No abstract

Spatial heterogeneities in soil hydrology have been confirmed as a key control on CO 2 and CH 4 fluxes in the Arctic tundra ecosystem. In this study, we applied a mechanistic ecosystem model, CLM-Microbe, to examine the microtopographic impacts on CO 2 and CH 4 fluxes across seven landscape types in Utqiaġvik, Alaska: trough, low-centered polygon (LCP) center, LCP transition, LCP rim, high-centered polygon (HCP) center, HCP transition, and HCP rim. We first validated the CLM-Microbe model against static-chamber measured CO 2 and CH 4 fluxes in 2013 for three landscape types: trough, LCP center, and LCP rim. Model application showed that low-elevation and thus wetter landscape types (i.e., trough, transitions, and LCP center) had larger CH 4 emissions rates with greater seasonal variations than highelevation and drier landscape types (rims and HCP center). Sensitivity analysis indicated that substrate availability for methanogenesis (acetate, CO 2 + H 2 ) is the most important factor determining CH 4 emission, and vegetation physiological properties largely affect the net ecosystem carbon exchange and ecosystem respiration in Arctic tundra ecosystems. Modeled CH 4 emissions for different microtopographic features were upscaled to the eddy covariance (EC) domain with an area-weighted approach before validation against EC-measured CH 4 fluxes. The model underestimated the EC-measured CH 4 flux by 20% and 25% at daily and hourly time steps, suggesting the importance of the time step in reporting CH 4 flux. The strong microtopographic impacts on CO 2 and CH 4 fluxes call for a model-data integration framework for better understanding and predicting carbon flux in the highly heterogeneous Arctic landscape.

We synthesized 1323 combinations of phospholipid fatty acid-derived fungal biomass C (FBC), bacterial biomass C (BBC), and fungi:bacteria (F:B) ratio in topsoil, spanning 11 major biomes. We found that the FBC, BBC, and F:B ratio display clear biogeographic patterns along latitude and environmental gradients including mean annual temperature, mean annual precipitation, net primary productivity, root C density, soil temperature, soil moisture, and edaphic properties. At the biome level, the highest FBC and BBC densities are observed in tundra, at 3684 (95% confidence interval: 1678˜8084) mg kg-1 and 428 (237˜774) mg kg-1, respectively. The lowest FBC and BBC densities were found in deserts, at 16.92 (14.4˜19.89) mg kg-1 and 6.83 (6.1˜7.65) mg kg-1, respectively. While the F:B ratio ranges from 1.8 (1.6˜2.1) in savanna to 8.6 (6.7˜11.0) in tundra. Combining an empirical model of F:B ratio with the global dataset of soil microbial biomass C, we then produced global maps for FBC and BBC in 0-30 cm topsoil. Global stock of C was estimated to be 12.6 (6.6˜16.4) Pg C in FBC and 4.3 (0.5˜10.3) Pg C in BBC in topsoil. This work creates a benchmark for explicit use of microbial data in modelling biosphere-atmosphere feedbacks in a changing environment.

Methane (CH 4 ) is a potent greenhouse gas which has 28 times global warming potential of CO 2 on a 100-years time frame (IPCC, 2013). Further, rising atmospheric CH 4 concentration has contributed to 20%-25% of

Abstract-Investigating and evaluating physical-chemicalbiological processes within an Earth system model (EMS) can be very challenging due to the complexity of both model design and software implementation. A virtual observation system (VOS) is presented to enable interactive observation of these processes during system simulation.Based on advance computing technologies, such as compiler-based software analysis, automatic code instrumentation, and high-performance data transport, the VOS provides run-time observation capability, in-situ data analytics for Earth system model simulation, model behavior adjustment opportunities through simulation steering. A VOS for a terrestrial land model simulation within the Accelerated Climate Modeling for Energy model is also presented to demonstrate the implementation details and system innovations.