loaded_climate_dice_update.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An update on the loaded climate dice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the recurring claims you will find in discussions of climate change is that the climate in general not only becomes warmer but also more extreme. For instance, the latest report on climate change by the U.S. Administration is covered in the news as follows:\n",
    "\n",
    "> \"While summers become more extreme, the harshness of winter is expected to ease.\" (Jason Samenow, [Washington Post, November 27](https://www.washingtonpost.com/weather/2018/11/27/weather-washingtons-future-hellish-heat-high-water-says-trump-administration-climate-report/?noredirect=on&utm_term=.7edba1564483)) \n",
    "\n",
    "In the last post, I have replicated a paper (Hansen et al. 2012) that shows in an elegant (because easy to see) way that the average temperatures during summer indeed rise (see [here](https://mschermann.github.io/serendipity/posts/loaded-climate-dice/)). Interestingly, there is a response (Rhines and Huybers 2013) to this paper that argues that while the average temperatures rise, they do not become more extreme. \n",
    "\n",
    "In other words, the mean of summer temperatures rises but the variance of the temperatures remains stable. The authors of the response suggest a series of corrections to show that the variance remains stable. I have replicated some of the corrections in my previous post. \n",
    "\n",
    "A big difference between my replication attempt and the papers by Hansen et al. (2012) and Rhines and Huybers (2013) is that I use the actual station data while the two papers use a gridded composition of the station data. Each grid represents a 250km square.\n",
    "\n",
    "Using the actual station data instead the gridded data has some implications:\n",
    "* An increase or decreased in available stations does not affect the results from the individual stations but will affect the gridded data.\n",
    "* The gridded data is also influenced by covariances between the stations while the individual station data is not. \n",
    "* Systematic measurement errors in one station do not get smoothed out. Thus, faulty stations may affect the results to a higher degree when compared to the gridded data.\n",
    "\n",
    "\n",
    "Here, I will replicate the results again and in addition to the corrections applied in the previous post, I will focus on the following issues:\n",
    "* Use stations that report temperature data on the full timeperiod between 1950 to today.\n",
    "* Detrend the temperature data for each station to correct for trend-induced variance components. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-19T17:00:53.553124Z",
     "start_time": "2018-04-19T17:00:53.545682Z"
    }
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets' set up our environment to allow [multiple outputs](https://stackoverflow.com/questions/34398054/ipython-notebook-cell-multiple-outputs) for each code cell. Also, we use the [magic](http://ipython.readthedocs.io/en/stable/interactive/magics.html) command to display [matplotlib](https://matplotlib.org) plots in the Jupyter notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T04:35:21.634739Z",
     "start_time": "2018-05-20T04:35:21.129355Z"
    }
   },
   "outputs": [],
   "source": [
    "# Multiple outputs\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = 'all'\n",
    "# Show matplotlib plots inline\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For data wrangling, we use the [Pandas](http://pandas.pydata.org) library, one of the most versatile workhorses for data analytics. Additionally, we need the [NumPy](http://www.numpy.org) library for general scientific computing. \n",
    "\n",
    "Scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We format the display of rows and the number of decimals. _(These commands setup Jupyter to my personal preferences. They are not required.)_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T04:35:21.945297Z",
     "start_time": "2018-05-20T04:35:21.637766Z"
    }
   },
   "outputs": [],
   "source": [
    "# Display up to 500 rows at a time\n",
    "pd.set_option('display.max_rows', 500)\n",
    "# Format the display of numbers to two decimals\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-19T23:36:04.090112Z",
     "start_time": "2018-05-19T23:36:04.080542Z"
    }
   },
   "source": [
    "Furthermore, we use [altair](https://altair-viz.github.io) to render visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T04:35:22.432451Z",
     "start_time": "2018-05-20T04:35:21.987279Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RendererRegistry.enable('notebook')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import altair as alt\n",
    "#Render altair charts inline \n",
    "alt.renderers.enable('notebook') # Change this depending on where you run the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-19T23:36:51.577078Z",
     "start_time": "2018-05-19T23:36:51.571396Z"
    }
   },
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's read the data. We use the same data as in the previous post."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T04:35:23.544088Z",
     "start_time": "2018-05-20T04:35:22.441859Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101603550000</td>\n",
       "      <td>1966</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1374</td>\n",
       "      <td>1144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101603550000</td>\n",
       "      <td>1967</td>\n",
       "      <td>1044</td>\n",
       "      <td>1164</td>\n",
       "      <td>1334</td>\n",
       "      <td>1474</td>\n",
       "      <td>1854</td>\n",
       "      <td>1974</td>\n",
       "      <td>2434</td>\n",
       "      <td>2594</td>\n",
       "      <td>2294</td>\n",
       "      <td>2054</td>\n",
       "      <td>1704</td>\n",
       "      <td>1114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101603550000</td>\n",
       "      <td>1968</td>\n",
       "      <td>1134</td>\n",
       "      <td>1274</td>\n",
       "      <td>1344</td>\n",
       "      <td>1644</td>\n",
       "      <td>1854</td>\n",
       "      <td>2114</td>\n",
       "      <td>2514</td>\n",
       "      <td>2454</td>\n",
       "      <td>2274</td>\n",
       "      <td>1954</td>\n",
       "      <td>1664</td>\n",
       "      <td>1324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101603550000</td>\n",
       "      <td>1969</td>\n",
       "      <td>1214</td>\n",
       "      <td>1184</td>\n",
       "      <td>1384</td>\n",
       "      <td>1534</td>\n",
       "      <td>1864</td>\n",
       "      <td>1984</td>\n",
       "      <td>2274</td>\n",
       "      <td>2454</td>\n",
       "      <td>2244</td>\n",
       "      <td>1894</td>\n",
       "      <td>1654</td>\n",
       "      <td>1124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101603550000</td>\n",
       "      <td>1970</td>\n",
       "      <td>1334</td>\n",
       "      <td>1144</td>\n",
       "      <td>1264</td>\n",
       "      <td>1424</td>\n",
       "      <td>1694</td>\n",
       "      <td>2154</td>\n",
       "      <td>2364</td>\n",
       "      <td>2534</td>\n",
       "      <td>2374</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1524</td>\n",
       "      <td>1186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0     1     2     3     4     5     6     7     8     9     10  \\\n",
       "0  101603550000  1966 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999   \n",
       "1  101603550000  1967  1044  1164  1334  1474  1854  1974  2434  2594  2294   \n",
       "2  101603550000  1968  1134  1274  1344  1644  1854  2114  2514  2454  2274   \n",
       "3  101603550000  1969  1214  1184  1384  1534  1864  1984  2274  2454  2244   \n",
       "4  101603550000  1970  1334  1144  1264  1424  1694  2154  2364  2534  2374   \n",
       "\n",
       "     11    12    13  \n",
       "0 -9999  1374  1144  \n",
       "1  2054  1704  1114  \n",
       "2  1954  1664  1324  \n",
       "3  1894  1654  1124  \n",
       "4 -9999  1524  1186  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw = pd.read_csv('https://raw.githubusercontent.com/mschermann/loaded_climate_dice/master/step2.v3.csv', header=None)\n",
    "raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a copy of the data, note missing values (-9999 in the dataset), and drop all missing values to ensure that we have only complete yearly datasets. Finally, we rename the columns to interpreting the data easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = raw.copy()\n",
    "data = data.replace(-9999,np.NaN)\n",
    "data = data.dropna()\n",
    "data.columns = ['station_id','year',\\\n",
    "               'jan','feb','mar','apr',\\\n",
    "               'may','jun','jul','aug',\\\n",
    "               'sep','oct','nov','dec']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also include the location of each station to make sure that we only analyze stations on the Northern hemisphere."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T04:35:37.608893Z",
     "start_time": "2018-05-20T04:35:37.528799Z"
    }
   },
   "outputs": [],
   "source": [
    "colspecs = [(0,11),(12,42),(43,49),(50,57)]\n",
    "stations = pd.read_fwf('https://raw.githubusercontent.com/mschermann/loaded_climate_dice/master/v3.temperature.inv.txt', colspecs=colspecs, names=['station_id', 'name', 'lat', 'lon'], header=None, index_col=0, skiprows=40).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a formatting mismatch for the `station_id`'s between the location data and the temperature data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T04:35:38.167858Z",
     "start_time": "2018-05-20T04:35:37.700654Z"
    }
   },
   "outputs": [],
   "source": [
    "#https://stackoverflow.com/questions/33034559/how-to-remove-last-the-two-digits-in-a-column-that-is-of-integer-type\n",
    "data['station_id'] = data['station_id'].astype(str).str[:-1].astype(np.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can merge the temperature data with the location data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T04:35:38.302853Z",
     "start_time": "2018-05-20T04:35:38.232233Z"
    }
   },
   "outputs": [],
   "source": [
    "loc_data = pd.merge(data, stations, how='left', left_on='station_id', right_on='station_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This allows us to filter the data to include only stations on the Northern hemisphere, that is where the latitude is positive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T04:35:38.469104Z",
     "start_time": "2018-05-20T04:35:38.306574Z"
    }
   },
   "outputs": [],
   "source": [
    "loc_data = loc_data[loc_data['lat']>0].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The number of stations over time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An important issue is that the number of stations that are included in the analysis, significantly varies over time. The following chart visualizes this problem.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"data\": {\"name\": \"data-069518d09d0a020b86d8cfee0415e5fc\"}, \"mark\": {\"type\": \"line\", \"color\": \"#333\"}, \"encoding\": {\"x\": {\"type\": \"quantitative\", \"axis\": {\"title\": \"Year\"}, \"field\": \"year\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"title\": \"Number of stations\"}, \"field\": \"station_id\"}}, \"height\": 200, \"width\": 400, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\", \"datasets\": {\"data-069518d09d0a020b86d8cfee0415e5fc\": [{\"year\": 1880, \"station_id\": 264}, {\"year\": 1881, \"station_id\": 277}, {\"year\": 1882, \"station_id\": 301}, {\"year\": 1883, \"station_id\": 313}, {\"year\": 1884, \"station_id\": 330}, {\"year\": 1885, \"station_id\": 331}, {\"year\": 1886, \"station_id\": 367}, {\"year\": 1887, \"station_id\": 381}, {\"year\": 1888, \"station_id\": 398}, {\"year\": 1889, \"station_id\": 430}, {\"year\": 1890, \"station_id\": 448}, {\"year\": 1891, \"station_id\": 490}, {\"year\": 1892, \"station_id\": 509}, {\"year\": 1893, \"station_id\": 582}, {\"year\": 1894, \"station_id\": 698}, {\"year\": 1895, \"station_id\": 771}, {\"year\": 1896, \"station_id\": 825}, {\"year\": 1897, \"station_id\": 882}, {\"year\": 1898, \"station_id\": 923}, {\"year\": 1899, \"station_id\": 987}, {\"year\": 1900, \"station_id\": 1013}, {\"year\": 1901, \"station_id\": 1048}, {\"year\": 1902, \"station_id\": 1101}, {\"year\": 1903, \"station_id\": 1100}, {\"year\": 1904, \"station_id\": 1168}, {\"year\": 1905, \"station_id\": 1168}, {\"year\": 1906, \"station_id\": 1202}, {\"year\": 1907, \"station_id\": 1246}, {\"year\": 1908, \"station_id\": 1285}, {\"year\": 1909, \"station_id\": 1345}, {\"year\": 1910, \"station_id\": 1359}, {\"year\": 1911, \"station_id\": 1397}, {\"year\": 1912, \"station_id\": 1421}, {\"year\": 1913, \"station_id\": 1433}, {\"year\": 1914, \"station_id\": 1495}, {\"year\": 1915, \"station_id\": 1529}, {\"year\": 1916, \"station_id\": 1523}, {\"year\": 1917, \"station_id\": 1511}, {\"year\": 1918, \"station_id\": 1496}, {\"year\": 1919, \"station_id\": 1520}, {\"year\": 1920, \"station_id\": 1510}, {\"year\": 1921, \"station_id\": 1577}, {\"year\": 1922, \"station_id\": 1600}, {\"year\": 1923, \"station_id\": 1630}, {\"year\": 1924, \"station_id\": 1690}, {\"year\": 1925, \"station_id\": 1699}, {\"year\": 1926, \"station_id\": 1745}, {\"year\": 1927, \"station_id\": 1783}, {\"year\": 1928, \"station_id\": 1801}, {\"year\": 1929, \"station_id\": 1815}, {\"year\": 1930, \"station_id\": 1846}, {\"year\": 1931, \"station_id\": 1955}, {\"year\": 1932, \"station_id\": 1935}, {\"year\": 1933, \"station_id\": 1952}, {\"year\": 1934, \"station_id\": 1976}, {\"year\": 1935, \"station_id\": 2030}, {\"year\": 1936, \"station_id\": 2047}, {\"year\": 1937, \"station_id\": 2044}, {\"year\": 1938, \"station_id\": 2119}, {\"year\": 1939, \"station_id\": 2157}, {\"year\": 1940, \"station_id\": 2170}, {\"year\": 1941, \"station_id\": 2155}, {\"year\": 1942, \"station_id\": 2157}, {\"year\": 1943, \"station_id\": 2193}, {\"year\": 1944, \"station_id\": 2213}, {\"year\": 1945, \"station_id\": 2229}, {\"year\": 1946, \"station_id\": 2259}, {\"year\": 1947, \"station_id\": 2293}, {\"year\": 1948, \"station_id\": 2354}, {\"year\": 1949, \"station_id\": 2370}, {\"year\": 1950, \"station_id\": 2465}, {\"year\": 1951, \"station_id\": 2889}, {\"year\": 1952, \"station_id\": 3038}, {\"year\": 1953, \"station_id\": 3123}, {\"year\": 1954, \"station_id\": 3225}, {\"year\": 1955, \"station_id\": 3284}, {\"year\": 1956, \"station_id\": 3328}, {\"year\": 1957, \"station_id\": 3326}, {\"year\": 1958, \"station_id\": 3394}, {\"year\": 1959, \"station_id\": 3443}, {\"year\": 1960, \"station_id\": 3474}, {\"year\": 1961, \"station_id\": 3567}, {\"year\": 1962, \"station_id\": 3618}, {\"year\": 1963, \"station_id\": 3683}, {\"year\": 1964, \"station_id\": 3672}, {\"year\": 1965, \"station_id\": 3786}, {\"year\": 1966, \"station_id\": 3822}, {\"year\": 1967, \"station_id\": 3812}, {\"year\": 1968, \"station_id\": 3786}, {\"year\": 1969, \"station_id\": 3805}, {\"year\": 1970, \"station_id\": 3852}, {\"year\": 1971, \"station_id\": 3774}, {\"year\": 1972, \"station_id\": 3751}, {\"year\": 1973, \"station_id\": 3740}, {\"year\": 1974, \"station_id\": 3742}, {\"year\": 1975, \"station_id\": 3721}, {\"year\": 1976, \"station_id\": 3641}, {\"year\": 1977, \"station_id\": 3585}, {\"year\": 1978, \"station_id\": 3618}, {\"year\": 1979, \"station_id\": 3551}, {\"year\": 1980, \"station_id\": 3477}, {\"year\": 1981, \"station_id\": 3368}, {\"year\": 1982, \"station_id\": 3345}, {\"year\": 1983, \"station_id\": 3308}, {\"year\": 1984, \"station_id\": 3207}, {\"year\": 1985, \"station_id\": 3159}, {\"year\": 1986, \"station_id\": 3104}, {\"year\": 1987, \"station_id\": 3112}, {\"year\": 1988, \"station_id\": 3125}, {\"year\": 1989, \"station_id\": 3013}, {\"year\": 1990, \"station_id\": 2662}, {\"year\": 1991, \"station_id\": 2027}, {\"year\": 1992, \"station_id\": 1972}, {\"year\": 1993, \"station_id\": 2007}, {\"year\": 1994, \"station_id\": 1897}, {\"year\": 1995, \"station_id\": 1817}, {\"year\": 1996, \"station_id\": 1846}, {\"year\": 1997, \"station_id\": 1846}, {\"year\": 1998, \"station_id\": 1863}, {\"year\": 1999, \"station_id\": 1872}, {\"year\": 2000, \"station_id\": 1766}, {\"year\": 2001, \"station_id\": 1503}, {\"year\": 2002, \"station_id\": 1497}, {\"year\": 2003, \"station_id\": 1651}, {\"year\": 2004, \"station_id\": 1478}, {\"year\": 2005, \"station_id\": 1579}, {\"year\": 2006, \"station_id\": 1597}, {\"year\": 2007, \"station_id\": 1575}, {\"year\": 2008, \"station_id\": 1610}, {\"year\": 2009, \"station_id\": 1566}, {\"year\": 2010, \"station_id\": 1587}, {\"year\": 2011, \"station_id\": 1610}, {\"year\": 2012, \"station_id\": 1561}, {\"year\": 2013, \"station_id\": 1533}, {\"year\": 2014, \"station_id\": 1577}, {\"year\": 2015, \"station_id\": 1513}, {\"year\": 2016, \"station_id\": 1540}, {\"year\": 2017, \"station_id\": 1345}]}};\n",
       "var opt = {};\n",
       "var type = \"vega-lite\";\n",
       "var id = \"57143bc6-2c69-41cd-829c-316e2cab853a\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
       "    \"  color: firebrick;\",\n",
       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
       "  ].join(\"\\\\n\");\n",
       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== \"scripterror\") {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x111221be0>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#57143bc6-2c69-41cd-829c-316e2cab853a"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#57143bc6-2c69-41cd-829c-316e2cab853a"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alt.Chart(loc_data.groupby('year')['station_id'].count().reset_index()).mark_line(color='#333').encode(\n",
    "    x=alt.X('year:Q', axis=alt.Axis(title='Year')),\n",
    "    y=alt.Y('station_id:Q', axis=alt.Axis(title='Number of stations')),\n",
    ").properties(\n",
    "    width=400,\n",
    "    height=200\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, after an almost linear growth, we have a significant increase of temperature stations between 1950 and 1970. Then, between 1970 and 1990 the number of available stations decreases. Around 1990, we have a sudden drop in the number of stations. Since about 2000, the number of stations remains fairly stable at a level that is similar to the 1920s (!)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obviously, the number of available stations that we either directly analyze or that goes into the gridded data influences the results. The fewer measurements we have per station, the larger is the impact of measurement errors, random noise, and other issues. \n",
    "\n",
    "Thus, we limit our analysis to stations that meet the following requirements:\n",
    "* We drop all missing data. We assume that stations that have missing data may have been affected by technical or political issues. We have already dropped all missing values in code cell `6` above.\n",
    "* We only include stations that have at least 65 measurements during the timeperiod of 1950 and 2017. This means, that stations with more than two years missing will be excluded from the analyis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We focus on the range of years analyzed by Hansen et al. (2012)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "relevant_years = list(range(1950,2018))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We filter the data to include on the relevant years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd = loc_data[loc_data['year'].isin(relevant_years)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We count the number of available years for each station."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs = cd.groupby('station_id')['year'].count().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We set the threshold to 65 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 65"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We filter our data accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete = cd[cd['station_id'].isin(cs[cs['year']>=threshold]['station_id'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "205"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(complete['station_id'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the Northern hemisphere, only 205 stations report complete data for at least 65 years between 1950 and 2017. The following map shows the locations of the 205 stations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
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       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
       "    \"  color: firebrick;\",\n",
       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
       "  ].join(\"\\\\n\");\n",
       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== \"scripterror\") {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x111221c50>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#708eca0c-7dae-4a30-85df-62873d0d4ea3"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#708eca0c-7dae-4a30-85df-62873d0d4ea3"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from vega_datasets import data\n",
    "\n",
    "countries = alt.topo_feature(data.world_110m.url, 'countries')\n",
    "\n",
    "base = alt.Chart(countries).mark_geoshape(\n",
    "     fill='#FFFFFF',\n",
    "     stroke='666666'\n",
    ")\n",
    "\n",
    "stations = alt.Chart(complete.groupby('station_id')[['station_id', 'name', 'lat', 'lon']].agg('first')).mark_circle(size=3).encode(\n",
    "    longitude='lon:Q',\n",
    "    latitude='lat:Q',\n",
    ").project(\n",
    "    type='mercator'\n",
    ").properties(\n",
    "    width=650,\n",
    "    height=400\n",
    ")\n",
    "\n",
    "base + stations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, most of the stations are in the US, Europe, and Japan. Very few stations report data on Canada, Russia, China, and India. The map alludes to a potential issue. The visible gaps in the available data means that we have almost no information on Canada, Russia, China, and India, which represent large areas of the planet. Thus, we should be careful about 'global' statements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distance between stations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nevertheless, the data is what it is. Another issue could be that the stations are too close to each other, which could result in co-variances in the gridded data.\n",
    "\n",
    "In the following, we analyze the distances between the stations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get a list of the stations, their names, and their location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_l = complete.groupby('station_id')[['name', 'lat', 'lon']].agg('first').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>name</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20550745000</td>\n",
       "      <td>QIQIHAR</td>\n",
       "      <td>47.38</td>\n",
       "      <td>123.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20552533000</td>\n",
       "      <td>JIUQUAN</td>\n",
       "      <td>39.77</td>\n",
       "      <td>98.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20553614000</td>\n",
       "      <td>YINCHUAN</td>\n",
       "      <td>38.48</td>\n",
       "      <td>106.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20554511000</td>\n",
       "      <td>BEIJING</td>\n",
       "      <td>39.93</td>\n",
       "      <td>116.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20556571000</td>\n",
       "      <td>XICHANG</td>\n",
       "      <td>27.90</td>\n",
       "      <td>102.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station_id      name   lat    lon\n",
       "0  20550745000   QIQIHAR 47.38 123.92\n",
       "1  20552533000   JIUQUAN 39.77  98.48\n",
       "2  20553614000  YINCHUAN 38.48 106.22\n",
       "3  20554511000   BEIJING 39.93 116.28\n",
       "4  20556571000   XICHANG 27.90 102.27"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_l.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We calculate the neighbors for each station."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import spatial\n",
    "s_coords = s_l[['lat','lon']].values\n",
    "neighbors = spatial.KDTree(s_coords)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following functions extracts the second-nearest station from the neighbors data (The nearest station would be the station itself) and returns the station name and the distance in kilometers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://stackoverflow.com/questions/19412462/getting-distance-between-two-points-based-on-latitude-longitude\n",
    "import geopy.distance\n",
    "\n",
    "def get_nearest_station_and_distance(x):\n",
    "    #print(x)\n",
    "    d, i = neighbors.query([x['lat'],x['lon']], k=2)\n",
    "    n = s_coords[i[1]]\n",
    "    #print(n[0], n[1])\n",
    "    station_id = s_l[(s_l['lat']==n[0]) & (s_l['lon']==n[1])]['station_id'].iloc[0]\n",
    "    #print(station_id)\n",
    "    #print(geopy.distance.distance([x['lat'],x['lon']], n).km)\n",
    "    return pd.Series([station_id, geopy.distance.distance([x['lat'],x['lon']], n).km])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We add the closest neighbor and the neighbor's distance to the station data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_l[['neighbor','distance']] = s_l.apply(lambda x: get_nearest_station_and_distance(x), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    205.00\n",
       "mean     294.15\n",
       "std      297.29\n",
       "min       13.75\n",
       "25%      128.92\n",
       "50%      184.83\n",
       "75%      330.88\n",
       "max     1830.19\n",
       "Name: distance, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "184.83280950507566"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_l['distance'].describe()\n",
    "s_l['distance'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On average, the stations are 294.15 km apart. However, the median is just 184.83 km. This means that we have a lot of stations that cluster and some stations that have very far away neighbors. We have to keep in mind that this may influence the results. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Replication"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have a better understanding of the where the data comes from. We prepare the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a copy of the station data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "c_d = complete.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We calculate the mean temperature for the summer months as proposed by Hansen et al. (2012)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "c_d['jja'] = c_d[['jun','jul', 'aug']].mean(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We focus the dataset on the relevant columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "c_d = c_d.loc[:,['station_id', 'year', 'jja', 'name', 'lat', 'lon']].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detrending"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous post, I have omitted the detrending correction of the data suggested by Rhines and Huybers (2013). [Detrending](http://www.stochtastic.com/climate-variability-and-extremes.html) did not appear to make a significant impact. However, since we only have the station data that reports over 65 years, the trend may actually increase the variance. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [`scipy`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.detrend.html) package offers a simple lineare detrending algorithm. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://stackoverflow.com/questions/43544895/warning-using-scipy-with-pandas\n",
    "from scipy import signal\n",
    "# We need this to avoid a warning message for the LAPACK package.\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\", module=\"scipy\", message=\"^internal gelsd\")\n",
    "\n",
    "detr = pd.DataFrame(c_d.groupby('station_id')['jja'].\\\n",
    "transform(lambda x: signal.detrend(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We rename the detrended summer temperatures as `detr`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "detr.rename(columns={'jja':'detr'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We add the detrended temperatures to the station data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdata = pd.merge(c_d, detr, how='left', \\\n",
    "                 left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>year</th>\n",
       "      <th>jja</th>\n",
       "      <th>name</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>detr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17908</th>\n",
       "      <td>20550745000</td>\n",
       "      <td>1950</td>\n",
       "      <td>2263.33</td>\n",
       "      <td>QIQIHAR</td>\n",
       "      <td>47.38</td>\n",
       "      <td>123.92</td>\n",
       "      <td>163.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17909</th>\n",
       "      <td>20550745000</td>\n",
       "      <td>1951</td>\n",
       "      <td>2120.00</td>\n",
       "      <td>QIQIHAR</td>\n",
       "      <td>47.38</td>\n",
       "      <td>123.92</td>\n",
       "      <td>18.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17910</th>\n",
       "      <td>20550745000</td>\n",
       "      <td>1952</td>\n",
       "      <td>2170.00</td>\n",
       "      <td>QIQIHAR</td>\n",
       "      <td>47.38</td>\n",
       "      <td>123.92</td>\n",
       "      <td>66.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17911</th>\n",
       "      <td>20550745000</td>\n",
       "      <td>1953</td>\n",
       "      <td>2193.33</td>\n",
       "      <td>QIQIHAR</td>\n",
       "      <td>47.38</td>\n",
       "      <td>123.92</td>\n",
       "      <td>87.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17912</th>\n",
       "      <td>20550745000</td>\n",
       "      <td>1954</td>\n",
       "      <td>2143.33</td>\n",
       "      <td>QIQIHAR</td>\n",
       "      <td>47.38</td>\n",
       "      <td>123.92</td>\n",
       "      <td>34.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        station_id  year     jja     name   lat    lon   detr\n",
       "17908  20550745000  1950 2263.33  QIQIHAR 47.38 123.92 163.87\n",
       "17909  20550745000  1951 2120.00  QIQIHAR 47.38 123.92  18.31\n",
       "17910  20550745000  1952 2170.00  QIQIHAR 47.38 123.92  66.07\n",
       "17911  20550745000  1953 2193.33  QIQIHAR 47.38 123.92  87.17\n",
       "17912  20550745000  1954 2143.33  QIQIHAR 47.38 123.92  34.94"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The detrended data appears to be completely different from the original data. To better understand the effect of detrending, the following chart compares the actual and the detrended data for station `20550745000`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tbJd/Z0ksa/en8vdZG/jp/v74lqIGmJGdy9zoBK5oH8DTI9oT5Otx/mR+Mj2LL9bG8+mqA4z/eB09Qhrx5Z29Sj3nPCE1nRveX4WbixP3DQ7jb12b4+LsRGZOLg/P3YyPhxv/GdUBgFZ+XtzaqyWfrT7AxD4hZOXmcSwts0gNOyzAi5Wx9nPmKyL1bBYLtiUxLirofALuGepLu6bezFh5gDGRQeeT46lz2dw+Yx2b4k/i6qwYEObHiE6BXBkRgLd7ycniZHoWm+JPMHVQm1LFVs/FmT5tHFd+/L3dublnMJ+tPsi3mw7Rxs+Lj2+PpEWjsv+vh8uaNaBD8wZMW7SX137fS/vABnQLbkTbpt4cS8sslAiST2cWWtfd1YmgRh4E+3rQq1Vjgnw9GNLOHzeX0h0v/cOa8OHyfZzJyGb1vuO0a+pdqmPYTp/WTVj/1BC05nwF41JX5xNEVKgv4QFezFpzkDGRQWitmf7nfl5csIuOzRvy6pgutPH3Or9804bOzL2rNw99tZnnftrJ8pgUlu5J4b7BbbjM6nYAGBcVzIyVB/h24yGHJ09HYpLPMPHT9SSeOMd7S+MY2NaPh64IP98MPXUumy/XxvPpqv0kn87ExUlxRfsAxkYF0T/Mr0oGtswJdAs/bz2Cs5NiUFt/xkUFMf3P/Xz45z7G925ZpMsj+XQG3248RP+wJqzdn8oDczbx6cSoEuNbuucoZzJyGN+7JSEX3afHx8ONewe14Y7+oXy26iAv/LKLudEJ3NLTfrpiQSfTs7h9xjrOZeXi7+3OP77ZyjtLYrl/cBh7j55hd9IZPp4QWagb4/4hYczbkMjLC3bTI9QXgAEXtQrDA7z5duOhSr+ga96GRLJy87i5wL4ppZjUN5R/zNvK6n3H6dO6Calnsxj/8VpiktN4btRlJKSm8/PWIyzefRQ3FyfuHdiGB64IK/a9/ow5Rp6Gy9v6V1r8d1/emm+iE+ke0oi3xnUtVaKyo5Tiqym9WX8glY0HT7Ah/gTfbkzkbFYuSkFgA3da+HrQP8yPYF8PgnzrW48e+HnVq9C00P5hfry7NI5le1OIPnCiVMdZSftSi2apVlidTxBKKcb3asm/ftjBhoOpfLMhkdnrEri6Y1Om3djFtuvEw82F927pzn8X7uH9ZXGEB3gxdXDhmld4gDfdgn2YvT6eO/qHlvogXbY3halfbMTdzZnZd/Zic8JJPlgex6h3VnJFe39aNPLg6+gEzmbl0rdNY/41MoItCSeZt/EQv+5IorlPfa6MCMDX0w2vei54u7vg4+HGwLZ+5b6yMyc3jwfnbGbB9iSmDmrD+N4tz8/iqO/qzM3T1/L1hkTG9yr85flkxX5y8vJ4YXRHVsYd48lvt/HG4hgevjK82Pf7ftNhmni50beYwcB6Ls7c0T+UX3ck8dbiWK7v1qLYPvmM7FzumBlNQuo5Pp8cRVSoL4t3HeXVRXt55OstAIyJbMGQ9gGF1vP1dOPewW14ecFuth8+Rbum3kUGEPMHqvcePUOPEN9i96208genI1uamnJB13Zpxsu/7mbGygO08fPilulriU9N56MJkVxutW6eHN6eTQknmf7nPl77fS/BjetzXVfHd0ZdsucoPh6uZeoGLElAA3fW/HMIHm7OFZ6771nPhYFt/RloJbDcPE3S6QyaeLlRz6Vyx2IK6tbS5/y03cycvFIPUP9V1PkEATC6a3NeWrCbW6ev41x2LlMHteHhK8OLbQY6OSmeGN6O/mFNCGniaXuQjo0K5h/fbGX9gRNEhZZ84vh89QGenb+T8ABvPp4QSTOf+vRu3ZhbewUzc9UBPly+j6V7UrimczPu6B96vsUyslMzHr2qLYt2JjNnXQJz1seTkV34AsCxPYJ4+fpOZftgMCeqf363jQXbk/jXyAgmX3QL5N6tG9M12If3l8YxtkfQ+SR06lw2X6yNZ0SnZgQ39iDIN4joAyd4648YugX7nP+iX+zUuWz+2HOUm6OCcSkhoSmleHRoW8Z9tIZZaw5yR3/7llpunuaBOZvYEH+Ct8d1o6fVh3xFRACD2/nz285kVsSm8LiDK1Nv7xPC56sPcujkOUbb3No7f6rr3uSiCWJr4km+33SYtMxszmTkkJaZw9nMHAJ96hPu7014gBdhAd6ENPYotL+r446z/9hZ7htctMvH3dWZm6OCeWdpLLuTTnM8LYtPJ0YVmpvv5KTo3rIRnVp05dbpa3li3jba+HnTsUXDItvLy9Ms35vCgCpofRY3XlARzk6qWm41kX/bjSV7UnBSENWqcioAdcVfIkF4u7syJjKIL9YeZNqNnbm+DPeg71tMP+zIToE8P38nc9bFl5gg3lwcw6uL9jKknT9vjuta6Ivl7e7K1MFhTOoXSmZ2nu1Mjnouzozs1IyR1mBmdm4eaRk5nMnI4YPlceby/74hZZr9o7XmhZ93MTc6kfuHhBVJDmBO0lMHtWHyzGh+2Hz4/P37Z605SFpmDndf3ur8cv8Z3YEdh0/x4Feb+em+frb90Qu3J5GVk8forqX7Hwu9Wzemf1gT3l0ax9ioYLwuOiFprXlu/g4W7kjmXyMjGNGp8IwbJyfFsA5NGdahqcP3cHd15vHh7bh/9qYiLQyA5j718XRzLnJXV601//hmK3EpaTT2rIeXu2nR1Xd1ZlviKX7ZduT8/XXcnJ1o5edJeIBJGqvijtOwvitXO7ja+NZeLXl/WRwn07OZdUdPh+NUrs5OvHtLN659eyVTPo/mx6n9ioxX7Th8mmNpWQxsW3T2kjDdTEv2pNCxhQ8NytlNVlf9JRIEwFMj2vP3Qa0r9eZ9Hm4uXNulGd9sSOSZay+jYX37g+tYWiZvL4llRKdA3hzb1WEtzsPNBY9Sjo+5OjvRyNONRp5uPHZVW+ZvOcxLv+xm5qSoUsf/9h+xTF+xn9v7hPBQMX3Yg9v5066pN+8ujeW6rs3Jzs1jxsoDDAj3KzQuU9/Nmfdv7c41b61g6peb+Obu3kVaCd9vPkRIYw8629R0HXlkaFtGv7OST6xplfny8jQv/7qbmasPcmf/UNsEV1rXdm5GVwc3l1NK0SbAu8iV1+sPnGB30hle/ltHxkYFF1kvPSuH2KNp7E1OI8aayrvh4Al+3HIYgDv7hzrsNmva0J1PJ0YR6ONOaz8v22XyNfaqxwfju3PD+6u494uNzLqj5/lBWq0v3FLbbnqruHDbjd5lnL30V/CXSRCuzk5VcmfXsT2C+WJtPN9vOsSEPiG2y3y2+iDZuXk8fGV4lQww+3i4cf+QMP7z8y7TleDgRJCXp9lx+DTL9h5l6Z4Uog+e4G/dmvP/RkYU24eslOLeQW24b/YmFu5IIvVsFsfSMrnn8tZFlg1p4smLf+vIfbM38f6yOKYOvnBCTzqVwep9x7lvcFiZ+qy7BPkwNCKAj5bv47beLfHxcCM9K4cH52zmt53JjO/VkieHty/19hxxdOdRgHB/L5bsKXzblpmrD9DA3cXhf5zzcHOhUwufIrdSSMvM4cCxs4UmR9gpyxTqDs0b8sr1nXhgzmae+HYr4QHebDh4gk3xJziWlkVky0Y0ucSnXFaVNv7evDG2C/2K6S34q/rLJIiq0rFFQzq3aMgHy+IYExlUZND7XFYun68+wBXtA0qsCVbE+N4tmbn6AC/+sou+bZoUSkRnM3N45dfd/Lz1CMfPmnvedGrRkEeHhnP35a1LNSXv6o6BvLZoL2//EcvZrBw6B/nQy0F/7TWdm7FwRxJvLI5hUDv/862M+VsOozWM7lLyHPWLPTK0LcPeWM77y/Zxe58QJs9cz64jp3nmmghu7xNS5Tc4a9vUm683JJJ6NgtfTzeSTmWwcHsSE/uGOLxGxBGvei50aF76FlRpjerSnJ2HT/PBcnN1eIh108ruLRsx7DLHXWwC+beyDkiCqARPjYhgzAereXdpLI8MbVvotW82JHAiPZu7yjgVtqzquTjz+LB2TP1yE/M2Jp6/5mP/sbPc/fkGYo6eYWSnZgxq50f/ML8y1yadnRR3D2zNP77ZCphZNMWdlJ8f1YG1+1N5ZO4Wfpjal3ouzny/+RCdWjSkVTkSZdum3ozq3IxPV+3nu02JpGXkMH1C5PmLGataWIGB6l6tGvPlunhytebWXhWbFlnZHh/WjiHtA2jl5yktBlFhdfZeTNUpKtSXUV2a8cHyfYXuiJmbp5m+Yj9dg33o3rJsV5eWx4iOgXQJ8mHab3tIz8ph8a5krn17BUfPZDBzUhRvjuvKdV1blPvEcV3X5jT3qU8rP0+GRhR/Ym7k6cYr13dkd9IZXv89htijZ9hx+HSFamoPXhFOTq7GxcmJeX/vU23JAQrfkykrJ48v18YzqK0/LRt7lrBm9XJyUkSF+kpyEJWixAShlApSSi1RSu1USu1QSj1glfsqpRYppWKsx0ZWuVJKvamUilVKbVVKdSuwrQnW8jFKqQlVt1vV78nh7XFxUjxX4J+v/LYjiYPH05nSv1W13ONdKcXTI9qTfDqTW6evZfLMaFo29uDHqf3oH1bxAUpXZydm39mLzyZFlapbanC7AG6KDOKDZXG88PMunBRc06l0N3GzE9LEk/n39ePn+/tV2b2aHGnawB3vei7sST7Dgu1HOJaWyW0O/teAEHVFaVoQOcAjWusIoBdwr1IqAngCWKy1DgMWW78DDAfCrJ8pwHtgEgrwDNATiAKeyU8qdUHThu7cPySM33cls2TPUbTWfLB8Hy0bezC0Gvt/I0N8GXZZUzbGn+SG7i345u4+xQ6+llVwY48y3U7h6ZHtCWxYnyV7UujTukmF72LZPrABPqWd6lWJlFKEN/Vmb3Ian60+SGgTTwZUQtIVojYrMUForY9orTdaz88Au4DmwChgprXYTGC09XwU8Jk21gA+SqlA4CpgkdY6VWt9AlgEDKvUvalhk/qG0qqJJ8/N38mquONsTjjJHf2q/57vr9zQiS/u6Mn/3dCp0u8IWlbe7q7878bOODspbows/fUntVF4gBeb40+y4eAJxvdqWWfutyOEI2Uag1BKhQBdgbVAgNY6/ybuSUB+h3BzoOA/g060yhyV1xluLk78v2sizMDwrA008nDlhu5BJa9YyRrWd6VvOe6CWlV6t25M9FNXcG0J/6O4tgvz9yYrNw8PN+cyXWwpxKWq1AlCKeUFzAMe1FqfLviaNv9KqVL+e45SaopSKlopFZ2SUvK/8qttBrb158qIAOtmdGWfAllXNfJ0qzUJq7zyb7lxXdfmDi+KFKIuKVWCUEq5YpLDF1rrb63iZKvrCOsx/yqiQ0DBanMLq8xReSFa6w+11pFa60g/v0uzj/fZay/j+m4tmNQ3pKZDEZWoe8tG3BQZxD0Di14gKERdVJpZTAr4GNiltX61wEs/AvkzkSYAPxQov82azdQLOGV1RS0EhiqlGlmD00OtsjqnuU99po3pXCODqaLq1Hdz5pUbOpXrfx4IcSkqzYVyfYHxwDal1Gar7J/Ay8BcpdRk4CAwxnrtF+BqIBZIByYCaK1TlVLPA+ut5Z7TWqdWyl4IIYSodErrShk6qBKRkZE6Ojq6psMQQohLilJqg9Y6sqLbkSuphRBC2JIEIYQQwpYkCCGEELYkQQghhLAlCUIIIYQtSRBCCCFsSYIQQghhSxKEEEIIW5IghBBC2JIEIYQQwpYkCCGEELYkQQghhLAlCUIIIYQtSRBCCCFsSYIQQghhSxKEEEIIW5IghBBC2JIEIYQQwpYkCCGEELYkQQghhLAlCUIIIYQtSRBCCCFsSYIQQghhSxKEEEIIW5IghBBC2JIEIYQQwpYkCCGEELYkQQghhLAlCUIIIYStEhOEUuoTpdRRpdT2AmXPKqUOKaU2Wz9XF3jtSaVUrFJqj1LqqgLlw6yyWKXUE5W/K0IIISpTaVoQnwLDbMpf01p3sX5+AVBKRQBjgcusdd5VSjkrpZyBd4DhQAQwzlpWCCFELeVS0gJa6+VKqZBSbm8UMEdrnQnsV0rFAlHWa7Fa630ASqk51rI7yxyxEEKIalGRMYipSqmtVhdUI6usOZBQYJlEq8xRuRBCiFqqvAniPaA10AU4AkyrrICUUlOUUtFKqeiUlJTK2qwQQogyKleC0Fona62uEzh2AAAfwElEQVRztdZ5wEdc6EY6BAQVWLSFVeao3G7bH2qtI7XWkX5+fuUJTwghRCUoV4JQSgUW+PU6IH+G04/AWKVUPaVUKBAGrAPWA2FKqVCllBtmIPvH8octhBCiqpU4SK2Umg0MBJoopRKBZ4CBSqkugAYOAHcBaK13KKXmYgafc4B7tda51namAgsBZ+ATrfWOSt8bIYQQlUZprWs6BociIyN1dHR0TYchhBCXFKXUBq11ZEW3I1dSCyGEsCUJQgghhC1JEEIIIWxJghBCCGFLEoQQQghbkiCEEELYkgQhhBDCliQIIYQQtiRBCCGEsCUJQgghhC1JEEIIIWxJghBCCGFLEoQQQghbkiCEEELYkgQhhBDCliQIIYQQtiRBCCGEsCUJQgghhC1JEEIIIWxJghBCCGFLEoQQQghbkiCEEELYkgQhhBDCliQIIYQQtiRBCCGEsCUJQgghhC1JEEIIIWxJghBCCGFLEoQQQghbkiCEEELYKjFBKKU+UUodVUptL1Dmq5RapJSKsR4bWeVKKfWmUipWKbVVKdWtwDoTrOVjlFITqmZ3hBBCVJbStCA+BYZdVPYEsFhrHQYstn4HGA6EWT9TgPfAJBTgGaAnEAU8k59UhBBC1E4lJgit9XIg9aLiUcBM6/lMYHSB8s+0sQbwUUoFAlcBi7TWqVrrE8AiiiYdIYQQtUh5xyACtNZHrOdJQID1vDmQUGC5RKvMUbkQQohaqsKD1FprDehKiAUApdQUpVS0Uio6JSWlsjYrhBCijMqbIJKtriOsx6NW+SEgqMByLawyR+VFaK0/1FpHaq0j/fz8yhmeEEKIiipvgvgRyJ+JNAH4oUD5bdZspl7AKasraiEwVCnVyBqcHmqVCSGEqKVcSlpAKTUbGAg0UUolYmYjvQzMVUpNBg4CY6zFfwGuBmKBdGAigNY6VSn1PLDeWu45rfXFA99CCCFqEWWGEGqnyMhIHR0dXdNhCCHEJUUptUFrHVnR7ciV1EIIIWxJghBCCGFLEoQQQghbkiCEEELYkgQhhBDCliQIIYQQtiRBCCGEsCUJQgghhC1JEEIIIWxJghBCCGFLEoQQQghbkiCEEELYkgQhhBDCliQIIYQQtiRBCCGEsCUJQgghhC1JEEIIIWxJghBCCGFLEoQQQghbkiCEEELYkgQhhBDCliQIIYQQtiRBCCGEsCUJQgghhC1JEEIIIWxJghBCCGFLEoQQQghbkiCEEELYkgQhhBDCVoUShFLqgFJqm1Jqs1Iq2irzVUotUkrFWI+NrHKllHpTKRWrlNqqlOpWGTsghBCialRGC2KQ1rqL1jrS+v0JYLHWOgxYbP0OMBwIs36mAO9VwnsLIYSoIlXRxTQKmGk9nwmMLlD+mTbWAD5KqcAqeH8hhBCVoKIJQgO/KaU2KKWmWGUBWusj1vMkIMB63hxIKLBuolUmhBCiFnKp4Pr9tNaHlFL+wCKl1O6CL2qttVJKl2WDVqKZAhAcHFzB8IQQQpRXhVoQWutD1uNR4DsgCkjO7zqyHo9aix8Cggqs3sIqu3ibH2qtI7XWkX5+fhUJTwghRAWUO0EopTyVUt75z4GhwHbgR2CCtdgE4Afr+Y/AbdZspl7AqQJdUUIIIWqZinQxBQDfKaXyt/Ol1vpXpdR6YK5SajJwEBhjLf8LcDUQC6QDEyvw3kIIIapYuROE1nof0Nmm/DgwxKZcA/eW9/2EEEJUL7mSWgghhC1JEEIIIWxJghBCCGFLEoQQQghbkiCEEELYkgQhhBDCliQIIYQQtiRBCCGEsCUJQgghhC1JEEIIIWxJghBCCGFLEoQQQghbkiCEEELYkgQhhBDCliQIIYQQtiRBCCGEsCUJQgghhC1JEEIIIWxJghBCCGGr7iaIzDOQnVHTUQgh1rwH274BrWs6ElFGdTNBpO6Dae1g+7yajkRcypb9Hyz6fzUdxaXtxAH49QmYNxk+HQHJO2s6IlEGdTNBNAqFBs0h+pOajkRcqvJyYc27sHl2TUdyacuvpA3+FxzdCe/3g4VPmRa+qPXqZoJQCiInwaFoOLKlpqMRl6Ijm+FcKpw9CumpNR3NpWv7txDUEwY8CvdthG7jYfU78G4fyDhd09GJEtTNBAHQ+SZwqV/5rYiDq2HmtXDuZOVuV9QusYsvPD+6q+biuJQd3Q3J26HDDeZ3D1+45g0YNxtOxcPeX2s2PlGiupsg6jeCDtfD1q8rr6aSeQa+nQL7l8G2rytnm6J2il0M3s3M85Q6mCCSd1T9oPH2eaCc4LLRhcvDrjKf7Y7vq/b9KyI9FeL++MsPrNfdBAGmmyn7LGybW7rl8/IgN8fx6789DacTzfjGxs8qJ0ZR+5w7AYnroMvN4OZtasJ1SfxaeK8PLP+/qnsPrWH7NxA6ALz8C7/m5AQR10Ls71U/FnEmGd7qDtPaw5td4b2+MP0KWPRM8ev98R/4/Dr49UkzHvUXVbcTRPNu0LQTrP+k+JrA0V3w+7/hjc7wcjDsml90mdjFsOFT6D0V+j0ESVvh8OYqC13UoH3LQOdB2JXg1xZS6liC2POLeVz6MiSsK/16uTlw9njplj2y2cwm7HC9/esRoyE3E/YuLP37l8f6j+B4HLQeBM26gU9Lk5RWvlH82NK+peDeENa+B1/fDtnnqjbOWqpuJ4j8weqjOyBxfeHX8vJg3UdmVsW7vWDl69AkDPzC4avx5rV8507Cj/dBk7Yw6CnoeAO4uMOmz6t3f0Tl0dpxpSFuMdRrCM0jwb9d9YxBHN4EvzxWPbXVmEXQvDs0bGGmn2acKnmdvDz48kZ4p0fplt/2DTi5Qvtr7F8P6gleTWHHd2WLvSyyz5kxyLZXw+h34YaPYdyXcM2bgIb9y+3XO5UIqXFw+eNw1YumwvjZ6L/kZIW6nSDAnMzdvAsPVmechq9ugV8eBScXGPYKPLIHxn8Lt/8CbYeb1xY9Y74YC5+CM0lw3Xvg6m7GN9pfa8Y3/qI1i0taThZ8OhK+v6foa1qb1mKry8HZBfzaQ/oxOHusamP64wVY92HV16hPJZoKU8RouH46nDoEPz1ccl/7qjdMn3z68cKVJzt5eebE3+YK812xU6ibKa18+1KSrV+ZeHtd9Hdu3h3qNYB9S+zXy08coZdD73vhxhkmgX881FzX8RdS9xNEPW/oNMZMt0tPNc3N6VeYL+Lw/8KdS6DX3Rf6Sd084KZZEDnZtCo+HQGbZ0G/B82Bla/beMg8BTt/LF9cR7bClzeZefbF1RrPJNVcH2j2udJd2FTeSQBam/2LXwNb5sCfr8KhjeXbVlksfQkOroAts82stIJSdsPpQ+bkBqYFkV9eVU4cMCdKgHUfVN37gGk9AIQNhaAoGPikGSvY+pXjdRKjTZ98xGiz3up3IOus4+UT1pjP0FH3Ur6I0ZCTATFVkBS1NldwN+0IIf0Kv+bsAiH9HQ9C718OHo3BP8L8ftl1cNv3Zsrz9CtNsqhseXlw+jAcXGXOCUteMp9zDav2BKGUGqaU2qOUilVKPVEtbxo50fR3/vIofDQIzqaYP3jPu0w31MWcnGHENBjyDMSvAv/LTHOzoJb9zAV55elm2j7P1EZiF8P3d8MHA8wJIv9gzc6ALV+Zg3FaWzO4turt6plam5dr+uC/vxf+Fw7v9YY4BzUtMMv+NxSiZ5Ttfda8By80Nfv3yVXw3V2w+N/m7zPrejOQWhUOrjaJv+MY8A6ERf8qfJLIP1G3GWIe/dqbx6rsZoqeYWb79LjD9H2n7Km694pZBA2DzdgKQP+HoWVf+PkRM2ZwsYxT8M0kM+vomjdgwGPm+pDi/t7b55kp5m2HFx9LcC/wCqia2Uxxf5ik3ute++9460FwMr7oPmttjunQAaaVk69lH5j0G7jUgxkjLiTaiso6a5Lvy0HwanuYMdycE5a9UvWtyVKo1gShlHIG3gGGAxHAOKVURJW/cdOO0CLKHLgNg2HKUnMAFEcp8+WZtBBunWcOjIKcnKDrrXDgT9MqKY28XNNt9c0kCOwMD22H6z82g2azrofPRpmZUq9FwHdTzBdx4JNm1tRvT8GrEeaLnBhtat7FzbgqK61hxevw2mXw2bWw8wfTf+wdCH9Oc7zesv9CXg4seNy0ikojL88kvMZhcPX/4JZ5MDUaHoszSfnwJvhkKMy8pnyJwlF3ScZp87n6BMPIV81nm7i+8KSE2MXg1870zwM0aGa6I6qqBZGTaSoZbYebeJzrma6mqnqvfUvN4Hv+SdPJGf72oXn8bJTpPsrv8tEafnrIdEtdPx3q+5hWR+gAWPWm/b3OcnPMCb/tMKjnVXw8Ts7mGItZVHyLpDzWvAue/tDhb/avtxpkHi/uZjoeB2cO258f/NvB5EXQuJVp/W+swBik1mac5u0eZjZZ2JUw4lVzrrlvIzydDBPK2TtRiaq7BREFxGqt92mts4A5wKhqeedhL0G/h2HyQmjUsvTrBfeCBoH2r3W5xdT8Ns0qeTvnTpqDauXrZuB8wnzwbmrGSKZGm3GQpG2w+l1TW7ntB1M+8AmYtACmLIOIUWZ67fQhpub9fBP4vzbwfn84sLL0+3QxreH3Z+H3Z8C/Pdz4KTwWYwb2ek81STAxuuh68WtNV03/R8xFUF/fXrppi/GrzHThfg9C1J0QdoWZIODZxCTlB7fB0BdMTfrTEXDiYOn35UwSvBMFHw2G/X8Wfm3B4+Zkd92Hpuuxyy1m4sHif0NutjlJHVx5oXsJzInUr13VTXXd+aPpJ4+cZPa/w/Wmi6E0A8FldXCVmfYdNrRwecMWMHY2eDQxrexXI8y428rXTaVq0JMQ3PPC8gMeg7Rk+9Zz3GIzZpN/cVxJIkZDzjmI+a38+3WxlD2mJRh1Z9GKXb7GraFhUNHW8f6l5jH0cvv1GgTCxAVmjOrHqaYrqKzXSiRGm5bCvMnmbz5pofnO9Zhsjr3GrR3HXc2qO0E0BxIK/J5olZ2nlJqilIpWSkWnpKRU3ju3iIQrngE3z8rbZoNA82Xb/GXJtfm5403tbeTrMPI1cHG78JqLmxkHeWg7PBZrxkBaDSzcNG7WxQySP7TTvD5iGlz+D2g3wpxgfrzPnOTKY9krFxLXrd+aPlfX+ua17hPA3QdWvFZ0vRWvQn1fkyCu/xhO7If5D5b8hdn6Fbh5mdkldtw8oc9UMz6knGD5f0u3H+dOmpbYqUMmUcwcCV+MMeMoO3+ALV+aWPNPds4ucMWzcDzWJN4DKyE360L3Uj7/dlV3sVz0J6arMr9G23OKOYlv/rLosnm55uRX3ou3YhaZFkpo/6KvhfSFO/8wNeSwK2Dt+6bSENLfVKwKLdvfzEJa+YYZ8M+3ZwF8M9l0RxVMssVp2Qc8/Sq3m2nNe2Y/u090vIxS5ju2/8/C3939y6FBC/Bt5Xjdet5w81xTwVj2sjnRlzRZJTPNTJP/4HJTwTsWY2ZT3bnEVEJrqVo3SK21/lBrHam1jvTz86vpcErW7TZIS4LYYvokE9aZA+/Kf5vxEEfcPE1NvDhefqZZ3uMOGPRP0y88YpqZlrdxZtnjX/GaGbTtcgtcPa1of209bzNWs/unwrXopO3mVgm97jFxh/Q1U4C3f2O+CI5kZ8AOq/vKzaP42Bo2N5/X5tkld+Nln4PZ48wJdOwsuG+DOfnHr4H3+8J3d0OzrkXHktoOh+De5pqAXT+YvvPgPoWX8WtvknBaJVZYwCSu+FVmH/P7u5t1Nd2h6z40XXHn9y8D5t5mWkdvdTN3mj2ZYL9dR2J+MwO2jipJSpkupBs+Ma24Ya/ADTNMV9DFyw14DE4lmGSfl2fimT3O1H7vWGRm+5XG+W6m3yArvWz7Yyc91Ux46DTGfFeK03qwmWiSP+icl2cSRqvL7cctCnJ2hVHvmC7R7d+aFsHpw0WXS9pmZolNawfzHzCVuKv/B/dvMpWviz/bWqa6E8QhIKjA7y2ssktX2FAz0LbmXcc1u5VvmFp4twlVE0P4MHNSW/py2a5MXfOeqSV2uAGufavwoFxBUXeBq4fZj3wrXjOtgKg7L5T1e9h86RY8br4YdmIWmi9lpzGli7Hfw+DsZlo5juTmmHGd+NWmP731YNMC6vcQPLAZev3djOP87SPzxS5IKbjyOTNDZdMscwK9+OR2fiZTJbcioj8xNd0utxYu73mXGTzNHzDPOA1f3GCSdM97zL4s+Q+83tGMG8SvKfm9UvfB8Zii3UuONGhmze5zcJJtc4UZR1vxKnx9m4mn000w6dcL4zelFTEastPL182UccpUVvb8asZPfphquqwuntpqp9VAQJkBbTD3jTqXWvL4ZL78ccqxX5oWwYeDIHGDSXSbvjCzJd/vZ46r9iPNIPc9K813xr1B2fe1BrhU8/utB8KUUqGYxDAWuLmaY6hczq7Q/1FY8Ji5QrXdiMKvH4uB3T+bu1mWNGhXXvknuY+vMIO/g560Xy4zDRLWmr7ogyvNCbX9NXDdB8XXZDwbm+S2/iPTasnNgh3fQp/7Cs9zd3Iy/fsf9IevJ8Ldf17oqsq3da5JqI76eC/mHQBRd5j96v/Ihdk3+bSG+febz37EtKKDkh6+cNUL5seRoCjzOeyab981cn4m0+6iJ4/UfabrJjcb8rJNstJ5pibdrKvpqrCrjWammZruZaPN51tQ+2vNZ7TuA7ONL643907620cXEuuJg2b9jTPhixtNV0WTNo73McZKNmFXOl6mLJQyx/3c8Waa7lUvmkRcUs3bTsu+Zn+/mWSmdra63Jy8m3c3LYJTiaa1cirBtJrO/54ImRdNsXZ2M12lAZeV/L4evibJ7VsCAx8391iD0ieIfO2uhsm/weyx8OnVZvwg4xQ0CYerXoLOY0vuGailqjVBaK1zlFJTgYWAM/CJ1npHdcZQJSInmdrgwn+aE0zBAaZVb5rfo+6q2hiCephB7FVvmXi8Ay68djLBNG/3LQWdC8rZjGlc/rj5kjuX4jDofa9JEKvfNnPXnVzNFMKLefnB6Pfg89Gw5EUY+vyF19JTzdS9nneVrWnd90Fzu5SlL5nBvHzZ58x+bf3KzADqcUfpt3mxK58zg9QRNnMmvJua2y7YtSC+u8fM+3fE3cec5Jt3M4/Nupna+bavIeuMud7mYi7WSW7pSzB9sOnaGjsbwgvU/hu1NCe1LuNMv/ZXt8Kdix13H8X8Br6tTeKqLO1GmmOoZV9zUi8vZxeY8JP5O+5fZmbNObpPVP1GpoXSKNSMhfgEmd8bBptHTz/HLWE7rQeZ70zmGdMN3DjM/H3KKuAyk6QXPG6SZPfbzedSnoRZi1R3CwKt9S/AL9X9vlXK2cXMkvp8tOlq6veQKT+TZGp5XceX3B9aGYY8Y1ory14x0zjBNL2/v9vUbPs+YLpQgnqWvTXjE2S6EDbMNEmm6/jCSaig1oPMF2T126Y2HNTDlO/4ztSyS9u9lM+zienu+HOaSWhNO5ja41e3mv7jQU+bFlpF+LaC8Q5u+6CUaUVcPJPp8CaTHK541pzQnVwvdGEd3QWHN5oL/w5vNFOItXXBo1eAmRrsf5lpvdjpPhGW/8/URG/73vFApk+wGTOY9Tf48X4zHfXik1JWupmJVtygbXk4OZkWZWXwC4ch/wL+Zfb5wErTTenld+Hk37BF5bfCWw0y3aVxS0zLuvPY8m/Ls4m5nUcdUu0Jos5qPQjajjBf6s7jTK1zzXvmRNBnavXE0Li1OTFHz4CoKbD5C9OCadrJ1LwrWnvs+4DZpnKGvvcXv+yVz5tujR/+Dnf9afr1t841J9qmncr+3r2nmj7mpS+Z53PHm4HbcXNKviCrMvi3MzOhtL5wAl77Ibh6muTg3rDw8oGdzE/3283v2efMCS8/YSTvMLPQHNUwvQNMYvAOLPnv1noQDH4aFj8HLXqYZFrQgRWm1RdeyvGHmube0HTbtHMwy60yBfcyExNWvApZaWXvXqrjJEFUpqHPmxv//f5vGP6K6XaKGFX8lLnKdvnjptXy4UAzWBc52fQPl3ZWSXH82pp+Zhd3aBRS/LLuDeDaN03NdulL5kSZsMa0csrT7PbwNd1cS18ys6d8WsLtPxcdk6gqfu3h3KeQdtScvNNSzIytbhOKJgc7rvVNa8FRi8HOxbeIKE7fh8wA6W9PmX714F7mdhdJ22DtB2aSQcu+pd/eX4VLPTPVNm4xoEy3lThPEkRlatzanEBXvm5u7ZF52tS6q5OXv6mZLp8Go98p+X44ZTXspdIv22aIOYGuetNcawDQ8cbyv3evv5vrFQI6mNlK9X3Kv62yKjiTyTvATOXNzTIttdrAyclcJ/PhQPhyjLkJ5bn8u48qcxFWLbn4qtZpPdgkiKYdL9nB5KoiCaKyDXjUXOS0fZ6ZqdOsa/XH0PcB0w1TG+ZYD/2PuX3F7p/M/at8gkpexxH3BvDA1tINqle2gjOZWvaF9dOh9RDTd15buDc0Uy5/fcK0sJp2NN15ARHmehZhr7V1kWJFBtrrKEkQla2et7kg7vt7zBzpmlIbkgNYXU1vwKwbzH9oq6iaSA5gWmb1G5kWxM4fzMWR175VM7EUx7+9uU2LKD3/CHPxmqMr+//ClK7F/3M1MjJSR0fb3APoUnAqsewXDNVlJ+PNvW8u5Wl/nww31zjoPHO/oakbyjalUohqopTaoLWOrOh25OiuKpIcCvMJvrSTA5hxiEPR5v9VR90lyUHUeXKEC1Fafu3NtGU3r8rpLhOilpMEIURp5c9k6nLzJXMvHSEqQgaphSitoJ5mdljvarrwUYgaJglCiNJyqVf8Tf+EqGOki0kIIYQtSRBCCCFsSYIQQghhSxKEEEIIW5IghBBC2JIEIYQQwpYkCCGEELYkQQghhLBVq+/mqpRKAQ5WYBNNgGOVFE51uRRjhkszbom5+lyKcV+KMYOJ21Nr7VfRDdXqBFFRSqnoyrjlbXW6FGOGSzNuibn6XIpxX4oxQ+XGLV1MQgghbEmCEEIIYauuJ4gPazqAcrgUY4ZLM26JufpcinFfijFDJcZdp8cghBBClF9db0EIIYQop0sqQSilPlFKHVVKbS9Q1lkptVoptU0pNV8p1cAqD1FKnVNKbbZ+3i+wTndr+Vil1JtKVe0/Sy5L3NZrnazXdlivu1d33GX8rG8p8DlvVkrlKaW61PKYXZVSM63yXUqpJwusM0wptceK+YmqireccbsppWZY5VuUUgMLrFOdn3WQUmqJUmqndZw+YJX7KqUWKaVirMdGVrmyYopVSm1VSnUrsK0J1vIxSqkJtSjmdtbfIFMp9ehF26q2Y6Qccd9ifcbblFKrlFKdyx231vqS+QEGAN2A7QXK1gOXW88nAc9bz0MKLnfRdtYBvQAFLACG16K4XYCtQGfr98aAc3XHXZaYL1qvIxBXE591GT/nm4E51nMP4IB1zDgDcUArwA3YAkTUouPjXmCG9dwf2AA41cBnHQh0s557A3uBCOC/wBNW+RPAK9bzq62YlBXjWqvcF9hnPTaynjeqJTH7Az2AF4BHC2ynWo+RcsTdJ/8zBIYX+KzLHHeVHfRV+GGFXPRFOsWFsZQgYKfdchd92LsL/D4O+KAWxX01MKs2xF3amC9a50XghdoesxXLfExCbmx96XyB3sDCAus/CTxZi46Pd4DxBZZbDETV1HFd4P1+AK4E9gCBBf7+e6znHwDjCiy/x3q9UJwXL1eTMRdY7lkKJ4gaOUbKGrdV3gg4VN64L6kuJgd2AKOs5zdivkz5QpVSm5RSy5RS/a2y5kBigWUSrbLq5ijucEArpRYqpTYqpf5hldeGuIv7rPPdBMy2ntfmmL8BzgJHgHjgf1rrVCu+hALr17bjYwtwrVLKRSkVCnS3Xquxz1opFQJ0BdYCAVrrI9ZLSUCA9dzR51ojn3cpY3akxo6RcsQ9GdNyg3LEXRcSxCTg70qpDZjmV5ZVfgQI1lp3BR4GvlQF+vlrAUdxuwD9gFusx+uUUkNqJsQiHMUMgFKqJ5Cutd5ut3INcRRzFJALNANCgUeUUq1qJkRbjuL+BPPFjgZeB1Zh9qNGKKW8gHnAg1rr0wVf06aaWuumSV6KMUPZ41ZKDcIkiMfL+54u5V2xttBa7waGAiilwoERVnkmkGk936CUisPUzg8BLQpsooVVVq0cxY358i/XWh+zXvsF0z89ixqOu5iY843lQusBasFnXUzMNwO/aq2zgaNKqZVAJKaGVbBlVKuOD611DvBQ/nJKqVWY7rETVPNnrZRyxZywvtBaf2sVJyulArXWR5RSgcBRq/wQ9p/rIWDgReVLa0nMjjjalypT1riVUp2A6ZhxqOPljfuSb0EopfytRyfgaeB963c/pZSz9bwVEAbss5pkp5VSvaxZHrdh+vRqRdzAQqCjUspDKeUCXI7pf67xuIuJOb9sDDAnv6yWxxwPDLZe88QMnO7GDA6HKaVClVJumKT3Y3XGXFzc1nHhaT2/EsjRWlf78WG9x8fALq31qwVe+hHIn4k0oUAMPwK3KaMXcMqKeSEwVCnVyJqFM9Qqqw0xO1Ktx0hZ41ZKBQPfYsaq9lYo7uoaWKmkwZnZmK6jbExNezLwAKYGtRd4mQsDe9dj+nE3AxuBawpsJxLYjhnRfzt/ndoQt7X8rVbs24H/1kTc5Yh5ILDGZju1MmbAC/ja+px3Ao8V2M7V1vJxwFO17LgOwQxO7gJ+B1rW0GfdD9OlsdX6jm22PrfGmIHzGCs+X2t5hRlgjwO2AZEFtjUJiLV+JtaimJtaf4/TwEnreYPqPkbKEfd0TIsyf9no8h7bciW1EEIIW5d8F5MQQoiqIQlCCCGELUkQQgghbEmCEEIIYUsShBBCCFuSIIQQQtiSBCGEEMKWJAghhBC2/j8oh7U1vpUXpwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(cdata[cdata['station_id']==20550745000]['year'], \\\n",
    "         cdata[cdata['station_id']==20550745000]['jja'])\n",
    "plt.plot(cdata[cdata['station_id']==20550745000]['year'], \\\n",
    "         cdata[cdata['station_id']==20550745000]['detr'])\n",
    "plt.show;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the pattern of the data the same. The only difference is that the 'slight' upward trend is removed. The impact of the trend on the variance for this station is as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.23"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round((cdata[cdata['station_id']==20550745000]['detr'].var() - \\\n",
    " cdata[cdata['station_id']==20550745000]['jja'].var()) / \\\n",
    " cdata[cdata['station_id']==20550745000]['jja'].var(), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variance for this station decreased by 23 percent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_overall = cdata.groupby('station_id')[['jja', 'detr']].var().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.16"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round((var_overall['detr'].mean() - var_overall['jja'].mean())/var_overall['jja'].mean(),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, detrending the data reduced the variance of the stations by 16 percent. However, this is across the whole dataset. Rhines and Huybers (2013) suggest to detrend for each timeperiod. This would be particularly important if the trend for the average temperature accelerates. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Corrected z-scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following Hansen et al. (2012) and Rhines and Huybers (2013), we analyze the following  timeperiods:\n",
    "1. A baseline period that covers the years between 1950 and 1979.\n",
    "2. A comparison period that covers the years between 1980 and 2009.\n",
    "3. We add a third timeperiod that covers the years between 2010 and today.\n",
    "4. We analyze each decade since 1980 independently.\n",
    "\n",
    "In contrast to Hansen et al. (2012), we shift the timeperiod by one year to include the year 1950)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each station, we calculate the following information:\n",
    "* We calculate the mean temperature for the baseline period.\n",
    "* We calculate the standard deviation for each period independently.\n",
    "* We detrend the data for each period independently."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "base = cdata[(cdata['year']>=1950) & (cdata['year']<1980)].copy().\\\n",
    "groupby('station_id')[['jja', 'detr']].\\\n",
    "agg({'jja': ['mean','std'], 'detr': ['std']}).reset_index()\n",
    "base.columns = base.columns.map('_'.join)\n",
    "base = base.rename(columns={'station_id_':'station_id'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>jja_mean</th>\n",
       "      <th>jja_std</th>\n",
       "      <th>detr_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20550745000</td>\n",
       "      <td>2130.44</td>\n",
       "      <td>63.52</td>\n",
       "      <td>69.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20552533000</td>\n",
       "      <td>2087.86</td>\n",
       "      <td>83.51</td>\n",
       "      <td>90.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20553614000</td>\n",
       "      <td>2208.67</td>\n",
       "      <td>65.21</td>\n",
       "      <td>75.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20554511000</td>\n",
       "      <td>2520.12</td>\n",
       "      <td>75.30</td>\n",
       "      <td>77.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20556571000</td>\n",
       "      <td>2186.88</td>\n",
       "      <td>57.88</td>\n",
       "      <td>57.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station_id  jja_mean  jja_std  detr_std\n",
       "0  20550745000   2130.44    63.52     69.74\n",
       "1  20552533000   2087.86    83.51     90.23\n",
       "2  20553614000   2208.67    65.21     75.88\n",
       "3  20554511000   2520.12    75.30     77.19\n",
       "4  20556571000   2186.88    57.88     57.26"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1980 to 2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "sd_2010 = cdata[(cdata['year']>=1980) & (cdata['year']<2010)].copy().\\\n",
    "groupby('station_id')[['jja', 'detr']].\\\n",
    "agg({'jja': ['mean','std'], 'detr': ['std']}).reset_index()\n",
    "sd_2010.columns = sd_2010.columns.map('_'.join)\n",
    "sd_2010 = sd_2010.rename(columns={'station_id_':'station_id'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2010 to today"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "sd_today = cdata[(cdata['year']>=2010)].copy().\\\n",
    "groupby('station_id')[['jja', 'detr']].\\\n",
    "agg({'jja': ['mean','std'], 'detr': ['std']}).reset_index()\n",
    "sd_today.columns = sd_today.columns.map('_'.join)\n",
    "sd_today = sd_today.rename(columns={'station_id_':'station_id'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decades since 1950"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "decades = cdata.copy()\n",
    "#https://stackoverflow.com/questions/48856982/divide-data-by-decade-then-plot-it-seaborn-box-and-whisker\n",
    "decades['decade'] = (decades['year'] // 10) * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "sd_decades = decades.\\\n",
    "groupby(['station_id','decade'])[['jja', 'detr']].\\\n",
    "agg({'jja': ['mean','std'], 'detr': ['std']}).reset_index()\n",
    "sd_decades.columns = sd_decades.columns.map('_'.join)\n",
    "sd_decades = sd_decades.rename(columns={'station_id_':'station_id', 'decade_':'decade'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>decade</th>\n",
       "      <th>jja_mean</th>\n",
       "      <th>jja_std</th>\n",
       "      <th>detr_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20550745000</td>\n",
       "      <td>1950</td>\n",
       "      <td>2146.67</td>\n",
       "      <td>79.33</td>\n",
       "      <td>82.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20550745000</td>\n",
       "      <td>1960</td>\n",
       "      <td>2113.67</td>\n",
       "      <td>49.88</td>\n",
       "      <td>51.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20550745000</td>\n",
       "      <td>1970</td>\n",
       "      <td>2131.00</td>\n",
       "      <td>60.12</td>\n",
       "      <td>60.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20550745000</td>\n",
       "      <td>1980</td>\n",
       "      <td>2150.67</td>\n",
       "      <td>96.72</td>\n",
       "      <td>97.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20550745000</td>\n",
       "      <td>1990</td>\n",
       "      <td>2186.67</td>\n",
       "      <td>71.02</td>\n",
       "      <td>68.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station_id  decade  jja_mean  jja_std  detr_std\n",
       "0  20550745000    1950   2146.67    79.33     82.86\n",
       "1  20550745000    1960   2113.67    49.88     51.80\n",
       "2  20550745000    1970   2131.00    60.12     60.25\n",
       "3  20550745000    1980   2150.67    96.72     97.96\n",
       "4  20550745000    1990   2186.67    71.02     68.49"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd_decades.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = pd.merge(cdata, base, how='left', on='station_id')\n",
    "c = pd.merge(c, sd_2010, how='left', on='station_id', suffixes=('_base', '_2010'))\n",
    "c = pd.merge(c, sd_today, how='left', on='station_id', suffixes=('_2010', '_today'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "c['decade'] = (c['year'] // 10) * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = pd.merge(c, sd_decades, how='left', on=['station_id', 'decade'], suffixes=('_today', '_decade') )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>year</th>\n",
       "      <th>jja</th>\n",
       "      <th>name</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>detr</th>\n",
       "      <th>jja_mean_base</th>\n",
       "      <th>jja_std_base</th>\n",
       "      <th>detr_std_base</th>\n",
       "      <th>jja_mean_2010</th>\n",
       "      <th>jja_std_2010</th>\n",
       "      <th>detr_std_2010</th>\n",
       "      <th>jja_mean_today</th>\n",
       "      <th>jja_std_today</th>\n",
       "      <th>detr_std_today</th>\n",
       "      <th>decade</th>\n",
       "      <th>jja_mean_decade</th>\n",
       "      <th>jja_std_decade</th>\n",
       "      <th>detr_std_decade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20550745000</td>\n",
       "      <td>1950</td>\n",
       "      <td>2263.33</td>\n",
       "      <td>QIQIHAR</td>\n",
       "      <td>47.38</td>\n",
       "      <td>123.92</td>\n",
       "      <td>163.87</td>\n",
       "      <td>2130.44</td>\n",
       "      <td>63.52</td>\n",
       "      <td>69.74</td>\n",
       "      <td>2191.67</td>\n",
       "      <td>96.34</td>\n",
       "      <td>91.24</td>\n",
       "      <td>2261.43</td>\n",
       "      <td>47.02</td>\n",
       "      <td>48.56</td>\n",
       "      <td>1950</td>\n",
       "      <td>2146.67</td>\n",
       "      <td>79.33</td>\n",
       "      <td>82.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20550745000</td>\n",
       "      <td>1951</td>\n",
       "      <td>2120.00</td>\n",
       "      <td>QIQIHAR</td>\n",
       "      <td>47.38</td>\n",
       "      <td>123.92</td>\n",
       "      <td>18.31</td>\n",
       "      <td>2130.44</td>\n",
       "      <td>63.52</td>\n",
       "      <td>69.74</td>\n",
       "      <td>2191.67</td>\n",
       "      <td>96.34</td>\n",
       "      <td>91.24</td>\n",
       "      <td>2261.43</td>\n",
       "      <td>47.02</td>\n",
       "      <td>48.56</td>\n",
       "      <td>1950</td>\n",
       "      <td>2146.67</td>\n",
       "      <td>79.33</td>\n",
       "      <td>82.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20550745000</td>\n",
       "      <td>1952</td>\n",
       "      <td>2170.00</td>\n",
       "      <td>QIQIHAR</td>\n",
       "      <td>47.38</td>\n",
       "      <td>123.92</td>\n",
       "      <td>66.07</td>\n",
       "      <td>2130.44</td>\n",
       "      <td>63.52</td>\n",
       "      <td>69.74</td>\n",
       "      <td>2191.67</td>\n",
       "      <td>96.34</td>\n",
       "      <td>91.24</td>\n",
       "      <td>2261.43</td>\n",
       "      <td>47.02</td>\n",
       "      <td>48.56</td>\n",
       "      <td>1950</td>\n",
       "      <td>2146.67</td>\n",
       "      <td>79.33</td>\n",
       "      <td>82.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20550745000</td>\n",
       "      <td>1953</td>\n",
       "      <td>2193.33</td>\n",
       "      <td>QIQIHAR</td>\n",
       "      <td>47.38</td>\n",
       "      <td>123.92</td>\n",
       "      <td>87.17</td>\n",
       "      <td>2130.44</td>\n",
       "      <td>63.52</td>\n",
       "      <td>69.74</td>\n",
       "      <td>2191.67</td>\n",
       "      <td>96.34</td>\n",
       "      <td>91.24</td>\n",
       "      <td>2261.43</td>\n",
       "      <td>47.02</td>\n",
       "      <td>48.56</td>\n",
       "      <td>1950</td>\n",
       "      <td>2146.67</td>\n",
       "      <td>79.33</td>\n",
       "      <td>82.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20550745000</td>\n",
       "      <td>1954</td>\n",
       "      <td>2143.33</td>\n",
       "      <td>QIQIHAR</td>\n",
       "      <td>47.38</td>\n",
       "      <td>123.92</td>\n",
       "      <td>34.94</td>\n",
       "      <td>2130.44</td>\n",
       "      <td>63.52</td>\n",
       "      <td>69.74</td>\n",
       "      <td>2191.67</td>\n",
       "      <td>96.34</td>\n",
       "      <td>91.24</td>\n",
       "      <td>2261.43</td>\n",
       "      <td>47.02</td>\n",
       "      <td>48.56</td>\n",
       "      <td>1950</td>\n",
       "      <td>2146.67</td>\n",
       "      <td>79.33</td>\n",
       "      <td>82.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station_id  year     jja     name   lat    lon   detr  jja_mean_base  \\\n",
       "0  20550745000  1950 2263.33  QIQIHAR 47.38 123.92 163.87        2130.44   \n",
       "1  20550745000  1951 2120.00  QIQIHAR 47.38 123.92  18.31        2130.44   \n",
       "2  20550745000  1952 2170.00  QIQIHAR 47.38 123.92  66.07        2130.44   \n",
       "3  20550745000  1953 2193.33  QIQIHAR 47.38 123.92  87.17        2130.44   \n",
       "4  20550745000  1954 2143.33  QIQIHAR 47.38 123.92  34.94        2130.44   \n",
       "\n",
       "   jja_std_base  detr_std_base  jja_mean_2010  jja_std_2010  detr_std_2010  \\\n",
       "0         63.52          69.74        2191.67         96.34          91.24   \n",
       "1         63.52          69.74        2191.67         96.34          91.24   \n",
       "2         63.52          69.74        2191.67         96.34          91.24   \n",
       "3         63.52          69.74        2191.67         96.34          91.24   \n",
       "4         63.52          69.74        2191.67         96.34          91.24   \n",
       "\n",
       "   jja_mean_today  jja_std_today  detr_std_today  decade  jja_mean_decade  \\\n",
       "0         2261.43          47.02           48.56    1950          2146.67   \n",
       "1         2261.43          47.02           48.56    1950          2146.67   \n",
       "2         2261.43          47.02           48.56    1950          2146.67   \n",
       "3         2261.43          47.02           48.56    1950          2146.67   \n",
       "4         2261.43          47.02           48.56    1950          2146.67   \n",
       "\n",
       "   jja_std_decade  detr_std_decade  \n",
       "0           79.33            82.86  \n",
       "1           79.33            82.86  \n",
       "2           79.33            82.86  \n",
       "3           79.33            82.86  \n",
       "4           79.33            82.86  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We calculate the z-score based on the mean of the timeperiod 1950 to 1979 and the corrected standard deviations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "c['z_base'] = (c['jja']-c['jja_mean_base'])/c['jja_std_base']\n",
    "c['z_base_detr'] = (c['jja']-c['jja_mean_base'])/c['detr_std_base']\n",
    "\n",
    "c['z_2010'] = (c['jja']-c['jja_mean_base'])/c['jja_std_2010']\n",
    "c['z_2010_detr'] = (c['jja']-c['jja_mean_base'])/c['detr_std_2010']\n",
    "\n",
    "c['z_today'] = (c['jja']-c['jja_mean_base'])/c['jja_std_today']\n",
    "c['z_today_detr'] = (c['jja']-c['jja_mean_base'])/c['detr_std_today']\n",
    "\n",
    "c['z_decade'] = (c['jja']-c['jja_mean_base'])/c['jja_std_decade']\n",
    "c['z_decade_detr'] = (c['jja']-c['jja_mean_base'])/c['detr_std_decade']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9535222321978685"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[(c['year']>=1950) & (c['year']<1980)]['z_base_detr'].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RTpTcpWjm1K2ngVgvt5RXnGazeEplgaXUKLlL0TQfOAhlFGbrO5EBRsld8q7bRhwqIyCSd0ruknfNBw4me+eJsWsRyS8ld5EcUMVKKTVK7iI5oKEmKTVaxCQiEkFK7iIiEaRhGSmMYGnfFZNUAVIkz9Rzl8IIlvZt2VnsaEQiT8ldRCSClNxFRCJIyV1EJIJ0Q1UKr3wS1JZr31SRPFJyl8KLz5SpqllHY230i4YFV65qsZMUipK7SJ6pro4UQ1Zj7mY2z8y2m9kOM6tJ8fy1ZrbFzDab2QtmdlLuQxURkWxlTO5mNhi4EzgfOAlYkCJ5P+ju1e4+DfhH4LacRyoSRvH7C6yoLnYkMsBkMyxzOrDD3d8CMLNVwEXAtkQDd3830H444LkMUiS0Eitx41vwiRRKNsm9AtgVeNwEzOzayMy+CfwDcASgu0YiQeWTaGRhbE/VcpVfkPwz95472WZ2CTDP3a+JP74CmOnui9O0Xwj8nbt/OcVzi4BFAOPHj5++atWqfoafXmtrKyNGjMjb9fMprLGni3tLcwvVg96GCdNixxXlHecrit+jLdT7vf0v7/Hh4XaqB73NdjuWE//DyH5dL6yfEwhv7KUQ99y5cze5+4yMDd29xy9gNvBU4PFNwE09tB8EtGS67vTp0z2fNmzYkNfr51NYY08X9+Qb17ovHdVxHDxfAgr+fi8dlZN/e1g/J+7hjb0U4gY2eob86u5ZzZZ5GTjezKaY2RHAZcCaYAMzOz7wcD7wZhbXFRmYyifRWLZQN1olrzImd3c/BCwGngJeBx52961mdouZXRhvttjMtprZZmLj7t2GZEQk7oYtzCl7PFkhc07d+mJHJBGU1SImd38SeLLLuSWB4+tzHJdIpCVXqtbGNhDvJl7/HtANWOkTrVAVKUXx+veAplFKnyi5i5SKrr11kX5QchcpFS07mVP2eGyYpg0q6tar0Jj0mZK7SJFVjB4Wq5BZFnusQmOSC0ruIsVUPomGloshXtu+4Qb11CU3lNxFikmzYCRPtM2eSIlKDNcAmgsvvaaeu0iJyjgXXqQH6rmLiESQkruISAQpuYuIRJDG3CV/4isuG+PT/ESkcNRzl/yJ10epantQU/7yYc+2WN0ZlQ6WFNRzFwmrwx+quJikpeQuBRWcu10xeliRoxGJLiV3KSgVwhIpDI25i4TYnLr1WsUqKSm5i4RY84GDySqSWsUqQUruIiIRpDF36bU5deuTvcSK0cNSjqPPqVtPA7Ga5LpxWgDlk2hkIdSiPVcFUHKXPggOBaTbUKL5wEEo69h4QvLshi2xDT/q5mtapAAalhERiSQldxGRCNKwjPTdimoay3Z2jPOe8s/FjkhE4tRzl75r2RmrG1PbAi072f6X96iqWaebqCIlQD13yZkPD7fTOP67sYJhbagSpEgRKblLbsUrQYpIcWlYRkQkgpTcRSIiWHFTdWZEwzIiEZFcKVyrOjOinrv0U6K32ORjqR70tm6iipQI9dylXzrqysxnR309LFBNE5FSoJ67iEgEqecuEjKJqpz3/ydtVSjpKblLWokk8sLQ66i0fbGT5ZOAuqLGNdAlqnLWP/RTbVsoaWlYRtJKJJFK29epzIAUWPkkGssWxkr5rqgudjQSEkruIqXuhi365Sq9llVyN7N5ZrbdzHaYWU2K5//BzLaZ2Wtm9qyZTc59qCIikq2Myd3MBgN3AucDJwELzOykLs1+D8xw908CjwL/mOtARUQke9n03E8Hdrj7W+7+IbAKuCjYwN03uPu/xx++CFTmNkwREekNc/eeG5hdAsxz92vij68AZrr74jTt7wD+4u7LUjy3CFgEMH78+OmrVq3qZ/jptba2MmLEiLxdP59KJfYtzS1UV5TD7s1saZ+S8pgJ05LtSyXu3gpD3Nv/8h4fHm7n47aLIXYIgNayCkYcOa574+D/UYkKw3ueSinEPXfu3E3uPiNjQ3fv8Qu4BPjvgcdXAHekafslYj33oZmuO336dM+nDRs25PX6+VQqsU++cW3sYOmotMdBpRJ3b0Uu7uD/UYmK3HteQMBGz5Bf3T2ree7NwMTA48r4uU7M7Bzge8Cn3P2DLK4rIiJ5ks2Y+8vA8WY2xcyOAC4D1gQbmNkpwL8AF7r7ntyHKSIivZExubv7IWAx8BTwOvCwu281s1vM7MJ4sx8DI4BHzGyzma1JczkJqWCtcC15Fyl9WZUfcPcngSe7nFsSOD4nx3FJiQnWCteSd5HSpxWqIiIRpMJh0jvlk2I1ThLHIlKSlNylk0QlSEgztn6DNuMQCQMld+kkUQlSRMJNY+4iIhGk5C4iEkEalhGJuK73UTSVdWBQcheJuOB9lMRCNIk+JXfpsKKaxrKdUEtsmqNmxoRT+SQaWdjx/9h1z9sV1R07Oun/ObI05i4dWnZ2bOcGsfnsteWazx42mbbla9kZe07b9kWaeu6SmnpzIqGm5C4ygFSMHgZtsbH3itHDaCh2QJI3Su4DiGZNDEBdxt8barZALTTWzWdO3Xqg+01WfTaiQcl9ANGsiYGjo0RzXUeyru287V7sHN1WJOuzEQ1K7iIRpJ63aLaMiEgEKbmLiESQkruISARpzH0g0kpUkchTz30gCq5E1QpFkUhSz11kIOu6baL+iosMJXeRgSyYzLvMg5dwU3IfoBKLXF4YOpbKYM+trbhxSR4leukqBDcgKLkPUB2LXOZTVbOuY5WiVidGl4ZcBhTdUBWRmHjPvrFsYazmu4SakruIxNywBWpbYjOpNIsq9JTcRUQiSGPuEZco8/vC0OtoLNsX2HpNRKJMyT3ikmV+axd2bJ8nIpGn5C6B2t/xnXpEJPSU3EW1v0UiSDdURUQiSMldRCSCNCwjIp1UjB4GbbG9VIObZWuD9XBRz11EOkkk7eBm6okb7o1182msm59M8lK61HMXkbTUOw+vrHruZjbPzLab2Q4zq0nx/Flm9oqZHTKzS3IfpvTJiupYnRBVAhQZcDL23M1sMHAncC7QBLxsZmvcfVug2U7gKuDb+QhSMlhR3VELJLjhQnzHpWTFR5G+SPf5kpKWzbDM6cAOd38LwMxWARcByeTu7o3x59rzEKNk0rKTOWWPx1ajsrBjfLRMi5IkB1p2dqxu1oYeoZFNcq8AdgUeNwEz8xOO9FVHmYGOG2HUasxU8k+zaEqTuXvPDWJj6PPc/Zr44yuAme6+OEXb+4G17v5ommstAhYBjB8/fvqqVav6F30PWltbGTFiRN6un0+9jn33Zra0T6G6ohx2b4YJ05Lnk8cFENb3XHGnkO5zFD/e0twS+7xB2uOe6D3vu7lz525y9xkZG7p7j1/AbOCpwOObgJvStL0fuCTTNd2d6dOnez5t2LAhr9fPp17HvnSUT75xbfI4eL6QwvqeK+4U0n2O4sfJz1sPxz3Re953wEbPIsdmMyzzMnC8mU0BmoHLgIV9+IUjhdB1N3uRQojfdG0sA1bopmspyJjc3f2QmS0GngIGA/e6+1Yzu4XYb5A1ZnYa8DhwJHCBmd3s7lPzGrkkxzo73TjVD5UUSjChMym2i1PNOhrV9ysJWc1zd/cn3f0Edz/W3ZfHzy1x9zXx45fdvdLdh7v7GCX2wkjeREU3TqUI4rNoqtoeVKeiBGmFqoh0p+G90FNyF5Hu+tgTT1d0TApPhcNEJGeCRcdUXKy4lNxFRCJIwzJhE6jz8cLQsYDqxkgBxcfiG8vQWHyJU3IPm3gxMCBW8VGkkOJj8VU162isVceilCm5h1CydsyKSSrnKyIpKbmHmeYWi0gaSu4lanftcUxgb+yYcUyo3VHkiESyFByXj5ci6Fo5cvkszeXINyX3EjWBvcka2hNqyzvVaBcpacFx+XgpguBq6thneXixohsw9OszJBIbE4uERaLeUWJBUyd7tsXuF62oLkJkA4N67iKSFw01n+68eUzQ4Q9jf5lqZ6e8UXIXkV6rGD0sOVSY9VaOgSqS9YNvTZ4OXkflCnJHyV1Eeq1PSThRRbJmHfcf3THm3nksXnJFyT0MVKFPRHpJyT0MNJ9dRHpJyV1E8ifFX50Vo4expbmFq2rWdZra+8LQ66A2NnVyN+OY3XZ7t8tpXD57Su4F0nURR6oPaKLNf60+xNkFjk8kL1L81dlQ82nq6+tpvPxsqO04X2n7Oq3tSDXLRuPy2VNyL5DgIo6mpcdC7b7YE+Udmwkn2tTX18ObRQpUpNSoEmqfKLkXQbCHonm+IhnEZ9kAVOrnJWtaoSoiEkHquZeAXi8GERHJQMm9BKhmjIjkmoZlREQiSD33ImvysR03iQIzZ0QGhMA8+CYfS2X8dLefC+k1JfciO/ODn3UMy2gmgAw0gc7MmTXraEwcB38uAtQZyp6Su4iExqXD/jW5GDCxEQjQaS68kn6MknsedV2VKiL902lld23gicBceP0FHKPknkerD/49lWXxlahDJwEZehOJ8ccTb9Y4o4j0i5J7jgV7641l6VeippzbnvhTsr4eFujPShlY+rQBSID2Ge5MyT3HgjVkOv3Z2KU6XmOt5raLBPW32mPKn7sBTMm9UHSDR6RgtHWfkruIhFRwWmRwjjxo6z5QcheRkArOhQ/OkU8nmz0VokTJXURCqbc3YIP3wwZCj17JvY/S9QKCW4VpOqNI/qTteQcmL2SzuUdUe/RK7inMqVsfm6Nu3XdLSki7sxJjO6Y/ikjhBX5WB9cel0z0vysbR7dEv6KahradUAaUT6Lq3+oKF2eeZVUV0szmmdl2M9thZjUpnh9qZqvjz79kZlW5DrSQmg8cTO6WVNX2YMey5oBYD728Y3pjbQvUtnDmBz8rcLQiks6E2h3Jn80J7E2eTw7ptOxkTtnjsTYtO5Pnq2rWMadufbL9nLr1VNWsY0tzS6fz6STad71OIWXsuZvZYOBO4FygCXjZzNa4+7ZAs6uBd9z9ODO7DPgRcGk+Ai60itHDaDrYvVhRcKu8S+vW06wNN0RCIzn0Uhs4Lp9EQ8vFyV78nA9up6pmHS8MvY4G2wdlUD/kVv7pwOSU1+w6vNNtfL/A9W+yGZY5Hdjh7m8BmNkq4CIgmNwvomPpwKPAHWZm7u45jLUoYv/xfwJi/0mdihV1aiMioRZMtrXlNNQmfgEs7Bhqfeinab+90wLGVApc/yab5F4B7Ao8bgJmpmvj7ofMrAUYA+wjD7K5AbL9L+9xVXAhw9Drk781m3wsZ37ws07f263IV1uaF4/frOk6r1ZESlxwlXjwXKa2wTaDj6CxbGGsK1vi1SctU+fazC4B5rn7NfHHVwAz3X1xoM0f4m2a4o//FG+zr8u1FgGL4g9PBLbn6h+Swljy9MulAMIau+IurLDGDeGNvRTinuzu4zI1yqbn3gxMDDyujJ9L1abJzP4GKAf2d72Qu/8C+EUWr9lvZrbR3WcU4rVyLayxK+7CCmvcEN7YwxR3NrNlXgaON7MpZnYEcBmwpkubNcCX48eXAOujMN4uIhJWGXvu8TH0xcBTwGDgXnffama3ABvdfQ1wD/A/zWwH8FdivwBERKRIslrE5O5PAk92ObckcNwGfDG3ofVbQYZ/8iSssSvuwgpr3BDe2EMTd8YbqiIiEj5ZrVAVEZFwiXRyN7MfmNlrZrbZzJ42s/9Y7JiyYWY/NrM/xmN/3MxGFzumbJnZF81sq5m1m1nJzyrIVFqjFJnZvWa2Jz4FOTTMbKKZbTCzbfHPyPXFjilbZlZmZv/XzF6Nx35zsWPKJNLDMmY2yt3fjR9fB5zk7tcWOayMzOw8YjOODpnZjwDc/cYih5UVM/sE0A78C/Btd99Y5JDSipfWeINAaQ1gQZfSGiXHzM4CWoEH3P3kYseTLTObAExw91fMbCSwCfh8qb/fAGZmwHB3bzWzIcALwPXu/mKRQ0sr0j33RGKPGw6E4jeZuz/t7ofiD1+E8CyGdffX3T2fi9NyKVlaw90/BBKlNUqauz9PbFZaqLj7bnd/JX78HvA6sdXtJc9jWuMPh8S/SjqfRDq5A5jZcjPbBVwOLMnUvgR9FfhNsYOIqFSlNUKRbMIuXjn2FOCl4kaSPTMbbGabgT3A/3b3ko499MndzJ4xsz+k+LoIwN2/5+4TgZXA4p6vVjiZ4o63+R5wiFj/bDnDAAABUklEQVTsJSOb2EXSMbMRwGPAf+ny13VJc/fD7j6N2F/Sp5tZSQ+JhX6zDnc/J8umK4nN1V+ax3CyliluM7sK+BzwmVJb7duL97zUZVNaQ3IoPl79GLDS3X9V7Hj6wt0PmNkGYB5Qsje1Q99z74mZHR94eBHwx2LF0htmNg/4b8CF7v7vxY4nwrIprSE5Er8peQ/wurvfVux4esPMxiVmrZnZMGI34Us6n0R9tsxjxKpPtgN/Bq5195LvmcXLOAylo/jai2GY5QNgZhcD/wyMAw4Am93974obVXpm9lngp3SU1lhe5JAyMrOHgLOJVSj8N2Cpu99T1KCyYGZnAr8FthD7mQT4bnwFfEkzs08C/4PY52QQ8LC731LcqHoW6eQuIjJQRXpYRkRkoFJyFxGJICV3EZEIUnIXEYkgJXcRkQhSchcRiSAldxGRCFJyFxGJoP8PAP3W7O9HMR8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c[(c['year']>=1950) & (c['year']<1980)]['z_base'].\\\n",
    "hist(bins=100, histtype = 'step', label='1950-1979', density=True)\n",
    "c[(c['year']>=1950) & (c['year']<1980)]['z_base_detr'].\\\n",
    "hist(bins=100, histtype = 'step', label='1950-1979 detrended', density=True).legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeveneResult(statistic=5.546793459057612, pvalue=0.01853043483528889)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as sc\n",
    "sc.levene(c[(c['year']>=1950) & (c['year']<1980)]['z_base'],\\\n",
    "          c[(c['year']>=1950) & (c['year']<1980)]['z_base_detr'], \\\n",
    "          center='median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c[(c['year']>=1980) & (c['year']<2010)]['z_2010'].\\\n",
    "hist(bins=100, histtype = 'step', label='1980-2009', density=True)\n",
    "c[(c['year']>=1980) & (c['year']<2010)]['z_2010_detr'].\\\n",
    "hist(bins=100, histtype = 'step', label='1980-2009 detrended', density=True).legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeveneResult(statistic=13.85822971800873, pvalue=0.0001980359268578363)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.levene(c[(c['year']>=1980) & (c['year']<2010)]['z_2010'],\\\n",
    "          c[(c['year']>=1980) & (c['year']<2010)]['z_2010_detr'], \\\n",
    "          center='median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c[(c['year']>=2010)]['z_today'].\\\n",
    "hist(bins=100, histtype = 'step', label='2010-today', density=True)\n",
    "c[(c['year']>=2010)]['z_today_detr'].\\\n",
    "hist(bins=100, histtype = 'step', label='2010-today detrended', density=True).legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeveneResult(statistic=0.08046936794091337, pvalue=0.7766818395331387)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.levene(c[(c['year']>=2010)]['z_today'],\\\n",
    "          c[(c['year']>=2010)]['z_today_detr'], \\\n",
    "          center='median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c[(c['year']>=1950) & (c['year']<1980)]['z_base_detr'].\\\n",
    "hist(bins=100, histtype = 'step', label='1950-1979', density=True)\n",
    "c[(c['year']>=1980) & (c['year']<2010)]['z_2010_detr'].\\\n",
    "hist(bins=100, histtype = 'step', label='1980-2009', density=True)\n",
    "c[(c['year']>=2010)]['z_today_detr'].\\\n",
    "hist(bins=100, histtype = 'step', label='2010-today', density=True).legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c[c['decade']==1950]['z_decade_detr'].\\\n",
    "hist(bins=100, histtype = 'step', label='1950', density=True)\n",
    "c[c['decade']==1960]['z_decade_detr'].\\\n",
    "hist(bins=100, histtype = 'step', label='1960', density=True)\n",
    "c[c['decade']==1970]['z_decade_detr'].\\\n",
    "hist(bins=100, histtype = 'step', label='1970', density=True)\n",
    "c[c['decade']==1980]['z_decade_detr'].\\\n",
    "hist(bins=100, histtype = 'step', label='1980', density=True)\n",
    "c[c['decade']==1990]['z_decade_detr'].\\\n",
    "hist(bins=100, histtype = 'step', label='1990', density=True)\n",
    "c[c['decade']==2000]['z_decade_detr'].\\\n",
    "hist(bins=100, histtype = 'step', label='2000', density=True)\n",
    "c[c['decade']==2010]['z_decade_detr'].\\\n",
    "hist(bins=100, histtype = 'step', label='2010', density=True).legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We calculated independent standard deviations and detrended the station data. Still, the variance of the temperature data increases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9092046472956058"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1.2324471771152585"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1.9358749526135184"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[(c['year']>=1950) & (c['year']<1980)]['z_base_detr'].var()\n",
    "c[(c['year']>=1980) & (c['year']<2010)]['z_2010_detr'].var()\n",
    "c[(c['year']>=2010)]['z_today_detr'].var()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The kurtosis of the distributions suggest that we can expect longer tails (more extreme temperature events). This is also visible in the histograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.1271642452398214"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.3400526263141632"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2.277009331459607"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[(c['year']>=1950) & (c['year']<1980)]['z_base_detr'].kurtosis()\n",
    "c[(c['year']>=1980) & (c['year']<2010)]['z_2010_detr'].kurtosis()\n",
    "c[(c['year']>=2010)]['z_today_detr'].kurtosis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, the increasing positive skewness sugges that we we can expect more extreme hot temperatures than more extreme cold temperatures.  This is also visible in the histograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16540508431056408"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.16188363599745012"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.9150190198457407"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[(c['year']>=1950) & (c['year']<1980)]['z_base_detr'].skew()\n",
    "c[(c['year']>=1980) & (c['year']<2010)]['z_2010_detr'].skew()\n",
    "c[(c['year']>=2010)]['z_today_detr'].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a similar trend in the decade data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://stackoverflow.com/questions/37345493/kurtosis-on-pandas-dataframe-doent-work\n",
    "stats = c.groupby('decade')['z_decade_detr'].agg(['mean', 'var', pd.DataFrame.kurt, 'skew']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(stats['decade'], stats['mean'], label='Mean')\n",
    "plt.plot(stats['decade'], stats['var'], label = 'Variance')\n",
    "plt.plot(stats['decade'], stats['kurt'], label = 'Kurtosis')\n",
    "plt.plot(stats['decade'], stats['skew'], label = 'Skewness')\n",
    "plt.legend(loc='upper left');\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the dramatic effects are primarily a result of the last two decades (2000-2010 and 2010 to today)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rival explanations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One issues could be that there a certain set of stations that influence the result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We calculate the stations in the timeperiods between 1980 and 2009 and 2010 and today that have temperature measurements that are more than 3 standard deviations away from the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_base = c[(c['year']>=1950) & \\\n",
    "             (c['year']<1980) & \\\n",
    "             (c['z_base_detr'] > (c['z_base_detr'].mean() + c['z_base_detr'].std()*3))]\n",
    "out_2010 = c[(c['year']>=1980) & \\\n",
    "             (c['year']<2010) & \\\n",
    "             (c['z_2010_detr'] > (c['z_2010_detr'].mean() + c['z_2010_detr'].std()*3))]\n",
    "out_today = c[(c['year']>=2010) & \\\n",
    "              (c['z_today_detr'] > (c['z_today_detr'].mean() + c['z_2010_detr'].std()*3))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(out_base.station_id.unique())\n",
    "len(out_2010.station_id.unique())\n",
    "len(out_today.station_id.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no outlier station in the baseline, 24 outliers in the timeperiod 1980 to 2009, and 45 outliers in the timeperiod 2010 to today."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "o_2010 = out_2010.groupby('station_id')['year'].agg(['count','min', 'max']).reset_index()\n",
    "o_today = out_today.groupby('station_id')['year'].agg(['count','min', 'max']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o_2010_2 = o_2010[o_2010['count']>=2]\n",
    "len(o_2010_2.station_id.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the timeperiod 1980 to 2009, no station reports more than one extreme event. This may indicate measurement errors or outlying events."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o_today_2 = o_today[o_today['count']>=2]\n",
    "len(o_today_2.station_id.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the timeperiod 2010 to today, there are 20 stations that report at least two exteme events."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
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"
     },
     "metadata": {
      "jupyter-vega": "#cb523c90-134b-4309-be68-ed50eef927c3"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from vega_datasets import data\n",
    "\n",
    "countries = alt.topo_feature(data.world_110m.url, 'countries')\n",
    "\n",
    "base = alt.Chart(countries).mark_geoshape(\n",
    "     fill='#FFFFFF',\n",
    "     stroke='666666'\n",
    ")\n",
    "\n",
    "stations_2010_1 = alt.Chart(out_2010[out_2010['station_id'].\\\n",
    "                              isin(o_2010['station_id'])].\\\n",
    "                          groupby('station_id')[['station_id', 'name', 'lat', 'lon']].\\\n",
    "                          agg('first')).mark_circle(fill='#ff0000', size=8).\\\n",
    "encode(\n",
    "    longitude='lon:Q',\n",
    "    latitude='lat:Q',\n",
    ").project(\n",
    "    type='mercator'\n",
    ").properties(\n",
    "    width=650,\n",
    "    height=400\n",
    ")\n",
    "\n",
    "stations_today_1 = alt.Chart(out_today[out_today['station_id'].\\\n",
    "                              isin(o_today['station_id'])].\\\n",
    "                          groupby('station_id')[['station_id', 'name', 'lat', 'lon']].\\\n",
    "                          agg('first')).mark_circle(fill='#800000', size=8).\\\n",
    "encode(\n",
    "    longitude='lon:Q',\n",
    "    latitude='lat:Q',\n",
    ").project(\n",
    "    type='mercator'\n",
    ").properties(\n",
    "    width=650,\n",
    "    height=400\n",
    ")\n",
    "\n",
    "stations_today_2 = alt.Chart(out_today[out_today['station_id'].\\\n",
    "                               isin(o_today_2['station_id'])].\\\n",
    "                           groupby('station_id')[['station_id', 'name', 'lat', 'lon']].\\\n",
    "                           agg('first')).mark_circle(fill='#0000ff', size=4).\\\n",
    "encode(\n",
    "    longitude='lon:Q',\n",
    "    latitude='lat:Q',\n",
    ").project(\n",
    "    type='mercator'\n",
    ").properties(\n",
    "    width=650,\n",
    "    height=400\n",
    ")\n",
    "\n",
    "base + stations_2010_1 + stations_today_1 + stations_today_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This map suggests that extreme events occur mostly in Europe and Asia along similar latitudes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.39"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "45.33"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "43.76"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(out_2010[out_2010['station_id'].isin(o_2010['station_id'])].\\\n",
    "                          groupby('station_id')[['station_id', 'name', 'lat', 'lon']].\\\n",
    "                          agg('first')['lat'].mean(),2)\n",
    "round(out_today[out_today['station_id'].isin(o_today['station_id'])].\\\n",
    "                          groupby('station_id')[['station_id', 'name', 'lat', 'lon']].\\\n",
    "                          agg('first')['lat'].mean(),2)\n",
    "round(out_today[out_today['station_id'].isin(o_today_2['station_id'])].\\\n",
    "                           groupby('station_id')[['station_id', 'name', 'lat', 'lon']].\\\n",
    "                           agg('first')['lat'].mean(),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average latitude ranges between 47.39 and 43.13 degrees latitude."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final thoughts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In an addition to the [previous post](https://mschermann.github.io/serendipity/posts/loaded-climate-dice/), we corrected the data for biases due to trends and analyzed only complete station data. Although both corrections reduce the variances but the data suggest that the variance still rises. The rise in variance seems to be induced by extreme temperature events of a few stations between 47.39 and 43.13 degrees latitude. These surface areas include the hottest areas on Earth. Only few extreme events are in North America. Thus, we can assume that here the weather gets hotter but not more extreme. A next analysis could focus only on the United States. Additionally, the analysis of the gridded temperature data could smooth over the extreme events in specific stations. But, this is something for another post, too."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Literature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Hansen, Sato, Ruedy (2012): [Perception of Climate Change](http://www.pnas.org/content/109/37/E2415). Proceedings of the National Academy of Sciences, 109(37) E2415-E2423; DOI: [10.1073/pnas.1205276109](https://doi.org/10.1073/pnas.1205276109)\n",
    "* Rhines and Huybers (2013): [Frequent summer temperature extremes reflect changes in the mean, not the variance](http://www.pnas.org/content/110/7/E546), Proceedings of the National Academy of Sciences, 110(7) E546; DOI: [10.1073/pnas.1218748110](https://doi.org/10.1073/pnas.1218748110)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project (including the data as of October 10, 2018) is available at [https://github.com/mschermann/loaded_climate_dice](https://github.com/mschermann/loaded_climate_dice)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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