Radiative transfer in CanAM5 includes the new specification of optical properties for the surface, cloud, and aerosol, in addition to the computation of radiative fluxes accounting for subgrid-scale surface variability.

The parameterized absorption by gases uses a correlated k-distribution model that is mostly unchanged from CanAM4, using the same wavelength intervals and quadrature points . A significant modification is the addition of a solar water vapour continuum , which resulted in improved simulation of absorption at solar wavelengths . The single-scattering properties of the ice clouds in CanAM4 were parameterized for thermal and solar wavelengths under the assumption that all the ice particles are hexagonal prisms . In CanAM5 the optical properties of ice particles use the parameterization of , assuming a mixture of ice habits that is based on spaceborne observations and assuming a moderately rough surface, which is found to improve retrievals . The single-scattering properties of pure liquid clouds remain the same but can be perturbed to account for internally mixed black carbon , allowing simulation of the semi-indirect effect.

Aerosol optical properties in CanAM5 use updated single-scattering properties as well as an improved method to mix aerosol optics. The single-scattering properties for organic carbon use the refractive index from HITRAN 2012 and the properties of black carbon from . Instead of externally mixing aerosols, it is assumed that sulfate and the hydrophilic components of black carbon and organic carbon are internally mixed . The refractive index of the internally mixed aerosol is computed based on the fraction, effective radius, and effective variance of each component aerosol, as well as relative humidity, which is used to compute hydrophilic growth.

The ocean optical properties are also changed in CanAM5. In CanAM4, the whitecap albedo was wavelength-invariant with a value of 0.3. In CanAM5, each wavelength interval in the solar radiative transfer model uses a different albedo (0.216, 0.134, 0.044, 0.005) based on . The parameterization of ocean albedo is similar to that in CanAM4, but the contents of the lookup table have been updated and now include a dependence on the solar zenith angle and partitioning of the incident downwelling solar radiation into direct and diffuse components . This partitioning is estimated using the vertically integrated aerosol and cloud optical depth. In CanAM4, the ocean albedo was computed using as input optical thickness and solar zenith angles from the last radiative transfer time step, which is 1 h earlier. To improve the consistency of the ocean albedo calculation and the radiative transfer calculations, especially near sunrise and sunset, in CanAM5 the ocean albedo is calculated using cloud and aerosol information from the previous dynamical time step, 15 min earlier, and the solar zenith angle from the current time step.

Over land, the dry bare-soil albedo in CanAM4 was set to a global constant combined with a parameterization to account for the effect of surface wetting . The use of a globally constant bare-soil albedo resulted in regional biases for clear-sky albedo at the top of atmosphere, especially over the Sahara and Australian interior. The constant albedo was replaced with a regionally varying soil colormap and associated albedos . These new location-dependent bare-soil albedo maps greatly reduced biases in clear-sky albedo relative to observations. In addition to the bare soil, the albedo and emissivity of snow and sea ice were also updated. The albedo of snow on land and sea ice in CanAM5 is computed using a lookup table accounting for snowpack properties. These include snow water equivalent, snow grain size, and black carbon simulated by the land surface model . Snow present on land or sea ice uses a single, wavelength-invariant emissivity, which was reduced from 1.0 to 0.97 . Similarly, the emissivity of sea ice was reduced from 1.0 to 0.97 to be consistent with the sea ice model used in CanESM5 .

Within a CanAM5 grid box, there can be multiple surface types including land, lake, ocean, and sea ice (Sect. ). When coupling the atmosphere with ocean, sea ice, and land models, it is necessary to have surface radiative fluxes that are consistent with each surface type. While it is possible to partition the grid-mean radiative fluxes at the surface using the tiled albedo, emissivity, and temperature, in CanAM5 radiative flux profiles are instead computed for each surface type and then averaged to a grid mean. It is assumed that the same atmosphere is present over each tile. Although this approach causes a small increase (<5 %) in the total time for global radiative transfer calculations in CanAM5, it maintains consistency between the surface and the atmosphere. The modest increase in computational time is possible because multiple surface tiles are only present in a portion of the CanAM5 grid boxes, e.g., there is only one surface type over sea-ice-free ocean.

The types of natural and anthropogenic aerosols in CanAM5 include sulfate, black and organic carbon, sea salt, and mineral dust, similar to CanAM4 . Parameterizations for aerosol emissions and transport, gas-phase and aqueous-phase chemistry, and dry and wet deposition account for interactions with simulated meteorology. Natural aerosol species are represented in the model using prognostic emission fluxes. In particular, a particle-size-dependent emission scheme is used to account for aeolian erosion in arid and semi-arid regions . Sea salt concentrations in two size modes are parameterized as a function of the wind speed near the surface of the ocean . Dimethyl sulfide emissions are predicted using specified climatological concentrations in the surface ocean . Sulfur oxidation in the gas and aqueous phases is simulated using specified climatological oxidant concentrations from CMAM20 .

The chemistry parameterizations in CanAM5 are unchanged relative to CanAM4 with the exception of stratospheric water vapour which can be produced by methane oxidation using a parameterization based on that described in .

The parameterization of clouds and cloud microphysics in CanAM5 is mostly the same as in CanAM4 . Like CanAM4 and other global climate models, CanAM5 continues to employ bulk cloud microphysical parameterizations which depend on mean water content and other moments of the droplet size distribution.

Autoconversion in liquid clouds is parameterized in CanAM4 using , but this has been replaced in CanAM5 with the autoconversion parameterization of , which is a modified version of the parameterization by , to simulate the collision and coalescence of cloud droplets. For convenience we reproduce here the main equations for and , since the adjustment of parameters in the equations is discussed in Sect. .

Autoconversion in CanAM4 is parameterized as 1∂qr∂t=AqL2.47‾Nd-1.79ρ-1.47, where A is a constant, qr is the rainwater content (in kg m-3), qL is the liquid cloud water content (in kg m-3), Nd is the cloud droplet number concentration (in m-3), and ρ is air density (in kg m-3). In CanAM5, the autoconversion is parameterized as 2∂qr∂t=EqL3‾Nd-1H(R6-R6C), where qr is the rainwater content (in kg m-3), qL is the liquid cloud water content (in kg m-3), Nd is the droplet number concentration (in m-3), and H() is the Heaviside function. Additionally, E=1.3×109β66, R6C=7.5/(qL‾1/6R61/2), R6=β6rv, and β6=[(rv+3)/rv], where rv is the mean volume radius (in µm). In order to account for the impacts of subgrid-scale variability in cloud liquid water content, the statistical cloud scheme in CanAM5 is used to determine the mean value of qL3, indicated by the bar in Eq. ().

Along with the new autoconversion parameterization, CanAM5 now accounts for indirect impacts of aerosols on cloud liquid water content and lifetime, i.e., the second aerosol indirect effect . This effect was not active in CanAM4, since it used a constant cloud droplet number concentration of 50 cm-3 in Eq. () . Given the uncertainty of applying the parameterizations at high altitudes, cloud processes are limited to pressures greater than 10 hPa.

The land component of CanAM5 is represented by the Canadian Land Surface Scheme (CLASS) and the Canadian Terrestrial Ecosystem Model (CTEM), which model physical and biogeochemical processes, respectively. CanAM5 uses version 3.6 of CLASS, which models the energy and moisture fluxes at the air–land surface interface .

Compared to its predecessor used in CanAM4 (CLASS 2.7; ) there are several major structural improvements in version 3.6 of CLASS. These include optional implementation of a user-specified number of soil layers rather than the previous hard-coded three layers, as well as the capability of supporting a mosaic of vegetation, soil, water, or ice tiles within grid cells. The capability of modelling fully organic soils has been introduced, with hydraulic properties assigned on the basis of the work of . The thermal conductivities of the organic and mineral soil layers are determined following and . The wet and dry albedos of the mineral soil are assigned based on a global soil reflectivity index described in and . Organic soil albedos are assigned following . The bare-soil surface evaporation efficiency parameter is calculated using a relation presented by . Empirical corrections are applied to the saturated hydraulic conductivity of each soil layer to take into account the viscosity of water at the layer temperature and the presence of ice . The field capacity of the lowest permeable soil layer and the baseflow at the bottom of the layer are obtained using relations derived from .

A new option is provided to model snowpack albedo and transmissivity in four wavelength intervals instead of two intervals. The thermal conductivity of snow is obtained from the snow density using a relationship derived by . The fresh snow density is calculated as an empirical function of the air temperature, using relations developed by and . The maximum snowpack density is calculated as a function of snow depth following . The amount of snowfall intercepted by vegetation and the unloading rate of intercepted snow are calculated following . The canopy interception capacity for snow is determined from the plant area index and the fresh snow density as described in and . Albedos of snow-covered vegetation canopies are set following . The sensible and latent heat fluxes between the vegetation, the underlying ground, and the overlying atmosphere are evaluated based on the analysis of , which incorporates an explicit treatment of the canopy air space.

Although CLASS 3.6 can represent vegetation as a mosaic, the composite approach of representing different vegetation types in a grid cell is employed in CanAM5. This implies that area-weighted grid-mean structural attributes of different vegetation types are used in energy and water balance calculations. The number of soil and bedrock layers remains three, the same as in CanAM4, with the first and second soil layers being 0.1 and 0.25 m thick. The maximum thickness of the permeable soil for the third layer is 3.75 m but varies geographically depending on the permeable soil depth specified following .

CTEM models vegetation as a dynamic component of the climate system and provides structural attributes of vegetation to CLASS for use in its physics calculations . These include leaf area index, vegetation height, rooting depth and distribution, and canopy mass. The biogeochemical component CTEM has not changed much from CanAM4 except the diagnostic calculation of wetland extent and methane emissions , none of which affects the physical land surface processes.

CanAM5 includes a parameterization for subgrid-scale lakes to improve surface fluxes of heat and moisture over land masses. The scheme is based on the Canadian Small Lake Model, CSLM . This scheme computes a nonlinear surface energy balance in a thin skin layer and then solves the heat equation based on thermal conduction and shortwave radiation extinction following Beer's law for both visible and near-infrared bands. A diurnal surface mixed layer is simulated based on the bulk turbulent kinetic energy approach, e.g., , developed by , , and for lakes. A seasonal thermocline arises naturally as a result of the daily excursions of the surface mixed layer. The equation of state follows , except that the effects of pressure and salinity are neglected.

The model allows for the formation of both black, i.e., congelation, and white ice. Black ice forms when the energy balance in a layer is sufficiently negative to cool it below 0 ∘C. White ice forms when the weight of the overlying snowpack is sufficient to crack the ice and allow lake water to flood a layer of snow, which is then assumed to freeze immediately and completely. Latent heat from the freezing of the pore water is first used to warm the snow crystals in the slush layer to 0 ∘C, with the remainder going into the overlying snowpack. Both white and congelation ice is assumed to be free of air bubbles and to have the same transmissivity.

Fractional ice cover, following , and fractional snow on ice are permitted, thus allowing for the simultaneous presence of open water, bare ice, and snow-covered ice. Fractional ice cover is especially important for larger lakes subject to sufficient wind stress, which can mechanically break ice to produce pressure ridges and open water leads. The presence of some open water will alter turbulent and radiative flux exchange with the atmosphere, as well as light availability at depth due to differences in roughness, albedo, and light extinction between water and ice. Snow itself is represented as in the Canadian Land Surface Scheme (CLASS; Sect. ), with the snowpack simulated as a layer thermally distinct from the underlying ice.

The properties and interaction of all lakes within a CanAM5 grid cell are modelled by one representative subgrid lake using CSLM. The properties of the representative lake in each CanAM5 grid cell are derived from the Global Lake Database version 2 (GLDv2) , which is provided at 1/120∘ × 1/120∘ resolution. The grid fraction covered by the representative lakes is derived from the aggregate area of lakes in GLDv2 that falls within each CanAM5 grid cell. This defines the unresolved lake tile (Sect. ). Lake dynamics are governed by three external geophysical parameters that must be specified: the visible light transparency, mean depth (or volume), and mean fetch. For all representative lakes, a constant transparency of 0.5 m-1 is assumed and the mean depth and mean fetch in each CanAM5 grid cell are derived in an aggregate manner from GLDv2.

To more easily facilitate conservative coupling, all previous versions of coupled atmosphere–ocean models developed at CCCma employed coincident grids with an identical binary land mask; e.g., CanESM2 employed CanAM4's land mask for its CMIP5 contribution. In these earlier versions, enhanced ocean resolution was achieved by prescribing multiple ocean grid cells below each CanAM atmospheric grid cell. For CanESM5, independent arbitrarily oriented grids are assumed for both the atmosphere and the ocean . This required the implementation of a fractional land mask in the atmospheric model and tiling of its underlying surface. In general, each CanAM5 grid cell can contain tiles representing land, ocean, sea ice, and unresolved lakes. The tiling approach used is a generalization of that discussed in Sect.  for the tiling of vegetation types over the land portion of atmospheric grid cells. For example, within each model grid cell, independent energy and water fluxes are derived over each underlying surface type given its unique properties, e.g., temperature and albedo. On the atmospheric side, these fluxes undergo a weighted aggregation based on the tile fraction to produce a single flux seen by the atmosphere. In fully coupled mode, if ocean and/or sea ice sits below some portion of an atmospheric grid cell, the flux of each representing each surface type is passed to the coupler, CanCPL, and is remapped and transferred to the underlying grid of the ocean and/or sea ice model.

Currently, surface tiling in CanAM5 has been implemented for the parameterization of radiative transfer, surface processes, and vertical diffusion. Aside from the radiation, the fluxes over each tile are derived from the prognostic variables in the lowest model level, e.g., temperature, specific humidity, and wind. For simplicity, the blending height at which the fluxes from each tile are aggregated is also taken to occur in the lowest atmospheric model level. For radiative transfer calculations, profiles of radiative fluxes are computed for each of the tiles and aggregated into a grid mean, while fluxes are maintained for each tile as described in Sect. .

For snow on sea ice in CanAM5, the parameterization of snow cover fraction was updated and a parameterization of wet-snow grain growth added to improve consistency with the treatment of snow on land.

In CanAM4, different parameterizations of snow cover were used on land and on sea ice, the snow cover on land being 3Xsnow=Zsnow/Zsnow,limif Zsnow≤Zsnow,lim1.0if Zsnow>Zsnow,lim and snow cover over sea ice being 4Xsnow=SWE/SWElimif SWE≤SWElim1.0if SWE>SWElim, where Xsnow is the fractional area of the land or sea ice covered with snow, Zsnow is the depth of the snow (in m), and SWE is the snow water equivalent (in kg m-2), with Zsnow,lim and SWElim being adjustable limits for each. In CanAM5, Eq. () is used to determine snow cover over land and sea ice. Note that in Eq. (), Zsnow is initially computed using Zsnow=SWE/ρsnow, where ρsnow is the snow density (in kg m-3). If Zsnow≤Zsnow,lim, then Zsnow=Zsnow,lim and the SWE is adjusted accordingly .

The computation of sea ice albedo includes a contribution from snowpack on sea ice when it is present. To calculate the albedo of snow, it is necessary to simulate the relevant physical properties of the snow, including the snow grain size. The approach used to parameterize these properties in CanAM5 is described in . Described here is the addition of a parameterization to CanAM5 so that the wet growth of snow grains is included for snow on sea ice, where previously only the dry growth of snow grains was considered.

To calculate the wet growth of snow grains, the same expression is used as over land (Eq. 3 of ), which requires the liquid water fraction in the snowpack. This was not available in CanAM5, so we added a parameterization of snowpack liquid water fraction using : 5Fliq=Fliq,minif ρsnow≥ρsnow,thresFliq,min+ΔFliqif ρsnow<ρsnow,thres, where Fliq is the fraction of liquid in the snow pack, ρsnow is the density of snow (in kg m-3), and ρsnow,thres is the snow density threshold at which Fliq,min occurs. The term ΔFliq is (Fliq,max-Fliq,min)×ρsnow,thres-ρsnowρsnow,thres, where Fliq,max and Fliq,min are the maximum and minimum allowed values of Fliq.

The near-global (equatorward of 60∘) cloud fraction as a function of cloud optical thickness and cloud top pressure is shown in Fig. . For the purposes of comparing more consistent model output from CanAM5 and CanAM4 with observations, output from the International Satellite Cloud Climatology Project (ISCCP) simulator is compared with ISCCP observations, both ISCCP-D and ISCCP-H . The two versions of ISCCP observations are used to illustrate the uncertainty in the cloud properties, an uncertainty that only increases once other cloud observations are considered . For example, showed that there are large differences between ISCCP and MODIS (larger than between the two versions of ISCCP shown here). Therefore, it is important that such differences be considered in the evaluation of models, especially for optically thin clouds.

With these caveats in mind, the histograms of biases indicate that CanAM5 generally simulates too much cloud with moderate optical thickness of high- and low-altitude clouds and simulates too little cloud at mid-level altitudes. Summing the histograms over cloud top pressure to look at clouds as a function of visible optical thickness, we see that there are more optically thin (τ<23) clouds in CanAM5 than in CanAM4. The structure of these biases relative to ISCCP is consistent with previous studies, for example .

The zonal mean structure for cloud amount is presented (Fig. ), which illustrates that the near-global mean biases are the result of regional biases which are a source of biases and improvements in the cloud radiative effect (CRE) (Fig. ). As seen in the near-global means, the differences between ISCCP-D and ISCCP-H are smaller than biases between CanAM and ISCCP-H and the change in biases between CanAM4 and CanAM5. Although there remain biases in the total cloud amount, there is a systematic reduction in biases in CanAM5 by ∼ 3.4 % in the near-global mean compared to biases in CanAM4. Parsing the biases in CanAM5 by the altitude of cloud top pressure, the increase in the CanAM5 total cloud amount mostly is caused by increased non-low (cloud top pressure <680 hPa) cloud amount. From CanAM4 to CanAM5, there is an increase in the amount of “thin” (cloud visible τ between 0.3 and 23) and reduction in “thick” (cloud visible τ>23) cloud at all latitudes, consistent with the near-global mean (Fig. ). This increase in cloud amounts is consistent with the change in CREs (Fig. ). The shift to more optically thin cloud would reduce the reflectively, doing so in a nonlinear manner, while the increase in the total cloud fraction will increase the CRE in a linear manner.

The CMIP6 protocol requested the additional diagnostic output consistent with retrievals based on lidar observations from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS) imager measurements . These are used to evaluate the vertical structure of the clouds in CanAM5 and the ability of CanAM5 to simulate the cloud phase (Fig. ). Biases in the cross section of cloud amount for CanAM5 relative to the GCM-Oriented CALIPSO Cloud Product (GOCCP) (upper row Fig. ) are consistent with biases between CanAM4 and GOCCP . There is generally too much cloud simulated at higher altitudes and too little cloud simulated at lower altitudes.

The middle and lower rows of Fig.  use diagnostics of GOCCP cloud-phase profiles and MODIS cloud top phase. In the tropics, CanAM5 underestimates the fraction of cloud that is ice in the middle troposphere; however, it occurs in a range of altitudes where CanAM5 is already simulating too few clouds. The more notable bias is that CanAM5 simulates too much ice cloud poleward of ∼50∘, seen in the GOCCP and MODIS diagnostics. These biases in the high-latitude cloud phase can have important consequences on the radiation budget and cloud feedbacks in these regions .

Precipitation biases are an important feature of any climate model. Although the structure of the biases in CanAM5 is similar to that in CanAM4, there are improvements in some key regions. The most noticeable improvement is the increased precipitation rate over the Amazon in CanAM5 for most seasons, although dry biases remain (Fig. ). This change in precipitation is consistent with a reduction in temperatures that are too warm over the Amazon in CanAM5 (Fig. ), which may be due to more moist conditions suppressing the near-surface temperature.

Radiative fluxes through the top of atmosphere (TOA) and bottom of atmosphere are evaluated using CERES observations . Figure  summarizes the global mean climatology for the solar and thermal flux components of the radiative energy budget for CanAM5 and CanAM4. Although a relatively short common period is used from the models and CERES observations (2003–2009), the results are very similar to those using longer periods from CERES (2003–2020) and CanAM (1979–2009).

We focus first on fluxes at TOA, which can be most directly compared with space-based observations from CERES. The global mean thermal fluxes are effectively identical in CanAM4 and CanAM5. The change in the downward solar flux is due to the use of updated solar forcing that has a reduced total solar irradiance, which is more consistent with observations and CERES. The upward solar flux is reduced by 3.4 W m-2. There is a small reduction in the clear-sky upward solar flux at TOA, ∼0.2 W m-2, so the remainder of this reduction is due to clouds (Fig. ).

Evaluated separately, the solar and thermal radiative fluxes are within the range of values from the CMIP6 simulations . When all fluxes are combined to compute the net flux imbalance at TOA, CanAM5 has a value that is larger than CERES and CanAM4 by 2.2 W m-2. In CanAM4 there is a compensation between the upward thermal (too small) and upward solar (too large) fluxes, resulting in a net imbalance that is in line with observations, while in CanAM5 both the upward thermal and solar fluxes are smaller than observations. We note that at least one other CMIP6 model documented a similar difference between AMIP and coupled simulations . To put the CanAM5 results into context with other models participating in CMIP6, we compared the net flux at TOA averaged over 2003–2009 for 34 models which had at least one AMIP and one historical coupled simulation. Of the 34 models, 18 AMIP simulations have absolute global mean differences relative to CERES that are greater than 1 W m-2 but only three historical coupled simulations have a difference greater than 1 W m-2 (not shown). This indicates that the behaviour seen in CanAM5 and CanESM5 is not unique among CMIP6 models.

The TOA flux imbalance is larger than that for coupled CanESM5 simulations, ∼1.1 W m-2, averaged over 2003–2009 (Fig. S6). This is mainly due to solar fluxes which are larger (99.3 W m-2) than when using observed SSTs (97.7 W m-2), since upward thermal fluxes at TOA are similar (239.8 W m-2 versus 239.5 W m-2). An in-depth analysis of why this occurs is beyond the scope of this paper. Preliminary analysis using CanAM5 with combinations of sea ice and SST specification, from observations and coupled CanESM5 simulations, suggests that differences in SSTs are the main factor which may be due to local and nonlocal responses affecting the TOA radiative fluxes.

While the TOA radiative fluxes were regularly evaluated during the development of CanAM, radiative fluxes at the surface and within the atmosphere were not. For both CanAM5 and CanAM4 the biases at the surface relative to CERES are consistent: downward and upward longwave fluxes that are too small and downward and upward shortwave fluxes that are too large. Altogether this results in too little absorption of radiation at the surface by 3 to 5 W m-2 and fairly consistent overestimation of net absorption in the atmosphere by 5 W m-2 due mainly to too much absorption of longwave radiation.

Clouds strongly modulate radiative fluxes, so we next examine the simulated cloud radiative effects (CREs), defined as CRE=Fclear sky-Fall sky, where Fclear sky is the radiative flux in the absence of clouds and Fall sky is the radiative flux with clouds present. The annual mean cloud radiative effects are generally positive for longwave and negative for shortwave at TOA and the surface, while the longwave strongly controls the zonal atmospheric CRE (Fig. ). The global mean CREs simulated by CanAM5 are less biased relative to CERES than CanAM4, especially for shortwave CRE, while the longwave CRE at TOA is slightly more biased than CanAM4 (Fig. ). Zonal mean CREs show that the improvements seen in the CanAM5 global means are due to reduced biases at most latitudes (Fig. ). In addition to improved global mean biases, the root mean square error is decreased (Fig. S1). These improved CREs suggest improved simulation of cloud properties in CanAM5 (Sect. ).

In this subsection we document the climatological properties of the winds, temperature, and surface pressure in CanAM5 for an AMIP experiment. Seasonal climatologies of latitude–height zonal-mean zonal wind fields and anomalies are presented in Fig. . While overall biases are similar relative to CanAM4, CanAM5 displays anomalously positive rather than negative wind biases in the mid- to high-latitude Northern Hemisphere DJF lower stratosphere. This is consistent with weaker planetary wave forcing of the Northern Hemisphere stratosphere in CanAM5. This wintertime positive zonal wind bias is associated with a weakening of the orographic gravity-wave drag due to a change in parameter values between the two model versions (Sect. ). Similarly, this weakening of the gravity wave drag contributes to a larger positive anomaly of zonal-mean zonal winds in CanAM5 in the Southern Hemisphere wintertime stratosphere. The near-surface zonal wind climatology is consistent with the coupled CanESM5 simulations , with biases relative to ERA5 generally smaller in CanAM5, with the most significant reductions in midlatitudes in both hemispheres (Fig. ).

In Fig. , seasonal climatologies of latitude–height zonal-mean temperature from ERA5, CanAM5, and CanAM4 are presented. In general, CanAM5 and CanAM4 have similar patterns of temperature bias in all seasons, including a warm tropical tropopause and cool extratropical tropopause. However, there are regional and seasonal differences; for example, temperatures between December and May poleward of 60∘ N in the stratosphere are not as systematically biased warm in CanAM5 relative to CanAM4. The pattern and magnitude of the temperature biases are similar to those in coupled configurations .

The seasonal mean sea-level pressure is presented in Fig. . Relative to CanAM4, CanAM5 displays larger DJF biases in the Aleutian Low and North Pacific High but lower bias in the whole of the Atlantic Ocean in all seasons.

Seasonal mean biases in near-surface temperature for CanAM5 and CanAM4 are presented in Fig. . Persistent cold biases are found over the Tibetan Plateau and North Africa in both models. The Tibetan Plateau bias is negatively correlated with snow cover bias (too much snow cover and temperatures that are too cold), a feature found in other CMIP6 models . The source of a snow cover that is too large is complex and is present to differing degrees in CanAM4 and CanAM5. That said, it does seem to be a robust feature of CanAM, given that the land model was significantly changed between CanAM4 and CanAM5, including the parameterization of snow albedo (Sect. ). In North Africa, cold biases are thought to be due to the change from a globally constant albedo for bare soil to a more realistic distribution based on local soil conditions .

Warm biases are apparent over the Brazil basin, although somewhat reduced in CanAM5, consistent with biases related to too little precipitation (Fig. ). Over central North America in JJA, the warm bias persists in CanAM5 and is more extensive than in CanAM4. This is a common warm bias among CMIP5 models during JJA for which the cause is thought to be a complex interplay between land–atmosphere coupling, radiation, and clouds that rapidly develop in climate models .