effect of the gulf of mexico’s mixed layer depth on hurricane …450... · 2020-04-09 · power...

Effect of the Gulf of Mexico’s Mixed Layer Depth on Hurricane Intensity in the Warming Environment

Kimberly R. Trent

Academic Affiliation, Fall 2006: Senior, Yale University

SOARS® Summer 2006

Science Research Mentors: Warren M. Washington, David A .Randall

Writing and Communication Mentor: Andrew Gettelman Community Mentor: Annette Lampert

Peer Mentor: Nancy Rivera Rivera

ABSTRACT

Due to the effect of mixed layer ocean depth in the Gulf of Mexico on hurricane intensity, it is important to understand how global warming will affect the ocean and how this in turn will affect hurricane intensity. We ran the ARW model for Hurricane Katrina with the Gulf of Mexico’s mixed layer depth distribution specified, and we compared the results to actual events to determine the accuracy of the model for its use in future predictions; then we reran the model adjusted for projected sea surface temperatures (SSTs) for the year 2100 due to global warming. The analysis showed that, due to the higher water temperature, the intensity of the hurricane started to increase more quickly in the beginning of the run which altered its track, causing it to cross over a deeper part of the mixed layer’s warm core rings (WCRs) which helped it to maintain its peak intensity for a longer period of time than in the present day simulation. This preliminary analysis demonstrates the importance of including the depth of the mixed layer when forecasting and simulating hurricanes, and implies that global warming may increase the intensity of hurricanes in the Gulf of Mexico.

The Significant Opportunities in Atmospheric Research and Science (SOARS) Program is managed by the University Corporation for Atmospheric Research (UCAR) with support from participating universities. SOARS is funded by the National Science Foundation, the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office, the NOAA Oceans and Human Health Initiative, and the Cooperative Institute for Research in Environmental Sciences. SOARS also receives funding from the National Center for Atmospheric Research (NCAR) Biogeosciences Initiative and the NCAR Earth Observing Laboratory. SOARS is a partner project with Research Experience in Solid Earth Science for Student (RESESS).

1. Introduction

The 2005 hurricane season in the North Atlantic basin has been an example of the disastrous impact hurricanes can have on coastal regions. Hurricanes (tropical cyclones with maximum wind speeds >33m/s) are fueled by heat which is released at high altitudes in the troposphere through the condensation of rising water vapor that is evaporated from the warm ocean surface. In areas where the upper, warmer mixed layer of the ocean is deeper, the hurricane is able to drastically increase in intensity because it does not run out of its energy source as quickly. In the Gulf of Mexico, the loop current and warm core rings (WCRs) are the areas with the deepest ocean mixed layer. This is why it is important to understand how these deep mixed layer features affect the intensity of hurricanes. In addition, increasing ocean heat content, which raises the temperature of the mixed layer, would provide even more energy to power hurricanes. Therefore it is also important to investigate how projected global warming due to anthropogenic activity will impact the effect of these features on hurricane intensity.

A significant increase in sea SST (Fig. 1.1) and ocean heat content has already been

observed during the last half of the century in all ocean basins (Crowley et al. 2003; Webster et al. 2005).

Figure 1.1. This graph shows the SSTs each year from 1970 to 2003 in all the ocean basins Webster et al. (2005). Model results confirmed that this increase in ocean temperature can only be explained by an anthropogenic increase in greenhouse gases such as carbon dioxide (Crowley et al. 2003, Meehl et al. 2006, and Knutson et al. 2006) (Fig. 1.2).

SOARS® 2006, Kimberly R. Trent, 2

Figure 1.2. This is a graph of the changes in global temperatures each year. The green and black lines represent observed data. The blue line represents the results of a simulation done with the Paleo-CSM (Climite Systems Model) where only natural forcing was taken into account. This involves solar and volcanic forcing along with CO2 levels that would exist without human presence. It shows that we would have experienced a decrease in global temperatures over the past fifty years. The red line represents a simulation done with the model that included anthropogenic forcing. This run very closely simulates observed values. This simulation was extended out to the year 2100 and it shows that we may continue to experience larger and larger increases in temperature each year (Caspar Ammann, personal communication). Since the primary source of energy for hurricanes is the release of heat from condensation, and higher SSTs makes evaporation from the ocean to the storm easier, a higher ocean heat content increases the maximum potential intensity of hurricanes. In the North Atlantic, the increase in SSTs coupled with a decrease in vertical wind shear have more than doubled the number of major hurricanes (Appendix A) seen each year, from 1995 to 2000 (Goldberg et al. 2001, Webster et al. 2005). The number of hurricane landfalls in the Caribbean and the United States is five times greater. In addition, the number of category 4 and 5 hurricanes has doubled from the 1970s in each basin while the number of category 1 hurricanes has decreased as a fraction of the total (Fig. 1.4).


Figure 1.4. These charts show the number and percent of hurricanes in each category during a four year time period from 1970 to 2004 along with the average value for that category for the whole 34 year time span. Webster et al. (2005). Due to the strong correlation between CO2 concentration and global surface temperatures, a continued increase in carbon dioxide emissions is likely to result in a continued increase in global surface temperatures. Assuming the rate of increase in anthropogenic green house gas (GHG) emissions will continue to rise at the current rate; Meehl et al. (2006) expect a 3.3ºC increase in global surface temperatures and a 2.5ºC increase in SSTs by 2100 along with a sea level rise of 31cm from thermal expansion. According to Knutson et al. (2004) and Emanuel et al. (2004), this will in turn affect the frequency of the strongest hurricanes just as we have already seen with the warming that has taken place so far. Emanuel et al. (2004) show that an increase in tropical SSTs of 1ºC will result in a 3.4 m/s increase in maximum wind speeds (potential intensity).

Increasing SSTs only affects the potential development of hurricanes. However, the factor that affects the actual intensity of a hurricane during its lifetime is the depth of the warm water beneath its path (Fig. 1.5).


Figure. 1.5. These plots show location and intensity of Katrina at intervals of six hours against SST and Sea Surface Height (SSH) as it passed through the Gulf of Mexico. Scharroo et al. (2005). In Gulf of Mexico, the loop current and WCRs have the deepest mixed layer. The loop current is a current that flows northward between Cuba and the Yucatán peninsula, moves around the Gulf of Mexico in the clockwise direction, and then exits to the east through the Florida Straits. WCRs develop when part of the loop current breaks off and forms eddies that travel to the west at speeds of around 5 cm/s.

The effect of the loop current and WCRs on hurricane intensity has been seen repeatedly in past events. Hurricane Camille (1969) was a category 3 when it entered the Gulf. It traveled along the loop current which allowed it to intensify to a category 5 before landfall in Mississippi. Hurricane Opal (1995) crossed a WCR and went from a category 2 to a category 4 in 14 hours a day before making landfall. Officials did not have enough time to adjust coastal evacuations (Hong et al., 2000). During the 2005 hurricane season, Katrina and Rita greatly increased in intensity when they passed over the loop current. Katrina’s increased from a category 1 to a category 5 (Shen et al., 2006). Rita passed over the same core ring and intensified from a category 2 to a category 5. That same year, Wilma was predicted to make landfall as a category 2 because mixed layer topography was not taken into account when the forecast was made; but as a result of passing over the loop current, it hit Florida as a category 3.


Due to recent hurricane disasters such as Katrina in 2005, and the predicted further increase in potential hurricane intensity due to global warming, it is now more important than ever to understand how the intensity of a hurricane changes due to deep mixed layer features and how this will be affected by their increasing thermal content. Emanuel et al. (2004) state that “the force of the wind increases as the square of the wind speed while engineering studies suggest that damage tends to rise more nearly as the cube of the wind speed.” According to Emanuel et al. (2004), the projected 2.5ºC increase in SST by 2100 may result in an 8.5m/s increase in potential hurricane intensity. Therefore, the objective of this research is to examine a hurricane’s response to passing over the loop current and warm core rings in the Gulf of Mexico so that better intensity predictions can be made in the last hours before a hurricane hits land, and to determine how the intensity of a hurricane in the Gulf of Mexico in the year 2100 would respond as it passes over the mixed layer features of that time period. In order to do this, a coupled atmospheric and ocean model was needed so that temperature as a function of ocean depth can be specified. The Advanced Research WRF (Weather Research and Forecasting) [ARW] was used because it can be coupled with a simple mixed-layer, isolated column ocean model (Davis et al., 2006). 2. Methods a. Overview First previous simulations were analyzed to determine their accuracy, and what could be improved. Next, simulations were done using the ARW model implementing these improvements. Finally, the best simulation, after making the improvements, was used to explore the effects of climate change due to global warming by increasing the temperature of the mixed layer, and rerunning the model. Each simulation that was done is summarized in Table 2.1, and is described in detail in the sections below.

Simulation

Name Model Used Initial

Conditions SSTs Ocean

Coupling Surface Friction

WRF WRF GFDL/GFS Observed No No ARW GFS ARW GFS Observed No No ARW GFS2 ARW GFS Observed Yes No ARW GFS3 ARW GFS Observed Yes Yes ARW GFDL ARW GFDL/GFS Observed Yes Yes ARW 2100 ARW GFS +2.5ºC Yes Yes

Table 2.1. This chart summarizes all of the runs that were done. b. Initial steps: Analysis of past results lots were made to analyze a previous simulation of Katrina. This simulation was run using an uncoupled WRF model. The simulated hurricane’s category and geographical position every six hours was plotted against the SST distribution. Then, the actual track and category of the hurricane, taken from taken from NHC official reports, was plotted with the SST distribution. The two plots were compared in order to determine the accuracy of the WRF simulation, and to figure out how the ARW coupled model could improve on it.


c. Intermediate steps: Simulation and analysis of ARW results for Katrina (2005)

1.) THE ADVANCED RESEARCH WRF (ARW) The ARW model (Skamarock et al., 2005) was designed to be used in the forecasting and research of mesoscale (2km to 2000km) weather systems. The model is composed of two main parts, the WRF Preprocessing System (WPS), and the ARW Solver. The WPS is the first part of the code that has to be run. It reformats the initial conditions data so that it can be understood by the model. The WPS does this by defining the simulation domains, and interpolating the terrestrial and meteorological input data to the domain. The ARW dynamics solver is the second part of the model. It initializes and runs the model using the reformatted input data generated by the WPS. For this study, the ARW was coupled with a simple, isolated-column ocean model so that within the meteorological input data, initial oceanic stratification in addition to initial SSTs could be specified.

2.) INITIAL CONDITIONS In the ARW model, initial atmospheric and oceanic conditions can be defined

analytically when running idealized simulations, or they can be defined realistically using observed data. In this case study, observed data was used for all initial atmospheric and oceanic conditions except the mixed layer depth in the outer and part of the inner domain.

Initial atmospheric conditions were set using Global Forecast System (GFS) or Geophysical and Fluid Dynamics Laboratory (GFDL) output data. Initial oceanic conditions were set using mixed layer altimeter-derived data (L. Shay, personal communication, 2006). This data specified Sea Height Anomalies (SHA), the depth of the 20ºC and 26ºC isotherms, and Ocean Heat Content (OHC) for the pre-Katrina state at 0000 UTC Aug 27 in 0.5 degree latitudinal-longitudinal grid format.

3.) OPERATIONAL DOMAIN The ARW has horizontal nesting which allows the area of interest to have a higher resolution. This is done by nesting a finer grid within with the coarser, parent grid. For these simulations, a 12km grid was used for the coarse domain which included the mid-Atlantic. A 4km nested domain was used for the area of interest which was the Gulf of Mexico (18ºN to 31ºN latitude and 80ºW to 98ºW longitude).

4.) THE ARW SIMULATION AND METHOD OF ANALYSIS The ARW was run with Hurricane Katrina initial conditions for various combinations of input data to observe the impact of each added input element on the predicted track and intensity development of the simulation. The results from these simulations were plotted in the same format as those for past results. In addition, the track and intensity were also plotted against the ocean mixed layer depth topography. Then, the track and category of Hurricane Katrina, taken from NHC official reports, was plotted against mixed layer depth to act as the control. All of the plots were compared to determine the accuracy of the ARW simulation to that of the WRF model. In addition, track and intensity for all the plots was analyzed to determine their correlation with SST and mixed layer depth. d. Final steps: Simulation and analysis of ARW results for 2100


The last part of this study involved rerunning the most accurate ARW Katrina simulation adjusted for one of the predicted effects of global warming by the year 2100 (A2 scenario). Meehl et al. (2006) suggest that SST in the Gulf will increase by about 2.5º C, which will result in a 31cm rise in sea level due to thermal expansion. There will also be an additional increase in sea level of several feet due to the melting of land glaciers. However, for this research, only the change in SST was taken into account without the resulting thermal expansion. In the ARW model, this resulted in a 2.5 º C increase in the temperature of the whole body of water which is a reasonable assumption. The first diagram of Figure 2.1 shows how the temperature of the water in the Gulf is related to depth. The second shows the idealized version of this that the model uses, and how this relationship was changed in the final run.

Fig. 2.1. The first graph shows a realistic stratification profile for a body of water such as the Gulf of Mexico. The second shows the idealized version of this profile used in the coupled ARW model. The model only describes the first 500m of the ocean. It also shows how the ocean temperatures changed in the year 2100 run. The 2.5ºC increase in SST resulted in a complete upward shift in the idealized ocean stratification. The track and category output from this simulation was plotted against the mixed layer depth topography and the SST distribution. This plot was then compared to the ARW Katrina simulation (ARW GFS3, Table 2.1) to determine the effects global warming may have. 3. Results and analysis

Figure 3.1 shows the Gulf of Mexico’s SSTs against the actual track and intensity of Hurricane Katrina. Figure 3.2 shows the track and the oceanic mixed layer depth. These plots depict the close correlation between that the development of the hurricane intensity and the depth of the warm mixed layer. As the hurricane passes over the WCR, located at 27ºN 90ºW, it increases and stays at a Category 5. Then, right as it comes off of this deeper warm water, it starts to decrease in intensity.


Fig. 3.1. This plot shows the actual track of Hurricane Katrina and the SSTs observed during the time the hurricane passed through the Gulf of Mexico. A point is plotted every six hours from August 27th 2005 at 0000 UTC to August 31st 2005 at 0000 UTC. The points show the location and the intensity of the hurricane at that time. Fig. 3.2. This plot shows the actual track of Hurricane Katrina and the SSTs observed during the time the hurricane passed through the Gulf of Mexico.


Figure 3.3 shows the WRF model’s simulation of Hurricane Katrina and SSTs.

Fig. 3.3. This plot shows the WRF model’s simulation of hurricane Katrina and the observed SSTs. SST was used as input in the WRF model for the simulation as the oceanic boundary condition. This plot shows the track over the same time period with the same point spacing is in Fig. 1 and 2. Since WRF was not coupled with an ocean model for this run, only the SST was taken into account. When the model is set up in this way without a finite depth attributed to the water temperature, the model behaves as if there is an infinite amount of this temperature water available underneath the hurricane. This is why the simulated hurricane continues to increase in intensity until it hits land. Therefore, even though the shape of the WRF simulation’s track is very close to the actual, the simulation is not an accurate portrayal of Katrina because it does not take into account the depth of the mixed layer. The runs that were done with the ARW model coupled with the ocean model attempted to take this aspect into account so that a more accurate simulation could be produced. First a run was done on the ARW model without ocean coupling to act as a comparison for the WRF run and the ocean coupled run. Figure 3.4 shows the output from this simulation.


Fig. 3.4. This plot shows the ARW model’s simulation of hurricane Katrina with GFS input data against the observed SSTs. In this run, GFS meteorological data was used to specify the atmospheric conditions throughout the simulation. In the beginning of the run, the points are very close together because the model is still trying to balance all of the parameters. This behavior is characteristic of all of the WRF and ARW runs. In the second run the ARW was coupled with an ocean model. The output from this simulation is shown in Figures 3.5 and 3.6.


Fig. 3.5. This plot shows the ARW model’s simulation of hurricane Katrina with GFS input data and ocean coupling against the observed SSTs.


Fig. 3.6. This plot shows the ARW model’s simulation of hurricane Katrina with GFS input data and ocean coupling against the observed ocean mixed layer depth. The charts show that with ocean coupling, the simulation overall produces a weaker hurricane. This is because as compared to the simulation with the uncoupled model where only SST is specified; this run attributed a finite depth to the temperature distribution of the water. Therefore, in the places where the mixed layer is shallower, the hurricane decreases in intensity, and where it is deeper, the hurricane increases in intensity. This is why the hurricane does not stay a Category 3 for as long as in the uncoupled run. In starts to decrease in intensity before hitting land after it comes off of the WCR. Therefore, this simulation more accurately depicts Katrina’s pattern of increasing and decreasing intensity as it passed through the Gulf. Still, this simulation is not fully accurate in depicting the actual track and the magnitude of intensity that Katrina reached.

In comparing Figure 3.6 and Figure 3.2, it becomes evident that in actual events, Katrina passed more directly over a deeper part of the WCR than the model simulates. Passing over this deeper part allowed Katrina to increase to a Category 5. Further comparison shows that the reason why Katrina passed farther out into the Gulf than the simulated hurricane did is because it reached a Category 3 earlier in its trajectory (around 1200 UTC on Aug 27th). This increase in wind speed may have allowed the hurricane to move further out into the Gulf. Therefore, the comparison shows that the wind speed of the hurricane may affect its track. This was taken into account in further simulations which tried to improve upon the accuracy of the model’s simulation.


In the next simulation, we changed the physics of the model to get a stronger hurricane by adding code that would accurately simulate the effects of ocean surface friction on hurricane intensity. As wind speed increases, the size and height of the waves increases which causes more friction between the air and the water. This generally acts as a negative feedback that decreases wind speed; however, this effect levels at ~30m/s. For winds greater than 30m/s, the wave height does not increase and therefore the amount of friction between the air and the water stays constant. In the previous simulations, it was assumed that the friction continued to increase. (Jimy Dudhia, personal communication, 2006). Figures 3.7 and 3.8 show the results from this simulation.

Fig. 3.7. This plot shows the ARW model’s simulation of hurricane Katrina with GFS input data, ocean coupling, and accurate surface friction effects against the observed SSTs.


Fig. 3.8. This plot shows the ARW model’s simulation of hurricane Katrina with GFS input data, ocean coupling, and accurate surface friction effects against the observed ocean mixed layer depth. In this simulation, the hurricane gets up to a Category 4 (August 29th at 1200 UTC), but having adjusted the physics did not do anything about increasing the intensity of the hurricane earlier on in its trajectory when wind speeds are in the 30m/s to 40m/s range. The effect of the surface friction leveling off, therefore may not become evident until the winds get well above 30m/s, into the 40m/s – 50m/s range. In order to increase the intensity of the hurricane from the beginning, we attempted to use GFDL output data to initialize the simulation. The GFDL forecasting system focuses specifically on hurricanes instead of on overall weather like the GFS model. Therefore, it more fully defines all of the characteristics of the hurricane, and it initialized the model with a Category 1 hurricane on Aug. 27 0000UTC as seen in actual events. The results from this simulation are shown in Figures 3.9 and 3.10.


Fig. 3.9. This plot shows the ARW model’s simulation of hurricane Katrina with GFDL input data, ocean coupling, and accurate surface friction effects against the observed SSTs.

Fig. 3.10. This plot shows the ARW model’s simulation of hurricane Katrina with GFDL input data, ocean coupling, and accurate surface friction effects against the observed ocean mixed layer depth.


In this run, the hurricane quickly jumps up to a Category 3 and then back down to a Category 1, and then it continues to oscillate between a Category 3 and 2 for the remainder of the simulation. This oscillation might be explained by the nature of the GFDL system which initialized the model with a hurricane that is not fully in balance with the GFS boundary conditions. The GFDL forecasting system contains a high resolution inner grid that moves with the hurricane vortex, and the input used from the model is only from the inner grid. Since this grid does not cover the whole ARW inner domain, this data was superimposed on GFS input data which covers the whole inner domain. Therefore, the input data may have a discontinuity in the wind field, and the model may have been trying to create equilibrium throughout the simulation.

Since this odd behavior occurred within the ARW GFDL run; the previous run, ARW GFS3, was used for the final simulation where we adjusted SSTs to reflect the effects of global warming up to the year 2100. Even though ARW GFS3 did not accurately depict Hurricane Katrina, it is still reasonable to use this simulation to explore the effects of climate change on hurricane intensity just as long as we only look at the differences between the two runs. This is because the model deals with the ocean in a realistic way, and the physics of the model is accurate. Figure 3.11 and Figure 3.12 plot the results from this simulation.

Fig. 3.11. This plot shows the ARW model’s simulation of a hurricane of the year 2100 with GFS input data, ocean coupling, and accurate surface friction effects against the predicted SST distribution which is the current SST field plus 2.5ºC.


Fig. 3.12. This plot shows the ARW model’s simulation of a hurricane of the year 2100 with GFDL input data, ocean coupling, and accurate surface friction effects against the ocean mixed layer depth. For this simulation the changes in mixed layer depth were not taken into account, therefore the mixed layer depth distribution is the same in this run as it was in the others. In this simulation, because only the SSTs were changed at the start of the simulation, the ocean temperatures were out of equilibrium with the atmosphere. This is why the simulated hurricane jumps from a tropical storm to a Category 2 and then back down to a Category 1. After this point the atmospheric temperatures are in equilibrium with the ocean, and the model output can be analyzed to determine the effects of increased global temperatures on hurricane intensity development. Due to the warmer water beneath the hurricane, its intensity quickly increased from a Category 1 to a Category 3. Since the hurricane increased to a Category 3 more quickly than in ARW GFS3, its track was altered. The hurricane moved farther out into the Gulf and passed over a deeper part of the WCR which caused it to increase in intensity, even more, to a Category 4.

As seen in Appendix B from Tables B5 and B7, the maximum wind speed that the ARW GFS3 hurricane obtains is 58.223m/s which occurs on Aug. 29 0600 UTC, and that of the ARW 2100 hurricane is 62.861m/s which occurs on Aug. 28th at 1800 UTC. The difference between these two values is 4.638m/s which is below the increase in potential hurricane intensity that Emanuel et al. (2004) projected for a 2.5ºC increase in SSTs. This value is lower than theirs because they also took into account other effects of global warming such as thermal expansion


and land glacier melting. The fact that our value is within the range of their calculated value further justifies using the ARW GFS3 simulation to investigate the effects of global warming.

Figure 3.13 is a graph of the difference in the wind speed between the ARW GFS3 and ARW 2100 simulations for each data point. The graph shows that the maximum difference in wind speed between the two runs is around 15m/s and that it occurs as the simulated hurricanes were over the WCR. This implies that the impact of the deep mixed layer features could become even greater in the warming environment.

Fig. 3.13. This graph shows the difference in the wind speeds of the ARW 2100 and the ARW GFS3 hurricane simulations for each data point along its trajectory as plotted in Fig. 3.8 and Fig. 3.12. 4. Conclusion Our results demonstrated the importance of including the depth of the mixed layer when forecasting. The ARW was only able to correctly simulate Katrina’s pattern of increasing and decreasing intensity when it was coupled with an ocean model. Our results also implied that global warming may increase the strength and intensity of hurricanes in the Gulf of Mexico.

Further research is needed to increase the accuracy of the model’s simulation so that it more closely reflects actual events. In addition, there are other effects of global warming, such as higher sea levels which may result in an altered mixed layer depth. Further research is needed to explore the effect of this climate change, due to global warming, on hurricane intensity.


REFERENCES

Crowley, Thomas J., et al. (2003), Modeling ocean heat content changes during the last millennium, Geophysical Research Letters, 30, Art. No. 1932. Davis, C. et al. (2006), Advanced Research WRF Developments for Hurricane Prediction, http://www.mmm.ucar.edu/events/2006wrfusers/agenda.php , WRF User's Workshop. Emanuel, K. (2004), Response of Tropical Cyclone Activity to Climate Change: Theoretical Basis, in Hurricanes and Typhoons: Past Present, and Future, edited by R. J. Murnane and K. B. Liu, pp. 395-407, Columbia Univ. Press, New York. Friedlingstein, P., and Solomon, S. (2005), Contributions of past and present human generations to committed warming caused by carbon dioxide, PNAS, 102, 10832-10836. Goldenberg, Stanley B., et al. (2001), The Recent Increase in Atlantic Hurricane Activity: Causes and Implications, Science Magazine, 293, 474-479. Hong, X. et al. (2000), The Interaction between Hurricane Opal (1995) and a Warm Core Ring in the Gulf of Mexico, American Meteorological Society, 128, 1347-1365. Knab, R. D., et al. (2005), Tropical Cyclone Report : Hurricane Katrina 23-30 August 2005, NHC Official Report. Knutson, T. R., et al. (2004), Impact of Climate Change on Hurricane Intensities as Simulated Using Regional Nested High-Resolution Models, in Hurricanes and Typhoons: Past Present, and Future, edited by R. J. Murnane and K. B. Liu, pp. 408-439, Columbia Univ. Press, New York. Knutson, T. R., et al. (2006), Assessment of Twentieth-Century Regional Surface Temperature Trends Using the GFDL CM2 Coupled Models, Journal of Climate, 19, 16-24-1651. Landsea, C. (2005), HRD Frequently Asked Questions: Tropical Cyclone Winds and Energy, http://www.aoml.noaa.gov/hrd/tcfaq/tcfaqD.html , AOML website. Meehl, G. A., Washington, W. M., et al. (2006), Climate Change Projections for the Twenty-First Century and Climate Change Commitment in the CCSM3, American Meteorological Society, 19, 2597-2616. Scharroo R., et al. (2005), Satellite Altimetry and the Intensification of Hurricane Katrina, EOS, 89, No. 40. Shen, B.-W., et al. (2006), Hurricane Forecasts with a Global Mesoscale-Resolving Model : Preliminary Results with Hurricane Katrina (2005), American Geophysical Union.


http://www.mmm.ucar.edu/events/2006wrfusers/agenda.php

http://www.aoml.noaa.gov/hrd/tcfaq/tcfaqD.html

Skamarock, W. C., et al. (2005), A Description of the Advanced Research WRF Version 2, NCAR/TN–468+STR NCAR Technical Note, 1-100. Webster, P. J., et al. (2005), Changes in Tropical Cyclone Number, Duration, and Intensity in a Warming Environment, Science Magazine, 309, 1844-1846.


5. Appendix A. a. Saffir/Simpson Hurricane Scale* Category Winds and Damage Pressure Storm Surge

1 Minimal

74-95 mph (33m/s - 42m/s; 64kts – 82kts) No real damage to building structures. Damage primarily to unanchored mobile homes, shrubbery, and trees. Some damage to poorly constructed signs. Also, some coastal road flooding and minor pier damage

>980 mb (>28.94 in)

3-5 ft (1.0m -1.7m)

2 Moderate

96-110 mph (43m/s – 49m/s; 83kts – 95kts) Some roofing material, door, and window damage of buildings. Considerable damage to shrubbery and trees with some trees blown down. Considerable damage to mobile homes, poorly constructed signs, and piers. Coastal and low-lying escape routes flood 2-4 hours before arrival of the hurricane center. Small craft in unprotected anchorages break moorings.

965-980 mb (28.50-

28.94 in)

6-8 ft (1.8m -2.6m)

3 Extensive

111-130 mph (50m/s – 58m/s; 96kts – 113kts) Some structural damage to small residences and utility buildings with a minor amount of curtainwall failures. Damage to shrubbery and trees with foliage blown off trees and large trees blown down. Mobile homes and poorly constructed signs are destroyed. Low-lying escape routes are cut by rising water 3-5 hours before arrival of the center of the hurricane. Flooding near the coast destroys smaller structures with larger structures damaged by battering from floating debris. Terrain continuously lower than 5 ft above mean sea level may be flooded inland 8 miles (13 km) or more. Evacuation of low-lying residences with several blocks of the shoreline may be required.

945-965 mb (27.91-

28.50 in)

9-12 ft (2.7m -3.8m)

4 Extreme

131-155 mph (59m/s – 69m/s; 114kts – 135kts) More extensive curtainwall failures with some complete roof structure failures on small

920-945 mb (27.17-

27.91 in)

13-18 ft (3.9m -5.6m)


residences. Shrubs, trees, and all signs are blown down. Complete destruction of mobile homes. Extensive damage to doors and windows. Low-lying escape routes may be cut by rising water 3-5 hours before arrival of the center of the hurricane. Major damage to lower floors of structures near the shore. Terrain lower than 10 ft above sea level may be flooded requiring massive evacuation of residential areas as far inland as 6 miles (10 km).

5 Catastrophic

>155 mph (>69m/s; >135kts) Complete roof failure on many residences and industrial buildings. Some complete building failures with small utility buildings blown over or away. All shrubs, trees, and signs blown down. Complete destruction of mobile homes. Severe and extensive window and door damage. Low-lying escape routes are cut by rising water 3-5 hours before arrival of the center of the hurricane. Major damage to lower floors of all structures located less than 15 ft above sea level and within 500 yards of the shoreline. Massive evacuation of residential areas on low ground within 5-10 miles (8-16 km) of the shoreline may be required.

<920 mb (< 27.17 in)

>18 ft (>5.6m)

Table A1. Definitions for tropical cyclone systems:

Tropical Storms: wind speed between 18 m/s and 33 m/s Hurricane: wind speeds >33 m/s Non-major Hurricane: wind speed between 33 m/s and 50 m/s (categories 1, and 2) Major Hurricane: wind speed >50 m/s (categories 3, 4, and 5 a.k.a. Intense Hurricane)

Notes:

• Hurricane strength is determined using the maximum 1-minute average sustained wind speed 10 meters above the ground. • Only wind speed is used to classify hurricane category. Classification by central pressure was ended in the 1990s. Values of central pressure and storm surge that accompany each category are estimates and are only used for reference. The actual storm surge of the hurricane will depend on offshore bathymetery and onshore terrain and construction.

http://www.crownweather.com/tropical.htmlhttp://www.aoml.noaa.gov/hrd/tcfaq/D1.html


http://www.crownweather.com/tropical.html

http://www.aoml.noaa.gov/hrd/tcfaq/D1.html

6. Appendix B a. Hurricane Katrina

Date/Time (UTC)

Latitude (ºN)

Longitude (ºW)

Pressure (mb)

Wind speed (m/s)

Stage/ Category

Aug. 27/0000 24.6 -83.3 959 46 1 Aug. 27/0600 24.4 -84.0 950 49 2 Aug. 27/1200 24.4 -84.7 942 51 3 Aug. 27/1800 24.5 -85.3 948 51 3 Aug. 28/0000 24.8 -85.9 941 51 3 Aug. 28/0600 25.2 -86.7 930 64 4 Aug. 28/1200 25.7 -87.7 909 75 5 Aug. 28/1800 26.3 -88.6 902 77 5 Aug. 29/0000 27.2 -89.2 905 72 5 Aug. 29/0600 28.2 -89.6 913 64 4 Aug. 29/1200 29.5 -89.6 923 57 3 Aug. 29/1800 31.1 -89.6 948 41 1 Aug. 30/0000 32.6 -89.1 961 26 TS Table B1. Data for Fig. 1and 2 in chart form from NHC reports (Knabb et al., 2005). The chart includes additional data about the hurricane such as maximum wind speed 10m from sea level which is used to determine the category of the hurricane, and the lowest pressure reading for the hurricane. b. WRF simulation of Hurricane Katrina

Date/Time (UTC)

Latitude (ºN)

Longitude (ºW)

Pressure (mb)

Wind speed (m/s)

Stage/ Category

Aug. 27/0000 25.259 -82.897 988.590 27.789 TS Aug. 27/0600 24.477 -84.174 976.103 41.868 1 Aug. 27/1200 24.307 -84.465 974.196 35.984 1 Aug. 27/1800 24.477 -84.746 971.447 39.425 1 Aug. 28/0000 24.761 -85.535 964.194 37.709 1 Aug. 28/0600 24.987 -86.729 956.376 38.344 1 Aug. 28/1200 25.428 -87.352 948.208 51.795 3 Aug. 28/1800 25.990 -88.380 940.015 55.717 3 Aug. 29/0000 26.873 -88.962 919.490 65.715 4 Aug. 29/0600 27.520 -89.356 899.154 76.641 5 Aug. 29/1200 28.710 -89.917 892.304 78.543 5 Aug. 29/1800 29.743 -90.177 891.004 83.127 5 Aug. 30/0000 31.247 -90.166 911.497 55.784 3 Table B2. Output data for Fig. 3 in chart form along with additional information. c. ARW Hurricane Simulation with GFS input data and no Ocean Coupling

Date/Time (UTC)

Latitude (ºN)

Longitude (ºW)

Pressure (mb)

Wind speed (m/s)

Stage/ Category

Aug. 27/0000 25.193 -83.302 996.351 29.507 TS Aug. 27/0600 24.694 -84.538 982.390 38.289 1 Aug. 27/1200 24.590 -84.465 976.318 36.370 1


Aug. 27/1800 24.713 -84.839 970.914 35.769 1 Aug. 28/0000 25.061 -85.431 967.048 34.358 1 Aug. 28/0600 25.718 -86.490 961.742 44.041 2 Aug. 28/1200 26.259 -87.103 953.371 45.423 2 Aug. 28/1800 26.891 -88.079 945.984 49.549 3 Aug. 29/0000 27.611 -88.650 934.253 50.991 3 Aug. 29/0600 28.837 -88.878 932.148 50.155 3 Aug. 29/1200 30.613 -89.013 967.109 33.069 1 Table B3. Output data for Fig. 4 in chart form. The chart includes additional data from the simulation such as maximum wind speed 10m from sea level, and the lowest pressure reading for the hurricane. d. ARW Hurricane Simulation with GFS input data and Ocean Coupling

Date/Time (UTC)

Latitude (ºN)

Longitude (ºW)

Pressure (mb)

Wind speed (m/s)

Stage/ Category

Aug. 27/0000 25.193 -83.302 996.351 29.507 TS Aug. 27/0600 24.684 -84.475 982.263 38.518 1 Aug. 27/1200 24.505 -84.662 977.188 36.971 1 Aug. 27/1800 24.760 -84.963 971.811 33.561 1 Aug. 28/0000 25.071 -85.659 967.575 39.588 1 Aug. 28/0600 25.680 -86.698 963.961 38.899 1 Aug. 28/1200 26.082 -87.290 957.511 42.669 2 Aug. 28/1800 26.492 -87.996 952.691 44.857 2 Aug. 29/0000 27.611 -88.754 940.364 49.269 3 Aug. 29/0600 28.472 -89.117 938.934 47.809 2 Aug. 29/1200 29.824 -89.107 939.277 43.519 2 Aug. 29/1800 30.676 -89.283 965.881 32.118 TS Table B4. Output data for Fig. 5 and 6 in chart form along with additional information. e. ARW Hurricane Simulation with GFS input data, Ocean Coupling and Surface Friction

Date/Time (UTC)

Latitude (ºN)

Longitude (ºW)

Pressure (mb)

Wind speed (m/s)

Stage/ Category

Aug. 27/0000 25.193 -83.302 996.351 29.507 TS Aug. 27/0600 24.760 -84.600 982.423 39.874 1 Aug. 27/1200 24.590 -84.725 978.015 37.641 1 Aug. 27/1800 25.014 -84.922 975.102 37.684 1 Aug. 28/0000 25.240 -85.306 973.390 34.842 1 Aug. 28/0600 25.802 -86.355 969.382 40.047 1 Aug. 28/1200 26.427 -87.051 966.078 41.051 1 Aug. 28/1800 27.224 -87.850 957.633 47.435 2 Aug. 29/0000 28.381 -88.318 947.948 49.473 3 Aug. 29/0600 29.055 -88.671 943.114 58.223 4 Aug. 29/1200 30.426 -88.556 947.852 46.910 2 Aug. 29/1800 30.524 -88.525 989.698 27.714 TS Table B5. Output data for Fig. 7 and 8 in chart form along with additional information.


f. ARW Hurricane Simulation with GFDL input data, Ocean Coupling and Surface Friction Date/Time

(UTC) Latitude

(ºN) Longitude

(ºW) Pressure

(mb) Wind speed

(m/s) Stage/

Category Aug. 27/0000 24.647 -83.229 977.731 41.269 1 Aug. 27/0600 24.599 -83.873 962.994 53.228 3 Aug. 27/1200 24.788 -84.392 969.753 43.541 2 Aug. 27/1800 24.835 -84.880 968.109 38.011 1 Aug. 28/0000 24.826 -85.306 967.594 40.461 1 Aug. 28/0600 25.587 -85.940 962.639 51.366 3 Aug. 28/1200 26.483 -86.729 960.427 47.424 2 Aug. 28/1800 27.011 -87.497 956.784 47.086 2 Aug. 29/0000 28.253 -88.183 953.004 50.130 3 Aug. 29/0600 29.445 -88.297 948.910 50.829 3 Aug. 29/1200 30.685 -88.359 956.069 45.428 2 Aug. 29/1800 30.649 -88.421 997.851 25.860 TS Table B6. Output data for Fig. 9 and 10 in chart form along with additional information. g. ARW Hurricane (2100) Simulation with GFS data, Ocean Coupling, Surface Friction and a 2.5ºC increase in SST

Date/Time (UTC)

Latitude (ºN)

Longitude (ºW)

Pressure (mb)

Wind speed (m/s)

Stage/ Category

Aug. 27/0000 25.193 -83.302 996.351 29.507 TS Aug. 27/0600 24.656 -84.309 979.522 42.986 2 Aug. 27/1200 24.420 -84.621 975.145 38.843 1 Aug. 27/1800 24.543 -85.451 968.271 36.865 1 Aug. 28/0000 24.750 -85.586 962.471 39.211 1 Aug. 28/0600 25.334 -86.531 953.724 49.482 3 Aug. 28/1200 26.054 -87.611 939.878 55.882 3 Aug. 28/1800 26.594 -88.588 927.730 62.861 4 Aug. 29/0000 27.510 -89.138 920.296 59.009 4 Aug. 29/0600 28.682 -88.688 917.418 61.057 4 Aug. 29/1200 30.094 -89.657 926.008 51.831 3 Aug. 29/1800 30.703 -89.771 955.688 36.946 1 Table B7. Output data for Fig. 11 and 12 in chart form along with additional information


effect of the gulf of mexico’s mixed layer depth on hurricane …450... · 2020-04-09 · power...

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