A numerical simulation method for evaluating the influence of cosmic rays on the composition of the tropospheric atmosphere

Abstract

This invention discloses a numerical simulation method for assessing the impact of cosmic rays on the composition of the troposphere. It constructs a negative ion chain reaction initiated by electron attachment and affecting ozone and water vapor, and fully couples the ionization process of cosmic rays and its triggered key ionic chemical reactions into a regional climate-chemical-ecological model. Through numerical comparison experiments, it quantitatively calculates the changes in ozone concentration and water vapor mixing ratio caused by the cosmic ionochemical process, and assesses the differences in this impact across different solar activity intensities, seasons, and latitudes. This invention solves the technical problem that existing climate-chemical numerical models cannot quantitatively assess the impact of cosmic rays on atmospheric composition through ionochemical reactions.

Description

Technical Field

[0001] This invention belongs to the field of atmospheric chemistry and climate numerical simulation technology, specifically involving a numerical simulation method for assessing the impact of cosmic rays on the composition of the troposphere. Background Technology

[0002] Assessing changes in atmospheric composition and their climate effects primarily relies on climate-chemistry coupled numerical models, such as global climate models or regional climate-chemistry models. The core mechanisms of these models include photochemical reactions, aerosol physicochemical processes, cloud microphysics, and interactions with ecosystems. They are widely used to study the impacts of anthropogenic emissions and traditional natural factors (such as solar ultraviolet radiation and volcanic eruptions) on ozone, aerosols, and other components, thereby assessing their role in climate change.

[0003] Current mainstream climate-chemistry models generally neglect or simplify cosmic rays, an important natural exogenous forcing process, when simulating and assessing the driving factors influencing climate change. Cosmic rays, upon entering the atmosphere, generate a large number of free electrons through ionization, which can trigger a series of complex gas-phase ionochemical reactions, directly affecting the concentrations of greenhouse gases such as ozone and water vapor. Because this physicochemical process is not incorporated into the model framework, the contribution of cosmic rays may be overlooked when analyzing upper tropospheric ozone changes, water vapor distribution, and their climate feedbacks, resulting in an incomplete assessment of the impact of natural factors on climate change. Summary of the Invention

[0004] Purpose of the invention: To overcome the shortcomings of existing regional climate-chemistry models in nitrous oxide simulation, this invention provides a numerical simulation method for assessing the impact of cosmic rays on the composition of the troposphere, solving the technical problem that existing climate-chemistry numerical models cannot quantitatively assess the impact of cosmic rays on atmospheric composition (especially ozone and water vapor in the upper troposphere) through ionic chemical reactions.

[0005] Summary of the Invention: To solve the above-mentioned technical problems, the present invention adopts the following technical solution: a numerical simulation method for evaluating the influence of cosmic rays on the composition of the troposphere, comprising the following steps:

[0006] S1, Data Acquisition and Driving Field Construction: Acquire cosmic ray flux observation data and cosmic ray modulation parameters for the target region and time period;

[0007] S2, Quantification of cosmic ray ionization process: Using the cosmic ray atmospheric shower and ionization model, and based on the cosmic ray flux observation data obtained in step S1, the electromagnetic cutoff stiffness of the target area and the standard atmospheric profile, an ionization rate calculation model is established, and then the spatiotemporal distribution of electron concentration in the target area is obtained.

[0008] The atmospheric ionization rate and electron concentration from the ground to the top of the model were calculated.

[0009] S3, Optimization of Climate-Chemical Coupling Model: Based on the regional climate-chemistry-ecology coupling model RegCM-Chem-YIBs, a cosmic ray-ion chemistry submodule is constructed within the original gas phase chemistry mechanism. This submodule is driven by electron attachment and influences the negative ion chain reaction of ozone and water vapor. The spatiotemporal distribution of electron concentration is obtained in step S2.

[0010] S4, Numerical experimental design and effect separation:

[0011] (1) Under the regional climate-chemistry-ecology coupling model, the cosmic ray-ion chemistry submodule is turned off, and the background field CTRL of atmospheric composition is simulated;

[0012] (2) Under the regional climate-chemistry-ecology coupling model and under the same conditions as in step (1), the cosmic ray-ion chemistry submodule is activated to simulate the distribution field CR of atmospheric components affected by cosmic rays;

[0013] (3) Subtract the results of the atmospheric component distribution field CR and the atmospheric component background field CTRL obtained in steps (2) and (1) respectively from each grid point and time to obtain the spatiotemporal distribution of the difference field.

[0014] Furthermore, the cosmic ray flux observation data mentioned in step S1 comes from the Global Neutron Monitor Network (NMDB).

[0015] Furthermore, the cosmic ray atmospheric shower and ionization model described in step S2 adopts the CRAC:CRII model (Cosmic-Ray Atmospheric Cascade: application to cosmic ray inducedionization).

[0016] Furthermore, the cosmic ray atmospheric shower and ionization model described in step S2 obtains the spatiotemporal distribution of electron concentration in the target region by calculating the atmospheric ionization rate and electron concentration from the ground to the top of the model.

[0017] Furthermore, the negative ion chain reaction of the cosmic ray-ion chemistry submodule described in step S3 includes:

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[0050] Beneficial effects: Compared with existing technologies, this invention embeds a cosmic ray-driven ionochemistry module into a regional climate-chemistry-ecology coupling model, which can fully couple the cosmic ray ionization process and the key ionochemistry reactions it triggers into the regional climate-chemistry-ecology model; through the design of numerical comparison experiments, the changes in ozone concentration and water vapor mixing ratio caused by cosmic ray ionochemistry processes can be quantitatively calculated; and the differences in this impact in different solar activity years (cosmic ray intensity years), different seasons, and different latitude regions can be evaluated. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating the numerical simulation method for evaluating the impact of cosmic rays on the composition of the troposphere, as described in this invention. Detailed Implementation

[0052] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading this invention, any modifications of the invention in various equivalent forms by those skilled in the art fall within the scope defined by the appended claims.

[0053] like Figure 1 As shown, the technical concept and process of the numerical simulation method for evaluating the influence of cosmic rays on the composition of the troposphere in this invention are as follows:

[0054] S1. Data Acquisition and Driving Field Construction Steps:

[0055] (1) Obtain cosmic ray flux observation data (sources such as the Global Neutron Monitor Network NMDB) and cosmic ray modulation parameters (Cosmic ray modulation potential) for the target region and time period.

[0056] (2) Obtain meteorological driving field data (such as the global reanalysis dataset ERA5 provided by the European Centre for Medium-Range Weather Forecasts (ECMWF)), high-resolution topographic data, and anthropogenic and natural source emission inventory data.

[0057] S2. Quantification steps of cosmic ray ionization process:

[0058] Using cosmic ray atmospheric shower and ionization models (such as the CRAC:CRII model, Cosmic-Ray Atmospheric Cascade: application to cosmic ray induced ionization), combined with cosmic ray data obtained in step S1, geomagnetic cutoff stiffness information of the target region, and standard atmospheric profiles, the atmospheric ionization rate and electron concentration from the ground to the model top are calculated. This step transforms the external forcing of cosmic ray flux into chemically driven parameters usable within the model.

[0059] S3. Steps to improve the climate-chemical coupling model:

[0060] Based on a mature regional climate-chemistry-ecology coupling model (such as RegCM-Chem-YIBs), a new "cosmic ray-ion chemistry" submodule is added to its original gas-phase chemical mechanism. This submodule contains a series of negative ion chain reactions initiated by electron attachment and ultimately affecting ozone (O3) and water vapor (H2O). These reactions may include (but are not limited to): the initial reaction of electrons with ozone to generate O3⁻, the decomposition of O3⁻, and subsequent hydration, transfer, and neutralization reactions of O⁻, O2⁻, etc., with molecules such as H2O, CO2, and NO₃⁻ (e.g., 32 key reaction formulas may be listed in a specific implementation). The chemical reaction rate of this submodule will depend on the real-time electron concentration calculated in step S2.

[0061] S4. Numerical experimental design and influence separation steps:

[0062] (1) Design control group experiment: run the original mode without the “Cosmic Ray-Ion Chemistry” submodule enabled (denoted as CTRL experiment) to simulate the background field of atmospheric composition.

[0063] (2) Design sensitivity test: Under the same initial conditions, boundary conditions and physical parameterization scheme, the improved mode of the “cosmic ray-ion chemistry” submodule was run (referred to as CR test).

[0064] (3) Calculation of influence: The results of the sensitivity test (CR) and the results of the control test (CTRL) are subtracted grid points and time points, i.e., Δ = result (CR) - result (CTRL). The resulting difference field (ΔO3, ΔH2O) represents the changes in atmospheric ozone concentration and water vapor mixing ratio caused solely by the cosmic ray ion chemical process, thus achieving the separation and quantification of the influence.

[0065] S5. Impact Assessment and Results Output Steps:

[0066] Output the spatial distribution map of the difference field to intuitively show the spatial distribution characteristics of the influence.

[0067] Example

[0068] like Figure 1 The diagram shown is a flowchart of the numerical simulation method based on the RegCM-Chem-YIBs model in this embodiment. This embodiment mainly simulates and evaluates the impact of cosmic rays on ozone and water vapor in the upper troposphere of East Asia in July 2009.

[0069] Step S1: Obtain cosmic ray data for July 2009: Obtain cosmic ray neutron count rate data from multiple stations, including Beijing Station (BEIJ) and Yangbajing Station (TIBT), from the Global Neutron Monitor Network (NMDB); Obtain cosmic ray modulation parameters for July 2009.

[0070] Meteorological driving field: Global reanalysis dataset ERA5 provided by the European Centre for Medium-Range Weather Forecasts (ECMWF)

[0071] Emissions Inventory: Obtain anthropogenic emission data from the China Multiscale Emissions Inventory Model (MEIC).

[0072] Step S2: Calculate the ionization rate of cosmic rays

[0073] The cosmic ray modulation parameter data obtained in step 1, along with the geomagnetic cutoff stiffness information for East Asia, were input into the CRAC:CRII physical model. Running the model, the atmospheric ionization rate and corresponding three-dimensional electron concentration distribution at a height of 250 hPa over East Asia in July 2009 were calculated.

[0074] Step S3: Set up a regional climate-chemical coupling model

[0075] The model is based on the regional climate-chemical-ecological coupled model RegCM-Chem(v4.6)-YIBs(v1.0), with a horizontal resolution of 60 km, 18 vertical layers, and a top pressure of 50 hPa.

[0076] Step S4: Run the control group test (CTRL)

[0077] In the mode set in step 3, the "Cosmic Ray-Ion Chemistry" reaction module is turned off. Using the meteorological field and emission inventory obtained in step S1 for July 2009, a one-month simulation is performed. After the simulation is complete, the three-dimensional fields of ozone concentration (O3) and specific humidity (Q) at each grid point during this period are output and denoted as CTRL_result.

[0078] Step S5: Implant the mechanism and run a sensitivity test (CR).

[0079] With the exact same mode configuration, enable the "Cosmic Ray-Ion Chemistry" reaction module. Add the 32 key negative ion reactions (whose reaction rates depend on electron concentration) required for this module to the gas-phase chemistry mechanism of the model. Input the spatiotemporal distribution field of electron concentration calculated in step 2 into this module as the driving variable for the reaction. Run the model again, outputting the ozone concentration and specific humidity field for the same period. This result includes the influence of cosmic rays and is denoted as CR_result.

[0080] Step 6: Calculate the effect of cosmic rays

[0081] The differences between the sensitivity test results and the control group results were calculated on a grid-by-grid basis and time-by-time basis:

[0082] ΔO3 = CR_Result(O3) - CTRL_Result(O3)

[0083] The ΔO3 and ΔQ fields calculated by ΔQ = CR_Result(Q) - CTRL_Result(Q) represent the changes in ozone concentration and specific humidity caused solely by the cosmic ray ionochemical process.

[0084] Step 7: Analyze the spatial distribution characteristics of the impact.

[0085] A horizontal spatial distribution map of ΔO3 and ΔQ was plotted at an altitude of 250 hPa (approximately the upper troposphere). Analysis of their spatial modes revealed a distinct ozone depletion center (negative ΔO3 region, reaching a maximum of -8 ppbv) over northwestern China and Mongolia, while a slight increase in ozone was observed in parts of central and eastern China. Simultaneously, an increase in specific humidity (positive ΔQ region) was observed in the ozone depletion region.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A numerical simulation method for evaluating the influence of cosmic rays on the composition of the tropospheric atmosphere, characterized by Includes the following steps: S1, Data Acquisition and Driving Field Construction: Acquire cosmic ray flux observation data and cosmic ray modulation parameters for the target region and time period; S2, Quantification of cosmic ray ionization process: Using the cosmic ray atmospheric shower and ionization model, and based on the cosmic ray flux observation data obtained in step S1, the electromagnetic cutoff stiffness of the target area and the standard atmospheric profile, an ionization rate calculation model is established, and then the spatiotemporal distribution of electron concentration in the target area is obtained. S3, Optimization of Climate-Chemical Coupling Model: Based on the regional climate-chemistry-ecology coupling model RegCM-Chem-YIBs, a cosmic ray-ion chemistry submodule is constructed within the original gas phase chemistry mechanism. This submodule is driven by electron attachment and influences the negative ion chain reaction of ozone and water vapor. The spatiotemporal distribution of electron concentration is obtained in step S2. S4, Numerical experimental design and effect separation: (1) Under the regional climate-chemistry-ecology coupling model, the cosmic ray-ion chemistry submodule is turned off, and the background field CTRL of atmospheric composition is simulated; (2) Under the regional climate-chemistry-ecology coupling model and under the same conditions as in step (1), the cosmic ray-ion chemistry submodule is activated to simulate the distribution field CR of atmospheric components affected by cosmic rays; (3) Subtract the results of the atmospheric component distribution field CR and the atmospheric component background field CTRL obtained in steps (2) and (1) respectively from each grid point and time to obtain the spatiotemporal distribution of the difference field.

2. The method of claim 1, wherein the method is characterized by: The cosmic ray flux observation data mentioned in step S1 comes from the Global Neutron Monitor Network (NMDB).

3. The method of claim 1, wherein the method is characterized by: The cosmic ray atmospheric shower and ionization model described in step S2 adopts the CRAC:CRII model.

4. The method of claim 3, wherein the method is characterized by: The cosmic ray atmospheric shower and ionization model described in step S2 calculates the atmospheric ionization rate and electron concentration from the ground to the top of the model to obtain the spatiotemporal distribution of electron concentration in the target region.

5. The method of claim 1, wherein the method is characterized by: The negative ion chain reaction of the cosmic ray-ion chemistry submodule described in step S3 includes: （1） （2） （3） （4） （5） （6） （7） （8） （9） （10） （11） （12） （13） （14） （15） （16） （17） （18） （19） （20） （21） （22） （23） （24） （25） （26） （27） （28） （29） （30） （31） （32）。

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