1,005 research outputs found
Correction to: Urbanization Impact on Regional Climate and Extreme Weather: Current Understanding, Uncertainties, and Future Research Directions (Advances in Atmospheric Sciences, (2022), 39, 6, (819-860), 10.1007/s00376-021-1371-9)
No full textThe article “Urbanization Impact on Regional Climate and Extreme Weather: Current Understanding, Uncertainties, and Future Research Directions”, written by Yun QIAN, TC CHAKRABORTY, Jianfeng LI, Dan LI, Cenlin HE, Chandan SARANGI, Fei CHEN, Xuchao YANG, and L. Ruby LEUNG was originally published electronically on the publisher’s internet portal on 25 of January 2022 with open access.</p
MJO-QBO Model Inter-comparison Data
No full textData in support of the paper "The Lack of a QBO-MJO Connection in Climate Models with a Nudged Stratosphere" by Zane K. Martin, Isla R. Simpson, Pu Lin, Clara Orbe, Qi Tang, Julie M. Caron, Chih-Chieh Chen, Hyemi Kim, L. Ruby Leung, Jadwiga H. Richter, and Shaocheng Xie, currently in preparation for submission.
Data is organized by model, then ensemble member, then the temporal data resolution.
Daily data are daily model OLR (olr/) and precipitation (precip/) in lat/lon/time format, over at least the tropical region spanning all longitudes and 20N to 20S. Daily data also include the Real-time Multivariate MJO index (RMM; RMM_index/) value from each model and ensemble members. OLR and precip files are provided on a 2.5 x 2.5 degree similar grid, rather than the models' native grid.
Monthly data are temperature (temp/, at all vertical levels and all longitudes, from at least 20N to 20S, and the 100 hPa temperature file, as described more in the paper) zonal wind (at all vertical levels, and the 50 hPa wind file; wind/), and TEM vertical velocity (wtem/)
Atmospheric_river_land_hydrology_western_US_HUC8_datasets
No full textDataset for the manuscript entitled "Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds".
It includes daily meteorological and surface hydrological data from western U.S. WRF simulation. Data is aggregated to 8-digit Hydrological Unit (HUC8) watersheds.
To use this dataset, please cite the following two publications:
Chen, X., Leung, L. R., Gao, Y., Liu, Y., Wigmosta, M., & Richmond, M. (2018). Predictability of extreme precipitation in western U.S. watersheds based on atmospheric river occurrence, intensity, and duration. Geophysical Research Letters, 45, 11,693–11,701. https://doi.org/10.1029/2018GL079831
Chen, X., Leung, L. R., Wigmosta, M., & Richmond, M. (2019). Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds. Journal of Geophysical Research: Atmospheres, https://doi.org/10.1029/2019JD03468</p
A multi-algorithm approach for modeling coastal wetland eco-geomorphology
No full textCoastal wetlands play an important role in the global water and biogeochemical
cycles. Climate change makes it more difficult for these ecosystems to adapt
to the fluctuation in sea levels and other environmental changes. Given the
importance of eco-geomorphological processes for coastal wetland resilience,
many eco-geomorphology models differing in complexity and numerical
schemes have been developed in recent decades. However, their divergent
estimates of the response of coastal wetlands to climate change indicate
that substantial structural uncertainties exist in these models. To investigate
the structural uncertainty of coastal wetland eco-geomorphology models,
we developed a multi-algorithm model framework of eco-geomorphological
processes, such as mineral accretion and organic matter accretion, within a
single hydrodynamics model. The framework is designed to explore possible
ways to represent coastal wetland eco-geomorphology in Earth system models
and reduce the related uncertainties in global applications. We tested this model
framework at three representative coastal wetland sites: two saltmarsh wetlands
(Venice Lagoon and Plum Island Estuary) and a mangrove wetland (Hunter
Estuary). Through the model–data comparison, we showed the importance
of using a multi-algorithm ensemble approach for more robust predictions of
the evolution of coastal wetlands. We also found that more observations of
mineral and organic matter accretion at different elevations of coastal wetlands
and evaluation of the coastal wetland models at different sites in diverse
environments can help reduce the model uncertainty
Correction to: Understanding what matters most to patients in acute care in seven countries, using the flash mob study design
No full textFollowing publication of the original article [1], the authors identified an error in the author name of Ling Yan LEUNG. The incorrect author name is: L. E. U. N. G. Ling Yan The correct author name is: Ling Yan LEUNG The author group has been updated above and the original article [1] has been corrected.</p
Cerebral atrophy in mild cognitive impairment and Alzheimer disease: rates and acceleration.
No full textOBJECTIVE: To quantify the regional and global cerebral atrophy rates and assess acceleration rates in healthy controls, subjects with mild cognitive impairment (MCI), and subjects with mild Alzheimer disease (AD). METHODS: Using 0-, 6-, 12-, 18-, 24-, and 36-month MRI scans of controls and subjects with MCI and AD from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we calculated volume change of whole brain, hippocampus, and ventricles between all pairs of scans using the boundary shift integral. RESULTS: We found no evidence of acceleration in whole-brain atrophy rates in any group. There was evidence that hippocampal atrophy rates in MCI subjects accelerate by 0.22%/year2 on average (p = 0.037). There was evidence of acceleration in rates of ventricular enlargement in subjects with MCI (p = 0.001) and AD (p < 0.001), with rates estimated to increase by 0.27 mL/year2 (95% confidence interval 0.12, 0.43) and 0.88 mL/year2 (95% confidence interval 0.47, 1.29), respectively. A post hoc analysis suggested that the acceleration of hippocampal loss in MCI subjects was mainly driven by the MCI subjects that were observed to progress to clinical AD within 3 years of baseline, with this group showing hippocampal atrophy rate acceleration of 0.50%/year2 (p = 0.003). CONCLUSIONS: The small acceleration rates suggest a long period of transition to the pathologic losses seen in clinical AD. The acceleration in hippocampal atrophy rates in MCI subjects in the ADNI seems to be driven by those MCI subjects who concurrently progressed to a clinical diagnosis of AD
Correction to:Understanding what matters most to patients in acute care in seven countries, using the flash mob study design (BMC Health Services Research, (2021), 21, 1, (474), 10.1186/s12913-021-06459-4)
No full textFollowing publication of the original article [1], the authors identified an error in the author name of Ling Yan LEUNG. The incorrect author name is: L. E. U. N. G. Ling Yan The correct author name is: Ling Yan LEUNG The author group has been updated above and the original article [1] has been corrected.</p
Correction to:Understanding what matters most to patients in acute care in seven countries, using the flash mob study design (BMC Health Services Research, (2021), 21, 1, (474), 10.1186/s12913-021-06459-4)
No full textFollowing publication of the original article [1], the authors identified an error in the author name of Ling Yan LEUNG. The incorrect author name is: L. E. U. N. G. Ling Yan The correct author name is: Ling Yan LEUNG The author group has been updated above and the original article [1] has been corrected.</p
Correction to:Understanding what matters most to patients in acute care in seven countries, using the flash mob study design (BMC Health Services Research, (2021), 21, 1, (474), 10.1186/s12913-021-06459-4)
No full textFollowing publication of the original article [1], the authors identified an error in the author name of Ling Yan LEUNG. The incorrect author name is: L. E. U. N. G. Ling Yan The correct author name is: Ling Yan LEUNG The author group has been updated above and the original article [1] has been corrected.</p
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