data:solar_satire [PMIP4]

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====== PMIP4 SATIRE-M solar forcing data ======

The files contain Solar Spectral Irradiance (SSI) reconstruction based on **14C** or **10Be** and the SATIRE-M model, with and without an adaptation of the spectral irradiance to the CMIP6 //historical// forcing. 

  * 14C data for the last 9000 years (6754.5 BC to December 2015)
  * 10Be data for the years 885 CE to December 2015

  * In both cases, the data is daily, starting on January 1st 1850, and yearly before

The **14C-based data set** scaled to the CMIP6 historical forcing is the **recommended forcing for the PMIP4-CMIP6 //tier-1// past1000 experiment**.

===== Data files =====

  * file names = ''SSI_<type>_cycle_yearly_cmip_v20160613_<scaling>.txt''
    * ''type'' = ''14C'' or ''10Be''
    * ''scaling'' =
      * ''fc'': adaptation of the spectral irradiance to the CMIP6 //historical// forcing
      * ''nfc'': no re-scaling

===== Data format =====

<WRAP center round alert 60%>
Be careful when working with the time axis, because the //float year values// do not really follow [[http://cfconventions.org/cf-conventions/v1.6.0/cf-conventions.html#time-coordinate|time axis and calendar conventions]]!
</WRAP>


The original data files are provided in simple text format, and we also provide the data in netCDF format. The text files' structure is as follows:
  * 1st array: **wavelength array** in [nm], listing the center of each wavelength bin\\ ''[ 115.5,  116.5,  117.5,  118.5,  119.5,  [...] 100000.0078125 ,  120000.0234375 ,  140000.015625  ,  160000.015625  ]''
  * 2nd array: **wavelength bin** in [nm], listing the bin width of each wavelength bin\\ ''[ 1., 1., 1., 1. [...]   40., 5010., 14990.00488281, 20000.00585938, 20000., 20000., 20000.00585938, 20000.00585938, 20000., 20000. ]''
  * 3rd array: **time** in [year] (floating numbers)
    * 69235 time steps for 14C: ''[-6754.5, -6753.5, -6752.5, -6751.5, [...]  2015.98632812,  2015.98901367,  2015.99182129, 2015.99450684,  2015.99731445]''
    * 61595 time steps for 10Be: ''[885.5, 886.5, 887.5, 888.5, [...] 2015.986, 2015.989, 2015.992, 
    2015.995, 2015.997]''
  * 4th array: **SSI reconstruction** in [W m-2 nm-1]. SSI is average SSI in corresponding bin.

We provide the following IDL and python code to read the ''.txt'' file and calculate TSI:
  * <code idl>;====================================================
;N=69235 for 14C reconstruction
;N=61595 for 10Be reconstruction
header=strarr(12)
satire_wl=dblarr(1070)
satire_dwl=dblarr(1070)
time=dblarr(N)
SSI=dblarr(1070,N)

openr,1,filename
 readf,1,header
 readf,1,satire_wl
 readf,1,satire_dwl
 readf,1,time
 readf,1,SSI
close,1

TSI=dblarr(N)
FOR i=0L,N-1 DO TSI[i]=TOTAL(satire_dwl*SSI[*,i])
;=====================================================
</code>

  * The following python code shows how to deal with the original compressed text data. You can also check the {{:data:solar_txt2nc.py|full python script}} that was used to generate the netCDF files
    * <code python># Get directly the data from the bz2 compressed file
file_in = bz2.BZ2File(input_full_path)

# Print the comments at the beginning of the file
print '\nData file header:'
for skip_header in range(nb_header_lines):
  hdr = file_in.readline().strip()
  print 'HEADER =>', hdr

# Get the wavelength data (on a single line) and store it in a numpy
# array
wl_str = file_in.readline()
wl = np.array(map(float, wl_str.strip().split()), dtype=np.float32)

# Get the wavelength bins data (on a single line) and store it in a
# numpy array
wl_bin_str = file_in.readline()
wl_bin = np.array(map(float, wl_bin_str.strip().split()), dtype=np.float32)

# Get the years (on a single line) and store them in a numpy array
year_str = file_in.readline()
year = np.array(map(float, year_str.strip().split()), dtype=np.float32)

# The rest of the data is the ssi data, with one line for each time
# steps! Read it in an array
ssi = np.loadtxt(file_in, dtype=np.float32)

file_in.close()

print '\nSize of the SSI matrix =', ssi.shape

# Compute the TSI
tsi = np.dot(ssi, wl_bin)
</code>
  * The following python example shows how to determine the indices of specific years, before Jan 1850 1st and after. Once you know the indices of Jan 1st and Dec 31st, and if a year is a leap year or not
    * <code python> # 1 value per day, AFTER (and including) Jan 1st 1850
>>> Jan_01_1850_idx = np.argwhere(year == 1850)[0, 0]
>>> Jan_01_1851_idx = np.argwhere(year == 1851)[0, 0]
>>> Jan_01_1850_idx, Jan_01_1851_idx
(8605, 8970)
>>> Jan_01_1851_idx - Jan_01_1850_idx
365
>>> Dec_31_1850 = Jan_01_1851_idx - 1
>>> year[Jan_01_1850_idx], year[Dec_31_1850_idx], year[Jan_01_1851_idx]
(1850.0, 1850.9973, 1851.0)

# 1 value per year, strictly BEFORE Jan 1st 1850
>>> year_50_idx = np.argwhere(year == 50.5)[0, 0] # DO NOT FORGET to use 'NNN.5' for the year value
>>> year_50_idx, year[year_50_idx]
(6805, 50.5)</code>
  * You can use the following if you want to do a weighted average over the time axis
    * <code python>>>> time_weights = np.ones((69235,))
>>> np.argwhere(year == 1850)
array([[8605]])
>>> tt[8605]
1850.0
>>> year[8604]
1849.5
# Note: it would be nicer to assign 366 to leap years below...
>>> time_weights[:8605] = 365 # Assign 365 to all time steps up to (but excluding) step 8605
>>> time_weights[8600:8620]
array([ 365.,  365.,  365.,  365.,  365.,    1.,    1.,    1.,    1.,
          1.,    1.,    1.,    1.,    1.,    1.,    1.,    1.,    1.,
          1.,    1.])
>>> ssi_average_weighted = np.average(ssi, axis=0, weights=time_weights)
</code>

===== References =====
 
  * Baroni, M., and ASTER Team (2015), **A new 10Be record recovered from an Antarctic ice core: validity and limitations to record the solar activity**, Geophysical Research Abstracts 17, [[http://meetingorganizer.copernicus.org/EGU2015/EGU2015-6357.pdf|EGU2015-6357]]
  * Vieira, L.E.A. et al. (2011), **Evolution of the solar irradiance during the Holocene**, Astron. Astroph., 531, A6, [[http://www.aanda.org/articles/aa/abs/2011/07/aa15843-10/aa15843-10.html|doi:10.1051/0004-6361/201015843]]
  * Usoskin, I.G. et al. (2014), **Evidence for distinct modes of solar activity**, Astron. Astrophys., 562, L10, [[http://www.aanda.org/articles/aa/abs/2014/02/aa23391-14/aa23391-14.html|doi:10.1051/004-6361/201423391]]
  * Usoskin, I.G. et al. (2016), **Solar activity during the Holocene: the Hallstatt cycle and its consequence for grand minima and maxima**, Astron. Astroph.,  587, A150, [[http://www.aanda.org/articles/aa/abs/2016/03/aa27295-15/aa27295-15.html|doi:10.1051/0004-6361/201527295]]

===== Download =====

<WRAP center round info 60%>
Once you have downloaded the compressed text data file, you can use the command ''bzmore'' to have a quick look at its content without having to uncompress it!
<code> > bzmore SSI_14C_cycle_yearly_cmip_v20160613_fc.txt.bz2
------> SSI_14C_cycle_yearly_cmip_v20160613_fc.txt.bz2 <------
;
;Solar Spectral Irradiance for last 9 millennia (added solar cycle), 6754.5 BC ~
 12.31.2015
;
;File structure
;1. wavelength: array[1070],nm
;2. wavelength bin: array[1070],nm
;3. year: array[69235], floating number
;   6754.5 BC~1849.5 AD, yearly cadence
;   1,1,1850 ~ 12,31,2015, daily cadence
;4. SSI(wavelength,date): array[1070,69235], W m-2 nm-1
;
; Note: SSI adjusted to CMIP
     115.500     116.500     117.500     118.500     119.500     120.500     121
.500     122.500     123.500 [...]</code>
</WRAP>

You will find below a table with all the available data files, and their md5sum checksum (if you want to check that you download was OK, you can just type ''md5sum file.nc'' and compare the result to what is displayed in the table).

If you want to download a file, click on the [[https://sharebox.lsce.ipsl.fr/index.php/s/LpiCUCkSmx0P6bb|PMIP4 SATIRE-M solar forcing data download link]] and then on the file you need.

/* The files are currently protected by a password. Get in touch with [[johann.jungclaus@mpimet.mpg.de|Johann Jungclaus]] or [[pmip3web@lsce.ipsl.fr|Jean-Yves Peterschmitt]] if you need to access them. */

^ md5sum output ^ Data file ^ Size ^
| 636519aa89b472a04748893d6f3ff1b3 | **SSI_14C_cycle_yearly_cmip_v20160613_fc.txt.bz2**\\ recommended forcing for the PMIP4-CMIP6 //tier-1// past1000 experiment | 506 Mb |
| af4d1c36647f094b38fd6d9bc8e6617b  | **SSI_14C_cycle_yearly_cmip_v20160613_fc.nc**\\ recommended forcing for the PMIP4-CMIP6 //tier-1// past1000 experiment | 283 Mb |
| 86bae35d2cd4d7f2c3dde9ee567d5a87 | SSI_14C_cycle_yearly_cmip_v20160613_nfc.txt.bz2 | 506 Mb |
| b5639df4fab6cac7d55972d7a066e2b9  | SSI_14C_cycle_yearly_cmip_v20160613_nfc.nc | 283 Mb |
| 96cdeb6a561f0be6b83e1b45a809f8ad | SSI_10Be_cycle_yearly_cmip_v20160613_fc.txt.bz2 | 450 Mb |
| 1f9075a93e58173281ee11731bdb97e5  | SSI_10Be_cycle_yearly_cmip_v20160613_fc.nc | 252 Mb |
| 0516a6a073c25365674a004034392130 | SSI_10Be_cycle_yearly_cmip_v20160613_nfc.txt.bz2 | 450 Mb |
| c2922684c81e839fb07de7017ed0d1f3  | SSI_10Be_cycle_yearly_cmip_v20160613_nfc.nc | 252 Mb |