Greenhouse gas emissions

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https://en.wikipedia.org/wiki/Greenhouse_gas_emissions 

Annual greenhouse gas emissions per person (height of vertical bars) and per country (area inside vertical bars)

 

In the highest-emitting countries, emission trends in recent decades sometimes diverge from longer-term historical trends.

Greenhouse gas emissions from human activities strengthen the greenhouse effect, causing climate change. Most is carbon dioxide from burning fossil fuels: coal, oil, and natural gas. The largest emitters include coal in China and large oil and gas companies, many state-owned by OPEC and Russia. Human-caused emissions have increased atmospheric carbon dioxide by about 50% over pre-industrial levels. The growing levels of emissions have varied, but it was consistent among all greenhouse gases. Emissions in the 2010s averaged 56 billion tons a year, higher than ever before.

Electricity generation and transport are major emitters, the largest single source being coal-fired power stations with 20% of GHG. Deforestation and other changes in land use also emit carbon dioxide and methane. The largest source of anthropogenic methane emissions is agriculture, closely followed by gas venting and fugitive emissions from the fossil-fuel industry. The largest agricultural methane source is livestock. Agricultural soils emit nitrous oxide partly due to fertilizers. Similarly, fluorinated gases from refrigerants play an outsized role in total human emissions.

At current emission rates averaging six and a half tonnes per person per year, before 2030 temperatures may have increased by 1.5 °C (2.7 °F) over pre-industrial levels, which is the limit for the G7 countries and aspirational limit of the Paris Agreement.

Measurements and calculations

Annual CO₂ emissions, total by country, not per capita (2017 data)

 

 

Global GHG Emissions by gas

Global greenhouse gas emissions are about 50 Gt per year (6.6t per person) and for 2019 have been estimated at 57 Gt CO2 eq including 5 Gt due to land use change. In 2019, approximately 34% [20 GtCO2-eq] of total net anthropogenic GHG emissions came from the energy supply sector, 24% [14 GtCO2-eq] from industry, 22% [13 GtCO2-eq]from agriculture, forestry and other land use (AFOLU), 15% [8.7 GtCO2-eq] from transport and 6% [3.3 GtCO2-eq] from buildings.

Carbon dioxide (CO₂), nitrous oxide (N
₂O), methane, three groups of fluorinated gases (sulfur hexafluoride (SF
₆), hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs)) are the major anthropogenic greenhouse gases, and are regulated under the Paris Agreement.

Although CFCs are greenhouse gases, they are regulated by the Montreal Protocol, which was motivated by CFCs' contribution to ozone depletion rather than by their contribution to global warming. Note that ozone depletion has only a minor role in greenhouse warming, though the two processes are sometimes confused in the media. In 2016, negotiators from over 170 nations meeting at the summit of the United Nations Environment Programme reached a legally binding accord to phase out hydrofluorocarbons (HFCs) in the Kigali Amendment to the Montreal Protocol.

There are several ways of measuring greenhouse gas emissions. Some variables that have been reported include:

• Definition of measurement boundaries: Emissions can be attributed geographically, to the area where they were emitted (the territory principle) or by the activity principle to the territory that produced the emissions. These two principles result in different totals when measuring, for example, electricity importation from one country to another, or emissions at an international airport.
• Time horizon of different gases: The contribution of given greenhouse gas is reported as a CO₂ equivalent. The calculation to determine this takes into account how long that gas remains in the atmosphere. This is not always known accurately and calculations must be regularly updated to reflect new information.
• The measurement protocol itself: This may be via direct measurement or estimation. The four main methods are the emission factor-based method, mass balance method, predictive emissions monitoring systems, and continuous emissions monitoring systems. These methods differ in accuracy, cost, and usability. Public information from space-based measurements of carbon dioxide by Climate Trace is expected to reveal individual large plants before the 2021 United Nations Climate Change Conference.

These measures are sometimes used by countries to assert various policy/ethical positions on climate change. The use of different measures leads to a lack of comparability, which is problematic when monitoring progress towards targets. There are arguments for the adoption of a common measurement tool, or at least the development of communication between different tools.

Emissions may be tracked over long time periods, known as historical or cumulative emissions measurements. Cumulative emissions provide some indicators of what is responsible for greenhouse gas atmospheric concentration build-up.

The national accounts balance tracks emissions based on the difference between a country's exports and imports. For many richer nations, the balance is negative because more goods are imported than they are exported. This result is mostly due to the fact that it is cheaper to produce goods outside of developed countries, leading developed countries to become increasingly dependent on services and not goods. A positive account balance would mean that more production was occurring within a country, so more operational factories would increase carbon emission levels.

Emissions may also be measured across shorter time periods. Emissions changes may, for example, be measured against the base year of 1990. 1990 was used in the United Nations Framework Convention on Climate Change (UNFCCC) as the base year for emissions, and is also used in the Kyoto Protocol (some gases are also measured from the year 1995). A country's emissions may also be reported as a proportion of global emissions for a particular year.

Another measurement is of per capita emissions. This divides a country's total annual emissions by its mid-year population. Per capita emissions may be based on historical or annual emissions.

While cities are sometimes considered to be disproportionate contributors to emissions, per-capita emissions tend to be lower for cities than the averages in their countries.

At current emission rates, before 2030 temperatures may have increased by 1.5 °C (2.7 °F) over pre-industrial levels, which is the limit for the G7 countries and aspirational limit of the Paris Agreement.

Sources

See also: Climate change and ecosystems

 

Modern global CO₂ emissions from the burning of fossil fuels.

Overview of main sources

Since about 1750, human activity has increased the concentration of carbon dioxide and other greenhouse gases. As of 2021, measured atmospheric concentrations of carbon dioxide were almost 50% higher than pre-industrial levels. Natural sources of carbon dioxide are more than 20 times greater than sources due to human activity, but over periods longer than a few years natural sources are closely balanced by natural sinks, mainly photosynthesis of carbon compounds by plants and marine plankton. Absorption of terrestrial infrared radiation by longwave absorbing gases makes Earth a less efficient emitter. Therefore, in order for Earth to emit as much energy as is absorbed, global temperatures must increase.

Burning fossil fuels is estimated to have emitted 62% of 2015 human GhG. The largest single source is coal-fired power stations, with 20% of GHG as of 2021.

The main sources of greenhouse gases due to human activity are:

• burning of fossil fuels and deforestation leading to higher carbon dioxide concentrations in the air.
• land use change (mainly deforestation in the tropics) accounts for about a quarter of total anthropogenic GHG emissions.
• livestock enteric fermentation and manure management, paddy rice farming, land use and wetland changes, man-made lakes, pipeline losses, and covered vented landfill emissions leading to higher methane atmospheric concentrations. Many of the newer style fully vented septic systems that enhance and target the fermentation process also are sources of atmospheric methane.
• use of chlorofluorocarbons (CFCs) in refrigeration systems, and use of CFCs and halons in fire suppression systems and manufacturing processes.
• agricultural activities, including the use of fertilizers, that lead to higher nitrous oxide (N
  ₂O) concentrations.

The seven sources of CO₂ from fossil fuel combustion are (with percentage contributions for 2000–2004):

• Liquid fuels (e.g., gasoline, fuel oil): 36%
• Solid fuels (e.g., coal): 35%
• Gaseous fuels (e.g., natural gas): 20%
• Cement production: 3 %
• Flaring gas industrially and at wells: 1%  
• Non-fuel hydrocarbons: 1%  
• "International bunker fuels" of transport not included in national inventories: 4%

The largest source of anthropogenic methane emissions is agriculture, closely followed by gas venting and fugitive emissions from the fossil-fuel industry. The largest agricultural methane source is livestock. Agricultural soils emit nitrous oxide partly due to fertilizers.

The major sources of Greenhouse gases (GHG) are:

• Land Use (CO2 emissions)
• Forestry (CO2-LULUCF)
• Nitrous Acid (N2O)
• Fluorinated gases (F-gases)
• Compromising hydrofluorocarbons (HFCs)
• Perfluorocarbons (PFCs)
• sulphur hexafluoride (SF6)
• nitrogen trifluoride (NF3)

A 2017 survey of corporations responsible for global emissions found that 100 companies were responsible for 71% of global direct and indirect emissions, and that state-owned companies were responsible for 59% of their emissions.

By socio-economic class

Fueled by the consumptive lifestyle of wealthy people, the wealthiest 5% of the global population has been responsible for 37% of the absolute increase in greenhouse gas emissions worldwide. Almost half of the increase in absolute global emissions has been caused by the richest 10% of the population. In the newest report from the IPCC 2022, it states that the lifestyle consumptions of the poor and middle class in emerging economies produce approximately 5–50 times less the amount that the high class in already developed high-income countries. Variations in regional, and national per capita emissions partly reflect different development stages, but they also vary widely at similar income levels. The 10% of households with the highest per capita emissions contribute a disproportionately large share of global household GHG emissions.

By energy source

Life-cycle greenhouse gas emissions of electricity supply technologies, median values calculated by IPCC

 

Life cycle CO₂ equivalent (including albedo effect) from selected electricity supply technologies according to IPCC 2014. Arranged by decreasing median (gCO₂eq/kWh) values.

Technology	Min.	Median	Max.
Currently commercially available technologies
Coal – PC	740	820	910
Gas – combined cycle	410	490	650
Biomass – Dedicated	130	230	420
Solar PV – Utility scale	18	48	180
Solar PV – rooftop	26	41	60
Geothermal	6.0	38	79
Concentrated solar power	8.8	27	63
Hydropower	1.0	24	22001
Wind Offshore	8.0	12	35
Nuclear	3.7	12	110
Wind Onshore	7.0	11	56
Pre‐commercial technologies
Ocean (Tidal and wave)	5.6	17	28

¹ see also environmental impact of reservoirs#Greenhouse gases.

Lifecycle GHG emissions, in g CO2 eq. per kWh, UNECE 2020

 

Lifecycle CO₂ emissions per kWh, EU28 countries, according to UNECE 2020.

Technology		gCO2eq/kWh
Hard coal	PC, without CCS	1000
	IGCC, without CCS	850
	SC, without CCS	950
	PC, with CCS	370
	IGCC, with CCS	280
	SC, with CCS	330
Natural gas	NGCC, without CCS	430
	NGCC, with CCS	130
Hydro	660 MW	150
	360 MW	11
Nuclear	average	5.1
CSP	tower	22
	trough	42
PV	poly-Si, ground-mounted	37
	poly-Si, roof-mounted	37
	CdTe, ground-mounted	12
	CdTe, roof-mounted	15
	CIGS, ground-mounted	11
	CIGS, roof-mounted	14
Wind	onshore	12
	offshore, concrete foundation	14
	offshore, steel foundation	13

List of acronyms:

• PC — pulverized coal
• CCS — carbon capture and storage
• IGCC — integrated gasification combined cycle
• SC — supercritical
• NGCC — natural gas combined cycle
• CSP — concentrated solar power
• PV — photovoltaic power

Relative CO₂ emission from various fuels

One liter of gasoline, when used as a fuel, produces 2.32 kg (about 1300 liters or 1.3 cubic meters) of carbon dioxide, a greenhouse gas. One US gallon produces 19.4 lb (1,291.5 gallons or 172.65 cubic feet).

The mass of carbon dioxide that is released when one MJ of energy is released from fuel can be estimated to a good approximation. For the chemical formula of diesel we use as an approximation C
_nH
_2n. Note that diesel is a mixture of different molecules. As carbon has a molar mass of 12 g/mol and hydrogen (atomic!) has a molar mass of about 1 g/mol, so the fraction by weight of carbon in diesel is roughly 12/14. The reaction of diesel combustion is given by:

2C
_nH
_2n + 3nO
₂ ⇌ 2nCO
₂ + 2nH
₂O

Carbon dioxide has a molar mass of 44g/mol as it consists of 2 atoms of oxygen (16 g/mol) and 1 atom of carbon (12 g/mol). So 12 g of carbon yield 44 g of Carbon dioxide. Diesel has an energy content of 42.6 MJ per kg or 23.47 gram of Diesel contain 1 MJ of energy. Putting everything together the mass of carbon dioxide that is produced by releasing 1MJ of energy from diesel fuel can be calculated as:

${\displaystyle 23.47\ \mathrm {g} {\text{ (Diesel)}}/\mathrm {MJ} \cdot {\frac {12}{14}}\cdot {\frac {44}{12}}=74\ \mathrm {g} {\text{ (carbon dioxide)}}/\mathrm {MJ} }$

For gasoline, with 22 g/MJ and a ratio of carbon to hydrogen atoms of about 6 to 14, the estimated value of carbon emission for 1MJ of energy is:

${\displaystyle 22\ \mathrm {g} {\text{ (gasoline)}}/\mathrm {MJ} \cdot {\frac {6\cdot 12}{6\cdot 12+14\cdot 1}}\cdot {\frac {44}{12}}=67.5\ \mathrm {g} {\text{ (carbon dioxide)}}/\mathrm {MJ} }$

Mass of carbon dioxide emitted per quantity of energy for various fuels

Fuel name	CO2 emitted (lbs/106 Btu)	CO2 emitted (g/MJ)	CO2 emitted (g/kWh)
Hydrogen gas	0	0.0	0.0
Natural gas	117	50.30	181.08
Liquefied petroleum gas	139	59.76	215.14
Propane	139	59.76	215.14
Aviation gasoline	153	65.78	236.81
Automobile gasoline	156	67.07	241.45
Kerosene	159	68.36	246.10
Fuel oil	161	69.22	249.19
Tires/tire derived fuel	189	81.26	292.54
Wood and wood waste	195	83.83	301.79
Coal (bituminous)	205	88.13	317.27
Coal (sub-bituminous)	213	91.57	329.65
Coal (lignite)	215	92.43	332.75
Petroleum coke	225	96.73	348.23
Coal (anthracite)	227	97.59	351.32

Emissions by sector

Greenhouse Gas Emissions by Economic Sector according to IPCC Fifth Assessment Report

 

2016 global greenhouse gas emissions by sector. Percentages are calculated from estimated global emissions of all Kyoto Greenhouse Gases, converted to CO₂ equivalent quantities (GtCO₂e).

Global greenhouse gas emissions can be attributed to different sectors of the economy. This provides a picture of the varying contributions of different types of economic activity to global warming, and helps in understanding the changes required to mitigate climate change.

Manmade greenhouse gas emissions can be divided into those that arise from the combustion of fuels to produce energy, and those generated by other processes. Around two thirds of greenhouse gas emissions arise from the combustion of fuels.

Energy may be produced at the point of consumption, or by a generator for consumption by others. Thus emissions arising from energy production may be categorized according to where they are emitted, or where the resulting energy is consumed. If emissions are attributed at the point of production, then electricity generators contribute about 25% of global greenhouse gas emissions. If these emissions are attributed to the final consumer then 24% of total emissions arise from manufacturing and construction, 17% from transportation, 11% from domestic consumers, and 7% from commercial consumers. Around 4% of emissions arise from the energy consumed by the energy and fuel industry itself.

The remaining third of emissions arise from processes other than energy production. 12% of total emissions arise from agriculture, 7% from land use change and forestry, 6% from industrial processes, and 3% from waste. Around 6% of emissions are fugitive emissions, which are waste gases released by the extraction of fossil fuels.

As of 2020 Secunda CTL is the world's largest single emitter, at 56.5 million tonnes CO₂ a year.

Agriculture

Agriculture contributes towards climate change through greenhouse gas emissions and by the conversion of non-agricultural land such as forests into agricultural land. In 2019 the IPCC reported that 13%-21% of anthropogenic greenhouse gasses came specifically from the Agriculture, Forestry, and Other Land Uses Sector (AFOLU). Emissions from agriculture of nitrous oxide, methane and carbon dioxide make up to half of the greenhouse-gases produced by the overall food industry, or 80% of agricultural emissions. Animal husbandry is a major source of greenhouse gas emissions.

The agricultural food system is responsible for a significant amount of greenhouse gas emissions. In addition to being a significant user of land and consumer of fossil fuel, agriculture contributes directly to greenhouse gas emissions through practices such as rice production and the raising of livestock. The three main causes of the increase in greenhouse gases observed over the past 250 years have been fossil fuels, land use, and agriculture. Farm animal digestive systems can be put into two categories: monogastric and ruminant. Ruminant cattle for beef and dairy rank high in greenhouse-gas emissions; monogastric, or pigs and poultry-related foods, are low. The consumption of the monogastric types may yield less emissions. Monogastric animals have a higher feed-conversion efficiency, and also do not produce as much methane.

There are many strategies that can be used to help soften the effects, and the further production of greenhouse gas emissions - this is also referred to as climate-smart agriculture. Some of these strategies include a higher efficiency in livestock farming, which includes management, as well as technology; a more effective process of managing manure; a lower dependence upon fossil-fuels and nonrenewable resources; a variation in the animals' eating and drinking duration, time and location; and a cutback in both the production and consumption of animal-sourced foods. A range of policies may reduce greenhouse gas emissions from the agriculture sector for a more sustainable food system.

Aviation

Approximately 3.5% of the overall human impacts on climate are from the aviation sector. The impact of the sector on climate in the late 20 years had doubled, but the part of the contribution of the sector in comparison to other sectors did not change because other sectors grew as well.

Buildings and construction

In 2018, manufacturing construction materials and maintaining buildings accounted for 39% of carbon dioxide emissions from energy and process-related emissions. Manufacture of glass, cement, and steel accounted for 11% of energy and process-related emissions. Because building construction is a significant investment, more than two-thirds of buildings in existence will still exist in 2050. Retrofitting existing buildings to become more efficient will be necessary to meet the targets of the Paris Agreement; it will be insufficient to only apply low-emission standards to new construction. Buildings that produce as much energy as they consume are called zero-energy buildings, while buildings that produce more than they consume are energy-plus. Low-energy buildings are designed to be highly efficient with low total energy consumption and carbon emissions—a popular type is the passive house.

The global design and construction industry is responsible for approximately 39 percent of greenhouse gas emissions. Green building practices that avoid emissions or capture the carbon already present in the environment, allow for reduced footprint of the construction industry, for example, use of hempcrete, cellulose fiber insulation, and landscaping.

In 2019, the building sector was responsible for 12 GtCO2-eq emissions. More than 95% of these emissions were carbon, and the remaining 5% were CH4 N20 and halocarbon.

Digital sector

Drip irrigation providing water to turmeric crop.

The digital sector produces between 2% and 4% of global GHG emissions, a large part of which is from chipmaking. However the sector reduces emissions from other sectors which have a larger global share, such as transport of people, and possibly buildings and industry.

Health care

The healthcare sector produces 4.4% - 4.6% of global greenhouse gas emissions.

Steel and aluminum

Steel and aluminum are key economic sectors for the carbon capture and storage. According to a 2013 study, "in 2004, the steel industry along emits about 590M tons of CO2, which accounts for 5.2% of the global anthropogenic GHG emissions. CO2 emitted from steel production primarily comes from energy consumption of fossil fuel as well as the use of limestone to purify iron oxides."

Electricity generation

See also: Life-cycle greenhouse gas emissions of energy sources

 

Global greenhouse gas emissions by gas.

Coal-fired power stations are the single largest emitter, with over 20% of global GhG in 2018. Although much less polluting than coal plants, natural gas-fired power plants are also major emitters, taking electricity generation as a whole over 25% in 2018. Notably, just 5% of the world's power plants account for almost three-quarters of carbon emissions from electricity generation, based on an inventory of more than 29,000 fossil-fuel power plants across 221 countries. In the 2022 IPCC report, it is noted that providing modern energy services universally would only increase greenhouse gas emissions by a few percent at most. This slight increase means that the additional energy demand that comes from supporting decent living standards for all would be far lower than current average energy consumption.

Plastics

Plastics are produced mainly from fossil fuels. It was estimated that between 3% and 4% of global GHG emissions are associated with plastics' life cycles. The EPA estimates as many as five mass units of carbon dioxide are emitted for each mass unit of polyethylene terephthalate (PET) produced—the type of plastic most commonly used for beverage bottles, the transportation produce greenhouse gases also. Plastic waste emits carbon dioxide when it degrades. In 2018 research claimed that some of the most common plastics in the environment release the greenhouse gases methane and ethylene when exposed to sunlight in an amount that can affect the earth climate.

Due to the lightness of plastic versus glass or metal, plastic may reduce energy consumption. For example, packaging beverages in PET plastic rather than glass or metal is estimated to save 52% in transportation energy, if the glass or metal package is single-use, of course.

In 2019 a new report "Plastic and Climate" was published. According to the report, the production and incineration of plastics will contribute in the equivalent of 850 million tonnes of carbon dioxide (CO₂) to the atmosphere in 2019. With the current trend, annual life cycle greenhouse gas emissions of plastics will grow to 1.34 billion tonnes by 2030. By 2050, the life cycle emissions of plastics could reach 56 billion tonnes, as much as 14 percent of the Earth's remaining carbon budget. The report says that only solutions which involve a reduction in consumption can solve the problem, while others like biodegradable plastic, ocean cleanup, using renewable energy in plastic industry can do little, and in some cases may even worsen it.

Sanitation sector

Wastewater as well as sanitation systems are known to contribute to greenhouse-gas emissions (GHG) mainly through the breakdown of excreta during the treatment process. This results in the generation of methane gas, that is then released into the environment. Emissions from the sanitation and wastewater sector have been focused mainly on treatment systems, particularly treatment plants, and this accounts for the bulk of the carbon footprint for the sector.

In as much as climate impacts from wastewater and sanitation systems present global risks, low-income countries experience greater risks in many cases. In recent years, attention to adaptation needs within the sanitation sector is just beginning to gain momentum.

Tourism

According to UNEP, global tourism is a significant contributor to the increasing concentrations of greenhouse gases in the atmosphere.

Trucking and haulage

Over a quarter of global transport CO₂ emissions are from road freight, so many countries are further restricting truck CO₂ emissions to help limit climate change.

Deforestation

Mean annual carbon loss from tropical deforestation.

 

Further information: Deforestation § Atmospheric, and Deforestation and climate change

Deforestation is a major source of greenhouse gas emissions. A study shows annual carbon emissions (or carbon loss) from tropical deforestation have doubled during the last two decades and continue to increase. (0.97 ±0.16 PgC per year in 2001–2005 to 1.99 ±0.13 PgC per year in 2015–2019)

Regional and national attribution of emissions

See also: Greenhouse gas inventory

From land-use change

Main article: Greenhouse gas emissions from agriculture

 

Substantial land-use change contributions to emissions have been made by Latin America, Southeast Asia, Africa, and Pacific Islands. Area of rectangles shows total emissions for that region.

Land-use change, e.g., the clearing of forests for agricultural use, can affect the concentration of greenhouse gases in the atmosphere by altering how much carbon flows out of the atmosphere into carbon sinks. Accounting for land-use change can be understood as an attempt to measure "net" emissions, i.e., gross emissions from all sources minus the removal of emissions from the atmosphere by carbon sinks.

There are substantial uncertainties in the measurement of net carbon emissions. Additionally, there is controversy over how carbon sinks should be allocated between different regions and over time. For instance, concentrating on more recent changes in carbon sinks is likely to favour those regions that have deforested earlier, e.g., Europe.

Greenhouse gas intensity

Greenhouse gas intensity is a ratio between greenhouse gas emissions and another metric, e.g., gross domestic product (GDP) or energy use. The terms "carbon intensity" and "emissions intensity" are also sometimes used. Emission intensities may be calculated using market exchange rates (MER) or purchasing power parity (PPP). Calculations based on MER show large differences in intensities between developed and developing countries, whereas calculations based on PPP show smaller differences. According to a study discussing the relationship between urbanization and carbon emissions, urbanization is becoming a huge player in the global carbon cycle. Depending on total carbon emissions done by a city that hasn't invested in carbon efficiency or improved resource management, the global carbon cycle is projected to reach 75% of the world population by 2030.

Cumulative and historical emissions

Cumulative CO2 emission by world region

 

Cumulative per person emissions by world region in 3 time periods

 

CO2 Emissions by Source Since 1880

Cumulative anthropogenic (i.e., human-emitted) emissions of CO₂ from fossil fuel use are a major cause of global warming, and give some indication of which countries have contributed most to human-induced climate change. In particular, CO₂ stays in the atmosphere for at least 150 years, whilst methane and nitrous oxides generally disappear within a decade or so. The graph gives some indication of which regions have contributed most to human-induced climate change. When these numbers are calculated per capita cumulative emissions based on then-current population the situation is shown even more clearly. The ratio in per capita emissions between industrialized countries and developing countries was estimated at more than 10 to 1.

Non-OECD countries accounted for 42% of cumulative energy-related CO₂ emissions between 1890 and 2007. Over this time period, the US accounted for 28% of emissions; the EU, 23%; Japan, 4%; other OECD countries 5%; Russia, 11%; China, 9%; India, 3%; and the rest of the world, 18%.

Overall, developed countries accounted for 83.8% of industrial CO₂ emissions over this time period, and 67.8% of total CO₂ emissions. Developing countries accounted for industrial CO₂ emissions of 16.2% over this time period, and 32.2% of total CO₂ emissions.

In comparison, humans have emitted more greenhouse gases than the Chicxulub meteorite impact event which caused the extinction of the dinosaurs.

Transport, together with electricity generation, is the major source of greenhouse gas emissions in the EU. Greenhouse gas emissions from the transportation sector continue to rise, in contrast to power generation and nearly all other sectors. Since 1990, transportation emissions have increased by 30%. The transportation sector accounts for around 70% of these emissions. The majority of these emissions are caused by passenger vehicles and vans. Road travel is the first major source of greenhouse gas emissions from transportation, followed by aircraft and maritime. Waterborne transportation is still the least carbon-intensive mode of transportation on average, and it is an essential link in sustainable multimodal freight supply chains.

Buildings, like industry, are directly responsible for around one-fifth of greenhouse gas emissions, primarily from space heating and hot water consumption. When combined with power consumption within buildings, this figure climbs to more than one-third.

Within the EU, the agricultural sector presently accounts for roughly 10% of total greenhouse gas emissions, with methane from livestock accounting for slightly more than half of 10%.

Estimates of total CO₂ emissions do include biotic carbon emissions, mainly from deforestation. Including biotic emissions brings about the same controversy mentioned earlier regarding carbon sinks and land-use change. The actual calculation of net emissions is very complex, and is affected by how carbon sinks are allocated between regions and the dynamics of the climate system.

Fossil fuel CO₂ emissions on a log (natural and base 10) scale

The graphic shows the logarithm of 1850–2019 fossil fuel CO₂ emissions; natural log on left, actual value of Gigatons per year on right. Although emissions increased during the 170-year period by about 3% per year overall, intervals of distinctly different growth rates (broken at 1913, 1945, and 1973) can be detected. The regression lines suggest that emissions can rapidly shift from one growth regime to another and then persist for long periods of time. The most recent drop in emissions growth - by almost 3 percentage points - was at about the time of the 1970s energy crisis. Percent changes per year were estimated by piecewise linear regression on the log data and are shown on the plot; the data are from The Integrated Carbon Observation system.

Changes since a particular base year

See also: Greenhouse gas inventory

The sharp acceleration in CO₂ emissions since 2000 to more than a 3% increase per year (more than 2 ppm per year) from 1.1% per year during the 1990s is attributable to the lapse of formerly declining trends in carbon intensity of both developing and developed nations. China was responsible for most of global growth in emissions during this period. Localised plummeting emissions associated with the collapse of the Soviet Union have been followed by slow emissions growth in this region due to more efficient energy use, made necessary by the increasing proportion of it that is exported. In comparison, methane has not increased appreciably, and N
₂O by 0.25% y⁻¹.

Using different base years for measuring emissions has an effect on estimates of national contributions to global warming. This can be calculated by dividing a country's highest contribution to global warming starting from a particular base year, by that country's minimum contribution to global warming starting from a particular base year. Choosing between base years of 1750, 1900, 1950, and 1990 has a significant effect for most countries. Within the G8 group of countries, it is most significant for the UK, France and Germany. These countries have a long history of CO₂ emissions (see the section on Cumulative and historical emissions).

Annual emissions

CO2 emissions vs GDP

Annual per capita emissions in the industrialized countries are typically as much as ten times the average in developing countries. Due to China's fast economic development, its annual per capita emissions are quickly approaching the levels of those in the Annex I group of the Kyoto Protocol (i.e., the developed countries excluding the US). Other countries with fast growing emissions are South Korea, Iran, and Australia (which apart from the oil rich Persian Gulf states, now has the highest per capita emission rate in the world). On the other hand, annual per capita emissions of the EU-15 and the US are gradually decreasing over time. Emissions in Russia and Ukraine have decreased fastest since 1990 due to economic restructuring in these countries.

Energy statistics for fast-growing economies are less accurate than those for industrialized countries.

The greenhouse gas footprint refers to the emissions resulting from the creation of products or services. It is more comprehensive than the commonly used carbon footprint, which measures only carbon dioxide, one of many greenhouse gases.

2015 was the first year to see both total global economic growth and a reduction of carbon emissions.

Top emitter countries

The top 40 countries emitting all greenhouse gases, showing both that derived from all sources including land clearance and forestry and also the CO₂ component excluding those sources. Per capita figures are included. "World Resources Institute data".. Note that Indonesia and Brazil show very much higher than on graphs simply showing fossil fuel use.

 

See also: List of countries by carbon dioxide emissions, List of countries by carbon dioxide emissions per capita, List of countries by greenhouse gas emissions, and List of countries by greenhouse gas emissions per person

Annual

In 2019, China, the United States, India, the EU27+UK, Russia, and Japan - the world's largest CO₂ emitters - together accounted for 51% of the population, 62.5% of global gross domestic product, 62% of total global fossil fuel consumption and emitted 67% of total global fossil CO₂. Emissions from these five countries and the EU28 show different changes in 2019 compared to 2018: the largest relative increase is found for China (+3.4%), followed by India (+1.6%). On the contrary, the EU27+UK (-3.8%), the United States (-2.6%), Japan (-2.1%) and Russia (-0.8%) reduced their fossil CO₂ emissions.

2019 Fossil CO₂ emissions by country

Country	total emissions (Mton)	Share (%)	per capita (ton)	per GDP (ton/k$)
Global Total	38,016.57	100.00	4.93	0.29
China	11,535.20	30.34	8.12	0.51
United States	5,107.26	13.43	15.52	0.25
EU27+UK	3,303.97	8.69	6.47	0.14
India	2,597.36	6.83	1.90	0.28
Russia	1,792.02	4.71	12.45	0.45
Japan	1,153.72	3.03	9.09	0.22
International Shipping	730.26	1.92	-	-
Germany	702.60	1.85	8.52	0.16
Iran	701.99	1.85	8.48	0.68
South Korea	651.87	1.71	12.70	0.30
International Aviation	627.48	1.65	-	-
Indonesia	625.66	1.65	2.32	0.20
Saudi Arabia	614.61	1.62	18.00	0.38
Canada	584.85	1.54	15.69	0.32
South Africa	494.86	1.30	8.52	0.68
Mexico	485.00	1.28	3.67	0.19
Brazil	478.15	1.26	2.25	0.15
Australia	433.38	1.14	17.27	0.34
Turkey	415.78	1.09	5.01	0.18
United Kingdom	364.91	0.96	5.45	0.12
Italy,  San Marino and the Holy See	331.56	0.87	5.60	0.13
Poland	317.65	0.84	8.35	0.25
France and  Monaco	314.74	0.83	4.81	0.10
Vietnam	305.25	0.80	3.13	0.39
Kazakhstan	277.36	0.73	14.92	0.57
Taiwan	276.78	0.73	11.65	0.23
Thailand	275.06	0.72	3.97	0.21
Spain and Andorra	259.31	0.68	5.58	0.13
Egypt	255.37	0.67	2.52	0.22
Malaysia	248.83	0.65	7.67	0.27
Pakistan	223.63	0.59	1.09	0.22
United Arab Emirates	222.61	0.59	22.99	0.34
Argentina	199.41	0.52	4.42	0.20
Iraq	197.61	0.52	4.89	0.46
Ukraine	196.40	0.52	4.48	0.36
Algeria	180.57	0.47	4.23	0.37
Netherlands	156.41	0.41	9.13	0.16
Philippines	150.64	0.40	1.39	0.16
Bangladesh	110.16	0.29	0.66	0.14
Venezuela	110.06	0.29	3.36	0.39
Qatar	106.53	0.28	38.82	0.41
Czechia	105.69	0.28	9.94	0.25
Belgium	104.41	0.27	9.03	0.18
Nigeria	100.22	0.26	0.50	0.10
Kuwait	98.95	0.26	23.29	0.47
Uzbekistan	94.99	0.25	2.90	0.40
Oman	92.78	0.24	18.55	0.67
Turkmenistan	90.52	0.24	15.23	0.98
Chile	89.89	0.24	4.90	0.20
Colombia	86.55	0.23	1.74	0.12
Romania	78.63	0.21	4.04	0.14
Morocco	73.91	0.19	2.02	0.27
Austria	72.36	0.19	8.25	0.14
Serbia and Montenegro	70.69	0.19	7.55	0.44
Israel and  Palestine	68.33	0.18	7.96	0.18
Belarus	66.34	0.17	7.03	0.37
Greece	65.57	0.17	5.89	0.20
Peru	56.29	0.15	1.71	0.13
Singapore	53.37	0.14	9.09	0.10
Hungary	53.18	0.14	5.51	0.17
Libya	52.05	0.14	7.92	0.51
Portugal	48.47	0.13	4.73	0.14
Myanmar	48.31	0.13	0.89	0.17
Norway	47.99	0.13	8.89	0.14
Sweden	44.75	0.12	4.45	0.08
Hong Kong	44.02	0.12	5.88	0.10
Finland	43.41	0.11	7.81	0.16
Bulgaria	43.31	0.11	6.20	0.27
North Korea	42.17	0.11	1.64	0.36
Ecuador	40.70	0.11	2.38	0.21
Switzerland and  Liechtenstein	39.37	0.10	4.57	0.07
New Zealand	38.67	0.10	8.07	0.18
Ireland	36.55	0.10	7.54	0.09
Slovakia	35.99	0.09	6.60	0.20
Azerbaijan	35.98	0.09	3.59	0.25
Mongolia	35.93	0.09	11.35	0.91
Bahrain	35.44	0.09	21.64	0.48
Bosnia and Herzegovina	33.50	0.09	9.57	0.68
Trinidad and Tobago	32.74	0.09	23.81	0.90
Tunisia	32.07	0.08	2.72	0.25
Denmark	31.12	0.08	5.39	0.09
Cuba	31.04	0.08	2.70	0.11
Syria	29.16	0.08	1.58	1.20
Jordan	28.34	0.07	2.81	0.28
Sri Lanka	27.57	0.07	1.31	0.10
Lebanon	27.44	0.07	4.52	0.27
Dominican Republic	27.28	0.07	2.48	0.14
Angola	25.82	0.07	0.81	0.12
Bolivia	24.51	0.06	2.15	0.24
Sudan and  South Sudan	22.57	0.06	0.40	0.13
Guatemala	21.20	0.06	1.21	0.15
Kenya	19.81	0.05	0.38	0.09
Croatia	19.12	0.05	4.62	0.16
Estonia	18.50	0.05	14.19	0.38
Ethiopia	18.25	0.05	0.17	0.07
Ghana	16.84	0.04	0.56	0.10
Cambodia	16.49	0.04	1.00	0.23
New Caledonia	15.66	0.04	55.25	1.67
Slovenia	15.37	0.04	7.38	0.19
Nepal	15.02	0.04	0.50	0.15
Lithuania	13.77	0.04	4.81	0.13
Côte d’Ivoire	13.56	0.04	0.53	0.10
Georgia	13.47	0.04	3.45	0.24
Tanzania	13.34	0.04	0.22	0.09
Kyrgyzstan	11.92	0.03	1.92	0.35
Panama	11.63	0.03	2.75	0.09
Afghanistan	11.00	0.03	0.30	0.13
Yemen	10.89	0.03	0.37	0.17
Zimbabwe	10.86	0.03	0.63	0.26
Honduras	10.36	0.03	1.08	0.19
Cameroon	10.10	0.03	0.40	0.11
Senegal	9.81	0.03	0.59	0.18
Luxembourg	9.74	0.03	16.31	0.14
Mozambique	9.26	0.02	0.29	0.24
Moldova	9.23	0.02	2.29	0.27
Costa Rica	8.98	0.02	1.80	0.09
North Macedonia	8.92	0.02	4.28	0.26
Tajikistan	8.92	0.02	0.96	0.28
Paraguay	8.47	0.02	1.21	0.09
Latvia	8.38	0.02	4.38	0.14
Benin	8.15	0.02	0.69	0.21
Mauritania	7.66	0.02	1.64	0.33
Zambia	7.50	0.02	0.41	0.12
Jamaica	7.44	0.02	2.56	0.26
Cyprus	7.41	0.02	6.19	0.21
El Salvador	7.15	0.02	1.11	0.13
Botswana	7.04	0.02	2.96	0.17
Brunei	7.02	0.02	15.98	0.26
Laos	6.78	0.02	0.96	0.12
Uruguay	6.56	0.02	1.89	0.09
Armenia	5.92	0.02	2.02	0.15
Curaçao	5.91	0.02	36.38	1.51
Nicaragua	5.86	0.02	0.92	0.17
Congo	5.80	0.02	1.05	0.33
Albania	5.66	0.01	1.93	0.14
Uganda	5.34	0.01	0.12	0.06
Namibia	4.40	0.01	1.67	0.18
Mauritius	4.33	0.01	3.41	0.15
Madagascar	4.20	0.01	0.16	0.09
Papua New Guinea	4.07	0.01	0.47	0.11
Iceland	3.93	0.01	11.53	0.19
Puerto Rico	3.91	0.01	1.07	0.04
Barbados	3.83	0.01	13.34	0.85
Burkina Faso	3.64	0.01	0.18	0.08
Haiti	3.58	0.01	0.32	0.18
Gabon	3.48	0.01	1.65	0.11
Equatorial Guinea	3.47	0.01	2.55	0.14
Réunion	3.02	0.01	3.40	-
Democratic Republic of the Congo	2.98	0.01	0.03	0.03
Guinea	2.92	0.01	0.22	0.09
Togo	2.85	0.01	0.35	0.22
Bahamas	2.45	0.01	6.08	0.18
Niger	2.36	0.01	0.10	0.08
Bhutan	2.12	0.01	2.57	0.24
Suriname	2.06	0.01	3.59	0.22
Martinique	1.95	0.01	5.07	-
Guadeloupe	1.87	0.00	4.17	-
Malawi	1.62	0.00	0.08	0.08
Guyana	1.52	0.00	1.94	0.20
Sierra Leone	1.40	0.00	0.18	0.10
Fiji	1.36	0.00	1.48	0.11
Palau	1.33	0.00	59.88	4.09
Macao	1.27	0.00	1.98	0.02
Liberia	1.21	0.00	0.24	0.17
Rwanda	1.15	0.00	0.09	0.04
Eswatini	1.14	0.00	0.81	0.11
Djibouti	1.05	0.00	1.06	0.20
Seychelles	1.05	0.00	10.98	0.37
Malta	1.04	0.00	2.41	0.05
Mali	1.03	0.00	0.05	0.02
Cabo Verde	1.02	0.00	1.83	0.26
Somalia	0.97	0.00	0.06	0.57
Maldives	0.91	0.00	2.02	0.09
Chad	0.89	0.00	0.06	0.04
Aruba	0.78	0.00	7.39	0.19
Eritrea	0.75	0.00	0.14	0.08
Lesotho	0.75	0.00	0.33	0.13
Gibraltar	0.69	0.00	19.88	0.45
French Guiana	0.61	0.00	2.06	-
French Polynesia	0.60	0.00	2.08	0.10
The Gambia	0.59	0.00	0.27	0.11
Greenland	0.54	0.00	9.47	0.19
Antigua and Barbuda	0.51	0.00	4.90	0.24
Central African Republic	0.49	0.00	0.10	0.11
Guinea-Bissau	0.44	0.00	0.22	0.11
Cayman Islands	0.40	0.00	6.38	0.09
Timor-Leste	0.38	0.00	0.28	0.10
Belize	0.37	0.00	0.95	0.14
Bermuda	0.35	0.00	5.75	0.14
Burundi	0.34	0.00	0.03	0.04
Saint Lucia	0.30	0.00	1.65	0.11
Western Sahara	0.30	0.00	0.51	-
Grenada	0.23	0.00	2.10	0.12
Comoros	0.21	0.00	0.25	0.08
Saint Kitts and Nevis	0.19	0.00	3.44	0.14
São Tomé and Príncipe	0.16	0.00	0.75	0.19
Saint Vincent and the Grenadines	0.15	0.00	1.32	0.11
Samoa	0.14	0.00	0.70	0.11
Solomon Islands	0.14	0.00	0.22	0.09
Tonga	0.13	0.00	1.16	0.20
Turks and Caicos Islands	0.13	0.00	3.70	0.13
British Virgin Islands	0.12	0.00	3.77	0.17
Dominica	0.10	0.00	1.38	0.12
Vanuatu	0.09	0.00	0.30	0.09
Saint Pierre and Miquelon	0.06	0.00	9.72	-
Cook Islands	0.04	0.00	2.51	-
Falkland Islands	0.03	0.00	10.87	-
Kiribati	0.03	0.00	0.28	0.13
Anguilla	0.02	0.00	1.54	0.12
Saint Helena,  Ascension and  Tristan da Cunha	0.02	0.00	3.87	-
Faroes	0.00	0.00	0.04	0.00

Embedded emissions

One way of attributing greenhouse gas emissions is to measure the embedded emissions (also referred to as "embodied emissions") of goods that are being consumed. Emissions are usually measured according to production, rather than consumption. For example, in the main international treaty on climate change (the UNFCCC), countries report on emissions produced within their borders, e.g., the emissions produced from burning fossil fuels. Under a production-based accounting of emissions, embedded emissions on imported goods are attributed to the exporting, rather than the importing, country. Under a consumption-based accounting of emissions, embedded emissions on imported goods are attributed to the importing country, rather than the exporting, country.

Davis and Caldeira (2010) found that a substantial proportion of CO₂ emissions are traded internationally. The net effect of trade was to export emissions from China and other emerging markets to consumers in the US, Japan, and Western Europe.

Fiscal decentralisation and carbon reductions

As carbon oxides are one important source of greenhouse gas, having means to reduce it is important. One suggestion, is to consider some means in relation to fiscal decentralisation. Previous research found that the linear term of fiscal decentralization promotes carbon emissions, while the non-linear term mitigates it. It verified the inverted U-shaped curve between fiscal decentralization and carbon emissions. Besides, increasing energy prices for non-renewable energy decrease carbon emission due to a substitution effect. Among other explanatory variables, improvement in the quality of institutions decreases carbon emissions, while the gross domestic product increases it. Strengthening fiscal decentralization, lowering non-renewable energy prices, and improving institutional quality to check the deteriorating environmental quality in the study sample and other worldwide regions can reduce carbon emissions.

Effect of policy

See also: Energy policy

Governments have taken action to reduce greenhouse gas emissions to mitigate climate change. Assessments of policy effectiveness have included work by the Intergovernmental Panel on Climate Change, International Energy Agency, and United Nations Environment Programme. Policies implemented by governments have included national and regional targets to reduce emissions, promoting energy efficiency, and support for a renewable energy transition, such as Solar energy, as an effective use of renewable energy because solar uses energy from the sun and does not release pollutants into the air.

Countries and regions listed in Annex I of the United Nations Framework Convention on Climate Change (UNFCCC) (i.e., the OECD and former planned economies of the Soviet Union) are required to submit periodic assessments to the UNFCCC of actions they are taking to address climate change.

Due to the COVID-19 pandemic, there was a significant reduction in CO₂ emissions globally in 2020.

In 2020, carbon dioxide (CO2) reductions were at an all-time low since World War II. However, by December 2020, carbon emissions surpassed those in 2019 by 2%.

Projections

See also: Carbon budget

Global CO₂ emissions and probabilistic temperature outcomes of different policies

Futures studies
Concepts
Accelerating change Cashless society Existential risk Future Earth Mathematics Race Climate Space exploration Universe Kardashev scale Moore's law Resource depletion Singularity Speculative evolution
Techniques
Backcasting Causal layered analysis Chain-linked model Consensus forecast Cross impact analysis Delphi Real-time Delphi Foresight Future-proof Futures wheel Futures workshops Horizon scanning Reference class forecasting Scenario planning Systems analysis Threatcasting Trend analysis
Technology assessment and forecasting
Critical design Design fiction Exploratory engineering Hype cycle Science fiction prototyping Speculative design Technology readiness level Technology scouting

Climate change scenarios or socioeconomic scenarios are projections of future greenhouse gas (GHG) emissions used by analysts to assess future vulnerability to climate change. Scenarios and pathways are created by scientists to survey any long term routes and explore the effectiveness of mitigation and helps us understand what the future may hold this will allow us to envision the future of human environment system. Producing scenarios requires estimates of future population levels, economic activity, the structure of governance, social values, and patterns of technological change. Economic and energy modelling (such as the World3 or the POLES models) can be used to analyze and quantify the effects of such drivers.

Scientists can develop separate international, regional and national climate change scenarios. These scenarios are designed to help stakeholders understand what kinds of decisions will have meaningful effects on climate change mitigation or adaptation. Most countries developing adaptation plans or Nationally Determined Contributions will commission scenario studies in order to better understand the decisions available to them.

International goals for mitigating climate change through international processes like the Intergovernmental Panel on Climate Change (IPCC), the Paris Agreement and Sustainable Development Goal 13 ("Take urgent action to combat climate change and its impacts") are based on reviews of these scenarios. For example, the Special Report on Global Warming of 1.5 °C was released in 2018 order to reflect more up-to-date models of emissions, Nationally Determined Contributions, and impacts of climate change than its predecessor IPCC Fifth Assessment Report published in 2014 before the Paris Agreement.

Rogue wave

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Rogue_wave

A merchant ship labouring in heavy seas as a large wave looms ahead, Bay of Biscay, ca. 1940

Rogue waves (also known as freak waves, monster waves, episodic waves, killer waves, extreme waves, and abnormal waves) are unusually large, unpredictable and suddenly appearing surface waves that can be extremely dangerous to ships, even to large ones. They are distinct from tsunamis, which are often almost unnoticeable in deep waters and are caused by the displacement of water due to other phenomena (such as earthquakes). A rogue wave appearing at the shore is sometimes referred to as a sneaker wave.

In oceanography, rogue waves are more precisely defined as waves whose height is more than twice the significant wave height (H_s or SWH), which is itself defined as the mean of the largest third of waves in a wave record. Therefore, rogue waves are not necessarily the biggest waves found on the water; they are, rather, unusually large waves for a given sea state. Rogue waves seem not to have a single distinct cause, but occur where physical factors such as high winds and strong currents cause waves to merge to create a single exceptionally large wave.

Rogue waves can occur in media other than water. They appear to be ubiquitous in nature and have also been reported in liquid helium, in quantum mechanics, in nonlinear optics, in microwave cavities, in Bose–Einstein condensation, in heat and diffusion, and in finance.

A 2012 study supported the existence of oceanic rogue holes, the inverse of rogue waves, where the depth of the hole can reach more than twice the significant wave height. Rogue holes have been replicated in experiments using water wave tanks, but have not been confirmed in the real world.

Background

Although commonly described as a tsunami, the titular wave in The Great Wave off Kanagawa by Hokusai is more likely an example of a large rogue wave.

Rogue waves are an open-water phenomenon, in which winds, currents, non-linear phenomena such as solitons, and other circumstances cause a wave to briefly form that is far larger than the "average" large wave (the significant wave height or "SWH") of that time and place. The basic underlying physics that makes phenomena such as rogue waves possible is that different waves can travel at different speeds, and so they can "pile up" in certain circumstances, known as "constructive interference". (In deep ocean the speed of a gravity wave is proportional to the square root of its wavelength, the peak-to-peak distance between adjacent waves.) However, other situations can also give rise to rogue waves, particularly situations where non-linear effects or instability effects can cause energy to move between waves and be concentrated in one or very few extremely large waves before returning to "normal" conditions.

Once considered mythical and lacking hard evidence for their existence, rogue waves are now proven to exist and known to be a natural ocean phenomenon. Eyewitness accounts from mariners and damage inflicted on ships have long suggested that they occur, however the first scientific evidence of their existence came with the recording of a rogue wave by the Gorm platform in the central North Sea in 1984. A stand-out wave was detected with a wave height of 11 metres (36 ft) in a relatively low sea state. However, what caught the attention of the scientific community was the digital measurement of a rogue wave at the Draupner platform in the North Sea on January 1, 1995; called the "Draupner wave", it had a recorded maximum wave height of 25.6 metres (84 ft) and peak elevation of 18.5 metres (61 ft). During that event, minor damage was inflicted on the platform far above sea level, confirming the validity of the reading made by a down-pointing laser sensor.

Their existence has also since been confirmed by video and photographs, satellite imagery, radar of the ocean surface, stereo wave imaging systems, pressure transducers on the sea-floor, and oceanographic research vessels. In February 2000, a British oceanographic research vessel, the RRS Discovery, sailing in the Rockall Trough west of Scotland, encountered the largest waves ever recorded by any scientific instruments in the open ocean, with a SWH of 18.5 metres (61 ft) and individual waves up to 29.1 metres (95 ft). "In 2004 scientists using three weeks of radar images from European Space Agency satellites found ten rogue waves, each 25 metres (82 ft) or higher."

A rogue wave is a natural ocean phenomenon that is not caused by land movement, only lasts briefly, occurs in a limited location, and most often happens far out at sea. Rogue waves are considered rare but potentially very dangerous, since they can involve the spontaneous formation of massive waves far beyond the usual expectations of ship designers, and can overwhelm the usual capabilities of ocean-going vessels which are not designed for such encounters. Rogue waves are, therefore, distinct from tsunamis. Tsunamis are caused by a massive displacement of water, often resulting from sudden movements of the ocean floor, after which they propagate at high speed over a wide area. They are nearly unnoticeable in deep water and only become dangerous as they approach the shoreline and the ocean floor becomes shallower; therefore, tsunamis do not present a threat to shipping at sea (e.g., The only ships lost in the 2004 Asian tsunami were in port.). They are also distinct from megatsunamis, which are single massive waves caused by sudden impact, such as meteor impact or landslides within enclosed or limited bodies of water. They are also different from the waves described as "hundred-year waves", which are a purely statistical prediction of the highest wave likely to occur in a hundred-year period in a particular body of water.

Rogue waves have now been proven to be the cause of the sudden loss of some ocean-going vessels. Well-documented instances include the freighter MS München, lost in 1978. Rogue waves have been implicated in the loss of other vessels, including the Ocean Ranger, a semi-submersible mobile offshore drilling unit that sank in Canadian waters on 15 February 1982. In 2007 the United States' National Oceanic and Atmospheric Administration (NOAA) compiled a catalogue of more than 50 historical incidents probably associated with rogue waves.

History of rogue wave knowledge

Early reports

In 1826, French scientist and naval officer Captain Jules Dumont d'Urville reported waves as high as 33 metres (108 ft) in the Indian Ocean with three colleagues as witnesses, yet he was publicly ridiculed by fellow scientist François Arago. In that era it was widely held that no wave could exceed 9 metres (30 ft). Author Susan Casey wrote that much of that disbelief came because there were very few people who had seen a rogue wave and survived; until the advent of steel double-hulled ships of the 20th century "people who encountered 100-foot [30 m] rogue waves generally weren't coming back to tell people about it."

Pre-1995 research

Unusual waves have been studied scientifically for many years (for example, John Scott Russell's wave of translation, an 1834 study of a soliton wave), but these were not linked conceptually to sailors' stories of encounters with giant rogue ocean waves, as the latter were believed to be scientifically implausible.

Since the 19th century, oceanographers, meteorologists, engineers and ship designers have used a statistical model known as the Gaussian function (or Gaussian Sea or standard linear model) to predict wave height, on the assumption that wave heights in any given sea are tightly grouped around a central value equal to the average of the largest third, known as the significant wave height. In a storm sea with a significant wave height of 12 metres (39 ft), the model suggests there will hardly ever be a wave higher than 15 metres (49 ft). It suggests one of 30 metres (98 ft) could indeed happen – but only once in ten thousand years. This basic assumption was well accepted, though acknowledged to be an approximation. The use of a Gaussian form to model waves had been the sole basis of virtually every text on that topic for the past 100 years.

The first known scientific article on "Freak waves" was written by Professor Laurence Draper in 1964. In that paper, he documented the efforts of the National Institute of Oceanography in the early 1960s to record wave height, and the highest wave recorded at that time, which was about 20 metres (67 ft). Draper also described freak wave holes.

However, even as late as the mid-1990s, most popular texts on oceanography such as that by Pirie did not contain any mention of rogue or freak waves. Even after the 1995 Draupner wave, the popular text on Oceanography by Gross (1996) only gave rogue waves a mention and simply stated that "Under extraordinary circumstances unusually large waves called rogue waves can form" without providing any further detail.

The 1995 Draupner wave

Measured amplitude graph showing the Draupner wave (spike in the middle)

The Draupner wave (or New Year's wave) was the first rogue wave to be detected by a measuring instrument. The wave was recorded in 1995 at Unit E of the Draupner platform, a gas pipeline support complex located in the North Sea about 160 kilometres (100 mi) southwest from the southern tip of Norway.

The rig was built to withstand a calculated 1-in-10,000-years wave with a predicted height of 20 metres (64 ft) and was fitted with a state-of-the-art set of sensors, including a laser rangefinder wave recorder on the platform's underside. At 3 pm on 1 January 1995, the device recorded a rogue wave with a maximum wave height of 25.6 metres (84 ft). Peak elevation above still water level was 18.5 metres (61 ft). The reading was confirmed by the other sensors. The platform sustained minor damage in the event.

In the area, the significant wave height was approximately 12 metres (39 ft), so the Draupner wave was more than twice as tall and steep as its neighbors, with characteristics that fell outside any known wave model. The wave caused enormous interest in the scientific community.

Subsequent research

Following the evidence of the Draupner wave, research in the area became widespread.

The first scientific study to comprehensively prove that freak waves exist, which are clearly outside the range of Gaussian waves, was published in 1997. Some research confirms that observed wave height distribution in general follows well the Rayleigh distribution, but in shallow waters during high energy events, extremely high waves are more rare than this particular model predicts. From about 1997 most leading authors acknowledged the existence of rogue waves with the caveat that wave models had been unable to replicate rogue waves.

Statoil researchers presented a paper in 2000, collating evidence that freak waves were not the rare realizations of a typical or slightly non-gaussian sea surface population (classical extreme waves), but rather they were the typical realizations of a rare and strongly non-gaussian sea surface population of waves (freak extreme waves). A workshop of leading researchers in the world attended the first Rogue Waves 2000 workshop held in Brest in November 2000.

In 2000 the British oceanographic vessel RRS Discovery recorded a 29-metre (95 ft) wave off the coast of Scotland near Rockall. This was a scientific research vessel fitted with high-quality instruments. Subsequent analysis determined that under severe gale force conditions with wind speeds averaging 21 metres per second (41 kn) a ship-borne wave recorder measured individual waves up to 29.1 metres (95.5 ft) from crest to trough, and a maximum significant wave height of 18.5 metres (60.7 ft). These were some of the largest waves recorded by scientific instruments up to that time. The authors noted that modern wave prediction models are known to significantly under-predict extreme sea states for waves with a significant height (H_s) above 12 metres (39.4 ft). The analysis of this event took a number of years, and noted that "none of the state-of-the-art weather forecasts and wave models – the information upon which all ships, oil rigs, fisheries, and passenger boats rely – had predicted these behemoths." Put simply, a scientific model (and also ship design method) to describe the waves encountered did not exist. This finding was widely reported in the press, which reported that "according to all of the theoretical models at the time under this particular set of weather conditions waves of this size should not have existed".

In 2004 the ESA MaxWave project identified more than ten individual giant waves above 25 metres (82 ft) in height during a short survey period of three weeks in a limited area of the South Atlantic. The ESA's ERS satellites have helped to establish the widespread existence of these "rogue" waves. By 2007, it was further proven via satellite radar studies that waves with crest to trough heights of 20 to 30 metres (66 to 98 ft), occur far more frequently than previously thought. It is now known that rogue waves occur in all of the world's oceans many times each day.

It is now well accepted that rogue waves are a common phenomenon. Professor Akhmediev of the Australian National University has stated that there are about 10 rogue waves in the world's oceans at any moment. Some researchers have speculated that approximately 3 of every 10,000 waves on the oceans achieve rogue status, yet in certain spots – like coastal inlets and river mouths – these extreme waves can make up 3 out of every 1,000 waves, because wave energy can be focused.

Rogue waves may also occur in lakes. A phenomenon known as the "Three Sisters" is said to occur in Lake Superior when a series of three large waves forms. The second wave hits the ship's deck before the first wave clears. The third incoming wave adds to the two accumulated backwashes and suddenly overloads the ship deck with tons of water. The phenomenon is one of various theorized causes of the sinking of the SS Edmund Fitzgerald on Lake Superior in November 1975.

In reference to extreme events, rogue waves and soliton theory

These are considered to be the most important discoveries in the twentieth and twenty first centuries mathematical and experimental physics.

Optical sciences group, Australian National University

Serious studies of the phenomenon of rogue waves only started after the 1995 Draupner wave and have intensified since about 2005. One of the remarkable features of the rogue waves is that they always appear from nowhere and quickly disappear without a trace. Recent research has suggested that there could also be "super-rogue waves", which are up to five times the average sea state. Rogue wave has now become a near-universal term used by scientists to describe isolated large-amplitude waves that occur more frequently than expected for normal, Gaussian-distributed, statistical events. Rogue waves appear to be ubiquitous in nature and are not limited to the oceans. They appear in other contexts and have recently been reported in liquid helium, in nonlinear optics, and in microwave cavities. It is now universally accepted by marine researchers that these waves belong to a specific kind of sea wave, not taken into account by conventional models for sea wind waves.

In 2012, researchers at the Australian National University proved the existence of rogue wave holes, an inverted profile of a rogue wave. Their research created rogue wave holes on the water surface, in a water wave tank. In maritime folklore, stories of rogue holes are as common as stories of rogue waves. They follow from theoretical analysis but had never been proven experimentally.

A 2015 paper studied the wave behavior around a rogue wave, including optical, and the Draupner wave, and concluded that "rogue events do not necessarily appear without a warning, but are often preceded by a short phase of relative order".

In 2019, researchers succeeded in producing a wave with similar characteristics to the Draupner wave (steepness and breaking), and proportionately greater height, using multiple wavetrains meeting at an angle of 120 degrees. Previous research had strongly suggested that the wave resulted from an interaction between waves from different directions ("crossing seas"). Their research also highlighted that wave-breaking behavior was not necessarily as expected. If waves met at an angle less than about 60 degrees, then the top of the wave "broke" sideways and downwards (a "plunging breaker"). But from about 60 degrees and greater, the wave began to break vertically upwards, creating a peak that did not reduce the wave height as usual, but instead increased it (a "vertical jet"). They also showed that the steepness of rogue waves could be reproduced in this manner. Finally, they observed that optical instruments such as the laser used for the Draupner wave might be somewhat confused by the spray at the top of the wave, if it broke, and this could lead to uncertainties of around 1 to 1.5 metres (3 to 5 ft) in the wave height. They concluded "that the onset and type of wave breaking play a significant role and differ significantly for crossing and non-crossing waves. Crucially, breaking becomes less crest-amplitude limiting for sufficiently large crossing angles and involves the formation of near-vertical jets".

Images from the 2019 simulation of the Draupner wave, showing how the steepness of the wave forms, and how the crest of a rogue wave breaks, when waves cross at different angles. (Click image for full resolution)

• In the first row (0 degrees), the crest breaks horizontally and plunges, limiting the wave size.
• In the middle row (60 degrees), there is somewhat upward lifted breaking behavior
• In the third row (120 degrees), described as the most accurate simulation achieved of the Draupner wave, the wave breaks upward, as a vertical jet, and the wave crest height is not limited by breaking.

Research efforts

There are a number of research programmes currently underway focussed on rogue waves, including:

• In the course of Project MaxWave, researchers from the GKSS Research Centre, using data collected by ESA satellites, identified a large number of radar signatures that have been portrayed as evidence for rogue waves. Further research is under way to develop better methods of translating the radar echoes into sea surface elevation, but at present this technique is not proven.
• The Australian National University, working in collaboration with Hamburg University of Technology and the University of Turin, have been conducting experiments in nonlinear dynamics to try to explain so-called rogue or killer waves. The "Lego Pirate" video has been widely used and quoted to describe what they call 'super rogue waves' which their research suggests can be up to five times bigger than the other waves around them.
• European Space Agency continues to do research into rogue waves by radar satellite.
• United States Naval Research Laboratory, the science arm of the Navy and Marine Corps published results of their modelling work in 2015.
• Massachusetts Institute of Technology. Research in this field is ongoing. Two researchers at the Massachusetts Institute of Technology partially supported by the Naval Engineering Education Consortium (NEEC) have considered the problem of short-term prediction of rare, extreme water waves and have developed and published their research on an effective predictive tool of about 25 wave periods. This tool can give ships and their crews a two-to-three minute warning of potentially catastrophic impact allowing crew some time to shut down essential operations on a ship (or offshore platform). The authors cite landing on an aircraft carrier as a prime example.
• University of Colorado and the University of Stellenbosch.
• Kyoto University.
• Swinburne University of Technology in Australia recently published work on the probabilities of rogue waves.
• University of Oxford. The Department of Engineering Science published a comprehensive review of the science of rogue waves in 2014. In 2019, A team from the Universities of Oxford and Edinburgh recreated the Draupner wave in a lab.
• University of Western Australia.
• Tallinn University of Technology in Estonia.
• Extreme Seas Project funded by the EU.
• Umeå University. A research group at the Umeå University in Sweden in August 2006 showed that normal stochastic wind driven waves can suddenly give rise to monster waves. The nonlinear evolution of the instabilities was investigated by means of direct simulations of the time-dependent system of nonlinear equations.
• Great Lakes Environmental Research Laboratory. GLERL did research in 2002 which dispelled the long-held contentions that rogue waves were of rare occurrence.
• University of Oslo. Has conducted research into: Crossing sea state and rogue wave probability during the Prestige accident; Nonlinear wind-waves, their modification by tidal currents, and application to Norwegian coastal waters; General Analysis of Realistic Ocean Waves (GROW); Modelling of currents and waves for sea structures and extreme wave events; Rapid computations of steep surface waves in three dimensions, and comparison with experiments; and Very large internal waves in the ocean.
• National Oceanography Centre in the United Kingdom.
• Scripps Institute of Oceanography in the United States.
• Ritmare project in Italy.

Causes

Because the phenomenon of rogue waves is still a matter of active research, it is premature to state clearly what the most common causes are or whether they vary from place to place. The areas of highest predictable risk appear to be where a strong current runs counter to the primary direction of travel of the waves; the area near Cape Agulhas off the southern tip of Africa is one such area; the warm Agulhas Current runs to the southwest, while the dominant winds are westerlies. However, since this thesis does not explain the existence of all waves that have been detected, several different mechanisms are likely, with localized variation. Suggested mechanisms for freak waves include the following:

Diffractive focusing

According to this hypothesis, coast shape or seabed shape directs several small waves to meet in phase. Their crest heights combine to create a freak wave.

Focusing by currents

Waves from one current are driven into an opposing current. This results in shortening of wavelength, causing shoaling (i.e., increase in wave height), and oncoming wave trains to compress together into a rogue wave. This happens off the South African coast, where the Agulhas Current is countered by westerlies.

Nonlinear effects (modulational instability)

It seems possible to have a rogue wave occur by natural, nonlinear processes from a random background of smaller waves. In such a case, it is hypothesized, an unusual, unstable wave type may form which 'sucks' energy from other waves, growing to a near-vertical monster itself, before becoming too unstable and collapsing shortly after. One simple model for this is a wave equation known as the nonlinear Schrödinger equation (NLS), in which a normal and perfectly accountable (by the standard linear model) wave begins to 'soak' energy from the waves immediately fore and aft, reducing them to minor ripples compared to other waves. The NLS can be used in deep water conditions. In shallow water, waves are described by the Korteweg–de Vries equation or the Boussinesq equation. These equations also have non-linear contributions and show solitary-wave solutions. A small-scale rogue wave consistent with the nonlinear Schrödinger equation (the Peregrine Solution) was produced in a laboratory water tank in 2011. In particular, the study of solitons, and especially Peregrine solitons, have supported the idea that non-linear effects could arise in bodies of water.

Normal part of the wave spectrum

Some studies argue that many waves classified as rogue waves (with the sole condition that they exceed twice the significant wave height) are not freaks, but just rare, random samples of the wave height distribution, and are as such statistically expected to occur at a rate of about 1 rogue wave every 28 hours. This is commonly discussed as the question "Freak Waves: Rare Realizations of a Typical Population Or Typical Realizations of a Rare Population?" According to this hypothesis, most real-world encounters with unusually large waves can be explained by linear wave theory (or weakly nonlinear modifications thereof), without the need for special mechanisms like the modulational instability. Recent studies analyzing billions of wave measurements by wave buoys demonstrate that rogue wave occurrence rates in the ocean can be explained with linear theory when the finite spectral bandwidth of the wave spectrum is taken into account. However, it is not yet known whether weakly nonlinear dynamics can explain even the largest rogue waves (such as those exceeding 3 times the significant wave height, which would be exceedingly rare in linear theory). This has also lead to some criticism questioning whether defining rogue waves using only their relative height is meaningful in practice.

Constructive interference of elementary waves

Rogue waves can result from the constructive interference (dispersive and directional focusing) of elementary 3D waves enhanced by nonlinear effects.

Wind wave interactions

While it is unlikely that wind alone can generate a rogue wave, its effect combined with other mechanisms may provide a fuller explanation of freak wave phenomena. As wind blows over the ocean, energy is transferred to the sea surface. When strong winds from a storm happen to blow in the opposing direction of the ocean current the forces might be strong enough to randomly generate rogue waves. Theories of instability mechanisms for the generation and growth of wind waves – although not on the causes of rogue waves – are provided by Phillips and Miles.

The spatio-temporal focusing seen in the NLS equation can also occur when the nonlinearity is removed. In this case, focusing is primarily due to different waves coming into phase, rather than any energy transfer processes. Further analysis of rogue waves using a fully nonlinear model by R. H. Gibbs (2005) brings this mode into question, as it is shown that a typical wavegroup focuses in such a way as to produce a significant wall of water, at the cost of a reduced height.

A rogue wave, and the deep trough commonly seen before and after it, may last only for some minutes before either breaking, or reducing in size again. Apart from one single rogue wave, the rogue wave may be part of a wave packet consisting of a few rogue waves. Such rogue wave groups have been observed in nature.

Other media

See also: Optical rogue waves

Researchers at UCLA observed rogue wave phenomena in microstructured optical fibers near the threshold of soliton supercontinuum generation, and characterized the initial conditions for generating rogue waves in any medium. Research in optics has pointed out the role played by a nonlinear structure called Peregrine soliton that may explain those waves that appear and disappear without leaving a trace.

Reported encounters

Main article: List of rogue waves

Many of these encounters are reported only in the media, and are not examples of open ocean rogue waves. Often, in popular culture, an endangering huge wave is loosely denoted as a rogue wave, while it has not been (and most often cannot be) established that the reported event is a rogue wave in the scientific sense – i.e. of a very different nature in characteristics as the surrounding waves in that sea state and with very low probability of occurrence (according to a Gaussian process description as valid for linear wave theory).

This section lists a limited selection of notable incidents.

19th century

• Eagle Island lighthouse (1861) – Water broke the glass of the structure's east tower and flooded it, implying a wave that surmounted the 40 metres (130 ft) cliff and overwhelmed the 26 metres (85 ft) tower.
• Flannan Isles Lighthouse (1900) – Three lighthouse keepers vanished after a storm that resulted in wave-damaged equipment being found 34 metres (112 ft) above sea level.

20th century

• SS Kronprinz Wilhelm, September 18, 1901 – The most modern German ocean liner of its time (winner of the Blue Riband) was damaged on its maiden voyage from Cherbourg to New York by a huge wave. The wave struck the ship head-on.
• RMS Lusitania (1910) – On the night of 10 January 1910, a 23-metre (75 ft) wave struck the ship over the bow, damaging the forecastle deck and smashing the bridge windows.
• Voyage of the James Caird (1916) – Sir Ernest Shackleton encountered a wave he termed "gigantic" while piloting a lifeboat from Elephant Island to South Georgia Island.
• RMS Homeric (1924) – Hit by a 24-metre (80 ft) wave while sailing through a hurricane off the East Coast of the United States, injuring seven people, smashing numerous windows and portholes, carrying away one of the lifeboats, and snapping chairs and other fittings from their fastenings.
• USS Ramapo (AO-12) (1933) – Triangulated at 34 metres (112 ft).
• RMS Queen Mary (1942) – Broadsided by a 28-metre (92 ft) wave and listed briefly about 52 degrees before slowly righting.
• SS Michelangelo (1966) – Hole torn in superstructure, heavy glass smashed 24 metres (80 ft) above the waterline, and three deaths.
• SS Edmund Fitzgerald (1975) – Lost on Lake Superior. A Coast Guard report blamed water entry to the hatches, which gradually filled the hold, or alternatively errors in navigation or charting causing damage from running onto shoals. However, another nearby ship, the SS Arthur M. Anderson, was hit at a similar time by two rogue waves and possibly a third, and this appeared to coincide with the sinking around ten minutes later.
• MS München (1978) – Lost at sea leaving only scattered wreckage and signs of sudden damage including extreme forces 20 metres (66 ft) above the water line. Although more than one wave was probably involved, this remains the most likely sinking due to a freak wave.
• Esso Languedoc (1980) – A 25-to-30-metre (80 to 100 ft) wave washed across the deck from the stern of the French supertanker near Durban, South Africa, and was photographed by the first mate, Philippe Lijour.
• Fastnet Lighthouse – Struck by a 48-metre (157 ft) wave in 1985.
• Draupner wave (North Sea, 1995) – The first rogue wave confirmed with scientific evidence, it had a maximum height of 25.6 metres (84 ft).
• Queen Elizabeth 2 (1995) – Encountered a 29-metre (95 ft) wave in the North Atlantic, during Hurricane Luis. The Master said it "came out of the darkness" and "looked like the White Cliffs of Dover." Newspaper reports at the time described the cruise liner as attempting to "surf" the near-vertical wave in order not to be sunk.

21st century

• U.S. Naval Research Laboratory ocean-floor pressure sensors detected a freak wave caused by Hurricane Ivan in the Gulf of Mexico, 2004. The wave was around 27.7 metres (91 ft) high from peak to trough, and around 200 metres (660 ft) long. Their computer models also indicated that waves may have exceeded 40 metres (130 ft) in the eyewall.
• Aleutian Ballad, (Bering Sea, 2005) footage of what is identified as an 18-metre (60 ft) wave appears in an episode of Deadliest Catch. The wave strikes the ship at night and cripples the vessel, causing the boat to tip for a short period onto its side. This is one of the few video recordings of what might be a rogue wave.
• In 2006, researchers from U.S. Naval Institute theorised rogue waves may be responsible for the unexplained loss of low-flying aircraft, such as U.S. Coast Guard helicopters during search and rescue missions.
• MS Louis Majesty (Mediterranean Sea, March 2010) was struck by three successive 8-metre (26 ft) waves while crossing the Gulf of Lion on a Mediterranean cruise between Cartagena and Marseille. Two passengers were killed by flying glass when a lounge window was shattered by the second and third waves. The waves, which struck without warning, were all abnormally high in respect to the sea swell at the time of the incident.
• In 2019, Hurricane Dorian's extratropical remnant generated a 30-metre (100 ft) rogue wave off the coast of Newfoundland.

Quantifying the impact of rogue waves on ships

The loss of the MS München in 1978 provided some of the first physical evidence of the existence of rogue waves. München was a state-of-the-art cargo ship with multiple water-tight compartments and an expert crew. She was lost with all crew and the wreck has never been found. The only evidence found was the starboard lifeboat, which was recovered from floating wreckage some time later. The lifeboats hung from forward and aft blocks 20 metres (66 ft) above the waterline. The pins had been bent back from forward to aft, indicating the lifeboat hanging below it had been struck by a wave that had run from fore to aft of the ship and had torn the lifeboat from the ship. To exert such force the wave must have been considerably higher than 20 metres (66 ft). At the time of the inquiry, the existence of rogue waves was considered so statistically unlikely as to be near impossible. Consequently, the Maritime Court investigation concluded that the severe weather had somehow created an 'unusual event' that had led to the sinking of the München.

In 1980 the MV Derbyshire was lost during Typhoon Orchid south of Japan along with all of her crew. The Derbyshire was an ore-bulk-oil combination carrier built in 1976. At 91,655 gross register tons, she was – and remains – the largest British ship ever to have been lost at sea. The wreck was found in June 1994. The survey team deployed a remotely operated vehicle to photograph the wreck. A private report was published in 1998 that prompted the British government to reopen a formal investigation into the sinking. The government investigation included a comprehensive survey by the Woods Hole Oceanographic Institution, which took 135,774 pictures of the wreck during two surveys. The formal forensic investigation concluded that the ship sank because of structural failure and absolved the crew of any responsibility. Most notably, the report determined the detailed sequence of events that led to the structural failure of the vessel. A third comprehensive analysis was subsequently done by Douglas Faulkner, professor of marine architecture and ocean engineering at the University of Glasgow. His 2001 report linked the loss of the Derbyshire with the emerging science on freak waves, concluding that the Derbyshire was almost certainly destroyed by a rogue wave.

Work by sailor and author Craig B. Smith in 2007 confirmed prior forensic work by Faulkner in 1998 and determined that the Derbyshire was exposed to a hydrostatic pressure of a "static head" of water of about 20 metres (66 ft) with a resultant static pressure of 201 kilopascals (29.2 psi). This is in effect 20 metres (66 ft) of seawater (possibly a super rogue wave) flowing over the vessel. The deck cargo hatches on the Derbyshire were determined to be the key point of failure when the rogue wave washed over the ship. The design of the hatches only allowed for a static pressure of less than 2 metres (6.6 ft) of water or 17.1 kilopascals (2.48 psi), meaning that the typhoon load on the hatches was more than ten times the design load. The forensic structural analysis of the wreck of the Derbyshire is now widely regarded as irrefutable.

In addition fast moving waves are now known to also exert extremely high dynamic pressure. It is known that plunging or breaking waves can cause short-lived impulse pressure spikes called Gifle peaks. These can reach pressures of 200 kilopascals (29 psi) (or more) for milliseconds, which is sufficient pressure to lead to brittle fracture of mild steel. Evidence of failure by this mechanism was also found on the Derbyshire. Smith has documented scenarios where hydrodynamic pressure of up to 5,650 kilopascals (819 psi) or over 500 metric tonnes per square metre could occur.

In 2004 an extreme wave was recorded impacting the Admiralty Breakwater, Alderney in the Channel Islands. This breakwater is exposed to the Atlantic Ocean. The peak pressure recorded by a shore-mounted transducer was 745 kilopascals (108.1 psi). This pressure far exceeds almost any design criteria for modern ships and this wave would have destroyed almost any merchant vessel.

Design standards

In November 1997 the International Maritime Organization (IMO) adopted new rules covering survivability and structural requirements for bulk carriers of 150 metres (490 ft) and upwards. The bulkhead and double bottom must be strong enough to allow the ship to survive flooding in hold one unless loading is restricted.

Rogue waves present considerable danger for several reasons: they are rare, unpredictable, may appear suddenly or without warning, and can impact with tremendous force. A 12-metre (39 ft) wave in the usual "linear" model would have a breaking force of 6 metric tons per square metre [t/m²] (8.5 psi). Although modern ships are designed to (typically) tolerate a breaking wave of 15 t/m², a rogue wave can dwarf both of these figures with a breaking force far exceeding 100 t/m². Smith has presented calculations using the International Association of Classification Societies (IACS) Common Structural Rules (CSR) for a typical bulk carrier which are consistent.

Peter Challenor, a leading scientist in this field from the National Oceanography Centre in the United Kingdom, was quoted in Casey's book in 2010 as saying: "We don’t have that random messy theory for nonlinear waves. At all." He added, "People have been working actively on this for the past 50 years at least. We don’t even have the start of a theory."

In 2006 Smith proposed that the International Association of Classification Societies (IACS) recommendation 34 pertaining to standard wave data be modified so that the minimum design wave height be increased to 19.8 metres (65 ft). He presented analysis that there was sufficient evidence to conclude that 20.1 metres (66 ft) high waves can be experienced in the 25-year lifetime of oceangoing vessels, and that 29.9 metres (98 ft) high waves are less likely, but not out of the question. Therefore, a design criterion based on 11.0 metres (36 ft) high waves seems inadequate when the risk of losing crew and cargo is considered. Smith has also proposed that the dynamic force of wave impacts should be included in the structural analysis. The Norwegian offshore standards now take into account extreme severe wave conditions and require that a 10,000-year wave does not endanger the ships integrity. Rosenthal notes that as at 2005 rogue waves were not explicitly accounted for in Classification Societies’ Rules for ships’ design. As an example, DNV GL, one of the world's largest international certification body and classification society with main expertise in technical assessment, advisory, and risk management publishes their Structure Design Load Principles which remain largely based on the 'Significant Wave height' and as at January 2016 still has not included any allowance for rogue waves.

The U.S. Navy historically took the design position that the largest wave likely to be encountered was 21.4 metres (70 ft). Smith observed in 2007 that the navy now believes that larger waves can occur and the possibility of extreme waves that are steeper (i.e. do not have longer wavelengths) is now recognized. The navy has not had to make any fundamental changes in ship design as a consequence of new knowledge of waves greater than 21.4 metres because they build to higher standards.

There are more than 50 classification societies worldwide, each with different rules, although most new ships are built to the standards of the 12 members of the International Association of Classification Societies, which implemented two sets of Common Structural Rules; one for oil tankers and one for bulk carriers; in 2006. These were later harmonised into a single set of rules.