Tianjin Climate Center

Tianjin, China

Tianjin Climate Center

Tianjin, China

Time filter

Source Type

Luo T.,CAS Institute of Tibetan Plateau Research | Li M.,CAS Institute of Tibetan Plateau Research | Li M.,Tianjin Climate Center | Luo J.,CAS Chengdu Institute of Mountain Hazards and Environment
Ecological Research | Year: 2011

It is still unclear to what extent variations in foliar δ13C and nitrogen can be used to detect seasonal changes in canopy productivity. We hypothesize that in a wet and cloudy fir forest, seasonally higher litterfall and lower leaf area index (LAI) are correlated with higher mass-based leaf nitrogen (Nmass) and net primary productivity (NPP), while foliar δ13C may change with specific leaf area (SLA), area-based leaf nitrogen (Narea), and/or starch concentration. In order to test our hypotheses, stand-level litterfall and the means of δ13C, Nmass, Narea, SLA, and starch concentration of canopy needles for a wet and cloudy Abies fabri forest in the Gongga Mountains were monthly measured during the growing season. Seasonal estimates of LAI were obtained from our previous work. A conceptual model was used to predict seasonal NPP of the fir forest. Seasonal mean δ13C and Nmass and climatic variables were used as inputs. The δ13C across 1-7-year-old needles increased from May to September associated with decreasing SLA and increasing Narea. There were no significant differences in seasonal starch concentration. With increasing litterfall and decreasing LAI, seasonal mean Nmass increased, while the δ13C varied little. The simulated NPP increased with increasing litterfall and related traits of Nmass and Narea. Our data generally supported the hypotheses. The results also suggest that in the forest with relatively moist and cloudy environment, the largest fraction of annual carbon gain may occur in the early part of the growing season when higher litterfall results in higher Nmass of canopy leaves. © 2010 The Ecological Society of Japan.


Zhao Y.,Chinese Academy of Meteorological Sciences | Chen S.,Chinese Academy of Meteorological Sciences | Chen S.,Tianjin Climate Center | Chen S.,Nanjing University of Information Science and Technology | Shen S.,Nanjing University of Information Science and Technology
Ecological Modelling | Year: 2013

The crop model (PyWOFOST) which coupled remote sensing information and a crop model (WOFOST) with Ensemble Kalman Filter (EnKF) was used to simulate maize growth and yield in Northeastern China with MODIS LAI as the coupling point. The assimilation plan focused on analyzing the impact of uncertainties of remote sensing observations (MODIS LAI) and crop model parameters (thermal time from emergence to anthesis, TSUM1) on the modeling results. First, the PyWOFOST model is used to simulate the maize LAI, yield and growth duration at site's scale; then the impact of remote sensing and crop model uncertainties on crop growth simulation is analyzed; finally, the regional maize yield is estimated with the PyWOFOST model, and the results are verified using the maize statistical yield. Results show that the simulated maize yield with assimilation has significantly improved compared to the one without assimilation. Under a business-as-usual scenario, the modeling results without assimilation has an error of 14.04%. The assimilated results show errors of 12.71%, 11.91%, 10.44%, and 10.48% at different TSUM1 uncertainty levels at 0, 10, 20, and 30. °C, respectively. The simulated LAI with assimilation agree better with the field observations than the one without assimilation. Without assimilation, the simulated growth duration has a mean deviation from the observed results at 3.4 days; with assimilation, the deviation would be 3.5, 4.3, 5.0, and 5.5 days respectively at different TSUM1 uncertainty levels. The results show that the errors for 58.82% areas are smaller than 15%. The simulated and statistical yields are highly correlated (R= 0.875), and the determination coefficient is at 0.806. The study shows that it is applicable to simulate crop growth using a crop model assimilated with remote sensing data based on EnKF and it is significant to estimate the remote sensing and crop model uncertainties in crop yield estimation. © 2013 Elsevier B.V.


Li Z.,Tianjin Climate Center | Wang T.,Tianjin Climate Center | Gong Z.,Tianjin Climate Center | Li N.,Tianjin Climate Center | Li N.,Nanjing University of Information Science and Technology
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2013

The Internet of Things has been wildly used in solar greenhouse. Most applications focus on facilities modern greenhouse environment monitoring and regulation, product traceability, and pest remote diagnostics. In fact, facility agriculture uses different methods to change microclimate in greenhouse to help crop grow anti - seasonally. This study focused on the Internet of Things (IOT) application in reducing the influence of low temperature disaster on solar greenhouse production in North China caused by strong cooling and successive overcast weather in winter and spring. We installed several sensors in greenhouse including air temperature, relative humidity, soil temperature, radiation (or light intensity), crop camera platform with synchronization photography, which composed the sensing layer of IOT. The equipments transferred data every 10 minutes to the server in our office. An application was developed to transfer data through socks programming, query and analyze data, and retrieve greenhouse cryogenic information. Data from different manufacturers are changed into a unified format, then SQL server 2000 sp4 is used to store data. A microclimate monitor data receiver software, based on GIS, was also developed to help people display and analyze data. A cryogenic disaster indicator for cucumber and real-time microclimate data analysis and processing system were established, which can provide low-temperature disaster warning. For example, if the cucumbers are planted in solar greenhouses during the flowering and fruiting period, and the lowest temperature outside is lower than -10°C and highest temperature outside is lower than -3°C, the cucumbers will stop growing or become damaged. Because most greenhouses share a few structures, when we make a low temperature disaster warning towards one kind's structure, it can be sent to the greenhouse manager group who owned same type greenhouse structure. The results were available via SMS (Short Message Service), LED/ LCD electronic display, website, and voice calls. We developed professional weather service website for real-time data and image display, microclimate data analysis and disaster warning in the greenhouse. We used flash/html5 to display data dynamically. When the greenhouse temperature goes down to threshold, people receive a warning by SMS. At the same time, the application platform triggers intelligent switch through SMS to start the heating equipment, and then prevents the crop from low-temperature disaster. We used an electric heater as heating equipment in this test. The temperature in heater outlet was stabilized at 7°C and wind speed stabilized at 3m/s. The results show that temperature in the test greenhouse is 4.2°C higher than in the reference greenhouse without heating. The average lowest temperature in test greenhouse is 4.5°C higher than reference greenhouse. The average temperature is 4.3°C higher than the reference in cold weather and 4.5°C higher in successive overcast weather. Because temperature distribution in space is uniform, it will not affect the uniformity of the crop population growth. This study effectively solved the low-temperature disaster monitoring and early warning problem in Tianjin. Using Internet of Things and cloud computing technology, it helped users to acquire relevant information through simple receiving terminal that could be used for disaster prevention. Effective monitoring and intelligent remote management in the groups of solar greenhouses will change the traditional management mode and improve management efficiency and capacity of calamity reduction.


Wang J.,Beijing Regional Climate Center | He L.,Tianjin Climate Center | Zhang X.,Harbin Meteorological Bureau
Dili Xuebao/Acta Geographica Sinica | Year: 2015

Using daily snowfall observations (1961-2012) of 227 meteorological observation stations which are treated by a series of climatic statistical methods, analysis is performed on the temporal and spatial characteristics of winter snowfall in the agri-pasture transitional zone of North China and its relations with circulation factors. The result shows that the high value center of snowfall is located in the northeast of Inner Mongolia in early winter, and then it moves to the southern part of North China at the end of winter and the beginning of spring. The periods of the 1960s and 1970s witnessed more snowfalls at all levels than normal, with high value centers moving eastward from West Inner Mongolia (in the 1960s) to most parts of Hebei and Shanxi provinces (in the 1970s). Since the 2000s, heavy snowfalls across North China have been the most significant in the north of Shanxi and Hebei provinces, with Hulunbuir coming next. Regarding variations of snowfall frequencies, there is a decline in the frequency of heavy snowfalls in different regions, with the most significant decrease occurring in Hebei province and the south of Shanxi province (named as the VI region in this paper). During the period with fewer snowfalls in North China (in the 1980s and 1990s), vapor transport was weak, moving from northwest to southeast; whereas during the years of strong vapor transport, the water transport in the past decade moved from southeast to northwest. The inter-decadal snowfall has a negative correlation with the air temperature and Arctic Oscillation (AO) index, whereas the snowfall at moderate and high levels is positively correlated with the air temperature and AO index in high latitude areas like the Greater Hinggan and Taihang Mountains in northeastern Inner Mongolia. © 2015, Science Press. All right reserved.


Zhang Y.,Chinese Academy of Meteorological Sciences | Zhao Y.,Chinese Academy of Meteorological Sciences | Zhao Y.,Shanghai Institute of Meteorological science | Wang C.,Hainan Meteorological Service | Chen S.,Tianjin Climate Center
Theoretical and Applied Climatology | Year: 2016

Assessment of the impact of climate change on crop productions with considering uncertainties is essential for properly identifying and decision-making agricultural practices that are sustainable. In this study, we employed 24 climate projections consisting of the combinations of eight GCMs and three emission scenarios representing the climate projections uncertainty, and two crop statistical models with 100 sets of parameters in each model representing parameter uncertainty within the crop models. The goal of this study was to evaluate the impact of climate change on maize (Zea mays L.) yield at three locations (Benxi, Changling, and Hailun) across Northeast China (NEC) in periods 2010–2039 and 2040–2069, taking 1976–2005 as the baseline period. The multi-models ensembles method is an effective way to deal with the uncertainties. The results of ensemble simulations showed that maize yield reductions were less than 5 % in both future periods relative to the baseline. To further understand the contributions of individual sources of uncertainty, such as climate projections and crop model parameters, in ensemble yield simulations, variance decomposition was performed. The results indicated that the uncertainty from climate projections was much larger than that contributed by crop model parameters. Increased ensemble yield variance revealed the increasing uncertainty in the yield simulation in the future periods. © 2016 Springer-Verlag Wien


Si P.,Tianjin Meteorological Information Center | Ren Y.,Tianjin Climate Center | Liang D.,Tianjin Climate Center | Lin B.,Xiamen Meteorological Bureau
Journal of Geographical Sciences | Year: 2012

Based on China homogenized land surface air temperature and the National Centers for Environmental Prediction/Department of Energy (NCEP/DOE) Atmospheric Model Intercomparison Project (AMIP)-II Reanalysis data (R-2), the main contributors to surface air temperature increase in Southeast China were investigated by comparing trends of urban and rural temperature series, as well as observed and R-2 data, covering two periods of 1954-2005 and 1979-2005. Results from urban-rural comparison indicate that urban heat island (UHI) effects on regional annual and autumn minimum temperature increases account for 10. 5% and 12. 0% since 1954, but with smaller warming attribution of 6. 2% and 10. 6% since 1979. The results by comparing observations with R-2 surface temperature data suggest that land use change accounts for 32. 9% and 28. 8% in regional annual and autumn minimum temperature increases since 1979. Accordingly, the influence of land use change on regional temperature increase in Southeast China is much more noticeable during the last 30 years. However, it indicates that UHI effect, overwhelmed by the warming change of background climate, does not play a significant role in regional warming over Southeast China during the last 50 years. © 2012 Science China Press and Springer-Verlag Berlin Heidelberg.


Si P.,Tianjin Meteorological Information Center | Zheng Z.,Institute of Urban Meteorology | Ren Y.,Tianjin Climate Center | Liang D.,Tianjin Climate Center | And 2 more authors.
Journal of Geographical Sciences | Year: 2014

The regional changes of daily temperature extremes in North China caused by urbanization are studied further from observed facts and model estimates on the basis of homogenized daily series of maximum and minimum temperature observations from 268 meteorological stations, NCEP/DOE AMIP-II reanalysis data (R-2), and the data of simulations by regional climate model (RegCM3). The observed facts of regional warming on long time scales are obtained by analyzing the indices of temperature extremes during two time periods of 1961-2010 and 1951-2010. For urbanization effect,^the contributions to decreases in annual and winter diurnal temperature range (DTR) are 56.0% and 52.9%, respectively, and increases in the lowest minimum temperature (TNn) are 35.7% and 26.2% by comparison of urban and rural observations. Obtained by R-2 data with observations for contrast, on the other hand, increase in the number of annual warm nights (TN90p) contributed by urbanization is 60.9%. And observed facts of regional warming in daily temperature extremes are also reflected in the simulations, but what difference is urbanization progress at rural areas in North China would be prominent in the next few years relative to urban areas to some extent from model estimates. © 2014 Science Press and Springer-Verlag Berlin Heidelberg.


Li M.,Tianjin Climate Center | Guo J.,Tianjin Climate Center | Tian Z.,Tianjin University | Shi J.,Tianjin Climate Center | And 2 more authors.
Building Services Engineering Research and Technology | Year: 2014

This paper concerns the impacts of future climate change under two forcing scenarios on energy demand of commercial building and residential buildings with different energy-saving levels in Tianjin. Heating load of commercial building will decrease under the two scenarios in the next 90 years but increase of cooling load is found. All residential buildings will decrease heating load in the future 90 years. In particular, the decreasing rate of energy demand during 2011-2100 by the residential building slows down from the first- to the third-stage energy-saving levels. Additionally, the difference in energy demand between the two scenarios becomes smaller as the energy-saving level increases. These suggest that higher energy-saving levels are beneficial for decreasing not only energy consumption but also its sensitivity to climate change.Practical application: Climate change in the future causes the large and significant increase in cooling energy demand but decrease in heating energy demand. This would be helpful for the adjustment of energy use strategy by government. Also, the possible changes in future energy demands for heating and cooling will be of interest to energy providers. The responses of heating energy demand to the future climate change show large difference among the residential buildings with different energy-saving levels. This will provide useful information for policy makers and building industry managers on how to make appropriate measures keep occupants comfort and reduce energy use. © The Chartered Institution of Building Services Engineers 2013.


PubMed | Tianjin University and Tianjin Climate Center
Type: Historical Article | Journal: PloS one | Year: 2015

Exploring changes of building energy consumption and its relationships with climate can provide basis for energy-saving and carbon emission reduction. Heating and cooling energy consumption of different types of buildings during 1981-2010 in Tianjin city, was simulated by using TRNSYS software. Daily or hourly extreme energy consumption was determined by percentile methods, and the climate impact on extreme energy consumption was analyzed. The results showed that days of extreme heating consumption showed apparent decrease during the recent 30 years for residential and large venue buildings, whereas days of extreme cooling consumption increased in large venue building. No significant variations were found for the days of extreme energy consumption for commercial building, although a decreasing trend in extreme heating energy consumption. Daily extreme energy consumption for large venue building had no relationship with climate parameters, whereas extreme energy consumption for commercial and residential buildings was related to various climate parameters. Further multiple regression analysis suggested heating energy consumption for commercial building was affected by maximum temperature, dry bulb temperature, solar radiation and minimum temperature, which together can explain 71.5 % of the variation of the daily extreme heating energy consumption. The daily extreme cooling energy consumption for commercial building was only related to the wet bulb temperature (R2= 0.382). The daily extreme heating energy consumption for residential building was affected by 4 climate parameters, but the dry bulb temperature had the main impact. The impacts of climate on hourly extreme heating energy consumption has a 1-3 hour delay in all three types of buildings, but no delay was found in the impacts of climate on hourly extreme cooling energy consumption for the selected buildings.


Wang C.-L.,Guangdong Climate Center | Guo J.,Tianjin Climate Center | Chen H.-H.,Guangdong Climate Center | Liu X.,Guangdong Climate Center
Chinese Journal of Ecology | Year: 2011

According to the principles of soil water balance, Penman-Monteith function was adopted to calculate potential evapotranspiration. The daily soil available moisture was simulated, and the daily dynamic drought-monitoring index was put forward based on soil moisture (SM). With the comparison of the standardized precipitation index (SPI), relative moisture index (MI), and combined meteorological drought index (CI) recommended by the 'meteorological drought levels of National Standards' (GB/T 20481-2006), the applicability of the daily dynamic drought-monitoring index based on SM in Guangdong was evaluated, and the results showed that the drought season (from November to next April) based on SM lagged one month, compared to rain-less period (from October to next March). The SM index could describe the lag effect and gradual change characteristics of drought relative to the annual change of precipitation, and objectively reflect the seasonal distribution of drought frequency in Guangdong. The drought index based on SM automatically implied the time cumulative effect of precipitation-evaporation process, and thereby, could describe the start, development, and end of drought process in detail. In terms of progressive development, precipitation sensitivity, and integrity of drought course, the drought index based on SM was of good application value.

Loading Tianjin Climate Center collaborators
Loading Tianjin Climate Center collaborators