It is essential to understand the factors driving the recent decline of dust activity in East Asia for future dust projections. Using a physically-based dust emission model, here we show that the weakening of surface wind and the increasing of vegetation cover and soil moisture have all contributed to the decline in dust activity during 2001 to 2017. The relative contributions of these three factors to the dust emission reduction during 2010–2017 relative to 2001 are 46%, 30%, and 24%, respectively. Much (78%) of the dust emission reduction is from barren lands, and a small fraction (4.6%) of the reduction is attributed to grassland vegetation increase that is partly ascribed to the ecological restoration. This suggests that the ecological restoration plays a minor role in the decline of dust activity. Rather, the decline is mainly driven by climatic factors, with the weakening of surface wind playing the dominant role.

The second version of Chinese Academy of Sciences Earth System Model (CAS‐ESM 2) is described with emphasis on the development process, strength and weakness, and climate sensitivities in simulations of the Coupled Model Intercomparison Project (CMIP6) DECK experiments. CAS‐ESM 2 was built as a numerical model to simulate both the physical climate system as well as atmospheric chemistry and carbon cycle. It is a newcomer in the international modeling community to provide sufficiently independent solutions of climate simulations from those of other models. Performances of the model in simulating the basic states of the radiation budget of the atmosphere and ocean, precipitation, circulations, variabilities, and the 20^th Century warming are presented. Model biases and their possible causes are discussed. Strength includes horizontal heat transport in the atmosphere and oceans, vertical profile of the Atlantic Meridional Overturning Circulation; weakness includes the double Inter‐Tropical Convergence Zone (ITCZ) and stronger amplitude of the El Niño‐Southern Oscillation (ENSO) that are also common in many other models. The simulated the 20^th Century warming shares a similar discrepancy with observations as in several other models—less warming in the 1920s and stronger cooling in the 1960s than in observation—at the time when there was a steep increase of anthropogenic aerosols. As a result, the 20^th Century warming is about 60% of the observed warming despite that the model simulated a similar slope of warming trend after 1980 to observation. The model has an equilibrium climate sensitivity of 3.4 K with a positive cloud feedback from the shortwave radiation.

The dust cycle is an important component of the Earth system and has been implemented in climate models and Earth system models (ESMs). An assessment of the dust cycle in these models is vital to address their strengths and weaknesses in simulating dust aerosol and its interactions with the Earth system and enhance the future model developments. This study presents a comprehensive evaluation of the global dust cycle in 15 models participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). The various models are compared with each other and with an aerosol reanalysis as well as station observations. The results show that the global dust emission in these models varies by a factor of 4–5 for the same size range. The models generally agree with each other and observations in reproducing the “dust belt”, which extends from North Africa, the Middle East, Central and South Asia to East Asia, although they differ greatly in the spatial extent of this dust belt. The models also differ in other dust source regions such as North America and Australia. We suggest that the coupling of dust emission with dynamic vegetation can enlarge the range of simulated dust emission. For the removal process, all the models estimate that wet deposition is smaller than dry deposition and wet deposition accounts for 12 %–39 % of total deposition. The models also estimate that most (77 %–91 %) dust particles are deposited onto continents and 9 %–23 % of dust particles are deposited into oceans. Compared to the observations, most models reproduce the dust deposition and dust concentrations within a factor of 10 at most stations, but larger biases by more than a factor of 10 are also noted at specific regions and for certain models. These results highlight the need for further improvements of the dust cycle especially on dust emission in climate models.

Dust emissions in climate and earth system models are associated with large uncertainties. These models often use the source erodibility (S) to constrain dust emissions and also lack explicit representations of the impact of surface roughness elements (SREs) on the threshold friction velocity (u_*t). This study presents a process‐oriented evaluation of dust emission parameterizations in the Community Earth System Model (CESM) by applying the model to simulate a severe dust storm during 19–22 March 2010 in East Asia. Through numerical experiments, we assess the applicability of S and investigate the impact of SREs on dust emissions by implementing the roughness correction factor (f_λ) to u_*t. Simulation results are compared against the surface synoptic observations and station observations of dust concentrations. We found that the model can capture the main dust emission regions and reproduce the temporal‐spatial evolution of surface dust concentrations in Mongolia and northern China. With a geomorphic S (S_g), the model tends to produce excessive dust emissions over the low‐lying basins. Moreover, the high‐resolution S_g performs worse with “point sources” of strong dust emissions than the low‐resolution one. With the inclusion of f_λ, total dust emissions are reduced by 24–34%, and the model reduces the overestimation of surface dust concentrations and improves their temporal variations over the vegetated regions. These results suggest that S_g may not be necessary when meteorology and land surface state are well simulated by the model and that f_λ provides an important constraint on dust emissions through SREs.

Dust aerosol plays an important role in the Earth System. As a natural aerosol, dust aerosol is often calculated interactively in global climate models and temporal variations of dust emission in the past century are far less constrained compared to those of anthropogenic aerosol emissions. Here we evaluate dust emission in East Asia simulated by 15 climate models participating in the Coupled Model Intercomparison Project Phase 5. The results show that none of the models can reproduce the observed decline of dust event frequency during 1961–2005 over East Asia. The models tend to simulate either much less decline or even increase of dust emission. The discrepancy is mainly ascribed to weaker or opposite trends of surface wind speeds and precipitation in the models. These results cast a doubt on the interpretation of long‐term variations of dust‐affected fields in climate models and highlight the need for further improvements of the models.

Subseasonal-to-seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging, but also has substantial societal impacts. Motivated by preliminary studies, the Global Energy and Water Exchanges (GEWEX)/Global Atmospheric System Study (GASS) has launched a new initiative called “Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction” (LS4P) as the first international grass-roots effort to introduce spring land surface temperature (LST)/subsurface temperature (SUBT) anomalies over high mountain areas as a crucial factor that can lead to significant improvement in precipitation prediction through the remote effects of land–atmosphere interactions. LS4P focuses on process understanding and predictability, and hence it is different from, and complements, other international projects that focus on the operational S2S prediction. More than 40 groups worldwide have participated in this effort, including 21 Earth system models, 9 regional climate models, and 7 data groups. This paper provides an overview of the history and objectives of LS4P, provides the first-phase experimental protocol (LS4P-I) which focuses on the remote effect of the Tibetan Plateau, discusses the LST/SUBT initialization, and presents the preliminary results. Multi-model ensemble experiments and analyses of observational data have revealed that the hydroclimatic effect of the spring LST on the Tibetan Plateau is not limited to the Yangtze River basin but may have a significant large-scale impact on summer precipitation beyond East Asia and its S2S prediction. Preliminary studies and analysis have also shown that LS4P models are unable to preserve the initialized LST anomalies in producing the observed anomalies largely for two main reasons: (i) inadequacies in the land models arising from total soil depths which are too shallow and the use of simplified parameterizations, which both tend to limit the soil memory; (ii) reanalysis data, which are used for initial conditions, have large discrepancies from the observed mean state and anomalies of LST over the Tibetan Plateau. Innovative approaches have been developed to largely overcome these problems.