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Christopher Potter, Steven Klooster, David Bubenheim, Hanwant B. Singh, and Ranga Myneni

Abstract

In recent years, oxygenated volatile organic chemicals (OVOCs) likeacetone have been recognized as important atmospheric constituents due to their ability to sequester reactive nitrogen in the form peroxyacetyl nitrate (PAN) and to be a source of hydroxyl radicals (HOx) in critical regions of the atmosphere. The potential biogenic sources of acetone include terrestrial plant canopies, oxidation of dead plant material, harvest of cultivated plants, biomass burning, and the oceans. These sources are poorly constrained at present in budgets of atmospheric chemistry. Based on reported laboratory, field, and satellite observations to date, an approach is presented for a biosphere model to estimate monthly emissions of acetone from the terrestrial surface to the atmosphere. The approach is driven by observed land surface climate and estimates of vegetation leaf area index (LAI), which are generated at 0.5o spatial resolution from the NOAA satellite Advanced Very High Resolution Radiometer (AVHRR). Seasonal changes in LAI are estimated using the Moderate Resolution Imaging Spectroradiometer (MODIS) radiative transfer algorithms to identify the probable times and locations of crop harvest in cultivated areas and leaf fall of newly dead plant material in noncultivated areas. Temperature-dependent emission factors are applied to derive global budgets of acetone fluxes from terrestrial plant canopies, oxidation of dead plant material, and harvest of cropland plants. The predicted global distribution of acetone emissions from live foliage is strongly weighted toward the moist tropical zones, where relatively warm temperatures and high LAI are observed in rain forest areas year-round. Predicted acetone emissions are estimated at between 54 and 172 Tg yr–1 from live foliage sources and between 7 and 22 Tg yr–1 from decay of dead foliage. These flux totals from vegetation are large enough to account for the majority of postulated biogenic acetone sources in models of global atmospheric chemistry, but our model predictions are subject to verification in subsequent flux control experiments using a variety of plant species, particularly those from humid tropical zones.

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Wolfgang Buermann, Jiarui Dong, Xubin Zeng, Ranga B. Myneni, and Robert E. Dickinson

Abstract

In this study the utility of satellite-based leaf area index (LAI) data in improving the simulation of near-surface climate with the NCAR Community Climate Model, version 3 (CCM3), GCM is evaluated. The use of mean LAI values, obtained from the Advanced Very High Resolution Radiometer Pathfinder data for the 1980s, leads to notable warming and decreased precipitation over large parts of the Northern Hemisphere lands during the boreal summer. Such warming and decreased rainfall reduces discrepancies between the simulated and observed near-surface temperature and precipitation fields. The impact of interannual vegetation extremes observed during the 1980s on near-surface climate is also investigated by utilizing the maximum and minimum LAI values from the 10-yr LAI record. Surface energy budget analysis indicates that the dominant impact of interannual LAI variations is modification of the partitioning of net radiant energy between latent and sensible heat fluxes brought about through changes in the proportion of energy absorbed by the vegetation canopy and the underlying ground and not from surface albedo changes. The enhanced latent heat activity in the greener scenario leads to an annual cooling of the earth land surface of about 0.3°C, accompanied by an increase in precipitation of 0.04 mm day−1. The tropical evergreen forests and temperate grasslands contribute most to this cooling and increased rainfall. These results illustrate the importance and utility of satellite-based vegetation LAI data in simulations of near-surface climate variability.

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Weile Wang, Bruce T. Anderson, Nathan Phillips, Robert K. Kaufmann, Christopher Potter, and Ranga B. Myneni

Abstract

Feedbacks of vegetation on summertime climate variability over the North American Grasslands are analyzed using the statistical technique of Granger causality. Results indicate that normalized difference vegetation index (NDVI) anomalies early in the growing season have a statistically measurable effect on precipitation and surface temperature later in summer. In particular, higher means and/or decreasing trends of NDVI anomalies tend to be followed by lower rainfall but higher temperatures during July through September. These results suggest that initially enhanced vegetation may deplete soil moisture faster than normal and thereby induce drier and warmer climate anomalies via the strong soil moisture–precipitation coupling in these regions. Consistent with this soil moisture–precipitation feedback mechanism, interactions between temperature and precipitation anomalies in this region indicate that moister and cooler conditions are also related to increases in precipitation during the preceding months. Because vegetation responds to soil moisture variations, interactions between vegetation and precipitation generate oscillations in NDVI anomalies at growing season time scales, which are identified in the temporal and the spectral characteristics of the precipitation–NDVI system. Spectral analysis of the precipitation–NDVI system also indicates that 1) long-term interactions (i.e., interannual and longer time scales) between the two anomalies tend to enhance one another, 2) short-term interactions (less than 2 months) tend to damp one another, and 3) intermediary-period interactions (4–8 months) are oscillatory. Together, these results support the hypothesis that vegetation may influence summertime climate variability via the land–atmosphere hydrological cycles over these semiarid grasslands.

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Arindam Samanta, Bruce T. Anderson, Sangram Ganguly, Yuri Knyazikhin, Ramakrishna R. Nemani, and Ranga B. Myneni

Abstract

Recent research indicates that the warming of the climate system resulting from increased greenhouse gas (GHG) emissions over the next century will persist for many centuries after the cessation of these emissions, principally because of the persistence of elevated atmospheric carbon dioxide (CO2) concentrations and their attendant radiative forcing. However, it is unknown whether the responses of other components of the climate system—including those related to Greenland and Antarctic ice cover, the Atlantic thermohaline circulation, the West African monsoon, and ecosystem and human welfare—would be reversed even if atmospheric CO2 concentrations were to recover to 1990 levels. Here, using a simple set of experiments employing a current-generation numerical climate model, the authors examine the response of the physical climate system to decreasing CO2 concentrations following an initial increase. Results indicate that many characteristics of the climate system, including global temperatures, precipitation, soil moisture, and sea ice, recover as CO2 concentrations decrease. However, other components of the Earth system may still exhibit nonlinear hysteresis. In these experiments, for instance, increases in stratospheric water vapor, which initially result from increased CO2 concentrations, remain present even as CO2 concentrations recover. These results suggest that identification of additional threshold behaviors in response to human-induced global climate change should focus on subcomponents of the full Earth system, including cryosphere, biosphere, and chemistry.

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Arindam Samanta, Sangram Ganguly, Eric Vermote, Ramakrishna R. Nemani, and Ranga B. Myneni

Abstract

The prevalence of clouds and aerosols and their impact on satellite-measured greenness levels of forests in southern and central Amazonia are explored in this article using 10 years of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) greenness data: normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). During the wet season (October–March), cloud contamination of greenness data is pervasive; nearly the entire region lacks uncorrupted observations. Even in the dry season (July–September), nearly 60%–66% of greenness data are corrupted, mainly because of biomass burning aerosol contamination. Under these conditions, spectrally varying residual atmospheric effects in surface reflectance data introduce artifacts into greenness indices; NDVI is known to artificially decrease, whereas EVI, given its formulation and use of blue channel surface reflectance data, shows artificial enhancement, which manifests as large patches of enhanced greenness. These issues render remote sensing of Amazon forest greenness a challenging task.

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Weile Wang, Bruce T. Anderson, Robert K. Kaufmann, and Ranga B. Myneni

Abstract

The authors use the notion of Granger causality to investigate the relationship between the North Atlantic Oscillation (NAO) index and the sea surface temperatures (SSTs) over the Northern Hemisphere. The Granger causality analysis ensures that any apparent oceanic influence upon the atmosphere (as measured by the NAO) is provided by the ocean and is not related to preexisting conditions within the NAO itself (and vice versa when looking at the atmospheric influence upon the ocean). Although this statistical technique does not imply physical forcing of one field on the other, it is generally more reliable compared to the simple lead/lagged correlation. Using this technique, the authors find that on seasonal time scales, the preceding NAO anomalies' influence on the wintertime SST field is rather restricted. Conversely, preceding SST anomalies have a statistically significant causal effect on the wintertime NAO. However, the causal relation between preceding SSTs and the wintertime NAO is limited to the Gulf Stream extension; in contrast to the canonical tripole SST pattern typically associated with the NAO, the authors do not find that SST anomalies in either the Greenland or subtropical regions have a significant causal effect on the NAO. These results suggest that the Gulf Stream SSTs have an important influence in initiating disturbances of the atmospheric circulation over the wintertime North Atlantic.

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Weile Wang, Bruce T. Anderson, Dara Entekhabi, Dong Huang, Yin Su, Robert K. Kaufmann, and Ranga B. Myneni

Abstract

This paper uses statistical and analytical techniques to investigate intraseasonal interactions between temperature and vegetation [surrogated by the normalized difference vegetation index (NDVI)] over the boreal forests. Results indicate that interactions between the two fields may be approximated as a coupled second-order system, in which the variability of NDVI and temperature of the current month is significantly regulated by lagged NDVI anomalies from the preceding two months. In particular, the influence from the one-month lagged NDVI anomalies upon both temperature and vegetation variability is generally positive, but the influence from the second-month lagged NDVI anomalies is often negative. Such regulations lead to an intrinsic oscillatory variability of vegetation at growing-season time scales across the study domain. The regulation of temperature variability by NDVI anomalies is most significant over interior Asia (Siberia), suggesting strong vegetation–atmosphere couplings over these regions. Physical mechanisms for these statistical results are investigated further with a stochastic model. The model suggests that the oscillatory variability of the temperature–NDVI system may reflect the dynamic adjustments between the two fields as they maintain a thermal balance within the soil and lower boundary layer of the atmosphere; the particular role vegetation plays in this scenario is mainly to dissipate heat and therefore reduce surface temperatures.

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Weile Wang, Bruce T. Anderson, Dara Entekhabi, Dong Huang, Robert K. Kaufmann, Christopher Potter, and Ranga B. Myneni

Abstract

A coupled linear model is derived to describe interactions between anomalous precipitation and vegetation over the North American Grasslands. The model is based on biohydrological characteristics in the semiarid environment and has components to describe the water-related vegetation variability, the long-term balance of soil moisture, and the local soil–moisture–precipitation feedbacks. Analyses show that the model captures the observed vegetation dynamics and characteristics of precipitation variability during summer over the region of interest. It demonstrates that vegetation has a preferred frequency response to precipitation forcing and has intrinsic oscillatory variability at time scales of about 8 months. When coupled to the atmospheric fields, such vegetation signals tend to enhance the magnitudes of precipitation variability at interannual or longer time scales but damp them at time scales shorter than 4 months; the oscillatory variability of precipitation at the growing season time scale (i.e., the 8-month period) is also enhanced. Similar resonance and oscillation characteristics are identified in the power spectra of observed precipitation datasets. The model results are also verified using Monte Carlo experiments.

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Zhenzhong Zeng, Shilong Piao, Laurent Z. X. Li, Tao Wang, Philippe Ciais, Xu Lian, Yuting Yang, Jiafu Mao, Xiaoying Shi, and Ranga B. Myneni

Abstract

Leaf area index (LAI) is increasing throughout the globe, implying Earth greening. Global modeling studies support this contention, yet satellite observations and model simulations have never been directly compared. Here, for the first time, a coupled land–climate model was used to quantify the potential impact of the satellite-observed Earth greening over the past 30 years on the terrestrial water cycle. The global LAI enhancement of 8% between the early 1980s and the early 2010s is modeled to have caused increases of 12.0 ± 2.4 mm yr−1 in evapotranspiration and 12.1 ± 2.7 mm yr−1 in precipitation—about 55% ± 25% and 28% ± 6% of the observed increases in land evapotranspiration and precipitation, respectively. In wet regions, the greening did not significantly decrease runoff and soil moisture because it intensified moisture recycling through a coincident increase of evapotranspiration and precipitation. But in dry regions, including the Sahel, west Asia, northern India, the western United States, and the Mediterranean coast, the greening was modeled to significantly decrease soil moisture through its coupling with the atmospheric water cycle. This modeled soil moisture response, however, might have biases resulting from the precipitation biases in the model. For example, the model dry bias might have underestimated the soil moisture response in the observed dry area (e.g., the Sahel and northern India) given that the modeled soil moisture is near the wilting point. Thus, an accurate representation of precipitation and its feedbacks in Earth system models is essential for simulations and predictions of how soil moisture responds to LAI changes, and therefore how the terrestrial water cycle responds to climate change.

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Robert E. Dickinson, Joseph A. Berry, Gordon B. Bonan, G. James Collatz, Christopher B. Field, Inez Y. Fung, Michael Goulden, William A. Hoffmann, Robert B. Jackson, Ranga Myneni, Piers J. Sellers, and Muhammad Shaikh

Abstract

Most evapotranspiration over land occurs through vegetation. The fraction of net radiation balanced by evapotranspiration depends on stomatal controls. Stomates transpire water for the leaf to assimilate carbon, depending on the canopy carbon demand, and on root uptake, if it is limiting. Canopy carbon demand in turn depends on the balancing between visible photon-driven and enzyme-driven steps in the leaf carbon physiology. The enzyme-driven component is here represented by a Rubisco-related nitrogen reservoir that interacts with plant–soil nitrogen cycling and other components of a climate model. Previous canopy carbon models included in GCMs have assumed either fixed leaf nitrogen, that is, prescribed photosynthetic capacities, or an optimization between leaf nitrogen and light levels so that in either case stomatal conductance varied only with light levels and temperature.

A nitrogen model is coupled to a previously derived but here modified carbon model and includes, besides the enzyme reservoir, additional plant stores for leaf structure and roots. It also includes organic and mineral reservoirs in the soil; the latter are generated, exchanged, and lost by biological fixation, deposition and fertilization, mineralization, nitrification, root uptake, denitrification, and leaching. The root nutrient uptake model is a novel and simple, but rigorous, treatment of soil transport and root physiological uptake. The other soil components are largely derived from previously published parameterizations and global budget constraints.

The feasibility of applying the derived biogeochemical cycling model to climate model calculations of evapotranspiration is demonstrated through its incorporation in the Biosphere–Atmosphere Transfer Scheme land model and a 17-yr Atmospheric Model Inter comparison Project II integration with the NCAR CCM3 GCM. The derived global budgets show land net primary production (NPP), fine root carbon, and various aspects of the nitrogen cycling are reasonably consistent with past studies. Time series for monthly statistics averaged over model grid points for the Amazon evergreen forest and lower Colorado basin demonstrate the coupled interannual variability of modeled precipitation, evapotranspiration, NPP, and canopy Rubisco enzymes.

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