RESUMEN
Extreme wildfires threaten human lives, air quality, and ecosystems. Meteorology plays a vital role in wildfire behaviors, and the links between wildfires and climate have been widely studied. However, it is not fully clear how fire-weather feedback affects short-term wildfire variability, which undermines our ability to mitigate fire disasters. Here, we show the primacy of synoptic-scale feedback in driving extreme fires in Mediterranean and monsoon climate regimes in the West Coast of the United States and Southeastern Asia. We found that radiative effects of smoke aerosols can modify near-surface wind, air dryness, and rainfall and thus worsen air pollution by enhancing fire emissions and weakening dispersion. The intricate interactions among wildfires, smoke, and weather form a positive feedback loop that substantially increases air pollution exposure.
RESUMEN
Ozone pollution that threatens human health and the ecosystem is a global environmental challenge. In megacities, ozone pollution has long been mainly attributed to anthropogenic sources. However, the processes and mechanisms of cross-regional transport of ozone and its precursors under interactions between mixed sources remain unclear. Here, we show that Northwest Pacific typhoons could intensify the chemical interactions between anthropogenic and biogenic emissions, resulting in extreme ozone pollution in two main city clusters in China. By integrating field and satellite observations together with model simulations, we show that biogenic emission and cross-regional ozone transport are greatly enhanced by approaching typhoons, with the increments reaching up to 78.0 and 22.5%, respectively. Ozone formation efficiency has more than doubled because of abundant precursors and active photochemistry. This study highlights the importance of natural emissions in areas with intensive human activity, which needs to be considered in future air pollution control in China.
RESUMEN
Low clouds play a key role in the Earth-atmosphere energy balance and influence agricultural production and solar-power generation. Smoke aloft has been found to enhance marine stratocumulus through aerosol-cloud interactions, but its role in regions with strong human activities and complex monsoon circulation remains unclear. Here we show that biomass burning aerosols aloft strongly increase the low cloud coverage over both land and ocean in subtropical southeastern Asia. The degree of this enhancement and its spatial extent are comparable to that in the Southeast Atlantic, even though the total biomass burning emissions in Southeast Asia are only one-fifth of those in Southern Africa. We find that a synergetic effect of aerosol-cloud-boundary layer interaction with the monsoon is the main reason for the strong semi-direct effect and enhanced low cloud formation in southeastern Asia.
RESUMEN
The El Niño-Southern Oscillation (ENSO) can significantly affect the rapid intensification of tropical cyclones over the western North Pacific (WNP). However, ENSO events have various durations, which can lead to different atmospheric and oceanic conditions. Here we show that during short duration El Niño events, the WNP tropical cyclone rapid-intensification mean occurrence position migrates westward by ~8.0° longitude, which is caused by reduced vertical wind shear, increased mid-tropospheric humidity, and enhanced tropical cyclone heat potential over the westernmost WNP. The changes in these factors are caused by westward advected upper ocean heat during the decaying phase of a short duration El Niño. As super El Niño events tend to have short durations and their frequency is projected to increase under global warming, our findings have important implications for future projections of WNP tropical cyclone activity.
RESUMEN
In this big data era, it is more urgent than ever to solve two major issues: (i) fast data transmission methods that can facilitate access to data from non-local sources and (ii) fast and efficient data analysis methods that can reveal the key information from the available data for particular purposes. Although approaches in different fields to address these two questions may differ significantly, the common part must involve data compression techniques and a fast algorithm. This paper introduces the recently developed adaptive and spatio-temporally local analysis method, namely the fast multidimensional ensemble empirical mode decomposition (MEEMD), for the analysis of a large spatio-temporal dataset. The original MEEMD uses ensemble empirical mode decomposition to decompose time series at each spatial grid and then pieces together the temporal-spatial evolution of climate variability and change on naturally separated timescales, which is computationally expensive. By taking advantage of the high efficiency of the expression using principal component analysis/empirical orthogonal function analysis for spatio-temporally coherent data, we design a lossy compression method for climate data to facilitate its non-local transmission. We also explain the basic principles behind the fast MEEMD through decomposing principal components instead of original grid-wise time series to speed up computation of MEEMD. Using a typical climate dataset as an example, we demonstrate that our newly designed methods can (i) compress data with a compression rate of one to two orders; and (ii) speed-up the MEEMD algorithm by one to two orders.
