The Bias Correction and Spatial Downscaling (BCSD) is a trend-preserving statistical downscaling algorithm, which has been widely used to generate accurate and high-resolution data set. We employ the BCSD technique to statistically downscale projected daily maximum temperature (DMT) over China from 13 general circulation models in Coupled Model Intercomparison Project Phase 5 (CMIP5) project to supplement the National Aeronautics and Space Administration Earth Exchange Global Daily Downscaled Projections data set under the Representative Concentration Pathway 2.6 (RCP2.6) scenario. We then compare the differences of DMT and four DMT-related indices (i.e., summer days (SU), annual maximum value of DMT (TXx), intensity, and frequency of heat wave) between before and after downscaling over eight subregions of China. The results indicate that the BCSD method reduces the cool bias of the DMT over the whole China compared with original CMIP5 simulations, especially over the Qinghai-Tibet plateau. The SU increases after downscaling for both China as a whole and most subregions except for South China. The BCSD also affects the mean value of TXx, intensity, and frequency of heat wave at subregional scales, although it shows little impact on China as a whole. Besides, the BCSD reduces the temporal variability of most indices except for the heat wave frequency. The most striking finding is that the intermodel spreads of DMT, SU, TXx, and heat wave intensity are dramatically reduced after downscaling compared with raw CMIP5 simulations. In summary, the BCSD method shows significant improvements to original CMIP5 climate projections under RCP2.6 scenario.

Abstract. The 2015 Paris Agreement set a goal to pursue a global mean temperature below 1.5 °C and well below 2 °C above preindustrial levels. Although it is an important surface hydrology variable, the response of snow under different warming levels has not been well investigated. This study provides a comprehensive assessment of the snow cover fraction (SCF) and snow area extent (SAE), as well as the associated land surface air temperature (LSAT) over the Northern Hemisphere (NH) based on the Community Earth System Model Large Ensemble project (CESM-LE), the CESM 1.5 and 2 °C projects, and the CMIP5 historical RCP2.6 and RCP4.5 products. The results show that the spatiotemporal variations in those modeled products are grossly consistent with observations. The projected SAE magnitude change in RCP2.6 is comparable to that in 1.5 °C, but lower than that in 2 °C. The snow cover differences between 1.5 and 2 °C are prominent during the second half of the 21st century. The signal-to-noise ratios (SNRs) of both SAE and LSAT over the majority of land areas are greater than 1, and for the long-term period, the dependences of SAE on LSAT changes are comparable for different ensemble products. The contribution of an increase in LSAT to the reduction of snow cover differs across seasons, with the greatest occurring in boreal autumn (49–55 %) and the lowest occurring in boreal summer (10–16 %). The snow cover uncertainties induced by the ensemble variability are invariant over time across CESM members but show an increase in the warming signal between the CMIP5 models. This feature reveals that the physical parameterization of the model plays the predominant role in long-term snow simulations, while they are less affected by internal climate variability.

Culture strategy is very important for transnational brand marketing. Functional near-infrared spectroscopy (fNIRS) is a promising brain imaging modality for neuromarketing research. In the present study, we used fNIRS to explore the neural correlates of consumers’ purchase decision on different cross-culture marketing strategies. Forty Chinese participants watched transnational brands and products advertised with photographs of the brands’ original culture (the original culture advertisements) and advertised with photographs of Chinese culture (the mixed culture advertisements), respectively. The behavioral results showed that the female participants showed significantly higher purchase rate when watching the original culture advertisements than the mixed culture advertisements, whereas the male participants did not show significant preference between these two types. The fNIRS results further revealed that for the female participants, watching mixed culture advertisements evoked significant positive activation in the left dorsolateral prefrontal cortex and negative activation in the medial prefrontal cortex, which was not found in the male participants. These findings suggest possible cognitive and emotional differences between men and women in purchase decision making toward different cross-culture marketing strategy. The present study also demonstrates the great potential of fNIRS in neuromarketing research.

Using observational analysis and numerical experiments, we identify that the dipole mode of spring surface wind speed (SWS) over the Tibetan Plateau (TP) could act as a trigger for subsequent winter El Niño–Southern Oscillation events. During the positive phase of spring SWS dipole mode (south‐positive and north‐negative), a self‐sustaining “negative sensible heating–baroclinic structure” prevails over the western TP, which is characterized by negative surface sensible heating anomalies, anomalous low‐level anticyclones, and mid–high‐level cyclones. The “negative sensible heating–baroclinic structure” stimulates the surface westerly wind anomalies over the tropical western Pacific in May through two pathways, favoring the occurrence of subsequent El Niño events. One is through weakening the zonal monsoon circulation over the tropical Indian Ocean and the Walker circulation over the tropical western Pacific. The other is modulating the air–sea interaction over the North Pacific through triggering Rossby waves. The negative SWS dipole mode tends to induce La Niña events.

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A Regional Extreme Climatic Change Index (RECCI), simultaneously considering the changes in intensity, frequency and interannual variability of three major extreme climatic variables (i.e., precipitation, temperature and wind speed), is constructed to represent regional changes of climate extremes in response to global warming. First, the daily outputs from 13 models in the Coupled Model Intercomparison Project phase 5 project in both historical and future simulations under the Representative Concentration Pathway 8.5 scenario are used to compute the extreme climatic indices. Second, the RECCI is computed on both annual and seasonal time scales during three periods (i.e., 2016–2035, 2046–2065 and 2080–2099) over 26 subregions. Finally, the spatiotemporal change of the RECCI is investigated, and then, the 26 subregions are classified into four categories for each period. The first category with the largest RECCI value is very sensitive to global warming, which is called hot spots of climate extremes. The results show that most hot spots are not time invariant on annual and seasonal time scales with some exceptions. On the annual time scale, the Amazon Basin is the only persistent hot spot in all three periods. For the seasonal time scale in March‐April‐May, the climate extremes in the Amazon Basin always display the strongest responsiveness to global warming, and the Eastern Africa is the only persistent hot spot in June‐July‐August in three periods. Similar results are also found for the other two seasons and periods. In addition, the change in extreme temperature is crucial over the East Asia with change in frequency prominent.

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