Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models

We examine the influence of increased resolution on four long-standing biases using five different climate models developed within the PRIMAVERA project. The biases are the warm eastern tropical oceans, the double Intertropical Convergence Zone (ITCZ), the warm Southern Ocean, and the cold North Atlantic. Atmosphere resolution increases from ∼100–200 to ∼25–50 km, and ocean resolution increases from ∼1∘ (eddy-parametrized) to ∼0.25∘ (eddy-present). For one model, ocean resolution also reaches 1/12∘ (eddy-rich). The ensemble mean and individual fully coupled general circulation models and their atmosphere-only versions are compared with satellite observations and the ERA5 reanalysis over the period 1980–2014. The four studied biases appear in all the low-resolution coupled models to some extent, although the Southern Ocean warm bias is the least persistent across individual models. In the ensemble mean, increased resolution reduces the surface warm bias and the associated cloud cover and precipitation biases over the eastern tropical oceans, particularly over the tropical South Atlantic. Linked to this and to the improvement in the precipitation distribution over the western tropical Pacific, the double-ITCZ bias is also reduced with increased resolution. The Southern Ocean warm bias increases or remains unchanged at higher resolution, with small reductions in the regional cloud cover and net cloud radiative effect biases. The North Atlantic cold bias is also reduced at higher resolution, albeit at the expense of a new warm bias that emerges in the Labrador Sea related to excessive ocean deep mixing in the region, especially in the ORCA025 ocean model. Overall, the impact of increased resolution on the surface temperature biases is model-dependent in the coupled models. In the atmosphere-only models, increased resolution leads to very modest or no reduction in the studied biases. Thus, both the coupled and atmosphere-only models still show large biases in tropical precipitation and cloud cover, and in midlatitude zonal winds at higher resolutions, with little change in their global biases for temperature, precipitation, cloud cover, and net cloud radiative effect. Our analysis finds no clear reductions in the studied biases due to the increase in atmosphere resolution up to 25–50 km, in ocean resolution up to 0.25∘, or in both. Our study thus adds to evidence that further improved model physics, tuning, and even finer resolutions might be necessary.

The Surface Longwave Cloud Radiative Effect derived from Space Lidar Observations

Clouds warm the surface in the longwave (LW) and this warming effect can be quantified through the surface LW cloud radiative effect (CRE). The global surface LW CRE is estimated using long-term observations from space-based radiometers (2000–2021) but has some bias over continents and icy surfaces. It is also estimated globally using the combination of radar, lidar and space-based radiometer over the 5–year period ending in 2011. To develop a more reliable long time series of surface LW CRE over continental and icy surfaces, we propose new estimates of the global surface LW CRE from space-based lidar observations. We show from 1D atmospheric column radiative transfer calculations, that surface LW CRE linearly decreases with increasing cloud altitude. These computations allow us to establish simple relationships between surface LW CRE, and five cloud properties that are well observed by the CALIPSO space-based lidar: opaque cloud cover and altitude, and thin cloud cover, altitude, and emissivity. We use these relationships to retrieve the surface LW CRE at global scale over the 2008–2020 time period (27 Wm−2). We evaluate this new surface LW CRE product by comparing it to existing satellite-derived products globally on instantaneous collocated data at footprint scale and on global averages, as well as to ground-based observations at specific locations. Our estimate appears to be an improvement over others as it appropriately capture the surface LW CRE annual variability over bright polar surfaces and it provides a dataset of more than 13 years long.

Earth’s Global Mean Energy Flow System as the Solution of Four Radiative Transfer Constraint Equations

Schwarzschild’s equation in two-stream approximation provides a constraint on the net radiation at the lower boundary, as unequivocally connects it to fluxes at the upper boundary (top-of-atmosphere): surface net radiation, balanced by the non-radiative fluxes in radiative-convective equilibrium, is equal to half of the outgoing longwave radiation (OLR), independent of the optical depth. This paper controls this equation on satellite observations and proved to be valid within -2.19 W/m2 for clear-sky, in the annual global mean. The all-sky version of the relationship can easily be constructed, separating atmospheric radiation transfer from the longwave cloud radiative effect (LWCRE); it is valid with a difference of 2.76 W/m2 on the same data set. The equation provides a total (greenhouse) relationship as well, where the surface energy income is given as a function of OLR and the optical depth. This equation is controlled at a particular optical depth of two, and found to be verified with a difference of -2.72 W/m2 for clear-sky in the global mean. An all-sky form is created by including LWCRE, justified within 2.42 W/m2 on the same data set. The four equations together have a mean bias of 0.07 W/m2. This set of equations, using evident definitions between the all-sky and clear-sky fluxes has a solution for the involved components of the global mean energy flow system. Implications on the global mean clear-sky and cloudy energy flow systems are assessed, and the all-sky energy budget is constructed as a weighted sum of the clear and cloudy fluxes.

Long-term Trend Comparison of Planetary Boundary Layer Height in Observations and CMIP6 models over China

The planetary boundary layer (PBL) plays an essential role in climate and air quality simulations. Nevertheless, large uncertainties remain in understanding the drivers for long-term trend of PBL height (PBLH) and its simulation. Here we combinate the radiosonde data and reanalysis datasets to analyze PBLH long-term trends over China, and to further explore the performance of CMIP6 climate models in simulating these trends. Results show that the observed long-term “positive to negative” trend shift of PBLH is related to the variation in the surface upward sensible heat flux (SHFLX), and the SHFLX is further controlled by the synergistic effect of low cloud cover (LCC) and soil moisture (SM) changes. Variabilities in LCC and SM directly influence the energy balance via surface net downward shortwave flux (SWF) and the latent heat flux (LHFLX), respectively. The CMIP6 climate models, however, cannot reproduce the observed PBLH long-term trend shift over China. The CMIP6 results illustrate an overwhelming continuous downward PBLH trend during the 1979–2014 period, which is largely caused by the poor capability in simulating long-term variations of cloud radiative effect. Our results reveal that the long-term cloud radiative effect simulation is critical for CMIP6 models in reproducing the long-term trend of PBLH. This study highlights the importance of processes associated with LCC and SM in modulating PBLH long-term variations and calls attentions to improve these processes in climate models in order to improve the PBLH long-term trend simulations.

Surface shortwave cloud radiative effect of Cumulus (Cu) and Stratocumulus-Cumulus (Sc-Cu) cloud types in the Caribbean area (Camagüey Cuba, 2010-2016)

The effects of cumulus clouds (Cu) and the combination of stratocumulus-cumulus clouds (Sc-Cu) on the solar radiation at the earth’s surface were evaluated at Camagüey (Cuba) for a 6-year period (from June 2010 to May 2016). Two methods to calculate the cloud radiative effect (CRE) were employed. The first method (CREm) uses solar irradiances in cloudy conditions from actinometric observations, where cloud information was also reported by visual observation. In the second method (CRE0) surface solar irradiances were estimated for both cloudy and clear sky conditions using a 1-D radiative transfer model, and cloud optical depth (COD) retrieved from an AERONET sun-photometer as the main input. A temporal correspondence criterion between COD retrievals and actinometric observations was performed in order to classify COD of each cloud type. After the application of this criterion, the COD belonging to the optically thin clouds was removed. Finally, 255 and 732 COD observations for Cu and Sc-Cu respectively, were found. Results show statistically significant difference at the 95% confidence level between CRE calculated for Sc-Cu and Cu, using the both methods. Mean values of CREm and CRE0 for Cu (Sc-Cu) were −442 W/m2 (−390 W/m2) and −460 W/m2 (−417 W/m2), respectively. CRE0 shows a linear relation with ln(COD), with stronger correlation at lower solar zenith angle. The shortwave cloud effect efficiency (CEE) for the two cloud types sharply decreases with the increase of the COD up to the value of 20. For larger COD, the CEE is less sensitive to the increase of COD.

Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models

We examine the impacts of increased resolution on four long-standing biases using five different climate models developed within the PRIMAVERA project. Atmospheric resolution is increased from ~100–200 km to ~25–50 km, and ocean resolution is increased from ~1° (i.e., eddy-parametrized) to ~0.25° (i.e., eddy-present). For one model, ocean resolution is also increased to 1/12° (i.e., eddy-rich). Fully-coupled general circulation models and their atmosphere-only versions are compared with observations and reanalysis of near-surface temperature, precipitation, cloud cover, net cloud radiative effect, and zonal wind over the period 1980–2014. Both the ensemble mean and individual models are analyzed. Increased resolution especially in the atmosphere helps reduce the surface warm bias over the tropical upwelling regions in the coupled models, with further improvements in the cloud cover and precipitation biases particularly over the tropical South Atlantic. Related to this and to the improvement in the precipitation distribution over the western tropical Pacific, the double Intertropical Convergence Zone bias also weakens with resolution. Overall, increased ocean resolution from ~1° to ~0.25° offers limited improvements or even bias degradation in some models, although an eddy-rich ocean resolution seems beneficial for reducing the biases in North Atlantic temperatures and Gulf Stream path. Despite the improvements, however, large biases in precipitation and cloud cover persist over the whole tropics as well as in the upper-troposphere zonal winds at mid-latitudes in coupled and atmosphere-only models at higher resolutions. The Southern Ocean warm bias also worsens or persists in some coupled models. And a new warm bias emerges in the Labrador Sea in all the high-resolution coupled models. The analysis of the PRIMAVERA models therefore suggests that, to reduce biases, i) increased atmosphere resolution up to ~25–50 km alone might not be sufficient and ii) an eddy-rich ocean resolution might be needed. The study thus adds to evidence that further improved model physics and tuning might be necessary in addition to increased resolution to mitigate biases.

Optically thin clouds in the trades

We develop a new method to describe the total cloud cover including optically thin clouds in trade wind cumulus cloud fields. Climate models as well as Large Eddy Simulations commonly underestimate the cloud cover, while estimates from observations largely disagree on the cloud cover in the trades. Currently, trade wind clouds contribute significantly to the uncertainty in climate sensitivity estimates derived from model perturbation studies. To simulate clouds well and especially how they change in a future climate we have to know how cloudy it is. In this study we develop a method to quantify the cloud cover from a clear-sky perspective. Using well-known radiative transfer relations we retrieve the clear-sky contribution in high-resolution satellite observations of trade cumulus cloud fields during EUREC4A. Knowing the clear-sky part, we can investigate the remaining cloud-related contributions consisting of areas detected by common cloud masking algorithms and those undetected areas related to optically thin clouds. We find that the cloud-mask cloud cover underestimates the total cloud cover by a factor of 2. Lidar measurements on board the HALO aircraft support our findings by showing a high abundance of optically thin clouds during EUREC4A. Mixing the undetected optically thin clouds into the clear-sky signal can cause an underestimation of the cloud radiative effect of up to −32 %. We further discuss possible artificial correlations in aersol-cloud cover interaction studies that might arise from undetected optically thin clouds. Our analysis suggests that the known underestimation of trade wind cloud cover and simultaneous overestiamtion of cloud brightness in models is even higher than assumed so far.