In previous posts I have shown plots of global temperature anomaly, volcano and Nino34 trends (here , here). In this post , I want to further explore the role of volcanic eruptions and Nino34 phases (El Nino, La Nina) on temperature anomalies.
This post shows a 5-panel chart of monthly climate trend data: 1) time line of major volcanoes and Volcanic Explosivity Index (VEI), 2) Mauna Loa Observatory (MLO) Atmospheric Transmission (AT) measurements, 3) Stratospheric Aerosol Optical Thickness (SATO) Index, 4) , Nino 34 as an indicator of ENSO and 5) GISS land-ocean temperature anomaly.
The RClimate script and Climate Time Series data file (CTS.csv) links are provided.
First, here is the 5-panel chart that I have made showing the monthly volcano time line with Volcano Explosivity Index (VEI) , Atmospheric Transmission at Mauna Loa Observatory, SATO Index as well as the Nino34 SSTA and GISS LOTA. (Click Image to Enlarge)
Laki Eruption , 1783: “For millennia humans no doubt have noticed that smoke and ash from volcanic eruptions can block sunlight for many days. But Benjamin Franklin went a step further in 1783, proposing that a massive volcanic eruption of the Laki fissure in Iceland caused months of unusually cold weather in Europe. By the early 1900s, scientists had begun trying to quantify how volcanic eruptions affect climate, but measurements and climate models were too crude to conclusively link the two. It wasn’t until the late 20th century that scientists understood precisely how Laki’s eruption and the subsequent strange blue haze that wafted over Europe cooled temperatures, and how this related to the “human volcano” of air pollution.” Source: NOVA Global Dimming
Volcanoes can inject large amounts of aerosols into the atmosphere, affecting the amount of solar radiation that reaches the Earth’s surface. Aerosols are a suspension of fine solid particles or liquid droplets in a gas.
Sulfur aerosols play an important role in reducing the amount of solar radiation that reaches the earth’s surface. Sulfur emissions from volcanoes vary widely, from 0.5% to 12% of all gaseous emissions ( link).
Here is part of Wikipedia’s discussion (link) of volcanic sulfur aerosols:
“Sulfur aerosols are common in the troposphere as a result of pollution with sulfur dioxide from burning coal, and from natural processes. Volcanos are the major source of particles in the stratosphere as the force of the volcanic eruption propels sulfur-containing gases into the stratosphere.”
“Stratospheric sulfur aerosols are tiny sulfur-rich particles of solid or liquid, or a mixture of the two, which exist in the stratosphere region of the Earth’s atmosphere. When present, after a strong volcanic eruption such as Mount Pinatubo, they produce a cooling effect for a few years before the particles fall out, by reflecting sunlight, and by modifying clouds as they fall out of the stratosphere.” Wikipedia link
The major volcano time line includes volcanoes with a VEI of 4 or more. The plot labels the 5 volcanic eruptions with VEI s of 5-6. Pinatubo was the only VEI @ 6 in the 1958-2011 period. It was followed by Hudson Cerro, VEI of 5, a little less than 2 months later.
El Chichon actually had 2 eruptions only 5 days apart (3/28/91 @ 4, 4/3/91 @ 5).
Volcanic Dimming Indicators
NASA uses the Stratospheric Aerosol Optical Thickness (SATO) index in their climate models (link).
“The optical depth expresses the quantity of light removed from a beam by scattering or absorption during its path through a medium. If I0 is the intensity of radiation at the source and I is the observed intensity after a given path, then optical depth τ is defined by the following equation:Wikipedia
This simple plot shows the optical depth – transmission relationship.
The SATO index reflects the portion of the solar radiation that passes through the stratosphere. Here is the SATO index data link.
The Maunal Loa Observatory measures clear-sky atmospheric transmission monthly (link). This data series shows the fraction of the top-of-atmosphere solar radiation that is reaching the surface. Here is the raw data link.
If we look at the MLO AT and SATO trend lines we can see significant decreases in AT and increases in SATO that coincide with the El Chichon and Pinatubo – Hudson Cerro eruptions. The El Chichon period AT decreased from approximately 0.92 to 0.79 2 months later. It took 28 months for the AT to return to its pre-Pinatubo level.
The Pinatubo period AT decreased from 0.93 to a low of 0.85 3 months later. It took 29 months for the AT to return back to the pre-El Chichon level.
It is important to note that the VEI is note a good indicator of volcanic dimming. The Mt St Helens eruption had a VEI of 5 with no noticeable change in either SATO or AT.
The Agung (VEI 5) eruption is interesting because it seems to have increased SATO and reduced the AT from pre-Agung levels of
Interplay of Nino 34 and Volcanic Dimming
Nino 34 is one of several ENSO (El Nino Southern Oscillation) indicators. I have a number of previous Nino 34 posts (here, here, here). El Nino conditions tend to increase global temperature anomalies and La Nina conditions tend to lower global temperature anomalies, after a multi-month lag. Major volcanic eruptions with high SATO index and lower AT will tend to lower global temperature.
The GISS anomaly series shows a variability caused by natural variation, and the aperiodic impacts of volcanic dimming and ENSO impacts.
RClimate Script and Data File
In working on this post, I developed a new Climate Time Series file (CTS.csv) that includes the 5 major land ocean temperature anomaly series, the climate oscillation series (Nino34, PDO, AMO, AO), MLO’s CO2 series, SATO, MLO Atmospheric Transmission as well as volcano VEIs.
I will update this file monthly to reflect the the latest agency updates for each series.
My RClimate script for this post chart uses my new CTS.csv file, greatly simplifying data download and preparation.
I continue to use traditional graphics for my R charts, even for my panel charts. For the 1st time, I used the layout() function to generate this 5-panel chart rather than the par(mfrow=) that I have used previously. I find that png() works better with the layout() so I’ll be sticking with layout().
Filed under: Citizen Climate Science, Do-it-yourself Climate Science, Global Warming, R Climate Data Analysis Tool, RClimate Script, Time Series Charts Tagged: Climate Trends, R scripts