Methane emissions from Arctic landscapes during 2000–2015: an analysis with land and lake biogeochemistry models

Wetlands and freshwater bodies (mainly lakes) are the largest natural sources of the greenhouse gas CH4 to the atmosphere. Great efforts have been made to quantify these source emissions and their uncertainties. Previous research suggests that there might be significant uncertainties coming from “double accounting” emissions from freshwater bodies and wetlands. Here we quantify the methane emissions from both land and freshwater bodies in the pan-Arctic with two process-based biogeochemistry models by minimizing the double accounting at the landscape scale. Two non-overlapping dynamic areal change datasets are used to drive the models. We estimate that the total methane emissions from the pan-Arctic are 36.46 ± 1.02 Tg CH4 yr−1 during 2000–2015, of which wetlands and freshwater bodies are 21.69 ± 0.59 Tg CH4 yr−1 and 14.76 ± 0.44 Tg CH4 yr−1, respectively. Our estimation narrows the difference between previous bottom-up (53.9 Tg CH4 yr−1) and top-down (29 Tg CH4 yr−1) estimates. Our correlation analysis shows that air temperature is the most important driver for methane emissions of inland water systems. Wetland emissions are also significantly affected by vapor pressure, while lake emissions are more influenced by precipitation and landscape areal changes. Sensitivity tests indicate that pan-Arctic lake CH4 emissions were highly influenced by air temperature but less by lake sediment carbon increase.</p

Peatlands and their carbon dynamics in northern high latitudes from 1990 to 2300: a process-based biogeochemistry model analysis

Northern peatlands have been a large C sink during the Holocene, but whether they will keep being a C sink under future climate change is uncertain. This study simulates the responses of northern peatlands to future climate until 2300 with a Peatland version Terrestrial Ecosystem Model (PTEM). The simulations are driven with two sets of CMIP5 climate data (IPSL-CM5A-LR and bcc-csm1-1) under three warming scenarios (RCPs 2.6, 4.5 and 8.5). Peatland area expansion, shrinkage, and C accumulation and decomposition are modeled. In the 21st century, northern peatlands are projected to be a C source of 1.2–13.3 Pg C under all climate scenarios except for RCP 2.6 of bcc-csm1-1 (a sink of 0.8 Pg C). During 2100–2300, northern peatlands under all scenarios are a C source under IPSL-CM5A-LR scenarios, being larger sources than bcc-csm1-1 scenarios (5.9–118.3 vs. 0.7–87.6 Pg C). C sources are attributed to (1) the peatland water table depth (WTD) becoming deeper and permafrost thaw increasing decomposition rate; (2) net primary production (NPP) not increasing much as climate warms because peat drying suppresses net N mineralization; and (3) as WTD deepens, peatlands switching from moss–herbaceous dominated to moss–woody dominated, while woody plants require more N for productivity. Under IPSL-CM5A-LR scenarios, northern peatlands remain as a C sink until the pan-Arctic annual temperature reaches −2.6 to −2.89 ∘C, while this threshold is −2.09 to −2.35 ∘C under bcc-csm1-1 scenarios. This study predicts a northern peatland sink-to-source shift in around 2050, earlier than previous estimates of after 2100, and emphasizes the vulnerability of northern peatlands to climate change.</p

An advanced meshless method for time fractional diffusion equation

Recently, because of the new developments in sustainable engineering and renewable energy, which are usually governed by a series of fractional partial differential equations (FPDEs), the numerical modelling and simulation for fractional calculus are attracting more and more attention from researchers. The current dominant numerical method for modeling FPDE is Finite Difference Method (FDM), which is based on a pre-defined grid leading to inherited issues or shortcomings including difficulty in simulation of problems with the complex problem domain and in using irregularly distributed nodes. Because of its distinguished advantages, the meshless method has good potential in simulation of FPDEs. This paper aims to develop an implicit meshless collocation technique for FPDE. The discrete system of FPDEs is obtained by using the meshless shape functions and the meshless collocation formulation. The stability and convergence of this meshless approach are investigated theoretically and numerically. The numerical examples with regular and irregular nodal distributions are used to validate and investigate accuracy and efficiency of the newly developed meshless formulation. It is concluded that the present meshless formulation is very effective for the modeling and simulation of fractional partial differential equations

Active optical clock based on four-level quantum system

Active optical clock, a new conception of atomic clock, has been proposed recently. In this report, we propose a scheme of active optical clock based on four-level quantum system. The final accuracy and stability of two-level quantum system are limited by second-order Doppler shift of thermal atomic beam. To three-level quantum system, they are mainly limited by light shift of pumping laser field. These limitations can be avoided effectively by applying the scheme proposed here. Rubidium atom four-level quantum system, as a typical example, is discussed in this paper. The population inversion between $6S_{1/2}$ and $5P_{3/2}$ states can be built up at a time scale of $10^{-6}$s. With the mechanism of active optical clock, in which the cavity mode linewidth is much wider than that of the laser gain profile, it can output a laser with quantum-limited linewidth narrower than 1 Hz in theory. An experimental configuration is designed to realize this active optical clock.Comment: 5 page

Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm

Reservoirs and dams are vital human-built infrastructures that play essential roles in flood control, hydroelectric power generation, water supply, navigation, and other functions. The realization of those functions requires efficient reservoir operation, and the effective controls on the outflow from a reservoir or dam. Over the last decade, artificial intelligence (AI) techniques have become increasingly popular in the field of streamflow forecasts, reservoir operation planning and scheduling approaches. In this study, three AI models, namely, the backpropagation (BP) neural network, support vector regression (SVR) technique, and long short-term memory (LSTM) model, are employed to simulate reservoir operation at monthly, daily, and hourly time scales, using approximately 30 years of historical reservoir operation records. This study aims to summarize the influence of the parameter settings on model performance and to explore the applicability of the LSTM model to reservoir operation simulation. The results show the following: (1) for the BP neural network and LSTM model, the effects of the number of maximum iterations on model performance should be prioritized; for the SVR model, the simulation performance is directly related to the selection of the kernel function, and sigmoid and RBF kernel functions should be prioritized; (2) the BP neural network and SVR are suitable for the model to learn the operation rules of a reservoir from a small amount of data; and (3) the LSTM model is able to effectively reduce the time consumption and memory storage required by other AI models, and demonstrate good capability in simulating low-flow conditions and the outflow curve for the peak operation period

Technical Note: An efficient method for accelerating the spin-up process for process-based biogeochemistry models

To better understand the role of terrestrial ecosystems in the global carbon cycle and their feedbacks to the global climate system, process-based biogeochemistry models need to be improved with respect to model parameterization and model structure. To achieve these improvements, the spin-up time for those differential equation-based models needs to be shortened. Here, an algorithm for a fast spin-up was developed by finding the exact solution of a linearized system representing the cyclo-stationary state of a model and implemented in a biogeochemistry model, the Terrestrial Ecosystem Model (TEM). With the new spin-up algorithm, we showed that the model reached a steady state in less than 10 years of computing time, while the original method requires more than 200 years on average of model run. For the test sites with five different plant functional types, the new method saves over 90 % of the original spin-up time in site-level simulations. In North American simulations, average spin-up time savings for all grid cells is 85 % for either the daily or monthly version of TEM. The developed spin-up method shall be used for future quantification of carbon dynamics at fine spatial and temporal scales.</p