We use atmospheric profiles from ERA5, JRA55 and MERRA2 between 1993 and 2023 to estimate Earth’s global clear-sky longwave feedback strength on the seasonal and interannual timescale. Differences in the relationship of relative humidity with skin temperature prior to 2008 lead to interannual feedback strengths between 1.34 W m−2 K−1 (JRA55) and 1.89 W m−2 K−1 (MERRA2). Restricting the analysis to the last 16 years yields more consistent interannual estimates of 2.05 W m−2 K−1 on average, which is larger than the overall seasonal estimate of 1.91 W m−2 K−1. The mid-tropospheric drying causing this difference suggest a substantial influence of ENSO variability on the interannual timescale. This indicates a long-term feedback strength smaller than 2.0 W m−2 K−1, which is already at the lower end of previous estimates; emphasizing the importance of accurate long-term RH measurements to reliably project Earth’s clear-sky feedback strength

Mid-tropospheric elevated moist layers (EMLs) near the melting level have been found in various regional observational studies in the tropics. Recently, a preponderance of EMLs in the presence of aggregated convection was found in cloud resolving simulations of radiative convective equilibrium (RCE), highlighting a significant circulation coupling. Here, we present global monthly EML occurrence rates based on reanalysis, yielding a broader view on where and when EMLs occur in the real world. Over the Atlantic, EML occurrence follows an annual cycle that maximizes in summer, aligning with maximized ITCZ intensity and organisation. Resembling the results in RCE, the large-scale circulation over the Atlantic shifts from a deep overturning in January to a bottom-heavy circulation in July. While EMLs embedded in the July cross-equatorial Hadley cell are found to be sourced from the ITCZ, EMLs north of the ITCZ emerge from the strongly sheared zonal flow over West Africa.

Aquaplanet experiments are used to investigate the physical convergence of a Global Storm-Resolving model (GSRM) under successive, two-fold horizontal grid spacing refinements from 160 km to 1.25 km. A methodology based on the Richardson extrapolation method is used with the aquaplanet hemispherical symmetry to quantify convergence. We use the symmetrical and anti-symmetrical solution components to estimate the asymptotic convergence pattern, the asymptotic estimate, and sampling uncertainty. Based on successive refinements, different climate statistics are explored to evaluate if they enter into a convergent regime and, if so, what their convergent value is. Our analysis focuses on global mean statistics related to the general circulation and aspects that influence the climate: the meridional overturning circulation, the tropical structure (the Inter-Tropical Convergence Zone (ITCZ), and the zonal mean thermodynamic state), and the energy and water budget. Our results show a kilometer and hectometer-scale horizontal grid spacing requirement for physical convergence of the meridional overturning circulation structure and global mean statistics. Distinctively, the tropical structure is estimated to be very near their asymptotic values at km-scale grid spacing, but the circulation intensity appears to converge more slowly, as do the storm track and jet-stream. As we increase the horizontal grid spacing, a better representation of clouds and zonal distribution of water vapor drives convergence in the energy and water budget. We conclude that simulations with a resolution of 2.5 km pose a great candidate for multi-decadal simulations within a compromise of the meridional overturning circulation structure convergence and intensity.

In this study we investigate whether a better representation of precipitation in the Amazon basin arises through an explicit representation of convection and whether it is related to the representation of organized systems. In addition to satellite data, we use ensemble simulations of the ICON-NWP model at storm-resolving (2.5-5.0 km) scales with explicit convection (E-CON) and coarse resolutions, with parameterized convection (P-CON). The main improvements in the representation of Amazon precipitation by E-CON are in the distribution of precipitation intensity and the spatial distribution in the diurnal cycle. By isolating precipitation from organized convective systems (OCS), it is shown that many of the well simulated precipitation features in the Amazon arise from the distribution of these systems. The simulated and observed OCS are classified into 6 clusters which distinguish nocturnal and diurnal OCS. While the E-CON ensembles capture the OCS, especially their diurnal cycle, their frequency is reduced compared to observations. Diurnal clusters are influenced by surface processes such as cold pools, which aid to the propagation of OCS. Nocturnal clusters are rather associated with strong low-level easterlies, possibly related to the Amazonian low-level jet. These particular environmental conditions provide insights on the processes that are important for OCS in the Amazon and should be further improved.

Using the global and coupled ICON-Sapphire model with a grid spacing of \SI{5}{\kilo\meter}, we describe seasonal and diurnal features of the tropical rainbelt and assess the limits of ICON-Sapphire in representing tropical precipitation. Aside from the meridional migration, the tropical rainbelt exhibits a seasonal enlargement and a zonal migration. Surprisingly, ICON-Sapphire reproduces these characteristics with better performance over land than over ocean and with a very high degree of agreement to observations. ICON-Sapphire especially struggles in capturing the seasonal features of the tropical rainbelt over the oceans of the Eastern Hemisphere, an issue associated with a cold SST bias at the equator. ICON-Sapphire also shows that a perfect representation of the diurnal cycle of precipitation over land is not a requirement to capture the seasonal features of the rainbelt over land, while over the ocean, 5km is sufficient to adequately represent the diurnal cycle of precipitation.

Clouds are primary modulators of Earth's energy balance. It is thus important to understand the links connecting variabilities in cloudiness to variabilities in other state variables of the climate system, and also describe how these links would change in a changing climate. A conceptual model of global cloudiness can help elucidate these points. In this work we derive simple representations of cloudiness, that can be useful in creating a theory of global cloudiness. These representations illustrate how both spatial and temporal variability of cloudiness can be expressed in terms of basic state variables. Specifically, cloud albedo is captured by a nonlinear combination of pressure velocity and a measure of the low-level stability, and cloud longwave effect is captured by surface temperature, pressure velocity, and standard deviation of pressure velocity. We conclude with a short discussion on the usefulness of this work in the context of global warming response studies.

Reducing the model spread in free-tropospheric relative humidity (RH) and its response to warming is a crucial step towards reducing the uncertainty in clear-sky climate sensitivity, a step that is hoped to be taken with recently developed global storm-resolving models (GSRMs). In this study we quantify the inter-model differences in tropical present-day RH across GSRMs, making use of DYAMOND, a first 40-day intercomparison. We find that the inter-model spread in tropical mean free-tropospheric RH is reduced compared to conventional atmospheric models, except from the the tropopause region and the transition to the boundary layer. We estimate the reduction to approximately 50-70% in the upper troposphere and 25-50% in the mid troposphere. However, the remaining RH differences still result in a spread of 1.2 Wm-2 in tropical mean clear-sky outgoing longwave radiation (OLR). This spread is mainly caused by RH differences in the lower and mid free troposphere, whereas RH differences in the upper troposphere have a minor impact. By examining model differences in moisture space we identify two regimes with a particularly large contribution to the spread in tropical mean clear-sky OLR: rather moist regimes at the transition from deep convective to subsidence regimes and very dry subsidence regimes. Particularly for these regimes a better understanding of the processes controlling the RH biases is needed.

We study how the vertical distribution of relative humidity (RH) affects climate sensitivity, even if it remains unchanged with warming. Using a radiative-convective equilibrium model, we show that the climate sensitivity depends on the shape of a fixed vertical distribution of humidity, tending to be higher for atmospheres with higher humidity. We interpret these effects in terms of the effective emission height of water vapor. Differences in the vertical distribution of RH are shown to explain a large part of the 0 to 30% differences in clear-sky sensitivity seen in climate and storm-resolving models. The results imply that convective aggregation reduces climate sensitivity, even when the degree of aggregation does not change with warming. Combining our findings with relative humidity trends in reanalysis data shows a tendency toward Earth becoming more sensitive to forcing over time. These trends and their height variation merit further study.

We conduct a series of eight 45-day experiments with a global storm-resolving model (GSRM) to test the sensitivity of relative humidity R in the tropics to changes in model resolution and parameterizations. These changes include changes in horizontal and vertical grid spacing as well as in the parameterizations of microphysics and turbulence, and are chosen to capture currently existing differences among GSRMs. To link the R distribution in the tropical free troposphere with processes in the deep convective regions, we adopt a trajectory-based assessment of the last-saturation paradigm. The perturbations we apply to the model result in tropical mean R changes ranging from 0.5% to 8% (absolute) in the mid troposphere. The generated R spread is similar to that in a multi-model ensemble of GSRMs and smaller than the spread across conventional general circulation models, supporting that an explicit representation of deep convection reduces the uncertainty in tropical R. The largest R changes result from changes in parameterizations, suggesting that model physics represent a major source of humidity spread across GSRMs. The R in the moist tropical regions is disproportionately sensitive to vertical mixing processes within the tropics, which impact R through their effect on the last-saturation temperature rather than their effect on the evolution of the humidity since last-saturation. In our analysis the R of the dry tropical regions strongly depends on the exchange with the extra-tropics. The interaction between tropics and extratropics could change with warming and presage changes in the radiatively sensitive dry regions.

Shallow cumulus clouds in the trade-wind regions cool the planet by reflecting solar radiation. The response of trade cumulus clouds to climate change is a major uncertainty in climate projections. Trade cumulus feedbacks in climate models are governed by changes in cloud fraction near cloud base, with high climate-sensitivity models suggesting a strong decrease in cloud-base cloudiness due to increased lower-tropospheric mixing. Here we show that novel observations from the EUREC4A field campaign refute this mixing-desiccation hypothesis. We find the dynamical increase of cloudiness through mixing to overwhelm the thermodynamic control through humidity. Because mesoscale motions and the entrainment rate contribute equally to variability in mixing, but have opposing effects on humidity, mixing does not desiccate clouds. The magnitude, variability, and coupling of mixing and cloudiness differ drastically among climate models and with the EUREC4A observations. Models with large trade cumulus feedbacks tend to exaggerate the dependence of cloudiness on relative humidity as opposed to mixing, and also exaggerate variability in cloudiness. Our observational analyses render models with large positive feedbacks implausible, and both support and explain at the process scale a weak trade cumulus feedback. Our findings thus refute an important line of evidence for a high climate sensitivity.

Understanding drivers of cloud organization is crucial for accurately estimating clouds’ feedback to a warming climate. Shallow mesoscale circulations are thought to play an important role in cloud organization, but they have not been observed. Here, we present observational evidence for shallow mesoscale overturning circulations (SMOCs) from divergence measurements made during the EUREC4A field campaign in the north-Atlantic trades. Meteorological reanalyses reproduce the observed low-level divergence well and confirm SMOCs to be mesoscale features (ca. 200 km). Large mesoscale variability, five-fold the mean, is shown to be associated with the ubiquity of SMOCs. Furthermore, time-lag correlations suggest that SMOCs amplify mesoscale moisture variance at cloud-base and in the sub-cloud layer. Through their modulation of cloud-base moisture, SMOCs influence the drying efficiency of entrainment, thus yielding moist ascending branches and dry descending branches. The observed moisture variance differs from expectations from large-eddy simulations, which show largest variance near cloud top and negligible sub-cloud variance. The ubiquity of SMOCS and their coupling to moisture and cloud fields suggest that the strength and scale of mesoscale circulations are important in determining how clouds couple to climate, something which is not considered by present theories.

Linking large-eddy simulations to local observations of clouds enables us to investigate clouds and microphysical processes and to improve our understanding as well as the respective representation in coarser models. Insights gained with large-eddy simulations can be applied within the development and evaluation of parameterizations for larger scale models, to bridge the gap between those models and the detailed local observations. In this study, various approaches for large-eddy simulations around local observations are explored and investigated with respect to remote sensing observations of clouds and their representation. By adding more ‘realism’ to the simulations and defining a well constrained setup, the representation of the daily variability at a mid-latitude site could be improved and shows promising results for continued research in the future. Especially the shown potential to investigate representativeness of column measurements will provide new insights into the analysis and construction of observational experiments.