[1] The parameterization of thermal roughness length z 0h plays a key role in land surface modeling. Previous studies have found that the daytime land surface temperature (LST) on dry land (arid and semiarid regions) is commonly underestimated by land surface models (LSMs). This paper presents two improvements of Noah land surface modeling for China's dry-land areas. The first improvement is the replacement of the model's z 0h scheme with a new one. A previous study has validated the revised Noah model at several dry-land stations, and this study tests the revised model's performance on a regional scale. Both the original Noah and the revised one are driven by the Global Land Data Assimilation System (GLDAS) forcing data. The comparison between the simulations and the daytime Moderate Resolution Imaging Spectroradiometer-(MODIS-) Aqua LST products indicates that the original LSM produces a mean bias in the early afternoon (around 1330, local solar time) of about −6 K, and this revision reduces the mean bias by 3 K. Second, the mean bias in early afternoon is further reduced by more than 2 K when a newly developed forcing data set for China (Institute of Tibetan Plateau Research, Chinese Academy of Sciences (ITPCAS) forcing data) is used to drive the revised model. A similar reduction is also found when the original Noah model is driven by the new data set. Finally, the original Noah model, when driven by the new forcing data, performs satisfactorily in reproducing the LST for forest, shrubland and cropland. It may be sensible to select the z 0h scheme according to the vegetation type present on the land surface for practical applications of the Noah LSM.

Land–atmosphere feedbacks occurring on daily to weekly time scales can magnify the intensity and duration of extreme weather events, such as droughts, heat waves, and convective storms. For such feedbacks to occur, the coupled land–atmosphere system must exhibit sufficient memory of soil moisture anomalies associated with the extreme event. The soil moisture autocorrelation e-folding time scale has been used previously to estimate soil moisture memory. However, the theoretical basis for this metric (i.e., that the land water budget is reasonably approximated by a red noise process) does not apply at finer spatial and temporal resolutions relevant to modern satellite observations and models. In this study, two memory time scale metrics are introduced that are relevant to modern satellite observations and models: the “long-term memory” τL and the “short-term memory” τS. Short- and long-term surface soil moisture (SSM) memory time scales are spatially anticorrelated at global scales in both a model and satellite observations, suggesting hot spots of land–atmosphere coupling will be located in different regions, depending on the time scale of the feedback. Furthermore, the spatial anticorrelation between τS and τL demonstrates the importance of characterizing these memory time scales separately, rather than mixing them as in previous studies.

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Terrestrial vegetation response to surface water availability is important for land-atmosphere interactions. However, current understanding of how the vegetation responds to surface water remains limited since the physical processes happening within the biosphere and hydrosphere are highly coupled. It is even more difficult to measure such interactions for the processes related to surface soil moisture (SSM) -the central variable that interacts the most intimately with vegetation -since the observations of SSM are often scarce and uneven. Here, we use the satellite observations of vegetation optical depth (VOD) and SSM to map the response time scales of vegetation to surface water anomalies. We use the stability theory to derive vegetation memory time () to reveal the global pattern of vegetation memory to surface water anomalies. That is, the time vegetation takes to return back to its equilibrium when an anomaly dissipates to a certain level (e.g., the e-folding level). We also estimate the plant reactive time () -the time when impacts of surface anomaly reach its peak to evaluate the overall resilience of terrestrial vegetation to surface water anomalies. The results show that tends to be longer in herbaceous biomes whereas the is longer in biomes with tree cover. Such anti-correlation of and indicates that the herbaceous biomes may be more vulnerable to surface water perturbations during climate extremes. Our study provides a global quantification on vegetation -soil moisture feedbacks, enabling comparison with Earth System Models (ESMs).

Near-surface hydrometeorological behaviors occurring at different timescales are important for regional weather and climate (Seneviratne et al., 2010). For example, the coupling of surface water and temperature anomalies can intensify the evolutions of extreme events such as droughts and heatwaves (

<p>Flux partitions between surface water and energy terms are essentially important to the climate system. They can potentially affect assessments of climate risk projections in the future. However, the characterization of surface flux partitioning in numerical models is rarely evaluated due to the absence of large-scale observational evidence. Here, we use long-term satellite datasets and observational meteorological records to evaluate the flux partitioning regime presented in four widely-used Land surface models (LSMs) over two study regions (i.e., China and Continental U.S.). We show that the regime in LSMs differs significantly from satellite-based estimations, which can be due to unrealistic representations of land surface characteristics. The biases in models’ flux partitioning regime may lead to the underestimated potential for climate risks, especially over regions with typical land surface characteristics. The results highlight that particular attention should be paid to the calibration of surface flux partitioning regimes in LSMs. Large model spreads in surface flux partitioning strength and climate risk maps are also reported.</p>

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Weather and climate forecast predictability relies on Land-Atmosphere (L-A) interactions occurring at different time scales. However, evaluation of L-A coupling parameterizations in current land surface models (LSMs) is challenging since the physical processes are complex, and large-scale observations are scarce and uncommon. Recent advancements in satellite observations, in this light, provide a unique opportunity to evaluate the models' performances at large spatial scales. Using 5-year soil moisture memory (SMM) from Soil Moisture Active and Passive (SMAP) observations, we evaluate L-A coupling performances in 4 prevailing LSMs with both coupled and offline simulations. Multi-model mean comparison at the global scale shows that current LSMs tend to overestimate SMM that is controlled by water-limited processes and vice versa. Large model spreads in SMM are also observed between individual models. The SMM biases are highly dependent on models' parameterizations, while showing minor relevance to the models' soil layer depths or the models' online/offline simulating schemes. Further analyses of two important terrestrial water cycle-related variables indicate current LSMs may underestimate soil moisture that is directly available for evapotranspiration and global flood risks. Finally, a comparison of two soil moisture thresholds indicates that the soil parameters employed in LSMs play an essential role in producing the model's biases. The satellite estimation of ET at the water-limited stage and soil hydraulic parameters provides readily available information to constrain LSMs, which are essentially important to improve the models' L-A coupling simulations, as well as other land surface processes such as terrestrial hydrological cycles.