Method, device and program for setting temperature parameter in simulation experiment

Abstract

The invention relates to a temperature parameter setting method, device and program in a simulation experiment, and the method comprises the steps: determining a temperature increasing target value as a target parameter, taking the time corresponding to the temperature increasing target value as a temperature increasing period, and dividing the temperature increasing period into m sub-time periods; according to the multi-year air temperature sequence data of the research area, determining the change trend of the air temperature in the research time period; determining a warming weight based on a slope value of a regression function for a time period with a linear change trend; for a time period in which the air temperature change trend is nonlinear, determining a temperature increasing weight through a temperature average value; and adjusting the target parameter based on the temperature increasing weight of each sub-time period, and obtaining a temperature increasing value of each sub-time period. According to the method, the day and night asymmetry warming trend of the hour scale is considered, and the asymmetry of the season scale is also considered, so that the local climate change characteristics can be more truly restored, and the influence of the climate change can be accurately simulated and researched.

Description

Technical Field

[0001] The invention belongs to the fields of meteorological analysis, limnology research, simulation experiments, and the like, and specifically relates to a method, equipment and program for setting temperature parameters in simulation experiments. Background Art

[0002] Lakes are important ecosystems for human survival. As climate change intensifies, lake ecosystems are facing the impact of climate warming and frequent extreme high temperature events. Understanding the impact of climate change on lake ecosystems and proposing corresponding mitigation strategies are scientific problems that need to be solved urgently.

[0003] There are many methods for studying the ecological effects of climate change. Among them, mesocosm simulation experiments have been widely used in limnology. Currently, there are two main methods for setting the temperature conditions of simulation experiments:

[0004] 1. Constant temperature method. This method simulates and compares the responses of organisms at different temperatures by setting multiple groups of constant temperatures. Constant temperature can provide stable experimental conditions, but it ignores the circadian rhythm of biological growth and the impact of seasonal changes on organisms. Its application scope is relatively limited and is generally only applicable to short-term indoor pure culture simulation studies.

[0005] 2. Fixed warming amplitude. This method sets a certain warming amplitude according to the needs of the scenario during the simulation experiment (for example, the IPCC predicts a 4°C warming in the next 100 years under the SSP5-8.5 scenario) to observe the response of organisms to a fixed warming amplitude. It can simulate seasonal and diurnal temperature changes, but ignores the seasonal and diurnal asymmetry of climate change.

[0006] Although the above two methods are widely used, they both have their own limitations. Constant temperature treatment ignores the diurnal rhythm of biological growth. Since constant temperature conditions rarely exist in nature and there is a large difference in temperature between day and night, constant temperature treatment cannot obtain the true response of organisms to climate change. Although the fixed warming amplitude can effectively take into account the changing laws of seasons and day and night, it ignores the fact that global climate change has seasonal asymmetry and diurnal asymmetry. From the perspective of global temperature change laws, the warming amplitude in winter and spring is significantly higher than that in summer and autumn, and the warming amplitude at night is about three times that of the warming during the day (Zhong Z, He B, Chen HW, et al. Reversed asymmetric warming of sub-diurnal temperature over land during recent decades [J]. Nat Commun, 2023, 14(1): 7189.). The fixed warming amplitude cannot truly restore the characteristics of climate change. Summary of the invention

[0007] The first purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a method for setting temperature parameters in a simulation experiment. The method recalculates and determines the temperature increase amplitude at different times based on historical observation data to achieve the purpose of truly simulating climate change characteristics.

[0008] To achieve the above object, the present invention adopts the following technical solution:

[0009] A method for setting temperature parameters in a simulation experiment, comprising:

[0010] Determine a temperature increase target value as a target parameter, the time corresponding to reaching the temperature increase target value is a temperature increase period, and divide the temperature increase period into m sub-time periods;

[0011] Based on the multi-year temperature series data of the study area, determine the temperature change trend during the study period;

[0012] For time periods with linear change trends, linear regression is performed on multi-year temperature series data and time to obtain the slope value of the linear regression function for each sub-time period, and the warming weight of each sub-time period is determined based on the slope value;

[0013] For time periods where the temperature change trend is nonlinear, variance analysis is used to determine whether the temperature deviation value of each sub-time period is significant: if it is not significant, equal warming weights are set; if it is significant, the multi-year average of the maximum warming amplitude of each sub-time period is calculated, and the warming weight of each sub-time period is determined based on the multi-year average;

[0014] The target parameters are adjusted based on the temperature increase weights of each sub-time period to obtain the temperature increase value of each sub-time period.

[0015] As a preferred implementation, the length of the sub-time period is determined based on a preset time scale.

[0016] Furthermore, the time scale is a monthly scale or a smaller time scale.

[0017] As a preferred implementation, the linear regression of multi-year temperature series data and time includes:

[0018] Based on multi-year temperature series data, obtain the mean temperature data for each sub-time period of each year;

[0019] A linear regression curve is established between the mean temperature data of the sub-time period and the corresponding year, and the slope of the regression curve of each sub-time period is obtained as the warming weight of each sub-time period.

[0020] As a preferred implementation, determining the temperature increase weight of each sub-time period based on the slope value includes:

[0021] Calculate the sum of the absolute values ​​of the slope values ​​of all sub-time periods included in the warming cycle;

[0022] The ratio of the slope value of each sub-time period to the sum of the absolute values ​​is used as the temperature increase weight of the corresponding sub-time period.

[0023] As a preferred implementation, the temperature anomaly value is the temperature anomaly value of each sub-time period, or the maximum temperature anomaly value calculated using a time scale smaller than the time length of the sub-time period;

[0024] The maximum temperature deviation value is obtained by dividing a sub-time period into several time lengths, calculating the deviation value of each time length, and obtaining the deviation values ​​of several time lengths corresponding to the sub-time period, the maximum value of which is the maximum temperature deviation value.

[0025] As a preferred implementation, determining the warming weight of each sub-time period based on the multi-year average includes calculating the multi-year average of the maximum warming amplitude of each sub-time period and the sum of the multi-year averages corresponding to all sub-time periods, and determining the warming weight of each sub-time period based on the ratio of the two.

[0026] As a preferred implementation, the temperature increase value of each sub-time period is obtained based on the following formula:

[0027]

[0028] In the formula, is the temperature increase value of sub-time period m; is the target parameter; is the temperature increase weight of sub-time period m; is the warming cycle; Refers to the number of sub-time periods included in the warming cycle.

[0029] A second object of the present invention is to provide a computer device, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0030] A third object of the present invention is to provide a computer program product, which comprises a computer program / instruction, and when the computer program / instruction is executed by a processor, the steps of the above method are implemented.

[0031] The present invention provides a method for determining the experimental conditions of warming simulation research by calculating the warming rate in different time periods based on historical data, with the purpose of taking into account the laws of global warming seasons and diurnal changes, and accurately setting the warming amplitude for climate change simulation experiments. The core of the method is to determine the warming intensity at each moment through historical data, and to assign different weights to the estimated value of future warming through the difference in warming intensity, thereby allocating different warming amplitudes at each moment. This method takes into account both the asymmetric warming trend of the diurnal scale at the hourly scale and the asymmetry of the seasonal scale, and can more realistically restore the local climate change characteristics.

[0032] According to the different time resolutions of the existing data, weights can be allocated from different dimensions such as time, day, week, month, and season. For cases with historical observation data with hourly resolution, hourly weights can be directly calculated through regression, variance decomposition and other methods, so as to achieve the allocation of hourly scale warming amplitudes and achieve the purpose of simulating seasonal and diurnal asymmetric warming at the same time. For cases with daily or monthly scale observation data, the monthly scale warming amplitude weights can be calculated first to achieve the purpose of simulating seasonal asymmetric warming; for diurnal asymmetry, weights are roughly allocated by the proportion of short-term hourly data (for example, obtained through downloading from the meteorological sharing network, short-term high-frequency observations, etc.).

[0033] The present invention has the following beneficial effects:

[0034] (1) In previous studies, ∆T was a constant that did not change with the season or day and night. That is, in simulation experiments, the temperature increase was directly increased by ∆T on the basis of the average temperature of previous years. However, the temperature increase actually varies with the season and day and night. The temperature increase in each month should be different. The present invention calculates the temperature increase based on historical observation data, fully considering the impact of seasonal and day-night changes on temperature, which is more in line with the actual temperature changes in the studied location.

[0035] (2) The distribution of hourly warming amplitude is calculated through regression, variance decomposition and other methods to eliminate the impact of seasonal and diurnal changes on temperature fluctuations.

[0036] (3) Ability to customize the research time scale to meet the needs of temperature simulation research at different scales. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 The flowchart of the method of the present invention is described by taking the monthly scale as an example.

[0038] Figure 2 It is a temperature sequence diagram during the study period of a specific implementation case of the present invention.

[0039] Figure 3It is a diagram of calculation results of deviation values ​​of a specific implementation case of the present invention.

[0040] Figure 4 It is a diagram of weight and temperature increase target calculation results for a specific implementation case of the present invention. DETAILED DESCRIPTION

[0041] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0042] Example 1

[0043] This embodiment takes the monthly time scale, that is, calculating the monthly temperature increase weight as an example, to specifically illustrate the implementation manner of the present invention.

[0044] (1) First, determine the future warming target (∆T), which can be obtained through models, literature, expert opinions, etc. In this example, the warming cycle is one year, and the annual warming target ∆T is the expected annual average temperature increase by ∆T compared with previous years.

[0045] (2) Collect multi-year temperature series data for the location to be studied, analyze the trend of the monthly average temperature during the study period, and determine whether it conforms to a linear trend.

[0046] If the warming trend is linear, the warming weight is determined by regression method. The steps include:

[0047] Linear regression was performed on the average monthly temperature over the years and time. ( is the average temperature per month, The 12 slope values ​​of the monthly average temperature over time were obtained. ( );

[0048] Use slope value Calculate the warming weights for each month of each year , ;

[0049] Calculate the monthly temperature increase Δ , Δ .

[0050] Similarly, the warming weights on daily and hourly scales can be calculated using the same idea.

[0051] For the case of nonlinear temperature increase, two schemes can be used for calculation:

[0052] The first method is to reselect the target period of interest for calculation, ensure that the temperature increase during this period is linear, and continue to use the above regression analysis;

[0053] The second method is the squared difference method, which uses variance analysis to determine whether the warming difference (temperature deviation value) at each moment is significant. If the difference is significant, weights are assigned according to the difference in mean values.

[0054] The time here should be a time scale less than or equal to the monthly scale. This embodiment takes the daily time scale as an example. The specific process is as follows:

[0055] Assuming t is the daily average temperature, subtract the average value of the corresponding day during the study period to obtain the temperature anomaly Δt;

[0056] a. Count the maximum value of the anomaly in each month and take the maximum value of the anomaly in each month as the potential maximum temperature increase in that month;

[0057] b. Use variance analysis to determine whether there is a significant difference in the maximum temperature increase of each month. If there is a significant difference, calculate the multi-year average of the maximum temperature increase from January to December. , use the obtained average value to calculate the warming weight of each month, and then calculate the warming amplitude;

[0058] If the variance test is not significant, it means that there is no difference in the warming amplitude between different months, and the warming weight can be set to the same value, that is, there is no seasonal difference in warming during the study period.

[0059] This embodiment takes the monthly scale as an example to illustrate the calculation method of the warming weight. For cases with historical observation data with hourly resolution, the hourly weights can be directly calculated through regression, variance decomposition and other methods, thereby realizing the distribution of the warming amplitude on the hourly scale and achieving the purpose of simulating seasonal and diurnal asymmetric warming at the same time. The calculation principles of other time scales are the same as those of the monthly scale. Calculations of different precisions can be performed according to the time resolution of the historical observation data and the needs.

[0060] For cases with daily or monthly scale observation data, to achieve simulation of day-night asymmetry, hourly data can be obtained based on short-term high-frequency observations, and a rough allocation can be made based on the proportion of each hour's warming to the calculated monthly / daily warming.

[0061] Example 2

[0062] This embodiment takes the calculation of the monthly temperature increase amplitude based on the daily historical observation data of the Wuxi Meteorological Station as an example to specifically illustrate the implementation method of the method of the present invention. The specific steps are as follows:

[0063] (1) Setting warming targets.

[0064] In this embodiment, the annual temperature increase target is set to ∆T = 3°C, that is, the annual average temperature is expected to be 3°C higher than in previous years.

[0065] (2) Collect daily meteorological data from Wuxi Meteorological Station. Analyze the trend of monthly average temperature changes during the study period. In this embodiment, two study periods are selected, namely 1955-1990 and 1980-2022. Taking January as an example, the temperature changes from 1955 to 1990 and from 1980 to 2022 are as follows: Figure 2 As shown, it can be seen that the temperature increase between 1955 and 1990 showed a fluctuating change pattern, and the temperature increase between 1980 and 2022 showed a significant linear trend. Therefore, the present invention will calculate the temperature increase amplitude of the two time periods at the same time.

[0066] (3) Calculate the daily temperature anomaly during the period 1955-1990, that is, the temperature on a certain day minus the average temperature of the corresponding day during the study period, and then calculate the maximum anomaly (that is, the potential maximum warming) for each month during 1955-1990.

[0067] by Figure 3 Taking the first row of data in the table as an example, the temperature of a certain day here refers to the daily average temperature on January 1, 1955. The corresponding average value of the daily temperature is: the daily average temperature on January 1 of each year from 1955 to 1990, a total average of 36 daily average temperatures. The difference between the daily average temperature on January 1, 1955 and the total average is calculated, that is, the temperature anomaly value on January 1, 1955. The maximum anomaly value in January 1955 is: the maximum value of the daily temperature anomaly values ​​from January 1, 1955 to January 31, 1955.

[0068] It should be noted that the monthly temperature anomaly values ​​from 1955 to 1990 can also be calculated here, and the significance judgment can be made using the monthly anomaly values, but the calculation error may increase in this case.

[0069] Alternatively, a smaller time scale (such as hourly) can be used to calculate the monthly temperature anomaly. In this case, the calculation method of the monthly temperature anomaly is the same as that of the daily scale, that is, calculate the hourly time anomaly. The maximum value of the hourly temperature anomaly from January 1, 1955 to January 31, 1955 is the maximum anomaly in January 1955.

[0070] Part of the calculation results of step (3) are as follows Figure 3 shown.

[0071] (4) Use variance analysis to determine whether there is a significant difference in the maximum deviation value of each month;

[0072] The maximum anomaly values ​​of the 12 months in each year from 1955 to 1990 were compared, and it was found that there were significant differences in the maximum anomaly values ​​of each month. Then the average value of the maximum temperature increase of each month from 1955 to 1990 was calculated, and the average value was used as the basis for calculating the weight. The calculation results of the weight and the temperature increase target are as follows: Figure 4 shown.

[0073] (5) The temperature increase between 1980 and 2022 is calculated using the regression method. First, the average temperature of each month is calculated, and the monthly average is linearly fitted with the year to obtain the slope value of the temperature increase for each of the 12 months between 1980 and 2022. The slope is used to calculate the temperature increase weight for each month between 1980 and 2022, and the temperature increase target value is further calculated. The calculation results are as follows: Figure 4 shown.

[0074] The calculation principle and process of temperature increase at other time scales are similar to this and will not be described in detail in this embodiment.

Claims

1. A method for setting temperature parameters in a simulation experiment, characterized in that: include, Determine a temperature increase target value as a target parameter, the time corresponding to reaching the temperature increase target value is a temperature increase period, and divide the temperature increase period into m sub-time periods; Based on the multi-year temperature series data of the study area, determine the temperature change trend during the study period; For time periods with linear change trends, linear regression is performed on multi-year temperature series data and time to obtain the slope value of the linear regression function for each sub-time period, and the warming weight of each sub-time period is determined based on the slope value; For the time period with nonlinear temperature change trend, variance analysis is used to determine whether the temperature anomaly value of each sub-time period is significant. If it is not significant, equal warming weight is set; If significant, calculate the multi-year average of the maximum temperature increase in each sub-time period, and determine the temperature increase weight of each sub-time period based on the multi-year average; The target parameters are adjusted based on the temperature increase weights of each sub-time period to obtain the temperature increase value of each sub-time period.

2. The method according to claim 1, characterized in that The length of the sub-time period is determined based on a preset time scale.

3. The method according to claim 2, characterized in that The time scale is a monthly scale or smaller.

4. The method according to claim 1, characterized in that: The linear regression of multi-year temperature series data and time includes: Based on multi-year temperature series data, obtain the mean temperature data for each sub-time period of each year; A linear regression curve is established between the mean temperature data of the sub-time period and the corresponding year, and the slope of the regression curve of each sub-time period is obtained as the warming weight of each sub-time period.

5. The method according to claim 1, characterized in that The step of determining the temperature increase weight of each sub-time period based on the slope value comprises: Calculate the sum of the absolute values ​​of the slope values ​​of all sub-time periods included in the warming cycle; The ratio of the slope value of each sub-time period to the sum of the absolute values ​​is used as the temperature increase weight of the corresponding sub-time period.

6. The method according to claim 1, characterized in that The temperature anomaly value is the temperature anomaly value of each sub-time period, or the maximum temperature anomaly value calculated using a time scale smaller than the time length of the sub-time period; The maximum temperature deviation value is obtained by dividing a sub-time period into several time lengths, calculating the deviation value of each time length, and obtaining the deviation values ​​of several time lengths corresponding to the sub-time period, the maximum value of which is the maximum temperature deviation value.

7. The method according to claim 1, characterized in that Determining the warming weight of each sub-time period based on the multi-year average includes calculating the multi-year average of the maximum warming amplitude of each sub-time period and the sum of the multi-year averages corresponding to all sub-time periods, and determining the warming weight of each sub-time period based on the ratio of the two.

8. The method according to claim 1, characterized in that The temperature increase value of each sub-time period is obtained based on the following formula: ； In the formula, is the temperature increase value of sub-time period m; is the target parameter; is the temperature increase weight of sub-time period m; is the warming cycle; Refers to the number of sub-time periods included in the warming cycle.

9. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

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