Two questions motivated this study: 1) Will meteorological droughts become more frequent and severe during the twenty-first century? 2) Given the projected global temperature rise, to what extent does the inclusion of temperature (in addition to precipitation) in drought indicators play a role in future meteorological droughts? To answer, we analyzed the changes in drought frequency, severity, and historically undocumented extreme droughts over 1981–2100, using the standardized precipitation index (SPI; including precipitation only) and standardized precipitation-evapotranspiration index (SPEI; indirectly including temperature), and under two representative concentration pathways (RCP4.5 and RCP8.5). As input data, we employed 103 high-resolution (0.44°) simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX), based on a combination of 16 global circulation models (GCMs) and 20 regional circulation models (RCMs). This is the first study on global drought projections including RCMs based on such a large ensemble of RCMs. Based on precipitation only, ~15% of the global land is likely to experience more frequent and severe droughts during 2071–2100 versus 1981–2010 for both scenarios. This increase is larger (~47% under RCP4.5, ~49% under RCP8.5) when precipitation and temperature are used. Both SPI and SPEI project more frequent and severe droughts, especially under RCP8.5, over southern South America, the Mediterranean region, southern Africa, southeastern China, Japan, and southern Australia. A decrease in drought is projected for high latitudes in Northern Hemisphere and Southeast Asia. If temperature is included, drought characteristics are projected to increase over North America, Amazonia, central Europe and Asia, the Horn of Africa, India, and central Australia; if only precipitation is considered, they are found to decrease over those areas.

A novel and simple electrochemical immunoassay for C-reactive protein was developed using metal-organic frameworks (Au-MOFs) as signal unit. In this study, we found MOFs could be used as signal probe. And this new class of signal probe differs from traditional probe. The signal of the copper ions (Cu) from MOFs could be directly detected without acid dissolution and preconcentration, which would greatly simplify the detection steps and reduce the detection time. Moreover, MOFs contain large amounts of Cu ions, providing high electrochemical signals. Our report represents the first example of using MOFs themselves as electrochemical signal probe for biosensors. Platinum nanoparticle modified covalent organic frameworks (Pt-COFs) with high electronic conductivity was employed as the substrate, which is the first time demonstrating the use of Pt-COFs for electrochemical immunoassay. Under the optimized experimental conditions, the proposed sensing strategy provides a linear dynamic ranging from 1 to 400 ng/mL. A detection limit of 0.2 ng/mL was obtained, indicating an improved analytical performance. With these merits, this stable, simple, low-cost, sensitive and selective electrochemical immunoassay shows promise for applications in the point-of-care diagnostics of dieses and environmental monitoring.

A 34 year (1979–2012) high-resolution (7 km grid) atmospheric hindcast over the Bohai Sea and the Yellow Sea (BYS) has been performed using COSMO-CLM (CCLM) forced by ERA-Interim reanalysis data (ERA-I). The accuracy of CCLM in surface wind reproduction and the added value of dynamical downscaling to ERA-I have been investigated through comparisons with the QuikSCAT Level2B 12.5 km version 3 (L2B12v3) swath data and in situ observations. The results revealed that CCLM has a reliable ability to reproduce the regional wind characteristics over the BYS. Added value to ERA-I has been detected in the coastal areas with complex orography. CCLM wind quality had strong seasonal variability, with better performance in the summer relative to ERA-I, even in the offshore areas. CCLM was better able to represent light and moderate winds but had even more added value for strong winds relative to ERA-I. The spatial digital filter method was used to investigate the scale of the added value, and the results show that CCLM adds value to ERA-I mainly in medium scales of wind variability. Furthermore, wind climatology was investigated, and significant increasing trends in the south Yellow Sea especially in winter and spring were found for seasonal mean wind speeds

Surface wind is significant for ocean state climate, ocean mixing, and viability of wind energy techniques. However, surface wind simulated from the regional climate model generally features substantial bias from observation. For the first time, this study compares the performance of five bias correction techniques, (1) linear scaling, (2) variance scaling, (3) quantile mapping based on empirical distribution, (4) quantile mapping based on Weibull distribution, and (5) cumulative distribution functions transformation, in reducing the statistical bias of a regional climate model wind output, which was downscaled from a global climate model CNRM‐CM5 during 1991–2000. The surface wind of JRA55 reanalysis data is used as reference. Results show that all bias correction methods are consistent in reducing the climatological mean bias in spatial patterns and intensities. The linear scaling method always performs the worst among all methods in correcting higher‐order statistical biases such as skewness, kurtosis, and wind power density. The other four bias correction methods are generally similar in reducing the statistical biases of different measures based on spatial distribution maps. However, when it comes to spatial averaged mean of statistical measures over CORDEX‐East Asia in January and July, the quantile mapping based on Weibull distribution generally shows the best skills among all methods in bias reduction.