Tracking Temperature Changes in Land-Surface, Air, and Sea-Water over 1753-2013

Today, a sudden curiosity struck me. How is global warming since the pre-industrial era?

Before introducing my figures and tables, I want to mention the IPCC's projections. The IPCC AR5 forecasts four different outcomes for global warming. Relative to the reference period of 1850−1900,  the global surface temperature had risen about 0.61 °C until 1986-2005 period. RCP2.6 is keeping the global warming below 2 °C by limiting the temperature rise at 1.61 °C. RCP8.5, the worst cast scenario, predicts the global warming could be 4.31 °C above the 1850-1900 average (Working Group I, 2013).

Currently, our world is walking the path of RCP8.5 (Le Quéré et al., 2014). It means that no climate policy is working. To make things worse, technology does not help the climate mitigation, either. A recent study revealed that the globally-welcomed technological breakthrough known as hydraulic fracturing (i.e., fracking) of shale gas might lower natural gas price but will fail to reduce global greenhouse gas emissions (McJeon et al., 2014). If we continue what we are doing now, the 2 °C goal would be shelved forever before the middle of this century.

Table 1. Global Mean Surface Temperature Change
(Relative to the reference period of 1850−1900)

Scenario	1986–2005	2046–2065	2081–2100
RCP2.6	0.61 °C	1.61 °C	1.61 °C
RCP4.5	0.61 °C	2.01 °C	2.41 °C
RCP6.0	0.61 °C	1.91 °C	2.81 °C
RCP8.5	0.61 °C	2.61 °C	4.31 °C

Source: Working Group I, 2013

Now, the following is my attempt to draw figures of long-term annual temperature changes. A recently launched global temperature database (Berkeley Earth, 2014) gave me a good time series data set for answering my question asked in the first sentence of this post. I tried to track down the trends of temperature in land-surface, air, and sea-water since before the Industrial Revolution.

There is a caveat, though. As I have explained in a previous post, temperature change estimates for any periods older than 1850 (in this case, 1753-1850) entail a high degree of uncertainties (You will understand what I mean by "uncertainties" when you see a graph below that shows a wide range of land-surface temperature fluctuations during the first century since the industrialization.). In addition to the annual recorded (or estimated) temperature data, therefore, I will calculate the range of global warming compared to the average temperature of 1851-1880 period. After the graphs, I will copy the data table for anyone interested in specific numbers (Table 3).

Land-surface temperature has shown the largest change among the three series. The global land-surface temperature rose from 7.874 °C in 1753 to 9.622 °C in 2013. It is a warming of 1.456 °C above the 1851-1880 average (8.166 °C).

However, most of our discussion about climate change is about atmospheric temperature. The temperature of air above sea-ice has warmed  from 14.330 °C in 1850 to 15.331 °C in 2013, which is 0.860 °C warmer than the 1851-1880 average (14.471 °C). If the world wants to keep the warming below 2 °C above the pre-industrial level, there is only a 1.140 °C margin remaining. In fact, the IPCC Working Group I's Fifth Assessment Report (AR5) stated that the globally averaged combined land and ocean surface temperature showed a warming of 0.85 °C over the period 1880 to 2012.

In addition, there might be some people who wonder if this data set shows an evidence of the global warming hiatus since 1998. (Trenberth, 2015). I made a comparison table for them, below. It seems that there was no such "hiatus" since 1998, although the latest 6 years were slightly cooler than the latest 11 years. Recent studies (e.g., Miller et al., 2014) are listing several reasons for the discrepancies between climate model (CMIP5) forecasts and actual temperature changes. They are (1) less solar radiation than the models estimated, (2) more effects from aerosols than the model simulation results, (3) a bias in the observations due to less data from the arctic regions than other regions, and (4) effects from ENSO (El Niño Southern Oscillation).

Table 2. Detecting a Likely Hiatus in Global Warming in Recent Years

Period		1851-1880	1981-2010	1998-2013	2003-2013	2008-2013
Average Temperature (°C)	Land-Surface	8.166	9.246	9.530	9.576	9.567
	Air	14.471	15.121	15.296	15.324	15.314
	Water	15.028	15.636	15.794	15.820	15.813
Anomaly relative to the 1851-1880 average (°C)	Land-Surface	0.000	1.079	1.364	1.410	1.401
	Air	0.000	0.650	0.825	0.853	0.843
	Water	0.000	0.608	0.766	0.792	0.785

Table 3. Historical Warming since Pre-Industrial Era

Year	Estimated Temperature (°C)			Annual Anomaly (°C) (Relative to the 1851-1880 average)
	Land-Surface (1753-2013)	Air above Sea-Ice (1850-2013)	Water below Sea-Ice (1850-2013)	Land-Surface (1753-2013)	Air above Sea-Ice (1850-2013)	Water below Sea-Ice (1850-2013)
1753	7.874			-0.292
1754	7.995			-0.171
1755	7.848			-0.318
1756	8.333			0.167
1757	8.465			0.299
1758	6.181			-1.985
1759	7.359			-0.807
1760	6.648			-1.518
1761	8.199			0.033
1762	7.980			-0.186
1763	7.083			-1.083
1764	8.360			0.194
1765	8.326			0.160
1766	8.428			0.262
1767	8.455			0.289
1768	6.988			-1.178
1769	7.957			-0.209
1770	7.854			-0.312
1771	8.061			-0.105
1772	8.308			0.142
1773	8.326			0.160
1774	8.840			0.674
1775	9.092			0.926
1776	8.139			-0.027
1777	7.952			-0.214
1778	8.177			0.011
1779	8.589			0.423
1780	9.372			1.206
1781	8.207			0.041
1782	7.968			-0.198
1783	7.835			-0.331
1784	8.051			-0.115
1785	7.532			-0.634
1786	8.498			0.332
1787	8.257			0.091
1788	8.677			0.511
1789	8.529			0.363
1790	8.138			-0.028
1791	8.344			0.178
1792	8.168			0.002
1793	8.334			0.168
1794	8.607			0.441
1795	8.537			0.371
1796	8.482			0.316
1797	8.699			0.533
1798	8.882			0.716
1799	8.683			0.517
1800	8.654			0.488
1801	8.740			0.574
1802	8.758			0.592
1803	8.598			0.432
1804	8.910			0.744
1805	8.649			0.483
1806	8.507			0.341
1807	8.356			0.190
1808	7.696			-0.470
1809	7.176			-0.990
1810	7.085			-1.081
1811	7.022			-1.144
1812	7.152			-1.014
1813	7.841			-0.325
1814	7.691			-0.475
1815	7.352			-0.814
1816	7.066			-1.100
1817	7.096			-1.070
1818	7.959			-0.207
1819	7.497			-0.669
1820	7.716			-0.450
1821	8.135			-0.031
1822	8.259			0.093
1823	7.768			-0.398
1824	8.605			0.439
1825	8.442			0.276
1826	8.412			0.246
1827	8.779			0.613
1828	8.190			0.024
1829	7.999			-0.167
1830	8.585			0.419
1831	7.699			-0.467
1832	7.477			-0.689
1833	8.031			-0.135
1834	8.226			0.060
1835	7.462			-0.704
1836	7.646			-0.520
1837	7.354			-0.812
1838	7.482			-0.684
1839	7.654			-0.512
1840	7.808			-0.358
1841	7.704			-0.462
1842	8.034			-0.132
1843	8.211			0.045
1844	7.678			-0.488
1845	7.898			-0.268
1846	8.587			0.421
1847	8.110			-0.056
1848	8.016			-0.150
1849	8.037			-0.129
1850	7.909	14.330	14.898	-0.257	-0.141	-0.130
1851	8.189	14.495	15.045	0.023	0.024	0.017
1852	8.111	14.489	15.048	-0.055	0.018	0.020
1853	8.066	14.439	14.997	-0.100	-0.032	-0.031
1854	8.229	14.502	15.052	0.063	0.031	0.024
1855	8.183	14.543	15.104	0.017	0.072	0.076
1856	8.070	14.338	14.905	-0.096	-0.133	-0.123
1857	7.825	14.235	14.809	-0.341	-0.236	-0.219
1858	8.157	14.378	14.936	-0.009	-0.093	-0.092
1859	8.302	14.439	14.989	0.136	-0.032	-0.039
1860	8.017	14.371	14.929	-0.149	-0.100	-0.099
1861	7.915	14.310	14.871	-0.251	-0.161	-0.157
1862	7.596	14.210	14.790	-0.570	-0.261	-0.238
1863	8.176	14.449	15.010	0.010	-0.022	-0.018
1864	8.004	14.381	14.940	-0.162	-0.090	-0.088
1865	8.196	14.482	15.039	0.030	0.011	0.011
1866	8.307	14.535	15.095	0.141	0.064	0.067
1867	8.433	14.559	15.103	0.267	0.088	0.075
1868	8.220	14.572	15.133	0.054	0.101	0.105
1869	8.388	14.565	15.108	0.222	0.094	0.080
1870	8.189	14.486	15.038	0.023	0.015	0.010
1871	8.076	14.475	15.039	-0.090	0.004	0.011
1872	8.141	14.488	15.044	-0.025	0.017	0.016
1873	8.303	14.528	15.082	0.137	0.057	0.054
1874	8.419	14.449	14.987	0.253	-0.022	-0.041
1875	7.852	14.398	14.965	-0.314	-0.073	-0.063
1876	8.042	14.378	14.939	-0.124	-0.093	-0.089
1877	8.496	14.767	15.314	0.330	0.296	0.286
1878	8.826	14.855	15.390	0.660	0.384	0.362
1879	8.141	14.542	15.102	-0.025	0.071	0.074
1880	8.116	14.471	15.035	-0.050	0.000	0.007
1881	8.297	14.561	15.116	0.131	0.090	0.088
1882	8.152	14.504	15.078	-0.014	0.033	0.050
1883	8.037	14.435	14.994	-0.129	-0.036	-0.034
1884	7.830	14.277	14.863	-0.336	-0.194	-0.165
1885	7.956	14.295	14.876	-0.210	-0.176	-0.152
1886	7.991	14.276	14.853	-0.175	-0.195	-0.175
1887	7.953	14.252	14.839	-0.213	-0.219	-0.189
1888	8.118	14.449	15.017	-0.048	-0.022	-0.011
1889	8.354	14.573	15.133	0.188	0.102	0.105
1890	8.011	14.284	14.860	-0.155	-0.187	-0.168
1891	8.055	14.396	14.967	-0.111	-0.075	-0.061
1892	8.097	14.353	14.910	-0.069	-0.118	-0.118
1893	8.085	14.386	14.938	-0.081	-0.085	-0.090
1894	8.200	14.380	14.940	0.034	-0.091	-0.088
1895	8.194	14.438	14.996	0.028	-0.033	-0.032
1896	8.303	14.566	15.130	0.137	0.095	0.102
1897	8.382	14.562	15.114	0.216	0.091	0.086
1898	8.229	14.357	14.917	0.063	-0.114	-0.111
1899	8.414	14.508	15.064	0.248	0.037	0.036
1900	8.543	14.627	15.170	0.377	0.156	0.142
1901	8.581	14.552	15.098	0.415	0.081	0.070
1902	8.315	14.411	14.975	0.149	-0.060	-0.053
1903	8.266	14.309	14.862	0.100	-0.162	-0.166
1904	8.114	14.276	14.829	-0.052	-0.195	-0.199
1905	8.264	14.425	14.978	0.098	-0.046	-0.050
1906	8.446	14.511	15.063	0.280	0.040	0.035
1907	8.000	14.334	14.898	-0.166	-0.137	-0.130
1908	8.218	14.310	14.854	0.052	-0.161	-0.174
1909	8.214	14.253	14.811	0.048	-0.218	-0.217
1910	8.257	14.280	14.841	0.091	-0.191	-0.187
1911	8.218	14.258	14.799	0.052	-0.213	-0.229
1912	8.202	14.340	14.898	0.036	-0.131	-0.130
1913	8.335	14.374	14.932	0.169	-0.097	-0.096
1914	8.633	14.547	15.098	0.467	0.076	0.070
1915	8.636	14.610	15.165	0.470	0.139	0.137
1916	8.275	14.366	14.923	0.109	-0.105	-0.105
1917	8.062	14.267	14.834	-0.104	-0.204	-0.194
1918	8.167	14.413	14.985	0.001	-0.058	-0.043
1919	8.420	14.490	15.029	0.254	0.019	0.001
1920	8.379	14.504	15.046	0.213	0.033	0.018
1921	8.588	14.581	15.120	0.422	0.110	0.092
1922	8.422	14.485	15.026	0.256	0.014	-0.002
1923	8.443	14.504	15.040	0.277	0.033	0.012
1924	8.523	14.521	15.056	0.357	0.050	0.028
1925	8.549	14.561	15.108	0.383	0.090	0.080
1926	8.749	14.704	15.256	0.583	0.233	0.228
1927	8.554	14.590	15.137	0.388	0.119	0.109
1928	8.664	14.604	15.146	0.498	0.133	0.118
1929	8.269	14.427	14.982	0.103	-0.044	-0.046
1930	8.650	14.653	15.185	0.484	0.182	0.157
1931	8.745	14.704	15.239	0.579	0.233	0.211
1932	8.736	14.664	15.202	0.570	0.193	0.174
1933	8.372	14.480	15.030	0.206	0.009	0.002
1934	8.668	14.622	15.167	0.502	0.151	0.139
1935	8.551	14.587	15.140	0.385	0.116	0.112
1936	8.587	14.639	15.185	0.421	0.168	0.157
1937	8.713	14.800	15.315	0.547	0.329	0.287
1938	8.884	14.801	15.312	0.718	0.330	0.284
1939	8.779	14.771	15.318	0.613	0.300	0.290
1940	8.790	14.869	15.385	0.624	0.398	0.357
1941	8.788	14.844	15.397	0.622	0.373	0.369
1942	8.758	14.804	15.340	0.592	0.333	0.312
1943	8.776	14.839	15.356	0.610	0.368	0.328
1944	8.869	14.942	15.462	0.703	0.471	0.434
1945	8.598	14.804	15.342	0.432	0.333	0.314
1946	8.698	14.737	15.276	0.532	0.266	0.248
1947	8.819	14.824	15.321	0.653	0.353	0.293
1948	8.766	14.724	15.274	0.600	0.253	0.246
1949	8.608	14.690	15.233	0.442	0.219	0.205
1950	8.384	14.612	15.149	0.218	0.141	0.121
1951	8.646	14.788	15.322	0.480	0.317	0.294
1952	8.662	14.851	15.375	0.496	0.380	0.347
1953	8.897	14.917	15.434	0.731	0.446	0.406
1954	8.583	14.718	15.242	0.417	0.247	0.214
1955	8.653	14.666	15.189	0.487	0.195	0.161
1956	8.305	14.596	15.119	0.139	0.125	0.091
1957	8.758	14.845	15.386	0.592	0.374	0.358
1958	8.800	14.845	15.395	0.634	0.374	0.367
1959	8.760	14.819	15.354	0.594	0.348	0.326
1960	8.608	14.773	15.304	0.442	0.302	0.276
1961	8.828	14.850	15.393	0.662	0.379	0.365
1962	8.772	14.807	15.338	0.606	0.336	0.310
1963	8.889	14.848	15.391	0.723	0.377	0.363
1964	8.437	14.574	15.127	0.271	0.103	0.099
1965	8.555	14.673	15.208	0.389	0.202	0.180
1966	8.628	14.741	15.279	0.462	0.270	0.251
1967	8.727	14.780	15.307	0.561	0.309	0.279
1968	8.544	14.723	15.278	0.378	0.252	0.250
1969	8.622	14.861	15.402	0.456	0.390	0.374
1970	8.738	14.791	15.347	0.572	0.320	0.319
1971	8.643	14.674	15.216	0.477	0.203	0.188
1972	8.543	14.767	15.310	0.377	0.296	0.282
1973	8.988	14.867	15.418	0.822	0.396	0.390
1974	8.510	14.649	15.183	0.344	0.178	0.155
1975	8.779	14.703	15.252	0.613	0.232	0.224
1976	8.393	14.597	15.147	0.227	0.126	0.119
1977	8.895	14.894	15.426	0.729	0.423	0.398
1978	8.738	14.771	15.317	0.572	0.300	0.289
1979	8.771	14.858	15.428	0.605	0.387	0.400
1980	9.027	14.977	15.504	0.861	0.506	0.476
1981	9.206	15.024	15.529	1.040	0.553	0.501
1982	8.687	14.806	15.358	0.521	0.335	0.330
1983	9.073	14.996	15.535	0.907	0.525	0.507
1984	8.736	14.831	15.359	0.570	0.360	0.331
1985	8.704	14.817	15.357	0.538	0.346	0.329
1986	8.887	14.866	15.401	0.721	0.395	0.373
1987	9.047	15.005	15.543	0.881	0.534	0.515
1988	9.245	15.053	15.571	1.079	0.582	0.543
1989	8.965	14.930	15.457	0.799	0.459	0.429
1990	9.277	15.120	15.644	1.111	0.649	0.616
1991	9.218	15.103	15.613	1.052	0.632	0.585
1992	8.871	14.920	15.468	0.705	0.449	0.440
1993	8.906	14.955	15.482	0.740	0.484	0.454
1994	9.076	15.004	15.550	0.910	0.533	0.522
1995	9.382	15.151	15.652	1.216	0.680	0.624
1996	9.070	15.050	15.537	0.904	0.579	0.509
1997	9.234	15.191	15.726	1.068	0.720	0.698
1998	9.552	15.342	15.840	1.386	0.871	0.812
1999	9.317	15.107	15.612	1.151	0.636	0.584
2000	9.232	15.127	15.623	1.066	0.656	0.595
2001	9.445	15.264	15.777	1.279	0.793	0.749
2002	9.595	15.341	15.838	1.429	0.870	0.810
2003	9.542	15.326	15.834	1.376	0.855	0.806
2004	9.356	15.230	15.769	1.190	0.759	0.741
2005	9.730	15.408	15.888	1.564	0.937	0.860
2006	9.546	15.353	15.818	1.380	0.882	0.790
2007	9.761	15.361	15.835	1.595	0.890	0.807
2008	9.458	15.221	15.726	1.292	0.750	0.698
2009	9.523	15.334	15.829	1.357	0.863	0.801
2010	9.724	15.404	15.897	1.558	0.933	0.869
2011	9.543	15.281	15.770	1.377	0.810	0.742
2012	9.530	15.312	15.801	1.364	0.841	0.773
2013	9.622	15.331	15.853	1.456	0.860	0.825

References:

Berkeley Earth. (2014). Time Series Data. [Data at http://berkeleyearth.org/data]

Le Quéré, C. et al. (2014). Global Carbon Budget 2014. Earth System Science Data Discussions, (In Review). [Full-text at http://dx.doi.org/10.5194/essdd-7-521-2014; Data at http://cdiac.ornl.gov/GCP/]

McJeon, H. et al. (2014). Limited impact on decadal-scale climate change from increased use of natural gas. Nature (In Press; advance online publication). [Full-text at http://dx.doi.org/10.1038/nature13837]

Miller, R. L. et al. (2014). CMIP5 historical simulations (1850–2012) with GISS ModelE2. Journal of Advances in Modeling Earth Systems, 6(2), 441-477. [Full-text at http://dx.doi.org/10.1002/2013MS000266]

Trenberth, K. E. (2015). Has there been a hiatus? Science, 349(6249), 691-692. [Full-text at http://dx.doi.org/10.1126/science.aac9225]

Working Group I. (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Bern, Switzerland: IPCC Working Group I. [Full-text at http://j.mp/WG1AR5]

Per Capita CO2 Emissions (Before- & After-Trade) Rankings of G20 Countries 1993-2013

China has been the world's number one carbon emitter since 2006. However, the country complains that developed countries' consumption of made-in-China goods is to blame for a big part of its CO₂ emissions. It is true that China's export-driven manufacturing has led to dramatic increases in CO₂ emissions. So a recent study (Le Quéré et al., 2014) analyzed how big the difference between before- and after-trade emissions in each country is. In those consumer countries such as the United States and the developed countries in Europe, the after-trade (consumption-based) CO₂ emissions per capita should be larger than the before-trade (production-based) CO₂ emissions per capita.
Because I have compared energy intensities of G20 countries, I decided to compare the G20 members by their CO₂ emissions per capita both in before- and after-trade terms. It was a simple task. I have divided each country's emissions (Le Quéré et al., 2014) by their population (UN DESA, 2013). The following figures show the results.
Let's take a look at the Table below. In 2012, French people emitted 56% more CO₂ by consumption-based (after-trade) emissions (8.32 tCO₂/person/year) than that by production-based (before-trade) emissions (5.34 tCO₂/person/year). China's per capita emissions decrease significantly using the same comparison. In 2012, Chinese people emitted 5.84 tonnes of CO₂ per person by after-trade emissions, which is 16% less than the before-trade emissions (6.97 tCO₂/person/year). (See also Figure 1 & Figure 2).
How about comparing those G20 countries by rankings? Before the trade-adjustment (Figure 3), China ranked 17th among G20 countries by the production-based per capita CO₂ emissions in 1993. In 2013, the country ranked 6th.
After adjusting the emissions by international trade (Figure 4), it is a different story. China was No. 17 in 1993 by the after-trade emissions per capita. In 2012, it stepped up only three ranks (No. 14). By comparison, South Korea leaped up 6 ranks. It was No. 11 in 1993 before its rank rose to No. 5 in 2012. Russia beat China in terms of rank ascension. The country rose by 4 ranks from No. 12 to No. 8.

Table. Difference between Before-Trade Emissions and After-Trade Emissions
([Difference] = [B-A]/A, where
A: CO₂ emissions per person before trade-adjustment,
B: CO₂ emissions per person after trade-adjustment)

	1993	1995	2000	2005	2010	2011	2012
Argentina	5.1%	3.2%	-2.4%	-16.3%	-7.2%	-6.5%	-4.8%
Australia	-14.3%	-13.8%	-16.8%	-12.6%	-9.5%	-8.5%	-4.2%
Brazil	7.6%	10.3%	3.3%	-2.8%	8.4%	8.0%	6.1%
Canada	2.9%	2.6%	-0.3%	0.5%	11.0%	11.4%	12.1%
China	-6.0%	-11.1%	-11.9%	-19.2%	-16.9%	-16.4%	-16.3%
France	33.1%	31.9%	38.0%	39.6%	48.2%	56.0%	55.8%
Germany	27.4%	27.3%	24.0%	23.6%	22.6%	26.1%	23.9%
India	-4.9%	-6.2%	-10.2%	-9.0%	-8.1%	-8.9%	-9.8%
Indonesia	-8.7%	-7.1%	-14.3%	-7.7%	-1.5%	-3.0%	-1.3%
Italy	36.7%	31.0%	30.9%	29.5%	44.3%	48.8%	51.8%
Japan	21.5%	22.6%	22.7%	21.4%	25.8%	29.6%	27.2%
Korea, South	22.3%	17.3%	10.6%	22.3%	15.9%	14.7%	14.2%
Mexico	2.8%	-5.7%	6.4%	10.3%	5.1%	4.8%	3.0%
Russia	-38.8%	-27.6%	-30.6%	-23.7%	-18.4%	-19.1%	-18.3%
Saudi Arabia	-7.4%	-5.5%	-30.9%	-27.0%	-13.0%	-16.5%	-17.0%
South Africa	-28.8%	-28.1%	-26.6%	-27.9%	-25.7%	-26.6%	-25.1%
Turkey	40.6%	28.4%	15.3%	16.7%	16.6%	15.2%	10.8%
United Kingdom	13.1%	13.1%	20.7%	28.5%	30.7%	37.0%	35.2%
United States	-1.1%	-0.9%	4.9%	8.1%	6.8%	7.6%	8.7%
EU28	22.7%	20.0%	21.7%	24.1%	28.9%	32.7%	33.0%

Figure 1


Figure 2

Figure 3


Figure 4


References:

Le Quéré, C., et al. (2014). Global Carbon Budget 2014. Earth System Science Data Discussions, (In Review). [Full-text at http://dx.doi.org/10.5194/essdd-7-521-2014; Data at http://cdiac.ornl.gov/GCP/]

United Nations Department of Economic and Social Affairs (UN DESA). (2013). World Population Prospects: The 2012 Revision. [Data at http://esa.un.org/wpp/]

What Made South Korea the Saddest Country in the OECD? (Part 3)

This is a third installment of my interest in how South Korea became the highest suicide rate country in the OECD (See the First and Second posts on this topic.). The timing of writing this post coincides with a publication of the latest global suicide rate report from the WHO (World Health Organization), the worst 20 countries by sex is listed below. So, South Korea is now the saddest country in the OECD and the third-saddest country in the world. (By the way, North Korea's suicide rates are higher than those of South Korea. Now I fear that Koreans are innately sad, then.... I leave this question to be answered later.)

Table. Suicide rates by sex in 2012 (OECD countries in boldface)
Suicide rate: Suicide mortality per 100,000 (age-standardized)

Rank	Both Sexes (rate)	Rank	Female (rate)	Rank	Male (rate)
1	Guyana (44.2)	1	Korea, North (35.1)	1	Guyana (70.8)
2	Korea, North (38.5)	2	Guyana (22.1)	2	Lithuania (51.0)
3	Korea, South (28.9)	3	Mozambique (21.1)	3	Sri Lanka (46.4)
4	Sri Lanka (28.8)	4	Nepal (20.0)	4	Korea, North (45.4)
5	Lithuania (28.2)	5	Tanzania (18.3)	5	Suriname (44.5)
6	Suriname (27.8)	6	Korea, South (18.0)	6	Korea, South (41.7)
7	Mozambique (27.4)	7	India (16.4)	7	Kazakhstan (40.6)
8	Nepal (24.9)	8	Sri Lanka (12.8)	8	Russia (35.1)
8	Tanzania (24.9)	8	South Sudan (12.8)	9	Mozambique (34.2)
10	Kazakhstan (23.8)	10	Burundi (12.5)	10	Burundi (34.1)
11	Burundi (23.1)	11	Uganda (12.3)	11	Belarus (32.7)
12	India (21.1)	12	Suriname (11.9)	12	Turkmenistan (32.5)
13	South Sudan (19.8)	13	Sudan (11.5)	13	Hungary (32.4)
14	Turkmenistan (19.6)	14	Bhutan (11.2)	14	Tanzania (31.6)
15	Russia (19.5)	15	Zambia (10.8)	15	Latvia (30.7)
15	Uganda (19.5)	16	Comoros (10.3)	16	Poland (30.5)
17	Hungary (19.1)	16	Myanmar (10.3)	17	Ukraine (30.3)
18	Japan (18.5)	18	Japan (10.1)	18	Nepal (30.1)
19	Belarus (18.3)	19	Zimbabwe (9.7)	19	Zimbabwe (27.2)
20	Zimbabwe (18.1)	20	Pakistan (9.6)	20	South Sudan (27.1)

Source: World Health Organization (2014)

In my first post, I found a significant relationship between suicide rates and income inequality. Here, I present the latest data on the income inequality.
The authors (Kim & Kim) of the paper I am citing used a method that's very similar to what was used by Thomas Piketty in his enormously successful book, "Capital in the Twenty-First Century." Realizing South Korea's household income survey is not reflecting actual income inequality due to sampling biases, Kim and Kim used global income tax data instead of the survey. The results reveals deeper income inequality.
To show historical changes in income inequality, I have drawn three figures covering the period from 1995 to 2012. By the way, Kim and Kim's database dates back all the way to 1933. If you are more interested, please visit the database website linked at the end of this post.
Here are the figures. I want to stress two points with them.
First, the shares of top income groups have been growing steadily (Figure 1). In 1995, the share of top 10% households was 29.2%. In 2012, it soared to 44.9%. In addition, the average growth rates of income shares were greater in higher income groups. For example, the top 0.01% households' income share have risen at 5.44% annually, while the CAGR of top 0.1%, 1%, 10% households' income shares were 4.80%, 3.44%, 2.56%, respectively.

Figure 1


Second, the top income groups' income has increased while the bottom 90% households' real income has actually decreased. For this explanation, I have drawn two figures containing the same data. Figure 2 was plotted with a logarithmic scale on Y-axis. Figure 3 is using a natural scale on Y-axis.
Top 0.01% families' average annual income was 917,193 dollars in 1995. In 2012, they earned $2,639,751 per family. It means 6.42% annual growth rate. Top 0.1%, 1%, 10% families have shown positive CAGRs, although the rates are descending with their income levels (5.70%, 4.35%, 3.45%, each).
However, the bottom 90% households could not catch up. Their income has shrunk by 0.60% annually. In 1995, the entire family in the group earned $10,840. In 2012, their income fell below $10k line. The average income of the bottom 90% households was $9784.

Figure 2


Figure 3


Reference:

Kim, N. N., & Kim, J. (2014). Top Incomes in Korea, 1933-2010: Evidence from Income Tax Statistics. WTID Working Paper, 2014/2. [Full-text at http://j.mp/Korea_Income_Inequality; Data from http://j.mp/Income_DB (updated by the authors after writing paper.)]

World Health Organization. (2014). Preventing Suicide: A Global Imperative. Geneva, Switzerland: World Health Organization. [Full-text at http://j.mp/WHO_Suicide_Report]

Energy Intensity (Primary & Final Energy) Rankings of G20 Countries 1990-2010

G20 (the Group of Twenty) refers to the 20 influential economies in the world. It consists of 19 countries and the European Union (EU). The members of the G20 are Argentina, Australia, Brazil, Canada, China, France, Germany, India, Indonesia, Italy, Japan, Republic of Korea (South Korea), Mexico, Russia, Saudi Arabia, South Africa, Turkey, the United Kingdom, the United States and the EU.
The term 'energy intensity' generally means the amount of energy consumed per unit of gross domestic product (GDP) in a country. It is calculated by dividing a country's total (primary or final) energy consumption by the country's GDP. It shows an overall energy efficiency of the country's economy.
By GDP at 2005 Purchasing Power Parity, the global primary energy intensity was improved from 10.215 MJ/dollar (or 0.244 toe/$1000) in 1990 to 7.904 MJ/dollar (0.189 toe/$1000) in 2010. The global final energy intensity passed almost the same direction as the primary one by enhancing the efficiency from 7.320 MJ/dollar (0.175 toe/$1000) to 5.376 MJ/dollar (0.128 toe/$1000) during the same period (See the last two figures of this post.).
Just like the global energy intensities, G20 members' energy intensities have generally improved over the last two decades. Although the American Council for an Energy-Efficient Economy (ACEEE) recently published an International Energy Efficiency Scorecard (Young et al., 2014), I wanted to compare countries not by scores but by absolute performances. Using the World Bank's data compiled for the United Nation's "Sustainable Energy for All" initiative, I compared energy intensities of the 19 countries (i.e., G20 excluding the EU) and ranked each country annually. Actual energy intensity numbers are also provided after the rankings figures.
The United Kingdom has achieved a tremendous gain in its economy's energy efficiency. It was 7th both in primary and final energy rankings in 1990. In 2010, it boasts the most energy-efficient economy.
Germany and India jumped up 4 ranks in both primary and final energy intensity rankings over 1990-2010. China climbed up the primary energy intensity rankings ladder by 3 steps. Australia improved its final energy intensity rankings by 3 steps.
By absolute ranks, Russia showed the poorest energy efficiency both in primary and final energy intensities. However, the worst performing country in energy efficiency improvement was Saudi Arabia. In 1990, the country was 11th in primary energy intensity rankings and 8th in final energy intensity rankings. Saudi Arabia's 2010 ranking was 18th by both energy intensities.
Brazil was the second most-demoted country in the primary energy intensity rankings by sliding 5 ranks over 1990-2010. France and South Korea fell by 3 steps in the same rankings. In the final energy intensity rankings, however, Turkey showed the second biggest lapse (5 steps) next to Saudi Arabia's, defeating Brazil's 4-step decline.








Notes:
(1) Energy intensity level of primary energy (MJ/$2005 PPP): A ratio between energy supply and gross domestic product measured at purchasing power parity. Energy intensity is an indication of how much energy is used to produce one unit of economic output. Lower ratio indicates that less energy is used to produce one unit of output.

(2) Energy intensity level of final energy (MJ/$2005 PPP): A ratio between final energy consumption and gross domestic product measured at purchasing power parity. Energy intensity is an indication of how much energy is used to produce one unit of economic output. Lower ratio indicates that less energy is used to produce one unit of output.

References:
The World Bank. (2014). Sustainable Energy for All. Washington, DC: The World Bank. [Data at http://j.mp/SE4ALL]
Young, R., Hayes, S., Kelly, M., Vaidyanathan, S., Kwatra, S., Cluett, R., & Herndon, G. (2014). The 2014 International Energy Efficiency Scorecard. Washington, DC: American Council for an Energy-Efficient Economy (ACEEE). [Full-text at http://j.mp/ACEEE_2014]