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  • View in gallery

    The bounds of all model domains discussed in the text. The outermost boxes represent the 54- and 18-km nests run in one model configuration. The innermost boxes represent the 18- and 6-km higher-resolution model configuration run without the 54-km outermost domain.

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    SST (°C) valid at 0000 UTC 11 Sep 2005, from a simulation with modification to the OML to allow it to work with time-varying SST and to only keep recently simulated cold wakes. At this time, a TC-induced cold anomaly is present off the eastern U.S. coast.

  • View in gallery

    Histogram of the frequency of occurrence of TCs with central pressures within the pressure thresholds (hPa) shown on the x axis. A central pressure is taken from each TC at every output time. The total number of pressures within each threshold are normalized by the total in all thresholds and expressed as a percentage, as labeled for all current 6- and 18-km ensemble members (represented by blue solid and dashed lines, respectively). The climatological central pressures distribution (in black) is taken from 6-hourly HURDAT observations only for storms that occurred during the month of September from 1988 to 2009. Note that both the 6-km 2005 and 2009 ensembles are binned together.

  • View in gallery

    Points of TC genesis for all ensemble members (shown as black hurricane symbols) in each of the (top) 18Current05, (middle) 6Current05, and (bottom) 6Current09 sets of ensembles.

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    An example of model-simulated radar reflectivity (dBZ) from a mature TC simulated with (left) 18- (18Current05 E1 valid at 0000 UTC 7 Sep 2005) and (right) 6-km grid spacing (6Current05 E1 valid at 1200 UTC 6 Sep 2005). Note that both plots are of the same spatial scale with 10° of latitude and longitude shown and that reflectivity associated with CP precipitation is included.

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    Temperature (K, solid line) and mixing ratio (g kg−1, dashed line) changes calculated for the A1B IPCC emission scenario. The computed SST anomaly, as projected for this scenario, is 2.21 K.

  • View in gallery

    Vertical profiles of the temperature (K) difference between 6Current05 and 6Warming05 ensembles averaged over the entire domain (excluding land) and over all ensemble members for 0000 UTC (left) 1 Sep and (right) 30 Sep. Each profile is plotted with pressure (hPa) as the vertical axis on a linear scale. Interior gridlines are shown on the x axis with the 0-K line emboldened.

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    The 6Current05 and 6Warming05 domain-averaged temperature (K) at (top) 850 and (bottom) 150 hPa shown with the cooler and warmer colors for the current and warming ensemble members, respectively, at each model output time. Ensemble means are shown as heavy lines.

  • View in gallery

    As in Fig. 8, but for (top) 850-hPa relative humidity (percent) and (bottom) column-total precipitable water vapor (g kg−1).

  • View in gallery

    As above, but for 850–200 hPa vertical wind shear (m s−1). Vertical shear from the 1° GFS-FNL analysis is averaged over an area corresponding to the 6-km domain and shown in black dots every 24 h.

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Atlantic Hurricanes and Climate Change. Part I: Experimental Design and Isolation of Thermodynamic Effects

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  • 1 North Carolina State University, Raleigh, North Carolina
  • | 2 AIR Worldwide Corporation, Boston, Massachusetts
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Abstract

The Weather Research and Forecasting (WRF) model is used in a downscaling experiment to simulate a portion of the Atlantic hurricane season both in present-day conditions and with modifications to include future thermodynamic changes.

Temperature and moisture changes are derived from an ensemble of climate simulations from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) A1B scenario and added to analyzed initial and lateral boundary conditions, leaving horizontal temperature gradients and vertical wind shear unaltered. This method of downscaling excludes future changes in shear and incipient disturbances, thereby isolating the thermodynamic component of climate change and its effect on tropical cyclone (TC) activity.

The North Atlantic basin is simulated with 18- and 6-km grid spacing, and a four-member physics ensemble is composed by varying microphysical and boundary layer parameterization schemes. This ensemble is used in monthly simulations during an active (2005) and inactive (2009) season, and the simulations are able to capture the change in activity between the different years. TC frequency is better reproduced with use of 6-km grid spacing and explicitly simulated convection, relative to simulations with 18-km grid spacing. A detailed comparison of present-day and future ensemble results is provided in a companion study.

Current affiliation: National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina.

Corresponding author address: Megan Mallard, U.S. EPA, Mail Drop E243-01, 109 T.W. Alexander Dr., Research Triangle Park, NC 27711. E-mail: megansmallard@gmail.com

Abstract

The Weather Research and Forecasting (WRF) model is used in a downscaling experiment to simulate a portion of the Atlantic hurricane season both in present-day conditions and with modifications to include future thermodynamic changes.

Temperature and moisture changes are derived from an ensemble of climate simulations from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) A1B scenario and added to analyzed initial and lateral boundary conditions, leaving horizontal temperature gradients and vertical wind shear unaltered. This method of downscaling excludes future changes in shear and incipient disturbances, thereby isolating the thermodynamic component of climate change and its effect on tropical cyclone (TC) activity.

The North Atlantic basin is simulated with 18- and 6-km grid spacing, and a four-member physics ensemble is composed by varying microphysical and boundary layer parameterization schemes. This ensemble is used in monthly simulations during an active (2005) and inactive (2009) season, and the simulations are able to capture the change in activity between the different years. TC frequency is better reproduced with use of 6-km grid spacing and explicitly simulated convection, relative to simulations with 18-km grid spacing. A detailed comparison of present-day and future ensemble results is provided in a companion study.

Current affiliation: National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina.

Corresponding author address: Megan Mallard, U.S. EPA, Mail Drop E243-01, 109 T.W. Alexander Dr., Research Triangle Park, NC 27711. E-mail: megansmallard@gmail.com

1. Introduction

It is anticipated that anthropogenic climate change will result in increased tropical cyclone (TC) strength along with decreased frequency (e.g., Knutson et al. 2010). However, the attribution of altered TC activity to physical processes impacted by climate change remains unclear, as future alterations in temperature, moisture, and momentum fields can have opposing effects on TC activity. For example, warming sea surface temperatures alone would lead to increased activity, but enhanced vertical wind shear or upper-tropospheric warming can offset this effect (e.g., Vecchi and Soden 2007; Hill and Lackmann 2011). Therefore, an advantageous approach is to simplify the problem by examining thermodynamic factors separately from the dynamical effects of climate change. The objective of this two-part study is to use the Weather Research and Forecasting (WRF) model for a downscaling experiment to better understand the impact of end-of-the-century changes in temperature and moisture alone on future TC activity.

The review article by Knutson et al. (2010), based on both theoretical and high-resolution downscaling modeling studies, estimates a 2%–11% increase in globally averaged TC intensity by 2100. In the same study, global TC frequency is shown to decrease by 6%–34%, a considerably broader range of projections compared with TC intensity change. Bender et al. (2010) also find an overall decrease in TC counts but a substantial increase in the frequency of the most intense hurricanes. Knutson et al. state that there is less confidence in projecting TC changes in individual basins. A review article by Grossmann and Morgan (2011) describes the difficulty in projecting future changes in TC activity and emphasizes both computational limitations and the uncertainty when projecting the regional response to climate change, as both thermodynamic and dynamic responses would affect the severity of future hurricane seasons. An overall increase in TC strength is understood to result from warming sea surface temperatures (SSTs) alone (e.g., Emanuel 1987, 2005). However, this effect would be somewhat mitigated by the impact of warming temperatures aloft discouraging TC intensification (e.g., Shen et al. 2000; Swanson 2008; Hill and Lackmann 2011). The impact of climate change on TC genesis is less understood, with past studies attributing decreased storm counts to many factors, including stronger wind shear (e.g., Vecchi and Soden 2007; Gualdi et al. 2008), increased vertical stability (e.g., Sugi et al. 2002; Oouchi et al. 2006; Bengtsson et al. 2007), and a weakening of vertical motion in the tropics (e.g., Sugi et al. 2002; Bengtsson et al. 2007; Lavendar and Walsh 2011; Held and Zhao 2011).

The present study implements a downscaling method to isolate the effect of temperature and moisture changes on future TC activity. In a downscaling approach, changes projected by general circulation models (GCMs) are included in simulations by a finer-resolution mesoscale numerical model. Typical grid spacing of GCMs is on the order of hundreds of kilometers (although some studies do employ finer resolutions, as discussed below). However, at such coarse resolutions, GCM representations of TCs bear only a vague similarity to real systems (e.g., Henderson-Sellers et al. 1998; Knutson and Tuleya 1999). Therefore, a number of time-slice methods have been used, where output from coarser-resolution GCMs at the end of the century is used to initialize higher-resolution simulations. Knutson et al. (2001) and Knutson and Tuleya (2004) initially used a downscaling approach in an idealized modeling framework with case studies of TCs simulated by the Geophysical Fluid Dynamics Laboratory (GFDL) model and found increased precipitation and storm intensity in a CO2-warmed environment. Emanuel et al. (2008) developed a statistical downscaling approach where incipient vortices were placed randomly and TC genesis and intensity were projected by the Coupled Hurricane Intensity Prediction System (CHIPS) model. Some GCM studies of future TC activity have been conducted at grid spacings from 50 to 20 km by taking a time-slice approach and utilizing SST information from coarser ocean-coupled global models (e.g., Zhao et al. 2009: Oouchi et al. 2006; Sugi et al. 2009; Murakami et al. 2012). Retrospective GCM simulations of decadal TC activity have even been conducted at 10-km grid spacing (Manganello et al. 2012). Nevertheless, TC intensity and structure have been shown to be sensitive to changes in grid spacing even at grid lengths below 4 km (Fierro et al. 2009; Gentry and Lackmann 2010). Therefore, even at relatively fine grid lengths, the results of downscaling studies could still be sensitive to changes in grid spacing, as concluded by Bender et al. (2010).

Hill and Lackmann (2011) projected changes in future TC maximum intensity using high-resolution 6- and 2-km WRF simulations of idealized TCs in a no-shear environment. In this study, end-of-the-century temperature and moisture changes, along with increased SST, were included to isolate the thermodynamic component of climate change in an idealized setting in which simulated TCs were allowed to develop to maximum intensity. Future thermodynamic changes were obtained from a 13-member GCM ensemble from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) with the B1, A1B, and A2 emission scenarios. Minimum central pressure reductions between 11% and 19% were found, along with increases in near-storm rainfall between 10% and 30%, depending on the emission scenario used. With warmer SST, increased intensity is expected (e.g., Emanuel 1987). However, warming aloft would have the opposite effect, increasing static stability as well as outflow temperatures and decreasing the potential intensity of the TC (Bister and Emanuel 2002). Hill and Lackmann demonstrate that enhanced upper-tropospheric warming in the tropical environment does offset the larger increase in intensity that would occur based on the projected SST change alone, consistent with the findings of other studies (e.g., Shen et al. 2000). A stronger potential vorticity (PV) tower is also found in future TCs, along with heavier precipitation, increased latent heating, and enhanced intensity. While idealized experiments, like those performed by Hill and Lackmann, are preferable when considering maximum TC intensity, this experimental design is unsuitable for addressing changes in hurricane frequency.

The downscaling approach used in this study involves modification of analyzed initial and boundary conditions with temperatures changes that are a function of pressure alone, allowing vertical wind shear to remain unchanged. SSTs are uniformly increased, and moisture changes are accounted for by assuming constant relative humidity (RH) (e.g., Charney et al. 1979; Allen and Ingram 2002; Held and Soden 2006). By adding the GCM-derived changes to analyzed data from a recent season, these future projections are based upon a realistic synoptic pattern, including an observed set of incipient disturbances. Thus, the effect of thermodynamic changes alone on seasonal hurricane activity is isolated.

While vertical wind shear changes are likely to be important to future TC activity, there is large variability in GCM-projected changes in shear for the Atlantic basin (Vecchi and Soden 2007; Garner et al. 2009; Talgo 2009). Therefore, it is advantageous to isolate TC changes due to the more robust thermodynamic signal. If the changes in future TC activity simulated here are similar to those found in prior studies (as summarized in Knutson et al. 2010), such a result would address whether or not these changes can occur from the thermodynamic component of climate change alone, regardless of future differences in vertical wind shear. However, a subsequent paper will present the main results with regard to the effects of climate change on TCs. The objectives of this part of the study are to 1) assess if WRF, in the model setup described below, is able to adequately simulate observed TC frequency and intensity; 2) document a method of downscaling that isolates the thermodynamic aspect of climate change; and 3) evaluate whether the imposed thermodynamic changes persist throughout the month-long future simulations.

The overall goal of this work is to devise a means of understanding the physical processes responsible for future changes in TC activity. The experimental design presented here will allow direct comparison of the same incipient disturbances in a similar synoptic environment but with thermodynamic modifications, allowing diagnosis of the specific processes responsible for future changes in the model simulations. Section 3 will focus on the first objective listed above, evaluation of simulations in the present-day environment. Section 4 will describe the modifications made to the future environment, demonstrating that the thermodynamic changes are robust and that vertical wind shear is left unaltered by the imposed changes.

2. Methods

a. Time periods of simulation

Two time periods of integration are used in this study. Most simulations were run for September 2005 (Table 1). All ensembles examined in this two-part study are listed in Table 1 with a designation that refers to their grid spacing, whether they simulate current or future (warming) conditions, and the time period of integration. This first time period was selected as a representative example of an “active” month, which took place during an Atlantic season with a record number of TCs and hurricanes (Beven et al. 2008). The rationale behind selecting this anomalous period is to maximize chances of isolating a statistically meaningful change in future storm activity. Also, it is of practical interest to understand how well WRF can simulate TC intensity and frequency during such an active period. The hurricane season of 2005 was the most active Atlantic season on record, with a total of 28 storms, 15 of which were hurricanes, and 7 of which were major hurricanes (Beven et al. 2008). September is the climatological peak of the Atlantic season (e.g., Klotzbach and Gray 2003), and this month in the 2005 season featured larger storm counts than in an average September, with six named storms, five hurricanes, and two major hurricanes occurring during September 2005 (Table 2). All simulations set during September 2005 are run from 0000 UTC 1 September to 0000 UTC 30 September.

Table 1.

Names of all ensembles, with marks to indicate the time period of the simulation, choice of grid spacing, and initial and boundary conditions.

Table 1.
Table 2.

Climatological, observed, and simulated storm counts; listed are the number of named storms, hurricanes, and major hurricanes. The September climatological storm counts are taken from Klotzbach and Gray (2003). Simulated storm counts are shown averaged across the four-member physics ensemble.

Table 2.

Another set of simulations was run for the month of September 2009 in order to assess whether the changes in future TCs would be of greater or lesser significance during a relatively inactive period and to see if the model could replicate observed changes to the sign of the TC activity anomaly between active and inactive periods (Table 1). The hurricane season of 2009 produced nine tropical storms and only three hurricanes (Berg and Avila 2011). The season included two major hurricanes. Notably, Hurricane Fred was present during late September and became a category 3 storm at its peak intensity. With 2009 being an inactive season, the month of September 2009 experienced few storms relative to climatology (Table 2). In addition to Fred, tropical storm Erika was the only other storm that occurred during the month. These simulations are run from 0000 UTC 27 August to 0000 UTC 30 September 2009 in order to allow some time for model spinup. However, only model output during September is used in the present study. As 2009 was an especially quiet season, the extra 4 days of spin-up time given does not produce a significant impact on the simulated TC activity. This will be discussed further in a subsequent section.

b. WRF simulations

The Advanced Research WRF model (WRF-ARW), version 3.0.1.1 (Skamarock et al. 2008), is used to simulate seasonal TC activity. WRF has been employed by several studies as a regional climate model (e.g., Bukovsky and Karoly 2009; Argüeso et al. 2012; Bowden et al. 2012). The version of WRF used in the present study does not account for some processes that become important on the temporal scale of climate-mode integrations, such as changing concentrations of ozone, aerosols, and greenhouse gases (Fita et al. 2010). On a monthly time scale, the results of this study are not found to be sensitive to whether increased CO2 concentrations are included in the future environment, as is discussed in a following section and in Mallard (2011). However, with longer integration lengths, it is unclear whether such sensitivity could become more significant. With this constraint recognized, along with computational limitations, the duration of these model runs is confined to a month.

In all model runs, the Community Atmospheric Model (CAM) (Collins et al. 2004) scheme is used for both longwave and shortwave radiation. Positive definite advection for moist variables is implemented. A four-member physics ensemble is run by alternating the microphysics (MP) and planetary boundary layer (PBL) parameterizations. The Morrison et al. (2009) two-moment MP scheme and the WRF Single-Moment 6-class (WSM6) (Hong and Lim 2006) parameterization are used (Table 3). Both the Yonsei University (YSU) (Hong et al. 2006) and the Mellor-Yamada-Janjić (MYJ) (Janjić 1994, 2002) PBL parameterizations are employed. The YSU scheme is used in conjunction with the alternative formula for exchange coefficients more appropriate for hurricane-force wind speeds (Skamarock et al. 2008, p. 72). This formulation of exchange coefficients was not available for use with the MYJ PBL scheme in this version of WRF.

Table 3.

The boundary layer and microphysical parameterizations used for each ensemble member.

Table 3.

Although the ensemble size used here is small because of constraints on computational resources, use of an ensemble mean provides a more robust signal than would a single model simulation (Reichler and Kim 2008). The ensemble is designed to eliminate the possibility of unrepresentative behavior in simulations with particular physics choices. Prior studies have shown that TC intensity is significantly sensitive to both the MP and PBL schemes with minimum central pressure differences of greater than 10 hPa due to changes in either one or both of these parameterizations (Braun and Tao 2000; Li and Pu 2008, Nolan et al. 2009a,b; Hill and Lackmann 2009; Fovell et al. 2009). Using this physics-based ensemble approach gives a range of possibilities for the intensity of the TCs and reveals systematic trends in TC activity due to the choice of model parameterizations.

For initial and lateral boundary conditions, these simulations utilize 1° National Centers for Environmental Prediction (NCEP) Global Forecast System Final Analysis (GFS FNL) and 0.5° Real-Time Global (RTG) SST analysis (Thiébaux et al. 2003). Lateral boundary conditions and SSTs are updated every 24 h, and output is produced every 12 h.

First, a set of simulations is run using an 18-km nested domain covering the Atlantic basin from approximately 2° to 45°N and 15° to 110°W (Fig. 1). An outer domain with 54-km grid spacing is implemented with one-way1 nesting applied to all grids. Subsequently, the effect of using finer resolution is investigated by nesting a 6-km domain within the 18-km domain. In the latter series of runs, the 54-km nest is excluded and the 18-km grid is the outermost domain. Further discussion of the domain configuration and sensitivity to this choice is found in Mallard (2011, section 3.1.2). Vertical resolution is held constant, with the default value of 28 vertical half-σ levels, and the model top is at 50 hPa. The Kain–Fritsch (KF) (Kain and Fritsch 1993) convective parameterization (CP) scheme is used for the 54- and 18-km domains, while convection is simulated explicitly within the 6-km innermost nest. Gentry (2007) found that the effect of CP can be detrimental on hurricane intensity at fine grid spacings where convection is partially resolved, and explicit treatment of convection has been employed at this resolution by Gentry and Lackmann (2010) in their simulations of Hurricane Ivan (2004).

Fig. 1.
Fig. 1.

The bounds of all model domains discussed in the text. The outermost boxes represent the 54- and 18-km nests run in one model configuration. The innermost boxes represent the 18- and 6-km higher-resolution model configuration run without the 54-km outermost domain.

Citation: Journal of Climate 26, 13; 10.1175/JCLI-D-12-00182.1

The question of how closely the present-day model runs simulate the observed environmental conditions over the course of the month is examined in Mallard (2011). Domain-averaged temperatures, SST, RH, vertical wind shear, and column-integrated precipitable water vapor from both the 18- and 6-km ensembles were compared to similar spatial averages of the GFS-FNL and RTG analyses (Mallard 2011, section 4.1). It should be noted that there is some tendency for lower tropospheric drying early in the integration of the coarser 18Current05 ensemble; however, the use of 6-km grid spacing and explicit convection does result in RH values similar to those analyzed (Mallard 2011, section 4.1.1).

In all cases, the finer-resolution domain being evaluated is nested within a relatively coarse outer domain. Without this nested setup, it is found that some TCs take unrealistic tracks where they are repelled from the northern edge of the domain as their tracks recurve to the northeast (Gentry and Lackmann 2008; Mallard 2011). As the model-simulated TC tracks diverge from those observed, the imposed lateral boundary conditions do not possess a representation of the approaching storms, and it becomes difficult for mature model-simulated TCs to exit the domain. Since the storm tracks simulated by the outer and inner grids are similar with the present configuration, TCs can recurve and exit the boundary of the nested domain.

c. Sea surface temperature evolution

The importance of the relationship between TC strength and SST has been explored extensively both in theoretical and observational studies (e.g., Ooyama 1982; Emanuel 1986; Rotunno and Emanuel 1987; Demaria and Kaplan 1994). Because of this relationship, it has been hypothesized that, in a warmer climate, increased SST might lead to enhanced TC activity (Emanuel 1987, 2005). Therefore, if a downscaling approach is to be used to address differences in TC activity due to climate change, the model being used should contain a realistic treatment of SST.

A static SST field is unrealistic for a month-long model simulation of TC activity. Therefore, SSTs are updated every 24 h with RTG analyses. In addition to a time-varying SST, a one-dimensional ocean mixed layer model (OML) (Pollard et al. 1973; Davis et al. 2008), available in this version of WRF, is used to partially account for the cold wakes generated by TCs. The OML is single-column model, where no lateral heat transfer occurs between columns, only vertical redistribution. The initial mixed layer depth and lapse rate used by the OML are left as the default values2 and are initialized with a constant value everywhere on the model grid.

The ocean's thermal structure, particularly the depth of the mixed layer, has been shown to impact simulated TC intensity (e.g., Wu et al. 2007). While a fully coupled ocean model would better simulate these cold wakes, the representation here is adequate for our purposes. In a case study of Hurricane Katrina (2005), Davis et al. (2008) found that inclusion of the OML to account for Katrina's cold wake resulted in improvement of the intensity forecast. Therefore, using the OML in concert with a time-varying observed SST is a preferred approach.

To use the OML in conjunction with time-varying SST analyses, some modification of the WRF model was required. Without this modification, changes made to the SST field by these separate parts of the model code would overwrite each other at different points during the model integration. To unify the two features of the model, the source code was altered such that the OML is essentially restarted every 24 h, with an updated SST that includes the OML-induced anomaly3 generated only over the previous 24 h. Therefore, analyzed SSTs are used, but with a short time scale component of the cold wakes generated by the model-simulated TCs included (Fig. 2). Additional description of this code modification and further discussion of sensitivity to it is available in Mallard (2011, section 3.1.4). Nevertheless, the treatment of SST is the same in all model runs and would not affect the comparison of current condition runs with future simulations (in which warmer SSTs are included, as described in section 4).

Fig. 2.
Fig. 2.

SST (°C) valid at 0000 UTC 11 Sep 2005, from a simulation with modification to the OML to allow it to work with time-varying SST and to only keep recently simulated cold wakes. At this time, a TC-induced cold anomaly is present off the eastern U.S. coast.

Citation: Journal of Climate 26, 13; 10.1175/JCLI-D-12-00182.1

d. Tropical cyclone detection

An objective algorithm is used to identify TCs in model output, similar to that of Knutson et al. (2007), which was based on Vitart et al. (1997, 2003). This algorithm is chosen because it has been used in the prior downscaling work of Knutson et al. with a similar model configuration that featured 18-km simulations. The detection criteria of Vitart et al. are compared with other objective detection methods in Walsh et al. (2007).

To qualify as a TC, the value of vorticity at the 850-hPa level must exceed 1.6 × 10−4 s−1. A 4-hPa radial increase in sea level pressure and a 0.8°C decrease in the layer-averaged temperature between 300 and 500 hPa must be present, as taken from the storm center to within a 5° radius away from the center. These criteria used are designed to eliminate candidates that are extratropical and lack a warm-core structure (Vitart et al. 1997). Threshold values employed in the current work are taken from Knutson et al. (2007), having been adapted for use with 18-km grid spacing model output in that prior study. In addition to the conditions used by the Knutson et al. (2007) algorithm, for a storm to qualify as a TC, 10-m winds of 17 m s−1 or greater must be found within approximately 500 km of the pressure minimum. To be classified as a hurricane, the TC must have 10-m winds in excess of 33 m s−1. The TC must be present in two consecutive model output times (with output every 12 h) in order to be included in the storm statistics. To adapt this algorithm for use with finer-resolution grids, the same vorticity, temperature gradient, and pressure gradient thresholds are used as stated above, but with output coarsened to 18-km grid spacing. Once a TC qualifies as a hurricane, it is placed into a Saffir–Simpson category based on the minimum4 central pressure, using the central pressure thresholds found in Landsea (1993).

A tracking algorithm is used that searches for the closest TC within 800 km of the previously known position in order to compose storm tracks. The position of a TC is defined by the location of its minimum sea level pressure. Subjective manual inspection and quality control were applied in situations where the algorithm performed poorly, as when multiple storm tracks are present within the previously defined search radius.

3. Evaluation of past simulated seasonal TC activity

a. Storm counts

The present-day 2005 ensemble run with 18-km grid spacing, 18Current05, simulates a very active month of September, with more named storms, hurricanes, and major hurricanes than the climatological September (Table 2). This increase in frequency relative to climatology is expected, as September 2005 was an especially active month. However, the ensemble-averaged number of storms also considerably overshoots the observed number of storms in September 2005. The 18Current05 ensemble-averaged counts exceed those observed by 4.75 named storms, 2.5 hurricanes, and 1.5 major hurricanes; overproduction of TCs is most pronounced when considering the number of named storms.

The storm counts are somewhat sensitive to the thresholds set in the detection algorithm, and it could be that the thresholds used by Knutson et al. (2007) in a similar downscaling experiment using the GFDL hurricane model are set too low for the model setup using WRF. Some sensitivity exists to the minimal time duration (12 h) used by the detection algorithm described above, especially among weak, short-lived TCs. However, even if a longer 24-h criterion is applied to eliminate some short-lived storms, all ensembles still produce more named storms than are observed (not shown). It is also difficult to compare the number of storms identified by an objective algorithm to those identified by the National Hurricane Center (NHC), because the naming of storms in the verification involves varied data sources and different criteria, and it can be somewhat subjective.

This overproduction of TCs could also be linked to the lack of accounting for convective momentum transport (CMT) within the KF scheme used in the 18-km simulation, as the CP was significantly active and produces approximately half of the precipitation within the model grid over the month (not shown). Previous studies (e.g., Inness and Gregory 1997; Richter and Rasch 2008) have concluded that inclusion of CMT reduces spurious TC development, as (in a warm-core system) momentum exchange through convection would result in a transfer of high momentum out of the lower troposphere and reduced low-level wind speeds (e.g., Han and Pan 2006). This hypothesis could be tested by including CMT in the KF scheme or by running higher-resolution simulations that explicitly represent convection, as is done here.

Relative to 18Current05 storm counts, the 6Current05 ensemble mean counts are reduced by 2.5 named storms, 3.5 hurricanes, and 2 major hurricanes (Table 2). Therefore, the overproduction of TCs present in the 18-km runs is lessened by the use of 6-km grid spacing. This could be partly due to the explicit simulation of convection, as discussed above. Even though the reduced grid spacing of 6Current05 does result in fewer TCs, this ensemble still simulates an active month of September relative to climatology (Table 2). Overall, the higher-resolution 6Current05 ensemble is better able to recreate the observed number of named storms, hurricanes, and major hurricanes. The occurrence of hurricanes and major hurricanes is slightly less than observed during 2005, by approximately one storm when comparing the ensemble mean counts.

When simulating the month of September 2009, the 6-km current ensemble correctly captures a decrease in TC activity, with the 6Current09 ensemble mean number of named storms and hurricanes reduced by almost 50%, relative to 6Current05 mean storm counts (Table 2). The number of major storms is significantly lower in 2009; with only 0.5 major hurricanes in the 6Current09 ensemble average, compared to 1.5 major TCs in the ensemble mean of the 6Current05 simulations. Named storms are still somewhat overproduced in the 6Current09 ensemble, both relative to the observed and climatological counts. However, during this inactive period, the number of hurricanes and major hurricanes in this ensemble is smaller than climatological counts by 0.4 and 0.8 storms, respectively.

b. Intensity

While previous studies have shown that grid spacing at or below 2 km is needed to simulate the full range of TC intensities (e.g., Chen et al. 2007; Gentry and Lackmann 2010), the 18- and 6-km grid spacing domains used here are nevertheless capable of producing a broad spectrum of TC minimum central pressures (Fig. 3). Surprisingly, the lowest central pressure attained in any of the present-day ensembles, 934 hPa, occurred in one of the 18-km members. However, the 6-km ensembles5 simulate a greater percentage of TCs with a central pressure deeper than 960 hPa. Past studies have generally found that intensity is increased by the use of higher resolution (e.g., Gentry and Lackmann 2010). However, the present model configuration is not ideal to study changes in intensity with grid spacing systematically, as the large domain allows comparison between TCs with divergent tracks in an environment that is not homogenous with respect to SST, shear, and other parameters affecting TC intensity.

Fig. 3.
Fig. 3.

Histogram of the frequency of occurrence of TCs with central pressures within the pressure thresholds (hPa) shown on the x axis. A central pressure is taken from each TC at every output time. The total number of pressures within each threshold are normalized by the total in all thresholds and expressed as a percentage, as labeled for all current 6- and 18-km ensemble members (represented by blue solid and dashed lines, respectively). The climatological central pressures distribution (in black) is taken from 6-hourly HURDAT observations only for storms that occurred during the month of September from 1988 to 2009. Note that both the 6-km 2005 and 2009 ensembles are binned together.

Citation: Journal of Climate 26, 13; 10.1175/JCLI-D-12-00182.1

Overall, the distribution of minimum central pressures6 for both ensembles compares well to the climatological distribution taken from the NHC's North Atlantic hurricanes database (HURDAT) for TCs during the month of September between 1988 and 2009. Using two-sample Kolmogrov–Smirnov tests performed at the 5% significance level, both the simulated distributions of central pressure plotted in Fig. 3 are found to be statistically similar to the shown climatological distribution.

c. Genesis location

Figure 4 shows the locations of the origins of all TC tracks for each set of ensembles. Some TCs originate in the northeastern part of the domain, poleward of 30°N, which is not an area typically favored for genesis. Some sensitivity of the simulated genesis locations to the TC detection algorithm is expected. It should be noted that the detection algorithm does include a criterion to test for the presence of a warm-core system. Knutson et al. (2007) noted a positive bias in TC genesis north of 30°N in their downscaling study, and the present work utilizes a set of TC detection criteria that are almost identical to that of Knutson et al. Therefore, these criteria may be somewhat prone to false positive TC detection at high latitudes.

Fig. 4.
Fig. 4.

Points of TC genesis for all ensemble members (shown as black hurricane symbols) in each of the (top) 18Current05, (middle) 6Current05, and (bottom) 6Current09 sets of ensembles.

Citation: Journal of Climate 26, 13; 10.1175/JCLI-D-12-00182.1

In 2005 simulations, TCs tend to originate farther east than observed (Fig. 4). According to NHC best-track data, all six observed storms in September 2005 developed west of 40°W, whereas approximately 67% (52%) of all simulated TCs form east of 40°W when considering the 18-km (6-km) 2005 ensemble. The use of 6-km grid spacing tends to shift genesis slightly westward (Fig. 4).

Kossin et al. (2010) objectively categorized storm tracks from 1950 to 2007 into four separate clusters to analyze intrabasin and intraseasonal variability in the pattern of TC formation. They conclude that the southeastern Atlantic is the favored region of TC genesis during the middle of the season, with these TCs accounting for approximately 40% of the storms developing in August and September. Therefore, while simulated TCs tend to form farther east than observed during September 2005, they do form in a climatologically favored area for genesis during this part of the hurricane season. While 6Current09 simulations tend to produce similar genesis locations to the previously discussed runs (58% of TCs formed eastward of 40°W), the observed genesis during this inactive period tended to be farther east than in 2005 (not shown). Hurricane Fred of September 2009 is on record as the most intense hurricane south of 30°N and east of 35°W (Berg and Avila 2011).

d. Simulated activity in 2005 versus 2009

In the present model configuration, WRF is able to reproduce the decreased activity in 2009 relative to 2005. Ensemble-mean storm counts are lower in 2009 for each category (Table 2). Also, the intensity of TCs is lower, with the average minimum central pressure of all hurricanes (averaged over every storm throughout its life and over all ensemble members) deeper7 by 13 hPa in 6Current05 than in 6Current09 (Mallard 2011, Table 4.4).

Berg and Avila (2011) conclude that the El Niño event that developed during June 2009 and corresponding increases in vertical wind shear over the tropical Atlantic resulted in decreased TC activity during 2009. As expected, shear increases in the 2009 simulations relative to the 2005 runs. Averaged over the domain (excluding land), ensemble-mean 850–200-hPa shear is consistently larger in 6Current09, being greater than 6Current05 shear during 26 out of the 30 days of the month (not shown). While it is possible that other factors also contributed to the differences in TC activity between these months, this topic is beyond the scope of the present study.

e. Storm structure at 18- and 6-km grid spacing

To briefly present the characteristics of TC structure at the grid lengths used in this study, Fig. 5 shows model-simulated radar reflectivity of two TCs, one at 18-km and the other at 6-km grid resolution. Each storm is shown approximately one week into the simulation, and both are tracking into the Caribbean and approaching Puerto Rico. Both TCs are examples of strong hurricanes with similar tracks. The 6-km TC is at category 3 intensity, while the 18-km example is a category 4 storm. The Saffir–Simpson category determined is based on the minimum central pressure, using the central pressure thresholds of Landsea (1993).

Fig. 5.
Fig. 5.

An example of model-simulated radar reflectivity (dBZ) from a mature TC simulated with (left) 18- (18Current05 E1 valid at 0000 UTC 7 Sep 2005) and (right) 6-km grid spacing (6Current05 E1 valid at 1200 UTC 6 Sep 2005). Note that both plots are of the same spatial scale with 10° of latitude and longitude shown and that reflectivity associated with CP precipitation is included.

Citation: Journal of Climate 26, 13; 10.1175/JCLI-D-12-00182.1

Overall, the 18-km TC is characterized by a more annular, symmetric shape and limited development of spiral band activity. While the reflectivity of the eyewall is smooth and symmetric in the coarse simulation, the higher-resolution run shows smaller-scale maxima embedded in the eyewall. The 6-km simulation also produces more spiral band activity surrounding the storm. In Gentry and Lackmann (2010), the effect of using different horizontal resolution to simulate Hurricane Ivan (2004) is studied using the WRF model at grid spacing between 8 and 1 km. In that study, as grid spacing decreased, spiral bands became more developed outside the eyewall and localized maxima of simulated radar reflectivity appeared within the eyewall. These maxima were smaller in spatial scale and larger in magnitude as resolution increased. The use of smaller grid spacing in the present study results in similar changes. A more detailed analysis of the simulated TC structure is found in Mallard (2011, section 4.3.5), along with discussion of 10-m winds, low-level vorticity, and vertical motion.

4. Downscaling GCM changes

To assess the impact of thermodynamic changes on TC activity, GCM-projected temperature and moisture changes for the end of the twenty-first century are added to the initial and boundary conditions for the previously described September 2005 and 2009 model runs (Table 1). In the following section, these temperature and moisture changes are described, with a subsequent discussion of the environmental vertical wind shear.

a. Method of downscaling temperature and moisture changes

Future temperature and moisture changes are computed using a 20-member ensemble of GCM simulations from the IPCC AR4 for the A1B emission scenario (Fig. 6). A1B is considered a moderately intensive, “middle of the road,” emission scenario, with a midcentury peak in population and energy sources that are balanced across both fossil fuel–intensive and nonfossil technologies (Nakicenovic and Swart 2000). Changes are computed using 10-yr spatial averages over the Atlantic main development region, taken at the beginning and end of the twenty-first century. The averaging area covers the region 8.5°–15°N and 60°–40°W, as discussed in the idealized studies of Hill et al. (2008) and Hill and Lackmann (2011). In those studies, temperature and moisture changes were added to a present-day environment typical of the Atlantic basin, and changes in TC intensity were studied using one storm in an idealized environment. The vertical profile of the thermodynamic changes exhibits strongest warming in the upper troposphere, which is a signal of tropical climate change found in many earlier studies (e.g., Held and Soden 2000; Santer et al. 2003; Cordero and Forster 2006). It should be noted that amount of warming in the upper troposphere is subject to the most model-to-model variability of the entire profile (e.g., Hill and Lackmann 2011).

Fig. 6.
Fig. 6.

Temperature (K, solid line) and mixing ratio (g kg−1, dashed line) changes calculated for the A1B IPCC emission scenario. The computed SST anomaly, as projected for this scenario, is 2.21 K.

Citation: Journal of Climate 26, 13; 10.1175/JCLI-D-12-00182.1

A uniform SST increase of 2.21 K is computed from the GCM ensemble described above and is added everywhere to the RTG analyses. Temperature changes are added to the GFS-FNL analyses uniformly at every horizontal grid point but are a function of pressure only. Thus, no change is imposed to the horizontal temperature gradient in initial or lateral boundary conditions. To the extent that the atmosphere is in approximate thermal wind balance, there is no modification of the environmental shear for the future replications of September 2005 and 2009, at least in the initial and lateral boundary conditions.

Moisture changes are introduced by keeping the RH constant and then recomputing the mixing ratio with a higher temperature. This approach has been used similarly in previous studies, including Frei et al. (1998) and Im et al. (2010). The assumption of constant RH is based on the work of Allen and Ingram (2002), Held and Soden (2006), and others; this aspect is consistent with model simulations (e.g., Charney et al. 1979; Mitchell and Cunningham 1987, Soden et al. 2002, Soden and Held 2006) and with some observational results (e.g., Trenberth et al. 2005; Dai 2006). Finally, after temperature and moisture changes have been incorporated in the modified GFS-FNL analyses as described above, virtual temperatures are recomputed at every vertical level and geopotential height fields are modified in accordance with the hypsometric equation. Apart from this adjustment of geopotential heights, which is done in order to maintain hydrostatic balance, the synoptic pattern in the initial and boundary conditions remains intact. Momentum fields are left unmodified.

Several components of the methods used in this study combine to create a novel approach compared to prior downscaling experimental designs. The first is the use of analyzed initial and boundary conditions to provide the incipient disturbances for TC development in a future environment. Here, the GFS-FNL analyzed initial disturbances are used in both present and future condition runs. Modification of the number of initial disturbances in the future is not considered in this study. Therefore, dynamical aspects of climate change (e.g., modification of vertical wind shear, initial disturbances, and synoptic pattern) are not considered, and the thermodynamic component is isolated.

In comparing the present downscaling method to past experiments, the isolation of thermodynamic impacts is similar to idealized studies like those of Shen et al. (2000), Nolan and Rappin (2008), Rappin et al. (2010), and Hill and Lackmann (2011). However, the use of a basinwide domain integrated over a larger time scale is similar to the more complex studies of Knutson et al. (2007), Garner et al. (2009), and others. A similar method to that used in this study has evolved independently, termed pseudo-global warming (PGW) (Schär et al. 1996; Kimura and Kitoh 2007; Hara et al. 2008). Here PGW refers to procedures where a future atmosphere is created by modifying analyzed initial and boundary conditions with GCM-projected changes. None of these prior PGW studies addresses changes in future hurricane activity. An independently developed method similar to the one used in the present study and to PGW has been applied to a case study of Hurricane Katrina and termed mean signal nesting (MSN) (Lynn et al. 2009). It should be noted that the terms PGW or MSN do not imply the isolation of the thermodynamic component of climate change, and that several studies using those methods do modify wind fields as well as temperature and moisture.

b. Persistence of downscaled changes

It is not obvious whether the superimposed thermodynamic changes will persist in a month-long model integration unless trace gas concentrations are adjusted consistently. However, even with present-day greenhouse gas and ozone concentrations, the superimposed changes are present throughout the month. While the implicit effects of increased CO2 have been accounted for by adding temperature and moisture changes, the amount of CO2 in the atmosphere has not been modified from present-day values in the model runs discussed presently. However, the increased water vapor concentration does mimic a water-vapor feedback in these simulations. An additional 18-km ensemble has been performed with explicitly increased CO2, consistent with the A1B scenario at the end of the century, the results of which are discussed in Mallard (2011, section 4.4.6). At a monthly time scale, the sensitivity of TC activity to this choice is small relative to that between ensemble members or projected future changes. Therefore, for the monthly simulations employed here, it is not required that the altered CO2 values be included in all model simulations to make the comparison to TC activity in the present-day environment.

The superimposed changes are present in the temperature profile after 30 days of model integration (Fig. 7). Domain averages of temperatures at lower and upper levels show that these changes persist at every output time, and the difference between current and future averaged temperature stays consistent throughout the month (Fig. 8). The warmer SST prescribed for the future run, 2.21 K, is present throughout the month, as expected (not shown). Column-integrated precipitable water, again averaged over the entire domain, is increased in the future simulations, but with more variation between ensemble members than is shown for temperature (Fig. 9). While some current and future ensemble members have similar amounts of moisture within the domain near the end of the simulation, it should be noted that these members use different physics options. RH values are similar in both the current and future ensembles, which is consistent with the assumption of constant RH in the future with higher temperatures resulting in more moisture (Fig. 9).

Fig. 7.
Fig. 7.

Vertical profiles of the temperature (K) difference between 6Current05 and 6Warming05 ensembles averaged over the entire domain (excluding land) and over all ensemble members for 0000 UTC (left) 1 Sep and (right) 30 Sep. Each profile is plotted with pressure (hPa) as the vertical axis on a linear scale. Interior gridlines are shown on the x axis with the 0-K line emboldened.

Citation: Journal of Climate 26, 13; 10.1175/JCLI-D-12-00182.1

Fig. 8.
Fig. 8.

The 6Current05 and 6Warming05 domain-averaged temperature (K) at (top) 850 and (bottom) 150 hPa shown with the cooler and warmer colors for the current and warming ensemble members, respectively, at each model output time. Ensemble means are shown as heavy lines.

Citation: Journal of Climate 26, 13; 10.1175/JCLI-D-12-00182.1

Fig. 9.
Fig. 9.

As in Fig. 8, but for (top) 850-hPa relative humidity (percent) and (bottom) column-total precipitable water vapor (g kg−1).

Citation: Journal of Climate 26, 13; 10.1175/JCLI-D-12-00182.1

c. Intact vertical wind shear

Some past studies have attributed decreases in future TC activity to an increase in Atlantic basin vertical wind shear (e.g., Garner et al. 2009). In the present study, no modification has been made to wind fields in the initial and boundary conditions. Small variations in shear within the domain can arise, due to the presence of TCs with differing intensity, track, and outflow. However, domain-averaged values of wind shear in the 6Warming05 ensemble do remain similar to those in 6Current05 throughout the month (Fig. 10). The correlation coefficient between the time series of ensemble-mean shear in the present-day and future runs is 0.89. Ensemble-mean shear values are also well correlated with wind shear averages from the GFS-FNL analysis, with a correlation coefficient of 0.75 (0.71) to the 6Current05 (6Warming05) ensemble mean.

Fig. 10.
Fig. 10.

As above, but for 850–200 hPa vertical wind shear (m s−1). Vertical shear from the 1° GFS-FNL analysis is averaged over an area corresponding to the 6-km domain and shown in black dots every 24 h.

Citation: Journal of Climate 26, 13; 10.1175/JCLI-D-12-00182.1

5. Summary

In this paper, we present an approach for isolating the effects of temperature and moisture changes on regional TC activity using high-resolution mesoscale model simulations. Replicating a past season with projected thermodynamic changes ensures realistic synoptic and incipient disturbance climatologies while removing sensitivity to changes in vertical wind shear. While it is acknowledged that changes in shear are important (e.g., Garner et al. 2009), the present experimental design will allow examination of the effect of thermodynamic changes alone on TCs activity. Such results are shown in a companion paper (Mallard et al. 2013).

First, the ability of WRF to recreate past seasonal-scale activity is assessed, both during the record-breaking season of 2005 and the inactive 2009 season. Ensembles with different grid spacing are run to assess the sensitivity of these results to changes in horizontal resolution (Table 1). A four-member physics ensemble is used to provide a more robust signal from North Atlantic basin simulations of September 2005 and 2009. The small physics ensemble is generated by varying the microphysical and PBL parameterization schemes (Table 3).

Overall, it is concluded that WRF is able to simulate TC activity on a monthly scale during the height of the season, with the intensity and spatial distribution of TCs satisfactorily reproduced (Figs. 3, 4). While most genesis occurs farther east than was observed during September 2005, the southeastern Atlantic is a climatologically favored area of genesis during September (Kossin et al. 2010). Our goal is not to replicate the exact observed occurrence of all Atlantic TCs during the simulated period, but rather to develop reliable comparison simulations of recent TC activity. While the frequency of storms is significantly overestimated in the case of the 18-km ensemble, the 6-km ensemble with explicit convection produces storm counts that better match observed and climatological values (Table 2). Also, the use of 6-km grid spacing and explicit convection results in the simulation of more realistic TC eyewall and spiral band structures (Fig. 5).

The downscaled WRF runs are evaluated to ensure that GCM-predicted temperature and moisture changes persist in the simulated future atmosphere. It is concluded that the warmer temperatures and additional moisture imposed on the future simulations are present throughout the month and that there is no need for additional forcing, such as nudging, to maintain the presence of these changes (Figs. 79). As these changes are functions of pressure alone, no modification is made to the wind fields, and vertical wind shear is intact in the initial and boundary conditions of the future simulations. In a companion paper, TC activity in future and present-day simulations will be compared (Mallard et al. 2013).

Acknowledgments

This research was supported by DOE Grant ER64448, awarded to North Carolina State University. The authors would also like to thank the Renaissance Computing Institute (RENCI) for making available their computing resources and technical support. Thanks also to Prof. Walt Robinson, who reviewed an earlier draft of this publication, and to four anonymous reviews for their helpful comments. Constructive input on the project was also provided by Prof. Fred Semazzi. The WRF model is made available by NCAR, funded by the National Science Foundation. We also thank the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the CMIP3 model output and the WCRP's Working Group on Coupled Modeling (WGCM) for organizing the model data analysis activity. The WCRP CMIP3 multimodel dataset is supported by the Office of Science, U.S. Department of Energy.

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1

Additional simulations were performed with the 54- and 18-km nested configuration using two-way nesting. Little sensitivity was found to this choice.

2

Default settings include a mixed layer depth of 50 m and a lapse rate of 0.14 K m−1.

3

Every 24 h, OML-generated anomalies are computed by taking a difference field of the current SST (which has evolved because of the action of only the OML alone) and the SST field from 24 h ago (at the time of the last OML restart). Therefore, only recently generated cold wakes are present in the anomalies, and OML-induced changes from prior to 24 h ago are not included.

4

Minima in central pressure are taken from a single point on the model grid.

5

When comparing the simulated intensity distribution, both the 6Current05 and 6Current09 ensembles are binned together. However, similar conclusions are drawn when the 6Current05 and 18Current05 distributions are compared, as the 6-km distribution shown in Fig. 3 is dominated by numerous TCs of the more active year.

6

The pressure–wind distribution simulated at both grid spacings compares well with the relationship derived from HURDAT observations (Mallard 2011, Fig. 4.16).

7

Consistent changes in ensemble-mean maximum 10-m winds and spatially averaged 850–700 PV are found between the 6Current05 and 6Current09 ensembles (Mallard 2011, Tables 4.5 and 4.12).

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