a volcano case study

Volcano case studies

Volcano case studies You should make sure you are familiar with 2 case studies: Either: Nyiragongo, Democratic Republic of Congo – Poor Country or Montserrat, Caribbean – Poor Country AND Either: Mount St. Helens, USA – Rich Country or Iceland – Rich Country

Key terms: Primary effects: the immediate effects of the eruption, caused directly by it Secondary effects: the after-effects that occur as an indirect effect of the eruption on a longer timescale Immediate responses: how people react as the disaster happens and in the immediate aftermath Long-term responses: later reactions that occur in the weeks, months and years after the event Nyiragongo Picture The video below contains more information on the primary and secondary effects of a volcano

On 17th January 2002 Nyiragongo volcano in the Democratic Republic of Congo (DRC) was disturbed by the movement of plates along the East African Rift Valley. This led to lava spilling southwards in three streams.

The primary effects – The speed of the lava reached 60kph which is especially fast. The lava flowed across the runway at Goma airport and through the town splitting it in half. The lava destroyed many homes as well as roads and water pipes, set off explosions in fuel stores and powerplants and killed 45 people

The secondary effects – Half a million people fled from Goma into neighbouring Rwanda to escape the lava. They spent the nights sleeping on the streets of Gisenyi. Here, there was no shelter, electricity or clean water as the area could not cope with the influx. Diseases such as cholera were a real risk. People were frightened of going back. However, looting was a problem in Goma and many residents returned within a week in hope of receiving aid.

Responses – In the aftermath of the eruption, water had to be supplied in tankers. Aid agencies, including Christian Aid and Oxfam, were involved in the distribution of food, medicine and blankets.

Montserrat – Poor country case study

Montserrat – Ledc Case Study from donotreply16 Mount St Helens – Rich country case study Picture Mount St. Helens is one of five volcanoes in the Cascade Range in Washington State, USA. The volcano erupted at 8:32am on 18th May 1980.

Effects – An earthquake caused the biggest landslide ever recorded and the sideways blast of pulverised rock, glacier ice and ash wiped out all living things up to 27km north of the volcano. Trees were uprooted and 57 people died.

Immediate responses – helicopters were mobilised to search and rescue those in the vicinity of the catastrophic blast. Rescuing survivors was a priority, followed by emergency treatment in nearby towns. Air conditioning systems were cleaned after by clogged with ash and blocked roads were cleared. Two million masks were ordered to protect peoples lungs.

Long-term responses – Buildings and bridges were rebuilt. Drains had to be cleared to prevent flooding. The forest which was damaged had to be replanted by the forest service. Roads were rebuilt to allow tourists to visit. Mount St. Helens is now a major tourist attraction with many visitor centres.

Iceland – Rich country case study Picture Location: Iceland lies on the Mid-Atlantic Ridge, a constructive plate margin separating the Eurasian plate from the North American plate. As the plates move apart magma rises to the surface to form several active volcanoes located in a belt running roughly SW-NE through the centre of Iceland. Eyjafjallajokull (1,666m high) is located beneath an ice cap in southern Iceland 125km south east of the capital Reykjavik

The Eruption: In March 2010, magma broke through the crust beneath Eyjafjallajokull glacier. This was the start of two months of dramatic and powerful eruptions that would have an impact on people across the globe. The eruptions in March were mostly lava eruptions. Whilst they were spectacular and fiery they represented very little threat to local communities, However, on 14th April a new phase began which was much more explosive. Over a period of several days in mid-April violent eruptions belched huge quantities of ash in the atmosphere.

Local impacts and responses: The heavier particles of ash (such as black gritty sand) fell to the ground close to the volcano, forcing hundreds of people to be evacuated (immediate response) from their farms and villages. As day turned to night, rescuers wore face masks to prevent them choking on the dense cloud of ash. These ash falls, which coated agricultural land with a thick layer of ash, were the main primary effects of the eruption. One of the most damaging secondary effects of the eruption was flooding. As the eruption occurred beneath a glacier, a huge amount of meltwater was produced. Vast torrents of water flowed out from under the ice. Sections of embankment that supported the main highway in Southern Iceland were deliberately breached by the authorities to allow floodwaters to pass through to the sea. This action successfully prevented expensive bridges being destroyed. After the eruption, bulldozers were quickly able to rebuild the embankments and within a few weeks the highway was reconstructed.

Local impacts: 800 people evacuated Homes and roads were damaged and services (electricity & water) disrupted Local flood defences had to be constructed Crops were damaged by heavy falls of ash Local water supplies were contaminated with fluoride from the ash

National impacts: Drop in tourist numbers – affected Iceland’s economy as well as local people’s jobs and incomes Road transport was disrupted as roads were washed away by floods Agricultural production was affected as crops were smothered by a thick layer of ash Reconstruction of roads and services was expensive

International impacts: Over 8 days – some 100,000 flights were cancelled 10 million air passengers affected Losses estimated to be £80 million Industrial production halted due to a lack of raw materials Fresh food could not be imported Sporting events such as the Japanese Motorcycle grand prix, Rugby leagues challenge cup and the Boston Marathon were affected

International impacts and responses: The eruption of Eyjafjallajokull became an international event in mid-April 2010 as the cloud of fine ash spread south-eastwards toward the rest of Europe. Concerned about the possible harmful effects of ash on aeroplane jet engines, large sections of European airspace closed down. Passenger and freight traffic throughout much of Europe ground to a halt. The knock-on effects were extensive and were felt across the world. Business people and tourists were stranded unable to travel in to or out of Western Europe. Industrial production was affected as raw materials could be flown in and products could not be exported by air. As far away as Kenya, farm workers lost their jobs or suffered pay cuts as fresh produce such as flowers and bean perished, unable to be flown to European supermarkets. The airline companies and airport operators lost huge amounts of money. Some people felt that the closures were an over-reaction and that aeroplanes could fly safely through low concentrations of ash. However, a scientific review conducted after the eruption concluded that under the circumstances it had been right to close the airspace. Further research will be carried out as a long-term response to find better ways of monitoring ash concentrations and improving forecast methods.

• Age: 14-16 MYP Individuals and Societies
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• 2.1 Earthquakes and volcanoes
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Volcano case study - Mount Etna (2002-2003), Italy

• Volcano case study - Mount Nyiragongo, Democratic Republic of Congo
• Volcanic hazard management - Mount Rainier, USA
• Earthquakes
• Earthquake case study - 2005 Kashmir
• Earthquake case study - Chuetsu Offshore Earthquake - 2007
• Why was the Haitian Earthquake so deadly?
• Earthquakes - Managing the hazard

Can you describe the location of Mount Etna? Could you draw a sketch map to locate Mount Etna?

Case study task

Use the resources and links that can be found on this page to produce a detailed case study of the 2002-2003 eruption of Mount Etna. You should use the 'Five W's" subheadings to give your case study structure.

What happened?

The Guardian - Sicilian city blanketed in ash [28 October 2002]

When did it happen?

Immediately before midnight on 26 October 2002 (local time=GMT+1), a new flank eruption began on Mount Etna. The eruption ended after three months and two days, on 28 January 2003.

Where did it happen?

The eruption occurred from fissures on two sides of the volcano: at about 2750 m on the southern flank and at elevations between 2500 and 1850 m on the northeastern flank.

Why did it happen?

Mount Etna is a volcano. The reasons why Mount Etna is located where it is are complex. Here are some of the theories:

• One theory envisages a hot spot or mantle-plume origin for this volcano, like those that produce the volcanoes in Hawaii.
• Another theory involves the subduction of the African plate under the Eurasian plate.
• Another group of scientists believes that rifting along the eastern coast of Sicily allows the uprise of magma.

Who was affected by it happening?

• The Italian Government declared a state of emergency in parts of Sicily, after a series of earthquakes accompanying the eruption of forced about 1,000 people flee their homes.
• A ship equipped with a medical clinic aboard was positioned off Catania - to the south of the volcano - to be ready in case of emergency.
• Emergency workers dug channels in the earth in an attempt to divert the northern flow away from the town of Linguaglossa.
• Schools in the town have been shut down, although the church has remained open for people to pray.
• Villagers also continued their tradition of parading their patron saint through the streets to the railway station, to try to ward off the lava flow.
• Civil protection officials in Catania, Sicily's second-biggest city, which sits in the shadow of Etna, surveyed the mountain by helicopter and were ready to send water-carrying planes into the skies to fight the fires.
• The tourist complex and skiing areas of Piano Provenzana were nearly completely devastated by the lava flows that issued from the NE Rift vents on the first day of the eruption.
• Heavy tephra falls caused by the activity on the southern flank occurred mostly in areas to the south of the volcano and nearly paralyzed public life in Catania and nearby towns.
• For more than two weeks the International Airport of Catania, Fontanarossa, had to be closed due to ash on the runways.
• Strong seismicity and ground deformation accompanied the eruption; a particularly strong shock (magnitude 4.4) on 29 October destroyed and damaged numerous buildings on the lower southeastern flank, in the area of Santa Venerina.
• Lava flows from the southern flank vents seriously threatened the tourist facilities around the Rifugio Sapienza between 23 and 25 November, and a few days later destroyed a section of forest on the southwestern flank.
• The eruption brought a heightened awareness of volcanic and seismic hazards to the Sicilian public, especially because it occurred only one year and three months after the previous eruption that was strongly featured in the information media.

Look at this video clip from an eruption on Mount Etna in November 2007.  What sort of eruption is it?

There is no commentary on the video - could you add your own explaining what is happening and why?

You should be able to use the knowledge and understanding you have gained about 2002-2003 eruption of Mount Etna to answer the following exam-style question:

In many parts of the world, the natural environment presents hazards to people. Choose an example of one of the following: a volcanic eruption, an earthquake, or a drought. For a named area, describe the causes of the example which you have chosen and its impacts on the people living there. [7 marks]

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An ash cloud rises over the lake as Taal Volcano erupts in the Philippines on January 12. The ongoing eruption is blanketing the region with debris and has already spurred evacuations, school closings, and flight cancellations.

What the Philippines volcano ‘worst-case scenario’ could look like

With millions of people at risk, experts are looking to past big eruptions to better understand the unique hazards this peak can produce.

Normally, the view from the webcam sitting inside Lake Taal in the Philippines shows clouds drifting over the lake’s placid waters, as verdant slopes rise in the distance. But on the afternoon of January 12, this peaceful scene was suddenly interrupted by a torrent of hot ash and gas, before the camera was smothered by darkness.

The outpouring marked the beginning of an unnerving eruption sequence at Taal Volcano, which sits on the island of Luzon. On the first day, steam-driven blasts flung ash nine miles into the sky . Startling displays of volcanic lightning ricocheted around this dark maelstrom , and a myriad of intense volcanic earthquakes rocked the region. On January 13, the eruption became somewhat more magmatic, as lava fountains started shooting up from the main crater.

Ash continues to blanket the Philippines as of press time, including in the capital city of Manila , about 62 miles north of the volcano. Flights have been cancelled, schools and other public institutions have closed, and tens of thousands of people have been evacuated from both the volcanic isle within Lake Taal and from the vast shorelines around it.

So far, no casualties have been reported, and there is a chance this eruption could fizzle out. Still, many people likely remain in high-risk zones, and “the biggest bang is not always at the beginning of an eruption,” says Jenni Barclay , a volcanologist at the University of East Anglia. “On a timescale much longer than the threat of a hurricane, something else could happen that’s even bigger.”

Past eruptions at Taal demonstrate that this volcano has a profoundly lethal capability, claiming thousands of lives throughout recorded history. If the latest event does become more explosive—a possibility that has scientists deeply concerned—it could yield a surfeit of volcanic hazards, from rocky debris bouncing across the lake to overwhelming tsunamis.

“This is definitely a volcano to be taken seriously,” says Beth Bartel , an outreach specialist at UNAVCO , a geoscientific consortium of universities and scientific institutions.

Telling Taal tales

With a plentiful supply of magma, Taal is one of the Philippines’ most active volcanoes , having erupted dozens of times in the past few centuries. Some of those past eruptions rank among the most powerful in the country’s history. But Taal Volcano is visually deceptive.

Many of these historical eruptions took place on the volcanic island in the middle of the more expansive Lake Taal. However, the entire volcano is far larger than this rocky outpost; it is a giant cauldron-shaped edifice known as a caldera. Much of the caldera is hidden by Lake Taal, and only a small portion of the volcano sits above the waves.

This is a problem not only for those who live on the central volcanic isle, but also for the 25 million people living within 60 miles of the volcano, including a huge number on Lake Taal’s shorelines.

Due to the continuing intense volcanic earthquakes and eruptive activity, the Philippine Institute of Volcanology and Seismology, or PHIVOLCS , has set the alert level to four , meaning that a hazardous explosive eruption is possible within hours to days.

A link to the past

To understand what that might mean, experts can look to the past for hints. The most recent past eruption at Taal was a minor steam-driven event in 1977, notes Ed Venzke , the database manager at the Smithsonian Institution’s Global Volcanism Program.

While there may not have been an eruption for four decades, the volcano has “clearly been restless for a very long time,” says Amy Donovan , an expert in volcanic risk at the University of Cambridge. Although often moderate when compared to other volcanic eruptions, many of Taal’s paroxysms have been violently explosive and, due to the huge number of people living on or close to it, frequently fatal.

Greater ash production that often accompanies bigger booms will exacerbate matters. Ash can pollute water supplies, damage electronic infrastructure, smother agriculture, and kill off farm animals and pets . It can also kill people if they inhale enough of it; breathing in glassy ash is always bad, but people with pre-existing respiratory ailments are most at risk, as are the very young and the elderly.

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Either through explosive mixing of magma and water, or through magmatic activity alone, Taal has also previously produced thundering, high-velocity clouds of hot ash, debris, and gas named pyroclastic flows that have killed thousands of people in mere moments. Boris Behncke , a volcanologist at Italy’s National Institute of Geophysics and Volcanology, shared some examples on Twitter , including flows from a 1911 eruption that killed 1,335 people on the central island.

A reasonable worst-case scenario would not just feature pyroclastic flows, but also low-altitude surges of ash and scorching gas that, due to their low density, can literally bounce over the water , says Donovan. These base surges—a term borrowed from nuclear explosion science—“can sandblast everything in their path, including the lake shore on the other side,” Bartel says.

What’s more, if explosions dislodge parts of the volcanic island that then fall into Lake Taal, that could generate tsunamis that will swamp the lake’s shorelines. As an eruption at Indonesia’s Anak Krakatau showed in December 2018 , it only takes a small volcanic collapse to generate a lethal tsunami.

Even if there is no tsunami, falling debris and volcanic earthquakes can cause peculiar and potentially destructive waves known as seiches ; if that debris has enough energy, it can miss the lake entirely and instead land directly on shore.

Back to Taal's future

Of course, forecasting eruptions is fraught with difficulty . Donovan points out that we don’t know how the properties of the magma under Taal have changed since the 1977 eruption. And while looking to old eruptions for clues is helpful, the past can only tell you so much.

“Every eruption is different,” Venzke says. “There’s nothing guaranteed.”

It’s possible that this grim future may not transpire, and that we’ve seen the worst of what Taal has to offer this time, Donovan says: “It might just generate a bit of ash, have a few fire fountains, then go back to sleep again.”

Every eruption is different. There's nothing guaranteed. Ed Venzke , Smithsonian Institution Global Volcanism Program

Alternatively, what we are seeing here could perhaps be the opening salvo of a far longer eruption sequence, says James Hickey , a geophysical volcanologist at the University of Exeter. And even if the eruption becomes more explosive, some, all, or none of these hazards may occur.

Still, it is sensible for people in the region to assume the worst-case scenario is unfolding and to take reasonable, responsible action, Donovan says. If you are still around Taal and haven’t yet heeded instructions to evacuate , it's best to immediately get away from low-lying areas near the volcano. Always listen to local authorities for updates.

In the meantime, volcanologists will wait with bated breath, since lessons from the past show just how dangerous this particular peak can be.

“When I saw yesterday that Taal was in eruption,” Bartel says,” I was somewhat horrified.”

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VOLCANO case study: Mt Soufriere, Montserrat 1997

Causes of eruption .

The island has been created because the Caribbean Plate and Atlantic Plate are moving towards each other and the dense oceanic plate is being subducted under the lighter continental plate.

Table of Contents

At destructive boundaries oceanic crust is destroyed as it is forced below the less dense continental crust. The partially melted rock forces its way to an area of lower pressure ready to erupt.

Before 1995 Mount Soufriere had been dormant for over 300 years. 

In 1995 the volcano began to give off warning signs of an eruption (small earthquakes and eruptions of dust and ash)

In 1997, Large eruptions continued with the dome collapsing and large pyroclastic flows affecting much of the island

Primary and Secondary effects of the Eruption

Responses to the Eruption

Short-term responses.

• Evacuation of the southern part of the island
• Abandonment of the capital city.
• The British government gave £41 million in aid although riots occurred as locals complained that the British were not doing enough to help the island  money for compensation and redevelopment.
• Unemployment rose due to the collapse of the tourist industry.

Long-term responses

• Money was given to individuals to help them move to other countries. 
• An exclusion zone was set up in the volcanic region.
• New roads and a new airport were built.
• Services in the north of the island were expanded.
• The presence of the volcano resulted in a growth in tourism.
• The MVO (Montserrat Volcano Observatory) was set up to study the volcano and provide warnings for the future 
• A Risk assessment was done to help islanders understand which areas are at risk and reduce problems for the future.

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• Published: 23 August 2024

Fingerprints of past volcanic eruptions can be detected in historical climate records using machine learning

• Johannes Meuer   ORCID: orcid.org/0009-0000-1998-6699 1 ,
• Claudia Timmreck   ORCID: orcid.org/0000-0001-5355-0426 2 ,
• Shih-Wei Fang   ORCID: orcid.org/0000-0003-3763-7753 2 , 3 , 4 &
• Christopher Kadow   ORCID: orcid.org/0000-0001-6537-3690 1  

Communications Earth & Environment volume  5 , Article number:  455 ( 2024 ) Cite this article

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• Atmospheric science
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Accurately interpreting past climate variability, especially distinguishing between forced and unforced changes, is challenging. Proxy data confirm the occurrence of large volcanic eruptions, but linking temperature patterns to specific events or origins is elusive. We present a method combining historical climate records with a machine learning model trained on climate simulations of various volcanic magnitudes and locations. This approach identifies volcanic events based solely on post-eruption temperature patterns. Validations with historical simulations and reanalysis products confirm the identification of significant volcanic events. Explainable artificial intelligence methods point to specific fingerprints in the temperature record that reveal key regions for classification and point to possible physical mechanisms behind climate disruption for major events. We detect unexpected climatic effects from smaller events and identify a northern extratropical footprint for the unidentified 1809 event. This provides an additional line of evidence for past volcanoes and refines our understanding of volcanic impacts on climate.

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Introduction.

Volcanic eruptions have been a major source of past climate variability 1 , 2 , 3 , causing regional to global cooling or warming, and altered dry-wet patterns depending on their location and strength 4 , 5 . Their impact on climate is a major source of uncertainty in predicting climate on seasonal to decadal timescales 6 , 7 . Notably, the Tambora eruption of 1815 stands as a stark illustration of its profound effect, evidenced by extreme cold records across Europe and North America in 1816 8 . These distinct volcanic climate effects can be identified in proxies and applied to reconstruct past volcanic events. However, uncertainties in reconstructing past events stem from the complex interaction between the signal and the inherent stochastic internal variability within our singular ensemble member - the Earth. In simpler terms, the challenge lies in untangling the influences of natural variability within our planet’s system, which can obscure the clarity of historical signals.

Traditionally, high-resolution analysis of ice cores combined with other palaeo-data has been used as an indicator of past volcanic eruptions 5 , 9 , 10 . However, the location of the volcanic source often remains unknown, and reconstructed climate variations cannot be accurately attributed to a specific volcanic event. A prominent example is the unidentified historical eruption of 1809, which is half the size of the well-known Tambora eruption of 1815 11 . This eruption was detected in ice core data 30 years ago 12 , but was not recorded in historical documents. Major surface cooling in 1810, probably in response to the eruption, has been detected in instrumental observations 13 , 14 and proxy records 15 , 16 , 17 and has in combination with the Tambora eruption led to strong climate responses 18 . Indeed, the cooling signature is spatially heterogeneous and inconsistent across different data sources 19 . This means that additional evidence is needed to have reliable attribution of reconstructed climate anomaly patterns to a (specific) volcanic eruption.

Recently, artificial neural networks (ANNs) have been used to distinguish between the forced and unforced (UF) signatures of ensemble climate simulations. Barnes et al. 20 have shown that ANNs can be trained to predict the year, given a map of annual mean temperature or precipitation from forced climate model simulations. In this way, ANNs can learn to identify forced patterns of change against a background of climate noise and model differences, providing reliable indicators of the underlying signal. Toms et al. 21 identify oceanic variability regions that contribute to predictability on decadal timescales in a fully coupled Earth system model. In addition, both approaches apply neural network explainability techniques, such as layer-wise relevance propagation (LRP), to help visualize these spatial patterns and highlight areas of disagreement between observed and simulated patterns. In addition, Kadow et al. 22 demonstrate the potential of using ANNs to reconstruct climate information of the past via transfer learning from numerical simulations. These studies have shown the potential of machine learning techniques as a valuable tool for identifying and reconstructing anomaly patterns in climate data.

In our study, due to the complex nature of climate data, we use a convolutional neural network (CNN) 23 architecture instead of simpler techniques such as linear regression. CNNs are well suited to capture the non-linear relationships and intricate spatio-temporal patterns in climate systems, such as the non-linear evolution of ENSO following volcanic eruptions. Their spatial invariance properties allow them to recognize patterns regardless of their position in the input data, which is crucial for global climate data. In addition, CNNs can automatically learn relevant features from raw data, whereas linear regression relies on manually constructed features that may miss important aspects of the data. This makes CNNs a robust and accurate choice for classifying volcanic eruption locations.

Data-driven identification of volcanic fingerprints in large ensemble simulations

To classify whether a volcanic eruption occurred and where it was located in the tropics or extratropics of each hemisphere, we train a CNN on the first post-volcanic boreal summer surface temperature anomalies.

Our CNN is constructed and evaluated on a suite of Max-Planck Institute Earth-System Model 1.1 24 large ensemble simulations with idealized volcanic radiative forcing (EVA-Ens 25 , 26 ), which are derived from the historical experiments of the Max-Planck-Institute Grand Ensemble (MPI-GE) 27 . The volcanic radiative forcing is prescribed in EVA-Ens and encompasses a combination of three eruption locations: NHE (northern hemispheric extra-tropical eruptions, 30°N–90°N), TR (tropical eruptions, 30°S–30°N), SHE (southern hemispheric extra-tropical eruptions, 30°S–90°S) and four different strengths, determined by the amount of sulphur injected into the atmosphere: 5, 10, 20, 40 Terra grams Sulphur (Tg S), and a UF (no volcanic forcing) scenario. The detailed setup is illustrated in Fig.  1 (see also ‘Methods’).

a Generating training and validation data for the classifier. The stratospheric aerosol optical depth (AOD) fields are shown for different volcanic eruption scenarios, northern hemispheric extratropical (NHE), tropical (TR), and southern hemispheric extratropical (SHE). These are prescribed in an earth system model (MPI-ESM1.1 24 ) to simulate the climate evolution after a volcanic eruption. b The convolutional neural network uses only the average boreal summer surface temperature anomalies one year after the volcanic eruption. The input layer is connected to three consecutive convolutional layers (Conv) + max-pooling layers (Pool), followed by two fully connected layers (FC). c The accuracy of the approach, given global surface temperature anomalies, by predicting each of the 100 ensemble members by 100 different trained models, dividing the accuracy scores into sulphur injection (volcanic strength) and the four predicted labels (NHE, TR, SHE, and UF). The overall average score is estimated from the mean of all correctly labelled samples.

The classification of excluded training data demonstrates strong performance, validated by the percentages depicted in Fig.  1 c. These percentages represent the ratio of correctly classified members to the total members within each ensemble. The correctly classified members are obtained by evaluating the excluded one member from each ensemble during training and repeating this process for each ensemble member. A detailed explanation can be found in the ‘Methods’ section. For eruptions greater than or equal to 10 Tg S, the classifications are almost perfect (>98%). The 5 Tg S eruption demonstrates accuracy exceeding 70% for both NHE and SHE classifications. This finding (detailed in Supplementary Table  1 ) carries two implications: Firstly, the signal-to-noise ratio for the 5 Tg S ensembles is smaller than for the other experiments and therefore the results are often influenced by internal variability, making it difficult to distinguish them from the UF scenarios 28 , 29 . Secondly, the NHE and SHE ensembles exhibit a closer resemblance to the TR ensemble compared to the larger eruptions. Notably, UF cases are correctly identified with 92% accuracy, underscoring our CNN’s capability to discern volcanic events based on subsequent boreal summer surface temperature anomaly patterns.

To understand how well our CNN performs on more realistic events, we assess the performance using 100 members from the historical runs of the MPI-GE, which is conducted with the same model as the EVA-Ens (Fig.  2 ). The volcanic forcing in the MPI-GE follows the Coupled Model Intercomparison Project Phase 5 (CMIP5) 30 , 31 protocol including different eruption locations and seasons (Fig.  2 a), which differs from the idealised volcanic forcing in EVA-Ens with three fixed locations and fixed timing mimicking the 1991 Pinatubo eruption (Fig.  1 a).

a The volcanic radiative forcing in the historical runs of the MPI-GE 27 in terms of monthly zonal mean AOD. b The probability of eruption location is predicted by our machine learning model. The distribution shows how many of the members were classified into the respective location, NHE, TR, and SHE, over the total ensemble members from each year of 1860–2000 for the Grand Ensemble. c Same as ( b ), but shows the percentage for the larger volcanic eruptions in numbers.

The input to the CNN are seasonal mean summer temperature anomalies (see ‘Methods’ for details), given for each year from 1860 to 2000. The CNN accurately detects the 1883 Krakatau eruption, identifying 87% (Fig.  2 c) of the members as a tropical eruption given the 1884 summer global surface temperature anomalies. As the climatic effects of volcanic eruptions diminish over several years, lower accuracies are found for 1884 (Fig.  2 b with darker colours indicating a high classification rate). The 1991 Pinatubo eruption, the 2nd largest eruption in the historical period in terms of radiative forcing after the Krakatau in 1883, is accurately identified by classifying 90% of the members as tropical eruptions. The Agung eruption of 1963, located in the tropics, is predominantly classified as SHE. This is consistent with the southerly shift of the tropical eruption’s aerosol load, into the southern hemisphere in the MPI-GE, which resembles more the AOD distribution of the southern hemisphere’s extra-tropical eruptions rather than the tropical ones in the training data set. Similarly, the CNN classifies most members of the tropical El Chichón eruption in 1982 as NHE, which can be attributed to the AOD displaying a pattern which is more similar to the northern hemispheric extra-tropical eruptions in the EVA-Ens.

Detection of historical volcanic events in observational records

How well does our CNN classify volcanic events based on observed temperature anomalies? To test the general applicability of our data-driven approach, we apply our CNN to six datasets (see ‘Methods’ for details). We focus on the European Reanalysis 5 (ERA5) 32 from 1960 to 2000 since there were four major volcanic eruptions in the period and only smaller eruptions before.

To estimate the uncertainty of our approach, we train 100 different CNNs, excluding each simulated ensemble member from the training. The ensemble accuracy of our approach results from the number of members identified as volcanic events by the 100 CNNs (see ‘Methods’ for details). Figure  3 a compares the mean accuracy of 100 CNN models on ERA5 data with the global mean stratospheric AOD from the CMIP6 volcanic forcing compilation 33 , 34 and the Global Space-based Stratospheric Aerosol Climatology (GloSSAC v2.0) dataset 35 ).

a The percentages of detected eruptions, shown as bars (red: NHE, purple: TR, and blue: SHE), are based on global surface temperature anomaly grids from ERA5 32 reanalysis. The monthly global mean AOD from CMIP6 66 climate models and the stratospheric aerosol observations (GloSSAC2.0 35 ) are in black. b The input mean surface temperature anomalies of the first post-volcano summer for 1963 Agung, 1982 El Chichón, and 1991 Pinatubo from ERA5 reanalysis. c The relevance heatmap of the corresponding surface temperature anomaly retrieved by LRP.

Our CNN model successfully identifies the presence or absence of eruptions for most years. The 1963 Agung eruption is again classified as a SHE instead of a tropical eruption with a hit rate of 100%, consistent with the classification for MPI-GE forced by the asymmetric AOD. In 1969, the figure shows a high probability ( ~80%) for an NHE eruption, which is not prominent in the CMIP6 AOD. However, previous studies 36 , 37 show that the 1968 tropical Fernandina eruption (0°S) had a large aerosol emission, exceeding, for example, the 1974 Fuego eruption.

The 1974 eruption of Fuego, which is located in the tropics, is classified as SHE in 1975 (>90%) and correctly as TR in 1976 (100%). This may be due to the fact that the eruption took place at the end of 1974 and had less of an impact on the climate in the first summer than in the second. Interestingly, the 1982 El Chichón eruption is correctly classified as TR (>90%), in contrast to the MPI-GE where the asymmetric AOD misleads our CNN. Furthermore, the eruption was already detected in 1982, which is consistent with the fact that El Chichón erupted in the spring of 1982 and already had an impact on global temperatures that summer 38 .

The 1991 Pinatubo eruption is successfully classified as tropical with an accuracy of  ~80% as TR and  ~20% as NHE. This high accuracy of identification not only demonstrates the applicability of our CNN, but also confirms the current understanding of the impact of volcanoes on global climate, since our classification model is constructed solely from physics-based numerical climate simulations with idealised volcanic forcing.

The results of the other reanalysis and observational datasets are shown in Supplementary Fig.  1 . The averaged result from all data shows a similar behaviour to that of ERA5. However, the differences in the individual data highlight the difficulties in classifying volcanic events, especially for the years preceding the Pinatubo eruption (1990 and 1991), where volcanic behaviour was detected in several cases. In addition, the HadCRUT5 and GISTEMP4 analyses for Pinatubo are classified as an NHE eruption, indicating problems with the lower resolution of these datasets.

A detailed examination of the decision-making mechanisms employed by our CNN could further uncover the reasons behind the accurate localization of certain eruptions in ERA5, such as El Chichón and Pinatubo, in contrast to the less consistent localization of Agung. To do so, we employ LRP 39 . This approach generates heatmaps that highlight the regions contributing to the machine learning model’s prediction (see ‘Methods’ for details).

Figure  3 b shows the following boreal summer surface temperature anomalies of the 1963 Agung (JJA 1964), 1982 El Chichón (JJA 1983), and 1991 Pinatubo (JJA 1992) eruptions from the ERA5 reanalysis, where the 1982 El Chichón and 1991 Pinatubo eruptions are mostly identified as TR eruptions, but the 1963 Agung eruption is incorrectly identified as a SHE eruption. For the 1964 input anomalies, the heatmap (Fig.  3 c) highlights the considerable focus of the CNN on negative anomalies in the tropical Pacific, southern Atlantic, and southern Indian Ocean—typical features associated with southern hemispheric extra-tropical eruptions (Supplementary Fig.  2 ). In the case of El Chichón, the map shows a substantial El-Niño event 40 in the tropical Pacific, to which the CNN only pays little attention. The attention is on regions with negative anomalies, equally in both hemispheres, a feature of tropical eruptions (Supplementary Fig.  2b ). This can also be seen in the 1992 temperature anomalies for Pinatubo, but in different regions (Fig.  3 b, c). In general, the heat maps point to the regions where the signal is strong. These relevant regions have also been identified in reanalysis data 38 and paleo reanalysis data (e.g. ref. 14 ).

Unlocking mysteries of the early 19th century

The good performance of the CNN model offers the potential to go even further back in time to classify and identify past volcanic eruptions in surface temperature reconstructions. One of the challenges is the limited availability of historical global surface temperature reconstructions 41 . Here we utilized the 20CR-v3 dataset 42 , the only global surface temperature reconstruction we found that is available back to 1806 with its experimental extension. The dataset uses only surface observations of synoptic pressure to assimilate global surface temperature grids and does not include information on external forcings such as volcanoes, solar radiation, greenhouse gases, and anthropogenic aerosols. This presents an additional challenge to our machine learning model in detecting volcanic fingerprints, as they are not artificially introduced. In addition, 20CR-v3 provides uncertainty estimates from 80 ensemble members using an ensemble Kalman filter, which facilitates uncertainty estimation for our approach. Over the 19th century, 12 major volcanic eruptions occurred according to the evolv2k-ENS 11 , which is an ensemble reconstruction of volcanic stratospheric sulphur injection and stratospheric AOD. We compared our machine learning model predictions with the reconstructed AOD field from evolv2k (Fig.  4 a). The accuracy of the CNN model was about 87% for identifying non-volcanic events, given an AOD threshold of 0.005, and 54% for identifying volcanic events (Fig.  4 b): 6 (6) out of 12 were clearly classified as volcanic (non-volcanic) events. The CNN had difficulties with the Icelandic high-latitude eruptions (1873 Grimsvötn and 1875 Askja) that were not included in our training data. The CNN also detects an NHE volcanic eruption in the summer of 1882 although the tropical eruption of Krakatau occurred in the summer of 1883. This could possibly be attributed to the weaker eruptions of Fuego in the summer of 1880 or Mayon in the summer of 1881. The Tarawera eruption in 1886 was not identified due to the previous Krakatau eruption, whose global cooling probably exacerbated the temperature anomalies calculated for 1887 (see ‘Methods’ for details). An interesting discovery is the 1831 eruption, which our model identifies as NHE. Although Sigl et al. 43 attribute the forcing to the tropical volcano Babuyan, there is some debate as to whether this eruption occurred near Sicily 44 , 45 , which would be consistent with our prediction.

a The AOD field is based on ref. 11 with the eruptions of the 19th century identified by Sigl et al. 43 . The unknown location of the first volcano eruption is marked as a dashed red line. The probability distributions from the predictions of our machine learning model were made on the 80 members of the 20CR-v3 reanalysis. b List of the largest volcano eruptions from the 19th century and the corresponding class probabilities, NHE, TR, SHE, and UF, estimated by our classifier.

We further focus on the early 19th century, which was one of the coldest periods of the past 500 years 3 , 46 , caused mainly by two strong volcanic eruptions. The unidentified eruption of 1809, that is likely located in the tropics 9 , 11 , 19 , and the well-known Tambora eruption of 1815 47 . The Tambora eruption is clearly classified by the CNN as TR with a score of 95% calculated from the 80 members of the 20CR-v3. The CNN also suggests a volcanic eruption in 1814 with a score of 79% as the Tambora eruption in April 1815 already influenced summer temperatures in the same year. Hence CNN predicts that a volcanic eruption had happened a year before.

The 1809 eruption was identified as NHE with a score of 83% (Fig.  5 a). Based on the surface temperature anomaly pattern in the experimental 20CR-v3 data, our CNN, therefore, suggests that the unidentified eruption from 1809 most likely has its aerosol loading concentrated in the northern extra-tropical region indicating that it was either a northern extratropical eruption or a tropical one but with an asymmetric forcing similar to the historic 1982 El Chichón eruption.

a Estimated average global AOD field from the evolv2k reconstruction 11 (top) and detected eruptions with predicted probabilities of eruption location (bottom) given the 80 members of the 20CR-v3 reanalysis 42 . b Average LRP heatmaps were retrieved from the 80 20CR-v3 reanalysis members of 1810 and 1816 and from 100 members of the 20 Tg S EVA-Ens of NHE and TR.

To analyse the classification of the 1809 event as NHE, we compare the average heatmaps generated from the 80 members of 20CR-v3 from summer 1810 with the heatmaps from summer 1816 for the 1815 Tambora eruption, as well as the heatmaps for the 20 Tg S NHE and TR EVA-Ens (Fig.  5 b). The 1810 and NHE heatmaps prominently highlight regions such as Greenland, northwest Africa, Central Asia and a part of central Russia. Interestingly, these areas do not show the same level of relevance in the 1816 and TR heatmaps, suggesting that the classification for 1809 is more similar to the 20 Tg S NHE EVA-Ens.

It should be noted that the surface temperature considered for the 1809 classification is under the experimental phase of the 20cr-v3 reanalysis. We also tested our CNN on the assimilation of tree-ring data 48 , which only covers the NH extratropics (30°N–90°N) and has a distinct temperature anomaly pattern compared to the 20CR-v3 (Fig.  6 ). In order to match these grids, we trained a new CNN model on simulated EVA-Ens surface temperature anomalies covering the same latitude range from 30°N to 90°N. Although this model achieves a lower skill score (72% compared to 92%), the results are still quite good, especially for larger eruptions. The classification of the 1809 eruption remains as NHE, with a skill of 99% confirming the results of the CNN trained on global data.

a The results on EVA-Ens of the machine learning model trained on surface temperature anomalies ranging from 30°N to 90°N, dividing the accuracy scores into sulphur injection (volcano strength) and the four predicted labels (NHE, TR, SHE, and UF). The total average score is estimated from the mean over all correctly labelled samples. b Tree-ring temperature anomalies that were assimilated using the MPI-ESM 48 and the corresponding relevance map.

The good performance of the north-extratropical-only CNN model on assimilated tree-ring data also highlights the application of our method not only on a global scale but also on a large hemispheric scale. This offers new pathways to identify volcanic eruptions and their hemispheric location in large-scale temperature reconstructions beyond the common era.

Overall, our data-driven approach demonstrates the feasibility of classifying past volcanic events from observational datasets but it has some limitations. As the eruptions in our training dataset all start only in July from only three locations, events with different eruption seasons or locations which were not included in our training simulations are more difficult to identify correctly, such as the high latitude and winter eruptions. Extended training data would therefore be essential to improve the classification skill.

With the potential to accurately detect past large eruptions and their possible eruption locations from observational data, CNN provides an additional line of evidence for identifying past volcanic events, helping the community to reconcile the historical record with climate responses. By using only temperature reconstructions from observed fields, our data-driven method also shows that our simulated volcanic fingerprints for training are reasonable. Even though we have a limited number of events in recent decades for which we have better observations, the study does indeed support the current understanding of volcanic impacts on climate, as well as their implementation in future projections and social impacts. In addition, this research enables historical investigations of past volcanic activity and its impact on human civilisation, ecosystems and the environment. As exemplified by the unidentified 1809 eruption, it has the potential to reveal previously overlooked or poorly documented volcanic events, enriching our understanding of the climate effects of historical eruptions.

Training data

For training the CNN, we used seasonal mean climate anomalies from different large ensemble simulations of idealized volcanic eruptions of different locations and strengths (EVA-Ens). The CNN is trained with boreal summer mean global surface temperature anomalies one year after the volcanic eruption. The idealized volcanic forcing experiments were performed with the Max-Planck-Institute Earth-System-Model (MPI-ESM1.1-LR), which is an intermediate version between the MPI-ESM CMIP5 version 24 and CMIP6 version 49 . The MPI-ESM1.1-LR has an atmospheric horizontal resolution of 1.8° with 47 vertical levels up to 0.01 hPa and a nominal ocean resolution of 1.5° with 64 vertical levels.

The EVA-Ens experiments were designed as sensitivity experiments to the Max Planck Institute Earth System Model Grand Ensemble (MPI-GE, 27 ) historical run, where the prescribed historical volcanic forcing was replaced in 1991 by more idealized volcanic forcing data. The EVA-Ens simulations were branched from 100 members of the historical experiments of the MPI-GE in January 1991 and for a time span of 3 years. We create large 100-member ensembles from combinations of four strengths (40 Tg S, 20 Tg S, 10 Tg S, and 5 Tg S) and three locations (tropical and northern/southern extra-tropical), for a Northern Hemisphere summer eruption in June 1991 25 , 26 , 28 . In addition, we also run one 100-member ensemble experiment without any volcanic forcing. To put the strengths into perspective, the recent Pinatubo eruption from 1991 had an estimated sulphur emission between 5 Tg S and 10 Tg S 50 . Other forcings, such as solar variations, greenhouse gases and anthropogenic aerosols remain for all experiments unchanged and are the same as in the historic simulation of the MPI-GE. Each of these simulations provides global monthly grids ranging from January 1991 to December 1993.

The volcanic forcing is prescribed in the MPI-ESM by zonally and monthly mean optical parameters (aerosol extinction, single scattering, albedo and asymmetry factor). In the MPI-GE historical runs, the PADS dataset 30 , 31 is used for the volcanic aerosol forcing of historic eruptions. For our idealized volcanic experiments, we compiled the radiative forcing with the easy volcanic aerosol (EVA) forcing generator 51 . EVA is based on a parameterized three-box model of stratospheric transport and scaling relationships that calculates stratospheric aerosol optical properties from eruption time and latitude, estimated stratospheric sulphur emission and wavelength. The stratospheric AOD fields generated with EVA for different emission strengths and locations can be seen in Supplementary Fig.  4 .

To ensure an even distribution of output classes in our training data, we included 100 members from the years 1991, 1992, 1993, and 1994 of the unperturbed runs to cover the no-volcano case. In total, this results in a dataset of 1600 training samples.

Reanalysis and observation of data

Our assessment included an ensemble of four reanalyses and two observational analyses. An overview of all datasets used for validation and evaluation can be seen in Supplementary Table  1 . Regarding the reanalyses, the ERA-5 reanalysis 32 is a comprehensive global atmospheric reanalysis produced under the auspices of the Copernicus Climate Change Service at the European Centre for Medium-Term Weather Forecasts. It covers the period from January 1940 to the present and provides hourly estimates of atmospheric, land and ocean climate variables at a spatial resolution of 0.25°/0.25°, covering 137 levels from the Earth’s surface to 80 km altitude. The 20CR-v3 42 is a four-dimensional weather reconstruction dataset covering the period 1836–2015, with an experimental phase covering the period 1806–1835, developed by NOAA’s Physical Sciences Laboratory. This reanalysis incorporates only surface pressure observations into NOAA’s Global Forecast System to predict various climate variables, including temperature, pressure, winds, humidity, solar radiation, and cloud cover. The data are presented as daily estimates with a spatial resolution of 0.7°/0.7° and include 80 ensemble members designed to estimate uncertainty. The Japanese 55-year reanalysis (JRA55) 52 , by the Japanese Meteorological Agency, integrates historical observations through a sophisticated operational data assimilation system. The dataset covers the period from 1958 to 2024, providing data at 3-h intervals and a spatial resolution of 1.25°/1.25°. In addition, NCEP1 53 is the result of a collaboration between the National Centers for Atmospheric Prediction (NCEP) and the National Center for Atmospheric Research and covers the period from 1948 to the present. The reanalysis uses multiple numerical weather prediction models, such as the global forecast system, the climate forecast system and others to assimilate observations. The data is given at 6-hourly, daily and monthly intervals with a spatial resolution of 1.875°/1.875°.

HadCRUT5 54 , a joint product of the Met Office Hadley Centre and the Climatic Research Unit at the University of East Anglia, contains historical surface temperature anomalies on a global scale, covering the period from 1961 to 1990. This dataset is available from 1850 onwards and provides monthly grids with a spatial resolution of 5°/5°. The most recent HadCRUT.5.0.2.0 analysis includes a total of 200 ensembles from which we only considered the ensemble mean. Finally, the GISTEMP4 55 , 56 analysis represents an estimate of global surface temperature changes produced by the National Aeronautics and Space Administration from 1880 to the present. This dataset is presented at a spatial resolution of 2°/2° on a monthly basis. The observational analyses both contain missing values, mainly concentrated near the poles due to a lack of observations. By simply setting these to zero, we found that our CNN still performed well for these observations. However, for future investigations, more sophisticated interpolation techniques could be used to improve the quality of the data.

Data pre-processing

All data were provided in NetCDF4 (Network Common Data Form) and pre-processed using the climate data operators 57 and Freva, a free evaluation system 58 . For the EVA-Ens dataset, we calculated anomalies from a reference period of 1985-1990 from the MPI-GE ensemble mean. This was subtracted from each of the EVA-Ens members. We chose this reference period, because it contains only small volcanic forcings, that would not interfere with the signals from the EVA simulations. For each resulting ensemble member anomaly, we calculated the boreal summer mean from 1992, one year after the simulated eruption. These were then each labelled as NHE, TR, SHE, and UF, forming our input-label pairs for training.

To address the potential influence of the global warming trend, anomalies in the MPI-GE, reanalyses, and observational data were calculated dynamically during the evaluation. In specific, for each year Y N , we selected the preceding years Y N −3 and Y N −2 as reference periods:

From these anomalies, we again calculated the boreal summer mean for each year, respectively, forming the input data for our evaluation. The short reference periods of only two years prevent the compensating effects of eruptions occurring in quick succession. However, if the eruptions are very close to each other, this might still pose a problem to the classifier. The gap year in between the reference period and the year under consideration is intended to deal with eruptions that occur in the early part of the year and have already had an impact on the same year’s boreal summer temperatures. This would reduce the anomalies of the following year and thus challenge our machine learning model. All datasets have been conservatively remapped to 1.8°/1.8° (192 × 92), the original resolution of the EVA-Ens dataset.

Deep-learning model

We used a CNN, a machine learning approach specifically designed to process gridded data by extracting spatial features. Our CNN consists of three convolutional layers with 5 × 5 kernels, a stride of 2 and global padding, which applies circular padding to the boundaries of the longitudes to account for the round shape of the Earth. Each convolutional layer is followed by a subsequent max-pooling operation (2 × 2) and rectified linear unit activation, and finally two fully connected (FC) layers. The implementation was done in Python 3.9, using the PyTorch deep learning framework. We trained each CNN over 10,000 iterations, using a batch size of 4 and a learning rate of 5e-5 with the Adam optimiser. For the loss function, we used the standard classification loss, which is the cross-entropy loss.

LRP is a technique for bringing accountability to complex deep-learning models. The trained CNN is propagated backwards after making a prediction, using a set of propagation rules. In this study, we used the interpretability library for PyTorch by Captum 59 . We applied the γ -propagation rule 60 , which aims to reduce noise and improve stability by favouring the effect of regions that contributed positively to the prediction. However, other propagation rules and neural network explainability techniques gave very similar results. In Supplementary Fig.  1 , we show a comparison of gradient-based interpretability techniques, including three different LRP propagation rules (Gamma, Alpha1Beta0, Epsilon) and Integrated Gradients 61 . In addition, we show the results of the post-hoc interpretability techniques SHAP 62 and Occlusion 63 . It is important to note that SHAP and Occlusion are fundamentally different from LRP-based methods and Integrated Gradients as they do not rely on the internal structure of the model.

Model tuning

The initial model was taken from the PyTorch documentation for an image classifier 64 designed to process images of size 3 × 32 × 32, denoting 3-channel colour images of 32 × 32 pixels. Our adaptation involved modifying the network architecture to handle images of size 1 × 192 × 192. Initially, using the original architecture with two convolutional layers and three FC layers yielded an accuracy of approximately 80% on the simulated dataset. Subsequently, through experimentation, we optimised the model by introducing an additional convolutional layer while removing an FC layer, resulting in a significant improvement to 92% accuracy. In addition, fine-tuning the learning rate to 5 e  − 5, as opposed to the original 1 e  − 3, significantly improved the training process. We also investigated the effect of the number of epochs and batch size on the effectiveness of the training. After extensive evaluation, we found that about 10,000 iterations and a batch size of four produced optimal results on our evaluation set. Deviating from this threshold resulted in either reduced performance due to underfitting or exacerbated overfitting due to excessive iterations.

Model validation

Typically, CNNs require extensive training data to effectively capture complex patterns and establish reliable classification capabilities. However, expanding our training dataset is challenging and constrained by the 100 members of the MPI-GE. When considering adding additional scenarios to the EVA-Ens for future exploration, we encountered potential redundancy issues, exemplified by the 2.5 Tg S ensemble (Supplementary Table  2 ), which we discuss further in the sensitivity experiments section.

To overcome this limitation, we developed an approach using leave-one-out cross-validation 65 . This method involves isolating a piece of data, using the remaining data for CNN training, and using the isolated data for validation. By iterating this process several times, each time isolating a different split, it is possible to train several different CNNs, allowing comprehensive validation without excessive data generation. While generating 100 ensemble members can be computationally demanding, our strategy mitigates this burden while ensuring robust training and validation, thereby increasing the accuracy and efficiency of our analysis. We observed a marginal improvement in results by reducing the validation data to a single member, thus maximising the sample availability for training the CNN model. This provided us with 100 trained CNN models, allowing us to estimate uncertainty when evaluating observational data without multiple members. By using the same data across our 100 trained CNNs, we can derive a probability distribution of predictions.

The validation metrics across all tables were computed by averaging the results of the 100 trained CNNs. Each CNN produced a single output for each scenario (e.g. TR—5 Tg S), resulting in 16 outputs per network. Aggregation of these outputs produced 100 results for each scenario and a cumulative total of 1600 validated results.

Sensitivity experiments

The CNN model exhibited sensitivity to data normalization when utilizing reanalysis and observational inputs. Specifically, the prediction accuracy varied depending on the reference period used for data normalization. Extending the reference period beyond three years into the past resulted in certain volcanoes being undetected. For instance, the 1982 El Chichón eruption was consistently misclassified as UF across multiple datasets. Conversely, selecting only a single year as a reference caused the model to overly prioritize NHE, TR, or SHE classifications.

We tried extending our training data with 2.5 Tg S ensembles (Supplementary Table  2 ). We retrained a new CNN model on this extended dataset, which increased the accuracy of the 5 Tg S ensembles. However, it significantly decreased the accuracy of the UF ensemble and the overall detection accuracy, even when excluding the 2.5 Tg S ensembles from the evaluation. This suggests that the weak 2.5 Tg S events are barely distinguishable from the internal variability in their surface temperature anomaly patterns and we do not include them in our CNN in order to maintain the high detection accuracy on UF events.

We further investigated how other variables performed compared to the surface temperature anomalies: precipitation (Supplementary Table  3 ), sea-level pressure (Supplementary Table  4 ) and all three variables combined (Supplementary Table  5 ). The CNN trained on precipitation anomalies achieved a total accuracy rate of over 83%, falling slightly short of the CNN trained solely with surface temperature anomalies. In contrast, the CNN trained with sea-level pressure anomalies exhibited the lowest accuracy, with a score of 75% correct classifications. This implies that volcanic eruptions leave a more identifiable signal in terms of their effect on global temperatures compared to precipitation and sea-level pressure. Interestingly, the CNN trained with all three variables—precipitation, sea-level pressure, and surface temperature anomalies—achieved slightly lower accuracy when compared to the CNN utilizing only surface temperature anomalies. This suggests that similar to including the ensembles of 2.5 Tg S, including additional variables alongside surface temperature anomalies may introduce redundancy rather than enhance the performance.

The observational datasets we examined posed challenges due to varying resolutions. Reanalysis datasets offered higher resolution compared to the simulations used for training our machine learning model. Conversely, the observational analyses HadCRUT5 and GISTEMP4 required conservative downscaling to align with the higher resolution. In Supplementary Fig.  1 , we observe a misclassification by the CNN, labelling the 1992 Pinatubo eruption as NHE in these datasets, potentially due to the resolution limitations posing a challenge to our CNN model. However, the majority of classifications align with the higher spatial resolution reanalysis datasets despite the considerable resolution gap.

Data availability

The primary data and scripts used in the analysis, along with other supplementary materials that are useful for reproducing the model simulations, have been archived by the Max Planck Institute for Meteorology. These materials can be accessed via the following links: • http://hdl.handle.net/21.11116/0000-0007-8B38-E • https://hdl.handle.net/21.11116/0000-000D-4B1F-E . Additionally, the following open-source reanalyses and analyses were used for this study, and are available through public repositories: •ERA5: https://doi.org/10.24381/cds.adbb2d47 •20CR-v3: https://doi.org/10.5065/H93G-WS83 •JRA55: https://doi.org/10.2151/jmsj.2015-001 •NCEP-1: https://downloads.psl.noaa.gov/Datasets/ncep.reanalysis/ •HadCRUT5: https://www.metoffice.gov.uk/hadobs/hadcrut5/data/HadCRUT.5.0.2.0/download.html •GISTEMP: https://data.giss.nasa.gov/gistemp/ These resources provide the necessary datasets for reproducing our analyses.

Code availability

The code used to perform the analysis and simulations, along with other supporting software to reproduce the results, is available at: https://doi.org/10.5281/zenodo.12755134 . This includes the scripts and documentation necessary to replicate our study’s results. It facilitates the validation and further exploration of the methods used in our research, as well as access to the trained models that generated the proposed results.

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Acknowledgements

The authors are grateful to the German Climate Computing Center (DKRZ) for providing the hardware for the calculations and all the data that were used for this study. Johannes Meuer and Claudia Timmreck are funded by the German National Funding Agency (DFG), provided by the research unit FOR 2820, titled “Revisiting The Volcanic Impact on Atmosphere and Climate-Preparations for the Next Big Volcanic Eruption" (VolImpact), with the project number 398006378. Shih-Wei Fang acknowledges support from the German Federal Ministry of Education and Research (BMBF), research programme “ROMIC-II, ISOVIC” (FKZ: 01LG1909B) and was supported by the Institute for Basic Science (IBS), Republic of Korea, under IBS-R028-D1. Support for the Twentieth Century Reanalysis Project version three dataset is provided by the U.S. Department of Energy, Office of Science Biological and Environmental Research (BER), the National Oceanic and Atmospheric Administration Climate Programme Office, and the NOAA Physical Sciences Laboratory. Thanks to ICDC, CEN, University of Hamburg for data support.

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Johannes Meuer conceived the study, designed the research methodology, conducted the data analysis, and collected and processed the data. Claudia Timmreck performed the simulations for the EVA-Ens dataset. Christopher Kadow contributed crucial methodological insights. Shih-Wei Fang provided valuable ideas for validation methods. All authors contributed to interpreting the results and offered critical feedback throughout the manuscript’s preparation. Additionally, all authors participated in drafting and editing the manuscript.

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Meuer, J., Timmreck, C., Fang, SW. et al. Fingerprints of past volcanic eruptions can be detected in historical climate records using machine learning. Commun Earth Environ 5 , 455 (2024). https://doi.org/10.1038/s43247-024-01617-y

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The Making of Our Alien Earth: The Undersea Volcanoes of Santorini, Greece

The following expedition marks the third installment of NASA Astrobiology's fieldwork series, the newly rebranded Our Alien Earth , streaming on NASA+ . Check out all three episodes following teams of astrobiologists from the lava fields of Holuhraun, Iceland, to the Isua Greenstone Belt of Greenland, and finally, the undersea volcanoes of Santorini, Greece. And stay tuned for the lava tubes of Mauna Loa, Hawaii in 2025.

THE VOYAGE BEGINS

My career at NASA has always felt like a mad scientist’s concoction of equal parts hard work, perseverance, absurd luck, and happenstance. It was due to this mad blend that I suddenly found myself on the deck of a massive tanker ship in the middle of the Aegean sea, watching a team of windburnt scientists, engineers, and sailors through my camera lens as they wrestled with a 5,000lb submersible hanging in the air.

“Let it out, Molly, slack off a little bit…” shouts deck boss Mario Fernandez, as he coordinates the dozen people maneuvering the vehicle. It’s a delicate dance as the hybrid remotely operated vehicle (ROV), Nereid Under Ice (NUI), is hoisted off the ship and deployed into the sea. “Tagline slips, line breaks… you’ve got a 5,000lb wrecking ball,” recounts Mario in an interview later that day.

How did I get here?

A few years ago I found myself roaming the poster halls of the Astrobiology Science Conference in Bellevue, Washington, struggling to decipher the jargon of a dozen disciplines doing their best to share their discoveries; phrases like lipid biomarkers, anaerobic biospheres, and macromolecular emergence floated past me as I walked. I felt like a Peanuts character listening to an adult speak.

Until I stumbled upon a poster by Dr. Richard Camilli entitled, Risk-Aware Adaptive Sampling for the Search for Life in Ocean Worlds . I was quickly enthralled in a whirlwind of icy moons, fleets of deep sea submersible vehicles, and life at sea.

“Are you free in November?”

“Absolutely,” I replied without checking a single calendar.

Five months and three flights later, I arrived at the port of Lavrio, Greece, as Dr. Camilli and his team were unloading their suite of vehicles from gigantic shipping crates onto the even more massive research vessel. I stocked up on motion sickness tablets, said a silent farewell to land, and boarded the ship destined for the undersea Kolumbo volcano.

Greece is a great place to study geology, because it's a kind of supermarket of natural disasters.

Dr. Paraskevi NomikoU

University of Athens

LIFE AT SEA

Documenting astrobiology fieldwork has taken me to some pretty remote and rough places. Sleeping in wooden shacks in Iceland without running water and electricity, or bundled up in a zero-degree sleeping bag in a tent while being buffeted by gale force winds in the wilderness of Greenland. But life at sea? Life at sea is GOOD.

I was fortunate to have a personal cabin all to myself: a set of bunk beds, a small bathroom with a shower, and a small desk with plenty of outlets for charging my gear. I would also be remiss if I didn’t mention the mess hall. Aside from a freshly rotated menu of three hot meals a day, it was open 24/7 with a constant lineup of snacks to keep bellies full and morale high. This was luxury fieldwork. The ability to live, work, and socialize all in the same place would make this trip special in its own right, and allowed me to really get to know the team and capture every angle of this incredibly complex and multi-faceted expedition.

SEARCHING FOR LIFE ON OCEAN WORLDS

“The goal of this program is cooperative exploration with under-actuated vehicles in hazardous environments,” explains Dr. Camilli as we stand on the bow of the ship, the sun beginning to set in the distance. “These vehicles work cooperatively in order to explore areas that are potentially too dangerous or too far away for humans to go.”

This is the problem at hand with exploring icy ocean worlds like Jupiter’s moon, Europa. The tremendous distance between Earth and Europa means we will barely be able to communicate and control vehicles that we send to the surface, and will face even more difficulty once those vehicles dive below the ice. This makes Earth’s ocean a perfect testbed for developing autonomous, intelligent robotic explorers.

“I've always been struck at how parallel ocean exploration and space exploration is,” says Brian Williams, professor from the Computer Science and Artificial Intelligence Laboratory at MIT. “Once you go through the surface, you can't communicate. So, somehow you have to embody the key insights of a scientist, to be able to look and see: is that evidence of life?”

MEET THE FLEET

Exploring anywhere in space begins with a few simple steps: first, you need to get a general map of the area, which is typically done by deploying orbiters around a celestial body. The next step is to get a closer look, by launching lander and rover missions to the surface. Finally, in order to understand the location best, you need to bring samples back to Earth to study in greater detail.

“So you can think of what we're doing here as being very parallel, that the ship is like the orbiter and is giving us a broad view of the Kolumbo volcano, right? Once we do that map, then we need to be able to explore interesting places to collect samples. So, the gliders are navigating around places that look promising from what the ship told us. And then, it looks to identify places where we might want to send NUI. NUI is very capable in terms of doing the samples, but it can't move around nearly as much. And so, we finally put NUI at the places where the gliders thought that they were interesting.”

THE SCIENTIST’S ROBOTIC APPRENTICE

As the espresso machine in the mess hall whirred away pouring out a much needed shot of caffeine, I sat with Eric Timmons, one of the expedition’s computer science engineers. Eric wears a few hats on the ship, but today we are discussing automated mission planning, the first step to true autonomy in robotic exploration.

“In any sort of scientific mission, you’re going to have a list of goals, each with their own set of steps, and a limited amount of time to achieve them. And so, Kirk works on automating that.” Kirk is the nickname of one of the many algorithms involved in the team’s automated mission planning. It’s joined by other algorithms, all named after Star Trek characters, collectively known as Enterprise , each responsible for different aspects of planning a mission and actively adapting to new mission parameters.

Dr. Richard Camilli explains further: “Basically, we have scientists onboard the ship that are feeding policies to these automated planners. [The planners] then take those policies plus historical information, the oceanographic context, and new information being transmitted by the vehicles here and now; they take all that information, and combine it to construct a mission that gets to the scientific deliverables, while also being safe.”

These are areas that humans aren't designed to go to. I guess the best analogy would be like hang gliding in Midtown Manhattan at night.

Dr. richard camilli

Woods Hole Oceanographic Institution

OK, let’s recap the story so far: the ship’s sonar and other instruments create a general map of the Kolumbo volcano. That information, along with data from previous missions, is fed to Enterprise ’s team of algorithms, which generates a mission for the gliders. The gliders are deployed, and using their sensors, provide higher-fidelity data about the area and transmit that knowledge back to the ship. The automated mission planners take in this new data, and revise their mission plan, ranking potential sites of scientific interest, which are then passed onto NUI, which will conduct its own mission to explore these sites, and potentially sample anything of interest.

DIVE, DIVE, DIVE

After a few days on the ship, the routine of donning my steel-toed boots and hard hat when walking around the deck has started to become second nature. My drone skills have greatly improved, as the magnetic field produced by the ship and its instruments forced me to take-off and land manually, carefully guiding the drone in and around the many hazards of the vessel. This morning, however, I’ve been invited to step off the ship for the first time to get a first-hand look at deploying the gliders. Angelos Mallios from the glider team leads me down into the bowels of the ship to the lower decks, as we arrive at a door that opens to the outside of the ship, waves lapping about six feet below. A zodiac pulls up to the door and we descend down a ladder into the small boat.

Meanwhile, the rest of the glider team is on the main deck of the ship, lifting the gliders with a large, motorized crane, and lowering them onto the surface of the water. The zodiac team approached to detach the glider and safely set it out into the sea, while I dipped a monopod-mounted action camera in and out of the water to capture the process. Unbeknownst to me at the time, this would become some of my favorite footage of the trip, sunlight dancing off the surface of the waves, while the gliders floated and dove beneath.

Angelos’ radio began to chatter. Eric Timmons was onboard the ship ready to command the gliders to begin their mission plan assigned by Enterprise . A moment passed and the yellow fin of the glider dipped below the water’s surface and disappeared.

NUI VERSUS THE VOLCANO

The following day, it was time to see the star of the show in action; the expedition team was ready to deploy the aforementioned 5,000lb wrecking ball, NUI. The gliders had been exploring the surrounding area day and night, using their suite of sensors to detect areas of scientific interest. Since this mission is about searching for life, the gliders know that warmer areas could indicate hydrothermal vent activity; a literal hotspot for life in the deep ocean. Kirk , along with the science planner algorithm, Spock , determined a list of possible candidates that fit that exact description.

“There's always a bit of tension in the operations, where, do you go strike out in an area that is unstudied and potentially come back with nothing? Or do you go to a site that you know and try to understand it a little bit more, that kind of incremental advance?” Dr. Camilli pauses to take a quick swig of sparkling water after a long day of diving operations, as he recounts a moment in the control room earlier that day. All the scientists onboard this expedition are extremely skilled and knowledgable, and this mission is asking them to put aside their instincts, and follow the suggestions of computer algorithms; a hard pill to swallow for some.

“We stuck with the Spock program, and it paid great dividends. And all of the scientists were amazed at what they saw. The first site that we went to was spectacular. The second site we went to was spectacular. Each of the five sites that it identified as interesting were interesting, and they were each interesting in a different way; totally different environments.”

Interesting, in this case, was quite the understatement. As the expedition team and I crowded into the ship’s control room to look at the camera feeds transmitted by NUI, now fully deployed to the seafloor, audible gasps erupted from multiple people. Bubbles filled the monitor as live fumaroles, active vents from the volcano, were pouring out heat and chemical-rich fluid into the water. Thick, microbial mats covered the surrounding rock, and multicellular lifeforms dotted the landscape. The expedition team had found a live hydrothermal vent, and life thriving around it.

SOUVENIRS FROM THE OCEAN FLOOR

“I've never seen anything like that before,” recalls Casey Machado, expedition lead and the main pilot for Nereid Under Ice (NUI). Casey is sitting in an office chair surrounded by glowing monitors, a joystick in their left hand, and a gaming controller in their right. Since NUI is a hybrid ROV, it can be controlled manually from the ship by remote, or receive autonomous instructions from the Enterprise mission planners. Today, the team plans on manually controlling NUI to retrieve samples from the first site of interest.

NUI is a strange looking vehicle. Only a small section of its body is watertight, where many of its critical components are housed. The remainder is fairly open, and upon arriving at the first site recommended by Spock, the front of the ROV opens up its front double doors to reveal a multi-jointed manipulator arm, stereo camera set, and other instruments. I’m instantly reminded of the space shuttle mission to repair the Hubble Space Telescope, which had a similar mechanism.

Casey deftly maneuvers each joint of the arm to approach a rock covered in microbial mats. The end of NUI’s arm is equipped with two sampling instruments: a claw-like grabbing mechanism and a vacuum-like hose called the “slurp gun”. The end of the arm twists and turns as Machado aligns it with the rock, eventually opening and closing it around the target. With a gentle pull, the rock comes loose, and with a few more careful manipulations places it delicately into NUI’s sample cache. I offer a high-five, which Casey nonchalantly returns like the whole task was nothing.

TEACHING A ROBOT TO FISH

At this point, the expedition team has collected dozens of samples and achieved multiple engineering milestones, enough to fill years' worth of scientific papers, but they are far from finished. A true mission to an ocean world will have to be pilotless, as Dr. Gideon Billings from MIT explains: “They need to operate without any human intervention. They need to be able to understand the scene through perception and then make a decision about how they want to manipulate to take a sample or achieve a task.”

Gideon sits in the control room to the left of the piloting station, working alongside Casey as they prepare to demonstrate NUI’s automated sampling capabilities. His laptop screen shows a live 3D-model of the craft, its doors open, arm extended. Projected around the craft is a 3D reconstruction, or point cloud, of the seafloor created from the stereo camera pair mounted inside the vehicle. Similarly to how our brains take the two visual feeds from both of our eyes to see three-dimensionally, a stereo camera pair uses two cameras to achieve the same effect. By clicking on the model and moving its position in the software, NUI performs the same action thousands of meters under the ocean.

“That is shared autonomy, where you could imagine a pilot indicating a desired pose

for the arm to move to, but then a planner taking over and coming up with the path that the arm should move to reach that goal. And then, the pilot just essentially hitting a button and the arm following that path.”

Over the course of multiple dives, Gideon tested various sampling techniques, directing the manipulator arm to use its claw-like device to grab different tools and perform a variety of tasks. “We were able to project the point cloud into that scene, and then command the arm to grab a push core and move it into a location within that 3D reconstruction. We verified that that location matched up. That showed the viability of an autonomous system.” This seemingly small victory is a huge step towards exploring planets beyond Earth. Since this expedition, the engineering team has not only improved this shared autonomy system, but has also implemented a natural language interface, allowing a user to use their normal speaking voice to give commands to the ROV, further blurring the lines between reality and science fiction.

SOMEWHERE BEYOND THE SEA

I cannot help but envy the life of those who chose to make the ocean their place of work. The time I’ve spent with oceanographers has me questioning all my life choices; clearly they knew something I didn’t.

Watching the sunrise every morning, peering through the murky depths of the deep sea, unlocking the secrets of Earth’s final frontier. All in a day’s work for Dr. Richard Camilli and his team of intrepid explorers. Watch Our Alien Earth and The Undersea Volcanoes of Santorini, Greece on NASA+ and follow the full story of this incredible expedition.

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Soufrière Hills Volcano, Montserrat, West Indies.

Soufrière Hills Volcano , Montserrat, West Indies. Synopsis of events by former Montserrat resident, photographer and Author Lally Brown. 

Where is Montserrat? Montserrat is a small tropical island of approximately 40 sq. miles in the Caribbean, fifteen minutes flying time from Antigua. It is a British Overseas Territory and relies on UK Government aid money to survive. It is of volcanic origin with the Soufrière Hills above the capital of Plymouth the highest point of the island.

How and when did the volcano erupt? Prior to 1995 the volcano in the Soufrière Hills had been dormant for 350 years but on the morning of 18th July 1995 steam and fine ash could be seen coming from the flanks of the Soufrière Hills accompanied by a roaring sound, described as being like a jet engine. In the capital of Plymouth there was a strong smell of ‘bad eggs’ the hydrogen sulphide being emitted by the awakening volcano.

Montserrat was totally unprepared. No-one had ever imagined the dormant volcano would erupt. The Soufrière Hills was the breadbasket of the island where farmers worked the fertile agricultural land, while the busy capital and island port of Plymouth nestled at the foot of the hills.

Scientists arrived from the University of the West Indies to assess the situation. They said the volcano was producing ‘acoustic energy explosions’ at approximately half-hour intervals sending ash and vapour three to four hundred metres into the air.

What happened next? Before July 1995 Montserrat was a thriving tourist destination with a population of 10,000 people but over several weeks there was a mass exodus from the island and a run on the banks with people withdrawing cash.

Several areas near the vent that had opened up in the hillside were declared exclusion zones and residents were evacuated to the safe north of the island into schools and churches.

It was evident the volcano was becoming more active when a series of small earthquakes shook the island. Heavy rain from passing hurricanes brought mudflows down the hillsides into Plymouth. Sulphide dioxide emissions increased, a sure sign of heightened activity.

The scientists hoped to be able to give a six hour warning of any eruptive activity but when they discovered the magma was less than 1 km below the dome they said this could not be guaranteed, saying there was a 50% chance of an imminent eruption. An emergency order was signed by the Governor and new exclusion zones were drawn with people evacuated north.

The years 1995 to 1997 The Soufrière Hills volcano became increasingly active and more dangerous.

Montserrat Volcano Observatory (MVO) was established to monitor activity and advise the Government.

December 1995 saw the first pyroclastic flow from the volcano.

The capital of Plymouth was evacuated for the last time in April 1996.

Acid rain damaged plants.

Two-thirds of Montserrat became the new exclusion zone , including the fertile agricultural land.

Population dropped to 4,000 with residents leaving for UK or other Caribbean islands.

Frequent heavy ashfalls covered the island with blankets of thick ash.

On the seismic drums at the MVO swarms of small hybrid earthquakes frequently registered. Also volcano-tectonic earthquakes (indicating fracture or slippage of rock) and ‘Broadband’ tremors (indicating movement of magma).

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MVO Seismograph printout Dec 1997

‘Spines’ grew rapidly out of the lava dome to heights of up to 15 metres before collapsing back.

Rainfall caused dangerous mudflows down the flanks of the Soufrière Hills.

Temporary accommodation was built to house evacuees living in churches and schools.

25th June 1997 Black Wednesday For a period of twenty minutes at 12.59 pm the volcano erupted without warning with devastating consequences. A massive pyroclastic flow swept across the landscape and boulders up to 4 metres in diameter were thrown out of the volcano. Over 4 sq.km was destroyed including nine villages and two churches. The top 300ft had been blown off the lava dome. Tragically nineteen people were caught in the pyroclastic flow and died.

Post Office and War Memorial 1997

Lateral blast December 1997 Midnight on Christmas Day 1997 the MVO reported that hybrid earthquakes had merged into a near-continuous signal clipping the sides of the seismic drum. At 3am on Boxing Day there was a massive collapse of the dome. Approximately 55 million cubic metres of dome material shot down the flanks of the volcano into the sea. Travelling at speeds of 250-300 km per hour it took less than a minute to slice a 7 km wide arc of devastation across southern Montserrat. The evacuated villages of Patrick’s and O’Garros were blasted out of existence. A delta 2 km wide spilled into the sea causing a small tsunami .

Police checkpoint Montserrat

March 1999 After a year of apparent inactivity at the volcano the Scientists declared the risk to populated areas had fallen to levels of other Caribbean islands with dormant volcanoes. Arrangements were made to encourage overseas residents to return. Plans were put in place to reopen the abandoned airport.

2000 to 2003 One year after the volcano had been declared dormant there was a massive collapse of the dome, blamed on heavy rainfall.

In July 2001 another massive collapse of the dome described as ‘a significant eruption’ caused airports on neighbouring Caribbean islands to close temporarily due to the heavy ashfall they experienced. A Maritime Exclusion Zone was introduced around Montserrat and access to Plymouth and the airport prohibited.

Soufrière Hills volcano was now described as a ‘persistently active volcano’ that could continue for 10, 20 or 30 years. (ie possibly to 2032).

In July 2003 ‘the worst eruption to date’ took place, starting at 8 pm 12th July and continuing without pause until 4 am morning of 13th July. Over 100 metres in height disappeared from the mountain overnight. It was the largest historical dome collapse since activity began in July 1995.

A period of relative quiet followed.

2006 The second largest dome collapse took place with an ash cloud reaching a record 55,000 metres into the air. Mudflows down the flanks of the Soufrière Hills was extensive and tsunamis were reported on the islands of Guadeloupe and Antigua.

Another period of relative quiet followed.

Soufriere Hills volcano 2007

2010 Another partial dome collapse with pyroclastic flows reaching 400 metres into the sea and burying the old abandoned airport. There was extensive ashfall on neighbouring islands.

Again followed by a period of relative quiet.

2018 Although the Soufrière Hills volcano is described as ‘active’ it is currently relatively quiet. It is closely monitored by a team at the Montserrat Volcano Observatory (MVO). They advise the Government and residents on the state of the volcano.

Negative effects of the volcano:

·       Approximately two-thirds of Montserrat now inaccessible (exclusion zone);

·       Capital of Plymouth including hospital, government buildings, businesses, schools etc. buried under ash;

·       Fertile farming land in the south in exclusion zone and buried under ash;

·       Population reduced from 10,000 to 4,000;

·       Businesses left Montserrat;

·       Tourism badly affected;

·       Concern over long term health problems due to ash;

·       Volcano Stress Syndrome diagnosed;

·       Huge financial cost to British Tax Payer (£400 million in aid);

·       Loss of houses, often not insured;

·       Relocation to the north of Montserrat by residents from the south.

Positive effects:

·       Tourists visiting Montserrat to see the volcano, MVO and Plymouth, now described as ‘Caribbean Pompeii’;

·       Geothermal energy being investigated;

·       Sand mining for export;

·       Plans for a new town and port in north;

·       New housing for displaced residents built;

·       New airport built (but can only accommodate small planes);

·       New Government Headquarters built;

·       Businesses opening up in the north of the island;

·       Ferry to Antigua operating.

Lally Brown

You can follow Lally Brown on Twitter.

If you are interested in reading a dramatic eyewitness account of life with this unpredictable and dangerous volcano then the book ‘THE VOLCANO, MONTSERRAT AND ME’ by Lally Brown is highly recommended. You can order a paper back or Kindle version on Amazon .

“As time moves on and memories fade, this unique, compelling book will serve as an important and accurate first-hand record of traumatic events, faithfully and sensitively recounted by Lally Brown.”

Prof. Willy Aspinall Cabot Professor in Natural Hazards and Risk Science, Bristol University.

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Taal volcano emits voluminous toxic gas, ‘vog’ reappears

Residents go on their daily life amid volcanic smog or vog from Taal Volcano in Agoncillo, Batangas on August 19, 2024. (INQUIRER file photo / RICHARD A. REYES)

LUCENA CITY — Taal Volcano in Batangas province emitted more sulfur dioxide (SO2) on Saturday, August 31, causing the reappearance of volcanic smog or “vog.”

The Philippine Institute of Volcanology and Seismology (Phivolcs) reported in its latest bulletin issued on Sunday morning, September 1, that the volcano released a “voluminous emission” of 9,645 metric tons (MT) of SO2 from the volcano’s main crater in the past 24 hours.

The plumes rose to 2,400 meters above Taal Volcano Island, the volcano’s crater landmass, locally known as “Pulo,” that sits within Taal Lake, before drifting northwest.

The latest emission was a huge increase from the recorded 2,921 MT from August 26 to 28 and 4,389 MT logged on August 29 to 30.

Taal has emitted an average of 7,777 tons/day of SO2 for the year and has been continuously degassing voluminous concentrations since 2021.

Phivolcs again noted an “upwelling of hot volcanic fluids” in the main crater lake.

No earthquake was recorded during this latest monitoring period.

State volcanologists also observed the renewed presence of “vog” during this time after it disappeared on August 21.

The vog returned on August 26 but disappeared again the next day.

Vog is composed of SO2 gas. It can irritate the eyes, nose and throat. People with respiratory conditions and pregnant women are at greater risk.

Authorities warned the public of the harmful effects of prolonged exposure to volcanic SO, such as irritation of the eyes, throat and respiratory tract, especially among those who have underlying health conditions such as asthma, lung and heart diseases.

Elderly individuals, pregnant women and children are also vulnerable to volcanic sulfur dioxide.

People exposed to vog are advised to use face masks, drink plenty of water to ease the discomfort from exposure, and consult a doctor if needed.

On August 19 and 20, the presence of volcanic smog from Taal forced local government officials in some towns in Calabarzon (Cavite, Laguna, Batangas, Rizal, Quezon) to suspend classes in their localities.

According to Phivolcs, Taal Volcano is still on alert level 1 or a low level of volcanic unrest.

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The agency reminded the public that the volcano remains in an “abnormal condition” and “should not be interpreted to have ceased unrest nor ceased the threat of eruptive activity.”

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Concentrations of f − , na + , and k + in groundwater before and after an earthquake: a case study on tenerife island, spain.

1. Introduction

2. data sources and methods, 2.1. study area, 2.2. characterization of geological units, 2.3. geological setting of f − , na + , and k + on volcanic islands, 2.4. water collection and sampling method, 3. results and discussion, 3.1. analysis of the water samples: f − , na + , and k +, 3.2. analysis of the variations in f − , na + , and k + , before and after 2012, 3.3. regional increase in fluoride concentration in groundwater linked to non-anthropogenic event, 3.3.1. precipitation, 3.3.2. earthquakes, 3.3.3. causes of fluoride variation, 3.3.4. analysis of the decrease in f − after the earthquake of 18 august 2012, 4. conclusions, author contributions, data availability statement, conflicts of interest.

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de Miguel-García, E.; Gómez-González, J.F. Concentrations of F − , Na + , and K + in Groundwater before and after an Earthquake: A Case Study on Tenerife Island, Spain. Hydrology 2024 , 11 , 138. https://doi.org/10.3390/hydrology11090138

de Miguel-García E, Gómez-González JF. Concentrations of F − , Na + , and K + in Groundwater before and after an Earthquake: A Case Study on Tenerife Island, Spain. Hydrology . 2024; 11(9):138. https://doi.org/10.3390/hydrology11090138

de Miguel-García, Eduardo, and José Francisco Gómez-González. 2024. "Concentrations of F − , Na + , and K + in Groundwater before and after an Earthquake: A Case Study on Tenerife Island, Spain" Hydrology 11, no. 9: 138. https://doi.org/10.3390/hydrology11090138

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