Abstract
Realistic simulation of the Earth’s mean state climate remains a major challenge and yet it is crucial for predicting the climate system in transition. Deficiencies in models’ process representations, propagation of errors from one process to another, and associated compensating errors can often confound the interpretation and improvement of model simulations. These errors and biases can also lead to unrealistic climate projections as well as incorrect attribution of the physical mechanisms governing the past and future climate change. Here we show that a significantly improved global atmospheric simulation can be achieved by focusing on the realism of process assumptions in cloud calibration and subgrid effects using the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1). The calibration of clouds and subgrid effects informed by our understanding of physical mechanisms leads to significant improvements in clouds and precipitation climatology, reducing common and longstanding biases across cloud regimes in the model. The improved cloud fidelity in turn reduces biases in other aspects of the system. Furthermore, even though the recalibration does not change the global mean aerosol and total anthropogenic effective radiative forcings (ERFs), the sensitivity of clouds, precipitation, and surface temperature to aerosol perturbations is significantly reduced. This suggests that it is possible to achieve improvements to the historical evolution of surface temperature over EAMv1 and that precise knowledge of global mean ERFs is not enough to constrain historical or future climate change. Cloud feedbacks are also significantly reduced in the recalibrated model, suggesting that there would be a lower climate sensitivity when running as part of the fully coupled E3SM. This study also compares results from incremental changes to cloud microphysics, turbulent mixing, deep convection, and subgrid effects to understand how assumptions in the representation of these processes affect different aspects of the simulated atmosphere as well as its response to forcings. We conclude that the spectral composition and geographical distribution of the ERFs and cloud feedback as well as the fidelity of the simulated base climate state are important for constraining the climate in the past and future.
Highlights
The Energy Exascale Earth System Model (E3SM) version 1 (E3SMv1) (Golaz et al, 2019;Caldwell et al, 2019) includes an 50 atmospheric component called the E3SM atmosphere model (EAM) version 1 (EAMv1) (Rasch et al, 2019)
The model shows general success in simulating the present-day climatology, producing improved simulation compared to atmospheric simulations of previous-generation Earth system models (ESMs) (Rasch et al, 2019) that participated in the Coupled Model Intercomparison Project (CMIP) phase 5 60 (CMIP5)(Taylor et al, 2012)
As shown in Rasch et al (2019), EAMv1 produces high annual mean precipitation over the global average, in high elevation regions, and in the central Pacific, but low annual mean precipitation over Amazonia and the tropical western Pacific (TWP). 70 EAMv1 contains the signature of a double intertropical convergence zone (ITCZ) that has been problematic in ESMs for over two decades (Mechoso et al, 1995;Dai, 2006)
Summary
The Energy Exascale Earth System Model (E3SM) version 1 (E3SMv1) (Golaz et al, 2019;Caldwell et al, 2019) includes an 50 atmospheric component called the E3SM atmosphere model (EAM) version 1 (EAMv1) (Rasch et al, 2019). The one-at-a-time and the short simulation ensemble approaches are complementary to each other, but for the purpose of tuning EAMv1, both approaches shared some common challenges: 1) insufficient computational and human resources to 130 explore and optimize parameter choices; 2) insufficient time to perform and analyze the simulations; and 3) improvements to one aspect of the simulation in general may be made at the price of degradation in other aspects, suggesting model structural deficiency in addition to parametric uncertainty (Qian et al, 2018) Reconciling these contradictory results and further improving the model fidelity have been great challenges for the model development team.
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