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Previous studies show that volcanic eruptions can intensify and extend drought events triggered by internal variability over Eastern China. However, it has remained unclear whether volcanic eruptions occurring in different drought phases have different impacts. Here, based on multiple reconstructions, simulations, as well as volcanic sensitivity experiments with volcanic forcing imposed in the early and late phases of droughts, we propose a nonlinear effect of volcanic eruptions on drought events. Late‐phase volcanic eruptions exert greater impact on drought persistence and intensity while early‐phase volcanic eruptions induce modest and weaker impacts. The evolutions of drought differ substantially from the typical volcanic‐only influence or the linear combination of the drought triggered by internal variability and volcanic‐only influences, which are hypothesized to be associated with positive feedbacks of soil moisture to precipitation, as well as its interaction with the evolution of the East Asia Summer monsoon. Plain Language Summary: The effects of volcanic eruptions on precipitation changes are usually considered only on short timescales, however, recent studies have found that volcanic eruptions can influence decadal mega‐drought when superposed on drought events triggered by internal variability in the climate system (Ning et al., 2019; F. Chen et al., 2020). Our modeling and observational studies further show that the response of droughts to volcanic eruptions also depends significantly on the timing of the eruption. A volcanic eruption occurs in the late‐phase of a drought intensify the drought and extends the drought the most, and this impact decreases when the followed eruptions occur toward the early‐phase. These nonlinear responses may be explained by the nonlinear feedbacks of local soil moisture to precipitation and nonlinear changes of East Asia Summer Monsoon after volcanic eruptions. Key Points: Droughts over eastern China responds nonlinearly to volcanic eruptions at different phases of droughts induced by internal variabilityThis nonlinear response can be attributed to the nonlinear influences of volcanic eruptions on EASMThe positive feedbacks between local soil moisture and precipitation can also contribute to this nonlinear response [ABSTRACT FROM AUTHOR]

Northern China (35°–50°N; 105°–125°E) has experienced a multidecadal aridification since 1950s, which was mainly manifested by multi‐decadal scale decreases of regional precipitation and soil moisture, and brought severe influences on societal and economic developments. However, the position of this aridification among the historical aridification events over northern China during the last millennium has not been fully assessed. Furthermore, the contributions from anthropogenic forcings, natural forcings, and internal variability on this aridification have not been quantitatively differentiated. In this study, observations, proxy reconstructions, and model simulations from community Earth system model last millennium ensemble archive were used to assess this aridification from a historical perspective, and differentiate the contributions from different forcings and internal variability. It was found that duration and magnitude of this aridification are more prominent than most of aridifications during the last millennium. The comparisons between control experiment and sensitivity experiments show that the external forcings, especially anthropogenic forcings, result in a significant larger aridification rate than other historical aridifications. Only combined impacts from internal variability (i.e., Pacific Decadal oscillation, PDO and Atlantic Multidecadal oscillation (AMO)) and anthropogenic forcings (e.g., greenhouses gas emission, anthropogenic aerosol emission, and land use cover changes) can result in such a severer aridification than historical aridifications. The results also confirm that the phase shifts of PDO and AMO trigger this aridification over the northern China, and, whether external forcings contribute to these phase shifts were also discussed. Key Points: Duration and magnitude of the current aridification are more prominent than most of historical aridifications during the last millenniumAnthropogenic forcings result in a significant larger aridification rate than other historical aridificationsOnly combined impacts from internal variability and anthropogenic forcings can result in such a severer aridification [ABSTRACT FROM AUTHOR]

The late Ming Dynasty Megadrought (LMDMD) (1637–1643) occurred at the end of Ming Dynasty and is the severest drought event in China in the last millennium. This unprecedented drought contributed significantly to the collapse of the Ming Dynasty in 1644, casting profound impacts on Chinese history. Here, the physical mechanism for the LMDMD is studied. Based on paleoclimate reconstructions, we hypothesize that this drought was initially triggered by a natural drought event starting in 1637 and was then intensified and extended by the tropical volcanic eruption at Mount Parker in 1641. This hypothesis is supported by the case study of the Community Earth System Model‐Last Millennium Experiment archive as well as sensitivity experiments with volcanic forcing superimposed on natural drought events. The volcano‐intensified drought was associated with a decreased land‐ocean thermal contrast, a negative soil moisture response, and a weakening and eastward retreating West Pacific Subtropical High. Plain Language Summary: The collapse of the Ming Dynasty at 1644 and, in turn, the historical transition from the Ming Dynasty to Qing Dynasty significantly changed the Chinese history into a long period of conservative policy. The collapse of Ming Dynasty at 1644 is greatly associated with the late Ming Dynasty Megadrought (LMDMD) (1637–1643). In this study, based on paleoclimate reconstructions and climate modeling, we show that the LMDMD is triggered by a natural drought event and is then intensified and extended by the strong volcanic eruption at Mt. Parker in 1641. This "superposition" mechanism of LMDMD and the spatiotemporal characteristics of this drought are reproduced by our volcanic sensitivity experiments with volcanic forcing superimposed on natural drought events, and the results demonstrate that the explosion of Mt. Parker at the end of a natural drought event amplified and extended the drought for 3 years, generating the megadrought. The volcano‐prolonged drought is associated with the failure of East Asia Summer Monsoon (EASM), which is caused directly by the decreasing land‐ocean thermal contrast after the volcanic eruption and indirectly by negative soil moisture feedback as well as weakening and eastward retreating of the West Pacific Subtropical High (WPSH). Key Points: The late Ming Dynasty Megadrought (LMDMD) is triggered by a natural drought event in 1637 and is then intensified and extended by the strong volcanic eruption in 1641Volcanic eruption superimposed on a drought triggered by internal variability can intensify drought conditions, such that the drought becomes severer than that triggered by internal variability or external forcing aloneThe intensification of LMDMD is related to the weakening of land‐ocean thermal contrast, the retreat of West Pacific Subtropical High, and the decrease of soil moisture after volcanic eruption [ABSTRACT FROM AUTHOR]

• Soil moisture of a semi-humid region was estimated from Sentinel-1 data. • Three machine learning algorithms were compared. • Random forest performed better than SVM and ANN. • Soil moisture is higher in the Yellow River National Wetland Park. • Soil moisture is affected by precipitation, temperature, and vegetation. Soil moisture (SM) plays a critical role in the growth and management of grain in semi-humid regions. However, little is known about how to integrate satellite data with machine learning to accurately retrieve SM information in these areas. This study compares the capability of three machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), to extract SM information over the Northwest Shandong Plain using multi-phase dual-polarized Sentinel-1A satellite data. The backscattering coefficients were obtained through standard intensity and phase processing to calculate the SAR indices, and several characteristic parameters were extracted as impact factors using the Cloude-Pottier decomposition. The importance of these factors was analyzed, while the performance of each machine learning algorithm was comprehensively evaluated using the K-fold cross-validation method. The best-performing model was utilized to retrieve the spatio-temporal changes in SM in the study area. The findings indicate the following: (1) The first eigenvalue has the greatest impact on retrieval accuracy, followed by entropy, where the intensity component of Shannon's entropy is more important than its polarization component; (2) The addition of more impact factors does not bring a continuous improvement in model performance, but the optimal factor combinations differ for different machine learning retrieval models; (3) The RF model trained using the IM12 combination demonstrates better performance than SVM and ANN in retrieving SM information, with a coefficient of determination (R2) of 0.55 and a root mean square error of 6.12 vol% on the validation set. The level of SM in the Yellow River National Wetland Park is higher than that of the surrounding areas, with substantial seasonal changes. Precipitation, temperature, and vegetation significantly influence the regional variations in SM at the macroscopic level. [ABSTRACT FROM AUTHOR]

Abstract: Given the important contributions of semiarid region to global land carbon cycle, accurate modeling of the interannual variability (IAV) of terrestrial gross primary productivity (GPP) is important but remains challenging. By decomposing GPP into leaf area index (LAI) and photosynthesis per leaf area (i.e., GPP_leaf), we investigated the IAV of GPP and the mechanisms responsible in a temperate grassland of northwestern China. We further assessed six ecosystem models for their capabilities in reproducing the observed IAV of GPP in a temperate grassland from 2004 to 2011 in China. We observed that the responses to LAI and GPP_leaf to soil water significantly contributed to IAV of GPP at the grassland ecosystem. Two of six models with prescribed LAI simulated of the observed IAV of GPP quite well, but still underestimated the variance of GPP_leaf, therefore the variance of GPP. In comparison, simulated pattern by the other four models with prognostic LAI differed significantly from the observed IAV of GPP. Only some models with prognostic LAI can capture the observed sharp decline of GPP in drought years. Further analysis indicated that accurately representing the responses of GPP_leaf and leaf stomatal conductance to soil moisture are critical for the models to reproduce the observed IAV of GPP_leaf. Our framework also identified that the contributions of LAI and GPP_leaf to the observed IAV of GPP were relatively independent. We conclude that our framework of decomposing GPP into LAI and GPP_leaf has a significant potential for facilitating future model intercomparison, benchmarking and optimization should be adopted for future data‐model comparisons. [ABSTRACT FROM AUTHOR]

Rainfall, temperature, and solar radiation are important climate factors, which determine crop growth, development and yield from instantaneous to decadal scales. We propose to identify year patterns of climate impact on yield on the basis of rain and non-rain weather. There are inter-related impacts of climatic factors on crop production within a specific pattern. Historical wheat yield data in Queensland during 1889-2004 were used. The influence of meteorological conditions on wheat yields was derived from statistical yield data which were detrended by 9-year-smoothing averages to remove the effects of technological improvements on wheat yields over time. Climate affects crop growth and development differently over different growth stages. Therefore, we considered the climate effects at both vegetative and reproductive stages (before and after flowering date, respectively) on yield. Cluster analysis was employed to identify the year patterns of climate impact. Five patterns were significantly classified. Precipitation during the vegetative stage was the dominant and beneficial factor for wheat yields while increasing maximum temperature had a negative influence. Crop yields were strongly dependent on solar radiation under normal rainfall conditions. As the effect of rainfall on soil water is relatively long-lasting, its beneficial effect in vegetative stage was higher than its effect during the reproductive stage. The Agricultural Production Systems sIMulator ( APSIM) was evaluated using long-term historical data to determine whether the model could reasonably simulate effects of climate factors for each year pattern. The model provided good estimates of wheat yield when conditions resulted in medium yield levels, however, in extremely low or high yield years, corresponding to extremely low or high precipitation in the vegetative stage, the model tended to underestimate or overestimate. Under high growing season precipitation, simulations responded more favourably to reproductive stage rainfall than measured yields. [ABSTRACT FROM AUTHOR]

The Earth's Critical Zone, where physical, chemical and biological systems interact, extends from the top of the canopy to the underlying bedrock. In this study, we investigated soil moisture controls on phenology and productivity of an Acacia woodland in semi-arid central Australia. Situated on an extensive sand plain with negligible runoff and drainage, the carry-over of soil moisture content (θ) in the rhizosphere enabled the delay of phenology and productivity across seasons, until conditions were favourable for transpiration of that water to prevent overheating in the canopy. Storage of soil moisture near the surface (in the top few metres) was promoted by a siliceous hardpan. Pulsed recharge of θ above the hardpan was rapid and depended upon precipitation amount: 150 mm storm − 1 resulted in saturation of θ above the hardpan (i.e., formation of a temporary, discontinuous perched aquifer above the hardpan in unconsolidated soil) and immediate carbon uptake by the vegetation. During dry and inter-storm periods, we inferred the presence of hydraulic lift from soil storage above the hardpan to the surface due to (i) regular daily drawdown of θ in the reservoir that accumulates above the hardpan in the absence of drainage and evapotranspiration; (ii) the dimorphic root distribution wherein most roots were found in dry soil near the surface, but with significant root just above the hardpan; and (iii) synchronisation of phenology amongst trees and grasses in the dry season. We propose that hydraulic redistribution provides a small amount of moisture that maintains functioning of the shallow roots during long periods when the surface soil layer was dry, thereby enabling Mulga to maintain physiological activity without diminishing phenological and physiological responses to precipitation when conditions were favourable to promote canopy cooling. [ABSTRACT FROM AUTHOR]