Improved Near‐Surface Continental Climate in IPSL‐CM6A‐LR by Combined Evolutions of Atmospheric and Land Surface Physics

Abstract

This work is motivated by the identification of the land-atmosphere interactions as one of the key sources of uncertainty in climate change simulations. It documents new developments in related processes, namely, boundary layer/convection/clouds parameterizations and land surface parameterization in the Earth System Model of the Institut Pierre Simon Laplace (IPSL). Simulations forced by prescribed oceanic conditions are produced with different combinations of atmospheric and land surface parameterizations. They are used to explore the sensitivity to the atmospheric physics and/or soil physics of • major biases in the near surface variables over continents, • the energy and moisture coupling established at the soil/atmosphere interface in not too wet (energy limited) and not too dry (moisture limited) soil moisture regions also known as transition or "hot-spot" regions, • the river runoff at the outlet of major rivers. The package implemented in the IPSL-Climate Model for the Phase 6 of the Coupled Models Intercomparison Project (CMIP6) allows us to reduce several biases in the surface albedo, the snow cover, and the continental surface air temperature in summer as well as in the temperature profile in the surface layer of the polar regions. The interactions between soil moisture and atmosphere in hotspot regions are in better agreement with the observations. Rainfall is also significantly improved in volume and seasonality in several major river basins leading to an overall improvement in river discharge. However, the lack of consideration of floodplains and human influences in the model, for example, dams and irrigation, impacts the realism of simulated discharge. Plain Language Summary Land surface-atmosphere interactions play an essential role in the climate system. They strongly modulate the regional climates and have impacts on the global scale for instance through freshwater release into the oceans. Climate hazards (heat waves, droughts) and their impacts on populations also strongly depend on interactions between land and atmosphere and on their evolution with climate change. Climate models are precious tools to investigate how the Earth climate behaves. The sixth phase of the Climate Model Intercomparison Project (CMIP6) provides important tools to measure the progress and address the remaining open questions regarding the continental climate modeling. The representation of the land-atmosphere coupled system by the IPSL-Climate Model involved in CMIP6 is thoroughly evaluated against observations and compared with simulations using the CMIP5 version. Several biases concerning the temperature over land and over the ice sheets and with the snow cover are significantly reduced. Numerous improvements were made developping advanced parameterizations and tuning of the radiation and of the turbulent mixing in the atmospheric model. The realism of the seasonal

Improved Representation of Clouds in the Atmospheric Component LMDZ6A of the IPSL‐CM6A Earth System Model

“…It now includes a 1.5 order closure K-gradient scheme and a prognostic equation for the TKE (turbulent kinetic energy). The K-gradient scheme is based on the work of Yamada (1983) and was improved for stable boundary layers (Cheruy et al, 2020;Vignon et al, 2017). The total water vapor mass mixing ratio q t is vertically mixed assuming a down-gradient Fick's type diffusion whose intensity Table 1 Architecture of the Physical Package, Showing All Cloud-Related Variables Note.…”

Multivariable evaluation of land surface processes in forced and coupled modes reveals new error sources to the simulated water cycle in the IPSL (Institute Pierre Simon Laplace) climate model

“…This probably arose from an incomplete atmospheric simulation of the local climate (Cheruy et al, 2020), such as an updrift along a mountain slope. Coarse spatial resolution of the atmospheric simulation in the coupled mode can also make it difficult to represent the impact of mountainous topography on local climate (Decharme and Douville, 2006). Theoretically, the overestimation of albedo should decrease the available energy at the surface, thereby decreasing ET and surface temperature.…”

“…We also evaluated the simulated precipitation because it is the primary factor that influences the hydrological variables (Qian et al, 2006;Decharme and Douville, 2006). To this end, we used the GPCC data set version 6 (Schneider et al, 2014), which was also used to bias correct the WFDEI meteorological forcing of the offline ORCHIDEE simulation (Sect.…”

“…Therefore, errors in the simulated values solely arise from deficiency model structure/parameterization, uncertainty in the forcing data (Yin et al, 2018), and mismatch in land cover between model and forcing data (Zabel et al, 2012). The foremost influential forcing factor on the water cycle is precipitation (Qian et al, 2006;Decharme and Douville 2006), although radiation and land cover (i.e., vegetation) can also affect hydrological variables (Dirmeyer, 2001;Guo et al, 2006) such as surface soil moisture (SSM) or evapotranspiration (ET), depending on the temporal scale (Guo et al, 2006) and the hydrometeorological condition of the region (i.e., energy limited or water limited; Nasonova et al, 2011;Zabel et al, 2012). Anthropogenic factors (e.g., irrigation) may also cause errors in the simulated variables when not accounted for by the LSM (Yin et al, 2018).…”

Alleviation of an Arctic Sea Ice Bias in a Coupled Model Through Modifications in the Subgrid‐Scale Orographic Parameterization

“…The scheme producing SSO gravity waves drag is also used to produce a shear production term in the prognostic turbulent kinetic equation of the planetary boundary layer scheme. This produces a turbulent orographic form drag, which was carefully validated over the Antarctica ice sheet (see details in the appendix of Cheruy et al, 2020). In LMDZ, the SSO parameterization applies gravity wave drag at upper levels and low‐level drag and lift forces at the model levels that intersect the SSO.…”