of ensemble forecast systems, and is especially the case for near-surface variables including surface temperature.
Here, we seek methods to perturb surface parameters and initial conditions that increase spread and are physically
based. The surface perturbations introduced here address two major sources of uncertainty in ensemble prediction.
To address initial condition uncertainty in the land model, we apply perturbations based on EOFs of differences
between normalized soil moisture and temperature states from different LSMs. To address uncertainty within the
land model, perturbations of parameters, including roughness lengths for heat and momentum, parameters related
to soil hydraulic conductivity, stomatal resistance, vegetation fraction and albedo, are applied, with the amplitude
and perturbation scales based on previous research. We also extend the stochastically perturbed physical tendencies
(SPPT) to include soil moisture and soil temperature. We find that overall surface perturbations have a modest
impact on near-surface temperature and other variables. In particular, soil initial condition perturbations have the
most impact on near surface temperature spread in arid and semi-arid regions. When considering bias-corrected
RMSE (i.e. with mean model error removed), the combined surface perturbations increase the spread to match RMSE
in the Tropics and the summer hemisphere. The results indicate that surface perturbations, through their impact on
near-surface spread, have a positive impact on the skill of short-range ensemble forecasts.