Greenhouse warming intensifies north tropical Atlantic sea surface temperature anomalies
IMAGE: OBSERVED NTA WARMING EVENTS AND THEIR IMPACT view more
CREDIT: IAP
North Tropical Atlantic (NTA) sea surface temperature anomalies are among the dominant and most consequential climate variations on Earth.
NTA warming events increase occurrences of extreme hurricanes and their landfall frequency along the U.S. East coast, induce severe droughts in Northeast Brazil, boost phytoplankton blooms in the Guinea Dome, and trigger La Niña events the following winter. Up until now, future changes in NTA variability and its underlying mechanisms have remained unknown.
A new study, however, has recently revealed that NTA variability is projected to increase in a warming climate. The research was conducted by an international team of 12 scientists from nine institutes around the world and results were published in Science Advances on August 25.
"The increase in NTA variability means not only strengthening of sea surface temperature anomalies but also increasing occurrences of extreme NTA events," said YANG Yun, associate professor at the College of Global Change and Earth System Science of Beijing Normal University and lead author of the study.
The increase in NTA variability and occurrences of extreme events mainly arises from an intensification of El Niño-Southern Oscillation (ENSO) influence, including ENSO-forced Pacific-North American pattern and tropospheric temperature anomalies.
ENSO-forced Pacific-North American pattern is enhanced in a warming climate because of the eastward shift of ENSO-related equatorial Pacific convection. This enhancement is further amplified by an increase in ENSO variability.
Co-author HUANG Gang from the Institute of Atmospheric Physics (IAP) of the Chinese Academy of Sciences further explained that "the strengthening of ENSO-induced temperature anomalies is due to combined effects of an increase in ENSO variability and amplified vapor response to ENSO."
"The consequence of an increase in ENSO variability and its teleconnections under greenhouse warming is more severe than previously thought, as the increase can energize dominant modes of climate variability remote from the Pacific, such as the NTA," said co-author CAI Wenju from the Centre for Southern Hemisphere Oceans Research, CSIRO Oceans and Atmosphere, Australia.
Given the profound climatic impact of NTA in inducing droughts, floods, and extreme hurricanes, this study adds to the urgency of reducing emissions of greenhouse gases.
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Sea level rise will harm coastal wetlands in the Chesapeake—A bad sign for people and property
Due to a combination of sea level rise and changing wetland distribution, even relatively weak storms in 2100 could have a greater impact on the Chesapeake Bay region than high-intensity storms today
Peer-Reviewed PublicationAs the planet warms, the severity and frequency of hurricanes is increasing—and with it, economic losses from storm surge–related flooding. A new paper, published earlier this month in the journal Natural Hazards Review, assesses the impacts of rising seas and wetland change on storm surge flooding in a region expected to be a “hot spot” for sea level rise—the Chesapeake Bay.
The study results suggest that, particularly for strong storms in the future, the combination of wetland loss and sea level rise spurs significantly more property damage and affects more people than sea level rise alone.
“In low-elevation communities, even a small change in sea level can have a big impact,” said Margaret Walls, study coauthor and senior fellow at Resources for the Future (RFF). “Wetlands provide an important buffer to storm surge. In combination, wetland loss and sea level rise can create, almost literally, conditions for a perfect storm. This has serious repercussions for insurance markets, developers, and people living in this highly populated region.”
To quantify damages, the study authors model two historic hurricanes that hit the Chesapeake Bay region at the turn of the century: a relatively weak storm—Hurricane Dennis, in 1999—and a relatively strong storm—Hurricane Isabel, in 2003. By running computer simulations of these storms, in combination with coastal land use change tools from the National Ocean and Atmospheric Administration (NOAA), the team could model the combined and isolated impacts of sea level rise and wetland loss on coastal flooding in 2100.
They came to the following conclusions:
- Under maximum sea level rise scenarios, even relatively weak storms in 2100 are projected to have a greater impact on the Chesapeake Bay region than high-intensity storms today.
- The loss of wetlands significantly increases the amount of flooding in the region during strong storms—flooded area increases by a factor of 3.6–6.0 compared to current conditions. Without considering wetland loss, flooded area increases by a factor of 1.3–2.3.
- In a scenario in which wetland loss is considered alongside sea level rise, a strong storm could put 789,000–1,971,000 people at risk of coastal inundation in the future.
- Wetland loss combined with sea level rise would create $2.5 billion–$13 billion in property damage during a strong storm in 2100.
Notably, the modeling also shows that Virginia is likely to lose more wetlands than Maryland. However, Anne Arundel County, Maryland—which is located between Washington, DC, and Baltimore—has the highest number of people at risk from coastal flooding.
“The results show that coastal communities should be braced for a future marked by climate change and sea level rise,” Walls said. “Hopefully, our research will support efforts to develop adaptation measures that include conservation, restoration, and floodplain management measures. Natural infrastructure can go a long way to protect us in an uncertain future.”
For more, read the article, “Quantifying the Impacts of Storm Surge, Sea Level Rise, and Potential Reduction and Changes in Wetlands in Coastal Areas of the Chesapeake Bay Region,” in the journal Natural Hazards Review. The paper was authored by Ali Mohammed Rezaire from George Mason University, Celso M. Ferreira from George Mason University, Margaret Walls from Resources for the Future, and Ziyan Chu from First Street Foundation.
JOURNAL
Natural Hazards Review
METHOD OF RESEARCH
Computational simulation/modeling
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Quantifying the Impacts of Storm Surge, Sea Level Rise, and Potential Reduction and Changes in Wetlands in Coastal Areas of the Chesapeake Bay Region
ARTICLE PUBLICATION DATE
9-Aug-2021
Artificial intelligence to help predict Arctic sea ice loss
IMAGE: ICENET FIGURE view more
CREDIT: BRITISH ANTARCTIC SURVEY
A new AI (artificial intelligence) tool is set to enable scientists to more accurately forecast Arctic sea ice conditions months into the future. The improved predictions could underpin new early-warning systems that protect Arctic wildlife and coastal communities from the impacts of sea ice loss.
Published this week (Thursday 26 August) in the journal Nature Communications, an international team of researchers led by British Antarctic Survey (BAS) and The Alan Turing Institute describe how the AI system, IceNet, addresses the challenge of producing accurate Arctic sea ice forecasts for the season ahead – something that has eluded scientists for decades.
Sea ice, a vast layer of frozen sea water that appears at the North and South poles, is notoriously difficult to forecast because of its complex relationship with the atmosphere above and ocean below. The sensitivity of sea ice to increasing temperatures has caused the summer Arctic sea ice area to halve over the past four decades, equivalent to the loss of an area around 25 times the size of Great Britain. These accelerating changes have dramatic consequences for our climate, for Arctic ecosystems, and Indigenous and local communities whose livelihoods are tied to the seasonal sea ice cycle.
IceNet, the AI predictive tool, is almost 95% accurate in predicting whether sea ice will be present two months ahead - better than the leading physics-based model.
Lead author Tom Andersson, Data Scientist at the BAS AI Lab and funded by The Alan Turing Institute, explains:
“The Arctic is a region on the frontline of climate change and has seen substantial warming over the last 40 years. IceNet has the potential to fill an urgent gap in forecasting sea ice for Arctic sustainability efforts and runs thousands of times faster than traditional methods.”
Dr Scott Hosking, Principal Investigator, Co-leader of the BAS AI Lab and Senior Research Fellow at The Alan Turing Institute, says:
“I’m excited to see how AI is making us rethink how we undertake environmental research. Our new sea ice forecasting framework fuses data from satellite sensors with the output of climate models in ways traditional systems simply couldn't achieve.”
Unlike conventional forecasting systems that attempt to model the laws of physics directly, the authors designed IceNet based on a concept called deep learning. Through this approach, the model ‘learns’ how sea ice changes from thousands of years of climate simulation data, along with decades of observational data to predict the extent of Arctic sea ice months into the future.
Tom Andersson concludes:
“Now we’ve demonstrated that AI can accurately forecast sea ice, our next goal is to develop a daily version of the model and have it running publicly in real-time, just like weather forecasts. This could operate as an early warning system for risks associated with rapid sea ice loss.”
Publication details:
Seasonal Arctic sea ice forecasting with probabilistic deep learning by Tom Andersson, Scott Hosking, Maria Pérez-Ortiz, Brooks Paige, Andrew Elliott, Chris Russell, Stephen Law, Dan Jones, Jeremy Wilkinson, Tony Phillips, James Byrne, Steffen Tietsche, Beena Sarojini, Eduardo Blanchard-Wrigglesworth, Yevgeny Aksenov, Rod Downie, and Emily Shuckburgh is published in the journal Nature Communications on Thursday 26 August. Read the paper here: http://dx.doi.org/10.1038/s41467-021-25257-4
Ends
JOURNAL
Nature Communications
ARTICLE TITLE
Seasonal Arctic sea ice forecasting with probabilistic deep learning
ARTICLE PUBLICATION DATE
25-Aug-2021
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