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304 results for “monsoons”
Climate model output for "The Unexpected Oceanic Peak in Energy Input to the Atmosphere and its Consequences for Monsoon Rainfall"
<p>Climate model output associated with the manuscript "The Unexpected Oceanic Peak in Energy Input to the Atmosphere and its Consequences for Monsoon Rainfall"</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>
Paleobotanical precipitation estimates used in Williams et al.: African hydroclimate monsoon during the early Eocene from the DeepMIP simulations, Paleoceanography and Paleoclimatology, 2022
<p>Paleobotanical precipitation estimates used in Williams et al.: African hydroclimate monsoon during the early Eocene from the DeepMIP simulations, Paleoceanography and Paleoclimatology, 2022.</p>
Dataset - Warming-induced monsoon precipitation phase change intensifies glacier mass loss in the southeastern Tibetan Plateau
<p>Materials and data results needed to reproduce the findings of the study published in (PNAS) Proceedings of the National Academy of Sciences of the United States of America:</p> <p>"<em>Warming-induced monsoon precipitation phase change intensifies glacier mass loss in the southeastern Tibetan Plateau"</em>. A. Jouberton, T. E. Shaw, E. Miles, M. McCarthy, S. Fugger, S. Ren, A. Dehecq, W. Yang and F. Pellicciotti</p> <p>It includes the meteorological forcing time-series, an exhaustive list of the model parameters, the outputs of TOPKAPI-ETH, and Matlab scripts allowing to reproduce the figures and compute the numbers given in the main manuscript as well as in the Supplementary Information.</p> <p>---------------</p> <p><strong>Contents</strong> :</p> <p>Folder : "Matlab_scripts"<br> '<strong>Climate_import.m</strong>' : Organizes meteorological forcing and generates Figure S9<br> <strong> 'TOPKAPI_result_import.m' :</strong> Imports TOPKAPI's reference run outputs and prepares them for analysis<br> <strong> 'Experiment_analysis.m' : </strong>Analyses the results of the forcing experiments, generates Figure 4 and Figure S25<br> <strong> 'Main_text_results.m' : </strong>Analyses the results of TOPKAPI's reference runs, generates Figure 1D, FIgure 2 and Figure 3<br> <strong> 'TOPKAPI_validation.m' : </strong>Compares TOPKAPI's reference run results with several validation datasets, generates the figures and performance metrics of the model calibration and validation procedure.<br> <strong> 'Parlung_albedo_regional_analysis.m'</strong>: Computes the mean glacier albedo per elevation band for each glacier within the Southeastern Tibean Plateau and compares it to the albedo of Parlung No.4 glacier.<br> <strong> 'Parlung_GMB_regional_analysis.m'</strong>: Computes the mean glacier mass balance per elevation band for each glacier within the Southeastern Tibean Plateau and compares it to the glacier mass balance of Parlung No.4 glacier.<br> <strong> 'Precipitation_phase_sensitivity_analysis.m'</strong>: Performs a sensitivity analysis on the simulated monsoon snowfall ratio per elevation band and on the attribution of glacier mass loss to precipitation<br> phase change using Monte Carlo simulations.<br> <strong> 'TOPKAPI_MODIS_validation.m'</strong>: Compares the snow cover at Parlung No.4 catchment simulated by TOPKAPI-ETH and observed by MODIS, generates Figure S19.</p> <p> </p> <p>Folder : "Remote_sensing" :</p> <p> Sub-Folder: 'Hugonnet' = Glacier mass balance averaged over 2000-2020 covering the Southeastern Tibetan Plateau, 100m resolution, derived from Hugonnet et al. 2021<br> Sub-Folder: 'MODIS' = contains the snow cover at Parlung No.4 derived from the daily product MOD10A1 version 61, for the period 2000-2018<br> Sub-Folder: 'Regional_glacier_albedo' = contains the annual glacier surface albedo from 2000 to 2020, covering the Southeastern Tibetan Plateau, 500m resolution.<br> Sub-Folder: 'Shapefiles' = contains the Parlung No.4 glacier outlines in 1974 and from the RGI 6.0<br> <strong>'ASTER_Nyainqentanglha_15m_utm.tif'</strong> = ASTER Digital elevation model at 15m resolution covering the Southeastern Tibetan Plateau<br> <strong> 'parlung_mask_1974.mat' </strong>= Parlung No.4 glacier mask as a matlab file<br> <strong> 'dh_ASTER_SRTM_30m.tif' </strong>= Mean elevation change rate from 2000 to 2016 at Parlung No.4 catchment.<br> <strong> 'Geodetic_map.mat'</strong> = Elevation change maps for the periods 1974-2000 and 1974-2014, as a matlab file<br> <strong> 'GMB_geodetic.mat' </strong>= Geodetic mass balance (glacier-wide mean and profile per elevation band) used in Figure S13<br> <strong> 'parlung_30m_catchment_mask.tif'</strong> = Parlung No.4 catchment mask<br> <strong>'parlung_1974_30m_dem.tif' </strong>= DEM of Parlung No.4 catchment, 30 m resolution<br> <strong> 'parlung_1974_30m_gla.tif'</strong> = Parlung No. 4 glacier mask, 30m resolution<br> <strong>'parlung_1974_30m_glah.tif' </strong>= Reconstructed ice thickness of 1975 for Parlung No.4 glacier<br> <strong>'parlung_1974_2000_diff_24m.tif' </strong>= Elevation change from DEM differencing at Parlung No.4 catchment for 1974-2000<br> <strong> 'parlung_1974_2014_diff_24m.tif' </strong>= Elevation change from DEM differencing at Parlung No.4 catchment for 1974-2014<br> <strong> 'Parlung_1974_bedrock_dem_30m.tif' </strong>= Bedrock surface digital elevation model of the catchment, 30m spatial resolution<br> <strong>'RGI_KangriKarpo_100m_utm_id.tif' </strong>= Glacier mask covering the Kangri Karpo mountain region, 100m resolution, with glacier IDs in the attribute table<br> <strong> 'RGI_Nyainqentanglha_100m_utm_id.tif' </strong>= = Glacier mask covering the Southeastern Tibetan Plateau, 100m resolution, with glacier IDs in the attribute table</p> <p> </p> <p>Folder : "TOPKAPI_forcing" :<br> <strong> CCT_AWS4600_extended.csv : </strong>Hourly cloud cover transmissivity from 1975 to 2018 reconstructed at AWSoff location <br> <strong> Climate.mat : </strong>Organizes meteorological forcings, output from the matlab script '<strong>Climate_import.m</strong>'<br> <strong> LR_AWS4600_extended.csv :</strong> Hourly temperature lapse-rates from 1975 to 2018 reconstructed at AWSoff location <br> <strong> Precipitation_AWS4600_extended.csv : </strong>Hourly precipitation from 1975 to 2018 reconstructed at AWSoff location <br> <strong> Ta_AWS4600_extended.csv :</strong> Hourly air temperature from 1975 to 2018 reconstructed at AWSoff location<br> Sub-Folder: 'National_meteorological_stations' = Contains the daily air temperature and precipitation measured at the national meteorological stations of Bomi, Zayu, Zuogong and Basu<br> Sub-Folder: 'Reference_run_inputs' = Contains the input files necessary to run TOPKAPI-ETH to obtain the outputs from which the results of this study are based on.</p> <p> </p> <p>Folder : "TOPKAPI_output":<br> <strong> </strong> Sub-Folder : "Forcing experiment" = organized TOPKAPI outputs from the forcing experiment<br> Sub-Folder :" Reference_run_outputs" = raw TOPKAPI outputs from the reference run (catchment average, spatial and grid cells)<br> Sub-Folder : "Reference_run_results" = organized TOPKAPI outputs from the reference run<br> Sub-Folder : "Snow_ice_cover" = contains TOPKAPI-ETH derived snow cover maps (daily map outputs)<br> Sub-Folder : "Regional_analysis" =<br> 'Alb'= Table containing the mean glacier albedo (2000-2020) per normalized elevation band, for each glacier in the SETP (RGI 6.0)<br> 'GMB'= Table containing the mean glacier mass balance (2000-2020) per normalized elevation band, for each glacier in the SETP (RGI 6.0)<br> 'Hypso_xxm' = Table containing the percentage of glacier area per normalized elevation band, for each glacier in the SETP (RGI 6.0), resolution of 100/500m<br> 'NormEl_100m' = Table containing the elevation per normalized elevation band, for each glacier in the SETP (RGI 6.0), resolution of 100/500m<br> Sub-Folder : "Semi_distributed_outputs" = Precipitation phase and amounts resulting from TOPKAPI-ETH simulation per elevation band, for the reference run and for the Monte Carlo sensitivity analysis</p> <p> </p> <p>Folder : "Validation_data"<br> <strong> 'topkapi.out_reference_discharge2016' </strong>= <strong> </strong>raw TOPKAPI outputs run in 2016 with AWSoff air temperature<br> <strong> </strong><strong> 'master_file_parlung.mat' </strong>=<strong> </strong>matlab structure containing AWS measurements, necessary for running <strong>'TOPKAPI_validation.m'</strong><br> <strong> 'Qdigit.mat' </strong>= Discharge measured at the Parlung No.4 glacier outlet, from Li et al., (2016)<br> <strong> 'Parlung_Q_1970.mat'</strong> = 'Discharge time-series used to run TOPKAPI-ETH (goes back to 1975, but filled with 0 when no measurements are available)</p> <p> </p> <p>In order to run the Matlab scripts, it is recommended to download all folders and gather them into the same folder. Any request about data or questions on how to run the Matlab scripts can be asked to the author of the paper (at achille.jouberton@wsl.ch).</p>
Paddy rice methane emissions across Monsoon Asia
<p>Although rice cultivation is one of the most important agricultural sources of methane and contributes ~8 % of total global anthropogenic emissions, large discrepancies remain among estimates of global methane emissions from rice cultivation due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5 degrees) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combined paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning, and produced gridded up-scaling estimates of rice methane emissions at 5000-m resolution at 8-day intervals across Monsoon Asia, where ~87% of global rice area is cultivated and ~90% of global rice production occurs.<br> </p>
Detection of abrupt changes in East Asian monsoon from Chinese loess and speleothem records
<p>There is a great interest concerning recent occurrences of tipping points in the climate system and great concern about those that could occur in the near future as a result of anthropogenic forcing. A lot of attention has been devoted to the study of past Dansgaard-Oeschger events, abrupt warmings of about 12°C on a time-scale of about 50 yrs that occurred during the last glacial period. Great effort is also dedicated to understanding the Atlantic Meridionnal overturning circulation and the Amazon forest dieback, which are already entering an unstable regime leading to tipping behavior.</p> <p>Instead, here we focus on the study of critical transitions in the SE Asian Monsoon that have occurred in the past 3.6 Myrs by a novel combination of advanced statistical tools (KS-test, recurrence quantification analysis). The SE Asian Monsoon is characterized by variations in the grain size with the occurrence of coarse material characterizing a strong winter monsoon mechanism with grains transported from the Chinese northern deserts by strong winds generated by the Siberian High located northward. By contrast intervals of fine grain size characterized periods during which the summer monsoon was rather reinforced. We have analyzed high-resolution grain-size datasets derived from Chinese loess sequences, i.e. the CHILOMOS and the LGS640 datasets, that we compare with the Chinese composite speleothem d<sup>18</sup>O records that arguably provide one the best representation of the Earth’s climate in the last 650 kyrs. Although visually observed rapid grain-size variations were previously interpreted as representing millennial-scale variations, our statistical analysis shows that both winter and summer monsoons co-varied at glacial-interglacial to millennial timescales. Analyzing a third dataset, i.e., MQSG, our statistical analysis shows that both winter and summer monsoon variations reflect a three-stage evolution of increasing intensity: (1) from 3.6 Ma to 2.6 Ma, (2) from 2.6 Ma to 1.2 Ma, and (3) from 1.2 Ma to present, with the winter monsoon strength increasing over these3 main steps.</p> <p>List of tables</p> <p><strong><span>Table 1</span></strong><span> KS test of the NGRIP and Hulu cave </span><span>d</span><sup><span>18</span></sup><span>O for the last climate cycle. Comparison of the dates of abrupt warmings/moistening (left) and cooling/drying transitions (right). Labels for NGRIP according to Rasmussen et al. </span><span><span>(2014)</span></span><span> and for Hulu cave according to Wang et al. </span><span><span>(2008)</span></span><span>. </span></p> <p><strong><span>Table 2</span></strong><span> KS test of the Chinese speleothem </span><span>d</span><sup><span>18</span></sup><span>O and of the CHILOMOS grain-size composite for the last two climate cycles. Comparison of the dates of abrupt moistening (left) and drying transitions (right). Labels for the moistening transitions in the Chinese speleothem are from Wang et al. </span><span><span>(2008)</span></span><span> and in CHILOMOS from Yang and Ding </span><span><span>(2014)</span></span><span>. The labels of the drying in CHILOMOS are from the present study</span></p> <p><strong><span>Table 3</span></strong><span> RQA of the Chinese speleothem </span><span>d</span><sup><span>18</span></sup><span>O and of the CHILOMOS grain-size composite for the last two climate cycles. Dates of the minima are identified by the RR prominence, shown together with the equivalent transitions detected by the KS method. For easier reading, the dates have been re-ordered from younger to older. The most significant minima are highlighted in yellow. The original ranking is given in Suppl. Tab 1.</span></p> <p><strong><span>Table 4 </span></strong><span>KS test of the Chinese speleothem<span> </span></span><span>d</span><sup><span>18</span></sup><span>O and of the LGS640 dataset for the last 640 Myrs. Comparison of the dates of abrupt moistening and drying transitions in both records. The dates found in both records are highlighted in red and in blue for drying or moistening events respectively.</span></p> <p><strong><span>Table 5 </span></strong><span>RQA of the LGS640 grain-size composite for the last seven climate cycles. Dates of the minima are identified by the RR prominence. The most significant minima (RR prominence >0.5) are highlighted in yellow. </span></p> <p><span><span> </span><strong>Table 6 </strong>KS test and<strong> </strong>RQA of MGSQ grain dataset for the last 3.6 Myrs. In this analysis the moistening and drying transitions is labeled as warming and cooling. On the left, KS results with the corresponding marine isotope stage (MIS) boundaries. On the right, RQA results with minima ordered according their prominence value. Dates with RR>0.6 are highlighted in yellow. </span></p> <p><strong><span>Table Supp.1.</span></strong><span> Abrupt transitions over the past 130 kyrs BP from the NGRIP, Chinese Speleothem and CHILOMOS records. Identification of the common abrupt warmings or moistenings on the left, and abrupt coolings or dryings on the right.<span> </span>Differences between the highest and lowest transition dates. Indication of the NGRIP and Chinese interstadials and stadials (GI-GS and A-SA respectively).</span></p> <p><strong><span>Table Supp.2.</span></strong><span> RQA of the 250 kyrs Chinese speleothem and CHILOMOS records ranked according the RR prominence, the chronology. Indication of the time difference between the identified transitions.</span></p> <p><strong><span>Table Supp.3.</span></strong><span> Comparison of the KS-test results from the LGS 640 and the Chinese speleothem over the past 650 kyrs. Indication of the Marine isotope stratigraphy and the number of cool and warm transitions and the percentage of drying events per climate cycle</span></p>
Figure 20 in Taxonomic revision of dragon lizards in the genus Diporiphora (Reptilia: Agamidae) from the Australian monsoonal tropics
Figure 20. Diporiphora granulifera sp. nov.: a, in life, Lawn Hill, Queensland (photo: S. Wilson). b, c, d, dorsal, ventral and lateral (head) views of holotype QM J96362 (formerly NMV D74060) Downs Road, 2 km from Barkly Highway, Queensland.
Figure 19 in Taxonomic revision of dragon lizards in the genus Diporiphora (Reptilia: Agamidae) from the Australian monsoonal tropics
Figure 19. Diporiphora gracilis sp. nov. Images of holotype (WAM R177291, formerly NMV D77540), Fairfield-Leopold Downs Road, south of Gibb River Road, Western Australia: a, in life (photo: J. Sumner); b, c, d, dorsal, ventral and, lateral (head) views. Pattern variation (individuals from Mornington Station, Western Australia: e, adult male with breeding colour; f, gravid female (plain); g, gravid female (patterned) (photos: Melissa Bruton, Australian Wildlife Conservancy).
Figure 18 in Taxonomic revision of dragon lizards in the genus Diporiphora (Reptilia: Agamidae) from the Australian monsoonal tropics
Figure 18. Diporiphora margaretae: a, adult male (NMV D73834), King Edward River, Kimberley, Western Australia (photo: J. Melville); b, holotype WAM R27648, Kalumburu, Western Australia.
Figure 22 in Taxonomic revision of dragon lizards in the genus Diporiphora (Reptilia: Agamidae) from the Australian monsoonal tropics
Figure 22. Diporiphora pallida sp. nov. Images of holotype (WAM R177292, formerly MNV D73853), Mitchell Plateau, Western Australia: a, in life (photo: J. Melville); b, c, dorsal and ventral views; d, collection location.
Figure 17 in Taxonomic revision of dragon lizards in the genus Diporiphora (Reptilia: Agamidae) from the Australian monsoonal tropics
Figure 17. Diporiphora magna: a, adult male, Larrimah, Northern Territory (photo: S. Wilson); b, holotype WAM R42786, Old Lissadell (now submerged by Lake Argyle), Western Australia.
Figure 15 in Taxonomic revision of dragon lizards in the genus Diporiphora (Reptilia: Agamidae) from the Australian monsoonal tropics
Figure 15. Diporiphora bilineata: a, adult in non-breeding colours, Casuarina, Northern Territory (photo: S. Wilson); b, syntypes BMNH 1946.8.12.75–76, Port Essington, Northern Territory.
Figure 14 in Taxonomic revision of dragon lizards in the genus Diporiphora (Reptilia: Agamidae) from the Australian monsoonal tropics
Figure 14. Distributions of D. magna, D. bilineata, D. lalliae, D. margaretae, D. gracilis sp. nov., D. granulifera sp. nov. and D. carpentariensis sp. nov. based on specimens examined and collection records.
Figure 13 in Taxonomic revision of dragon lizards in the genus Diporiphora (Reptilia: Agamidae) from the Australian monsoonal tropics
Figure 13. Diporiphora sobria: a, adult male with breeding colouration, Halls Creek, Western Australia (photo: S. Wilson); b, holotype WAM R23180 from Pine Creek, Northern Terrotory.
Figure 12 in Taxonomic revision of dragon lizards in the genus Diporiphora (Reptilia: Agamidae) from the Australian monsoonal tropics
Figure 12. Holotype of Diporiphora perplexa sp. nov. (WAM R177290, formerly NMV D73819): a, in life – adult male in breeding colouration from Gibb River Road, west of Ellenbrae Station, Western Australia; b, c, d, preserved specimen in dorsal, ventral and lateral (head) views. Yellow arrow highlights a key diagnostic character: dark pigment "smear" on posterior of tympanum spreading onto neighbouring head scales (photos: J. Melville).
Figure 11 in Taxonomic revision of dragon lizards in the genus Diporiphora (Reptilia: Agamidae) from the Australian monsoonal tropics
Figure 11. Diporiphora bennettii: a, adult from Little Mertens Falls area, Mitchell Plateau, Western Australia. (photo: S. Wilson); b, holotype BMNH 1946.8.12.77, from the "NW coast of Australia", showing dorsal view.
Figure 16 in Taxonomic revision of dragon lizards in the genus Diporiphora (Reptilia: Agamidae) from the Australian monsoonal tropics
Figure 16. Diporiphora lalliae: a, adult male, Three Ways, Northern Territory (photo: S. Wilson); b, holotype – WAM R23020, Langley Crossing, Western Australia.
Figure 10 in Taxonomic revision of dragon lizards in the genus Diporiphora (Reptilia: Agamidae) from the Australian monsoonal tropics
Figure 10. Images of dorsal scales of D. albilabris and D. sobria, depicting: a, heterogeneous dorsal scales in D. albilabris; b, homogeneous dorsal scales in D. sobria from Western Australia; c, moderately heterogeneous dorsal scales in D. sobria from northern and eastern Northern Territory.
Figure 8 in Taxonomic revision of dragon lizards in the genus Diporiphora (Reptilia: Agamidae) from the Australian monsoonal tropics
Figure 8. Distributions of D. albilabris, D. bennettii, D. sobria and D. perplexa sp. nov. based on specimens examined and collection records.
Figure 21 in Taxonomic revision of dragon lizards in the genus Diporiphora (Reptilia: Agamidae) from the Australian monsoonal tropics
Figure 21. Diporiphora carpentariensis sp. nov. Image of holotype (QM J88197), Littleton National Park, northern Queensland, a, in life (photo: E. Vanderduys); b, c, d, dorsal, ventral and lateral (head) view of paratype NMV D74068.
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