File:Mean precipitation 240ma 1.png

From Wikimedia Commons, the free media repository
Jump to navigation Jump to search

Original file (1,800 × 900 pixels, file size: 1.43 MB, MIME type: image/png)

Captions

Captions

Annual mean precipitation 240 Ma

Summary

[edit]
Description
English: Annual mean precipitation 240 Ma, Trias period.
Date
Source Own work
Author Merikanto

This image is based data from exoplasim simulation and Scotese paleodem maps.

https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/

PaleoDEM Resource – Scotese and Wright (2018) 11 August, 2018 by Sabin Zahirovic

https://www.earthbyte.org/webdav/ftp/Data_Collections/Scotese_Wright_2018_PaleoDEM/Scotese_Wright_2018_Maps_1-88_1degX1deg_PaleoDEMS_nc.zip

PALEOMAP Paleodigital Elevation Models (PaleoDEMS) for the Phanerozoic

Scotese, Christopher R, & Wright, Nicky M. (2018). PALEOMAP Paleodigital Elevation Models (PaleoDEMS) for the Phanerozoic [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5460860

Scotese, Christopher R; Wright, Nicky M https://zenodo.org/record/5460860

https://zenodo.org/record/5460860/files/Scotese_Wright_2018_Maps_1-88_6minX6min_PaleoDEMS_nc.zip?download=1

Params from exoplasim 

       triassic middle

a_eccentricity1=0.0167022 a_obliquity1=23.441 a_lonvernaleq1=102.7

a_pCO21=1400.0e-6

Codes to produce pre-datra for this

https://commons.wikimedia.org/wiki/File:World_200ma_6.webp

Creeata matplotlib plot

    1. python draw mean precip map from exoplasim output
  2. 19.6.2022 v 0000.0000

import netCDF4 as nc import numpy as np from scipy import interpolate from scipy.interpolate import griddata

import matplotlib.pyplot as plt from matplotlib.pylab import * import matplotlib.mlab as mlab

def ncread(fn1, var1): ds2 = nc.Dataset(fn1) varr1=ds2[var1] return(varr1)

def savenetcdf_single_frommem(outfilename1, outvarname1, xoutvalue1,xoutlats1,xoutlons1): nlat1=len(xoutlats1) nlon1=len(xoutlons1) #indata_set1=indata1 print(outfilename1) ncout1 = nc.Dataset(outfilename1, 'w', format='NETCDF4') outlat1 = ncout1.createDimension('lat', nlat1) outlon1 = ncout1.createDimension('lon', nlon1) outlats1 = ncout1.createVariable('lat', 'f4', ('lat',)) outlons1 = ncout1.createVariable('lon', 'f4', ('lon',)) outvalue1 = ncout1.createVariable(outvarname1, 'f4', ('lat', 'lon',)) outvalue1.units = 'Unknown' outlats1[:] = xoutlats1 outlons1[:] = xoutlons1 outvalue1[:, :] =xoutvalue1[:] ncout1.close() return 0

def ncsave_T21(oname1, ovar1, ovals1): newx=64 newy=32 X = np.arange(-180, 180, 360/newx) Y = np.arange(-90, 90, 180/newy) savenetcdf_single_frommem(oname1, ovar1,ovals1,Y, X)


from matplotlib.colors import LinearSegmentedColormap

cm_data = [[0.2081, 0.1663, 0.5292], [0.2116238095, 0.1897809524, 0.5776761905], 

[0.212252381, 0.2137714286, 0.6269714286], [0.2081, 0.2386, 0.6770857143], 
[0.1959047619, 0.2644571429, 0.7279], [0.1707285714, 0.2919380952, 
 0.779247619], [0.1252714286, 0.3242428571, 0.8302714286], 
[0.0591333333, 0.3598333333, 0.8683333333], [0.0116952381, 0.3875095238, 
 0.8819571429], [0.0059571429, 0.4086142857, 0.8828428571], 
[0.0165142857, 0.4266, 0.8786333333], [0.032852381, 0.4430428571, 
 0.8719571429], [0.0498142857, 0.4585714286, 0.8640571429], 
[0.0629333333, 0.4736904762, 0.8554380952], [0.0722666667, 0.4886666667, 
 0.8467], [0.0779428571, 0.5039857143, 0.8383714286], 
[0.079347619, 0.5200238095, 0.8311809524], [0.0749428571, 0.5375428571, 
 0.8262714286], [0.0640571429, 0.5569857143, 0.8239571429], 
[0.0487714286, 0.5772238095, 0.8228285714], [0.0343428571, 0.5965809524, 
 0.819852381], [0.0265, 0.6137, 0.8135], [0.0238904762, 0.6286619048, 
 0.8037619048], [0.0230904762, 0.6417857143, 0.7912666667], 
[0.0227714286, 0.6534857143, 0.7767571429], [0.0266619048, 0.6641952381, 
 0.7607190476], [0.0383714286, 0.6742714286, 0.743552381], 
[0.0589714286, 0.6837571429, 0.7253857143], 
[0.0843, 0.6928333333, 0.7061666667], [0.1132952381, 0.7015, 0.6858571429], 
[0.1452714286, 0.7097571429, 0.6646285714], [0.1801333333, 0.7176571429, 
 0.6424333333], [0.2178285714, 0.7250428571, 0.6192619048], 
[0.2586428571, 0.7317142857, 0.5954285714], [0.3021714286, 0.7376047619, 
 0.5711857143], [0.3481666667, 0.7424333333, 0.5472666667], 
[0.3952571429, 0.7459, 0.5244428571], [0.4420095238, 0.7480809524, 
 0.5033142857], [0.4871238095, 0.7490619048, 0.4839761905], 
[0.5300285714, 0.7491142857, 0.4661142857], [0.5708571429, 0.7485190476, 
 0.4493904762], [0.609852381, 0.7473142857, 0.4336857143], 
[0.6473, 0.7456, 0.4188], [0.6834190476, 0.7434761905, 0.4044333333], 
[0.7184095238, 0.7411333333, 0.3904761905], 
[0.7524857143, 0.7384, 0.3768142857], [0.7858428571, 0.7355666667, 
 0.3632714286], [0.8185047619, 0.7327333333, 0.3497904762], 
[0.8506571429, 0.7299, 0.3360285714], [0.8824333333, 0.7274333333, 0.3217], 
[0.9139333333, 0.7257857143, 0.3062761905], [0.9449571429, 0.7261142857, 
 0.2886428571], [0.9738952381, 0.7313952381, 0.266647619], 
[0.9937714286, 0.7454571429, 0.240347619], [0.9990428571, 0.7653142857, 
 0.2164142857], [0.9955333333, 0.7860571429, 0.196652381], 
[0.988, 0.8066, 0.1793666667], [0.9788571429, 0.8271428571, 0.1633142857], 
[0.9697, 0.8481380952, 0.147452381], [0.9625857143, 0.8705142857, 0.1309], 
[0.9588714286, 0.8949, 0.1132428571], [0.9598238095, 0.9218333333, 
 0.0948380952], [0.9661, 0.9514428571, 0.0755333333], 
[0.9763, 0.9831, 0.0538]]

parulamap = LinearSegmentedColormap.from_list('parula', cm_data)

parulamap_r = LinearSegmentedColormap.from_list('parula_r',cm_data[::-1])

infilename1="./origo/input.nc" varname1="pr" kaption1="Annual mean precipitation mm, 240 Ma" infilename2="./origo/dem.nc" savename1="mean_precipitation_240ma_1.png"

varr1=ncread(infilename1, varname1)

  1. varr1=np.flipud(varr0)

mask0=ncread(infilename1, "lsm") mask1=mask0[1]

varr2=ncread(infilename2, "z")

mask2=np.flipud(np.array(varr2))

  1. mask2=mask2[mask2>0]=1
  1. plt.imshow(varr1)
  1. plt.show()
  1. quit(-1)

print(varr1)

means1=[]

for month in range(1,12): varmonth1=varr1[month] mean1=np.mean(varmonth1) means1.append(mean1)

varmean1=np.mean(varr1,axis=0)

print(varmean1) print (len(varmean1))

  1. plt.imshow(varmean1)
  1. plt.show()
  1. quit(-1)

print (means1) mini1=np.min(means1) maxi1=np.max(means1)

minlok1=means1.index(mini1)

maxlok1=means1.index(maxi1)

print(mini1,maxi1,minlok1, maxlok1)

  1. minitable1=varr1[minlok1]
  2. maxitable1=varr1[maxlok1]

minitable1=varr1[minlok1]-273.15 maxitable1=varr1[maxlok1]-273.15

  1. plt.imshow(minitable1)

ncsave_T21("tas_min.nc", "pr", minitable1)

coloris = parulamap_r

contourrange1=[0,200,400,800,1000,1200,1500,2000]

contourrange2=[0,2000]

contourrange3=np.arange(0,2000,50)

range3=np.arange(0,2000,2)

    1. dravar1=minitable1

dravar00=varmean1 dravar01=np.flipud(dravar00)

    1. precip ms-1 to mm/yr

dravar1=dravar01*3600*24*365*1000

  1. plt.imshow(dravar1)
  1. plt.show()
  1. quit(-1)

meanmean1=np.mean(dravar1)

print("Mean avg.", meanmean1)

plt.rcParams["figure.figsize"] = (18,9)

plt.title(kaption1, fontsize=24)

plt.xlabel("Longitude", fontsize=18) plt.xticks(fontsize=18) plt.ylabel("Latitude", fontsize=18) plt.yticks(fontsize=18)

  1. plt.imshow( minitable1, extent=(-180,180,-90,90), cmap=kmap2, vmin=-50, vmax=50 )
  2. plt.imshow( mask1, extent=(-180,180,-90,90) )

cs2 = plt.contourf( dravar1,extent=(-180,180,-90,90), levels=range3, cmap=coloris, extend='both')

cs = plt.contour(dravar1, extent=(-180,180,-90,90), inline=True, cmap=plt.cm.binary, alpha=0.7, levels=contourrange1)

cs0a = plt.contour(dravar1, extent=(-180,180,-90,90), inline=True, cmap=plt.cm.binary, alpha=0.7, linestyles=':',linewidths=0.4, levels=contourrange3)

plt.clabel(cs, fontsize=20, inline=1,fmt = '%3.0f')


cs3 = plt.contour( mask2,extent=(-180,180,-90,90), width=10, levels=[1], cmap=plt.cm.binary, extend='both')

plt.savefig(savename1)

plt.show()



Licensing

[edit]
I, the copyright holder of this work, hereby publish it under the following license:
w:en:Creative Commons
attribution share alike
This file is licensed under the Creative Commons Attribution-Share Alike 4.0 International license.
You are free:
  • to share – to copy, distribute and transmit the work
  • to remix – to adapt the work
Under the following conditions:
  • attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • 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.

File history

Click on a date/time to view the file as it appeared at that time.

Date/TimeThumbnailDimensionsUserComment
current12:50, 19 June 2022Thumbnail for version as of 12:50, 19 June 20221,800 × 900 (1.43 MB)Merikanto (talk | contribs)Uploaded own work with UploadWizard

You cannot overwrite this file.

There are no pages that use this file.

Metadata

This file contains additional information such as Exif metadata which may have been added by the digital camera, scanner, or software program used to create or digitize it. If the file has been modified from its original state, some details such as the timestamp may not fully reflect those of the original file. The timestamp is only as accurate as the clock in the camera, and it may be completely wrong.

Retrieved from "https://commons.wikimedia.org/w/index.php?title=File:Mean_precipitation_240ma_1.png&oldid=839998838"