Short-term modulation of Indian summer monsoon rainfall bywest Asian dust

Transcription

1 SUPPLEMENTARY INFORMATION DOI: /NGEO2107 Short-term modulation of Indian summer monsoon rainfall bywest Asian dust V Vinoj 1,2, Philip J Rasch 1*, Hailong Wang 1, Jin-Ho Yoon 1, Po-Lun Ma 1, Kiranmayi Landu 1 and Balwinder Singh 1 1 Atmospheric Sciences and Global Change Division Pacific Northwest National Laboratory Richland, WA , USA 2 School of Earth, Ocean and Climate Sciences Indian Institute of Technology Bhubaneswar Odisha, INDIA correspondence to philip.rasch@pnnl.gov * NATURE GEOSCIENCE 1

2 S1. Observations The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the NASA Terra and Aqua satellites have produced aerosol optical depth (AOD) over a wide spectral range and cloud information at high spatial and temporal resolutions globally 30 for over a decade, providing an excellent dataset to study aerosol-cloudprecipitation interactions at different scales. We use MODIS Terra dataset (collection 5, level 3 at 1x1 degree resolution) as it provides a slightly longer time series in comparison to that from the MODIS Aqua. However, results are consistent using either dataset. Also, aerosol retrieval over land is complex due to varying surface reflectance characteristics that induces additional uncertainty. Therefore we used the aerosol information from Multi-angle Imaging SpectroRadiometer (MISR) 31 at half-degree spatial resolution, which is considered better for assessing aerosols over land with high reflectance characteristics (West Asian/Arabian/Somali coast regions). In addition to AOD, we also use MODIS fine-mode fraction dataset. The precipitation data is obtained from the global precipitation climatology project (GPCP, at 1x1 degree daily) dataset over the central India region. GPCP utilizes multi-satellite observations as well as rain gauge to estimate precipitation at 1.0 degree resolution 32. The GPCP data for the present study was obtained from climate data 44 archive at Aerosol data was obtained from 45 and Giovanni data archive at Goddard Space Flight 46 Centre (GSFC). The aerosol and precipitation datasets were selected during the 47 overlapping period between 2000 to 2009 summer monsoon (June to August). 2

3 A brief description regarding the correlation analysis shown in Fig 1(a & b) in the main text is provided here. Correlations between aerosol and precipitation fields were calculated based on weekly averaged datasets after removing the linear trends in MODIS, MISR AOD and GPCP precipitation (for the years 2000 to 2009). Lead-lag analysis showed maximum correlation with a zero lag time. Our model simulations indicate that the discussed effect of upstream aerosols on downstream rainfall is mostly a consequence of dynamic feedback provided by the aerosols on circulation and moisture convergence. Therefore, we do not discuss/differentiate or quantify the various local aerosol (direct, semi-direct or indirect) effects in modulating the downstream monsoon precipitation S2. Model Simulations The Community Atmosphere Model version 5 (CAM5) developed at National Centre for Atmospheric Research (NCAR) with some new capabilities developed at the Pacific Northwest National Laboratory (PNNL) is used in this study. The model is run 63 with fixed climatological sea surface temperatures (SSTs). CAM5 simulations are conducted at a spatial resolution of 2.5 x 1.9 degrees with 30 vertical levels (on a η- coordinate system). The CAM5 model has a prognostic aerosol scheme with explicit treatment of 3 modes of differing size (Aitken, accumulation, and coarse) comprised of sea-salt, dust, sulfate, black carbon, organic carbon, and secondary organic aerosols. More details of the model and its description has been given in the literature 33,34 and hence not repeated. Each of the simulation is carried out for 10 years (equilibrium runs) and only data during the summer period (June to August, a total of 30 months) is used for 3

4 the analysis. In the dust pulse ensemble experiment, model simulations were branched/restarted every 5 days (for the period from June 1 st to August 31 st ) with dust emissions turned off. Each simulation was run for a period of 10 days. Fig S1 shows the model simulated spatial distribution of precipitation and AOD from (a & b) CAM5 in free running mode and (c & d) observations from GPCP and MODIS Terra, respectively. The simulated precipitation is reasonable for a global climate model (at 2 degree resolution) with both the high precipitation centers over Western 78 Ghats and Bay of Bengal simulated reasonably well. AOD over Arabian Sea (AS) is also well simulated, although the model is biased low over the Indian land region. These details would have been very important if we were specifically focusing on the absolute intensity of aerosol induced precipitation changes. In this study, we focus on an observed feature (co-variability between aerosols and monsoon precipitation), which is well simulated by the model, and investigate how this feature and the resultant precipitation responds to aerosol forcing thereby effectively avoiding issues related to model biases inherent in most global climate models. Also, multiple lines of evidence from both observations and model simulations (in different configurations) are used to support the results we discuss. A note on the optical properties of dust used in CAM5 is provided as the physical mechanism suggested here relies heavily on the dust induced warming over the west Asian/Arabian region. The optical properties for the aerosol mixture in each mode are calculated using the MAM3 aerosol module 35. The optical properties for pure dust is based on the software package OPAC 36 and Mie theory. Studies using AERONET observations 37 have shown that the OPAC dust absorption is too strong. The absorption 4

5 has size-mode and wavelength dependence, but the estimate is about 10-20% low bias in terms of single scattering albedo (SSA) for shortwave bands. However, like many other global models, the CAM5 underestimates absorption AOD over Africa and S. Asia by more than 50% according to AERONET observations 38, which compensates the low bias in SSA and hence total heat absorption in the model. In addition, observational studies have shown that dust absorption efficiency over North Africa/West Asia is highest among the dust laden regions 39. Hence it may be stated that the biases if any in the dust absorption efficiency is not expected to change the qualitative conclusion discussed in the present study S3 The effect of Sea-salt Aerosols The sea-salt aerosol produces a large surface radiative forcing over the oceanic regions with the highest forcing (in the tropics) over the AS. The resultant surface radiative effect does not change the SSTs in the present configuration of the model. However on shorter time scales, the presence of sea-salt aerosol leads to an increase in CCN and cloud droplet number concentration. The diabatic heating within the clouds (Fig S5b) also increases as a consequence of latent heat release. The subsequent warming of the atmosphere leads to large-scale convergence (as observed over land for dust aerosol, see Fig S5a) over the ocean (south of AS and over Indian Ocean) with convergent flow in the lower troposphere (Fig S5b). This circulation (in the opposite direction) weakens the climatological monsoon Westerly over AS leading to decreased moisture flux convergence over the coastal and central part of India and resultant decrease in precipitation. 5

6 S4. Summary of Supplementary Figures Fig S4a reveals a strong dust-induced atmospheric shortwave warming over the West Asian region. The atmospheric absorption along with moist processes within clouds leads to strong diabatic heating (Fig. S5a) in the atmosphere, which consequently alters the pressure gradient significantly (Fig. S6a) and alters the strength of winds (Figs S7a & S8a) over the AS. The dust strengthens the monsoon westerly (see Figs. S7a & S8a) leading to additional moisture convergence (Fig S9a), and the resultant monsoon rainfall. This increased westerly winds can provide additional positive feedback by increasing sea-salt and also dust over the AS. However, the resultant effect on precipitation depends on their relative strengths. On the other hand, sea-salt induced change in the atmospheric warming is mostly a consequence of moist processes and is confined over the southern Indian Ocean (Fig S5b). Following similar processes as for dust, sea-salt weakens the monsoon westerly over AS because the sea-salt layer is located south of the Indian landmass, resulting in weakly reduced rainfall. The schematic in Fig S11 shows the conceptual diagram summarizing the dust and sea-salt induced changes

7 138 Supplementary Figures Fig S1: The spatial distribution of precipitation and aerosol optical depth (June to August climatology) over Indian region from (a & b) CAM5 simulation and (c & d) observations. The box shows the central India region

8 Fig: S2: Correlation between precipitation over CI and Angstrom Exponent (alpha). The black dots represent statistical significance at 95% level The inverse relationship between Angstrom exponent (an indicator of the aerosol size) and central India rainfall shows that the negative correlation is not due to small size aerosol particles. This indicates that the observational relationship may not necessarily be due to anthropogenic aerosols (mostly in the small size range). However our analysis (not shown) reveals a positive correlation between coarse mode fraction (1- fine mode fraction) and precipitation, indicating that the observation (Fig 1) may be either due to sea-salt and/or dust aerosols. 8

9 Fig: S3: Lead-lag correlation between aerosol optical depth (leading) over AS (8 different sub-regions) and rainfall over central India. The correlation peaks at zero lag for any sub-region chosen over AS The co-variability between aerosols over Arabian Sea and rainfall over Central India does not show any lead or lag, suggesting that both the quantities rise and fall almost simultaneously within a week. This observation reveals the short-term nature of the aerosol-monsoon rainfall co-variability. 9

10 Fig. S4: Atmospheric clear-sky shortwave forcing (W m -2 ) due to the presence of (a) dust and (b) Sea-salt aerosols. The black dots represent significance at the 95% confidence level The presence of dust aerosol leads to warming of the atmosphere due to the absorption of short-wave radiation over West Asia / Arabian Sea region. The forcing is greater than 30Wm -2 over the regions to the west (north) of 60 o E (15 o N). Such large absorption combined with latent heat release due to dust induced cloud / moist processes lead to a large warming (see Fig S5a) over these regions. 10

11 Fig. S5: Changes to diabatic heating (K day -1 ) within the atmospheric column averaged over longitudes 50 to 70 E (over Arabian Sea) due to the presence of (a) dust and (b) sea- salt aerosols. The black dots represent significance at the 95% confidence level The dust induced diabatic heating (both short-wave absorption and moist processes) leads to warming of the atmosphere at a rate of up to 2 K day -1. Such warming leads to changes in atmospheric circulation. On the other hand, as mentioned earlier, sea-salt is purely scattering in nature and does not warm the atmosphere. However, they act as cloud condensation nuclei (CCN), thereby leading to moist processes that release more latent heat into the atmosphere (mostly to the south of equator) altering the upper atmospheric north-south temperature gradient, which slows down the monsoon circulation to some extent (see Fig S7 & S8)

12 Fig. S6: Changes to the surface pressure (Pa) due to the presence of (a) dust and (b) sea- salt aerosols. The black dots represent significance at the 95% confidence level The largest change to surface pressure (lowering by > 100 Pa) is observed to the west of India over North Africa and Arabian region as a consequence of dust forcing. The changes in pressure gradient (north-south) due to dust aerosols strengthen the monsoon circulation

13 Fig. S7: Changes to the meridional circulation (Pa day -1 ) within the atmospheric column averaged over longitudes 50 to 70 o E (over Arabian Sea) due to the presence of (a) dust and (b) sea-salt aerosols The atmospheric warming due to dust aerosols (shown in Fig S5a) modifies the atmospheric circulation as shown in Fig S7a (meridional cross-section). The circulation change induced by dust (sea-salt) amplifies (dampens) monsoon circulation that is also a conduit for moisture (see Fig S9) over Indian land region, thereby increasing (decreasing) the monsoon rainfall (see Fig 4)

14 Fig. S8: Changes to winds at 850 mb level over the wider monsoon domain as a response to (a) dust and (b) sea-salt aerosols. The shaded colors indicate the changes to wind speed (m s -1 ), compared to the control simulation The changes to circulation induced by dust (sea-salt) increase (decrease) the wind speeed over Arabian Sea by as much as 2 to 5 m s -1 (-1 to -2 m s -1 ), thereby, increasing (decreasing) the moisture transport to the central India region

15 Fig. S9: Changes to moisture flux (divergent mode, kg m -1 s -1 ) at 850 mb level over the wider monsoon domain as a response to (a) dust and (b) sea-salt aerosols. The shaded colors indicate the magnitude of precipitation in comparison to the control simulation It s shown that dust (sea-salt) leads to increased (decreased) moisture convergence over India. Changes in the source of moisture are consistent with the increased (decreased) rainfall due to dust (sea salt). 15

16 Fig. S10: Schematic showing the Dust Pulse Experiment (DPE) design Each ensemble member represents a 10-day model run with either control or no dust configuration starting every 5 days (from 1 st June to 31 st August), so there are a total of 2x19 simulations in the ensemble. Each of these simulations is considered as a separate ensemble member. The results discussed are averages over all individual ensemble members. The design of DPE also tries to exclude potential influence of intraseasonal oscillations, which is an important mechanism modulating monsoon life cycle. 16

17 Fig. S11: Conceptual diagram summarizing various physical processes through which dust and sea-salt induce changes to monsoon precipitation over India. The numbers in the bracket corresponds to figures. The (+/-) sign indicates increase/decrease, and the L, O, and CI refers to changes occurring generally over land, ocean and central India, respectively

18 Tables Table S1: Details of the model experiments using CAM5 Experiment Name Emissions Description and number of simulations (in parentheses) 1 All Species (PD) Control Present Day (PD) All aerosol species including prognostic sea-salt and dust (1) 2 No Sea Salt (PD) PD 3 No Dust (PD) PD 4 No Sea-salt & Dust (PD) PD 5 No Sea Salt (PI) Pre-industrial (PI) 6 No Dust (PI) PI All aerosol species excluding sea-salt emissions (1) All aerosol species excluding dust emissions (1) All aerosol species excluding sea-salt and dust emissions (1) All aerosol species excluding sea-salt emissions (1) All aerosol species excluding dust emissions (1) All aerosol species included, the model 7 CAM5-ERA PD simulation uses prescribed meteorology from ERA Interim reanalysis [2000 to 2009] (1) 8 Pulse Experiment All Species (PD) PD 10-day simulations started every 5 days from 1 st June to 31 st Aug (19) 9 Pulse Experiment No Dust (PD) PD Same as above (19)

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