Computer Models of the Troposphere Vs Global Warming Fractal Art
The most common measure of global temperature ascent is here on the Globe's surface, but scientists besides gather data on how temperatures in the atmosphere high above u.s.a. are irresolute.
Of particular interest is the troposphere – the lowest layer of the atmosphere where well-nigh all of our conditions occurs. To rails temperatures, scientists employ satellites, which have been providing data since they were first launched in the late 1970s.
Since around the start of the 21st century, the tropospheric warming recorded by satellites has been slower than the rate projected by climate models. In a new written report, published in Nature Geoscience, researchers find that these differences are outside the range of what we would look from natural variability.
Instead, they say the differences could be downward to recent changes in greenhouse gas emissions, solar output, volcanic eruptions and air pollution that weren't anticipated in the assumptions made by climate modellers.
Overall, the study suggests that while tropospheric warming has non accelerated to the extent that models have predicted in recent years, there'due south little testify that information technology has slowed down.
Temperatures from satellites
Much of our historical temperature data comes from weather stations, ships, and buoys on the Earth's surface. Since 1979 temperature records of the atmosphere are as well bachelor from satellite-based microwave sounding units (MSU). These measure the "brightness" of microwave radiation bands in the atmosphere, from which scientists can estimate air temperatures.
Even so, the bands measured past the satellite instruments cannot hands provide the temperature of a specific layer of the atmosphere. Researchers take identified item sets of bands that stand for to the temperature of the lower troposphere (TLT) spanning roughly 0 to 10 km, the centre troposphere (TMT) spanning around 0 to twenty km and the lower stratosphere (TLS) spanning 10 to xxx km.
Unfortunately, these bands tend to overlap a fleck. For example, TMT estimates will include role of the lower stratosphere, while TLT estimates will include some surface temperature. These overlaps thing because different parts of the temper are expected to react very differently to climatic change.
When greenhouse gases trap incoming solar radiation, they tend to increment the temperature of the surface and lower temper, and decrease the temperature of the upper atmosphere as less solar radiation is escaping. We run across this in satellite observations and data from weather balloons, where the lower stratosphere is cooling while the underlying troposphere and surface are warming.
Because the tropospheric temperature estimates from satellites overlap with part of the stratosphere, they terminate up combining a flake of stratospheric cooling with tropospheric warming and can underestimate the true charge per unit of warming. To avoid this issue, the new study applies a correction to remove some of the stratospheric cooling from the TMT serial. The approach they utilize for this is described in a previous newspaper published in the Journal of Climate.
Correcting errors in the data
Dealing with stratospheric contagion is non the merely challenge when working with satellite data. Dissimilar on the surface where there are tens of thousands of individual ascertainment stations, in that location are simply around 2 to three MSU satellites taking measurements at any given time, and the satellites only last about v-to-ten years before they need to exist replaced.
While the satellites are designed to laissez passer over the same part of the earth at the same fourth dimension every day, that changes equally their orbits decay. A satellite that in one case took the temperature over London at 2pm 10 years ago, for example, might at present be taking information technology at 8pm. Changing the observation times has a big effect on the temperatures measured, and researchers need to correct their measurements for this.
Similarly, a replacement satellite might mensurate temperatures slightly differently from its predecessor. Around the year 2000, for case, the musical instrument in the satellites was changed to an upgraded version of the sensor. All of these tin potentially innovate bias into measurements that need to be addressed.
At that place are two primary groups that process the same underlying MSU data to judge atmospheric temperatures: the University of Alabama, Huntsville (UAH) and Remote Sensing Systems (RSS). Each grouping has a different fix of assumptions to correct for diverse issues in the information, and they end up with adequately different results. You can see how the UAH (yellow line) and RSS (blue) figures differ in the chart beneath – particularly subsequently the year 2000.
Annual global mean centre tropospheric temperatures from RSS and UAH from 1979 through 2016, covering from 82.five N–82.5 S. No stratospheric adjustments are included equally an adjusted UAH dataset is not available. Chart by Carbon Brief using Highcharts.
While RSS generally agrees with the rate of warming seen globally in surface temperature records, UAH shows much less warming – including a more pronounced slowdown in temperature rise afterwards 1998. The differences between satellite records are much larger than those between different surface temperature estimates. Co-author Dr Carl Mears, the co-founder of RSS, suggests that:
"In general, I call back that the surface datasets are likely to exist more accurate than the satellite datasets. The betwixt-research spreads are much larger than for the surface data, suggesting larger structural dubiousness."
These large uncertainties between satellite datasets somewhat complicate any comparison of tropospheric temperatures with climate models, as it makes it unclear if the disagreement is due to issues in the models or in the observations, and leaves open the possibility that additional corrections to the data may happen in the future.
Comparisons with climate models
In their paper, the researchers employed a number of different statistical tests to compare climate models and observations of TMT. They corrected both models and observations for stratospheric cooling influence, and compared the two over the period from 1979 through 2016 every bit shown in the figure below. The upper nautical chart shows the model output and the lower nautical chart shows the observations from RSS.
Superlative console shows stratosphere-corrected RSS TMT compared to the CMIP5 multimodel average TMT. Bottom panel shows the difference between the two over time. Source: Santer et al. (2017)
While the rate of warming in the models and observations is pretty close prior to the year 2000, the differences after 2000 are much larger. Some of these differences are explained by short-term natural variability, such equally El Niño events, which practise not necessarily occur at the aforementioned time in the models as in the observations and tend to average out. However, even with this removed from the observations, the researchers find that notable differences remain.
To explain these differences, the researchers tested a number of unlike possible factors. First, they looked to see if the difference could be explained by longer-term multi-decadal natural variability from El Nino and body of water temperature oscillations that was not captured in the model average.
They constitute that while natural internal variability tin can explicate well-nigh of the relatively small differences between modeled and observed tropospheric warming in the last two decades of the 20th century, just can't fully explain why model tropospheric warming is larger than in the satellite data during much of the early 21st century.
2d, they looked to meet if the difference might be caused by models existence too sensitive to CO2. They found no discernable relationship between model sensitivity and their ability to accurately predict tropospheric temperatures over this period.
The conclusion the researchers came to was that the model-ascertainment discrepancy isn't downwardly to a unmarried gene, merely a combination. Specifically, they posit that information technology is due to a combination of internal variability and that models got some climate forcings wrong in contempo years.
Climate models used historic data for factors like greenhouse gas concentrations, solar output, volcanic eruptions, air pollution, and other factors that tin touch on the climate through 2005 or and so, just after that bespeak made assumptions of how these would change in the future. Recent research has suggested that a serial of moderate volcanic eruptions, a long and unusually depression minimum in the sun's energy output during the last solar bike, and an uptick in particulate pollution from Chinese coal-fired ability plants accept all inverse these forcings in ways unanticipated by the modelers.
These forcings will be updated in current modeling effort, called CMIP6, being washed in preparation for the side by side Intergovernmental Console on Climate change study. This new generation of models, featuring forcings closer to observations in recent years, will likely show ameliorate correspondence with tropospheric temperature observations, but may not be any more or less sensitive to CO2 than the prior generation of models (CMIP5).
According to Dr Gavin Schmidt, director of NASA'due south Goddard Establish for Space Studies, who was not involved in the paper, there are even plans to rerun the older CMIP5 generation of climate models with updated forcings to see what happens if those are updated in isolation without changing other factors.
Ultimately, the paper finds that while at that place is a mismatch between climate models and observations in the troposphere since the year 2000, in that location is little evidence to-date that the model/observation differences imply that the climate is less sensitive to greenhouse gases. The results suggest that while these curt-term differences between models and observations are a subject area of swell scientific interest, information technology does not diminish the reality of long-term human-driven warming.
Santer, B. D. et al. (2017) Causes of differences in model and satellite tropospheric warming rates, Nature Geoscience, doi:ten.1038/ngeo2973
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Source: https://www.carbonbrief.org/study-why-troposphere-warming-differs-between-models-and-satellite-data
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