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1 Aerosol-induced intensification of rain from the tropics to the mid-latitudes Ilan Koren 1, Orit Altaratz 1, Lorraine A. Remer 2, Graham Feingold 3, J. Vanderlei Martins 2,4, and Reuven Heiblum 1 1. Department of Environmental Sciences Weizmann Institute, Rehovot 76100, Israel 2. Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA 3. NOAA Earth System Research Laboratory (ESRL), Chemical Sciences Division, Boulder, Colorado 80305, USA 4. Department of Physics and Joint Center for Earth Systems Technology, University of Maryland Baltimore County This supplement contains: 1) Information on the databases used in this study 2) Preparing the A-Train-like TRMM and GDAS products 3) Narrowing the meteorological variance 4) Cloud top pressure and cloud fraction analysis 5) Analysis with restricted cloud fraction 1. Information on the databases used in this study In this study we used 3 global daily databases for 92 days in June to August All datasets were projected to a 1 spatial resolution. The databases are: 1) MODIS Level 3, Collection 5 Aerosol_Optical_Depth_Land_And Ocean (AOD), and cloud properties (cloud fraction and cloud top pressure) onboard the AQUA satellite1 1, 2. As part of the A-train 3 the MODIS-AQUA instrument covers most of the globe with a local equatorial crossing time of approximately 1:30 p.m. 2) For rain-rate measurements we used the TRMM 3B42 version 6 product 4. This product merges rain-radars (and other microwave) measurements with continuous IR precipitation estimates from geostationary satellites and with additional information from NATURE GEOSCIENCE 1

2 rain-gauge data. The IR precipitation estimates from geostationary satellites are calibrated by precipitation estimates from the TMI onboard TRMM, SSM/I onboard DMSP, ASMU-E onboard AQUA and ASMU-B onboard NOAA satellites 4. The estimates are gridded to a by spatial resolution over a global belt between 50N and 50S and have a 3-hour temporal resolution. Rain events tend to be sporadic and of relatively short duration (on the order of hours). This makes the rain estimations on daily time scales a challenge 5, 6. Despite this the TRMM-3B42 has been able to successfully capture specific short term events such as an Eastern Pacific Typhoon 7, monsoon rain bands over China 8, Indian Monsoon rain events 9 and ITCZ rain events 4. Moreover, the 3B42 product has been used to conduct a study of global diurnal precipitation regimes 10, which displayed the diurnal cycle in several domains around the globe. However, this product might have some difficulties in estimating short-lived orographic induced precipitation such as seen over the Indian Western Ghats 9, underestimating precipitation on the windward side and overestimating precipitation on the lee side. 3) For meteorological information, the NOAA-NCEP Global Data Assimilation System (GDAS) was used 11. The GDAS data set is assimilated 4 times a day, for 13 pressure levels from the surface up to 20 hpa using surface and satellite observations with model data 11, 12. It is the 25 final run in the series of NCEP operational model runs. 2. Preparing the A-Train-like TRMM and GDAS products The equivalent to 1:30 pm local-time product was calculated by weighted interpolation of the 2 relevant out of the 8 daily measurements (every 3hrs) of the TRMM rain and out of the 4 meteorology databases (every 6 hrs) of the GDAS. For each point over the globe the 2 NATURE GEOSCIENCE

3 GMT time equivalent to 1:30 pm local-time was calculated. Next, for each point and for each variable the A-Train-like value was calculated as the weighted interpolation of the two datasets closest in time to 1:30 local-time. For example, say that for a given point with coordinates (x,y) the equivalent GMT time is 11:00. For the TRMM rain rate calculations the 9:00 am GMT (R 9am ) and the 12 noon GMT were used and R 11am (x,y) = (2 R 12noon (x,y) + R 9am (x,y)) 1/3. For the GDAS temperature at 850 hpa (T 850 ) the 6:00 GMT and the 12:00 noon GMT were used and T am (x,y) = (5 T noon (x,y) + T 850-6am (x,y)) 1/6 3. Narrowing the meteorological variance A method to isolate aerosol effects on clouds from the environmental ones using reanalysis model data as a proxy for the meteorological conditions was recently developed 13. Correlations between the reanalysis variables and the observed cloud properties are calculated. The key meteorological variables that correlate well with the cloud properties are likely to be important in controlling the cloud properties. By restricting the range of these specific factors we narrow the meteorological variance. This provides some meteorological context for the observed aerosol-r relationships. Here we have used the NOAA-NCEP Global Data Assimilation System (GDAS) at 1 resolution to provide the meteorological context. The GDAS data set is assimilated 4 times a day, for 13 pressure levels from the surface to 20 hpa using surface and satellite observations with model data 11. The GDAS is the final run in the series of NCEP operational model runs. We have produced the A-Train-like GDAS dataset of 1:30 pm local time set which is collocated in time with the A-Train-like TRMM and MODIS datasets. Figure NATURE GEOSCIENCE 3

4 S1 shows the absolute correlations between all GDAS model variables (286 variables 13 ) and the global TRMM rain rate. The pressure vertical velocity (ω) of the upper atmosphere ( hpa, marked in red line) shows the best correlations with the rain rate (> 0.3 for all data 13,14 ). Figure S1. Correlations (in absolute values) between TRMM rain-rate and the GDAS assimilation variables. The zero correlation data points are those variables that did not pass the p-value <0.001 test. 4. Cloud top pressure and cloud fraction analysis Similar analysis done for R is presented here for Cloud Top Pressure (CTP) and Cloud Fraction (CFR). Both MODIS retrievals are robust and do not depend strongly on the cloud microphysical and optical properties. Changes in the average CTP and CFR can 4 NATURE GEOSCIENCE

5 serve as an approximation for changes in the cloud vertical and horizontal extent as a function of aerosol loading and meteorology. The results are shown in Figure S2 in the same manner as was shown for rain: The ω units are in Pascals per second [Pa/s] and are divided to 3 cases with similar numbers of samples. Since pressure goes down with increasing altitude, negative values of ω refer to upward motion. In the upper panel the data were restricted to ω < -0.4 [Pa/s]. The middle panel shows the neutral vertical motion cases where -0.4 < ω < 0.4 [Pa/s] and the lower panel represent the more stable, subsidence cases of ω >0.4 [Pa/s]. As in the paper s Figure 2, the red bars on the left column show the distribution without zero R grid squares and the empty bars show those with zeros. Note how the meteorological filtering is represented by the mean and the range of the differences. For the updraft case (upper panel) the red bar average differences are ~0.1 mm/hr, ~0.07 mm/hr, and ~0.05 mm/hr for the updraft, neutral, and stable regions, respectively. Figure S2. Distribution of differences in TRMM rain rates (left), cloud fraction (middle) and cloud top pressure (right) between polluted and clean cases grouped by the GDAS vertical motion (ω) at 400 hpa pressure height. NATURE GEOSCIENCE 5

6 The middle and right columns of Figure S2 show differences in the cloud top pressure (middle) and cloud fraction (right) between the polluted-clean sets sliced by the 3 vertical velocity regimes. All vertical velocity sets clearly show that polluted clouds are taller (by ~ hpa higher) and have larger cloud fraction (by ~ 15-30%). Deeper clouds have greater amounts of condensate and therefore possess the potential for heavier rain rates. The clear and consistent intensification of rain rate, increase in cloud height and area when the aerosol loading increases suggests that the same mechanism that invigorates clouds is responsible for the rain rate intensification. Note that by no means does this imply that total amounts of precipitation differ substantially. Note that as the vertical pressure velocities ω become more negative (enhanced rising motion) the absolute aerosol effect on rainrate, ΔR, is doubled from ~0.05 to ~0.1 mm/hr while the relative aerosol effect (ΔR/R) decreases from 36% in the stable regimes to 9% when ω400hpa < Pa/s. The decrease in the relative influence of the aerosol on rainrate intensification in more convective conditions points to an increasingly dominant role of dynamics in determining R. 5. Analysis with restricted cloud fraction Correlations between aerosol loading and clouds might be affected by aerosol humidification 15, by a higher likelihood of contribution of undetectable clouds 16 and from extra photons scattered from nearby clouds 17. All of these processes may contribute to the observed correlations between clouds and aerosol that are not due to aerosol effects on clouds. 6 NATURE GEOSCIENCE

7 In an attempt to evaluate this effect we have performed an analysis in which the data have been sorted by cloud fraction. This forces the data, to first approximation, to suffer to similar degree from the aforementioned artifacts. However limiting the cloud fraction range of the data does pose an inherent problem. It is well known that cloud size and cloud fraction correlate well with cloud vertical extent. It has been shown 13, that regardless of aerosol properties, the average cloud fraction increases for deeper clouds (see Figure S3 for the link between cloud top pressure and fraction for the clouds in this study). Therefore by restricting cloud fraction we inherently restrict the cloud vertical development and therefore the derived rain-rate. In other words, following the suggested line of thinking in this paper that polluted clouds are invigorated i.e., will be deeper and therefore have larger cloud fraction, then, by restricting cloud fraction we degenerate the expression of the effect cloud top pressure [hpa] cloud fraction Figure S3. Cloud fraction vs. cloud top pressure for all clouds in the study. Each point represents an average of ~50 pixels. NATURE GEOSCIENCE 7

8 In spite of the problem described above we did run the analysis for 4 cloud fraction ranges, averaged around 0.66, 0.75, 0.80 and 0.86 with mean standard deviation in cloud fraction of ~2-3% for each group. The cloud fraction ranges were defined with the aim of having similar numbers of samples, and with small enough variance, in each group. rain rate differences [mm/hr] cloud fraction cloud top pressure differences [hpa] cloud fraction Figure S4. Differences in cloud properties and rain-rate (polluted-clean) for 4 ranges of cloud fraction. Red, excluding zero R; Blue, including zero R. Bars indicates standard errors. Note (Figure S4) that for all cloud fraction sets the polluted grid-boxes have higher rainrates and higher clouds. The average results for the restricted sets are: mm/h for the 8 NATURE GEOSCIENCE

9 sets with zeros, and mm/h for the sets without zeros. The polluted cloud tops are higher by an average of 58 hpa and despite the restriction in cloud fraction the polluted set average cloud fraction is larger by 3%. Thus even after sorting the data into cloud fraction subsets, which to a first approximation dictates similar average retrieval problems, the same associations between deeper clouds, stronger rain-rates and larger cloud fraction are observed. The magnitude of the effects is weaker due to the inherent limitation that binning by cloud fraction poses on the proposed invigoration process. References 1. Platnick, S. et al. The MODIS cloud products: Algorithms and examples from Terra. IEEE Transactions on Geoscience and Remote Sensing 41, , doi: /tgrs (2003). 2. Remer, L.A., Kaufman, Y.J., Tanre, D., et al., The MODIS aerosol algorithm, products and validation. Journal of Atmospheric Sciences, 62, pp (2005). 3. Anderson, T. L., R. J. Charlson, et al. "An A-Train Strategy for Quantifying Direct Climate Forcing by Anthropogenic Aerosols." Bulletin of the American Meteorological Society 86(12): (2005). 4. Huffman, G. J. et al. The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of Hydrometeorology 8, 38-55, doi: /jhm560.1 (2007). 5. Ruane, A.C., and J.O. Roads. 6-hour to 1-year variance of five global precipitation sets. Earth Interactions, 11, doi: /ei225.1, (2007). NATURE GEOSCIENCE 9

10 6. Villarini, G. and W.F. Krajewski. Evaluation of the research-version TMPA three-hourly rainfall estimates over Oklahoma, Geophysical Research Letters, 34, L05402, doi: /2006gl029147, (2007). 7. Wang, Y.-Q., Y. Wang, and H. Fudeyasu. Roles of Typhoon Songda (2004) in producing distantly located heavy rainfall in Japan. Mon. Wea. Rev., 137, (2009). 8. Xu, W., E. J. Zipser, and C. Liu. Rainfall Characteristics and Convective Properties of Mei-Yu Precipitation Systems over South China and Taiwan,Part I: TRMM observations. Mon. Wea. Rev., 137(12), (2009). 9. Nair, S. G. Srinivasan, R. Nemani, Evaluation of Multi-Satellite TRMM Derived Rainfall Estimates over a Western State of India Sushma NAIR, Govindrajan Srinivasan, Ramakrishna Nemani Journal of the Meteorological Society of Japan, Vol. 87, No. 6, pp , (2009). 10. Kikuchi, K., and B. Wang, 2008: Diurnal precipitation regimes in the global tropics. J. Climate, 21 (11), (2008). 11. Parrish, D. F. & Derber, J. C. THE NATIONAL-METEOROLOGICAL- CENTERS SPECTRAL STATISTICAL-INTERPOLATION ANALYSIS SYSTEM. Monthly Weather Review 120, (1992). 12. Saha, S. et al. THE NCEP CLIMATE FORECAST SYSTEM REANALYSIS. Bulletin of the American Meteorological Society 91, , doi: /2010bams (2010). 10 NATURE GEOSCIENCE

11 13. Koren, I., Feingold, G. & Remer, L. A. The invigoration of deep convective clouds over the Atlantic: aerosol effect, meteorology or retrieval artifact? Atmos. Chem. Phys. 10, , doi: /acp (2010). 14. Takayabu, Yukari N., Shoichi Shige, Wei-Kuo Tao, Nagio Hirota, Shallow and Deep Latent Heating Modes over Tropical Oceans Observed with TRMM PR Spectral Latent Heating Data. J. Climate, 23, doi: /2009JCLI (2010) 15. Quaas, J., Stevens, B., Stier, P., and Lohmann, U.: Interpreting the cloud cover aerosol optical depth relationship found in satellite data using a general circulation model, Atmos. Chem. Phys., 10, , doi: /acp , (2010). 16. Koren, I., L. A. Remer, Y. J. Kaufman, Y. Rudich, and J. V. Martins, On the twilight zone between clouds and aerosols, Geophys. Res. Lett., 34, L08805, doi: /2007gl (2007). 17. Wen, G. Y., Marshak, A., Cahalan, R. F., Remer, L. A., and Kleidman, R. G.: 3-D aerosol-cloud radiative interaction observed in collocated MODIS and ASTER images of cumulus cloud fields, Journal of Geophysical Research-Atmospheres, 112, /2006jd008267, (2007). NATURE GEOSCIENCE 11