AbstractThe occurrence of extreme hot and dry summer conditions in the Pacific Northwest region of North America (PNW) has been known to be influenced by climate modes of variability such as the El Niño-Southern Oscillation and other variations in tropospheric circulation such as stationary waves and blocking. However, the extent to which the subseasonal remote tropical driver influences summer heat extremes and fire weather conditions across the PNW remains elusive. Our investigation reveals that the occurrence of heat extremes and associated fire-conducive weather conditions in the PNW is significantly heightened during the boreal summer intraseasonal oscillation (BSISO) phases 6-7, by ~50–120% relative to the seasonal probability. The promotion of these heat extremes is primarily attributed to the enhanced diabatic heating over the tropical central-to-eastern North Pacific, which generates a wave train traveling downstream toward North America, resulting in a prominent high-pressure system over the PNW. The ridge, subsequently, promotes surface warming over the region primarily through increased surface radiative heating and enhanced adiabatic warming. The results suggest a potential pathway to improving subseasonal-to-seasonal predictions of heatwaves and wildfire risks in the PNW by improving the representation of BSISO heating over the tropical-to-eastern North Pacific.

The Risk Analysis Framework for Tropical Cyclones (RAFT)'s comprehensive and unified simulation of 40,000 synthetic North Atlantic tropical cyclone (TC) events are presented in this dataset. RAFT meticulously models these events based on large-scale environmental conditions, providing a valuable tool for in-depth TC impact analysis. The dataset encompasses detailed 6-hourly track information, along-track intensity metrics (including maximum wind speed and minimum pressure), the radius of maximum winds, and cumulative precipitation for each event. The primary dataset is encapsulated in a NetCDF4 file, "RAFT.NA.v20231016.nc", which contains a complete array of variables pertinent to the 40,000 synthetic TCs. These variables, detailed in Table 1 of the accompanying paper and summarized below, offer a comprehensive view of each TC event: Basin ID: Identifies the basin (1 for North Atlantic) Storm ID: Unique identification number for each TC, starting from 0 Year: Year of the environmental conditions used for modeling Jday: Julian day of the year, ranging from 0 to 365 Longitude (lon): Geographical longitude in degrees Latitude (lat): Geographical latitude in degrees Maximum Wind Speed (vmax): Measured in knots Minimum Pressure (mslp): Measured in hectopascals (hPa) Radius of Maximum Wind (rmax): Measured in nautical miles (nmi) Additionally, the dataset offers individualized accumulated rainfall data for each TC event, stored in NetCDF4 files named according to the convention "modeled_rainfall_ERA5_syn_{i}.h5", where "{i}" is the synthetic storm's ID. "ERA5" signifies the reanalysis input source, and "syn" indicates a synthetic track. This component of the dataset includes the following variables, all measured in total millimeters of precipitation: Total Accumulated Rainfall (p_accum) Frictional Precipitation Component (p_accum_f) Topographic Precipitation Component (p_accum_h) Shear-related Precipitation Component (p_accum_s) Vortex Stretching Precipitation Component (p_accum_t) The rainfall dataset is curated to focus on TC events within 600 km of the U.S. coast, reducing the number of rainfall events to 17,010 from the original 40,000, thereby enhancing its relevance and manageability. For user convenience, these events are compressed into grouped archives named "RAFT_accum_rainfall_{index}.tar.gz", where each "{index}" represents the index of the zipfile, containing up to 2,000 files for efficient data retrieval. The accumulated rainfall data is provided on a regular spatial grid, detailed in "RAFT_rainfall_latlon_grid.h5", which outlines the grid coordinates ('lat' and 'lon'). For comprehensive usage guidelines and further insights into this dataset, users are encouraged to refer to the associated paper. This dataset is not only a significant resource for researchers and analysts in the field of meteorology but also serves as a pivotal tool for understanding and predicting the impacts of tropical cyclones.

This is the RAFT synthetic tropical cyclone (TC) dataset generated for the paper "Increased US coastal hurricane risk under climate change" submitted to the journal Science Advances in 2022. Each file contains 50,000 synthetic TCs from RAFT either for the historical period (1980-2014) or the future period (2066-2100) under “SSP585”, and from a CMIP6 global climate model. intensity_model_output_corrVMPI_11vars_alltcs_cutoff15_CMIP6_{PERIOD} _{MODEL}.mat To read a .mat file in Python, one can use “scipy.io.loadmat”. There are several variables included in each file, and all have the same dimension [number of storms, number of timesteps]. Here are a list of variable names and what they represent: ‘lat’: Storm latitude; ‘lon’: Storm longitude; ‘year’: year; ‘jday_syn’: Julian day in the year; ‘vs0_syn’: maximum surface wind (knot). Please note that this version of synthetic TC dataset is only intended for assessing the large-scale change of hurricane risk under climate change (through statistical-dynamical downscaling of CMIP6 GCMs), which is addressed in the above mentioned paper. Due to the model biases in CMIP6 and the low temporal resolution (monthly) used for RAFT inputs, the synthetic TCs' life-time maximum intensity is underestimated. Therefore, the synthetic TCs here should not be treated directly as "example TCs of current or future climate" without bias correction on the TC intensity. The authors plan to release a separate version of RAFT simulated synthetic TCs with proper bias correction for localized TC impact assessment. Please email authors if you have questions.

While El Niño–Southern Oscillation (ENSO) influences eastern North Pacific (ENP) tropical cyclones (TCs) through a variety of atmospheric processes when examined concurrently, ocean pathways dominate at longer lead times. The eastward displacement of the warm pool during an El Niño, which carries warm water into the ENP basin, is the primary oceanic mechanism. Despite this, the question of whether an accurate knowledge of preseason ENSO conditions enhances predictability of ENP TCs has not been addressed specifically. In this study, we show that relative to traditional indices of ENSO, the ENSO Longitude Index (ELI) captures changes in the location of deep convection and associated thermocline processes more accurately. Consequently, the ELI explains more variability in the upper‐ocean heat content, and thus TC activity, at lead times of several months in the ENP basin. These results motivate the need to further explore the predictability of ENP TCs associated with ENSO.