Challenges and opportunities for improved understanding of regional climate dynamics

Dynamical processes in the atmosphere and ocean are central to determining the large-scale drivers of regional climate change, yet their predictive understanding is poor. Here, we identify three frontline challenges in climate dynamics where significant progress can be made to inform adaptation: response of storms, blocks and jet streams to external forcing; basin-to-basin and tropical–extratropical teleconnections; and the development of non-linear predictive theory. We highlight opportunities and techniques for making immediate progress in these areas, which critically involve the development of high-resolution coupled model simulations, partial coupling or pacemaker experiments, as well as the development and use of dynamical metrics and exploitation of hierarchies of models

Impacts of convective treatment on tropical rainfall variability in realistic and idealized simulations

The prediction of precipitation in the tropics is a challenge for numerical weather prediction (NWP), meaning very low practical predictability there. However, previous studies indicated that intrinsic predictability in the tropics is up to a few weeks and thus longer than in the extratropics. Equatorial waves (EWs) from the linear shallow-water theory are considered the source of this long predictability. Most weather and climate models still struggle to accurately capture EWs, which is often attributed to parameterized convection. With advanced computing power, model development is moving toward high-resolution models with explicit convection. To evaluate the value of these high-resolution models, this thesis aims to provide important insights into the behavior of tropical precipitation due to the treatment of deep and shallow convection using the ICOsahedral Nonhydrostatic (ICON) model. First, we examine the sensitivity of EWs to model configuration using realistic ICON simulations with varying horizontal grid spacings (80-2.5 km) and with different convectivetreatments between parameterized versus explicit deep and shallow convection. To robustly identify wave signals, we use two objective methods, one filtering rainfall using a fast Fourier transform and the other projecting two-dimensional wind and geopotential onto theoretical wave patterns. The results demonstrate that large-scale EWs are surprisingly consistent in terms of phase speed and wave amplitude with little sensitivity to model resolution, convective treatment and wave identification method. Rainfall signals of westward inertio-gravity waves (WIGs), however, show a large difference between parameterized and explicit convection with the latter showing marked rainfall signals but with no corresponding wind patterns. A composite analysis to link rainfall and wind fields of waves reveals that the identified signals in rainfall appear to be associated with mesoscale convective systems, the spatiotemporal scales of which overlap with those of WIGs, and thus are isolated as waves through space-time filtering. Secondly, we analyze idealized ICON simulations in a tropical aquachannel configuration with zonally symmetric sea surface temperatures and with rigid walls at 30°N/S. The aquachannel simulations vary in the representation of deep and shallow convection but with the same horizontal grid spacing of 13 km. All aquachannel simulations have maximum rainfall at the equator, showing an intertropical convergence zone (ITCZ) there, but the rainfall amount increases by 35% with explicit deep convection. To physically understand this difference, we adapt a diagnostic based on a conceptual model by Emanuel (2019), assuming boundary-layer quasi-equilibrium (BLQE), the weak temperature gradient approximation, and mass and energy conservation. BLQE implies that moist entropy is in balance between surface enthalpy fluxes, which import high moist entropy to the BL, and convective downdrafts, which transport low moist entropy from the free troposphere into the BL. The results reveal that the rainfall differences are primarily associated with surface enthalpy fluxes through BLQE, while precipitation efficiency is surprisingly constant in the ITCZ. Further detailed analysis demonstrates that mean surface wind speed, which is closely related to the large-scale circulation, contributes most to the differences in surface enthalpy fluxes. Thus, the treatment of deep convection alters mean rainfall through tight links between surface winds, associated surface fluxes and convective mass flux. Lastly, variability associated with EWs is examined in the aquachannel simulations by using the same wave identification methods used for the realistic simulations. All simulations show prominent signals of Kelvin waves (KWs) with large variations among them. Parameterized deep convection produces various eastward propagation with speeds of 5–27 m/s, while explicit deep convection exhibits a dominance of KWs with a zonal wavenumber of one and with a propagation speed of 24 m/s. Furthermore, explicit deep convection causes more pronounced structures of zonal wind and temperature in the lower stratosphere and a stronger link of wind-induced surface enthalpy flux exchange to the development of convection. Meanwhile, the treatment of shallow convection plays a role for temperature variation below 2.5 km. However, BL warming is in phase with maximum rainfall associated with KWs, which is opposite to observations. Parameterized deep convection generates a feature sharing similarities with the Madden Julian Oscillation, which is not found in the other aquachannel simulations. The novelty of this thesis lies in understanding the behavior of tropical rainfall in both realistic and idealized simulations by using diagnostics adapted for systematic comparisons between different simulations, mainly due to different convective treatments. This allows us to obtain valuable insights into the sensitivity of tropical rainfall and its variability to model configuration, ultimately paving the way for developing more accurate weather and climate predictions in the tropics

Monsoons, ITCZs, and the Concept of the Global Monsoon

Earth's tropical and subtropical rainbands, such as Intertropical Convergence Zones (ITCZs) and monsoons, are complex systems, governed by both large‐scale constraints on the atmospheric general circulation and regional interactions with continents and orography, and coupled to the ocean. Monsoons have historically been considered as regional large‐scale sea breeze circulations, driven by land‐sea contrast. More recently, a perspective has emerged of a global monsoon, a global‐scale solstitial mode that dominates the annual variation of tropical and subtropical precipitation. This results from the seasonal variation of the global tropical atmospheric overturning and migration of the associated convergence zone. Regional subsystems are embedded in this global monsoon, localized by surface boundary conditions. Parallel with this, much theoretical progress has been made on the fundamental dynamics of the seasonal Hadley cells and convergence zones via the use of hierarchical modeling approaches, including aquaplanets. Here we review the theoretical progress made and explore the extent to which these advances can help synthesize theory with observations to better understand differing characteristics of regional monsoons and their responses to certain forcings. After summarizing the dynamical and energetic balances that distinguish an ITCZ from a monsoon, we show that this theoretical framework provides strong support for the migrating convergence zone picture and allows constraints on the circulation to be identified via the momentum and energy budgets. Limitations of current theories are discussed, including the need for a better understanding of the influence of zonal asymmetries and transients on the large‐scale tropical circulation

Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary Conditions

We explore the potential of feed-forward deep neural networks (DNNs) for emulating cloud superparameterization in realistic geography, using offline fits to data from the Super Parameterized Community Atmospheric Model. To identify the network architecture of greatest skill, we formally optimize hyperparameters using ~250 trials. Our DNN explains over 70 percent of the temporal variance at the 15-minute sampling scale throughout the mid-to-upper troposphere. Autocorrelation timescale analysis compared against DNN skill suggests the less good fit in the tropical, marine boundary layer is driven by neural network difficulty emulating fast, stochastic signals in convection. However, spectral analysis in the temporal domain indicates skillful emulation of signals on diurnal to synoptic scales. A close look at the diurnal cycle reveals correct emulation of land-sea contrasts and vertical structure in the heating and moistening fields, but some distortion of precipitation. Sensitivity tests targeting precipitation skill reveal complementary effects of adding positive constraints vs. hyperparameter tuning, motivating the use of both in the future. A first attempt to force an offline land model with DNN emulated atmospheric fields produces reassuring results further supporting neural network emulation viability in real-geography settings. Overall, the fit skill is competitive with recent attempts by sophisticated Residual and Convolutional Neural Network architectures trained on added information, including memory of past states. Our results confirm the parameterizability of superparameterized convection with continents through machine learning and we highlight advantages of casting this problem locally in space and time for accurate emulation and hopefully quick implementation of hybrid climate models.Comment: 32 Pages, 13 Figures, Revised Version Submitted to Journal of Advances in Modeling Earth Systems April 202

Understanding the dependence of mean precipitation on convective treatment and horizontal resolution in tropical aquachannel experiments

The Intertropical Convergence Zone (ITCZ) is a key circulation and precipitation feature in the tropics. There has been a large spread in the representation of the ITCZ in global weather and climate models for a long time, the reasons for which remain unclear. This paper presents a novel approach with which we disentangle different physical processes responsible for the changeable behavior of the ITCZ in numerical models. The diagnostic tool is based on a conceptual framework developed by Emanuel (2019) and allows for physically consistent estimates of convective mass flux and precipitation efficiency for simulations with explicit and parameterized convection. We apply our diagnostic tool to a set of tropical aquachannel experiments using the ICOsahedral Nonhydrostatic (ICON) model with horizontal grid spacings of 13 and 5 km and with various representations of deep and shallow convection. The channel length corresponds to the Earth\u27s circumference and has rigid walls at 30∘ N/S. Zonally symmetric sea surface temperatures are prescribed. All experiments simulate an ITCZ at the Equator coinciding with the ascending branch of the Hadley circulation and descending branches at 15∘ N/S with subtropical jets and easterly trade wind belts straddling the ITCZ. With explicit deep convection, however, rainfall in the ITCZ increases and the Hadley circulation becomes stronger. Increasing horizontal resolution substantially reduces the rainfall maximum in the ITCZ, while the strength of the Hadley circulation changes only marginally. Our diagnostic framework reveals that boundary-layer quasi-equilibrium (BLQE) is a key to physically understanding those differences. At 13 km, enhanced surface enthalpy fluxes with explicit deep convection are balanced by increased convective downdrafts. As precipitation efficiency is hardly affected, convective updrafts and rainfall increase. The surface enthalpy fluxes are mainly controlled by mean surface winds, closely linked to the Hadley circulation. These links also help understand rainfall differences between different resolutions. At 5 km, the wind–surface-fluxes–convection relation holds, but additionally explicit convection dries the mid-troposphere, which increases the import of air with lower moist static energy into the boundary layer, thereby enhancing surface fluxes. Overall, the different model configurations create little variations in precipitation efficiency and radiative cooling, the effects of which are compensated for by changes in dry stability. The results highlight the utility of our diagnostic tool to pinpoint processes important for rainfall differences between models, suggesting applicability for climate model intercomparison projects

Model Hierarchies for Understanding Atmospheric Circulation

This is the final version. Available from Wiley via the DOI in this record.In this review, we highlight the complementary relationship between simple and comprehensive models in addressing key scientific questions to describe Earth’s atmospheric circulation. The systematic representation of models in steps, or hierarchies, connects our understanding from idealized systems to comprehensive models, and ultimately the observed atmosphere. We define three interconnected principles that can be used to characterize the model hierarchies of the atmosphere. We explore the rich diversity within the governing equations in the dynamical hierarchy, the ability to isolate and understand atmospheric processes in the process hierarchy, and the importance of the physical domain and resolution in the hierarchy of scale. We center our discussion on the large scale circulation of the atmosphere and its interaction with clouds and convection, focusing on areas where simple models have had a significant impact. Our confidence in climate model projections of the future is based on our efforts to ground the climate predictions in fundamental physical understanding. This understanding is, in part, possible due to the hierarchies of idealized models that afford the simplicity required for understanding complex systems.Natural Environment Research Council (NERC)US National Science FoundationUS Department of Energy Office of Biological and Environmental ResearchNatural Science and Engineering Research Council of CanadaAustralian Research CouncilSimons FoundationGerman Ministry of Education and Research (BMBF)FONA: Research for Sustainable DevelopmentState Research Agency of Spai

Deep learning to represent sub-grid processes in climate models

The representation of nonlinear sub-grid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric sub-grid processes in a climate model by learning from a multi-scale model in which convection is treated explicitly. The trained neural network then replaces the traditional sub-grid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multi-year simulations are stable and closely reproduce not only the mean climate of the cloud-resolving simulation but also key aspects of variability, including precipitation extremes and the equatorial wave spectrum. Furthermore, the neural network approximately conserves energy despite not being explicitly instructed to. Finally, we show that the neural network parameterization generalizes to new surface forcing patterns but struggles to cope with temperatures far outside its training manifold. Our results show the feasibility of using deep learning for climate model parameterization. In a broader context, we anticipate that data-driven Earth System Model development could play a key role in reducing climate prediction uncertainty in the coming decade.Comment: View official PNAS version at https://doi.org/10.1073/pnas.181028611

Evidence for added value of convection-permitting models for studying changes in extreme precipitation

Climate model resolution can affect both the climate change signal and present-day representation of extreme precipitation. The need to parametrize convective processes raises questions about how well the response to warming of convective precipitation extremes is captured in such models. In particular, coastal precipitation extremes can be sensitive to sea surface temperature (SST) increase. Taking a recent coastal precipitation extreme as a showcase example, we explore the added value of convection-permitting models by comparing the response of the extreme precipitation to a wide range of SST forcings in an ensemble of regional climate model simulations using parametrized and explicit convection. Compared at the same spatial scale, we find that the increased local intensities of vertical motion and precipitation in the convection-permitting simulations play a crucial role in shaping a strongly nonlinear extreme precipitation response to SST increase, which is not evident when convection is parametrized. In the convection-permitting simulations, SST increase causes precipitation intensity to increase only until a threshold is reached, beyond which further SST increase does not enhance the precipitation. This flattened response results from an improved representation of convective downdrafts and near-surface cooling, which damp the further intensification of precipitation by stabilizing the lower troposphere locally and also create cold-pools that cause subsequent convection to be triggered at sea, rather than by the coastal orography. These features are not well represented in the parametrized convection simulations, resulting in precipitation intensity having a much more linear response to increasing SST