• Bjerknes, J., 1966: A possible response of the atmospheric Hadley circulation to equatorial anomalies of ocean temperature. Tellus, 18 , 820829.

    • Search Google Scholar
    • Export Citation
  • Chambers, L. H., , B. Lin, , and D. F. Young, 2002: Examination of new CERES data for evidence of tropical iris feedback. J. Climate, 15 , 37193726.

    • Search Google Scholar
    • Export Citation
  • Fu, C., , H. F. Diaz, , and J. O. Fletcher, 1986: Characteristics of the response of sea surface temperature in the central Pacific associated with warm episodes of the Southern Oscillation. Mon. Wea. Rev., 114 , 17161739.

    • Search Google Scholar
    • Export Citation
  • Fu, R., , A. D. Del Genio, , W. B. Rossow, , and W. T. Liu, 1992: Cirrus-cloud thermostat for tropical sea surface temperatures tested using satellite data. Nature, 358 , 394397.

    • Search Google Scholar
    • Export Citation
  • Fu, R., , A. D. Del Genio, , and W. B. Rossow, 1994: Influence of ocean surface conditions on atmospheric vertical thermodynamic structure and deep convection. J. Climate, 7 , 10921108.

    • Search Google Scholar
    • Export Citation
  • Graham, N. E., , and T. P. Barnett, 1987: Sea surface temperature, surface wind divergence, and convection over tropical oceans. Science, 238 , 657659.

    • Search Google Scholar
    • Export Citation
  • Hall, A., , and S. Manabe, 1999: The role of water vapor feedback in unperturbed climate variability and global warming. J. Climate, 12 , 23272346.

    • Search Google Scholar
    • Export Citation
  • Hartmann, D. L., , and M. L. Michelsen, 1993: Large-scale effects on the regulation of tropical sea surface temperature. J. Climate, 6 , 20492062.

    • Search Google Scholar
    • Export Citation
  • Hartmann, D. L., , and M. L. Michelsen, 2002a: No evidence for iris. Bull. Amer. Meteor. Soc., 83 , 249254.

  • Hartmann, D. L., , and M. L. Michelsen, 2002b: Reply. Bull. Amer. Meteor. Soc., 83 , 13491352.

  • Hartmann, D. L., , and M. L. Michelsen, 2002c: A two-box model of cloud-weighted sea surface temperature: The semiautomatic negative correlation with mean cloud fraction. Bull. Amer. Meteor. Soc., 83 .(Suppl.), ES70–ES71.

    • Search Google Scholar
    • Export Citation
  • Houghton, J. T., , Y. Ding, , D. J. Griggs, , M. Noguer, , P. J. van der Linden, , X. Dai, , K. Maskell, , and C. A. Johnson, 2001: Climate Change 2001: The Scientific Basis, Cambridge University Press, 892 pp.

  • Iguchi, T., , T. Kozu, , R. Meneghini, , J. Awaka, , and K. Okamoto, 2000: Rain-profiling algorithm for the TRMM precipitation radar. J. Appl. Meteor., 39 , 20382052.

    • Search Google Scholar
    • Export Citation
  • Inamdar, A. K., , and V. Ramanathan, 1994: Physics of greenhouse effect and convection in warm oceans. J. Climate, 7 , 715731.

  • Jackson, D. L., , and G. L. Stephens, 1995: A study of SSM/I-derived columnar water vapor over the global oceans. J. Climate, 8 , 20252038.

    • Search Google Scholar
    • Export Citation
  • Krueger, A. F., , and T. I. Gray, 1969: Long-term variations in equatorial circulation and rainfall. Mon. Wea. Rev., 97 , 700711.

  • Kummerow, C., , W. Barnes, , T. Kozu, , J. Shiue, , and J. Simpson, 1998: The Tropical Rainfall Measuring Mission (TRMM) sensor package. J. Atmos. Oceanic Technol., 15 , 808816.

    • Search Google Scholar
    • Export Citation
  • Lau, K-M., , and H. T. Wu, 2003: Warm rain processes over tropical oceans and climate implications. Geophys. Res. Lett., 30 , 22902294.

  • Lau, K-M., , C-H. Sui, , M-D. Chou, , and W-K. Tao, 1994: An inquiry into the cirrus-cloud thermostat effect for tropical sea surface temperature. Geophys. Res. Lett., 21 , 11571160.

    • Search Google Scholar
    • Export Citation
  • Lin, B., , B. A. Wielicki, , L. H. Chambers, , Y. Hu, , and K-M. Xu, 2002: The iris hypothesis: A negative or positive cloud feedback? J. Climate, 15 , 37.

    • Search Google Scholar
    • Export Citation
  • Lindzen, R. S., , M-D. Chou, , and A. Hou, 2001: Does the earth have an adaptive infrared iris? Bull. Amer. Meteor. Soc., 82 , 417432.

  • Manabe, S., , and R. T. Weatherald, 1967: Thermal equilibrium of the atmosphere with a given distribution of relative humidity. J. Atmos. Sci., 24 , 241259.

    • Search Google Scholar
    • Export Citation
  • Pierrehumbert, R. T., 1995: Thermostats, radiator fins, and the local runaway greenhouse. J. Atmos. Sci., 52 , 17841806.

  • Ramanathan, V., 1981: The role of ocean-atmosphere interactions in the CO2 climate problem. J. Atmos. Sci., 38 , 918930.

  • Ramanathan, V., , and W. Collins, 1991: Thermodynamic regulation of ocean warming by cirrus clouds deduced from observations of the 1987 El Niño. Nature, 351 , 2732.

    • Search Google Scholar
    • Export Citation
  • Rasool, S. I., , and S. H. Schneider, 1971: Atmospheric carbon dioxide and aerosols: Effects of large increases on global climate. Science, 173 , 138141.

    • Search Google Scholar
    • Export Citation
  • Raval, A., , and V. Ramanathan, 1989: Observational determination of the greenhouse effect. Nature, 342 , 758762.

  • Reynolds, R. W., , and D. C. Marsico, 1993: An improved real-time global sea surface temperature analysis. J. Climate, 6 , 114119.

  • Reynolds, R. W., , and T. M. Smith, 1994: Improved global sea surface temperature analyses using optimum interpolation. J. Climate, 7 , 929948.

    • Search Google Scholar
    • Export Citation
  • Riehl, H., , and J. S. Malkus, 1958: On the heat balance in the equatorial trough zone. Geophysica, 6 , 503538.

  • Simpson, J., , C. Kummerow, , W. K. Tao, , and R. F. Adler, 1996: On the tropical rainfall measuring mission (TRMM). Meteor. Atmos. Phys., 60 , 1936.

    • Search Google Scholar
    • Export Citation
  • Sun, D-Z., , and R. S. Lindzen, 1993: Distribution of tropical tropospheric water vapor. J. Atmos. Sci., 50 , 16431660.

  • Tokay, A., , D. A. Short, , C. R. Williams, , W. L. Ecklund, , and K. S. Gage, 1999: Tropical rainfall associated with convective and stratiform clouds: Intercomparison of disdrometer and profiler measurements. J. Appl. Meteor., 38 , 302320.

    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., 1996: Formation and limiting mechanisms for very high sea surface temperature: Linking the dynamics and the thermodynamics. J. Climate, 9 , 161188.

    • Search Google Scholar
    • Export Citation
  • Wallace, J. M., 1992: Effect of deep convection on the regulation of tropical sea surface temperature. Nature, 357 , 230231.

  • Wentz, F. J., , and T. Meissner, 2000: AMSR Ocean Algorithm: ATBD Version 2. NASA Reference Publication, 66 pp. [Available online at http://www.ssmi.com/papers/AMSR_Ocean_Algorithm_Version_2.pdf.].

  • View in gallery

    (a) VIRS brightness temperature, (b) matched PR rain rates, (c) cloud identification number, and (d) number of convective cores in the cloud.

  • View in gallery

    (a) Slope and (b) monthly correlation coefficients between SST and single-core convective cloud size normalized by rainfall for 30°S–30°N, 130°E–170°W.

  • View in gallery

    Single-core convective cloud size normalized by rainfall regressed against SST for clouds identified with Tb thresholds of (a) 250, (b) 260, (c) 270, and (d) 280 K for Jan 1998–Aug 1999. The slopes of the regression lines, the correlation coefficients (R), and the number of points included in the regressions are labeled in each panel.

  • View in gallery

    Single-core convective cloud size normalized by rainfall regressed against SST for clouds identified with a Tb threshold of 260 K for (a) 10°S–10°N, (b) 20°S–20°N, and (c) 30°S–30°N. The slopes of the regression lines, the correlation coefficients (R), and the number of points included in the regressions are labeled in each panel.

  • View in gallery

    Single-core convective cloud size normalized by rainfall regressed against SST for clouds with mean Tbs between (a) 250 and 260 K, (b) 260 and 270 K, and (c) 270 and 280 K for Jan 1998–Aug 1999. The slopes of the regression lines, the correlation coefficients (R), and the number of points included in the regressions are labeled in each panel.

  • View in gallery

    Same as in Fig. 3, but for multicore convective cloud size.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 44 44 10
PDF Downloads 12 12 1

An Evaluation of the Proposed Mechanism of the Adaptive Infrared Iris Hypothesis Using TRMM VIRS and PR Measurements

View More View Less
  • 1 Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado
© Get Permissions
Full access

Abstract

Significant controversy surrounds the adaptive infrared iris hypothesis put forth by Lindzen et al., whereby tropical anvil cirrus detrainment is hypothesized to decrease with increasing sea surface temperature (SST). This dependence would act as an iris, allowing more infrared radiation to escape into space and inhibiting changes in the surface temperature. This hypothesis assumes that increased precipitation efficiency in regions of higher sea surface temperatures will reduce cirrus detrainment. Tropical Rainfall Measuring Mission (TRMM) satellite measurements are used here to investigate the adaptive infrared iris hypothesis. Pixel-level Visible and Infrared Scanner (VIRS) 10.8-μm brightness temperature data and precipitation radar (PR) rain-rate data from TRMM are collocated and matched to determine individual convective cloud boundaries. Each cloudy pixel is then matched to the underlying SST. This study examines single- and multicore convective clouds separately to directly determine if a relationship exists between the size of convective clouds, their precipitation, and the underlying SSTs. In doing so, this study addresses some of the criticisms of the Lindzen et al. study by eliminating their more controversial method of relating bulk changes of cloud amount and SST across a large domain in the Tropics. The current analysis does not show any significant SST dependence of the ratio of cloud area to surface rainfall for deep convection in the tropical western and central Pacific. Results do, however, suggest that SST plays an important role in the ratio of cloud area and surface rainfall for warm rain processes. For clouds with brightness temperatures between 270 and 280 K, a net decrease in cloud area normalized by rainfall of 5% per degree SST was found.

Corresponding author address: Anita D. Rapp, Dept. of Atmospheric Science, Colorado State University, Fort Collins, CO 80523-1371. Email: rapp@atmos.colostate.edu

Abstract

Significant controversy surrounds the adaptive infrared iris hypothesis put forth by Lindzen et al., whereby tropical anvil cirrus detrainment is hypothesized to decrease with increasing sea surface temperature (SST). This dependence would act as an iris, allowing more infrared radiation to escape into space and inhibiting changes in the surface temperature. This hypothesis assumes that increased precipitation efficiency in regions of higher sea surface temperatures will reduce cirrus detrainment. Tropical Rainfall Measuring Mission (TRMM) satellite measurements are used here to investigate the adaptive infrared iris hypothesis. Pixel-level Visible and Infrared Scanner (VIRS) 10.8-μm brightness temperature data and precipitation radar (PR) rain-rate data from TRMM are collocated and matched to determine individual convective cloud boundaries. Each cloudy pixel is then matched to the underlying SST. This study examines single- and multicore convective clouds separately to directly determine if a relationship exists between the size of convective clouds, their precipitation, and the underlying SSTs. In doing so, this study addresses some of the criticisms of the Lindzen et al. study by eliminating their more controversial method of relating bulk changes of cloud amount and SST across a large domain in the Tropics. The current analysis does not show any significant SST dependence of the ratio of cloud area to surface rainfall for deep convection in the tropical western and central Pacific. Results do, however, suggest that SST plays an important role in the ratio of cloud area and surface rainfall for warm rain processes. For clouds with brightness temperatures between 270 and 280 K, a net decrease in cloud area normalized by rainfall of 5% per degree SST was found.

Corresponding author address: Anita D. Rapp, Dept. of Atmospheric Science, Colorado State University, Fort Collins, CO 80523-1371. Email: rapp@atmos.colostate.edu

1. Introduction

Anthropogenic effects on climate have become an increasingly important topic in atmospheric and environmental sciences. Projected climate change has significant implications for socioeconomic activity and environmental policy. In the Third Assessment Report by the Intergovernmental Panel on Climate Change (IPCC; Houghton et al. 2001), globally averaged surface temperature is projected to rise between 1.4° and 5.8°C over the next 100 yr. This increase in temperature is due largely to the current and predicted emissions of greenhouse gases such as carbon dioxide. Carbon dioxide, however, is only indirectly responsible for the projected increase in temperature. By fixing the atmospheric lapse rate, relative humidity, stratospheric temperature, and cloudiness, Rasool and Schneider (1971) computed only a 0.8°C temperature increase in a carbon dioxide doubling experiment. The real atmosphere responds to changes in the surface temperature with feedbacks that further modify the initial forcing caused by the increased carbon dioxide. The large uncertainty in the IPCC projected temperature increase arises mostly from the unknown response of these feedbacks in the climate system, especially those due to water vapor and clouds. Raval and Ramanathan (1989), Inamdar and Ramanathan (1994), and Jackson and Stephens (1995) all showed that column water vapor increases with increasing sea surface temperature (SST). Water vapor, much like carbon dioxide, has a positive feedback on temperature because it serves to trap terrestrial radiation in the atmosphere. This water vapor feedback is thought to account for more than half the projected warming simulated by climate models (Manabe and Weatherald 1967; Ramanathan 1981; Hall and Manabe 1999). Increased water vapor, in turn, is likely to impact clouds whose effect on the climate system is even less certain. More importantly, convective clouds have been postulated (Ramanathan and Collins 1991; Lindzen et al. 2001) to have a large impact on regulating the west Pacific SSTs, which rarely exceed 303 K (Fu et al. 1986; Waliser 1996). In this region, convection is strongly coupled to the SST (e.g., Bjerknes 1966; Krueger and Gray 1969; Graham and Barnett 1987; Fu et al. 1994) because of the fact that water vapor and latent energy of an air parcel increase with temperature and allow the parcel to overcome the gravitational potential energy and rise to the upper troposphere. The tropical Pacific is therefore ideal to investigate cloud and water vapor feedbacks because of this strong relationship between the surface temperature and radiative feedback effects through convection.

The feedback of cirrus clouds produced by convection has been the subject of many studies, although the proposed mechanisms for this feedback differ. The two most prominent and disputed studies utilizing satellite data both offer hypotheses for this cirrus cloud feedback, which this study will term the “thermostat hypothesis” (Ramanathan and Collins 1991) and the “iris hypothesis” (Lindzen et al. 2001). The thermostat hypothesis suggests that as the greenhouse effect increases with surface temperature, deep convection produces more reflective cirrus clouds. These more reflective cirrus clouds shield the surface from solar radiation and act like a thermostat to regulate the SST. Ramanathan and Collins (1991) attempted to predict the climate implications of the thermostat hypothesis and calculated a maximum SST between 303 and 305 K, in agreement with observations. They also suggested that this thermostat would require carbon dioxide concentrations to increase by an order of magnitude before the SST could be substantially increased from its current value.

The proposed thermostat hypothesis was met with significant criticism. A number of papers (Fu et al. 1992; Lau et al. 1994; Wallace 1992; Hartmann and Michelsen 1993) suggested that convection is much more sensitive to the large-scale circulation than SST. Most of these papers also suggested that evaporational cooling is a much more likely candidate for the regulation of SST. Pierrehumbert (1995) found that neither clouds nor evaporational cooling could provide regulation of SST. He reported instead on a strong connection between SST and the distribution of dry and moist regions in the Tropics.

As suggested by Pierrehumbert (1995), Lindzen et al. (2001; hereafter LCH) investigated changes in the relative areas of high- and low-humidity regions with SST. Dry regions typically occur in regions of large-scale subsidence, while the moist regions are associated with ascent that is concentrated in convective “hot towers” (Riehl and Malkus 1958). Near the top of the convective tower, ice particles are detrained by the upper-level air motions to form the cirrus anvil. These ice particles are detrained downstream of the original cumulus tower, and the anvil cirrus cloud grows quite large relative to the size of the cumulus updraft. It is with these clouds that LCH found an inverse relationship between cloud area and the cloud-weighted SST in the tropical western and central Pacific. The production of cirrus depends on how much water is available for detrainment into the anvil, which in turn can be thought of as a competition for cloud water between detrainment and precipitation. Citing earlier findings from Sun and Lindzen (1993) that raindrop growth rate increases with temperature, LCH hypothesized that increases in surface temperature result in increased precipitation efficiency in the cumulus tower, which leads to a decrease in cirrus detrainment. LCH assumed that on average, the longwave effect of the cirrus would dominate. These cirrus clouds are more transparent to solar radiation than the cumulus towers and allow some of the incoming shortwave radiation to reach the surface. Since they exist in the cold, upper levels of the atmosphere, however, they emit less radiation to space and trap terrestrial radiation in the atmosphere, increasing the greenhouse effect. A decrease in the amount of detrained cirrus with increasing temperature would therefore decrease the cirrus cloud greenhouse effect by allowing more outgoing longwave radiation (OLR) to escape into space and thus provide a negative feedback to regulate the underlying SST. This dependence of cirrus detrainment on temperature that adapts by opening and closing dry regions to inhibit changes in the surface temperature, is likened to the eye’s iris, which adapts to different light intensities by increasing or decreasing the size of the iris.

LCH used infrared (IR) brightness temperatures (Tb) from the Geostationary Meteorological Satellite-5 (GMS-5) operated by the Japan Meterological Agency at two different wavelengths, 11 and 12 μm, to determine high cirrus clouds over the tropical west and central Pacific (30°S–30°N, 130°E–170°W). Satellite 11-μm pixels less than 260 K were considered to be filled with high clouds. If the difference between the 11- and 12-μm Tbs was less than 1.5 K, then the high cloud was also considered to be thick (i.e., a cumulus tower). To estimate the cumulus area of a cloud, an 11-μm Tb threshold of 220 K was used. The mean cloud amount, Ac, for their entire domain of study was represented by
i1520-0442-18-20-4185-e1
where A is high cloud area and θ is the latitude, and subscript n represents each 1° × 1° region. The cloud-weighted SST (CWT) for a grid box was then calculated by
i1520-0442-18-20-4185-e2
where Tn is the SST in each grid box. LCH found that high-cloud area, cumulus-cloud area, and high-cloud area normalized by cumulus area all have negative correlations with CWT. From their results, they estimated the amount of cirrus decreases by 17%–27% per 1°C decrease in CWT. This negative correlation led to a cooling of −0.45° to −1.1°C in the model used by the authors.

Both the methodology and the radiative calculations in the LCH study have been criticized. The criticisms of the radiative calculations (e.g., Lin et al. 2002; Chambers et al. 2002) focus on the radiative fluxes’ input to the climate model that LCH used to assess the iris feedback. Of more interest to the present study are the methodological criticisms. In a series of papers, Hartmann and Michelsen (2002a, b,c, hereafter HM) suggested that the negative correlation between SST and cloud area found by LCH is due to the use of CWT and not from a negative climate feedback. Using the same data as LCH, HM showed that deep convective cores are separated from the subtropical cloud variations that are producing the changes in CWT. This problem arises because the study by LCH was limited to IR data, and the use of Tb < 220 K to represent the convective core area is inadequate, especially in the subtropical regions where convective cores tend to be warmer.

To address criticisms of the methodology adopted by LCH, this study examines individual clouds determined to be convective using Tropical Rainfall Measuring Mission (TRMM; Simpson et al. 1996) satellite pixel-level data, not gridded cloud fraction. The same domain as in LCH is used. Identification of convective cells eliminates the possibility that anvil cirrus can be decoupled from the convective cores. Rain-rate information from the TRMM precipitation radar is used to determine if the cloud has a convective core. LCH hypothesized that the decrease in the size of the cloud is due to an increase in precipitation efficiency with SST. Not only does the rain-rate information allow the determination of convective activity, but it also allows examination of the specific mechanism of the iris using rain rate and cloud area as a proxy for precipitation efficiency. It should be noted that this is only a proxy for precipitation efficiency and not the ratio of moisture source to precipitation. The definition used in this paper goes toward examining the specific mechanism of the iris and trying to show that the cloud area and precipitation are related as suggested by LCH. Precipitation efficiency is italicized when used in this context throughout the paper to remind the reader of this definition. Besides addressing the issue of convective cloud identification, this study eliminates the arguable cloud-weighted SST. By developing a dataset of convective clouds with information on the cloud size, convective activity, rainfall, and underlying SST of each cloud, the criticisms by HM of LCH are reexamined, and the hypothesized mechanism for the iris is explored. It is not the aim of this study to quantify any radiative feedback effects by the iris, only to determine whether such a feedback exists.

2. Data and methods

Twenty months (January 1998–August 1999) of TRMM satellite measurements from the same region used in the LCH study, 30°S–30°N, 130°E–170°W, are examined to investigate the iris hypothesis. The TRMM satellite operates in a circular precessing orbit providing only instantaneous “snapshots” of clouds, which prohibits the examination of the full life cycle of the cloud. However, since the sampling is random and over such a long time period, this dataset should provide sampling over the entire system’s life cycle and reduce the temporal sampling issues. Pixel-level Visible Infrared Scanner (VIRS; Kummerow et al. 1998) 10.8-μm brightness temperatures and precipitation radar (PR) 2A25 rain-rate data (Iguchi et al. 2000) are used to determine individual convective cloud boundaries. The VIRS instrument has a 2.11-km field of view at nadir and a swath width of 720 km, while the PR has a 4.3-km field of view at nadir and 215-km swath width. Since VIRS and PR have different fields of view, the data are collocated and matched before analysis. Because it is impossible to determine the convective activity outside of the PR swath, all clouds intersecting an edge of the swath or edge of the domain are eliminated from the analysis. Also, because clouds over land likely respond to more complicated dynamics, they are also eliminated. Once the matched PR rain-rate and VIRS IR brightness temperature dataset is produced, the data are examined for convective clouds. A pixel is determined to be cloudy if its brightness temperature (Tb) is less than a defined threshold. In the original study by LCH, the existence of the iris was not dependent on the Tb threshold chosen. However, since the aim of this study is to reexamine the validity of the iris hypothesis itself, thresholds other than the 260-K value used by LCH are examined. Four Tb thresholds at 250, 260, 270, and 280 K are used to identify discrete cloud clusters and determine if the existence of the iris is dependent on the threshold chosen for cloud identification. Despite the availability of higher temporal and spatial resolution SST datasets, the National Centers for Environmental Prediction (NCEP) Reynolds Optimal Interpolation SST (Reynolds and Marsico 1993; Reynolds and Smith 1994) dataset is used to maintain consistency with the original study by LCH. Comparing a sample 3-day average TRMM Microwave Imager (TMI) SST (Wentz and Meissner 2000) to the corresponding weekly NCEP SST shows that over the entire domain there is a root-mean-square error of 0.43, which is only a 1%–2% error depending on the SST. NCEP SSTs are averaged within each cloud boundary as determined by VIRS to calculate the mean underlying SST for every cloud. This eliminates the need for CWT.

After all the clouds within a swath have been identified, each cloud is examined to determine the existence of contiguous convective cores within the cloud. For this study, convective pixels are defined as those meeting a rain-rate threshold of 10 mm h−1 or greater. While this rain rate may seem low, studies using profilers and disdrometers (e.g., Tokay et al. 1999) have shown almost all cases of rain rates greater than 10 mm h−1 to be convective. It should also be noted that the PR rain rate is defined for a much larger area (∼16 km2) than what would be seen using a profiler and disdrometer. A PR rain rate of 10 mm h−1, therefore, represents a conservative estimate of convection. For this study, a convective core is defined by at least one pixel that meets the rain-rate threshold. In cases where there is more than one pixel in the cloud meeting this threshold, each contiguous area of pixels meeting the threshold is considered a convective core. A sample of results from the cloud and convective core identification algorithm are shown in Fig. 1. The total rainfall for each cloud is then calculated by summing the instantaneous PR rain rates matched to that cloud.

Having identified clouds and their convective cores, they are divided into two categories: single-core and multicore systems. Examining single-core convective systems allows a direct investigation of the proposed mechanism for the iris hypothesis. Since the suggested mechanism for the iris is that the size of the detrained anvil cloud decreases due to an increase in precipitation efficiency, it is necessary to isolate the convective precipitating area from the nonconvective precipitating and nonprecipitating area of the cloud. In examining multicore systems, there is no quantitative method to isolate which part of the anvil cirrus results from any given convective core. Single-core convective systems, therefore, provide a way to look at this hypothesized mechanism without being concerned by ambiguities caused by systems with more than one convective core. It should be noted that an unintentional result of examining single-core clouds could be to limit results to convective systems that are not fully developed. However, the numbers of samples, as well as examination of other systems, should limit the problems associated with the spatial sampling of the instruments used in this study.

Though single-core convective systems, as defined by this study, are ideal for examining the mechanism of the iris, they only account for about 4.5% of rainfall in the domain of interest. Multicore systems account for about 91% of the rainfall in this domain and play a much larger role in the hydrologic cycle, as well as being the dominant source of cloud feedbacks in the Tropics. Therefore, these are examined separately. To interpret the results of this study, a regression analysis as in LCH is performed.

3. Results

Since LCH assumed that the clouds are responding to the underlying SST, it is necessary to test the sensitivity of the iris to changes in SST regime. SSTs can respond to locations of the clouds, as well as large-scale seasonal and interseasonal regime changes. To test the sensitivity of the iris to SST regime, the regression analyses are performed on a variety of temporal scales, from monthly to the entire 20-month period. The regression analysis is performed for clouds identified at each of the cloud-top temperature thresholds. As the threshold is increased, the cloud areas identified by lower Tb thresholds are included, as well as more of the cloud edge area. In addition, the LCH domain is also broken into smaller geographic subdomains, ±10° and ±20°.

Figure 2 summarizes the monthly slopes and correlation coefficients for the four different Tb thresholds used for cloud identification. This figure shows that the relationship between cloud size normalized by rainfall and SST is extremely variable from month to month. Note that the 280-K threshold is the only one that is frequently negative. The other thresholds show primarily positive correlations, most being less than 0.15, but opposite in sign to the correlations presented by LCH. These consistently negative correlations for 280 K and positive correlations at the other thresholds indicate that some type of cloud is observed using the 280-K threshold that is not observed by the other thresholds. Because the 280-K threshold is inclusive of all the clouds identified with the lower thresholds, this implies that the observed cloud temperature must be warm, between 270 and 280 K, and that the cloud is raining heavily enough to be considered convective by this study’s definition. One possible scenario that could account for this observation is that as SST increases, the amount of cirrus between 270 and 280 K associated with a convective core decreases. However, a comparison of the percentage of observed cloud area between 270 and 280 K shows no substantial decrease with SST. The single-core clouds examined in this study show that this percentage of cloud area is around 90% of the total area regardless of the SST. The other possibility is that heavily raining warm clouds with cloud-top temperatures between 270 and 280 K, possibly cumulus congestus, are included in this analysis and may be driving this negative correlation. Since these warm clouds have a larger effect in the shortwave than in the longwave, a positive feedback would likely result from a reduction in these clouds.

Regressions of SST and cloud size normalized by rainfall amount for the entire 20-month time period are seen in Fig. 3. Again, the 280-K threshold for the whole domain has a negative correlation. All the correlation coefficients are near zero regardless of the domain or threshold. Examination of the confidence interval of the slopes shows that the given slopes are valid to ±0.002 at the 95% confidence level, indicating that the sign of the slopes and correlations is valid, despite being very small. An examination of the three latitudinal bands is shown in Fig. 4 for clouds identified with a Tb threshold of 260 K. Not surprisingly, this figure shows little variation with latitude in the weak slopes and correlations. Separate examination of data for 1998 and 1999 shows similar results despite the end of the strong El Niño occurring in early 1998. These results, from Figs. 2 –4, thus show no inverse relationship between SST and single-core convective cloud size normalized by rainfall amount on time scales of a year or longer for the entire domain, as well as the subdomains.

The negative correlation found at the 280-K threshold in the overall statistics is examined next. Figure 5 shows regressions of SST and single-core clouds identified by this study with all Tbs between 250 and 260 K (Fig. 5a), 260 and 270 K (Fig. 5b), and 270 and 280 K (Fig. 5c). The relationship between these heavily raining warm clouds and SST is much stronger than that observed when analyzing all of the convective clouds. This finding is consistent with that of Lau and Wu (2003), who showed that precipitation efficiency increases with SST for warm rain systems. The current study suggests that warm cloud area, normalized by rainfall, decreases by approximately 5% per degree SST. While not shown here, similar results were found for the geographical subdomains, ±20° and ±10°. These results, along with the fact that 76% of the warm rain systems are occurring between ±20°, also suggest that these changes are occurring in the Tropics and are not being driven by warm subtropical systems.

Since most of the rainfall in the Tropics is from multicore convective systems, not single-core convection, these multicore systems have the most impact on the hydrologic and energy cycles and are also investigated. The narrow width of the PR swath limits the number of multicore convective systems available for use in this study since 75% of them intersect the edges and it is not possible to determine the convective activity outside of the swath. Because of this, one plot for the entire period is created.

The multicore convective cloud size normalized by the amount of rainfall from the cloud regressed against SST is shown in Fig. 6. This figure shows slightly positive correlations between SST and multicore cloud size normalized by rainfall amount for all thresholds and all regions except for clouds with Tb less than 280 K for the whole domain, and even this correlation is almost zero. Examination of the confidence intervals of the slopes at the 95% level shows that the slopes are valid to within ±0.004, which means that the sign of the slopes and correlations are valid, except at 280 K. This indicates that not only is the size of multicore convective clouds not decreasing with SST, but the amount of rainfall from the cloud, or precipitation efficiency, is not well correlated with the cloud size.

Results from both the single-core and multicore convective cloud analyses do not support the adaptive infrared iris hypothesis except for the possible cumulus congestus clouds (270 K < Tb < 280 K). Results from colder clouds show almost no correlation, or even a slightly positive correlation, between the size of a cloud normalized by its rainfall amount and its underlying SST. While not shown here, regressing cloud area with SST or cloud area normalized by the core area with SST results in similar lack of correlation.

4. Conclusions

The adaptive infrared iris hypothesis suggests that moist and dry regions in the Tropics open and close to regulate the outgoing longwave radiation and provide a negative feedback on the sea surface temperature. LCH proposed that as the SST rises, precipitation efficiency of convective clouds increases, which results in a decrease in the amount of cirrus detrainment. Using the TRMM dataset of IR brightness temperatures and radar rainfall estimates, this hypothesis has been directly examined.

Defining the effective size of a cloud as the area of cloud divided by the total rainfall from that cloud, this study finds very weak correlations between the effective size and the underlying SST. Examination of the confidence interval of the slopes indicates that the sign of the slopes and correlations is valid. Since the majority of the results shows a positive correlation, this indicates that the interaction between the SST and effective cloud size may even have a slight positive relationship, not the inverse relationship suggested by LCH. The effective size is negatively correlated with SST only for clouds identified using the threshold of 280 K. Further examination of the systems driving this negative correlations reveals that cloud size and rainfall from warm cloud systems are more highly correlated with SST than in cold, deep convection. These warm systems show a 5% decrease in the ratio of cloud area and rainfall per degree rise in SST. These results suggest it is possible that interactions between precipitation efficiency and cloud size with SST are acting on shallow, warm clouds, not deep convection. This is supported by the Lau and Wu (2003) finding that precipitation efficiency in warm clouds is more sensitive to the underlying SST than deep, cold clouds, in which strong updrafts dominate the conversion of cloud water into precipitation, not SST. Sun and Lindzen (1993) used a simple Bowen model and Lau and Wu (2003) used TRMM retrievals combined with model parameterization to calculate the rate of conversion of cloud water to precipitation. Both show that raindrop growth is faster at higher SSTs. This shift in the drop size distribution with SST could account for some of the reduction in the cloud area to rainfall ratio observed in this study. These warm rain systems are prevalent in the Tropics and are not only important to the hydrologic cycle, but could also produce a considerable effect on the radiation balance. A reduction in the areal coverage of these systems would likely yield a positive feedback on temperature since this would reduce the amount of reflected solar radiation.

A result similar to single-core clouds is found for multicore convective clouds. Complicated interactions between convective cores and anvils occur within the cloud, making it more difficult to interpret results. However, it is important to investigate these clouds since they account for the majority of deep convection and precipitation in the Tropics. As with the single-core clouds, effective cloud size for multicore convective clouds is not correlated with SST. SST explains less than 0.2% of the variance regardless of the cloud identification threshold. However, because of swath width limitations by the PR, small multicore convective clouds are examined and should not be interpreted as being representative of the multicore convective clouds larger than the PR swath, such as mesoscale convective systems.

The lack of any significant correlation between cloud size normalized by rainfall amount and SST for both single- or multicore convective clouds suggests that it is likely that the LCH use of CWT, as proposed by HM, was responsible for the negative correlations found in the original iris study. Both single-core and multicore results indicate that changes in precipitation efficiency with SST are not likely affecting the size of deep convective clouds. However, examination of warm rain systems shows a much stronger inverse relationship between rainfall, cloud size, and SST. Since these warm rain systems are so prevalent, it is possible that changes in these systems could have a large impact on the global energy budget. Further examination of the mechanisms affecting multicore cloud size, as well as the interaction between precipitation efficiency, cloud size and SST for warm, shallow clouds, is needed to fully understand the complex cloud radiative feedback effects in the Tropics.

Acknowledgments

This research was supported by Grant NA17RJ1228 from NOAA OGP and NASA Grants NAG5-12273 and NAG5-13694.

REFERENCES

  • Bjerknes, J., 1966: A possible response of the atmospheric Hadley circulation to equatorial anomalies of ocean temperature. Tellus, 18 , 820829.

    • Search Google Scholar
    • Export Citation
  • Chambers, L. H., , B. Lin, , and D. F. Young, 2002: Examination of new CERES data for evidence of tropical iris feedback. J. Climate, 15 , 37193726.

    • Search Google Scholar
    • Export Citation
  • Fu, C., , H. F. Diaz, , and J. O. Fletcher, 1986: Characteristics of the response of sea surface temperature in the central Pacific associated with warm episodes of the Southern Oscillation. Mon. Wea. Rev., 114 , 17161739.

    • Search Google Scholar
    • Export Citation
  • Fu, R., , A. D. Del Genio, , W. B. Rossow, , and W. T. Liu, 1992: Cirrus-cloud thermostat for tropical sea surface temperatures tested using satellite data. Nature, 358 , 394397.

    • Search Google Scholar
    • Export Citation
  • Fu, R., , A. D. Del Genio, , and W. B. Rossow, 1994: Influence of ocean surface conditions on atmospheric vertical thermodynamic structure and deep convection. J. Climate, 7 , 10921108.

    • Search Google Scholar
    • Export Citation
  • Graham, N. E., , and T. P. Barnett, 1987: Sea surface temperature, surface wind divergence, and convection over tropical oceans. Science, 238 , 657659.

    • Search Google Scholar
    • Export Citation
  • Hall, A., , and S. Manabe, 1999: The role of water vapor feedback in unperturbed climate variability and global warming. J. Climate, 12 , 23272346.

    • Search Google Scholar
    • Export Citation
  • Hartmann, D. L., , and M. L. Michelsen, 1993: Large-scale effects on the regulation of tropical sea surface temperature. J. Climate, 6 , 20492062.

    • Search Google Scholar
    • Export Citation
  • Hartmann, D. L., , and M. L. Michelsen, 2002a: No evidence for iris. Bull. Amer. Meteor. Soc., 83 , 249254.

  • Hartmann, D. L., , and M. L. Michelsen, 2002b: Reply. Bull. Amer. Meteor. Soc., 83 , 13491352.

  • Hartmann, D. L., , and M. L. Michelsen, 2002c: A two-box model of cloud-weighted sea surface temperature: The semiautomatic negative correlation with mean cloud fraction. Bull. Amer. Meteor. Soc., 83 .(Suppl.), ES70–ES71.

    • Search Google Scholar
    • Export Citation
  • Houghton, J. T., , Y. Ding, , D. J. Griggs, , M. Noguer, , P. J. van der Linden, , X. Dai, , K. Maskell, , and C. A. Johnson, 2001: Climate Change 2001: The Scientific Basis, Cambridge University Press, 892 pp.

  • Iguchi, T., , T. Kozu, , R. Meneghini, , J. Awaka, , and K. Okamoto, 2000: Rain-profiling algorithm for the TRMM precipitation radar. J. Appl. Meteor., 39 , 20382052.

    • Search Google Scholar
    • Export Citation
  • Inamdar, A. K., , and V. Ramanathan, 1994: Physics of greenhouse effect and convection in warm oceans. J. Climate, 7 , 715731.

  • Jackson, D. L., , and G. L. Stephens, 1995: A study of SSM/I-derived columnar water vapor over the global oceans. J. Climate, 8 , 20252038.

    • Search Google Scholar
    • Export Citation
  • Krueger, A. F., , and T. I. Gray, 1969: Long-term variations in equatorial circulation and rainfall. Mon. Wea. Rev., 97 , 700711.

  • Kummerow, C., , W. Barnes, , T. Kozu, , J. Shiue, , and J. Simpson, 1998: The Tropical Rainfall Measuring Mission (TRMM) sensor package. J. Atmos. Oceanic Technol., 15 , 808816.

    • Search Google Scholar
    • Export Citation
  • Lau, K-M., , and H. T. Wu, 2003: Warm rain processes over tropical oceans and climate implications. Geophys. Res. Lett., 30 , 22902294.

  • Lau, K-M., , C-H. Sui, , M-D. Chou, , and W-K. Tao, 1994: An inquiry into the cirrus-cloud thermostat effect for tropical sea surface temperature. Geophys. Res. Lett., 21 , 11571160.

    • Search Google Scholar
    • Export Citation
  • Lin, B., , B. A. Wielicki, , L. H. Chambers, , Y. Hu, , and K-M. Xu, 2002: The iris hypothesis: A negative or positive cloud feedback? J. Climate, 15 , 37.

    • Search Google Scholar
    • Export Citation
  • Lindzen, R. S., , M-D. Chou, , and A. Hou, 2001: Does the earth have an adaptive infrared iris? Bull. Amer. Meteor. Soc., 82 , 417432.

  • Manabe, S., , and R. T. Weatherald, 1967: Thermal equilibrium of the atmosphere with a given distribution of relative humidity. J. Atmos. Sci., 24 , 241259.

    • Search Google Scholar
    • Export Citation
  • Pierrehumbert, R. T., 1995: Thermostats, radiator fins, and the local runaway greenhouse. J. Atmos. Sci., 52 , 17841806.

  • Ramanathan, V., 1981: The role of ocean-atmosphere interactions in the CO2 climate problem. J. Atmos. Sci., 38 , 918930.

  • Ramanathan, V., , and W. Collins, 1991: Thermodynamic regulation of ocean warming by cirrus clouds deduced from observations of the 1987 El Niño. Nature, 351 , 2732.

    • Search Google Scholar
    • Export Citation
  • Rasool, S. I., , and S. H. Schneider, 1971: Atmospheric carbon dioxide and aerosols: Effects of large increases on global climate. Science, 173 , 138141.

    • Search Google Scholar
    • Export Citation
  • Raval, A., , and V. Ramanathan, 1989: Observational determination of the greenhouse effect. Nature, 342 , 758762.

  • Reynolds, R. W., , and D. C. Marsico, 1993: An improved real-time global sea surface temperature analysis. J. Climate, 6 , 114119.

  • Reynolds, R. W., , and T. M. Smith, 1994: Improved global sea surface temperature analyses using optimum interpolation. J. Climate, 7 , 929948.

    • Search Google Scholar
    • Export Citation
  • Riehl, H., , and J. S. Malkus, 1958: On the heat balance in the equatorial trough zone. Geophysica, 6 , 503538.

  • Simpson, J., , C. Kummerow, , W. K. Tao, , and R. F. Adler, 1996: On the tropical rainfall measuring mission (TRMM). Meteor. Atmos. Phys., 60 , 1936.

    • Search Google Scholar
    • Export Citation
  • Sun, D-Z., , and R. S. Lindzen, 1993: Distribution of tropical tropospheric water vapor. J. Atmos. Sci., 50 , 16431660.

  • Tokay, A., , D. A. Short, , C. R. Williams, , W. L. Ecklund, , and K. S. Gage, 1999: Tropical rainfall associated with convective and stratiform clouds: Intercomparison of disdrometer and profiler measurements. J. Appl. Meteor., 38 , 302320.

    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., 1996: Formation and limiting mechanisms for very high sea surface temperature: Linking the dynamics and the thermodynamics. J. Climate, 9 , 161188.

    • Search Google Scholar
    • Export Citation
  • Wallace, J. M., 1992: Effect of deep convection on the regulation of tropical sea surface temperature. Nature, 357 , 230231.

  • Wentz, F. J., , and T. Meissner, 2000: AMSR Ocean Algorithm: ATBD Version 2. NASA Reference Publication, 66 pp. [Available online at http://www.ssmi.com/papers/AMSR_Ocean_Algorithm_Version_2.pdf.].

Fig. 1.
Fig. 1.

(a) VIRS brightness temperature, (b) matched PR rain rates, (c) cloud identification number, and (d) number of convective cores in the cloud.

Citation: Journal of Climate 18, 20; 10.1175/JCLI3528.1

Fig. 2.
Fig. 2.

(a) Slope and (b) monthly correlation coefficients between SST and single-core convective cloud size normalized by rainfall for 30°S–30°N, 130°E–170°W.

Citation: Journal of Climate 18, 20; 10.1175/JCLI3528.1

Fig. 3.
Fig. 3.

Single-core convective cloud size normalized by rainfall regressed against SST for clouds identified with Tb thresholds of (a) 250, (b) 260, (c) 270, and (d) 280 K for Jan 1998–Aug 1999. The slopes of the regression lines, the correlation coefficients (R), and the number of points included in the regressions are labeled in each panel.

Citation: Journal of Climate 18, 20; 10.1175/JCLI3528.1

Fig. 4.
Fig. 4.

Single-core convective cloud size normalized by rainfall regressed against SST for clouds identified with a Tb threshold of 260 K for (a) 10°S–10°N, (b) 20°S–20°N, and (c) 30°S–30°N. The slopes of the regression lines, the correlation coefficients (R), and the number of points included in the regressions are labeled in each panel.

Citation: Journal of Climate 18, 20; 10.1175/JCLI3528.1

Fig. 5.
Fig. 5.

Single-core convective cloud size normalized by rainfall regressed against SST for clouds with mean Tbs between (a) 250 and 260 K, (b) 260 and 270 K, and (c) 270 and 280 K for Jan 1998–Aug 1999. The slopes of the regression lines, the correlation coefficients (R), and the number of points included in the regressions are labeled in each panel.

Citation: Journal of Climate 18, 20; 10.1175/JCLI3528.1

Fig. 6.
Fig. 6.

Same as in Fig. 3, but for multicore convective cloud size.

Citation: Journal of Climate 18, 20; 10.1175/JCLI3528.1

Save