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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. Changes in climatic factors mainly drive the decline of East Asian dust activity in the past two decades. The weakening of surface winds plays a dominant role, and the increasing of vegetation cover and soil moisture also has key contribution

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.

The impact of Arctic Sea ice melting on weather and climate extremes in the Northern Hemisphere has garnered widespread attention. Existing research has convincingly demonstrated the importance of this impact in mid-high latitudes, while its influence in areas beyond remains controversial. This study reveals the indirect influence of Kara Sea ice reduction on cold surge (CS) over the tropical western Pacific (TWP), with the East Asian jet stream serving as the connecting link. The leading mode of CSs over the TWP exhibits a zonal dipole characteristic, which is associated with cyclonic anomaly over the Philippine Sea. The enhanced cyclonic anomaly is caused by strengthened and northward-moved subtropical East Asian jet stream and weakened polar jet stream, which can lead to more CSs over the South China Sea and fewer CSs over the Philippine Sea. Such variations in the jet stream are contributed by the facilitated atmospheric blockings west of the Ural Mountains, which suppressed the circumpolar westerly winds and increased meridional temperature gradient in Northeast Asia. The connection between atmospheric blockings and Kara Sea ice can be confirmed through local vertical energy exchange. Simulations of the atmospheric response to the forcing of decreased Kara Sea ice support the proposed connection. Although there is no statistically significant correlation between tropical CSs and Kara Sea ice, this study highlights the potential impacts of Arctic climate change signal on weather and climate extremes over tropical regions.

Central Asia is prone to wildfires, but the relationship between wildfires and climatic factors in this area is still not clear. In this study, the spatiotemporal variation in wildfire activities across Central Asia during 1997–2016 in terms of the burned area (BA) was investigated with Global Fire Emission Database version 4s (GFED4s). The relationship between BA and climatic factors in the region was also analyzed. The results reveal that more than 90% of the BA across Central Asia is located in Kazakhstan. The peak BA occurs from June to September, and remarkable interannual variation in wildfire activities occurs in western central Kazakhstan (WCKZ). At the interannual scale, the BA is negatively correlated with precipitation (correlation coefficient r = −0.66), soil moisture (r = −0.68), and relative humidity (r = −0.65), while it is positively correlated with the frequency of hot days (r = 0.37) during the burning season (from June to September). Composite analysis suggests that the years in which the BA is higher are generally associated with positive geopotential height anomalies at 500 hPa over the WCKZ region, which lead to the strengthening of the downdraft at 500 hPa and the weakening of westerlies at 850 hPa over the region. The weakened westerlies suppress the transport of water vapor from the Atlantic Ocean to the WCKZ region, resulting in decreased precipitation, soil moisture, and relative humidity in the lower atmosphere over the WCKZ region; these conditions promote an increase in BA throughout the region. Moreover, the westerly circulation index is positively correlated (r = 0.53) with precipitation anomalies and negatively correlated (r = −0.37) with BA anomalies in the WCKZ region during the burning season, which further underscores that wildfires associated with atmospheric circulation systems are becoming an increasingly important component of the relationship between climate and wildfire.

Spaceborne microwave radiometer observations play vital roles in surface parameter retrievals and data assimilation, but widespread radio-frequency interference (RFI) signals in the C-band channel result in a lack of valuable data over large areas. Establishing repaired data based on existing observation information is crucial. In this study, Advanced Microwave Scanning Radiometer (AMSR)-2 C-band data affected by RFI were accurately repaired through the iterative principal component analysis (PCA) method in 2016 over the U.S. land area. The standard deviation (STD) and bias characteristics of the brightness temperature in the C-band vertical polarization channel were compared and analyzed before and after the restoration to verify the assimilation application prospect of the repaired data. Not only was the spatial continuity of the microwave imager observations significantly improved following restoration; the STD and bias of the observation minus background (OMB) of the restored data were basically consistent with those of the RFI-free data. The STD of OMB exhibited obvious seasonal variations, which were approximately 4.0 K from January to May and 3.0 K from June to December, whereas the biases were near zero in winter but negative (approximately −2.0 K) in summer. The surface type and terrain height also critically affected the STD and bias. The STD decreased with increasing terrain height, whereas the bias exhibited the opposite trend. The STD was largest in low-vegetation areas (4.0 K) but only approximately 2.0–3.0 K in pine forest and brush areas. These results show that the restored data have a high prospect for retrieval application and assimilation, and the STD and bias estimation results also provide a reference for land-based AMSR-2 data assimilation.

Soil moisture is a key variable of the climate system. Microwave remote sensing has become an essential means of obtaining soil moisture because of the unique advantages of its all-day and all-weather observation capability. Theoretically, low-frequency C-band observations are highly suitable for soil moisture retrieval because of their high sensitivity to soil moisture at vegetation roots. However, the quality of C-band observations suffers from radio frequency interference (RFI) over the United States. This paper used the iterative principal component analysis (PCA) method to repair RFI-contaminated second-generation Advanced Microwave Scanning Radiometer (AMSR-2) C-band observations, and the results of soil moisture retrieval based on restored data were evaluated. It was found that RFI could lead to nonconvergence in the retrieval of a large amount of data, and the application of repaired data in retrieval could result in the recovery of more than 80% of nonconvergent data, especially in spring and autumn. The retrieval results based on restored data attained a satisfactory correlation with ERA5 reanalysis data and European Space Agency Climate Change Initiative (ESA-CCI) soil moisture data and suitably agreed with precipitation observation data. Soil moisture generally exhibited a gradual increase from west to north and east. This feature was weakened due to the influence of monsoons in the east in summer. The western side of the Cascade Mountains is the wettest area of the United States, with soil moisture exceeding 0.4 m3 m−3. The driest region of the United States is located between the Rockies and Cordillera Mountains, and the soil moisture value is lower than 0.1 m3 m−3.

MultiCal is an affordable, high-precision measuring device designed for the on-site calibration of industrial robots. Its design features a long measuring rod with a spherical tip that is attached to the robot. By restricting the rod’s tip to multiple fixed points under different rod orientations, the relative positions of these points are accurately measured beforehand. A common issue with MultiCal is the gravitational deformation of the long measuring rod, which introduces measurement errors into the system. This problem becomes especially serious when calibrating large robots, as the length of the measuring rod needs to be increased to enable the robot to move in a sufficient space. To address this issue, we propose two improvements in this paper. Firstly, we suggest the use of a new design of the measuring rod that is lightweight yet has high rigidity. Secondly, we propose a deformation compensation algorithm. Experimental results have shown that the new measuring rod improves calibration accuracy from 20% to 39%, while using the deformation compensation algorithm, the accuracy increases from 6% to 16%. In the best configuration, the calibration accuracy is similar to that of a measuring arm with a laser scanner, producing an average positioning error of 0.274 mm and a maximum positioning error of 0.838 mm. The improved design is cost-affordable, robust, and has sufficient accuracy, making MultiCal a more reliable tool for industrial robot calibration.

This study investigates the variation of seasonal streamflow and streamflow extremes in five catchments of the Mahaweli River Basin (MRB) Sri Lanka from 1990 to 2014, and the relationship between streamflow and seasonal rainfall in each catchment is then examined. Furthermore, the influence of Indian Ocean Dipole (IOD) and El Nino and Southern Oscillation (ENSO) on the seasonal rainfall and streamflow in the upper (UMRB) and lower reaches (LMRB) of MRB are explored. It’s found that the rainfall amount in southwest monsoon (SWM) season contributes 29.7% out of annual total rainfall in the UMRB, while the LMRB records 41% of the total rainfall during the northeast monsoon (NEM) season. The maximum streamflow of upper (lower) Mahaweli catchments is observed in the SWM (NEM) season. Catchments in the UMRB (LMRB) recorded strong interannual variability of seasonal overall flow (Q50), Maximum 10-day, and 30-day flows during the SWM (NEM) season. It’s further revealed that the catchment streamflow in the UMRB is closely correlated with the SWM rainfall in the interannual time scale, while streamflow of catchments in the LMRB is closely associated with the NEM rainfall. The effects of ENSO and IOD on streamflow are consistent with their impacts on rainfall for all catchments in MRB, with strong seasonal dependent. These suggested that the sea surface temperature anomalies in the both Indian Ocean and tropical Pacific Ocean are important factors affecting the streamflow variability in the MRB, especially during the SWM season.

Various cloud cover products have been developed over the past few decades, but their uncertainties have not been sufficiently assessed, especially at a regional scale, which is vital for the application of satellite products to climate studies. In this study, we compare the spatial–temporal variability of the cloud amount over China from the 11 datasets provided by the Global Energy and Water Cycle Experiment (GEWEX) cloud assessment project at a horizontal resolution of 1° × 1° from the 1980s to 2000s, using the site data as a reference. The differences among these datasets are quantified in terms of the standard deviations and the correlation coefficients between different datasets. Most of the datasets show a similar spatial distribution of total cloud amounts (TCAs), but their magnitudes differ. The standard deviations of the annual, winter, and summer mean TCA are approximately 9–18% for the regional mean TCAs over the four typical regions of China, including the northwestern region (NW), northeastern region (NE), Tibetan Plateau region (TP), and southern China region (SC), with the largest standard deviations of 13–18% in the TP. By analyzing the factors that influence the satellite inversion data, such as the observation instrument, inversion algorithm, and observation time, we found that the difference caused by the observation instrument or algorithm is greater than the effect of the observation time, and the satellite cloud datasets with better recognition capability for cloud types show lower uncertainties when compared with the station observation. In terms of seasonal cycle, except HIRS and MODIS-ST, most satellite datasets can reproduce the observed seasonal cycle with the largest TCA in summer and the smallest TCA in autumn and winter. For the interannual variation, ISCCP-D1, MODIS-CE, and MODIS-ST are most consistent with the site data for the annual mean TCA, and two of the remaining datasets (PATMOSX and TOVSB) show more consistent temporal variations with the site observation in summer than in winter, especially over NW and NE regions. In general, MODIS-CE shows the best performance in reproducing the spatial pattern and interannual variation of TCA amongst the 11 satellite datasets, and PATMOSX, MODIS-ST, CALIPSO-GOCCP, and CALIPSO-ST also show relatively good performance.

The deposition of light-absorbing aerosols (LAAs), such as black carbon (BC) and dust, onto snow cover has been suggested to reduce the snow albedo and modulate the snowpack and consequent hydrologic cycle. In this study we use the variable-resolution Community Earth System Model (VR-CESM) with a regionally refined high-resolution (0.125°) grid to quantify the impacts of LAAs in snow in the Rocky Mountain region during the period 1981–2005. We first evaluate the model simulation of LAA concentrations both near the surface and in snow and then investigate the snowpack and runoff changes induced by LAAs in snow. The model simulates similar magnitudes of near-surface atmospheric dust concentrations as observations in the Rocky Mountain region. Although the model underestimates near-surface atmospheric BC concentrations, the model overestimates BC-in-snow concentrations by 35 % on average. The regional mean surface radiative effect (SRE) due to LAAs in snow reaches up to 0.6–1.7 W m−2 in spring, and dust contributes to about 21–42 % of total SRE. Due to positive snow albedo feedbacks induced by the LAA SRE, snow water equivalent is reduced by 2–50 mm and snow cover fraction by 5–20 % in the two regions around the mountains (eastern Snake River Plain and southwestern Wyoming), corresponding to an increase in surface air temperature by 0.9–1.1 °C. During the snow melting period, LAAs accelerate the hydrologic cycle with monthly runoff increases of 0.15–1.00 mm day−1 in April–May and reductions of 0.04–0.18 mm day−1 in June–July in the mountainous regions. Of all the mountainous regions, the Southern Rockies experience the largest reduction of total runoff by 15 % during the later stage of snowmelt (i.e., June and July). Compared to previous studies based on field observations, our estimation of dust-induced SRE is generally 1 order of magnitude smaller in the Southern Rockies, which is ascribed to the omission of larger dust particles (with the diameter > 10 µm) in the model. This calls for the inclusion of larger dust particles in the model to reduce the discrepancies. Overall these results highlight the potentially important role of LAA interactions with snowpack and the subsequent impacts on the hydrologic cycles across the Rocky Mountains.