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MERRA2_dustmod_19x25_exec.R
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#############################################################
#############################################################
# dust emission code version 1: 2006 MERRA2 1.9x2.5 data for dust emissions
# extract N Africa for calculation only (or specify other spatial domains)
# allow 1-hourly, 2-hourly data or other time resolution simulations (can skip some timestep and multiply dt at the end if one think e.g. 2-hourly sampling is good enough)
# allow showing actual dust emissions and annually scaled emissions (Tg / yr)
# set up spatial and temporal domain
# 8 Oct 2021
# remaining issues:
# 1. Now all MERRA2 variables are 2-hourly and are directly averaged instead of area-weighted averaged. Will fix this next time.
# 2. Check if the boundaries have too much emissions and whether it is needed to multiply by a land fraction to discount those emissions, or else the upscaled variables over the seashores will misbehave (very large or very small).
# 3. Now I have a big time loop for everything; but if later on find that the loop takes too long, one can easily change the script to several smaller time loops and run one by one.
# 4. indices are too bad when extracting MERRA2 variables. Please fix it (8 Oct 2021)
# 18 Nov 2021
# The code has been revised to include Zender et al. (2003) as the dust production model (DPM). Other codes mostly only have Kok et al. (2014) as the DPM.
# set working directory
setwd('/Volumes/GoogleDrive/My Drive/CESM/CESMDUSTinR')
# load libraries
library(ncdf4); library(fields); library(maps); library(Metrics); library(pracma)
# load functions
source("get_geo.R"); source("get_met.R"); source("sptial_plot_fns.R")
source("dust_research_fns.R")
# load color scheme
load("WBGYR_scheme.RData"); load('TEMP_DIFF.RData')
##################
# user input here
# specialize temporal domain as YYYYMMDD
startdate = 20060101 # YYYYMMDD
enddate = 20061231 # YYYYMMDD
# specialize temporal resolution (dt = 1 as hourly, dt = 3 as 3-hourly etc.)
dt = 2 # try 2-hourly
# specialize spatial domain as indices
indi = 1:144; indj = 18:93
# choose clay dataset: 'FAO' or 'SG' (SoilGrids)
clay_dataset = 'FAO'
# choose the wind scale for dust emission, including default (aerodynamic + nonneutral) ustar (default), aeolian + nonneutral star (aeo+nonneu), and aeolian + neutral ustar (aeo+neu)
wind.scale='default'
# choose land cover averaging method: LC0 (rock and veg) or LC1 (rock, veg and mix)
LC = 'LC0'
# choose the averaging method of the coarse input variables: 'mean' or 'aw_mean' (area-weighted mean)
avg.method = 'aw_mean'
# choose whether default Kok et al. (2014) scheme (K14) or intermittency (K14+Comola et al., 2019) is used. 'FALSE' is K14, 'TRUE' is K14+C19 intermittency scheme.
use.intermittency = TRUE
# fragmentation exponent limit (CESM simulations can blow up when frag_expt is too large when using impact threshold for dust emission equation)
frag_expt_lim = 3
# dust production model (DPM): Kok et al. (2014) K14 or Zender et al. (2003) Z03, added 18 Nov 2021
DPM = 'Z03'
if (DPM=='Z03') use.intermittency = 'FALSE' # by default Z03 has no intermittency eff
# if source function is needed (for Z03), choose 'Zender' or 'Ginoux'. For K14, type 'FALSE'
src_fn = 'Ginoux'
##################
# calculate model parameters based on user inputs
date_vec = make.date.vec(startdate, enddate) # make date vec
time = 1:(length(date_vec)*24/dt) # a vector of timesteps
indt = seq(dt, 24, by=dt) # hour of day
# get CESM lon lat
files.clm = Sys.glob('/Volumes/GoogleDrive/My Drive/CESM_tempfiles/renewmod5_20042005/new_intermittency/*.clm*.nc'); nc = nc_open(files.clm[1])
LON.CESM = seq(-180,177.5,by=2.5)[indi]; LAT.CESM = ncvar_get(nc, 'lat')[indj]
indlon = c(73:144,1:72); LON.r = LON.CESM[indlon]; LON.r[141:144]=LON.r[141:144]+360
nc_close(nc)
###################
# constant parameters
C_tune = 0.05 # global tuning constant
gamma = 5e-4; # soil aggregation effect on dust emission threshold
u_star_st0 = 0.16; # m s-1, minimum value of u_star_st
g = 9.81 # m/s^2, gravitational acceleration
dns_slt = 2650 # kg/m3, soil particle density
D_p = 130e-6 # m, soil median diameter
z0s = D_p / 15 # m, smooth roughness length, White (2006) equation
X = 10 # m, we assume interobstacle distance ~ 10 m
LAI_thr = 1 # Mahowald et al. (2010) set as 0.3 but we changed to 1
U_0 = 0.32 # SSR in the immediate lee of a plant for Okin's scheme, dimensionless
c = 4.8 # e-folding distance SSR recovery for Okin's scheme, dimensionless
k = 0.387 # von Karman constant for high Reynolds number flows
SHR_CONST_RHOFW = 1000 # kg / m3, pure water density
B_it = 0.81 # impact threshold to dry fluid threshold ratio
c_p = 1005 # J/kg/K specific heat of air at constant pressure
rho_a0 = 1.225; # kg m-3, standard atmospheric density
ustar_st0 = 0.16; # m s-1, minimum value of standardized fluid threshold
C_d0 = 4.4e-5; C_e = 2.0; C_alpha = 2.7 # dust emission coefficient parameters
cst_slt = 2.61 # saltation constant for Z03, added 18 Nov 2021
flx_mss_fdg_fct = 5.0e-4 # Empirical emission tuning factor for Z03, added 18 Nov 2021
###################
# load required time-invariant datasets
.env = new.env() # set a new environment for storing data temporarily
# load GLCNMO LULC data
load('GLCNMO_frc_area_19x25.RData',envir=.env); ls(.env)
frc.r.CESM = .env$frc.r.CESM[indi,indj]; frc.veg.CESM = .env$frc.veg.CESM[indi,indj]
frc.mix.CESM = .env$frc.mix.CESM[indi,indj]
#A_r = frc.r.CESM / (frc.r.CESM+frc.veg.CESM+frc.mix.CESM)
#A_veg = frc.veg.CESM / (frc.r.CESM+frc.veg.CESM+frc.mix.CESM)
#A_mix = frc.mix.CESM / (frc.r.CESM+frc.veg.CESM+frc.mix.CESM)
#rm('frc.r.CESM','frc.veg.CESM','frc.mix.CESM')
#plot.field(frc.mix.CESM, LON.CESM, LAT.CESM, col=WBGYR)
# load landfilt
load('landfilt_19x25.RData',envir=.env)
landfilt.CESM = .env$landfilt.CESM[indi,indj]
# get Prigent 2005 roughness length and rock drag partition factor
load('Pr05_z0.min_19x25.RData',envir=.env)
z0.r = .env$z0.CESM[indi,indj]/100 # in cm, divide by 100 to convert to m
# in this formulation, set globally constant z0s because soil median diameter ~ 130 um
f_eff.r = 1 - ( log(z0.r/z0s) / log(0.7*(X/z0s)^0.8) )
f_eff.r = f_eff.r*landfilt.CESM # take away values in Caspian sea
#plot.field(f_eff.r, LON.CESM, LAT.CESM)
# image(f_eff.r)
# get porosity (issue: MERRA2 porosity may be different from CESM's effective porosity)
load('MERRA2_porosity_19x25.RData', envir=.env)
poros = .env$poros.CESM[indi,indj] # porosity (m3 / m3, or volumetric soil water)
#image(poros)
#plot.field(poros, LON.CESM, LAT.CESM)
# get bulk density of dry soil material [kg/m^3]
bulk_den = (1 - poros)*dns_slt
#plot.field(bulk_den, LON.CESM, LAT.CESM, legend.mar=4)
# use CESM's FAO clay fraction
for (aa in 1) {
if (clay_dataset == 'FAO') {
nc = nc_open('/Volumes/GoogleDrive/My Drive/CESM/CESMDUSTinR/CESMmetinput/surfdata_1.9x2.5_simyr1850_c091108.nc') # CESM's FAO (2012) clay
f_clay.FAO.19x25 = ncvar_get(nc, 'PCT_CLAY')[c(73:144,1:72),,1]*0.01 # 0-1, fraction of #plot.field(f_clay.FAO.05x06, LON.CESM, LAT.CESM, type='def', zlim=c(0,0.65))
f_clay.FAO.19x25 = f_clay.FAO.19x25[indi,indj]
# threshold gravimetric moisture and clay limitation on dust emission
gwc_thr = 0.01*(0.17*f_clay.FAO.19x25*100 + 0.0014*(f_clay.FAO.19x25*100)^2)*landfilt.CESM
#plot.field(gwc_thr, LON.CESM, LAT.CESM)
mss_frc_cly_vld = f_clay.FAO.19x25 * landfilt.CESM
mss_frc_cly_vld[which(f_clay.FAO.19x25>0.2)] = 0.2
#plot.field(mss_frc_cly_vld, LON.CESM, LAT.CESM)
print('used FAO clay data')
} else if (clay_dataset == 'SG') {
load('f_clay_SoilGrids_19x25.RData',envir=.env)
f_clay.SG.19x25 = .env$f_clay.SG.CESM
#plot.field(f_clay.SG.19x25, LON.CESM, LAT.CESM, type='def', zlim=c(0,0.65))
# threshold gravimetric moisture and clay limitation on dust emission
gwc_thr = 0.01*(0.17*f_clay.SG.19x25*100 + 0.0014*(f_clay.SG.19x25*100)^2)
#plot.field(gwc_thr, LON.CESM, LAT.CESM)
mss_frc_cly_vld = f_clay.SG.19x25
mss_frc_cly_vld[which(mss_frc_cly_vld>0.2)] = 0.2
#plot.field(mss_frc_cly_vld, LON.CESM, LAT.CESM)
print('used SoilGrids clay data')
}
}
# calculate sandblasting efficiency if Z03 is used, added 18 Nov 2021
for (aa in 1) {
if (DPM=='Z03') {
# sandblasting efficiency
dst_slt_flx_rat_ttl = 100*exp( log(10) * (13.4*mss_frc_cly_vld - 6))
}
#plot.field(dst_slt_flx_rat_ttl, LON.CESM, LAT.CESM)
}
# choose source function if using Z03 dust emission equation, added 18 Nov 2021
for (aa in 1) {
if (src_fn=='Ginoux') {
load('ginoux_dust_source_19x25.RData',envir=.env)
mbl_bsn_fct = .env$mbl_bsn_fct_G.CESM[indi,indj]
print('used Ginoux source function')
} else if (src_fn=='Zender') {
load('zender_dust_source_19x25.RData',envir=.env)
mbl_bsn_fct = .env$mbl_bsn_fct_Z.CESM[indi,indj]
print('used Zender source function')
}
}
#plot.field.log(mbl_bsn_fct, LON.CESM, LAT.CESM, legend.mar=5)
###################
# scrap files for selected dates
files = NULL # character strings storing the file names
for (dd in 1:length(date_vec)) {
date_vec[dd]
files = c(files, Sys.glob(paste('/Volumes/SEAGATE/MERRA2/upscale_2006/REGRIDDED_MERRA2/',avg.method,'/merra2_regridded_met_19x25_', date_vec[dd], '.RData', sep='')))
}
###################
# start the main loop
# for days in d
d = 1 # d-th day
ptm = proc.time()
for (d in 1:length(date_vec)) {# d-th day
print(paste('day ', d, ': ', date_vec[d], sep=''))
#print('extract MERRA2 data', sep='')
###################
# get 3D data in time and space, subsetted as regional data with variable time resolutions
i1 = indi[1]; j1 = indj[1]; t1 = indt[1]
il = length(indi); jl = j1+length(indj)-1; tl = t1+length(indt)
#ptm = proc.time()
#nc = nc_open(files_flx[d])#; names(nc$var)#, start=c(11,26), count=c(5,5)
load(files[d], envir=.env)#; ls(.env)
USTAR = .env$USTAR.CESM[i1:il,j1:jl,] # m/s
Z0M = .env$Z0M.CESM[i1:il,j1:jl,] # m
RHOA = .env$RHOA.CESM[i1:il,j1:jl,] # kg/m3
PBLH = .env$PBLH.CESM[i1:il,j1:jl,] # kg/m3
#DISPH = .env$DISPH # m
T10M = .env$T10M.CESM[i1:il,j1:jl,] # K
#U10 = .env$U10.CESM[i1:il,j1:jl,] # m/s. Define U10 as the total wind speed of U10M and V10M. Don't confuse U10 with U10M.
#proc.time() - ptm
SFMC = .env$SFMC.CESM[i1:il,j1:jl,]
LAI = .env$LAI.CESM[i1:il,j1:jl,]
SHLAND = .env$SHLAND.CESM[i1:il,j1:jl,]
#plot.field(DISPH[,,3], LON.CESM, LAT.CESM)
#plot.field(RHOA[,,3], LON.CESM, LAT.CESM)
#plot.field(Z0M[,,3], LON.CESM, LAT.CESM)
#plot.map.log((Z0M[,,3]),LON.CESM, LAT.CESM,type='def',zlim=c(-4,0), legend.mar=5)
#print('finished loading MERRA2 met fields; now calculate hybrid drag partition and hybrid roughness length')
###################
# get drag partition factors and hybrid roughness length (later on, make this as a separate time loop independent of dust emission calculation?)
# get vegetation roughness length following Okin's scheme using LAI
# first use LAI to get bare land fraction using Natalie Mahowald's equation
# and vegetation drag partition using Okin-Pierre parameterization
# Natalie's bare land fraction approximation
f_bare = 1 - LAI/LAI_thr; f_bare[which(f_bare<0)] = NaN
f_bare = sweep(f_bare, c(1,2), landfilt.CESM, '*') # optional
#f_bare_0.3 = 1 - LAI[,,3]/0.3; f_bare_0.3[which(f_bare_0.3<0)] = NaN
#plot.field(f_bare[,,6], LON.CESM, LAT.CESM)
#plot.field(f_bare_0.3, LON.CESM, LAT.CESM)
#plot.field.log(LAI[,,6], LON.CESM, LAT.CESM, zlim=c(-2,1), legend.mar=5)
# make a filter for dust emission sources
#emisfilt = f_bare/f_bare # this filter is 3D and changes with time; or one can make a annual, 2D one.
#image(emisfilt)
# Okin's vegetation drag partition
# Using Caroline Pierre (2014)'s formulation of Okin's scheme
K = pi/2 * (1/LAI - 1); K[which(K<0)] = 0
SSR = (K + U_0 * c) / (K + c) # Pierre-Okin shear stress ratio (SSR)
#plot.field(SSR[,,3], LON.CESM, LAT.CESM, type='def', zlim=c(0.6,0.9))
# combine rock and vegetation drag partition factors
# then combine drag partition factors
#F_eff.LC = array(NaN, dim=c(length(indi), length(indj), length(indt)))
#F_eff.LC2 = array(NaN, dim=c(length(indi), length(indj), length(indt)))
#for (h in 1:length(indt)) {
#F_eff.LC1[,,h] = (frc.r.CESM*f_eff.r^3 + frc.veg.CESM*SSR[,,h]^3 + frc.mix.CESM*(SSR[,,h]*f_eff.r)^3)^(1/3)
# if (LC=='LC1') {F_eff.LC[,,h] = (A_r*f_eff.r^3 + A_veg*SSR[,,h]^3 + A_mix*(SSR[,,h]*f_eff.r)^3)^(1/3)}
# else if (LC=='LC0') {F_eff.LC[,,h] = (frc.r.CESM*f_eff.r^3 + (frc.veg.CESM+frc.mix.CESM)*SSR[,,h]^3)^(1/3)} # newly define LC0, removed mixed regime
#}
if (LC=='LC1') {
f_eff_veg.comp = sweep(SSR^3, c(1,2), (frc.veg.CESM), '*')
f_eff_mix.comp = sweep(SSR^3, c(1,2), (frc.mix.CESM*(f_eff.r^3)), '*')
F_eff.LC = (sweep(f_eff_veg.comp+f_eff_mix.comp, c(1,2), (frc.r.M2*f_eff.r^3), '+')) ^ (1/3)
rm('f_eff_veg.comp','f_eff_mix.comp')
} else if (LC=='LC0') {
f_eff_veg.comp = sweep(SSR^3, c(1,2), (frc.veg.CESM+frc.mix.CESM), '*')
F_eff.LC = (sweep(f_eff_veg.comp, c(1,2), (frc.r.CESM*f_eff.r^3), '+')) ^ (1/3)
rm(f_eff_veg.comp)
}
#plot.field(F_eff.LC[,,6], LON.CESM, LAT.CESM, type='def', zlim=c(0,1), col=WBGYR)
# then get hybrid aeolian roughness length (Z0.LC1) from hybrid F_eff, using Marticorena's equation
#f_eff.r = 1 - ( log(z0.r/z0s) / log(0.7*(X/z0s)^0.8) )
#Z0.LC = z0s * exp( (1 - F_eff.LC) * log(0.7*(X/z0s)^0.8) ) # in m
#plot.field.log(Z0.LC1[,,6], LON.CESM, LAT.CESM, type='def', zlim=c(-5,0), col=tim.colors(32)[32:1], legend.mar=5)
#plot.field.log(Z0M[,,6], LON.CESM, LAT.CESM, type='def', zlim=c(-5,0), col=tim.colors(32)[32:1], legend.mar=5)
#plot.field.log(z0.r*landfilt.CESM, LON.CESM, LAT.CESM, type='def', zlim=c(-5,0), col=tim.colors(32)[32:1], legend.mar=5) # also in m
#print('finished drag partition calculation; now select friction velocity at relevant scale')
###################
# reconstruct friction velocity with different meanings and choose which to use
# test if U10, USTAR, and Z0M are consistent with each other
for (aa in 1) {
if (wind.scale=='default') {print('default MERRA2 ustar is used')}
else {
Z0.LC = z0s * exp( (1 - F_eff.LC) * log(0.7*(X/z0s)^0.8) ) # in m
U10 = .env$U10.CESM[i1:il,j1:jl,] # m/s. Define U10 as the total wind speed of U10M and V10M. Don't confuse U10 with U10M.
if (wind.scale=='aeo_nonneu') {
KSI_m = k*U10/USTAR - log(10 / Z0M) # first determine MOST instability (KSI)
USTAR.aeo.nonneu = k * U10 / (log(10 / Z0.LC)+KSI_m); print('aeolian+non-neutral ustar is used')} # reconstruct USTAR from U10 and Z0M
#USTAR.aero.neu = k * U10 / log(10 / Z0M) # reconstruct USTAR from U10 and Z0M; aero means aerodynamic, neu means neutral (log wind profile)
# calculate aeolian ustar assuming neutral condition
else if (wind.scale=='aeo_neu') {USTAR.aeo.neu = k * U10 / log(10 / Z0.LC); print('aeolian+neutral ustar is used')} # reconstruct USTAR from U10 and Z0M
}
}
#plot.field(USTAR.aero.neu[,,8] / USTAR[,,8], LON.CESM, LAT.CESM, type='def', zlim=c(0,2), col=TEMP_DIFF_65)
# then also reconstruct ustar under non-neutral condition
#plot.field(USTAR[,,6]*landfilt.CESM, LON.CESM, LAT.CESM, type='def', zlim=c(0,0.8), col=WBGYR)
#plot.field(USTAR.aeo.nonneu[,,6], LON.CESM, LAT.CESM, type='def', zlim=c(0,0.8), col=WBGYR)
#plot.field(USTAR.aeo.nonneu[,,6]/USTAR.aeo.neu[,,6], LON.CESM, LAT.CESM, type='def', zlim=c(0,2), col=TEMP_DIFF_65)
#plot.field(USTAR.aeo.neu[,,6], LON.CESM, LAT.CESM, type='def', zlim=c(0,0.8), col=WBGYR)
#A = apply(USTAR, c(1,2), mean, na.rm=T)
#plot.field(A*landfilt.CESM, LON.CESM, LAT.CESM, legend.mar=5, type='def', zlim=c(0,0.7))
#A = apply(USTAR.aeo.nonneu, c(1,2), mean, na.rm=T)
#plot.field(A, LON.CESM, LAT.CESM, legend.mar=5, type='def', zlim=c(0,0.7))
#A = apply(USTAR.aeo.neu, c(1,2), mean, na.rm=T)
#plot.field(A, LON.CESM, LAT.CESM, legend.mar=5, type='def', zlim=c(0,0.7))
#print('finished threshold calculation; now calculate fluid and impact thresholds')
###################
# construct dust emission thresholds
# first get dry fluid threshold
ustar_ft.dry = 1.0*sqrt(0.0123 * (dns_slt*g*D_p + gamma/D_p)) / sqrt(RHOA)
#plot.field(ustar_ft.dry[,,6], LON.CESM, LAT.CESM)
# to get wet fluid threshold,
# get gravimetric water content from volumetric water content
gwc_sfc = SHR_CONST_RHOFW*sweep(SFMC, c(1,2), bulk_den, '/')
#plot.field(gwc_sfc.yr, LON.CESM, LAT.CESM)
#plot.field(SFMC.yr, LON.CESM, LAT.CESM)
#plot.field.log(gwc_sfc[,,6], LON.CESM, LAT.CESM, type='def',zlim=c(-3,0), legend.mar=5)
# then get soil moisture effect on fluid threshold
#ptm = proc.time()
#frc_thr_wet_fct = array(NaN, dim=c(length(indi), length(indj), length(indt)))
#for (h in 1:length(indt)) {
# A = sqrt(1.0 + 1.21*(100.0*(gwc_sfc[,,h] - gwc_thr))^0.68)# A[which(gwc_sfc[,,h] < gwc_thr)] = 1
# frc_thr_wet_fct[,,h] = A
#}
#proc.time() - ptm
#rm(A) # remove dummy
#ptm = proc.time()
frc_thr_wet_fct = sqrt(1.0 + 1.21*(100.0* (sweep(gwc_sfc, c(1,2), gwc_thr, '-')) )^0.68)
frc_thr_wet_fct[which(sweep(gwc_sfc, c(1,2), gwc_thr, '<'))] = 1
#proc.time() - ptm
#plot.field(frc_thr_wet_fct[,,6], LON.CESM, LAT.CESM, type='def', zlim=c(1,3.6))
# then get fluid and impact thresholds (now assume using FAO's moisture effect)
ustar_ft.wet = ustar_ft.dry * frc_thr_wet_fct
#ustar_it = ustar_ft.dry * B_it * landfilt.CESM
ustar_it = B_it * sweep((ustar_ft.dry), c(1,2), landfilt.CESM, '*')
#plot.field(ustar_it[,,6], LON.CESM, LAT.CESM)
#plot.field(ustar_ft.wet[,,6], LON.CESM, LAT.CESM)
#A = apply(USTAR - ustar_it, c(1,2), mean, na.rm=T)
#plot.field(A*landfilt.CESM, LON.CESM, LAT.CESM, legend.mar=5, type='sign', zlim=c(-0.5,0.5), col=TEMP_DIFF_65)
#A = apply(USTAR.aeo.nonneu - ustar_it, c(1,2), mean, na.rm=T)
#plot.field(A, LON.CESM, LAT.CESM, legend.mar=5, type='sign', zlim=c(-0.5,0.5), col=TEMP_DIFF_65)
#print('finished threshold calculation; now calculate intermittency effect')
###################
# construct intermittency
# choose a friction velocity. Aeolian/Aerodynamic? Neutral/Non-neutral?
# USTAR.aeo.neu, USTAR.aeo.nonneu, USTAR.aero.neu, USTAR (aero+nonneu, default MERRA2 output)
for (aa in 1) {
if (wind.scale=='default') {wnd_frc_slt = USTAR*F_eff.LC; print('default MERRA2 ustar is used for intermittency and emission')} # default MERRA2 (aero+nonneu)output
else if (wind.scale=='aeo+neu') {wnd_frc_slt = USTAR.aeo.neu*F_eff.LC; print('aeolian+neutral ustar is used for intermittency and emission')} # reconstructed aeo+neu USTAR
else if (wind.scale=='aeo+nonneu') {wnd_frc_slt = USTAR.aeo.nonneu*F_eff.LC; print('aeolian+neutral ustar is used for intermittency and emission')} # reconstructed aeo+nonneu USTAR
}
if (use.intermittency=='TRUE') {
# get Monin-Obukhov length in m (Donald Golder, 1972; Khaled Essa, 1999)
# we use aerodynamic non-neutral USTAR here
Obu_L = - RHOA * c_p * T10M * USTAR^3 / (k * g * SHLAND)
u_mean_slt = (wnd_frc_slt/k) * log(0.1 / 1e-4) # We used z0a = 1e-4 m in CESM and assumed. Should we change to aeolian data here?
# sd of lowpass-filtered wind speed
# u_sd_slt = wnd_frc_slt * (12 )^0.333 # note that there is no buoyancy term here; need to add in later when Obukhov length is obtained (1 Oct 2021)
u_sd_slt = wnd_frc_slt * (12 - 0.5 * PBLH/Obu_L)^0.333 # buoyancy term is added
# then get thresholds at saltation height
u_fld_thr = (ustar_ft.wet/k) * log(0.1 / 1e-4) # to avoid model error
u_impct_thr = (ustar_it/k) * log(0.1 / 1e-4) # to avoid model error
# threshold crossing rate
thr_crs_rate = (exp((u_fld_thr^2 - u_impct_thr^2 - 2 * u_mean_slt * (u_fld_thr - u_impct_thr)) / (2 * u_sd_slt^2)) + 1)^(-1)
# probability that lowpass-filtered wind speed does not exceed u_ft
prb_crs_fld_thr = 0.5 * (1 + erf((u_fld_thr - u_mean_slt) / (1.414 * u_sd_slt)))
# probability that lowpass-filtered wind speed does not exceed u_it
prb_crs_impct_thr = 0.5 * (1 + erf((u_impct_thr - u_mean_slt) / (1.414 * u_sd_slt)))
# intermittency factor (from 0 to 1)
intrmtncy_fct = 1 - prb_crs_fld_thr + thr_crs_rate * (prb_crs_fld_thr - prb_crs_impct_thr)
}
#plot.field(u_mean_slt[,,6], LON.CESM, LAT.CESM)
#plot.field(u_sd_slt[,,6], LON.CESM, LAT.CESM)
#plot.field(u_fld_thr[,,6], LON.CESM, LAT.CESM)
#plot.field(u_impct_thr[,,6], LON.CESM, LAT.CESM)
#plot.field(intrmtncy_fct[,,6], LON.CESM, LAT.CESM)
#A = apply(intrmtncy_fct, c(1,2), mean, na.rm=T)
#plot.field(A, LON.CESM, LAT.CESM, col=WBGYR, legend.mar=5, type='def', zlim=c(0,1))
#print('finished intermittency; now calculate dust emission flux')
###################
# a few more essential variables for dust emission equations
# a few more essential variables for dust emission equations, modified 18 Nov 2021
if (DPM=='K14') {
# standardized fluid threshold (m/s)
ustar_st = ustar_ft.wet * sqrt(RHOA/rho_a0)
# dust emission coefficient (a measure of soil erodibility)
C_d = C_d0 * exp(-C_e * (ustar_st-ustar_st0)/ustar_st0);
#A = apply(C_d, c(1,2), mean, na.rm=T)
#plot.field.log(A/C_d0, lon.M2, lat.M2, type='def', zlim=c(-5,0), legend.mar=5)
# get fragmentation exponent and limit to frag_expt_lim and below
frag_expt = (C_alpha*(ustar_st-ustar_st0)/ustar_st0)
frag_expt[which(frag_expt>frag_expt_lim)] = frag_expt_lim
#A = apply(frag_expt, c(1,2), mean, na.rm=T)
#plot.field(A, lon.M2, lat.M2, legend.mar=5)
} #else if (DPM=='Z03') { # dml added 18 Nov 2021
# sandblasting efficiency
#dst_slt_flx_rat_ttl = 100*exp( log(10) * (13.4*mss_frc_cly_vld - 6))
#}
###################
# compute 3-D dust emissions
# compute dust emissions
for (aa in 1) {
if (DPM == 'K14') {
if (use.intermittency == 'TRUE') {
F_d = array(0, dim=dim(C_d)) # using dimension of C_d to define F_d dimension
ind = which(wnd_frc_slt > ustar_it) # 3-D index for which wind speed > threshold
F_d[ind] = C_tune * C_d[ind] * f_bare[ind] * RHOA[ind] * (wnd_frc_slt[ind]^2-ustar_it[ind]^2)/ustar_it[ind] * (wnd_frc_slt[ind]/ustar_it[ind])^(frag_expt[ind]) * intrmtncy_fct[ind] # calculate F_d for indices with wind speed > threshold
F_d = sweep(F_d, c(1,2), mss_frc_cly_vld, '*') # multiply 3-D fields by 2-D fields
#F_d_sum = apply(F_d, c(1,2), sum, na.rm=T) * 3600 * dt * 365
#plot.field.log(F_d_sum, LON.CESM, LAT.CESM, legend.mar=5, col=WBGYR)
} else if (use.intermittency == 'FALSE') {
F_d = array(0, dim=dim(C_d)) # using dimension of C_d to define F_d dimension
ind = which(wnd_frc_slt > ustar_ft.wet) # 3-D index for which wind speed > threshold
F_d[ind] = C_tune * C_d[ind] * f_bare[ind] * RHOA[ind] * (wnd_frc_slt[ind]^2-ustar_ft.wet[ind]^2)/ustar_ft.wet[ind] * (wnd_frc_slt[ind]/ustar_ft.wet[ind])^(frag_expt[ind]) # calculate F_d for indices with wind speed > threshold
F_d = sweep(F_d, c(1,2), mss_frc_cly_vld, '*') # multiply 3-D fields by 2-D fields
}
filename = paste('/Volumes/SEAGATE/MERRA2/upscale_2006/OUTPUTS_',clay_dataset,'_',LC,'_globe_fragexpt=',frag_expt_lim,'_intmtncy=',use.intermittency,'/dust_emis_flx_19x25_', date_vec[d], '.RData', sep='')
save('F_d', 'LON.CESM', 'LAT.CESM', file=filename)
}
else if (DPM == 'Z03') { # dml added 18 Nov 2021
wnd_frc_rat = ustar_ft.wet / wnd_frc_slt
F_d = array(0, dim=dim(ustar_ft.wet)) # using dimension of ustar_ft.wet to define F_d dimension
ind = which(wnd_frc_slt > ustar_ft.wet) # 3-D index for which wind speed > threshold
F_d[ind] = flx_mss_fdg_fct * cst_slt * f_bare[ind] * RHOA[ind] * (wnd_frc_slt[ind]^3) * (1 - wnd_frc_rat[ind]) * (1 + wnd_frc_rat[ind]) * (1 + wnd_frc_rat[ind]) / g
F_d = sweep(F_d, c(1,2), dst_slt_flx_rat_ttl*mbl_bsn_fct , '*') # multiply 3-D fields by 2-D fields
filename = paste('/Volumes/SEAGATE/MERRA2/upscale_2006/OUTPUTS_',clay_dataset,'_',LC,'_',DPM,'_src_fn=',src_fn,'/dust_emis_flx_19x25_', date_vec[d], '.RData', sep='')
save('F_d', 'LON.CESM', 'LAT.CESM', file=filename)
print('computed Z03 emission')
}
}
print('emission and other variables saved')
}
proc.time() - ptm