Suchergebnisse

Abstract. Understanding city residents' collective geotagged behaviors (CGTBs) in
response to hazards and emergency events is important in disaster
mitigation and emergency response. It is a challenge, if not impossible, to
directly observe CGTBs during a real-time matter. This study used the number
of location requests (NLR) data generated by smartphone users for a variety
of purposes such as map navigation, car hailing, and food delivery to
infer the dynamics of CGTBs in response to rainstorms in eight Chinese cities. We examined rainstorms, flooding, and NLR anomalies, as well as the
associations among them, in eight selected cities across mainland China.
The time series NLR clearly reflects cities' general diurnal rhythm, and the
total NLR is moderately correlated with the total city population. Anomalies
of the NLR were identified at both the city and grid scale using the Seasonal Hybrid Extreme Studentized Deviate (S-H-ESD) method. Analysis results demonstrated that the NLR anomalies at the city and
grid levels are well associated with rainstorms, indicating that city residents
request more location-based services (e.g., map navigation, car hailing, food delivery, etc.) when there is a rainstorm. However, the sensitivity of the city residents' collective geotagged behaviors in response to rainstorms varies in different cities as shown by different peak rainfall intensity
thresholds. Significant high peak rainfall intensity tends to trigger city
flooding, which leads to increased location-based requests as shown by
positive anomalies in the time series NLR.

Abstract
The Tibetan Plateau holds the largest mass of snow and ice outside of the polar regions. The deposition of light-absorbing particles (LAPs) including mineral dust, black carbon and organic carbon and the resulting positive radiative forcing on snow (RFSLAPs) substantially contributes to glacier retreat. Yet how anthropogenic pollutant emissions affect Himalayan RFSLAPs through transboundary transport is currently not well known. The COVID-19 lockdown, resulting in a dramatic decline in human activities, offers a unique test to understand the transboundary mechanisms of RFSLAPs. This study employs multiple satellite data from the moderate resolution imaging spectroradiometer and ozone monitoring instrument, as well as a coupled atmosphere–chemistry–snow model, to reveal the high spatial heterogeneities in anthropogenic emissions-induced RFSLAPs across the Himalaya during the Indian lockdown in 2020. Our results show that the reduced anthropogenic pollutant emissions during the Indian lockdown were responsible for 71.6% of the reduction in RFSLAPs on the Himalaya in April 2020 compared to the same period in 2019. The contributions of the Indian lockdown-induced human emission reduction to the RFSLAPs decrease in the western, central, and eastern Himalayas were 46.8%, 81.1%, and 110.5%, respectively. The reduced RFSLAPs might have led to 27 Mt reduction in ice and snow melt over the Himalaya in April 2020. Our findings allude to the potential for mitigating rapid glacial threats by reducing anthropogenic pollutant emissions from economic activities.

Abstract
The northern hemisphere has experienced regional cooling, especially during the global warming hiatus (1998–2012) due to ocean energy redistribution. However, the lack of studies about the natural cooling effects hampers our understanding of vegetation responses to climate change. Using 15,125 ground phenological time series at 3,620 sites since the 1950s and 31-year satellite greenness observations (1982–2012) covering the warming hiatus period, we show a stronger response of leaf onset date (LOD) to natural cooling than to warming, i.e. the delay of LOD caused by 1°C cooling is larger than the advance of LOD with 1°C warming. This might be because cooling leads to larger chilling accumulation and heating requirements for leaf onset, but this non-symmetric LOD response is partially offset by warming-related drying. Moreover, spring greening magnitude, in terms of satellite-based greenness and productivity, is more sensitive to LOD changes in the warming area than in the cooling. These results highlight the importance of considering non-symmetric responses of spring greening to warming and cooling when predicting vegetation-climate feedbacks.