Vertical cloud radiative heating in the tropics: Confronting the EC-Earth model with satellite observations

. Understanding the coupling of clouds to large-scale circulation is one of the grand challenges for the global climate research community. In this context, realistically modelling the vertical structure of cloud radiative heating/cooling (CRH) in Earth system models is a key premise to understand these couplings. Here, we evaluate CRH in two versions of the European Community Earth System Model (EC-Earth) using retrievals derived from the combined radar and lidar data from the CloudSat and CALIPSO satellites. One model version is also used with two different horizontal resolutions. Our study evaluates large- 5 scale intraseasonal variability in the vertical structure of CRH and cloud properties and investigates the changes in CRH during different phases of the El Niño Southern Oscillation (ENSO), a process that dominates the interannual climate variability in the tropics. active zones the compared to the Further CRH shortwave and noticeable structure. The component of the radiative heating is overestimated by all model versions in the lowermost troposphere and underestimated in the middle troposphere. These over- and underestimates of shortwave heating are partly compensated by an overestimate of longwave cooling in the 15 lowermost troposphere and heating in the middle troposphere. The biases in CRH can be traced back to disagreements in cloud amount and cloud water content. There is no noticeable improvement of CRH by increasing the horizontal resolution in the model alone. Our ﬁndings highlight the importance of evaluating models with satellite observations that resolve the vertical structure of clouds and cloud properties.


Introduction
Due to ground clutter contamination in the CloudSat data, the lowest 750m are excluded. When the differences between the different data and model outputs are calculated, the dataset with higher resolution is averaged to the lower resolution. For both the satellite retrievals and the model output, the CRH is calculated by subtracting the shortwave and longwave clear sky values from the cloudy sky values (in models typically referred to as "all-sky") (Johansson et al., 2015). CRH = ((SW HR cloudy + LW HR cloudy ) − (SW HR clear + LW HR clear )) (1) 125 where SW HR is the shortwave heating rate and LW HR is the longwave heating rate. A two-sided student t-test is used on the monthly mean values and the 95% confidence interval is highlighted with black dots in the figures. We also did a Mann-Whitney rank test with similar results as the student t-test and these results are therefore not shown. Even so, the small sample size, especially for the ENSO study, will limit the interpretation of the statistical test.
The cloud water content in EC-Earth is given in kgkg −1 , while the satellite dataset provide the water content in gm −3 . We 130 therefore need to recalculate the water content from the model by multiplying with density where W C is the cloud water content (either ice or liquid) and ρ is the density. ρ is calculated from the ideal gas law (Holton and Hakim, 2012, chapter 2.9) 135 and where p is the pressure, T v is the virtual temperature, T is the temperature, q v is the specific humidity, and R d and R v is the gas constant for dry air and water vapour. We use the constants 287.04Jkg −1 K −1 and 461Jkg −1 K −1 for R d and R v respectively while the model is providing the other variables for each vertical level.

140
The phase of the ENSO is obtained from the National Oceanic and Atmospheric Administration (NOAA; see    Figure 1 shows the vertical structure of CRH derived from the satellite observations. During all seasons, clouds have a net heating effect in the upper troposphere, from 5 to 10-15km. Close to the equator, the intertropical convergence zone constitutes the rising branch of the Hadley Cell. This zone has a prevalent 155 cloud cover with small seasonal variations within the ±30 • latitude band and will, therefore, contribute to CRH throughout the year. Over the warm tropical waters, large convective systems exist and these clouds will generate strong CRH. This CRH is especially pronounced at higher altitudes during seasons when the convection is most active. The highest ocean surface temper-6 https://doi.org/10.5194/gmd-2020-277 Preprint. Discussion started: 11 November 2020 c Author(s) 2020. CC BY 4.0 License. atures are found in the western Pacific and eastern Indian Ocean (sometimes referred to as the tropical warm pool; 70 • -160 • ) and the convective cloud systems here contribute to the radiative heating (1.5Kday −1 ) with a small seasonal cycle. In DJF 160 and MAM, during the rainy period over south-central Africa (340 • -45 • ), there is strong CRH (1.5Kday −1 and 1Kday −1 , respectively) compared to 0.5Kday −1 during the dry season (JJA). The signature of the South Asian monsoon (∼ 90 • ) is also visible in the CRH (1.5Kday −1 ) during JJA and SON (also see Johansson et al., 2015). During the SON months, the intense but local CRH (1.5Kday −1 ) over south-central America (280 • -315 • ) also stands out.
West of the continents, cold upwelling water creates vast areas with marine stratocumulus clouds that are present on average 165 60% of the time. These clouds generate the sloping wedges of low-level radiative cooling seen in the lower troposphere, mostly below 2km, with maxima sloping down towards the west coast of the continents at low altitude. Marine stratocumuli have a seasonal cycle with peaks in cloud fraction in August -October (Hahn andWarren, 2007, updated 2009), leading to intense cooling (−1.5-−2Kday −1 ) in JJA and SON close to South America and Africa (see Figures 1c-d).
We now explore if the high-resolution version of EC-Earth can simulate the large-scale CRH features retrieved from satellite  There are, however, noticeable differences between the satellite retrievals and the model simulations. First, within most parts between 10 and 15km, the magnitude of the CRH is much stronger in EC-Earth3P-HR, almost twice as high as in the satellite observations. The seasonally averaged heating from the model-simulated convection sometimes reaches above 2Kday −1 while in the satellite data, it typically stays around 1Kday −1 . This overestimate by the model occurs over all convectively active 180 regions, but the CRH is also overestimated in the stratocumulus regions, where the modelled cooling is too weak. The second striking disagreement between the model and observations relates to the vertical structure. The modelled CRH is vertically located in the upper troposphere, predominantly 12-14km, where convective outflow and detrainment typically occurs. In the middle troposphere the CRH from the model simulations is weaker than in the observations. It should be noted that these disagreements are visible even though the satellite observations are matched to the interpolated CRHs from the nearest 3-hourly 185 time steps in the model simulations.
8 https://doi.org/10.5194/gmd-2020-277 Preprint. Discussion started: 11 November 2020 c Author(s) 2020. CC BY 4.0 License. Figure 3 address the question if the high-resolution version of EC-Earth offer any improvement when compared to its standard counterpart, showing the difference in CRH between EC-Earth3P-HR and EC-Earth3P. Over the American and the African continents, there are considerable differences for the simulations with a lower resolution. When the convection is strong, the CRH in EC-Earth3P-HR is 0.5Kday −1 higher compared to EC-Earth3P. During the dry period (JJA), the differences 190 are reduced, but still reaching up to 0.3Kday −1 . Similarly, when the Indian monsoon is at its peak, during JJA, the CRH is larger in EC-Earth3P-HR than in EC-Earth3P. Even though the convection over the west Pacific and the East Indian Ocean is persistently active, there are only small differences between the two model versions over this area.
The difference in CRH between EC-Earth3 and EC-Earth3P is shown in Figure 4. EC-Earth3 has a slightly higher CRH over the American continent for all seasons (0.3-0.4Kday −1 ) and for the west Pacific Ocean in MAM (0.2Kday −1 ). On the other 195 hand, EC-Earth3P has slightly higher CRH over the Indian Ocean, especially during SON. The maritime stratocumulus regions have slightly higher CRH in EC-Earth3P than in EC-Earth3. The differences are in general small, with low significance, but both EC-Earth3 and EC-Earth3P are closer to the satellite observations than EC-Earth3P-HR, despite the higher horizontal resolution in the latter.

Meridional and Zonal average 200
In order to understand the differences between model simulations and observations, we explore the vertical structure of CRH and the clouds (Figures 5 and 6). The plots show the CRH divided into its shortwave and longwave components, as well as the vertical structure of cloud fraction and cloud water content, for all four seasons.
Examining the vertical structure of the shortwave and longwave components of CRH reveals clear differences ( Figure 5). In the lowermost troposphere (up to about 2km), all model versions overestimate shortwave CRH (0.2Kday −1 difference), while 205 the CRH is strongly underestimated in the middle and upper troposphere (up to 12km) by ∼ −0.4Kday −1 . Above 12km, the differences are negligible. These shortwave over-and underestimates are to some extent compensated in the longwave spectrum by overestimated cooling in the lowermost troposphere and heating in the middle-to-upper troposphere. The resulting net CRH shows stronger heating for the satellite ∼ 0.4Kday −1 ) between 2 and 10km compared to the models (∼ 0.2Kday −1 ) while the net CRH in the upper troposphere (between 10-14km) is higher in the models (1Kday −1 ) compared to the observations 210 (0.5Kday −1 ). This large difference in the upper troposphere is mainly due to the strong overestimate of longwave heating by the models and appears in all seasons. Figure 6 shows that the models underestimate the cloud fraction as well as cloud liquid and ice water content in the lower and middle parts of the troposphere. This underestimate results in suppressed shortwave heating and longwave cooling in the middle troposphere in the models, thus, at least partly, explaining the differences observed Figure 5. It is interesting to note 215 that even though the difference in cloudiness is substantial in the middle part of the troposphere between the models (0.05) compared to satellite (0.1), the difference in liquid water content is smaller which implies that the models have fewer but denser clouds compared to observations. In the upper troposphere, although the models do have similar cloudiness as in the observations, the ice water content is much lower in the models (peaks at 0.002gm −3 ) compared to observations (0.011gm −3 ).
This leads to a significant underestimate of cloud top longwave cooling in the models in the upper troposphere ( Figure 5). The   anomalies. During ENSOP, trade winds over the Pacific weaken, allowing a surge of warm water eastwards, leading to colder sea surface temperatures (SST) in the western Pacific Ocean while central and eastern Pacific will experience warmer than usual SST (see Timmermann et al., 2018). The increase in SST leads to additional and more vigorous convection. These will, in turn, generate stronger atmospheric radiative heating (0.75Kday −1 ) in the central Pacific atmosphere. Over the western Pacific, the decrease in SST will instead generate less convection, leading to a decrease in the CRH (−0.5Kday −1 ). In contrast, for 235 ENSON, the surface water in the western Pacific and eastern Indian Ocean is warmer than usual, while central and eastern parts of the Pacific Ocean are colder than usual. This leads to an increase of CRH with 0.25Kday −1 over West Pacific and East Indian Ocean and a decrease with −0.25Kday −1 over the central Pacific Ocean. The response to ENSON is expected to be smaller than the response to ENSOP when compared to the average since ENSON is an enhancement of the normal mode.
11 https://doi.org/10.5194/gmd-2020-277 Preprint. Discussion started: 11 November 2020 c Author(s) 2020. CC BY 4.0 License. ENSO also has implications for clouds over the Atlantic Ocean. Madenach et al. (2019) found that close to the equator, high 240 clouds in the Atlantic decreased during ENSOP and increased during ENSON, while the opposite was observed for low-level clouds. For ENSOP the CRH decreases by −0.5Kday −1 in the Atlantic middle and high troposphere and the radiative cooling due to the maritime stratocumulus clouds outside Angola increases with −0.75Kday −1 .     there is a larger CRH over the Indian Ocean in EC-Earth3P-HR while the differences are small in the Pacific Ocean and the Atlantic.   thick cirrus. In the model simulations, the CRH anomaly continues to increase with height and then changes sign abruptly 285 compared to the observations at around 14km. As a result of these overestimations of both shortwave and longwave heating, the net CRH anomaly in the models (1.2Kday −1 ) is almost three times higher compared to observations (0.4Kday −1 ) in the upper troposphere in ENSOP for the Nino4 region.
As the Walker circulation is enhanced during the ENSON phase, the reduction in convection and associated clouds lead to a reduction in CRH in the upper troposphere over the Nino4 region (Figures 11d-f). In this case, although the reduced shortwave 290 heating (peak at −0.4Kday −1 ) is captured relatively well by the models, the magnitude of the reduction in longwave heating (−0.4Kday −1 ), in the models, is large enough to result in a net CRH anomaly that is half of that from the observations (−0.8Kday −1 compared to −0.4Kday −1 ). Over the Nino3 region the CRH and cloud property anomalies are low both in the observations and model simulations, during both phases of the ENSO (Figure 12).
Overall the models tend to underestimate the anomaly in cloud ice water content and overestimate the change in liquid water 295 content for both phases of ENSO over both the Nino3 and Nino4 areas (Figures 13 and 14). The variability in the satellite observations is however large for both liquid and ice water content compared to all three versions of the model investigated here.

Conclusions
In the past, cloud radiative forcing in climate models has mainly been evaluated at the top of the atmosphere or the surface since 300 the net fluxes are more readily observed there, either by satellite or by surface instruments. A rigorous evaluation of the vertical structure of cloud radiative heating/cooling has only been possible since the last few years as the retrievals from the combined active radar and lidar sensors onboard CloudSat and CALIPSO satellites have matured enough to allow a quantitative analysis (Cesana et al, 2019). Understanding the vertical structure of CRH and evaluating climate models from this aspect is especially thereby influencing the atmospheric circulation, but it also influences the troposphere-to-stratosphere transport.
In this study, we therefore investigated two versions of the EC-Earth climate model, where one version was used with two different horizontal resolutions (EC-Earth3P-HR and EC-Earth3P). In addition to a traditional statistical comparison, we also carried out a more process-oriented evaluation by examining how different versions of the EC-Earth model simulate CRH during different phases of ENSO. The following conclusions can be drawn from the evaluations: (c) There are two noticeable differences between EC-Earth (EC-Earth3P-HR) and the satellite retrievals. First, the magnitude of net CRH (above 2Kday −1 ) is much stronger in the model in the upper part of the troposphere over convectively active zones, almost twice as high as in the satellite observations. Second, the net CRH in the models is vertically limited to the upper troposphere, predominantly between 12-14km, whereas, the satellite observations also show pronounced heating 320 in the middle troposphere. These disagreements are the largest in EC-Earth3P-HR.
(d) There are substantial differences in the vertical structure of cloud fraction and cloud water content between the models and observations. In the upper part of the troposphere, the cloud fraction is similar but the models have less ice water content. In the lower and middle parts of the troposphere, the models underestimate the cloud fraction. These differences are vital in understanding the disagreements in the magnitude and vertical distribution of the CRH between the models 325 and the observations.
(e) The spatial CRH variability associated with the ENSO phases and shifts in the Walker circulation is also reasonably captured by the models. However, the differences in the magnitude and vertical structure of the CRH between the models and satellite data mentioned above remain or even increase.
Our results highlight the importance of having satellite observations that resolve the vertical structure of clouds for evaluating 330 climate models and the importance of realistically simulating the vertical structure of cloud properties. We could, however, only notice negligible differences in the simulation of vertical cloudiness and CRH over most parts of the tropics when changing the horizontal resolution in EC-Earth. An increase in the vertical resolution could potentially further improve the representation of clouds and CRH in the models and such an investigation would be of interest in a prospective study. A longer time series from the observations, especially for the ENSO analysis, would be preferable for future studies.