The performance of climate and weather forecasting models depend on the availability of high-resolution accurate surface radiation datasets and their assimilation into climate and weather forecasting models (Rahaman et al., 2019). The atmosphere and ocean interact through mass, momentum, and heat fluxes at the ocean–atmosphere interface. Heat fluxes modulate intraseasonal oscillations in tropical oceans (Vialard et al., 2008; Sobel et al., 2008; Jayakumar et al., 2011). Shortwave radiation and latent heat flux are the principal contributors to heat flux variation in the tropics (Koberle et al. 1994). Tropical oceans receive the largest solar irradiance in the form of shortwave radiation. The excess heat over the tropical ocean balances through turbulent mixing, longwave radiation, and transport to the higher latitudes through ocean circulation for more extended periods (Trenberth et al. 2004). On weekly time scales, tropical and extratropical cyclones make up the most prominent energy transfer from the ocean to the atmosphere through the release of latent heat (Ma et al., 2015). Heat fluxes also influence large-scale tropical coupled processes such as the El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Atlantic Meridional Mode (AMM) (Xie and Philander 1994; Wang et al. 1999; Murtugudde et al. 2001; Vialard et al. 2001; Hendon 2003; Vimont and Kossin 2007). Ocean general circulation models (OGCMs) use the near-surface atmospheric state to parameterize the heat and momentum fluxes (Large et al., 2009). Similarly, uncertainties in turbulent and radiative heat fluxes could affect the heat balance in the boundary conditions prescribed in the OGCMs (Venugopal et al. 2016, Thandlam and Rahaman, 2019). These limitations in the forcing fields are partially responsible for the OGCMs making unrealistic simulations of intraseasonal, seasonal, interannual and climate variability (McWilliams et al. 1996). Hence, accurate near-surface atmospheric fields are essential for realistic simulations in a forced ocean and climate model.
The sum of the net radiative flux and turbulent flux adds up to the net air–sea flux at the sea surface (Fairall et al. 1996; Curry et al. 2004; Pinker et al. 2014). Longwave and shortwave radiation are the two components of radiative flux. The downwelling fluxes of shortwave (QS) and longwave radiation (QL), together with other meteorological variables and the initial ocean state, are used to force the OGCMs. A significant extent of the ocean model simulation accuracy depends on the accuracy of these downwelling radiative fluxes. At present, the ocean modeling community uses Common Reference Ocean–Ice Experiments (CORE-II) and JRA-do datasets as the prime source to run the global ocean and sea–ice model (Large et al. 2004, 2009; Tsujino et al. 2018; Kobayashi. et al. 2015). The National Centre for Environmental Prediction (NCEP-R2) released corrected data as CORE-II (Kanamitsu et al. 2002), and the improved JRA reanalysis is called JRA-do. However, CORE-II uses radiation data obtained from the International Satellite Cloud Climatology Project (ISCCP) (Zhang et al. 2004), available only until 2009. Hence, CORE-II forcing fields are not available beyond 2009. Therefore, climate forecasting system reanalysis (CFSR, CFSv2) (Saha et al. 2010, 2014) replaced the NCEP-R2 data. Although other radiation datasets have taken over the ISCCP since 2009, there are still gaps in this area.
Ramesh et al. (2017) used satellite data (MODIS) as the reference to evaluate the radiative fluxes from the Ocean Moored Network for the Northern Indian Ocean (OMNI). Rahaman et al. (2013) evaluated the near-surface air temperature and humidity from CORE-II, Objectively Analyzed Air–Sea Fluxes (OAFLUX), and Tropical Flux (TropFlux) to find a better dataset to use in the model forcing. An evaluation of the surface radiation fluxes with buoy observations in the Pacific Ocean during 2000–2012 concluded that satellite data match well with the in situ observations, followed by reanalysis and model data (Pinker et al. 2017a, 2017b, 2018). Trolliet et al. (2018) compared the irradiance data from MERRA-2 and ERA5 and three other satellite-derived datasets: HelioClim-3v5, Surface Solar Radiation Parameters (SARAH-2), and Copernicus Atmosphere Monitoring Service (CAMS) with five buoys in the Atlantic for the period 2012–2013. At the ocean–atmosphere interface in the Atlantic, heat budgets derived from satellites and blended products were compared with in situ observations during 2003–2005 (Pinker et al. 2014). While the performances are similar between the three satellite-derived datasets, existing reanalysis data have significant biases, errors, and poor correlation values compared with independent in situ observations. In addition, the Earth’s radiation balance from satellite observations has a more significant bias over the ocean than their better agreement over land (Ma et al., 2012). These differences are due to frequent changes in satellite observing systems, the degradation of sensors, the restricted spectral intervals and viewing geometry of sensors, and changes in the quality of atmospheric inputs that drive the inference schemes.
1.1 Global Tropical Moored Buoy Array
Moored buoy observing systems in all three tropical oceans comprise the Global Tropical Moored Buoy Array (GTMBA). This program is a multinational effort to obtain surface meteorological and subsurface oceanic near-real-time data for research and applications. It has three components, namely, the Tropical Atmosphere Ocean/Triangle Trans‐Ocean Buoy Network (TAO/TRITON) in the tropical Pacific (McPhaden et al. 1998), the Pilot Research Moored Array in the Tropical Atlantic (PIRATA) in the tropical Atlantic (Bourlès et al. 2008) and the Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA) in the tropical Indian Ocean (McPhaden et al. 2009). The GTMBA program in the Indian Ocean (RAMA) is relatively new, as this program was first started in the Pacific and later followed by the Atlantic. The GTMBA data undergo rigorous three-stage quality control (daily, weekly, and monthly) procedures to ensure high accuracy standards before being delivered to users (Freitag et al. 1999, 2001; Payne et al. 2002; Medovaya et al.2002; Lake et al. 2003).
The uncertainty in Qs in the GTMBA data is 2% due to drift criteria, and the reported monthly mean accumulation biases due to high-dust accumulation can reach -200 Wm-2 in the Atlantic. Similarly, record-length mean biases in the Qs from the moorings in the Atlantic (PIRATA) can reach -10 Wm-2, potentially leading to significant negative Qs biases (Foltz et al., 2013; Thandlam and Rahaman (2019) and the references detailed the GTMBA project). Among the 27 mooring sites in the Indian Ocean, daily averaged (0000 UTC to 2300 UTC) QL data are available only at 0°N, 80.5°E; 15°N, 90°E and 8°S, 67°E (three locations), while daily averaged QS data (0000 UTC to 2300 UTC) are available at 19 sites during the study period. Among the 7 stations delivering QL in the Atlantic Ocean, only 4 have consistent data, and QS is available from 17 of 21 locations during the study period. The Pacific Ocean has more moorings with better data availability than other oceans. We use the QL data delivered from 11 moorings and QS data obtained from 32 of 34 sites in the Pacific Ocean. We use all available mooring locations for QS and QL in the tropical oceans during 2000–2015. As a result, more QS observations with a wider distribution are available than fewer and more sparsely distributed QL observations in the tropical oceans. However, many buoy locations have data gaps, and few stations exist where only QS or QL are available. Therefore, the data availability period of all mooring sites is not the same. Data gaps and the spuriousness of mooring data are considered when validating satellite data with GTMBA data. Supplementary Figure S1 shows the present status of GTMBA and the location of buoys.
1.2 Clouds and Earth's Radiant Energy System (CERES/MODIS)
The Clouds and Earth's Radiant Energy System (CERES) project started in 1997 was conceived as a successor to the Earth Radiation Budget Experiment (ERBE) to compile a data record for the investigation of interannual variations in climate (Harrison et al. 1990; Wielicki et al. 1998; Kato et al. 2013; Ramanathan et al. 1989; Barkstrom et al. 1986; Barkstrom et al. 1989). This program also provides an alternative for the radiative flux components available from 2000 to the present (Barkstrom et al. 1989; Wielicki et al. 1998; Kato et al. 2013). In total, 7 instruments have been launched, with the latest instrument (FM6) launched in 2017 on the National Oceanic and Atmospheric Administration's (NOAA) Joint Polar Satellite System 1 (JPSS-1). The release of the instrument and ERBE-like data from FM6 occurred in June 2018. The instruments and platforms used to collect these data include imaging radiometers on the Geostationary Satellites platform; CERES Flight Model 1 (FM1), CERES FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and CERES FM3, CERES FM4, and MODIS on Aqua.
The CM project produces a long-term, integrated global climate data record for detecting decadal changes in the Earth’s radiation budget from the surface to the top of the atmosphere. Thus, the CM program supports climate model evaluation and improvement through model–observation intercomparisons. CM is the only project to produce global climate data records of Earth’s radiation budget using polar-orbiting and geostationary satellites accounting for variations in radiation at hourly, daily, and monthly timescales and at spatial scales ranging from 20 km to 1°. The CM program focuses on measuring outgoing longwave radiation radiances to an accuracy of 1% and reflected solar radiances to 2%. CM estimates of incident solar radiation agree better with surface measurements at monthly rather than at daily timescales. These estimates can also capture the seasonal variation in incident solar radiation very well (Wielicki et al. 1996). Barkstrom (1999) and Smith et al. (2011) presented complete technical details on the status of CM.
Past studies have evaluated radiative fluxes from different satellites with observations over the land and ocean (Pinker 2009, 2014, 2017a, 2017b and 2018). Rutan et al. (2015) compared CM surface radiation flux data at 85 globally distributed land (37) and ocean buoy (48) surface observations as well as several other publicly available products on global surface radiation flux data. The downward fluxes from SYN1deg have a monthly bias (standard deviation) of 3.0 Wm-2 (5.7%) for QS and -4.0 Wm-2 (2.9%) for QL compared to surface observations. Inclusion of the diurnal cycle of cloud changes minimized the standard deviation between surface QS flux calculations and observations at the 3-hourly time scale. Kato et al. (2013) estimated the bias (RMSE) between computed and observed monthly mean irradiances calculated with ten years of CM data as 4.7 (13.3) Wm-2 for QS and -2.5 (7.1) Wm-2 for QL over global oceans. Nevertheless, all these studies either have a single downwelling radiation parameter or focus on a narrow region for a brief period. Venugopal et al. (2016) and Thandlam and Rahaman (2019) performed a similar analysis in the global tropical oceans, with a subset of CERES/MODIS data, hereafter CM, version 3 datasets during 2000–2009. In the present work, we evaluate both the components of downwelling radiation (QS and QL) from CM version 3 (CMv3) and CM version 4 (CMv4) for a longer period (2000–2017). To our knowledge, no study has focused on validating near-surface QS and QL from updated CMv4 data in tropical oceans with in situ observations for such a long period.
Hence, this study aims to undertake a comparison of the QS and QL from the regularly updated downwelling radiation from satellite data over tropical oceans with in situ observations to assess whether CMv4 data can be complementary to reanalysis data to force OGCMs and can be used to evaluate climate models. Evaluating both versions with independent in situ observations over the global tropical oceans could give a glimpse of their performance and help to choose them in developing hybrid/blended forcing data, including other atmospheric and ocean variables. We also study the spatial variability of CM (QS and QL) available during 2000–2017. This paper is organized as follows. Section 2 describes the various datasets used in the study. Section 3 discusses the evaluation of satellite data with in situ observations. Sections 4 and 5 focus on spatial variability and quantify the annual SST change due to annual trends in QS and QL from CMv4. We also illustrate the details and results of the validation process with reference data from the GTMBA (McPhaden et al., 2010). The main conclusions and a summary are offered in Section 6.