中国登路热带气旋的季节预测模型

The year-to-year increment prediction approach proposed by was applied to forecast the annual number of tropical cyclones (TCs) making landfall over China.The year-to-year increase or decrease in the number of land-falling TCs (LTCs) was first predicted to yield a net number of LTCs between successive years.The statistical prediction scheme for the year-to-year increment of annual LTCs was developed based on data collected from 1977 to 2007,which includes five predictors associated with high latitude circulations in both Hemispheres and the circulation over the local,tropical western North Pacific Ocean.The model shows reasonably high predictive ability,with an average root mean square error (RMSE) of 1.09,a mean absolute error (MAE) of 0.9,and a correlation coefficient between the predicted and observed annual number of LTCs of 0.86,accounting for 74% of the total variance.The cross-validation test further demonstrated the high predictive ability of the model,with an RMSE value of 1.4,an MAE value of 1.2,and a correlation coefficient of 0.74 during this period.     

Seasonal Prediction of Summer Temperature over Northeast China Using a Year-to-Year Incremental Approach

We present a model for predicting summertime surface air temperature in Northeast China (NESSAT) using a year-to-year incremental approach. The predicted value for each year's increase or decrease of NESSAT is added to the observed value within a particular year to yield the net forecast NESSAT. The seasonal forecast model for the year-to-year increments of NESSAT is constructed based on data from 1975-2007. Five predictors are used: an index for sea ice cover over the East Siberian Sea, an index for central Pacific tropical sea surface temperature, two high latitude circulation indices, as well as a North American pressure index. All predictors are available by no later than March, which allows for compilation of a seasonal forecast with a two-month lead time. The prediction model accurately captures the interannual variations of NESSAT during 1977-2007 with a correlation coefficient between the predicted and observed NESSAT of 0.87 (accounting for 76% of total variance) and a mean absolute error (MAE) of 0.3℃. A cross-validation test during 1977 2008 demonstrates that the model has good predictive skill, with MAE of 0.4℃ and a correlation coefficient between the predicted and observed NESSAT of 0.76.     

A Seasonal Prediction Model for the Summer Rainfall in Northeast China Using the Year-To-Year Increment Approach

Using the year-to-year increment approach, this study investigated the relationship of selected climatic elements with the increment time series of the summer rainfall between successive years in Northeast China, including the soil moisture content, sea surface temperature, 500 hPa geopotential height, and sea level pressure in the preceding spring for the period 1981–2008. Two spring predictors were used to construct the seasonal prediction model: the area mean soil moisture content in Northwest Eurasia and the 500 hPa geopotential height over Northeast China. Both the cross-validation and comparison with previous studies showed that the above two predictors have good predicting ability for the summer rainfall in Northeast China.     

从东北玉米冷害预测模型展望农业气象灾害预测技术的发展

以东北玉米冷害预测为例,介绍了近年发展的基于热量指数预测的热量年型统计预测模型和基于玉米生长模式的机理性预测模型,比较了两类模型在预报原理、预报对象、预报时效及准确率等方面的各自差别和利弊,探讨了两类模型取长补短优势互补的可能方法,展望了农业气象灾害预测预警技术发展的若干问题。     

Assessing the impact of climate changes on the potential yields of maize and paddy rice in Northeast China by 2050

Northeast China is the main crop production region in China, and future climate change will directly impact crop potential yields, so exploring crop potential yields under future climate scenarios in Northeast China is extremely critical for ensuring future food security. Here, this study projected the climate changes using 12 general circulation models (GCMs) under two moderate Representative Concentration Pathway (RCP) scenarios (RCP 4.5 and 6.0) from 2015 to 2050. Then, based on the Global Agro-ecological Zones (GAEZ) model, we explored the effect of climate change on the potential yields of maize and paddy rice in Northeast China during 2015–2050. The annual relative humidity increased almost throughout the Northeast China under two RCPs. The annual precipitation increased more than 400 mm in some west, east, and south areas under RCP 4.5, but decreased slightly in some areas under RCP 6.0. The annual wind speed increased over 2 m/s in the west region. The annual net solar radiation changes varied significantly with latitude, but the changes of annual maximum temperature and minimum temperature were closely related to the terrain. Under RCP 4.5, the average maize potential yield increased by 34.31% under the influence of climate changes from 2015 to 2050. The average rice potential yield increased by 16.82% from 2015 to 2050. Under RCP 6.0, the average maize and rice potential yields increased by 25.65% and 6.34% respectively. The changes of maize potential yields were positively correlated with the changes of precipitation, wind speed, and net solar radiation (the correlation coefficients were > 0.2), and negatively correlated with the changes of relative humidity, minimum and maximum temperature under two RCPs. The changes of rice potential yields were positively correlated with the changes of precipitation (correlation coefficient = 0.15) under RCP 4.5. Under RCP 6.0, it had a slight positive correlation with net solar radiation, relative humidity, and wind speed.     

我国东北地区玉米冷害风险评估

根据年平均寒积温水平,将东北地区的玉米冷害分成4个区域。在分析各冷害区域年寒积温距平与年减产率关系的基础上,确定东北地区各地无冷害影响年、一般冷害年、严重冷害年的年寒积温距平指标。将危险度、脆弱度和暴露度作为冷害风险的评估因子,建立了冷害风险评估指标体系,并应用层次分析法对各指标赋予权重。建立了冷害危险度与年平均寒积温的回归方程,利用地理因子构建了年平均寒积温空间格点化模型,应用地理信息系统推算东北地区玉米冷害危险度,由危险度、脆弱度和暴露度得到我国东北地区玉米冷害风险指数,并开展风险评估。结果表明:东北地区的玉米冷害风险空间分布呈北侧和南部低、东部中等、西部高,松嫩平原东北部和西北部以及吉林省中北部主要为较高或高风险区,三江平原主要为中等风险区,黑龙江省北部、吉林省东南部和辽宁省主要为较低或低风险区。     

Influence of springtime North Atlantic Oscillation on crops yields in Northeast China

Forecasting grain production is of strategic importance in considerations of climate change and growing population. Here we show that the springtime North Atlantic Oscillation (NAO) is significantly correlated to the year-to-year increment of maize and rice yield in Northeast China (NEC). The physical mechanism for this relationship was investigated. Springtime NAO can induce sea surface temperature anomalies (SSTAs) in the North Atlantic, which display a tripole pattern and are similar to the empirical mode pattern in spring. The spring Atlantic SSTA pattern that could persists to summer, can trigger a high-level tropospheric Rossby wave response in the Eurasia continent, resulting in atmospheric circulation anomalies over the Siberia-Mongolia region, which is unfavorable (favorable) for cold surges that affect NEC. Weaker (stronger) cold surges can accordingly reduce (increase) cloud amount, resulting in an increase (a decrease) in daily maximum temperature and a decrease (an increase) in daily minimum temperature, thereby leading to an increase (a decrease) in diurnal temperature range. And summer-mean daily minimum temperature and diurnal temperature range are most significantly related to the NEC crop yields.     

中国气象科学研究院农业气象研究50年进展

该文在简要回顾20世纪我国农业气象学科发展历程基础上, 重点阐述了50年来中国气象科学研究院在农业气象各主要研究领域, 包括农业气候资源与区划、农业产量气象预测与卫星遥感估产、农业气象灾害、气候变化影响评估、作物生长模拟与模式以及农业气象情报信息服务等所取得的若干重大进展, 并从当前面临的挑战与机遇出发, 探讨了中国气象科学研究院未来发展中在农业气象研究领域的可能热点趋势。     

Improve the prediction of summer precipitation in the Southeastern China by a hybrid statistical downscaling model

We attempt to apply year-to-year increment prediction to develop an effective statistical downscaling scheme for summer (JJA, June–July–August) rainfall prediction at the station-to-station scale in Southeastern China (SEC). The year-to-year increment in a variable was defined as the difference between the current year and the previous year. This difference is related to the quasi-biennial oscillation in interannual variations in precipitation. Three predictors from observations and six from three general circulation models (GCMs) outputs of the development of a European multi-model ensemble system for seasonal to interannual prediction (DEMETER) project were used to establish this downscaling model. The independent sample test and the cross-validation test show that the downscaling scheme yields better predicted skill for summer precipitation at most stations over SEC than the original DEMETER GCM outputs, with greater temporal correlation coefficients and spatial anomaly correlation coefficients, as well as lower root-mean-square errors.     

东北春玉米积温模型的改进与比较

积温是农业气象科研和业务工作中最常使用的指标之一,但由于受其他环境条件的影响,农作物生育期间的积温在年际间和地区间均表现出不稳定性。因此,如何对已有积温模型进行修正,使农作物生育期间积温计算值趋于稳定并反映实际情况,对农业生产和气象服务均有重要意义。该文以东北春玉米四单19为例,应用沈国权提出的非线性积温模型（简称NLM）进行拟合,分析了参数选择对积温稳定性的影响,提出使用平均温度的二次函数对线性积温模型（简称LM）进行修正（修正后模型称TRM）并进行效果分析,与NLM进行比较。结果表明:NLM拟合时参数P越小,模拟有效积温越稳定;NLM积温在年际间、地区间均存在差异,造成积温不稳定的主要因子是温度强度,与其他因子相关性较差;有效积温与生育期平均温度呈二次曲线关系,对LM的温度二次方修正结果与NLM结果比较发现,二次方修正方法具有可行性。     

不同物候模型对作物发育期模拟的对比分析

作物发育期预报在农业气象业务中具有重要意义。通过比较4种作物发育期模型的模拟效果,为中国东北地区作物发育期预报提供参考。基于东北地区玉米、水稻和大豆的发育期观测数据及其对应的气象资料,利用模拟退火算法估算了4个发育期模型的参数值,并对模型进行内外部验证。结果表明：在参数本地化过程中,高亮之模型和沈国权模型的效果较好,均方根误差平均分别为3.31d和3.72d。在模型验证过程中,沈国权模型的模拟效果较好,均方根误差平均为5.22d,因此,相对而言,沈国权模型对作物发育期模拟效果较好。     

气候适宜指数在早稻产量动态预报上的应用

结合早稻生理特性和前人研究成果,分别构建了早稻温度、降水及日照适宜度模型,在此基础上,为反映多因子对早稻产量的协同效应,建立了早稻气候适宜度模型。根据不同时段的早稻气候适宜度,利用加权法,构建了早稻气候适宜指数。基于气候适宜指数与早稻产量的关系,建立了早稻气候适宜指标。并利用历年不同时段的气候适宜指数和早稻产量建立了早稻产量动态预报模型。结果表明,模型回代检验和预报检验的丰歉趋势正确率、实际预报准确率均较高,能够满足业务服务的需要。     

基于年际增量法的卷积神经网络模型对中国夏季降水的预测研究

将前冬的500 hPa位势高度、向外长波辐射和海表温度的年际增量作为预测因子,建立基于卷积神经网络(Convolutional Neural Network, CNN)的非线性预测模型,对中国160个测站夏季降水展开预测研究,并与基于线性奇异值分解(Singular Value Decomposition, SVD)的预测模型进行效果对比。结果表明：CNN在1981—2020年的交叉检验中所回报的降水平均PS评分和距平相关系数(ACC)分别为74.33和0.12,比SVD高2.15和0.06,说明CNN比SVD在整体上对夏季降水具有更好的预测能力。其中,CNN对SVD预测较好年份的预测效果提升较为明显,对SVD预测较差的年份则改进不大。CNN对中国降水预测存在一定的系统性偏差,订正后CNN对拉尼娜年的降水预测改进较大。结果表明,基于年际增量法的CNN预测模型展示出较好的潜在应用价值。     

春玉米产量动态预报技术的改进方法探索

为及时、准确地进行玉米产量预报,为吉林省玉米产量预报业务提供参考依据,为政府调控提供科技支撑,利用吉林省19802016年春玉米产量和50个气象站逐日气温、降水量、日照时数等资料,基于相似距离和相关系数构建综合诊断指标筛选气温、降水量、日照时数等各类气象要素历史相似年,根据各类气象要素历史相似年与预报年的玉米产量丰歉气象影响指数之间的关系,建立吉林省春玉米产量动态预报模型。同时,对历史相似年的筛选方法进行改进,利用欧氏距离直接筛选综合气候历史相似年,根据气候历史相似年与预报年的玉米产量丰歉气象影响指数之间的关系,构建春玉米产量预报模型。对比改进前、后的产量预报模型的预报,结果表明:两种方法在吉林省玉米单产预报中,预报准确率均较高,普遍在85%以上。产量预报模型对20022013年的预报检验结果表明,改进方法后20022013年单产预报平均准确率提高了3.9个百分点,均方根误差降低了4个百分点,标准差降低了2。对20142016年的预报检验结果表明,改进方法的玉米产量预报结果优于传统方法的预报结果。改进方法比传统方法准确率更高,稳定性更强,应用价值更高。     

淮河流域夏季极端降水事件的统计预测模型研究

采用年际增量预测方法, 通过考察与淮河流域夏季极端降水事件发生频次(HRF)年际增量相关的环流, 确定了5个预测因子:冬季北太平洋涛动、12月南极涛动、春季3～4月南印度洋气压、春季3～4月白令海气压、春季3～4月印尼—澳洲附近经向风垂直切变;然后利用这5个预测因子, 通过多元线性回归方法建立HRF年际增量的预测模型, 进而预测HRF。交叉检验表明, 在1962～2005年的后报中, 这个预测模型对HRF显示了较高的预测技巧, 预测结果与实测间的相关系数为0.67, 表现出较高的预测潜力, 对淮河流域夏季极端降水事件的预测具有较大的应用价值。     

基于涡度相关的春玉米逐日作物系数及蒸散模拟

作物系数是计算作物蒸散量的关键参数。利用2006—2008年和2011年辽宁锦州玉米农田生态系统的涡度相关、气象、作物发育期及叶面积指数观测数据,分析不受水分胁迫条件下玉米逐日作物系数特征及其与叶面积指数的关系。研究表明:作物系数与玉米农田实际蒸散均呈单峰型变化,约在7月末至8月初达到最大值 (玉米开花吐丝期)。在此基础上,建立了不受水分胁迫条件下玉米逐日作物系数与叶面积指数关系 (达到0.01显著性水平), 同时,采用积温表示的标准化生育期方法模拟相对叶面积指数,并建立了逐日作物系数与相对叶面积指数关系 (达到0.01显著性水平),解决了无叶面积观测地区玉米逐日实际蒸散量的计算。研究结果可为玉米农田用水管理以及灌溉措施的制定提供参考。     

Storylines: an alternative approach to representing uncertainty in physical aspects of climate change

Investigating the relationships between climate extremes and crop yield can help us understand how unfavourable climatic conditions affect crop production. In this study, two statistical models, multiple linear regression and random forest, were used to identify rainfall extremes indices affecting wheat yield in three different regions of the New South Wales wheat belt. The results show that the random forest model explained 41–67% of the year-to-year yield variation, whereas the multiple linear regression model explained 34–58%. In the two models, 3-month timescale standardized precipitation index of Jun.–Aug. (SPI_JJA), Sep.–Nov. (SPI_SON), and consecutive dry days (CDDs) were identified as the three most important indices which can explain yield variability for most of the wheat belt. Our results indicated that the inter-annual variability of rainfall in winter and spring was largely responsible for wheat yield variation, and pre-growing season rainfall played a secondary role. Frequent shortages of rainfall posed a greater threat to crop growth than excessive rainfall in eastern Australia. We concluded that the comparison between multiple linear regression and machine learning algorithm proposed in the present study would be useful to provide robust prediction of yields and new insights of the effects of various rainfall extremes, when suitable climate and yield datasets are available.     

基于气候适宜指数的江西晚稻产量动态预报模型构建及应用

本文利用1981—2014年江西91个气象观测站的地面气象观测资料和同期的分县晚稻产量资料,结合江西地区气候特点及晚稻生理特性,构建适用于江西地区晚稻降水、温度、日照及综合适宜度模型,并根据适宜度与产量的相关关系,确定气候适宜指数,建立基于气候适宜指数的江西晚稻产量动态预报模型,并对模型进行了回代检验及预报检验,从而实现对江西地区晚稻气候适宜度诊断及晚稻产量动态预报的目的。结果表明:模型的回代检验、产量丰歉趋势、产量动态预报检验的准确率均较高,能够满足业务的需要。     

Potential impact of future climate change on crop yield in northeastern China

We evaluated the potential impact of future climate change on spring maize and single-crop rice in northeastern China(NEC) by employing climate and crop models. Based on historical data, diurnal temperature change exhibited a distinct negative relationship with maize yield, whereas minimum temperature correlated positively to rice yield. Corresponding to the evaluated climate change derived from coupled climate models included in the Coupled Model Intercomparison Project Phase 5(CMIP5) under the Representative Concentration Pathway 4.5 scenario(RCP4.5), the projected maize yield changes for three future periods [2010–39(period 1), 2040–69(period 2), and 2070–99(period 3)] relative to the mean yield in the baseline period(1976–2005) were 2.92%, 3.11% and 2.63%, respectively. By contrast, the evaluated rice yields showed slightly larger increases of 7.19%, 12.39%, and 14.83%, respectively. The uncertainties in the crop response are discussed by considering the uncertainties obtained from both the climate and the crop models. The range of impact of the uncertainty became markedly wider when integrating these two sources of uncertainty. The probabilistic assessments of the evaluated change showed maize yield to be relatively stable from period 1 to period 3, while the rice yield showed an increasing trend over time. The results presented in this paper suggest a tendency of the yields of maize and rice in NEC to increase(but with great uncertainty) against the background of global warming, which may offer some valuable guidance to government policymakers.     

基于东北玉米区域动力模型的低温冷害预报应用研究

在田间试验资料基础上，采用改进的发育模型和分区作物参数，结合前人有关研究成果建立了东北玉米区域动力模型，并利用模型模拟了12站40年 (1961~2000年) 玉米生长发育过程。确定抽雄期延迟天数为低温冷害指标，分析了历史低温冷害年及减产情况。模拟了典型冷害年和40年气候平均的0.25°×0.25°网格点玉米生长发育过程, 探讨了与区域气候模式结合进行低温冷害预报的方法。主要结论有:①玉米发育模型能够较好地模拟玉米发育期和发育期对低温冷害的响应，以抽雄期延迟天数为冷害指标评估的历史冷害发生状况基本符合历史实况。②模型有一定的模拟玉米生长量对低温冷害响应的能力，但还需要更多的试验数据校正品种参数，完善模型。③利用GIS技术，结合区域化的作物参数运行区域作物模型，是作物模型区域化应用的一种解决方案。④东北玉米区域动力模型解释性好，根据确定的害指标，以区域气候模式输出结果驱动玉米模型可以模拟和预测低温冷害，是农业气象灾害预测预报的一个有益的尝试。