scholarly journals High predictability of terrestrial carbon fluxes from an initialized decadal prediction system

2019 ◽  
Vol 14 (12) ◽  
pp. 124074 ◽  
Author(s):  
Nicole S Lovenduski ◽  
Gordon B Bonan ◽  
Stephen G Yeager ◽  
Keith Lindsay ◽  
Danica L Lombardozzi
2012 ◽  
Vol 39 (22) ◽  
pp. n/a-n/a ◽  
Author(s):  
W. A. Müller ◽  
J. Baehr ◽  
H. Haak ◽  
J. H. Jungclaus ◽  
J. Kröger ◽  
...  

Author(s):  
Daniel Senftleben ◽  
Veronika Eyring ◽  
Axel Lauer ◽  
Mattia Righi

2018 ◽  
Vol 32 (1) ◽  
pp. 127-143 ◽  
Author(s):  
Dongmin Kim ◽  
Myong-In Lee ◽  
Eunkyo Seo

Abstract The Q10 value represents the soil respiration sensitivity to temperature often used for the parameterization of the soil decomposition process has been assumed to be a constant in conventional numerical models, whereas it exhibits significant spatial and temporal variation in the observations. This study develops a new parameterization method for determining Q10 by considering the soil respiration dependence on soil temperature and moisture obtained by multiple regression for each vegetation type. This study further investigates the impacts of the new parameterization on the global terrestrial carbon flux. Our results show that a nonuniform spatial distribution of Q10 tends to better represent the dependence of the soil respiration process on heterogeneous surface vegetation type compared with the control simulation using a uniform Q10. Moreover, it tends to improve the simulation of the relationship between soil respiration and soil temperature and moisture, particularly over cold and dry regions. The modification has an impact on the soil respiration and carbon decomposition process, which changes gross primary production (GPP) through controlling nutrient assimilation from soil to vegetation. It leads to a realistic spatial distribution of GPP, particularly over high latitudes where the original model has a significant underestimation bias. Improvement in the spatial distribution of GPP leads to a substantial reduction of global mean GPP bias compared with the in situ observation-based reference data. The results highlight that the enhanced sensitivity of soil respiration to the subsurface soil temperature and moisture introduced by the nonuniform spatial distribution of Q10 has contributed to improving the simulation of the terrestrial carbon fluxes and the global carbon cycle.


2014 ◽  
Vol 14 (11) ◽  
pp. 5807-5824 ◽  
Author(s):  
H. F. Zhang ◽  
B. Z. Chen ◽  
I. T. van der Laan-Luijk ◽  
T. Machida ◽  
H. Matsueda ◽  
...  

Abstract. Current estimates of the terrestrial carbon fluxes in Asia show large uncertainties particularly in the boreal and mid-latitudes and in China. In this paper, we present an updated carbon flux estimate for Asia ("Asia" refers to lands as far west as the Urals and is divided into boreal Eurasia, temperate Eurasia and tropical Asia based on TransCom regions) by introducing aircraft CO2 measurements from the CONTRAIL (Comprehensive Observation Network for Trace gases by Airline) program into an inversion modeling system based on the CarbonTracker framework. We estimated the averaged annual total Asian terrestrial land CO2 sink was about −1.56 Pg C yr−1 over the period 2006–2010, which offsets about one-third of the fossil fuel emission from Asia (+4.15 Pg C yr−1). The uncertainty of the terrestrial uptake estimate was derived from a set of sensitivity tests and ranged from −1.07 to −1.80 Pg C yr−1, comparable to the formal Gaussian error of ±1.18 Pg C yr−1 (1-sigma). The largest sink was found in forests, predominantly in coniferous forests (−0.64 ± 0.70 Pg C yr−1) and mixed forests (−0.14 ± 0.27 Pg C yr−1); and the second and third large carbon sinks were found in grass/shrub lands and croplands, accounting for −0.44 ± 0.48 Pg C yr−1 and −0.20 ± 0.48 Pg C yr−1, respectively. The carbon fluxes per ecosystem type have large a priori Gaussian uncertainties, and the reduction of uncertainty based on assimilation of sparse observations over Asia is modest (8.7–25.5%) for most individual ecosystems. The ecosystem flux adjustments follow the detailed a priori spatial patterns by design, which further increases the reliance on the a priori biosphere exchange model. The peak-to-peak amplitude of inter-annual variability (IAV) was 0.57 Pg C yr−1 ranging from −1.71 Pg C yr−1 to −2.28 Pg C yr−1. The IAV analysis reveals that the Asian CO2 sink was sensitive to climate variations, with the lowest uptake in 2010 concurrent with a summer flood and autumn drought and the largest CO2 sink in 2009 owing to favorable temperature and plentiful precipitation conditions. We also found the inclusion of the CONTRAIL data in the inversion modeling system reduced the uncertainty by 11% over the whole Asian region, with a large reduction in the southeast of boreal Eurasia, southeast of temperate Eurasia and most tropical Asian areas.


2019 ◽  
Vol 71 (1) ◽  
pp. 1618678 ◽  
Author(s):  
Hendrik Feldmann ◽  
Joaquim g. Pinto ◽  
Natalie Laube ◽  
Marianne Uhlig ◽  
Julia Moemken ◽  
...  

2014 ◽  
Vol 27 (20) ◽  
pp. 7550-7567 ◽  
Author(s):  
Jeff R. Knight ◽  
Martin B. Andrews ◽  
Doug M. Smith ◽  
Alberto Arribas ◽  
Andrew W. Colman ◽  
...  

Abstract Decadal climate predictions are now established as a source of information on future climate alongside longer-term climate projections. This information has the potential to provide key evidence for decisions on climate change adaptation, especially at regional scales. Its importance implies that following the creation of an initial generation of decadal prediction systems, a process of continual development is needed to produce successive versions with better predictive skill. Here, a new version of the Met Office Hadley Centre Decadal Prediction System (DePreSys 2) is introduced, which builds upon the success of the original DePreSys. DePreSys 2 benefits from inclusion of a newer and more realistic climate model, the Hadley Centre Global Environmental Model version 3 (HadGEM3), but shares a very similar approach to initialization with its predecessor. By performing a large suite of reforecasts, it is shown that DePreSys 2 offers improved skill in predicting climate several years ahead. Differences in skill between the two systems are likely due to a multitude of differences between the underlying climate models, but it is demonstrated herein that improved simulation of tropical Pacific variability is a key source of the improved skill in DePreSys 2. While DePreSys 2 is clearly more skilful than DePreSys in a global sense, it is shown that decreases in skill in some high-latitude regions are related to errors in representing long-term trends. Detrending the results focuses on the prediction of decadal time-scale variability, and shows that the improvement in skill in DePreSys 2 is even more marked.


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