• Energy Research

The latent heat flux (LE) between the terrestrial biosphere and atmosphere is a major driver of the global hydrological cycle. In this study, we evaluated LE simulations by 45 general circulation models (GCMs) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) by a comparison with eddy covariance (EC) observations from 240 globally distributed sites from 2000 to 2009. In addition, we improved global terrestrial LE estimates for different land cover types by synthesis of seven best CMIP5 models and EC observations based on a Bayesian model averaging (BMA) method. The comparison results showed substantial differences in monthly LE among all GCMs. The model CESM1-CAM5 has the best performance with the highest predictive skill and a Taylor skill score (S) from 0.51–0.75 for different land cover types. The cross-validation results illustrate that the BMA method has improved the accuracy of the CMIP5 GCM’s LE simulation with a decrease in the averaged root-mean-square error (RMSE) by more than 3 W/m2 when compared to the simple model averaging (SMA) method and individual GCMs. We found an increasing trend in the BMA-based global terrestrial LE (slope of 0.018 W/m2 yr−1, p < 0.05) during the period 1970–2005. This variation may be attributed directly to the inter-annual variations in air temperature (Ta), surface incident solar radiation (Rs) and precipitation (P). However, our study highlights a large difference from previous studies in a continuous increasing trend after 1998, which may be caused by the combined effects of the variations of Rs, Ta, and P on LE for different models on these time scales. This study provides corrected-modeling evidence for an accelerated global water cycle with climate change.

A number of studies have estimated turbulent heat fluxes by assimilating sequences of land surface temperature (LST) observations into the strong constraint-variational data assimilation (SC-VDA) approaches. The SC-VDA approaches do not account for the structural model errors and uncertainties in the micrometeorological variables. In contrast to the SC-VDA approaches, the WC-VDA approach (the so-called weak constraint-VDA) accounts for the effects of structural and model errors by adding a model error term. In this study, the WC-VDA approach is tested at six study sites with different climatic and vegetative conditions. Its performance is also compared with that of SC-VDA at the six study sites. The results show that the WC-VDA produces 10.16% and 10.15% lower root mean square errors (RMSEs) for sensible and latent heat flux estimates compared with the SC-VDA approach. The model error term can capture errors in the turbulent heat flux estimates due to errors in LST and micrometeorological measurements, as well as structural model errors, and does not allow those errors to adversely affect the turbulent heat flux estimates. The findings also indicate that the estimated model error term varies reasonably well, so as to capture the misfit between predicted and observed net radiation in different hydrological and vegetative conditions. Finally, synthetically generated positive (negative) noises are added to the hydrological input variables (e.g., LST, air temperature, air humidity, incoming solar radiation, and wind speed) to examine whether the WC-VDA approach can capture those errors. It was found that the WC-VDA approach accounts for the errors in the input data and reduces their effect on the turbulent heat flux estimates.

AbstractEstimation of turbulent heat fluxes via variational data assimilation (VDA) approaches has been the subject of several studies. The VDA approaches need an adjoint model that is difficult to derive. In this study, remotely sensed land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are assimilated into the heat diffusion equation within an ensemble Kalman smoother (EnKS) approach to estimate turbulent heat fluxes. The EnKS approach is tested in the Heihe River Basin (HRB) in northwest China. The results show that the EnKS approach can estimate turbulent heat fluxes by assimilating low temporal resolution LST data from MODIS. The findings indicate that the EnKS approach performs fairly well in various hydrological and vegetative conditions. The estimated sensible (H) and latent (LE) heat fluxes are compared with the corresponding observations from large aperture scintillometer systems at three sites (namely, Arou, Daman, and Sidaoqiao) in the HRB. The turbulent heat flux estimates from EnKS agree reasonably well with the observations, and are comparable to those of the VDA approach. The EnKS approach also provides statistical information on the H and LE estimates. It is found that the uncertainties of H and LE estimates are higher over wet and/or densely vegetated areas (grassland and forest) compared to the dry and/or slightly vegetated areas (cropland, shrubland, and barren land).

AbstractIn view of future changes in climate, it is important to better understand how different plant functional groups (PFGs) respond to warmer and drier conditions, particularly in temperate regions where an increase in both the frequency and severity of drought is expected. The patterns and mechanisms of immediate and delayed impacts of extreme drought on vegetation growth remain poorly quantified. Using satellite measurements of vegetation greenness, in‐situ tree‐ring records, eddy‐covariance CO2 and water flux measurements, and meta‐analyses of source water of plant use among PFGs, we show that drought legacy effects on vegetation growth differ markedly between forests, shrubs and grass across diverse bioclimatic conditions over the temperate Northern Hemisphere. Deep−rooted forests exhibit a drought legacy response with reduced growth during up to 4 years after an extreme drought, whereas shrubs and grass have drought legacy effects of approximately 2 years and 1 year, respectively. Statistical analyses partly attribute the differences in drought legacy effects among PFGs to plant eco‐hydrological properties (related to traits), including plant water use and hydraulic responses. These results can be used to improve the representation of drought response of different PFGs in land surface models, and assess their biogeochemical and biophysical feedbacks in response to a warmer and drier climate.

AbstractWinter snow is an important driver of tree growth in regions where growing‐season precipitation is limited. However, observational evidence of this influence at larger spatial scales and across diverse bioclimatic regions is lacking. Here, we investigated the interannual effects of winter (here defined as previous October to current February) snow depth on tree growth across temperate China over the period of 1961–2015, using a regional network of tree ring records, in situ daily snow depth observations, and gridded climate data. We report uneven effects of winter snow depth on subsequent growing‐season tree growth across temperate China. There shows little effect on tree growth in drier regions that we attribute mainly to limited snow accumulation during winter. By contrast, winter snow exerts important positive influence on tree growth in stands with high winter snow accumulation (e.g., in parts of cold arid regions). The magnitude of this effect depends on the proportion of winter snow to pre‐growing‐season (previous October to current April) precipitation. We further observed that tree growth in drier regions tends to be increasingly limited by warmer growing‐season temperature and early growing‐season water availability. No compensatory effect of winter snow on the intensifying drought limitation of tree growth was observed across temperate China. Our findings point toward an increase in drought vulnerability of temperate forests in a warming climate.