N. Andela

Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle

The land and ocean act as a sink for fossil-fuel emissions, thereby slowing the rise of atmospheric carbon dioxide concentrations. Although the uptake of carbon by oceanic and terrestrial processes has kept pace with accelerating carbon dioxide emissions until now, atmospheric carbon dioxide concentrations exhibit a large variability on interannual timescales, considered to be driven primarily by terrestrial ecosystem processes dominated by tropical rainforests. We use a terrestrial biogeochemical model, atmospheric carbon dioxide inversion and global carbon budget accounting methods to investigate the evolution of the terrestrial carbon sink over the past 30 years, with a focus on the underlying mechanisms responsible for the exceptionally large land carbon sink reported in 2011 (ref. 2). Here we show that our three terrestrial carbon sink estimates are in good agreement and support the finding of a 2011 record land carbon sink. Surprisingly, we find that the global carbon sink anomaly was driven by growth of semi-arid vegetation in the Southern Hemisphere, with almost 60 per cent of carbon uptake attributed to Australian ecosystems, where prevalent La Niña conditions caused up to six consecutive seasons of increased precipitation. In addition, since 1981, a six per cent expansion of vegetation cover over Australia was associated with a fourfold increase in the sensitivity of continental net carbon uptake to precipitation. Our findings suggest that the higher turnover rates of carbon pools in semi-arid biomes are an increasingly important driver of global carbon cycle inter-annual variability and that tropical rainforests may become less relevant drivers in the future. More research is needed to identify to what extent the carbon stocks accumulated during wet years are vulnerable to rapid decomposition or loss through fire in subsequent years.

How Much Global Burned Area Can Be Forecast on Seasonal Time Scales Using Sea Surface Temperatures

Home Search Collections Journals About Contact us My IOPscience How much global burned area can be forecast on seasonal time scales using sea surface temperatures? This content has been downloaded from IOPscience. Please scroll down to see the full text. 2016 Environ. Res. Lett. 11 045001 (http://iopscience.iop.org/1748-9326/11/4/045001) View the table of contents for this issue, or go to the journal homepage for more Download details: IP Address: 128.200.24.108 This content was downloaded on 28/03/2016 at 21:39 Please note that terms and conditions apply.

A pan-tropical cascade of fire driven by El Niño/Southern Oscillation

The El Niño/Southern Oscillation (ENSO) has a pronounced influence on year-to-year variations in climate1. The response of fires to this forcing2 is complex and has not been evaluated systematically across different continents. Here we use satellite data to create a climatology of burned-area and fire-emissions responses, drawing on six El Niño and six La Niña events during 1997–2016. On average, reductions in precipitation and terrestrial water storage increased fire emissions in pan-tropical forests by 133% during and following El Niño as compared with La Niña. Fires peaked in equatorial Asia early in the ENSO cycle when El Niño was strengthening (Aug–Oct), before moving to southeast Asia and northern South America (Jan–Apr), Central America (Mar–May) and the southern Amazon (Jul–Oct) during the following year. Large decreases in fire occurred across northern Australia during Sep–Oct of the second year from a reduced fuel availability. Satellite observations of aerosols and carbon monoxide provided independent confirmation of the spatiotemporal evolution of fire anomalies. The predictable cascade of fire across different tropical continents described here highlights an important time delay in the Earth system’s response to precipitation redistribution. These observations help to explain why the growth rate of atmospheric CO2 increases during El Niño3 and may contribute to improved seasonal fire forecasts.Reductions in precipitation and water storage increased fire emissions in pan-tropical forests by 133% during and following El Niño events (1997–2016). Fires follow a predictable cascade across tropical continents that may improve seasonal fire forecasts.

Emergent relationships on burned area in global satellite observations and fire-enabled vegetation models

Abstract. Recent climate changes increases fire-prone weather conditions and likely affects fire occurrence, which might impact ecosystem functioning, biogeochemical cycles, and society. Prediction of how fire impacts may change in the future is difficult because of the complexity of the controls on fire occurrence and burned area. Here we aim to assess how process-based fire-enabled Dynamic Global Vegetation Models (DGVMs) represent relationships between controlling factors and burned area. We developed a pattern-oriented model evaluation approach using the random forest (RF) algorithm to identify emergent relationships between climate, vegetation, and socioeconomic predictor variables and burned area. We applied this approach to monthly burned area time series for the period 2005–2011 from satellite observations and from DGVMs from the Fire Model Inter-comparison Project (FireMIP) that were run using a common protocol and forcing datasets. The satellite-derived relationships indicate strong sensitivity to climate variables (e.g. maximum temperature, number of wet days), vegetation properties (e.g. vegetation type, previous-season plant productivity and leaf area, woody litter), and to socioeconomic variables (e.g. human population density). DGVMs broadly reproduce the relationships to climate variables and some models to population density. Interestingly, satellite-derived responses show a strong increase of burned area with previous-season leaf area index and plant productivity in most fire-prone ecosystems which was largely underestimated by most DGVMs. Hence our pattern-oriented model evaluation approach allowed to diagnose that current fire-enabled DGVMs represent some controls on fire to a large extent but processes linking vegetation productivity and fire occurrence need to be improved to accurately simulate the role of fire under global environmental change.