Averaging-Related Biases in Monthly Latent Heat Fluxes

Abstract

Seasonal-to-multidecadal applications that require ocean surface energy fluxes often require accuracies of surface turbulent fluxes to be 5 W m 22 or better. While there is little doubt that uncertainties in the flux algorithms and input data can cause considerable errors, the impact of temporal averaging has been more controversial. The biases resulting from using monthly averaged winds, temperatures, and humidities in the bulk aerodynamic formula (i.e., the so-called classical method) to estimate the monthly mean latent heat fluxes are shown to be substantial and spatially varying in a manner that is consistent with most prior work. These averaging-related biases are linked to nonnegligible submonthly covariances between the wind, temperature, and humidity. To provide additional insight into the averaging-related bias, the methodology behind the third-generation Florida State University monthly mean surface flux product (FSU3) is detailed to highlight additional sources of errors in gridded datasets. The FSU3 latent heat fluxes suffer from this averaging-related bias, which can be as large as 90 W m 22 in western boundary current regions during winter and can exceed 40 W m 22 in synoptically active portions of the tropics. The regional impacts of these biases on the mixed layer temperature tendency are shown to demonstrate that the error resulting from applying the classical method is physically substantial.

Fast Warming of the Surface Ocean Under a Climatological Scenario

“…The difference in the mixed-layer depth computed by the KPP scheme is very similar to what is shown in Figure 1 for the AML experiments. This is not consistent with previous studies (Gulev, 1994(Gulev, , 1997Hughes et al, 2012), where the lack of high-frequency wind variance is expected to significantly reduce the magnitude of turbulent air-sea fluxes. In fact, because the atmospheric temperature is prescribed in this experiment, the ocean-atmosphere temperature differences increase, as shown in Figure 3 (bottom right panel) for the subtropical gyre.…”

“…This question arises from the recognized contribution of the fast-varying winds associated with synoptic weather systems to maintain realistic turbulent air-sea fluxes (Gulev, 1994;Hughes et al, 2012;Jung et al, 2014;Ponte & Rosen, 2004;Wu et al, 2016;Zhai et al, 2012) and upper-ocean vertical mixing (Condron & Renfrew, 2013;Holdsworth & Myers, 2015;Wu et al, 2016). This question arises from the recognized contribution of the fast-varying winds associated with synoptic weather systems to maintain realistic turbulent air-sea fluxes (Gulev, 1994;Hughes et al, 2012;Jung et al, 2014;Ponte & Rosen, 2004;Wu et al, 2016;Zhai et al, 2012) and upper-ocean vertical mixing (Condron & Renfrew, 2013;Holdsworth & Myers, 2015;Wu et al, 2016).…”

Global Impacts of Subseasonal (<60 Day) Wind Variability on Ocean Surface Stress, Buoyancy Flux, and Mixed Layer Depth

“…Second, the annual mean and seasonal cycle of the various components of the stress and buoyancy flux in the CORE normal year forcing products are reasonably well documented (Griffies et al, ; Large and Yeager, ), and the global patterns are, to a first approximation, consistent with other estimates, although there are significant outstanding uncertainties and differences, for example, in high‐latitude buoyancy fluxes (Bourassa et al, ; Cerovečki et al, ). In addition, subseasonal wind variability has a significant rectified impact on the mean and seasonal cycle of the air‐sea fluxes (e.g., Gulev, ; Hughes et al, ; Lin et al, ; Ponte and Rosen, ). Hence, it is important that this forcing product includes a climatological representation of temporal variability on periods ranging from 1 year to 12 hr (i.e., 6‐hr resolution), and therefore, it includes the majority of the subseasonal atmospheric variance of interest to this study, including 2‐ to 10‐day synoptic variability (wind speed power spectra are shown in the Figure S8).…”

High-Latitude Ocean and Sea Ice Surface Fluxes: Challenges for Climate Research

“…Important cases of undersampling also occur when observations are made conditionally, for example, only in clear or nonprecipitating conditions. Undersampling can result in large and non-Gaussian errors (Gulev et al 2007a,b) and spatial/temporal inhomogeneity in error characteristics (Schlax et al 2001;Hughes et al 2012). Furthermore, scales smaller than the scale of the observation network can alias onto the larger scales that are resolved.…”