File:Ngrip dryas delta o18 1.svg

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Original file ‎(SVG file, nominally 1,461 × 599 pixels, file size: 112 KB)

Captions

Captions

Delta-O18 of Younger Dryas period in NGRIP ice core

Summary[edit]

Description
English: Delta-O18 of Younger Dryas period in NGRIP ice core
Date
Source Own work
Author Merikanto
SVG development
InfoField
 
The source code of this SVG is invalid due to an error.
 
This W3C-invalid diagram was created with an unknown SVG tool.

Late Glacial δ18O and Calcium data on the GICC05 time scale.

Calcium concentration and δ18O isotope ratios from the Greenland NGRIP, GRIP, and GISP2 ice cores on the GICC05 time scale covering the period 11.6 - 32.44 ka b2k.

http://www.iceandclimate.nbi.ku.dk/data/GICC05_NGRIP_GRIP_GISP2_data_20y_27nov2006.xls http://www.iceandclimate.nbi.ku.dk/data/

% DATA REFERENCES: % % NGRIP Calcium data: M. Bigler, 2004. Hochauflösende Spurenstoffmessungen an polaren Eisbohrkernen: % Glaziochemische und klimatische Prozessstudien, Ph.D. dissertation, University of Bern, Switzerland. % % NGRIP d18O: NGRIP members, 2004. High-resolution record of Northern Hemisphere climate extending into % the last interglacial period. Nature 431, 147–151. % % GRIP Calcium: K. Fuhrer et al., 1993. Continuous measurements of hydrogen peroxide, formaldehyde, calcium % and ammonium concentrations along the new GRIP ice core from Summit, Central Greenland. % Atmospheric Environment 27A (12), 1873–1880. % % GRIP d18O: S.J. Johnsen et al., 1997. The d18O record along the Greenland Ice Core Project deep ice core % and the problem of possible Eemian climatic instability. Journ. Geophys. Res. 102 (C12), 26397–26410. % % GISP2 Calcium: P. Mayewski et al., 1997. Major features and forcing of high-latitude northern hemisphere % atmospheric circulation using a 110,000-year-long glaciochemical series. Journ. Geophys. Res. 102, 26345–26366. % % GISP2 d18O: M. Stuiver and P.M. Grootes, 2000. GISP2 Oxygen isotope ratios. Quat. Res. 53, 277–284. % P.M. Grootes and M. Stuiver, 1997. Oxygen 18/16 variability in Greenland snow and ice with % 10^-3 to 10^5-year time resolution. Journ. Geophys. Res. 102, 26455–26470.

Code

    3. drawing climate diagram in python 3
    4. version 2.11
    5. 11.9.2020

import matplotlib.pyplot as plt import numpy as np import pandas as pd from scipy import interpolate from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)

datafilename="ngrip20.csv" captioni="Younger dryas in NGRIP ice core" savename="ngrip_dryas.svg"

figsizex=16 figsizey=8

x = [] y = [] y2= []

dfin0=pd.read_csv(datafilename, sep=";")

lst1=['Age_BP','NGRIP_dO18','GISP_dO18','GISP2_dO18']

dfin1 = dfin0[dfin0.columns.intersection(lst1)]

x0=dfin1['Age_BP'] y0=dfin1['NGRIP_dO18'] y20=dfin1['GISP_dO18'] y30=dfin1['GISP2_dO18']

x=np.array(x0) y=np.array(y0) y2=np.array(y20) y3=np.array(y30)

list1=[]

list1.append(y) list1.append(y2) list1.append(y3)

data1=np.array(list1)

print (np.shape(data1))

data_avg1=np.average(data1, axis=0)

  1. print(x)
  2. print(y)
  1. quit(0)

size0=14 size1=16 size2=18 size3=24

x_sm = np.array(x) y_sm = np.array(y) x_smooth = np.linspace(x_sm.min(), x_sm.max(), 20000) funk1 = interpolate.interp1d(x_sm, y_sm, kind="cubic") y_smooth = funk1(x_smooth)

fig, ax1 = plt.subplots()

  1. ax1.axis((11600,14000,0,ymax1))

ax1.set_xlim(11500,13000) ax1.set_ylim(-36.0, -43.0)

plt.gca().invert_xaxis() plt.gca().invert_yaxis()

ax1.set_ylabel('delta-O18', color='#0000ff', fontsize=size2+2)

  1. ax1.plot(x,y, color="#0000ff", linewidth=3,label="NGRIP delta-O18")

ax1.plot(x_smooth,y_smooth, color="#0000ff", linewidth=3,label="NGRIP delta-O18")

  1. ax1.plot(x,data_avg1, color="#ff0000", linewidth=2, linestyle=":", label="Average of NGRIP, GISP, GISP2 delta-O18")

ax1.tick_params(axis='both', which='major', labelsize=size2)

ax1.xaxis.set_minor_locator(MultipleLocator(100)) ax1.xaxis.set_minor_locator(MultipleLocator(20))

ax1.yaxis.set_minor_locator(MultipleLocator(1.0)) ax1.yaxis.set_minor_locator(MultipleLocator(0.1))

ax1.grid(which='major', linestyle='-', linewidth='0.1', color='black') ax1.grid(which='minor', linestyle=':', linewidth='0.1', color='black')


ax1.set_xlabel('Age BP', color="darkgreen", fontsize=size2)


ax1.set_title(captioni, fontsize=size3, color="#0000af")

plt.legend(fontsize=size0)

fig = plt.gcf() fig.set_size_inches(figsizex, figsizey, forward=True)

plt.savefig(savename, format="svg", dpi = 100)

plt.show()

Data Age_BP;NGRIP_dO18;GISP_dO18;GISP2_dO18 11620;-36.41;-37.14;-36.73 11640;-36.57;-36.6;-36.44 11660;-36.68;-36.81;-36.01 11680;-38.49;-37.87;-37.78 11700;-39.04;-38.88;NaN 11720;-39.6;-40.56;NaN 11740;-39.72;-40.32;-39.71 11760;-40.85;-39.4;-39.43 11780;-40.55;-39.69;-38.51 11800;-39.87;-40.33;-39.71 11820;-40.45;-41.34;-40.84 11840;-40.97;-39.57;-39.38 11860;-39.17;-40.91;-39.39 11880;-38.93;-40.16;-40.07 11900;-41.52;-40.25;-40.89 11920;-39.73;-41.77;-40.35 11940;-41.94;-40.79;-40.51 11960;-40.34;-40.18;-39.63 11980;-41.54;-40.33;NaN 12000;-40.54;-39.25;-39.48 12020;-39.6;-40.79;NaN 12040;-40.17;-40.26;-39.70 12060;-39.32;-40.18;-37.59 12080;-40.77;-40.08;-39.08 12100;-40.74;-40.13;NaN 12120;-41.89;-39.81;-40.63 12140;-40.61;-40.62;-39.76 12160;-41.76;-40.68;-39.36 12180;-42.09;-41.51;-41.21 12200;-38.91;-39.52;-38.85 12220;-41.4;-41.63;-41.48 12240;-40.9;-40.86;-39.59 12260;-39.9;-38.56;-39.65 12280;-40.71;-41.18;-39.87 12300;-41.01;-40.88;-40.90 12320;-40.41;-40.79;-38.54 12340;-40.95;-39.58;-38.05 12360;-41.73;-41.14;-41.57 12380;-41.17;-40.29;-40.59 12400;-40.73;-41.8;-40.63 12420;-41.21;-38.83;-38.84 12440;-41.05;-40.29;-39.17 12460;-41.39;-41.1;-40.51 12480;-40.02;-39.65;-41.01 12500;-41.91;-40.2;-40.34 12520;-40.62;-40.86;-40.50 12540;-39.09;-40.48;-40.64 12560;-40.95;-41.57;-40.56 12580;-40.53;-41.61;-40.28 12600;-41.43;-41.11;-40.09 12620;-42.71;-41.64;-41.79 12640;-40.59;-39.96;-40.67 12660;-41.82;-42.35;-41.38 12680;-40.92;-40.53;-40.46 12700;-42.27;-41.82;-41.02 12720;-41.82;-41.82;NaN 12740;-41.95;-42.04;NaN 12760;-41.06;-41.86;NaN 12780;-39.86;-40.31;NaN 12800;-40.17;-40.64;-40.29 12820;-40.36;-41.21;-39.86 12840;-39.88;-39.22;-39.33 12860;-38.98;-39.53;-39.93 12880;-39.81;-38.7;-39.08 12900;-40.11;-39.35;-38.41 12920;-39.42;-37.89;-38.36 12940;-37.79;-38.01;-38.39 12960;-38.25;-38.95;-37.30 12980;-39.11;-37.68;-38.63 13000;-38.96;-38.24;-38.36 13020;-39.87;-38.79;-37.46 13040;-39.55;-38.72;-38.67 13060;-39.49;-38.8;-38.23 13080;-40.52;-38.96;-37.93 13100;-39.77;-39.41;-39.39 13120;-40.57;-39.65;-39.75 13140;-40.17;-40.51;-40.47 13160;-41.51;-40.19;-41.34 13180;-40.08;-39.46;-38.44 13200;-40.53;-39.93;-39.73 13220;-40.1;-40.54;-39.21 13240;-40.58;-39.08;-39.91 13260;-39.45;-39.45;-38.39 13280;-39.3;-39.39;-38.49 13300;-38.97;-38.32;NaN 13320;-39.03;-39.46;NaN 13340;-39.35;-38;-38.00 13360;-39.07;-37.53;-38.01 13380;-38.56;-38.16;-36.82 13400;-38.36;-37.83;-37.85 13420;-38.76;-38.35;-37.43 13440;-38.14;-37.25;-38.27 13460;-38.56;-37.85;-37.78 13480;-38.79;-38.08;-37.62 13500;-38.33;-37.72;-37.28 13520;-37.74;-36.41;-37.25 13540;-38.08;-36.96;-37.43 13560;-38.29;-37.18;-37.37 13580;-38.99;-37.63;-37.22 13600;-39.36;-37.62;-38.19 13620;-39.64;-39.61;-38.76 13640;-40.05;-39.17;-37.74 13660;-39.21;-37.73;-37.14 13680;-39.09;-37.8;-38.30 13700;-38.67;-37.4;-37.20 13720;-38.93;-38.17;-38.07 13740;-37.66;-37.39;-37.70 13760;-38.45;-37.31;-37.40 13780;-39.42;-37.64;-37.51 13800;-38.81;-37.89;-36.69 13820;-37.86;-37.72;-37.49 13840;-39.05;-37.87;-38.29 13860;-37.96;-38.09;-37.44 13880;-38;-37.02;-37.86 13900;-38.43;-38.3;-38.09 13920;-38.21;-36.96;-37.37 13940;-38.61;-37.39;-37.43 13960;-38.03;-37.22;-37.54 13980;-39.99;-38.17;-36.73 14000;-39.03;-39.19;-39.80 14020;-39.73;-39.98;-39.81 14040;-40.21;-38.94;-38.96 14060;-39.9;-39.67;-39.24 14080;-40.94;-38.59;-38.91 14100;-38.05;-37.12;-37.63 14120;-38.34;-37.38;-37.66 14140;-38.01;-37.11;-37.16 14160;-38.68;-37.55;-37.81 14180;-38.04;-38.15;-38.03 14200;-37.59;-37.19;-37.19 14220;-37.46;-36.97;-37.37 14240;-38.42;-37.06;-36.68 14260;-37.76;-36.62;-37.12 14280;-37.19;-36.56;-36.82 14300;-37.13;-35.76;-35.88 14320;-37.05;-35.65;-35.40 14340;-37.54;-35.36;-36.09 14360;-36.81;-36.52;-36.27 14380;-37.49;-36.75;-36.99 14400;-36.07;-35.63;-35.87 14420;-37.17;-36.42;-35.88 14440;-37.29;-35.91;-36.01 14460;-37.04;-35.73;-35.29 14480;-36.86;-36.26;-35.84 14500;-36.26;-34.72;-34.97 14520;-36.11;-34.98;-34.93 14540;-36.14;-35.74;-34.98 14560;-36.14;-35.09;-35.81 14580;-36.98;-35.87;-36.05 14600;-35.81;-35.23;-35.76 14620;-36.74;-36.23;-36.16 14640;-37.48;-36.94;-37.23 14660;-37.52;-35.81;-36.07 14680;-37.97;-37.5;-37.34 14700;-40.64;-38.57;-38.39 14720;-41.96;-38.59;-38.88 14740;-42.47;-39.85;-40.56 14760;-40.81;-40.51;-39.85 14780;-41.03;-40.41;-40.95 14800;-39.92;-39.06;-39.30 14820;-40.68;-38.75;-38.55 14840;-42.44;-40.03;-39.81 14860;-40.78;-39.34;-40.81 14880;-41.18;-39.55;-39.58 14900;-40.14;-39.7;-39.33 14920;-41.47;-41.28;-38.19 14940;-41.76;-38.17;-39.60 14960;-41.65;-39.38;-40.40 14980;-40.88;-39.15;-40.51 15000;-42.61;-39.76;-39.00 15020;-42.37;-38.55;-39.14 15040;-41.54;-40.55;-40.38 15060;-43.17;-39.62;-40.73 15080;-39.98;-39.51;-39.57 15100;-43.51;-40.49;-39.78 15120;-42.76;-39.75;-38.90 15140;-40.12;-39.27;-36.26 15160;-42.26;-40.24;-38.27 15180;-42.99;-40.13;-39.22 15200;-42.08;-39.42;-38.08 15220;-40.57;-39.32;-38.84 15240;-43.57;-39.52;-38.98 15260;-40.55;-38.58;-38.33 15280;-44.91;-38.55;-39.11 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