This study investigates the usefulness of MODIS (Moderate Resolution Imaging Spectroradiometer) satellite imagery for determining the start, end, and length of the growing season of selected ...deciduous tree species. Vegetation indices derived from satellite imagery provide consistent observations in a similar temporal sequence and are useful for determining phenological phases. Using time series of NDVI (Normalised Difference Vegetation Index) vegetation index from MODIS imagery, phenological patterns were detected at several points in Slovenia and different approaches to determine seasonal phases were compared. In addition, the derived seasonal phases with field phenological and meteorological data were also compared. It has been found that the success of determining phenological phases from satellite imagery depends on many factors: the spatial resolution of the satellite data, the smoothing method for the time series data, the method for determining phenological parameters, and the field data used for comparison. The results of the study show that phenological phases determined by using MODIS data with a resolution of 250 m best match the phenological data maintained by the Slovenian Forestry Institute using the mean seasonal values method.
In this study, we examine the transitions in the monsoon phases (onset, active, break and the withdrawal) during an entire monsoon season. This makes use of a host of observational tools that come ...from GPM (Global Precipitation Measurement) and TRMM (Tropical Rainfall Measuring Mission) satellites for precipitation estimates, the vertical structure of rain, hydrometeors and cloud types from TRMM and CloudSat datasets. During onset, the mean moisture convergence, especially over west and south-west coast of India is 2 × 10
−4
kg m
−1
s
−1
; however, it carries much higher value of >4 × 10
−4
kg m
−1
s
−1
during the active phase over central eastern India. Much lesser moisture convergence (<1 × 10
−4
kg m
−1
s
−1
) is noted over Western Ghats area during the break phase. However, there are northeasterly moisture fluxes present over southern part of India during withdrawal phase. The tall cumulonimbus clouds that extend out to 16 km are illustrate during onset, the active phase is dominated by alto stratus and nimbostratus type clouds that are somewhat shallower. In general, we noted an absence of such clouds during the break and the withdrawal phases. Those structures were consistent in a number of derived fields such as the moisture convergence, moisture fluxes, the energy conversions between the rotational and the divergent kinetic energy and the corresponding phases of the intra-seasonal oscillations.
We study a class of chain-binomial metapopulation models, giving special attention to the ‘mainland–island’ configuration, where patches receive immigrants from an external source. We evaluate the ...distribution of the number
n
t
of occupied patches at any census time
t
and establish a law of large numbers that identifies a deterministic trajectory which can be used to approximate the process when the number of patches is large. We also establish a central limit law, which shows that the fluctuations about this trajectory are approximately normally distributed. We describe briefly much finer results that can be used for model calibration.
Division of flood seasonal phases for reservoir can achieve reasonable adjustments in the flood control levels,and the purpose of improving water use efficiency. This paper refers the recent ...developments of the division of flood seasonal phases for reservoir research results, applications currently used three kinds of quantitative analysis method for reservoir flood phases. Tanghe Reservoir in Liaoning Province as an application example of the flood seasonal phases. The comprehensive analysis of 3 kinds of theoretical and computational methods based on the results obtained for the optimization of Tanghe Reservoir flood season program.
This paper analyzes the effect of the Australian High (AH) on the seasonal phase locking of Indian Ocean Dipole (IOD) events. The anomalous strong AH associated with the positive phase of the ...Antarctic Oscillation can cause significant easterly wind anomalies and northward cross‐equatorial flow over the western Maritime Continent (MC) by strengthening the Australian winter monsoon during May–August. The AH‐associated easterly anomalies and northward cross‐equatorial flow can create thermodynamic air‐sea feedback and contribute to a significant cooling anomaly in the western MC and the tropical eastern Indian Ocean. Without considering the effect of ENSO, these processes contribute to the occurrence of positive IOD events, which begin in early summer, peak in late summer, and decay rapidly thereafter. The effect of ENSO can extend the peak period of IOD into the boreal autumn of that year. An anomalous weak AH corresponds to the occurrence and seasonal phase locking of negative IOD events. Through combined empirical orthogonal function analysis, we find that the effect of the AH can well explain the seasonal phase locking of 34 IOD events (40 in total), which provides an important theoretical basis for the prediction of IOD events.
Plain Language Summary
Seasonal phase locking is a critical characteristic of typical Indian Ocean Dipole (IOD) events. The IOD usually develops in boreal summer, peaks in autumn, and decays rapidly in winter. Note that the Australian High (AH) plays a key role on the seasonal phase locking of IOD events. It is found that The anomalous strong AH associated with the positive phase of the Antarctic Oscillation (AAO) can cause significant easterly wind anomalies and northward cross‐equatorial flow over the western Maritime Continent by strengthening the Australian winter monsoon during May–August.
Key Points
Investigate the effect of Australian High (AH) the on the seasonal phase locking of Indian Ocean Dipole events
The Antarctic Oscillation can strengthen AH and Australian winter monsoon
The AH induced easterlies and cross‐equatorial flow contribute to a significant cooling anomaly in the tropical eastern Indian Ocean
The mechanisms of coupled model bias in seasonal ENSO phase locking are investigated using versions 1.0 and 1.3 of the CSIRO–BOM ACCESS coupled model (hereafter, ACCESS1.0 and ACCESS1.3, ...respectively). The two ACCESS coupled models are mostly similar in construction except for some differences, the most notable of which are in the cloud and land surface schemes used in the models. ACCESS1.0 simulates a realistic seasonal phase locking, with the ENSO variability peaking in December as in observations. On the other hand, the simulated ENSO variability in ACCESS1.3 peaks in March, a bias shown to be shared by many other CMIP5 models. To explore the mechanisms of this model bias, we contrast the atmosphere–ocean feedbacks associated with ENSO in both ACCESS model simulations and also compare the key feedbacks with those in other CMIP5 models. We find evidence that the ENSO phase locking bias in ACCESS1.3 is primarily caused by incorrect simulations of the shortwave feedback and the thermocline feedback in this model. The bias in the shortwave feedback is brought about by unrealistic SST–cloud interactions leading to a positive cloud feedback bias that is largest around March, in contrast to the strongest negative cloud feedback found in ACCESS1.0 simulations and observations at that time. The positive cloud feedback bias in ACCESS1.3 is the result of a dominant role played by the low-level clouds in its modeled SST–cloud interactions in the tropical eastern Pacific. Two factors appear to contribute to the dominance of low-level clouds in ACCESS1.3: the occurrence of a stronger mean descending motion bias and, to a lesser extent, a larger mean SST cold bias during March–April in ACCESS1.3 than in ACCESS1.0. A similar association is found between the positive cloud feedback bias and the biases in spring-time mean descending motion and SST for a group of CMIP5 models that show a seasonal phase locking bias similar to ACCESS1.3. Significant differences are also found between the thermocline feedbacks simulated by ACCESS1.0 and ACCESS1.3 that appear to reinforce the seasonal ENSO phase locking bias in the latter model. We discuss a mechanism by which the thermocline feedback differences could arise from atmospheric forcing differences in the two models.
Traditional seasonal color matching methods of ornamental plants only study the color attributes of plants, but not through qualitative and quantitative research on seasonal color matching of plants, ...resulting in imperfect matching methods. Five landscape units were selected from five representative streets in Harbin, namely, Central Avenue, Gogoli, Zhongshan Road, Xinyang Road, and Huanghe Road. Seasonal periods of Harbin from 2014 to 2018 were defined using the difference in seasonal time division and duration characteristics by the method of climate temperature division, and the beginning time of each season in 2019 was deduced. Meanwhile, according to the NCS color value data of each species in different seasons and the length of each season, the seasonal color matching results of each landscape unit case were established. The results of seasonal color matching of each landscape unit case were evaluated by weighted method. The results showed that the richer the NCS color value of plant species ornamental traits in plant landscape, the more the color seasonal encounter of ornamental traits and the longer the duration, the better the matching results of plant seasonal color. Based on the research results, the NCS color value and its ornamental period of ornamental characters of plant species, the color matching scheme of landscape color, and the selection of plant species were proposed.
The tropical Indian Ocean climate variability is investigated using an artificial neural network analysis called self-organizing map (SOM) for both observational data and coupled model outputs. The ...SOM successfully captures the dipole sea surface temperature anomaly (SSTA) pattern associated with the Indian Ocean Dipole (IOD) and basin-wide warming/cooling associated with ENSO. The dipole SSTA pattern appears only in boreal summer and fall, whereas the basin-wide warming/cooling appears mostly in boreal winter and spring owing to the phase-locking nature of these phenomena. Their occurrence also undergoes significant decadal variation. Composite diagrams constructed for nodes in the SOM array based on the simulated SSTA reveal interesting features. For the nodes with the basin-wide warming, a strong positive SSTA in the eastern equatorial Pacific, a negative Southern Oscillation, and a negative precipitation anomaly in East Africa are found. The nodes with the positive IOD are associated with a weak positive SSTA in the central equatorial Pacific or positive SSTA in the eastern equatorial Pacific, a positive (negative) sea level pressure anomaly in the eastern (western) tropical Indian Ocean, and a positive precipitation anomaly over East Africa. The warming in the central equatorial Pacific appears to correspond to El Niño Modoki discussed recently. These results suggest usefulness of SOM in studying large-scale ocean-atmosphere coupled phenomena.
A new North Atlantic Oscillation (NAO) index, the NAOI, is defined as the differences of normalized sea level pressures regionally zonal-averaged over a broad range of longitudes 80°W-30°E. A ...comprehensive comparison of six NAO indices indicates that the new NAOI provides a more faithful representation of the spatial-temporal variability associated with the NAO on all timescales. A very high signal-to-noise ratio for the NAOI exists for all seasons, and the life cycle represented by the NAOI describes well the seasonal migration for action centers of the NAO. The NAOI captures a larger fraction of the variance of sea level pressure over the North Atlantic sector (20°-90°N, 80°W-30°E), on average 10% more than any other NAO index. There are quite different relationships between the NAOI and surface air temperature during winter and summer. A novel feature, however, is that the NAOI is significantly negative correlated with surface air temperature over the North Atlantic Ocean between 10°-25°N and 70°-30°W, whether in winter or summer. From 1873, the NAOI exhibits strong interannual and decadal variability. Its interannual variability of the twelve calendar months is obviously phase-locked with the seasonal cycle.Moreover, the annual NAOI exhibits a clearer decadal variability in amplitude than the winter NAOI. An upward trend is found in the annual NAOI between the 1870s and 1910s, while the other winter NAOindices fail to show this tendency. The annual NAOI exhibits a strongly positive epoch of 50 years between 1896 and 1950. After 1950, the variability of the annual NAOI is very similar to that of the winter NAO indices.
Many coral reef ecosystems experience shifts in benthic community composition from scleractinian corals to algae. However, consequences of such phase shifts on O₂ availability, important for many ...reef organisms, are unresolved. This study therefore comparatively investigated potential in situ effects of different benthic cover by reef macroalgae and scleractinian corals on water column O₂ concentrations in a Northern Red Sea fringing reef. Findings revealed that mean daily O₂ concentrations at algae-dominated sites were significantly lower compared to coral-dominated sites. Minimum O₂ concentrations were significantly negatively correlated, while diurnal variability in O₂ concentration was significantly positively correlated, with increasing benthic cover by algae. In contrast, no correlation with coral cover was found. These results indicate that shifts from corals to benthic algae may likely affect both in situ O₂ availability and variability. This may be particularly pronounced in reef systems with low water exchange (e.g. closed lagoons) or under calm weather conditions and suggests potential O₂-mediated effects on reef organisms.
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