A method for simulating heat waves based on a comprehensive climate change assessment model

Abstract

This invention belongs to the field of integrated climate change assessment modeling technology, specifically involving a heat wave simulation method based on an integrated climate change assessment model. This method, based on climatological techniques, combines the Integrated Climate Change Assessment (IAM) model with features from multiple complex Earth system models and historical observed temperature characteristics. It first simulates the global annual mean temperature, then transforms it into monthly, daily, and intraday global geographic grid temperatures. During the transformation process, it simulates the monthly seasonal effects and daily mean characteristics of the Earth system model, capturing historically observed intraday temperature anomalies and diurnal temperature range characteristics. Finally, by superimposing mean and wave dynamic characteristics, it simulates daily extreme temperatures, thereby simulating regional short-term heat wave characteristics consistent with long-term global climate change trends. This invention achieves simplified simulation of regional extreme temperatures using IAM, improves the ability and efficiency of IAM in assessing extreme climate risks, and facilitates efficient climate policy analysis.

Description

Technical Field

[0001] This invention belongs to the field of integrated climate change assessment and modeling technology, specifically involving a heat wave simulation method based on an integrated climate change assessment model. Background Technology

[0002] The Integrated Climate Change Assessment (IAM) approach is a modeling method that integrates knowledge from two or more fields within a single framework. It typically couples economic models, energy system models, climate models, and climate impact models to form Integrated Climate Change Assessment Models (IAMs). IAMs can be used to assess the impacts and risks of climate change under different socio-economic development paths and climate policy controls, providing decision support for adaptation actions to address climate change.

[0003] Climate system simulation models are a key component of Earth system models (IAMs), primarily used to simulate the impact of greenhouse gas emissions on various variables within the climate system. Existing mainstream IAMs climate simulation models can be broadly categorized into two types: one is the moderately complex Earth system model, which can characterize in detail the physical processes of interaction between the atmosphere, ocean, cryosphere, and land surface within the Earth system. This type of model can discretize the Earth's surface into a series of gridded regions, enabling the simulation of climate variables such as temperature at high three-dimensional spatiotemporal resolution. An example is the MESM climate module in the IGSM model.

[0004] Another type is simplified climate models. These models use more complex Earth system models as benchmarks, simplifying the physicochemical processes while retaining the basic physical transmission mechanism of "emissions-concentration-radiative forcing-temperature change" found in Earth system models. They are primarily used to simulate the impact of greenhouse gas emissions on annual global average temperature changes. They are fast to run, low in cost, and can simulate the behavior and simulation effects of complex Earth system models to a certain extent. Examples include the MAGICC climate module in the MESSAGE and IMAGE models, and the Hector climate module in the GCAM model.

[0005] While moderately complex Earth system models can characterize physical processes in detail and simulate climate variables at high three-dimensional spatiotemporal resolution, their relatively long operating times and high operating costs make them less efficient for conducting climate change policy simulations. Therefore, most International Motion Models (IAMs) employ simplified climate models. However, these simplified models primarily simulate long-term climate change trends and focus on climate mean states, such as annual global average temperature, without considering regional differences and intra-annual variations. This prevents IAMs from capturing short-term weather variability and thus ignores the adverse effects of short-term extreme events (such as...). Figure 1However, the impacts and damages of climate change on the economy, society, and natural ecosystems may not be simply proportional to the climate mean; they are often functions of the extreme values ​​of climate factor distributions. This suggests that the lack of simulation of “extreme climate” by IAMs may significantly underestimate the damage caused by climate change.

[0006] Heat waves (extreme heat waves) are a typical type of extreme event, usually referring to extreme high temperatures lasting for several days. In the context of climate change, the frequency and duration of heat waves will increase dramatically, adversely affecting labor productivity, human health, energy infrastructure, and transportation infrastructure. Therefore, simulating heat waves in IAMs (Information and Weather Assemblies) is crucial for understanding the socio-economic impacts of climate change. Climate science research typically defines a heat wave as an event occurring when the daily maximum temperature exceeds the 98th percentile of the daily maximum temperature distribution for two or more consecutive days within a reference year. As the definition shows, simulating heat waves requires refining the temperature to the daily level on a time scale; furthermore, heat waves are not determined by absolute temperature values, but by relative daily temperatures within a region. A temperature normal for a warmer climate region may be a heat wave for a typically colder region. Therefore, simulating heat waves also needs to consider different regional characteristics on a spatial scale.

[0007] Furthermore, the uncertainties surrounding the response of climate change to emissions are crucial for discussing the risk characteristics of climate change impacts. These uncertainties manifest primarily in the differences in structural parameters of complex Earth system models (IAMs) and the variations in climate scenarios simulated by these models. Therefore, when simplifying extreme climate simulations using IAMs, it is necessary to integrate the structural, parameter, and operational scenario uncertainties of multiple complex Earth system models. However, existing IAMs lack the ability to simulate multiple scenarios from various complex Earth system models within a single integrated assessment framework, making it difficult to characterize the uncertainties in regional daily temperature variations and limiting their ability to simulate potential heat wave risks under future socio-economic 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 accurately simulate 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 of which is as follows:

[0011] Simulation to obtain global annual average temperature By selecting a complex Earth system model and determining socio-economic scenarios, and combining the simplified climate and socio-economic modules of the IAM, the global annual average temperature under certain socio-economic scenarios was simulated and obtained. year∈{y0,y0+1,…,y end});

[0012] Simulation to obtain global monthly average temperature Seasonal model factors are calculated based on historical temperature simulation data from complex Earth system models, and then the seasonal model factors are used in conjunction with the global annual average temperature. Calculate the global monthly average temperature month∈{1,2,…,12}, gcm∈{1,2,…,n});

[0013] Get global monthly grid temperature Based on future scenario temperature simulation data from complex Earth system models, a statistical relationship is established between the monthly global average temperature change and the monthly global grid average temperature change for selected complex Earth system models. Based on this relationship and the global monthly average temperature... The corresponding monthly grid temperature was calculated. year∈{y0,y0+1,…,y end}, month∈{1,2,…,12}, gcm∈{1,2,…,n});

[0014] Get the highest daily temperature in the future Based on the global monthly grid temperature Calculate the daily temperature variation trend Daily temperature anomaly HisA calculated based on historical temperature observation data. x,y,year,month,d Based on the aforementioned daily temperature variation trend and the daily temperature anomaly HisA x,y,year,month,d Calculate the global grid daily average temperature A relationship between daily average temperature and daily maximum temperature was established based on historical temperature observation data. This relationship was then used in conjunction with global grid daily average temperatures. The daily maximum temperature of the global grid was calculated.

[0015] Heat wave simulation: First, a heat wave is defined as follows: a heat wave event is considered to have occurred if the daily maximum temperature exceeds the 98th percentile of the daily maximum temperature distribution of the three warmest months within the reference year for two or more consecutive days. Second, grid-based daily temperature simulation results with characteristics of various complex models are integrated. By analyzing heat wave frequency (the number of heat waves that occur in a year), heat wave duration (the average number of days that occur in all heat wave events in a year), and their probability distribution characteristics, the regional heat wave uncertainty characteristics under a certain socio-economic scenario can be characterized in a single IAM.

[0016] Furthermore, the simulation described in this 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] Identify n sets of complex Earth system models (gcm∈{1,2,…,n}) that need to be simulated, and combine them with the IAM's simplified simulation model of global annual average temperature to identify n sets of sub-modules that can simplify the simulation of global annual average temperature.

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

[0020] Obtain the global average temperature averaged for all years within the historical base period under each complex Earth system model.

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

[0022] Furthermore, the simulation described in this invention obtains the global annual average temperature. year∈{y0,y0+1,…,y end The process is as follows:

[0023] Step 1: Determine the socioeconomic scenario S and the relevant parameters that characterize this scenario;

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

[0025] Step 3: Determine n sets 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 in IAM, obtain the simulation parameters of n sets of complex Earth system models, and form n sub-modules 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 to be simulated, y0~ ... end In 2018, annual global average temperature simulation data for each of the aforementioned complex Earth system models within the historical base period were obtained through the CMIP6 website database (the Sixth Coupled Model Intercomparison Project). For each Earth system model, the global annual average temperature for all years within the historical base period was calculated.

[0028] Step 6: Input the global greenhouse gas emission pathway G into the above n sub-modules to obtain... Then, based on the results obtained in step 5 according to Further simulations yielded n sets of global annual average temperatures for that time period.

[0029] Furthermore, the simulation described in this invention obtains the global monthly average temperature. The specific process for month∈{1,2,…,12}, gcm∈{1,2,…,n} is as follows:

[0030] Global average temperature based on the average of all years within the historical base period and the average temperature of each month in all years within the historical base period. Calculate seasonality pattern factors

[0031] According to the seasonal pattern factors The simulation in step 6 Transformed into seasonal global monthly average temperature

[0032] Furthermore, the simulation described in this invention obtains the global monthly average temperature. The specific process for month∈{1,2,…,12}, gcm∈{1,2,…,n} is as follows:

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

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

[0035]

[0036] Step 9: Based on the seasonal pattern factors The simulation in step 6 Transformed into monthly global average temperature with seasonal characteristics

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

[0038]

[0039] Furthermore, obtain global monthly grid temperature data. year∈{y0,y0+1,…,y end The specific process for (month∈{1,2,…,12}, gcm∈{1,2,…,n}) is as follows:

[0040] Obtain global grid monthly mean temperature data for each complex Earth system model in the historical scenario within the historical base period from the CMIP6 database (the geographic latitude and longitude grid sizes vary between models), and calculate the global mean temperature for each month of each model within all annual averages of the historical base period. Temperature in each month within the historical base period, calculated using a 0.5 × 0.5° geographic latitude and longitude grid.

[0041] Obtain monthly global temperature gridded data for future time periods from each complex Earth system model in the CMIP6 database for 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 geographic latitude and longitude grid sizes vary between models), and calculate the monthly global average temperature for each model in each future time period under each scenario. And monthly temperature using a 0.5×0.5° geographic latitude and longitude grid.

[0042] Based on the monthly global average temperature values ​​within the historical baseline period and the monthly global grid temperature field within the future scenario, the monthly global average temperature change is calculated. and monthly grid temperature changes

[0043] Establish statistical relationships between monthly global average temperature changes and monthly global grid temperature change fields for each model over future time periods. Based on these relationships, calculate the global monthly average temperature values ​​under the global greenhouse gas emission pathway G. Converted into corresponding global monthly grid temperature

[0044] Furthermore, this invention obtains global monthly grid temperature data. year∈{y0,y0+1,…,y end The specific process for (month∈{1,2,…,12}, gcm∈{1,2,…,n}) is as follows:

[0045] Step 10: Collect monthly grid data of global near-surface temperature for each Earth system model within the historical base period of the historical scenario using the CMIP6 database;

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

[0047] Step 12: Calculate the global average temperature value for all years in each month of the historical base period based on the monthly global near-surface temperature grid data.

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

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

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

[0051] Step 16: For each complex Earth system model, calculate the difference between the global average temperature in the future time period and the historical base period under the future scenario; this is the monthly global average temperature change.

[0052] Step 17: For each complex Earth system model, calculate the difference between the monthly 0.5×0.5° geographic latitude and longitude grid temperature in the future time period and the historical base period under the future scenario. This 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 for each model over future time periods:

[0054] Step 19: Calculate the global monthly average temperature value generated in Step 9 corresponding to the global greenhouse gas emission pathway G. Transformed into changes relative to the historical base period And calculate the global monthly grid temperature variation based on the statistical relationship described in step 18: Finally, the global monthly grid temperature corresponding to the global greenhouse gas emission pathway G was obtained:

[0055] Furthermore, the present invention describes the acquisition of the highest daily temperature of a future grid. Where N(year, month) represents the total number of days in the year and month:

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

[0057] Acquire HisT grid daily average temperature observation data within the historical base period x,y,year,month,d Based on this, the HisLTS long-term moving average temperature within the historical base period is calculated. x,y,month,d ;

[0058] The daily temperature anomaly HisA is obtained by subtracting the grid-based daily average temperature observation data within the historical base period from the long-term moving average 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] Obtain HisMaxT daily maximum temperature observation data within the historical base period x,y,yrar,month,dThe daily maximum temperature observation data is compared with the historical base period grid daily average temperature observation data HisT. x,y,year,month,d By combining these factors, a statistical relationship between daily average temperature and daily maximum temperature as they vary with the seasons can be established.

[0061] Based on the statistical relationship between the daily average temperature and the daily maximum temperature as they change with the seasons, the future daily average temperature will be... Converted to daily maximum temperature

[0062] Furthermore, the present invention describes the method for obtaining the highest daily temperature in the future. The specific process is as follows:

[0063] Step 20: Obtain the monthly grid temperature from Step 19. Interpolation is daily grid temperature Then, the daily grid temperature The temperature trend was obtained by smoothing the interpolation results using a 31-day moving average method.

[0064] Step 21: Obtain the HisT daily average temperature observation data within the historical base period from the WFDEI database. x,y,year,month,d And calculate the 31-day moving average temperature value HisTS for each day within the historical base period. x,y,year,month,d ;

[0065] Step 22: Calculate the long-term moving average temperature HisLTS by averaging the 31-day moving average temperature value over all annual values ​​within the historical base period. x,y,month,d ;

[0066] Step 23: Calculate the daily temperature anomaly by subtracting the daily temperature data from the historical base period from the long-term moving average temperature of 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 anomalies from the historical base period obtained in Step 23 onto the smoothed daily temperature series from Step 20 to obtain the future daily average temperature with fluctuation characteristics that is consistent with the long-term annual temperature change trend.

[0069]

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

[0071] Step 26: To address the differences in daily temperature range across different seasons, establish statistical relationships between daily average temperature and daily maximum temperature for each season, in order to capture the characteristics of intraday temperature variations with seasonality.

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

[0073] Step 27: Based on the model established in Step 26, simulate the future daily average temperature results obtained in Step 24. Converted to daily maximum temperature

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

[0075] First, a heat wave is defined as follows: a heat wave event is considered to have occurred if the daily maximum temperature exceeds the 98th percentile of the daily maximum temperature distribution of the three warmest months within the reference year for two or more consecutive days. Second, the target region R is determined. Based on the results of step 27, the grid daily maximum temperatures simulated based on the characteristics of each set of complex Earth system models are aggregated to the regional level. Then, the daily maximum temperatures from all n complex Earth system models are integrated. Finally, under the socio-economic scenario S, the annual heat wave frequency (the number of heat waves in a year), the heat wave duration (the average number of days of all heat wave events in a year), and their probability distribution characteristics are calculated for region R.

[0076] Beneficial effects:

[0077] Traditional IAMs' simplified climate simulations focus on the annual average state of temperature, lacking descriptions of intra-annual variations and regional characteristics. As a result, they are unable to depict extreme temperature states, leading to an underestimation of potential climate risks.

[0078] First, this invention, based on climatological statistics techniques and combining multi-climate scenario simulation results from multiple complex Earth system models with historical observed temperature data, transforms the global annual mean temperature trend simulated by traditional IAM into monthly, daily, and intraday global 0.5×0.5 degree grid temperature fields. The transformation process simulates the monthly seasonal effects and daily mean characteristics of complex Earth system models. Simultaneously, it calibrates and simulates intraday temperature anomalies and seasonal fluctuations in diurnal temperature range based on historical observation data. By superimposing mean characteristics with wave dynamics characteristics, it captures the extreme characteristics of short-term daily temperatures.

[0079] Second, within the comprehensive evaluation and modeling framework, this invention simulates the response capabilities of multiple complex Earth system models to greenhouse gas emissions related to temperature, and characterizes the regional short-term daily temperature uncertainty characteristics consistent with the long-term global climate change trend under any socio-economic scenario, thereby simplifying the simulation of regional heat wave characteristics under the background of climate change. Attached Figure Description

[0080] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0081] Figure 1 Climate simulation for traditional Integrated Assessment Models (IAMs);

[0082] Figure 2 This scheme represents an improvement over traditional Integrated Assessment Models (IAMs) for climate simulation.

[0083] Figure 3 The accompanying drawings show the key steps of the technical solution in the embodiments of this application. Detailed Implementation

[0084] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0085] It should be noted that, in the absence of conflict, the following embodiments and features can be combined with each other; and, based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

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

[0087] Climate science research typically defines a heat wave as follows: if the daily maximum temperature exceeds the 98th percentile of the daily maximum temperature distribution for two or more consecutive days within a reference year, a heat wave event is considered to have occurred. According to this definition, simulating heat wave characteristics requires the ability to simulate the impact of greenhouse gas emissions on regional daily maximum temperatures.

[0088] To address the deficiency of traditional IAM (Integrated Climate Change Assessment) methods in simplifying temperature change simulations by lacking regional daily temperature uncertainty characteristics, this application provides a heat wave simulation method based on an integrated climate change assessment model, such as... Figure 2 As shown, the specific process is as follows:

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

[0090] Simulation to obtain global monthly average temperature Global average temperature based on the average of all years within the historical base period and the average temperature of each month in all years within the historical base period. Calculate seasonality pattern factors Based on the factors Global annual average 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 grid temperature Obtain global grid monthly mean temperature data for each complex Earth system model in the historical scenario within the historical base period from the CMIP6 database (the geographic latitude and longitude grid sizes vary between models), and calculate the global mean temperature for each month of each model within all annual averages of the historical base period. Temperature in each month within the historical base period, calculated using a 0.5 × 0.5° geographic latitude and longitude grid. Obtain monthly global temperature gridded data for future time periods from each complex Earth system model in the CMIP6 database for 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 geographic latitude and longitude grid sizes vary between models), and calculate the monthly global average temperature for each model in each future time period under each scenario. And monthly temperature using a 0.5×0.5° geographic latitude and longitude grid. Based on the monthly global average temperature values ​​within the historical base period and the monthly global grid temperature within the future scenario, calculate the monthly global average temperature change. and monthly grid temperature changes Establish statistical relationships between monthly global average temperature changes and monthly global grid temperature changes for each model over future time periods. Based on these relationships, calculate the global monthly average temperature values ​​under the global greenhouse gas emission pathway G. Converted into corresponding global monthly grid temperature year∈{y0,y0+1,…,y end}, month∈{1,2,…,12}, gcm∈{1,2,…,n});

[0092] Get the highest daily temperature in the future Based on the global monthly grid temperature Calculate temperature change trend Acquire HisT grid daily average temperature observation data within the historical base period x,y,year,month,d Based on this, the HisLTS long-term moving average temperature within the historical base period is calculated. x,y,month,d The daily temperature anomaly HisA is obtained by subtracting the grid-based daily average temperature observation data within the historical base period from the long-term moving average 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 Obtain HisMaxT daily maximum temperature observation data within the historical base period x,y,year,month,d The daily maximum temperature observation data is compared with the historical base period grid daily average temperature observation data HisT. x,y,year,month,dBy combining these factors, a statistical relationship is established between the daily average temperature and the daily maximum temperature as they vary with the seasons. Based on this statistical relationship, the future daily average temperature is projected. Converted to daily maximum temperature

[0093] Heatwave simulation: By fusing grid-based daily temperature simulation results with characteristics of various complex models and performing probabilistic analysis, the uncertainty characteristics of regional heatwaves under a certain socio-economic scenario can be characterized in a single IAM. (Step 28)

[0094] This embodiment has the following effects:

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

[0096] Secondly, further simulations of the complex model's characteristics on a daily timescale do not establish a direct statistical relationship between annual temperature (long-term trend) and daily temperature (short-term characteristics), as this might overlook the regional characteristics of temperature on a spatial scale and the true fluctuation characteristics on a temporal scale. Instead, a layered transformation is performed from "year to month, month to day," gradually converting long-term climate trends into short-term weather fluctuation characteristics. Specifically, the monthly seasonal fluctuation characteristics simulated by the complex Earth system model under historical scenarios are captured through simulation results (step 7), and the simulation results of annual global long-term average temperature changes under certain socio-economic scenarios are refined to the monthly level (steps 8 and 9). Based on the characteristic of traditional spatial scaling methods that can map global average temperature from complex Earth system models to local average temperature, this embodiment further trains the spatial scaling model using simulation results of complex Earth system models 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 range of potential future temperature rise currently known to humankind and helps to improve the generalization ability of the model), thereby obtaining the monthly temperature geographic grid field under the corresponding socio-economic scenarios (steps 18 and 19).

[0097] Next, a daily temperature grid field is obtained using meteorological interpolation techniques, and the mean state characteristics of daily temperatures are identified using statistical methods. Historical observational data is combined to capture real-world daily temperature anomalies (steps 20, 21, 22, 23) and the fluctuation characteristics of intraday temperatures with seasonal variations in the real world (steps 25, 26). The simulated long-term annual trend is then overlaid with the short-term real fluctuation characteristics to ensure consistency between short-term weather characteristics and long-term climate change trends. Thus, within the same IAM framework, short-term daily temperature variation characteristics consistent with long-term temperature change trends under a given socio-economic scenario are simulated (steps 24, 27).

[0098] Finally, by integrating the grid-based daily temperature simulation results with the characteristics of each complex model and performing probabilistic analysis, the uncertainty characteristics of regional heat waves under a certain socio-economic scenario can be characterized in a single IAM.

[0099] (Step 28)

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

[0101] Step 1: Determine the socioeconomic scenario S and the relevant parameters that characterize this scenario;

[0102] A key objective of the Integrated Assessment of Climate Change (IAM) is to understand how the development of human socioeconomic systems will affect the climate. Socioeconomic scenario drivers can effectively describe future socioeconomic development trends, depicting the paths of changes in factors such as the economy, population, technology, and policy over time. Combining these drivers with the socioeconomic modules of IAMs further yields corresponding climate change mitigation strategies in areas such as the economy, energy, and land use, as well as greenhouse gas emission pathways (trajectories over time). The socioeconomic scenario S to be assessed is determined, and its quantitative parameters across macroeconomic, technological change, and climate policy dimensions are obtained.

[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) for scenario S, with the start year being y0 and the end year being y0. end .

[0104] Step 3: Differences in structure and parameters among climate models introduce uncertainties into climate simulations. To simulate the uncertainty of global mean temperature change, n groups (generally n>20) of global complex Earth system models are selected.

[0105] Step 4: Based on the existing simplified climate models for global annual average temperature in IAM, and considering the characteristics of the simulation results for each group of complex Earth system models, determine the key parameter values ​​for these n groups of complex Earth system models, such as climate sensitivity and feedback parameters. This will create n sub-modules in IAM capable of simplifying the simulation of global annual average temperature.

[0106] Step 5: Determine the historical base period and the future time period for simulation. Set the historical base period for the relative change in global average temperature as 1981-2010 and the future time period as y0~y end Year (y0, y end ∈[2020,2100] and y0 <y end The system retrieves annual global average temperature data for each complex Earth system model's historical scenario simulation within its historical base period from the CMIP6 website database, and calculates the global annual average temperature for all years within the historical base period (1981-2010) for each model.

[0107] Step 6: Input the global greenhouse gas emission path G into the above n sub-modules that can simplify the simulation of global annual average temperature, and simulate the annual global average temperature changes of n groups. Then, based on the results obtained in step 5 According to the following formula (1):

[0108]

[0109] Further simulations yielded n sets of global annual average temperatures for that time period. year∈{y0,y0+1,…,y end}).

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

[0111]

[0112] in, It is the monthly global average temperature of the year and month in the base period.

[0113] Step 8: Based on the base period global monthly average temperature of each complex Earth system model determined in Step 7. And the global average temperature averaged for all years within the historical base period for each complex Earth system model determined in step 5. Calculate seasonal model factors that reflect the relationship between annual global average temperature and monthly global average temperature within a 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 simulation in step 6 can be Transformed 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 monthly grid data of global near-surface temperature for each group of global complex Earth system models in the historical base period (January 1981 - December 2010) using the CMIP6 database.

[0120] Step 11: Spatially interpolate the data collected in Step 10 to obtain the annual average temperature data for all months within the historical base period using a global 0.5×0.5° geographic latitude and longitude grid. month{1,2,…,12}, gcm∈{1,2,…,n}).

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

[0122] Step 13: Using the CMIP6 database, collect monthly gridded global near-surface temperature data (January y0 - y0) for future time periods under scenarios SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5 for each Earth system model group. end(December 2018). Here, SSPs represent socioeconomic paths, and the following numbers represent the radiative forcing in 2100 under a certain level of greenhouse gas emissions; a larger radiative forcing indicates a greater degree of global warming. These warming scenarios almost cover the potential temperature rise range of global warming under current understanding, helping to make the constructed temperature mapping model more generalizable.

[0123] Step 14: Spatially interpolate the monthly global near-surface temperature grid data collected in Step 13 for each Earth system model under each scenario to obtain monthly global 0.5×0.5° geographic latitude and longitude grid temperature data for each future scenario. year∈{y0,y0+1,…,y end}, month∈{1,2,…,12}, gcm∈{1,2,…,n}).

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

[0125] Step 16: For each complex Earth system model, calculate the difference between the global average temperature in the future time period and the historical base period under the future scenario; this is the monthly global average temperature change.

[0126] Step 17: For each complex Earth system model, calculate the difference between the monthly 0.5×0.5° geographic latitude and longitude grid temperature in the future time period and the historical base period under the future scenario. This is the monthly grid average temperature variation field.

[0127] Step 18: Existing research has demonstrated that, under the forcing of greenhouse gases, although climate change exists in global climate models, the spatial distribution of near-surface temperature changes in these models remains relatively stable. Furthermore, there is an approximately linear relationship between global average temperature changes and regional temperature changes in these models. Therefore, such linear statistical models can be constructed for various complex Earth system models; these models are called spatial scaling models. These models can conveniently and quickly obtain regional-level temperature changes through global average temperature changes, thus avoiding the significant costs and time required to run complex Earth system models with numerous physical processes. In view of this, this method further integrates the scenario data under each complex Earth system model according to their corresponding time points to form a dataset for regression, and estimates the above linear statistical model using the following formula (5):

[0128]

[0129] in, These are the simulation data of each model under all the above future scenarios, representing the grid temperature changes of the gcm-th (gcm=1,2,3…,n) complex Earth system model in year and month (reflecting the change of grid temperature at this time point relative to the grid temperature during the baseline period); It also represents the simulation data of each model under the above scenario, representing the global average temperature change of the gcm (gcm=1,2,3…,n) complex Earth system model in year month (reflecting the change of the global average temperature at this point in time relative to the global average temperature during the baseline period); This represents the year-invariant grid temperature mapping coefficient for the gcm-th (gcm = 1, 2, 3, ..., n) complex Earth system model in the month of month.

[0130] Step 19: Calculate the global monthly average temperature value generated in Step 9 corresponding to the global greenhouse gas emission pathway G. The expression month∈{1,2,…,12} and gcm∈{1,2,…,n} are transformed into the change value relative to the historical base period through the following formula (6).

[0131]

[0132] And calculate the global monthly grid temperature variation field based on the statistical relationship described in step 18: Finally, the global monthly grid temperature field corresponding to the global greenhouse gas emission pathway G was obtained:

[0133] Step 20: To ensure that the temperature does not fluctuate seasonally, this step first uses cubic spline interpolation to calculate the monthly temperature. Interpolation is performed to a daily scale. This interpolation method can capture this natural fluctuation well. The interpolation control point is defined as the midpoint of each month. Assign the midpoint date of the month. Based on the start, midpoint, and end points, calculate the daily average temperature for each day using this interpolation method.

[0134] To eliminate random fluctuations in the daily temperature series and retain long-term seasonal variations, providing a more stable temperature trend, this step uses a 31-day moving average method to smooth the interpolation results, as shown in formula (7).

[0135]

[0136] in, This represents the processed daily smoothed temperature. In particular, for days near the boundary, the sliding window range can be narrowed; for example, the sliding average for day 1 only considers data from days 1 to 16.

[0137] Step 21: Smoothing the temperature curve lacks real-world daily temperature fluctuations. Temperature anomalies can reveal abnormal temperature changes and reflect the temperature variation characteristics of a particular location. To make the daily temperature series more closely resemble real-world daily fluctuations, this step adds temperature anomaly features to the smoothed daily temperature values ​​based on historically observed daily temperature anomalies. First, daily average temperature observation data within the historical base period are obtained 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 near the boundary, the sliding window range can be narrowed; for example, the sliding average of day 1 only considers data from days 1 to 16.

[0140] Step 22: To extract long-term climate trends, it is necessary to average the daily moving averages over the 30 years from 1981 to 2010 annually to obtain the long-term moving average temperature HisLTS. x,y,month,d The calculation process is as shown in formula (9):

[0141]

[0142] This formula represents the annual average of the 31-day moving average over a 30-year period, calculated for the d-th day of the 1st month of each year.

[0143] Step 23: Next, calculate the daily temperature anomaly. Daily temperature anomaly HisA x,y,year,month,d 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 moving average temperature within the historical base period, it 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, overlay the historical daily average temperature anomalies onto the data from y0 to y0. end Smoothed daily temperatures over the years. 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 be used as the basis for determining the annual mean temperature anomaly.x,y,year,month,d The smoothed daily temperature series superimposed on step 20 This yields the future daily average temperature (with fluctuation characteristics) that is consistent with the long-term annual temperature variation trend. The following formula (11):

[0146]

[0147] Step 25: According to the definition of a heat wave: if the daily maximum temperature exceeds the 98th percentile of the daily maximum temperature distribution of the three warmest months within the reference year period for two or more consecutive days, a heat wave event is considered to have occurred. Simulating heat wave characteristics requires further simulation of daily maximum temperature values. Therefore, the daily average temperature is further adjusted based on the historical base period simulation data of each model. Specifically, firstly, the HisT daily average temperature data for 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 diurnal temperature range varies in different seasons, different regression models can be established for each season to capture seasonal variations, 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 For regression model parameters

[0151] Step 27: Extrapolate based on the historical relationships in Step 26, and apply the simulation results of future daily average temperatures obtained in Step 24. Converted to daily maximum temperature

[0152] Where (x,y)∈Grid(global 0.5×0.5° geographic latitude and longitude 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: A heat wave event is considered to have occurred if the daily maximum temperature exceeds the 98th percentile of the daily maximum temperature distribution of the three warmest months within the reference year for two or more consecutive days. Second, determine the target region R of the study. Based on the results of Step 27, aggregate the grid daily maximum temperatures simulated based on the characteristics of each set of complex Earth system models to the regional level. Then, integrate the daily maximum temperatures from all n complex Earth system models to simulate the uncertainty of daily maximum temperatures in this region, thereby estimating the annual frequency and duration of heat waves (the average number of days with heat waves in a year), and using their correlation probability distribution to characterize the regional heat wave uncertainty.

[0154] Ultimately, this method enables the simulation of the uncertainty characteristics of regional heat waves under a given socio-economic scenario S.

[0155] This application has the following characteristics:

[0156] First: Based on climate statistics technology and combined with the simulation results of multiple climate scenarios from multiple complex Earth system models and historical observed temperature data, this invention transforms the global annual average temperature trend simulated by the traditional IAM model into monthly, daily, and intraday global 0.5×0.5 degree grid temperature fields.

[0157] Second: In order to simplify the simulation of the response of daily temperature to global greenhouse gas emissions, this invention does not directly establish a statistical relationship between annual temperature (long-term trend) and daily temperature (short-term characteristics) for complex Earth system models, as this might ignore the regional characteristics of temperature at the spatial scale and the real fluctuation characteristics at the temporal scale. Instead, it transforms the data layer by layer from "year to month, month to day," while also gradually converting long-term climate trends into short-term weather fluctuation characteristics.

[0158] Third: This invention simulates the monthly seasonal effects and daily mean characteristics of complex Earth system models under multiple climate scenarios using simulation data from complex Earth system models. Simultaneously, it calibrates and simulates the seasonal characteristics of daytime temperature anomalies and diurnal temperature range based on historical observation data, thereby capturing the extreme characteristics of short-term daily temperatures. By superimposing the mean characteristics with wave dynamics characteristics, it further captures the extreme characteristics of short-term daily temperatures.

[0159] Fourth: In a single comprehensive evaluation model, this invention simulates the response of multiple complex Earth system models to greenhouse gas emissions in terms of temperature, thereby characterizing the uncertainty of regional daily temperature changes.

[0160] Fifth: In the comprehensive evaluation and modeling framework, this invention simulates the response capability of multiple complex Earth system models to greenhouse gas emissions related to temperature, and characterizes the short-term regional daily temperature uncertainty characteristics consistent with the long-term global 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] To address the deficiency of traditional IAM in simplifying temperature variation simulations by lacking regional diurnal temperature uncertainty characteristics:

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

[0164] This scheme first identifies multiple complex Earth system models (step 3), and then uses the existing simplified simulation module of annual global average temperature in IAM to simulate the simulation capability of annual global average temperature changes of each complex model, and then simulates the uncertainty of annual global average temperature changes under certain socio-economic scenarios (steps 4 and 6).

[0165] (2) By using the simulation results of complex Earth system models under historical scenarios, the monthly seasonal fluctuation characteristics simulated by complex models are captured, and the simulation results of annual global long-term average temperature changes under certain socio-economic scenarios are refined to monthly.

[0166] To simplify the simulation of the response of daily temperature to global greenhouse gas emissions, this approach does not directly establish a statistical relationship between annual temperature (long-term trend) and daily temperature (short-term characteristics) for complex Earth system models. This would potentially overlook the regional characteristics of temperature at the spatial scale and the actual fluctuations at the temporal scale. Instead, it uses a step-by-step transformation from "year to month, month to day," gradually converting long-term climate trends into short-term weather fluctuations. Specifically, this invention captures the monthly seasonal fluctuation characteristics simulated by complex Earth system models under historical scenarios (step 7), refining the annual global long-term average temperature change simulation results under certain socio-economic scenarios to the monthly level (steps 8 and 9).

[0167] (3) Using simulation results from 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 range of potential future warming currently known to humankind) as training datasets to train a spatial scaling model reflecting the relationship between "monthly global average temperature - monthly global grid average temperature field" can improve the model's generalization ability. Simultaneously, it transforms the monthly global average temperature under certain socio-economic scenarios into a monthly temperature geographic grid field, thereby refining regional characteristics. Employing statistical models can not only restore the simulation effects of complex physical models to a certain extent but also significantly improve the speed of integrated climate change assessments, avoiding many complex physical processes. This avoids the relatively slow and inefficient operation of complex Earth System Models, facilitating the rapid simulation of the impacts of various climate policies.

[0168] Based on the characteristic of traditional spatial scaling methods that can map global average temperature from complex Earth system models to local average temperature, this scheme further trains the spatial scaling model using simulation results of complex Earth system models 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 range of potential future temperature rise currently known to humankind and helps to improve the generalization ability of the model), thereby obtaining 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 historical observation data to capture the daily temperature fluctuation characteristics (short-term weather characteristics) in the real world. By superimposing the average state and wave dynamics, the consistency between short-term weather characteristics and long-term climate change trends is achieved, thereby simulating short-term daily temperature change characteristics that are consistent with long-term temperature change trends under certain socio-economic scenarios.

[0170] This scheme further obtains a daily temperature grid field through meteorological interpolation technology and identifies the mean state characteristics of daily temperatures through statistical methods. It combines historical observational data to capture real-world daily temperature anomalies (steps 20, 21, 22, 23), and overlays the simulated long-term annual trend with short-term real fluctuation characteristics (step 24), thereby simulating the real-world intraday temperature fluctuation characteristics with seasonal variations (steps 25, 26). This scheme ensures consistency between short-term weather characteristics and long-term climate change trend characteristics, thus simulating short-term daily temperature variation characteristics consistent with long-term temperature change trends under a certain socio-economic scenario within the same IAM framework (steps 24, 27).

[0171] (5) By simulating the temperature variation characteristics of multiple complex Earth system models under various scenarios using a single IAM model, the uncertainty characteristics of daily maximum temperature are further characterized. Then, based on the definition of heat waves, the uncertainty characteristics of regional heat waves are simulated.

[0172] By integrating the grid-based daily temperature simulation results with characteristics of various complex patterns and performing probabilistic analysis, the uncertainty characteristics of regional heat waves under a certain socio-economic scenario can be characterized in a single IAM (Step 28).

[0173] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection 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 as follows: Simulation to obtain global annual average temperature By selecting a complex Earth system model and determining socio-economic scenarios, and combining the simplified climate and socio-economic modules of the IAM, the global annual average temperature under the socio-economic scenarios was simulated and obtained. Simulation to obtain global monthly average temperature Seasonal model factors are calculated based on historical temperature simulation data from complex Earth system models, and then the seasonal model factors are used in conjunction with the global annual average temperature. Calculate the global monthly average temperature Get global monthly grid temperature Based on future scenario temperature simulation data from complex Earth system models, a statistical relationship is established between the monthly global average temperature change and the monthly global grid average temperature change for selected complex Earth system models. Based on this relationship and the global monthly average temperature... The corresponding monthly grid temperature was calculated. Get the highest daily temperature in the future Based on the global monthly grid temperature Calculate the daily temperature variation trend Daily temperature anomaly HisA calculated based on historical temperature observation data. x,y,year,month,d Based on the aforementioned daily temperature variation trend and the daily temperature anomaly HisA x,y,year,month,d Calculate the global grid daily average temperature A relationship between daily average temperature and daily maximum temperature was established based on historical temperature observation data. This relationship was then used in conjunction with global grid daily average temperatures. The daily maximum temperature was calculated. Heatwave simulation: Integrating grid-based daily temperature simulation results with characteristics of various complex models By analyzing the frequency, duration, and probability distribution characteristics of heat waves, the uncertainty characteristics of regional heat waves under a certain socio-economic scenario can be characterized in a single IAM.

2. The heat wave simulation method based on the integrated climate change assessment model according to claim 1, characterized in that, The simulation obtained the global annual average temperature. The specific process is as follows: Determine the socio-economic scenario S that needs to be simulated, and calculate the global greenhouse gas emission path G under this scenario based on the socio-economic module of IAM. Identify n sets of complex Earth system models that need to be simulated, and combine them with the IAM's simplified simulation model of global annual average temperature to identify n sets of sub-modules that can simplify the simulation of global annual average temperature. Determine the range of the historical base year and the simulated future time period y0~y end In 2018, the global greenhouse gas emission pathway G was input into the above n sub-modules to obtain the global annual average temperature change. Obtain the global average temperature averaged for all years within the historical base period under each complex Earth system model. According to the average temperature and the average temperature change Obtain the global annual average temperature under socioeconomic scenario S 3. The heat wave simulation method based on the integrated climate change assessment model according to claim 2, characterized in that, The simulation obtained the global annual average temperature. The process is as follows: Step 1: Determine the socioeconomic scenario S and the relevant parameters that characterize this 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 sets 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 in IAM, obtain the simulation parameters of n sets of complex Earth system models, and form n sub-modules 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 to be simulated, y0~ ... end In 2018, annual global average temperature simulation data for each of the aforementioned complex Earth system models within the historical base period were obtained from the CMIP6 website database. For each Earth system model, the global annual average temperature for all years within the historical base period was calculated. Step 6: Input the global greenhouse gas emission pathway G into the above n sub-modules to obtain... Then, based on the results obtained in step 5 according to Further simulations yielded n sets of global annual average temperatures for that time period.

4. The heat wave simulation method based on the integrated climate change assessment model according to claim 3, characterized in that, The simulation obtained the global monthly average temperature. The specific process is as follows: Global average temperature based on the average of all years within the historical base period and the average temperature of each month in all years within the historical base period. Calculate seasonality pattern factors According to the seasonal pattern factors The simulation in step 6 Transformed into seasonal global monthly average temperature 5. The heat wave simulation method based on the integrated climate change assessment model according to claim 4, characterized in that, The simulation obtained the global monthly average temperature. The specific process is as follows: Step 7: Obtain monthly global average temperature simulation data for each complex Earth system model within the historical base period using the CMIP6 database. Calculate the average temperature of each month over all years within the historical base period. Step 8: Global average temperature based on the average of all years within the historical base period and the average temperature of each month in all years within the historical base period. Calculate seasonality pattern factors Step 9: Based on the seasonal pattern factors The simulation in step 6 Transformed into monthly global average temperature with seasonal characteristics 6. The heat wave simulation method based on the integrated climate change assessment model according to claim 5, characterized in that, The global monthly average temperature for:

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

9. The heat wave simulation method based on the integrated climate change assessment model according to claim 5, characterized in that, The method for obtaining the highest temperature of future days For the monthly grid temperature Perform interpolation and smoothing operations to obtain the daily temperature trend. Acquire HisT grid daily average temperature observation data within the historical base period x,y,year,month,d Based on this, the HisLTS long-term moving average temperature within the historical base period is calculated. x,y,month,d ; The daily temperature anomaly HisA is obtained by subtracting the grid-based daily average temperature observation data within the historical base period from the long-term moving average 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 Obtain HisMaxT daily maximum temperature observation data within the historical base period x,y,year,month,d The daily maximum temperature observation data is compared with the historical base period grid daily average temperature observation data HisT. x,y,year,month,d By combining these factors, a statistical relationship between daily average temperature and daily maximum temperature as they vary with the seasons can be established. Based on the statistical relationship between the daily average temperature and the daily maximum temperature as they change with the seasons, the future daily average temperature will be... Converted to daily maximum temperature 10. The heat wave simulation method based on the integrated climate change assessment model according to claim 9, characterized in that, The method for obtaining the highest temperature of future days The specific process is as follows: Step 20: Obtain the monthly grid temperature from Step 19. Interpolation is daily grid temperature Then, the daily grid temperature The temperature trend was obtained by smoothing the interpolation results using a 31-day moving average method. Step 21: Obtain the HisT daily average temperature observation data within the historical base period from the WFDEI database. x,y,year,month,d And calculate the 31-day moving average temperature value HisTS for each day within the historical base period. x,y,year,month,d ; Step 22: Calculate the long-term moving average temperature HisLTS by averaging the 31-day moving average temperature value over all annual values ​​within the historical base period. x,y,month,d ; Step 23: Calculate the daily temperature anomaly by subtracting the daily temperature data from the historical base period from the long-term moving average temperature of 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 anomalies from the historical base period obtained in Step 23 onto the smoothed daily temperature series from Step 20 to obtain the future daily average temperature with fluctuation characteristics that is consistent with the long-term annual temperature change trend. Step 25: Obtain the daily maximum temperature observation data HisMaxT from the WFDEI database within the historical base period. x,y,year,month,d ; Step 26: To address the differences in diurnal temperature range across different seasons, establish statistical relationships between daily average temperature and daily maximum temperature for each season to capture the characteristics of intraday temperature variations with seasonality. HisMaxT x,y,season =a season HisT x,y,year,month,d +b season Step 27: Based on the model established in Step 26, simulate the future daily average temperature results obtained in Step 24. Converted to daily maximum temperature

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