Estimated future global lightning strokes (2010-2100)

Estimated future lightning stroke density for FireMIP/ISIMIP

Jed O. Kaplan, Alexander Koch, and Katie Hong-Kiu Lau
Department of Earth Sciences, The University of Hong Kong
[e-mail]

Introduction

This dataset contains scenarios of global lightning density (strokes km-2 day-1) for the period 2010-2100. The lightning density estimates are available on an unprojected global half-degree grid with monthly temporal resolution. Each pixel represents the monthly mean of daily lightning stroke density. The estimates of lightning density cover all global land and ocean areas. This dataset may be used in a range of applications, for example, to understand the influence of lightning on wildfire ignitions or atmospheric composition.

Methods

The estimate of lightning density is based on future climate simulations from an earth system model and an emprical relationship between Convective Available Potential Energy (CAPE) and lightning strokes based on the WWLLN Global Lightning Climatology and timeseries (WGLC) (Kaplan and Lau 2021, 2022).

Future climate simulations performed with the UKESM1 earth system model for the 6th Coupled Model Intercomparison Project (CMIP6) provided the following meteorological outputs on pressure levels from the surface to the top of the troposphere required for calculating CAPE:

air temperature (ta, K)

air pressure (p, Pa)

specific humidity (hus, kg kg-1).

We derived dewpoint temperature (Td, K) based on ta and hus. CAPE was then estimated using the getcape.F (Version 1.02) code by George Bryan that is part of the NCAR CM1 atmosphere model .

After experimenting with climate model output on different timesteps (6-hourly, daily, monthly) we decided to use daily mean values for calculating CAPE as it provided a reasonable balance of accuracy and computational time. The calculated CAPE values were linearly interpolated from the native UKESM grid resolution to a global half-degree grid using the CDO software

We estimated lightning stroke density based on an emprical relationship between daily CAPE and observed lightning over the period 2015-2020. Lightning observations were provided by the WGLC. The relationship between CAPE and lightning is described by a linear regression on log(CAPE) vs. log(WGLC) for each gridcell and month of the year. Where <10 observations of daily lightning were available over the calibration period, we used global mean regression parameters.

Using the relationship described above, we generated an estimate of future lightning. To better maintain the spatial structure of lightning observed at present, we created our dataset using future lightning anomalies added to the present-day lightning climatology. We generated anomalies of the lightning estimate for each month from 2015-2100 (made with the emprical relationship described above) relative to the simulated 2015-2020 multi-year monthly mean (i.e., climatological mean lightning) using simple subtraction. We then added these anomalies using to the 2015-2020 climatological mean lightning from the WGLC. For the period 2010-2014 we used the WGLC, and for 2015-2020 we over-wrote the estimated lightning with observed data from the WGLC.

We estimate lighting for four future scenarios using the Shared Socioeconomic Pathways (SSPs; Riahi et al., 2017) SSP126, SSP245, SSP370, and SSP585.

Data specification

The data are stored in a  NetCDF (version 4) files following the CF conventions and have the following attributes:

Spatial extent: Entire Earth

Spatial reference system (SRS): Unprojected (geographic, WGS84)

Spatial resolution: 30 arc-minutes (half degree)

Temporal extent: 2010-2100

Temporal resolution: 1 month (1080 elements)

File naming convention:

[model name]-[version]_futurelght_[scenario]_[period]_[method].nc

e.g., UKESM1-0-LL_futurelght_ssp370_2010-2100_Aday.nc

means

UKESM1 version 0-LL,

with the SSP370 future scenario,

covering the period 2010-2100,

using mean daily meteorology (Aday) to calculate CAPE.

NB in this release the SSP126 scenario covers only 2010-2099.

Further information

For further information about this dataset, please contact Jed Kaplan [e-mail].

References

Kaplan, J. O., & Lau, K. H.-K. (2022). World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series, 2022 update. Earth System Science Data, 14(12), 5665-5670. doi:10.5194/essd-14-5665-2022

Kaplan, J. O., & Lau, K. H.-K. (2021). The WGLC global gridded lightning climatology and time series. Earth System Science Data, 13(7), 3219-3237. doi:10.5194/essd-13-3219-2021

Kaplan, Jed O., & Lau, Katie Hong-Kiu. (2021). The WWLLN Global Lightning Climatology and timeseries (WGLC) (v2022.0.0) [Data set]. Zenodo. doi:10.5281/zenodo.6007052

Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J. C., Kc, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Da Silva, L. A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D., Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G., Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M., Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A., & Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153-168. doi:10.1016/j.gloenvcha.2016.05.009