Method for evaluating elasticity index of human radiation forcing effect on air temperature seasonality

Abstract

The present application relates to a kind of greenhouse gas influence temperature seasonality elasticity index method of evaluating, steps include: 1) determining research area, research time, obtain human radiation forcing index, meteorological element index and climate system index and carry out pretreatment;2) determine the spatio-temporal variation of temperature seasonality in research time;3) using human radiation forcing index, meteorological element index and climate system index to the spatio-temporal variation of temperature seasonality is attributed;4) using human radiation forcing index, meteorological element index and climate system index to the contribution of temperature seasonality assesses the influence of greenhouse gas on temperature seasonality.This method can effectively solve the limitation that human radiation forcing and natural factors are not considered in the commonly used temperature seasonality attribution method, cannot be used to research and judge how temperature seasonality changes with greenhouse gas emissions, has wide application in vegetation phenology, livable temperature and ecosystem service and the like.

Description

Technical Field

[0001] The present invention relates to a method for evaluating the impact of anthropogenic radiative forcing on temperature seasonality, and in particular to an elasticity index method for evaluating the impact of anthropogenic radiative forcing on temperature seasonality. Background Art

[0002] Global warming, a key characteristic of the climate system, has weakened temperature seasonality worldwide over the past few decades. Understanding the dynamics of temperature seasonality under global warming is essential for the survival of human society and the development of an ecological civilization. To assess the dynamics of temperature seasonality, it is necessary to identify the primary drivers of this weakening.

[0003] Although the weakening of temperature seasonality is generally attributed to both anthropogenic radiative forcing and natural variability, the dominant factor in temperature seasonality remains controversial. Some studies suggest that anthropogenic radiative forcing dominates the weakening of temperature seasonality in the Northern Hemisphere, while others suggest that changes in natural systems are the primary driving force. This suggests that the extent to which anthropogenic radiative forcing and natural climate system factors contribute to the weakening of temperature seasonality is currently unclear. Therefore, further quantification of the contributions of anthropogenic radiative forcing and natural factors to temperature seasonality is crucial.

[0004] Two statistical methods are commonly used in studying temperature seasonality: the optimal fingerprint method and the inference method. The optimal fingerprint method can be implemented using multiple regression; the inference method combines deduction with hypothetical estimation of scalar factors and can be divided into the standard highest frequency method and the Bayesian method. The standard method first tests whether the hypothesized climate change signal is significantly different from zero and then compares the observed responses to the forcing with the model simulations. The Bayesian method requires the ability to aggregate information and comprehensively analyze independent information. While these two methods are effective in analyzing rising temperature trends, they are insufficient in explaining the causes of observed temperature seasonality. This suggests that forcing indicators are poorly expressed or omitted. The main reason for the current divergence in understanding the weakening of temperature seasonality is the lack of effective methods to analyze the mechanisms of action of anthropogenic radiative forcing and natural factors on temperature seasonality. This, on the one hand, leads to one-sided attribution of the weakening of temperature seasonality and on the other hand, misjudgment of the further evolution of temperature seasonality.

[0005] In view of this, the present invention is proposed. Summary of the Invention

[0006] The purpose of the present invention is to provide an elasticity index method for evaluating the impact of anthropogenic radiative forcing on temperature seasonality; the temperature seasonality evaluation method of the present invention integrates the impact of anthropogenic radiative forcing and natural changes on temperature seasonality and has strong adaptability.

[0007] The present invention aims to provide a method for evaluating the elasticity index of the impact of anthropogenic radiative forcing on temperature seasonality, the method comprising the following steps:

[0008] Step 1: Determine the study area and time, obtain human radiative forcing indicators, meteorological element indicators, and climate system indicators, and perform preprocessing;

[0009] Step 2: Determine the spatiotemporal variation of temperature seasonality during the study period;

[0010] Step 3: Use human radiative forcing indicators, meteorological element indicators, and climate system indicators to attribute the spatiotemporal changes in temperature seasonality;

[0011] Step 4: Assess the impact of anthropogenic radiative forcing on temperature seasonality using the contributions of anthropogenic radiative forcing indicators, meteorological element indicators, and climate system indicators to temperature seasonality.

[0012] As a further technical solution, in step 1, determining the research area and research time, and obtaining human radiation forcing indicators, meteorological element indicators, and climate system indicators include:

[0013] The study area should be located in the mid-latitudes of the Northern Hemisphere and have seasonal temperature;

[0014] The research period should be greater than 30 years and include information on seasonal temperature changes;

[0015] The anthropogenic radiative forcing indicators include anthropogenic black carbon emissions, black carbon biomass burning emissions, dust extinction aerosol optical depth, dust scattering aerosol optical depth, anthropogenic organic carbon emissions, organic carbon biomass burning emissions, anthropogenic sulfur dioxide emissions, sulfur dioxide biomass burning emissions, anthropogenic sulfur tetroxide emissions, and carbon dioxide concentration;

[0016] The meteorological element indicators include air temperature, D20 evaporation dish evaporation, 0cm surface temperature, precipitation, air pressure, relative humidity, sunshine hours, and wind speed;

[0017] The climate system indicators include the Atlantic Multidecadal Oscillation, the Eastern Pacific or North Pacific Oscillation, the Arctic Oscillation, the North Atlantic Oscillation, the extreme eastern tropical Pacific sea surface temperature, the eastern tropical Pacific sea surface temperature, the central tropical Pacific sea surface temperature, the eastern and central tropical Pacific sea surface temperature, the Pacific Decadal Oscillation, the Polar or Eurasian mode, the Quasi-Biennial Oscillation, the Scandinavian mode, the Southern Oscillation Index, the Pacific North American Index, the Trans-Nino Index, the Tropical North Atlantic Index, and the Tropical South Atlantic Index.

[0018] As a further technical solution, in step 1, the process of preprocessing the human radiative forcing index, meteorological element index and climate system index is as follows:

[0019] Step 101: Use the standard normal uniformity test to homogenize the meteorological element indicators to improve the consistency of the data set;

[0020] Step 102: Average the monthly climate system indicator values ​​into annual and seasonal mean time series;

[0021] Step 103: Use the linear prediction plus random perturbation method to expand the time span of the human radiative forcing index to the study period;

[0022] Step 104: Use the spatiotemporal adaptive reflectivity fusion model combined with the global CO2 concentration to extend the CO2 time span to the study period. The calculation formula is as follows:

[0023]

[0024] Where C represents the study area, t p represents the predicted time within the extended time period, Indicates the predicted time t for the study area C p The predicted CO2 concentration, G represents global, Denotes the global G prediction time t p CO2 concentration, T e Indicates the end time of CO2 concentration data in the study area, T s Indicates the start time of CO2 concentration data in the study area, t b Represents the CO2 concentration data T in the study area e With T s The reference time between Indicates the benchmark time t of the study area b CO2 concentration, represents the global reference time t b This method uses the global CO2 concentration during the extended time to represent the temporal variation of CO2 concentration in the study area, and uses the CO2 concentration data at all times in the study area as a benchmark to predict the CO2 concentration during the extended time, which can eliminate the uncertainty of the forecast at a single benchmark time.

[0025] Step 105: Use the grid area weighted average method to process the CO2, human radiative forcing, and meteorological element indicator datasets to obtain the annual and seasonal average anomaly time series averaged over the study area. The calculation method is as follows:

[0026]

[0027] Where x n is the anomaly value of CO2, human radiative forcing or meteorological element index in year n, x in is the average value of the indicator data in the i-th grid in the n-th year, is the multi-year average index value of the index data in the i-th grid, I represents the number of grids, and w i is the weighting coefficient of the ith grid, calculated based on the internationally recognized cosine relationship between the latitudes of each grid, and the calculation formula is as follows:

[0028]

[0029] Where, lat i is the center latitude of the grid, and the grid size is set to 2.5° latitude × 2.5° longitude.

[0030] As a further technical solution, in step 2, the specific steps of determining the spatiotemporal variation of seasonal temperature during the study period are:

[0031] Step 201: Calculate the daily mean temperature of the study area for 5 years before and after the study time;

[0032] Step 202: Based on the daily average temperature of the study area for the first five years, the 60th, 152nd, 244th, and 305th days are used as the start dates Ds of spring, summer, autumn, and winter in the five years from the start of the study. The mean of the daily average temperature of the two days before and after the start date is the temperature T of the corresponding season start date using the pentad temperature method. as5 Based on the daily temperature average of the study area over the past five years, the linear regression equation was obtained with the 40 Julian days centered on the 60th, 152nd, 244th, and 305th days and the daily temperature average of the corresponding Julian days as independent variables and dependent variables respectively. The linear regression equation was used to calculate the daily temperature average of the study area over the past five years, which is equal to T. as5 The corresponding Julian day is the start date De of spring, summer, autumn and winter in the study area for the past five years; De-Ds is the change in the start date of spring, summer, autumn and winter in the study area during the study period. De-Ds>0 indicates that the start date of the season is delayed, and De-Ds<0 indicates that the start date of the season is advanced.

[0033] Step 203: With March to May as spring, June to August as summer, September to November as autumn, and December to the following February as winter, calculate the seasonal temperature mean for the first five years of each 2.5° latitude zone in the study area and form a seasonal temperature mean latitude gradient curve, and calculate the seasonal temperature mean for the first five years and the last five years of the entire study area;

[0034] Step 204: Project the seasonal temperature means of the entire study area for the first five years and the last five years onto the seasonal temperature mean latitude gradient curve. The resulting projection latitudes are denoted as Ls and Le, respectively. Le-Ls>0 indicates that the seasonal temperature mean is shifted northward, and De-Ds<0 indicates that the seasonal temperature mean is shifted toward the equator.

[0035] As a further technical solution, in step 3, the specific steps of attributing the seasonal spatiotemporal changes of temperature using the human radiation forcing index, meteorological element index, and climate system index are as follows:

[0036] Step 301: The seasonal spatiotemporal variation of the temperature in the study area is attributed to the trend of the annual mean temperature in the study area. The time series of the annual mean temperature in the study area is recorded as:

[0037] Ta N×1 =[t a,1 ,…,t a,i ,…,t a,N ]′

[0038] Where, t a,i represents the annual mean temperature of the study area in year i, N represents the number of years of the study, Ta N×1 It represents the time series consisting of the annual mean temperature of the study area over N years;

[0039] Step 302: Using stepwise regression analysis, select the indicators that have a significant impact on the trend of the annual mean temperature in the study area from different combinations of human radiation forcing indicators, meteorological element indicators, and climate system indicators, and record them as:

[0040]

[0041] Where p represents the number of indicators that have a significant impact on the trend of the annual mean temperature in the study area selected from different combinations of human radiation forcing indicators, meteorological element indicators, and climate system indicators using stepwise regression analysis, and x in represents the annual average value of the i-th selected indicator study area in the n-th year, X N×p A matrix of N rows and p columns representing the annual mean values ​​of all p selected indicators in the study area over all N years;

[0042] Step 303: To Ta N×1 and X N×p Standardize by column, that is, Ta N×1 The annual mean temperature of the study area in each year minus the mean annual mean temperature of the study area in N years is divided by the standard deviation of the annual mean temperature of the study area in N years, and X is obtained. N×p The annual average value of each indicator in column p of each year is deducted from the mean of the annual average values ​​of the indicators in the column over N years and then divided by the standard deviation of the annual average values ​​of the indicators in the column over N years;

[0043] Step 304: normalize X N×p Perform principal component analysis to obtain X N×p The singular value decomposition of is as follows:

[0044] X=UΔV

[0045] Where, Δ p×p represents the non-negative singular values ​​of X, and U N×p and V p×p The columns of are the left and right singular vectors of X;

[0046] Step 305: Perform spectral decomposition on X′X. The spectral decomposition formula is as follows:

[0047] X'X=VΛV'

[0048] Where, represents the non-negative eigenvalues ​​of X′X, and the columns of V are orthogonal sets of eigenvectors;

[0049] Step 306: Transform X into a p×k matrix consisting of the first k orthogonal columns of V N×p Convert to W N×k , the conversion formula is:

[0050] W N×k =X N×p V p×k

[0051] Where W N×k Contains the first k principal components as its columns, k is the predicted and observed Ta N×1 The root mean square error between them reaches the minimum value;

[0052] Step 307: N×1 In W N×k The regression coefficients on can be obtained through least squares regression as follows:

[0053]

[0054] Where, For Ta N×1 In W N×k The regression coefficient on ;

[0055] Step 308: N×1 In X N×p The regression coefficient can be obtained by the following formula:

[0056]

[0057] Where, β p×1 For Ta N×1 In X N×p The regression coefficient on .

[0058] As a further technical solution, in step 4, the specific steps of using the contribution of human radiative forcing indicators, meteorological element indicators, and climate system indicators to temperature seasonality to assess the impact of human radiative forcing on temperature seasonality are as follows:

[0059] Step 401: j 1≤j≤p The contribution of the selected indicators to the annual mean or seasonal mean trend of temperature in the study area C j By its trend T j and the regression coefficient β j The product of and the trend of all selected indicators T j|1≤j≤p and coefficient β p×1 The ratio of the sum of the products is determined by the following formula:

[0060]

[0061] Step 402: For any season, the contribution and trend of the selected human radiative forcing indicators, meteorological element indicators, and climate system indicators are combined to evaluate the impact of the human radiative forcing indicators on the temperature seasonality using the temperature seasonal elasticity index. The temperature seasonal elasticity index is defined as follows:

[0062]

[0063] Where, the subscript s represents spring, summer, autumn or winter. For season s, β_ARF i and T_ARF i denote the regression coefficient and standardized trend of the i-th selected human radiative forcing indicator, and β_NAT j and T_NAT j They represent the regression coefficient and standardized trend of the jth selected meteorological element index or climate system index, TSRI s It represents the ratio of the combined control of human radiative forcing index and natural index on the s season;

[0064] Step 403: Using the concept of exchanging factors for time, construct the series of seasonal elasticity indices of temperature for spring, summer, autumn, and winter, and the series of the sum of trends of the corresponding selected indicators of human radiative forcing. The construction method is as follows:

[0065]

[0066] Step 404: Regress x and y to obtain the following regression model:

[0067] y=κx+b

[0068] Step 405: Using y=κx+b, the degree of control of anthropogenic radiative forcing on temperature seasonality can be revealed as the trend of anthropogenic radiative forcing changes.

[0069] The beneficial effects brought about by the present invention are: the present invention provides an elasticity index method for evaluating the impact of anthropogenic radiative forcing on temperature seasonality. This method can effectively solve the limitations of commonly used temperature seasonality attribution methods, such as insufficient consideration of anthropogenic radiative forcing and natural factors, and inability to be used to study how temperature seasonality changes with greenhouse gas emissions. It has wide applications in the fields of vegetation phenology, habitable temperature and ecosystem services. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is a flow chart of a method for evaluating the elasticity index of the impact of anthropogenic radiative forcing on temperature seasonality according to the present invention;

[0071] Figure 2 This is a flowchart for the specific implementation of step 1 of the present invention;

[0072] Figure 3 This is a flowchart for the specific implementation of step 2 of the present invention;

[0073] Figure 4 This is a flowchart for the specific implementation of step 3 of the present invention;

[0074] Figure 5 This is a specific implementation flow chart of step 4 of the present invention. DETAILED DESCRIPTION

[0075] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0076] Figure 1 A flow chart of the method in the embodiment provided by the present invention, such as Figure 1 As shown, this embodiment provides a method for evaluating the elasticity index of the impact of anthropogenic radiative forcing on temperature seasonality, the method comprising the following steps:

[0077] Step 1: Determine the study area and time, obtain human radiative forcing indicators, meteorological element indicators, and climate system indicators, and perform preprocessing;

[0078] Step 2: Determine the spatiotemporal variation of temperature seasonality during the study period;

[0079] Step 3: Use human radiative forcing indicators, meteorological element indicators, and climate system indicators to attribute the spatiotemporal changes in temperature seasonality;

[0080] Step 4: Assess the impact of anthropogenic radiative forcing on temperature seasonality using the contributions of anthropogenic radiative forcing indicators, meteorological element indicators, and climate system indicators to temperature seasonality.

[0081] As a further technical solution, in step 1, determining the research area and research time, and obtaining human radiation forcing indicators, meteorological element indicators, and climate system indicators include:

[0082] The study area should be located in the mid-latitudes of the Northern Hemisphere and have seasonal temperature;

[0083] The research period should be greater than 30 years and include information on seasonal temperature changes;

[0084] The anthropogenic radiative forcing indicators include anthropogenic black carbon emissions, black carbon biomass burning emissions, dust extinction aerosol optical depth, dust scattering aerosol optical depth, anthropogenic organic carbon emissions, organic carbon biomass burning emissions, anthropogenic sulfur dioxide emissions, sulfur dioxide biomass burning emissions, anthropogenic sulfur tetroxide emissions, and carbon dioxide concentration;

[0085] The Human Radiative Forcing dataset can be downloaded from https: / / disc.gsfc.nasa.gov / datasets. It covers the period 1980-2017, has a global coverage, a spatial resolution of 0.5°×0.625°, and a monthly temporal resolution.

[0086] The CO2 dataset can be downloaded at https: / / disc.gsfc.nasa.gov / api / jobs / results. It covers the period 2002-2017, has a global coverage, a spatial resolution of 2°×2.5°, and a monthly temporal resolution.

[0087] The meteorological element indicators include air temperature, D20 evaporation dish evaporation, 0cm surface temperature, precipitation, air pressure, relative humidity, sunshine hours, and wind speed;

[0088] The meteorological element dataset can be downloaded at https: / / www.ncei.noaa.gov / access / search / index. The data can be traced back to the beginning of the last century, with global coverage, spatial resolution of meteorological stations, and temporal resolution of annual, monthly, and daily.

[0089] The climate system indicators include the Atlantic Multidecadal Oscillation, the Eastern or North Pacific Oscillation, the Arctic Oscillation, the North Atlantic Oscillation, the Extreme Eastern Tropical Pacific Sea Surface Temperature, the Eastern Tropical Pacific Sea Surface Temperature, the Central Tropical Pacific Sea Surface Temperature, the East-Central Tropical Pacific Sea Surface Temperature, the Pacific Decadal Oscillation, the Polar or Eurasian Mode, the Quasi-Biennial Oscillation, the Scandinavian Mode, the Southern Oscillation Index, the Pacific North American Index, the Trans-Nino Index, the Tropical North Atlantic Index, and the Tropical South Atlantic Index;

[0090] The climate system dataset can be downloaded from https: / / www.esrl.noaa.gov / psd / data / climate indices / list. The data can be traced back to the beginning of the last century, with global coverage and annual and monthly temporal resolution.

[0091] Figure 2 The flowchart of step 1 in the embodiment provided by the present invention is as follows: Figure 2 As shown, the process of step 1 is as follows:

[0092] Step 101: Use the standard normal uniformity test to homogenize the meteorological element indicators to improve the consistency of the data set;

[0093] Step 102: Average the monthly climate system indicator values ​​into annual and seasonal mean time series;

[0094] Step 103: Use the linear prediction plus random perturbation method to expand the time span of the human radiative forcing index to the study period;

[0095] Step 104: Use the spatiotemporal adaptive reflectivity fusion model combined with the global CO2 concentration to extend the CO2 time span to the study period. The calculation formula is as follows:

[0096]

[0097] Where C represents the study area, t p represents the predicted time within the extended time period, Indicates the predicted time t for the study area C p The predicted CO2 concentration, G represents global, Denotes the global G prediction time t p CO2 concentration, T e Indicates the end time of CO2 concentration data in the study area, T s Indicates the start time of CO2 concentration data in the study area, t b Represents the CO2 concentration data T in the study area e With T s The reference time between Indicates the benchmark time t of the study area b CO2 concentration, represents the global reference time t b This method uses the global CO2 concentration during the extended time to represent the temporal variation of CO2 concentration in the study area, and uses the CO2 concentration data at all times in the study area as a benchmark to predict the CO2 concentration during the extended time, which can eliminate the uncertainty of the forecast at a single benchmark time.

[0098] Step 105: Use the grid area weighted average method to process the CO2, human radiative forcing, and meteorological element indicator datasets to obtain the annual and seasonal average anomaly time series averaged over the study area. The calculation method is as follows:

[0099]

[0100] Where x n is the anomaly value of CO2, human radiative forcing or meteorological element index in year n, x in is the average value of the indicator data in the i-th grid in the n-th year, is the multi-year average factor value of the indicator data in the i-th grid, I represents the number of grids, and w i is the weighting coefficient of the ith grid, calculated based on the internationally recognized cosine relationship between the latitudes of each grid, and the calculation formula is as follows:

[0101]

[0102] Where, lat i is the center latitude of the grid, and the grid size is set to 2.5° latitude × 2.5° longitude.

[0103] Figure 3 The flowchart of step 2 in the embodiment provided by the present invention is as follows: Figure 3 As shown, the process of step 2 is as follows:

[0104] Step 201: Calculate the daily mean temperature of the study area for 5 years before and after the study time;

[0105] Step 202: Based on the daily average temperature of the study area for the first five years, the 60th, 152nd, 244th, and 305th days are used as the start dates Ds of spring, summer, autumn, and winter in the five years from the start of the study. The mean of the daily average temperature of the two days before and after the start date is the temperature T of the corresponding season start date using the pentad temperature method. as5 Based on the daily temperature average of the study area over the past five years, the linear regression equation was obtained with the 40 Julian days centered on the 60th, 152nd, 244th, and 305th days and the daily temperature average of the corresponding Julian days as independent variables and dependent variables respectively. The linear regression equation was used to calculate the daily temperature average of the study area over the past five years, which is equal to T. as5 The corresponding Julian day is the start date De of spring, summer, autumn and winter in the study area for the past five years; De-Ds is the change in the start date of spring, summer, autumn and winter in the study area during the study period. De-Ds>0 indicates that the start date of the season is delayed, and De-Ds<0 indicates that the start date of the season is advanced.

[0106] Step 203: With March to May as spring, June to August as summer, September to November as autumn, and December to the following February as winter, calculate the seasonal temperature mean for the first five years of each 2.5° latitude zone in the study area and form a seasonal temperature mean latitude gradient curve. Calculate the seasonal temperature mean for the first five years and the last five years of the entire study area.

[0107] Step 204: Project the seasonal temperature means of the entire study area for the first five years and the last five years onto the seasonal temperature mean latitude gradient curve. The resulting projection latitudes are denoted as Ls and Le, respectively. Le-Ls>0 indicates that the seasonal temperature mean is shifted northward, and De-Ds<0 indicates that the seasonal temperature mean is shifted toward the equator.

[0108] Figure 4 The flowchart of step 3 in the embodiment provided by the present invention is as follows: Figure 4 As shown, the process of step 3 is as follows:

[0109] Step 301: The seasonal spatiotemporal variation of the temperature in the study area is attributed to the trend of the annual mean temperature in the study area. The time series of the annual mean temperature in the study area is recorded as:

[0110] Ta N×1 =[t a,1 ,…,t a,i ,…,t a,N ]′

[0111] Where, t a,i represents the annual mean temperature of the study area in year i, N represents the number of years of the study, Ta N×1 It represents the time series consisting of the annual mean temperature of the study area over N years;

[0112] Step 302: Using stepwise regression analysis, select the indicators that have a significant impact on the trend of the annual mean temperature in the study area from different combinations of human radiation forcing indicators, meteorological element indicators, and climate system indicators, and record them as:

[0113]

[0114] Where p represents the number of indicators that have a significant impact on the trend of the annual mean temperature in the study area selected from different combinations of human radiation forcing indicators, meteorological element indicators, and climate system indicators using stepwise regression analysis, and x in represents the annual average value of the i-th selected indicator study area in the n-th year, X N×p A matrix of N rows and p columns representing the annual mean values ​​of all p selected indicators in the study area over all N years;

[0115] Step 303: To Ta N×1 and X N×p Standardize by column, that is, Ta N×1The annual mean temperature of the study area in each year minus the mean annual mean temperature of the study area in N years is divided by the standard deviation of the annual mean temperature of the study area in N years, and X is obtained. N×p The annual average value of each indicator in column p of each year is deducted from the mean of the annual average values ​​of the indicators in the column over N years and then divided by the standard deviation of the annual average values ​​of the indicators in the column over N years;

[0116] Step 304: normalize X N×p Perform principal component analysis to obtain X N×p The singular value decomposition of is as follows:

[0117] X=UΔV

[0118] Where, Δ p×p represents the non-negative singular values ​​of X, and U N×p and V p×p The columns of are the left and right singular vectors of X;

[0119] Step 305: Perform spectral decomposition on X′X. The spectral decomposition formula is as follows:

[0120] X'X=VΛV'

[0121] Where, represents the non-negative eigenvalues ​​of X′X, and the columns of V are orthogonal sets of eigenvectors;

[0122] Step 306: Transform X into a p×k matrix consisting of the first k orthogonal columns of V N×p Convert to W N×k , the conversion formula is:

[0123] W N×k =X N×p V p×k

[0124] Where W N×k Contains the first k principal components as its columns, k is the predicted and observed Ta N×1 The root mean square error between them reaches the minimum value;

[0125] Step 307: N×1 In W N×k The regression coefficients on can be obtained through least squares regression as follows:

[0126]

[0127] Where, For Ta N×1 In W N×k The regression coefficient on ;

[0128] Step 308: N×1 In X N×pThe regression coefficient can be obtained by the following formula:

[0129]

[0130] Where, β p×1 For Ta N×1 In X N×p The regression coefficient on .

[0131] Figure 5 The flowchart of step 4 in the embodiment provided by the present invention is as follows: Figure 5 As shown, the process of step 4 is as follows:

[0132] Step 401: j 1≤j≤p The contribution of the selected indicators to the annual mean or seasonal mean trend of temperature in the study area C j By its trend T j and the regression coefficient β j The product of and the trend of all selected indicators T j|1≤j≤p and coefficient β p×1 The ratio of the sum of the products is determined by the following formula:

[0133]

[0134] Step 402: For any season, the contribution and trend of the selected human radiative forcing indicators, meteorological element indicators, and climate system indicators are combined to evaluate the impact of the human radiative forcing indicators on the temperature seasonality using the temperature seasonal elasticity index. The temperature seasonal elasticity index is defined as follows:

[0135]

[0136] Where, the subscript s represents spring, summer, autumn or winter. For season s, β_ARF i and T_ARF i denote the regression coefficient and standardized trend of the i-th selected human radiative forcing indicator, and β_NAT j and T_NAT j They represent the regression coefficient and standardized trend of the jth selected meteorological element index or climate system index, TSRI s It represents the ratio of the combined control of human radiative forcing index and natural index on the s season;

[0137] Step 403: Using the concept of exchanging factors for time, construct the series of seasonal elasticity indices of temperature for spring, summer, autumn, and winter, and the series of the sum of trends of the corresponding selected indicators of human radiative forcing. The construction method is as follows:

[0138]

[0139] Step 404: Regress x and y to obtain the following regression model:

[0140] y=κx+b

[0141] Step 405: Using y=κx+b, the degree of control of anthropogenic radiative forcing on temperature seasonality can be revealed as the trend of anthropogenic radiative forcing changes.

[0142] It should be noted that the above-described embodiments are only preferred embodiments of the present invention and are used to illustrate the technical solutions of the present invention. Those skilled in the art may make changes, modifications or equivalent substitutions to the technical solutions of the present invention, which should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for evaluating the elasticity index of the impact of anthropogenic radiative forcing on temperature seasonality, characterized by: The following steps are involved: Step 1: Determine the study area and time, obtain human radiative forcing indicators, meteorological element indicators, and climate system indicators, and perform preprocessing; Step 2: Determine the spatiotemporal variation of temperature seasonality during the study period; Step 3: Use human radiative forcing indicators, meteorological element indicators, and climate system indicators to attribute the spatiotemporal changes in temperature seasonality, including the following steps: Step 301: The seasonal spatiotemporal variation of the temperature in the study area is attributed to the trend of the annual mean temperature in the study area. The time series consisting of the annual mean temperature in the study area over N years is recorded as Ta N×1 ; Step 302: Using stepwise regression analysis, select the indicator that has a significant impact on the trend of the annual mean temperature in the study area from different combinations of human radiation forcing indicators, meteorological element indicators, and climate system indicators, and record it as X N×p , X N×p A matrix of N rows and p columns representing the annual mean values ​​of all p selected indicators in the study area over all N years; Step 303: To Ta N×1 and X N×p Normalize by columns; Step 304: normalize X N×p Perform principal component analysis to obtain X N×p Singular value decomposition of ; Step 305: Perform spectral decomposition X′X=VΛV′ on X′X; Step 306: Transform X into a p×k matrix consisting of the first k orthogonal columns of V N×p Convert to W N×k =X N×p V p×k ; Step 307: N×1 In W N×k The regression coefficient on It can be obtained through least squares regression; Step 308: N×1 In X N×p The regression coefficient on Step 4: Assess the impact of anthropogenic radiative forcing on temperature seasonality using the contributions of anthropogenic radiative forcing indicators, meteorological element indicators, and climate system indicators to temperature seasonality. This includes the following steps: Step 401: Calculate the contribution C of the selected indicators to the annual mean or seasonal mean trend of temperature in the study area. j ; Step 402: Calculate the Temperature Seasonal Elasticity Index (TSRI) s , the calculation formula is as follows: Where, subscript s represents season, β_ARF i and T_ARF i denote the regression coefficient and standardized trend of the ith selected human radiative forcing indicator in season s, respectively, while β_NAT j and T_NAT j They represent the regression coefficient and standardized trend of the jth selected meteorological element index or climate system index in season s, TSRI s It represents the ratio of the combined control of human radiative forcing index and natural index on the s season; Step 403: Using the concept of exchanging factors for time, construct the seasonal elasticity index series y of temperature for spring, summer, autumn, and winter, and the corresponding series of the sum of trends of selected human radiative forcing indicators x; Step 404: Regress x and y to obtain a regression model: y=κx+b Step 405: Use y=κx+b to reveal the degree of control of anthropogenic radiative forcing on temperature seasonality as the trend of anthropogenic radiative forcing changes.

2. The elasticity index method for assessing the impact of anthropogenic radiative forcing on temperature seasonality according to claim 1, characterized in that: In step 1: The study area should be located in the mid-latitudes of the Northern Hemisphere and have seasonal temperature; The research period should be greater than 30 years and include information on seasonal temperature changes; The anthropogenic radiative forcing indicators include anthropogenic black carbon emissions, black carbon biomass burning emissions, dust extinction aerosol optical depth, dust scattering aerosol optical depth, anthropogenic organic carbon emissions, organic carbon biomass burning emissions, anthropogenic sulfur dioxide emissions, sulfur dioxide biomass burning emissions, anthropogenic sulfur tetroxide emissions, and carbon dioxide concentration; The meteorological element indicators include air temperature, D20 evaporation dish evaporation, 0cm surface temperature, precipitation, air pressure, relative humidity, sunshine hours, and wind speed; The climate system indicators include the Atlantic Multidecadal Oscillation, the Eastern Pacific or North Pacific Oscillation, the Arctic Oscillation, the North Atlantic Oscillation, the extreme eastern tropical Pacific sea surface temperature, the eastern tropical Pacific sea surface temperature, the central tropical Pacific sea surface temperature, the eastern and central tropical Pacific sea surface temperature, the Pacific Decadal Oscillation, the Polar or Eurasian mode, the Quasi-Biennial Oscillation, the Scandinavian mode, the Southern Oscillation Index, the Pacific North American Index, the Trans-Nino Index, the Tropical North Atlantic Index, and the Tropical South Atlantic Index.

3. The elasticity index method for assessing the impact of anthropogenic radiative forcing on temperature seasonality according to claim 1, characterized in that: The process of pre-processing the human radiation forcing index, meteorological element index and climate system index in step 1 is as follows: Step 101: Use the standard normal uniformity test to homogenize the meteorological element indicators to improve the consistency of the data set; Step 102: Average the monthly climate system indicator values ​​into annual and seasonal mean time series; Step 103: Use the linear prediction plus random perturbation method to expand the time span of the human radiative forcing index to the study period; Step 104: Use the spatiotemporal adaptive reflectivity fusion model combined with the global CO2 concentration to extend the CO2 time span to the study period. The calculation formula is as follows: Where C represents the study area, t p represents the predicted time within the extended time period, Indicates the predicted time t for the study area C p The predicted CO2 concentration, G represents global, Denotes the global G prediction time t p CO2 concentration, T e Indicates the end time of CO2 concentration data in the study area, T s Indicates the start time of CO2 concentration data in the study area, t b Represents the CO2 concentration data T in the study area e With T s The reference time between Indicates the benchmark time t of the study area b CO2 concentration, represents the global reference time t b This method uses the global CO2 concentration during the extended time to represent the temporal variation of CO2 concentration in the study area, and uses the CO2 concentration data at all times in the study area as a benchmark to predict the CO2 concentration during the extended time, which can eliminate the uncertainty of the forecast at a single benchmark time. Step 105: Use the grid area weighted average method to process the CO2, human radiative forcing, and meteorological element indicator datasets to obtain the annual and seasonal average anomaly time series averaged over the study area. The calculation method is as follows: Where x n is the anomaly value of CO2, human radiative forcing or meteorological element index in year n, x in is the average value of the indicator data in the i-th grid in the n-th year, is the multi-year average index value of the index data in the i-th grid, I represents the number of grids, and w i is the weighting coefficient of the ith grid, calculated based on the internationally recognized cosine relationship between the latitudes of each grid, and the calculation formula is as follows: Where, lat i is the center latitude of the grid, and the grid size is set to 2.5° latitude × 2.5° longitude.

4. The elasticity index method for evaluating the impact of anthropogenic radiative forcing on temperature seasonality according to claim 1, characterized in that: The specific steps of step 2 are: Step 201: Calculate the daily mean temperature of the study area for the first and last five years of the study period; Step 202: Based on the daily temperature average of the first five years of the study area, the 60th, 152nd, 244th, and 305th days are used as the start dates Ds of spring, summer, autumn, and winter in the five years from the start of the study period. The mean of the daily temperature averages of the two days before and after the start date is used as the temperature T on the start date of the corresponding season using the pentad temperature method. as5 Based on the daily temperature average of the last five years in the study area, the 40 Julian days centered on the 60th, 152nd, 244th, and 305th days and the daily temperature average of the corresponding Julian days were used as independent variables and dependent variables to obtain a linear regression equation. The linear regression equation was used to calculate the daily temperature average of the last five years in the study area, which is equal to T. as5 The corresponding Julian day is the start date De of spring, summer, autumn and winter in the study area for the past five years; De-Ds is the change in the start date of spring, summer, autumn and winter in the study area during the study period. De-Ds>0 indicates that the start date of the season is delayed, and De-Ds<0 indicates that the start date of the season is advanced. Step 203: With March to May as spring, June to August as summer, September to November as autumn, and December to the following February as winter, calculate the seasonal temperature mean for the first five years of each 2.5° latitude zone in the study area and form a seasonal temperature mean latitude gradient curve, and calculate the seasonal temperature mean for the first five years and the last five years of the entire study area; Step 204: Project the seasonal temperature means of the first five years and the last five years of the study area onto the seasonal temperature mean latitude gradient curve. The resulting projection latitudes are denoted as Ls and Le, respectively. Le-Ls>0 indicates that the seasonal temperature mean is shifted northward, and De-Ds<0 indicates that the seasonal temperature mean is shifted toward the equator.

5. The elasticity index method for evaluating the impact of anthropogenic radiative forcing on temperature seasonality according to claim 1, characterized in that: The specific steps of step 3 are: Step 301: Attribute the seasonal spatiotemporal variation of the study area temperature to the trend of the annual mean temperature in the study area. The time series of the annual mean temperature in the study area is Ta N×1 : Are N×1 =[t a,1 ,…,t a,i ,…,t a,N ]′ Where, t a,i represents the annual mean temperature of the study area in year i, N represents the number of years of the study, Ta N×1 It represents the time series consisting of the annual mean temperature of the study area over N years; Step 302: Using stepwise regression analysis, select the indicator that has a significant impact on the trend of the annual mean temperature in the study area from different combinations of human radiation forcing indicators, meteorological element indicators, and climate system indicators, and record it as X N×p : Where p represents the number of indicators that have a significant impact on the trend of the annual mean temperature in the study area selected from different combinations of human radiation forcing indicators, meteorological element indicators, and climate system indicators using stepwise regression analysis, and x in represents the annual average value of the i-th selected indicator study area in the n-th year, X N×p A matrix of N rows and p columns representing the annual mean values ​​of all p selected indicators in the study area over all N years; Step 303: To Ta N×1 and X N×p Standardize by column, that is, Ta N×1 The annual mean temperature of the study area in each year minus the mean annual mean temperature of the study area in N years is divided by the standard deviation of the annual mean temperature of the study area in N years, and X is obtained. N×p The annual average value of each indicator in column p of each year is deducted from the mean of the annual average values ​​of the indicators in the column over N years and then divided by the standard deviation of the annual average values ​​of the indicators in the column over N years; Step 304: normalize X N×p Perform principal component analysis to obtain X N×p The singular value decomposition of is as follows: X=UΔV Where Δp × p represents the non-negative singular value of X, corresponding to Δ in the formula, and U N×p and V p×p The columns are the left and right singular vectors of X, corresponding to U and V in the formula respectively; Step 305: Perform spectral decomposition on X′X. The spectral decomposition formula is as follows: X'X=VΛV' Where, represents the non-negative eigenvalues ​​of X′X, and the columns of V are orthogonal sets of eigenvectors; Step 306: Transform X into a p×k matrix consisting of the first k orthogonal columns of V N×p Convert to W N×k , the conversion formula is: W N×k =X N×p V p×k Where W N×k Contains the first k principal components as its columns, k is the predicted and observed Ta N×1 The root mean square error between them reaches the minimum value; Step 307: N×1 In W N×k The regression coefficient on It can be obtained by least squares regression as follows: Step 308: N×1 In X N×p The regression coefficient can be obtained by the following formula:

6. The elasticity index method for evaluating the impact of anthropogenic radiative forcing on temperature seasonality according to claim 1, characterized in that: The specific steps of step 4 are: Step 401: Contribution C of the jth selected indicator to the annual mean or seasonal mean trend of temperature in the study area j By its trend T j and the regression coefficient β j The product of and the trend of all selected indicators T i and coefficient β i The ratio of the sum of products is determined, where 1≤j≤p, 1≤i≤p, and the calculation formula is as follows: Step 402: For any season, the contribution and trend of the selected human radiative forcing indicators, meteorological element indicators, and climate system indicators are combined to evaluate the impact of the human radiative forcing indicators on the temperature seasonality using the temperature seasonal elasticity index. The temperature seasonal elasticity index is defined as follows: Where, the subscript s represents spring, summer, autumn or winter. For season s, β_ARF i and T_ARF i denote the regression coefficient and standardized trend of the i-th selected human radiative forcing indicator, and β_NAT j and T_NAT j They represent the regression coefficient and standardized trend of the jth selected meteorological element index or climate system index, TSRI s It represents the ratio of the combined control of human radiative forcing index and natural index on the s season; Step 403: Using the concept of exchanging factors for time, construct the series of seasonal elasticity indices of temperature for spring, summer, autumn, and winter, and the series of the sum of trends of the corresponding selected indicators of human radiative forcing. The construction method is as follows: y={TSRI spring ,TSRI summer ,TSRI autumn ,TSRI winter } x={ARF spring ,ARF summer ,ARF autumn ,ARF winter } Step 404: Regress x and y to obtain the following regression model: y=κx+b Step 405: Use y=κx+b to reveal the degree of control of anthropogenic radiative forcing on temperature seasonality as the trend of anthropogenic radiative forcing changes.

7. The elasticity index method for assessing the impact of human radiative forcing on temperature seasonality according to any one of claims 1 to 6 is applied in the fields of global warming attribution, assessment of spatiotemporal changes in temperature seasonality, and attribution of temperature seasonal changes.

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