Assessment of Regional Aerosol Radiative Effects under SWAAMI Campaign – PART 2: Clear-sky Direct Shortwave Radiative Forcing using Multi-year Assimilated Data

Abstract. Clear-sky, direct shortwave Aerosol Radiative Forcing (ARF) has been estimated over the Indian region, for the first time employing multi-year (2009–2013) gridded, assimilated aerosol products. The aerosol datasets have been constructed following statistical assimilation of concurrent data from a dense network of ground-based observatories, and multi-satellite products, as described in Part-1 of this two-part paper. The ARF, thus estimated, are assessed for their superiority or otherwise over other ARF estimates based on satellite-retrieved aerosol products, over the Indian region, by comparing the radiative fluxes (upward) at Top of Atmosphere (TOA) estimated using assimilated products with spatio-temporally matched radiative flux values provided by CERES (Clouds and Earth's Radiant Energy System) Single Scan Footprint (SSF) product. This clearly demonstrated improved accuracy of the forcing estimates using the assimilated vis-a-vis satellite-based aerosol datasets; at regional, sub-regional and seasonal scales. The regional distribution of diurnally averaged ARF estimates has revealed (a) significant differences from similar estimates made using currently available satellite data, not only in terms of magnitude but also sign of TOA forcing; (b) largest magnitudes of surface cooling and atmospheric warming over IGP and arid regions from north-western India; and (c) negative TOA forcing over most parts of the Indian region, except for three sub-regions – the Indo-Gangetic plains (IGP), north-western India and eastern parts of peninsular India where the TOA forcing changes to positive during pre-monsoon season. Aerosol induced atmospheric warming rates, estimated using the assimilated data, demonstrate substantial spatial heterogeneities (~ 0.2 to 2.0 K day−1) over the study domain with the IGP demonstrating relatively stronger atmospheric heating rates (~ 0.6 to 2.0 K day−1). There exists a strong seasonality as well; with atmospheric warming being highest during pre-monsoon and lowest during winter seasons. It is to be noted that the present ARF estimates demonstrate substantially smaller uncertainties than their satellite counterparts, which is a natural consequence of reduced uncertainties in assimilated vis-a-vis satellite aerosol properties. The results demonstrate the potential application of the assimilated datasets and ARF estimates for improving accuracies of climate impact assessments at regional and sub-regional scales.



Introduction
The uncertainties in aerosol radiative forcing (ARF) pose primary challenges in the assessment of climatic implications of atmospheric aerosols at global, regional and even sub-regional scales (Schwartz, 2004;Boucher et al., 2013). In order to improve the estimates of aerosol climate sensitivity, at least three-fold reduction in the uncertainties in aerosol radiative forcing is necessary (Schwartz, 2004). Despite efforts towards this, significant uncertainties still persist in the estimates of even direct 5 aerosol radiative forcing (DARF) (Penner et al., 1994;Boucher and Anderson, 1995;Ramanathan and Carmichael, 2008), leave alone the indirect forcing. This calls for improvement in the accuracy of primary aerosol inputs to DARF estimation at sub-regional and regional scales.
There have been several estimates of global aerosol radiative forcing (ARF) by employing general circulation models (GCM) or chemistry transport models (CTM) making use of aerosol emission inventories (Jacobson, 2001;Takemura et al., 2002;Myhre et al., 2007Myhre et al., , 2009Kim et al., 2008). These studies have highlighted the regional and temporal heterogeneity in aerosol forcing, but the actual forcing values reported have significant uncertainties emanating mainly from those in the input inventories, meteorology and assumptions made in aerosol-chemistry processing. (Schulz et al., 2006) have shown that the model (GCM or CTM) based estimates of radiative forcing differ significantly amongst themselves, even in terms of sign of radiative forcing despite using identical emission inventories. Recognizing this issue, (Chung et al., 2005(Chung et al., , 2010 have produced 15 global and regional maps of aerosol forcing employing observationally constrained aerosol datasets, which were constructed by integrating satellite and ground based observations of aerosol optical depth (AOD) with those derived from the global chemistry-transport model, GOCART. They provided somewhat more realistic estimates of aerosol forcing as compared those incorporating model-derived aerosol parameters, because of the improvements in the input datasets arising from their assimilation efforts. Nevertheless, due to limited number of regional ground stations involved in the assimilation process by (Chung 20 et al., 2005), (for example only 2 stations over Indian region), the assimilated AODs over large parts of the globe remained largely represented by satellite-retrieved and model-derived AODs, which suffer from significant uncertainties and biases emanating from a variety of sources (cloud contamination, spatial heterogeneities in surface albedo, sparse temporal sampling, various assumption made during retrievals procedure and sensor degradation etc. (Zhang and Reid, 2006;Jethva et al., 2014).
As a result, large uncertainties still prevailed at regional and sub-regional scales. 25 Location-specific estimates are more accurate as these employ highly accurate ground-based measurements of aerosol properties (spectral AOD/ aerosol absorption/ altitude profiles etc.) and generate aerosol models constrained with measurements and use them in a radiative transfer scheme (Babu and Moorthy, 2002;Babu et al., 2007;Satheesh et al., 2006;Pathak et al., 2010;Sinha et al., 2013). Due to smaller uncertainties in the direct measurements, these aerosol forcing estimates tend to have lesser uncertainties vis-a-vis forcing estimates from satellite-retrieved and model-derived aerosol parameters. However, due to 30 limited spatial representativeness of each ground station, and the limited density of ground networks, these ARF estimates are highly location specific. They lack the much-needed regional representativeness for climate assessment, unless they involve a large number of ground locations, from a highly dense network, which has associated practical difficulties. Recognizing these scenarios, especially over the Indian region which has large spatio-temproal variations in aerosol properties, a careful assim-ilation of moderately dense network data with satellite data to generate a gridded data set which is more or less continuous in space and time, has been envisioned as one of the key objectives by the South West Asian Aerosols Monsoon Interactions (SWAAMI) (Morgan, 2016) (https://gtr.ukri.org/projects?ref=NE%2FL013886%2F1), a co-ordinated field campaign jointly undertaken by the scientists from India and the United Kingdom.
In part-1 of this two-part paper, we have presented, the gridded, assimilated datasets of AOD and SSA (Single Scattering 5 Albedo) over India, which provide spatio-temporally continuous data, generated for the first time by harmonizing long-term (2009)(2010)(2011)(2012)(2013) measurements from a dense network of ground-based aerosol observatories and multi-satellite datasets following statistical assimilation techniques (Pathak et al., 2019). The resulting improvement in accuracies of the gridded products (over the parent data) in reproducing the spatio-temporal characteristics of aerosol properties at sub-regional scales over the Indian domain was also demonstrated. In this part-2 of the paper, we have estimated direct shortwave aerosol radiative forcing (ARF) 10 over the Indian region using the above gridded data and examined its features. A comparison of these estimates is made with similar estimates made using the parent satellite data to demonstrate the effectiveness of the assimilated data in betterquantifying ARF over the Indian region with its characteristic spatio-temporal features (section 3.1). Further, we have compared the top-of-the-atmosphere (TOA) radiances estimated using the assimilated data with the radiance values measured by Clouds and Earth's Radiant Energy System (CERES) instrument (section 3.2) and the seasonal contrast in ARF is then presented for 15 various geographically homogeneous sub-regions (section 3.3). The primary findings of the present work are then summarised in section 4.

Database
Accuracy of estimation of direct aerosol radiative effect depends strongly on the accuracies of three key optical properties of aerosols, namely AOD, SSA and single-scatter phase function, and the availability of these continuously in space and time over 20 the domain of interest. Accordingly, in this work, we have used the gridded data over the Indian domain for AOD and SSA at 1 • × 1 • resolution. These datasets are generated by assimilating long-term data from ground-based network observatories and space-borne sensors following statistical assimilation techniques (3D-VAR and Weighted Interpolation methods) as described in Pathak et al 2019 (the part-1 paper) and are available at http://dccc.iisc.ac.in/aerosoldata/ (Pathak et al., 2019). These assimilated AOD and SSA are henceforth respectively denoted as AS AOD and AS SSA. However, such a comprehensive 25 dataset for aerosol phase function is not available over the study domain, and as such we relied on 'Optical Properties of Aerosols and Clouds' (OPAC) model (Hess et al., 1998), which provides optical properties for variety aerosol species and their mixtures (under externally mixed assumption). As our study domain mainly comprises of land areas, an average value of aerosol phase function (at 550 nm) corresponding to all continental aerosol mixtures in OPAC is considered, and its Legendre polynomial coefficients (8 streams) are employed for determining the Legendre moments of the phase function. 30 In addition to assimilated aerosol products, we have also employed satellite-retieved AOD and SSA (denoted as SR AOD and SR SSA respectively) for ARF estimation. The satellite-retrieved AODs employed here are constructed by combining by monthly averaged AOD products (1 • × 1 • resolution) from (L3, collection 6, https://modis.gsfc.nasa.gov/data/ ) MODerate Imaging Spectroradiometer (MODIS) onboard AQUA and TERRA satellites as well as from Multiangle Imaging SpectroRadiometer (MISR) onboard TERRA satellite (L3, https://misr.jpl.nasa.gov/ ) (Diner et al., 1998), as detailed in the Part-1 of the two-part paper (Pathak et al., 2019). The satellite-retrieved SSA are provided by monthly mean SSA (at 500 nm) datasets constructed from upwelling radiance measurements (in the range of 270-500 nm) performed by OMI on-board AURA satellite (Torres et al., 2002;Levelt et al., 2006).

5
The spatial distribution the gridded aerosol properties (assimilated as well as satellite-retrieved; AODs and SSA) are shown in Figure for two typical months; January -typical winter, when majority aerosols are trapped near the surface due to the shallow atmospheric boundary layer; and May -typical summer/pre-monsoon when the strong thermal convection ensures a thorough vertical mixing within the deep Atmospheric Boundary Layer (ABL). These two months also signify periods when the local emissions dominate in the aerosol loading (winter) and when advected aerosols (dust and sea-salt) also contributes significantly 10 to the aerosol loading (summer/pre-monsoon). Sub-regional differences in the spatial distribution of these parameters between the assimilated and satellite retrieved are noticeable in Figure ; the most conspicuous being in SSA where the assimilated data shows stronger absorption than the satellite retrieved over many parts of the domain. For estimating aerosol radiative forcing, we have used the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) radiative heat transfer model, which uses the DIScrete Ordinate Radiative Transfer (DISORT) algorithm to solve the radiative heat transfer equation with plane-parallel assumption, in the atmosphere with vertical inhomogeneities (Ricchiazzi et al., 1998).
The above (described in section 2) data are used as inputs. The surface reflectance data needed has been taken from MODIS, 5 while vertical distribution of atmospheric gases, except columnar Ozone and water vapour, is specified using the tropical environment model provided by SBDART. For columnar ozone and water vapour, datasets for the corresponding period provided respectively by OMI and MODIS are used (Ziemke et al., 2006;Gao and Kaufman, 2003). The upward and downward shortwave fluxes at the TOA and surface, (in the wavelength range 0.2 to 4 µm) are computed using SBDART. The direct aerosol radiative forcing (corresponding to assimilated aerosol products) is then estimated as shown in the following equations 1 and 10 2.
Here, AS ARF denotes aerosol radiative forcing estimated using assimilated aerosol properties while the subscripts TOA and srf denote the top of atmosphere and ground surface respectively. The upward and downward fluxes over the shortwave 15 spectrum are respectively denoted by F ↑ and F ↓. The corresponding atmospheric forcing (denoted by AS ARF atm ) due to aerosols absorption is then estimated as shown in the following equation 3.
With a view to assessing the improvements (or otherwise) of the current estimates over such estimates made using conventional datasets (satellite retrieved or ground measured), we have also estimated direct ARF by using only the satellite-retrieved 20 aerosol products (AOD and SSA) in SBDART for the same period, over the same domain. All other input parameters are kept the same as for AS dataset. The ARF estimates corresponding to satellite products, at TOA, surface and within atmosphere are henceforth referred to as SR ARF TOA , SR ARF srf and SR ARF atm .
Further, from each of the forcing estimates made using the two different datasets, we also estimated the difference between the two (referred to as dARF) as, 25 dARF TOA = AS ARF TOA − SR ARF TOA (4) 4 Results and discussion 5 4.1 Spatial distribution of aerosol radiative forcing The regional distribution of the different components of aerosol radiative forcing, estimated as above, are shown in figure 2 to 4 for the representative winter month (January) and in figures 5 to 7 for the representative summer/ pre-monsoon month (May).
Each figure shows the spatial distribution of ARF determined using assimilated datasets (AS ARF), satellite retrieved datasets (SR ARF) and the difference between the two; for TOA, Surface and atmosphere As the dARF calculation has to account for 1. Over most of the study domain, ARF estimated using the assimilated datasets show stronger surface cooling and higher atmospheric warming than those yielded from the satellite retrieved data, except over outflow region of Indo-Gangetic plain. The higher atmospheric forcing when assimilated dataset is sued is mainly contributed by the lower SSA in the assimilated data (owing to assimilation with ground-based measurements of aerosol absorption). During summer ( Figures 5-7), the seasonal transformation of radiative impacts is clearly seen in the assimilated forcing (mainly due to seasonal change in the aerosol types with anthropogenic aerosols being abundant during winter and natural aerosols during summer). The salient features are: 1. Significantly large increase in all the three components of aerosol forcing compared to the winter-time values, primarily due to enhanced aerosol loading in summer.
2. This enhancement is seen more conspicuously in the maps using assimilated data (AOD and SSA) as inputs, than those generated using satellite derived data as input, over most parts of the region.
3. In sharp contrast to the winter case, during summer the TOA forcing using assimilated data shows positive values over a

Comparison with CERES measurements
The above described spatial distribution of aerosol direct forcing, and the large season-dependent differences in between the estimates made using assimilated and satellite retrieved datasets calls for a quantitative verification based on independent measurements, which would delineate the datasets that has better accuracy over the study domain. This exercise would also qualify the superior datasets as inputs to climate models for impact assessment. With a view to accomplishing this, we have compared the TOA fluxes, estimated using the assimilated and satellite-based datasets with spatio-temporally collocated measurements by CERES (Clouds and Earth's Radiant Energy System) onboard AQUA satellite for clear-sky conditions. CERES is a scanning broadband radiometer which measures the upwelling radiances at TOA over three spectral regimes: the short-5 wave (0.3-5 µm), the infrared window (8-12 µm) and the total (0.3 to 200 µm). These measured radiances are then converted into the radiative fluxes using the scene-dependent empirical angular distribution models (Loeb et al., 2003), which are then re-gridded to 1 • ×1 • grid. In the present study, we have used the re-gridded, instantaneous flux measurements at TOA provided by CERES-SSF (Single Scan Footprint) product for clear-sky conditions.
As the equatorial crossing time (ECT) of AQUA (local solar time for ascending orbit) varies slightly about its mean value   this, we have performed the regional level estimation of the aerosol induced atmospheric heating rate (columnar) using the atmospheric absorption corresponding to assimilated aerosol datasets and compared them to those estimated using satellite aerosol products.

Atmospheric heating rate estimation
The atmospheric forcing component of ARF is the amount of energy absorbed by the atmosphere, which heats the atmosphere.

5
The heating rate due to aerosol induced atmospheric absorption is calculated as shown in equation .
Here, ∂T ∂t is the atmospheric heating rate (K S −1 ), g is the acceleration due to gravity, C p is the specific heat of air at constant pressure (∼ 1005 J kg −1 K −1 ), ∆F is the aerosol-induced atmospheric absorption and P is the atmospheric pressure. Here, ∆P is considered to be 300 hPa (pressure varying from 1000 hPa to 700 hPa) implying that atmospheric aerosols are largely 10 concentrated in the first 3 km above the ground which is also supported by observations (Parameswaran et al., 1995;Müller et al., 2001).
The spatial distribution of diurnally averaged atmospheric heating rates estimated using assimilated datasets (HR AS ) and its difference from similar estimates made using satellite retrieved aerosol datasets (HR AS -HR SR ) are shown in figure 9, for two representative months, January and May-2099.  K day -1 K day -1 K day -1 K day -1 Figure 9. Spatial Variation of aerosol-induced atmospheric heating rate (in K day −1 ) estimated using assimilated aerosol products (HRAS) for January -2009 and May-2009 (a and c respectively), difference between aerosol-induced atmospheric heating rate corresponding to assimilated and satellite aerosol products (HRdiff = HRAS -HRSR) for January -2009 andMay-2009 (b andd respectively) The figure reveals consistently higher heating rates (∼ 0.6 to 0.7 K day −1 during Jan-2009 and ∼ 1.5 to 2.0 K day −1 during May) over the Indo-Gangetic plains (IGP) than rest of the sub-regions. As indicated by positive values of HR diff , the heating rate estimates using assimilated datasets are consistently higher than those estimated using satellite derived aerosol products.
Further examination of figure 9 reveals that there is significant seasonality in the atmospheric heating rate with pre-monsoonal 18 https://doi.org/10.5194/acp-2020-454 Preprint. Discussion started: 25 May 2020 c Author(s) 2020. CC BY 4.0 License. month, May exhibiting substantially stronger warming than that during the winter month of January, over most of the Indian region. In view of this, we have examined the seasonal variation in the aerosol radiative forcing over the Indian region, in the next section.

Seasonal and sub-regional features
Aerosol types and their properties over Indian region are known to exhibit significant seasonal variation, both at regional and 5 sub-regional scales, primarily due to seasonality in the nature of aerosol sources, advection pathways as well as synoptic and meso-scale meteorology (Jethva et al., 2014;Moorthy et al., 2007;Babu et al., 2013;Vaishya et al., 2018;Pathak et al., 2019).
We examine the signatures of these in direct ARF. For this analysis, we have considered four sub-regions of the spatial domain based on he homogeneity of broad-scale geographical features as detailed in Figure 10 and Table 1.  The climatological seasonal variation of TOA forcing and atmospheric absorption estimated employing assimilated aerosol products and averaged over four sub-regions are presented in Figures 11 and 12 respectively, with the panels a to d respectively representing the IGP, NE, PI and WAR. Please note that the vertical lines over the bars in Figures 11 and 12 indicate the spatio-temporal variation in ARF over the respective sub-region and not the uncertainties in radiative forcing. Further, we have estimated the uncertainties in the ARF estimated using assimilated as well as satellite aerosol products and the exercise revealed that the uncertainties in AS ARF are substantially smaller than those in SR ARF which is a direct consequence of smaller uncertainties in assimilated AOD and SSA vis-a-vis respective satellite products (Pathak et al., 2019).

5
The corresponding details are provided in the Appendix A.

Summary
We have estimated shortwave, clear-sky direct aerosol radiative forcing over the Indian region by incorporating gridded, assimilated, multi-year (2009-2013) datasets for monthly AOD and SSA in SBDART and compared its spatio-temporal features with those in ARF estimated using presently available satellite-retrieved aerosol products. This work is the first of its kind over the 10 Indian region that computes regional ARF estimates that employs assimilated, gridded datasets constrained by highly accurate aerosol measurements performed with a dense network of ground-based observatories spanned across the Indian region. In order to examine the accuracy of these ARF estimates, the monthly instantaneous TOA fluxes estimated using assimilated and satellite retrieved products are compared against the monthly averaged, instantaneous CERES measurements. Finally, we have estimated the aerosol-induced atmospheric warming rates and discussed its spatio-temporal features. The primary findings of 15 the present work are: 1. The TOA fluxes estimated using the assimilated datasets conform better with independent and concurrent space-borne measurements performed by CERES as compared to that shown by TOA fluxes corresponding to satellite retrieved datasets. This establishes the higher accuracy of ARF estimated using assimilated vis-a-vis satellite aerosol products.
2. The diurnally averaged ARF corresponding to assimilated aerosol properties depicts significant spatial and temporal 20 diversity not only in terms of the magnitudes but also in the sign of ARF at TOA. Indo-Gangetic plains, north-eastern parts as well as southern parts of peninsular India exhibit either negative forcing with smaller magnitudes or positive forcing, as compared to rest of the region demonstrating negative TOA forcing of relatively larger magnitudes.
3. The regional distribution of radiative forcing also reveals increased surface cooling and atmospheric absorption over Indo-Gangetic plain and arid regions from north-western India vis-a-vis rest of the region. 4. Similar large-scale spatial features are also shown by ARF estimated using satellite products, however they differ from their assimilated counterparts in terms of magnitude as well as sign of TOA forcing.
In most of the cases, the heating rates corresponding to assimilated products demonstrate substantially increased lower- 30 tropospheric warming vis-a-vis those corresponding to satellite aerosol products.
6. Over most parts of the region, the TOA forcing is negative throughout the year with maximum magnitudes occurring during winter and minimum during pre-monsoon, except Indo-Gangetic plain and western arid regions over which the sign of TOA forcing flips from positive (during pre-monsoon) to negative (during post-monsoon and winter). However, the atmospheric forcing due to aerosols is highest during pre-monsoon and lowest during winter, over almost entire Indian region. 5 7. The uncertainties in ARF estimated using assimilated aerosol products are substantially lower than those in ARF estimated using satellite products, which is a natural consequence of smaller uncertainties in assimilated vis-a-vis satellite aerosol products.
On the background of these benefits, the present ARF estimates and the corresponding assimilated aerosol products can be potentially applied for improving the accuracy of aerosol climate impact assessment at regional, sub-regional and seasonal 10 scales.

Appendix A: Uncertainties in ARF
One of the prime challenges in the accurate climate impact assessment of aerosols is posed by the uncertainties in the estimation of direct aerosol radiative effect. These uncertainties primarily emanate from those in the gridded datasets for aerosol properties, mainly AOD and SSA. Past studies have shown that small changes in SSA can even change the sign of aerosol radiative forcing 15 at TOA Shine, 1995, 1997;Heintzenberg et al., 1997;Russell et al., 2000;Takemura et al., 2002;Loeb and Su, 2010;Babu et al., 2016). Against this backdrop, it becomes imperative to assess the uncertainties in the radiative forcing estimates presented in the current study.
For estimation of uncertainties in aerosol radiative forcing, we have derived multiple realizations of diurnally averaged ARF (at TOA, surface and within atmosphere) at each grid point over the Indian region, with each of these ARF realizations 20 corresponding to a particular AOD and SSA which are perturbed from the original values within their respective uncertainty limits. The uncertainties in assimilated AOD and SSA are estimated as discussed in Part-1 of the two-part paper (Pathak et al., 2019). The uncertainties in SR AOD (which largely comprises of MODIS AODs) are estimated as ±(0.03 + 0.2τ M ) (Sayer et al., 2013) where τ M is the corresponding MODIS AOD. The uncertainty in OMI SSA is considered to be ±0.05 following (Torres et al., 2002) and (Jethva et al., 2014). For a given grid-point, the standard deviation across the multiple realizations of 25 ARF is then considered to be the uncertainty in the radiative forcing estimate. Thus, we have estimated the uncertainties in ARF correspoding to assimilated and satellite-based aerosol products.
The RMS uncertainty in the ARF at TOA, surface and atmospheric absorption estimated using assimilated aerosol products are presnted and compared with those in ARF estimated using satellite derived aerosol products in Table A1 Table A1 and A2 demonstrate that uncertainties in AS ARF are substantially smaller than those in SR ARF, which is the consequence of smaller uncertainties in assimilated aerosol products as compared their satellite counterparts. It can further be seen from table A1 and A2 that uncertainties in aerosol radiative forcing estimated using assimilated datasets are least for the forcing at TOA as compared to surface forcing and atmospheric absorption. This is in contrast with the ARF corresponding to satellite products for which the TOA forcing exhibit highest uncertainty vis-a-vis its surface and atmospheric counterparts,