CN113837620A - A method for evaluating changes in temperature extremes - Google Patents
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Abstract
本发明提供了一种用于评估温度极值变化的方法，首先，获取温度极值指标数据、环流场数据、大尺度环流模式指标数据；接着，基于不同时间尺度和不同季节对上述数据进行EOF分解，进而根据最小二乘回归和Mann‑Kendall检验展开温度极值的趋势变化分析；然后，通过Pearson相关分析识别影响温度极值的单个环流场和大尺度环流模式指标的因子；最后，基于因子分析方法识别对温度极值有显著贡献的因子，并量化这些关键因子之间的互动效应对温度极值带来的影响。
The present invention provides a method for evaluating temperature extreme value changes. First, obtain temperature extreme value index data, circulation field data, and large-scale circulation pattern index data; EOF is decomposed, and then the trend change analysis of temperature extremes is carried out according to the least squares regression and Mann-Kendall test; then, the factors affecting the single circulation field and large-scale circulation pattern indicators of temperature extremes are identified by Pearson correlation analysis; finally, Based on the factor analysis method, the factors that have a significant contribution to the temperature extreme value are identified, and the interaction effect between these key factors is quantified.
Description
Technical Field
The invention belongs to the field of climate research, and particularly relates to a method for evaluating temperature extreme value change.
Background
Over the past few decades, temperature and extreme temperature anomalies have had extremely serious negative impacts on society, economy, and people's life. The research on the extreme climate value and the influence mechanism thereof at home and abroad is attached with importance, and the understanding of the change mechanism of the extreme climate is very important for predicting the occurrence of the extreme climate event and taking measures to reduce the relevant influence. There are intricate and complex interaction effects among various elements (such as potential height, wind field, remote correlation factor, etc.) which affect the extreme temperature, but these effects are difficult to be characterized by the traditional functional form. Currently, most of the research in this area is only directed to one or more individual elements, and the influence of the interaction between different elements on the temperature extreme value is rarely considered.
Object of the Invention
The invention aims to solve the problems in the prior art and provide a method for evaluating the change of the extreme temperature value, thereby providing favorable support for the future extreme climate change.
Disclosure of Invention
The invention provides a method for evaluating temperature extreme value change, which comprises the following steps:
step a, respectively acquiring temperature extreme value data, circulation field data and large-scale circulation mode index data; the definition of the temperature extreme value data is formulated by a world weather organization WMO climate change detection and index expert group ETCCDI, and is obtained by a land climate extreme event index data set HadEX3 covering the land climate extreme event data set with the precision of 1.875 multiplied by 1.25 longitude and latitude from 1901 to 2018, and specific indexes comprise a daily maximum air temperature value TXx, a daily minimum air temperature value TNx, a daily maximum air temperature value TXn, a daily minimum air temperature value TNn, a cold night TN10p, a warm night TN90p, a cold day TX10p and a hot day TX90 p; the circulation field data comprises sea level pressure SLP, 500hPa wind field and 500hPa potential height of ERA5 reanalysis data; the large-scale circulation pattern data are indexes for measuring a natural variability pattern, are time sequence data with a scale of days and comprise index items of Erleno and southern billow ENSO, Pacific decade billow PDO, North Atlantic billow NAO, Arctic billow AO and Pacific-North Atlantic related PNA;
b, analyzing temperature extreme values from a time sequence and a space scale respectively according to different time dimensions of each year, years, summer and winter by using an empirical orthogonal function EOF method, and identifying areas with extremely high temperature and low temperature in summer in the northern hemisphere area; the specific processing procedure of the EOF method is as follows: setting the number of lattice points of the temperature extreme value as m and the time sequence length as n, firstly, processing the temperature extreme value data into a distance form to obtain a matrix X related to the temperature extreme value time and spacem×n＝[xij]I is 1,2, …, m, j is 1,2, …, n, where the ith row represents the value of the temperature extremum x at location i and the jth column represents the value of the temperature extremum x at time j; then, for the matrix Xm×nPerforming orthogonal decomposition, wherein the feature vector of the orthogonal decomposition corresponds to a space function S capable of reflecting the space distribution characteristics of the variable, the main component of the orthogonal decomposition corresponds to a time function T capable of reflecting the change of the space mode along with the time, and the orthogonal decomposition is expressed as Xm×nSxt ═ sxt; the jth spatial field of the temperature extreme value is represented as linear superposition of m typical spatial fields according to different time coefficients, wherein each column of a spatial function S represents a typical spatial field, each row of a time function T represents a time coefficient, and when a research variable is a summer temperature extreme value of 30 years in the northern hemisphere area, a first feature vector obtained by orthogonal decomposition by using the EOF method is a feature field with the 30-year summer temperature extreme value most similar to a flat field, so that the extremely high-temperature and low-temperature area in summer in the northern hemisphere area is identified;
c, respectively calculating linear trends of the temperature extreme value time sequence according to different time dimensions of every year, years, summer and winter by a least square regression method, and evaluating the statistical significance of the calculated linear trends of the temperature extreme value time sequence on the significance level of 0.05 by a nonparametric Mann-Kendall test method;
d, determining the relationship between the temperature extreme value data and the annular flow field data as well as the large-scale annular flow mode index data by adopting a Pearson correlation analysis method;
and e, quantifying the statistical significance of the annular flow field data and the large-scale circulation mode index data based on a factor analysis method, respectively identifying factors which contribute to the temperature extreme values, and reflecting the interactive relationship among the factors, namely quantifying the relationship between a single annular flow field or a single large-scale circulation mode and the temperature extreme values, the influence of the interaction among a plurality of annular flow fields on the temperature extreme values, and the influence of the interaction of a plurality of large-scale circulation modes or positive and negative phases thereof on the temperature extreme values.
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Fig. 1 is a flowchart of a method for evaluating a change in an extreme temperature value according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings.
The invention provides a method for evaluating temperature extreme value change, which comprises the following steps:
step a, respectively acquiring temperature extreme value data, circulation field data and large-scale circulation mode index data; the definition of the temperature extreme value data is formulated by a world weather organization WMO climate change detection and index expert group ETCCDI, and is obtained by a land climate extreme event index data set HadEX3 covering the land climate extreme event data set with the precision of 1.875 multiplied by 1.25 longitude and latitude from 1901 to 2018, and specific indexes comprise a daily maximum air temperature value TXx, a daily minimum air temperature value TNx, a daily maximum air temperature value TXn, a daily minimum air temperature value TNn, a cold night TN10p, a warm night TN90p, a cold day TX10p and a hot day TX90 p; the circulation field data comprises sea level pressure SLP, 500hPa wind field and 500hPa potential height of ERA5 reanalysis data; the large-scale circulation pattern data are indexes for measuring a natural variability pattern, are time sequence data with a scale of days and comprise index items of Erleno and southern billow ENSO, Pacific decade billow PDO, North Atlantic billow NAO, Arctic billow AO and Pacific-North Atlantic related PNA;
b, utilizing an empirical orthogonal function EOF method to respectively perform different time dimensions of every year, years, summer and winterAnalyzing the temperature extreme value from the time sequence and the spatial scale, and identifying the regions with extremely high temperature and low temperature in the northern hemisphere region in summer; the specific processing procedure of the EOF method is as follows: setting the number of lattice points of the temperature extreme value as m and the time sequence length as n, firstly, processing the temperature extreme value data into a distance form to obtain a matrix X related to the temperature extreme value time and spacem×n＝[xij]I is 1,2, …, m, j is 1,2, …, n, where the ith row represents the value of the temperature extremum x at location i and the jth column represents the value of the temperature extremum x at time j; then, for the matrix Xm×nPerforming orthogonal decomposition, wherein the feature vector of the orthogonal decomposition corresponds to a space function S capable of reflecting the space distribution characteristics of the variable, the main component of the orthogonal decomposition corresponds to a time function T capable of reflecting the change of the space mode along with the time, and the orthogonal decomposition is expressed as Xm×nSxt ═ sxt; the jth spatial field of the temperature extreme value is represented as linear superposition of m typical spatial fields according to different time coefficients, wherein each column of a spatial function S represents a typical spatial field, each row of a time function T represents a time coefficient, and when a research variable is a summer temperature extreme value of 30 years in the northern hemisphere area, a first feature vector obtained by orthogonal decomposition by using the EOF method is a feature field with the 30-year summer temperature extreme value most similar to a flat field, so that the extremely high-temperature and low-temperature area in summer in the northern hemisphere area is identified;
c, respectively calculating linear trends of the temperature extreme value time sequence according to different time dimensions of every year, years, summer and winter by a least square regression method, and evaluating the statistical significance of the calculated linear trends of the temperature extreme value time sequence on the significance level of 0.05 by a nonparametric Mann-Kendall test method;
d, determining the relationship between the temperature extreme value data and the annular flow field data as well as the large-scale annular flow mode index data by adopting a Pearson correlation analysis method;
and e, quantifying the statistical significance of the annular flow field data and the large-scale circulation mode index data based on a factor analysis method, respectively identifying factors which contribute to the temperature extreme values, and reflecting the interactive relationship among the factors, namely quantifying the relationship between a single annular flow field or a single large-scale circulation mode and the temperature extreme values, the influence of the interaction among a plurality of annular flow fields on the temperature extreme values, and the influence of the interaction of a plurality of large-scale circulation modes or positive and negative phases thereof on the temperature extreme values.
In order to make the objects, features and advantages of the present invention more comprehensible, the present invention is described in detail with reference to the accompanying drawings and examples.
Example (b): as shown in fig. 1, the present invention provides a method for evaluating the change in the temperature extremes. Firstly, acquiring extreme value index data related to temperature through a high-precision lattice point land climate extreme event index data set of HadEX 3; analyzing data again through ERA5 to obtain circulation field data such as 500hPa wind field, 500hPa potential altitude, sea level pressure and the like; large-scale circulation mode index data such as ENSO, PDO, PNA, AO, NAO and the like are obtained through the national weather administration. Secondly, performing EOF decomposition on the data, and further performing trend change analysis of the developed temperature extreme value according to minimum two-component regression and Mann-Kendall test based on different time scales and different seasons; extracting corresponding annular flow field and large-scale annular flow mode data according to the time of the temperature extreme value, and identifying factors of a single annular flow field and large-scale annular flow mode indexes which influence the temperature extreme value through Pearson correlation analysis; further, factors which significantly contribute to the temperature extreme value are identified based on a factor analysis method, and the influence of the interaction effect between the key factors on the temperature extreme value is quantified. The invention can simultaneously consider single and multiple circulation fields and large-scale circulation mode indexes, and quantize the significance of the influence of the interaction between the single and multiple circulation fields on the change of the temperature extreme value, thereby disclosing the mechanism causing the trend change of the temperature extreme value.
Compared with the prior art, the invention has the beneficial effects that: the method for analyzing the temperature extreme values and the influence mechanisms of the temperature extreme values in different time scales is provided, and the influence of interaction of a plurality of influence factors on the temperature extreme values is quantified simultaneously.
Claims (1)
1. A method for evaluating a change in an extreme temperature value, comprising the steps of:
step a, respectively acquiring temperature extreme value data, circulation field data and large-scale circulation mode index data; the definition of the temperature extreme value data is formulated by a world weather organization WMO climate change detection and index expert group ETCCDI, and is obtained by a land climate extreme event index data set HadEX3 covering the land climate extreme event data set with the precision of 1.875 multiplied by 1.25 longitude and latitude from 1901 to 2018, and specific indexes comprise a daily maximum air temperature value TXx, a daily minimum air temperature value TNx, a daily maximum air temperature value TXn, a daily minimum air temperature value TNn, a cold night TN10p, a warm night TN90p, a cold day TX10p and a hot day TX90 p; the circulation field data comprises sea level pressure SLP, 500hPa wind field and 500hPa potential height of ERA5 reanalysis data; the large-scale circulation pattern data are indexes for measuring a natural variability pattern, are time sequence data with a scale of days and comprise index items of Erleno and southern billow ENSO, Pacific decade billow PDO, North Atlantic billow NAO, Arctic billow AO and Pacific-North Atlantic related PNA;
b, analyzing temperature extreme values from a time sequence and a space scale respectively according to different time dimensions of each year, years, summer and winter by using an empirical orthogonal function EOF method, and identifying areas with extremely high temperature and low temperature in summer in the northern hemisphere area; the specific processing procedure of the EOF method is as follows: setting the number of lattice points of the temperature extreme value as m and the time sequence length as n, firstly, processing the temperature extreme value data into a distance form to obtain a matrix X related to the temperature extreme value time and spacem×n＝[xij]I is 1,2, …, m, j is 1,2, …, n, where the ith row represents the value of the temperature extremum x at location i and the jth column represents the value of the temperature extremum x at time j; then, for the matrix Xm×nPerforming orthogonal decomposition, wherein the feature vector of the orthogonal decomposition corresponds to a space function S capable of reflecting the space distribution characteristics of the variable, the main component of the orthogonal decomposition corresponds to a time function T capable of reflecting the change of the space mode along with the time, and the orthogonal decomposition is expressed as Xm×nSxt ═ sxt; the jth spatial field of the temperature extremum is represented as m typical spatial fields according to different time coefficientsLinear superposition, wherein each column of the space function S represents a typical space field, each line of the time function T represents a time coefficient, and when the research variable is a summer temperature extreme value of the northern hemisphere area for 30 years, a first feature vector obtained by orthogonal decomposition by using the EOF method is a feature field with the 30-year summer temperature extreme value most similar to a flat field, so that the area with extremely high temperature and low temperature in summer in the northern hemisphere area is identified;
c, respectively calculating linear trends of the temperature extreme value time sequence according to different time dimensions of every year, years, summer and winter by a least square regression method, and evaluating the statistical significance of the calculated linear trends of the temperature extreme value time sequence on the significance level of 0.05 by a nonparametric Mann-Kendall test method;
d, determining the relationship between the temperature extreme value data and the annular flow field data as well as the large-scale annular flow mode index data by adopting a Pearson correlation analysis method;
and e, quantifying the statistical significance of the annular flow field data and the large-scale circulation mode index data based on a factor analysis method, respectively identifying factors which contribute to the temperature extreme values, and reflecting the interactive relationship among the factors, namely quantifying the relationship between a single annular flow field or a single large-scale circulation mode and the temperature extreme values, the influence of the interaction among a plurality of annular flow fields on the temperature extreme values, and the influence of the interaction of a plurality of large-scale circulation modes or positive and negative phases thereof on the temperature extreme values.
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| CN114462247A (en) * | 2022-02-14 | 2022-05-10 | 中国人民解放军61540部队 | Method and system for identifying annual modal associations of surface salinity of North Pacific ocean |
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| CN114462247A (en) * | 2022-02-14 | 2022-05-10 | 中国人民解放军61540部队 | Method and system for identifying annual modal associations of surface salinity of North Pacific ocean |
| CN114462247B (en) * | 2022-02-14 | 2022-10-21 | 中国人民解放军61540部队 | Method and system for identifying annual representative modality of sea surface salinity of North Pacific ocean |
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