Biochar application to soils may increase carbon (C) sequestration due to the inputs of recalcitrant organic C. However, the effects of biochar application on the soil greenhouse gases (GHGs) fluxes appear variable among many case studies; therefore the efficacy of biochar as a carbon sequestration agent for climate change mitigation remains uncertain. We performed a meta-analysis of 91 published papers with 552 paired comparisons to obtain a central tendency of three main GHG fluxes (i.e., CO 2 , CH 4 , and N 2 O) in response to biochar application. Our results showed that biochar application significantly increased soil CO 2 fluxes by 22.14%, but decreased N 2 O fluxes by 30.92% and did not affect CH 4 fluxes. As a consequence, biochar application may significantly contribute to increased global warming potential (GWP) of total soil GHG fluxes due to the large stimulation of CO 2 fluxes. However, soil CO 2 fluxes were suppressed when biochar was added to fertilized soils, indicating that Accepted ArticleThis article is protected by copyright. All rights reserved. biochar application is unlikely to stimulate CO 2 fluxes in the agriculture sector, in which N fertilizer inputs are common. Responses of soil GHG fluxes mainly varied with biochar feedstock source and soil texture, and the pyrolysis temperature of biochar. Soil and biochar pH, biochar applied rate and latitude also influence soil GHG fluxes, but to a more limited extent.Our findings provide a scientific basis for developing more rational strategies towards widespread adoption of biochar as a soil amendment for climate change mitigation.

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Himalayan glaciers are a vital water source for people in the high regions of Asia. Their complete melting would be a crisis for approximately 1 billion people. Albedo is one of the key parameters that affect the energy balance of the snow and ice surfaces. Since 2000, albedos have been retrieved from satellite data for eleven representative Himalayan glaciers. It was found that most of the glaciers showed declining trends in the albedo of their upper areas, indicating that they have generally become darker in the past decade. A simulation case study in conjunction with in situ measurements showed that light-absorbing constituents (e.g., black carbon and dust) could be partly responsible for this phenomenon during late springtime; the background regional warming could also be responsible. The current surface radiation absorption in Himalayan glaciers could lead to significant melting, causing most of them to be in danger of rapid mass loss.

The terrestrial carbon (C) cycle has been commonly represented by a series of C balance equations to track C influxes into and effluxes out of individual pools in earth system models (ESMs). This representation matches our understanding of C cycle processes well but makes it difficult to track model behaviors. It is also computationally expensive, limiting the ability to conduct comprehensive parametric sensitivity analyses. To overcome these challenges, we have developed a matrix approach, which reorganizes the C balance equations in the original ESM into one matrix equation without changing any modeled C cycle processes and mechanisms. We applied the matrix approach to the Community Land Model (CLM4.5) with vertically-resolved biogeochemistry. The matrix equation exactly reproduces litter and soil organic carbon (SOC) dynamics of the standard CLM4.5 across different spatial-temporal scales. The matrix approach enables effective diagnosis of system properties such as C residence time and attribution of global change impacts to relevant processes. We illustrated, for example, the impacts of CO fertilization on litter and SOC dynamics can be easily decomposed into the relative contributions from C input, allocation of external C into different C pools, nitrogen regulation, altered soil environmental conditions, and vertical mixing along the soil profile. In addition, the matrix tool can accelerate model spin-up, permit thorough parametric sensitivity tests, enable pool-based data assimilation, and facilitate tracking and benchmarking of model behaviors. Overall, the matrix approach can make a broad range of future modeling activities more efficient and effective.