Intensification of daily tropical precipitation extremes from more organized convection.

Tropical precipitation extremes and their changes with surface warming are investigated using global storm resolving simulations and high-resolution observations. The simulations demonstrate that the mesoscale organization of convection, a process that cannot be physically represented by conventional global climate models, is important for the variations of tropical daily accumulated precipitation extremes. In both the simulations and observations, daily precipitation extremes increase in a more organized state, in association with larger, but less frequent, storms. Repeating the simulations for a warmer climate results in a robust increase in monthly-mean daily precipitation extremes. Higher precipitation percentiles have a greater sensitivity to convective organization, which is predicted to increase with warming. Without changes in organization, the strongest daily precipitation extremes over the tropical oceans increase at a rate close to Clausius-Clapeyron (CC) scaling. Thus, in a future warmer state with increased organization, the strongest daily precipitation extremes over oceans increase at a faster rate than CC scaling.

The role of baroclinic activity in controlling Earth's albedo in the present and future climates.

Clouds are one of the most influential components of Earth's climate system. Specifically, the midlatitude clouds play a vital role in shaping Earth's albedo. This study investigates the connection between baroclinic activity, which dominates the midlatitude climate, and cloud-albedo and how it relates to Earth's existing hemispheric albedo symmetry. We show that baroclinic activity and cloud-albedo are highly correlated. By using Lagrangian tracking of cyclones and anticyclones and analyzing their individual cloud properties at different vertical levels, we explain why their cloud-albedo increases monotonically with intensity. We find that while for anticyclones, the relation between strength and cloudiness is mostly linear, for cyclones, in which clouds are more prevalent, the relation saturates with strength. Using the cloud-albedo strength relationships and the climatology of baroclinic activity, we demonstrate that the observed hemispheric difference in cloud-albedo is well explained by the difference in the population of cyclones and anticyclones, which counter-balances the difference in clear-sky albedo. Finally, we discuss the robustness of the hemispheric albedo symmetry in the future climate. Seemingly, the symmetry should break, as the northern hemisphere's storm track response differs from that of the southern hemisphere due to Arctic amplification. However, we show that the saturation of the cloud response to storm intensity implies that the increase in the skewness of the southern hemisphere storm distribution toward strong storms will decrease future cloud-albedo in the southern hemisphere. This complex response explains how albedo symmetry might persist even with the predicted asymmetric hemispheric change in baroclinicity under climate change.

Strong cloud-circulation coupling explains weak trade cumulus feedback.

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 key uncertainty in climate projections1-4. Trade cumulus feedbacks in climate models are governed by changes in cloud fraction near cloud base5,6, with high-climate-sensitivity models suggesting a strong decrease in cloud-base cloudiness owing to increased lower-tropospheric mixing5-7. Here we show that new observations from the EUREC4A (Elucidating the role of cloud-circulation coupling in climate) field campaign8,9 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 markedly 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 sensitivity10,11.

Visualization of Climate Science Simulation Data.

Climate simulations belong to the most data-intensive scientific disciplines and are-in relation to one of humankind's largest challenges, i.e., facing anthropogenic climate change-ever more important. Not only are the outputs generated by current models increasing in size, due to an increase in resolution and the use of ensembles, but the complexity is also rising as a result of maturing models that are able to better describe the intricacies of our climate system. This article focuses on developments and trends in the scientific workflow for the analysis and visualization of climate simulation data, as well as on changes in the visualization techniques and tools that are available.

Clouds and Convective Self-Aggregation in a Multimodel Ensemble of Radiative-Convective Equilibrium Simulations.

The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative-convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique among intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud-resolving models (CRMs), large eddy simulations (LES), and global cloud-resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self-aggregation in large domains and agree that self-aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self-aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than unaggregated simulations.

Sugar, Gravel, Fish, and Flowers: Dependence of Mesoscale Patterns of Trade-Wind Clouds on Environmental Conditions.

Trade-wind clouds exhibit a large diversity of spatial organizations at the mesoscale. Over the tropical western Atlantic, a recent study has visually identified four prominent mesoscale patterns of shallow convection, referred to as flowers, fish, gravel, and sugar. We show that these four patterns can be identified objectively from satellite observations by analyzing the spatial distribution of infrared brightness temperatures. By applying this analysis to 19 years of data, we examine relationships between cloud patterns and large-scale environmental conditions. This investigation reveals that on daily and interannual timescales, the near-surface wind speed and the strength of the lower-tropospheric stability discriminate the occurrence of the different organization patterns. These results, combined with the tight relationship between cloud patterns, low-level cloud amount, and cloud-radiative effects, suggest that the mesoscale organization of shallow clouds might change under global warming. The role of shallow convective organization in determining low-cloud feedback should thus be investigated.

The scientific challenge of understanding and estimating climate change.

Given the slow unfolding of what may become catastrophic changes to Earth's climate, many are understandably distraught by failures of public policy to rise to the magnitude of the challenge. Few in the science community would think to question the scientific response to the unfolding changes. However, is the science community continuing to do its part to the best of its ability? In the domains where we can have the greatest influence, is the scientific community articulating a vision commensurate with the challenges posed by climate change? We think not.

Assessing the scales in numerical weather and climate predictions: will exascale be the rescue?

We discuss scientific features and computational performance of kilometre-scale global weather and climate simulations, considering the Icosahedral Non-hydrostatic (ICON) model and the Integrated Forecast System (IFS). Scalability measurements and a performance modelling approach are used to derive performance estimates for these models on upcoming exascale supercomputers. This is complemented by preliminary analyses of the model data that illustrate the importance of high-resolution models to gain improvements in the accuracy of convective processes, a better understanding of physics dynamics interactions and poorly resolved or parametrized processes, such as gravity waves, convection and boundary layer. This article is part of the theme issue 'Multiscale modelling, simulation and computing: from the desktop to the exascale'.

Deep learning and process understanding for data-driven Earth system science.

Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.

A New Look at the Daily Cycle of Trade Wind Cumuli.

A description of the daily cycle of oceanic shallow cumulus for undisturbed boreal winter conditions in the North Atlantic trades is presented. Modern investigation tools are used, including storm-resolving and large-eddy simulations, runover large domains in realistic configurations, and observations from in situ measurements and satellite-based retrievals. Models and observations clearly show pronounced diurnal variations in cloudiness, both near cloud base and below the trade inversion. The daily cycle reflects the evolution of two cloud populations: (i) a population of nonprecipitating small cumuli with weak vertical extent, which grows during the day and maximizes around sunset, and (ii) a population o deeper precipitating clouds with a stratiform cloud layer below the trade inversion, which grows during the night and maximizes just before sunrise. Previous studies have reported that cloudiness near cloud base undergoes weak variations on time scales longer than a day. However, here we find that it can vary strongly at the diurnal time scale. This daily cycle could serve as a critical test of the models' representation of the physical processes controlling cloudiness near cloud base, which is thought to be key for the determination of the Earth's climate response to warming.

The role of the permanent wilting point in controlling the spatial distribution of precipitation.

Convection-permitting simulations on an idealized land planet are performed to understand whether soil moisture acts to support or impede the organization of convection. Initially, shallow circulations driven by differential radiative cooling induce a self-aggregation of the convection into a single band, as has become familiar from simulations over idealized sea surfaces. With time, however, the drying of the nonprecipitating region induces a reversal of the shallow circulation, drawing the flow at low levels from the precipitating to the nonprecipitating region. This causes the precipitating convection to move over the dry soils and reverses the polarity of the circulation. The precipitation replenishes these soils with moisture at the expense of the formerly wet soils which dry, until the process repeats itself. On longer timescales, this acts to homogenize the precipitation field. By analyzing the strength of the shallow circulations, the surface budget with its effects on the boundary layer properties, and the shape of the soil moisture resistance function, we demonstrate that the soil has to dry out significantly, for the here-tested resistance formulations below 15% of its water availability, to be able to alter the precipitation distribution. We expect such a process to broaden the distribution of precipitation over tropical land. This expectation is supported by observations which show that in drier years the monsoon rains move farther inland over Africa.

Mechanisms and Model Diversity of Trade-Wind Shallow Cumulus Cloud Feedbacks: A Review.

Shallow cumulus clouds in the trade-wind regions are at the heart of the long standing uncertainty in climate sensitivity estimates. In current climate models, cloud feedbacks are strongly influenced by cloud-base cloud amount in the trades. Therefore, understanding the key factors controlling cloudiness near cloud-base in shallow convective regimes has emerged as an important topic of investigation. We review physical understanding of these key controlling factors and discuss the value of the different approaches that have been developed so far, based on global and high-resolution model experimentations and process-oriented analyses across a range of models and for observations. The trade-wind cloud feedbacks appear to depend on two important aspects: (1) how cloudiness near cloud-base is controlled by the local interplay between turbulent, convective and radiative processes; (2) how these processes interact with their surrounding environment and are influenced by mesoscale organization. Our synthesis of studies that have explored these aspects suggests that the large diversity of model responses is related to fundamental differences in how the processes controlling trade cumulus operate in models, notably, whether they are parameterized or resolved. In models with parameterized convection, cloudiness near cloud-base is very sensitive to the vigor of convective mixing in response to changes in environmental conditions. This is in contrast with results from high-resolution models, which suggest that cloudiness near cloud-base is nearly invariant with warming and independent of large-scale environmental changes. Uncertainties are difficult to narrow using current observations, as the trade cumulus variability and its relation to large-scale environmental factors strongly depend on the time and/or spatial scales at which the mechanisms are evaluated. New opportunities for testing physical understanding of the factors controlling shallow cumulus cloud responses using observations and high-resolution modeling on large domains are discussed.

Climate research must sharpen its view.

Human activity is changing Earth's climate. Now that this has been acknowledged and accepted in international negotiations, climate research needs to define its next frontiers.

EUREC4A: A Field Campaign to Elucidate the Couplings Between Clouds, Convection and Circulation.

Trade-wind cumuli constitute the cloud type with the highest frequency of occurrence on Earth, and it has been shown that their sensitivity to changing environmental conditions will critically influence the magnitude and pace of future global warming. Research over the last decade has pointed out the importance of the interplay between clouds, convection and circulation in controling this sensitivity. Numerical models represent this interplay in diverse ways, which translates into different responses of trade-cumuli to climate perturbations. Climate models predict that the area covered by shallow cumuli at cloud base is very sensitive to changes in environmental conditions, while process models suggest the opposite. To understand and resolve this contradiction, we propose to organize a field campaign aimed at quantifying the physical properties of trade-cumuli (e.g., cloud fraction and water content) as a function of the large-scale environment. Beyond a better understanding of clouds-circulation coupling processes, the campaign will provide a reference data set that may be used as a benchmark for advancing the modelling and the satellite remote sensing of clouds and circulation. It will also be an opportunity for complementary investigations such as evaluating model convective parameterizations or studying the role of ocean mesoscale eddies in air-sea interactions and convective organization.

Thermodynamic control of anvil cloud amount.

General circulation models show that as the surface temperature increases, the convective anvil clouds shrink. By analyzing radiative-convective equilibrium simulations, we show that this behavior is rooted in basic energetic and thermodynamic properties of the atmosphere: As the climate warms, the clouds rise and remain at nearly the same temperature, but find themselves in a more stable atmosphere; this enhanced stability reduces the convective outflow in the upper troposphere and decreases the anvil cloud fraction. By warming the troposphere and increasing the upper-tropospheric stability, the clustering of deep convection also reduces the convective outflow and the anvil cloud fraction. When clouds are radiatively active, this robust coupling between temperature, high clouds, and circulation exerts a positive feedback on convective aggregation and favors the maintenance of strongly aggregated atmospheric states at high temperatures. This stability iris mechanism likely contributes to the narrowing of rainy areas as the climate warms. Whether or not it influences climate sensitivity requires further investigation.

Response to Comment on "The Atlantic Multidecadal Oscillation without a role for ocean circulation".

Zhang et al interpret the mixed-layer energy budget in models as showing that "ocean dynamics play a central role in the AMO." Here, we show that their diagnostics cannot reveal the causes of the Atlantic Multidecadal Oscillation (AMO) and that their results can be explained with minimal ocean influence. Hence, we reaffirm our findings that the AMO in models can be understood primarily as the upper-ocean thermal response to stochastic atmospheric forcing.

Prospects for narrowing bounds on Earth's equilibrium climate sensitivity.

The concept of Earth's Equilibrium Climate Sensitivity (ECS) is reviewed. A particular problem in quantifying plausible bounds for ECS has been how to account for all of the diverse lines of relevant scientific evidence. It is argued that developing and refuting physical storylines (hypotheses) for values outside any proposed range has the potential to better constrain these bounds and to help articulate the science needed to narrow the range further. A careful reassessment of all important lines of evidence supporting these storylines, their limitations, and the assumptions required to combine them is therefore required urgently.