Global Observationally-Based Land-Atmosphere Coupling Metrics (GOLAM)

The Global Observationally-Based Land-Atmosphere Coupling Metrics (GOLAM) provide a comprehensive representation of land-atmosphere (L-A) coupling indices based on observationally derived (mainly satellite-based) gridded datasets. This dataset includes L-A coupling indices such as Pearson correlation coefficients and terrestrial coupling indices between daily soil moisture (SM) and surface heat fluxes, including sensible heat flux (H), evaporation (E), and evaporative fraction (EF), as well as SM memory. These indices are designed to account for random observational errors present in satellite SM measurements by employing a Markovian framework. The global patterns of surface and sub-surface SM regimes and SM-EF indicators, such as wilting point, critical SM, saturated EF, and SM-EF slope, are also quantified through segmented regression analysis. Multiple SM and flux data products are used to develop these datasets, including: two SM satellite datasets: SMAP and ESA CCI, a machine learning SM dataset: SoMo, and three surface heat flux datasets: GLEAM, CAMELE, and FluxCom-X-BASE. For consistency, the Land-Atmosphere Coupling Metrics datasets are provided at a spatial resolution of 0.25˚*0.25˚; however, the SMAP-GLEAM and SMAP-only combinations are available at both 0.1˚*0.1˚ and 0.25˚*0.25˚. The temporal coverage of these metrics depends on the input datasets and is provided across four different seasons: June-August (JJA), September-November (SON), December-February (DJF), and March-May (MAM), as well as composited warm seasons across three regions: May through September (MJJAS) for 23˚N-60˚N, November through March (NDJFM) for 23˚S-60˚S, and all months for 23˚S-23˚N. However, SM regimes are only calculated during warm seasons, depending on the latitude bands. The source code for generating GOLAM is available at https://doi.org/10.5281/zenodo.17702299.