A Multimodal Method for Predicting Arctic Climate Change and Analyzing its Regulation Mechanisms

Abstract

This invention relates to the field of meteorological forecasting, providing a multimodal method for predicting Arctic climate change and analyzing its regulatory mechanisms. The aim is to predict Arctic climate change by understanding the relationship between NPOs, AMOCs, and Arctic near-surface temperatures (SATs). This method constructs and validates a large-sample dataset containing AMOC proxy data and historical Arctic SAT observation data to evaluate the performance and biases of the CMIP6 model in simulating AMOC and Arctic SAT changes. Next, it reveals the physical mechanisms by which AMOCs influence Arctic SATs and explores the regulatory role of NPO internal variability in the relationship between AMOCs and Arctic SATs. Based on this, it simulates future climate change scenarios, adjusts model parameters to accurately reflect the impacts of AMOCs and NPOs on Arctic SATs, and predicts future trends in Arctic SAT and AMOC changes to assess the reliability of the model predictions. This method, used to predict Arctic climate change, improves the accuracy of predicting future Arctic climate changes by gaining a deeper understanding of the interactions between different climate modes.

Description

Technical Field

[0001] This invention relates to the field of meteorological forecasting and provides a method for predicting and analyzing the regulation mechanisms of Arctic climate change using a multimodal approach. Background Technology

[0002] In recent years, the impacts of climate change on the Arctic region have attracted widespread attention. Particularly between 1950 and 2014, Arctic surface air temperature (SAT) and the Atlantic Meridional Overturning Circulation (AMOC) exhibited significant in-phase changes on decadal timescales. This phenomenon reveals two key factors: rapid warming in the Arctic region and the influence of changes in the Atlantic Meridional Overturning Circulation on Arctic climate.

[0003] Rapid warming in the Arctic, known as polar intensification, is associated with a variety of physical processes, including local radiation feedback and polar-directed oceanic and atmospheric heat transport. These processes primarily affect Arctic sea surface temperature (SAT) on interannual timescales, while on decadal timescales, changes in Arctic SAT show a more significant positive correlation with the Arctic Oceanic Heat Exchange Center (AMOC). The enhancement of the AMOC is accompanied by increased oceanic heat transport to the mid-to-high latitudes of the Atlantic Ocean, leading to warming of sea surface temperatures and melting of Arctic sea ice at the North Atlantic basin scale.

[0004] Furthermore, variations in Arctic SAT are also influenced by Pacific sea surface temperature variability, particularly its relationship with the Pacific Decadal Oscillation (PDO). Observations and simulations indicate that co-phase changes in the PDO and AMO can cause Arctic warming or cooling. Local Arctic atmospheric circulation and atmospheric circulation in other regions also adjust the interdecadal variations of Arctic SAT and sea ice.

[0005] However, current research still lacks a comprehensive understanding of how low-frequency climate modes influence Arctic warming. To gain a deeper understanding of these relationships, researchers have employed various proxy data and climate model simulations to explore the connection between AMOC and Arctic SAT and its possible physical mechanisms.

[0006] On decadal timescales, the North Pacific Oscillation (NPO) influences the relationship between the AMOC and the Arctic SAT through polar water vapor transport and transbasin atmospheric circulation processes. Specifically, synchronous phase variations between the NPO and AMOC can explain 14%–41% of the AMOC-Arctic SAT covariance.

[0007] In summary, changes in the Arctic SAT are a complex process influenced by multiple factors, including variations in the AMOC, Pacific sea surface temperature variability, and the North Pacific Oscillation. The interactions of these factors have a significant impact on future Arctic climate change.

[0008] In summary, changes in the Arctic SAT are a complex process influenced by multiple factors, including variations in the AMOC, Pacific sea surface temperature variability, and the North Pacific Oscillation. The interactions of these factors have a significant impact on future Arctic climate change. Summary of the Invention

[0009] The purpose of this invention is to predict climate change in the Arctic region by analyzing the relationship between the North Pacific Oscillation (NPO), the Atlantic Meridian Overturning Circulation (AMOC), and the Arctic Near-Surface Temperature (SAT).

[0010] To achieve the above objectives, the present invention employs the following technical solution:

[0011] A method for predicting and analyzing the regulation mechanisms of multimodal Arctic climate change, characterized by the following steps:

[0012] Step 1: Dataset Construction and Validation

[0013] 1.1 Collect and organize observational data, including AMOC proxy data and historical observational data of Arctic SAT;

[0014] 1.2 Construct a large sample dataset in CMIP6 mode to ensure data quality and integrity;

[0015] 1.3 Compare the observation data and model output to verify the reliability of the dataset;

[0016] 1.4 The consistency and accuracy of the dataset are assessed through statistical tests (such as correlation coefficient and mean squared error).

[0017] Step 2: Comparison of model simulation and observation data

[0018] 2.1 The constructed dataset was used to simulate the CMIP6 model, and simulation results of AMOC and Arctic SAT were obtained;

[0019] 2.2 Compare the simulation results with observational data to evaluate the model's ability to simulate changes in AMOC and Arctic SAT;

[0020] 2.3 Through comparative analysis, the deviations and uncertainties of the model during the simulation process are identified.

[0021] Step 3: Revealing the physical mechanisms

[0022] 3.1 By comparing and analyzing observational and simulated data, the physical processes underlying the impact of AMOC on Arctic SAT are diagnosed based on the formula for calculating the meridional overturning stream function (MSF):

[0023]

[0024] Where lev B It is the depth of the ocean floor, lon w It is the western boundary of the ocean. e It is the eastern boundary of the ocean. v(time, lev, lat, lon) represents the northward flow velocity of the ocean. lev represents the vertical layer, indicating different depth layers in the ocean. lat represents latitude, lon represents longitude, dlon represents the infinitesimal element of longitude, used for integration calculations, and dlev represents the infinitesimal element of the vertical layer, used for integration calculations.

[0025] The AMOC index is calculated as the maximum MSF value north of 20°N and below 500m in the Atlantic region, based on the definition of the Arctic Gradient Index (AGI):

[0026] AGI = SAT 60N-90N -SAT 0-90N

[0027] Among them, SAT 60N-90N and SAT 0-90N This is an area-weighted average, where 60N-90N represents the area from 60°N to 90°N, and 0-90N represents the area from the equator to 90°N (the North Pole).

[0028] 3.2 Through thermodynamic and kinetic analysis, the mechanism of atmospheric-ocean heat transfer to the poles and the atmospheric-ocean heat transfer between the atmosphere and the ocean are revealed. The calculation formula for atmospheric heat transfer (AHT) is as follows:

[0029]

[0030] Where φ is latitude, φ s It's the latitude of Antarctica, Flx s and Flx TOA R represents the net heat flux downwards from the Earth's surface and the net incident radiation at the top of the atmosphere, respectively. E λ represents the Earth's radius, λ represents the radial longitude, and ocean heat transport is the output of the model diagnostics.

[0031] 3.3 Establish a quantitative relationship between physical processes and changes in AMOC and Arctic SAT. The energy budget formula for SAT is as follows:

[0032]

[0033] On the left side of the equation, T air S represents SAT, Δ represents annual anomaly, and positive anomalies correspond to a downward direction. u / S d F represents upward / downward shortwave radiation. latent / F sensible Represents latent heat / sensible heat flux, l dIt is long-wave radiation, R is the residual term, and T is the long-wave radiation. s σ is the surface temperature, σ is the Stephen-Boltzmann constant, and ε is the surface emissivity.

[0034] Step 4: The regulatory role of internal variables

[0035] 4.1 Analysis of the influence of internal variability such as NPO on the relationship between AMOC and Arctic SAT. The NPO index is defined as the normalized sea level anomaly in the mid-latitudes of the North Pacific (45°-65°N, 170°E-140°W) minus the normalized sea level pressure anomaly in the subtropics (20°-40°N, 170°E-140°W). The formula for calculating the NPO index is as follows:

[0036]

[0037] SLP mid It is a standardized sea-level pressure anomaly in the mid-latitude region of the North Pacific (45°-65°N, 170°E-140°W);

[0038] SLP sub It is a standardized sea-level pressure anomaly in the subtropical region (20°-40°N, 170°E-140°W);

[0039] This represents the average difference between sea level pressure anomalies in two regions.

[0040] σSLP mid and σSLP sub These are the standard deviations of sea level pressure anomalies in the two regions.

[0041] 4.2 Through statistical analysis, the correlation between NPO and AMOC and Arctic SAT was determined:

[0042]

[0043] r NPO，AMOC : Represents the correlation coefficient between NPO and AMOC; r NPO，SAT : Represents the correlation coefficient between NPO and SAT; NPO i AMOC i SAT i These are the NPO index, AMOC index, and Arctic SAT value at the i-th time point, respectively; n is the total number of time points.

[0044] 4.3 Investigate how NPOs regulate atmospheric and oceanic circulation, thereby affecting Arctic SAT.

[0045] 4.3.1 Assessment of the impact of NPO on meridional atmospheric heat transport (AHT):

[0046] ΔAHT=CΔv

[0047] Where ΔAHT represents the change in meridional atmospheric heat transport, C is a proportionality constant representing the sensitivity of meridional atmospheric heat transport to changes in the northward ocean current velocity, and Δv represents the change in northward current velocity between the positive and negative phases of the NPO:

[0048]

[0049] and These represent the northward ocean current speeds during the positive and negative phases of the NPO, respectively.

[0050] 4.3.2 How does the NPO influence the Atlantic Meridian Overturning Circulation (AMOC) through air-sea interaction, and consequently the Arctic Saturn Atmosphere (SAT)? Here, changes in Δv may lead to changes in AMOC, expressed as:

[0051]

[0052] ΔAMOC reflects the change in AMOC intensity caused by the NPO phase change. The change in Δv affects ΔAMOC through the following pathways:

[0053] If Δv > 0, it indicates that the northward flow velocity increases, which will lead to more heat being transported to the North Atlantic and enhance AMOC, i.e., ΔAMOC > 0.

[0054] If Δv < 0, it indicates that the northward flow velocity is reduced, which will lead to a decrease in heat transport and weaken AMOC, i.e., ΔAMOC < 0;

[0055] The NPO influences the Arctic SAT by modulating atmospheric and oceanic circulation, affecting northward flow velocity Δv and AMOC intensity ΔAMOC.

[0056] Step 5: Predicting Future Climate Change

[0057] 5.1. Based on the compared models and the revealed physical mechanisms and internal variability regulation, we will simulate future climate change scenarios.

[0058] 5.1.1 When simulating future climate change, incorporate the impact mechanism of AMOC on Arctic SAT revealed in step 3 into the model, and accurately represent changes in heat transport, salinity, and ocean productivity in the model;

[0059] 5.1.2 Integrate the regulatory effect of NPO internal variability analyzed in step 4 into the simulation, including setting a parameterization scheme in the model that can simulate NPO changes, and the NPO changes will be fed back to AMOC and Arctic SAT;

[0060] 5.2 Adjust model parameters to better reflect the observed physical processes and the effects of internal variability;

[0061] 5.2.1 Based on the results of steps 3 and 4, adjust the model parameters to ensure that the model can better simulate the impact of AMOC and NPO on Arctic SAT, including adjusting the air-sea coupling parameters to reflect the transport of heat and salinity.

[0062] 5.2.2 Adjust the internal variability parameters in the model, including the characterization of NPO, to ensure that the model can simulate changes in internal variability and their impact on AMOC and Arctic SAT;

[0063] 5.3 Input future RCP or SSP emission scenarios to simulate changes in AMOC and Arctic SAT over the next few decades to hundreds of years.

[0064] 5.4 Perform statistical analysis on the simulation results, extract the predicted trends of future Arctic SAT and AMOC, and compare them with the findings in steps 3 and 4 to evaluate the reliability of the model predictions.

[0065] Because the present invention employs the above-mentioned technical means, it has the following beneficial effects:

[0066] By constructing and validating a large-sample dataset containing AMOC proxy data and historical SAT observation data from the Arctic, the problem of constructing and validating datasets in Arctic climate change prediction has been solved, thereby improving the reliability and accuracy of the data. Compared with existing technologies, it takes multi-source data more comprehensively.

[0067] By simulating and comparing the output of the CMIP6 model with observational data, the model's performance and biases in simulating AMOC and Arctic SAT changes were evaluated and identified. This solved the matching problem between model simulation and actual observation, thus improving the accuracy of model simulation. Compared with existing technologies, it is better able to identify and adjust biases in the simulation process.

[0068] This study reveals the physical mechanisms underlying the impact of AMOC on Arctic SAT. Through diagnostic analysis, thermodynamic and kinetic studies, quantitative relationships are established, and relevant physical parameters are calculated. This addresses the ambiguity of physical mechanisms in Arctic climate change prediction, thereby improving the scientific rigor of predictions and providing a deeper understanding of the internal interactions of the climate system compared to existing technologies.

[0069] This study explores the moderating effect of internal variability of NPOs on the relationship between AMOC and Arctic SAT, determines their correlation through statistical analysis, and assesses how NPOs regulate Arctic SAT by influencing atmospheric and oceanic circulation. This addresses the lack of clarity regarding the role of internal variability in Arctic climate change prediction, thereby improving prediction accuracy and taking into account the complexity of the climate system more comprehensively than existing technologies. Attached Figure Description

[0070] Figure 1 The study presents the temporal evolution of the AMOC and AGI indices observed from 1995 to 2014.

[0071] Figure 2 Quantifying the moderating effect of NPO on the correlation between AMOC and AGI at the decadal scale. Scatter plots of AMOC and AGI indices (unit: °C) at the decadal scale, under conditions where NPO and AMOC indices are in the same (opposite) or opposite phases. (a, b) Multiple AMOC proxy indices; (c, d) FGOALS-g3 large sample set simulation; (e, f) piControl large set simulated by FGOALS-g3; "Same phase" ("opposite phase") refers to NPO and AMOC indices being in the same (opposite) phase at the decadal scale, and their absolute values ​​being greater than 0.5 times the standard deviation. Red and blue lines represent regression lines, and black lines represent 95% confidence intervals. The linear correlation coefficient (r), p-value, and number of sample points are shown on the horizontal and vertical axes.

[0072] Figure 3 Composite analysis of near-surface air temperature (SAT, unit: °C, shaded) and sea level pressure (SLP) anomalies (unit: Pa, isopleths, interval: 5 Pa) simulated by the FGOALS-g3 large sample ensemble under different NPO and AMOC phases. (a) NPO- and AMOC+ phases; (b) NPO+ and AMOC+ phases; (c) NPO- and AMOC- phases; (d) NPO+ and AMOC- phases. The standardized NPO and AMOC indices are both greater than ±0.5 standard deviations, and the NPO index leads the AMOC index by three years. (c) is (a) minus (b), and (f) is (d) minus (e). Dotted lines represent negative SLP anomalies, solid lines represent positive anomalies, and thick black lines represent zero lines. The black / red lines in the subplots are the zonal averages of the SAT / SLP anomalies. The dotted areas passed the signal-to-noise ratio test (signal greater than noise). Detailed Implementation

[0073] The embodiments of the present invention will be described in detail below. Although the present invention will be described and illustrated in conjunction with some specific embodiments, it should be noted that the present invention is not limited to these embodiments. On the contrary, any modifications or equivalent substitutions made to the present invention should be covered within the scope of the claims of the present invention.

[0074] Furthermore, to better illustrate the present invention, numerous specific details are set forth in the following detailed embodiments. Those skilled in the art will understand that the present invention can be practiced without these specific details.

[0075] This invention provides a multimodal method for predicting Arctic climate change and analyzing its regulatory mechanisms. In this embodiment, the SAT (Sea Surface Temperature) data used is HadCRUT5 (Morice et al. 2021), and similar conclusions are obtained from the GISTEMP (England et al. 2021; Hansen et al. 2010) dataset. Sea surface heat flux and radiation data are referenced from JRA55 (Kobayashi et al. 2015) and NCEP-NCAR reanalysis data (Kalnay et al. 1996). Sea surface temperature is observed using ERSSTv5, and sea level pressure is observed using Hadley Centre Sea Level Pressure (Ailan and Ansell 2006). Due to the lack of early (pre-1950) oceanographic observations and their susceptibility to instrumental uncertainties (Chanet et al. 2019; Kennedy 2014), only observations and simulations after 1950 are analyzed (e.g., Zhao et al. 2022). Unless otherwise specified, all observational data in this paper have been linearly detrended. The climatological period is 1961–1990, and climate anomalies are defined as deviations from the climatological period.

[0076] FGOALS-g3 and CESM2 large sample sets

[0077] FGOALS-g3 is a climate model (Li et al. 2020) with a large ensemble of 110 samples for historical simulations (1850–2014) and future climate projections (2015–2099) (Lin et al. 2022). The horizontal resolution of the FGOALS-g3 atmospheric / oceanic grid is approximately 2° × 2.25° / 1° × 0.76°. Each sample begins with values ​​from 1850, and the only difference between samples is the initial value. Historical external forcing uses the CMIP6 standard external forcing field (Eyring et al. 2016). The initial values ​​for the samples are 110 different years from 900–2000, simulated using pre-industrial controlled experiments. Previous assessments indicated that the FGOALS-g3 ensemble could reasonably simulate similar observed SAT variations (Lin et al. 2022).

[0078] To validate the FGOALS-g3 results, a large historical sample set (100 samples) of CESM2 (Rodgerset al. 2021) was used. CESM2 is an Earth system model with an atmospheric / oceanic latitude and longitude grid horizontal resolution of approximately 1.25° × 0.9° / 1.125° × 0.44°, and historical forcing is based on the CMIP6 standard. The CESM2 analysis results are similar to those of FGOALS-g3. Therefore, unless otherwise specified, the main body of the text presents the simulation results of FGOALS-g3.

[0079] To validate historical simulation results, a large piContro1 dataset was constructed based on the pre-industrial control experiments (piControl) of FGOALS-g3 and CESM2. Referring to previous studies (Maher et al. 2018; Zhao et al. 2022), each sample in the piContro1 dataset is a truncated segment of piContro1 over a period of 65 years. The FGOALS-g3 / CESM2 piContro1 dataset contains 43 / 89 samples. The appendix provides a more detailed description of the model simulations; unless otherwise emphasized, all model variables in the analysis have been subtracted from the ensemble mean.

[0080] AMOC, AGI, NPO, and PDO indices

[0081] The meridional flip stream function (MSF) is calculated as follows:

[0082]

[0083] Where lev B It is the depth of the ocean floor, lon w / lon eThe west / east boundary of the ocean is represented by v(time, lev, lat, lon), which represents the northward ocean current velocity. The AMOC index of the model is calculated as the maximum MSF value at a depth of 500m north of 20°N in the Atlantic region (Zhang and Wang 2013).

[0084] Many studies have quantified Arctic polar strengthening from different perspectives (Bekryaev et al. 2010; Davy et al. 2018; Ono et al. 2022). The Arctic polar strengthening index used is referenced from Davy et al. (2018), which is defined as the area-weighted average of Arctic (60°N–90°N) SAT minus the area-weighted average of Northern Hemisphere SAT. This index represents the change in Arctic SAT relative to lower latitude SAT, and is therefore called the Arctic Gradient Index (AGI).

[0085] AGI = SAT 60N-90N -SAT 0-90N #(2)

[0086] SAT 60N-90N and SAT 0-90N As an area-weighted average, other definitions of AA exhibit a similar phase shift to the AGI exponent on the interdecadal timescale (see Davy et al. 2018).

[0087] Referring to the work of Furtado et al. (2012), the NPO index is defined as: the normalized sea-level anomaly in the mid-latitudes of the North Pacific (45°–65°N, 170°E–140°W) minus the normalized sea-level pressure anomaly in the subtropics (20°–40°N, 170°E–140°W). Other NPO indices (Yeh et al. 2018) yielded similar results. The observed NPO modes can be well simulated in the FGOALS-g3 and CESM2 models.

[0088] AMOC and NPO in-phase (opposite-phase) is defined as the standardized AMOC and NPO indices being in phase, and their absolute values ​​being greater than 0.5 times the standard deviation. Similar conclusions are reached for standard deviation thresholds of different intensities.

[0089] Referring to the work of Newman et al. (2016), the calculation of the PDO index involves the following steps: First, the global mean sea surface temperature anomaly is subtracted to eliminate the effects of global warming. Then, the first EOF of the residual sea surface temperature anomaly in the North Pacific (20°-70°N, 110°E-110°W) is calculated as the PDO mode, and the corresponding standardized principal component time series is defined as the PDO index.

[0090] Atmospheric heat transport (AHT)

[0091] Referring to He et al. (2019), the formula for calculating the longitudinal AHT is:

[0092]

[0093] Where φ is latitude, φ s It's the latitude of Antarctica, Flx s and Flx TOA R represents the net heat flux downwards from the Earth's surface and the net incident radiation at the top of the atmosphere, respectively. E λ represents the Earth's radius, and λ represents the radial longitude. Oceanic heat transport is a model diagnostic output. The climatology of polar oceanic / atmospheric heat transport simulated by the FGOALS-g3 and CESM2 models is similar (not shown).

[0094] SAT Energy Balance

[0095] Because the Arctic Sat's temperature (SAT) is temporally correlated with surface temperature, the energy balance of the SAT anomaly is...

[0096] (Lee et al. 2017) can be calculated as follows:

[0097]

[0098] On the left side of the equation, T air S represents SAT, Δ represents annual anomalies, and S u / S d F represents upward / downward shortwave radiation. latent / F sensible Represents latent heat / sensible heat flux (positive anomalies correspond to a downward direction), l d It represents downward longwave radiation, R is the residual term, T is the surface temperature, σ is the Stephen-Boltzmann constant, and ε is the surface emissivity.

[0099] The moderating effect of NPO on the relationship between AMOC and Arctic SAT

[0100] To explore the potential impact of NPOs. Figure 2 Figures a and b show scatter plots of AMOC and AGI when AMOC and NPO are in the same or opposite phase, respectively, during observation. As shown, when AMOC and NPO are in opposite phases, the correlation coefficient between AMOC and AGI is relatively large (r = 0.77), while when AMOC and NPO are in the same phase, the correlation coefficient is smaller (r = 0.42). This indicates that the negative phase of NPO contributes more significantly to the relationship between AMOC and AGI. Based on historical simulations and the piControl ensemble, this relationship was re-examined using the same calculation method. Figure 2The results also show that when AMOC and NPO are in opposite phases, the correlation coefficient between AMOC and AGI (historical simulation r = 0.61, piControl r = 0.48) is greater than the correlation coefficient when they are in the same phase (historical simulation r = 0.35, piControl r = -0.02). Both observational and FGOALS-g3 large-sample ensemble results indicate that the correlation coefficients between AMOC and AGI differ significantly (exceeding the 95% confidence level, Table 2) under different and the same phases of NPO and AMOC. Furthermore, the simulation results of the CESM2 historical large-sample ensemble and the corresponding piControl ensemble are similar to those of FGOALS-g3 (Table 2). To quantify the impact of NPO phase changes on the interdecadal timescale association between AMOC and Arctic SAT, based on observations and simulations, we calculated the explained covariance (the square of the correlation coefficient, i.e., r) caused by NPO phase changes. 2 The phase changes of NPO and AMOC are shown in Table 3. The results indicate that the phase changes of NPO and AMOC contribute 14%-41% to the explained covariance between AMOC and AGI, suggesting that NPO plays an important role.

[0101] Table 2. Figure 2 The correlation coefficients (outside parentheses) and their Fisher's r-to-z test values ​​(in parentheses). The values ​​in parentheses are confidence intervals estimated based on Fisher's r-to-z test (95% significance level). HIST(PI) represents the historical (piControl) large sample set.

[0102]

[0103]

[0104] Table 3. Squared values ​​of correlation coefficients (r) in Table 2 2 HIST(PI) represents the historical (piControl) large sample set.

[0105]

[0106] The moderating effect of NPO on the influence of AMOC on SAT was further investigated. Figure 3 This represents the spatial synthesis field of SAT and SLP anomalies under different combinations of AMOC and NP0, where NPO leads AMOC by three years. In the positive phase of AMOC ( Figure 3 (a) The North Atlantic and Arctic Oceans show significant SAT warming anomalies. Conversely, in the negative phase of AMOC (b) Figure 3The aforementioned region exhibits a significant SAT cold anomaly. This indicates that the AMOC dominates the interdecadal SAT variation in the Arctic. When the AMOC is in a certain phase, the NPO- phase is more conducive to warming in northern North America and the Arctic Ocean than the NPO+ phase. Figure 3 (c, f). Furthermore, when AMOC remains in the negative phase, the SAT amplitude in North America and the Arctic Ocean is in the NPO-phase ( Figure 3 f) is better than NPO+phase ( Figure 3 The increase in SAT (c) is greater (approximately 0.1°C). During the NPO- phase, the zonal averages of SAT and SLP north of 60°N are also greater than during the NPO+ phase. In the NPO- phase, the increase in TCWV is greater than in the NPO+ phase. This verifies that in different phases of the AMOC, the NPO- phase promotes Arctic SAT warming through meridional water vapor advection. Furthermore, the influence of NPO is greater during the negative phase of the AMOC.

[0107] Figure 1 This diagram illustrates the physical processes by which different NPO phases and AMOC types influence the Arctic SAT. (Left) NPO negative phase (NPO-) and trans-basin AMOC; (Right) NPO positive phase (NPO+) and intra-Atlantic circulation AMOC.

[0108] Example 1

[0109] A method for predicting and analyzing the regulation mechanisms of multimodal Arctic climate change includes the following steps:

[0110] Step 1: Construct and validate a large-sample dataset containing AMOC proxy data and historical Arctic SAT observation data, and ensure its reliability and accuracy through comparison and statistical testing;

[0111] Step 1.1 Collect and organize observational data, including AMOC proxy data and historical observational data of Arctic SAT;

[0112] Step 1.2 Construct a large sample dataset in CMIP6 mode to ensure data quality and integrity;

[0113] Step 1.3 Compare the observation data and model output to verify the reliability of the dataset;

[0114] Step 1.4 assesses the consistency and accuracy of the dataset through statistical tests.

[0115] Step 2: By simulating and comparing the CMIP6 model output with observational data, evaluate and identify the model's performance and biases in simulating AMOC and Arctic SAT changes;

[0116] Step 2.1 Use the constructed dataset to simulate the CMIP6 model and obtain the simulation results of AMOC and Arctic SAT;

[0117] Step 2.2 compares the simulation results with the observational data to evaluate the model's ability to simulate changes in AMOC and Arctic SAT;

[0118] Step 2.3 Identify the deviations and uncertainties of the model during the simulation process through comparative analysis.

[0119] Step 3: Reveal the physical mechanism of AMOC's influence on Arctic SAT, establish quantitative relationships through diagnostic analysis, thermodynamic and kinetic studies, and calculate relevant physical parameters;

[0120] Step 3.1 Compare and analyze observational and simulated data to diagnose the physical processes by which AMOC affects Arctic SAT, based on the formula for calculating the meridional overturning stream function (MSF):

[0121]

[0122] Where lev B It is the depth of the ocean floor, lon w It is the western boundary of the ocean. e It is the eastern boundary of the ocean. v(time, lev, lat, lon) represents the northward flow velocity of the ocean. lev represents the vertical layer, indicating different depth layers in the ocean. lat represents latitude, lon represents longitude, dlon represents the infinitesimal element of longitude, used for integration calculations, and dlev represents the infinitesimal element of the vertical layer, used for integration calculations.

[0123] The AMOC index is calculated as the maximum MSF value north of 20°N and below 500m in the Atlantic region, based on the definition of the Arctic Gradient Index (AGI):

[0124] AGI = SAT 60N-90N -SAT 0-90N

[0125] Among them, SAT 60N-90N and SAT 0-90N This is an area-weighted average, where 60N-90N represents the area from 60°N to 90°N, and 0-90N represents the area from the equator to 90°N (the North Pole).

[0126] Step 3.2 Through thermodynamic and kinetic analysis, the mechanism of atmospheric-ocean heat transfer to the poles and the interaction between the ocean and the atmosphere are revealed. The calculation formula for atmospheric heat transfer (AHT) is as follows:

[0127]

[0128] Where φ is latitude, φ s It's the latitude of Antarctica, Flx s and Flx TOA R represents the net heat flux downwards from the Earth's surface and the net incident radiation at the top of the atmosphere, respectively. E λ represents the Earth's radius, λ represents the radial longitude, and ocean heat transport is the output of the model diagnostics.

[0129] Step 3.3 Establish a quantitative relationship between physical processes and changes in AMOC and Arctic SAT. The energy budget calculation formula for SAT is as follows:

[0130]

[0131] On the left side of the equation, T air S represents SAT, Δ represents annual anomaly, and positive anomalies correspond to a downward direction. v / S d F represents upward / downward shortwave radiation. latent / Fs ensible Represents latent heat / sensible heat flux, l d It is long-wave radiation, R is the residual term, and T is the long-wave radiation. s σ is the surface temperature, σ is the Stephen-Boltzmann constant, and ε is the surface emissivity.

[0132] Step 4: Investigate the regulatory role of NPO internal variability on the relationship between AMOC and Arctic SAT, determine its correlation through statistical analysis, and assess how NPO regulates Arctic SAT by influencing atmospheric and oceanic circulation;

[0133] Step 4.1 Analyze the impact of internal variability such as NPO on the relationship between AMOC and Arctic SAT. The NPO index is defined as the normalized sea level anomaly in the mid-latitudes of the North Pacific (45°-65°N, 170°E-140°W) minus the normalized sea level pressure anomaly in the subtropics (20°-40°N, 170°E-140°W). The formula for calculating the NPO index is as follows:

[0134]

[0135] SLP mid It is a standardized sea-level pressure anomaly in the mid-latitude region of the North Pacific (45°-65°N, 170°E-140°W);

[0136] SLP sub It is a standardized sea-level pressure anomaly in the subtropical region (20°-40°N, 170°E-140°W);

[0137] This represents the average difference between sea level pressure anomalies in two regions.

[0138] σSLP mid and σSLP sub These are the standard deviations of sea level pressure anomalies in the two regions.

[0139] Step 4.2 uses statistical analysis to determine the correlation between NPO and AMOC and Arctic SAT:

[0140]

[0141] r NPO，AMOC : Represents the correlation coefficient between NPO and AMOC; r NPO，SAT : Represents the correlation coefficient between NPO and SAT; NPO i AMOC i SAT i These are the NPO index, AMOC index, and Arctic SAT value at the i-th time point, respectively; n is the total number of time points.

[0142] Step 4.3 Investigate how NPOs regulate atmospheric and oceanic circulation, thereby affecting Arctic SAT.

[0143] Step 4.3.1: Assess the impact of NPO on meridional atmospheric heat transport (AHT):

[0144] ΔAHT=CΔv

[0145] Where ΔAHT represents the change in meridional atmospheric heat transport, C is a proportionality constant representing the sensitivity of meridional atmospheric heat transport to changes in the northward ocean current velocity, and Δv represents the change in northward current velocity between the positive and negative phases of the NPO:

[0146]

[0147] and These represent the northward ocean current speeds during the positive and negative phases of the NPO, respectively.

[0148] 4.3.2 How does the NPO influence the Atlantic Meridian Overturning Circulation (AMOC) through air-sea interaction, and consequently the Arctic Saturn Atmosphere (SAT)? Here, changes in Δv may lead to changes in AMOC, expressed as:

[0149]

[0150] ΔAMOC reflects the change in AMOC intensity caused by the NPO phase change. The change in Δv affects ΔAMOC through the following pathways:

[0151] If Δv > 0, it indicates that the northward flow velocity increases, which will lead to more heat being transported to the North Atlantic and enhance AMOC, i.e., ΔAMOC > 0.

[0152] If Δv < 0, it indicates that the northward flow velocity is reduced, which will lead to a decrease in heat transport and weaken AMOC, i.e., ΔAMOC < 0;

[0153] The NPO influences the Arctic SAT by modulating atmospheric and oceanic circulation, affecting northward flow velocity Δv and AMOC intensity ΔAMOC.

[0154] Step 5: Based on the understanding of physical mechanisms and internal variability regulation, simulate future climate change scenarios, adjust model parameters to accurately reflect the impact of AMOC and NPO on Arctic SAT, and input emission scenarios to predict future trends in Arctic SAT and AMOC in order to assess the reliability of model predictions.

[0155] Step 5.1, and the compared models, combined with the revealed physical mechanisms and internal variability regulation, to simulate future climate change scenarios;

[0156] Step 5.1.1: When simulating future climate change, incorporate the impact mechanism of AMOC on Arctic SAT revealed in Step 3 into the model, and accurately represent changes in heat transport, salinity, and ocean productivity in the model.

[0157] Step 5.1.2: Integrate the regulatory effect of the internal variability of NPO analyzed in Step 4 into the simulation, including setting a parameterization scheme in the model that can simulate the changes of NPO. The changes of NPO will be fed back to AMOC and Arctic SAT.

[0158] Step 5.2 Adjust the model parameters to better reflect the observed physical processes and the effects of internal variability;

[0159] Step 5.2.1: Based on the results of Steps 3 and 4, adjust the model parameters to ensure that the model can better simulate the impact of AMOC and NPO on Arctic SAT, including adjusting the air-sea coupling parameters to reflect the transport of heat and salinity.

[0160] Step 5.2.2: Adjust the internal variability parameters in the model, including the characterization of NPO, to ensure that the model can simulate changes in internal variability and their impact on AMOC and Arctic SAT.

[0161] Step 5.3 Input future RCP or SSP emission scenarios to simulate changes in AMOC and Arctic SAT over the next few decades to hundreds of years.

[0162] Step 5.4 Perform statistical analysis on the simulation results, extract the predicted trends of future Arctic SAT and AMOC, and compare them with the findings in Steps 3 and 4 to evaluate the reliability of the model predictions.

Claims

1. A method for predicting and analyzing the regulatory mechanisms of multimodal Arctic climate change, characterized in that, Includes the following steps: Step 1: Construct and validate a large-sample dataset containing AMOC proxy data and historical Arctic SAT observation data, and ensure its reliability and accuracy through comparison and statistical testing; Step 2: By simulating and comparing the CMIP6 model output with observational data, evaluate and identify the model's performance and biases in simulating AMOC and Arctic SAT changes; Step 3: Reveal the physical mechanism of AMOC's influence on Arctic SAT, establish quantitative relationships through diagnostic analysis, thermodynamic and kinetic studies, and calculate relevant physical parameters; Step 4: Investigate the regulatory role of NPO internal variability on the relationship between AMOC and Arctic SAT, determine its correlation through statistical analysis, and assess how NPO regulates Arctic SAT by influencing atmospheric and oceanic circulation; Step 5: Based on the understanding of physical mechanisms and internal variability regulation, simulate future climate change scenarios, adjust model parameters to accurately reflect the impact of AMOC and NPO on Arctic SAT, and input emission scenarios to predict future trends in Arctic SAT and AMOC in order to assess the reliability of model predictions.

2. The method for predicting and analyzing the multimodal Arctic climate change and its regulatory mechanisms according to claim 1, characterized in that, Step 1 includes the following steps: Step 1.1: Collect and organize observational data, including AMOC proxy data and historical observational data of Arctic SAT; Step 1.2: Construct a large sample dataset in CMIP6 mode to ensure data quality and integrity; Step 1.3: Compare the observation data and model output to verify the reliability of the dataset; Step 1.4: Evaluate the consistency and accuracy of the dataset through statistical tests.

3. The method for predicting and analyzing the multimodal Arctic climate change and its regulatory mechanisms according to claim 1, characterized in that, Step 2 includes the following steps: Step 2.1: Use the constructed dataset to simulate the CMIP6 model and obtain the simulation results of AMOC and Arctic SAT; Step 2.2: Compare the simulation results with the observational data to evaluate the model's ability to simulate changes in AMOC and Arctic SAT; Step 2.3: Identify the deviations and uncertainties of the model during the simulation process through comparative analysis.

4. The method for predicting and analyzing the multimodal Arctic climate change and its regulatory mechanisms according to claim 1, characterized in that, Step 3 includes the following steps: Step 3.1: Compare and analyze observational and simulated data to diagnose the physical processes by which AMOC affects Arctic SAT, based on the calculation formula of the meridional overturning stream function (MSF): Where lev B It is the depth of the ocean floor, lon w It is the western boundary of the ocean. e It represents the eastern boundary of the ocean. v(time, lev, lat, lon) represents the northward flow velocity of the ocean, lev represents the vertical layer indicating different depths in the ocean, 1at represents latitude, 1on represents longitude, and d1o... n The infinitesimal element representing longitude is used for integration operations, and d1ev represents the infinitesimal element representing the vertical level, which is also used for integration operations. The AMOC index is calculated as the maximum MSF value north of 20°N and below 500m in the Atlantic region, based on the definition of the Arctic Gradient Index (AGI): AGI=SAT 60N-90N -VILLAGE 0-90N Among them, SAT 60N-90N and SAT 0-90N This is an area-weighted average, where 60N-90N represents the area from 60°N to 90°N, and 0-90N represents the area from the equator to 90°N (the North Pole). Step 3.2: Through thermodynamic and kinetic analysis, the atmospheric-ocean interaction and the mechanism of heat transport to the poles are revealed. The calculation formula for atmospheric heat transport (AHT) is as follows: Where φ is latitude, φ s It's the latitude of Antarctica, Flx s and Flx TOA R represents the net heat flux downwards from the Earth's surface and the net incident radiation at the top of the atmosphere, respectively. E λ represents the Earth's radius, λ represents the radial longitude, and ocean heat transport is the output of the model diagnostics. Step 3.3: Establish a quantitative relationship between physical processes and changes in AMOC and Arctic SAT. The energy budget calculation formula for SAT is as follows: On the left side of the equation, T air S represents SAT, Δ represents annual anomaly, and positive anomalies correspond to a downward direction. u / S d F represents upward / downward shortwave radiation. latent / F sensible Represents latent heat / sensible heat flux, l d It is long-wave radiation, R is the residual term, and T is the long-wave radiation. s σ is the surface temperature, σ is the Stephen-Boltzmann constant, and ε is the surface emissivity.

5. The method for predicting and analyzing the multimodal Arctic climate change and its regulatory mechanisms according to claim 1, characterized in that, Step 4 includes the following steps: Step 4.1: Analyze the impact of internal variability such as NPO on the relationship between AMOC and Arctic SAT. The NPO index is defined as the normalized sea level anomaly in the mid-latitudes of the North Pacific (45°-65°N, 170°E-140°W) minus the normalized sea level pressure anomaly in the subtropics (20°-40°N, 170°E-140°W). The formula for calculating the NPO index is as follows: SLP mid It is a standardized sea-level pressure anomaly in the mid-latitude region of the North Pacific (45°-65°N, 170°E-140°W); SLP sub It is a standardized sea-level pressure anomaly in the subtropical region (20°-40°N, 170°E-140°W); This represents the average difference between sea level pressure anomalies in two regions. σSLP mid and σSLP sub These are the standard deviations of sea-level pressure anomalies in the two regions, respectively. Step 4.2: Through statistical analysis, determine the correlation between NPO, AMOC, and Arctic SAT: r NPO,AMOC : Represents the correlation coefficient between NPO and AMOC; r NPO,SAT : Represents the correlation coefficient between NPO and SAT; NPO i AMOC i SAT i These are the NPO index, AMOC index, and Arctic SAT value at the i-th time point, respectively; n is the total number of time points. Step 4.3: Investigate how NPOs regulate atmospheric and oceanic circulation, thereby affecting Arctic SAT.

6. The method for predicting and analyzing the multimodal Arctic climate change and its regulatory mechanisms according to claim 5, characterized in that, Step 4.3 includes the following steps: Step 4.3.1: Assess the impact of NPO on meridional atmospheric heat transport (AHT): ΔAHT=CΔ v Where ΔAHT represents the change in meridional atmospheric heat transport, C is a proportionality constant representing the sensitivity of meridional atmospheric heat transport to changes in the northward ocean current velocity, and Δv represents the change in northward current velocity between the positive and negative phases of the NPO: and These represent the northward ocean current velocities during the positive and negative phases of the NPO, respectively. Step 4.3.2: How does the NPO influence the Atlantic Meridian Overturning Circulation (AMOC) through air-sea interaction, and consequently affect the Arctic Saturn Atmosphere (SAT)? Here, changes in Δv may lead to changes in AMOC, expressed as: ΔAMOC reflects the change in AMOC intensity caused by the NPO phase change. The change in Δv affects ΔAMOC through the following pathways: If Δv>0, it indicates that the northward flow velocity increases, which will lead to more heat being transported to the North Atlantic and enhance AMOC, i.e., ΔAMOC>0. If Δv < 0, it indicates that the northward flow velocity is reduced, which will lead to a decrease in heat transport and weaken AMOC, i.e., ΔAMOC < 0; The NPO influences the Arctic SAT by modulating atmospheric and oceanic circulation, affecting northward flow velocity Δv and AMOC intensity ΔAMOC.

7. The method for predicting and analyzing the regulation mechanism of multimodal Arctic climate change according to claim 1, characterized in that, Step 5 includes the following steps: Step 5.1, and the compared models, combined with the revealed physical mechanisms and internal variability regulation, to simulate future climate change scenarios; Step 5.2: Adjust the model parameters to better reflect the observed physical processes and the influence of internal variability; Step 5.3: Input future RCP or SSP emission scenarios to simulate changes in AMOC and Arctic SAT over the next few decades to hundreds of years; Step 5.4: Perform statistical analysis on the simulation results, extract the predicted trends of future Arctic SAT and AMOC, and compare them with the findings in Steps 3 and 4 to evaluate the reliability of the model predictions.

8. The method for predicting and analyzing the multimodal Arctic climate change and its regulatory mechanisms according to claim 7, characterized in that, Step 5.1 includes the following steps: Step 5.1.1: When simulating future climate change, incorporate the impact mechanism of AMOC on Arctic SAT revealed in Step 3 into the model, and accurately represent changes in heat transport, salinity, and ocean productivity in the model. Step 5.1.2: Integrate the regulatory effect of the internal variability of NPO analyzed in Step 4 into the simulation, including setting a parameterization scheme in the model that can simulate the changes of NPO. The changes of NPO will be fed back to AMOC and Arctic SAT.

9. The method for predicting and analyzing the multimodal Arctic climate change and its regulatory mechanisms according to claim 7, characterized in that, Step 5.2 includes the following steps: Step 5.2.1: Based on the results of Steps 3 and 4, adjust the model parameters to ensure that the model can better simulate the impact of AMOC and NPO on Arctic SAT, including adjusting the air-sea coupling parameters to reflect the transport of heat and salinity. Step 5.2.2: Adjust the internal variability parameters in the model, including the characterization of NPO, to ensure that the model can simulate changes in internal variability and their impact on AMOC and Arctic SAT.

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