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Variability of fire emissions on interannual to multi-decadal timescales in two Earth System models
Connections between wildfires and modes of variability in climate are sought as a means for predicting fire activity on interannual to multi-decadal timescales. Several fire drivers, such as temperature and local drought index, have been shown to vary on these timescales, and analysis of tree-ring data suggests covariance between fires and climate oscillation indices in some regions. However, the shortness of the satellite record of global fire events limits investigations on larger spatial scales. Here we explore the interplay between climate variability and wildfire emissions with the preindustrial long control numerical experiments and historical ensembles of CESM1 and the NOAA/GFDL ESM2Mb. We find that interannual variability in fires is underpredicted in both Earth System models (ESMs) compared to present day fire emission inventories. Modeled fire emissions respond to the El Niño/southern oscillation (ENSO) and Pacific decadal oscillation (PDO) with increases in southeast Asia and boreal North America emissions, and decreases in southern North America and Sahel emissions, during the ENSO warm phase in both ESMs, and the PDO warm phase in CESM1. Additionally, CESM1 produces decreases in boreal northern hemisphere fire emissions for the warm phase of the Atlantic Meridional Oscillation. Through analysis of the long control simulations, we show that the 20th century trends in both ESMs are statistically significant, meaning that the signal of anthropogenic activity on fire emissions over this time period is detectable above the annual to decadal timescale noise. However, the trends simulated by the two ESMs are of opposite sign (CESM1 decreasing, ESM2Mb increasing), highlighting the need for improved understanding, proxy observations, and modeling to resolve this discrepancy.
Variability of fire emissions on interannual to multi-decadal timescales in two Earth System models
Connections between wildfires and modes of variability in climate are sought as a means for predicting fire activity on interannual to multi-decadal timescales. Several fire drivers, such as temperature and local drought index, have been shown to vary on these timescales, and analysis of tree-ring data suggests covariance between fires and climate oscillation indices in some regions. However, the shortness of the satellite record of global fire events limits investigations on larger spatial scales. Here we explore the interplay between climate variability and wildfire emissions with the preindustrial long control numerical experiments and historical ensembles of CESM1 and the NOAA/GFDL ESM2Mb. We find that interannual variability in fires is underpredicted in both Earth System models (ESMs) compared to present day fire emission inventories. Modeled fire emissions respond to the El Niño/southern oscillation (ENSO) and Pacific decadal oscillation (PDO) with increases in southeast Asia and boreal North America emissions, and decreases in southern North America and Sahel emissions, during the ENSO warm phase in both ESMs, and the PDO warm phase in CESM1. Additionally, CESM1 produces decreases in boreal northern hemisphere fire emissions for the warm phase of the Atlantic Meridional Oscillation. Through analysis of the long control simulations, we show that the 20th century trends in both ESMs are statistically significant, meaning that the signal of anthropogenic activity on fire emissions over this time period is detectable above the annual to decadal timescale noise. However, the trends simulated by the two ESMs are of opposite sign (CESM1 decreasing, ESM2Mb increasing), highlighting the need for improved understanding, proxy observations, and modeling to resolve this discrepancy.
Variability of fire emissions on interannual to multi-decadal timescales in two Earth System models
D S Ward (Autor:in) / E Shevliakova (Autor:in) / S Malyshev (Autor:in) / J-F Lamarque (Autor:in) / A T Wittenberg (Autor:in)
2016
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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