Wintertime nutrients in the North Atlantic—new approaches and implications for new production estimates

Observations of wintertime nutrient concentrations in surface waters are scarce in the temperate and subarctic North Atlantic Ocean. Three new methods of their estimation from spring or early summer observations are described and evaluated. The methods make use of a priori knowledge of the vertical distribution of oxygen saturation and empirical relationships between nutrient concentrations and oxygen saturation. A south–north increase in surface water winter nutrient concentration is observed. Winter nitrate concentrations range from very low levels of about 0.5 μmol dm⁻³ at 33°N to about 13.5 μmol dm⁻³ at 60°N. Previous estimates of winter nitrate concentrations have been overestimates by up to 50%. At the Biotrans Site (47°N, 20°W), a typical station in the temperate Northeast Atlantic, a mean winter nitrate concentration of 8 μmol dm⁻³ is estimated, compared to recently published values between 11 and 12.5 μmol dm⁻³. It is shown that most of the difference is due to a contribution of remineralised nitrate that had not been recognized in previous winter nutrient estimates. Mesoscale variation of wintertime nitrate concentrations at Biotrans are moderate (less than ±15% of the regional mean value of about 8 μmol dm⁻³). Interannual variation of the regional mean is small, too. In the available dataset, there was only 1 year with a significantly lower regional mean winter nitrate concentration (7 μmol dm⁻³), presumably due to restricted deep mixing during an atypically warm winter. The significance of winter nitrate estimates for the assessment of spring-bloom new production and the interpretation of bloom dynamics is evaluated. Applying estimates of wintertime nitrate concentrations of this study, it is found that pre-bloom new production (0.275 mol N m⁻²) at Biotrans almost equals spring-bloom new production (0.3 mol N m⁻²). Using previous estimates of wintertime nitrate yields unrealistically high estimates of pre-bloom new production (1.21–1.79 mol N m⁻²) which are inconsistent with observed levels of primary production and the seasonal development of biomass.     

Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China

The significance of crop yield estimation is well known in agricultural management and policy development at regional and national levels. The primary objective of this study was to test the suitability of the method, depending on predicted crop production, to estimate crop yield with a MODIS-NDVI-based model on a regional scale. In this paper, MODIS-NDVI data, with a 250 m resolution, was used to estimate the winter wheat (Triticum aestivum L.) yield in one of the main winter-wheat-growing regions. Our study region is located in Jining, Shandong Province. In order to improve the quality of remote sensing data and the accuracy of yield prediction, especially to eliminate the cloud-contaminated data and abnormal data in the MODIS-NDVI series, the Savitzky–Golay filter was applied to smooth the 10-day NDVI data. The spatial accumulation of NDVI at the county level was used to test its relationship with winter wheat production in the study area. A linear regressive relationship between the spatial accumulation of NDVI and the production of winter wheat was established using a stepwise regression method. The average yield was derived from predicted production divided by the growing acreage of winter wheat on a county level. Finally, the results were validated by the ground survey data, and the errors were compared with the errors of agro-climate models. The results showed that the relative errors of the predicted yield using MODIS-NDVI are between −4.62% and 5.40% and that whole RMSE was 214.16 kg ha⁻¹ lower than the RMSE (233.35 kg ha⁻¹) of agro-climate models in this study region. A good predicted yield data of winter wheat could be got about 40 days ahead of harvest time, i.e. at the booting-heading stage of winter wheat. The method suggested in this paper was good for predicting regional winter wheat production and yield estimation.     

一次造成乌鲁木齐市电网瑶线铁塔倒塌事故的气象原因分析

为了解2004年11月24日乌鲁木齐市两座电网瑶线铁塔倒塌事故的气象原因，通过实地调查，并对高低空环流形势场、加密气象站信息等进行了分析。结果表明，乌鲁木齐地区不同地理位置风速差异很大，红雁池和头屯河区瞬间极大风速分别比市区大12．8m／s和18．4m／s。纠正了过去认为东南大风的强中心只出现在红雁池附近的说法，为今后制作大风分区预报提供了参考，也可为城区高层建筑及相关设计规范的修正提供借鉴。     

近地层O3和CO2浓度变化对冬小麦影响的数值模拟：Ⅱ模拟结果和分析

针对CO2和O3浓度变化对冬小麦影响,改进了农田生态系统碳氮生物化学模型(DNDC),并利用模型模拟了O3和CO2浓度变化对冬小麦生长发育和产量的影响,检验了模型的模拟效果.通过对原DNDC模型适用性的调整,使之适用于固城站,为进一步改进作物模型打下了可靠的基础.通过试验资料验证表明,模型较好地反映了O3和CO2浓度变化对冬小麦生长发育和产量形成的影响.通过敏感性分析得出,模型对温度变化反映灵敏;在CO2浓度倍增情况下,O3浓度变化对冬小麦的复合影响分析看出,一定浓度范围内,CO2可缓解O3对作物影响的负效应,O3对CO2带来的正效应有削弱作用.     

O3浓度增加对冬小麦影响的试验研究

利用OTC 1型开顶式气室对冬小麦进行不同O3 浓度处理的试验研究。结果表明 ,O3 浓度增加 ,冬小麦发育期表现为开花前期有所延迟 ,开花后期的各发育期明显提前 ,生育期缩短 ,植株矮化 ,干物质累积量明显下降。无论是长时期通气处理还是阶段性通气处理 ,产量均明显降低     

旋转风螺旋度在广西春季一次冰雹大风天气分析中的应用

利用旋转风螺旋度对 2 0 0 1年 4月发生在桂中地区的冰雹大风天气进行分析 ,结果表明 :低层旋转风螺旋度对冰雹大风等强对流天气有一定的指示意义 ,冰雹大风天气易产生于低层正旋转风螺旋度大值中心与中低层对流性不稳定能量储存区所包围的区域 ,其上空 5 0 0 h Pa以下旋转风螺旋度为正 ,40 0 h Pa以上为负     

一次冬季锋面暴风雪天气过程的斜压边界层特征的观测分析

对STORM-FESTIOP17一次冬季锋面暴风雪天气过程的斜压边界层结构演变及特征进行了分析。发现：暖湿空气沿锋面抬升凝结成云，产生降水过程中释放的大量潜热显著增加锋两侧的水平温度差异，产生锋生。与锋生相伴，在锋前产生低空急流和高空急流。当锋生至最强时，锋两侧温差可达20K，锋前低空急流开始减弱，锋后低空急流增强，锋后冷平流开始主导锋两侧的环流系统。该冷平流削弱锋两侧的温度水平梯度，产生锋消作用。对这次锋面斜压对流边界层的湍流特征分析表明：在边界层之上切应力wv明显增大；湍能收支分析表明在边界层之上的风切变产生项很强，即大尺度天气系统有利于斜压对流边界层的发展，边界层内各量充分混合。这次冬季锋面暴风雪天气过程，冷锋前的低空南风急流从墨西哥湾携带来的充足水汽及锋区边界层大气的强斜压性是其产生的关键因子：冷锋过后，大尺度高空急流的作用更有利于对流边界层的充分发展。     

新疆大风的时空统计特征

统计了1961－1999年39a新疆90个气象观测站的气表－1资料，给出新疆大风的时空分布特征，结果表明：（1）年平均大风日数的高值区在北疆西北部，东疆和南疆西部，阿拉山口，达坂城大风最多，准噶尔盆地中心，塔里木盆地南缘最少。（2）大风年总日数的变化有明显的波动性，大部分地区80年代起大风日数有减少的趋势。（3）春，夏季大风最多，以5，6月最为频敏，大风主要出现在上年10时到午夜23时，半数以上的大风持续时间在1h以上，以0.5h以内最多。     

北京一次大风和强降水天气过程形成机理的数值模拟

利用3维强风暴冰雹分档模式(IPA—HBM)对2001年8月23日北京的一次伴有大风、暴雨和冰雹的强对流天气过程进行模拟和分析，并与部分观测资料进行了比较分析。结果表明，该模式对此次强风暴的生命史、降水分布、降雹的大小等要素做了较好的模拟，并能够模拟出伴随强风暴过程所产生的强下沉气流和及地面强风速切变(下去暴流)。从云微物理学角度分析了此次局地性大风的形成原因，认为由高空冰雹粒子的拖曳产生的负浮力作用是促发强下沉气流产生的主要原因，其次是冰雹的融化和雨水蒸发冷却对下沉气流起加速作用，冰雹的拖曳和融化作用对下沉气流具有决定性作用。强风暴所产生的爆发性强下沉气流最终导致了局地大风的形成。     

华北地区农业干旱预测模型及其应用研究

对华北地区冬小麦进行了水分胁迫实验，确定了冬小麦光合作用速率对水分胁迫的响应曲线，提出了农业干旱指数和农业干旱预警指数两个基本概念，在此基础上建立了具有明确生物学机理的华北农业干旱预测数值模式。对北京、石家庄、郑州和济南1998年至2000年432旬的农业干旱模拟结果表明:农业干旱预警定性准确率为90.7%，定量准确率在87.5%左右；此外，模拟表明模式也可以对区域农业干旱进行准确有效的预测。利用1961~2000年气象资料对北京等地区历年农业干旱进行数值分析，结果表明:不同于大气干旱，在自然气象条件下，北京等地区作物生长期内几乎每年都存在农业干旱现象，特别是冬小麦灌浆至成熟期每年均存在一次较为严重的农业干旱胁迫过程，但农业干旱胁迫程度年际间存在一定波动，功率谱分析表明其具有3~6年的周期变化规律。对平均气候状况下华北地区农业干旱进行了时空动态分析，结果发现在自然条件下，华北大部分地区冬小麦4月下旬至5月下旬，即大约在冬小麦开花、灌浆至成熟期，农业干旱胁迫指数存在一种自然的逐渐加强的动态过程，这与华北地区的农业生产实践是基本一致的。