Vegetation phenology is a sensitive indicator of the dynamic response of terrestrial ecosystems to climate change. In this study, the spatiotemporal pattern of vegetation dormancy onset date (DOD) and its climate controls over temperate China were examined by analysing the satellite-derived normalized difference vegetation index and concurrent climate data from 1982 to 2010. Results show that preseason (May through October) air temperature is the primary climatic control of the DOD spatial pattern across temperate China, whereas preseason cumulative precipitation is dominantly associated with the DOD spatial pattern in relatively cold regions. Temporally, the average DOD over China's temperate ecosystems has delayed by 0.13 days per year during the past three decades. However, the delay trends are not continuous throughout the 29-year period. The DOD experienced the largest delay during the 1980s, but the delay trend slowed down or even reversed during the 1990s and 2000s. Our results also show that interannual variations in DOD are most significantly related with preseason mean temperature in most ecosystems, except for the desert ecosystem for which the variations in DOD are mainly regulated by preseason cumulative precipitation. Moreover, temperature also determines the spatial pattern of temperature sensitivity of DOD, which became significantly lower as temperature increased. On the other hand, the temperature sensitivity of DOD increases with increasing precipitation, especially in relatively dry areas (e.g. temperate grassland). This finding stresses the importance of hydrological control on the response of autumn phenology to changes in temperature, which must be accounted in current temperature-driven phenological models.

Water-limited ecosystems, covering~50% of the global land, are controlled primarily by hydrologic factors. Because climate change is predicted to markedly alter current hydroclimatic conditions later this century, a better hydrological indicator of ecosystem performance is warranted to improve understanding of hydrological controls on vegetation and to predict changes in the future. Here we show that the observed total water storage anomaly (TWSA) from the Gravity Recovery and Climate Experiment (GRACE) can serve as this indicator. Using the Australian mainland as a case study, where ecosystems are generally water limited, we found that GRACE-observed TWSA can explain changes in surface greenness (as measured by the normalized difference vegetation index, NDVI) both interannually and seasonally. In addition, we found that TWSA shows a significant decreasing trend during the millennium drought from 1997 through 2009 in the region. However, decline in annual mean NDVI during the same period was mainly driven by decline in annual minimum monthly NDVI, whereas annual maximum monthly NDVI remained relatively constant across biomes. This phenomenon reveals an intrinsic sensitivity of ecosystems to water availability that drought-induced reductions in surface greenness are more likely expressed through its influence on vegetation during lower NDVI months, whereas ecosystem activities tend to recover to their maximum level during periods when the combined environmental conditions favor vegetation growth within a year despite the context of the prolonged drought.

No abstract

[1] Quantifying carbon fluxes at large spatial scales has attracted considerable scientific attentions. In this study, a novel approach was proposed to estimate the terrestrial ecosystem gross primary production (GPP) using imagery from the satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The new model (named Temperature and Greenness Rectangle, TGR) uses a combination of MODIS Enhanced Vegetation Index and Land Surface Temperature products as well as in situ measurement of photosynthetically active radiation to estimate GPP at a 16 day interval. Three major advantages are included in the model: (1) the model follows strictly the logic of the light use efficiency model and each parameter has physical meaning; (2) the model reduces the dependency on ground-based meteorological measurements; and (3) the overlap of information in correlated explanatory variables is avoided. The model was calibrated with data from 17 sites within the Ameriflux network and validated at another 13 sites, covering a wide range of climates and eight major vegetation types. Results show that the TGR model explains reasonably well the tower-based measurements of GPP for all vegetation types, except for the evergreen broadleaf forest, with the coefficient of determination in a range from 0.67 to 0.91 and the root mean square error from 9.0 to 31.9 g C/m 2 /16 days. Comparisons with other two models (the TG and GR model) show that the TGR model generally gives better GPP estimates in nearly all vegetation types, especially under dry climate conditions. These results indicate that the TGR model can be potentially used to estimate GPP at regional scale.

No abstract

No abstract

We investigate the relationship between investor attention and financial market anomalies. We find that anomaly returns tend to be higher following high-attention days. The result is robust after controlling for the effect of news and in a natural experiment setting in which a stock market regulation and rounding errors generate exogenous variations in attention. An analysis of order imbalances suggests that large traders trade on anomaly signals more aggressively upon observing higher attention. We discuss the extent to which the findings are driven by inattention-driven underreaction, bias amplification, or coordinated arbitrage mechanisms, thereby providing insight into the understanding of anomalies.

We study the interdependencies between transaction costs, portfolio characteristics, and mutual fund performance. Using a novel data set of actual mutual fund trades, we find that, controlling for investment style, larger funds realize lower percentage transaction costs than smaller funds. Larger mutual funds trade less frequently and hold bigger stocks to actively avoid incurring higher trading costs. Gross returns of larger funds are lower than those of smaller funds due, in part, to the characteristics of their holdings, which suggests that decreasing returns to scale could arise due to limited investment opportunities because of trading cost constraints. Taken together, our results highlight the trade-offs faced by mutual funds between trading costs, portfolio characteristics, and fund performance as they grow in size. This paper was accepted by Karl Diether, finance.