Guiyun Zhou

Bio: Guiyun Zhou is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Computer science & Point cloud. The author has an hindex of 3, co-authored 3 publications receiving 408 citations.

Declining global warming effects on the phenology of spring leaf unfolding.

Abstract: Spring leaf unfolding has been occurring earlier in the year because of rising temperatures; however, long-term evidence in the field from 7 European tree species studied in 1,245 sites shows that this early unfolding effect is being reduced in recent years, possibly because the reducing chilling and/or insolation render trees less responsive to warming. Spring leaf unfolding has been occurring earlier in the year because of rising temperatures, but some experimental evidence has suggested that the effect is becoming less marked because trees are not receiving the necessary chilling required to trigger leaf unfolding. Shilong Piao and colleagues present evidence based on long-term field observations of seven European tree species studied in 1,245 locations across Europe confirming that a weakening of temperature sensitivity of leaf unfolding is indeed occurring. The authors provide model-based evidence that the chilling effect is at least partially responsible. Earlier spring leaf unfolding is a frequently observed response of plants to climate warming1,2,3,4. Many deciduous tree species require chilling for dormancy release, and warming-related reductions in chilling may counteract the advance of leaf unfolding in response to warming5,6. Empirical evidence for this, however, is limited to saplings or twigs in climate-controlled chambers7,8. Using long-term in situ observations of leaf unfolding for seven dominant European tree species at 1,245 sites, here we show that the apparent response of leaf unfolding to climate warming (ST, expressed in days advance of leaf unfolding per °C warming) has significantly decreased from 1980 to 2013 in all monitored tree species. Averaged across all species and sites, ST decreased by 40% from 4.0 ± 1.8 days °C−1 during 1980–1994 to 2.3 ± 1.6 days °C−1 during 1999–2013. The declining ST was also simulated by chilling-based phenology models, albeit with a weaker decline (24–30%) than observed in situ. The reduction in ST is likely to be partly attributable to reduced chilling. Nonetheless, other mechanisms may also have a role, such as ‘photoperiod limitation’ mechanisms that may become ultimately limiting when leaf unfolding dates occur too early in the season. Our results provide empirical evidence for a declining ST, but also suggest that the predicted strong winter warming in the future may further reduce ST and therefore result in a slowdown in the advance of tree spring phenology.

Semantic Segmentation of 3D Point Clouds Based on High Precision Range Search Network

Abstract: Semantic segmentation for 3D point clouds plays a critical role in the construction of 3D models. Due to the sparse and disordered natures of the point clouds, semantic segmentation of such unstructured data yields technical challenges. A recently proposed deep neural network, PointNet, delivers attractive semantic segmentation performance, but it only exploits the global features of point clouds without incorporating any local features, limiting its ability to recognize fine-grained patterns. For that, this paper proposes a deeper hierarchical structure called the high precision range search (HPRS) network, which can learn local features with increasing contextual scales. We develop an adaptive ball query algorithm that designs a comprehensive set of grouping strategies. It can gather detailed local feature points in comparison to the common ball query algorithm, especially when there are not enough feature points within the ball range. Furthermore, compared to the sole use of either the max pooling or the mean pooling, our network combining the two can aggregate point features of the local regions from hierarchy structure while resolving the disorder of points and minimizing the information loss of features. The network achieves superior performance on the S3DIS dataset, with a mIoU declined by 0.26% compared to the state-of-the-art DPFA network.

Strong but Intermittent Spatial Covariations in Tropical Land Temperature

Abstract: Surface temperature variations across the tropics exhibit different levels of spatial coherence, yet this is poorly characterized. Years of high temperature anomalies occurring simultaneously across large geographical regions have the potential to adversely impact food production and societal well‐being. Using cluster analysis of correlations between extensive temperature measurements from the last six decades, we find a major change occurs in the late 1970s. Two spatial clusters merge to a single dominant one, and therefore, warmer years are experienced at the same time across most tropical land regions. Noting this change occurs at the same time as the Pacific Decadal Oscillation shifts a warm phase, we investigate this potential driver by a range of coupled ocean‐atmosphere‐land climate models. These simulations verify that stronger spatial tropical land temperature coherence tends to occur in Pacific Decadal Oscillation warm phases, although model differences exist in projections of how climate change may modulate this dependence.

Long-term linear trends mask phenological shifts

Abstract: Climate warming has substantially advanced the timing of leaf unfolding, while the temperature sensitivity of leaf unfolding (ST) was significantly reduced over the past three decades according to our recent study (Fu et al. 2015). We are very happy to see that the article from Wang et al. (2016) confirmed these findings using a 15year window, despite using only 927 species-site combinations, which are about one sixth of the species sites (5472) used in our study. However, we cannot agree with the highlighted conclusion that the significant decrease in ST using the 15-year window is not sustained when examining longer-term phenological responses to climate warming. On the contrary, we argue that the long-term linear trends may mask short-term phenological shifts. According to the IPCCAR5, the period since the 1980s was very likely the warmest 30-year period of the last 800 years in the Northern Hemisphere (IPCC, Climate Change 2013); we therefore investigated the phenological changes during this warmest period (Fu et al. 2015). We welcome Wang et al’s study that extended our analyses back to the 1950s and found that ST slightly increased before the 1980s. However, they provided no explanation for this increase and did not quantify the intensity of chilling during this extended period. In addition, it should be noted that previous analyses of phenological data since 1753 in Switzerland also suggested long-term changes in ST based on 30-year windows (Rutishauser et al. 2008). This result actually supports our conclusion that changes in the number of chilling days can elicit changes in ST. As shown

Effects of the Gully Land Consolidation Project on Runoff and Peak Flow Rate on the Loess Plateau, China

Abstract: The Gully Land Consolidation (GLC) project, aiming to create land for agriculture on the Loess Plateau, heavily interfered with the underlying surface and thus affected the hydrological process. The purpose of this study was to investigate the effects of the GLC on the surface runoff and peak flow rates of watershed on the Loess Plateau under different rainfall events and hydrological years. A GIS-based Soil Conservation Service Curve Number (SCS-CN) model was used. The results showed that GLC reduced the mean event surface runoff by 6.2–24.7%, and the reducing efficiency was the highest under light rain events. GLC also decreased annual surface runoff, and the reducing efficiency was 12.04% (normal year) > 7.63% (wet year) > 4.45% (dry year). In addition, GLC decreased the peak flow rate of the watershed by 8.1–30.2% and prolonged the time to peak flow rate. The efficiency of GLC in reducing the peak flow rate was higher under light rain events than that under extraordinary storm events. The reason for the decrease in runoff and peak flow rate after GLC was that the GLC decreased the slope gradient and hydrological connectivity of the watershed. The results will provide guidance for the application of GLC on the Loess Plateau and watershed management for similar regions.

Plant phenology and global climate change: Current progresses and challenges

Abstract: Plant phenology, the annually recurring sequence of plant developmental stages, is important for plant functioning and ecosystem services and their biophysical and biogeochemical feedbacks to the climate system. Plant phenology depends on temperature, and the current rapid climate change has revived interest in understanding and modeling the responses of plant phenology to the warming trend and the consequences thereof for ecosystems. Here, we review recent progresses in plant phenology and its interactions with climate change. Focusing on the start (leaf unfolding) and end (leaf coloring) of plant growing seasons, we show that the recent rapid expansion in ground- and remote sensing- based phenology data acquisition has been highly beneficial and has supported major advances in plant phenology research. Studies using multiple data sources and methods generally agree on the trends of advanced leaf unfolding and delayed leaf coloring due to climate change, yet these trends appear to have decelerated or even reversed in recent years. Our understanding of the mechanisms underlying the plant phenology responses to climate warming is still limited. The interactions between multiple drivers complicate the modeling and prediction of plant phenology changes. Furthermore, changes in plant phenology have important implications for ecosystem carbon cycles and ecosystem feedbacks to climate, yet the quantification of such impacts remains challenging. We suggest that future studies should primarily focus on using new observation tools to improve the understanding of tropical plant phenology, on improving process-based phenology modeling, and on the scaling of phenology from species to landscape-level.

ENSO representation in climate models: from CMIP3 to CMIP5

Abstract: We analyse the ability of CMIP3 and CMIP5 coupled ocean–atmosphere general circulation models (CGCMs) to simulate the tropical Pacific mean state and El Nino-Southern Oscillation (ENSO). The CMIP5 multi-model ensemble displays an encouraging 30 % reduction of the pervasive cold bias in the western Pacific, but no quantum leap in ENSO performance compared to CMIP3. CMIP3 and CMIP5 can thus be considered as one large ensemble (CMIP3 + CMIP5) for multi-model ENSO analysis. The too large diversity in CMIP3 ENSO amplitude is however reduced by a factor of two in CMIP5 and the ENSO life cycle (location of surface temperature anomalies, seasonal phase locking) is modestly improved. Other fundamental ENSO characteristics such as central Pacific precipitation anomalies however remain poorly represented. The sea surface temperature (SST)-latent heat flux feedback is slightly improved in the CMIP5 ensemble but the wind-SST feedback is still underestimated by 20–50 % and the shortwave-SST feedbacks remain underestimated by a factor of two. The improvement in ENSO amplitudes might therefore result from error compensations. The ability of CMIP models to simulate the SST-shortwave feedback, a major source of erroneous ENSO in CGCMs, is further detailed. In observations, this feedback is strongly nonlinear because the real atmosphere switches from subsident (positive feedback) to convective (negative feedback) regimes under the effect of seasonal and interannual variations. Only one-third of CMIP3 + CMIP5 models reproduce this regime shift, with the other models remaining locked in one of the two regimes. The modelled shortwave feedback nonlinearity increases with ENSO amplitude and the amplitude of this feedback in the spring strongly relates with the models ability to simulate ENSO phase locking. In a final stage, a subset of metrics is proposed in order to synthesize the ability of each CMIP3 and CMIP5 models to simulate ENSO main characteristics and key atmospheric feedbacks.

PCT: Point cloud transformer

Abstract: The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer(PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation and normal estimation tasks.

A typology of compound weather and climate events

Abstract: Compound weather and climate events describe combinations of multiple climate drivers and/or hazards that contribute to societal or environmental risk. Although many climate-related disasters are caused by compound events, the understanding, analysis, quantification and prediction of such events is still in its infancy. In this Review, we propose a typology of compound events and suggest analytical and modelling approaches to aid in their investigation. We organize the highly diverse compound event types according to four themes: preconditioned, where a weather-driven or climate-driven precondition aggravates the impacts of a hazard; multivariate, where multiple drivers and/or hazards lead to an impact; temporally compounding, where a succession of hazards leads to an impact; and spatially compounding, where hazards in multiple connected locations cause an aggregated impact. Through structuring compound events and their respective analysis tools, the typology offers an opportunity for deeper insight into their mechanisms and impacts, benefiting the development of effective adaptation strategies. However, the complex nature of compound events results in some cases inevitably fitting into more than one class, necessitating soft boundaries within the typology. Future work must homogenize the available analytical approaches into a robust toolset for compound-event analysis under present and future climate conditions. Research on compound events has increased vastly in the last several years, yet, a typology was absent. This Review proposes a comprehensive classification scheme, incorporating compound events that are preconditioned, multivariate, temporally compounding and spatially compounding events.

Dryland climate change: Recent progress and challenges

Abstract: Drylands are home to more than 38% of the world's population and are one of the most sensitive areas to climate change and human activities. This review describes recent progress in dryland climate change research. Recent findings indicate that the long-term trend of the aridity index (AI) is mainly attributable to increased greenhouse gas emissions while anthropogenic aerosols exert small effects but alter its attributions. Atmosphere-land interactions determine the intensity of regional response. The largest warming during the last 100 years was observed over drylands and accounted for more than half of the continental warming. The global pattern and inter-decadal variability of aridity changes are modulated by oceanic oscillations. The different phases of those oceanic oscillations induce significant changes in land-sea and north-south thermal contrasts, which affect the intensity of the westerlies and planetary waves and the blocking frequency, thereby altering global changes in temperature and precipitation. During 1948-2008, the drylands in the Americas became wetter due to enhanced westerlies, whereas the drylands in the Eastern Hemisphere became drier because of the weakened East Asian summer monsoon. Drylands as defined by the AI have expanded over the last sixty years and are projected to expand in the 21st century. The largest expansion of drylands has occurred in semi-arid regions since the early 1960s. Dryland expansion will lead to reduced carbon sequestration and enhanced regional warming. The increasing aridity, enhanced warming and rapidly growing population will exacerbate the risk of land degradation and desertification in the near future in developing countries.