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Afforestation is an important human activity that changes land use, and would affect regional climate. However, the impact of afforestation on the regional climate in arid and semi-arid regions remains a subject of controversy. By ensemble sensitivity simulations using Advanced Research Weather Research and Forecasting (WRF) model, this study investigates the influence of vegetation category change within a 100 km width along the middle and upper reaches of the Yellow River (YR) on regional climate. Results suggest that afforestation would reduce summer surface temperature by -0.05 ~ -0.8℃ and increase summer convective precipitation by 3 ~ 45 mm in the afforestation regions, instead, summer non-convective precipitation would increase (3 ~ 35 mm) in the upper reaches of the YR. The increased non-convective precipitation in the upper reaches of the YR could be attributed to the increased integral water vapor convergence, cloud hydrometeor, and decreased cloud top temperature. In the afforested region, the increased convective precipitation could be related to the enhanced thermodynamic and water vapor conditions. These findings emphasize that even on regional scale in semi-arid and arid regions, greening can lead to evident regional climate impacts, these also provide insight for policymakers in formulating sustainable afforestation strategies. [ABSTRACT FROM AUTHOR]

Arid, semi-arid, and semi-humid regions (drylands) with fragile ecological balance have undergone dramatic climate change in past decades, and how the desertification will change under a continuous warming background still remain uncertain. In this study, the bias-corrected Community Earth System Model outputs from Coupled Model Intercomparison Project phase 5 were dynamically downscaled using the Weather Research and Forecasting (WRF) model, based on which the evolution trend of desertification over northern China (NC) in the past (1972–2000) and the near future period (2021–2050) under the RCP8.5 scenario were analyzed using the dune mobility index, and the impacts of climate change on the intensification or reversal of desertification over NC in the near future were explored. The results show that WRF downscaling can reproduce the desertification changes over NC in the past. The regions with a high risk of desertification are mainly located on the border of the desert and gobi. Under a rapidly warming climate in the near future, desertification will likely reverse in most regions of NC, especially for regions north of 40°N over NC. Potential evapotranspiration changes will exacerbate desertification, while precipitation changes will promote rehabilitation, and wind speed changes show obvious local impacts on desertification. The results in this study imply that, with rising temperatures in the future, the extent of desertification will not always continue, desertification will likely reverse at the front and margin of deserts and gobi, and responses of desertification to climate change have significant spatial differences. [ABSTRACT FROM AUTHOR]

Soil moisture can be an important preceding signal in seasonal precipitation prediction because of its persistence and influence on the energy and water balance between land and atmosphere. The thawing process of frozen ground on the Tibetan Plateau (TP) during spring makes it difficult to accurately simulate soil variables (e.g., soil moisture and temperature) due to defective parameterizations in numerical models during spring. In this study, we investigate the effectiveness of soil moisture correction using an indirect nudging scheme to simulate the coupling between spring soil moisture and the subsequent precipitation with an advanced research version of the weather research and forecasting (WRF) model. The results without assimilation show that extreme cold and dry land surface states during spring cause a large bias in the spatial pattern of summer precipitation over almost the entire TP. However, the experiments with indirect assimilation in spring show that the spatial pattern of summer precipitation improved significantly. This comparison emphasizes the importance of correcting the spring soil moisture during the freeze–thaw period to significantly adjust the heat and water vapour exchange between the land and atmosphere. Additionally, changes in the circulation of the subtropical–tropical jet, vertical ascent, and region‐related moisture recycling can support an active convection environment over the eastern TP, thereby increasing summer precipitation. Furthermore, the pattern shift in water vapour convergence in summer highlights the regional horizontal transportation of water vapour. The differences between experiments with and without deep‐soil temperature nudging illustrate the significance of soil temperature in soil moisture simulation in the freeze–thaw process. More so, the deep‐soil temperature nudging scheme requires in‐depth studies to correct its variation and quantify its degree of impact on soil moisture. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) plays an important role in the climate system, and the effects of SM anomalies on climate can persist from month to season. The seasonal frozen-thawing zone (SFTZ) in the northern hemisphere (NH), which is associated with large inter-annual variability in spring SM, is important from land–atmosphere interaction perspective. In this study, by assimilating spring SM in the SFTZ through indirect soil nudging (ISN) in the Weather Research and Forecasting (WRF) model, the effects of correcting spring SM biases in the SFTZ on subsequent summer precipitation simulations in the NH are investigated. The results indicated that correcting spring SM biases in the SFTZ improves the subsequent summer precipitation simulations in the NH. Correcting spring SM biases in the SFTZ significantly adjusts energy and moisture evolution on the land surface from spring to summer. Specifically, the correction of SM biases by assimilating SM in SFTZ in the spring can clearly reduce the biases of sensible heat flux (SH) and latent heat flux (LH) in the summer. This affects land–atmosphere interactions over NH, leading to correcting the negative biases of the geopotential height in the middle troposphere in June and July, as well as larger biases of water vapor transport and its divergence during the summer. The results imply that spring SM in the SFTZ is a potential signal for predicting summer precipitation in the NH. [ABSTRACT FROM AUTHOR]

Accurate forecast and simulation of near-surface wind is a great challenge for numerical weather prediction models due to the significant transient and intermittent nature of near-surface wind. Based on the analyses of the impact of assimilating in situ and Advanced Tiros Operational Vertical Sounder (ATOVS) satellite radiance data on the simulation of near-surface wind during a severe wind event, using the new generation mesoscale Weather Research and Forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation system, the dynamic downscaling of near-surface wind is further investigated by coupling the microscale California Meteorological (CALMET) model with the WRF and its 3DVAR system. Results indicate that assimilating in situ and ATOVS radiance observations strengthens the airflow across the Alataw valley and triggers the downward transport of momentum from the upper atmosphere in the downstream area of the valley in the initial conditions, thus improving near-surface wind simulations. Further investigations indicate that the CALMET model provides more refined microtopographic structures than the WRF model in the vicinity of the wind towers. Although using the CALMET model achieves the best simulation of near-surface wind through dynamic downscaling of the output from the WRF and its 3DVAR assimilation, the simulation improvements of near-surface wind speed are mainly within 1 m s−1. Specifically, the mean improvement proportions of near-surface wind speed are 64.8% for the whole simulation period, 58.7% for the severe wind period, 68.3% for the severe wind decay period, and 75.4% for the weak wind period. The observed near-surface wind directions in the weak wind conditions are better simulated in the coupled model with CALMET downscaling than in the WRF and its 3DVAR system. It is concluded that the simulation improvements of CALMET downscaling are distinct when near-surface winds are weak, and the downscaling effects are mainly manifested in the simulation of near-surface wind directions. [ABSTRACT FROM AUTHOR]

There is general uncertainty about the representation of clouds and convective precipitation in almost all models. Efforts to handle the convective parametrization scheme (CPS) in the grey zone (∼1–5 km horizontal grid spacing) are still imperative to the community, especially for the case of weakly forced deep convection. This study proposes a new convective adjustment time‐scale (τ) for the Kain–Fritsch eta (KFeta) scheme in the Weather Research and Forecasting (WRF) model at high resolution, which differs from the current scale‐aware function. The validation was made in three cases of weakly forced deep convection over the Tibetan Plateau. The results confirmed that modifying τ improves the simulation performance. In particular, the modified τ improved the simulated precipitation pattern, horizontal scale and intensity over the original scheme, which could be attributed to the enhanced rate at which convective instability generated by external factors (e.g. large‐scale advection and surface turbulent fluxes) is removed by the parametrized convection. When τ is appropriately chosen, the CPS can be implemented in high‐resolution simulations. [ABSTRACT FROM AUTHOR]

A moderate precipitation event occurring in northern Xinjiang, a region with a continental climate with little rainfall, and in leeward slope areas influenced by topography is important but rarely studied. In this study, the performance of lightning data assimilation is evaluated in the short-term forecasting of a moderate precipitation event along the western margin of the Junggar Basin and eastern Jayer Mountain. Pseudo-water vapor observations driven by lightning data are assimilated in both single and cycling analysis experiments of the Weather Research and Forecast (WRF) three-dimensional variational (3DVAR) system. Lightning data assimilation yields a larger increment in the relative humidity in the analysis field at the observed lightning locations, and the largest increment is obtained in the cycling analysis experiment. Due to the increase in water vapor content in the analysis field, more suitable thermal and dynamic conditions for moderate precipitation are obtained on the leeward slope, and the ice-phase and raindrop particle contents increase in the forecast field. Lightning data assimilation significantly improves the short-term leeward slope moderate precipitation prediction along the western margin of the Junggar Basin and provides the best forecast skill in cycling analysis experiments. [ABSTRACT FROM AUTHOR]

A dual-resolution, hybrid, three-dimensional ensemble-variational (3DEnVAR) data assimilation method combining static and ensemble background error covariances is used to assimilate radar data, and pseudo-water vapor observations to improve short-term severe weather forecasts with the Weather Research and Forecast (WRF) model. The higher-resolution deterministic forecast and the lower-resolution ensemble members have 3 and 9 km horizontal resolution, respectively. The water vapor pseudo-observations are derived from the combined use of total lightning data and cloud top height from the Fengyun-4A(FY-4A) geostationary satellite. First, a set of single-analysis experiments are conducted to provide a preliminary performance evaluation of the effectiveness of the hybrid method for assimilating multisource observations; second, a set of cycling analysis experiments are used to evaluate the forecast performance in convective-scale high-frequency analysis; finally, different hybrid coefficients are tested in both the single and cycling experiments. The single-analysis results show that the combined assimilation of radar data and water vapor pseudo-observations derived from the lightning data is able to generate reasonable vertical velocity, water vapor and hydrometeor adjustments, which help to trigger convection earlier in the forecast/analysis and reduce the spin-up time. The dual-resolution hybrid 3DEnVAR method is able to adjust the wind fields and hydrometeor variables with the assimilation of lightning data, which helps maintain the triggered convection longer and partially suppress spurious cells in the forecast compared with the three-dimensional variational (3DVAR) method. A cycling analysis that introduced a large number of observations with more frequent small adjustments is able to better resolve the observed convective events than a single-analysis approach. Different hybrid coefficients can affect the forecast results, either in the single deterministic or cycling analysis experiments. Overall, we found that a static coefficient of 0.4 and an ensemble coefficient of 0.6 yields the best forecast skill for this event. [ABSTRACT FROM AUTHOR]