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Excessive anthropogenic emissions of greenhouse gas have led to global climate change in recently years. Methane is an important greenhouse gas after carbon dioxide. Methane emissions increase with the global temperature, which further intensifies global warming. Soil is a huge source and sink of methane, and methanotrophic bacteria play a key role in reducing methane emissions. Exploring the effect of temperature on soil methane-oxidizing ability has been a research hotspot in recent years. This paper reviewed the effects of temperature on soil methane oxidation and methanotrophic bacteria; analyzes and compares the soil methane oxidation process and methanotrophic bacteria in various ecosystems at different temperatures, and the growth temperature range of methanotrophic bacteria strains. The results showed that various ecosystems have different temperature ranges for methane oxidation; the rate of methane oxidation increases with increasing temperature in their particular temperature ranges. When the cultivation temperature is closer to the in-situ temperature, the methane oxidation response will be faster. Similar to the effect of temperature on methane oxidation, the diversity and abundance of methanotrophic bacteria also increase with increasing temperature and are closely related to the magnitude of temperature increase and the adaptation temperature of dominant bacteria. Type II methanotrophic bacteria in the soil are more sensitive to temperature. As the temperature rises, the abundance of type II methanotrophs increases. Therefore, temperature affects the abundance and community structure of methanotrophs, thereby affecting the rate of methane oxidation and methane emissions. However, whether temperature can only change the methane oxidation capacity of the soil by regulating the replacement of dominant bacteria is still unconfirmed. This review discussed the response of methane oxidation to temperature and its microbial mechanism, in order to provides a comprehensive analysis for methane oxidation capacity under the context of future climate change. [ABSTRACT FROM AUTHOR]

The greenhouse climate model has the characteristics of nonlinearity, strong disturbance and time variance. The commonly used methods to deal with the complex model are to linearize the original nonlinear models or to do some offline identification research. Without considering the complex characteristics completely, the usual modeling methods cannot predict the greenhouse climate dynamic behaviour effectively. In this paper, on account of the complex characteristics of greenhouse climate system, taking the temperature model as the example, the continuous-discrete recursive error algorithm was used to identify combined parameter and state online. Firstly, describe the greenhouse temperature model. The greenhouse temperature is affected by the heat load imposed on the greenhouse by the sun, the energy lost to the external air because of transmission through the greenhouse cover, the heat transfer between the internal air and soil, the heat loss by crop transpiration, the heat lost through natural ventilation of the roof windows and the energy supply from the heating system. In all of the model parameters, due to the fact that external solar radiation has a great effect on greenhouse temperature, radiation conversion factor changes over time and the parameters related to heating and ventilation are fundamental but difficult to obtain, this paper attempted to estimate and update 5 key parameters online. Secondly, continuous-discrete recursive prediction error algorithm to estimate combined parameters and states online was developed. This algorithm is appropriate for a continuous-discrete system, which is defined as a dynamic system with a continuous state function, and the observation function is discrete. The algorithm estimates the parameters and states by minimizing the error sum of squares between predicted values and measured values, which is usual in this technology. Compared with other traditional estimation algorithm such as the extended Kalman filter, the big difference of the algorithm is that there is no need to set the initial value of system noise precisely. It defines the system noise in real time by introducing an extra parameter as gain matrix and estimating it online. The algorithm adjusts the system noise to match the model predicted values and actual values by regulating the gain matrix. As estimating the system noise for the greenhouse temperature system beforehand is extremely difficult, the advantage of the proposed algorithm enhances the feasibility of its application in practice. At last, in order to test the developed algorithm, the model identification results were compared between the continuous-discrete recursive prediction error algorithm and the extended Kalman filter in MATLAB. The simulation was based on the measured data containing outside temperature, outside solar radiation, control inputs and temperature of an experiment greenhouse. The results showed that the proposed algorithm could lead to a higher model fit value of 93.7% compared with 89.5% of the extended Kalman filter. The gain matrix varied from zero to non-zero, and it nearly maintained stable at a non-zero constant in the end of each test day. The changing process indicated that there were errors between the model predicted value and the measured value in the initial, and the errors could be compensated by regulating the gain matrix. The different values of gain matrix in 2 days showed that system noise may vary largely in different condition and it should not be set as a constant. From the simulation results and the data analysis, it can be known that the proposed continuous-discrete recursive prediction error algorithm can estimate the temperature well and improve the model accuracy and validity. [ABSTRACT FROM AUTHOR]

Over the past hundred years, remarkable changes occurred around global climate. Two significant characteristics of climate changes are temperature mutation and warming hiatus, which led to a variety of extreme climate events of all scales, such as in the north of China. Therefore, revealing the features and rules of temperature mutation and warming hiatus in northern China could provide some basis to deeply understand climate changes, prevent and mitigate disasters as well as improve ecological environment in northern China and in the whole world. Based on the seasonal (monthly) average minimum temperature, average temperature and average maximum temperature data at 357 meteorological stations in northern China and surrounding areas during 1951-2014, this paper analyzes the spatio- temporal variation of three types of seasonal temperature (the average minimum temperature, average temperature and average maximum temperature) mutation and warming hiatus over northern China by using Mann-Kendall test, slide t test and other methods. The results indicate: according to the average minimum temperature, average temperature and average maximum temperature, the abrupt change of temperature and warming hiatus became late and the cycle from abrupt change to warming hiatus became short as the latitude reduces. In spring and winter, the temperature mutation and warming hiatus in the Northeast China were the earliest (1970s- 1980s, 1993- 2002), in North China were the second and in the Northwest China were the latest (1990s-2000s, 1996- 2010). While in summer and autumn, the temperature mutation and warming hiatus in North China were the earliest, in Northeast China were the second and in Northwest China were the latest (1990s- 2010s). There was little distinction among areas in warming hiatus year. Meteorological stations with no mutation and no hiatus were centralized in mountainous regions, high- latitude areas and southern part of North China Plain where the surrounding areas were relatively late in mutation and warming hiatus. The mutations and warming hiatus of same kinds of temperature became late in the order of winter (1981- 1990), spring, autumn and summer (1994-2008) and winter (1995-2008), autumn, summer and spring (1998-2010). The cycle from temperature mutated to warming hiatus became short in the sequence of winter, spring, autumn and summer, during which the average minimum temperature period was the longest in winter (9- 24 a). The average minimum temperature mutated earliest in spring, summer and winter, which occurred during 1972-1999, 1987-1999 and 1971-2000, the average temperature was the second to mutate, while the average maximum temperature was the latest to mutate which occurred during 1975- 2008, 1994- 2008, 1972- 2006. However, in autumn, the mutation of the average temperature was the earliest (1982-2001), the mutation of the lowest average temperature was the second, and the mutation of the highest average temperature was the latest (1987-2001). The warming hiatus of the average minimum temperature, average temperature and average maximum temperature in spring and summer became late successively, which were during 1994- 2008, 1997-2008 and 1997-2010, respectively, while the sequence was opposite in autumn and winter. The lengths of periods from temperature mutation to warming hiatus in each season in order were that of the average minimum temperature (9-18 a), that of the average temperature and that of the average maximum temperature (5- 12 a). In particular, the three kinds of temperatures all mutated earliest in the southern of North China in summer, which is inconsistent with the rule in the whole area that the mutation became late as latitude decreases, and the temperature at most stations in this area did not mutate, which is also inconsistent with the general rule in the whole area. [ABSTRACT FROM AUTHOR]