International Center for Climate and Global Change Research CHESS Cluster Leading Center

Fire-induced changes in 10-year average NDVI during 2006–2015 (Yang et al., 2017)

A new paper titled “Continental-scale quantification of post-fire vegetation greenness recovery in temperate and boreal North America” was published in Remote Sensing of Environment by Yang et al., 2017.

Knowledge and information of post-fire vegetation recovery are essential for our understanding of ecosystem stability and resilience in response to present and future disturbances. Although previous studies have examined the post-fire vegetation recovery at landscape and regional levels, the lack of continent-wide analysis limits our understanding of fire effect on vegetation recovery across ecoregions and climate zones. With advanced technology in satellite observations as well as large-scale database development, it is possible for us to explore the large-scale effect of concurrent and lagged responses of terrestrial ecosystems to fire disturbances. In this study, we investigated the recovery of vegetation greenness in the early post-fire period in North America by integrating two national-level fire perimeter datasets and multiple Moderate Resolution Imaging Spectroradiometer (MODIS) products. We developed a new algorithm to estimate fire effect on the Normalized Difference Vegetation Index (NDVI) by comparing fire pixels with their surrounding unburned pixels. Our results showed the fire effect on needleleaf trees was stronger than on other vegetation types, and the high-latitude ecoregions had larger NDVI reduction and longer recovery time than the mid-latitude ecoregions. We also found the variations in vegetation composition and fire severity can explain a large fraction of post-fire vegetation greenness recovery. Our satellite-based analysis showed a longer NDVI recovery time than the existing estimates, which indicates a stronger fire effect on ecosystems than previously thought. Findings in this study indicate that more sophisticated parameterization schemes of fire severity and post-fire vegetation recovery are needed for the vegetation-fire models to better simulate the terrestrial carbon cycling and climate-ecosystem feedbacks.

Preindustrial nitrous oxide emissions (Xu et al., 2017)

A new paper titled “Preindustrial nitrous oxide emissions from the land biosphere estimated by using a global biogeochemistry model” was published in Climate of the Past by Xu et al., 2017.

To accurately assess how increased global nitrous oxide (N2O) emission has affected the climate system requires a robust estimation of the preindustrial N2O emissions since only the difference between current and preindustrial emissions represents net drivers of anthropogenic climate change. However, large uncertainty exists in previous estimates of preindustrial N2O emissions from the land biosphere, while preindustrial N2O emissions on the finer scales, such as regional, biome, or sector scales, have not been well quantified yet. In this study, we applied a processbased Dynamic Land Ecosystem Model (DLEM) to estimate the magnitude and spatial patterns of preindustrial N2O fluxes at the biome, continental, and global level as driven by multiple environmental factors. Uncertainties associated with key parameters were also evaluated. Our study indicates that the mean of the preindustrial N2O emission was approximately 6.20 Tg N yr−1, with an uncertainty range of 4.76 to 8.13 Tg N yr−1. The estimated N2O emission varied significantly at spatial and biome levels. South America, Africa,and Southern Asia accounted for 34.12, 23.85, and 18.93 %, respectively, together contributing 76.90 % of global total emission. The tropics were identified as the major source of N2O released into the atmosphere, accounting for 64.66 % of the total emission. Our multi-scale estimates provide a robust reference for assessing the climate forcing of anthropogenic N2O emission from the land biosphere.

Methane emission estimates from ruminant livestock during 1890-2014 available (Dangal et al. 2017)

A new paper titled “Methane emission from global livestock sector during 1890–2014: Magnitude, trends and spatiotemporal patterns” was published in Global Change Biology by Dangal et al., 2017.

Human demand for livestock products has increased rapidly during the past few decades largely due to dietary transition and population growth, with significant impact on climate and the environment. The contribution of ruminant livestock to greenhouse gas (GHG) emissions has been investigated extensively at various scales from regional to global, but the long-term trend, regional variation and drivers of methane (CH4) emission remain unclear. In this study, we use Intergovernmental Panel on Climate Change (IPCC) Tier II guidelines to quantify the evolution of CH4 emissions from ruminant livestock during 1890–2014. We estimate that total CH4 emissions in 2014 was 97.1 million tonnes (MT) CH4 or 2.72 Gigatonnes (Gt) CO2-eq (1 MT = 1012 g, 1 Gt = 1015 g) from ruminant livestock, which accounted for 47%–54% of all non-CO2 GHG emissions from the agricultural sector. Our estimate shows that CH4 emissions from the ruminant livestock had increased by 332% (73.6 MT CH4 or 2.06 Gt CO2-eq) since the 1890s. Our results further indicate that livestock sector in drylands had 36% higher emission intensity (CH4 emissions/km2) compared to that in nondrylands in 2014, due to the combined effect of higher rate of increase in livestock population and low feed quality. We also find that the contribution of developing regions (Africa, Asia and Latin America) to the total CH4 emissions had increased from 51.7% in the 1890s to 72.5% in the 2010s. These changes were driven by increases in livestock numbers (LU units) by up to 121% in developing regions, but decreases in livestock numbers and emission intensity (emission/km2) by up to 47% and 32%, respectively, in developed regions. Our results indicate that future increases in livestock production would likely contribute to higher CH4 emissions, unless effective strategies to mitigate GHG emissions in livestock system are implemented.