The Robot Ocean Network
Automated underwater vehicles go where people cannot, filling in crucial details about weather, ecosystems, and Earth’s changing climate.
Riding a Hurricane
Together, the three types of ocean robots deployed throughout the world’s ocean are bringing into scientific reach processes that are not accessible using ships or satellites. For example, these robots can study the ocean’s response to, and feedback from, large storms such as hurricanes and typhoons. All three of us live in the mid-Atlantic region of the United States and have experienced Hurricanes Irene and Sandy, so we are all too familiar with storm aftermath in our local communities.
Hurricane Irene, a category-1 storm offshore, moved rapidly northward along the U.S. East Coast in August 2011, resulting in torrential rains and significant flooding on inland waterways. Hurricane Sandy, a much larger category-2 storm offshore, made an uncharacteristic left turn and approached perpendicular to the coast in October 2012, causing significant damage to coastal communities. The U.S. National Hurricane Center ranks Sandy as the second-costliest hurricane ever in this country, producing over $60 billion of damage; Irene comes in eighth place, with at least $15 billion in damages.
Path forecasts by the U.S. National Hurricane Center for Irene and Sandy were extremely accurate even several days in advance, enabling evacuations that saved many lives. Hurricane intensity forecasts were less precise. The force of Irene was significantly overpredicted, and the rapid acceleration and strengthening of Sandy just before landfall was underpredicted. A more accurate forecast for Sandy would have triggered more effective preparations, which might have reduced the amount of damage.
The cause of the discrepancy between track forecasts and intensity forecasts remains an open research question. Global atmospheric model development over the past 20 years has successfully reduced forecast hurricane track errors by factors of two to three. The predictive skill of hurricane intensity forecasts has remained flat, however.
One possible reason is that more information is required about the interactions between the ocean and the atmosphere during storms, because the heat content of the upper ocean provides fuel for hurricanes. The expanding array of robotic ocean-observing technologies is providing a means for us to study storm interactions in the coastal ocean just before landfall, accessing information in ways not possible using traditional oceanographic sampling.
During the summer, the surface waters of the mid-Atlantic are divided into a thin, warm upper layer (10 to 20 meters deep and 24 to 26 degrees Celsius) overlying much colder bottom water (8 to 10 degrees). Gliders were navigating the ocean waters beneath both Hurricanes Irene and Sandy, collecting hydrographic profiles. Data taken during Irene suggest that as the leading edge of the storm approached the coast, the hurricane-induced increase in the flow of water onto the shore was compensated by an offshore flow below the thermocline (the region of maximum temperature change in the water column) in a downwelling flow created by high winds. This phenomenon minimized the potential storm surge. Simultaneously, storm-induced mixing of the water layers broadened the thermocline and cooled the ocean surface ahead of Irene by up to 8 degrees in a few hours, shortly before the eye of the storm passed over. This cold bottom water potentially weakened the storm as it came ashore. When data from a glider that measured the colder surface water were retrospectively input into the storm forecast models, that adjustment eliminated the overprediction of Irene’s intensity.
In contrast to Irene, Hurricane Sandy arrived in the late fall, after seasonal cooling had already decreased the ocean surface temperatures by 8 degrees. As the storm came ashore, it induced mixing of cold water from the bottom to the surface—just as Hurricane Irene did—but because of the seasonal declines in temperature, the surface water temperature dropped by only around 1 degree. Such a small change did little to reduce the intensity of Hurricane Sandy as it approached the New Jersey and New York coastlines.
Robotic platforms have thus demonstrated their potential to sample storms and possibly aid future forecasts of hurricane intensity. The gliders operate effectively under rough ocean conditions that are not safe for people, and the mobility of gliders allows their positions to be adjusted as the storm moves. Their long deployment lifetime means these robots can be in place well before the storm’s arrival until well after conditions calm down. Real-time data from the gliders should improve hurricane intensity forecast models and potentially help coastal communities proactively mitigate storm damage.