State of Non-Renewable Energy Resources at the Beginning of 2014

The latest global estimates of the reserves and resources of fossil and nuclear energy resources are published by the BGR (Bundesanstalt für Geowissenschaften und Rohstoffe; Federal Institute for Geosciences and Natural Resources). So I updated my previous post.
You may find there are two tables below (Due to a technical reason, I couldn't separate them entirely). The first one is in exajoules, just as the source. The second is in million tonnes of oil equivalents. At the beginning of 2014, the world had approximately 73 years' supply of non-renewable energy resources that could be economically exploited with contemporary technologies.
(According to the BGR's glossaries,
Reserves = Proven volumes of energy commodities economically exploitable at today’s prices and using today’s technology (Initial reserves: cumulative production plus remaining reserves);
Resources = Proven amounts of energy resources which cannot currently be exploited for technical and/or economic reasons, as well as unproven but geologically possible energy resources which may be exploitable in future. For coal this term is used for all in-place resources.)

State of Non-Renewable Energy Resources, at the End of 2013
Unit:	EJ (= 1018 joules)
	Crude oil		Natural gas		Coal		Nuclear energy		Total
	conventional	non- conventional	conventional [1]	non- conventional [2]	hard coal	lignite	uranium	thorium
Reserves	9,137		7,526		20,378		606		37,646
	7,126	2,011	7,318	208	17,148	3,230	606	–
Resources	13,959		24,257		489,766		9,859		537,840
	6,745	7,214	12,099	12,158	438,034 [3]	51,732	6,681	3,178 [4]
Production in 2013	175.6		130.0		179.0		29.8		514.5
					168.7	10.3	29.8
Consumption in 2013	176.7		130.5		178.4		32.5		518.1
					168.4	10.0	32.5
Reserves-to-production ratio (years)	52		58		114		20		73
					102	314	20
[1] including tight gas [2] without natural gas in gas hydrates and aquifer gas (7,904 EJ) [3] including hard coal in the Antarctic (3,825 EJ) [4] including thorium without country allocation (62 EJ)
State of Non-Renewable Energy Resources, at the End of 2013
Unit:	million toe
	Crude oil		Natural gas		Coal		Nuclear energy		Total
	conventional	non- conventional	conventional	non- conventional	hard coal	lignite	uranium	thorium
Reserves	218,233		179,755		486,720		14,474		899,159
	170,202	48,032	174,787	4,968	409,573	77,147	14,474	–
Resources	333,405		579,368		11,697,860		235,478		12,846,088
	161,102	172,303	288,980	290,389	10,462,262	1,235,598	159,573	75,905
Production in 2013	4,194		3,105		4,275		712		12,289
					4,029	246	712
Consumption in 2013	4,220		3,117		4,261		776		12,375
					4,022	239	776
Reserves-to-production ratio (years)	52		58		114		20		73
					102	314	20
Conversion factor:
	1 EJ = 23,884,589.66275 toe

Reference:
BGR. (2014). Energy Study 2014: Reserves, Resources and Availability of Energy Resources. Hannover, Germany: Bundesanstalt für Geowissenschaften und Rohstoffe (Federal Institute for Geosciences and Natural Resources). [Full-text at http://j.mp/FF_RR_2015]

An Indirect Look at Radioactive Material Leaks from Fukushima Dai-ichi Nuclear Power Plant

It's been four years since Fukushima Dai-ichi Nuclear Power Plant was ravaged by a tsunami that was triggered by a historic earthquake in March 2011. Radioactive materials leaked from the accident are forecasted to raise the radiation levels in coastal waters of North America as high as 3–5 becquerels per cubic meter (Bq/m³) during 2015-2016 (Smith et al., 2014). The same study is expecting the seawater radiation levels will drop to normal levels (1 Bq/m³) in 2021. Then can we expect that there will be no more leaking of radioactive materials from the Fukushima Dai-ichi Nuclear Power Plant?
The following figure visually summarizes the levels of radioactive cesium in seawater, sediment, and fish tissue that had been sampled at the Fukushima Dai-ichi Nuclear Power Plant port from the pre-accident period to three years after the accident.
Firstly, before the accident, seawater radiation levels resulting from radioactive cesium were lower than 0.01 becquerel per liter (Bq/liter). In 2014, the activity concentrations of ^134,137Cs were still around 10 Bq/liter, more than 1,000 times the pre-accident radiation levels.
Secondly, fishes caught before the 2011 accident showed radioactive cesium concentrations of lower than 0.1 becquerel per kilogram (Bq/kg). Even three years after the accident, the fish tissue radioactivity levels were recorded between 10 to 100,000 Bq/kg.
Thirdly, radioactive cesium activity concentrations in sediment didn't drop significantly at least until 2014, compared to the post-accident 2011 levels, either. Only highest activity concentrations seem to have declined.
All in all, leaking of radioactive materials from Fukushima Dai-ichi Nuclear Power Plant is not being controlled yet, apparently. I expect more thorough monitoring data will come out soon. Until then, let us not be so decisive about the effects of the tragic accident, whether being optimistic or pessimistic.

Figure. ^134,137Cs activity concentrations in seawater, sediment, and muscle tissue samples from four species, of similar trophic levels (TLs), from the Fukushima Dai-ichi Nuclear Power Plant port. Lines represent means of data over 60-day intervals.

Source: Johansen, et al., 2015


References:

Johansen, M. P., et al. (2015). Radiological Dose Rates to Marine Fish from the Fukushima Daiichi Accident: The First Three Years Across the North Pacific. Environmental Science & Technology, 49, 1277–1285. [Full-text at http://dx.doi.org/10.1021/es505064d]

Smith, J. N., et al. (2015). Arrival of the Fukushima radioactivity plume in North American continental waters. Proceedings of the National Academy of Sciences, 112(5), 1310–1315. [Full-text at http://dx.doi.org/10.1073/pnas.1412814112]

Particle (PM2.5) Pollution by Country in 2005 and 2010

Outdoor air pollution is becoming a serious health threat in East Asia. A former Beijing TV reporter's presentation about Chin'a heavy PM_2.5 pollution (https://youtu.be/T6X2uwlQGQM) is said to generate a national sensation. To cite a serious study, a 10 µg/m³ rise in ambient PM_2.5 concentration increases the relative risks of developing two types of lung cancer: adenocarcinoma by 40% and squamous cell carcinoma by 11% (Hamra et al., 2014). In addition, a recent study (Rohde & Muller, 2015) even estimated that about 17% of total annual deaths in China is due to premature deaths caused by people's chronic exposure to high PM_2.5 concentrations.
So I looked at pollution data provided by the World Bank. Although just two years' data cannot show any meaningful direction for further explanation, the global particle pollution seems getting worse. The global mean annual exposure increased from 29.9 µg/m³ in 2005 to 31.3 µg/m³ in 2010. Pollution from industrial and transport sectors might be the main cause.
However, there is one important condition that we can easily overlook. Although countries with vast area of deserts recorded higher pollution levels, the effects of natural dust and sea salt should be removed to better assess the health impacts of PM_2.5. So, after the final PM_2.5 concentration map from SEDAC below (Figure 1), I am introducing additional images (Figure 2) from a recent study (van Donkelaar et al., 2015) that used the same satellite observation data.

Table. PM_2.5 Pollution (mean annual exposure)
Unit: µg/m³

Country	2005	2010
United Arab Emirates	65.68166	79.51939
China	63.99531	72.56515
Qatar	59.87048	69.02888
Mauritania	69.93466	65.18214
Saudi Arabia	62.20242	61.68340
Kuwait	49.41866	50.39333
Bahrain	48.01391	49.33072
Turkmenistan	48.76600	48.28830
Cabo Verde	43.36331	42.86366
Senegal	41.36001	41.19929
Pakistan	37.28355	38.10374
Korea, South	40.09238	37.52048
Libya	36.42195	37.18039
Niger	37.36272	36.82437
Gambia, The	35.93841	35.75570
Oman	34.12933	35.29841
Mali	34.62133	34.11404
Chad	34.03407	33.35825
Nepal	30.66388	32.66240
Egypt, Arab Rep.	33.59428	32.58492
India	31.46866	32.02058
Korea, North	30.72270	31.51703
World	29.86480	31.25349
Guinea-Bissau	31.11515	31.21841
Bangladesh	29.84423	31.13156
Iraq	30.63495	30.39379
Yemen, Rep.	30.81548	30.16242
Vietnam	28.87354	29.80268
Iran, Islamic Rep.	30.02963	29.69507
Jordan	29.53331	28.77559
Burkina Faso	27.48525	27.35054
Nigeria	26.93633	27.07458
Djibouti	26.51410	26.83520
Israel	26.88058	26.18212
Syrian Arab Republic	26.77977	25.99799
Sudan	26.07869	25.86403
West Bank and Gaza	26.06656	25.37601
Eritrea	24.44714	24.53869
Afghanistan	24.32812	23.89933
Lebanon	24.58599	23.79930
Lao PDR	21.19542	22.45218
Guinea	22.65570	22.28595
Cameroon	22.08601	22.17291
Uzbekistan	23.72026	22.10496
Algeria	22.43683	22.04645
Benin	22.20245	21.92649
Japan	22.79315	21.81006
Myanmar	21.72509	21.77255
Bhutan	20.19818	21.72453
Malta	22.76602	21.30753
Thailand	20.87660	21.07797
Togo	21.33735	20.98064
Morocco	20.13554	19.97916
Singapore	20.85270	19.82913
Barbados	19.47597	19.41978
Central African Republic	19.76822	19.21593
Italy	21.74040	19.04609
Tunisia	19.76945	19.04604
Cyprus	19.76426	18.96764
Belgium	22.08128	18.80821
Armenia	19.92665	18.73134
Netherlands	21.72581	18.54935
Ghana	18.37616	18.01068
Dominica	17.78881	17.98825
St. Lucia	18.18060	17.96446
Sierra Leone	17.85903	17.63314
Antigua and Barbuda	17.41343	17.45214
Turkey	18.49803	17.44953
Cambodia	17.54926	17.43361
Azerbaijan	18.74119	17.28889
Romania	20.75279	17.25078
St. Vincent and the Grenadines	17.39695	17.09740
Greece	18.90921	16.89706
Macedonia, FYR	19.14504	16.85793
Bulgaria	19.53040	16.81881
Tajikistan	18.57619	16.68379
Mexico	16.83631	16.64726
Hungary	19.84951	16.24823
Kyrgyz Republic	18.22999	15.98221
Montenegro	18.57360	15.89186
Serbia	18.57360	15.89186
Germany	19.04302	15.85773
Poland	18.98603	15.78261
Maldives	15.19199	15.75179
Czech Republic	19.45999	15.66124
Ethiopia	15.39432	15.41976
Grenada	16.07247	15.27145
Cote d'Ivoire	15.46996	15.24135
Slovenia	17.64982	15.23634
Congo, Dem. Rep.	15.12717	15.13202
Slovak Republic	18.22347	15.00319
Croatia	16.92603	14.36613
France	15.61563	14.33079
Albania	16.41100	14.29622
Rwanda	14.13845	14.16464
Congo, Rep.	14.78158	14.02519
Spain	14.81681	13.98518
Switzerland	15.91973	13.86089
Indonesia	13.93691	13.80772
Moldova	16.84122	13.79449
United Kingdom	14.91859	13.70214
Kazakhstan	13.17328	13.39266
United States	13.73764	13.38303
Luxembourg	15.67862	13.29090
Austria	15.01844	13.22534
Bahamas, The	13.24748	13.02847
Andorra	13.67137	12.99452
Malaysia	13.08630	12.94251
Ukraine	15.49109	12.69998
Portugal	11.77354	12.53817
Bosnia and Herzegovina	13.42289	12.41069
Georgia	11.77928	12.00855
Jamaica	10.17643	11.96345
Guatemala	10.63070	11.85139
Denmark	11.53602	11.70216
Haiti	12.10771	11.42943
Burundi	11.49712	11.23896
Angola	10.35762	11.23079
Belarus	10.29214	10.64201
Lithuania	7.88313	10.15811
Canada	10.28616	10.14480
Uganda	10.87613	10.03688
Peru	10.16026	9.80432
Russian Federation	8.75458	9.59945
Mongolia	9.32179	9.20861
Latvia	5.01399	9.14117
Dominican Republic	9.56640	8.90070
Liberia	8.21280	8.78300
Ireland	7.04813	8.67139
Sri Lanka	10.24467	8.61689
Somalia	8.13699	8.21147
Costa Rica	5.02749	8.19678
Chile	8.12389	8.13966
Venezuela, RB	6.27769	8.09122
Marshall Islands	5.08027	7.85952
South Africa	7.20781	7.80041
Estonia	5.00164	7.24874
Philippines	6.96318	7.06607
Cuba	6.85605	6.94057
Equatorial Guinea	7.34036	6.85246
Honduras	6.70937	6.72552
Kiribati	6.34917	6.42433
Guyana	6.36976	6.26987
Gabon	6.36254	6.09124
Bolivia	5.85383	6.04628
Sweden	5.85217	5.99107
Lesotho	5.00127	5.93226
Kenya	5.08719	5.84695
Iceland	5.68715	5.83887
Solomon Islands	5.02314	5.81406
Uruguay	5.00428	5.80678
Australia	5.01004	5.68590
Zambia	5.08932	5.63978
Ecuador	5.83868	5.61844
New Zealand	5.13576	5.58406
Belize	5.45297	5.54806
Timor-Leste	5.00523	5.44113
Tanzania	5.29027	5.42503
Colombia	5.26264	5.40619
Brunei Darussalam	5.01483	5.40484
Papua New Guinea	5.00340	5.37060
Fiji	5.02250	5.35870
Nicaragua	5.09545	5.34375
Panama	5.00599	5.30016
El Salvador	5.05292	5.24743
Seychelles	4.92666	5.24212
Finland	4.99448	5.22406
Madagascar	5.00159	5.21810
Vanuatu	5.00859	5.21081
Botswana	5.00322	5.16612
Tonga	5.08818	5.14790
Mozambique	4.99959	5.11382
Mauritius	5.09764	5.07905
Brazil	5.25013	5.07802
Suriname	5.29545	5.05235
Argentina	5.21216	4.98674
Sao Tome and Principe	4.95706	4.97077
Samoa	4.99461	4.92124
Swaziland	5.00995	4.91671
Malawi	5.00105	4.87758
Zimbabwe	5.00225	4.77891
Comoros	4.95402	4.72747
Micronesia, Fed. Sts.	4.99973	4.69715
Paraguay	5.00445	4.47555
Namibia	5.25051	4.44832
Norway	5.56339	4.40615
Trinidad and Tobago	5.26734	4.39580

Source: World Bank. (2015). World Development Indicators. Washington, DC: The World Bank. [Data at http://data.worldbank.org/data-catalog/world-development-indicators]

Figure 1. Global Annual Average PM_2.5 Grids (2001–2010).

Source: SEDAC, 2013


Figure 2. Comparison of PM_2.5 concentration with and without natural dust and sea salt (2001–2010).

Source: van Donkelaar et al., 2015


References:

Hamra, G. B., et al. (2014). Outdoor Particulate Matter Exposure and Lung Cancer: A Systematic Review and Meta-Analysis. Environmental Health Perspectives, 122(9), 906–911. DOI: 10.1289/ehp/1408092. [Full-text at http://dash.harvard.edu/bitstream/handle/1/12987239/4154221.pdf]

Rohde, R. A., & Muller, R. A. (2015). Air Pollution in China: Mapping of Concentrations and Sources. PLoS ONE, 10(8), e0135749. [Full-text at http://dx.doi.org/10.1371/journal.pone.0135749]

Socioeconomic Data and Applications Center (SEDAC). (2013). Global Annual Average PM_2.5 Grids from MODIS and MISR Aerosol Optical Depth (AOD), v1 (2001 – 2010). [Map image at http://sedac.ciesin.columbia.edu/data/set/sdei-global-annual-avg-pm2-5-2001-2010]

van Donkelaar, A., Martin, R. V., Brauer, M., & Boys, B. L. (2015). Use of Satellite Observations for Long-Term Exposure Assessment of Global Concentrations of Fine Particulate Matter. Environmental Health Perspectives, 123(2), 135–143. [Full-text at http://dx.doi.org/10.1289/ehp.1408646]

Efficiency of Energy Conversion and Delivery in G20 Countries, 2000-2012

How do we measure the efficiency of energy conversion and delivery at a country level? I am here using the 'final to primary energy ratios,' which is coined by the UN's Sustainable Energy for All initiative. As the name implies, it is simply calculated by dividing a country's 'total primary energy supply' (TPES) by her 'total final energy consumption' (TFEC or TFC).
I compared the ratios between G20 countries (except the European Union). The following figure shows a gradual decline of the energy conversion and delivery efficiency. The latest IEA/World Bank report for the Sustainable Energy for All initiative ("Global Tracking Framework Report")) explains that main causes would be "the growth in coal use for electricity generation, and coal, oil, and gas consumption for heat provision relative to other primary resources" (p. 123). In my opinion, electric heating (instead of heating by direct combustion of gas/coal/oil/biomass) is another cause of the declining shares of final energy consumption in primary energy supply due to increased number of energy conversion stages.
As for coal power's effects on lowering energy conversion efficiency, the reason is clear. Recently, a dutch energy consultancy Ecofys reviewed the performance of fossil power plants in Australia, China, France, Germany, India, Japan, Nordic countries (Denmark, Finland, Sweden and Norway aggregated), South Korea, United Kingdom and Ireland (aggregated), and the United States. The study (2014) finds, in 2011, the weighted average energy efficiency of coal-fired power plants was 35%, that of gas-fired power plants was 48%, and that of oil power plants was nearly 40%.
Increasing consumption of the low-efficiency coal power explains why China's efficiency is getting worse every year. According to the World Bank's database for the Sustainable Energy for All initiative, China's coal power production grew at an annual growth rate of 11.8% from 2000–2010, whereas the country's primary energy supply rose by 8% annually during the same period.
South Africa's heavy dependence on coal power (94% of electricity production in 2010) might be keeping the country at a remote bottom among G20 countries in terms of energy transformation efficiency.
I cannot explain Saudi Arabia's sudden efficiency improvement in 2011 and 2012. Because Canada's efficiency has also improved recently, I suspect management strategies of the surplus crude oil by the two oil exporting countries have any effect on the final to primary energy ratios.
However, there's a problem in this accounting method of aggregating different energy resources. Because the overarching unit of the IEA's energy balance, tonnes of oil equivalent (TOE), cannot appropriately deal with renewable and nuclear energy sources. While fossil fuels (coal, oil, natural gas) can relatively easily compared among each other by their thermal energy content, it is very difficult to compare renewable and nuclear energy sources. Therefore, most renewable energy sources and the energy in nuclear fuel rods (mostly, processed uranium) are measured by the amount of electricity generated by each source.
The prime example is Brazil. Because the country's electricity mostly comes from hydro power (87% of electricity production in 2000; 78% in 2010), the final to primary energy ratio has been number one until outranked by Canada, another heavyweight producer of hydro power (59% of electricity production in 2012).
I think this mixed dealing with fuel and non-fossil energy sources are distorting the overall energy transformation efficiency statistics. Or am I unaware of a simple solution of my frustration over this problem?





References:

Hussy, C. Klaassen, E., Koornneef, J., & Wigand, F. (2014). International Comparison of Fossil Power Efficiency and CO₂ Intensity - Update 2014. Utrecht, The Netherlands: Ecofys. [Full-text at http://j.mp/Powerplants_EE]

International Energy Agency. (2003–2014). Energy Balances of OECD Countries. Paris, France: IEA Publications.

International Energy Agency. (2003–2014). Energy Balances of Non-OECD Countries. Paris, France: IEA Publications.

International Energy Agency, & World Bank. (2014). Sustainable Energy for All 2013-2014: Global Tracking Framework Report.  Washington, DC: World Bank. [Full-text at http://j.mp/SE4All_13-14]

World Bank. (2014). Sustainable Energy for All. Washington, DC: The World Bank. [Data at http://j.mp/SE4ALL]

Cost of Energy Comparison, Including Levelized Cost of Energy (LCOE) - 2015 Update

I updated the list in a new post for the year of 2016. Please move to the post cited below.

Park, H. (2016). Cost of Energy Comparison, Including Levelized Cost of Energy (LCOE) - 2016 Update [Blog post]. Retrieved from http://j.mp/LCOE_2016

Female Thyroid Cancer Rates near Nuclear Power Plants

I think this is my third post about the risk of living near nuclear power plants. Previous two posts on this topic are:
(1) Exposure to Radiation Causes Birth Defects or Abnormal Sex Ratios http://j.mp/Dangerous_Nuclear
(2) Infant Cancer Rates near Nuclear Power Plants http://j.mp/Infants_vs_NPPs

Now, let's go back to female thyroid cancer. The study I am citing here is two years old. But I think the results could explain the peculiar increase in thyroid cancer incidents in South Korea. According to the study:

In South Korea,
“Thyroid cancer incidence in women in the exposed and Control-1 was as high as 2.5 and 1.8 times, respectively, than in Control-2 cohort. And the trend in risk was statistically significant (p for trend = 0.03).” (Ahn et al., 2012)
Note:
a. Exposed: the group living within a 5 km radius from the Nuclear Power Plants
b. Control-1: the inter-mediate proximity (5-30 km radius) group
c. Control-2: the far-distance (more than 30 km) group

It is NOT 2.5% or 1.8%, BUT 2.5 times and 1.8 times! If a woman is living within a 5 km radius from a nuclear power plant, she has 150% more possibility of getting thyroid cancer.

Thyroid cancer incidence among Korean females are definitely a serious concern. Let's see Table 1. Although the annual percentage change of overall cancer incidence was 5.4% over 1999-2011, that of thyroid cancer was unbelievably high 23.3%. If we are aware of the above study, researchers must find one of main causes of high thyroid cancer increase rate from the female patients' proximity to nuclear power plants.

Table 1. Trends in cancer incidence rates in females from 1999 to 2011 in Korea
Unit: Age-standardized incidence rate per 100,000

Site/Type	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	APC
All sites	61.1	157.4	169.0	174.6	184.3	193.4	207.8	16.4	232.0	249.0	263.0	273.6	286.2	5.4%
Bladder	1.6	1.6	1.7	1.7	1.8	1.7	1.7	1.6	1.7	1.6	1.6	1.5	1.5	-0.8%
Brain and CNS	2.6	2.5	2.5	2.4	2.5	2.6	2.8	2.7	2.9	2.7	2.6	2.7	2.4	0.3%
Breast	20.9	20.9	24.7	27.2	27.8	29.3	32.0	33.3	35.6	37.1	38.7	40.4	43.8	6.3%
Cervix uteri	16.3	15.1	15.8	14.8	14.2	13.1	12.4	12.2	11.1	11.5	10.6	10.8	10.1	-4.0%
Colon and rectum	16.4	16.4	17.9	18.8	20.5	21.5	23.0	24.1	24.5	25.1	26.3	26.0	26.4	4.5%
Corpus uteri	2.8	2.6	3.0	3.3	3.8	3.7	3.9	4.0	4.2	4.7	5.1	5.1	5.3	6.1%
Esophagus	0.6	0.6	0.6	0.5	0.6	0.5	0.4	0.5	0.5	0.5	0.4	0.4	0.4	-2.6%
Gallbladder	5.3	5.5	5.7	5.8	5.8	5.9	6.0	5.5	5.6	5.5	5.8	5.4	5.5	-0.1%
Hodgkin lymphoma	0.1	0.2	0.2	0.2	0.2	0.2	0.2	0.3	0.3	0.3	0.3	0.3	0.4	6.3%
Kidney	1.7	1.8	1.9	2.0	2.1	2.2	2.5	2.7	2.8	3.0	3.2	3.0	3.4	6.0%
Larynx	0.4	0.3	0.3	0.3	0.3	0.3	0.3	0.2	0.2	0.2	0.2	0.2	0.2	-7.6%
Leukemia	3.9	3.8	4.1	4.0	4.1	4.1	4.0	4.4	4.2	4.3	4.2	4.1	4.5	0.9%
Lip, oral cavity, and pharynx	1.6	2.4	1.7	1.7	1.7	1.9	1.9	1.8	1.9	1.9	1.8	2.2	2.1	1.2%
Liver	12.3	11.8	12.2	11.8	11.5	11.3	11.4	11.1	11.1	10.7	10.6	10.3	10.3	-1.5%
Lung	12.4	12.5	12.3	12.6	12.4	13.0	13.5	14.0	13.9	14.2	14.1	14.6	15.1	1.7%
Multiple myeloma	0.8	0.8	0.9	0.8	1.0	1.0	1.2	1.1	1.2	1.2	1.2	1.3	1.1	3.9%
Non-Hodgkin lymphoma	3.4	3.2	3.4	3.5	3.9	4.1	4.4	4.4	4.4	4.7	5.1	5.1	5.5	4.5%
Ovary	5.0	4.8	4.8	5.0	5.1	5.2	5.4	5.4	5.9	5.6	5.4	5.8	5.7	1.6%
Pancreas	4.0	4.0	4.0	4.2	4.5	4.5	4.7	4.7	4.8	4.9	4.9	5.0	5.1	2.2%
Stomach	26.7	25.2	26.2	26.3	25.9	24.7	26.8	25.1	24.8	25.1	25.7	25.2	25.1	-0.4%
Thyroid	10.4	10.1	13.2	16.2	21.8	29.5	35.3	43.3	55.6	69.4	80.6	88.6	96.8	23.3%
Other and ill-defined	11.8	11.5	11.8	11.5	12.8	13.1	13.9	14.1	14.8	14.9	14.8	15.5	15.7	3.0%

Source: Ahn et al. (2012)
Notes:
APC = annual percentage change (age-standardized)
CNS = central nervous system

Although there is no statistically significant evidence about overall cancer incident rate and proximity to nuclear power plants, I suspect that presence of nuclear power plants could be a clue how to explain Korea's skyrocketing cancer mortality, as manifested in the figure below. While the average OECD countries showed 15% decline in cancer mortality rates during the past two decades, South Korea showed 6% increase. It was the 2nd highest increase rate in the OECD.

According to an IAEA data, South Korea has the most number of nuclear reactors per unit land area (Table 2). Korean people have more chance of finding themselves living near a nuclear power plants than any other country in the world. South Korea might urgently need a comprehensive re-examination of the relationship between people's proximity to nuclear power plants and other kinds of cancer.

Maybe some people will ask a question,
"Why Belgium, the No. 2 country in terms of the reactor density per area in the world (Table 2), showed a decreasing cancer mortality rates in the figure below?"
I don't know. However, at least as for thyroid cancer, the country must be worried. In a recent study (Bollaerts et al., 2014), Belgian people living in the vicinity (20 km radius) of 3 nuclear sites (out of total 5 sites studied) have shown to have 15-47% more chance of getting thyroid cancer than the people living outside the radius. If the study has taken accounted for sex-specific incidence rates, female thyroid cancer rates might have been higher, I suspect.




Table 2. Reactors in operation, in long term shutdown, or under construction (as of December 31, 2013)

Country	Reactors in operation or long term shutdown	Reactors under construction	Total	Land area	Reactors per land area
	No. of units	N. of units	N. of units	km2	No. of units/ 1000 km2
Korea, South	23	5	28	97,100	0.2884
Belgium	7		7	30,280	0.2312
Taiwan	6	2	8	36,191	0.2210
Japan	49	2	51	364,500	0.1399
Switzerland	5		5	40,000	0.1250
Slovak Republic	4	2	6	48,091	0.1248
France	58	1	59	547,660	0.1077
Czech Republic	6		6	77,240	0.0777
United Kingdom	16		16	241,930	0.0661
Slovenia	1		1	20,140	0.0497
Hungary	4		4	90,530	0.0442
Armenia	1		1	28,480	0.0351
Netherlands	1		1	33,730	0.0296
Ukraine	15	2	17	579,320	0.0293
Germany	9		9	348,570	0.0258
Sweden	10		10	410,340	0.0244
United Arab Emirates	0	2	2	83,600	0.0239
Bulgaria	2		2	108,560	0.0184
Finland	4	1	5	303,900	0.0165
Spain	8		8	498,800	0.0160
United States	100	5	105	9,147,420	0.0115
India	21	6	27	2,973,190	0.0091
Romania	2		2	230,050	0.0087
Pakistan	3	2	5	770,880	0.0065
China	20	29	49	9,327,490	0.0053
Belarus	0	1	1	202,900	0.0049
World	436	72	508	129,710,379	0.0039
Russia	33	10	43	16,376,870	0.0026
Canada	19		19	9,093,510	0.0021
South Africa	2		2	1,213,090	0.0016
Argentina	2	1	3	2736690	0.0011
Mexico	2		2	1,943,950	0.0010
Iran	1		1	1,628,550	0.0006
Brazil	2	1	3	8,459,420	0.0004

Sources:
a. nuclear reactors: International Atomic Energy Agency (2014)
b. land area: The World Bank (2014)


References:

Ahn, Y.-O. et al. (2012). Cancer Risk in Adult Residents near Nuclear Power Plants in Korea - A Cohort Study of 1992-2010. Journal of Korean Medical Science, 27(9), 999-1008. [Full-text at http://dx.doi.org/10.3346/jkms.2012.27.9.999]

Bollaerts, K. et al. (2014). Thyroid cancer incidence in the vicinity of nuclear sites in Belgium, 2000–2008. Thyroid, 24(5), 906-917. [Full-text at http://dx.doi.org/10.1089/thy.2013.0227]

International Atomic Energy Agency. (2014). Nuclear Power Reactors in the World: 2014 Edition. Vienna, Austria: International Atomic Energy Agency. [Full-text at http://j.mp/NPRs_2014]

Jung, K.-W. et al. (2014). Cancer Statistics in Korea: Incidence, Mortality, Survival, and Prevalence in 2011. Cancer Research and Treatment, 46(2), 109-123. [Full-text at http://dx.doi.org/10.4143/crt.2014.46.2.109]

OECD. (2013). Health at a Glance 2013: OECD Indicators. Paris, France: OECD Publishing. [Full-text at http://dx.doi.org/10.1787/health_glance-2013-en]

The World Bank. (2014). World Development Indicators 2014. Washington, DC: The World Bank. [Data at http://j.mp/WDI_Data]

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]