SiB2和SiB3对高寒草甸和茶树地表能量通量模拟的比较

运用简单生物圈模式第2版(SiB2)和第3版(SiB3),分别模拟青藏高原两个观测站(那曲、安多)和长江三角洲苏州东山观测站的近地面能量收支,并与相应观测数据进行比较研究,分析SiB2、SiB3模拟结果和观测资料产生差异的原因,以此来认识上述地区地表能量收支特点。结果表明,SiB2和SiB3模拟的近地面能量通量与观测数据有较好的一致性。对感热通量,那曲和安多站SiB3比SiB2模拟的结果更接近观测资料,但苏州站SiB2模拟的结果与观测资料更吻合;对潜热通量,SiB3比SiB2模拟的日变化与观测资料更一致,SiB3的模拟结果与观测资料(除苏州站外)相关系数都在0.8以上;对地表土壤热通量,SiB2和SiB3模拟结果与观测数据相关系数都在0.8以上;对净辐射通量,SiB2和SiB3模拟结果与观测资料相关系数接近1.0。与SiB2相比,SiB3引用通用陆面模式的土壤描述并增加对冠层空间层温度、湿度和痕量气体的预报,使其能够改善潜热通量和土壤热通量的模拟,但对复杂下垫面的感热和净辐射通量模拟能力提高不明显。     

不均匀地表产生的中尺度通量的数值试验

采用北京大学三维的复杂地形中尺度模式,结合陆面过程模式(SiB),模拟了草原和沙漠并存的下垫面的边界层大气运动.利用SiB模式计算了地表辐射、感热、潜热通量,并且预报地表温度.中尺度模式则模拟了沙漠地区受热抬升,形成的辐合运动,垂直速度的分布,不同高度上水平流场的变化以及中尺度动量和热量通量,把中尺度通量跟湍流通量进行了比较,以确定这种中尺度运动在GCM模式的参数化过程中的重要性.试验表明中尺度通量尤其是热量通量要比湍流通量大很多.     

利用简单生物圈模式SiB2模拟青藏高原地表能量收支

利用简单生物圈模式SiB2模拟了西藏短草大草原安多观测站1998年7月15日至9月10日期间的地表能量分配、 地表有效辐射温度和土壤湿度。季风期, 平均冠层高度和叶面积指数大约分别为0.05 m和0.5。实验地点基本代表了夏季藏北高原大面积特征。所用资料为全球能量与水循环实验GEWEX (Global Energy and Water Cycle Experiment) 亚洲季风实验GAME (Asian Monsoon Experiment) 的西藏观测期间安多观测站微气象实地测量。采用这些资料确定SiB2所需要的参数和初始值后, 由该资料中半小时一次的太阳短波辐射、大气长波辐射、水汽压、气温、水平风速和降水驱动SiB2, 最后将模拟结果与实际测量的湍流通量、 地表温度和土壤湿度进行了对比, 进一步检验SiB2对季风期间青藏高原稀疏草原地表能量分配的模拟能力。对比结果表明: (1) 当模式低估净辐射0.2%时, 模式分别高估感热、 潜热和土壤热通量4%、 13%和8%; (2) 模式得出的地表温度偏高5%; (3) 土壤湿度估计合理。总的来说, SiB2对辐射分量模拟结果的偏差相对较小, 相关程度普遍比感热通量、 潜热通量和土壤热通量的模拟结果高。     

基于FLUXNET数据集对陆面模式CoLM能量通量的单点评估

模式评估是模式发展中的重要一环。本文利用来自FLUXNET2015数据集的30个站点的涡动相关系统观测数据,重点关注能量通量,对通用陆面模式（Common Land Model version 2014,CoLM2014）在不同典型下垫面的模拟能力进行评估。结果表明,模式总体上能抓住感热、潜热和净辐射通量在日、季节和年平均等不同时间尺度上的变化特征,对感热、潜热和净辐射通量都有较好的模拟能力,净辐射的模拟效果最好,潜热通量次之。季节变化模拟中,感热、潜热通量在夏季不同植被型下站点的空间离散程度大于冬季,不同站点间模拟效果相差较大,净辐射多站点标准差变化幅度要小于感热、潜热,不同站点间模拟效果偏差较小。CoLM在常绿针叶林、稀树林地、草地、农田模拟感热、潜热通量的效果相对较好,在永久湿地、落叶阔叶林下模拟感热通量较差。本研究对CoLM2014在未来的改进和发展中提供了有用的参考。     

CoLM模式在中国温带混交林和亚热带人工针叶林的水热通量模拟性能评估

利用中国陆地生态系统通量观测研究网络(ChinaFLUX)在长白山温带混交林观测站点和千烟洲亚热带人工针叶林观测站点的强化观测资料,考察了一个较为完善的陆面过程模式(Common LandModel,CoLM)在这两种不同植被类型下垫面的模拟性能,特别是其对潜热通量的模拟。模拟结果与强化观测资料的对比表明,CoLM能较好地模拟出实验站点地表能量平衡的基本特征以及潜热通量的日变化和季节变化。其中长白山站和千烟洲站潜热通量日平均的观测值与模拟值的相关系数分别为0.804和0.692,均通过了0.001的显著性水平检验。总体而言,CoLM在长白山站的模拟效果更好一些,模式对不同植被覆盖类型的土壤—植被—大气间水热传输过程的处理可能是造成差别的原因。     

WRF模式对金塔绿洲效应的数值模拟

利用美国NCAR中心的天气研究与预报模式WRF,对金塔绿洲的温度场、环流场、能量场的结构及其日变化特征进行了较为细致的模拟研究。结果表明,WRF模式能较好地模拟出非均匀下垫面上绿洲和戈壁的近地面温度、风场、净辐射、感热和潜热等要素的变化特征及日变化规律,较为完整地呈现出绿洲"冷岛效应",模拟的近地面风向和观测值吻合较好。通过对能量场的时空分析,发现下垫面的植被类型、土壤类型和土壤湿度对绿洲白天感热、潜热、土壤热通量和净辐射等有很大影响,绿洲白天净辐射峰值比戈壁大,潜热通量比感热通量大;白天大气向地面传输热量,绿洲地表获得的热通量大;而夜间地表向上传递热量,绿洲释放的热通量比戈壁大。更加细致地研究这些现象对深入了解绿洲气候的形成和维持机理具有重要的意义。     

北京郊区草地夏季能量收支平衡的数值模拟

北京郊区地表能量分配可能影响北京地区的天气和气候。为了进一步检验陆面过程模式对北京郊区具有代表性的稀疏草地地表能量分配的模拟能力,利用原版和改进版简化生物圈模式(SiB2,Simple Biosphere Model 2)模拟了2010年7月22日-8月5日期间北京郊区阳坊镇坦克打靶场草地的辐射平衡、能量收支以及地表热通量。并将模拟结果与实际测量的数据进行对比,结果表明:1)原版SiB2低估净辐射11.32%,改进版SiB2则低估净辐射5.81%,主要原因是改进版SiB2更新了土壤热传导率计算方法,从而提高了土壤温度(包括地表温度)模拟结果的精度,进而改善了地表向上的长波辐射模拟结果的准确性;2)改进版SiB2同时改善了感热通量和潜热通量的模拟结果,但是原版SiB2和改进版SiB2均低估了土壤热通量。     

黑河试验中测站设置（夏季）的数值模拟研究

本文用具有较高水平与垂直分辨率的数值模式诊断计算了黑河试验区的感热通量和潜热通量,并通过对这些诊断通量的统计分析研究了黑河试验中观测站点的代表性和设置问题,得到在试验区内不同下垫面上设置观测站点的具体方案,以及选择有较好代表性的观测站点的一般原则。     

黑河实验区非均匀地表能量通量的数值模拟

利用三维非静力RAMS模拟研究了黑河实验区非均匀地表能量通量，模拟结果表明：绿洲地表净辐射通量较沙漠戈壁大；绿洲及沙漠戈壁下垫面上的Bowen比分别为0．4和4．0；夜间绿洲上整晚维持蒸发，并有负感热通量。模拟结果与测站实测结果与卫星反演值的对比研究指出，RAMS对绿洲下垫面潜热通量的模拟和沙漠戈壁下垫面感热通量的模拟与实测值基本一致。卫星遥感反演及数值模拟方法对净辐射的估算与实测较吻合，绿洲地表感热通量的卫星反演值较数值模拟结果更接近于实际，但潜热通量的模拟值则较卫星估算值更接近于实际；沙漠戈壁地表则是感热通量的模拟值较卫星反演值更接近于实际。上述分析为今后结合卫星遥感改进RAMS陆面过程参数化，使使用于模拟研究干旱区非均匀下垫面地气相互作用提供了可靠依据。     

GLDAS资料驱动的Noah-MP陆面模式青藏高原地表能量交换模拟性能评估

使用Noah-MP陆面模式,在GLDAS数据的驱动下,对青藏高原不同区域6个观测站点的感热通量和潜热通量进行了模拟,并与实测值进行了对比分析。研究结果表明:使用默认参数化方案选项模拟的感热通量除在珠峰站冬春季偏高而夏秋季偏低以外,在其他站点均偏高;潜热通量的模拟在不同站点和不同季节存在较大差异,在藏东南站模拟结果较好,在珠峰站偏高,在慕士塔格站偏低,在纳木错站秋冬季偏低、在阿里站春季偏高、在那曲站夏季偏低。通过进一步分析模拟结果对各个参数化方案选项的敏感性,选出了更适合各站点感热通量及潜热通量模拟的参数化方案选项组合,提高了模式对各个站点全年整体模拟水平,使得模拟结果和站点观测相比具有更小的均方根误差和更高相关系数,但对一些站点部分季节的模拟可能会带来更大的误差。本研究为这些站点的陆面及耦合模式的模拟提供了参考,同时也能对青藏高原热源变化的模拟提供更准确的地气交换信息。     

呼和浩特市白塔机场气象观测要素分析

利用呼和浩特市白塔机场气象站1977--2010年月平均最高、最低气温,月降水量资料和1979--2010年月平均风速资料,分析了近34a气象要素变化特征。得出：呼和浩特白塔机场近34a来,（1）年平均气温、年降水量都没有明显的变化趋势,进入21世纪以来气候变率明显变大,气候不稳定性增加;（2）气温的年日较差明显增大,气候倾向率为0．4℃／10a;（3）年平均相对湿度在三个时段存在不同的变化特征;（4）年平均风速呈现出明显的减小趋势,平均每10a减小了0．3m·s^-1（5）四个要素中,气温年日较差由小变大的突变特征最为明显,1990年前后出现突变点;平均风速则存在由大转小的突变过程,突变时间点在1996年;年平均气温南低向高的突变过程较弱,且持续时间较短;年降水量和年平均相对湿度在近34a罩均不存在气候突变现象,气候状态相对稳定。呼和浩特机场气候与市区气候存在明显的差异．还需进一步深入分析。     

淮河流域南部丘陵森林地区土壤-植被-大气相互作用:长期野外试验观测的初步分析

本文简要介绍了包括三部分观测的安徽淮南长期野外试验观测站,特别是土壤-植被-大气的集中观测,对小塔运行前三个月(2018年6月至8月)的数据,并结合同一时段大塔获得的数据,进行了初步分析.结果表明这些资料有合理的变化特征,日变化和夏季值特征显著,各月份间气象变化有明显差异.土壤水分和温度受降雨影响,在不同的下垫面条件下表现出不同的变化.土壤CO2日平均浓度在2 cm和10 cm处分别为1726和4481 ppm.2018年夏季土壤CO2浓度随土壤体积含水量的变化而变化,但与土壤温度呈弱相关.     

Quantifying the value of historical climate knowledge and climate forecasts using agricultural systems modelling

A well tested agricultural systems model was used together with 114 years of historical climate data to study the performance of a dryland wheat–fallow system as impacted by climate variations and nitrogen input levels in southeast Australia, and to investigate the value of: (1) historical climate knowledge, (2) a perfect climate forecast, and (3) various forecasts of targeted variables. The potential value of historical climate records increases exponentially with the number of years of data. In order to confidently quantify the long term optimal nitrogen application rate at the study site at least 30 years of climate data are required. For nitrogen management only, the potential value of a perfect climate forecast is about $54/ha/year with a reduction of excess nitrogen application of 20 kg N/ha/year. The value of an ENSO based forecast system is $2/ha/year. Perfect forecasting of three or six categories of growing season rainfall would have a value of $10–12/ha/year. Perfect forecasts of three or six categories of simulated crop yield would bring about $33–34/ha/year. Choosing integrated variables as a forecasting target, for example crop yield derived from agricultural modelling, has the potential to significantly increase the value of forecasts.     

The potential effects of climate change on ecosystem processes and cattle production on U.S. rangelands

In spite of the uncertainties of potential climate change, a scientific consensus is emerging that increasing concentrations of atmospheric CO₂ could alter global temperatures and precipitation patterns. Changes in global climate as predicted by General Circulation Models (GCM) could therefore, have profound implications for global agriculture. The objective of this study was to assess the impacts of potential climate change on livestock and grassland production in the major producing regions of the United States. Simulation sites were selected for the study on the basis of the region's economic dependence on rangeland livestock production. Five thirty-year simulations were conducted on each site using the Simulation of Production and Utilization of Rangelands model and Colorado Beef Cattle Production Model. Climate change files were obtained by combining historic weather data from each site with predicted output from three GCM's. Results from nominal runs were compared with the three climate change scenarios and a doubled CO₂ run. The magnitude and direction of ecosystem response to climate change varied among the GCM's and by geographic region. Simulations demonstrated that changes in temperature and precipitation patterns caused an increase in above-ground net primary production for most sites. Increased decomposition rates were recorded for northern regions. Similarly, animal production in northern regions increased, implying an increase in economic survivability. However, because decreases in animal production indicators were recorded for the southern regions, economic survivability in southern regions is less certain.     

Statistical downscaling of daily climate variables for climate change impact assessment over New South Wales, Australia

This paper outlines a new statistical downscaling method based on a stochastic weather generator. The monthly climate projections from global climate models (GCMs) are first downscaled to specific sites using an inverse distance-weighted interpolation method. A bias correction procedure is then applied to the monthly GCM values of each site. Daily climate projections for the site are generated by using a stochastic weather generator, WGEN. For downscaling WGEN parameters, historical climate data from 1889 to 2008 are sorted, in an ascending order, into 6 climate groups. The WGEN parameters are downscaled based on the linear and non-linear relationships derived from the 6 groups of historical climates and future GCM projections. The overall averaged confidence intervals for these significant linear relationships between parameters and climate variables are 0.08 and 0.11 (the range of these parameters are up to a value of 1.0) at the observed mean and maximum values of climate variables, revealing a high confidence in extrapolating parameters for downscaling future climate. An evaluation procedure is set up to ensure that the downscaled daily sequences are consistent with monthly GCM output in terms of monthly means or totals. The performance of this model is evaluated through the comparison between the distributions of measured and downscaled climate data. Kruskall-Wallis rank (K-W) and Siegel-Tukey rank sum dispersion (S-T) tests are used. The results show that the method can reproduce the climate statistics at annual, monthly and daily time scales for both training and validation periods. The method is applied to 1062 sites across New South Wales (NSW) for 9 GCMs and three IPCC SRES emission scenarios, B1, A1B and A2, for the period of 1900–2099. Projected climate changes by 7 GCMs are also analyzed for the A2 emission scenario based on the downscaling results.     

Yield estimation and sowing date optimization based on seasonal climate information in the three CLARIS sites

The present article is a contribution to the CLARIS WorkPackage “Climate and Agriculture”, and aims at testing whether it is possible to predict yields and optimal sowing dates using seasonal climate information at three sites (Pergamino, Marcos Juarez and Anguil) which are representative of different climate and soil conditions in Argentina. Considering that we focus on the use of climate information only, and that official long time yield series are not always reliable and often influenced by both climate and technology changes, we decided to build a dataset with yields simulated by the DSSAT (Decision Support System for Agrotechnology Transfer) crop model, already calibrated in the selected three sites and for the two crops of interest (maize and soybean). We simulated yields for three different sowing dates for each crop in each of the three sites. Also considering that seasonal forecasts have a higher skill when using the 3-month average precipitation and temperature forecasts, and that regional climate change scenarios present less uncertainty at similar temporal scales, we decided to focus our analysis on the use of quarterly precipitation and temperature averages, measured at the three sites during the crop cycle. This type of information is used as input (predictand) for non-linear statistical methods (Multivariate Adaptive Regression Splines, MARS; and classification trees) in order to predict yields and their dependency to the chosen sowing date. MARS models show that the most valuable information to predict yield amplitude is the 3-month average precipitation around flowering. Classification trees are used to estimate whether climate information can be used to infer an optimal sowing date in order to optimize yields. In order to simplify the problem, we set a default sowing date (the most representative for the crop and the site) and compare the yield amplitudes between such a default date and possible alternative dates sometimes used by farmers. Above normal average temperatures at the beginning and the end of the crop cycle lead to respectively later and earlier optimal sowing. Using this classification, yields can be potentially improved by changing sowing date for maize but it is more limited for soybean. More generally, the sites and crops which have more variable yields are also the ones for which the proposed methodology is the most efficient. However, a full evaluation of the accuracy of seasonal forecasts should be the next step before confirming the reliability of this methodology under real conditions.     

Impact of Climate, Moisture Regime, and Nutrient Regime on the Productivity of Douglas-Fir in Coastal British Columbia, Canada

Future climate change is expected to have many impacts on forest ecosystems. It is important to have some understanding of these impacts in order to make informed forest management decisions. A major consideration in making forest management decisions is the productivity of a site, as measured by site index. In this study, I relate Douglas-fir site index to accumulated growing degree-days greater than 5^°C (DD5), as well as to soil moisture and nutrient regime. This allows the impact of climate change on forest productivity to be estimated. A two step approach was followed. The first step derived models to estimate various climate variables to latitude, longitude, and elevation using data from climate stations. Then, these climate variables were used along with soil moisture and nutrient data to predict site index for the site index plots. A two step approach was taken because climatic data were not available for the site index plots. The trend was for site index to increase with both increasing soil moisture and nutrients, although the site index decreased on the wetter sites. Site index also increased with DD5 at the rate of 1.2 m for every increase of 100 units in DD5. These models can be used together to evaluate the impact of various climate change scenarios on site index.     

Modelling the impacts of climate change on wheat yield and field water balance over the Murray�CDarling Basin in Australia

The study used a modelling approach to assess the potential impacts of likely climate change and increase in CO₂ concentration on the wheat growth and water balance in Murray?CDarling Basin in Australia. Impacts of individual changes in temperature, rainfall or CO₂ concentration as, well as the 2050 and 2070 climate change scenarios, were analysed. Along an E?CW transect, wheat yield at western sites (warmer and drier) was simulated to be more sensitive to temperature increase than that at eastern sites; along the S?CN transect, wheat yield at northern warmer sites was simulated to be more sensitive to temperature increase, within 1?C3°C temperature increase. Along the E?CW and S?CN transects, wheat at drier sites would benefit more from elevated [CO₂] than at wetter sites, but more sensitive to the decline in rainfall. The increase in temperature only did not have much impact on water balance. Elevated [CO₂] increased the drainage in all the sites, whilst rainfall reduction decreased evapotranspiration, runoff and drainage, especially at drier sites. In 2050, wheat yield would increase by 1?C10% under all climate change scenarios along the S?CN transect, except for the northernmost site (Dalby). Along the E?CW transect, the most obvious increase of wheat yields under all climate change scenarios occurred in cooler and wetter eastern sites (Yass and Young), with an average increase rate of 7%. The biggest loss occurred at the driest sites (Griffith and Swan Hill) under A1FI and B2 scenarios, ranging from ?5% to ?16%. In 2070, there would be an increased risk of yield loss in general, except for the cool and wet sites. Water use efficiency was simulated to increase at most of the study sites under all the climate change scenarios, except for the driest site. Yield variability would increase at drier sites (Ardlethan, Griffith and Swan Hill). Soil types would also impact on the response of wheat yield and water balance to future climate change.     

Mapping the climate sceptical blogosphere

While mainstream scientific knowledge production has been extensively examined in the academic literature, comparatively little is known about alternative networks of scientific knowledge production. Online sources such as blogs are an especially under-investigated site of knowledge contestation. Using degree centrality and node betweenness tests from social network analysis, and thematic content analysis of individual posts, this research identifies and critically examines the climate sceptical blogosphere and investigates whether a focus on particular themes contributes to the positioning of the most central blogs. A network of 171 individual blogs is identified, with three blogs in particular found to be the most central: Climate Audit, JoNova and Watts Up With That. These blogs predominantly focus on the scientific element of the climate debate, providing either a direct scientifically-based challenge to mainstream climate science, or a critique of the conduct of the climate science system. This overt scientific framing, as opposed to explicitly highlighting differences in values, politics, or ideological worldview, appears to be an important contributory factor in the positioning of the most central blogs. It is suggested that these central blogs are key protagonists in a process of attempted expert knowledge de-legitimisation and contestation, acting not only as translators between scientific research and lay audiences, but, in their reinterpretation of existing climate science knowledge claims, are acting themselves as alternative public sites of expertise for a climate sceptical audience.