Final Report: 0301186

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Final Report for Period: 02/2003 - 12/2004 Submitted on: 03/22/2005

Principal Investigator: Mears, Carl A. Award ID: 0301186

Organization: RSS

Title:SGER: The Impact of a Sea Surface Skin Temperature Parameterization on Tropical Convection in Climate System Model (CSM)

Project Participants

Senior Personnel

Name: Mears, Carl

Worked for more than 160 Hours: No

Contribution to Project: Name: Ricciardulli, Lucrezia

Worked for more than 160 Hours: Yes

Contribution to Project:

Post-doc

Graduate Student

Undergraduate Student

Technician, Programmer

Other Participant

Research Experience for Undergraduates

Organizational Partners

Other Collaborators or ContactsWe established a collaboration with Dr. James Hack at NCAR (CGD). We needed to have access to the atmospheric component (CAM3) of thelatest version of the NCAR Community Climate System Model. Thanks to the collaboration with James Hack, we could access CAM3 fewmonths before its public release, which was in June 2004. Dr. Hack also provided valuable comments about setting up the numericalexperiment and interpreting the results. John Truesdale at NCAR (CGD) also provided prompt and very efficient assistance with the new version of the model. In setting up the numerical experiment, the PIs greatly benefited from conversations with Dr. William Large at NCAR (CGD) and Dr. ChrisFairall at NOAA (ETL), about the parameterization of air-sea heat fluxes.

Activities and Findings

Research and Education Activities: (See PDF version submitted by PI at the end of the report)

Findings: (See PDF version submitted by PI at the end of the report)

Training and Development:

Status: Approved, User: Jay S. Fein, Date: 05/01/05

Final Report: 0301186

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In addition to gaining insight in the scientific aspects of the research topic, the principal investigators had the opportunity to become morefamiliar with the atmospheric component of the Community Climate System Model, as their numerical experiment required the modification ofseveral modules in the code.

Outreach Activities:

Journal Publications

Books or Other One-time Publications

Web/Internet Site

Other Specific Products

Product Type:

Data or databases

Product Description:The model output (history files) of the numerical experiments conducted are stored on the Mass Storage System (MSS) at the National Centerfor Atmospheric Research (NCAR).

Sharing Information:The model output is easily available to anybody who has access the the MSS at NCAR. The principal investigators will provide details abouthow to access the history files to anybody who will request it.

Product Type:

Software (or netware)

Product Description:The PIs modified several modules of the original code for the CAM3 model. Sharing Information:The modified code has already been shared with other scientists at NCAR, and it will be shared with any other scientist who will request it.

Contributions

Contributions within Discipline: The investigation described in this report deals with a topic of interest to all climate modelers. Recent observations have shown that the oceansurface temperature is not constant during the day, but has a diurnal cycle which, in regions of low wind and strong solar radiation (like in theIndian Ocean, or the West Tropical Pacific), can increase the temperature by up to 3 degrees C. This study was a preliminary investigation ofthe impact that the diurnal warming of the ocean surface temperatures has on the climate simulated by the models. Hopefully these preliminaryresults will stimulate interest in the topic and initiate more studies about the impact of diurnal modulation of ocean temperature on climatemodel simulations. Our results are specific for the Community Climate System Model, but the implementation of the diurnal cycle of the SeaSurface Temperature (described in the report) can be easily tested in any other climate model.

Contributions to Other Disciplines: Contributions to Human Resource Development: Contributions to Resources for Research and Education: Contributions Beyond Science and Engineering:

Final Report: 0301186

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Categories for which nothing is reported: Organizational Partners

Activities and Findings: Any Outreach Activities

Any Journal

Any Book

Any Web/Internet Site

Contributions: To Any Other Disciplines

Contributions: To Any Human Resource Development

Contributions: To Any Resources for Research and Education

Contributions: To Any Beyond Science and Engineering

Figure 1: Average amplitude oft the diurnal warming estimated as the difference between daytime and nighttime observations with the AVHRR sensor (Pathfinder dataset, 1988). Panel b shows the surface wind speed observed with the Special Sensor Microwave Imager SSMI (1988). From Gentemann et al., 2003.

Figure 2: Diurnal cycle of skin SST determined from the empirical parameterization in Eq. (1), for Q= 350 W/m2 and three sample wind speeds: 1.5, 3, and 5 m/s.

CAM3 (1 year)

OBSERVATIONS (SSMI, 1995-1999)

Figure 3: Percentage of low surface winds ( less than 6 m/s) from one year of 3-hourly output with CAM3 (panel a) , and from 5 years observations with SSMI (panel b).

(a)

(b)

Figure 4: Annual average amplitude of the diurnal warming implemented in CAM3, using Eq. (1).

Figure 5: Timeseries of the SST diurnal warming and the surface wind speeds in the CAM3perturbed simulation. The figure shows a 10 day period in July, for a sample location at (90E, EQ).

Figure 6: Panel a shows the amplitude of the diurnal warming as simulated by CAM3 forthe DJF season. Panel b refers to the amplitude of the diurnal cycle implemente by AiguoDai in CAM2 for the same season (see text).

(a)

(b)

Figure 7: Difference of the latent heat flux between the perturbed and the control simulations (5 year),for two seasons, DJF and JJA.

(a) DJF

(b) JJA

Figure 8: Difference in tropical precipitation rates between the perturbed and controlsimulations (5 years), for DJF and JJA.

(b) JJA

(a) DJF

Figure 9: Diurnal composites for the latent heat flux for the perturbed and control simulations, for four selected regions (see text). The diurnal composites are determined rom one year of 3-hourly output.

Figure 10: Similar to Fig. 9, but for the Planetary Boundary Layer Height.

Figure 11: Similar to Fig. 9, but for the comvective precipitation rate.

Figure 12: Similar to Fig. 9, but for the stratiform precipitation rate.

Figure 13: Global tropics (30N-30S) average diurnal composites for the PlanetaryBoundary Layer Height (panel a) and the convective precipitation rate (b), from theperturbed and control simulation.

(b)

(a)

Figure 14: Standard deviation about the mean convective precipitation rate simulated fromone year of 3-hourly output from the control (a) and perturbed (b) simulations. Panel c showsthe standard deviation about mean precipitation determined from 1998-2003 observationswith TRMM (dataset 3A25), at the T42 resolution. Note the different range for the color barsin (a) and (b) compared to (c).

(c)

(b)

(a)

Figure 15: Average duration of convective precipitation events determined from 3-hourly output from the control and perturbed simulations (panel a and b, respectively). Panel c shows the estimated duration for the TRMM 3A25 dataset. Note the different range for thecolor bars in (a) and (b) compared to (c).

(c)

(a)

(b)

THE IMPACT OF A SEA SURFACE SKIN TEMPERATURE PARAMETERIZATION ON

TROPICAL CONVECTION IN CCSM

FINAL REPORT, PROJECT ATM-0301186

Principal Investigators: Carl Mears, Lucrezia Ricciardulli

1. Aim of proposed research

The lower boundary condition for standard atmospheric climate models uses monthly Sea Surface Temperature (SST) datasets determined from a combination of observations. In the atmospheric component CAM of the Community Climate System Model (CCSM) the boundary SST dataset is determined from a careful blending of measurements taken at different depths, from few microns to 10 m, and then normalized to 1-m depth (bulk SST). These SSTs are then used in the model to determine the air-sea heat fluxes. However, in the real world, the air-sea fluxes are determined by the thin layer (~ microns) at the interface between ocean and air, the “skin” layer. During the daytime and in conditions of low surface wind speeds, the skin layer is warmed by solar radiation and its temperature can be significantly warmer that the bulk temperature. In this investigation we implemented an interactive empirical parameterization of diurnal warming of skin SST into the atmospheric component of the model, CAM3. Our goal was to study the impact of this SST “adjustment” on the simulated surface heat fluxes and tropical precipitation in the model.

2. Diurnal warming of SST: Observations and empirical parameterization

Recent in-situ observations analyzed in great detail the vertical profile of the upper ocean and its thermal properties (Webster et al., 1996; Wick et al., 1996; Donlon and Robinson, 1997; Emery et al., 2001; Donlon et al., 2002). During the nighttime or in moderate to high wind conditions (greater than 6 m/s, approximately) the upper ocean is well mixed and turbulent motions result in a homogeneous thermal structure, with ocean temperatures almost independent on depth in the upper 10 m. During the day and in regions of low wind and strong solar heating, thermal stratification occurs and the skin temperature can be significantly higher than the bulk one, by as much as 3 oC. As illustrated in Fig. 1, global satellite observations (Gentemann et al., 2003) reveal a strong correlation between regions with low surface wind speed and areas significantly affected by diurnal warming of the skin SST, approximated by the difference between day and night SST retrievals with the infrared sensor AVHRR. Most of the warming is localized in the Indian Ocean and in the Inter-Tropical Convergence Zone (ITCZ), with annual averages of the peak amplitude close to 0.5 oC. In order to reconcile differences

observed when measuring the SST with different methods and therefore referring to different depths, it is important to carefully evaluate the amplitude of this diurnal warming of the skin SST. With this goal in mind, Gentemann et al. (2003) developed a simple empirical parameterization of the ocean diurnal warming based on 18 years of satellite observations. They stratified the satellite observations in terms of wind speed, time of the day, and insolation, and developed an empirical model of the amplitude of the SST diurnal warming which depends only on surface wind speed U (m/s) and the daily average insolation at the top of the atmosphere Q (W/m2), and it varies according to the time of the day t (hrs): 2

1 2 3( ) ( ) [ ( 154) ( 154) ] exp( )DIURNALSST t f t c Q c Q c U∆ = ⋅ − − − ⋅ − (1) where c1=1.23459; c2=1.56088e-3; c3=-0.44. f(t) is the following function of time:

50 1

( ) [ cos( ) sin( )]i iif t a a ih b ih

== + +� , with 2 / 24h tπ= . The coefficients are:

a0=4.118e-3; a1=-4.132e-3; a2=0.8746e-3; a3=-0.246e-3; a4=0.2762e-3; a5=-0.0609e-3; b1=-5.093e-3; b2=2.583e-3; b3=-0.5143e-3; b4=-0.3355e-3; b5=0.2269e-3. For Q< 154 W/m2, the diurnal warming was set to zero. The equation (1) is slightly different from the one in Gentemann et al. (2003) because the authors have recently modified it (Gentemann, personal communication).

Fig. 2 shows an example of the diurnal warming of skin SST calculated from the empirical parameterization in eq. (1) for Q=350 W/m2 and three sample surface wind speeds.

3. Diurnal warming parameterization: implementation in CAM3

Accurate understanding of the diurnal warming of the SSTs has great advantages also for climate modeling, as the specification of SST is one of the most important boundary conditions imposed in these models. The atmospheric component of the Community Climate System Model (CCSM) is constrained with a monthly climatological dataset (Shea et al., 1992) or monthly observed SSTs (Fiorino, 2000), both derived from the Reynolds SSTs (Reynolds and Smith, 1994; Reynolds et al., 2002), which are normalized to 1-m depth (bulk SST). These SSTs, assigned as lower boundary condition, do not contain any diurnal signal, and are used when calculating the air-sea fluxes (latent heat flux E and sensible heat flux H), parameterized as follows in the atmospheric model: ( )A H SH C U T Tρ= − (2) ( )A E SE C U q qρ= − (3) Where Aρ is the atmospheric surface density, U is the 10-m wind speed, q and T are the lowest model atmospheric specific humidity and temperature, respectively, and CH and CE are air-sea transfer coefficients determined from observations (see section 4.10.2 in http://www.ccsm.ucar.edu/models/atm-cam/docs/description/). In Eqs. (2-3) Ts is the surface temperature and qS refers to the saturation specific humidity at the surface-atmosphere interface, which is a function of TS. In the standard version of the model, TS

over the ocean is approximated by the SST assigned as lower boundary condition (bulk SST). However, the skin SST represents a better approximation for the real temperature at the ocean-atmosphere interface. As already mentioned in the previous section, the skin SST during the day can be significantly warmer than the bulk SST. Therefore, using the monthly observed or climatological SST derived from bulk SST in Eqs. (2-3) can result in significant errors in the estimation of the air-sea fluxes in the atmospheric model, with potential impact on tropical precipitation. Our investigation focused on the impact of using “skin SST” to determine air-sea fluxes in the model and its effects on the simulated tropical precipitation. For our experiment we used version 3 of the atmospheric model CAM, with the Eulerian dynamical core and a triangular spectral truncation T42. We performed a 5-year simulation with standard climatological SSTs (control run) and a 5-year perturbed simulation where we adjusted the climatological SSTs with the addition of the parameterized diurnal cycle (SST-diurnal run). For these simulations we collected the standard monthly output. In addition, we extended both the control and the SST-diurnal simulation for one extra year with a three-hourly output, which allowed a detailed investigation of the diurnal cycle of some selected variables. A realistic simulation of the surface winds in CAM3 (represented by the 10-m neutral stability winds, parameterized according to Large et al., 1994) is critical to the success in implementing the empirical parameterization of the diurnal cycle developed by Gentemann et al (2003). Therefore, as a preliminary check, we compared the frequency of occurrence for low (less than 6 m/s) surface winds in the model with the one from observations. Fig. 3 shows the percentage of low winds for one simulated year with CAM3 (3a), compared to 5 years of observations obtained with SSMI (3b). The geographical distribution of low-wind regions in model and observations is very similar, with slightly higher frequency of low winds in the tropics in the model. This could lead to slightly overestimated amplitude of the SST diurnal cycle in the model, but is not a source of concern for our purpose. The amplitude of the diurnal cycle expressed in eq. (1) was then implemented interactively in the model, at each time step t and gridpoint. The daily average insolation at the top of the atmosphere Q was calculated from an analytical expression (Liou, 1980).

The annual average SST diurnal perturbation determined with the empirical parameterization (1) implemented in CAM3 is illustrated in Fig. 4. The amplitudes, with annual averages up to 0.5 oC, and the geographical distribution are comparable to the observed ones, in Fig. 1. However, the maximum amplitudes for a single day can reach 2 oC or more. As an example, Fig. 5 shows the diurnal amplitudes in CAM3 for a 10-day period in July in the equatorial Indian Ocean, with greater warming corresponding to days with lower surface winds.

The SST diurnal amplitude implemented in the model was then added to the standard SSTs used as lower boundary condition at each time step, except for gridpoints with an SST lower than 7 oC, in order to exclude high latitudes.

A similar experiment was conducted by Aiguo Dai at NCAR, with CAM2 (presented at the IUGG meeting in Sapporo, Japan, 2003; Dai, personal communication). The perturbation on the SSTs in Dai’s experiment was imposed to be a sinusoidal function, with maximum amplitudes determined from COADS data for each season, not calculated interactively in the model as a function of wind speed like in our experiment. In order to make sure our experiment was not a repetition of Dai’s one, in Fig. 6 we compare the average diurnal amplitudes for the December-January-February season in our experiment (a) with those implemented by Dai (b). The figure shows that our interactive parameterization gives rise to much greater amplitudes, mostly in the Indian ocean and the ITCZ, in regions usually affected by low winds. These amplitudes are consistent with satellite observations, but very different from those in Dai’s numerical experiment. Therefore we proceeded with our experiment with CAM3.

1. Model’s response:

Here we analyze the impact that our interactive SST adjustment due to diurnal warming has on surface fluxes and tropical convection in CAM3.

a. Impact on Seasonal averages (5 years)

Fig. 7 shows the 5-year average difference for the surface latent heat flux in the perturbed simulation minus the control one, for DJF (a) and JJA (b). In both seasons we observed a significant impact in the perturbed simulation in the tropics, with changes in latent heat flux up to 30 W/m2 (10-20%), both positive and negative. The regions mostly affected by positive changes are those where the surface water flux increases (QFLX, not shown) due to enhanced evaporation (where evaporation minus precipitation, E-P, increases up to 3 mm/day, not shown). On the other hand, areas of negative change in surface latent heat flux correspond to the combination of two effects: a decrease in E-P, due to increased precipitation, and the presence of moderate to high surface winds which enhance the impact on latent heat flux (see Eq. 3). Minor changes were found in the surface sensible heat flux over ocean (not shown). In Fig. 8 we illustrate the impact of the diurnal SST perturbation on 5-year seasonal averages of the tropical precipitation rate, for DJF and JJA. Increased precipitation up to 3 mm/day (20%) is observed in the Western ITCZ in winter and in the northern Indian Ocean in summer (reaching 5 mm/day), in areas where the SST diurnal amplitude is significant. Other tropical areas present a decrease in precipitation, keeping the global average precipitation unchanged in the tropics.

b. Impact on Diurnal cycles

In addition to 5 years of monthly output (standard history files) from the perturbed and control runs, we stored one year of 3-hourly output of the following variables: convective and stratiform precipitation rates (PRECC and PRECL, respectively), planetary boundary layer height (PBLH), latent and sensible heat fluxes (LHFLX and SHFLX, respectively), the SST diurnal adjustment (SST_DATA), the 10-m wind speed (U_10), and the temperature tendency due to moist processes (DTCOND, three dimensional field). We selected four areas over the ocean to analyze how the SST diurnal adjustment affected the diurnal cycle: the northern Indian Ocean (80E-100E; 5S-10N), the Western ITCZ (150E-170E; EQ-15N), the Eastern ITCZ (240E-260E; EQ-15N), and the Atlantic ITCZ (320E-340E; EQ-15N). Fig. 9 shows the composite diurnal cycle for the latent heat flux, for all the selected areas; the mean LHFLX for each region is also indicated for the control and perturbed simulations. A significant impact on the amplitude of the diurnal cycle of LHFLX is found when the diurnal SST adjustment is included, with peak-to- peak amplitudes of the order of 10 W/m2 (or greater in the Indian ocean). Fig. 9 shows that the LHFLX has a diurnal cycle even in the control run, when the SSTs are unaffected

by diurnal warming (they are only interpolated from monthly files). The reason for this minor diurnal cycle, which peaks in the early morning, is likely due to the diurnal variation of the air temperature and moisture. However, when the SSTs diurnal adjustment is added to the monthly values, the LHFLX diurnal cycle increases in amplitude and peaks in the early afternoon, at about 1500LT, in correspondence to the maximum of the observed (and parameterized) SSTs diurnal cycle (see Fig. 2). Fig. 10 shows the composite diurnal cycle of the PBLH, for the perturbed and the control run. The diurnal variation of PBLH in the perturbed run follows the variation of the SST and LHFLX, because an increase in surface temperature enhances the atmospheric boundary layer turbulence and therefore moves the PBLH towards greater altitudes. The average peak-to-peak amplitude of the PBLH composite diurnal cycle over the ocean is about 80 m. The composite diurnal cycle of convective precipitation (PRECC) over the selected oceanic regions is illustrated in Fig. 11. The addition of a diurnal cycle of SSTs in the model results in an enhanced diurnal cycle of PRECC, with average peak-to-peak amplitudes of approximately 1 mm/day or more (10 to 20% increase), with the exception of the western ITCZ where the peak-to-peak amplitude reaches 2.4 mm/day (almost 50% increase). The time of the maximum in the perturbed run is not affected, and is consistent with observations, which show an early morning maximum for convective rain events over the ocean (Janowiak et al., 1994; Imaoka and Spencer, 2000; Yang and Slingo, 2001; Ricciardulli and Sardeshmuk, 2002). Stratiform precipitation rates over the ocean in the perturbed run are not significantly different from the control one, as shown in Fig. 12. Finally, Fig. 13 shows the impact of the SST diurnal warming on the global tropical (30N-30S) composites for the convective precipitation rate and the PBLH. Both the variables are unaffected in terms of global mean, but the amplitude of the diurnal cycle increases from 0.55 to 0.7 mm/day for PRECC and from 15 to 45 m for the PBLH. An estimation of the observed diurnal cycle of tropical convection over ocean in Dai and Trenberth (2004) reports a peak-to-peak amplitude of showery precipitation of approximately 1.5 mm/day, for latitudes 25S-25N (see their figure 14). Therefore, we can conclude that in CAM3 (both the control and perturbed simulations) the diurnal cycle of oceanic convection is present but underestimated compared to observations.

c. Impact on short-term variability of tropical convection In this section we analyze the change in short-term variability of convective precipitation. Previous studies (Ricciardulli and Garcia, 2000; Rasch et al., 2005) showed that the CCSM (current and previous versions) significantly underestimates the variability of tropical precipitation at time scales shorter than 2 days. The lack of variability is particularly evident over the oceans. One goal of our investigation was to determine if the addition of a diurnal cycle for the SST has an impact on the variability of precipitation. Fig. 14 shows the standard deviation about the mean convective precipitation, determined from the 3-hourly output for the control (a) and perturbed (b) simulations, and for

observations with TRMM (c) (for a dataset with the T42 spatial resolution; Rasch et al, 2005). The figure clearly shows no significant changes when the SST diurnal warming is taken into account. In the model, the standard deviation about mean convective precipitation is on the order of 6-8 mm/day in the oceanic convective regions, quite different from the TRMM observations where it exceeds 15 mm/day in the same regions. Similar conclusions can be drawn from Fig. 15, which shows an extremely long duration of convective rain events for the perturbed and control simulations (36 hrs or more, in the active regions), in sharp contrast with the TRMM observations (between 10 and 14 hrs in the same regions).

2. Conclusions and Findings Our main goal with this investigation was to evaluate the impact on tropical dynamics of a parameterization of the diurnal cycle of the Sea Surface Temperatures in the Community Atmospheric Model CAM3, the atmospheric component of the Community Climate System Model. In particular we focused on the impact on surface heat fluxes and tropical precipitation. A simple empirical parameterization was implemented in the model. The parameterization is interactive at each time step and grid point, and it only depends on the local surface wind speed, the insolation, and the time of the day. Analysis of the characteristics of the diurnal warming simulated in the model using the parameterization showed a close similarity to features of the observed diurnal cycle of SSTs. We performed two 5-year simulations, with the standard model (control) and the one including the diurnal warming (perturbed), and analyzed the monthly climatology. In addition to this, we extended the simulation to one extra year with 3-hourly output for some selected variables of interest in order to investigate the impact on their diurnal cycle. For the two simulations, we compared precipitation (convective and stratiform), latent and sensible heat fluxes, and planetary boundary layer height (PBLH). Analysis of the 3-hourly output from the model showed that the diurnal cycles of latent heat flux, PBLH and convective precipitation were clearly impacted by the SST diurnal adjustment. The implementation of a diurnal warming of the SSTs also affected seasonal averages of latent heat flux, with changes up to 30 W/m2 in some tropical regions (corresponding to a 10-20% change). Convective precipitation rate was not affected in terms of global average, but major regional changes up to +/-3 mm/day occurred in the Inter-Tropical Convergence Zone (ITCZ) and +5 mm/day in the Indian Ocean. However, we did not observe changes in the short-term (less than 2-days) variability of tropical convection. For example, the standard deviation about the mean convective precipitation and the average duration of precipitation events are mostly unaffected by the SST adjustment. These results are somehow surprising, as we expected that the diurnal warming of the SSTs would enhance the surface forcing and increase the variability of tropical convection over the ocean. We can consider this investigation as a preliminary study

indicating the need to investigate in more detail the model’s behavior of the moist physics (i.e., initiation of convection over ocean) at the process level. Motivated by the results presented here, the Principal Investigators are considering submitting to a funding agency a proposal for a more in-depth investigation on this subject.

3. Presentations These results were presented at the CCSM Atmospheric Model Working Group Meeting in March 2005, in Boulder, Colorado. A manuscript for publication in a peer-reviewed scientific journal is in preparation, but will require additional model simulations and analysis in order to better understand the lack of response of oceanic convection to the SST diurnal perturbation.

References

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Donlon, C. J., P. J. Minnett, C. L. Gentemann, T. J. Nightingale, I. J. Barton, B. Ward, and M. J. Murray, 2002: Toward improved validation of satellite sea surface skin temperature measurements for climate research, J. Climate, 15, 353-369.

Emery,W. J., S. Castro, G. A. Wick, P. Schluessel, and C. Donlon, 2001: Estimating sea surface temperature from infrared satellite and in situ temperature data, Bull. Amer. Meteor. Soc., 82, 2773-2785.

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Rasch, P. J., M. J. Stevens, L. Ricciardulli, A. Dai, A. Negri, R. Wood, B. A Boville, B. Eaton, and J. J. Hack, 2005: A characterization of tropical transient activity in the CAM3 atmospheric hydrologic cycle. Submitted to J. Climate.

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Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Wang, 2002: An improved in situ and satellite analysis for climate, J. Climate, 15, 1609-1625.

Ricciardulli, L., and R. R. Garcia, 2000: The excitation of equatorial waves by deep convection in the NCAR Community Climate Model (CCM3). J. Atmos. Sci., 57, 3461-3487.

Ricciardulli, L. and P.D. Sardeshmukh, 2002: Local space and time scales of organized tropical convection. J. Climate, 15, 2775-2790.

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