Search Results

You are looking at 1 - 10 of 19 items for

  • Author or Editor: Yongjiu Dai x
  • Refine by Access: All Content x
Clear All Modify Search
Liming Zhou
,
Yuhong Tian
,
Haishan Chen
,
Yongjiu Dai
, and
Ronald A. Harris

Abstract

This paper uses the empirical orthogonal function (EOF) analysis to decompose satellite-derived nighttime land surface temperature (LST) for the period of 2003–11 into spatial patterns of different scales and thus to identify whether (i) there is a pattern of LST change associated with the development of wind farms and (ii) the warming effect over wind farms reported previously is an artifact of varied surface topography. Spatial pattern and time series analysis methods are also used to supplement and compare with the EOF results. Two equal-sized regions with similar topography in west-central Texas are chosen to represent the wind farm region (WFR) and nonwind farm region (NWFR), respectively. Results indicate that the nighttime warming effect seen in the first mode (EOF1) in WFR very likely represents the wind farm impacts due to its spatial coupling with the wind turbines, which are generally built on topographic high ground. The time series associated with the EOF1 mode in WFR also shows a persistent upward trend over wind farms from 2003 to 2011, corresponding to the increase of operating wind turbines with time. Also, the wind farm pixels show a warming effect that differs statistically significantly from their upwind high-elevation pixels and their downwind nonwind farm pixels at similar elevations, and this warming effect decreases with elevation. In contrast, NWFR shows a decrease in LST with increasing surface elevation and no warming effects over high-elevation ridges, indicating that the presence of wind farms in WFR has changed the LST–elevation relationship shown in NWFR. The elevation impacts on Moderate Resolution Imaging Spectroradiometer (MODIS) LST, if any, are much smaller and statistically insignificant than the strong and persistent signal of wind farm impacts. These results provide further observational evidence of the warming effect of wind farms reported previously.

Full access
Yongjiu Dai
,
Robert E. Dickinson
, and
Ying-Ping Wang

Abstract

The energy exchange, evapotranspiration, and carbon exchange by plant canopies depend on leaf stomatal control. The treatment of this control has been required by land components of climate and carbon models. Physiological models can be used to simulate the responses of stomatal conductance to changes in atmospheric and soil environments. Big-leaf models that treat a canopy as a single leaf tend to overestimate fluxes of CO2 and water vapor. Models that differentiate between sunlit and shaded leaves largely overcome these problems.

A one-layered, two-big-leaf submodel for photosynthesis, stomatal conductance, leaf temperature, and energy fluxes is presented in this paper. It includes 1) an improved two stream approximation model of radiation transfer of the canopy, with attention to singularities in its solution and with separate integrations of radiation absorption by sunlit and shaded fractions of canopy; 2) a photosynthesis–stomatal conductance model for sunlit and shaded leaves separately, and for the simultaneous transfers of CO2 and water vapor into and out of the leaf—leaf physiological properties (i.e., leaf nitrogen concentration, maximum potential electron transport rate, and hence photosynthetic capacity) vary throughout the plant canopy in response to the radiation–weight time-mean profile of photosynthetically active radiation (PAR), and the soil water limitation is applied to both maximum rates of leaf carbon uptake by Rubisco and electron transport, and the model scales up from leaf to canopy separately for all sunlit and shaded leaves; 3) a well-built quasi-Newton–Raphson method for simultaneous solution of temperatures of the sunlit and shaded leaves.

The model was incorporated into the Common Land Model (CLM) and is denoted CLM 2L. It was driven with observational atmospheric forcing from two forest sites [Anglo-Brazilian Amazonian Climate Observation Study (ABRACOS) and Boreal Ecosystem–Atmosphere Study (BOREAS)] for 2 yr of simulation. The simulated fluxes by CLM 2L were compared with the observations, and with the results by the CLM with a single big-leaf scheme (CLM 1L) and by the CLM with the assimilation–stomatal conductance scheme of NCAR Land Surface Model (LSM). The results showed that CLM 2L was an improvement compared to the CLM 1L and the CLM for the test cases of tropical evergreen broadleaf land cover and coniferous boreal forest.

Full access
Xubin Zeng
,
Muhammad Shaikh
,
Yongjiu Dai
,
Robert E. Dickinson
, and
Ranga Myneni

Abstract

The Common Land Model (CLM), which results from a 3-yr joint effort among seven land modeling groups, has been coupled with the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM3). Two 15-yr simulations of CCM3 coupled with CLM and the NCAR Land Surface Model (LSM), respectively, are used to document the relative impact of CLM versus LSM on land surface climate. It is found that CLM significantly reduces the summer cold bias of surface air temperature in LSM, which is associated with higher sensible heat fluxes and lower latent heat fluxes in CLM, and the winter warm bias over seasonally snow-covered regions, especially in Eurasia. CLM also significantly improves the simulation of the annual cycle of runoff in LSM. In addition, CLM simulates the snow mass better than LSM during the snow accumulation stage. These improvements are primarily caused by the improved parameterizations in runoff, snow, and other processes (e.g., turbulence) in CLM. The new land boundary data (e.g., leaf-area index, fractional vegetation cover, albedo) also contribute to the improvement in surface air temperature simulation over some regions. Overall, CLM has little impact on precipitation and surface net radiative fluxes.

Full access
Lu Li
,
Yongjiu Dai
,
Wei Shangguan
,
Nan Wei
,
Zhongwang Wei
, and
Surya Gupta

Abstract

Accurate spatiotemporal predictions of surface soil moisture (SM) are important for many critical applications. Machine learning models provide a powerful method for building an accurate and reliable predictive model of SM. However, the models used in recent studies have some limitations, including lack of spatial autocorrelation (SAC), vague representation of important features, and primarily focused on the one-step forecast. Thus, we proposed an attention-based convolutional long short-term memory model (AttConvLSTM) for multistep forecasting. The model includes three layers, spatial compression, axial attention, and encoder–decoder prediction, which are used for compressing spatial information, feature extraction, and multistep prediction, respectively. The model was trained using surface SM from the Soil Moisture Active Passive L4 product at 18-km spatial resolution over the United States. The results show that AttConvLSTM predicts 24 h ahead SM with mean R 2 and RMSE is equal to 0.82 and 0.02, respectively. Compared with LSTM, AttConvLSTM improves the model performance over 73.6% of regions, with an improvement of 8.4% and 17.4% in R2 and RMSE, respectively. The performance of the model is mainly influenced by temporal autocorrelation (TAC). Moreover, we also highlight the importance of SAC on model performance, especially over regions with high SAC and low TAC. Our model is also competent for SM predictions from several hours to several days, which could be a useful tool for predicting all meteorological variables and forecasting extremes.

Open access
Lu Li
,
Yongjiu Dai
,
Wei Shangguan
,
Zhongwang Wei
,
Nan Wei
, and
Qingliang Li

Abstract

The accurate prediction of surface soil moisture (SM) is crucial for understanding hydrological processes. Deep learning (DL) models such as the long short-term memory model (LSTM) provide a powerful method and have been widely used in SM prediction. However, few studies have notably high success rates due to lacking prior knowledge in forms such as causality. Here we present a new causality-structure-based LSTM model (CLSTM), which could learn time interdependency and causality information for hydrometeorological applications. We applied and compared LSTM and CLSTM methods for forecasting SM across 64 FLUXNET sites globally. The results showed that CLSTM dramatically increased the predictive performance compared with LSTM. The Nash–Sutcliffe efficiency (NSE) suggested that more than 67% of sites witnessed an improvement of SM simulation larger than 10%. It is highlighted that CLSTM had a much better generalization ability that can adapt to extreme soil conditions, such as SM response to drought and precipitation events. By incorporating causal relations, CLSTM increased predictive ability across different lead times compared to LSTM. We also highlighted the critical role of physical information in the form of causality structure to improve drought prediction. At the same time, CLSTM has the potential to improve predictions of other hydrometeorological variables.

Free access
Xubin Zeng
,
Michael Barlage
,
Robert E. Dickinson
,
Yongjiu Dai
,
Guiling Wang
, and
Keith Oleson

Abstract

In arid and semiarid regions most of the solar radiation penetrates through the canopy and reaches the ground, and hence the turbulent exchange coefficient under canopy Cs becomes important. The use of a constant Cs that is only appropriate for thick canopies is found to be primarily responsible for the excessive warm bias of around 10 K in monthly mean ground temperature over these regions in version 2 of the Community Climate System Model (CCSM2). New Cs formulations are developed for the consistent treatment of undercanopy turbulence for both thick and thin canopies in land models, and provide a preliminary solution of this problem.

Full access
Qiaoling Ren
,
Kevin I. Hodges
,
Reinhard Schiemann
,
Yongjiu Dai
,
Xingwen Jiang
, and
Song Yang

Abstract

Using an objective feature-tracking algorithm and the fifth major global reanalysis produced by ECMWF data (ERA5), the seasonal behaviors of cyclonic transient eddies (cyclones) at different levels around the Tibetan Plateau (TP) were examined to understand the effects of the TP on cyclones. Results show that the TP tends to change the moving directions of the remote cyclones when they are close to the TP, with only 2% of the 250-hPa eastward-moving cyclones directly passing over the TP. The sudden reductions of their moving speeds and relative vorticity intensities around the TP suggest a suppression effect of the plateau. Over 70% of these cyclones perish over the TP regardless of the altitude. This percentage decreases to around 65% during summertime, exhibiting a weaker summer suppression effect. On the other hand, the TP has a stimulation effect on local cyclones through its dynamic forcing in winter, thermodynamic forcing in summer, and both forcings in the transitional seasons. The numbers of locally generated cyclones, especially at 500 hPa, just above the TP, are significantly larger than those of the remote cyclones during all seasons. Although about one-half of the local cyclones dissipate over the TP, the cyclones moving off the plateau significantly outnumber the moving-in cyclones, with the differences ranging from 0 to 6 cyclones per month. Only the 250-hPa wintertime moving-off cyclones are fewer than the cyclones entering the TP, which may be caused by the weaker stimulation effect and stronger suppression effect of the TP on the wintertime upper-level cyclones.

Significance Statement

Cyclonic transient eddies (cyclones), steered by westerly jet streams, can influence climate and induce extreme weather processes under certain conditions. The Tibetan Plateau (TP), the highest and largest obstacle embedded in the westerly jet streams, suppresses remote cyclones entering the TP region, destroying over 70% of these cyclones. However, because of the excitation effect of the TP on local cyclones, the numbers of cyclones moving off the TP are still larger than or equal to those of the moving-in cyclones, except at the upper levels in winter. This feature suggests that the TP cannot significantly decrease the total cyclone numbers in most cases, but it indeed weakens the mean intensity and moving speed of the cyclones.

Open access
Yongjiu Dai
,
Wei Shangguan
,
Qingyun Duan
,
Baoyuan Liu
,
Suhua Fu
, and
Guoyue Niu

Abstract

The objective of this study is to develop a dataset of the soil hydraulic parameters associated with two empirical soil functions (i.e., a water retention curve and hydraulic conductivity) using multiple pedotransfer functions (PTFs). The dataset is designed specifically for regional land surface modeling for China. The authors selected 5 PTFs to derive the parameters in the Clapp and Hornberger functions and the van Genuchten and Mualem functions and 10 PTFs for soil water contents at capillary pressures of 33 and 1500 kPa. The inputs into the PTFs include soil particle size distribution, bulk density, and soil organic matter. The dataset provides 12 estimated parameters and their associated statistical values. The dataset is available at a 30 × 30 arc second geographical spatial resolution and with seven vertical layers to the depth of 1.38 m. The dataset has several distinct advantages even though the accuracy is unknown for lack of in situ and regional measurements. First, this dataset utilizes the best available soil characteristics dataset for China. The Chinese soil characteristics dataset was derived by using the 1:1 000 000 Soil Map of China and 8595 representative soil profiles. Second, this dataset represents the first attempt to estimate soil hydraulic parameters using PTFs directly for continental China at a high spatial resolution. Therefore, this dataset should capture spatial heterogeneity better than existing estimates based on lookup tables according to soil texture classes. Third, the authors derived soil hydraulic parameters using multiple PTFs to allow flexibility for data users to use the soil hydraulic parameters most preferable to or suitable for their applications.

Full access
Han Zhang
,
Xin-Zhong Liang
,
Yongjiu Dai
,
Lianchun Song
,
Qingquan Li
,
Fang Wang
, and
Shulei Zhang

Abstract

This study investigates skill enhancement in operational seasonal forecasts of Beijing Climate Center’s Climate System Model through regional Climate-Weather Research and Forecasting (CWRF) downscaling and improved land initialization in China. The downscaling mitigates regional climate biases, enhancing precipitation pattern correlations by 0.29 in spring and 0.21 in summer. It also strengthens predictive capabilities for interannual anomalies, expanding skillful temperature forecast areas by 6% in spring and 12% in summer. Remarkably, during seven of ten years with relative high predictability, the downscaling increases average seasonal precipitation anomaly correlations by 0.22 and 0.25. Additionally, substitution of initial land conditions via a Common Land Model integration reduces snow cover and cold biases across the Tibetan Plateau and Mongolia-Northeast China, consistently contributing to CWRF’s overall enhanced forecasting capabilities.

Improved downscaling predictive skill is attributed to CWRF’s enhanced physics representation, accurately capturing intricate regional interactions and associated teleconnections across China, especially linked to the Tibetan Plateau’s blocking and thermal effects. In summer, CWRF predicts an intensified South Asian High alongside a strengthened East Asian Jet compared to CSM, amplifying cold air advection and warm moisture transport over central to northeast regions. Consequently, rainfall distributions and interannual anomalies over these areas experience substantial improvements. Similar enhanced circulation processes elucidate skill improvement from land initialization, where accurate specification of initial snow cover and soil temperature within sensitive regions persists in influencing local and remote circulations extending beyond two seasons. Our findings emphasize the potential of improving physics representation and surface initialization to markedly enhance regional climate predictions.

Restricted access
Lu Li
,
Yongjiu Dai
,
Zhongwang Wei
,
Wei Shangguan
,
Yonggen Zhang
,
Nan Wei
, and
Qingliang Li

Abstract

Accurate prediction of hydrological variables (HVs) is critical for understanding hydrological processes. Deep learning (DL) models have shown excellent forecasting abilities for different HVs. However, most DL models typically predicted HVs independently, without satisfying the principle of water balance. This missed the interactions between different HVs in the hydrological system and the underlying physical rules. In this study, we developed a DL model based on multitask learning and hybrid physically constrained schemes to simultaneously forecast soil moisture, evapotranspiration, and runoff. The models were trained using ERA5-Land data, which have water budget closure. We thoroughly assessed the advantages of the multitask framework and the proposed constrained schemes. Results showed that multitask models with different loss-weighted strategies produced comparable or better performance compared to the single-task model. The multitask model with a scaling factor of 5 achieved the best among all multitask models and performed better than the single-task model over 70.5% of grids. In addition, the hybrid constrained scheme took advantage of both soft and hard constrained models, providing physically consistent predictions with better model performance. The hybrid constrained models performed the best among different constrained models in terms of both general and extreme performance. Moreover, the hybrid model was affected the least as the training data were artificially reduced, and provided better spatiotemporal extrapolation ability under different artificial prediction challenges. These findings suggest that the hybrid model provides better performance compared to previously reported constrained models when facing limited training data and extrapolation challenges.

Restricted access