Zihan Liu, Genta Indra Winata, Peng Xu, Pascale Fung. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.

A comprehensive comparison of the trends and drivers of global surface and canopy urban heat islands (termed I_s and I_c trends, respectively) is critical for better designing urban heat mitigation strategies. However, such a global comparison remains largely absent. Using spatially continuous land surface temperatures and surface air temperatures (2003–2020), here we find that the magnitude of the global mean I_s trend (0.19 ± 0.006°C/decade, mean ± SE) for 5,643 cities worldwide is nearly six‐times the corresponding I_c trend (0.03 ± 0.002°C/decade) during the day, while the former (0.06 ± 0.004°C/decade) is double the latter (0.03 ± 0.002°C/decade) at night. Variable importance scores indicate that global daytime I_s trend is slightly more controlled by surface property, while background climate plays a more dominant role in regulating global daytime I_c trend. At night, both global I_s and I_c trends are mainly controlled by background climate.

The FengYun-2F (FY-2F) geostationary satellite land surface temperature (LST) and its diurnal variation are important when evaluating climate change, the land-atmosphere energy budget, and the hydrological cycle. However, the presence of clouds generates numerous meaningless pixels that constrain the potential application of the available satellite LST products. These pixels covered by cloud are assigned –2, and otherwise are the LST values, based on the result of a double-channel threshold cloud detection algorithm. This paper proposes a combined temporal and spatial information reconstruction method for the missing FY-2F LST data reconstruction with a good spatial continuity, where cloud detection has already been undertaken. Compared with the methods used in the past, the main characteristics of the proposed method are: 1) the consideration of a free parameter $\delta T$ when modeling the diurnal temperature cycle curve; 2) the introduction of the genetic algorithm for solving the parameters; 3) the adoption of the spectral multimanifold clustering algorithm for clustering the multitemporal geostationary satellite LST data; and 4) the accurate and efficient combined temporal and spatial reconstruction method. The proposed combined temporal and spatial reconstruction method was tested and quantitatively assessed with both simulated and real data experiments, using the FY-2F LST products. The results indicate that the combined reconstruction method is accurate to within about 2 °C, which can significantly improve the practical value of FY-2F LST datasets.

Knowledge of the day-to-day dynamics of surface urban heat island (SUHI) as well as their underlying determinants is crucial to a better design of effective heat mitigation. However, there remains a lack of a globally comprehensive investigation of the responsiveness of SUHI variations to meteorological variables. Based on the MODIS land surface temperature and auxiliary data in 2017, here we investigated 10 000+ cities worldwide to reveal day-to-day SUHI intensity (SUHII) variations (termed as SUHII_dv) in response to meteorological variables using Google Earth Engine. We found that: (a) meteorological variables related to the thermal admittance, e.g. precipitation, specific humidity (SH) and soil moisture (SM) (represented by daily temperature range in rural area, DTR_r), reveal a larger regulation on SUHII_dv than those related to the air conditions (e.g. wind speed and near-surface air temperature) over a global scale. (b) Meteorological regulations on SUHII_dv can differ greatly by background climates. The control of SH on SUHII_dv is significantly strengthened in arid zones, while that of wind speed is weakened prominently in equatorial zones. SUHII_dv is more sensitive to SM in cities with higher background temperatures. (c) All meteorological variables, except that related to SM (DTR_r), show larger impact on SUHII_dv with antecedent precipitation over the global scale. Precipitation is observed to mitigate the SUHII_dv globally, and such effects are even more pronounced in equatorial and arid zones. We consider that our findings should be helpful in enriching the knowledge of SUHI dynamics on multiple timescales.

Biodiversity has important implications for the sustainable development of cities. Given the paucity of ground-based experiments, the responses of biodiversity to urbanization and its associated controls on a global scale remain largely unexplored. We present a novel conceptual framework for quantifying the direct and indirect impacts of urbanization on biodiversity in 1523 cities worldwide using the global 100 m grid biodiversity intactness index data (2017–2020) as a proxy for biodiversity. The results show a pervasive positive impact of urbanization on biodiversity in global cities, with a global mean direct and indirect impact of 24.85 ± 9.97% and 16.18 ± 10.92%, respectively. The indirect impact is relatively large in highly urbanized cities in the eastern United States, Western Europe, and the Middle East. The indirect impact is predominantly influenced by urbanization intensity, population density, and background climate. The correlation between urbanization intensity and indirect impact is most pronounced across all climate zones, while the other driving variables influencing the indirect effect exhibited considerable variations. Furthermore, our findings indicate that the biodiversity responses to urbanization are influenced by the biodiversity and development conditions of cities. Our findings have important implications for understanding the impact of urbanization on biodiversity and for future sustainable urban biodiversity.