Effect of land model ensemble versus coupled model ensemble on the simulation of precipitation climatology and variability

Abstract

Through a series of model simulations with an atmospheric general circulation model coupled to three different land surface models, this study investigates the impacts of land model ensembles and coupled model ensemble on precipitation simulation. It is found that coupling an ensemble of land models to an atmospheric model has a very minor impact on the improvement of precipitation climatology and variability, but a simple ensemble average of the precipitation from three individually coupled land-atmosphere models produces better results, especially for precipitation variability. The generally weak impact of land processes on precipitation should be the main reason that the land model ensembles do not improve precipitation simulation. However, if there are big biases in the land surface model or land surface data set, correcting them could improve the simulated climate, especially for well-constrained regional climate simulations.

Validation of Terrestrial Biogeochemistry in CMIP6 Earth System Models: A Review

“…Replicate simulations with different initial conditions allow for the attribution of uncertainty from unforced variability, such as performed by Danabasoglu et al (2020), which accounted for half of the inter-model spread in key variables previously (Deser et al, 2020;Eyring et al, 2019). In addition, replicate simulations with different forcing datasets can indicate the role of forcing uncertainty (Wei et al, 2018), which Lawrence and Bonan et al (2019) found to be significant. Further, sensitivity analyses or perturbed parameter analyses involving replicated simulations with one or more variables fixed as performed by Hajima et al (2020) and Lawrence et al…”

Validation of terrestrial biogeochemistry in CMIP6 Earth system models: a review

“…Replicate simulations with different initial conditions, such as those performed by Danabasoglu et al (2020), allow for the attribution of uncertainty from unforced variability, which accounted for half of the inter-model spread in key variables previously (Deser et al, 2020;Eyring et al, 2019). In addition, replicate simulations with different forcing datasets can indicate the role of forcing uncertainty (Wei et al, 2018), which Lawrence et al (2019) found to be significant. Further, sensitivity analyses or perturbed parameter analyses involving replicated simulations with one or more variables fixed as performed by Hajima et al (2020) and illuminate structural uncertainty.…”