File:Late glacial temperature curve bolling2.png

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Summary[edit]

Description
Suomi: Late glacial temperature curve. This is based on NGRIP

Greenland Ice core oxygen isotope ratio data. The "ka" means 1000 year calendar years ago. So 14 ka means 14 000 calenrad years ago ("cal BP"). Major warmings in curve are beginning of the Bölling initial warming ca 14.5 ka cal BP and

beginning of the Holocene ca 11,6 Ka BP.
Date
Source Own work
Author Merikanto

Licensing[edit]

I, the copyright holder of this work, hereby publish it under the following license:
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  • share alike – If you remix, transform, or build upon the material, you must distribute your contributions under the same or compatible license as the original.

Additional information[edit]

Data for this file is from http://www.iceandclimate.nbi.ku.dk/data/NGRIP_d18O_and_dust_5cm.xls http://www.iceandclimate.nbi.ku.dk/data/ processed to csv

δ18O values and dust concentrations


The dataset provides NGRIP δ18O values, dust concentrations, and GICC05 ages in 5cm depth resolution for the period 0-60 ka (δ18O) and 10-60 ka (dust).

The dataset accompany the following papers:

NGRIP members, Nature, 431, 147-151, 2004. DOI: 10.1038/nature02805

Gkinis et al., Earth Planet. Sci. Lett., 405, 132-141, 2014. DOI: 10.1016/j.epsl.2014.08.022

Ruth et al., J. Geophys. Res., 108, 4098, 2003. DOI: 4010.1029/2002JD002376

    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) import scipy.signal

def running_mean(x, N): 

   cumsum = np.cumsum(np.insert(x, 0, 0)) 
   return (cumsum[N:] - cumsum[:-N]) / float(N)


datafilename="ngrip1.csv" captioni="Late Glacial period in NGRIP ice core" savename="ngrip_dryas.svg"

figsizex=16 figsizey=8

  1. x = []
  2. y = []
  3. y2= []


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

lst1=['gicc05_age','delta_O18']

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

x0=dfin1['gicc05_age'] y0=dfin1['delta_O18']

  1. y20=dfin1['GISP_dO18']
  2. y30=dfin1['GISP2_dO18']


x=np.array(x0) y=np.array(y0)

  1. y2=np.array(y20)
  2. y3=np.array(y30)
  1. list1=[]
  1. list1.append(y)
  2. list1.append(y2)
  3. list1.append(y3)
  1. data1=np.array(list1)
  1. print (np.shape(data1))
  1. data_avg1=np.average(data1, axis=0)
  1. print(x)
  2. print(y)
  1. quit(0)

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


  1. y_savgol = scipy.signal.savgol_filter(y,31, 3)

y_savgol = scipy.signal.savgol_filter(y,71, 3)


  1. y_running = running_mean(y, 31)


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(10000,20000) ax1.set_ylim(-32.0, -45.0)

  1. ax1.set_ylim(-35.0, -42.0)

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

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


ax1.plot(x,y, color="#a0a0ff", linewidth=1,label="NGRIP delta-O18")

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

ax1.plot(x,y_savgol, color="#FF0000", linewidth=3, label="SavGol filter, 71 and 3")

  1. ax1.plot(x,y_running, color="#FF0000", linewidth=3)


  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(500)) ax1.xaxis.set_minor_locator(MultipleLocator(50))

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()


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Date/TimeThumbnailDimensionsUserComment
current17:13, 12 September 2020Thumbnail for version as of 17:13, 12 September 20201,600 × 766 (329 KB)Merikanto (talk | contribs)New time span, data and layout
12:48, 30 November 2015Thumbnail for version as of 12:48, 30 November 2015910 × 579 (53 KB)Merikanto (talk | contribs)User created page with UploadWizard

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