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23 results for “Indian monsoon”
Tracks of Indian Summer monsoon Low-Pressure Systems from ERA5
<p>This repository contains the codes and dataset as explained below:</p> <p>1) This dataset contains downstream and in situ LPS tracks over the Bay of Bengal (BoB) classified using the algorithm developed by Srujan et al. (2021) from 1979-2017 using the ERA5 reanalysis dataset. The LPS are tracked from mean sea level pressure (MSLP) using the algorithm developed by Praveen et al. (2015).</p> <p>2) This also contains Principal components (PCs) of Rossby filtered OLR. </p> <p>3) The code to compute Transfer Entropy between PC1 of Rossby filtered OLR over West Pacific region and MSLP anomaly over BoB. </p> <p><strong>References:</strong></p> <p>Praveen, V., Sandeep, S., & Ajayamohan, R. S. (2015). <strong>On the relationship between mean monsoon precipitation and low pressure systems in climate model simulations</strong>. <em>Journal of Climate</em>, <em>28</em>(13), 5305-5324.</p> <p>Srujan, K. S. S. S., Sandeep, S., & Suhas, E. (2021). <strong>Downstream and In Situ Genesis of Monsoon Low‐Pressure Systems in Climate Models</strong>. <em>Earth and Space Science</em>, <em>8</em>(9), e2021EA001741.</p>
Tracks of Indian Summer monsoon Low-Pressure Systems from ERA5
<p>This repository contains the codes and dataset as explained below:</p> <p>1) This dataset contains downstream and in situ LPS tracks over the Bay of Bengal (BoB) classified using the algorithm developed by Srujan et al. (2021) from 1979-2017 using the ERA5 reanalysis dataset. The LPS are tracked from mean sea level pressure (MSLP) using the algorithm developed by Praveen et al. (2015).</p> <p>2) This also contains Principal components (PCs) of Rossby filtered OLR. </p> <p>3) The code to compute Transfer Entropy between PC1 of Rossby filtered OLR over West Pacific region and MSLP anomaly over BoB. </p> <p>4) Codes and data to perform Kolmogorov Smirnov (KS) test.</p> <p><strong>References:</strong></p> <p>Praveen, V., Sandeep, S., & Ajayamohan, R. S. (2015). <strong>On the relationship between mean monsoon precipitation and low pressure systems in climate model simulations</strong>. <em>Journal of Climate</em>, <em>28</em>(13), 5305-5324.</p> <p>Srujan, K. S. S. S., Sandeep, S., & Suhas, E. (2021). <strong>Downstream and In Situ Genesis of Monsoon Low‐Pressure Systems in Climate Models</strong>. <em>Earth and Space Science</em>, <em>8</em>(9), e2021EA001741.</p>
Indian Summer monsoon Low-Pressure Systems Dataset from ERA5
<p>This dataset contains downstream and in situ LPS genesis dates and their location over the Bay of Bengal classified using the algorithm developed by Srujan et al. (2021) from 1979-2017 using the ERA5 reanalysis dataset. The LPS are tracked from mean sea level pressure using the algorithm developed by Praveen et al. (2015).</p> <p><strong>References:</strong></p> <p>Praveen, V., Sandeep, S., & Ajayamohan, R. S. (2015). <strong>On the relationship between mean monsoon precipitation and low pressure systems in climate model simulations</strong>. <em>Journal of Climate</em>, <em>28</em>(13), 5305-5324.</p> <p>Srujan, K. S. S. S., Sandeep, S., & Suhas, E. (2021). <strong>Downstream and In Situ Genesis of Monsoon Low‐Pressure Systems in Climate Models</strong>. <em>Earth and Space Science</em>, <em>8</em>(9), e2021EA001741.</p>
Data for "Opposing changes in Indian Summer Monsoon Rainfall variability produced by orbital and anthropogenic forcing"
<p>The dataset for the CAM5 and LBM experiments is presented in the manuscript titled "Opposing changes in Indian summer monsoon rainfall variability produced by orbital and anthropogenic forcing. And the proxy data for the paper.</p>
Data from: River interlinking alters land-atmosphere feedback and changes the Indian summer monsoon.
<p>The dataset contains post-processed output from two experiments performed for Indian Summer Monsoon (June-September) from 1991-2012 using WRF-CLM4: CTL and IRR. Here, CTL represents WRF-CLM4 simulation with irrigation currently practiced in India. We use a modified irrigation module in CLM4 that better represents the Indian practices of irrigation by incorporating groundwater withdrawal and flood irrigation practiced over paddy fields. The module can be found at <a href="https://github.com/IMMM-SFA/WRF_CLM4_Irrigation">https://github.com/IMMM-SFA/WRF_CLM4_Irrigation</a> and <a href="https://doi.org/10.1029/2019GL083875">https://doi.org/10.1029/2019GL083875</a>. IRR simulation adds additional irrigation to CTL by increasing the percentage of irrigated area to 80% in regions where India's river-interlinking projects target an increase in the culturable command area.</p> <p>The post-processed output contains the following variables:</p> <ol> <li>Mean Daily Temperature</li> <li>Daily Maximum Temperature</li> <li>Latent Heat Flux</li> <li>Sensible Heat Flux</li> <li>Relative Humidity</li> <li>U-Wind at Pressure levels</li> <li>V-Wind at Pressure levels</li> <li>Net-Solar Radiation on Land</li> <li>Soil Moisture</li> </ol> <p>Irrigation input files for CTL and IRR simulations of WRF-CLM4 are also included.</p>
RAEI Data for Sensitivity of the Indian Monsoon to regional aerosol emissions
<p>This folder contains the data from bespoke air quality simulations that were analyzed in the paper "Sensitivity of the Indian Monsoon to regional aerosol emissions".</p>
PDRMIP Data for Sensitivity of the Indian Monsoon to regional aerosol emissions
<p>This folder contains the data from the Precipitation Driver Response Model Intercomparison Project (PDRMIP) that were analyzed in the paper "Sensitivity of the Indian Monsoon to regional aerosol emissions".</p>
Unprecedented failure of the Northeastern Indian Monsoon and recent water scarcity
<p>Dataset used in the publication on "<strong>Unprecedented failure of the Northeastern Indian Monsoon and recent water scarcity" submitted in GRL.</strong></p>
Abrupt Indian summer monsoon shifts aligned with Heinrich events and D-O cycles since MIS 3
<p>We present a new high-resolution speleothem-based record of Indian summer monsoon (ISM) variability ranging from ~ 45,000 to 34,000 yr BP combined with a published record up to 5,500 yr BP from Mawmluh cave, NE (North East) India</p>
Monsoon Mission Coupled Forecast System Version 2.0: Model Description and Indian Monsoon Simulations Figures
<p>Monsoon Mission Coupled Forecast System Version 2.0: Model Description and Indian Monsoon Simulations Figures</p>
Coupled Ocean-atmospheric forcing on Indian Summer Monsoon variability during the middle Holocene: Insights from the Core Monsoon Zone speleothem record.
<p>Stable oxygen and carbon isotope data of stalagmite sample during middle Holocene time from Mahadev cave of Jagdalpur region in Central India.</p>
GFDL AM2.1 Model Output -Changes in Monsoon Meteorological Elements Due to the Warming of the Tropical Indian Ocean
<p>Changes in Climatological Mean Stream Function, Eddy Flux, Wind Field, Moisture Flux, and Precipitation from May to September Due to the Warming of the Indian Ocean</p>
Extreme events in Indian Monsoon linked to Global warming scenario during Bølling–Allerød
<p>Stable Oxygen isotope data from stalagmite samples of Kailash cave, Central India, during the Bølling-Allerød warmth </p>
Supporting Data for "Coccolithophore Assemblage Response to Indian Monsoon-ENSO Teleconnections During the Last Century" - AGU Paleoceanography and Paleoclimatology
<p>Data (Tables S1-S3) supporting the manuscript "Coccolithophore Assemblage Response to Indian Monsoon-ENSO Teleconnections During the Last Century". Files are uploaded as .csv format. Below are descriptions of the data generated from this study. In Table S1, the thickness data of von Rad et al. (1999), publicly available in <a href="https://doi.pangaea.de/10.1594/PANGAEA.63129" target="_blank" rel="noopener">PANGAEA,</a> were used to construct the revised core composite stratigraphy for Core SO90-39KG. Please refer to the manuscript for details of the referenced publication.</p> <p> </p> <table> <tbody> <tr> <td>Table</td> <td>Table Description</td> </tr> <tr> <td>S1</td> <td>Revised composite stratigraphy of core SO90-39KG.</td> </tr> <tr> <td>S2</td> <td>Raw count data of all coccolithophore species identified in sediment samples in core SO90-39KG.</td> </tr> <tr> <td>S3</td> <td>Relative abundance data of all coccolithophore species identified in sediment samples in core SO90-39KG.</td> </tr> </tbody> </table>
CESM data: Response of the low-level jet to precession and itsimplications for proxies of the Indian monsoon
<p>This repository contains the simulation output of precession minimum and maximum experiments carried out using the CESM 1.2.0. The experimental setup is the same as that of Bosmans et al. (2014). Each simulation is run of 100 years, and the Jun-Jul-Aug mean of the last 50 years of the simulation is provided here. The climatological monthly mean of total precipitation from the pre-industrial control simulation is also deposited here. </p> <p> </p> <p>Contact: Chetankumar Jalihal<br> email: jalihal@iisc.ac.in</p> <p> </p> <p>Acknowledgments:<br> These simulations were carried out as a part of Chetankumar Jalihal's Ph.D. thesis, funded by the Ministry of Human Resource Development, Government of India, and the Centre for Excellence in the Divecha Centre for Climate Change (DCCC), supported by the Department of Science and Technology, Government of India. We also acknowledge the Supercomputer Education and Research Centre (SERC), Indian Institute of Science, Bangalore, for making available the computation facilities to carry out the simulations.</p>
Supporting Data for "The Toba Eruption 74,000 Years ago Strengthened the Indian Winter Monsoon - Evidence from Coccolithophores" - AGU Paleoceanography and Paleoclimatology
<p>All data tables (Tables S1-S9) supporting the manuscript "The Toba Eruption 74,000 Years Ago Strengthened the Indian Winter Monsoon - Evidence from Coccolithophores". Files are uploaded as .csv or in Excel (.xlsx) formats, both compressed as a .zip file. See below for a brief description of the contents of each table found in both formats. Please refer to the manuscript for details of the referenced publications in the dataset.</p> <p> </p> <table> <tbody> <tr> <td>Table</td> <td>Table Description</td> </tr> <tr> <td>S1</td> <td>Ages used for calculating the error-weighted mean age of the YTT eruption (Crick et al., 2021; Du et al., 2019; Mark et al., 2017; Svennson et al., 2013).</td> </tr> <tr> <td>S2</td> <td>RAMPFIT parameters used to construct the modified age model in this study.</td> </tr> <tr> <td>S3</td> <td>Resampled L* reflectance data (Deplazes et al., 2013c) used for RAMPFIT modeling.</td> </tr> <tr> <td>S4</td> <td>Modified age model used in this study.</td> </tr> <tr> <td>S5</td> <td><em>Florisphaera profunda</em> and <em>Helicosphaera carteri</em> relative abundance, estimated primary productivity, and paleo-temperature estimates based on the <em>Gephyrocapsa </em>transfer function.</td> </tr> <tr> <td>S6</td> <td>Calculations of the duration of the enclosed interval and the lower limb using various age models.</td> </tr> <tr> <td>S7</td> <td>Summary of calculations of sedimentation rates and the estimated duration of the enclosed interval.</td> </tr> <tr> <td>S8</td> <td>Comparison of selected "warm" and "cold" periods between the dataset of Rogalla & Andruleit (2005) and this study.</td> </tr> <tr> <td>S9</td> <td>Original and adjusted age models of selected marine (Deplazes et al., 2013c) and terrestrial proxy records (Andersen et al., 2004; Du et al., 2019; Svensson et al., 2013) in this study.</td> </tr> </tbody> </table>
Indian summer monsoon variability during the late Quaternary northeastern Arabian Sea
<p>This dataset contains benthic foraminifera abundance and Stable isotope values from northeastern Arabian sea since the 18 cal Kyr BP.</p>
The linear baroclinic model ouput forced by Indian summer monsoonal diabatic heating.
<p>The linear baroclinic model (Watanabe and Kimoto 2000, 2001) ouput forced by Indian summer monsoonal diabatic heating. The LBM source code can be requested via https://ccsr.aori.u-tokyo.ac.jp/~lbm/sub/lbm.html.</p> <p>Forcing file: ISM_forcing.nc<br> Simulation output files: LBM_T42_xxx.nc; each file denotes different monthly mean atmohpsheric basic state is used from May to September.</p> <p>The data are generated and analyzed in the following study:</p> <p>Li, S., Sato, T., Nakamura, T. <em>et al.</em> East Asian summer rainfall stimulated by subseasonal Indian monsoonal heating. <em>Nat Commun</em> <strong>14</strong>, 5932 (2023). https://doi.org/10.1038/s41467-023-41644-5</p>
Dataset: Sea-level and monsoonal control on the Maldives carbonate platform (Indian Ocean) over the last 1.3 million years
<p>Datasets used in the article "Sea-level and monsoonal control on the Maldives carbonate platform (Indian Ocean) over the last 1.3 million years" by M. Alonso-Garcia et al. (2023)</p> <p>In this study, we used elemental geochemical compositional records, obtained by X-ray fluorescence (XRF) core-scanning, from IODP Site U1467, in the Maldives Sea (Indian Ocean), to investigate how sea-level and coupled ocean-atmosphere dynamics affected the production and export of carbonate platform sediments to the Maldives Inner Sea over the last 1.3 Ma. The Sr/Ca ratio has been interpreted as a proxy for neritic carbonate production at the Maldives platform and its export to the periplatform sediments. The record of the Sr/Ca ratio has been combined with the Br normalized record, as a proxy for organic matter content linked to pelagic primary productivity and water column mixing, and with other proxies from Site U1467 that indicate variations in the monsoon dynamics, such as the Fe/K ratio, as a proxy for summer monsoon intensity, and the Fe input for winter monsoon intensity (Kunkelova et al., 2018). The combination of all those proxies suggests that during the last 1.3 Ma changes in the carbonate production and export in the Maldives region responded to sea-level variations but also to climate fluctuations related to monsoon dynamics. Moreover, the long-term patterns observed in the records can be related to the MPT and MBE events.</p>
Impact of improved ocean initial condition on the seasonal prediction of Indian summer monsoon
<p><span><span><span><span><span><span><span><span><span><span><span><span>In this study, an effort has been made to show the impact of improved ocean initial condition in the coupled forecast system (CFSv2) on the seasonal prediction skill of Indian summer monsoon rainfall (ISMR). CFSv2 is used as an operational dynamical model for the seasonal prediction of ISMR. The new improved ocean initial condition is based on <span>Global Ocean Data Assimilation System</span> (GODAS) analysis and is produced by assimilating vertical profiles of observed temperature and salinity from all the sources (XBTs, buoys and Argo profiling floats) over the global ocean using 3Dvar assimilation scheme and MOM4p1 ocean model. This new analysis is improved compared to the NCEP GODAS which uses earlier generation MOM4p0d and assimilates observed temperature and synthetic salinity. Twin sets of identical model experiments differing in initial conditions (IC) with the former (later) using NCEP IC (new IC; NIC) are performed. The NIC experiment shows consistent enhancement of ENSO skill compared to NCEP IC. This advancement leads to the improvement of ISMR skill. We found that the significant improvement of surface and sub-surface temperature, thermocline depth, and heat content over the global ocean particularly in the Nino3 region in the NIC compared to NCEP IC contributed to the improved ISMR skills. This enhanced ISMR skill score might be the result of reduced model drift in the NIC even on 4 month lead and capturing the ISMR – ENSO teleconnection with great fidelity.</span></span></span></span></span></span></span></span></span></span></span></span></p>
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