Vegetation-induced asymmetric diurnal land surface temperatures changes across global climate zones.

Unprecedented global vegetation greening during past decades is well known to affect annual and seasonal land surface temperatures (LST). However, the impact of observed vegetation cover change on diurnal LST across global climatic zones is not well understood. Using global climatic time-series datasets, we investigated the long-term growing season daytime and nighttime LST changes globally and explored associated dominant contributors including vegetation and climate factors including air temperature, precipitation, and solar radiation. Results revealed asymmetric growing season mean daytime and nighttime LST warming (0.16 °C/10a and 0.30 °C/10a, respectively) globally from 2003 to 2020, as a result, the diurnal LST range (DLSTR) declined at 0.14 °C/10a. The sensitivity analysis indicated the LST response to changes in LAI, precipitation, and SSRD mainly concentrated during daytime instead of nighttime, however, which showed comparable sensitivities for air temperature. Combining the sensitivities results and the observed LAI and climate trends, we found rising air temperature contributes to 0.24 ± 0.11 °C/10a global daytime LST warming and 0.16 ± 0.07 °C/10a nighttime LST warming, turns to be the dominant contributor to the LST changes. Increased LAI cooled global daytime LST (-0.068 ± 0.096 °C/10a) while warmed nighttime LST (0.064 ± 0.046 °C/10a); hence LAI dominates declines in DLSTR trends (-0.12 ± 0.08 °C/10a), despite some day-night process variations across climate zones. In Boreal regions, reduced DLSTR was due to nighttime warming from LAI increases. In other climatic zones, daytime cooling, and DLSTR decline, was induced by increased LAI. Biophysically, the pathway from air temperature heats the surface through sensible heat and increased downward longwave radiation during day and night, while the pathway from LAI cools the surface by enhancing energy redistribution into latent heat rather than sensible heat during the daytime. These empirical findings of diverse asymmetric responses could help calibrate and improve biophysical models of diurnal surface temperature feedback in response to vegetation cover changes in different climate zones.

Prediction of fishing intensity and trends across South China Sea biogeographic zones.

The volume of industrial fishing in the South China Sea ranks among the top global sustainable fisheries concerns of the Food and Agriculture Organization (FAO). To better understand the scale of management challenges, biogeographic zones of the SCS were characterized, and within each a multivariate GAM (General Additive Model) was fitted to predict and map the complete fishing activities from 2017 to 2020. Model variables, some incomplete or with gaps, included: VIIRS DNB night-time light imagery; Global Fisheries Watch (GFW) data; satellite Ocean Colour; Sea Surface Temperature; and bathymetry data. Four biogeographic zones with differing fishing patterns and trends were identified. We used cross-validation and the GAM model's own tuning method for model prediction accuracy determination, which performed well in four biogeographic zones (R2 respectively: 0.62, 0.68, 0.74 and 0.71). High-intensity fishing grounds are mainly distributed in offshore continental shelf areas. From 2017 to 2019, high-intensity fishing grounds were located near the Beibu Gulf of Vietnam, south Vietnam, part of the Gulf of Thailand and the central Java Sea, where fishing effort greater than 50 h exceeded average annual SCS fishing intensity for several years. By season, intensity and extent of fishing in Spring were largest. In 2020, due to the impact of COVID-19, except for Spring, fishing volume generally decreased. Our experimental results provide new insights and an adaptable biogeographic modelling methodology to map the scale and intensity of regional fishing activities more accurately and completely. This more comprehensive database, that takes account of intrinsic biogeographic fishery context, will help improve and strengthen the regulation of fishing activities around the world.

Widespread global disparities between modelled and observed mid-depth ocean currents.

The mid-depth ocean circulation is critically linked to actual changes in the long-term global climate system. However, in the past few decades, predictions based on ocean circulation models highlight the lack of data, knowledge, and long-term implications in climate change assessment. Here, using 842,421 observations produced by Argo floats from 2001-2020, and Lagrangian simulations, we show that only 3.8% of the mid-depth oceans, including part of the equatorial Pacific Ocean and the Antarctic Circumpolar Current, can be regarded as accurately modelled, while other regions exhibit significant underestimations in mean current velocity. Knowledge of ocean circulation is generally more complete in the low-latitude oceans but is especially poor in high latitude regions. Accordingly, we propose improvements in forecasting, model representation of stochasticity, and enhancement of observations of ocean currents. The study demonstrates that knowledge and model representations of global circulation are substantially compromised by inaccuracies of significant magnitude and direction, with important implications for modelled predictions of currents, temperature, carbon dioxide sequestration, and sea-level rise trends.

Rapid greening response of China's 2020 spring vegetation to COVID-19 restrictions: Implications for climate change.

The 2019 novel coronavirus pandemic (COVID-19) negatively affected global public health and socioeconomic development. Lockdowns and travel restrictions to contain COVID-19 resulted in reduced human activity and decreased anthropogenic emissions. However, the secondary effects of these restrictions on the biophysical environment are uncertain. Using remotely sensed big data, we investigated how lockdowns and traffic restrictions affected China's spring vegetation in 2020. Our analyses show that travel decreased by 58% in the first 18 days following implementation of the restrictions across China. Subsequently, atmospheric optical clarity increased and radiation levels on the vegetation canopy were augmented. Furthermore, the spring of 2020 arrived 8.4 days earlier and vegetation 17.45% greener compared to 2015-2019. Reduced human activity resulting from COVID-19 restrictions contributed to a brighter, earlier, and greener 2020 spring season in China. This study shows that short-term changes in human activity can have a relatively rapid ecological impact at the regional scale.