Inconsistencies of interannual variability and trends in long-term satellite leaf area index products

Understanding the long-term performance of global satellite leaf area index (LAI) products is important for global change research. However, few effort has been devoted to evaluating the long-term time-series consistencies of LAI products. This study compared four long-term LAI products (GLASS, GLOBMAP, LAI3g, and TCDR) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products (GEOV1, MERIS, MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long-term LAI products. In general, there were marked discrepancies between the four long-term LAI products. During the pre-MODIS period (1982-1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS>LAI3g>TCDR>GLOBMAP. The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP. During the overlap period (2003-2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI3g an increasing trend. GEOV1, MERIS, and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI3g. During both periods, the R2 of detrended anomalies between the four long-term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long-term LAI products were considerably different over the two periods, and the differences followed the order GLASS>LAI3g>TCDR>GLOBMAP. Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long-term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long-term LAI products

Spatiotemporal patterns of terrestrial gross primary production: A review

This is the final version of the article. Available from American Geophysical Union via the DOI in this record.There is another record for this publication in ORE at http://hdl.handle.net/10871/21007Great advances have been made in the last decade in quantifying and understanding the spatiotemporal patterns of terrestrial gross primary production (GPP) with ground, atmospheric, and space observations. However, although global GPP estimates exist, each data set relies upon assumptions and none of the available data are based only on measurements. Consequently, there is no consensus on the global total GPP and large uncertainties exist in its benchmarking. The objective of this review is to assess how the different available data sets predict the spatiotemporal patterns of GPP, identify the differences among data sets, and highlight the main advantages/disadvantages of each data set. We compare GPP estimates for the historical period (1990-2009) from two observation-based data sets (Model Tree Ensemble and Moderate Resolution Imaging Spectroradiometer) to coupled carbon-climate models and terrestrial carbon cycle models from the Fifth Climate Model Intercomparison Project and TRENDY projects and to a new hybrid data set (CARBONES). Results show a large range in the mean global GPP estimates. The different data sets broadly agree on GPP seasonal cycle in terms of phasing, while there is still discrepancy on the amplitude. For interannual variability (IAV) and trends, there is a clear separation between the observation-based data that show little IAV and trend, while the process-based models have large GPP variability and significant trends. These results suggest that there is an urgent need to improve observation-based data sets and develop carbon cycle modeling with processes that are currently treated either very simplistically to correctly estimate present GPP and better quantify the future uptake of carbon dioxide by the world's vegetation.European Commission's Seventh Framework Programme. Grant Numbers: 238366, 28267

A model-based constraint on CO₂ fertilisation

We derive a constraint on the strength of CO2 fertilisation of the terrestrial biosphere through a “top-down” approach, calibrating Earth system model parameters constrained by the post-industrial increase of atmospheric CO2 concentration. We derive a probabilistic prediction for the globally averaged strength of CO2 fertilisation in nature, for the period 1850 to 2000 AD, implicitly net of other limiting factors such as nutrient availability. The approach yields an estimate that is independent of CO2 enrichment experiments. To achieve this, an essential requirement was the incorpo- ration of a land use change (LUC) scheme into the GENIE Earth system model. Using output from a 671-member ensemble of transient GENIE simulations, we build an emulator of the change in atmospheric CO2 concentration change since the preindustrial period. We use this emulator to sample the 28-dimensional input parameter space. A Bayesian calibration of the emulator output suggests that the increase in gross primary productivity (GPP) in response to a doubling of CO2 from preindustrial values is very likely (90 % confidence) to exceed 20 %, with a most likely value of 40–60 %. It is important to note that we do not represent all of the possible contributing mechanisms to the terrestrial sink. The missing processes are subsumed into our calibration of CO2 fertilisation, which therefore represents the combined effect of CO2 fertilisation and additional missing processes. If the missing processes are a net sink then our estimate represents an upper bound. We derive calibrated estimates of carbon fluxes that are consistent with existing estimates. The present-day land–atmosphere flux (1990–2000) is estimated at −0.7 GTC yr−1 (likely, 66 % confidence, in the range 0.4 to −1.7 GTC yr−1). The present-day ocean–atmosphere flux (1990–2000) is estimated to be −2.3 GTC yr−1 (likely in the range −1.8 to −2.7 GTC yr−1). We estimate cumulative net land emissions over the post-industrial period (land use change emissions net of the CO2 fertilisation and climate sinks) to be 66 GTC, likely to lie in the range 0 to 128 GTC

Generalization and evaluation of the process-based forest ecosystem model PnET-CN for other biomes

Terrestrial ecosystems play an important role in carbon, water, and nitrogen cycling. Process-based ecosystem models, including PnET-CN, have been widely used to simulate ecosystem processes during the last two decades. PnET-CN is a forest ecosystem model, originally designed to predict ecosystem carbon, water, and nitrogen dynamics of temperate forests under a variety of circumstances. Among terrestrial ecosystem models, PnET-CN offers unique benefits, including simplicity and transparency of its structure, reliance on data-driven parameterization rather than calibration, and use of generalizeable relationships that provide explicit linkages among carbon, water and nitrogen cycles. The objective of our study was to apply PnET-CN to non-forest biomes: grasslands, shrublands, and savannas. We determined parameter values for grasslands and shrublands using the literature and ecophysiological databases. To assess the usefulness of PnET-CN in these ecosystems, we simulated carbon and water fluxes for six AmeriFlux sites: two grassland sites (Konza Prairie and Fermi Prairie), two open shrubland sites (Heritage Land Conservancy Pinyon Juniper Woodland and Sevilleta Desert Shrubland), and two woody savanna sites (Freeman Ranch and Tonzi Ranch). Grasslands and shrublands were simulated using the biome-specific parameters, and savannas were simulated as mixtures of grasslands and forests. For each site, we used flux observations to evaluate modeled carbon and water fluxes: gross primary productivity (GPP), ecosystem respiration (ER), net ecosystem productivity (NEP), evapotranspiration (ET), and water yield. We also evaluated simulated water use efficiency (WUE). PnET-CN generally captured the magnitude, seasonality, and interannual variability of carbon and water fluxes as well as WUE for grasslands, shrublands, and savannas. Overall, our results show that PnET-CN is a promising tool for modeling ecosystem carbon and water fluxes for non-forest biomes (grasslands, shrublands, and savannas), and especially for modeling GPP in mature biomes. Limitations in model performance included an overestimation of seasonal variability in GPP and ET for the two shrubland sites and overestimation of early season ER for the two shrubland sites and Freeman Ranch. Future modifications of PnET-CN for non-forest biomes should focus on belowground processes, including water storage in dry shrubland soils, root growth and respiration in grasslands, and soil carbon fluxes for all biomes

Spatiotemporal patterns of terrestrial gross primary production: A review

This is the final version of the article. Available from American Geophysical Union via the DOI in this record.There is another record for this publication in ORE at http://hdl.handle.net/10871/30934Great advances have been made in the last decade in quantifying and understanding the spatiotemporal patterns of terrestrial gross primary production (GPP) with ground, atmospheric, and space observations. However, although global GPP estimates exist, each data set relies upon assumptions and none of the available data are based only on measurements. Consequently, there is no consensus on the global total GPP and large uncertainties exist in its benchmarking. The objective of this review is to assess how the different available data sets predict the spatiotemporal patterns of GPP, identify the differences among data sets, and highlight the main advantages/disadvantages of each data set. We compare GPP estimates for the historical period (1990-2009) from two observation-based data sets (Model Tree Ensemble and Moderate Resolution Imaging Spectroradiometer) to coupled carbon-climate models and terrestrial carbon cycle models from the Fifth Climate Model Intercomparison Project and TRENDY projects and to a new hybrid data set (CARBONES). Results show a large range in the mean global GPP estimates. The different data sets broadly agree on GPP seasonal cycle in terms of phasing, while there is still discrepancy on the amplitude. For interannual variability (IAV) and trends, there is a clear separation between the observation-based data that show little IAV and trend, while the process-based models have large GPP variability and significant trends. These results suggest that there is an urgent need to improve observation-based data sets and develop carbon cycle modeling with processes that are currently treated either very simplistically to correctly estimate present GPP and better quantify the future uptake of carbon dioxide by the world's vegetation.European Commission's Seventh Framework Programme. Grant Numbers: 238366, 28267

Global vegetation variability and its response to elevated CO2, global warming, and climate variability – a study using the offline SSiB4/TRIFFID model and satellite data

Abstract. The climate regime shift during the 1980s had a substantial impact on the terrestrial ecosystems and vegetation at different scales. However, the mechanisms driving vegetation changes, before and after the shift, remain unclear. In this study, we used a biophysical-dynamic vegetation model to estimate large-scale trends in terms of carbon fixation, vegetation growth, and expansion during the period 1958–2007, and to attribute these changes to environmental drivers including elevated atmospheric CO2 concentration (hereafter eCO2), global warming, and climate variability (hereafter CV). Simulated Leaf Area Index (LAI) and Gross Primary Product (GPP) were evaluated against observation-based data. Significant spatial correlations are found (correlations > 0.87), along with regionally varying temporal correlations of 0.34–0.80 for LAI and 0.45–0.83 for GPP. More than 40 % of the global land area shows significant trends in LAI and GPP since the 1950s: 11.7 % and 19.3 % of land has consistently positive LAI and GPP trends, respectively; while 17.1 % and 20.1 % of land, saw LAI and GPP trends respectively, reverse during the 1980s. Vegetation fraction cover (FRAC) trends, representing vegetation expansion/shrinking, are found at the edges of semi-arid areas and polar areas. Overall, eCO2 consistently contributes to positive LAI and GPP trends in the tropics. Global warming is shown to mostly affected LAI, with positive effects in high latitudes and negative effects in subtropical semi-arid areas. CV is found to dominate the variability of FRAC, LAI, and GPP in the semi-humid and semi-arid areas. The eCO2 and global warming effects increased after the 1980s, while the CV effect reversed during the 1980s. In addition, plant competition is shown to have played an important role in determining which driver dominated the regional trends. This paper presents a new insight into ecosystem variability and changes in the varying climate since the 1950s

Modeling and Monitoring Terrestrial Primary Production in a Changing Global Environment: Toward a Multiscale Synthesis of Observation and Simulation

There is a critical need to monitor and predict terrestrial primary production, the key indicator of ecosystem functioning, in a changing global environment. Here we provide a brief review of three major approaches to monitoring and predicting terrestrial primary production: (1) ground-based field measurements, (2) satellite-based observations, and (3) process-based ecosystem modelling. Much uncertainty exists in the multi-approach estimations of terrestrial gross primary production (GPP) and net primary production (NPP). To improve the capacity of model simulation and prediction, it is essential to evaluate ecosystem models against ground and satellite-based measurements and observations. As a case, we have shown the performance of the dynamic land ecosystem model (DLEM) at various scales from site to region to global. We also discuss how terrestrial primary production might respond to climate change and increasing atmospheric CO2 and uncertainties associated with model and data. Further progress in monitoring and predicting terrestrial primary production requires a multiscale synthesis of observations and model simulations. In the Anthropocene era in which human activity has indeed changed the Earth’s biosphere, therefore, it is essential to incorporate the socioeconomic component into terrestrial ecosystem models for accurately estimating and predicting terrestrial primary production in a changing global environment