 1950 to 2010 was 206 Tg C yr À1 with 87% (180 Tg C yr À1 ) of the sink in living biomass. Compared with the simulation of all factors combined, the estimated C sink would be reduced by 95 Tg C yr À1 if disturbance factors were omitted, and reduced by 50 Tg C yr À1 if non-disturbance factors were omitted. Our study also showed diverse regional patterns of C sinks related to the importance of driving factors. During 1980-2010, disturbance effects dominated the C changes in the South and Rocky Mountain regions, were almost equal to non-disturbance effects in the North region, and had minor effects compared with non-disturbance effects in the West Coast region.

Sun‐induced chlorophyll fluorescence (SIF) has been regarded as a promising proxy for gross primary productivity (GPP) over land. Considerable uncertainties in GPP estimation using remotely sensed SIF exist due to variations in the Sun‐satellite view observation geometry that could induce unwanted variations in SIF observation. In this study, we normalize the far‐red Global Ozone Monitoring Experiment‐2 SIF observations on sunny days to hot spot direction (SIFh) to represent sunlit leaves and compute a weighted sum of SIF (SIFt) from sunlit and shaded leaves to represent the canopy. We found that SIFh is better correlated with sunlit GPP simulated by a process‐based ecosystem model and SIFt is better correlated with the simulated total GPP than the original SIF observations. The coefficient of determination (R2) are increased by 0.04 ± 0.03, and 0.07 ± 0.04 on a global average using SIFh and SIFt, respectively. The most significant increases of the R2 (0.09 ± 0.04 for SIFt and 0.05 ± 0.03 for SIFh) appear in deciduous broadleaf forests.

Evapotranspiration (ET) is commonly estimated using the Penman‐Monteith equation, which assumes that the plant canopy is a big leaf (BL) and the water flux from vegetation is regulated by canopy stomatal conductance (Gs). However, BL has been found to be unsuitable for terrestrial biosphere models built on the carbon‐water coupling principle because it fails to capture daily variations of gross primary productivity (GPP). A two‐big‐leaf scheme (TBL) and a two‐leaf scheme (TL) that stratify a canopy into sunlit and shaded leaves have been developed to address this issue. However, there is a lack of comparison of these upscaling schemes for ET estimation, especially on the difference between TBL and TL. We find that TL shows strong performance (r2 = 0.71, root‐mean‐square error = 0.05 mm/h) in estimating ET at nine eddy covariance towers in Canada. BL simulates lower annual ET and GPP than TL and TBL. The biases of estimated ET and GPP increase with leaf area index (LAI) in BL and TBL, and the biases of TL show no trends with LAI. BL miscalculates the portions of light‐saturated and light‐unsaturated leaves in the canopy, incurring negative biases in its flux estimation. TBL and TL showed improved yet different GPP and ET estimations. This difference is attributed to the lower Gs and intercellular CO2 concentration simulated in TBL compared to their counterparts in TL. We suggest to use TL for ET modeling to avoid the uncertainty propagated from the artificial upscaling of leaf‐level processes to the canopy scale in BL and TBL.