{ "cells": [ { "cell_type": "markdown", "id": "0f872ec4", "metadata": {}, "source": [ "# Statistical comparison global gridded climate datasets and their influence on LPJ-GUESS model outputs\n", "Geocomputation course VT2021, Individual project\n", "\n", "Alexandra Pongracz \n", "\n", "alexandra.pongracz@nateko.lu.se" ] }, { "cell_type": "markdown", "id": "5d6cb96c", "metadata": {}, "source": [ "## Background & Introduction\n", "Gridded climate data products are imperative resources for global environmental research. These continuous datasets are prduced by compiling and interpolating observational datasets, reanalysis products or a hybrid product of these two approaches. Such climate datasets enable to investigate long term processes, causality and atmosphere-biosphere interactions on a global scale. They are widely used as forcing in models to investigate ecosystem response to changing climatic conditions (New et al. 2000). Nowadays there are several gridded climatic datasets available - for instance CEDA Archive, NOAA - and updated on a regular basis. \n", "\n", "Meteorological input variables have a large influence of simulated biogeochemical and hydrological model outputs, such as ecosystem productivity or the computed carbon balance (McGuire et al 2001, Wu et al. 2018). The choice of forcing climate dataset is a large source of uncertainty in ecosystem modelling (McGuire et al. 2018). Model intercomparison projects – such as the Coupled Model Intercomparison Project (CMIP) -,therefore, assign a common simulation protocol to avoid output bias due to the differences in the climatic inputs (Taylor 2012, CMIP5). Finding of state-of-the-art global modelling studies are presented in the IPCC (Intergovermental Panel on Climate Change) climate assessment reports and provide the basis for discussion on climate change adoptation and mitigation procedures. \n", "The chosen climate forcing does not only effect scientific research but has a direct impact on decision making. Assessing the the potential bias and incertainty in model outputs due to different climatic forcing is critical to evaluate.\n", "\n", "These issues concern my own PhD project where I investigate climate change impact on the Arctic carbon cycle using a dynamic vegetation model (LPJ-GUESS). Future climate change scenarios forecast largest changes in temperature and precipitation at northern high latitudes - the region that is already undergoing unprecedented environmental changes (Box et al.2019). It is important to get a more comprehensive understanding on the impact of changing environmental conditions on biogeochemical and hydrological cycles. As these cycles are strongly dependent on the climatic forcing, \n", "the choice of model climatic forcing needs to be carefully chosen. A number of studies evaluated the fit between observations and gridded climate datasets with the conclusion that depending on region and time period, there is a substantial bias between gridded climate data and observations (Mehran et al. 2014, Li et al. 2013). This bias may propagate to model uncertainty. Wu et al. (2018) suggested that the large climate bias induced uncertainties in modelled carbon balance estimated need to be further investigated and minimised. Similarily, the study of Ahlström et al. (2013) emphesised that the modelled carbon balance strongly depend on climatic forcing variables. It is useful to analyse the impact of different climatic forcing on the model simulations to (1) evaluate the outputs accordingly and to (2) keep the climate forcing derived differences in mind when comparing to modelling studies using different simulation set-ups. \n", "\n", "One of the most widely used global gridded climate datasets is the CRUNCEP (v7) product, that is combined from CRU TS 3.2 observational and NCEP reanalysis products (Viovy et al. 2016). CRUJRA (v.2.1) is a recently developed dataset, that is planned to replace CRUNCEP in the future (Harris et al. 2019). CRUJRA is constructed from the Japanese Reanalysis data (JRA) and CRU TS 4.03 datasets, and has a global coverage just as CRUNCEP. Currenly, LPJ-GUESS offline historical simulations are forced by CRUNCEP data, with plans to migrate to the CRUJRA dataset in the near future. Understanfing the differences between these two dataset would be valuable prior to this update.\n", "\n", "In this project, the focus is on investigating the spatio-temporal differences on current and future model climate forcing datasets and their impact on modelled variables. This project is purely geocomputational, as my aim is to analyse two well known gridded climate datasets and their impact on LPJ-GUESS model outputs. Details on the two studied datasets are shown in Table 1.\n", "\n", "Table 1. Description of studied global gridded climate datasets.\n", "\n", " - | CRUNCEP v.7 | CRU JRA 2.1 \n", "--|:---------:|:-----------:\n", "data coverage |1901-2015 | 1901-2019 \n", "spatial resolution | 0.5° x 0.5°| 0.5° x 0.5°\n", "temporal resolution | daily | daily\n", "reference| Viovy et al. 2016 | Harris et al. 2019\n", "available variables| air T, precipitation, humidity,| incoming solar radiation, surface winds, pressure" ] }, { "cell_type": "markdown", "id": "2351ca5c", "metadata": {}, "source": [ "The objectives of this project are:\n", "
    \n", "
  1. to compute and compare the amplitude and interannual variation in key variables for the two studied datasets
  2. \n", "
  3. to identify areas with largest differences (hotspots with largest deviations)
  4. \n", "
  5. to evaluate the impact on selected modelled variables (snow depth, near surface soil T, ecosystem productivity)
  6. \n", "
\n", "\n" ] }, { "cell_type": "markdown", "id": "55ff259c", "metadata": {}, "source": [ "## Methods" ] }, { "cell_type": "markdown", "id": "15a6b7d1", "metadata": {}, "source": [ "#### Data aquisition\n", "Both datasets are available to the public free of charge upon registration on the data repository sites of CEDA (CRUJRA 2.1.CEDA https://catalogue.ceda.ac.uk/uuid/7f785c0e80aa4df2b39d068ce7351bbb) and UCAR (CRUNCEP v.7.https://rda.ucar.edu/datasets/ds314.3/). The downloaded data was stored on an external harddrive.\n", "\n", "#### Data exploration \n", "As the CRUJRA data already had a daily temporal resolution I did not have conduct pre-processing or data alignment. CRUNCEP data was dowloaded with a 6-hourly temporal resolution. Prior to the data analysis the datafiles were combined and aggregated to a single time series dataset. Due to time limitation, I selected a confined study region and focused on surface temperature variable between the 1970-2019 period. Data was clipped to the selected spatial and temporal extent to facilitate the further processing and analysis steps (using Python xarray tools). The study area was delinated by a bounding box with coordinates lat 4.0,54.0, lon. 33.0,72.0." ] }, { "cell_type": "markdown", "id": "abd52b55", "metadata": {}, "source": [ "#### Data processing \n", "Climate data was processed using the Climate data operator (CDO) software. CDO is a well known data processing toolset, custimised to climate data processing and analysis (Schulzweida 2019). Firstly, I used CDO tools to have an overview of the data specifics (ncdump, ncview). I computed a set of descriptive statistical indices to compare the datasets.\n", "
    \n", "
  1. Descriptive statistics, monthly, global/regional scale
  2. \n", " relative frequency distribution (max, min, mean, median, percentile)\n", "
  3. Spatial patterns, visualising areas with largest differences by the difference in mean daily temperature
  4. \n", "
  5. Temporal patterns, time-series analysis
  6. \n", "DTW: currently, the most popular method for elastic matching of sequences is DTW, which minimizes the Euclidean distance of corresponding points. \n", "
" ] }, { "cell_type": "code", "execution_count": null, "id": "7bd69320", "metadata": {}, "outputs": [], "source": [ "############################################\n", "# # Downloading CRUNCEP data from UCAR\n", "############################################\n", "import sys, os\n", "import requests\n", "\n", "def check_file_status(filepath, filesize):\n", " sys.stdout.write('\\r')\n", " sys.stdout.flush()\n", " size = int(os.stat(filepath).st_size)\n", " percent_complete = (size/filesize)*100\n", " sys.stdout.write('%.3f %s' % (percent_complete, '% Completed'))\n", " sys.stdout.flush()\n", "\n", "# Try to get password\n", "if len(sys.argv) < 2 and not 'RDAPSWD' in os.environ:\n", " try:\n", " import getpass\n", " input = getpass.getpass\n", " except:\n", " try:\n", " input = raw_input\n", " except:\n", " pass\n", " pswd = input('Password: ')\n", "else:\n", " try:\n", " pswd = sys.argv[1]\n", " except:\n", " pswd = os.environ['RDAPSWD']\n", "\n", "url = 'https://rda.ucar.edu/cgi-bin/login'\n", "values = {'email' : 'alexandra.pongracz@nateko.lu.se', 'passwd' : pswd, 'action' : 'login'}\n", "# Authenticate\n", "ret = requests.post(url,data=values)\n", "if ret.status_code != 200:\n", " print('Bad Authentication')\n", " print(ret.text)\n", " exit(1)\n", "dspath = 'https://rda.ucar.edu/data/ds314.3/'\n", "filelist = [\n", "'2010_2016/clmforc.cruncep.V7.c2016.0.5d.TPQWL.*.nc']\n", "for file in filelist:\n", " filename=dspath+file\n", " file_base = os.path.basename(file)\n", " print('Downloading',file_base)\n", " req = requests.get(filename, cookies = ret.cookies, allow_redirects=True, stream=True)\n", " filesize = int(req.headers['Content-length'])\n", " with open(file_base, 'wb') as outfile:\n", " chunk_size=1048576\n", " for chunk in req.iter_content(chunk_size=chunk_size):\n", " outfile.write(chunk)\n", " if chunk_size < filesize:\n", " check_file_status(file_base, filesize)\n", " check_file_status(file_base, filesize)\n", " print()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9c36b6c7", "metadata": {}, "outputs": [], "source": [ "############################################\n", "# Function to extract study area and period\n", "############################################\n", "def raw_to_extent(directory):\n", " for filename in os.listdir(directory):\n", " if filename.endswith(\".nc\"):\n", " data_path = os.path.join(directory, filename)\n", " raw = xr.open_dataset(data_path)\n", " area_clipped = raw.sel(lon=slice(4, 33), lat=slice(54, 72))\n", " clipped = area_clipped.sel(time=slice(\"1970-01-01\", \"2019-12-31\"))\n", "\n", " new_filename = '/media/VM_shared/clim_out/extent/' + str(filename) + '_ext.nc'\n", " print ('saving to ', new_filename)\n", " clipped.to_netcdf(path=new_filename)\n", "\n", "# Clipping raw data files to extent\n", "directory = r'/media/data/CRU_JRA2_1'\n", "raw_to_extent(directory)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "d89e6ab7", "metadata": {}, "outputs": [], "source": [ "ds = xarray.open_mfdataset('E:/VM_shared/NCEP/re_prc/clmforc.*.nc',combine = 'by_coords', concat_dim=\"time\")\n", "daily = ds.resample(time=\"24H\").sum()\n", "daily.PRECTmms.to_netcdf('E:/VM_shared/NCEP/prec_1970_2016sum.nc')" ] }, { "cell_type": "code", "execution_count": 6, "id": "6fdc8ee8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: ecCodes 2.18.0 or higher is recommended. You are running version 2.6.0\n", "Importing the dtw module. When using in academic works please cite:\n", " T. Giorgino. Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package.\n", " J. Stat. Soft., doi:10.18637/jss.v031.i07.\n", "\n" ] } ], "source": [ "# Import nessesary Python packages\n", "import os\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib as mpl\n", "import matplotlib.colors as colors\n", "import matplotlib.pyplot as plt\n", "import xarray as xr\n", "import cartopy.crs as ccrs\n", "import cartopy.feature as cfeature\n", "import seaborn as sns\n", "from dtw import *\n", "import xskillscore as xs\n", "#import help_func_AP as fun\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": 7, "id": "74a65e2a", "metadata": {}, "outputs": [], "source": [ "################################################\n", "## Calculating seasonal time series\n", "################################################\n", "def calc_season(indata, varname, dataset):\n", " time = \"time.season\" \n", " out = indata.groupby(time)\n", " DJF = out[\"DJF\"].to_series().to_frame().rename(columns={varname: 'data'})\n", " DJF[\"seas\"] = \"DJF\"\n", " MAM = out[\"MAM\"].to_series().to_frame().rename(columns={varname: 'data'})\n", " MAM[\"seas\"] = \"MAM\"\n", " JJA = out[\"JJA\"].to_series().to_frame().rename(columns={varname: 'data'})\n", " JJA[\"seas\"] = \"JJA\"\n", " SON = out[\"SON\"].to_series().to_frame().rename(columns={varname: 'data'})\n", " SON[\"seas\"] = \"SON\"\n", " x = pd.concat([DJF, MAM, JJA, SON])\n", " x[\"dataset\"] = dataset\n", " return x" ] }, { "cell_type": "code", "execution_count": 7, "id": "c5a51341", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cdo addc: Started child process \"setattribute,tmp@units=degC /media/sf_VM_shared/clim_out/extent/crujra.v2.1.5d.tmp.1901-2019.365d.noc.daymean.chunked_rew.nc_ext.nc (pipe1.1)\".\n", "cdo(2) setattribute: Processed 35819640 values from 1 variable over 17155 timesteps [11.54s]\n", "cdo addc: Processed 35819640 values from 1 variable over 17155 timesteps [11.54s 607MB]\n", "cdo addc: Started child process \"setattribute,TBOT@units=degC /media/sf_VM_shared/NCEP/tmp_1970_2016.nc (pipe1.1)\".\n", "^C\n" ] } ], "source": [ "# Computing statistical properties from raw netcdf data using cdo\n", "\n", "# Temperature----------------------------------------------------\n", "# Convert from K to Celsius and change attributes\n", "!cdo -addc,-273.15, -setattribute,tmp@units=\"degC\" /media/sf_VM_shared/clim_out/extent/crujra.v2.1.5d.tmp.1901-2019.365d.noc.daymean.chunked_rew.nc_ext.nc /media/sf_VM_shared/CRUJRA/tmp_1970_2016_C.nc\n", "!cdo -addc,-273.15, -setattribute,TBOT@units=\"degC\" /media/sf_VM_shared/NCEP/tmp_1970_2016.nc /media/sf_VM_shared/NCEP/tmp_1970_2016_C.nc" ] }, { "cell_type": "code", "execution_count": 26, "id": "171f7866", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "saving to /media/sf_VM_shared/NCEP/tmp_1970_2016_C_ext.nc\n" ] } ], "source": [ "raw2 = xr.open_dataset(\"/media/sf_VM_shared/NCEP/tmp_1970_2016_C.nc\")\n", "area_clipped = raw2.sel(lon=slice(4, 33), lat=slice(54, 72))\n", "new_filename = '/media/sf_VM_shared/NCEP/tmp_1970_2016_C_ext.nc'\n", "print ('saving to ', new_filename)\n", "area_clipped.to_netcdf(path=new_filename)" ] }, { "cell_type": "code", "execution_count": 7, "id": "348a41af", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cdo ydaymean: Processed 4446576000 values from 1 variable over 17155 timesteps [29.43s 1517MB]\n", "cdo ydaymean: Processed 35819640 values from 1 variable over 17155 timesteps [7.14s 294MB]\n" ] } ], "source": [ "# Calculate daily annual cycle\n", "!cdo -ydaymean /media/sf_VM_shared/CRUJRA/tmp_1970_2016_C.nc /media/sf_VM_shared/CRUJRA/tmp_ydaymean365.nc" ] }, { "cell_type": "code", "execution_count": 27, "id": "feeefe78", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cdo ydaymean: Processed 35819640 values from 1 variable over 17155 timesteps [3.38s 495MB]\n" ] } ], "source": [ "!cdo -ydaymean /media/sf_VM_shared/NCEP/tmp_1970_2016_C_ext.nc /media/sf_VM_shared/NCEP/tmp_ydaymean365.nc" ] }, { "cell_type": "code", "execution_count": 100, "id": "55b7000f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cdo monmean: Processed 35819640 values from 1 variable over 17155 timesteps [1.78s 1594MB]\n" ] } ], "source": [ "!cdo -monmean /media/sf_VM_shared/NCEP/prec_cut.nc /media/sf_VM_shared/clim_out/stat/tmpCCRUJRA_ydays.nc" ] }, { "cell_type": "code", "execution_count": 104, "id": "8ff182b2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cdo ydayvar: Processed 35819640 values from 1 variable over 17155 timesteps [3.80s 1594MB]\n" ] } ], "source": [ "!cdo -ydayvar /media/sf_VM_shared/clim_out/ncepT_1970_2016_scandi_C.nc /media/sf_VM_shared/NCEP/tmpNCEP_ydayvar365.nc" ] }, { "cell_type": "markdown", "id": "494ea3cd", "metadata": {}, "source": [ "#### CRU NCEP ---------------------------------------------------------" ] }, { "cell_type": "code", "execution_count": null, "id": "eb9fb2f4", "metadata": {}, "outputs": [], "source": [ "# test a global stat\n", "!cdo -daymean /media/sf_VM_shared/clim_out/sliced_raw/crujra.v2.1.5d.tmp.1901-2019.365d.noc.daymean.chunked_rew.nc /media/sf_VM_shared/clim_out/stat/tmp_global_daymean.nc" ] }, { "cell_type": "code", "execution_count": 101, "id": "22ab3666", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cdo ydayvar: Processed 35819640 values from 1 variable over 17155 timesteps [1.51s 1594MB]\n" ] } ], "source": [ "!cdo -ydayvar /media/sf_VM_shared/NCEP/tmp_cut.nc /media/sf_VM_shared/NCEP/tmpNCEP_ydayvar.nc" ] }, { "cell_type": "markdown", "id": "e134751c", "metadata": {}, "source": [ "pre_yvar = xr.open_dataset(\"/media/sf_VM_shared/clim_out/stat/pre_daymean.nc\")## Data processing" ] }, { "cell_type": "markdown", "id": "2da6b2e4", "metadata": {}, "source": [ "### Time series analysis (DTW)\n", "The Python dwt function allows the selection of different arbitary windows, which define the shape of the matching window and the diagonal alignment of the least cost path. In this project I test the Sakoe-Chiba band and Itakura parallelogram windows. Slope contraints can be set by the *step patterns* parameter. This parameter controls the number of time series elements (Y) to be matched with X, which influences the curve of the warping path. In this project I tested (Toni Giorgino, 2009).\n", "\n", "### Basic model evaluation\n", "To assess the impact of changing the climatic input in an ecosystem model, I conducted a set of simulations with the LPJ-GUESS DGVM v.4. Due to time limitations, I selected a number of sites randomly within the study region, and focused on the simulated snow depth, near surface soil temperature and GPP outputs.\n" ] }, { "cell_type": "markdown", "id": "78180569", "metadata": {}, "source": [ "## Results & Discussion" ] }, { "cell_type": "markdown", "id": "6808bb9a", "metadata": {}, "source": [ "The computed statistical characteristics of the two datasets are shown in Table 2. There are marginal differences in mean global temperature, with the CRUJRA dataset having higher variance. There are large differences in precipitation, with CRUJRA dataset daily precipitation being significantly lower than the CRUNCEP values." ] }, { "cell_type": "markdown", "id": "0131927f", "metadata": {}, "source": [ "*Table 2. Basic statistical indices of daily near surface temperature and daily precipitation variables for the two studied datasets.*\n", " - | CRUJRA | CRUNCEP\n", "--|:---------:|:-----------:\n", "T min (°C) |-27.38 | -19.43 \n", "T max (°C) | 21.39 | 17.02\n", "T mean (°C)| 3.27 | 3.39\n", "T var (°C) | 74.65 | 51.83\n", "Prec min (mm) |0.0 | 0.0 \n", "Prec max (mm) | 10.1| 39.06\n", "Prec mean (mm) | 2.1 | 8.64\n", "Prec var (mm) | 1.74 | 20.62" ] }, { "cell_type": "markdown", "id": "66454468", "metadata": {}, "source": [ "pip install netCDF4pip install netCDF4#### Examining data distribution and seasonal patterns\n", "When reviewing climate data we are not solely interested in the descriptive statistics, such as mean an variance of datasets, but the distribution of datapoints is extremely important to be able to capture rare and extreme conditions as well. To visualise the distribution, temperature and precipitation was plotted using violin plots.\n", "\n", "The violin plots (Fig X and Y) show the comparison of mean daily temperature data over the 1970-2016 period for both climate datasets. The violin plot presents the mean and quantiles of data and the kernel density estimation of data distribution. Fig X shows a similar mean annual near surface temperature distibution for the two datasets, however, the CRU JRA dataset contains more values outside of the interquartile range." ] }, { "cell_type": "code", "execution_count": 8, "id": "3fdd2075", "metadata": {}, "outputs": [], "source": [ "jra_mon = xr.open_dataset(\"/media/sf_VM_shared/CRUJRA/tmp_1970_2016_C.nc\", engine = \"netcdf4\")\n", "x = jra_mon.tmp.to_dataframe()\n", "xx = jra_mon.mean([\"lat\",\"lon\"])\n", "cru_time = xx.tmp" ] }, { "cell_type": "code", "execution_count": 9, "id": "e9b476b4", "metadata": {}, "outputs": [], "source": [ "cru_time_df = cru_time.to_dataframe()\n", "cru_time_df[\"dataset\"] = \"CRUJRA\"\n", "cru_time_df = cru_time_df.rename(columns={'tmp': 'data'})" ] }, { "cell_type": "code", "execution_count": 10, "id": "7b16e29f", "metadata": {}, "outputs": [], "source": [ "# NCEP timeseries\n", "ncep_mon = xr.open_dataset(\"/media/sf_VM_shared/NCEP/tmp_1970_2016_C_ext.nc\")\n", "x = ncep_mon.TBOT.to_dataframe()\n", "xx = ncep_mon.mean([\"lat\",\"lon\"])\n", "ncep_time = xx.TBOT" ] }, { "cell_type": "code", "execution_count": 11, "id": "0c293065", "metadata": {}, "outputs": [], "source": [ "ncep_time_df = ncep_time.to_dataframe()\n", "ncep_time_df[\"dataset\"] = \"CRUNCEP\"\n", "ncep_time_df = ncep_time_df.rename(columns={'TBOT': 'data'})" ] }, { "cell_type": "code", "execution_count": 12, "id": "c7fb6ec0", "metadata": {}, "outputs": [], "source": [ "T_viol = pd.concat([ncep_time_df,cru_time_df])" ] }, { "cell_type": "code", "execution_count": 13, "id": "783f458b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'Dataset')" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "h = sns.violinplot(x=\"dataset\", y=\"data\", data=T_viol,palette = \"Set1\",inner=\"quartile\")\n", "h.set_ylabel('Tmean$_{annual}$ (°C)', fontweight='bold', fontsize = 15)\n", "h.set_xlabel('Dataset', fontweight='bold', fontsize = 15)" ] }, { "cell_type": "markdown", "id": "725e81df", "metadata": {}, "source": [ "*Figure 1. Violin plot showing the kernel density distribution of daily near surface temeprature for the CRUJRA and CRUNEP datasets.*" ] }, { "cell_type": "markdown", "id": "86cccf19", "metadata": {}, "source": [ "### Seasonal differences" ] }, { "cell_type": "code", "execution_count": 17, "id": "0f5e002f", "metadata": {}, "outputs": [], "source": [ "# Mean difference between T\n", "da_a = xr.open_dataset(\"/media/sf_VM_shared/NCEP/tmp_ydaymean365.nc\")\n", "da_b = xr.open_dataset(\"/media/sf_VM_shared/CRUJRA/tmp_ydaymean365.nc\")\n", "x = da_a.mean(dim = \"time\")\n", "y = da_b.mean(dim = \"time\")\n", "# absolute difference between mean annual T (NCEP-crujra)\n", "diff = x.TBOT - y.tmp " ] }, { "cell_type": "code", "execution_count": null, "id": "cc25ca1a", "metadata": {}, "outputs": [], "source": [ "cmap = \"YlOrRd\"\n", "norm = colors.TwoSlopeNorm(vmin=-0.3, vcenter=0.0, vmax=0.3) \n", "ax = plt.axes(projection=ccrs.Robinson())\n", "\n", "plt.contourf(diff.lon,diff.lat,diff,cmap=cmap,norm = norm,transform=ccrs.PlateCarree())\n", "\n", "ax.coastlines()\n", "\n", "fig.colorbar(mpl.cm.ScalarMappable(cmap=cmap),\n", " ax=ax, orientation='vertical', label='T diff. (°C)', fraction = 0.033)\n", "#plt.colorbar(mpl.cm.ScalarMappable(cmap=cmap),fraction=0.046, pad=0.04, pad=0.04)\n", "ax.set_title(\"$T_{mean}$ difference (CRUNCEP-CRUJRA)\")\n", "plt.savefig(\"E:/VM_shared/spatial_diff.png\", dpi = 300)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "id": "f7de6c36", "metadata": {}, "outputs": [], "source": [ "viol_mon_ncep = calc_season(ncep_time, \"TBOT\", \"CRUNCEP\")\n", "viol_mon_jra = calc_season(cru_time, \"tmp\", \"CRUJRA\")\n", "viol_mon = pd.concat([viol_mon_ncep, viol_mon_jra])" ] }, { "cell_type": "code", "execution_count": 15, "id": "abde4433", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fig. 2. Violon plot showing the seasonal distribution of surface temperature for the CRUNCEP and CRUJRA datasets.\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "h = sns.violinplot(x=\"seas\", y=\"data\",hue = \"dataset\", data=viol_mon,\n", " palette = \"Set1\",inner=\"quartile\",split = True)\n", "h.set_ylabel('Seasonal surface temperature$_{mean}$ (°C)', fontweight='bold', fontsize = 12)\n", "h.set_xlabel('Season', fontweight='bold', fontsize = 12)\n", "plt.legend(loc='lower right')\n", "plt.savefig(\"/media/sf_VM_shared/seasT_viol.png\", dpi = 300)\n", "print(\"Fig. 2. Violon plot showing the seasonal distribution of surface temperature for the CRUNCEP and CRUJRA datasets.\")\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "c1834617", "metadata": {}, "outputs": [], "source": [ "# Corr\n", "da_a = xr.open_dataset(\"/media/sf_VM_shared/NCEP/tmp_ydaymean365.nc\")\n", "da_b = xr.open_dataset(\"/media/sf_VM_shared/CRUJRA/tmp_ydaymean365.nc\")\n", "x = da_a.mean(dim = \"time\")\n", "y = da_b.mean(dim = \"time\")\n", "# absolute difference between mean annual T (NCEP-crujra)\n", "diff = x.TBOT - y.tmp " ] }, { "cell_type": "markdown", "id": "9cb99aa2", "metadata": {}, "source": [ "Precipitation (only for testing purposses)" ] }, { "cell_type": "code", "execution_count": 17, "id": "728fa82e", "metadata": {}, "outputs": [], "source": [ "prec_raw_crujra = xr.open_dataset(\"/media/sf_VM_shared/CRUJRA/prec_cut.nc\")\n", "prec_jra = prec_raw_crujra.pre\n", "prec_jra_time = prec_jra.mean([\"lat\",\"lon\"])" ] }, { "cell_type": "code", "execution_count": 18, "id": "ec98d94e", "metadata": {}, "outputs": [], "source": [ "prec_raw_ncep = xr.open_dataset(\"/media/sf_VM_shared/NCEP/prec_cut.nc\")\n", "prec_ncep = prec_raw_ncep.PRECTmms * 60*60\n", "prec_ncep_time = prec_ncep.mean([\"lat\",\"lon\"])" ] }, { "cell_type": "code", "execution_count": 19, "id": "daac841d", "metadata": {}, "outputs": [], "source": [ "violP_mon_ncep = calc_season(prec_ncep_time, \"PRECTmms\", \"CRUNCEP\")\n", "violP_mon_jra = calc_season(prec_jra_time, \"pre\", \"CRUJRA\") \n", "violP_mon = pd.concat([violP_mon_ncep, violP_mon_jra])" ] }, { "cell_type": "code", "execution_count": 20, "id": "055d3979", "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fig. 3. Violon plot showing the seasonal distribution of precipitation for the CRUNCEP and CRUJRA datasets.\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "h = sns.violinplot(x=\"seas\", y=\"data\",hue = \"dataset\", data=violP_mon,\n", " palette = \"Set1\",inner=\"quartile\",split = False, legend_out=False)\n", "h.set_ylabel('Seasonal surface temperature$_{mean}$ (°C)', fontweight='bold', fontsize = 12)\n", "h.set_xlabel('Season', fontweight='bold', fontsize = 12)\n", "print(\"Fig. 3. Violon plot showing the seasonal distribution of precipitation for the CRUNCEP and CRUJRA datasets.\")\n" ] }, { "cell_type": "markdown", "id": "788ec58a", "metadata": {}, "source": [ "### Dynamic Time Warping (DTW)" ] }, { "cell_type": "code", "execution_count": 21, "id": "32c022a0", "metadata": {}, "outputs": [], "source": [ "# assign arrays monthly\n", "Tcrujra = xr.open_dataset(\"/media/sf_VM_shared/CRUJRA/tmp_1970_2016_C.nc\")\n", "x = Tcrujra.resample(time=\"1MS\").mean(dim=\"time\")\n", "xx = Tcrujra.groupby(\"time.month\").mean(\"time\")\n", "TCRU = xx.mean([\"lat\",\"lon\"])\n", "TCRU = TCRU.tmp.values" ] }, { "cell_type": "code", "execution_count": 22, "id": "2edc7bf6", "metadata": {}, "outputs": [], "source": [ "T_NCEP= xr.open_dataset(\"/media/sf_VM_shared/NCEP/tmp_1970_2016_C_ext.nc\")\n", "xx = T_NCEP.groupby(\"time.month\").mean(\"time\")\n", "TNCEP = xx.mean([\"lat\",\"lon\"]).TBOT.values" ] }, { "cell_type": "code", "execution_count": 23, "id": "f66ec065", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sakoe-Chiba, Normalised distance 0.8617017467816671\n" ] } ], "source": [ "alignment1 = dtw(TCRU, TNCEP, keep_internals=True,window_type=\"sakoechiba\",window_args={'window_size':10},\n", " step_pattern = asymmetric)\n", "print(\"Sakoe-Chiba, Normalised distance\",alignment1.normalizedDistance)" ] }, { "cell_type": "code", "execution_count": 24, "id": "ef9f7c05", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sakoe-Chiba, Normalised distance 0.8859652280807495\n" ] } ], "source": [ "alignment2 = dtw(TCRU, TNCEP, keep_internals=True,window_type=\"sakoechiba\",window_args={'window_size':10},\n", " step_pattern=symmetric2)\n", "print(\"Sakoe-Chiba, Normalised distance\",alignment2.normalizedDistance)" ] }, { "cell_type": "code", "execution_count": 25, "id": "38537980", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sakoe-Chiba, Normalised distance 0.9817056854565939\n" ] } ], "source": [ "alignment3= dtw(TCRU, TNCEP, keep_internals=True,window_type=\"sakoechiba\",step_pattern=symmetricP05,window_args={'window_size':10})\n", "print(\"Sakoe-Chiba, Normalised distance\",alignment3.normalizedDistance)" ] }, { "cell_type": "code", "execution_count": 26, "id": "e5c54afe", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sakoe-Chiba, Normalised distance 0.9948395689328512\n" ] } ], "source": [ "alignment4 = dtw(TCRU, TNCEP, keep_internals=True,window_type=\"sakoechiba\", \n", " step_pattern=rabinerJuangStepPattern(5, \"c\"),window_args={'window_size':10})\n", "print(\"Sakoe-Chiba, Normalised distance\",alignment4.normalizedDistance)" ] }, { "cell_type": "code", "execution_count": 28, "id": "3a123b91", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Figure 4. DWT matrices with the contrsucted best alignment path shown by the blue line. Applied the Sakoe-Chiba window contraint and tested four different step patterns, asymmetric, symmetric patterns with different slopes and Rabiner-Juang set.\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot cost matrices\n", "fig, axs = plt.subplots(2,2,figsize=(12,12))\n", "\n", "axs[0, 0].imshow(alignment1.localCostMatrix,alpha=0.5,origin='lower', cmap = \"coolwarm\")\n", "axs[0, 0].plot(alignment1.index2,alignment1.index1)\n", "axs[0, 0].set_ylabel(\"month CRUJRA\", fontweight='bold', fontsize = 12)\n", "axs[0, 0].set_title(\"Asymmetric\",fontweight='bold', fontsize = 14)\n", "axs[0, 0].set_xticklabels([\"Jan\",\"Mar\",\"May\",\"Jul\",\"Sep\",\"Nov\"])\n", "axs[0, 0].set_xticks([0,2,4,6,8,10])\n", "axs[0, 0].set_yticklabels([\"Jan\",\"Mar\",\"May\",\"Jul\",\"Sep\",\"Nov\"])\n", "axs[0, 0].set_yticks([0,2,4,6,8,10])\n", "for (j,i),label in np.ndenumerate(np.round(alignment1.localCostMatrix,1)):\n", " axs[0, 0].text(i,j,label,ha='center',va='center')\n", " \n", "axs[0, 1].imshow(alignment2.localCostMatrix,alpha=0.5,origin='lower', cmap = \"coolwarm\")\n", "axs[0, 1].plot(alignment2.index2,alignment2.index1)\n", "\n", "axs[0, 1].set_title(\"Symmetric2\",fontweight='bold', fontsize = 14)\n", "axs[0, 1].set_xticklabels([\"Jan\",\"Mar\",\"May\",\"Jul\",\"Sep\",\"Nov\"])\n", "axs[0, 1].set_xticks([0,2,4,6,8,10])\n", "axs[0, 1].set_yticklabels([\"Jan\",\"Mar\",\"May\",\"Jul\",\"Sep\",\"Nov\"])\n", "axs[0, 1].set_yticks([0,2,4,6,8,10])\n", "for (j,i),label in np.ndenumerate(np.round(alignment2.localCostMatrix,1)):\n", " axs[0, 1].text(i,j,label,ha='center',va='center')\n", " \n", "axs[1, 0].imshow(alignment3.localCostMatrix,alpha=0.5,origin='lower', cmap = \"coolwarm\")\n", "axs[1, 0].plot(alignment3.index2,alignment3.index1)\n", "axs[1, 0].set_ylabel(\"month CRUJRA\", fontweight='bold', fontsize = 12)\n", "axs[1, 0].set_xlabel(\"month CRUNCEP\", fontweight='bold', fontsize = 12)\n", "axs[1, 0].set_title(\"SymmetricP05\",fontweight='bold', fontsize = 14)\n", "axs[1, 0].set_xticklabels([\"Jan\",\"Mar\",\"May\",\"Jul\",\"Sep\",\"Nov\"])\n", "axs[1, 0].set_xticks([0,2,4,6,8,10])\n", "axs[1, 0].set_yticklabels([\"Jan\",\"Mar\",\"May\",\"Jul\",\"Sep\",\"Nov\"])\n", "axs[1, 0].set_yticks([0,2,4,6,8,10])\n", "for (j,i),label in np.ndenumerate(np.round(alignment3.localCostMatrix,1)):\n", " axs[1, 0].text(i,j,label,ha='center',va='center')\n", " \n", "axs[1, 1].imshow(alignment4.localCostMatrix,alpha=0.5,origin='lower', cmap = \"coolwarm\")\n", "axs[1, 1].plot(alignment4.index2,alignment4.index1)\n", "axs[1, 1].set_xlabel(\"month CRUNCEP\", fontweight='bold', fontsize = 12)\n", "axs[1, 1].set_title(\"Rabiner-Juang set\",fontweight='bold', fontsize = 14)\n", "axs[1, 1].set_xticklabels([\"Jan\",\"Mar\",\"May\",\"Jul\",\"Sep\",\"Nov\"])\n", "axs[1, 1].set_xticks([0,2,4,6,8,10])\n", "axs[1, 1].set_yticklabels([\"Jan\",\"Mar\",\"May\",\"Jul\",\"Sep\",\"Nov\"])\n", "axs[1, 1].set_yticks([0,2,4,6,8,10])\n", "for (j,i),label in np.ndenumerate(np.round(alignment4.localCostMatrix,1)):\n", " axs[1, 1].text(i,j,label,ha='center',va='center')\n", "plt.savefig(\"/media/sf_VM_shared/dtw.png\", dpi = 300)\n", "print(\"Figure 4. DWT matrices with the contrsucted best alignment path shown by the blue line. Applied the Sakoe-Chiba window contraint and tested four different step patterns, asymmetric, symmetric patterns with different slopes and Rabiner-Juang set.\")\n" ] }, { "cell_type": "markdown", "id": "2e105df7", "metadata": {}, "source": [ "\n", "Fig. X above shows the DWT matrix computed using four different step patterns for the least path computing. The figures show that the largest difference between the mean monthly temperature series is between June to August, with the largest deviation in July. There is also a considerable deviation in the winter months (Dec-Jan-Feb). The time-normalised cumulative distances are not significantly different (0.886 and 0.907 for the Sakoe-Chiba and Itakura windowing, respectively), which shows the dissimilarity between the two timeseries.\n", "\n", "Discovering the difference between time series during winter months is important for modelling purposes at northern high latitudes. Air temperature forcing directly affects soil thermodynamics, which governs permafrost conditions. Soil temperature this influences carbon emission from soils, which carry a significant portion of the overall modern uncertainty." ] }, { "cell_type": "markdown", "id": "34a1f716", "metadata": {}, "source": [ "## Impact on model outputs" ] }, { "cell_type": "markdown", "id": "1d576a1d", "metadata": {}, "source": [ "### Near surface soil temperature\n", "There is only a marginal difference (max 0.2 °C) in the calculated mean monthly near surface soil temperature. The largest differences are in the summer months, which align with the time series analysis from the DTW analysis. " ] }, { "cell_type": "code", "execution_count": 34, "id": "444ef679", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Fig x. Mean monthly difference in near surface soil T\n" ] } ], "source": [ "# Read in model outputs\n", "raw=pd.read_table('/media/sf_VM_shared/lpj_guess_runs/jra/msnowsoiltemp.out', sep= \"\\s+\")\n", "\n", "rawncep=pd.read_table('/media/sf_VM_shared/lpj_guess_runs/ncep/msnowsoiltemp.out', sep= \"\\s+\")\n", "\n", "test = rawncep.groupby([\"Mth\",\"Lat\",\"Lon\"]).mean()\n", "del test['Year']\n", "temp = test.groupby([\"Mth\"]).mean()\n", "\n", "test2 = raw.groupby([\"Mth\",\"Lat\",\"Lon\"]).mean()\n", "del test2['Year']\n", "temp2 = test2.groupby([\"Mth\"]).mean()\n", "\n", "diff = temp[\"10\"] - temp2[\"10\"]\n", "\n", "\n", "fig = plt.figure()\n", "diff.plot.bar()\n", "plt.title(\"T difference per month\")\n", "plt.ylabel(\"T diff (°C))\")\n", "plt.xlabel(\"Month\")\n", "plt.show()\n", "print(\"Fig 5. Mean monthly difference in near surface soil T\")" ] }, { "cell_type": "markdown", "id": "5232d6df", "metadata": {}, "source": [ "### Snow depth\n", "There is a marginal difference in snow depth, which can be product of precipitation and temperature differences in the two datasets. Snow cover is an important governing factor over soil thermodynamics, also influencing the rate of greenhouse gas emissions. The negative difference during the spring can be caused by slightly warmer conditions in the CRUJRA dataset." ] }, { "cell_type": "code", "execution_count": 32, "id": "e22cf51a", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Fig. 6. Snow depth difference \n" ] } ], "source": [ "# Read in model outputs\n", "raw = pd.read_table('/media/sf_VM_shared/lpj_guess_runs/jra/msnowdepth.out', sep= \"\\s+\")\n", "rawncep = pd.read_table('/media/sf_VM_shared/lpj_guess_runs/ncep/msnowdepth.out', sep= \"\\s+\")\n", "\n", "sd_ncep = rawncep.groupby([\"Lat\",\"Lon\"]).mean()\n", "sd_jra = raw.groupby([\"Lat\",\"Lon\"]).mean()\n", "diff_sd = sd_ncep - sd_jra\n", "del diff_sd['Year']\n", "\n", "fig = plt.figure()\n", "diff_sd.mean().plot.bar(color = \"royalblue\")\n", "plt.ylabel(\"Snow depth difference (m)\")\n", "plt.title(\"Snow depth difference (CRUNCEP-CRUJRA)\")\n", "\n", "plt.savefig(\"/media/sf_VM_shared/sd.png\", dpi = 300)\n", "plt.show()\n", "\n", "print(\"Fig. 6. Snow depth difference \")" ] }, { "cell_type": "markdown", "id": "b681d93b", "metadata": {}, "source": [ "### Annual GPP" ] }, { "cell_type": "code", "execution_count": 7, "id": "b9f110b1", "metadata": {}, "outputs": [], "source": [ "# agpp\n", "raw = pd.read_table('/media/sf_VM_shared/lpj_guess_runs/jra/agpp.out',sep= \"\\s+\")\n", "rawncep = pd.read_table('/media/sf_VM_shared/lpj_guess_runs/ncep/agpp.out',sep= \"\\s+\")" ] }, { "cell_type": "code", "execution_count": 24, "id": "4fe5ebc6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.23250266500092348" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw.Total.var()" ] }, { "cell_type": "code", "execution_count": 10, "id": "be7debae", "metadata": {}, "outputs": [], "source": [ "agpp_ncep = rawncep.groupby([\"Lat\",\"Lon\"]).mean()\n", "agpp_jra = raw.groupby([\"Lat\",\"Lon\"]).mean()" ] }, { "cell_type": "code", "execution_count": 31, "id": "23ced7f0", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Fig. X. Average annual GPP difference/site (CRUNCEP-CRUJRA)\n" ] } ], "source": [ "diff = agpp_ncep - agpp_jra\n", "fig = plt.figure()\n", "diff.Total.plot.bar(color = \"#006400\")\n", "plt.ylabel(\"GPP difference ($g C \\ m² $)\")\n", "plt.title(\"Annual GPP difference (CRUNCEP-CRUJRA)\")\n", "plt.savefig(\"/media/sf_VM_shared/agpp_site.png\", dpi = 300)\n", "plt.show()\n", "print(\"Fig. 7. Average annual GPP difference/site (CRUNCEP-CRUJRA)\")\n" ] }, { "cell_type": "code", "execution_count": 30, "id": "c369c70d", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Fig. 8. Average monthly GPP difference (CRUNCEP-CRUJRA)\n" ] } ], "source": [ "# Read in model outputs\n", "raw=pd.read_table('/media/sf_VM_shared/lpj_guess_runs/jra/mgpp.out', sep= \"\\s+\")\n", "\n", "rawncep=pd.read_table('/media/sf_VM_shared/lpj_guess_runs/ncep/mgpp.out', sep= \"\\s+\")\n", "\n", "test = rawncep.groupby([\"Lat\",\"Lon\"]).mean()\n", "test2 = raw.groupby([\"Lat\",\"Lon\"]).mean()\n", "fig = plt.figure()\n", "diff = test-test2\n", "del diff['Year']\n", "diff.mean().plot.bar(color = \"#006400\")\n", "plt.ylabel(\"GPP difference ($g C \\ m² $)\")\n", "plt.title(\"Monthly GPP difference (CRUNCEP-CRUJRA)\")\n", "plt.savefig(\"/media/sf_VM_shared/mgpp.png\", dpi = 300)\n", "plt.show()\n", "print(\"Fig. 8. Average monthly GPP difference (CRUNCEP-CRUJRA)\")\n" ] }, { "cell_type": "markdown", "id": "4370eda8", "metadata": {}, "source": [ "## Summary" ] }, { "cell_type": "markdown", "id": "c786141c", "metadata": {}, "source": [ "In this short project, I found that the mean statistical indices for surface temperature are similar for the two compared datasets. The DTW time series analysis showed a deviation in winter and summer months. These differences have the ability to influence modelled biogeochemical variables. The model output analysis showed that there is a non-significant difference in soil temeprature, snow depth and GPP outputs. However to draw conclusions over a larger region, a more thorough analysis needed with global extent simulations.\n", "\n", "\n", "An obvious shortcoming of this project is the limited spatial and temporal extent. Given more time, more climate variables could be compared on a global scale which would aid in deciding whether the CRUJRA dataset is a suitable to replace the current LPJ-GUESS climate forcing dataset. Additionally, the two climate datasets could be evaluated against observations to evaluate the regional and global fit of the gridded climate datasets." ] }, { "cell_type": "markdown", "id": "85f6f1e0", "metadata": {}, "source": [ "## References" ] }, { "cell_type": "markdown", "id": "3cc36010", "metadata": {}, "source": [ "Ahlström A, Smith B, Lindström J, Rummukainen M and Uvo C B 2012 GCM characteristics explain the majority of uncertainty in projected 21st century terrestrial ecosystem carbon balance Biogeosciences 10 1517–28\n", "\n", "Box, J. E., Colgan, W. T., Christensen, T., Schmidt, N. M., Lund, M., Parmentier, F.-J. W., Brown, R., Bhatt, U. S., Euskirchen, E. S.,5 Romanovsky, V. E., Walsh, J. E., Overland, J. E., Wang, M., Corell, R., Meier, W. N., Wouters, B., Mernild, S. H., Mård, J., Pawlak, J.,\n", "and Olsen, M. S.: Key indicators of Arctic climate change: 1971–2017., Environmental Research Letters, 14, 2019. Bruhwiler, L., Parmentier, F.-J.W., Crill, P., Leonard, M., and Palmer, P. I.: The Arctic Carbon Cycle and Its Response to\n", "\n", "McGuire, A. D., et al. (2001), Carbon balance of the terrestrial biosphere in the Twentieth Century: Analyses of CO2, climate and land use effects with four process-based ecosystem models, Global Biogeochem. Cycles, 15( 1), 183– 206, doi:10.1029/2000GB001298. \n", "\n", "Mehran A, Agha, Kouchak A and Phillips T J 2014 Evaluation of CMIP5 continental precipitation simulations relative to satellite-based gauge-adjusted observations J. Geophys. Res.: Atmos. 119 1695–707\n", "\n", "New, M., Hulme, M., & Jones, P. (2000). Representing Twentieth-Century Space–Time Climate Variability. Part II: Development of 1901–96 Monthly Grids of Terrestrial Surface Climate, Journal of Climate, 13(13), 2217-2238. 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