Sustained upregulation of widespread hippocampal-neocortical coupling following memory encoding

Systems consolidation of new experiences into lasting episodic memories involves interactions between hippocampus and the neocortex. Evidence of this process is seen already during early awake post-encoding rest periods. Functional MRI (fMRI) studies have demonstrated increased hippocampal coupling with task-relevant perceptual regions and reactivation of stimulus-specific encoding patterns following intensive encoding tasks. Here we investigate the spatial and temporal characteristics of these hippocampally anchored post-encoding neocortical modulations. Eighty-nine adults participated in an experiment consisting of interleaved memory task- and resting-state periods. As expected, we observed increased post-encoding functional connectivity between hippocampus and individually localized neocortical regions responsive to stimulus categories encountered during memory encoding. Post-encoding modulations were however not restricted to stimulus-selective cortex, but manifested as a nearly system-wide upregulation in hippocampal coupling with all major functional networks. The spatial configuration of these extensive modulations resembled hippocampal-neocortical interaction patterns estimated from active encoding operations, suggesting hippocampal post-encoding involvement by far exceeds reactivation of perceptual aspects. This reinstatement of encoding patterns during immediate post-encoding rest was not observed in resting-state scans collected 12 hours later, nor in control analyses estimating post-encoding neocortical modulations in functional connectivity using other candidate seed regions. The broad similarity in hippocampal functional coupling between online memory encoding and offline post-encoding rest suggests reactivation in humans may involve a spectrum of cognitive processes engaged during experience of an event.

Spatial and temporal characteristics of evaporation trends over India during 1971-2000

Evaporation and rainfall data for the period 1971-2000 for 58 well distributed stations over India were selected for this study. Trends of these two parameters for the country as a whole and for individual stations for annual (January – December), winter (December, January and February), summer (March – May), monsoon (June – September) and post-monsoon (October, November) period were analysed and tested for significance at 95% level of confidence. The analysis shows that for the country as a whole, the evaporation has significantly decreased in all seasons while there is no significant trend in rainfall. Out of 58 stations, numbers of stations having significant decrease in evaporation are 45 (annual), 30 (winter), 42 (summer) and 35 (monsoon and post monsoon seasons). Decadal analysis of trends shows that the variability of evaporation towards the decreasing trend is steadily maintained throughout the period but more in the decade 1991-2000. Spatial analysis of the seasonal trends of evaporation indicates the decreasing trends over all parts of India except northeast where it is increasing. Regions of significant decrease in evaporation viz., North, Southwest and Southeast and increase in evaporation viz., Northeast emerge from the spatial analysis of trends over the country. Spatial analysis of seasonal rainfall trends indicates the increasing trends in southern parts and decreasing trends in central and northeastern parts of the country. Evaporation trends of nearly 50% stations (mostly in southern parts of India) show complimentary relation with rainfall of the same period. Rest of the long term trends in evaporation may be due to the variation in other parameters like wind speed, cloud cover, sunshine duration etc. which needs further examination.

Spatial and temporal characteristics of drought and its correlation with climate indices in Northeast China

The spatial and temporal characteristics of drought in Northeast China are investigated, using monthly meteorological data from 140 stations over the period 1970–2014. The study area was divided into three regions using hierarchical cluster analysis based on the precipitation and potential evapotranspiration data. The standardized precipitation evapotranspiration index (SPEI) was calculated for each station on 3-month and 12-month time scales. The Mann-Kendall (MK) trend test and Sen’s slope method were applied to determine the trends for annual and seasonal SPEI time series. Periodic features of drought conditions in each sub-region and possible relationship with large-scale climate patterns were respectively identified using the continuous wavelet transform (CWT) and cross wavelet transform. The results show mitigations in spring and winter droughts and a significant increasing trend in autumn drought. On the annual scale, droughts became more severe and more intense in the western regions but were mitigated in the eastern region. CWT analysis showed that droughts in Northeast China occur predominantly in 14- to 42-month or 15- to 60-month cycles. Annual and seasonal droughts have 2- to 6-year cycles over the three defined regions. Cross wavelet analysis also shows that the statistically significant influence of large-scale climate patterns (the Southern Oscillation, the Atlantic Multidecadal Oscillation, the Arctic Oscillation, and the Polar–Eurasian Pattern) on drought in Northeast China is concentrated in a 16- to 50-month period, possibly causing drought variability in the different regions. The Southern Oscillation, Polar–Eurasia pattern, and Arctic Oscillation are significantly correlated with drought on decadal scales (around 120-month period). The findings of this study will provide valuable reference for regional drought mitigation and drought prediction.

The Variabilities of Convective Precipitation and Large-scale Precipitation in Southern China under Global Warming

In this paper, the spatial and temporal characteristics of convective precipitation (CP) and large-scale precipitation (LSP) in southern China during 1980-2020 are analyzed using monthly mean precipitation data from MERRA-2. In addition, the possible effects of relative humidity on CP and LSP are explored. The results indicate the following. (1) The LSP dominates the proportion of total precipitation (TP). Both LSP and CP are more prevalent in the south and less prevalent in the north, but there is a difference in the regions of their maximum centers. (2) Significant interannual and seasonal variations are observed in precipitation. TP and LSP tended to be higher than average after the 1990s, while for the CP, a negative trend has dominated the past years with considerable fluctuation. There are obvious increasing trends for TP and LSP, with area-averaged linear trends of 7.0 mm/year and 8.9 mm/year, respectively, while that of CP is -1.9 mm/year. The increasing trends of LSP are mainly contributed by the precipitation of summer and autumn. (3) The variations of LSP are affected by relative humidity in the troposphere, while CP is only influenced by the changes in relative humidity due to air temperature or specific humidity. The trend of relative humidity is -0.32%/decade, mainly due to rising temperature in the troposphere. (4) Changes in specific humidity caused by temperature or specific humidity alone act on large-scale precipitation through both interannual and interdecadal processes, causing large-scale precipitation to increase. And the convective precipitation is mainly affected by the interdecadal processes.

Using feature-based verification methods to explore the spatial and temporal characteristics of the 2019 chlorophyll-<i>a</i> bloom season in a model of the European Northwest Shelf

Two feature-based verification methods, thus far only used for the diagnostic evaluation of atmospheric models, have been applied to compare ∼7 km resolution pre-operational analyses of chlorophyll-a (Chl-a) concentrations to a 1 km gridded satellite-derived Chl-a concentration product. The aim of this study was to assess the value of applying such methods to ocean models. Chl-a bloom objects were identified in both data sets for the 2019 bloom season (1 March to 31 July). These bloom objects were analysed as discrete (2-D) spatial features, but also as space–time (3-D) features, providing the means of defining the onset, duration and demise of distinct bloom episodes and the season as a whole. The new feature-based verification methods help reveal that the model analyses are not able to represent small coastal bloom objects, given the coarser definition of the coastline, also wrongly producing more bloom objects in deeper Atlantic waters. Model analyses' concentrations are somewhat higher overall. The bias manifests itself in the size of the model analysis bloom objects, which tend to be larger than the satellite-derived bloom objects. The onset of the bloom season is delayed by 26 d in the model analyses, but the season also persists for another month beyond the diagnosed end. The season was diagnosed to be 119 d long in the model analyses, compared to 117 d from the satellite product. Geographically, the model analyses and satellite-derived bloom objects do not necessarily exist in a specific location at the same time and only overlap occasionally.