The main drivers that have acted over the last century are:. Of these, solar fluctuations and volcanic eruptions are entirely natural, while the other four are predominantly caused by human influences.
The human-induced drivers have been dominant over the past century Figure 3. Changes in greenhouse gas concentrations, dominated by CO 2 , caused a large warming contribution. Some of this has been offset by the net cooling effects of increased aerosol concentrations and their impact on clouds.
Black carbon or soot has probably exerted a smaller, warming influence. The net effect of all aerosol types including soot remains hard to quantify accurately.
Quantifying human contributions to past and future ocean warming and thermosteric sea level rise
Among the natural influences, the effect of changes in the brightness of the Sun has been very small Box 3. Volcanic influences are highly intermittent, with major eruptions such as Pinatubo in causing significant cooling for a year or two, but their average effects over the past century have been relatively small. In comparison with other influences, the effects of solar variations on present global warming are small.
Indirect estimates suggest that changes in the brightness of the Sun have contributed only a few percent of the global warming since Direct measurements show a decreasing solar intensity over recent decades, opposite to what would be required to explain the observed warming. Solar activity has declined significantly over the last few years, and some estimates suggest that weak activity will continue for another few decades, in contrast with strong activity through the 20th century.
Nevertheless, the possible effects on warming are modest compared with anthropogenic influences. Using climate models, it is possible to separate the effects of the natural and human-induced influences on climate. This information tells scientists that fossil fuel emissions are the largest contributor of CO 2 concentrations since the pre-industrial era.
Volcanic events and some types of human-made pollution, both of which inject sunlight-reflecting aerosols i. Human activity drives climate change. Volcanic eruptions account for the cooling spikes seen in the graph in and Among natural drivers, a large volcanic eruption can have a sharp cooling influence as it spews tiny particles high into the stratosphere the layer of the atmosphere above the troposphere where weather typically occurs.
The massive explosions from Krakatoa Indonesia in and Mount Pinatubo Philippines in , for example, can be seen as the two largest downward spikes in the volcanic data depicted in the figure to the right. These particles prevented the full energy of the sun from reaching the surface of Earth and created a cooling trend for several years. Fossil fuel burning by humans emits tiny particles in addition to releasing CO 2 in the atmosphere.
Some particles reflect sunlight back to space aerosols , similar to the volcanic particles, having a cooling effect. Other particles such as soot black carbon absorb the sunlight and drive temperature rise, leading to local warming of the atmosphere level where the soot particles circulate. Very likely, there would have been even more warming in the past 60 years if it were not for these human-made and natural tiny particles. Some of these climate drivers result in warming and others lead to cooling, but when all the natural and human-induced climate drivers are stacked up and compared to one another, the accumulation of human-released heat-trapping gases in the atmosphere is so large that it has very likely swamped other climate drivers over the past half century, leading to observed global warming.
Much as the Air Force develops computer programs to simulate aircraft flight under different conditions, climate scientists develop computer programs to simulate global climate changes under different conditions.
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These programs use our knowledge of physical, chemical, and biological processes that occur within Earth's atmosphere and oceans and on its land surfaces. Mathematical models allow scientists to simulate the behavior of complex systems like climate and explore how these systems respond to natural and human factors.
When models take into account both natural and human drivers, they better reflect the observed changes in temperature. For a computer model to accurately project the future climate, scientists must first ensure that it accurately reproduces already observed temperature changes i.
Scientists use climate computer models to compare these observed changes with natural climate drivers and human climate drivers. Scientists reached this conclusion after contrasting observed changes to different types of drivers. When climate models include only recorded natural climate drivers, the models cannot accurately reproduce the observed warming of the past half century.
10 Human Causes of Global Warming for - phodishendphenci.gq
When the models also include human-induced climate drivers, then they accurately capture recent temperature increases in the atmosphere and in the oceans. This approach can also be applied to land temperatures, as shown in the figure below. Here, land temperatures are shown between and , with post forcings also from RCP6.
This is seen in the model results, where land warms by around 4C by compared to 3C globally in the RCP6.
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There is a wide range of future warming possible from different RCP scenarios and different values for the sensitivity of the climate system , but all show a similar pattern of declining future aerosol emissions and a larger role for greenhouse gas forcing in future temperatures. While natural forcings from solar and volcanoes do not seem to play much of a role in long-term warming, there is also natural variability associated with ocean cycles and variations in ocean heat uptake.
As the vast majority of energy trapped by greenhouse gases is absorbed by the oceans rather than the atmosphere, changes in the rate of ocean heat uptake can potentially have large impacts on the surface temperature. While human factors explain all the long-term warming, there are some specific periods that appear to have warmed or cooled faster than can be explained based on our best estimates of radiative forcing.
For example, the modest mismatch between the radiative forcing-based estimate and observations during the mids might be evidence of a role for natural variability during that period. A number of researchers have examined the potential for natural variability to impact long-term warming trends. They have found that it generally plays a limited role.
But that is a weak argument: you can, of course, never rule out the unknown unknown. The question is whether there is strong, or even any evidence for it.
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And the answer is no, in my view. Models get the short-term temperature variability approximately right. In many cases, they even have too much. But the forced response pretty much explains the observations, so there is no evidence from the 20th century that we are missing something…. Similarly, Dr Martin Stolpe and colleagues, also at IAC, recently analysed the role of multidecadal natural variability in both the Atlantic and Pacific oceans.
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Internal variability is likely to have a much larger role in regional temperatures. For example, in producing unusually warm periods in the Arctic and the US in the s. However, its role in influencing long-term changes in global surface temperatures appears to be limited. The global warming witnessed over the past years matches nearly perfectly what is expected from greenhouse gas emissions and other human activity, both in the simple model examined here and in more complex climate models.
Some uncertainty remains due to the role of natural variability, but researchers suggest that ocean fluctuations and similar factors are unlikely to be the cause of more than a small fraction of modern global warming.
The simple statistical model used in this article is adapted from the Global Warming Index published by Haustein et al In turn, it is based on the Otto et al model. The model estimates contributions to observed climate change and removes the impact of natural year-to-year fluctuations by a multiple linear regression of observed temperatures and estimated responses to total human-induced and total natural drivers of climate change.
The forcing responses are provided by the standard simple climate model given in Chapter 8 of IPCC , but the size of these responses is estimated by the fit to the observations. An Excel spreadsheet containing their model is also provided.
The model was adapted by calculating forcing responses for each of the different major climate forcings rather than simply total human and natural forcings, using the Berkeley Earth record for observations. The decay time of thermal response used in converting forcings to forcing responses was adjusted to be one year rather than four years for volcanic forcings to better reflect the fast response time present in observations. The temperature response for each individual forcing was calculated by scaling their forcing responses by the total human or natural coefficients from the regression model.
The regression model was also run separately for land temperatures.
Temperature responses for each forcing between and were estimated using forcing data from RCP6. Uncertainties in total human and total natural temperature response was estimated using a Monte Carlo analysis of different forcing series, as well as the uncertainties in the estimated regression coefficients.
The Python code used to run the model is archived with GitHub and available for download. Observational data from shown in the figures is based on the average of the first 10 months of the year and is likely to be quite similar to the ultimate annual value. Get a Daily or Weekly round-up of all the important articles and papers selected by Carbon Brief by email. Tailpipe exhaust, Anchorage, Alaska, Credit: Daniel H.