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Soil organic matter (SOM) is enriched on the eastern Tibetan Plateau, but its effects on the hydrothermal state of the coupled land–atmosphere system remain unclear. This study comprehensively investigates these effects during summer from multiple perspectives based on regional climate modeling, land surface modeling, and observations. Using a regional climate model, we show that accounting for SOM effects lowers cold and wet biases in simulations of this region. SOM increases 2-m air temperature, decreases 2-m specific/relative humidity, and reduces precipitation in coupled simulations. Inclusion of SOM also warms the shallow soil while cooling the deep soil, which may help to preserve frozen soil in this region. This cooling effect is captured by both observations and offline land surface simulations, but it is overestimated in the offline simulations due to no feedback from the atmosphere compared to the coupled ones. Including SOM in coupled climate models could therefore not only imrove their representations of atmospheric energy and water cycles, but also help to simulate the past, present, and future evolution of frozen soil with increased confidence and reliability. Note that these findings are from one regional climate model and do not apply to wetlands. Significance Statement The eastern Tibetan Plateau is rich in soil organic matter (SOM), which increases the amount of water the soil can hold while decreasing the rate at which heat moves through it. Although SOM is expected to preserve frozen soil by insulating it from atmospheric warming, researchers have not yet tested the effects of coupled land–atmosphere interactions on this relationship. Using a regional climate model, we show that SOM typically warms and dries the near-surface air, warms the shallow soil, and cools the deep soil by modifying both soil properties and energy exchanges at the land–atmosphere interface. The results suggest that the cooling effect of SOM on deep soil is overestimated when atmospheric feedbacks are excluded.

Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface ­temperatures show a lag correlation with summer precipitation in several remote regions, but current global land–atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hotspot” regions identified here; the ensemble means in some “hotspots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.

Deforestation can impact precipitation through biophysical processes and such effects are commonly examined by models. However, previous studies mostly conduct deforestation experiments with a single model and the simulated precipitation responses to deforestation diverge across studies. In this study, 11 Earth system models are used to robustly examine the biophysical impacts of deforestation on precipitation, precipitation extremes, and the seasonal pattern of the rainy season through a comparison of a control simulation and an idealized global deforestation simulation with clearings of 20 million km2 of forests. The multimodel mean suggests decreased precipitation, reduced frequency and intensity of heavy precipitation, and shortened duration of rainy seasons over deforested areas. The deforestation effects can even propagate to some regions that are remote from deforested areas (e.g., the tropical and subtropical oceans and the Arctic Ocean). Nevertheless, the 11 models do not fully agree on the precipitation changes almost everywhere. In general, the models exhibit higher consistency over the deforested areas and a few regions outside the deforested areas (e.g., the subtropical oceans) but lower consistency over other regions. Such intermodel spread mostly results from divergent responses of evapotranspiration and atmospheric moisture convergence to deforestation across the models. One of the models that has multiple simulation members also reveals considerable spread of the precipitation responses to deforestation across the members due to internal model variability. This study highlights the necessity of robustly examining precipitation responses to deforestation based on multiple models and each model with multiple simulation members.

The arid and semiarid areas of the Loess Plateau are extremely sensitive to climate change. Land–atmosphere interactions of these regions play an important role in the regional climate. However, most present land surface models (LSMs) are not reasonable and accurate enough to describe the surface characteristics in these regions. In this study, we investigate the effects of three key land surface parameters including surface albedo, soil thermal conductivity, and additional damping on the Noah LSM in simulating the land surface characteristics. The observational data from June to September from 2007 to 2009 collected at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) station in northwest China are used to validate the Noah LSM simulations. The results suggest that the retrieved values of surface albedo, soil thermal conductivity, and additional damping based on observations are in closer agreement with those of the MULT scheme for surface albedo, the J75_NOAH scheme for soil thermal conductivity, and the Y08 scheme for additional damping, respectively. Furthermore, the model performance is not obviously affected by surface albedo parameterization schemes, while the scheme of soil thermal conductivity is vital to simulations of latent heat flux and soil temperature and the scheme of additional damping is crucial for simulating net radiation flux, sensible heat flux, and surface soil temperature. A set of optimal parameterizations is proposed for the offline Noah LSM at the SACOL station when the MULT scheme for surface albedo, the J75_NOAH scheme for soil thermal conductivity, and the Y08 scheme for additional damping are combined simultaneously, especially in the case of sensible heat flux and surface soil temperature simulations.

Regional climate models have been widely used to examine the biophysical effects of afforestation, but their performances in this respect have rarely been evaluated. To fill this knowledge gap, an evaluation method based on the “space for time” strategy is proposed here. Using this method, we validate the performances of three regional models, the Regional Climate Model (RegCM), Weather Research and Forecasting (WRF) model and the WRF model run at a convection-permitting resolution (WRF-CP), in representing the local biophysical effects of afforestation over continental China against satellite observations. The results show that WRF and WRF-CP can not accurately describe afforestation-induced changes in surface biophysical properties, e.g. albedo or leaf area index. Second, all models exhibit poor simulations of afforestation-induced changes in latent and sensible heat fluxes. In particular, the observed increase in the summer latent heat due to afforestation is substantially underestimated by all models. Third, the models are basically reasonable in representing the biophysical impact of afforestation on temperature. The cooling of the daily mean surface temperature and 2-meter temperature in summer are reproduced well. Nevertheless, the mechanism driving the cooling effect may be improperly represented by the models. Moreover, the models perform relatively poorly in representing the response of the daily minimum surface temperature to afforestation. This highlights the necessity of evaluating the representation of the biophysical effects by a model before the model is employed to carry out afforestation experiments. This study serves as a test bed for validating regional model performance in this respect.

Urban heat islands (UHIs) are caused by a multitude of changes induced by urbanization. However, the relative importance of biophysical and atmospheric factors in controlling the UHI intensity remains elusive. In this study, we quantify the magnitude of surface UHIs (SUHIs), or surface urban cool islands (SUCIs), and elucidate their biophysical and atmospheric drivers on the basis of observational data collected from one urban site and two rural grassland sites in and near the city of Nanjing, China. Results show that during the daytime a strong SUCI effect is observed when the short grassland site is used as the reference site whereas a moderate SUHI effect is observed when the tall grassland is used as the reference site. We find that the former is mostly caused by the lower aerodynamic resistance for convective heat transfer at the urban site and the latter is primarily caused by the higher surface resistance for evapotranspiration at the urban site. At night, SUHIs are observed when either the short or the tall grassland site is used as the reference site and are predominantly caused by the stronger release of heat storage at the urban site. In general, the magnitude of SUHI is much weaker, and even becomes SUCI during daytime, with the short grassland site being the reference site because of its larger aerodynamic resistance. The study highlights that the magnitude of SUHIs and SUCIs is mostly controlled by urban–rural differences of biophysical factors, with urban–rural differences of atmospheric conditions playing a minor role.

The Inner Tibetan Plateau (ITP; also called the Qiangtang Plateau) appears to have experienced an overall wetting in summer (June, July, and August) since the mid-1990s, which has caused the rapid expansion of thousands of lakes. In this study, changes in atmospheric circulations associated with the wetting process are analyzed for 1979–2018. These analyses show that the wetting is associated with simultaneously weakened westerlies over the Tibetan Plateau (TP). The latter is further significantly correlated with the Atlantic multidecadal oscillation (AMO) on interdecadal time scales. The AMO has been in a positive phase (warm anomaly of the North Atlantic Ocean sea surface) since the mid-1990s, which has led to both a northward shift and weakening of the subtropical westerly jet stream at 200 hPa near the TP through a wave train of cyclonic and anticyclonic anomalies over Eurasia. These anomalies are characterized by an anomalous anticyclone to the east of the ITP and an anomalous cyclone to the west of the ITP. The former weakens the westerly winds, trapping water vapor over the ITP while the latter facilitates water vapor intruding from the Arabian Sea into the ITP. Accordingly, summer precipitation over the ITP has increased since the mid-1990s.

China is several decades into large-scale afforestation programs to help address significant ecological and environmental degradation, with further afforestation planned for the future. However, the biophysical impact of afforestation on local surface temperature remains poorly understood, particularly in midlatitude regions where the importance of the radiative effect driven by albedo and the nonradiative effect driven by energy partitioning is uncertain. To examine this issue, we investigated the local impact of afforestation by comparing adjacent forest and open land pixels using satellite observations between 2001 and 2012. We attributed local surface temperature change between adjacent forest and open land to radiative and nonradiative effects over China based on the Intrinsic Biophysical Mechanism (IBM) method. Our results reveal that forest causes warming of 0.23°C (±0.21°C) through the radiative effect and cooling of −0.74°C (±0.50°C) through the nonradiative effect on local surface temperature compared with open land. The nonradiative effect explains about 79% (±16%) of local surface temperature change between adjacent forest and open land. The contribution of the nonradiative effect varies with forest and open land types. The largest cooling is achieved by replacing grasslands or rain-fed croplands with evergreen tree types. Conversely, converting irrigated croplands to deciduous broadleaf forest leads to warming. This provides new guidance on afforestation strategies, including how these should be informed by local conditions to avoid amplifying climate-related warming.

Numerical simulations of the atmospheric boundary layer require careful representation of the surface heterogeneity, which involves the upscaling parameterization scheme for the heterogeneous surface parameters. In this study, the sensitivity comparisons of an effective aerodynamic parameter scheme against the area-weighted average scheme in simulating the land–atmosphere interaction over heterogeneous terrain were carried out by conducting multinested simulations with the Weather Research and Forecasting (WRF) Model at coarse and fine resolutions, for a typical sea–land breeze case in the Bohai Gulf of China. The results show that the limited-area model is sensitive to the aerodynamic parameter scheme and the effective aerodynamic parameter scheme exhibits a better performance in simulating the variables and parameters in the land–atmosphere interaction process, such as surface wind speed, sensible heat flux, latent heat flux, friction velocity, and surface air temperature, among others, for short-term simulations. Particularly, the underestimation of sensible heat flux and overestimation of latent heat flux over heterogeneous terrain with area-weighted average scheme for aerodynamic parameters can be improved with the effective parameter scheme in the coastal regions, where the mean simulation error with the effective parameter scheme is about one-half of that with the average scheme for sensible heat flux and one-third for latent heat flux.

The impacts of land-use and land-cover change (LULCC) on tropospheric temperatures are investigated in this study using the fully coupled Community Earth System Model. Two simulations are performed using potential and current vegetation cover. The results show that LULCC can induce detectable changes in the tropospheric air temperature. Although the influence of LULCC on tropospheric temperature is weak, a significant influence can still be found below 300 hPa in summer over land. Compared to the global-mean temperature change, LULCC-induced changes in the regional-mean air temperature can be 2–3 times larger in the middle–upper troposphere and approximately 8 times larger in the lower troposphere. In East Asia and South Asia, LULCC is shown to produce significant decreases (0.2° to 0.4°C) in air temperature in the middle–upper troposphere in spring and autumn due to the largest decrease in the latent heat release from precipitation. In Europe and North America, the most significant tropospheric cooling occurs in summer, which can be attributed to the significant decrease in the absorbed solar radiation and sensible heat flux during this season. In addition to local effects, LULCC also induces nonlocal responses in the tropospheric air temperature that are characterized by significant decreases over the leeward sides of LULCC regions, which include East Asia–western North Pacific Ocean, Mediterranean Sea–North Africa, North America–Atlantic Ocean, and North America–eastern Pacific. Cooling in the leeward sides of LULCC regions is primarily caused by an enhanced cold advection induced by LULCC.

On the basis of the similarity theory of the atmospheric surface layer and the mass conservation principle, a new scheme using a variational method is developed to estimate the surface momentum and sensible and latent heat fluxes. In this scheme, the mass conservation is introduced into the cost function as a weak physical constraint, which leads to an overdetermined system. For the variational method with mass conservation constraint, only the conventional meteorological observational data are taken into account. Data collected in the Yellow River Source Region Climate and Environment Observation and Research Station at Maqu, China, during 11–25 August 2010 are used to test this new scheme. Results indicate that this scheme is more reliable and accurate than both the flux-profile method and the variational method without mass conservation constraint. In addition, the effect of the weights in the cost function is examined. Sensitivity tests show that the fluxes estimated by the proposed scheme are insensitive to the stability functions explored in the cost function and measurement errors.