FEMA flood maps are not particularly glamorous or technologically exciting. They have done their work for many years and, provided that they are up to date, are an effective way of communicating a generalized level of flood risk. FEMA flood maps have been the primary, flood insurance underwriting tool for the National Flood Insurance Program (NFIP). That program is currently billions of dollars in debt to the U.S. Treasury due to premium subsidization.
Also, FEMA flood maps have been used extensively by local governments in their efforts to keep development out of the floodiest areas of the U.S. That has not worked out so well, either, as coastal and riverine developments in the most flood-prone areas have abounded over the past 30-years. As we saw with the Hurricane Harvey disaster in 2017, FEMA flood maps are far from perfect. Nearly a hundred thousand homeowners in the FEMA X Zone (500-year, or 0.2% annual risk) in Houston, who were told that they did not need or were not required to purchase flood insurance, had their homes severely damaged by Harvey’s heavy rainfall flooding.
In 2016, we at Coastal Risk Consulting observed that FEMA flood maps did not provide comprehensive enough flood risk assessments to allow individuals and businesses to make accurate “buy, sell, protect and insure” decisions at the property level. So, Coastal Risk identified a number of improvements that could be made to the FEMA flood maps, including downscaling risk mapping to the individual property level, and made them publicly available for purchase at www.floodscores.com for any property in the U.S.
FEMA flood maps don’t tell the whole story about your risk of flooding.
First, as was seen with Hurricane Harvey flooding in Houston, FEMA flood maps don’t include heavy rainfall flooding risks to your home or business. Properties in FEMA X Zones, which don’t require flood insurance for federally insured mortgages, may definitely need to be insured to protect them from heavy rainfall flooding. Coastal Risk models heavy rainfall flood risk for every property in the U.S.; FEMA flood maps do not.
FEMA flood maps also don’t include coastal tidal flooding risks to your home or business. Tidal flooding is a property damage “threat multiplier.” When hurricanes come ashore at high tide or even a King Tide, which often occurs in the fall on the Atlantic Coast of the U.S., properties with existing tide flooding are at much greater risk of damage and loss than those that don’t experience tidal flooding. FEMA flood maps do not take this type of flooding into account, now and into the future as sea levels rise. King Tides occurs during the height of hurricane season. Because King Tides are due to astronomy and not weather, scientists know precisely when they will occur.
FEMA flood maps also underestimate the height of hurricane storm surge, as compared with NOAA models. NOAA surge models typically show higher water heights than the FEMA Base Flood Elevations (BFEs), which are a key component of the FEMA flood maps. The higher the surge, the greater the economic damage and loss to properties, and the greater the risk of injury and death to those who don’t evacuate in advance of these storms.
The 2017 Atlantic hurricane season was remarkable, including five landfalls of Category 5 storms in the Caribbean Basin and three Category 4 strikes on the U.S. coastline. The 2017 landfalls cost hundreds of lives and record-breaking economic losses, exceeding $250 billion. These losses are sober reminders of hurricane vulnerability and the importance of hurricane prediction for public safety and the management of insurance and other economic risks.
Hurricane forecasts have continued to improve in recent years, but they are not yet as good as they could be. Continued advances in weather and climate computer models used to make forecasts and improved observations from satellites and aircraft are driving these improvements. Also essential to progress are advances in understanding of weather and climate dynamics.
Short-term track and wind intensity forecasts
The National Hurricane Center (NHC) provides five-day forecasts of hurricane tracks and wind intensity that guide emergency management. Technological improvements from higher resolution weather forecast models and improved satellite observations are helping improve hurricane forecasts. The figure below shows the improvement over several decades in the NHC’s forecasted location of storms, referred to as “track error.” See figure 1 below.
An average track error at 48 hours of about 50 nautical miles is impressive in a meteorological context. However, a track uncertainty of just 50 nautical miles for Hurricane Irma’s predicted Florida landfall in 2017 meant the difference between a costly Miami landfall or a relatively benign Everglades landfall. As seen in the figure above, we are approaching a track forecast accuracy limit for one to two days, arising from the inherent unpredictability of weather. Over the past decade, the greatest improvements have been in the three- to five-day track forecasts.
A recent analysis conducted by Climate Forecast Applications Network (CFAN) compared track errors from different global and regional weather forecast models, all of which are considered by the NHC in preparing its forecast. The European model, operated by the European Center for Medium Range Weather Forecasting (ECMWF) and supported by 22 European countries, consistently outperformed the U.S. models maintained by the National Oceanic and Atmospheric Administration (NOAA) for track forecasts beyond two days. At five days lead time, the ECMWF model had an average track error for the 2017 season of 120 nautical miles, compared with 148 nm for the official NHC forecasts.
Innovators in the private sector apply proprietary algorithms to improve upon the NOAA and ECMWF model forecasts. At CFAN, ECMWF forecasts are corrected for model biases based on historical track errors. For 2017, CFAN’s bias-corrected storm tracks resulted in five-day average track error of 114 nautical miles – 26% lower than the average track error for the official NHC forecast.
Forecasts beyond five days (120 hours) are becoming increasingly important to the insurance community, especially with the development of insurance-linked securities and catastrophe bonds. The superior global weather forecasts provided by the European model (ECMWF) produced Atlantic hurricane tracks for 2017 with an average track error of 200 nautical miles out to eight days in advance. The proprietary track calibrations and synthetic tracks produced by CFAN from the European model maintain an average track error of 200 nautical miles even beyond 10 days, for the longest-lived storms.
Forecasting of storm wind intensity (as measured by maximum sustained wind speed) is also of key importance. The NHC’s intensity forecasts are slowly improving – the NHC’s intensity forecast errors at time horizons of two to five days average from 10 to 20 knots (10 knots = 11.5 mph) over the past several years. The greatest challenge in short-term hurricane forecasting remains the prediction of rapid intensification, as occurred with Hurricane Harvey in August 2017, immediately before landfall. The NHC has invested considerable resources in the development of high-resolution regional models to improve prediction of hurricane intensity. The prediction of rapid intensification remains elusive, although there is considerable research underway on this topic.
Seasonal and longer-term forecasts
Advances have been made in forecasting the probability of track locations on weekly timescales out to a month in advance. Monthly forecasts based on global weather forecast models are provided by several private sector weather forecast providers. Beyond the timescale of about a month, however, global models show little skill in predicting hurricanes. Hence, most seasonal forecasting efforts, particularly beyond timescales of six months, focus on data-driven statistical methods that examine longer-term trends in the global atmosphere.
Sea surface temperatures in the Atlantic and the tropical Pacific are key predictors for seasonal forecasts of Atlantic hurricane activity. El Niño (warmer tropical Pacific sea surface temperatures) and La Niña (cooler tropical Pacific sea surface temperatures) patterns have a strong influence, with La Niña being associated with higher levels of Atlantic hurricane activity.
Atmospheric circulation patterns also have some long-term “memory” that is useful for seasonal forecasts. CFAN’s research has identified additional predictors of seasonal Atlantic hurricane activity through examination of global and regional interactions among circulations in the ocean and in the lower and upper atmosphere. The predictors are identified through data mining, interpreted in the context of climate dynamics analysis, and then subjected to statistical tests in historical forecasts.
While forecasts issued around June 1 for the coming season generally have skill that is better than climatology, different outcomes are suggested by late May/early June forecasts for the 2018 Atlantic hurricane season. Predictions range from low activity (CFAN and Tropical Storm Risk ) to average activity (Colorado State University ) to near or above normal activity (NOAA Climate Prediction Center ). While many of the late May/early June 2017 forecasts predicted an above-normal season, none of the publicly reported forecasts predicted the extreme activity that was observed during the 2017 season.
At the longer forecast horizons, forecast skill is increasingly diminished. The greatest challenge in making seasonal forecasts in April and earlier is the springtime “predictability barrier” for El Niño/La Niña, whereby random spring weather events in the tropical Pacific Ocean during spring can determine its longer seasonal evolution. Seasonal forecasts from the latest version of the European model show substantially improved forecast skill of El Niño and La Niña across the spring predictability barrier, which improves the prospects for seasonal hurricane forecasts issued in late March/early April. La Niña generally heralds an active hurricane season, whereas El Niño is generally associated with a weak hurricane season. However, the occurrence of El Niño or La Niña accounts for only about half of year-to-year variation in Atlantic hurricane activity. In particular, the extremely active years such as 2017, 2004 and 2005 were not characterized by much of a signal from La Niña.
The greatest challenge for seasonal predictions of hurricane activity is to forecast the possibility of an extremely active hurricane season such as observed during 2017, 2005, 2004 and 1995. CFAN’s seasonal forecast models capture the extremes in 1995 and 2017 but not 2004 and 2005. Improved understanding of the causes of the extreme activity during 2004 and 2005 is an active area of research.
The most important target of seasonal forecasts is the number of landfalling hurricanes and their likely locations. The number of U.S. landfalling hurricanes in one year has varied from zero to six since 1920. The number of landfalling hurricanes is only moderately correlated with overall seasonal activity. It is notable that the period of overall elevated hurricane activity from 1995 to 2014 overlapped with a historic 2006-2014 drought of major hurricane and Florida landfalls.
Several seasonal forecasters provide a prediction of landfalls. These forecasts may specify the number of U.S. and Caribbean landfalls, the probability of a U.S. landfall (tropical storm, hurricane, major hurricane). Few forecast providers attempt to predict location of the landfalls. New research conducted by CFAN scientists has uncovered strong relationships between U.S. landfall totals and spring atmospheric circulation over the Arctic, which tends to precede summer dynamic conditions in the western North Atlantic and the Gulf of Mexico.
Certain insurance contracts with hurricane exposure typically take effect Jan. 1 of each year, and for this reason there has been a desire for Atlantic hurricane forecasts to be issued in December for the following season. Such contracts often are written for a period of a year or even longer time horizons. Because of the apparent lack of hurricane predictability on this time scale, in December Colorado State University provides a qualitative forecast discussion, rather than a forecast. CFAN research has identified some sources of hurricane predictability on timescales from 12 to 48 months. Research is underway to exploit this predictability into skillful annual and inter-annual predictions of Atlantic hurricane activity.
Five- to 10-year outlooks
Some atmospheric modelers provide a five-year outlook of annual hurricane activity, focused on landfall frequency. A key element of such for such outlooks is the state of the Atlantic Multidecadal Oscillation (AMO). The AMO is an ocean circulation pattern and related Atlantic sea surface temperature that changes in 25- to 40-year periods with increased or suppressed hurricane activity.
The year 1995 marked the transition to the warm phase of the AMO, which has been an active period for Atlantic hurricanes. In the warm phase of the AMO, sea surface temperatures in the tropical Atlantic are anomalously warm compared with the rest of the global tropics. These conditions produce weaker vertical wind shear and a stronger West African monsoon system that are conducive to increased hurricane activity in the North Atlantic.
There has been a great deal of uncertainty about the status of the AMO, complicated by the overall global warming trend. According to the AMO index produced by NOAA, the current positive (warm) AMO phase has not yet ended. In contrast, an alternative AMO definition, the standardized Klotzbach and Gray AMO Index, indicates the AMO has generally been in a negative (cooler) phase since 2014 – and May 2018 had the lowest value since 2015.
An intriguing development is underway in the Atlantic in 2018. The figure below shows sea surface temperature anomalies in the Atlantic for May. You see an arc of cold blue temperature anomalies extending from the equatorial Atlantic, up the coast of Africa and then in an east-west band just south of Greenland and Iceland. This pattern is referred to as the Atlantic ARC pattern. See figure 2 below.
A time series of sea surface temperature anomalies in the ARC region since 1880, depicted below, shows that temperature changes occur in sharp shifts occurring in 1902, 1926, 1971 and 1995. On the bottom graph, the ARC temperatures show a precipitous drop over the past few months. Is this just a cool anomaly, similar to 2002? Or does this portend a shift to cool phase of the AMO? See figure 3 below.
Figure 3. Top: ARC temperatures from 1880-2017. The black lines reflect the cold and warm regimes of the Atlantic Multidecadal Oscillation. Bottom: ARC temperatures from 1982 through June 2017.
Based on past shifts, a forthcoming shift to the cool phase of the AMO is expected to have profound impacts:
diminished Atlantic hurricane activity
increased U.S. rainfall
decreased rainfall over India and the Sahel region of Africa
shift in north Atlantic fish stocks
acceleration of sea level rise on northeast U.S. coast.
The figures below depict how the AMO has a substantial impact on Atlantic hurricanes. The top figure shows the time series of the number of major hurricanes since 1920. The warm phases of the AMO are shaded in yellow. There are substantially higher numbers of major hurricanes during the periods shaded in yellow. A similar effect of the AMO is seen on the Accumulated Cyclone Energy (ACE). Seasonal ACE is a measure of the overall activity of a hurricane season that accounts for the number, strength and duration of all of tropical storms in the season. See figure 4 below.
These variations in Florida landfalls associated with changes in the AMO have had a substantial impact on development in Florida. The spate of hurricanes starting in 1926 killed the economic boom that started in 1920. Florida’s population and development accelerated in the 1970s, aided by a period of low hurricane activity. By contrast, the warm versus cool phase of the AMO has little impact on the frequency of U.S. landfalling hurricanes generally. However, the phase of the AMO has a substantial impact on Florida landfalls. During the previous cold phase, no season had more than one Florida landfall, while during the warm phase there have been multiple years with as many as three landfalls. A major hurricane striking Florida is more than twice as likely during the warm phase relative to the cool phase.
New developments in decadal scale prediction are combining global climate model simulations with statistical models. Such predictions have shown improved skill relative to climatological and persistence forecasts on the decadal time scale.
The recent tropic Pacific Ocean La Niña event is now over; the tropical Pacific is trending to neutral with an El Niño watch underway. Sea surface temperatures in the subtropical Atlantic are currently the lowest that have been seen since 1982. For the 2018 Atlantic hurricane season, many forecasters who predicted a normal or active season previously are now lowering their forecasts, considering the trend toward El Niño and the cool temperatures observed in the tropical Atlantic.
Based on the overall expectations for low Atlantic hurricane activity in 2018, combined with forecasts of a U.S. landfall ranging from 50% to 100%, we can expect 2018 to be a year with smaller economic loss from landfalling hurricanes relative to the average.
Looking at longer time horizons, there is a potential game-changer in play – a possible shift to the cold phase of the Atlantic Multidecadal Oscillation that would herald multiple decades of suppressed Atlantic hurricane activity that would have a substantial impact on reduced landfalls, particularly in Florida.
“It was the best of times, it was the worst of times…” Charles Dickens, “A Tale of Two Cities”
On Aug. 12, 2016, it was the best of times in Denham Springs, (Livingston Parish) LA. By Monday, Aug. 15, 2015, it was the worst of times.
Denham Springs was a community of 10,000-plus people in a parish of 132,000. Driving your car through Main Street, you’d see small-town America. Driving through the suburbs, you’d discover a booming town. On Aug. 12, Denham Springs was a bedroom community for Baton Rouge, LA. It had it all — great schools, young families, new homes and commercial developments everywhere. You’d agree — it was the best of times.
After three days and 30 inches of rain, you could still drive through the town — by boat. It was the worst of times.
From the Livingston Parish News website: “The 30-inch downpour that has devastated nearly 90% of Denham Springs and flooded more than 70% of Livingston Parish has led NOAA [National Oceanic and Atmospheric Administration] to classify the rain event as a once-in-every 500-year flood.
For Livingston Parish, it may have exceeded the statistics of even the 500-year event.
In a parish in which an estimated 40,000 homes were flooded — and 90% of them considered possibly a “total loss” in Denham Springs — observers from NOAA believe the damage, based on population and statistics, could surpass the devastation New Orleans and the Mississippi Gulf Coast suffered in Hurricane Katrina.”
If you can’t wrap your head around such devastation, remember the difference between New York City on Sept. 10, 2001, and on Sept. 11. Think about New Orleans, which was not destroyed by wind but rather by water. Think Flint, MI, where failed decision making and neglect resulted in destruction of the “water” and severe damage to the health and the future of her citizens. Drive through your town and imagine 70% of it wiped out. I could go on, but I won’t — I assume you get the picture.
Now let’s leave the flood waters of Louisiana and move back to your reality. You are a successful professional or a business owner. Things are going great or, at least, good enough. You are in your comfort zone.
If you’re in the business of risk or insurance, you talk constantly about risk management. In my simple mind, risk is uncertainty. Uncertainty is the difference between good things and bad things happening. Management is control. Risk management is control of uncertainty. This is all about maximizing the good and minimizing the bad in our clients’ lives.
Be selfish. Exercise this risk management process and discipline on your own shop and your own future. I’d ask you to do one thing differently: Over the next few paragraphs, measure your reality not as the wild-eyed, optimistic, successful entrepreneur you are but measure it in the hard reality of “misery.”
Consider what would happen if you and your agency failed to open today because (like in Denham Springs) water has risen to the ceiling of your office and to the ceiling of 70% of the homes and offices in your city. Consider what would happen if the city and the state is on lockdown because terrorists have set off a dirty bomb. You’re driving away but are hearing rumors of the community being uninhabitable for at least three years (think Chernobyl).
What do you do? How do you do it? Where’s your staff? Your future? Your value?
Draining the swamp is difficult when you’re up to your butt in alligators. Consider: “Job” and “job” are spelled the same way. Most often, when we think of “job” we are thinking of what we do (“a piece of work, especially a specific task done as part of the routine of one’s occupation or for an agreed price.”) When we are going through the worst of times, some think of the other “Job,” “the central figure in an Old Testament parable of the righteous sufferer.”
About 20 years ago, Dave Hamilton spoke at the IIAL convention. He was excellent. His theme was “No bad days.” His message was that we all have bad moments where bad stuff happens — but there are no bad days. He closed with the following, “The merchant of misery is either at your door, has just left or is soon to arrive. Still there are no bad days.” Dave is wise!
If you’re enjoying the “best of times,” thank God. If you’re suffering through the “worst of times,” pray to God. Remember: Life is a streaming video, not a snapshot. Even after a 1,000-year flood, the sun shines again. Be prepared.
So goes the famous Australian bush poem by John O’Brien about the plight of farmers going from drought to flood to bush fire – one extreme weather situation after another. And though we are nearly 100 years on since that poem was written, we seem to be no further along in being able to predict weather with any certainty more than a few days into the future. In fact, extreme weather seems to be hitting more frequently and with greater ferocity because of the apparent effects of global climate change. The extended 2013 winter in Europe cost the economy there more than $7 billion – that is just from being cold for a month longer than usual.
Extreme weather events have dominated the headlines, especially where they impinge on highly developed insurance markets such as North America and Europe. But from the perspective of the impact on human lives, the greatest risk lies in Asia and Africa, where a vast majority of people depend upon subsistence farming and there is very little penetration of traditional financial services. A number of governments in the region, in partnership with semi-government, educational institutions and private organizations, have established a range of programs to foster the development of sustainable microfinance and microinsurance services for the most at-risk segments of their communities. In India alone, there are more than 700 million farmers and farm workers who struggle with extreme weather risk every season.
Building sustainable programs, now there’s the trick!
In one program in India that ran from the mid-’80s through to the end of the twentieth century, the cumulative premiums were $80 million, while the cumulative claims were $461 – hardly a sustainable proposition. In another program, a World Bank study showed that the microinsurance proposition was advantageous to farmers only in very extreme situations, so in most cases it was uneconomic for farmers to buy the insurance.
From 2001, the Indian government has ensured the growth of microinsurance through a regulatory framework set up as part of the entry of private insurers into the market. Popular products in the sector are weather index policies, where payouts occur if rainfall is below a trigger level, in a particular area. The premium for these types of policy have proven to be expensive, but, with government subsidies and an education program, awareness and acceptability of this kind of financial service have grown in communities in rural India. Governments in China, Bangladesh, Indonesia and the Philippines are following suit, by introducing their own agricultural insurance programs.
The major problem for insurers writing this kind of business is getting a good handle on the risk, to enable correct pricing. For the most part, insurers have to rely on historical data, which really only establishes a wide range of outcomes; with extreme weather trends continuing, insurers tend to be very conservative in risk pricing.
This is where big data and analytics come in.
In the commodities sector, at least one player has marketed reports that help predict the price of commodity futures. A case in point is the recent U.S. drought. By using National Oceanic and Atmospheric Administration (NOAA) and NASA remote Earth-sensing data, and coupling with advanced climate predictive analytics, the U.S. drought was predicted three months in advance of the U.S. government’s declaring drought. The model enabled the assessment of the weather impact on particular areas of the U.S., as well as the impact on the particular commodity crop grown in that area (corn), and, consequently, a prediction of the price of the commodity at harvest time based on the expected overall yield, with drought factored in.
Currently more than 15 petabytes of public data is produced annually that is global in nature, and the amount of data is set to increase to more 300 petabytes as more satellites come on line over the next few years. The computing power and technology to cope with huge data sets continues to improve each year with big data solutions.
These rich data sources and new technology solutions represent an unparalleled opportunity for governments and communities to turn microinsurance from a subsidized, unprofitable activity, to a sustainable model to spread economic stability and prosperity. By enabling the weather risk to be more accurately modeled, underwriters will be able to price policies on a more accessible basis.
Studies are showing that, where microinsurance is in operation, microfinancing is supported, as farmers can have certainty around being able to meet their loan commitments. These financial services are being used by farmers to improve their farms’ yield by investing in appropriate weather risk mitigation (irrigation, soil moisture conservation, etc) and productivity enhancements (planting automation, genetically modified seeds, fertilizers, improved pest control, etc). This virtuous cycle lifts this sector from depending on subsidies and government programs to being commercially viable and self sustaining. Definitely a win, win, win proposition.
Far from the pessimistic, doom-mongering of Hanrahan, I see a world more in line with Peter Diamandis’s vision as outlined in his book Abundance: The Future Is Better Than You Think.
I don’t know about you, but I for one would love the bragging rights to say my industry is helping to improve the lives of billions on planet Earth, while still making a commercially reasonable profit.