WO2020107520A1 - 计算气温与气候因子遥相关关系的方法及装置 - Google Patents

计算气温与气候因子遥相关关系的方法及装置 Download PDF

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WO2020107520A1
WO2020107520A1 PCT/CN2018/119943 CN2018119943W WO2020107520A1 WO 2020107520 A1 WO2020107520 A1 WO 2020107520A1 CN 2018119943 W CN2018119943 W CN 2018119943W WO 2020107520 A1 WO2020107520 A1 WO 2020107520A1
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climate
temperature data
temperature
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孙立群
李晴岚
李广鑫
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Shenzhen Institute of Advanced Technology of CAS
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

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  • the invention belongs to analysis and measurement control technology, and particularly relates to a method and a device for calculating the remote correlation between air temperature and climatic factors.
  • the study of climate change in a certain area is mainly based on the calculation of the relationship between temperature and climatic factors, and it is mainly based on the difference of regions and the selection of different climatic factors for calculation.
  • there are sometimes more than one climatic factor affecting the temperature change in a certain area and sometimes it may be restricted by several climatic factors at the same time.
  • Mutual interference of multiple climatic factors is not conducive to predicting changes in temperature, and it is impossible to accurately provide reference for agricultural and forestry production and meteorological risks in specific areas.
  • the existing technology is insufficient.
  • the purpose of the present invention is to provide a method and device capable of removing interference to calculate the remote correlation between air temperature and climatic factor, aiming to solve the problem of how to accurately predict the response relationship between air temperature and climatic factor in a certain area.
  • the present invention provides a method for calculating the remote correlation between air temperature and climatic factors, including the following steps:
  • a third-order partial correlation coefficient is used to calculate the variable relationship between each of the climatic factors and the temperature data in the spatial grid;
  • step s3 draw a response diagram of the temperature data of the global region and the climate factor.
  • the present invention also provides a device for calculating the remote correlation between air temperature and climatic factors, including:
  • Input unit to receive selected climate factors with global influence and temperature data with credibility
  • the pre-processing unit groups the selected climate factors with global impact and credible temperature data by month to calculate the tie value and deviation of each season within the time period;
  • a variable relationship calculation unit based on a geographic information system, calculating a variable relationship between each of the climatic factors and the temperature data in the spatial grid through a third-order partial correlation coefficient;
  • the interference removal unit sets the confidence level, excludes interference factors with a confidence level lower than 95% in the calculation results, and outputs the climate factor in the spatial grid that is most affected by temperature changes.
  • the device further includes: a mapping unit, based on a geographic information system, plotting a response relationship graph of the temperature data and the climate factor in the global region.
  • the present invention When analyzing the correlation between air temperature and multiple climatic factors, the present invention adopts climatic factors with global influence and temperature data with credibility; and calculates each climatic factor and temperature in the spatial grid through the third-order partial correlation coefficient The variable relationship of the data; the interference factor in the calculation result is eliminated by the confidence degree, and the climate factor that has the greatest influence on the temperature change is obtained.
  • This solution can more accurately reflect the temperature changes in the selected area, and which climate factors affect the temperature in the area. Based on the changes of several climatic factors at present, it is possible to estimate the temperature change in the specific area in the future, provide certain reference information for the prediction of the vegetation biological yield in the area, and improve the accuracy of agricultural and forestry production risk prediction .
  • 1 is the main flow chart of the method for calculating the remote correlation between air temperature and climatic factor of the present invention
  • FIG. 3 is a frame diagram of the device for calculating the remote correlation between temperature and climatic factors of the present invention
  • FIG. 5 is a graph showing the response relationship of the average surface temperature data drawn by the present invention to climate factors.
  • a method for calculating the remote correlation between air temperature and climate factor includes the following steps:
  • a third-order partial correlation coefficient is used to calculate the variable relationship between each of the climatic factors and the temperature data in the spatial grid;
  • the confidence interval of a probability sample is the interval estimation of a certain overall parameter of this sample.
  • the confidence interval shows how much the true value of this parameter falls around the measurement result with a certain probability.
  • the confidence interval gives the credibility of the measured value of the measured parameter, that is, the "certain probability" required earlier.
  • This probability is called the confidence level, or confidence.
  • the partial correlation coefficients of temperature data and ENSO, IOD, AO and AAO are calculated, and the correlation coefficient between temperature and ENSO is more than 95%, and the remaining few are not exceeded, indicating that the temperature is controlled by ENSO and the remaining several climate factors It has little effect on temperature, and can be regarded as interference factor being excluded.
  • the calculation method also includes the following steps:
  • step s3 draw a response diagram of the temperature data of the global region and the climate factor.
  • the temperature data uses monthly average grid data of the highest and lowest temperatures of 0.5 degrees in the world; the climatic factors use El Ni ⁇ o-Southern Oscillation, Indian Ocean Dipole, Arctic Oscillation and Antarctic Oscillation; The climate factor with confidence below 95%.
  • the world's highest and lowest temperature month of 0.5 degrees is currently a widely used set of data in the scientific community, with high credibility and data quality.
  • the present invention selects four climatic factors that can characterize global climate change. They are the El Ni ⁇ o-Southern Oscillation (ENSO) in the Pacific Ocean, the Indian Ocean Dipole (IOD, Indian Ocean Dipole) in the Indian Ocean, and the Northern Hemisphere Arctic Oscillation (AO: Arctic Oscillation), Antarctic Oscillation in the Southern Hemisphere (AAO: Antarctic Oscillation).
  • ENSO El Ni ⁇ o-Southern Oscillation
  • IOD Indian Ocean Dipole
  • AO Northern Hemisphere Arctic Oscillation
  • AAO Antarctic Oscillation
  • the step s1 further includes pre-processing the air temperature data; the pre-processing is to group the air temperature data and the climatic factor index by month to calculate the seasonal Draw value and deviation.
  • the time period is 1982 to 2016.
  • the seasonal averages from 1982 to 2016 were calculated according to natural months. March, April, and May were spring, June, 7, and 8 were summer, 9, 10, and 11 were autumn, and 12, 1, 2 were winter. .
  • the draw value is the multi-year average of the temperature in each season from 1982 to 2016, and the deviation is the temperature anomaly obtained by subtracting the multi-year average of the annual value of each quarter.
  • step s2 is performed based on a geographic information system with grid calculation function.
  • the geographic information system is an ArcGIS environment. Write programs directly under this system to calculate partial correlation. Using the Raster Calculator tool in ArcGIS 9.3 software to write a script can directly calculate the meteorological data layer and climate factor data, and calculate the partial correlation coefficient efficiently and conveniently. The advantage of this is that every time related calculations, raster (Raster) files are generated, the relationship between variables can be better analyzed, understood and explained.
  • 1 is the highest temperature
  • 2 is the El Ni ⁇ o-Southern Oscillation
  • 3 is the Indian Ocean dipole
  • 4 is the Arctic Oscillation
  • 5 is the Antarctic Oscillation.
  • R stands for relationship, that is, relationship.
  • R12,345 represents the relationship between variables 1 and 2, while eliminating the interference of variables 3,4,5.
  • the grid calculation function of the geographic information system can use the data of each month as a variable, and apply the formula of partial correlation calculation, you can directly implement the calculation process on arcgis and present the calculation results, without the need to write an external program and import the arcgis software Show results.
  • the correlation between the highest and lowest temperatures at any point in the world and a certain subsequent factor can be obtained, and the interference of the other three subsequent factors is excluded at the same time.
  • This method can better explain global and local climate change, determine that the temperature at any location is most closely related to that climate factor, and through the fluctuation of climate factors, make predictions about the future long-term climate trends in the region.
  • step s4 includes the following specific steps:
  • each spatial grid corresponds to a pixel, and the layers of the calculated temperature and the correlation coefficient of each climatic factor are separately loaded, and different layers are distinguished by different patterns;
  • the transparency of all layers is set to 50%, which is conducive to displaying the overlapping areas affected by climate factors, and clarifying the range of common influence of multiple climate factors.
  • dark gray represents the influence area of the Pacific El Ni ⁇ o phenomenon (ENSO)
  • light gray represents the influence area of the Indian Ocean dipole (IOD)
  • diagonal grid represents the influence area of the Arctic Oscillation (AO)
  • orthogonal grid represents Area of influence of the Antarctic Oscillation (AAO).
  • All layers are done with 50% transparency to facilitate the observation of some overlapping areas.
  • the histogram illustration is the percentage of the total area of the surface area affected by each climatic factor after projecting on an equal area.
  • Picture b is the same as picture a, but represents summer
  • picture c is the same as picture a, but represents autumn
  • picture d is the same as picture a, but represents winter.
  • the response graph of temperature and climate factors will have a positive reference significance for the prediction of grain and agricultural and forestry products, and it can give a certain degree of reference for agricultural futures trading.
  • the response graph of temperature and climate factors will have a positive reference significance for the prediction of grain and agricultural and forestry products, and it can give a certain degree of reference for agricultural futures trading.
  • a device for calculating the remote correlation between air temperature and climatic factors includes:
  • the input unit receives the selected climate factors with global influence and the temperature data with credibility.
  • the pre-processing unit groups the selected climate factors with global impact and the temperature data with credibility by month to calculate the average value and deviation of each season within the time period.
  • the draw value is the multi-year average of the temperature in each season from 1982 to 2016, and the deviation is the temperature anomaly obtained by subtracting the multi-year average of the annual value of each quarter.
  • variable relationship calculation unit calculates the variable relationship between each climate factor and the temperature data in the spatial grid by inputting a third-order partial phase relationship formula.
  • the interference removal unit sets the confidence level, excludes interference factors with a confidence level lower than 95% in the calculation results, and outputs the climate factor in the spatial grid that is most affected by temperature changes.
  • the device further includes: a mapping unit, based on a geographic information system, plotting a response relationship graph of the temperature data and the climate factor in the global region.
  • each unit of the device for calculating the remote correlation between air temperature and climatic factors may be implemented by a corresponding hardware or software unit, and each unit may be an independent software or hardware unit, or may be integrated into one software or hardware unit , Not used here to limit the invention.
  • each unit may be implemented by a corresponding hardware or software unit, and each unit may be an independent software or hardware unit, or may be integrated into one software or hardware unit , Not used here to limit the invention.
  • the present invention performs well on the prediction of global surface temperature.
  • the present invention calculates the response relationship between the temperature and the four climatic factors by inputting the formula for calculating the third-order partial correlation relationship in the ground information system, that is, calculating the relationship between the temperature and a certain climatic factor to exclude interference from other climatic factors.
  • the response graph of global gas temperature and four climatic factors is drawn to indicate which climatic factors are mainly or jointly affected by the temperature in any area. Introducing confidence to finally determine one or more climatic factors that are most relevant to changes in air temperature improves the accuracy of the teleconnection (response relationship) between the two.
  • Based on the changes of several climatic factors at present it is possible to estimate the temperature change in the specific area in the future, provide certain reference information for the prediction of the vegetation biological yield in the area, and improve the accuracy of agricultural and forestry production risk prediction .

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Abstract

一种计算气温与气候因子遥相关关系的方法及装置，在分析气温和多个气候因子的相关性时，采用有全球性影响的气候因子和有公信度的气温数据（S1）；并通过三阶偏相关系数计算空间栅格内的每一个气候因子与气温数据的变量关系（S2）；再通过置信度排除计算结果中的干扰因子（S3），得到对气温变化影响最大的气候因子。该方法能更准确反应选定区域内的气温变化情况，以及该区域的气温受到哪些气候因子的影响。实现了根据当下几个气候因子的变化情况，就可以估计未来该特定区域的温度变化情况，为该区域内的植被生物产量的预测提供一定的参考信息，提升了农林业生产风险预测的准确性。使用该方法的装置也同样具有上述效果。

Description

计算气温与气候因子遥相关关系的方法及装置 技术领域
本发明属于分析及测量控制技术，尤其涉及计算气温与气候因子遥相关关系的方法及装置。
背景技术
目前在全球范围内，除了厄尔尼诺(ENSO事件)这一全球性的表征气候变化的指数外，科学家在全球目前已经发现了数十个气候因子。这些气候因子都会在不同程度对不同范围内的天气系统造成不同程度的影响。这些影响最直观的体现则在气温变化上。而且这些气候因子相互作用，形成制约机制，有时一个地区的温度变化，可能同时受多个外部气候因子的影响，产生遥相关关系，进而影响这一地区的植被生长与农产品产量。
研究某一地区的气候变化主要是依据计算气温与气候因子的关系，而且主要是根据地域的不同，选取不同的气候因子做计算。但是影响某一地区的气温变化的气候因子有时不止一个，有时可能同时受到几个气候因子的相互制约。多个气候因子相互干扰不利于预测气温的变化，无法准确的对特定区域的农林业生产及气象风险提供参考，现有技术存在不足。
发明内容
本发明的目的在于提供一种能够去除干扰的计算气温与气候因子遥相关关系的方法及装置，旨在解决如何准确预测出某一区域内气温与气候因子的响应关系问题。
一方面，本发明提供了一种计算气温与气候因子遥相关关系的方法包括下 述步骤：
S1.选取具有全球性影响的气候因子和有公信度的气温数据；
S2.通过三阶偏相关系数计算空间栅格内的每一个所述气候因子与所述气温数据的变量关系；
S3.通过置信度排除计算结果中的干扰因子。
进一步的，还包括以下步骤：
S4.根据所述步骤s3的结果绘制全球区域的所述气温数据与所述气候因子的响应关系图。
另一方面，本发明还提供一种计算气温与气候因子遥相关关系的装置，包括：
输入单元，接收选取的具有全球性影响的气候因子和有公信度的气温数据；
预处理单元，将选取的具有全球性影响的气候因子和有公信度的气温数据按月分组，计算时间周期内每个季节的平局值及偏差；
变量关系计算单元，基于地理信息系统，通过三阶偏相关系数计算空间栅格内的每一个所述气候因子与所述气温数据的变量关系；
去干扰单元，设置置信度，排除计算结果中置信度低于95％的干扰因子，输出该空间栅格内对气温变化影响最大受的气候因子。
进一步的，该装置还包括：制图单元，基于地理信息系统，绘制全球区域的所述气温数据与所述气候因子的响应关系图。
本发明在分析气温和多个气候因子的相关性时，采用有全球性影响的气候因子和有公信度的气温数据；并通过三阶偏相关系数计算空间栅格内的每一个气候因子与气温数据的变量关系；再通过置信度排除计算结果中的干扰因子，得到对气温变化影响最大的气候因子。该方案能更准确反应选定区域内的气温变化情况，以及该区域的气温受到哪些气候因子的影响。实现了根据当下几个 气候因子的变化情况，就可以估计未来该特定区域的温度变化情况，为该区域内的植被生物产量的预测提供一定的参考信息，提升了农林业生产风险预测的准确性。
附图说明
图1是本发明计算气温与气候因子遥相关关系的方法的主要流程图；
图2是本发明计算方法的完整流程图；
图3是本发明计算气温与气候因子遥相关关系装置的框架图；
图4是本发明计算方法的具体工作流程示意图；
图5是本发明绘制的地表平均气温数据对气候因子的响应关系图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图1-5及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
以下结合具体实施例对本发明的具体实现进行详细描述：
实施例一：
如附图1所示的一种计算气温与气候因子遥相关关系的方法，包括下述步骤：
S1.选取具有全球性影响的气候因子和有公信度的气温数据，有利于保证结果的准确性。
S2.通过三阶偏相关系数计算空间栅格内的每一个所述气候因子与所述气温数据的变量关系；
S3.通过置信度排除计算结果中的干扰因子。
关于本发明的置信度，在统计学中，一个概率样本的置信区间(Confidence interval)是对这个样本的某个总体参数的区间估计。置信区间展现的是这个参数的真实值有一定概率落在测量结果的周围的程度。置信区间给出的是被测量参数的测量值的可信程度，即前面所要求的“一定概率”。这个概率被称为置信水平，即置信度。比如计算温度数据与ENSO、IOD、AO和AAO的偏相关系数，得到温度与ENSO的相关系数置信度超过95％，其余几个都没有超过，则说明温度受ENSO影响控制，其余几个气候因子对温度影响不大，可视为干扰因子被排除。
如附图2所示，计算方法还包括以下步骤：
S4.根据所述步骤s3的结果绘制全球区域的所述气温数据与所述气候因子的响应关系图。
进一步的，所述气温数据采用全球0.5度最高、最低气温月平均格点数据；所述气候因子采用厄尔尼诺-南方涛动，印度洋偶极子，北极涛动和南极涛动；所述干扰因子为置信度低于95％的所述气候因子。
全球0.5度最高、最低气温月是目前科学界使用较为广泛的一套数据，公信度和数据质量较高。
本发明选取了四个可以表征全球气候变化的气候因子，他们分别是处于太平洋的厄尔尼诺-南方涛动(ENSO)，处于印度洋的印度洋偶极子(IOD，Indian Ocean Dipole)，北半球的北极涛动(AO：Arctic Oscillation)，南半球的南极涛动(AAO：Antarctic Oscillation)。将通过建立数学模型，系统地对全球陆地的0.5度最高、最低气温的与这四个气候因子进行偏相关分析。
如附图4所示的具体工作流程，所述步骤s1还包括对所述气温数据进行 预处理；所述预处理为将气温数据与气候因子指数按月分组，计算时间周期内每个季节的平局值及偏差。
优选的，所述时间周期为1982至2016年。在该周期内，按自然月分别计算1982至2016年间季节平均值，3、4、5月为春季，6、7、8为夏季，9、10、11为秋季，12、1、2为冬季。并把气温数据与气候因子指数数据减季节平均值得到每一个季节的偏差。其中平局值为1982至2016年每个季节温度的多年平均值，偏差为每个季度每年的值减去多年平均值所得到的温度距平。
进一步的，所述步骤s2的计算基于具有栅格计算功能的地理信息系统进行。
该地理信息系统为ArcGIS环境。在该系统下直接编写程序计算偏相关。利用ArcGIS 9.3软件中的Raster Calculator工具编写脚本可以对气象数据图层和气候因子数据直接进行计算，高效便捷地计算偏相关系数。这样做的好处是每一次相关计算，都有栅格(Raster)文件生成，对于变量之间的关系能更好地进行分析、理解并解释。
进一步的，需要编写进脚本的三阶偏相关的计算公式为：
Figure PCTCN2018119943-appb-000001
Figure PCTCN2018119943-appb-000002
其中，1为最高气温，2为厄尔尼诺-南方涛动，3为印度洋偶极子，4为北极涛动，5为南极涛动。R代表relationship，也就是关系。R12，345表示变量1与2的关系，同时排除了变量3，4，5的干扰。
该地理信息系统的栅格计算功能可以把每个月的数据当成一个变量，套用偏相关计算的公式，便可以直接在arcgis上实现计算过程并呈现计算结果，不需要外部编写程序再导入arcgis软件显示结果。
通过上述偏相关关系计算，可以得到全球任何一个点的最高、最低气温与某一个其后因子的相关性，并同时排除了其它三个其后因子的干扰。该方法可以更好对全球及局地气候变化进行解释，确定任意地点的气温与那个气候因子关系最密切，并通过气候因子的波动，该地区的未来较长时间的气候趋势做出预测。
进一步的，所述步骤s4包括以下具体步骤：
S41.基于所述地理信息系统，将每个空间栅格与像素对应，分别加载计算得到的温度与每个气候因子的相关系数的图层，以不同花纹区分不同的图层；
S42.设置置信度，去除所述图层中置信度低于95％的像素；
S43.所有图层透明度设置为50％，有利于显示气候因子影响重叠区域，明确部分受多个气候因子共同影响的范围。
如附图5所示，图中所有影响区域的置信度均超过95％。其中图a中深灰色代表太平洋厄尔尼诺现象(ENSO)的影响区域，浅灰色代表印度洋偶极子(IOD)的影响区域，斜纹网格代表北极涛动(AO)的影响区域，正交网格代表南极涛动(AAO)的影响区域。所有图层都做了50％透明度处理，以便于一些重叠区域的观察。柱状图插图为按照等面积投影后，各个气候因子的影响区域占地表总面积的百分比。图b同图a，但表示夏季；图c同图a，但表示秋季；图d同图a，但表示冬季。
该气温与气候因子的响应关系图对于粮食及农林产品预测会有积极参考意义，对于农产品期货交易可以给出一定程度的参考。准确的说就是根据当下几个气候因子的变化情况，就可以估计未来特定区域的温度变化情况，进而为植被生物产量的预测提供一定的参考信息。
实施例二：
如附图3所示的一种计算气温与气候因子遥相关关系的装置，包括：
输入单元，接收选取的具有全球性影响的气候因子和有公信度的气温数据。
预处理单元，将选取的具有全球性影响的气候因子和有公信度的气温数据按月分组，计算时间周期内每个季节的平局值及偏差。
其中平局值为1982至2016年每个季节温度的多年平均值，偏差为每个季度每年的值减去多年平均值所得到的温度距平。
变量关系计算单元，基于地理信息系统，通过输入三阶偏相关系公式计算空间栅格内的每一个所述气候因子与所述气温数据的变量关系。
去干扰单元，设置置信度，排除计算结果中置信度低于95％的干扰因子，输出该空间栅格内对气温变化影响最大受的气候因子。
进一步的，该装置还包括：制图单元，基于地理信息系统，绘制全球区域的所述气温数据与所述气候因子的响应关系图。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，所述的程序可以存储于一计算机可读取存储介质中，所述的存储介质，如ROM/RAM、磁盘、光盘等。
在本发明实施例中，该计算气温与气候因子遥相关关系的装置的各单元可由相应的硬件或软件单元实现，各单元可以为独立的软、硬件单元，也可以集成为一个软、硬件单元，在此不用以限制本发明。各单元的具体实施方式可参考实施例一、二、三的描述，在此不再赘述。
在具体实施过程中，本发明在2015年全球超强厄尔尼诺的影响下，对全球地表气温预测表现良好。
本发明通过在地里信息系统中输入计算3阶偏相关关系的公式，计算气温与4个气候因子的响应关系，即计算气温与某一气候因子的关系而排除其它气候因子的干扰。且根据计算结果绘制全球气温度与4个气候因子的响应关系 图，表征任意地区的气温同时受到哪些气候因子的主要影响或者共同影响。引入置信度最终确定与气温变化关联最大的一个或多个气候因子，提升了二者遥相关(响应关系)的准确性。实现了根据当下几个气候因子的变化情况，就可以估计未来该特定区域的温度变化情况，为该区域内的植被生物产量的预测提供一定的参考信息，提升了农林业生产风险预测的准确性。
以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。

Claims (10)

  1. 一种计算气温与气候因子遥相关关系的方法，其特征在于，包括下述步骤：
    S1.选取具有全球性影响的气候因子和有公信度的气温数据；
    S2.通过三阶偏相关系数计算空间栅格内的每一个所述气候因子与所述气温数据的变量关系；
    S3.通过置信度排除计算结果中的干扰因子。
  2. 如权利要求1所述的方法，其特征在于，还包括以下步骤：
    S4.根据所述步骤s3的结果绘制全球区域的所述气温数据与所述气候因子的响应关系图。
  3. 如权利要求2所述的方法，其特征在于，所述气温数据采用全球0.5度最高、最低气温月平均格点数据；所述气候因子采用厄尔尼诺-南方涛动，印度洋偶极子，北极涛动和南极涛动；所述干扰因子为置信度低于95％的所述气候因子。
  4. 如权利要求2所述的方法，其特征在于，所述步骤s1还包括对所述气温数据进行预处理；所述预处理为将气温数据与气候因子指数按月分组，计算时间周期内每个季节的平局值及偏差。
  5. 如权利要求4所述的方法，其特征在于，所述时间周期为1982-2016年。
  6. 如权利要求2所述的方法，其特征在于，所述步骤s2的计算基于具有栅格计算功能的地理信息系统进行。
  7. 如权利要求6所述的方法，其特征在于，所述三阶偏相关的计算公式为：
    Figure PCTCN2018119943-appb-100001
    其中，1为最高气温，2为厄尔尼诺-南方涛动，3为印度洋偶极子，4为北极涛动，5为南极涛动。
  8. 如权利要求7所述的方法，其特征在于，所述步骤s4包括以下具体步骤：
    S41.基于所述地理信息系统，将每个空间栅格与像素对应，分别加载计算得到的温度与每个气候因子的相关系数的图层，以不同花纹区分不同的图层；
    S42.设置置信度，去除所述图层中置信度低于95％的像素；
    S43.所有图层透明度设置为50％。
  9. 一种计算气温与气候因子遥相关关系的装置，其特征在于，包括：
    输入单元，接收选取的具有全球性影响的气候因子和有公信度的气温数据；
    预处理单元，将选取的具有全球性影响的气候因子和有公信度的气温数据按月分组，计算时间周期内每个季节的平局值及偏差；
    变量关系计算单元，基于地理信息系统，通过三阶偏相关系数计算空间栅格内的每一个所述气候因子与所述气温数据的变量关系；
    去干扰单元，设置置信度，排除计算结果中置信度低于95％的干扰因子，输出该空间栅格内对气温变化影响最大受的气候因子。
  10. 如权利要求9所述的装置，其特征在于，还包括：
    制图单元，基于地理信息系统，绘制全球区域的所述气温数据与所述气候因子的响应关系图。
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