The Canadian Earth System Model version 5 (CanESM5.0.3)

Abstract

Abstract. The Canadian Earth System Model version 5 (CanESM5) is a global model developed to simulate historical climate change and variability, to make centennial-scale projections of future climate, and to produce initialized seasonal and decadal predictions. This paper describes the model components and their coupling, as well as various aspects of model development, including tuning, optimization, and a reproducibility strategy. We also document the stability of the model using a long control simulation, quantify the model's ability to reproduce large-scale features of the historical climate, and evaluate the response of the model to external forcing. CanESM5 is comprised of three-dimensional atmosphere (T63 spectral resolution equivalent roughly to 2.8∘) and ocean (nominally 1∘) general circulation models, a sea-ice model, a land surface scheme, and explicit land and ocean carbon cycle models. The model features relatively coarse resolution and high throughput, which facilitates the production of large ensembles. CanESM5 has a notably higher equilibrium climate sensitivity (5.6 K) than its predecessor, CanESM2 (3.7 K), which we briefly discuss, along with simulated changes over the historical period. CanESM5 simulations contribute to the Coupled Model Intercomparison Project phase 6 (CMIP6) and will be employed for climate science and service applications in Canada.

Implementation of Yale Interactive terrestrial Biosphere model v1.0 into GEOS-Chem v12.0.0: a tool for biosphere–chemistry interactions

“…Predicted summertime surface [O 3 ] instead shows positive biases in eastern USA and Europe (Fig. S4), consistent with previous evaluations using the GC model (Travis et al, 2016;Schiferl and Heald, 2018;Yue and Unger, 2018).…”

“…Benchmark GPP product of 2010-2012 is estimated by upscaling ground-based FLUXNET eddy covariance data using a model tree ensemble approach, a type of machine learning technique (Jung et al, 2009). Although these products may have certain biases, they have been widely used to evaluate land surface models because direct observations of GPP and LAI are not available on the global scale (Yue and Unger, 2015;Slevin et al, 2017;Swart et al, 2019). Measurements of surface [O 3 ] over North America and Europe are provided by the global gridded surface ozone data set of Sofen et al (2016), and those over China are interpolated from data at ∼ 1500 sites operated by China's Ministry of Ecology and Environment (http://www.cnemc.cn/en/, last access: 10 January 2020).…”

Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models

“…The physical core shares almost the same structure and characteristics with the latest model MIROC6 (Tatebe et al, 2019), except for the atmospheric spatial resolution and treatment of cumulus clouds. This model interactively couples an atmospheric general circulation model (CCSR-NIES AGCM; Tatebe et al, 2019) including an on-line aerosol component (SPRINTARS; Takemura et al, 2000), an ocean general circulation model (GCM) with a sea ice component (COCO; Hasumi, 2015), and a land physical surface model (MATSIRO; Takata et al, 2003). The land and ocean biogeochemical components are represented by VISIT (Ito and Inatomi, 2012) and OECO2 (Hajima et al, 2020), respectively, which are interactively coupled to the atmospheric component.…”

Reductions in daily continental-scale atmospheric circulation biases between generations of global climate models: CMIP5 to CMIP6

“…The mean inter-reanalysis error is used as an estimate of observational uncertainty. Finally, errors are also calculated for 50 members of a large initial condition ensemble of CanESM5 historical simulations (Swart et al 2019). The standard deviation of errors across the CanESM5 large ensemble (CanESM5 LE) serves as an estimate of internal variability.…”