Can atmospheric reanalyses (CRA and ERA5) represent cloud spatiotemporal characteristics?

Yao, Bin; Teng, Shiwen; Lai, Ruize; Xu, Xiangde; Yin, Yan; Shi, Chunxiang; Liu, Chao

doi:10.1016/j.atmosres.2020.105091

Cited by 31 publications

(24 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At these midlatitudes, higher values of transmissivity were observed due to global climatological mechanisms that correspond to arid regions (e.g., the Sahara Desert) and low cloud cover. These patterns are also consistent with the recent findings of Yao et al [41], who showed that the reanalysis cloud cover CRA and MODIS estimates were smaller over the subtropical high-pressure zones and arid and semi-arid regions due to atmospheric circulation. Values of cloud cover in these regions are consistent with Lu et al [70]; Siedel et al [71]; and Trentberth [73].…”

Section: Latitudinal Pattern Of Transmissivity and Cloud Coversupporting

confidence: 92%

“…The trend obtained in this study was also consistent with the pattern observed by Warren et al [75]. Yao et al [41] showed similar patterns of cloud cover by using the MODIS and CRA reanalysis products. However, a few variations were observed due to lack of observational sites at those particular latitudes, and also due to the presence of the Hadley cell.…”

Section: Latitudinal Pattern Of Transmissivity and Cloud Coversupporting

confidence: 92%

“…Transmissivity values for equatorial climates ranged from 0.35 to 0.63; cloud cover showed values between 44% and 89%. These estimates from the different climatic regions are well in line with the findings from Yao et al [41]. Given this, there was low transmissivity in this area due to the presence of more cloud cover since precipitation and cloud cover are directly inter-related.…”

Section: Atmospheric Transmissivity Based On Köppen Classificationsupporting

confidence: 90%

“…Longman et al [56] showed that for low zenith angle (i.e., for more vertical sun and minimum optical path length), underestimation of atmospheric transmission was greatest, while for high zenith angle (low sun, long optical path length), it overestimated transmissivity for some of the stations. Similar findings of the seasonal mean cloud cover were reported by Yao et al [41] using ERA5, CRA, and MODIS. Additionally, the findings in this study are also in line with results obtained by Poudyal et al [91], who reported average τ values for winter, spring, summer, and autumn of 0.75, 0.61, 0.33, and 0.55, respectively.…”

Section: Monthly Variations Of Transmissivity With Cloud Coversupporting

confidence: 89%

See 3 more Smart Citations

Global Analysis of Atmospheric Transmissivity Using Cloud Cover, Aridity and Flux Network Datasets

et al. 2021

View full text Add to dashboard Cite

Atmospheric transmissivity (τ) is a critical factor in climatology, which affects surface energy balance, measured at a limited number of meteorological stations worldwide. With the limited availability of meteorological datasets in remote areas across different climatic regions, estimation of τ is becoming a challenging task for adequate hydrological, climatic, and crop modeling studies. The availability of solar radiation data is comparatively less accessible on a global scale than the temperature and precipitation datasets, which makes it necessary to develop methods to estimate τ. Most of the previous studies provided region specific datasets of τ, which usually provide local assessments. Hence, there is a necessity to give the empirical models for τ estimation on a global scale that can be easily assessed. This study presents the analysis of the τ relationship with varying geographic features and climatic factors like latitude, aridity index, cloud cover, precipitation, temperature, diurnal temperature range, and elevation. In addition to these factors, the applicability of these relationships was evaluated for different climate types. Thus, empirical models have been proposed for each climate type to estimate τ by using the most effective factors such as cloud cover and aridity index. The cloud cover is an important yet often overlooked factor that can be used to determine the global atmospheric transmissivity. The empirical relationship and statistical indicator provided the best performance in equatorial climates as the coefficient of determination (r2) was 0.88 relatively higher than the warm temperate (r2 = 0.74) and arid regions (r2 = 0.46). According to the results, it is believed that the analysis presented in this work is applicable for estimating the τ in different ecosystems across the globe.

show abstract

Section: Latitudinal Pattern Of Transmissivity and Cloud Coversupporting

confidence: 92%

Section: Latitudinal Pattern Of Transmissivity and Cloud Coversupporting

confidence: 92%

Section: Atmospheric Transmissivity Based On Köppen Classificationsupporting

confidence: 90%

Section: Monthly Variations Of Transmissivity With Cloud Coversupporting

confidence: 89%

See 2 more Smart Citations

Global Analysis of Atmospheric Transmissivity Using Cloud Cover, Aridity and Flux Network Datasets

et al. 2021

View full text Add to dashboard Cite

show abstract

“…For ecologists, satellite remote sensing has become a potential goldmine for monitoring and predicting changes in vegetation activities over large regions in a repeatable manner [6,43,44], which is also widely used by climatologists [24][25][26][45][46][47] and researchers in natural disasters [48][49][50]. Normalized Difference Vegetation Index (NDVI), as an effective indicator to monitor vegetation and natural environment at regional and global scales, has been widely used in the research on vegetation activity [51][52][53].…”

Section: Ndvi and Land Use/land Cover Datamentioning

confidence: 99%

Impact of Three Gorges Reservoir Water Impoundment on Vegetation–Climate Response Relationship

et al. 2020

View full text Add to dashboard Cite

In recent years, the impact of global climate change and human activities on vegetation has become increasingly prominent. Understanding vegetation change and its response to climate variables and human activities are key tasks in predicting future environmental changes, climate changes and ecosystem evolution. This paper aims to explore the impact of Three Gorges Reservoir (TGR) water impoundment on the vegetation–climate response relationship in the Three Gorges Reservoir Region (TGRR) and its surrounding region. Firstly, based on the SPOT/VEGETATION NDVI and ERA5 reanalysis datasets, the correlation between climatic factors (temperature and precipitation) and NDVI was analyzed by using partial correlation coefficient method. Secondly, nonlinear fitting method was used to fit the mapping relationship between NDVI and climatic factors. Then, the residual analysis was conducted to evaluate the impact of TGR impoundment on vegetation–climate response relationship. Finally, sensitivity index (SI), sensitivity variation index (SVI) and difference index (DI) were defined to quantify the variation of vegetation–climate response relationship before and after water impoundment. The results show that water impoundment might have some impacts on the response of vegetation–climate, which gradually reduced with increasing distance from the channel; comparing with the residual analysis method, the SI and DI index methods are more intuitive, and combining these two methods may provide new ideas for the study of the impact of human activities on vegetation.

show abstract

Revisiting mechanisms of the Mesoamerican Midsummer drought

et al. 2022

View full text Add to dashboard Cite

Observations show that the seasonal cycle of precipitation in parts of southern Mexico and Central America exhibits a bimodal signal, known as the Midsummer drought (MSD), but there is no consensus on which processes are most relevant for the two-peak structure of the rainy season. This paper evaluates three hypotheses that could explain the MSD: the SST cloud-radiative feedback, the solar declination angle and the Caribbean Low-Level Jet (CLLJ) moisture transport hypotheses. Model experiments produced by the Met Office Hadley Centre (MOHC) for CMIP6 as well as ERA5 reanalysis data are used to critically assess the predictions of each hypothesis. The simulations capture the double peak signal of precipitation well and reasonably simulate the spatial and temporal variations of the MSD and other relevant climate features such as the CLLJ. Evidence from our analysis suggests that the Eastern Pacific SSTs do not increase in late summer in ERA5 data and only slightly increase in the simulations. More importantly, the Eastern Pacific SST variability in ERA5 and in the model experiments cannot explain the differences in the seasonality of precipitation. The net shortwave radiation at the surface shows a two-peak seasonal cycle; however, this behaviour appears to result from a strong anti-correlation of the incoming shortwave and convective activity due to cloud radiative-effects. There was no evidence found by this study of a causal link in which absorption of shortwave energy forces precipitation variations, as suggested by the solar declination angle hypothesis. The moisture convergence, CLLJ and the precipitable water vapor variations best explain the characteristics of the observed and simulated MSD, particularly for the onset of the MSD. The diagnosed variations of moisture convergence, which are synchronous with the timing of the MSD, point to a dynamic mechanism in which the low-level inflow from the Caribbean is more important for the MSD than other radiative mechanisms.

show abstract

Can atmospheric reanalyses (CRA and ERA5) represent cloud spatiotemporal characteristics?

Cited by 31 publications

References 29 publications

Global Analysis of Atmospheric Transmissivity Using Cloud Cover, Aridity and Flux Network Datasets

Global Analysis of Atmospheric Transmissivity Using Cloud Cover, Aridity and Flux Network Datasets

Impact of Three Gorges Reservoir Water Impoundment on Vegetation–Climate Response Relationship

Revisiting mechanisms of the Mesoamerican Midsummer drought

Contact Info

Product

Resources

About