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Outputs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models have been widely used in studies of climate changes related to scenarios at global and regional scales. However, CMIP5 outputs cannot be used directly in analysis of climate changes due to coarse spatial resolution. Here, we proposed a new statistical downscaling method for the downscaling practice of the CMIP5 outputs, i.e. Bias-corrected and station-based Non-linear Regression Downscaling method based on Randomly-Moving Points (BNRD). And up to now, there are only two global downscaled CMIP5 precipitation datasets, i.e. NASA daily downscaled CMIP5 precipitation product and BCSD-based (Bias Correction Spatial Disaggregation) monthly downscaled CMIP5 precipitation product available online, which are both based on BCSD downscaling method. Hence, we evaluated downscaling performance of BNRD by comparing it with the downscaled CMIP5 outputs using the BCSD method in this current study. The results indicate that: (1) during the period for development of the model (1964–2005), the error between downscaled CMIP5 precipitation and GPCC ranges between −50 mm–50 mm at monthly scale. When compared to BCSD-downscaled CMIP5 precipitation, BNRD-downscaled CMIP5 precipitation well reduces errors and avoids underestimation and overestimation of GPCC by BCSD-downscaled CMIP5 precipitation; (2) during period for verification of the downscaling models (2006–2013), the maximum (182 mm), minimum (15 mm) and average (68 mm) RMSEs between BNRD-downscaled CMIP5 precipitation and GPCC are all lower than those between BCSD-downscaled CMIP5 precipitation and GPCC at continental scales. Besides, from the average precipitation viewpoint, BNRD-downscaled CMIP5 precipitation is in higher correlation (around 0.75) with GPCC than BCSD-downscaled CMIP5 precipitation under RCP4.5 and RCP8.5 scenarios at continental scales; (3) BNRD resolved the negative relation to GPCC in the areas near equator, including north part of the South America, southern Africa, northern Australia. In all, BNRD downscaling method developed in this study performs better in describing GPCC changes in both space and time when compared to BCSD and can be used for downscaling practice of CMIP5 and even potentially CMIP6 precipitation outputs over the globe. Unlabelled Image • Propose and develop a new downscaling technique • Compare and corroborate downscaling performance of the proposed downscaling technique • Provide a new candidate downscaling method for precipitation downscaling over globe [ABSTRACT FROM AUTHOR]

The assessment of climate change impacts is usually done by calculating the change in drought conditions between future and historical periods by using multiple climate model simulations. However, this approach usually focuses on anthropogenic climate changes (ACCs) while ignoring the internal climate variability (ICV) caused by the chaotic nature of the climate system. Recent studies have shown that ICV plays an important role in the projected future climate change. To evaluate that role, this study quantifies the contribution of ICV to climate change impacts on regional droughts by using the signal-to-noise ratio (SNR) and the fraction of standard deviation (FOSD) as metrics for China. The internal climate variability or noise (i.e. ICV) is estimated as the inter-member variability of two climate models' large-member ensembles; the signal (i.e. ACC) and the climate model uncertainty (or inter-model uncertainty, IMU) are estimated as the ensemble mean and inter-model variability of 29 global climate models, respectively. The drought conditions are characterized by drought frequency, duration and severity, which are quantified by using the theory of run based on the standardized precipitation evapotranspiration index (SPEI). The results show that deteriorated drought conditions induced by ACCs are projected to occur over China. From the perspective of the SNR, the ICV impacts are less significant compared to the ACC impacts for drought metrics. Remarkable spatial variations of SNRs for future drought metrics are found, with values varying from 0.001 to exceeding 10. In terms of the FOSD, ICV contributions relative to the IMU are large, as FOSDs are >1 for around 22% grids. These results imply the significance of taking into account the impacts of ICV in drought assessment, any study ignores the influence of ICV may be biased. Unlabelled Image • This study quantifies the contribution of internal climate variability to climate change impacts on drought. • The internal climate variability is less influential compared to anthropogenic climate change on future droughts. • The internal climate variability contributes significantly to the uncertainty envelope of drought estimations. [ABSTRACT FROM AUTHOR]

Abstract Good knowledge of the surface air temperature (SAT) is critical for scientific understanding of ecological environment changes and land-atmosphere thermodynamic interactions. However, sparse and uneven spatial distribution of the temperature gauging stations introduces remarkable uncertainties into analysis of the SAT pattern. From a geo-intelligent perspective, here we proposed a new SAT reconstruction method based on the multisource data and machine learning technique which was developed by considering autocorrelation of the in situ observed SAT in both space and time, or simply STAML, i.e. Geoi-SVM (Geo-Intelligent Support Vector Machine), Geoi-BPNN (Geo-Intelligent Back Propagation Neural Network) and Geoi-RF (Geo-Intelligent Random Forest). The multisource data used in this study include the in situ observed SAT and multisource remotely sensed data such as MODIS land surface temperature, NDVI (Normalized Difference Vegetation Index) data. Intermodel comparisons amidst reconstructed SAT data were done to evaluate reconstructing performance of abovementioned models. Besides, the SAT reconstructed by CART (Classification and Regression Tree) was also included to evaluate the reconstructing performance of the models considered in this study when compared to SAT data by CART algorithm. We found that the estimation error of the reconstructed SAT by the STAML is smaller than 0.5 K (Kelvin). In addition, it is interesting to note that the Geoi-RF performs better with Mean Absolute Error (MAE) of lower than 0.25 K, and Root Mean Squared Error (RMSE) and Standard Deviation (SD) of lower than 0.5 K respectively. Correlation coefficients between the reconstructed SAT by Geoi-RF and the observed SAT are close to 1. Besides, the estimation accuracy of the SAT by the Geoi-RF technique is 18.51–63.17% higher than that by the other techniques considered in this study. This study provides a new idea and technique for reconstruction of SAT over large spatial extent at regional and even global scale. Graphical abstract Unlabelled Image Highlights • Estimation of surface air temperature considering multisource data; • Consider spatio-temporal autocorrelation and to reconstruct surface air temperature; • Significantly improve the inversion accuracy of high spatial resolution surface air temperature in a wide range [ABSTRACT FROM AUTHOR]

Lakes and vegetation are important factors of the Earth's hydrological cycle and can be called an “indicator” of climate change. In this study, long-term changes of lakes' area and vegetation coverage in the Qinghai-Tibetan Plateau (QTP) and their relations to the climate change were analyzed by using Mann-Kendall method during the past 30 years. Results showed that: 1) the lakes' area of the QTP increased significantly during the past 30 years as a whole, and the increasing rates have been dramatically sped up since the year of 2000. Among them, the area of Ayakekumu Lake has the fastest growing rate of 51.35%, which increased from 618 km 2 in the 1980s to 983 km 2 in the 2010s; 2) overall, the Normalized Difference Vegetation Index (NDVI) increased in the QTP during the past 30 years. Above 79% of the area in the QTP showed increasing trend of NDVI before the year of 2000; 3) the air temperature increased significantly, the precipitation increased slightly, and the pan evaporation decreased significantly during the past 30 years. The lake area and vegetation coverage changes might be related to the climate change. The shifts in the temporal climate trend occurred around the year 2000 had led the lake area and vegetation coverage increasing. This study is of importance in further understanding the environmental changes under global warming over the QTP. [ABSTRACT FROM AUTHOR]

Abstract Wetlands are thought to be the most unique ecosystem in the world which plays an important role in water and material circulation. However, investigation of ecosystem dynamics in those lake floodplain wetlands that suffering rapid and significant short-term water level fluctuation is quite a challenge. In this study, the short- and long-term characteristics of vegetation NPP (net primary productivity) and their driving mechanism were investigated in the Poyang Lake floodplain wetland, an important international wetland that listed in the Global Eco-region by the World Wildlife Fund (WWF). Attempts were achieved through validating the Carnegie-Ames-Stanford Approach (CASA) model based on observed biomasses of different vegetation types and reconstructed continuous high spatiotemporal resolution (30 m and 16 days) of NDVI data during 2000–2015 according to the fused Landsat and MODIS data. Major result indicates that the intra-annual variation of NPP of most vegetation types shows two peaks in a year due to combined effects of vegetation growth rhythm and seasonal exposure of the lake floodplain. Annual NPP of the lake floodplain ranges in 360.09–735.94 gC/m2 and shows an increasing trend during the study period. The change of NPP in space indicates that the distribution elevation of the major vegetation types decreased and evoluted toward the center lake floodplain. Different from the terrestrial ecosystem, inundation duration is the dominant factor that controls NPP dynamics in the lake floodplain, while the influences of other meteorological factors are much weakened. Recent decline of lake water level was the major reason for the spatio-temporal evolution of annual and seasonal vegetation NPP in the lake floodplain. Graphical abstract Unlabelled Image Highlights • Spatio-temporal evolution of NPP was investigated in a typical lake floodplain. • The increase of NPP in the lake floodplain was significant in autumn. • The distribution elevation of major vegetation types decreased in the lake floodplain. • NPP evolution in the floodplain was dominated by the decline of lake water level. [ABSTRACT FROM AUTHOR]

Abstract The Himalayan-Tibetan Plateau (HTP), often known as the "Third Pole" and the "Asian Water Tower", is the source of water resources for many Asian rivers and in turn for hundreds of millions of people living downstream. The HTP has direct impacts on the establishment and maintenance of Asian monsoon, and therefore on the climate of its surrounding areas. Besides, soil moisture plays a critical role in the hydrological cycle and is a critical link between land surface and atmosphere. Hence, soil moisture was greatly emphasized by Global Climate Observing System Programme as an Essential Climate Variable. However, little is known about soil moisture changes on the HTP from a long-term perspective. By comparing remotely sensed and modelled soil moisture datasets against in-situ observations from 100 observation stations, here we find that Noah performed better than other soil moisture datasets. In past years, soil moisture first decreased and then increased obviously. In most regions on HTP, precipitation changes can be taken as the major cause behind soil moisture variations. In future, there is persistently decreasing soil moisture trend since ~2010 with a decreasing rate of −0.044 kg/m2/10a, −0.031 kg/m2/10a and −0.0p 88 kg/m2/10a under RCP2.6, RCP4.5 and RCP8.5 scenarios, respectively, in CMIP5 (Coupled Model Intercomparision Project Phase 5). Specifically, a sudden decrease of soil moisture with a rate of −0.372 kg/m2/10a can be expected after ~2080 under RCP8.5 scenario. Amplifying terrestrial aridity due to increasing precipitation but more significant increasing potential evapotranspiration potentially results in drying HTP. Potential water deficiency for Asian rivers due to drying HTP should arouse considerable concerns. Graphical abstract Unlabelled Image Highlights • New finding about impacts of soil moisture on near-surface air temperature via evaporation fraction. • Identification of different response regimes via relations between soil moisture and evaporation fraction. • Vegetation coverage is the major influencing factor between coupling between soil moisture and temperature. [ABSTRACT FROM AUTHOR]

Uncertainty is recognized as a critical consideration for accurately predicting stream water nitrogen (N) loading, but identifying the relative contribution of individual uncertainty sources within the total uncertainty remains unclear. In this study, a powerful method, referred to as the Bayesian inference combined with analysis of variance (BayeANOVA) was adopted to detect the timing and magnitude of multiple uncertainty sources and their relative contributions to total uncertainty in simulating daily loadings of three stream water N species (ammonium-N: NH 4 + -N, nitrate-N: NO 3 − -N and total N: TN) in a rice agricultural watershed (the Tuojia watershed) as influenced by non-point source N pollution. Five sources of uncertainty have been analyzed in this study, which arise from model structure, parameters, inputs, interaction effects between parameters and inputs, and internal variability (induced by random errors of model or environment). The results show that uncertainty in parameters relating to the processes of both N and hydrologic cycles contributed the largest fractions of total uncertainty in N loading simulations (58.83%, 63.48% and 61.64% for NH 4 + -N, NO 3 − -N and TN loading, respectively). Additionally, three of the largest uncertainties (i.e. parameters, inputs and interaction effects) in all three simulated N loadings were on average significantly greater in the rice-growing season relative to the fallow season, primarily due to the excess fertilization application during the rice-growing season. The predicted TN uncertainty was mainly attributed to the inaccuracy of NO 3 − -N simulation, which contributed to 75.48% of predicted TN uncertainty. It is concluded that reducing the parameter uncertainty of NO 3 − -N loading simulation during the rice-growing season is the key factor to improving stream water N modeling precision in rice agricultural watersheds. [ABSTRACT FROM AUTHOR]

The rise of global mean temperature has aroused wide attention in scientific communities. To reduce the negative climate change impact, the United Union's Intergovernmental Panel on Climate Change (IPCC) set a goal to limit global warming to 1.5 °C relative to pre-industrial levels based on the previous 2.0 °C target in October 2018. To understand the necessity of more stringent emission reduction, this study investigates the impacts of additional 0.5 °C global warming from 1.5 to 2.0 °C on global extreme precipitation, and especially its socioeconomic consequences. The extreme precipitation is represented by extreme precipitation frequency (R95pF), extreme precipitation percentage (R95pT), and maximum one-day precipitation (RX1day) as indicators, calculated based on daily precipitation data extracted from 29 Coupled Model Inter-comparison Project Phase 5 (CMIP5) global climate models (GCMs) under two representative concentration pathways: RCP4.5 and RCP8.5. The exposures of economy and population to extreme precipitation events are also computed and compared for two warming levels by using the Shared Socioeconomic Pathways (SSPs). The results show that most regions in the world are likely to suffer from increasing extreme precipitation hazards in a warming climate, with ascending gross domestic product (GDP) and population being exposed to extreme dangers with an additional 0.5 °C warming. R95pT and RX1day are projected to increase overwhelmingly throughout all continents, directly leading to intensified precipitation extremes and flash floods. In middle and low latitudes, the annual total wet-day precipitation (PRCPTOT) shows a rich-get-richer trend and R95pF decreases, which will reinforce the intensified trend of the magnitude of extreme precipitation. The exposures of GDP and population in regions with extensive exposure to extreme precipitation events at the 1.5 °C warming increase more remarkably with the additional 0.5 °C warming. In particular, Asia and Africa show lager sensitivity to global warming than other regions. These findings could provide information for mitigation and adaptation policymaking. Unlabelled Image • Response of extreme precipitation was assessed for 1.5 and 2.0 °C warming levels. • Exposures of economy and population to extreme precipitation were investigated. • Dynamic socioeconomic scenarios are used, instead of a fixed socioeconomic scenario. • Most regions would suffer from increasing extreme precipitation hazards with warming. • Asia and Africa show lager sensitivity to global warming than other regions. [ABSTRACT FROM AUTHOR]