Jet feature data from PAMIP model simulations

Author: Yvonne Anderson Contact: [e-mail] Dataset created: 21/08/2023 Paper title: Minimal influence of future Arctic sea ice loss on North Atlantic jet stream morphology <strong>Dataset information</strong> CSV files contain arrays of daily jet feature data for all ensemble member winters for a given model. Dimensions of the arrays are (number of ensemble members, 90 winter days). <strong>Filename structure</strong> Filenames of CSV files can be interpreted as: timeperiod_jetfeature_model.csv <strong>Example filename structure</strong> Time period Jet feature Model Example filename Present-day Latitude AWI-CM-1-1-MR present-day_jet_latitude_AWI-CM-1-1-MR.csv Future Speed HadGEM3-GC31-MM future_jet_speed_HadGEM3-GC31-MM.csv <strong>Jet feature description</strong> Jet feature data are for the largest mass jet region found on each day of winter, where jet mass is the area weighted jet speed. The jet features and corresponding units contained in the csv files are as follows: Jet feature Units Latitude ° Speed ms^-1 Mass ms^-1 Tilt ° Area m² <strong>Time periods</strong> Time periods are present-day and future, which refer to simulations forced by present-day and future sea ice concentrations, from which the jet features have been extracted. <strong>Models</strong> Models are AWI-CM-1-1-MR, CanESM5, FGOALS-f3-L, HadGEM3-GC31-MM, IPSL-CM6A-LR and MIROC6 from the Polar Amplification Model Intercomparison Project (PAMIP; https://doi.org/10.5194/gmd-12-1139-2019) <strong>Spatial and temporal information</strong> Arrays contain daily jet feature data that has been constrained to the North Atlantic region (0-60 &amp;deg; W, 15-75 &amp;deg; N) and to the winter period (December, January and February) <strong>Prior processing</strong> Original dataset: netcdf files of daily zonal wind data from Polar Amplification Model Intercomparison Project simulations forced by present-day and future sea ice concentrations 850 hPa wind speed data was extracted and regridded to 2.81 ° x 2.81 ° resolution Constrained to North Atlantic region and winter period Wind speed data was filtered using a 10-day Lanczos filter with a 61 day window Jet feature data was extracted for each day in ensemble member winters and saved to numpy arrays <strong>Example code for loading jet variables from csv file</strong> To generate a numpy array of jet variable arrays contained in the csv file: <pre><code class="language-python">loaded_jet_variable_arrays = np.genfromtxt((path_to_file/filename.csv'), delimiter=',')</code></pre> To combine arrays for all ensemble member winters, which allows plotting of daily jet feature distributions: <pre><code class="language-python">jet_variable_array_all_winters = np.concatenate(loaded_jet_variable_arrays)</code></pre>作者：伊冯·安德森（Yvonne Anderson） 联系方式：[e-mail] 数据集创建时间：2023年8月21日 论文标题：北极未来海冰减少对北大西洋急流形态的影响极小 **数据集说明** CSV文件中存储了特定模式下所有集合成员冬季的每日急流特征数据数组。数组维度为（集合成员数量，90个冬季日）。 **文件名结构** CSV文件的文件名可按如下规则解析：timeperiod_jetfeature_model.csv **示例文件名** 时间阶段为“现代”、急流特征为“纬度”、模式为AWI-CM-1-1-MR的文件名为`present-day_jet_latitude_AWI-CM-1-1-MR.csv`；时间阶段为“未来”、急流特征为“风速”、模式为HadGEM3-GC31-MM的文件名为`future_jet_speed_HadGEM3-GC31-MM.csv`。 **急流特征说明** 本数据集的急流特征数据取自每个冬季每日识别出的最大质量急流区域，其中急流质量为面积加权后的急流风速。CSV文件中包含的急流特征及其对应单位如下： | 急流特征 | 单位 | | ---- | ---- | | 纬度 | 度（°） | | 风速 | 米每秒（ms⁻¹） | | 急流质量 | 米每秒（ms⁻¹） | | 倾斜角 | 度（°） | | 面积 | 平方米（m²） | **时间阶段说明** 时间阶段分为“现代”与“未来”两类，分别对应以现代和未来海冰浓度作为强迫场的模式模拟试验，急流特征数据即从上述试验中提取得到。 **模式列表** 本数据集所用模式均来自极地放大模式比对计划（Polar Amplification Model Intercomparison Project, PAMIP; https://doi.org/10.5194/gmd-12-1139-2019），具体包括：AWI-CM-1-1-MR、CanESM5、FGOALS-f3-L、HadGEM3-GC31-MM、IPSL-CM6A-LR以及MIROC6。 **时空范围说明** 数组中存储的每日急流特征数据已被约束至北大西洋区域（西经0°~60°，北纬15°~75°），且仅包含冬季（12月、1月、2月）的数据。 **预处理流程** 原始数据集为来自极地放大模式比对计划的、以现代和未来海冰浓度为强迫场的模拟试验的逐日纬向风场NetCDF文件（NetCDF）。具体处理步骤如下： 1. 提取850百帕风速数据，并将其重网格化至2.81°×2.81°的分辨率； 2. 将数据约束至北大西洋区域与冬季时段； 3. 使用窗口长度为61天的10天兰科斯（Lanczos）滤波器对风速数据进行滤波； 4. 提取每个集合成员冬季每日的急流特征数据，并保存为NumPy数组（NumPy）。 **CSV文件急流变量加载示例代码** 若要生成CSV文件中存储的急流变量数组的NumPy数组，可使用如下代码： python loaded_jet_variable_arrays = np.genfromtxt(path_to_file/filename.csv, delimiter=',') 若要合并所有集合成员冬季的数组以绘制每日急流特征分布，可使用如下代码： python jet_variable_array_all_winters = np.concatenate(loaded_jet_variable_arrays)