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Accurate quantification of turbulent heat fluxes [THF, comprising sensible heat flux (SHF) and latent heat flux (LHF)] and Bowen ratio ( β , ratio of SHF and LHF) is essential for understanding the air–sea interaction. However, the biased estimates of SHF and LHF by widely applied bulk aerodynamic models, and the separate estimates of SHF and LHF by data-driven models, both may result in unrealistic β estimates. This study for the first time innovatively proposes a Bowen ratio-informed data-driven model (BrTHF) for coordinating the estimations of THF and β using the multivariate random forest (RF) technique and a combination of eddy covariance flux observations and meteorological and oceanic observations collected from 53 historical cruises. The result shows that the BrTHF model could not only achieve high-accuracy SHF and LHF estimates, but also avoid the outliers of β estimates that were commonly found in un-synergistic RF and well-known Coupled Ocean Atmosphere Response Experiment 3.5 models.

Accurate quantification of terrestrial gross primary production (GPP) is integral for enhancing our understanding of the global carbon budget and climate change. The light use efficiency (LUE) model is undoubtedly the most extensively applied method for GPP estimation. However, the two-leaf (TL)-LUE model using a ‘potential’ sunlit leaf area index (LAI _su ) can separate a portion of LAI _su even when the canopy does not receive any direct radiation, leading to the underestimation of GPP under cloudy and overcast days. Here, we developed a dynamic-leaf (DL) LUE model by introducing an ‘effective’ LAI _su to improve GPP estimation, which considers the comprehensive contribution of LAI _su when the canopy does and does not receive direct radiation. In particular, the new model decreases LAI _su to zero when direct radiation reaches zero. Our evaluation at eight ChinaFLUX sites showed that (1) the DL-LUE model outperformed the most well-known BL-LUE (namely, the MOD17 GPP algorithm) and TL-LUE models in reproducing the daily in situ GPP, especially at four forest sites [reducing the root mean square error (RMSE) from 1.74 g C m ^−2 d ^−1 and 1.53 g C m ^−2 d ^−1 to 1.36 g C m ^−2 d ^−1 and increasing the coefficient of determination ( R ^2 ) from 0.74 and 0.79–0.82, respectively]. Moreover, the improvements were particularly pronounced at longer temporal scales, as indicated by the RMSE decreasing from 29.32 g C m ^−2 month ^−1 and28.11 g C m ^−2 month ^−1 to 25.81 g C m ^−2 month ^−1 at a monthly scale and from 231.82 g C m ^−2 yr ^−1 and 221.60 g C m ^−2 yr ^−1 –200.00 g C m ^−2 yr ^−1 at a yearly scale; (2) the DL-LUE model mitigated the systematic underestimation of the in situ GPP by both the TL-LUE and BL-LUE models when the clearness index (CI) was below 0.5, as indicated by the Bias reductions of 0.25 g C m ^−2 d ^−1 and 0.46 g C m ^−2 d ^−1 , respectively; and (3) the contributions of the shaded GPP to the total GPP from the DL-LUE model were higher by 0.07–0.16 than those from the TL-LUE model across the eight ChinaFLUX sites. The proposed parsimonious and effective DL-LUE model not only has great potential for improving global GPP estimations but also provides a more mechanism-based approach for partitioning the total GPP into its shaded and sunlit components.