414 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
Get PDFCoastal 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
Atmospheric_river_precipitation_predictability_data
No full text<p>Data for the manuscript entitled "Predictability of Extreme Precipitation Associated With Atmospheric Rivers in Western U.S. Watersheds".</p>
<p> </p>
<p>It includes daily precipitation data from WRF and PRISM. Also includes atmospheric river information derived from ARTMIP Tier 1 archive.</p>
<p> </p>
<p>The tools used to generate the figures in the paper is at: <a href="https://github.com/lucas-uw/Chen-2018-GRL">https://github.com/lucas-uw/Chen-2018-GRL</a></p>
<p> </p>
<p>If you use this dataset, please cite the following paper:</p>
<p> </p>
<p>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. <a href="http://doi.org/10.1029/2018GL079831">https://doi.org/10.1029/2018GL079831</a></p>
<p> </p>
<p>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, <a href="http://doi.org/10.1029/2019JD03468">https://doi.org/10.1029/2019JD03468</a></p>
Precipitation objects under the current and future climate: WRF 6-km hydroclimate simulation of the western US
No full textThis folder includes the precipitation objects that are used in the following manuscript:
Chen et al., Sharpening of Cold Season Storms over the Western US.
It is generated using WRF V3.8 at PNNL. A historical simulation ("NARR") is done for 1981-2010, and five future simulations ("CanESM2", "CESM1-CAM5", "GFDL-ESM2M", "HadGEM2-ES", "MPI-ESM-MR") are done for 2041-2070 using the Pseudo Global Warming (PGW) approach. For the WRF model configuration and the simulation details, please refer to the abovementioned manuscript and Chen et al. (2018).
This is the preliminary version of the dataset that contains the precipitation object features as analyzed in the manuscript. More data (including the WRF raw precipitation output) and the finalized scripts will be included here before the manuscript is published.
Reference:
Chen, X., L. R. Ruby, Y. Gao, Y. Liu, M. Wigmosta, and M. Richmond (2018), Predictability of Extreme Precipitation in Western U.S. Watersheds Based on Atmospheric River Occurrence, Intensity, and Duration, Geophys. Res. Lett. doi: 10.1029/2018GL079831
Chen, X., L. R. Ruby, Y. Gao, Y. Liu, and M. Wigmosta (xxxx), Sharpening of Cold Season Storms over the Western US, Nat. Clim. Change
Author contributions can be clarified
No full textKey Points
Author order is an ambiguous indicator of contribution
Greater clarity is needed
The optional CrediT standard provides greater clarit
Threat by marine heatwaves to adaptive large marine ecosystems in an eddy-resolving model.
Get PDFMarine heatwaves (MHWs), episodic periods of abnormally high sea surface temperature (SST), severely affect marine ecosystems. Large Marine Ecosystems (LMEs) cover ~22% of the global ocean but account for 95% of global fisheries catches. Yet how climate change affects MHWs over LMEs remains unknown, because such LMEs are confined to the coast where low-resolution climate models are known to have biases. Here, using a high-resolution Earth system model and applying a "future threshold" that considers MHWs as anomalous warming above the long-term mean warming of SSTs, we find that future intensity and annual days of MHWs over majority of the LMEs remain higher than in the present-day climate. Better resolution of ocean mesoscale eddies enables simulation of more realistic MHWs than low-resolution models. These increases in MHWs under global warming poses a serious threat to LMEs, even if resident organisms could adapt fully to the long-term mean warming
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