Zonally contrasting shifts of the tropical rainbelt in response to climate change.

Future changes in the position of the intertropical convergence zone (ITCZ; a narrow band of heavy precipitation in the tropics) with climate change could affect the livelihood and food security of billions of people. Although models predict a future narrowing of the ITCZ, uncertainties remain large regarding its future position, with most past work focusing on zonal-mean shifts. Here we use projections from 27 state-of-the-art (CMIP6) climate models and document a robust zonally-varying ITCZ response to the SSP3-7.0 scenario by 2100, with a northward shift over eastern Africa and the Indian Ocean, and a southward shift in the eastern Pacific and Atlantic Oceans. The zonally-varying response is consistent with changes in the divergent atmospheric energy transport, and sector-mean shifts of the energy flux equator. Our analysis provides insight about mechanisms influencing the future position of the tropical rainbelt, and may allow for more robust projections of climate change impacts.

Safety, security, and serviceability in road engineering.

Road engineers have special responsibilities to design and maintain roads that are safe, secure, and serviceable. This paper explores some of the challenges such responsibilities pose, especially from the vantage point of non-engineers whose lives are deeply affected by the work of road engineers. It also supports the thesis that road engineers need to be prepared to consult and work with professionals in other fields than engineering in order to fulfill their responsibilities well.

Deep learning to represent subgrid processes in climate models.

The representation of nonlinear subgrid 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 subgrid processes in a climate model by learning from a multiscale model in which convection is treated explicitly. The trained neural network then replaces the traditional subgrid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multiyear 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.

Rainfall From Resolved Rather Than Parameterized Processes Better Represents the Present-Day and Climate Change Response of Moderate Rates in the Community Atmosphere Model.

Deficiencies in the parameterizations of convection used in global climate models often lead to a distorted representation of the simulated rainfall intensity distribution (i.e., too much rainfall from weak rain rates). While encouraging improvements in high percentile rainfall intensity have been found as the horizontal resolution of the Community Atmosphere Model is increased to ∼25 km, we demonstrate no corresponding improvement in the moderate rain rates that generate the majority of accumulated rainfall. Using a statistical framework designed to emphasize links between precipitation intensity and accumulated rainfall beyond just the frequency distribution, we show that CAM cannot realistically simulate moderate rain rates, and cannot capture their intensification with climate change, even as resolution is increased. However, by separating the parameterized convective and large-scale resolved contributions to total rainfall, we find that the intensity, geographic pattern, and climate change response of CAM's large-scale rain rates are more consistent with observations (TRMM 3B42), superparameterization, and theoretical expectations, despite issues with parameterized convection. Increasing CAM's horizontal resolution does improve the representation of total rainfall intensity, but not due to changes in the intensity of large-scale rain rates, which are surprisingly insensitive to horizontal resolution. Rather, improvements occur through an increase in the relative contribution of the large-scale component to the total amount of accumulated rainfall. Analysis of sensitivities to convective timescale and entrainment rate confirm the importance of these parameters in the possible development of scale-aware parameterizations, but also reveal unrecognized trade-offs from the entanglement of precipitation frequency and total amount.

Vertical structure and physical processes of the Madden-Julian Oscillation: Biases and uncertainties at short range.

An analysis of diabatic heating and moistening processes from 12 to 36 h lead time forecasts from 12 Global Circulation Models are presented as part of the "Vertical structure and physical processes of the Madden-Julian Oscillation (MJO)" project. A lead time of 12-36 h is chosen to constrain the large-scale dynamics and thermodynamics to be close to observations while avoiding being too close to the initial spin-up of the models as they adjust to being driven from the Years of Tropical Convection (YOTC) analysis. A comparison of the vertical velocity and rainfall with the observations and YOTC analysis suggests that the phases of convection associated with the MJO are constrained in most models at this lead time although the rainfall in the suppressed phase is typically overestimated. Although the large-scale dynamics is reasonably constrained, moistening and heating profiles have large intermodel spread. In particular, there are large spreads in convective heating and moistening at midlevels during the transition to active convection. Radiative heating and cloud parameters have the largest relative spread across models at upper levels during the active phase. A detailed analysis of time step behavior shows that some models show strong intermittency in rainfall and differences in the precipitation and dynamics relationship between models. The wealth of model outputs archived during this project is a very valuable resource for model developers beyond the study of the MJO. In addition, the findings of this study can inform the design of process model experiments, and inform the priorities for field experiments and future observing systems.

Engineering ethics: looking back, looking forward.

The eight pieces constituting this Meeting Report are summaries of presentations made during a panel session at the 2011 Association for Practical and Professional Ethics (APPE) annual meeting held between March 3rd and 6th in Cincinnati. Lisa Newton organized the session and served as chair. The panel of eight consisted both of pioneers in the field and more recent arrivals. It covered a range of topics from how the field has developed to where it should be going, from identification of issues needing further study to problems of training the next generation of engineers and engineering-ethics scholars.

Radar observations of individual rain drops in the free atmosphere.

Atmospheric remote sensing has played a pivotal role in the increasingly sophisticated representation of clouds in the numerical models used to assess global and regional climate change. This has been accomplished because the underlying bulk cloud properties can be derived from a statistical analysis of the returned microwave signals scattered by a diverse ensemble comprised of numerous cloud hydrometeors. A new Doppler radar, previously used to track small debris particles shed from the NASA space shuttle during launch, is shown to also have the capacity to detect individual cloud hydrometeors in the free atmosphere. Similar to the traces left behind on film by subatomic particles, larger cloud particles were observed to leave a well-defined radar signature (or streak), which could be analyzed to infer the underlying particle properties. We examine the unique radar and environmental conditions leading to the formation of the radar streaks and develop a theoretical framework which reveals the regulating role of the background radar reflectivity on their observed characteristics. This main expectation from theory is examined through an analysis of the drop properties inferred from radar and in situ aircraft measurements obtained in two contrasting regions of an observed multicellular storm system. The observations are placed in context of the parent storm circulation through the use of the radar's unique high-resolution waveforms, which allow the bulk and individual hydrometeor properties to be inferred at the same time.