Thermal wave simulation method based on climatic change comprehensive evaluation model

Abstract

The invention belongs to the technical field of climate change comprehensive evaluation modeling, and particularly relates to a thermal wave simulation method based on a climate change comprehensive evaluation model. The method is based on a climate statistics technology, a climate change comprehensive assessment model (IAM) is combined with multiple groups of complex earth system mode characteristics and historical observation air temperature characteristics, global annual average state air temperature is simulated firstly, and then the global annual average state air temperature is converted into monthly, daily and intraday global geographic grid air temperature. In the conversion process, the monthly seasonal effect and daily average state characteristics of an earth system mode are simulated, and historically observed daytime temperature anomaly and daily temperature poorer characteristics are captured; and finally, simulating day-degree extreme air temperature by superposing average state and wave dynamic characteristics, thereby simulating regional short-term heat wave characteristics consistent with the global long-term climate change trend. According to the method, simplified simulation of the IAM on the regional extreme air temperature is realized, the capability and efficiency of evaluating the extreme climate risk by the IAM are improved, and efficient climate policy analysis is facilitated.

Description

Technical Field

[0001] The present invention belongs to the technical field of comprehensive assessment modeling of climate change, and specifically relates to a heat wave simulation method based on a comprehensive assessment model of climate change. Background Art

[0002] The integrated assessment method of climate change is a method that integrates knowledge from two or more fields into a model under the same framework. It usually couples economic models, energy system models, climate models, climate impact models, etc. to form an integrated climate change assessment model (IAMs). IAMs can be used to assess the impacts and risks of climate change under different socio-economic development paths and climate policy management, and can provide decision-making support for adaptation actions to address climate change.

[0003] Climate system simulation models are key components of IAMs, and are mainly used to simulate the impact of greenhouse gas emissions on various variables in the climate system. Climate simulation models in existing mainstream IAMs are mainly divided into two categories: one is the medium-complexity Earth system model, which can describe in detail the physical processes of the interaction between the atmosphere, ocean, cryosphere and land surface in the Earth system, and can discretize the Earth's surface into a series of grid regions, and can simulate climate variables such as temperature at a three-dimensional high spatiotemporal resolution. For example, the climate module MESM in the IGSM model.

[0004] The other type is simplified climate models, which use the Earth system model with more complex processes as a benchmark, simplify the physical and chemical processes, retain the basic physical conduction mechanism of "emission-concentration-radiative forcing-temperature change" in the Earth system model, and are mainly used to simulate the impact of greenhouse gas emissions on annual global average temperature changes. They run fast, have low consumption costs, and can simulate the behavior and simulation effects of complex Earth system models to a certain extent. For example, the climate module MAGICC in the MESSAGE and IMAGE models, and the climate module Hector in the GCAM model.

[0005] Although medium-complexity earth system models can describe physical processes in detail and simulate climate variables at three-dimensional high spatiotemporal resolution, they have relatively long running times and high running costs, and are less efficient when conducting climate change policy simulation studies. In view of this, the vast majority of IAMs use simplified climate models. However, these simplified climate models mainly simulate future long-term climate change trends and focus on the average climate state, such as the annual global average temperature, without taking into account regional differences and intra-annual changes. This makes it impossible for IAMs to capture short-term weather change characteristics, thereby ignoring the adverse effects of short-term extreme events (such as Figure 1). However, the impacts and damages caused by climate change on the economy, society and natural ecosystems may not be simply proportional to the average climate state. They are often a function of the extreme values ​​of the distribution of climate factors, which indicates that the lack of simulation of "extreme climate" in IAMs may greatly underestimate the damage caused by climate change.

[0006] Heat waves (extreme high temperatures) are a typical type of extreme event, which usually refers to extremely high temperature weather that lasts for several days. In the context of climate change, the frequency and duration of heat waves will increase dramatically, which will have adverse effects on labor productivity, human health, energy infrastructure, and transportation infrastructure. Therefore, simulating heat waves in IAMs is crucial to understanding the socioeconomic impacts of climate change. Climate science research usually defines a heat wave as: if the daily maximum temperature for two consecutive days or more exceeds the 98% quantile of the daily maximum temperature distribution in the warmest three months of the reference year, a heat wave event is considered to have occurred. From the definition, it can be seen that simulating heat waves requires, on the one hand, refining the temperature to daily degrees on a time scale; on the other hand, heat waves are not determined by the absolute value of temperature, but by the relative value of daily temperature in a region. A temperature that is normal for a hotter climate region may be a heat wave for a generally colder region. Therefore, simulating heat waves also needs to consider different regional characteristics at a spatial scale.

[0007] In addition, there are uncertainties in the response of climate change to emissions, which are crucial for discussing the risk characteristics of climate change impacts. These uncertainties are mainly reflected in the differences in the structural parameters of complex earth system models and the differences in climate scenarios simulated by complex models during the simulation process. Therefore, when simplifying the simulation of extreme climate through IAMs, it is necessary to integrate the uncertainty characteristics of the structure, parameters and operation of multiple complex earth system models. However, existing IAMs lack multiple sets of scenario outputs that simulate multiple complex earth system models under a single comprehensive assessment framework, making it difficult to characterize the uncertainty characteristics of regional daily temperature changes, limiting their simulation of potential heat wave risks under future socioeconomic development paths. Summary of the invention

[0008] In view of this, a heat wave simulation method based on a comprehensive climate change assessment model can achieve accurate simulation of heat waves.

[0009] The technical solution for implementing the present invention is as follows:

[0010] A heat wave simulation method based on a comprehensive climate change assessment model, the specific process is as follows:

[0011] Simulate the global annual average temperature Select a complex earth system model, determine the socio-economic scenario, and combine the simplified climate module and socio-economic module of IAM to simulate the global annual average temperature under a certain socio-economic scenario. year∈{y0,y0+1,…,y end});

[0012] Simulate the global monthly average temperature The seasonal pattern factor is calculated based on the historical temperature simulation data of the complex earth system model, and the seasonal pattern factor and the global annual average temperature are calculated based on the seasonal pattern factor and the global annual average temperature. Calculate the global monthly average temperature month∈{1,2,…,12}, gcm∈{1,2,…,n});

[0013] Get global monthly gridded temperatures Based on the future scenario temperature simulation data of the complex earth system model, the statistical relationship between the monthly global average temperature change value and the monthly global grid average temperature change of the selected complex earth system model is established. Calculate the corresponding monthly grid temperature year∈{y0,y0+1,…,y end}, month∈{1,2,…,12}, gcm∈{1,2,…,n});

[0014] Get the maximum temperature of the next day Based on the global monthly gridded temperature Calculate daily temperature trends Calculation of daily temperature anomaly HisA based on historical temperature observation data x,y,year,month,d Based on the daily temperature trend and the daily temperature anomaly HisA x,y,year,month,d Calculate the global grid daily average temperature The relationship between the daily average temperature and the daily maximum temperature is established based on historical temperature observation data. Calculate the maximum daily temperature of the global grid

[0015] Heat wave simulation: First, a heat wave is defined: if the daily maximum temperature for two or more consecutive days exceeds the 98% quantile of the daily maximum temperature distribution of the three warmest months in the reference year, a heat wave event is considered to have occurred. Second, the grid daily temperature simulation results with complex pattern characteristics of each group are integrated By analyzing the frequency of heat waves (the number of heat waves in a year), the duration of heat waves (the average number of days for all heat wave events in a year), and their probability distribution characteristics, the uncertainty characteristics of regional heat waves under certain socioeconomic scenarios can be characterized in a single IAM.

[0016] Furthermore, the simulation of the present invention obtains the global annual average temperature year∈{y0,y0+1,…,y end The specific process is as follows:

[0017] Determine the socio-economic scenario S that needs to be simulated, and calculate the global greenhouse gas emission path G (time series) under this scenario based on the socio-economic module of IAM;

[0018] Determine n groups of complex earth system models (gcm∈{1,2,…,n}) that need to be simulated, combine the simplified simulation model of global annual average temperature of IAM, and determine n groups of submodules that can simplify the simulation of global annual average temperature;

[0019] Determine the historical base year range and the simulated future time period y0~y end In 2018, the global greenhouse gas emission pathway G is input into the above n groups of submodules to obtain the global annual average temperature change (relative to the historical base period) year∈{y0,y0+1,…,y end})

[0020] Obtain the global mean temperature averaged over all years in the historical base period for each set of complex Earth system models

[0021] According to the average temperature and the average temperature change year∈{y0,y0+1,…,y end}), obtain the global annual average temperature under socioeconomic scenario S year∈{y0,y0+1,…,y end}).

[0022] Furthermore, the simulation of the present invention obtains the global annual average temperature year∈{y0,y0+1,…,y end}) The process is:

[0023] Step 1: Determine the socio-economic scenario S and the relevant parameters that characterize the scenario;

[0024] Step 2: Input the relevant parameters into the socio-economic module of IAM to calculate the global greenhouse gas emission pathway G (time series);

[0025] Step 3: Determine n groups of complex earth system models that need to be simulated;

[0026] Step 4: Based on n sets of complex earth system models and the simplified simulation model of global annual average temperature of IAM, obtain n sets of simulation parameters of complex earth system models, and form n submodules in IAM that can simplify the simulation of global annual average temperature;

[0027] Step 5: Determine the historical base year range and the future time period y0~y end In 2017, the annual global average temperature simulation data of each group of complex earth system models in the historical base period were obtained through the CMIP6 website database (the sixth international coupled model intercomparison project: Coupled Model Intercomparison Project, CMIP6). For each group of earth system models, the global annual average temperature of all years in the historical base period was calculated.

[0028] Step 6: Input the global greenhouse gas emission pathway G into the above n groups of submodules to obtain According to step 5, according to Further simulations were performed to obtain n groups of global annual average temperatures during this period.

[0029] Furthermore, the simulation of the present invention obtains the global monthly average temperature The specific process of month∈{1,2,…,12},gcm∈{1,2,…,n}) is as follows:

[0030] Global mean temperature based on the average of all years in the historical base period And the average temperature of each month in all years within the historical base period Calculate seasonal pattern factors

[0031] According to the seasonal pattern factor The simulated Converted into global monthly average temperature with seasonal characteristics

[0032] Furthermore, the simulation of the present invention obtains the global monthly average temperature The specific process of month∈{1,2,…,12},gcm∈{1,2,…,n}) is as follows:

[0033] Step 7: Obtain the monthly global average temperature simulation data of each complex earth system model during the historical base period through the CMIP6 database Based on this, the average temperature of each month in all years within the historical base period is calculated.

[0034] Step 8: Global mean temperature based on the average of all years in the historical base period And the average temperature of each month in all years within the historical base period Calculate seasonal pattern factors

[0035]

[0036] Step 9: Based on the seasonal pattern factor The simulated Converted into monthly global average temperature with seasonal characteristics

[0037] Furthermore, the global monthly average temperature of the present invention month∈{1,2,…,12},gcm∈{1,2,…,n}) is:

[0038]

[0039] Furthermore, the global monthly grid temperature is obtained year∈{y0,y0+1,…,y end The specific process is:

[0040] Obtain the global grid monthly average temperature data for each complex earth system model in the historical scenario in the CMIP6 database (the sizes of geographical longitude and latitude grids of different models are inconsistent), and calculate the global average temperature of each model in each month over all years in the historical base period. and the average annual temperature of each month in the historical base period at a 0.5×0.5° geographic latitude and longitude grid

[0041] Obtain the global temperature grid monthly data for each complex earth system model in the CMIP6 database in multiple future global warming scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5) (the sizes of geographical longitude and latitude grids of different models are inconsistent), and calculate the monthly global average temperature in the future time period of each model under each scenario. and monthly 0.5×0.5° geographic longitude and latitude grid temperature

[0042] Based on the monthly global average temperature values ​​and monthly global grid temperature fields in the historical base period and future scenarios, the monthly global average temperature change values ​​are calculated. and monthly grid temperature changes

[0043] Establish a statistical relationship between the monthly global average temperature change value and the monthly global grid temperature change field for each model in the future time period, and based on the relationship, convert the global monthly average temperature value under the global greenhouse gas emission path G into Transformed into the corresponding global monthly gridded temperature

[0044] Furthermore, the present invention obtains the global monthly grid temperature year∈{y0,y0+1,…,y end The specific process is:

[0045] Step 10: Collect the global near-surface temperature grid monthly data for each group of Earth system models in the historical scenario through the CMIP6 database;

[0046] Step 11: Calculate the annual average temperature data of the global 0.5×0.5° geographic longitude and latitude grid for each month in the historical base period based on the global near-surface temperature grid monthly data

[0047] Step 12: Calculate the global mean temperature value for all months of the historical base period based on the global near-surface temperature grid monthly data

[0048] Step 13: Collect the global near-surface temperature grid monthly data for the future time period under the SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5 scenarios for each group of Earth system models through the CMIP6 database;

[0049] Step 14: Based on the global near-surface temperature grid monthly data described in step 13, obtain the global 0.5×0.5° geographic longitude and latitude grid temperature data for each future scenario

[0050] Step 15: Based on the monthly global surface temperature grid data, obtain the monthly global average temperature value in each future scenario

[0051] Step 16: For each set of complex earth system models, calculate the difference between the global average temperature in the future time period and the historical base period under the future scenario, which is the monthly global average temperature change value.

[0052] Step 17: For each set of complex earth system models, calculate the difference between the monthly 0.5×0.5° geographic longitude and latitude grid temperatures in the future time period and the historical base period under the future scenario, which is the monthly grid average temperature change

[0053] Step 18: Establish the statistical relationship between the monthly global average temperature change and the monthly global grid temperature change of each model in the future time period:

[0054] Step 19: Convert the global monthly average temperature values ​​corresponding to the global greenhouse gas emission pathway G generated in step 9 to Converted into change value relative to historical base period And calculate the global monthly grid temperature change according to the statistical relationship described in step 18: Finally, the global monthly grid temperature corresponding to the global greenhouse gas emission path G is obtained:

[0055] Furthermore, the present invention obtains the maximum daily temperature of the future grid Where N(year,month) represents the total number of days in year year and month month:

[0056] For the monthly grid temperature Perform interpolation and smoothing operations to obtain daily temperature trends

[0057] Obtain the grid daily average temperature observation data HisT within the historical base period x,y,year,month,d , and based on this, the long-term sliding mean temperature HisLTS in the historical base period is calculated x,y,month,d ;

[0058] The daily temperature anomaly HisA is obtained by subtracting the grid daily average temperature observation data within the historical base period from the long-term sliding mean temperature within the historical base period. x,y,year,month,d ;

[0059] Add the daily temperature anomaly to the daily temperature trend Get the average daily temperature in the future

[0060] Get the daily maximum temperature observation data HisMaxT within the historical base period x,y,yrar,month,d, compare the daily maximum temperature observation data with the grid daily average temperature observation data HisT x,y,year,month,d Combined, establish the statistical relationship between daily mean temperature and daily maximum temperature with seasonal changes;

[0061] Based on the statistical relationship between the daily average temperature and the daily maximum temperature with seasonal changes, the future daily average temperature Converted to daily maximum temperature

[0062] Furthermore, the present invention obtains the maximum temperature of the future day The specific process is:

[0063] Step 20: Add the monthly grid temperature from step 19 Interpolation to daily grid temperature Then calculate the grid temperature for that day Use the 31-day moving average method to smooth the interpolation results to obtain the temperature trend

[0064] Step 21: Obtain the daily average temperature observation data HisT during the historical base period through the WFDEI database x,y,year,month,d , and calculate the 31-day sliding mean temperature value HisTS for each day in the historical base period x,y,year,month,d ;

[0065] Step 22: Based on the 31-day sliding mean temperature value, average all annual values ​​in the historical base period to calculate the long-term sliding mean temperature HisLTS x,y,month,d ;

[0066] Step 23: Calculate the daily temperature anomaly by subtracting the daily temperature data within the historical base period from the long-term sliding mean temperature within the historical base period:

[0067] HisA x,y,year,month,d =HisT x,y,year,month,d -HisLTS x,y,month,d

[0068] Step 24: Superimpose the daily temperature anomaly of the historical base period observation obtained in step 23 onto the smoothed daily temperature sequence of step 20 to obtain the future daily average temperature with fluctuation characteristics that is consistent with the future long-term annual temperature change trend:

[0069]

[0070] Step 25: Obtain the daily maximum temperature observation data HisMaxT during the historical base period through the WFDEI database x,y,year,month,d ;

[0071] Step 26: According to the daily temperature range differences in different seasons, the statistical relationship between the daily average temperature and the daily maximum temperature is established for each season to capture the characteristics of the daily temperature changes with seasonality.

[0072] HisMaxT x,y,season =aseason·HisT x,y,year,month,d +b season

[0073] Step 27: Based on the model built in step 26, the simulation results of the future daily average temperature obtained in step 24 are Converted to daily maximum temperature

[0074] Furthermore, the specific process of heat wave simulation of the present invention is as follows:

[0075] First, a heat wave is defined: if the daily maximum temperature for two or more consecutive days exceeds the 98% quantile of the daily maximum temperature distribution of the three warmest months in the reference year, a heat wave event is considered to have occurred. Secondly, the target region R of the study is determined. According to the results of step 27, the grid daily maximum temperatures simulated based on the characteristics of each group of complex earth system models are aggregated to the regional level, and then the daily maximum temperatures of all n complex earth system models are integrated to calculate the annual heat wave frequency (the number of heat waves in a year), heat wave duration (the average number of days for all heat wave events in a year), and their probability distribution characteristics in region R under the socio-economic scenario S.

[0076] Beneficial effects:

[0077] The simplified climate simulations of traditional IAMs focus on the annual average temperature and lack descriptions of intra-annual variations and regional characteristics. Therefore, it is difficult to depict the characteristics of temperature extremes, leading to underestimation of potential climate risks.

[0078] First, based on climate statistics technology, the present invention combines the multi-climate scenario simulation results of multiple sets of complex earth system models and historical observation temperature data to convert the global annual average temperature trend simulated by traditional IAM into monthly, daily, and daily global 0.5×0.5 degree grid temperature fields. The conversion process simulates the monthly seasonal effects and daily average characteristics of the complex earth system model. At the same time, the fluctuation characteristics such as daytime temperature anomaly characteristics and seasonal daily temperature difference are calibrated and simulated according to historical observation data. By superimposing the average state characteristics with the wave state characteristics, the extreme characteristics of short-term daily temperature are captured.

[0079] Second, in the comprehensive assessment modeling framework, the present invention simulates the response capacity of temperature to greenhouse gas emissions of multiple sets of complex Earth system models, and characterizes the regional short-term daily temperature uncertainty characteristics consistent with the global long-term climate change trend under any socio-economic scenario, thereby simplifying the simulation of regional heat wave characteristics under the background of climate change. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0081] Figure 1 Climate simulations for traditional integrated assessment models (IAMs);

[0082] Figure 2 This is an improvement of the traditional integrated assessment models (IAMs) climate simulations;

[0083] Figure 3 The following are the key steps of the technical solution of the embodiment of this application. DETAILED DESCRIPTION

[0084] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0085] It should be noted that the following embodiments and features in the embodiments may be combined with each other in the absence of conflict; and, based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making any creative work are within the scope of protection of the present disclosure.

[0086] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0087] Climate science studies usually define heat waves as: if the daily maximum temperature for two or more consecutive days exceeds the 98th percentile of the distribution of daily maximum temperatures in the three warmest months of the reference year, a heat wave event is considered to have occurred. According to the definition, simulating heat wave-related characteristics requires being able to simulate the impact of greenhouse gas emissions on regional daily maximum temperatures.

[0088] In view of the defect that the traditional IAM lacks the uncertainty characteristics of regional daily temperature when simplifying the simulation of temperature changes, the embodiment of the present application provides a heat wave simulation method based on a comprehensive assessment model of climate change, such as Figure 2 As shown, the specific process is:

[0089] Simulate the global annual average temperature Determine the socio-economic scenario S that needs to be simulated, combine the simplified climate module and socio-economic module of IAM to determine the global greenhouse gas emission path G (time series) under the scenario; determine n groups of complex earth system models that need to be simulated, combine the simplified simulation model of global annual average temperature of IAM, and determine n groups of sub-modules that can simplify the simulation of global annual average temperature; determine the historical base year range and the simulated future time period y0~y end In 2017, the global greenhouse gas emission pathway G is input into the above n groups of submodules to obtain the global annual average temperature change (relative to the historical base period) year∈{y0,y0+1,…,y end}); Get the global average temperature averaged over all years in the historical base period under each set of complex earth system models According to the average temperature and the average temperature change Get the global annual average temperature under socioeconomic scenario S year∈{y0,y0+1,…,y end}).

[0090] Simulate the global monthly average temperature Global mean temperature based on the average of all years in the historical base period And the average temperature of each month in all years within the historical base period Calculate seasonal pattern factors Based on the factors The global annual mean temperature Convert to n sets of global monthly average temperatures with seasonal characteristics month∈{1,2,…,12}, gcm∈{1,2,…,n}).

[0091] Get global monthly gridded temperatures Obtain the global grid monthly average temperature data for each complex earth system model in the historical scenario in the CMIP6 database (the sizes of geographical longitude and latitude grids of different models are inconsistent), and calculate the global average temperature of each model in each month over all years in the historical base period. and the average annual temperature of each month in the historical base period at a 0.5×0.5° geographic latitude and longitude grid Obtain the global temperature grid monthly data for each complex earth system model in the CMIP6 database in multiple future global warming scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5) (the sizes of geographical longitude and latitude grids of different models are inconsistent), and calculate the monthly global average temperature in the future time period of each model under each scenario. and monthly 0.5×0.5° geographic longitude and latitude grid temperature Calculate the monthly global average temperature change based on the monthly global average temperature values ​​and monthly global grid temperature in the historical base period and future scenarios and monthly grid temperature changes Establish a statistical relationship between the monthly global average temperature change value and the monthly global grid temperature change in each model in the future time period, and based on the relationship, convert the global monthly average temperature value under the global greenhouse gas emission path G into Transformed into the corresponding global monthly gridded temperature year∈{y0,y0+1,…,y end}, month∈{1,2,…,12}, gcm∈{1,2,…,n});

[0092] Get the maximum temperature of the next day Based on the global monthly gridded temperature Calculate temperature trend Obtain the grid daily average temperature observation data HisT within the historical base period x,y,year,month,d , and based on this, the long-term sliding mean temperature HisLTS in the historical base period is calculated x,y,month,d ; Subtract the grid daily average temperature observation data within the historical base period from the long-term sliding mean temperature within the historical base period to obtain the daily temperature anomaly HisA x,y,year,month,d ; Add the daily temperature anomaly to the daily temperature trend Get the average daily temperature in the future Get the daily maximum temperature observation data HisMaxT within the historical base period x,y,year,month,d , compare the daily maximum temperature observation data with the grid daily average temperature observation data HisT x,y,year,month,dCombined with the above, a statistical relationship between the daily average temperature and the daily maximum temperature with seasonal changes is established; based on the statistical relationship between the daily average temperature and the daily maximum temperature with seasonal changes, the future daily average temperature Converted to daily maximum temperature

[0093] Heat wave simulation: By integrating the grid daily temperature simulation results with various complex pattern characteristics and performing probabilistic analysis, the uncertainty characteristics of regional heat waves under certain socioeconomic scenarios can be characterized in a single IAM. (Step 28)

[0094] This embodiment has the following effects:

[0095] First, multiple groups of complex Earth system models are identified (step 3). The annual global average temperature simplified simulation model of the existing IAM is used to simulate the simulation capability of each group of complex models in terms of annual global average temperature changes. The annual global average temperature under certain socio-economic scenarios is then simulated to reflect the uncertainty of temperature changes (steps 4 and 6).

[0096] Secondly, to further simulate the characteristics of the complex model on a daily time scale, it is not to establish a direct statistical relationship between annual temperature (long-term trend) and daily temperature (short-term characteristics), which may ignore the regional characteristics of temperature at the spatial scale and the real fluctuation characteristics of the time scale. Instead, it is transformed layer by layer from "year to month, month to day", and the long-term climate trend is gradually transformed into short-term weather fluctuation characteristics. Specifically, through the simulation results of the complex earth system model under the historical scenario (step 7), the monthly seasonal fluctuation characteristics of the complex model simulation are captured, and the simulation results of the annual global long-term average temperature changes under a certain socio-economic scenario are refined to the monthly level (steps 8 and 9). Based on the traditional spatial scaling method, the global average temperature of the complex earth system model can be mapped to the characteristics of the local average temperature. This embodiment further trains the spatial scaling model through the simulation results of the complex earth system model under the latest SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5 climate scenarios of CMIP6 (step 13, which can cover the potential future temperature rise range currently known to humans and help improve the generalization ability of the model), and then obtains the monthly temperature geographic grid field under the corresponding socio-economic scenario (steps 18 and 19).

[0097] Again, the daily temperature grid field is obtained through meteorological interpolation technology, and the average characteristics of daily temperature are identified through statistical methods. Combined with real data from historical observations, the daily temperature anomalies in the real world are captured (steps 20, 21, 22, and 23), as well as the fluctuation characteristics of daily temperature in the real world with seasonal changes (steps 25 and 26). The simulated annual long-term trend is then superimposed with the short-term real fluctuation characteristics, so that the short-term weather characteristics are consistent with the long-term climate change trend characteristics, thereby simulating the short-term daily temperature change characteristics that are consistent with the long-term temperature change trend characteristics under a certain socio-economic scenario in the same IAM framework (steps 24 and 27).

[0098] Finally, by integrating the grid daily temperature simulation results with various sets of complex pattern characteristics and performing probabilistic analysis, the uncertainty characteristics of regional heat waves under certain socioeconomic scenarios can be portrayed in a single IAM.

[0099] (Step 28).

[0100] Examples, such as Figure 3 As shown:

[0101] Step 1: Determine the socio-economic scenario S and the relevant parameters that characterize the scenario;

[0102] A key goal of the integrated assessment of climate change is to understand how the development of human socio-economic systems will affect the climate. Socio-economic scenario drivers can better describe future socio-economic development trends. They describe the paths of changes in economic, demographic, technological and policy factors over time. Combining these drivers with the socio-economic modules of IAMs can further obtain climate change mitigation plans corresponding to these factors in terms of economy, energy, land use, etc., as well as greenhouse gas emission paths (trajectories of change over time). Determine the socio-economic scenario S that needs to be evaluated and obtain its quantitative parameters in terms of macroeconomics, technological change, climate policy and other dimensions.

[0103] Step 2: Input the relevant parameters into the socio-economic module of IAM to calculate the future global greenhouse gas emission path G (time series) of scenario S, with the starting year of the path being y0 and the ending year being y end .

[0104] Step 3: The differences in structure and parameters between climate models will lead to uncertainty in climate simulation. In order to simulate the uncertainty of global average temperature change, n groups (generally n>20) of global complex earth system models are selected.

[0105] Step 4: Based on the existing simplified climate model of global annual average temperature in IAM, for each group of complex earth system models mentioned above, simulate the characteristics of the simulation results of the complex earth system model, and determine the key parameter values ​​for the simulation of these n groups of complex earth system models, such as climate sensitivity, feedback parameters, etc. Form n submodules in IAM that can simplify the simulation of global annual average temperature.

[0106] Step 5: Determine the historical base period and the simulated future time period, setting the historical base period of the relative change of global average temperature to 1981-2010 and the future time period to y0~y end Year(y0,y end ∈[2020,2100] and y0 <y end ), and obtain the annual global average temperature data of each set of complex earth system models in the historical scenario simulation (Historical scenario) in the historical base period in the CMIP6 website database, and calculate the global annual average temperature of each model in all years in the historical base period (1981-2010).

[0107] Step 6: Input the global greenhouse gas emission pathway G into the above n groups of simplified simulation submodules of global annual average temperature, and simulate n groups of annual global average temperature changes According to step 5, According to the following formula (1):

[0108]

[0109] Further simulations were performed to obtain n groups of global annual average temperatures during this period. year∈{y0,y0+1,…,y end}).

[0110] Step 7: Obtain the monthly global average temperature of each complex earth system model in the historical base period through the CMIP6 database month∈{1,2,…,12},gcm∈{1,2,…,n}). Based on this, the mean temperature of each month in each group of Earth system models in all years in the historical base period is calculated. The following formula (2) is used for calculation:

[0111]

[0112] in, It is the monthly global mean temperature in month month in year year during the base period.

[0113] Step 8: Base period global monthly mean temperature of each complex earth system model determined in step 7 and the global mean temperature averaged over all years of the historical base period for each complex Earth system model determined in step 5 Calculate the seasonal pattern factor that reflects the relationship between the annual global average temperature and the monthly global average temperature during the historical base period for:

[0114]

[0115] in, It indicates the degree of deviation of the average temperature of each month from the annual average temperature and is used to capture seasonal changes.

[0116] Step 9: Based on the above seasonal pattern factors The simulated Converted into monthly global average temperature with seasonal characteristics The calculation process is as shown in formula (4):

[0117]

[0118] where year∈{y0,y0+1,…,y end}, month∈{1,2,…,12}, gcm∈{1,2,…,n}.

[0119] Step 10: Collect the global near-surface temperature grid monthly data for the historical base period (January 1981-December 2010) of each group of global complex Earth system models in the historical scenario Historical through the CMIP6 database.

[0120] Step 11: Use spatial interpolation to obtain the annual average temperature data of the global 0.5×0.5° geographic longitude and latitude grid for each month in the historical base period. month{1,2,…,12}, gcm∈{1,2,…,n}).

[0121] Step 12: Based on the data collected in step 10, calculate the global average temperature values ​​for all months in the historical base period.

[0122] Step 13: Collect the gridded monthly data of global near-surface temperature in the future time period (January to January y0) for each group of Earth system models under the SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5 scenarios through the CMIP6 database. endAmong them, SSPs stands for socioeconomic pathways, and the numbers behind it represent the radiative forcing in 2100 under a certain greenhouse gas emission level. The greater the radiative forcing, the greater the degree of global warming. These groups of warming scenarios almost cover the potential temperature rise range of global warming under current cognition, which helps the temperature mapping model to be more generalized.

[0123] Step 14: Perform spatial interpolation on the global near-surface temperature grid monthly data of each group of earth system models under each scenario collected in step 13 to obtain the monthly global 0.5×0.5° geographic longitude and latitude grid temperature data in each future scenario. year∈{y0,y0+1,…,y end}, month∈{1,2,…,12}, gcm∈{1,2,…,n}).

[0124] Step 15: Based on the global near-surface temperature grid monthly data collected in step 13 for each set of earth system models under each scenario, calculate the monthly global average temperature value in each future scenario month∈{1,2,…,12}, gcm∈{1,2,…,n}).

[0125] Step 16: For each set of complex earth system models, calculate the difference between the global average temperature in the future time period and the historical base period under the future scenario, which is the monthly global average temperature change value.

[0126] Step 17: For each set of complex earth system models, calculate the difference between the monthly 0.5×0.5° geographic longitude and latitude grid temperatures in the future time period and the historical base period under the future scenario, which is the monthly grid average temperature change field.

[0127] Step 18: Existing studies have proven that under the forcing of greenhouse gases, although there is climate change in the global climate model, the spatial distribution of near-ground temperature changes in the model is relatively stable. At the same time, there is an approximately linear relationship between the global average temperature change and the regional temperature change in the model. Therefore, such linear statistical models can be constructed for each complex earth system model, and such models are called spatial scaling models. This type of model can easily and quickly obtain temperature changes at the regional level through global average temperature changes, thereby avoiding the cost and time of running complex earth system models with many physical processes. In view of this, this method further integrates the scenario data in each complex earth system model at the corresponding time point to form a data set for regression, and estimates the above linear statistical model through the following formula (5):

[0128]

[0129] in, is the simulated data of each model under all the above future scenarios, representing the grid temperature change in month year (reflecting the change of grid temperature at that time point relative to the grid temperature in the baseline period) of the gcm-th (gcm=1,2,3…,n) complex earth system model; It is also the simulated data of each model under the above scenario, representing the global average temperature change in month month of year year (reflecting the change of the global average temperature at that time point relative to the global average temperature in the baseline period) of the gcm-th (gcm=1,2,3…,n) complex earth system model; Represents the grid temperature mapping coefficient of the gcm-th (gcm=1,2,3…,n) complex Earth system model in month month that does not change with the year.

[0130] Step 19: Convert the global monthly average temperature values ​​corresponding to the global greenhouse gas emission pathway G generated in step 9 to month∈{1,2,…,12}, gcm∈{1,2,…,n}) is converted into the change value relative to the historical base period through the following formula (6):

[0131]

[0132] And calculate the global monthly grid temperature change field according to the statistical relationship described in step 18: Finally, the global monthly grid temperature field corresponding to the global greenhouse gas emission path G is obtained:

[0133] Step 20: To ensure seasonal fluctuations in temperature, this step first uses cubic spline interpolation to interpolate monthly temperatures. Interpolation to daily scale. This interpolation method can better capture this natural fluctuation. The interpolation control point is defined as the midpoint of each month. Assign the midpoint of the month. Based on the starting point, midpoint, and end point, calculate the daily average temperature for each day through this interpolation method

[0134] In order to eliminate random fluctuations in the daily temperature series, retain long-term seasonal changes, and provide a smoother temperature change trend. This step uses a 31-day moving average method to smooth the interpolation results, as shown in the following formula (7):

[0135]

[0136] in, Represents the processed daily smoothed temperature. In particular, for days close to the boundary, the sliding window range can be reduced, for example, the sliding average of day 1 only considers data from days 1 to 16.

[0137] Step 21: The smoothed temperature curve lacks the daily temperature fluctuations in reality. The temperature anomaly can reveal the abnormal changes in temperature and reflect the temperature change characteristics of a certain place. In order to make the daily temperature series closer to the daily volatility in reality, this step will add the temperature anomaly characteristics based on the daily temperature smoothing value according to the daily temperature anomaly values ​​observed in history. First, obtain the daily average temperature observation data in the historical base period from the WFDEI database Calculate the 31-day moving average HisTS for each day in the historical base period (1981-2010) x,y,year,month,d , the smoothing process is shown in the following formula (8):

[0138]

[0139] In particular, for days close to the boundaries, the sliding window range can be reduced, for example, the sliding average of day 1 only considers data from days 1 to 16.

[0140] Step 22: To extract the long-term climate trend, the sliding mean of each day in the 30 years from 1981 to 2010 needs to be averaged annually to obtain the long-term sliding mean temperature HisLTS x,y,month,d , the calculation process is as follows:

[0141]

[0142] This formula means that for the month and day of each year, the annual average of the corresponding 31-day sliding average over a 30-year period is calculated.

[0143] Step 23: Next, calculate the daily temperature anomaly. Daily temperature anomaly HisA x,y,year,month,d It is defined as the difference between the actual daily average temperature on a certain day of a certain month of a certain year and the long-term sliding average temperature in the historical base period, which can be expressed by the following formula (10):

[0144] HisA x,y,year,month,d =HisT x,y,year,month,d -HisLTS x,y,month,d (10)

[0145] Step 24: Finally, superimpose the historical daily average temperature anomalies to the period from y0 to y end Assuming that the daily mean temperature anomaly pattern of the historical base period (1981-2010) remains unchanged under future climate conditions, the daily temperature anomaly HisA obtained in step 23 can bex,y,year,month,d Superimposed on the smoothed daily temperature series of step 20 The future daily average temperature (with fluctuation characteristics) with fluctuation characteristics consistent with the future long-term annual temperature change trend can be obtained. The following formula (11):

[0146]

[0147] Step 25: According to the definition of heat wave: if the daily maximum temperature for two or more consecutive days exceeds the 98% quantile of the daily maximum temperature distribution in the three warmest months of the reference year, a heat wave event is considered to have occurred. To simulate the characteristics of heat waves, it is necessary to further simulate the daily maximum temperature value. To this end, the daily average temperature is further adjusted according to the historical base period simulation data of each model. Specifically, first, the daily average temperature data HisT in the historical base period (1981-2010) is obtained from the WFDEI database x,y,year,month,d And the daily maximum temperature data HisMaxT x,y,year,month,d .

[0148] Step 26: Next, establish the statistical relationship between the daily average temperature and the daily maximum temperature. Since the daily temperature range is different in different seasons, a different regression model can be established for each season to capture seasonal changes, as shown in the following formula (12):

[0149] HisMaxT x,y,season =a season ·HisT x,y,year,month,d +b season (12)

[0150] Among them, a season and b season is the regression model parameter

[0151] Step 27: Extrapolate the historical relationship in step 26 and use the simulated results of the future daily average temperature obtained in step 24 Converted to daily maximum temperature

[0152] Among them, (x, y) ∈ Grid (global 0.5×0.5° geographic longitude and latitude grid set), year ∈ {y0, y0+1, …, y end}, month∈{1,2,…,12}, d∈{1,2,…,N(year,month)}.

[0153] Step 28: First, define a heat wave: if the daily maximum temperature for two or more consecutive days exceeds the 98% quantile of the daily maximum temperature distribution of the three warmest months in the reference year, a heat wave event is considered to have occurred. Secondly, determine the target area R for the study, and according to the results of step 27, aggregate the grid daily maximum temperatures simulated based on the characteristics of each group of complex earth system models to the regional level. Then integrate the daily maximum temperatures of all n complex earth system models to simulate the uncertainty of the daily maximum temperature in the region, and then estimate the frequency of heat waves that occur each year and the duration of heat waves (the average number of days when all heat waves occur in a year), and use their related probability distributions to characterize the regional heat wave uncertainty characteristics.

[0154] Finally, this method achieves the simulation of regional heat wave uncertainty characteristics under a certain socio-economic scenario S.

[0155] This application has the following features:

[0156] First: Based on climate statistics technology, the present invention combines the multi-climate scenario simulation results of multiple sets of complex earth system models and historical observed temperature data to convert the global annual average temperature trend simulated by traditional IAM into monthly, daily, and global 0.5×0.5 degree grid temperature fields within the day.

[0157] Second: In order to simplify the characteristics of the simulated daily temperature response to global greenhouse gas emissions, the present invention does not directly establish the statistical relationship between annual temperature (long-term trend) and daily temperature (short-term characteristics) for complex earth system models, which may ignore the regional characteristics of temperature in the spatial scale and the real fluctuation characteristics of the time scale. Instead, it is transformed layer by layer from "year to month, month to day", and the long-term climate trend is gradually transformed into short-term weather fluctuation characteristics.

[0158] Third: The present invention simulates the monthly seasonal effects and daily average characteristics of the complex earth system model through the simulation data of the complex earth system model under multiple climate scenarios. At the same time, the seasonal characteristics of the daytime temperature anomaly and the daily temperature difference are calibrated and simulated according to the historical observation data, so as to capture the extreme characteristics of the short-term daily temperature. By superimposing the average characteristics and the wave characteristics, the extreme characteristics of the short-term daily temperature are captured.

[0159] Fourth: The present invention simulates the response capacity of temperature to greenhouse gas emissions of multiple sets of complex earth system models in a single comprehensive evaluation model, thereby characterizing the uncertainty characteristics of regional daily temperature changes.

[0160] Fifth: In the comprehensive assessment modeling framework, the present invention simulates the response capacity of multiple sets of complex Earth system models regarding temperature to greenhouse gas emissions, and depicts the short-term regional daily temperature uncertainty characteristics consistent with the global long-term climate change trend under any socio-economic scenario, thereby simplifying the simulation of regional heat wave characteristics under the background of climate change.

[0161] Specifically:

[0162] In view of the defect that traditional IAM lacks regional daily temperature uncertainty characteristics when simplifying the simulation of temperature changes:

[0163] (1) Using a single IAM, we simulated the response of multiple complex Earth system models to greenhouse gas emissions, thereby characterizing the uncertainty of temperature.

[0164] This plan first determines multiple groups of complex Earth system models (step 3), and simulates the simulation capabilities of annual global average temperature changes of each group of complex models through the existing simplified simulation module of annual global average temperature in IAM, and then simulates the uncertainty of annual global average temperature changes under certain socio-economic scenarios (steps 4 and 6).

[0165] (2) Through the simulation results of the complex earth system model under historical scenarios, the monthly seasonal fluctuation characteristics of the complex model simulation are captured, and the annual global long-term average temperature change simulation results under certain socio-economic scenarios are refined to monthly

[0166] In order to simplify the characteristics of the simulated daily temperature response to global greenhouse gas emissions, this solution does not directly establish the statistical relationship between annual temperature (long-term trend) and daily temperature (short-term characteristics) for the complex earth system model, which may ignore the regional characteristics of the temperature in the spatial scale and the real fluctuation characteristics of the time scale. Instead, it is converted layer by layer through "year to month, month to day", and the long-term climate trend is also gradually converted into short-term weather fluctuation characteristics. Specifically, the present invention captures the monthly seasonal fluctuation characteristics of the complex model simulation through the simulation results of the complex earth system model under the historical scenario (step 7), and refines the annual global long-term average temperature change simulation results under a certain socio-economic scenario to the monthly level (steps 8 and 9).

[0167] (3) The simulation results of the complex earth system model under the latest SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5 climate scenarios of CM IP6 (covering the current potential temperature rise range known to humans) are used as training data sets to train a spatial scaling model that reflects the relationship between "monthly global average temperature and monthly global grid average temperature field", which can improve the generalization ability of the model; at the same time, the monthly global average temperature under certain socio-economic scenarios is converted into a monthly temperature geographic grid field, thereby refining regional characteristics. The use of statistical models can not only restore the simulation effect of complex physical models to a certain extent, but also significantly improve the operating speed of comprehensive climate change assessments, avoid many complex physical processes, and thus avoid the shortcomings of the relatively slow operating speed and low efficiency of complex earth system models, which is conducive to the rapid simulation of the impact of various climate policies.

[0168] Based on the traditional spatial scaling method, the global average temperature of the complex earth system model can be mapped to the characteristics of the local average temperature. This scheme further trains the spatial scaling model through the simulation results of the complex earth system model under the latest SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5 climate scenarios of CMIP6 (step 13, which can cover the potential future temperature rise range currently known to humans and help improve the generalization ability of the model), and then obtains the monthly temperature geographic grid field under the corresponding socio-economic scenarios (steps 18 and 19).

[0169] (4) The average state characteristics (long-term climate trend) of the daily temperature grid field are obtained through statistical techniques, and combined with real historical observation data to capture the daily temperature fluctuation characteristics (short-term weather characteristics) in the real world. The average state and the fluctuation state are superimposed to achieve the consistency of short-term weather characteristics with long-term climate change trends, and then to simulate the short-term daily temperature change characteristics that are consistent with the long-term temperature change trend characteristics under certain socio-economic scenarios.

[0170] This scheme further obtains the daily temperature grid field through meteorological interpolation technology, and identifies the average characteristics of daily temperature through statistical methods. Combined with real data from historical observations to capture daily temperature anomalies in the real world (steps 20, 21, 22, and 23), the simulated annual long-term trend is superimposed with the short-term real fluctuation characteristics (step 24), thereby simulating the fluctuation characteristics of daily temperature with seasonal changes in the real world (steps 25 and 26). This scheme allows short-term weather characteristics to be consistently matched with long-term climate change trend characteristics, thereby simulating short-term daily temperature change characteristics that are consistent with long-term temperature change trend characteristics under certain socio-economic scenarios in the same IAM framework (steps 24 and 27).

[0171] (5) Through a single IAM simulation of the temperature change characteristics of multiple complex earth system models under multiple scenarios, the uncertainty characteristics of the daily maximum temperature are further characterized. Then, based on the definition of heat waves, the uncertainty characteristics of regional heat waves can be simulated.

[0172] By integrating the grid daily temperature simulation results with various complex pattern characteristics and performing probabilistic analysis, the uncertainty characteristics of regional heat waves under certain socioeconomic scenarios can be characterized in a single IAM (step 28).

[0173] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A heat wave simulation method based on a comprehensive climate change assessment model, characterized in that: The specific process is: Simulate the global annual average temperature Select a complex earth system model, determine the socio-economic scenario, and combine the simplified climate module and socio-economic module of IAM to simulate the global annual average temperature under the socio-economic scenario. Simulate the global monthly average temperature The seasonal pattern factor is calculated based on the historical temperature simulation data of the complex earth system model, and the seasonal pattern factor and the global annual average temperature are calculated based on the seasonal pattern factor and the global annual average temperature. Calculate the global monthly average temperature Get global monthly gridded temperatures Based on the future scenario temperature simulation data of the complex earth system model, the statistical relationship between the monthly global average temperature change value and the monthly global grid average temperature change of the selected complex earth system model is established. Calculate the corresponding monthly grid temperature Get the maximum temperature of the next day Based on the global monthly gridded temperature Calculate daily temperature trends Calculation of daily temperature anomaly HisA based on historical temperature observation data x,y,year,month,d ; Based on the daily temperature trend and the daily temperature anomaly HisA x,y,year,month,d Calculate the global grid daily average temperature Based on the historical temperature observation data, the relationship between the daily average temperature and the daily maximum temperature is established. Calculate the maximum daily temperature of the global grid Heat wave simulation: Fusion of gridded daily temperature simulation results with complex pattern characteristics By analyzing the frequency and duration of heat waves and their probability distribution characteristics, the uncertainty characteristics of regional heat waves under certain socioeconomic scenarios can be characterized in a single IAM.

2. The heat wave simulation method based on the comprehensive assessment model of climate change according to claim 1 is characterized in that: The simulation obtains the global annual average temperature The specific process is: Determine the socio-economic scenario S that needs to be simulated, and calculate the global greenhouse gas emission pathway G under this scenario based on the socio-economic module of IAM; Determine n groups of complex earth system models that need to be simulated, combine with IAM's simplified simulation model of global annual average temperature, and determine n groups of submodules that can simplify the simulation of global annual average temperature; Determine the historical base year range and the simulated future time period y0~y end In 2018, the global greenhouse gas emission pathway G is input into the above n groups of submodules to obtain the global annual average temperature change. Obtain the global mean temperature averaged over all years in the historical base period for each set of complex Earth system models According to the average temperature and the average temperature change Get the global annual average temperature under socioeconomic scenario S 3. The heat wave simulation method based on the comprehensive assessment model of climate change according to claim 2 is characterized in that: The simulation obtains the global annual average temperature The process is: Step 1: Determine the socio-economic scenario S and the relevant parameters that characterize the scenario; Step 2: Input the relevant parameters into the socio-economic module of IAM to calculate the global greenhouse gas emission pathway G; Step 3: Determine n groups of complex earth system models that need to be simulated; Step 4: Based on n sets of complex earth system models and the simplified simulation model of global annual average temperature of IAM, obtain n sets of simulation parameters of complex earth system models, and form n submodules in IAM that can simplify the simulation of global annual average temperature; Step 5: Determine the historical base year range and the future time period y0~y end In 2017, the annual global average temperature simulation data of each complex earth system model in the historical base period was obtained through the CMIP6 website database. For each group of earth system models, the global annual average temperature of all years in the historical base period was calculated. Step 6: Input the global greenhouse gas emission pathway G into the above n groups of submodules to obtain According to step 5, according to Further simulations were performed to obtain n groups of global annual average temperatures during this period.

4. The heat wave simulation method based on the comprehensive assessment model of climate change according to claim 1 is characterized in that: The simulation obtains the global monthly average temperature The specific process is: Global mean temperature based on the average of all years in the historical base period And the average temperature of each month in all years within the historical base period Calculate seasonal pattern factors According to the seasonal pattern factor The simulated Converted into global monthly average temperature with seasonal characteristics 5. The heat wave simulation method based on the climate change comprehensive assessment model according to claim 4 is characterized in that: The simulation obtains the global monthly average temperature The specific process is: Step 7: Obtain the monthly global average temperature simulation data of each complex earth system model during the historical base period through the CMIP6 database Calculate the average temperature of each month in all years within the historical base period Step 8: Global mean temperature based on the average of all years in the historical base period And the average temperature of each month in all years within the historical base period Calculate seasonal pattern factors Step 9: Based on the seasonal pattern factor The simulated Converted into monthly global average temperature with seasonal characteristics 6. The heat wave simulation method based on the climate change comprehensive assessment model according to claim 5 is characterized in that: The global average monthly temperature for:

7. The heat wave simulation method based on the climate change comprehensive assessment model according to claim 5, characterized in that: Get global monthly gridded temperatures The specific process is: Obtain the global grid monthly mean temperature data for each complex earth system model in the historical scenario in the CMIP6 database during the historical base period, and calculate the global mean temperature for each model month over all years during the historical base period. and the average annual temperature of each month in the historical base period at a 0.5×0.5° geographic latitude and longitude grid Obtain the gridded monthly global temperature data for each complex earth system model in the CMIP6 database in multiple future global warming scenarios SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5, and calculate the monthly global average temperature for each model in the future time period under each scenario. and monthly 0.5×0.5° geographic longitude and latitude grid temperature Based on the monthly global average temperature values ​​and monthly global grid temperature fields in the historical base period and future scenarios, the monthly global average temperature change values ​​are calculated. and monthly grid temperature changes Establish a statistical relationship between the monthly global average temperature change value and the monthly global grid temperature change field for each model in the future time period, and based on the relationship, convert the global monthly average temperature value under the global greenhouse gas emission path G into Transformed into the corresponding global monthly gridded temperature 8. The heat wave simulation method based on the comprehensive assessment model of climate change according to claim 7 is characterized in that: Get global monthly gridded temperatures The specific process is: Step 10: Collect the global near-surface temperature grid monthly data for each group of Earth system models in the historical scenario through the CMIP6 database; Step 11: Calculate the annual average temperature data of the global 0.5×0.5° geographic longitude and latitude grid for each month in the historical base period based on the global near-surface temperature grid monthly data Step 12: Calculate the global mean temperature value for all months of the historical base period based on the global near-surface temperature grid monthly data Step 13: Collect the global near-surface temperature grid monthly data for the future time period under the SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5 scenarios for each group of Earth system models through the CMIP6 database; Step 14: Based on the global near-surface temperature grid monthly data described in step 13, obtain the global 0.5×0.5° geographic longitude and latitude grid temperature data for each future scenario Step 15: Based on the monthly global surface temperature grid data, obtain the monthly global average temperature value in each future scenario Step 16: For each set of complex earth system models, calculate the difference between the global average temperature in the future time period and the historical base period under the future scenario, which is the monthly global average temperature change value. Step 17: For each set of complex earth system models, calculate the difference between the monthly 0.5×0.5° geographic longitude and latitude grid temperatures in the future time period and the historical base period under the future scenario, which is the monthly grid average temperature change Step 18: Establish the statistical relationship between the monthly global average temperature change and the monthly global grid temperature change of each model in the future time period: Step 19: Convert the global monthly average temperature values ​​corresponding to the global greenhouse gas emission pathway G generated in step 9 to Converted into change value relative to historical base period And calculate the global monthly grid temperature change according to the statistical relationship described in step 18: Finally, the global monthly grid temperature corresponding to the global greenhouse gas emission path G is obtained:

9. The heat wave simulation method based on the comprehensive assessment model of climate change according to claim 5, characterized in that: Obtaining the future grid daily maximum temperature For the monthly grid temperature Perform interpolation and smoothing operations to obtain daily temperature trends Obtain the grid daily average temperature observation data HisT within the historical base period x,y,year,month,d , and based on this, the long-term sliding mean temperature HisLTS in the historical base period is calculated x,y,month,d ; The daily temperature anomaly HisA is obtained by subtracting the grid daily average temperature observation data within the historical base period from the long-term sliding mean temperature within the historical base period. x,y,year,month,d ; Add the daily temperature anomaly to the daily temperature trend Get the average daily temperature in the future Get the daily maximum temperature observation data HisMaxT within the historical base period x,y,year,month,d , compare the daily maximum temperature observation data with the grid daily average temperature observation data HisT x,y,year,month,d Combined, establish the statistical relationship between daily mean temperature and daily maximum temperature with seasonal changes; Based on the statistical relationship between the daily average temperature and the daily maximum temperature with seasonal changes, the future daily average temperature Converted to daily maximum temperature 10. The heat wave simulation method based on the climate change comprehensive assessment model according to claim 9, characterized in that: Obtaining the maximum daily temperature in the future The specific process is: Step 20: Add the monthly grid temperature from step 19 Interpolation to daily grid temperature Then calculate the grid temperature for that day Use the 31-day moving average method to smooth the interpolation results to obtain the temperature trend Step 21: Obtain the daily average temperature observation data HisT during the historical base period through the WFDEI database x,y,year,month,d , and calculate the 31-day sliding mean temperature value HisTS for each day in the historical base period x,y,year,month,d ; Step 22: Based on the 31-day sliding mean temperature value, average all annual values ​​in the historical base period to calculate the long-term sliding mean temperature HisLTS x,y,month,d ; Step 23: Calculate the daily temperature anomaly by subtracting the daily temperature data within the historical base period from the long-term sliding mean temperature within the historical base period: HisA x,y,year,month,d =HisT x,y,year,month,d -HisLTS x,y,month,d Step 24: Superimpose the daily temperature anomaly of the historical base period observation obtained in step 23 onto the smoothed daily temperature sequence of step 20 to obtain the future daily average temperature with fluctuation characteristics that is consistent with the future long-term annual temperature change trend: Step 25: Obtain the daily maximum temperature observation data HisMaxT during the historical base period through the WFDEI database x,y,year,month,d ; Step 26: According to the daily temperature range differences in different seasons, the statistical relationship between the daily average temperature and the daily maximum temperature is established for each season to capture the characteristics of the daily temperature changes with seasonality. HisMaxT x,y,season =a season ·HisT x,y,year,month,d +b season Step 27: Based on the model built in step 26, the simulation results of the future daily average temperature obtained in step 24 are Converted to daily maximum temperature

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