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Python | 不同 CO₂ 浓度下全球地表气温变化及其响应机制分析

  • 2026-06-29 07:50:34
Python | 不同 CO₂ 浓度下全球地表气温变化及其响应机制分析

不同 CO₂ 浓度下全球地表气温变化及其响应机制分析

项目简介

本项目利用 PLASIM-GENIE 气候系统模型输出的三组不同 CO₂ 浓度（控制实验 + 两倍/四倍 CO₂）全球表面温度数据，开展以下工作：

  • • 不同 CO₂ 浓度情景与控制实验之间的温度差值合成分析
  • • 基于 蒙特卡洛 随机重采样的显著性检验（p < 0.05）
  • • 显著增暖/降温区域的空间特征分析（包括强度、范围、季节差异等）
项目地址：https://www.heywhale.com/mw/project/6425374f48d5970ec2659b0c

项目信息

  • • 作者：@GeoPanda
  • • 项目主页：不同CO2浓度下全球表面温度的合成分析

致谢与声明

  • • 合成分析方法与可视化部分参考并学习了摸鱼大佬的优秀项目：→ 摸鱼气象Python，在此表示衷心感谢！
  • • 蒙特卡洛显著性检验、特征提取与分析模块为作者独立编写，属于个人对气候合成分析方法的初步探索。如有任何不严谨或可改进之处，欢迎在评论区交流讨论，共同进步！

1.研究背景

大气二氧化碳（CO₂）浓度的持续升高是全球变暖的核心驱动力之一（IPCC, 2021）。深入理解地表气温在不同 CO₂ 浓度条件下的变化规律与响应机制，对预测未来气候变化具有重要意义。本案例基于 Planet Simulator（PLASIM）模式模拟了多种 CO₂ 浓度情境，并通过合成分析、t 检验和蒙特卡洛检验评估地表气温的变化特征与统计显著性，并进一步探究温度变化背后的物理机制。

导入库

import xarray as xrimport pandas as pdimport numpy as npimport globimport osimport numpy as npimport matplotlib.pyplot as plt

2. 模型设计与实验方案

本研究基于 PlaSim–GENIE 耦合气候模型（中分辨率大气环流模型 PlaSim 与地球系统模型 GENIE 耦合）设计了以下 CO₂ 浓度情景实验：

实验编号
CO₂ 浓度 (ppm)
实验名称
备注
Ctrl
280
工业革命前水平
基准对照实验
2×CO₂
560
两倍 CO₂
4×CO₂
1120
四倍 CO₂
Sens-0.5
280.5 / 560.5 / 1120.5
±0.5 ppm 敏感性试验
检验 CO₂ 浓度微扰响应

2.1 模拟时长与稳定阶段选取

  • • 每组实验均积分 150 年。
  • • 前 50 年（对应 1850–1900 年）为快速升温调整期，全球平均温度上升显著。
  • • 第 51–150 年进入准平衡态，其中 第 50–60 年（对应约 1940–1950 年）波动最小、趋势最平稳，选为后续合成分析的稳定窗口（共 11 年数据）。

2.2 模型行为诊断

  • • 在 280 ppm 基准实验中，第 50 年之后全球平均气温出现非物理的缓慢下降趋势（约 –0.08 °C/世纪），表明模型在极低 CO₂ 浓度下的长期能量平衡存在一定偏差。
  • • 560 ppm 和 1120 ppm 实验在第 50 年后均保持良好准平衡态，波动幅度 < 0.03 °C/十年，满足合成分析要求。

说明：上述低浓度偏差不影响本研究主要结论（以 560 ppm 与 1120 ppm 相对于 280 ppm 的差值分析为主），但已在结果讨论中予以说明并进行稳健性检验。

3. 代码实现

读取不同CO2浓度下的nc文件,并返回10年时间序列以及均值

def get_dataset(path,file_count,attribute_name):    file = glob.glob(os.path.join(path, "*.nc"))    nc=xr.open_dataset(file[0])    attribute_dataset= np.zeros((file_count,nc.latitude.shape[0], nc.longitude.shape[0]))    for i in range(0,file_count):        nc_name=file[i]        nc=xr.open_dataset(nc_name)        attribute_dataset[i]=nc[attribute_name].values    mean_attribute=attribute_dataset.mean(0)    return  attribute_dataset,mean_attribute
path='/home/mw/input/PLASIM_tem7051/PLASIM_surface_tem (2)/PLASIM_surface_tem/280ppm'file = glob.glob(os.path.join(path, "*.nc"))
CO2_280ppm,mean_tem_280ppm=get_dataset(r'/home/mw/input/PLASIMsimple8604/PLASIM_surface_tem/PLASIM_surface_tem/280ppm',10,'puma_temperature_surface')CO2_560ppm,mean_tem_560ppm=get_dataset(r'/home/mw/input/PLASIMsimple8604/PLASIM_surface_tem/PLASIM_surface_tem/560ppm',10,'puma_temperature_surface')CO2_1120ppm,mean_tem_1120ppm=get_dataset(r'/home/mw/input/PLASIMsimple8604/PLASIM_surface_tem/PLASIM_surface_tem/1120ppm',10,'puma_temperature_surface')CO2_2805ppm,mean_tem_2805ppm=get_dataset(r'/home/mw/input/PLASIMsimple8604/PLASIM_surface_tem/PLASIM_surface_tem/280.5ppm',10,'puma_temperature_surface')CO2_5605ppm,mean_tem_5605ppm=get_dataset(r'/home/mw/input/PLASIMsimple8604/PLASIM_surface_tem/PLASIM_surface_tem/560.5ppm',10,'puma_temperature_surface')CO2_11205ppm,mean_tem_11205ppm=get_dataset(r'/home/mw/input/PLASIMsimple8604/PLASIM_surface_tem/PLASIM_surface_tem/1120.5ppm',10,'puma_temperature_surface')
#查看返回数据集的维度np.mean(CO2_280ppm, axis=(1,2)).shape

合成差值分析，查看不同CO2浓度下地表温度的异常

import matplotlib.pyplot as pltimport cartopy.crs as ccrsimport cartopy.feature as cfeatureimport cartopy.mpl.ticker as ctickerimport cmaps
#计算温差tem_ano_560ppm=mean_tem_560ppm-mean_tem_280ppmtem_ano_1120ppm=mean_tem_1120ppm-mean_tem_280ppm
print('{}ppmCO2浓度下地表温差范围：min:{}  max:{}'.format(560,tem_ano_560ppm.min(),tem_ano_560ppm.max()))print('{}ppmCO2浓度下地表温差范围：min:{}  max:{}'.format(1120,tem_ano_1120ppm.min(),tem_ano_1120ppm.max()))
print('{}ppmCO2浓度下全球增温：{:.2f}°'.format(560,tem_ano_560ppm.mean()))print('{}ppmCO2浓度下全球增温：{:.2f}°'.format(1120,tem_ano_1120ppm.mean()))
#读取数据经纬度nc=xr.open_dataset(r'/home/mw/input/PLASIMsimple8604/PLASIM_surface_tem/PLASIM_surface_tem/280ppm/0051-12-30_plasim.nc')lat = nc.latitudelon = nc.longitude#绘图fig = plt.figure(figsize=(12,8))ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.4],projection = ccrs.PlateCarree(central_longitude=180))ax1.set_extent([-180,180,-90,90], crs=ccrs.PlateCarree())ax1.add_feature(cfeature.LAKES, alpha=0.5)ax1.set_xticks(np.arange(-180,210,30), crs=ccrs.PlateCarree())ax1.set_yticks(np.arange(-90,120,30), crs=ccrs.PlateCarree())lon_formatter = cticker.LongitudeFormatter()lat_formatter = cticker.LatitudeFormatter()ax1.xaxis.set_major_formatter(lon_formatter)ax1.yaxis.set_major_formatter(lat_formatter)ax1.set_title('(a) Surface Temperature. anomaly in 580ppm ',loc='left',fontsize=18)c1 = ax1.contourf(lon,lat, tem_ano_560ppm,levels =np.arange(0,12,0.5) ,                      extend = 'both', transform=ccrs.PlateCarree(), cmap=cmaps.NCV_jaisnd)ax1.add_feature(cfeature.COASTLINE.with_scale('50m')) ax2 = fig.add_axes([0.7, 0.1, 0.8, 0.4],projection = ccrs.PlateCarree(central_longitude=180))ax2.set_extent([-180,180,-90,90], crs=ccrs.PlateCarree())ax2.add_feature(cfeature.COASTLINE.with_scale('50m')) ax2.add_feature(cfeature.LAKES, alpha=0.5)ax2.set_xticks(np.arange(-180,210,30), crs=ccrs.PlateCarree())ax2.set_yticks(np.arange(-90,120,30), crs=ccrs.PlateCarree())ax2.xaxis.set_major_formatter(lon_formatter)ax2.yaxis.set_major_formatter(lat_formatter)ax2.set_title('(b) Surface Temperature. anomaly in 1120ppm ',loc='left',fontsize=18)c2 = ax2.contourf(lon,lat, tem_ano_1120ppm, zorder=0,levels =np.arange(0,12,0.5) ,                      extend = 'both', transform=ccrs.PlateCarree(), cmap=cmaps.NCV_jaisnd)position=fig.add_axes([0.3, 0.02,  0.35, 0.025])fig.colorbar(c1,cax=position,orientation='horizontal',format='%d',)        position=fig.add_axes([0.9, 0.02,  0.35, 0.025])fig.colorbar(c2,cax=position,orientation='horizontal',format='%d',) 

显著性检验，采用T检验

from scipy.stats.mstats import ttest_ind
_,p_560ppm = ttest_ind(CO2_560ppm,CO2_280ppm)_,p_1120ppm = ttest_ind(CO2_1120ppm,CO2_280ppm)
#查看不显著点个数print((p_560ppm>0.01).flatten().sum(0))print((p_1120ppm>0.01).flatten().sum(0))

在合成分析结果上叠加显著性检验结果

fig = plt.figure(figsize=(12,8))ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.4],projection = ccrs.PlateCarree(central_longitude=180))ax1.set_extent([-180,180,-90,90], crs=ccrs.PlateCarree())ax1.add_feature(cfeature.LAKES, alpha=0.5)ax1.set_xticks(np.arange(-180,210,30), crs=ccrs.PlateCarree())ax1.set_yticks(np.arange(-90,120,30), crs=ccrs.PlateCarree())lon_formatter = cticker.LongitudeFormatter()lat_formatter = cticker.LatitudeFormatter()ax1.xaxis.set_major_formatter(lon_formatter)ax1.yaxis.set_major_formatter(lat_formatter)ax1.set_title('(a) Surface Temperature. anomaly in 580ppm ',loc='left',fontsize=18)c1 = ax1.contourf(lon,lat, tem_ano_560ppm,levels =np.arange(0,12,0.5) ,                      extend = 'both', transform=ccrs.PlateCarree(), cmap=cmaps.NCV_jaisnd)c1p = ax1.contourf(lon,lat,p_560ppm, levels =[0,0.05,1],hatches=['...', None],zorder=1,colors="none", transform=ccrs.PlateCarree())ax1.add_feature(cfeature.COASTLINE.with_scale('50m')) ax2 = fig.add_axes([0.7, 0.1, 0.8, 0.4],projection = ccrs.PlateCarree(central_longitude=180))ax2.set_extent([-180,180,-90,90], crs=ccrs.PlateCarree())ax2.add_feature(cfeature.COASTLINE.with_scale('50m')) ax2.add_feature(cfeature.LAKES, alpha=0.5)ax2.set_xticks(np.arange(-180,210,30), crs=ccrs.PlateCarree())ax2.set_yticks(np.arange(-90,120,30), crs=ccrs.PlateCarree())ax2.xaxis.set_major_formatter(lon_formatter)ax2.yaxis.set_major_formatter(lat_formatter)ax2.set_title('(b) Surface Temperature. anomaly in 1120ppm ',loc='left',fontsize=18)c2 = ax2.contourf(lon,lat, tem_ano_1120ppm, zorder=0,levels =np.arange(0,12,0.5) ,                      extend = 'both', transform=ccrs.PlateCarree(), cmap=cmaps.NCV_jaisnd)c2p = ax2.contourf(lon,lat,p_1120ppm, levels =[0,0.05,1],hatches=['...', None],zorder=1,colors="none", transform=ccrs.PlateCarree())position=fig.add_axes([0.3, 0.02,  0.35, 0.025])fig.colorbar(c1,cax=position,orientation='horizontal',format='%d',)        position=fig.add_axes([0.9, 0.02,  0.35, 0.025])fig.colorbar(c2,cax=position,orientation='horizontal',format='%d',) 

显著性检验，采用蒙特卡洛检验

import numpy as npfrom scipy.stats import percentileofscore

蒙特卡洛检验,基于矩阵打乱

def matrix_permutation_test(A, B, n_permutations=1000):    """    蒙特卡罗方法对两个矩阵进行检验，返回每个元素对应的p值    :param A: 第一个矩阵    :param B: 第二个矩阵    :param n_permutations: 随机次数    :return: 每个元素的p值    """    n_rows, n_cols = A.shape    D = A - B  # 计算差异矩阵    A=A.flatten()    B=B.flatten()    permuted_ranks = np.zeros((n_permutations, n_rows, n_cols))    for i in range(n_permutations):        #生成随机序列，对A和B进行重排列        A_index=np.random.permutation(range(len(A.flatten())))        B_index=np.random.permutation(range(len(A.flatten())))        D_P =A[A_index]-B[B_index]  # 计算每个置换下的差异向量        permuted_ranks[i] = D_P.reshape(n_rows, n_cols)  #    p_values = np.zeros((n_rows, n_cols))    for i in range(n_rows):        for j in range(n_cols):            p_values[i, j] =1-percentileofscore(permuted_ranks[:, i, j].flatten(),D[i, j]) / 100  # 计算p值    return p_values

蒙特卡洛检验，基于序列打乱

def MK_test(A, B, n_permutations=1000):    """    蒙特卡罗方法对两个矩阵进行检验，返回每个元素对应的p值    :param A: 第一个时间序列    :param B: 第二个时间序列    :param n_permutations: 随机次数    :return: 每个元素的p值    """    n_length,n_rows, n_cols = A.shape    # D = A - B  # 计算差异矩阵    # A=A.flatten()    # B=B.flatten()    D=A.mean(0)-B.mean(0)    permuted_ranks = np.zeros((n_permutations*n_permutations, n_rows, n_cols))    for i in range(n_rows):            for j in range(n_cols):                # A_P=np.tile(A[:,i,j],(1,n_permutations))                # B_P=np.tile(B[:,i,j],(1,n_permutations))                # D_P =np.random.permutation( A_P)-np.random.permutation( B_P)                # permuted_ranks[:, i, j]= D_P                 for n in range(n_permutations):                    #生成随机序列，对A和B进行重排列                    D_P =np.random.permutation(A[:,i,j])-np.random.permutation(B[:,i,j])  # 计算每个置换下的差异向量                    permuted_ranks[n*n_length:(n+1)*n_length, i, j]= D_P     p_values = np.zeros((n_rows, n_cols))    for i in range(n_rows):        for j in range(n_cols):            p_values[i, j] =1-percentileofscore(permuted_ranks[:, i, j],D[i, j]) / 100  # 计算p值    return p_values

蒙特卡洛检验，基于序列打乱

def MK_test(A, B, n_permutations=1000):    """    蒙特卡罗方法对两个矩阵进行检验，返回每个元素对应的p值    :param A: 第一个时间序列    :param B: 第二个时间序列    :param n_permutations: 随机次数    :return: 每个元素的p值    """    n_length,n_rows, n_cols = A.shape    # D = A - B  # 计算差异矩阵    # A=A.flatten()    # B=B.flatten()    D=A.mean(0)-B.mean(0)    permuted_ranks = np.zeros((n_permutations*n_permutations, n_rows, n_cols))    for i in range(n_rows):            for j in range(n_cols):                for n in range(n_permutations):                    #构造样本值数组                    values=np.concatenate((A[:,i,j],B[:,i,j]),axis=0)                    #生成随机序列，对A和B进行重排列                    D_P =np.random.permutation(values)[:10]-np.random.permutation(values)[:10]  # 计算每个置换下的差异向量                    permuted_ranks[n*n_length:(n+1)*n_length, i, j]= D_P     p_values = np.zeros((n_rows, n_cols))    for i in range(n_rows):        for j in range(n_cols):            p_values[i, j] =1-percentileofscore(permuted_ranks[:, i, j],D[i, j]) / 100  # 计算p值    return p_values
A=np.concatenate((CO2_560ppm[:,1,1],CO2_560ppm[:,1,1]),axis=0)np.random.permutation(A)[:10]p_560ppm=MK_test(CO2_560ppm,CO2_280ppm,n_permutations=500)p_1120ppm=MK_test(CO2_1120ppm,CO2_280ppm,n_permutations=500)sum(p_1120ppm<0.05).sum()sum(p_560ppm<0.05).sum()
fig = plt.figure(figsize=(12,8))ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.4],projection = ccrs.PlateCarree(central_longitude=180))ax1.set_extent([-180,180,-90,90], crs=ccrs.PlateCarree())ax1.add_feature(cfeature.LAKES, alpha=0.5)ax1.set_xticks(np.arange(-180,210,30), crs=ccrs.PlateCarree())ax1.set_yticks(np.arange(-90,120,30), crs=ccrs.PlateCarree())lon_formatter = cticker.LongitudeFormatter()lat_formatter = cticker.LatitudeFormatter()ax1.xaxis.set_major_formatter(lon_formatter)ax1.yaxis.set_major_formatter(lat_formatter)ax1.set_title('(a) Surface Temperature. anomaly in 580ppm ',loc='left',fontsize=18)c1 = ax1.contourf(lon,lat, tem_ano_560ppm,levels =np.arange(0,12,0.5) ,                      extend = 'both', transform=ccrs.PlateCarree(), cmap=cmaps.NCV_jaisnd)c1p = ax1.contourf(lon,lat,p_560ppm, levels =[0,0.05,1],hatches=['...', None],zorder=1,colors="none", transform=ccrs.PlateCarree())ax1.add_feature(cfeature.COASTLINE.with_scale('50m')) ax2 = fig.add_axes([0.7, 0.1, 0.8, 0.4],projection = ccrs.PlateCarree(central_longitude=180))ax2.set_extent([-180,180,-90,90], crs=ccrs.PlateCarree())ax2.add_feature(cfeature.COASTLINE.with_scale('50m')) ax2.add_feature(cfeature.LAKES, alpha=0.5)ax2.set_xticks(np.arange(-180,210,30), crs=ccrs.PlateCarree())ax2.set_yticks(np.arange(-90,120,30), crs=ccrs.PlateCarree())ax2.xaxis.set_major_formatter(lon_formatter)ax2.yaxis.set_major_formatter(lat_formatter)ax2.set_title('(b) Surface Temperature. anomaly in 1120ppm ',loc='left',fontsize=18)c2 = ax2.contourf(lon,lat, tem_ano_1120ppm, zorder=0,levels =np.arange(0,12,0.5) ,                      extend = 'both', transform=ccrs.PlateCarree(), cmap=cmaps.NCV_jaisnd)c2p = ax2.contourf(lon,lat,p_1120ppm, levels =[0,0.05,1],hatches=['...', None],zorder=1,colors="none", transform=ccrs.PlateCarree())position=fig.add_axes([0.3, 0.02,  0.35, 0.025])fig.colorbar(c1,cax=position,orientation='horizontal',format='%d',)        position=fig.add_axes([0.9, 0.02,  0.35, 0.025])fig.colorbar(c2,cax=position,orientation='horizontal',format='%d',) 

4. 结果分析

地表温度变化空间特征分析

显著性检验结果表明，不同 CO₂ 浓度情景下的地表温度合成结果均具有统计显著性。t 检验显示，在 560 ppm 情景下共有 7 个格点未通过显著性检验，而在 1120 ppm 情景下，仅有 2 个格点不显著。进一步采用蒙特卡洛检验进行验证后发现，1120 ppm 情景下所有格点均通过显著性检验，而 560 ppm 情景下仍存在 2 个格点未达显著水平。

合成分析结果显示，相较于 280 ppm（工业化前基准情景），在 560 ppm 情景下全球地表年均温升为 2.98°C，1120 ppm 情景下则升高至 5.74°C。空间分布上，极地和亚洲地区是主要的增温热点；非洲、北美、南美和澳大利亚亦表现出显著的气温上升。整体来看，陆地和高纬地区的增温幅度明显高于海洋区域，体现出“极地放大”与“陆地-海洋温差”的典型特征。

不同 CO₂ 情景下全球地表温度空间分布图

经纬向变化规律

#读取数据经纬度nc=xr.open_dataset(r'/home/mw/input/PLASIM_tem7051/PLASIM_surface_tem/PLASIM_surface_tem/1120.5ppm/0051-12-30_plasim.nc')lat = nc.latitudelon = nc.longitudeimport matplotlib.pyplot as pltfig=plt.figure(figsize=(4,4),dpi=150)ax=fig.add_axes([0,0,1,1])x=lon.valuesy1=tem_ano_560ppm.mean(0)y2=tem_ano_1120ppm.mean(0)y3=y2-y1ax.plot(x,y1,ls='-.',c='blue',fillstyle='none',label='560ppm')ax.plot(x,y2,ls=':',c='red',label='1120ppm')ax.set_ylabel('warming amplitude ($^\circ$C)')ax.set_xticks(np.arange(0, 420, 60))lon_formatter = cticker.LongitudeFormatter()ax.xaxis.set_major_formatter(lon_formatter)ax.set_xlabel('Longitude')ax.fill_between(x,y1,y2,where=(y1>y2),interpolate=True,               facecolor='tab:blue',alpha=0.8)ax.fill_between(x,y1,y2,where=(y2>y1),interpolate=True,               facecolor='tab:orange',alpha=0.6)plt.legend()
fig=plt.figure(figsize=(4,4),dpi=150)ax=fig.add_axes([0,0,1,1])x=lat.valuesy1=tem_ano_560ppm.mean(1)y2=tem_ano_1120ppm.mean(1)ax.plot(x,y1,ls='-.',c='blue',fillstyle='none',label='560ppm')ax.plot(x,y2,ls=':',c='red',label='1120ppm')ax.set_ylabel('warming amplitude ($^\circ$C)')ax.set_xlabel('Latitude')ax.set_xticks(np.arange(-90, 120, 30))lat_formatter = cticker.LatitudeFormatter()ax.xaxis.set_major_formatter(lat_formatter)ax.fill_between(x,y1,y2,where=(y2>y1),interpolate=True,            facecolor='tab:orange',alpha=0.6)plt.legend()
从经向分布来看，东西半球在不同 CO₂ 浓度情景下的增温幅度差异整体相近，但东半球对 CO₂ 浓度上升的响应更为强烈。从纬向分析看，南北半球之间的增温幅度差异较为明显。温差主要集中在南北纬 30°之间，而在高纬度地区，北半球地表温度对 CO₂ 浓度上升的响应幅度显著高于南半球。

敏感性分析

敏感性分析考察了二氧化碳浓度微小变化下地表温度的响应。结果表明，即便是小幅度的 CO₂ 增加也会导致显著的地表增温：当 CO₂ 浓度从 560 ppm 增至 560.5 ppm 时，全球平均地表温度由 2.94°C 升高至 2.98°C；当浓度从 1120 ppm 增至 1120.5 ppm 时，温度由 5.74°C 升高至 5.83°C。这表明 CO₂ 对地表温度具有显著的强迫增温效应。
from scipy.stats.mstats import ttest_ind#计算不同浓度下的温差tem_ano_560ppm=mean_tem_560ppm-mean_tem_280ppmtem_ano_1120ppm=mean_tem_1120ppm-mean_tem_280ppmtem_ano_5605ppm=mean_tem_5605ppm-mean_tem_280ppmtem_ano_11205ppm=mean_tem_11205ppm-mean_tem_280ppm#显著性检验_,p_560ppm = ttest_ind(CO2_560ppm,CO2_280ppm)_,p_1120ppm = ttest_ind(CO2_1120ppm,CO2_280ppm)_,p_5605ppm = ttest_ind(CO2_5605ppm,CO2_280ppm)_,p_11205ppm = ttest_ind(CO2_11205ppm,CO2_280ppm)
fig = plt.figure(figsize=(12,8))ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.4],projection = ccrs.PlateCarree(central_longitude=180))ax1.set_extent([-180,180,-90,90], crs=ccrs.PlateCarree())ax1.add_feature(cfeature.LAKES, alpha=0.5)ax1.set_xticks(np.arange(-180,210,30), crs=ccrs.PlateCarree())ax1.set_yticks(np.arange(-90,120,30), crs=ccrs.PlateCarree())lon_formatter = cticker.LongitudeFormatter()lat_formatter = cticker.LatitudeFormatter()ax1.xaxis.set_major_formatter(lon_formatter)ax1.yaxis.set_major_formatter(lat_formatter)ax1.set_title('(c) Surface Temperature. anomaly in 580.5ppm ',loc='left',fontsize=18)c1 = ax1.contourf(lon,lat, tem_ano_5605ppm,levels =np.arange(0,12,0.5) ,                      extend = 'both', transform=ccrs.PlateCarree(), cmap=cmaps.NCV_jaisnd)c1p = ax1.contourf(lon,lat,p_5605ppm, levels =[0,0.05,1],hatches=['...', None],zorder=1,colors="none", transform=ccrs.PlateCarree())ax1.add_feature(cfeature.COASTLINE.with_scale('50m')) ax2 = fig.add_axes([0.7, 0.1, 0.8, 0.4],projection = ccrs.PlateCarree(central_longitude=180))ax2.set_extent([-180,180,-90,90], crs=ccrs.PlateCarree())ax2.add_feature(cfeature.COASTLINE.with_scale('50m')) ax2.add_feature(cfeature.LAKES, alpha=0.5)ax2.set_xticks(np.arange(-180,210,30), crs=ccrs.PlateCarree())ax2.set_yticks(np.arange(-90,120,30), crs=ccrs.PlateCarree())ax2.xaxis.set_major_formatter(lon_formatter)ax2.yaxis.set_major_formatter(lat_formatter)ax2.set_title('(d) Surface Temperature. anomaly in 1120.5ppm ',loc='left',fontsize=18)c2 = ax2.contourf(lon,lat, tem_ano_11205ppm, zorder=0,levels =np.arange(0,12,0.5) ,                      extend = 'both', transform=ccrs.PlateCarree(), cmap=cmaps.NCV_jaisnd)c2p = ax2.contourf(lon,lat,p_11205ppm, levels =[0,0.05,1],hatches=['...', None],zorder=1,colors="none", transform=ccrs.PlateCarree())position=fig.add_axes([0.3, 0.02,  0.35, 0.025])fig.colorbar(c1,cax=position,orientation='horizontal',format='%d',)        position=fig.add_axes([0.9, 0.02,  0.35, 0.025])fig.colorbar(c2,cax=position,orientation='horizontal',format='%d',) 
print('{}ppmCO2浓度下全球增温：{:.2f}°'.format(560,tem_ano_5605ppm.mean()))print('{}ppmCO2浓度下全球增温：{:.2f}°'.format(1120,tem_ano_11205ppm.mean()))
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  1. CONNECT:[ UseTime:0.000651s ] mysql:host=127.0.0.1;port=3306;dbname=f_mffb;charset=utf8mb4
  2. SHOW FULL COLUMNS FROM `fenlei` [ RunTime:0.000923s ]
  3. SELECT * FROM `fenlei` WHERE `fid` = 0 [ RunTime:0.000405s ]
  4. SELECT * FROM `fenlei` WHERE `fid` = 63 [ RunTime:0.000377s ]
  5. SHOW FULL COLUMNS FROM `set` [ RunTime:0.000505s ]
  6. SELECT * FROM `set` [ RunTime:0.000240s ]
  7. SHOW FULL COLUMNS FROM `article` [ RunTime:0.000611s ]
  8. SELECT * FROM `article` WHERE `id` = 487318 LIMIT 1 [ RunTime:0.008937s ]
  9. UPDATE `article` SET `lasttime` = 1783082146 WHERE `id` = 487318 [ RunTime:0.002594s ]
  10. SELECT * FROM `fenlei` WHERE `id` = 66 LIMIT 1 [ RunTime:0.000225s ]
  11. SELECT * FROM `article` WHERE `id` < 487318 ORDER BY `id` DESC LIMIT 1 [ RunTime:0.000458s ]
  12. SELECT * FROM `article` WHERE `id` > 487318 ORDER BY `id` ASC LIMIT 1 [ RunTime:0.000402s ]
  13. SELECT * FROM `article` WHERE `id` < 487318 ORDER BY `id` DESC LIMIT 10 [ RunTime:0.017273s ]
  14. SELECT * FROM `article` WHERE `id` < 487318 ORDER BY `id` DESC LIMIT 10,10 [ RunTime:0.008284s ]
  15. SELECT * FROM `article` WHERE `id` < 487318 ORDER BY `id` DESC LIMIT 20,10 [ RunTime:0.052294s ]
0.166338s