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8 results for “Jet Stream”
Dataset for: Wood et al Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing
<p>This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article Wood et al (2020) 'Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing' published in Environmental Research Letters (<a href="https://doi.org/10.1088/1748-9326/abce27">https://doi.org/10.1088/1748-9326/abce27</a>).</p> <p>To isolate the role of sea surface temperature (SST) patterns for the Southern Hemisphere circulation response in the abrupt-4xCO2 experiments in CMIP5 and CMIP6, we perform experiments using IGCM4.</p> <p>Five 120-year long simulations were performed following a 5-year spin-up period. In the control simulation (CTRL) we prescribe an annually repeating cycle of climatological monthly mean SSTs using the multi-model mean (MMM) of the ‘ts’ field for the first 200 years of the CMIP5 piControl simulations. Following the CMIP6 protocol (Eyring et al., 2016), greenhouse gas (CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O) concentrations are set at preindustrial (year 1850) values and ozone is prescribed as a zonally averaged monthly mean preindustrial climatology.</p> <p>In two perturbation simulations (4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub>) the same boundary conditions are used as in CTRL, but with an annually repeating cycle of climatological monthly mean SST anomalies added using the MMM ‘ts’ field for either the CMIP5 or CMIP6 FAST (years 4-10) responses. In both the 4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub> simulations CO<sub>2</sub> is quadrupled from its preindustrial concentration. This enables a like-for-like comparison with the CMIP5 and CMIP6 abrupt-4xCO2 simulations. Two further perturbation simulations (SHET-only<sub>CMIP5</sub> and SHET-only<sub>CMIP6</sub>) are used to isolate the effect of differences in SH extratropical SST patterns alone. In both simulations CO<sub>2</sub> is kept at preindustrial values, and CTRL SSTs are used with the SST anomalies from either 4xCO2-FULL<sub>CMIP5</sub> or 4xCO2-FULL<sub>CMIP6</sub> added poleward of 18°S. Similarly to McCrystall et al. (2020), the SST anomalies are smoothed between 18°S and 29°S using a cosine squared weighting function with weights of 0 at 18°S and 1 at 29°S. This minimizes sharp gradients in SST across the tropical-extratropical boundary.</p> <p>To enable a clean determination of the effects of SST patterns alone, in all perturbation simulations we keep sea ice fixed at preindustrial values by only adding SST anomalies where the MMM sea ice concentration in the CMIP5 piControl simulations is less than 15% (i.e., equatorward of the sea ice edge). Furthermore, to remove the effect of differences in the change in global mean SST, the SST anomalies in each CMIP model are normalised by the respective global mean SST anomaly and then scaled to a global mean value of 2.2 K (the pooled MMM of CMIP5 and CMIP6). The CMIP6 FAST SST anomalies are added to the CMIP5 preindustrial control SSTs, so as to isolate the effect of differences in the fast SST responses between CMIP5 and CMIP6, and not the effect of differences in the base state.</p>
North Atlantic jet stream clusters: daily and seasonal occurence
<p>This dataset contains the time series used in Madonna et al 2020 (Reconstructing winter climate anomalies in the Euro-Atlantic sector using circulation patterns, DOI: 10.5194/wcd-2021-6)</p> <p><br> Filenames:</p> <p>1) seasonal_timeseries.txt</p> <p>Time series of the occurrence (in % = days/season*100) of time during winter of each jet cluster, blocking and the NAO.<br> Winters are defined as December, January and February (DJF). The season name is given by the last month (i.e. 1980 is December 1979, January 1980 and February 1980). 29 February is removed from the data so that each winter season has 90 days.</p> <p>Jet clusters are calculated following Madonna et al 2017. The five clusters are named as in Madonna et al 2017: Northern (N), Central (C), Mixed (M), Southern (S) and Tilted (T).<br> Blocking are calculated following Scherrer et al. 2006 and averaged over Greenland (GB), offshore of the Iberian Peninsula also called Iberian wave breaking (IWB) and over Scandinavia (SBL). The exact definition of the regions can be found in Madonna et al 2020.</p> <p>The NAO index was downloaded from ftp://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.nao.index.b500101.current.ascii. Positive (NAO+) and negative (NAO-) days are defined as those that exceed 0.5 DJF standard deviation, corresponding to values greater than 0.613 and lower than -0.177, respectively.</p> <p>Example: during winter 1980, 7.78% of the days were in the North jet cluster. This is equivalent to 7 days -> 7.78 * 90 (days per season) /100</p> <p><br> 2) daily_inverse_distance_from_centroid.txt contains information about the similarity of the 2D zonal wind field to the cluster centroids which is used to determine the jet state.</p> <p>The file has 12 columns, labelled as follow:<br> date, lat, speed, N4, C4, M4, S4, N5, C5, M5, S5, T5</p> <p>The first column (date) shows the day in YYYYMMDD format, the second (lat) is the latitude (in °N) of the maximum zonal wind in the 60°W-0°W sector (i.e. the jet latitude index, see Woollings et al. 2010 or Madonna et al. 2017 for more details), and the third (speed) is the zonal averaged (60°W-0°) zonal wind speed (in m/s) at the latitude given by column 2.</p> <p>Columns 4-7 give the inverse distance from each cluster centroids using four (4) clusters: Northern (N4), Central (C4), Mixed (M4), Southern (S4) and is normalized from 0 to 1. Values close to 1 means that the clusters are similar to its centroid. The distances sum up to 1.</p> <p>Columns 8-12 show similar to 4-7 the inverse distance from the centroids using five (5) clusters: Northern (N5), Central (C5), Mixed (M5), Southern (S5) and Tilted (T5). Distances are also normalized and sum up to 1.</p> <p>In the study of Madonna et al 2020, a day has a defined cluster X (X=N, C, M, S, T), if the inverse distance from the cluster centroid X exceeds 0.5 and it clearly dominates over the other clusters.</p> <p><br> Example: 1 January 1979, the zonal mean zonal wind is maximum at 47°N and has a value of 15.61 m/s.<br> Considering 4 clusters, the jet resembles most the Mixed cluster (M4=0.36), followed by the Southern (S4=0.23), Northern (N4=0.22) and Central (C4=0.18). The sum of the distances (0.36 + 0.23 + 0.22 + 0.18 = 0.99 due to decimal approximation) is equal to 1. Using 4 clusters, this day would be assigned to cluster M4. The day is, however, not clearly identified as a Mixed jet, as the inverse distance (M4=0.36) is smaller than 0.5. The threshold of 0.5 is set to identify days where a centroid clearly leads over the others.<br> If we consider 5 clusters, the jet on 1 Jan 1979 resembles the tilted jet (T5 = 0.73) and has very little in common with the other centroids (values of 0.05-0.08). Thus, considering 5 clusters, this day is classified as a tilted jet. It is also clearly defined, as 0.73 > 0.5.</p> <p><br> References:</p> <p>Madonna, E., Li, C., Grams, C.M. and Woollings, T. (2017), The link between eddy‐driven jet variability and weather regimes in the North Atlantic‐European sector. Q.J.R. Meteorol. Soc, 143: 2960-2972. https://doi.org/10.1002/qj.3155</p> <p>Scherrer, S. C., Croci‐Maspoli, M., Schwierz, C., and Appenzeller, C. (2006). Two‐dimensional indices of atmospheric blocking and their statistical relationship with winter climate patterns in the Euro‐Atlantic region. International Journal of Climatology, 26(2), 233-249</p> <p>Woollings T, Hannachi A and Hoskins B. (2010). Variability of the North Atlantic eddy‐driven jet stream. Q. J. R. Meteorol. Soc. 136: 856– 868.</p> <p> </p> <p> </p> <p> </p>
Data accompanying Using Neural Networks to Learn the Forced Response of the Jet-Stream to Tropospheric Temperature Tendencies
<p>Data used to train and evaluate a CNN. Details about data and the preprocessing can be found in the citation given below</p> <p>Charlotte Connolly, Elizabeth A. Barnes, Pedram Hassanzadeh, and Mike Pritchard: Using Neural Networks to Learn the Jet Stream Forced Response from Natural Variability, accepted to Artificial Intelligence for the Earth Systems 03/2023. Preprint available at <a href="https://arxiv.org/abs/2301.00496">https://arxiv.org/abs/2301.00496</a>.</p> <p>Code found at https://doi.org/10.5281/zenodo.7796266.</p> <p> </p>
Integration-based Extraction and Visualization of Jet Stream Cores - Supplemental Video
<p>Supplemental video for the publication "Integration-based Extraction and Visualization of Jet Stream Cores", which extracts jet stream core lines from ERA5 data using a predictor corrector algorithm.</p>
Jet stream controls on driven European climate extremes and agricultural agriculture
<p>The code is used to analyze data and produce the main figures for the manuscript (Xu et al., Jet stream controls on European climate and agriculture since 1300 CE. Nature, 2024, https://doi.org/10.1038/s41586-024-07985-x). We used three temperature-sensitive tree-ring maximum chronologies from Europe to reconstruct the North-Atlantic-Europe Jet Stream Latitude (EU JSL). The EU JSL can capture the variability of dipolar patterns in temperature and precipitation extremes between Southern Europe and Northern Europe in summer (July-August). We extended the EU JSL to 1300 CE based on a multiple linear regression transfer function, which can explain the total variance of about 38.5% over the instrumental period (1945-2005, from NCEP reanalysis dataset V1.0). We then explore the relationships between EU JSL and temperature and precipitation over a long-term scale. We also explored the relationships between EU JSL extremes, crop failure events, and some social events over the past seven centuries. We found that the climate and societal extremes have been influenced by a summertime climatic dipole between northwestern and southeastern Europe, driven by EU JSL. Using the reconstruction, we explore relationships between EU JSL and climate and societal extremes using independent historical documentary datasets. We highlight the imprint of the EU JSL climate dipole on not only historical climate extremes, but also on the dipole's biophysical (e.g., grape harvest, wildfire), economic (e.g., wine quality and grain price), and even demographic (e.g., mortality and epidemics) impacts.</p> <p>The code is for the data analysis and visualization. We used R 4.2 version in the Windows 11 platform. Before re-running the code, please read the following instructions:</p> <p>1. Download all the input data and the R folder (all of the code). Please put the input data into a folder named input. We uploaded the temp and precip reconstruction data for the European domain.<br>2. Create a similar structure for the folders.<br>3. Before running the code, please unzip the crop data in the folder "./input/crop/*.zip", two large crop NetCDF fromat dataset.<br>4. Before running the main code "test all.R", please set you-work-path and run "setwd ("YOURPATH")" command line. <br>5. Some results or data in the output folder can be produced when run the code, you can also directly input them from the input folder in the next step to save time.<br>6. For the visualization, some of the arrangements are finished in Adobe Illustrator or Adobe Photoshop.<br>7. This is the main code for the manuscript, we would be improved or modified during the peer review.<br>8. If you have any questions or warnings during the re-run, please get in touch with me (guobaoxu@nwu.edu.cn; xgb234@lzb.ac.cn).</p> <p>If you have any suggestions or comments, please also let me know.</p> <p>Best wishes,<br>Guobao</p>
Integration-based Extraction and Visualization of Jet Stream Cores - Demo Data
<p>Demo data for the publication "Integration-based Extraction and Visualization of Jet Stream Cores", containing the meteorological attirbutes for September 01, 2016 at 00:00. The data is derived from ERA5.</p> <p>The ERA5 data is courtesy of the European Centre for Medium-Range Weather Forecasts (ECMWF) and is documented here: <a href="https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation">https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation</a> The data is available under the Copernicus License Agreement: <a href="https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</a></p>
Dataset belonging to "The Influence of Large-scale Spatial Warming on Jet Stream Extreme Waviness on an Aquaplanet"
Open the record for dataset details and reuse information.
The interaction between subpolar and subtropical jet stream leads to extreme rainfall events over North India in 2013 and 2023
<p>..ini.csv: These files contain the starting location of the Lagrangian trajectories</p> <p>..out.csv: These files contain the ending location of the Lagrangian trajectories</p> <p>..run.csv: These files contain the trajectory location at each spatial grid crossing</p> <p>era5_air*.csv: These are the trajectory files for the simulation where we have backtracked the upper level air southwards to identify the subpolar and subtropical jet stream interaction</p> <p>era5_north_air*.csv: These are the trajectory files for the simulation where we have backtracked the upper level air northwards from the flood locations to identify the wind pathways responsible for upper level meridional wind divergence.</p> <p>era5_water*.csv: These are the trajectory files for the simulation where we have backtracked the surface precipitation to identify atmospheric water sources and pathways.</p>
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