{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "b8f745f5-ac17-493a-a5c5-e726f9bcbc64", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import xarray as xr\n", "import cmocean.cm as cmo\n", "import numpy as np\n", "import my_functions as my\n", "import gsw\n", "from matplotlib.dates import date2num\n", "import matplotlib.dates as mdates\n", "import pandas as pd\n", "import glidertools as gt\n", "from sklearn.linear_model import LinearRegression\n", "import math\n", "import scipy\n", "\n", "import my_plot_params\n", "\n", "years = mdates.YearLocator() # every year\n", "months = mdates.MonthLocator(interval=1) # every month\n", "week = mdates.WeekdayLocator(byweekday=mdates.MO, interval=1)\n", "weeks = mdates.WeekdayLocator(byweekday=mdates.MO, interval=3)\n", "\n", "yearsFmt = mdates.DateFormatter(\"%d/%m\")\n", "\n", "mnthFmt = mdates.DateFormatter(\"%B\")\n", "\n", "lightblue = '#5499c7'\n", "blue = '#21618c'\n", "orange = '#f39c12'\n", "green = '#27ae60'\n", "red = '#cb4335'" ] }, { "cell_type": "code", "execution_count": 2, "id": "64630cd4-1bdf-49ec-b116-751e80227e92", "metadata": {}, "outputs": [], "source": [ "# note these are the 1D time series\n", "dat_saz = xr.open_dataset('../data/dat_saz_1D.nc')\n", "dat_pfz = xr.open_dataset('../data/dat_pfz_1D.nc')\n", "dat_miz = xr.open_dataset('../data/dat_miz_1D.nc')\n", "\n", "# note these are the 6H time series\n", "dat_saz_6H = xr.open_dataset('../data/dat_saz_6H.nc')\n", "dat_pfz_6H = xr.open_dataset('../data/dat_pfz_6H.nc')\n", "dat_miz_6H = xr.open_dataset('../data/dat_miz_6H.nc')" ] }, { "cell_type": "markdown", "id": "2b1513d8-1002-40df-9c73-519af615e6e9", "metadata": {}, "source": [ "#### Mean buoyancy of the mixed layer" ] }, { "cell_type": "code", "execution_count": 3, "id": "d46217ed-c934-40c6-837f-e3950d75cc3e", "metadata": {}, "outputs": [], "source": [ "# get the mean buoyancy within the mixed layer\n", "for dat in [dat_saz, dat_pfz, dat_miz]:\n", "\n", " xb=[]\n", " \n", " for i, m in enumerate(np.round(dat['mld_03'].astype(int))):\n", " \n", " xb += dat.buoyancy.sel(depth=slice(5, m-5)).mean(dim='depth').values[i],\n", " \n", " dat['ml_b'] = (('time'), xb)" ] }, { "cell_type": "markdown", "id": "fe28af61-9fa5-4ea0-8804-6622f00fa89f", "metadata": {}, "source": [ "Determine the lateral buoyancy bouyancy gradient from the interpolated values of buoyancy to a common grid. The grid used here is 2 km. " ] }, { "cell_type": "code", "execution_count": 4, "id": "db564175-9cd3-4644-9654-802abffc9886", "metadata": {}, "outputs": [], "source": [ "sites = ['saz', 'pfz', 'miz']\n", "\n", "for i, dat in enumerate([dat_saz, dat_pfz, dat_miz]):\n", " \n", " if i==0:\n", " dat_raw=dat_saz_6H\n", " if i==1:\n", " dat_raw=dat_pfz_6H\n", " if i==2:\n", " dat_raw=dat_miz_6H\n", " \n", " dat_raw['buoyancy'] = -9.81*(dat_raw['density']-1025)/1025\n", " \n", " xb=[]\n", " xt=[]\n", " xs=[]\n", " \n", " for c, m in enumerate(np.round(dat_raw['mld_03'].astype(int))):\n", " \n", " xb += dat_raw.buoyancy.sel(depth=slice(10, m-5)).mean(dim='depth').values[c], \n", " xt += dat_raw.temp.sel(depth=slice(10, m-5)).mean(dim='depth').values[c],\n", " xs += dat_raw.salt.sel(depth=slice(10, m-5)).mean(dim='depth').values[c],\n", " \n", " dat_raw['ml_s'] = (('time'), xs)\n", " dat_raw['ml_t'] = (('time'), xt)\n", " dat_raw['ml_b'] = (('time'), xb)\n", " \n", " for c, val in enumerate(dat_raw.ml_b):\n", " \n", " if val<-0.027:\n", " dat_raw['ml_b'][c] = dat_raw['ml_b'][c-1] \n", " \n", " # determin the along-track distance \n", " d = gsw.distance(dat_raw.lon.values, dat_raw.lat.values)\n", " d_sum = np.append(0, np.cumsum(d))\n", " \n", " # make a new grid from cumulative sum of the distances\n", " new_grid = np.arange(0, d_sum[-1], 6000)\n", " \n", " t = date2num(dat_raw.time.values)\n", " \n", " # interpolate the mixed layer buoyancy and depth to the new grid\n", " ml_b_6km = scipy.interpolate.griddata(d_sum, dat_raw['ml_b'].values, new_grid)\n", " mld_6km = scipy.interpolate.griddata(d_sum, dat_raw['mld_03'].values, new_grid)\n", " time_6km = scipy.interpolate.griddata(d_sum, t, new_grid)\n", " \n", " # find the buoyancy gradients\n", " ml_bx_6km = np.diff(ml_b_6km)/6000\n", " \n", " # Submesoscale MLI equivalent heat flux\n", " \n", " #set the parameters for the equivalent heat flux equation\n", " \n", " c = 0.06 # emperically defined coefficient\n", " bx = ml_bx_6km # buoyancy gradient\n", " H = mld_6km[1:] # mixed layer depth\n", " f = gsw.f(dat_raw.lat).mean().values # coriolis freq\n", " Cp = 3850 # specific heat capacity\n", " rho0 = 1027 # density\n", " alpha = gsw.alpha(dat_raw.ml_s, dat_raw.ml_t, 0).mean().values # thermal expansion coeff\n", " g = 9.81 # gravitational acceleration\n", " \n", " # determine the MLI flux\n", " \n", " mli = c * ( (bx**2 * H**2)/np.abs(f) ) * ( (Cp*rho0)/(alpha*g) )\n", " \n", " dat_raw['mli'] = (('time'), scipy.interpolate.griddata(time_6km[:-1], mli, t))\n", " \n", " ###################\n", " \n", " # convert the tendency units of C/s and psu/s to W m-2\n", " \n", " hm = dat['mld_03'] # mixed layer depth\n", " beta = -gsw.beta(dat.ml_s_smooth, dat.ml_t_smooth, 0).mean() # haline contraction coeff\n", " alpha = gsw.alpha(dat.ml_s_smooth, dat.ml_t_smooth, 0).mean() # haline contraction coeff\n", "\n", " dat['hf_wm2'] = dat['dT_hf'] * (rho0*Cp*hm) # heat flux\n", " dat['en_t_wm2'] = dat['ent_dT'] * (rho0*Cp*hm) # entrainment\n", " dat['ek_t_wm2'] = dat['ek_trans_dT'] * (rho0*Cp*hm) # ekman adv\n", " \n", " dat['ff_wm2'] = dat['dS_ff'] * (rho0*Cp*hm*(beta/alpha)) # freshwater flux\n", " dat['ek_s_wm2'] = dat['ek_trans_dS'] * (rho0*Cp*hm*(beta/alpha)) # entrainment\n", " dat['en_s_wm2'] = dat['ent_dS'] * (rho0*Cp*hm*(beta/alpha)) # ekman adv\n", " \n", " dat['mli_wm2'] = dat_raw['mli'].interp_like(dat)" ] }, { "cell_type": "markdown", "id": "c352df5d-2688-4d19-80bd-9fae89db1b99", "metadata": {}, "source": [ "#### Plot the pie chars" ] }, { "cell_type": "code", "execution_count": 17, "id": "7f04a8a8-34ad-4a44-ab04-76fbc47eda41", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('Heat Flux', 'Freshwater Flux', 'Entrainment', 'Ek Adv', 'MLI')\n", "[0.46360655700985004, 0.0807190454415762, 0.22417895216294875, 0.22569859862807892, 0.005796846757546111]\n", "('Heat Flux', 'Freshwater Flux', 'Entrainment', 'Ek Adv', 'MLI')\n", "[0.5943616358711414, 0.2237896448904379, 0.024745220615348024, 0.13937125554441362, 0.017732243078659088]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/n5/s6yw8y811f7bqlr9nb7w5rsr0000gn/T/ipykernel_99754/2454746998.py:22: MatplotlibDeprecationWarning: normalize=None does not normalize if the sum is less than 1 but this behavior is deprecated since 3.3 until two minor releases later. After the deprecation period the default value will be normalize=True. To prevent normalization pass normalize=False \n", " ax[i].pie(sizes,\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "('Heat Flux', 'Freshwater Flux', 'Entrainment', 'Ek Adv', 'MLI')\n", "[0.35627682082561485, 0.15939331915036364, 0.45372328560515973, 0.02788163819071361, 0.002724936228148221]\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 3, figsize=[18, 6], sharey=True)\n", "\n", "for i, dat in enumerate([dat_saz, dat_pfz, dat_miz]):\n", " \n", " # now plot the pie chart\n", " \n", " labels = 'Heat Flux', 'Freshwater Flux', 'Entrainment', 'Ek Adv', 'MLI'\n", " clrs = orange, 'mediumseagreen', 'steelblue', 'firebrick', '0.75'\n", " \n", " hf = np.nanmean(np.abs(dat['hf_wm2']))\n", " ff = np.nanmean(np.abs(dat['ff_wm2']))\n", " \n", " en = np.nanmean(np.abs(dat['en_t_wm2']) + np.abs(dat['en_s_wm2']))\n", " ek = np.nanmean(np.abs(dat['ek_t_wm2']) + np.abs(dat['ek_s_wm2']))\n", " \n", " sm = np.nanmean(np.abs(dat['mli_wm2']))\n", " \n", " total = hf+ff+en+ek+sm \n", " \n", " sizes = [hf/total, ff/total, en/total, ek/total, sm/total]\n", " \n", " ax[i].pie(sizes, \n", "# labels=labels, \n", "# autopct='%1.1f%%', \n", " colors=clrs,\n", " startangle=90)\n", " \n", " print(labels)\n", " \n", " print(sizes)\n", " \n", " ax[i].axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.\n", " \n", " props = dict(fontsize=22, fontweight='bold')\n", " if i==1:\n", " \n", " ax[i].text(-2.9,-1.6,'Heat Flux', c=orange, **props)\n", " ax[i].text(-1.9,-1.6,'Freshwater Flux', c=green, **props)\n", " ax[i].text(-0.4,-1.6,'Entrainment', c=blue, **props)\n", " ax[i].text(0.8, -1.6,'Ekman advection', c=red, **props)\n", " ax[i].text(2.4, -1.6,'MLI', c='0.65', **props)\n", " \n", "# ax.legend(ncol=6, bbox_to_anchor=(0, 1))\n", "\n", " ax[1].set_title('Relative contribution of the budgets terms to absolute changes in buoyancy', fontweight='bold', pad=80, fontsize=22)\n", " props_per = dict(fontsize=19,fontweight='bold')\n", " props_wm2 = dict(fontsize=16)\n", "\n", " ### SAZ ###\n", " \n", " #heat flux \n", " ax[0].text(-0.6, 0.2, '46%', **props_per)\n", " ax[0].text(-0.9, 0, '140 $\\pm$ 81 W m$^{-2}$', **props_wm2)\n", " \n", " #MLI\n", " ax[0].text(0, 1.3, '<1%', **props_per, c='0.65')\n", " ax[0].text(-0.15, 1.1, '2 $\\pm$ 6 W m$^{-2}$', **props_wm2, c='0.65')\n", " \n", " #Ekman advection\n", " ax[0].text(0.3, 0.6, '23%', **props_per, c='k')\n", " ax[0].text(0.09, 0.4, '-6 $\\pm$ 5 W m$^{-2}$', **props_wm2, c='k')\n", " \n", " #entrainment \n", " ax[0].text(0.4, -0.2, '22%', **props_per, c='k')\n", " ax[0].text(0.2, -0.4, '-68 $\\pm$ 110 W m$^{-2}$', **props_wm2, c='k')\n", " \n", " #entrainment \n", " ax[0].text(0, -1.2, '8%', **props_per, c=green)\n", " ax[0].text(-0.15, -1.4, '25 $\\pm$ 25 W m$^{-2}$', **props_wm2, c=green)\n", " \n", " ### PFZ ###\n", " \n", " #heat flux \n", " ax[1].text(-0.6, 0, '59%', **props_per)\n", " ax[1].text(-0.9, -0.2, '146 $\\pm$ 57 W m$^{-2}$', **props_wm2)\n", " \n", " #MLI\n", " ax[1].text(0, 1.3, '2%', **props_per, c='0.65')\n", " ax[1].text(-0.15, 1.1, '4 $\\pm$ 7 W m$^{-2}$', **props_wm2, c='0.65')\n", " \n", " #Ekman advection\n", " ax[1].text(0.2, 0.72, '14%', **props_per, c='k')\n", " ax[1].text(0.09, 0.52, '-25 $\\pm$ 17 W m$^{-2}$', **props_wm2, c='k')\n", " \n", " #entrainment \n", " ax[1].text(0.95, 0.45, '3%', **props_per, c=blue)\n", " ax[1].text(0.85, 0.65, '-6 $\\pm$ 39 W m$^{-2}$', **props_wm2, c=blue)\n", " \n", " #freshwater flux \n", " ax[1].text(0.4, 0, '22%', **props_per, c='k')\n", " ax[1].text(0.2, -0.2, '55 $\\pm$ 41 W m$^{-2}$', **props_wm2, c='k')\n", " \n", " \n", " ### MIZ ###\n", " \n", " #heat flux \n", " ax[2].text(-0.6, 0.2, '36%', **props_per)\n", " ax[2].text(-0.9, 0, '118 $\\pm$ 77 W m$^{-2}$', **props_wm2)\n", " \n", " #MLI\n", " ax[2].text(-0.4, 1.3, '<1%', **props_per, c='0.65')\n", " ax[2].text(-0.55, 1.1, '1 $\\pm$ 1 W m$^{-2}$', **props_wm2, c='0.65')\n", " \n", " #Ekman advection\n", " ax[2].text(0.25, 1.3, '3%', **props_per, c=red)\n", " ax[2].text(0.15, 1.1, '3 $\\pm$ 9 W m$^{-2}$', **props_wm2, c=red)\n", " \n", " #entrainment \n", " ax[2].text(0.3, 0.1, '45%', **props_per, c='k')\n", " ax[2].text(0.05, -0.1, '-158 $\\pm$ 290 W m$^{-2}$', **props_wm2, c='k')\n", " \n", " #freshwater flux \n", " ax[2].text(-0.4, -0.5, '16%', **props_per, c='k')\n", " ax[2].text(-0.7, -0.7, '55 $\\pm$ 63 W m$^{-2}$', **props_wm2, c='k')\n", " \n", " ax[0].text(-1.0, 0.9, 'a) SAZ', fontweight='bold', fontsize=20)\n", " ax[1].text(-1.0, 0.9, 'b) PFZ', fontweight='bold', fontsize=20)\n", " ax[2].text(-1.0, 0.9, 'c) MIZ', fontweight='bold', fontsize=20)\n", " \n", " plt.savefig('../figs_submission2/fig10.png', dpi=300, bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 5, "id": "25fc4834-7987-448e-bbe6-b543175c1931", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-----\n", "heat flux: 140.0 std 81.0\n", "fresh flx: 25.0 std 26.0\n", "sms mli : 2.0 std 4.0\n", "entrainmt: -68.0 std 110.0\n", "ekmanadvn: -6.0 std 5.0\n", "-----\n", "heat flux: 146.0 std 57.0\n", "fresh flx: 55.0 std 41.0\n", "sms mli : 4.0 std 7.0\n", "entrainmt: -6.0 std 39.0\n", "ekmanadvn: -25.0 std 17.0\n", "-----\n", "heat flux: 118.0 std 77.0\n", "fresh flx: 56.0 std 63.0\n", "sms mli : 1.0 std 1.0\n", "entrainmt: -159.0 std 291.0\n", "ekmanadvn: 3.0 std 9.0\n" ] } ], "source": [ "for i, dat in enumerate([dat_saz, dat_pfz, dat_miz]):\n", " \n", " print('-----')\n", " \n", " print('heat flux: ' + str(np.round(np.nanmean(dat['hf_wm2'] .values))) + ' std ' + str(np.round(np.nanstd(dat['hf_wm2'] .values))))\n", " print('fresh flx: ' + str(np.round(np.nanmean(dat['ff_wm2'] .values))) + ' std ' + str(np.round(np.nanstd(dat['ff_wm2'] .values))))\n", " print('sms mli : ' + str(np.round(np.nanmean(dat['mli_wm2'].values))) + ' std ' + str(np.round(np.nanstd(dat['mli_wm2'].values))))\n", " \n", " en = dat['en_t_wm2'] + dat['en_s_wm2'] # substract the salinity equiv. W m-2 from the temperature as neg salinity means freshening\n", " ek = dat['ek_t_wm2'] + dat['ek_s_wm2'] # substract the salinity equiv. W m-2 from the temperature as neg salinity means freshening\n", " \n", " print('entrainmt: ' + str(np.round(np.nanmean(en.values))) + ' std ' + str(np.round(np.nanstd(en.values))))\n", " print('ekmanadvn: ' + str(np.round(np.nanmean(ek.values))) + ' std ' + str(np.round(np.nanstd(ek.values))))" ] }, { "cell_type": "code", "execution_count": null, "id": "54f62c25-c312-4d16-9ac4-4a55c6739a27", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "20290578-0512-48aa-9cfc-64cf47bc1c8c", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }