Coastal Ocean Observing Systems The GCOOS-RA Facilitating Climate Change Studies in the Gulf of Mexico C. Simoniello 1, J. Swaykos 2, G. Mitchum 1, R. Weisberg 1, G. Jeffress 3, and A. Jochens 4 1 University of South Florida College of Marine Science, th Avenue South, St. Petersburg, FL University of Southern Mississippi Center of Higher Learning, Stennis Space Center, MS Texas A&M University-Corpus Christi College of Science and Technology, Corpus Christi, TX Texas A&M University Department of Oceanography, College Station, TX Introduction A challenge to implementing innovative climate change management strategies for the Gulf of Mexico region is aggregating and disseminating information in a way that is meaningful and easily accessible to a variety of stakeholders. The developing Gulf of Mexico Coastal Ocean Observing System Regional Association (GCOOS-RA; Fig. 1), one of 11 regional associations of the U.S. Integrated Ocean Observing System (IOOS), can be instrumental in promoting the use of these data. With 13 IOOS data management and communication-compliant parameters currently available, the GCOOS data portal can provide the tool with which we can integrate and manage the data and products. Please contact Capt. Joe Swaykos, USN (Ret.) for more Fig. 1 Observing system assets in the Gulf of Mexico region. Table 1 Serious datum problems exist and correction factors need to be applied. Shown are the correction factors used when comparing various historic datums in the New Orleans area (Denny, 2002). Blanket corrections can no longer be made to adjust elevations to NAVD , which is the most oft cited datum currently used in New Orleans. The reason for these disparities is the gross differential settlement between reference benchmarks, which can be an order of magnitude different. What we should’ve seen? The global ocean, via its interactions with the atmosphere, is responsible for climate. When the global climate warms, sea level (SL) rises. The net change is a balance between the thermal expansion of water, the addition of water to the oceans from melting continental ice sheets, and the rebound effect of the land as ice melts, among other processes. The average rate of sea level rise (SLR) for the past century prior to 1993 is 1.8 mm/year. Since then, rates are on the order of 3.1 mm/year (Fig. 2). While some debate the relative contributions of natural vs. anthropogenic causes, there is no doubt that the planet is warming at a rate unprecedented in human history. Fig. 2 The rate of sea level rise has increased from a century-average of 1.8 mm/year prior to 1993, to 3.1 +/- 0.4 mm/yr. Many residents of the Gulf Coast live at or, in some cases, below sea level. With high-end SLR estimates into 2100 on the order of 1.5 meters, the threat of inundation, particularly related to storm surge, is of great concern. Three aspects of coastal inundation are presented here: 1) Identifying major inland and coastal datum concerns; 2) Improving forecasting capabilities with 3-D vs. 2-D numerical models; and 3) Communicating model output data more effectively. Major Datum Concerns Sea surface height (SSH) is strictly speaking only measured by the satellite altimeters. The SSH is the deviation of the vertical position of the sea surface relative to a known reference surface, ideally the oceanic geoid. Sea level is measured by tide gauges and referenced to the height of the adjacent land. While it is useful to think of SL as the value of SSH at the ocean/land boundaries, this is not quite appropriate because the reference surfaces are not the same. Major terrestrial and tidal datum concerns arise because adjacent land height levels are not easily transferred into any global reference frame, and they are not always vertically stable, changing in a way that is not known precisely. Other sources of uncertainty arise from lack of leveling precision, historical records, and trend and time information since last epoch, and from relative SL changes and gauge uncertainty. Figure 3 shows the uncertainties in typical reference datums on a coastal navigation project. Table one shows the correction factors that need to be used when comparing various historic datums. Fig. 3 Typical reference datums on a coastal navigation or shore protection project. Image credit: U.S. Army Corps of Engineers. Please contact Dr. Gary Jeffress for more Improving Forecasting Capabilities Surge is the redistribution of a water mass. Hurricane storm surge is sensitive to intensity (Saffir-Simpson scale), point of landfall, direction of approach, and speed of approach, as well as to the hurricane eye radius. Because surge is a localized phenomenon on the basis of storm attributes relative to the regional geometry, the two-dimensional Sea, Lake and Overland Surges from Hurricanes (SLOSH) model used by the National Oceanic and Atmospheric Administration (NOAA) to simulate storm surge for all U.S. coastal regions has its drawbacks. These include: 1) only worst case flooding is routinely disseminated; 2) results may be misleading because depending on landfall location, sea level can either be set up or set down; and 3) for some locations, resolution is too course (about 2 km), so the bathymetry, elevations, and conveyances of mass cannot be accurately resolved. The Tampa Bay region (Fig. 4) is an example where these are not resolved by the SLOSH model. Figure 5 shows the sub-aerial configurations for the Tampa Bay region under uniform rises of sea level by 5 ft and 20 ft above mean water. The 5 ft level is essentially a state of no flooding since this level approximates that of the sea walls. The 20 ft level shows that much of the Tampa Bay surroundings are inundated. While a 20 ft storm surge is within the range of possibilities, the way water piles up along rigid boundaries results in a spatially inhomogeneous surge evolution so that Fig. 5b inundation is not realizable. Because the evolution entails a complex, three-dimensional, time- dependent, non-linear process dependent on local geometry relative to the hurricane winds, it is necessary to model the surge evolution. Figure 6 shows model output for the Tampa Bay region from a high resolution, 3-D, primitive equation, finite volume coastal ocean model with flooding and drying capabilities, supported by a merged bathymetric/ topographic data set, and driven by Hurricane Ivan wind and atmospheric pressure fields directed just north of Tampa Bay instead of the actual point of landfall near Pensacola. Fig. 5 Sub-aerial configurations for the Tampa Bay region under uniform rises of sea level by (a) 5 ft and (b) 20 ft above mean water [from the merged NOAA/USGS bathymetric/topographic Tampa Bay demonstration project data set (Hess, 2001)]. Image credit: Weisberg and Zheng, 2006 Fig. 4 Map showing the Gulf of Mexico region. Inset shows the Tampa Bay estuary and the adjacent west Florida shelf. Filled circles denote the study areas. Longitude Please contact Dr. Robert Weisberg for more Prior to landfall (model hour 27) we see the onset of inundation. The surge levels then builds with landfall at Indian Rocks Beach. (model hour 30) and mass is then distributed to varying degrees across the bay as the storm transits the bay (shown through model hours 31 and 32). The middle panels give the inundation levels relative to the land elevations. Not only can St. Petersburg become an island for a brief period of time, the lower lying elevations can potentially be covered by substantially high water levels. For instance, northeast St. Petersburg shows 3-4m of water above the land level, and Apollo Beach shows water levels exceeding 4m above the land levels. Inundation potential is therefore enormous, even for a category 3 storm. The coupling of the inundation with the accompanying waves (not shown) shows that the potential for destruction is massive, as large as what was observed for coastal Mississippi by Hurricane Katrina. Fig. 7 Absolute (black) and percent (red) differences between 3-D and 2-D surge simulations at four positions from mouth to head of the bay. At peak surge, the 2-D model is in error by up to 35% relative to the 3-D model with all else the same. Image credit: Weisberg and Zheng, 2008 Communicating Model Output Data More Effectively Properly forecasting and displaying storm surge information makes it “easier” to decide when to evacuate and by which route evacuation is safest. Fig. 8 is an example of SLOSH model output for the Gulf Coast, used by FEMA and local emergency managers during Hurricane Katrina. Fig. 8 SLOSH model output for Hurricane Katrina storm surge along the Louisiana coast. Image credit: David Welch and Dave Ramirez, National Weather Service, Lower Mississippi River Forecast Center While providing useful information, the SLOSH model did not accurately resolve the Mississippi Gulf Coast bathymetry and elevations on scales smaller than approximately 2 km. Figure 9 is a case study demonstrating the value of 3-D coastal ocean modeling as a planning tool for emergency response planning. The Advanced Circulation Model (ADCIRC) for shelves, coasts, and estuaries shows a realistic simulation of Hurricane Katrina storm surge. Benefits of the model are 3-D dynamics, forcing from tides, winds, waves and rivers, shoreline inundation and recession, and the use of unstructured grids. The ADCIRC model is being applied in Southern Louisiana by the U.S. Army Corps of Engineers New Orleans District to design levee heights and alignments, by FEMA to establish flooding probabilities for insurance purposes, by the State of Louisiana at the Center for the Study of Public Health Impacts of Hurricanes to operationally predict storm surge, and by the Louisiana State Department of Natural Resources to assess coastal restoration projects. Fig. 9 Advanced Circulation Model (ADCIRC) output of Hurricane Katrina storm surge. The x indicates the storm center. The asterisk indicates landfall in Waveland, MS. Image credit: Cheryl Ann Blain and Chris Massey, Naval Research Lab Oceanography Division In the words of Waveland, MS Hurricane Katrina survivors David and Kimberly King “Learn. Heed. Live.” The Future: Coupling Forecast Model Output with Visualization Biloxi storm surge forecast depiction Gulfport storm surge forecast depiction The future of making communities more resilient to inundation is intricately linked to enabling them to make informed decisions. To this end, the University of Southern Mississippi Center of Higher Learning Data Visualization team is working to couple model output with visualization to provide forecast depictions that are meaningful and easy to understand. Figure 10 shows depictions of Hurricane Katrina-induced high water marks in Biloxi and Gulfport, MS. Providing local references makes it easier for people to determine if evacuation is necessary and helps them identify the safest route. Fig. 10 Depictions of Hurricane Katrina-induced high water marks in coastal Mississippi. Image credit: USM Center of Higher Learning Data Visualization Lab DatumConversion to Mean Sea Level 1929 Ellet Datum of 1850unknown Delta Survey Datum of Old Memphis Datum of Old Cairo Datum of New Memphis Datum of Mean Gulf Level Datum (preliminary) Mean Gulf Level Datum of New Cairo Datum of Mean Low Gulf Level Datum of Fig. 6 Planar view of model-simulated elevation snapshots at hours 24, 28, 29, 30, 32, and 36 for a prototypical category 4 hurricane, approaching from the west at 5 meters per second, and making landfall at Indian Rocks Beach (asterisk). The filled circles denote the location of storm center. The bold lines are elevation contours at 1 m intervals. Longitude x-axis; Latitude y-axis. Image credit: Weisberg and Zheng, 2008 By comparing such 3-D model simulations with those performed using a 2-D model with all else the same (Fig. 7) we see that a 2-D model (such as SLOSH) may significantly underestimate the surge height (35% in this case). This suggests that we reconsider how storm surges are modeled, as both NOAA and FEMA presently use 2-D models.