Model Simulations and Methodology The Model

The modelling data employed in this work are time series obtained from climate simulations carried out with the SINTEX-G (SXG) coupled atmosphere-ocean general circulation model (AOGCM), which is an evolution of the SINTEX and

SINTEX-F models (Gualdi et al., 2003a, 2003b; Guilyardi et al., 2003, Luo et al. 2003, Masson et al. 2005, Behera et al. 2005).

The ocean model component is the reference version 8.2 of the Ocean Parallelise (OPA; Madec et al. 1998) with the ORCA2 configuration. To avoid the singularity at the North Pole, it has been transferred to two poles located on Asia and North America. The model longitude-latitude resolution is 2° x 2° cosine(latitude) with increased meridional resolutions to 0.5° near the equator. The model has 31 vertical levels, ten of which lie in the top 100 m.

Model physics includes a free-surface configuration (Roullet and Madec 2000) and the Gent and McWilliams (1990) scheme for isopycnal mixing. Horizontal eddy viscosity coefficient in open oceans varies from 40000 m2/s in high latitudes to 2000 m2/s in the equator. Vertical eddy diffusivity and viscosity coefficients are calculated from a 1.5-order turbulent closure scheme (Blanke and Delecluse 1993). For more details about the ocean model and its performance, readers are referred to Madec et al. (1998) or online to the web-site http://www.lodyc.jussieu.fr/opa/.

The evolution of the sea-ice is described by the LIM (Louvain-La-Neuve sea-ice model; Fichefet and Morales Maqueda, 1999), which is a thermodynamic-dynamic snow sea-ice model, with three vertical levels (one for snow and two for ice). The model allows for the presence of leads within the ice pack. Vertical and lateral growth and decay rates of the ice are obtained from prognostic energy budgets at both the bottom and surface boundaries of the snow-ice cover and in leads. When the snow load is sufficiently large to depress the snow-ice interface under the sea-water level, sea-water is supposed to infiltrate the entirety of the submerged snow and to freeze there, forming a snow ice cap. For the momentum balance, sea-ice is considered as a two-dimensional continuum in dynamical interaction with the atmosphere and ocean. The ice momentum equation is solved on the same horizontal grid as the ocean model. LIM has been thoroughly validated for both Arctic and Antarctic conditions, and has been used in a number of process studies and coupled simulations (Timmermann et al. 2005 and references therein).

The atmospheric model component is the latest version of ECHAM-4 in which the Message Passing Interface is applied to parallel computation (Roeckner et al. 1996). We adopted a horizontal resolution T106, corresponding to a gaussian grid of about 1.12° x 1.12°. In the pantheon of long coupled climate simulations, this is a considerably high horizontal resolution. A hybrid sigma-pressure vertical coordinate is used with 4-5 of a total of 19 levels lying in the planetary boundary layer. The parameterization of convection is based on the mass flux concept (Tiedtke, 1989), modified following Nordeng (1994). The Morcrette (1991) radiation scheme is used with the insertion of greenhouse gases (ghg) and a revised parameterization for the water vapour and the optical properties of clouds. A detailed discussion of the model physics and performances can be found in Roeckner et al. (1996).

The ocean and atmosphere components exchange SST, surface momentum, heat and water fluxes every 1.5 hours. The coupling and the interpolation of the coupling fields is made through the OASIS2.4 coupler (Valcke et al., 2000). No flux corrections are applied to the coupled model.

The Climate Scenario Simulations

With respect to the previous versions of the SINTEX model, SXG includes a model of the sea-ice, which allows the production of fully coupled climate scenario experiments. In this paper, we present results obtained from the analysis of four climate simulations (Table 1).

In order to assess the capability of the model to reproduce a reasonably realistic TC activity and to evaluate the effectiveness of our TC detection methodology, the tropical cyclone-like vortices produced during the last 30 years of a 20th Century simulation have been analyzed and compared with observations. The simulation has been conducted integrating the model with forcing agents, which include greenhouse gases (CO2, CH4, N2O and CFCs) and sulfate aerosols, as specified in the protocol for the 20C3M experiment defined for the IPCC simulations (for more details see also the web-site http://www-pcmdi.llnl.gov/ipcc/aboutX_ipcc. php). The integration starts from an equilibrium state obtained from a long coupled simulation of the pre-industrial climate, and has been conducted throughout the period 1870-2000.

Once the skill of the model to reproduce TC-like vortices has been evaluated using the present climate simulation, the possible effects induced by greenhouse global warming on the simulated TCs have been explored using 30 years of twice-daily data from climate scenario experiments. Specifically, a simulation with atmospheric CO2 concentration 287 ppm, corresponding to the pre-industrial period (PREIND), a climate simulation with CO2 concentration doubled with respect to the PREIND period (2CO2), and a climate simulation with atmospheric CO2 concentration quadrupled with respect to the PREIND period (4CO2). The transition between PREIND and 2CO2 and between 2CO2 and 4CO2 periods has been produced by a 1%/year increment of the CO2 concentration. At the end of the two transition periods, the model has been integrated for 100 years with constant values of CO2 concentration, i.e. 574 ppm and 1148 ppm respectively.

A greenhouse warming scenario based on a doubling and a quadrupling of atmospheric CO2 is certainly an idealized experiment and does not represent a realistic forecast of future radiative forcing. The motivation of this choice resides in the fact that large concentration of atmospheric CO2 might emphasize and make more evident the response of simulated TCs to greenhouse warming. Also, the advisability of this kind of idealized experiments in the framework of TC studies has been discussed by Michaels et al. (2005) and Knutson and Tuleya (2005). Furthermore, the possible impacts of a doubling of atmospheric CO2 concentration has been explored in a

Table 1 Summary of the climate simulations used in this study

CLIMATE SIMULATIONS AND SCENARIOS

PREIND

Preindustrial ghg concentration

30 years

20C3M

20th Century ghg conc. + aerosols

30 years (1970-1999)

2CO2

2 x PREIND CO2 conc.

30 years

4CO2

4 x PREIND CO2 conc.

30 years

number of previous works (e.g., Broccoli and Manabe 1990, Haarsma et al. 1993, Bengtsson et al. 1996, Royer 1998, Sugi et al. 2002, Knutson and Tuleya 2004, McDonald et al. 2005, Yoshimura et al. 2006, Chauvin et al. 2006), but so far no analysis has been performed on the effects of its further increase.

Reference Data

The simulated TC-like vortices and the main features of their climatology are evaluated comparing the model results with observational data sets. Specifically, we use data from the National Hurricane Center (NHC) and the U.S. Joint Typhoon Warning Center (JTWC). Furthermore, the capability of the model to reproduce the observed mean climate is assessed using the ECMWF 40-year Re-Analysis (ERA40; more information available at the web-site http://www.ecmwf.int/ research/era), the observational sea-surface temperature data set HadISST (Global Sea-Ice and Sea Surface Temperature Dataset produced at the Hadely Centre, Rayner et al. 2003) and the observed precipitation data set produced by Xie and Arkin (1997). For the sake of simplicity, in the rest of the paper we will refer to all of these data as observations.

Method of Detection of the Simulated Tropical Cyclones

Basically, two methods for detecting TCs have been commonly used in the analysis of general circulation model (GCM) experiment results. The first technique produces an estimate of the TC activity based on a genesis parameter computed from seasonal means of large scale fields (Gray 1979, Watterson et al. 1995, Royer 1998). This method has been used especially in the analysis of low-resolution model runs, as it obviates the explicit simulation of individual TCs.

The second method is the location and tracking of individual TCs based on objective criteria for the identification of specific atmospheric conditions that characterize a TC with respect to other atmospheric disturbances. In particular, TCs are identified and tracked as centres of maximum relative vorticities and minimum of surface pressure, with a warm core in high levels and maximum wind in the low layers of the atmosphere (Haarsma 1993, Bengtsson et al. 1995, Walsh 1997). In the existing literature, the definition of the criteria, i.e. the thresholds and the domain over which they are computed, varies from work to work. A discussion and a short summary for the criteria of objective TCs detection in atmospheric analysis and model simulations is given in Walsh (1997) and Chauvin et al. (2006) respectively.

In this study, we use a TC location and tracking method based on the approach defined in Bengtsson et al. (1995) and Walsh (1997). Specifically we assume that a model TC is active over a certain grid point A if the following conditions are satisfied:

• in A relative vorticity at 850 hPa is >3 • 10-5 1/s;

• there is a relative minimum of surface pressure and wind velocity is >14 m/s in an area of 2.25° around A;

• wind velocity at 850 hPa is > wind velocity at 300 hPa;

• the sum of temperature anomalies at 700, 500 and 300 hPa is >2°K. Where the anomalies are defined as the deviation from a spatial mean computed over an area of 13 grid points in the east-west and 2 grid points in the north-south direction;

• temperature anomaly at 300 hPa is > temperature anomaly at 850 hPa;

• the above conditions persist for a period longer than 1.5 days;

Conditions 3, 4 and 5 distinguish TCs from other low-pressure systems and particularly the extra-tropical cyclones, which are characterized by strongest winds near the tropopause and a tropospheric cold core. The choice of the parameters in conditions 1-6 are very similar to the value indicated by Bengtsson et al. (1995) and Walsh (1997) and optimize the detection of simulated TCs in our model compared with the observations. Also, we checked the sensitivity of our results to small changes in these parameters. We found that the number of detected TCs is scarcely sensitive to the threshold values, but exhibits some sensitivity to the size of the areas over which means are computed. For a complete discussion of these criteria and their sensitivity to the parameters used the reader is addressed to Walsh (1997).

Simulation of the Tropical Climate and TC Climatology

As a first step, we analyze the results obtained from a simulation of the 20th Century, as described in Section 2.2, comparing the model results with observations (re-analysis) for the period 1970-1999.

The TC occurrence has a pronounced seasonal character, with more intense activity found in the summer hemisphere (Emanuel 2006), namely in the Northern Hemisphere from June to October and in the Southern Hemisphere from December to April. Therefore, we will focus our attention on the specific seasons (and regions) of intense TC activity.

Simulation of Mean State and High-frequency Variability in the Tropics

Figure 1 shows the seasonal means of SST and precipitation as obtained from the observations and the model, for the extended northern summer (June-October, JJASO) and southern summer (December-April, DJFMA).

a) OBS SST JJASO MEAN

a) OBS SST JJASO MEAN

c) OBS prec JJASO MEAN
e) OBS SST DJFMA MEAN
g) OBS prec DJFMA MEAN
b) SXG SST JJASO MEAN
d) SXG prec JJASO MEAN
f) SXG SST DJFMA MEAN
h) SXG prec DJFMA MEAN

Fig. 1 Seasonal means of sea-surface temperature (SST) and precipitation as obtained from the observations (left panels) and the model (right panels). The upper panels (a-d) show the extended Northern Hemisphere summer means June-July-August-September-October (JJASO); the lower panels (e-h) are the means obtained for the extended Southern Hemisphere summer December-January-February-March-April (DJFMA). The SST contours (panels a, b, e and f) is 2°C for the SST interval 10-20°C and 1°C for the SST interval 21-29°C. The precipitation contours (panels c, d, g and h) are 2 mm/day. Rainfall values lower than 2 mm/day are not plotted

In general the model overestimates the SSTs in the tropical regions, during both seasons. The seasonal mean SST, averaged over the tropics is 0.26°C and 0.32°C warmer than observed in JJASO and in DJFMA respectively. The warm bias is visible both in the tropical Indian Ocean and Atlantic Ocean and it is particularly evident in the central-eastern Pacific, south of the equator. In this region, over the warm SSTs, the model overestimates also the rainfall, tending to produce a double ITCZ, which is a common error of most AOGCMs. In the equatorial Pacific, on the other hand, the model cold tongue is clearly too strong and extends too far west. Correspondingly, the simulated precipitation is too weak in the equatorial Pacific, especially west of the date line.

In the tropical Atlantic, the model rainfall is reasonably close to observations in JJASO, whereas during DJFMA it appears to be shifted south (by about 10° of latitude), probably as a consequence of the excessively warm SSTs found in the subtropical southern Atlantic, off the Brazilian coast. Interestingly, in the tropical Indian Ocean, the model precipitation is generally weaker than observed. During northern summer, the model shows a clear rainfall deficit in the area affected by the Asian summer monsoon, extending from the Bay of Bengal, through South-East Asia and South China Sea, up to the region east of the Philippines archipelago. Simulated precipitation appears to be too weak also over the eastern equatorial Indian Ocean, whereas it tends to be too intense in the western part of the basin, between the equator and 10° S. Also during northern winter (Fig. 1, panels g and h) model rainfall is too weak over the eastern Indian Ocean and the Indonesian region.

Simulation of Tropical Cyclones

In this Section we analyze the ability of the model to simulate tropical cyclones-like vortices (that we will refer to simply as TCs), following the methodology discussed in Section 2.4. As a first step, we compare the total number of TCs per year detected in the model simulation and in the observations over the period 1970-1999 (Table 2). In general, the number of simulated TCs per year is almost 30% lower than the number detected in the observations, whereas its standard deviation is quite well captured by the model.

The geographical distribution of the TC formation positions is shown in Fig. 2. In the observations (panel a) there are four distinct regions of TC formation in the Tropics of the Northern Hemisphere: North Indian Ocean (NI), West-North Pacific (WNP), East-North Pacific (ENP) and North Atlantic (NA); and three regions in the

Table 2 Total number of Tropical Cyclones found in the observations and in the 20th Century model simulation during the period 1970-1999

NUMBER OF TCs 1970-1999

OBS

SXG 20C3M

TOT

2813

1986

MEAN

93.8

66.2

STD

10.9

9.2

OBS TC track starting points 1970-1999

OBS TC track starting points 1970-1999

Fig. 2 Distribution of the TC track starting points for the period 1970-1999 for the observations (panel a) and model (panel b). Each point corresponds to the geographical location of a TC at the time of its first detection. Following Camargo et al. (2004) seven regions of TC genesis have been defined. In the pictures these regions are delimited by thick black lines

Fig. 2 Distribution of the TC track starting points for the period 1970-1999 for the observations (panel a) and model (panel b). Each point corresponds to the geographical location of a TC at the time of its first detection. Following Camargo et al. (2004) seven regions of TC genesis have been defined. In the pictures these regions are delimited by thick black lines

Southern Hemisphere: Southern Indian Ocean (SI), the ocean North of Australia (AUS) and the Southern Pacific (SP). Based on these regions of TC genesis and following Camargo et al. (2004), we define seven basins (demarked by the boxes in Fig. 2) that will be used to delimit and characterize the different areas of TCs activity.

The model (Fig. 2, panel a) reproduces well the patterns of TCs genesis, especially in the Northern Hemisphere. The major contrast with the results obtained from the observations occurs in the southern Atlantic, where the model generates some TC, though no TCs are observed in this region during the considered period (1970-1999). This model error might be related to the too warm SSTs and intense convective activity found in this region (Fig. 1). However, it is noteworthy that in March 2004 the first ever observed TC in South America, named Catarina, hit the Brazilian coast (Pezza and Simmonds, 2005).

A comparison with the results obtained with atmospheric GCMs forced with observed prescribed SSTs (Camargo et al. 2004, Fig. 1) shows a substantial improvement in the patterns of TC genesis obtained with the coupled simulation. Interestingly, the comparison is made more valid by the fact that one of the atmospheric models used in Camargo et al. (Echam4) is basically the same as the one we use as atmospheric component in our coupled model. An important difference, however, is the horizontal resolution, which is T42 in Camargo et al. and T106 in our case. The enhanced model resolution might explain some of the improvements we find with our model, such as, for example, the increased global number of TCs, accompanied by a significant reduction of the number of TCs near the equator, which is a rather unrealistic feature (e.g., Camargo et al. 2004, Oouchi et al. 2006).

In Fig. 3, the box plots representing the mean number of TCs per year for each area are shown both for the observations (left panel) and the model (right panel). The plots confirm that in the simulation there is a lower number of TCs, especially in the tropical North Pacific (WNP and ENP). However, in general the difference with the observations is relatively small, and, for each area, the model simulates a fairly realistic mean year-to-year variability (see also STD in Table 2). More importantly, the simulation appears to capture the basic features of the TC distribution among the different areas. Specifically, the region with the highest mean number of TCs per year is the north-western tropical Pacific (WNP) both in the model and observations. Also the mean number of TCs in the North Indian Ocean

Fig. 3 Box plots of the number of TCs per year for the observations (left panel) and model simulation (right panel). The number of TCs (y-axis) is plotted for each area of TC-genesis (x-axis) defined in Fig. 4. In a box-plot, the box represents the interquartile (IQR) and contains the 50% of the data; the upper edge of the box represents the 75th percentile (upper quartile, UQ), while the lower edge is the 25th percentile (lower quartile, LQ). The horizontal lines within the box are the median. The vertical dashed lines indicate the range of the non-outliers. The values indicated with the crosses are the outliers, i.e. values that are either larger than UQ + 1.5 • IQR or smaller than LQ - 1.5 • IQR

(NI) and Atlantic region (ATL) are well reproduced, whereas the TC activity in the north-eastern Pacific (ENP) is clearly underestimated.

The results shown in Figs. 1-3 indicate that the model reproduces a quite realistic tropical mean state (at least in terms of SST and precipitation) and number of simulated TC-like vortices. Furthermore, the geographic distribution of TCs appears to be in good agreement with the observations. However, so far nothing has been said about how realistic the features of the simulated TCs are.

In order to have a closer look at the structure of the model TCs, Fig. 4 depicts the composite patterns of precipitation and low-level wind field obtained from the 100 most intense simulated TCs in the Northern Hemisphere. The composites were calculated by averaging the fields over the period of occurrence of the TCs and over the 100 events. The means have been computed for a domain centered on the core of the cyclones and extending 10° each side.

From these patterns it turns out that the model simulates TCs with a somewhat realistic structures. When averaged over the 100 events and their lifetimes, the mean TC has intense mean precipitation and surface winds that extend for about 300-400 Km from the centre ("eye") of the cyclone. The amplitude of the fields is substantially smaller than observed, but consistent with the results obtained from high-resolution atmospheric GCMs experiments (e.g., Bengtsson et al. 1995, wind [m/s] total precipitation [mm/day]

wind [m/s] total precipitation [mm/day]

10-8-6-4-2 0 2 4 6 8 10 -10 -8 -6 -4 -2 0 2 4 8 B 10

Fig. 4 Composite patterns of 850-hPa wind and total precipitation associated with the simulated TCs. The composites have been computed by averaging the fields of the 100 most intense (in terms of precipitation) model TCs in the Northern Hemisphere. The fields have been averaged over the period of occurrence of the TCs and over the 100 events. The mean fields have been computed over a spatial domain centred in the core of the cyclone and extending 10° each side. In panel (a) the of 850-hPa wind (arrows) is plotted along with the intensity of the wind (contour). The contour interval is 2 m/s. Contours larger than 10 m/s are shaded. Panel (b) shows the 850-hPa wind (arrows) along with the total precipitation rate. The contour interval is 5 mm/day

10-8-6-4-2 0 2 4 6 8 10 -10 -8 -6 -4 -2 0 2 4 8 B 10

Fig. 4 Composite patterns of 850-hPa wind and total precipitation associated with the simulated TCs. The composites have been computed by averaging the fields of the 100 most intense (in terms of precipitation) model TCs in the Northern Hemisphere. The fields have been averaged over the period of occurrence of the TCs and over the 100 events. The mean fields have been computed over a spatial domain centred in the core of the cyclone and extending 10° each side. In panel (a) the of 850-hPa wind (arrows) is plotted along with the intensity of the wind (contour). The contour interval is 2 m/s. Contours larger than 10 m/s are shaded. Panel (b) shows the 850-hPa wind (arrows) along with the total precipitation rate. The contour interval is 5 mm/day

Chauvin et al. 2006). In agreement with observational studies (e.g., Frank 1977, Gray 1979, Willoughby et al. 1982), the strongest wind velocities are located to the right-front sector of the core, though the maxima in the model is much too far away from the "eye". This model error is most likely due to the model resolution, which does not allow to resolve the fine and tight structures observed in "real" TCs, as suggested in McDonald et al. (2005) Chauvin et al. (2006) and shown in Bengtsson et al. (2007). For the same reason, the minimum surface pressure at the center of the storm (not shown) tends to be rather high (—990 hPa) and the simulated TC does not exhibits the "eye" in precipitation, though in general the rainfall pattern is reasonably realistic.

An important feature of the observed TCs is their marked seasonal character (Emanuel 2003). Fig. 5 shows the seasonality of TC occurrence for both observations and model simulations in the Northern Hemisphere and Southern Hemisphere and for specific regions of activity described in Fig. 2. In general the model reproduces well the seasonal behaviour of TCs, especially in the Southern Hemisphere, the northern Indian and Atlantic Oceans. In the Northern Hemisphere, and

Fig. 5 Seasonal modulation of the TC occurrence for the observations (dashed lines) and model simulation (solid lines) and for different region of the Tropics. Upper panels: tropical region of the Southern Hemisphere (left) and of the Northern Hemisphere (right). Middle and lower panels: northern Indian Ocean, western tropical Pacific, eastern tropical Pacific and tropical Atlantic

Fig. 5 Seasonal modulation of the TC occurrence for the observations (dashed lines) and model simulation (solid lines) and for different region of the Tropics. Upper panels: tropical region of the Southern Hemisphere (left) and of the Northern Hemisphere (right). Middle and lower panels: northern Indian Ocean, western tropical Pacific, eastern tropical Pacific and tropical Atlantic particularly in the North-West and North-East Pacific the annual phase of the TC activity is captured but the amplitude is much smaller, consistent with the reduced number of simulated TCs previously discussed.

Beside the seasonal modulation, the TC activity exhibits a rather strong year-to-year variability. As it has been shown in a number of studies (Gray 1984, Chan 2000, Chia and Ropelewski 2002, Frank and Young 2007, among the others), this interannual variability has a strong link with ENSO. Changes in the SST distribution in the tropical Pacific and the associated changes in the large scale circulation, in fact, appear to have a strong impact on the number of TCs that occur in different regions of the globe. The relationship between ENSO and TCs activity differs depending on the region considered. Frank and Young 2007 have shown that the number of observed TCs and the NINO3 ENSO index are negatively correlated in the North Atlantic (r = -0.55), whereas they appear to be positively correlated in the North-East Pacific (r = 0.38) and Indian Ocean (r = 0.24).

Figure 6 shows the interannual variation of the number of TCs in the North Atlantic, North-East Pacific and southern Indian Ocean (solid curves), along with the NINO3 SSTA index (dotted curves). Here, the value of the NINO3 index is computed for the season of maximum TC activity, i.e. JJASO for the Northern

Fig. 6 Time series of the number of TCs along with the NINO-3 index for different regions of the Tropics. The dashed lines show the interannual variation of the number of simulated TCs in the northern tropical Atlantic (upper panel), northern tropical eastern Pacific (middle panel) and Southern Indian Ocean (lower panel). The solid line show the value of NINO-3 SSTA index defined as the average of the SST anomaly over the NINO-3 region (5°S-5°N ; 150°W-90°W). The values of the NINO-3 index plotted in the ATL and ENP case have been obtained for JJASO, whereas for the SI case it has been computed for DJFMA. The value of the correlation between the two curves (r) is also shown

Fig. 6 Time series of the number of TCs along with the NINO-3 index for different regions of the Tropics. The dashed lines show the interannual variation of the number of simulated TCs in the northern tropical Atlantic (upper panel), northern tropical eastern Pacific (middle panel) and Southern Indian Ocean (lower panel). The solid line show the value of NINO-3 SSTA index defined as the average of the SST anomaly over the NINO-3 region (5°S-5°N ; 150°W-90°W). The values of the NINO-3 index plotted in the ATL and ENP case have been obtained for JJASO, whereas for the SI case it has been computed for DJFMA. The value of the correlation between the two curves (r) is also shown

Hemisphere and DJFMA for the Southern Hemisphere. The curves shown in Fig. 6 indicate that the model simulates a fairly realistic interannual modulation of the number of TCs and that this interannual variability is correlated with ENSO similarly to what is found in the observations.

All these results indicate that the model simulates intense convective disturbances with characteristics similar to the basic features of observed TCs, reassuring about its suitability to investigate how climate change might impact on the TC activity, which will be the subject of the next Section.

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