Through an oceanic mixed-layer heat budget analysis, the dominant processes contributing to the largest decay rate (− 0.37 °C/mon) in EP El Nino, the moderate delay rate (− 0.22 °C/mon) in CP El Nino and the smallest decay rate (0.13 °C/mon) in La Nina, are identified. The result shows that both dynamic (wind induced equatorial ocean waves and thermocline changes) and thermodynamic (net surface solar radiation and latent heat flux changes) processes contribute to a fast decay and thus phase transition in EP El Niño composite, whereas the thermodynamic process has less effect on the decay rate for both CP El Niño and La Niña due to the westward shift of sea surface temperature anomaly (SSTA) centers. Thus, the difference in surface wind stress forcing is critical in contributing to evolution asymmetry between CP El Niño and La Niña, while the difference in both the wind stress and heat flux anomalies contribute to evolution asymmetry between EP El Niño and La Niña. It is interesting to note that El Nino induced anomalous anticyclone over the western North Pacific is stronger and shifts more toward the east during EP El Niño than during CP El Niño, while compared to CP El Niño, the center of an anomalous cyclone during La Niña shifts further to the west. As a consequence, both EP and CP El Niño decay fast and transform into a La Niña episode in the subsequent year, whereas La Niña has a much slower decay rate and re-develops in the second year.
Rapidly accelerating climate change in the Himalaya is projected to have major implications for montane species, ecosystems, and mountain farming and pastoral systems. A geospatial modeling approach based on a global environmental stratification is used to explore potential impacts of projected climate change on the spatial distribution of bioclimatic strata and ecoregions within the transboundary Kailash Sacred Landscape (KSL) of China, India and Nepal. Twenty-eight strata, comprising seven bioclimatic zones, were aggregated to develop an ecoregional classification of 12 ecoregions (generally defined by their potential dominant vegetation type), based upon vegetation and landcover characteristics. Projected climate change impacts were modeled by reconstructing the stratification based upon an ensemble of 19 Earth System Models (CIMP5) across four Representative Concentration Pathways (RCP) emission scenarios (i.e. 63 impact simulations), and identifying the change in spatial distribution of bioclimatic zones and ecoregions. Large and substantial shifts in bioclimatic conditions can be expected throughout the KSL area by the year 2050, within all bioclimatic zones and ecoregions. Over 76 % of the total area may shift to a different stratum, 55 % to a different bioclimatic zone, and 36.6 % to a different ecoregion. Potential impacts include upward shift in mean elevation of bioclimatic zones (357 m) and ecoregions (371 m), decreases in area of the highest elevation zones and ecoregions, large expansion of the lower tropical and sub-tropical zones and ecoregions, and the disappearance of several strata representing unique bioclimatic conditions within the KSL, with potentially high levels of biotic perturbance by 2050, and a high likelihood of major consequences for biodiversity, ecosystems, ecosystem services, conservation efforts and sustainable development policies in the region. 相似文献