Land–atmosphere interactions have been investigated at daily or longer timescales due to limited data availability and large errors for measuring high-frequency variations. Yet coupling at the subdaily timescale is characterized by the diurnal cycle of incoming solar radiation and surface fluxes. Based on flux tower observations, this study investigates the climatology of observed land–atmosphere interactions on subdaily timescales during the warm season. Process-based multivariate metrics are employed to quantitatively measure segmented coupling processes, and mixing diagrams are adopted to demonstrate the integrative moist and thermal energy budget evolution in the atmospheric mixed layer. The land, atmosphere, and combined couplings for the entire daily mean, midday, and midnight periods show different situations to which surface latent and sensible heat fluxes are relevant, and they also reveal the climate sensitivity to soil moisture and surface air temperature. The 24 h coevolution of the moist and thermal energy within the boundary layer traces a particular path on mixing diagrams, exhibiting different degrees of asymmetry (time shifts) in water- and energy-limited locations. Water- and energy-limited processes also show opposing long tails of low humidity during the daytime and nighttime, related to the impact on land and atmospheric couplings of latent heat flux and other diabatic processes like radiative cooling. This study illustrates the necessity of considering the entire diurnal cycle to understand land–atmosphere coupling processes comprehensively in observations and models.

Rapidly developing droughts, including flash droughts, have frequently occurred throughout East Asia in recent years, causing significant damage to agricultural ecosystems. Although many drought monitoring and warning systems have been developed in recent decades, the short-term prediction of droughts (within 10 days) is still challenging. This study has developed drought prediction models for a short-period of time (one pentad) using remote-sensing data and climate variability indices over East Asia (20°–50°N, 90°–150°E) through random forest machine learning. Satellite-based drought indices were calculated using the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture, Tropical Rainfall Measuring Mission (TRMM) precipitation, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST), and normalized difference vegetation index (NDVI). The real-time multivariate (RMM) Madden–Julian oscillation (MJO) indices were used because the MJO is a short timescale climate variability and has important implications for droughts in East Asia. The validation results show that those drought prediction models with the MJO variables (r ~ 0.7 on average) outperformed the original models without the MJO variables (r ~ 0.4 on average). The predicted drought index maps showed similar spatial distribution to actual drought index maps. In particular, the MJO-based models captured sudden changes in drought conditions well, from normal/wet to dry or dry to normal/wet. Since the developed models can produce drought prediction maps at high resolution (5 km) for a very short timescale (one pentad), they are expected to provide decision makers with more accurate information on rapidly changing drought conditions.

This study investigates the physical mechanisms that contributed to the 2016 Eurasian heat wave during boreal summer season (July–August, JA), characterized by much higher than normal temperatures over eastern Europe, East Asia, and the Kamchatka Peninsula. It is found that the 2016 JA mean surface air temperature, upper-tropospheric height, and soil moisture anomalies are characterized by a tri-pole pattern over the Eurasia continent and a wave train-like structure not dissimilar to recent (1980–2016) trends in those quantities. A series of forecast experiments designed to isolate the impacts of the land, ocean, and sea ice conditions on the development of the heat wave is carried out with the Global Seasonal Forecast System version 5. The results suggest that the tri-pole blocking pattern over Eurasia, which appears to be instrumental in the development of the 2016 summer heat wave, can be viewed as an expression of the recent trends, amplified by record-breaking oceanic warming and internal land-atmosphere interactions.

This study investigates the performance of the latter NCEP Unified Forecast System (UFS) Coupled Model prototype simulations (P5–P8) during boreal summer 2011–17 in regard to coupled land–atmosphere processes and their effect on model bias. Major land physics updates were implemented during the course of model development. Namely, the Noah land surface model was replaced with Noah-MP and the global vegetation dataset was updated starting with P7. These changes occurred along with many other UFS improvements. This study investigates UFS’s ability to simulate observed surface conditions in 35-day predictions based on the fidelity of model land surface processes. Several land surface states and fluxes are evaluated against flux tower observations across the globe, and segmented coupling processes are also diagnosed using process-based multivariate metrics. Near-surface meteorological variables generally improve, especially surface air temperature, and the land–atmosphere coupling metrics better represent the observed covariance between surface soil moisture and surface fluxes of moisture and radiation. Moreover, this study finds that temperature biases over the contiguous United States are connected to the model’s ability to simulate the different balances of coupled processes between water-limited and energy-limited regions. Sensitivity to land initial conditions is also implicated as a source of forecast error. Above all, this study presents a blueprint for the validation of coupled land–atmosphere behavior in forecast models, which is a crucial model development task to assure forecast fidelity from day one through subseasonal time scales.

High temperature extremes accompanied by drought have led to serious ramifications for environmental and socio‐economic systems. Thus, improving the predictability of heat‐wave events is a high priority. One key to achieving this is to better understand land‐atmosphere interactions. Recent studies have documented a hypersensitive regime in the soil moisture‐temperature relationship: when soil dries below a critical low threshold, called the soil moisture breakpoint, air temperatures increase at a greater rate as soil moisture declines. Whether such a hypersensitive regime is rooted in land surface processes and whether this soil moisture breakpoint corresponds to a known plant critical value, the permanent wilting point (WP), below which latent heat flux almost ceases, remains unclear. In this study, we explore the mechanisms linking low soil moisture to high air temperatures. From in situ observations, we confirm that the hypersensitive regime acts throughout the chain of energy processes from land to atmosphere. A simple energy‐balance model indicates that the hypersensitive regime occurs when there is a dramatic drop in evaporative cooling, which happens when soil moisture dries toward the permanent WP, suggesting that the soil moisture breakpoint is slightly above the permanent WP. Precisely how a model represents the relationship between evapotranspiration and soil moisture is found to be essential to describe the occurrence of the hypersensitive regime. Thus, we advocate that weather and climate models should ensure a realistic representation of land‐atmosphere interactions to obtain reliable forecasts of extremes and climate projections, aiding the assessment of heatwave vulnerability and adaptation.