Terrestrial Biosphere Model Performance for Inter-Annual Variability of Land-Atmosphere CO2 Exchange

Interannual variability in biosphere-atmosphere exchange of CO2 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. Herein, 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 CO2 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.Organismic and Evolutionary Biolog

Seasonal variation in the canopy color of temperate evergreen conifer forests

Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near‐surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated. Here, we integrate on‐the‐ground phenological observations, leaf‐level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower‐based CO₂ flux measurements, and a predictive model to simulate seasonal canopy color dynamics. We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter‐dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy‐level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature‐based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color. These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color‐based vegetation indices

Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery

Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate change and variability. Here we present a series of datasets, together consisting of almost 750 years of observations, characterizing vegetation phenology in diverse ecosystems across North America. Our data are derived from conventional, visible-wavelength, automated digital camera imagery collected through the PhenoCam network. For each archived image, we extracted RGB (red, green, blue) colour channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 min) imagery, we derived time series characterizing vegetation colour, including “canopy greenness”, processed to 1- and 3-day intervals. For ecosystems with one or more annual cycles of vegetation activity, we provide estimates, with uncertainties, for the start of the “greenness rising” and end of the “greenness falling” stages. The database can be used for phenological model validation and development, evaluation of satellite remote sensing data products, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems

Above-ground biomass and structure of 260 African tropical forests.

We report above-ground biomass (AGB), basal area, stem density and wood mass density estimates from 260 sample plots (mean size: 1.2 ha) in intact closed-canopy tropical forests across 12 African countries. Mean AGB is 395.7 Mg dry mass ha⁻¹ (95% CI: 14.3), substantially higher than Amazonian values, with the Congo Basin and contiguous forest region attaining AGB values (429 Mg ha⁻¹) similar to those of Bornean forests, and significantly greater than East or West African forests. AGB therefore appears generally higher in palaeo- compared with neotropical forests. However, mean stem density is low (426 ± 11 stems ha⁻¹ greater than or equal to 100 mm diameter) compared with both Amazonian and Bornean forests (cf. approx. 600) and is the signature structural feature of African tropical forests. While spatial autocorrelation complicates analyses, AGB shows a positive relationship with rainfall in the driest nine months of the year, and an opposite association with the wettest three months of the year; a negative relationship with temperature; positive relationship with clay-rich soils; and negative relationships with C : N ratio (suggesting a positive soil phosphorus-AGB relationship), and soil fertility computed as the sum of base cations. The results indicate that AGB is mediated by both climate and soils, and suggest that the AGB of African closed-canopy tropical forests may be particularly sensitive to future precipitation and temperature changes

Earlier snowmelt may lead to late season declines in plant productivity and carbon sequestration in Arctic tundra ecosystems

Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO2 later in the season

To what extent do welfare states compensate for the cost of children? The joint impact of taxes, benefits and public goods and services

In order to alleviate child poverty, contemporary European welfare states have shifted their focus increasingly towards child-centred investment strategies. However, studies examining the generosity of welfare states to families with children focus mainly on cash benefit packages, or on government expenditure, while not taking into account the actual out-of-pocket costs families have to make to fulfil their needs. This article aims at contributing to existing studies by: (1) empirically assessing the needs and costs of children across welfare states by making use of cross-nationally comparable reference budgets, while taking into account publicly provided or subsidised services; (2) simulating the cash benefits and taxes that affect households with children through the tax–benefit system, by making use of the new Hypothetical Household Tool (HHoT) in EUROMOD; and (3) combining both types of information in order to compare the essential out-of-pocket costs for children between 6 and 18 years old with the simulated cash benefit packages. We propose a new indicator that can be used to assess welfare state generosity to families with children: the child cost compensation indicator. The use of the indicator is empirically illustrated by comparing six European welfare states: Belgium, Finland, Greece, Hungary, Italy and Spain. The article shows that, even though with important cross-national variation, cash transfers generally amount to less than 60 percent of the cost of children. Although in five out of six countries support for families is higher at the lower end of the income distribution, for households living on a low gross wage, the income of a family with children is less adequate compared to a similar childless family and is in many cases insufficient to participate adequately in society

To what extent do welfare states compensate for the cost of children? The joint impact of taxes, benefits and public goods and services

In order to alleviate child poverty, contemporary European welfare states have shifted their focus increasingly towards child-centred investment strategies. However, studies examining the generosity of welfare states to families with children focus mainly on cash benefit packages, or on government expenditure, while not taking into account the actual out-of-pocket costs families have to make to fulfil their needs. This article aims at contributing to existing studies by: (1) empirically assessing the needs and costs of children across welfare states by making use of cross-nationally comparable reference budgets, while taking into account publicly provided or subsidised services; (2) simulating the cash benefits and taxes that affect households with children through the tax–benefit system, by making use of the new Hypothetical Household Tool (HHoT) in EUROMOD; and (3) combining both types of information in order to compare the essential out-of-pocket costs for children between 6 and 18 years old with the simulated cash benefit packages. We propose a new indicator that can be used to assess welfare state generosity to families with children: the child cost compensation indicator. The use of the indicator is empirically illustrated by comparing six European welfare states: Belgium, Finland, Greece, Hungary, Italy and Spain. The article shows that, even though with important cross-national variation, cash transfers generally amount to less than 60 percent of the cost of children. Although in five out of six countries support for families is higher at the lower end of the income distribution, for households living on a low gross wage, the income of a family with children is less adequate compared to a similar childless family and is in many cases insufficient to participate adequately in society. </jats:p

Impacts and uncertainties of upscaling of remote sensing data validation for a semi-arid woodland.

Over the last decade, remote-sensing products found their use in numerous public applications and scientific research. Frequent validation of remote-sensing products is necessary to ensure the quality and accuracy. An important step in this process is the upscaling from field measurements of leaf area index (LAI) or other biogeophysical variables to the scale of a high-resolution satellite image. Unlike other vegetation types, semi-arid woodlands are often characterized by a distinct vegetation pattern, a low vegetation cover and site variations in bare soil reflectance which influence the upscaling process. This paper focuses on the upscaling of LAI field measurements of a semi-arid woodland in northwest China and the uncertainties associated with it. The LAI measurements were scaled up using two different approaches. The first upscaling method uses an ordinary average of the LAI measurements. The alternative method uses an area-based weighted average. The vegetation characteristics showed abundant small vegetation patches which were not accurately reflected in the systematic LAI field measurements. Therefore, the alternative method which reflected the vegetation structure more accurately needs to be considered. We conclude that an area-weighted average based on the fractional green vegetation cover is to be preferred over a simple average. If possible destructive sampling of the LAI is be preferred over indirect LAI measurements to reduce the errors associated with vegetation clumping and the presence of abundant non-green biomass. © 2008 Elsevier Ltd. All rights reserved.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Tracking forest phenology and seasonal physiology using digital repeat photography : a critical assessment

Digital repeat photography is becoming widely used for near-surface remote sensing of vegetation. Canopy greenness, which has been used extensively for phenological applications, can be readily quantified from camera images. Important questions remain, however, as to whether the observed changes in canopy greenness are directly related to changes in leaf-level traits, changes in canopy structure, or some combination thereof. We investigated relationships between canopy greenness and various metrics of canopy structure and function, using five years. 2008-2012. of automated digital imagery, ground observations of phenological transitions, leaf area index (LAI) measurements, and eddy covariance estimates of gross ecosystem photosynthesis from the Harvard Forest, a temperate deciduous forest in the northeastern United States. Additionally, we sampled canopy sunlit leaves on a weekly basis throughout the growing season of 2011. We measured physiological and morphological traits including leaf size, mass (wet/dry), nitrogen content, chlorophyll fluorescence, and spectral reflectance and characterized individual leaf color with flatbed scanner imagery. Our results show that observed spring and autumn phenological transition dates are well captured by information extracted from digital repeat photography. However, spring development of both LAI and the measured physiological and morphological traits are shown to lag behind spring increases in canopy greenness, which rises very quickly to its maximum value before leaves are even half their final size. Based on the hypothesis that changes in canopy greenness represent the aggregate effect of changes in both leaf-level properties (specifically, leaf color) and changes in canopy structure (specifically, LAI), we developed a two end-member mixing model. With just a single free parameter, the model was able to reproduce the observed seasonal trajectory of canopy greenness. This analysis shows that canopy greenness is relatively insensitive to changes in LAI at high LAI levels, which we further demonstrate by assessing the impact of an ice storm on both LAI and canopy greenness. Our study provides new insights into the mechanisms driving seasonal changes in canopy greenness retrieved from digital camera imagery. The nonlinear relationship between canopy greenness and canopy LAI has important implications both for phenological research applications and for assessing responses of vegetation to disturbances.12 page(s