Evaluation of Different Heat Flux Products Over the Tropical Indian Ocean

Abstract

Net heat flux (Qnet) and its components from four reanalysis (NCEP-2, CFSR, ERA5, and MERRA) and two blended products (OAFlux & TropFlux) are compared with in situ observation (two Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction buoys and one Woods Hole Oceanographic Institution buoy) over the north Indian Ocean to quantify their uncertainties in daily, seasonal, and annual scales. These comparisons provide the present status of Qnet error in most state of the art reanalysis/blended products. The root-mean-square error (RMSE) remains similar to the RMSE a decade earlier, despite more observation and improved models and reanalysis methods. However, there is a clear separation of flux quality from the older generation of reanalysis (NCEP-2) to the newer production of reanalysis (MERRA, CFSR, and ERA5). While individually ERA5 provides the best estimate, the ensemble mean (i.e., average of ERA5, CFSR, MERRA, TropFlux, and OAFlux) is very close to ERA5 both in terms of correlations and RMSE and provides the most reliable estimate by virtue of removal of some of the uncertainties in estimation of flux by each of the flux products. A significant reduction of RMSE in Qnet estimates from 100 W/m 2 (in NCEP-2) to 45 W/m 2 (in the ensemble mean) is considerable progress. It is noteworthy that all the recent flux products estimate the increasing trend of Qnet in the north Bay of Bengal and subseasonal fluctuations with significant fidelity. Also, in the south of equator location vigorous subseasonal fluctuations in boreal winter are well captured. We believe that this is significant progress in the estimation of Qnet over the Indian Ocean.

Attribution of Observed Recent Decrease in Low Clouds Over the Northeastern Pacific to Cloud‐Controlling Factors

“…The authors acknowledge that uncertainties exist for all data sets used, and that their magnitude is likely to vary between the different products and reanalysis parameters. Surface flux products from reanalyses are, for example, expected to contain much larger uncertainties than better observed quantities like the SST (Bentamy et al., 2017; Luo & Minnett, 2020; Martens et al., 2020; Pokhrel et al., 2020). However, all data sets are expected to capture the general patterns and trends adequately for this study.…”

Seasonal to Intraseasonal Variability of the Upper Ocean Mixed Layer in the Gulf of Oman

“…Data from ERA5, the fifth generation of the European Centre for Medium‐Range Weather Forecasts (ECMWF) atmospheric reanalyses of global climate, are used in this study to assess the local surface buoyancy forcing and wind stress (Hersbach et al., 2020). ERA5 is highly accurate, representing the magnitude and variability of near‐surface air temperature and wind regimes (Pokhrel et al., 2020). A 0.25° × 0.25° grid and hourly data provide high spatial and temporal resolution.…”

Local ocean–atmosphere interaction in Indian summer monsoon multi-decadal variability

“…Where, NSWR = DSWR − USWR and NLWR = ULWR -DLWR, together with, Downward shortwave radiation (DSWR), Upward shortwave radiation (USWR), Upward longwave radiation (ULWR), Downward longwave radiation (DLWR) (e.g., Pokhrel et al 2020) A simple thermodynamic understanding about the upper ocean is that the rate of change of SST is proportional to net heat ux. Such a balance can be expressed as…”

“…To be consistent with SST and precipitation (ISMR), here we use the mean JJAS Q net between 1901 to 2007 to estimate its contribution to the SST trends during the two periods, P1 and P2. However, we recognize that all heat ux products, whether from reanalysis or 'observations' have their own biases (Pokhrel et al 2020). To have an idea of biases in Q net climatology from NCEPv3, we compare the climatology of JJAS Q net from NECPv3 for the two periods (Fig.…”