Model prediction of biome‐specific global soil respiration from 1960 to 2012

Abstract

Biome-specific soil respiration (Rs) has important yet different roles in both the carbon cycle and climate change from regional to global scales. To date, no comparable studies related to global biome-specific Rs have been conducted applying comprehensive global Rs databases. The goal of this study was to develop artificial neural network (ANN) models capable of spatially estimating global Rs and to evaluate the effects of interannual climate variations on 10 major biomes. We used 1976 annual Rs field records extracted from global Rs literature to train and test the ANN models. We determined that the best ANN model for predicting biome-specific global annual Rs was the one that applied mean annual temperature (MAT), mean annual precipitation (MAP), and biome type as inputs (r 2 = 0.60). The ANN models reported an average global Rs of 93.3 ± 6.1 Pg C yr −1 from 1960 to 2012 and an increasing trend in average global annual Rs of 0.04 Pg C yr −1 . Estimated annual Rs increased with increases in MAT and MAP in cropland, boreal forest, grassland, shrubland, and wetland biomes. Additionally, estimated annual Rs decreased with increases in MAT and increased with increases in MAP in desert and tundra biomes, and only significantly decreased with increases in MAT (r 2 = 0.87) in the savannah biome. The developed biome-specific global Rs database for global land and soil carbon models will aid in understanding the mechanisms underlying variations in soil carbon dynamics and in quantifying uncertainty in the global soil carbon cycle.

Future Global Soil Respiration Rates Will Swell Despite Regional Decreases in Temperature Sensitivity Caused by Rising Temperature

“…Our historical rate of increase (0.04 Pg C year À1 ; Figure 4a) was consistent with prior estimates of historical R SG increase (averaging 0.04 to 0.10 Pg C year À1 ; Bond-Lamberty & Thomson, 2010aThomson, , 2010bHashimoto et al, 2015;Zhao et al, 2017) and similar between the two respiration model types we examined (Figure 4a). Our historical rate of increase (0.04 Pg C year À1 ; Figure 4a) was consistent with prior estimates of historical R SG increase (averaging 0.04 to 0.10 Pg C year À1 ; Bond-Lamberty & Thomson, 2010aThomson, , 2010bHashimoto et al, 2015;Zhao et al, 2017) and similar between the two respiration model types we examined (Figure 4a).…”

“…The estimates were consistent with previous estimates based on monthly R S data but lower than previous estimates based on annual R S data (Bond-Lamberty & Thomson, 2010a, 2010bHashimoto et al, 2015;Zhao et al, 2017). The estimates were consistent with previous estimates based on monthly R S data but lower than previous estimates based on annual R S data (Bond-Lamberty & Thomson, 2010a, 2010bHashimoto et al, 2015;Zhao et al, 2017).…”

“…For example, temperature has risen much faster than the global average in high-latitude regions, 0.6°C per decade over the last 30 years (Hartmann et al, 2013). At the regional scale, a recent study found that R S rates in specific biomes are already less sensitive to temperature increases than during past decades (Zhao et al, 2017). While R S generally increases with temperature, R S rate can level off or begin to decline when temperature rises above a certain threshold (Lellei-Kovacs et al, 2011;Parker et al, 1983).…”

“…A major obstacle that causes uncertainty in regional and R SG modeling is insufficient annual R S field records with unequal distribution across localities and biomes (Raich et al, 2002). Using the updated data, Zhao et al (2017) estimated R S for 10 biomes across the globe using artificial neural network models. Based on the SRDB, Bond-Lamberty and Thomson (2010a, 2010b) developed boreal-, temperate-, and tropical-specific empirical models to estimate global R S and analyze how R S responds to global warming.…”

“…Fortunately, the number of site R S measurements has increased substantially over the past several decades, and a continuously updated annual timescale global R S database (SRDB) was developed (Bond-Lamberty & Thomsom, 2014). Nonetheless, some biomes such as deserts, savannas, shrublands, tundras, and wetlands still have less than 100 R S records to support R S modeling (Zhao et al, 2017). Building on the updated R S data in the SRDB are a new compendium of R S data for Africa (Epule, 2015), as well as field R S records from China (Song et al, 2014).…”