The Center for the Study of Ethics in Society at Twenty-Five

Center for the Study of Ethics in Society: Celebrating 25 Years - Presented November 15, 2010

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

Using Sequential Two-Part Focus Groups As A Supplemental Instrument For Student Course Evaluations

This paper describes the use of a sequential two-part focus group program designed to be a supplemental instrument for eliciting student suggestions to improve the teaching/learning process.   The sequential two-part focus group program provides students with two opportunities to provide feedback and have that feedback both noted and mirrored back to them. Many of the student comments and suggestions elicited during the focus group sessions were positive and constructive.  Moreover, use of the sequential two-part focus group program resulted in statistically significant improvement in student evaluations in all but one of the seven major sections of the Educational Testing Service’s SIR II, the primary instrument utilized to obtain the student course evaluations.  The “Overall Evaluation” of the course, wherein students are asked to, “Rate the quality of instruction in this course as it contributed to your learning,” improved most significantly.&nbsp

Safety, the Preface Paradox and Possible Worlds Semantics

This paper contains an argument to the effect that possible worlds semantics renders semantic knowledge impossible, no matter what ontological interpretation is given to possible worlds. The essential contention made is that possible worlds semantic knowledge is unsafe and this is shown by a parallel with the preface paradox

Everolimus plus exemestane in postmenopausal patients with HR(+) breast cancer: BOLERO-2 final progression-free survival analysis.

IntroductionEffective treatments for hormone-receptor-positive (HR(+)) breast cancer (BC) following relapse/progression on nonsteroidal aromatase inhibitor (NSAI) therapy are needed. Initial Breast Cancer Trials of OraL EveROlimus-2 (BOLERO-2) trial data demonstrated that everolimus and exemestane significantly prolonged progression-free survival (PFS) versus placebo plus exemestane alone in this patient population.MethodsBOLERO-2 is a phase 3, double-blind, randomized, international trial comparing everolimus (10 mg/day) plus exemestane (25 mg/day) versus placebo plus exemestane in postmenopausal women with HR(+) advanced BC with recurrence/progression during or after NSAIs. The primary endpoint was PFS by local investigator review, and was confirmed by independent central radiology review. Overall survival, response rate, and clinical benefit rate were secondary endpoints.ResultsFinal study results with median 18-month follow-up show that median PFS remained significantly longer with everolimus plus exemestane versus placebo plus exemestane [investigator review: 7.8 versus 3.2 months, respectively; hazard ratio = 0.45 (95% confidence interval 0.38-0.54); log-rank P < 0.0001; central review: 11.0 versus 4.1 months, respectively; hazard ratio = 0.38 (95% confidence interval 0.31-0.48); log-rank P < 0.0001] in the overall population and in all prospectively defined subgroups, including patients with visceral metastases, [corrected] and irrespective of age. The incidence and severity of adverse events were consistent with those reported at the interim analysis and in other everolimus trials.ConclusionThe addition of everolimus to exemestane markedly prolonged PFS in patients with HR(+) advanced BC with disease recurrence/progression following prior NSAIs. These results further support the use of everolimus plus exemestane in this patient population. ClinicalTrials.gov #NCT00863655

Zonally opposing shifts of the intertropical convergence zone in response to climate change

Future changes in the location of the intertropical convergence zone (ITCZ) due to climate change are of high interest since they could substantially alter precipitation patterns in the tropics and subtropics. Although models predict a future narrowing of the ITCZ during the 21st century in response to climate warming, uncertainties remain large regarding its future position, with most past work focusing on the zonal-mean ITCZ shifts. Here we use projections from 27 state-of-the-art climate models (CMIP6) to investigate future changes in ITCZ location as a function of longitude and season, in response to climate warming. We document a robust zonally opposing response of the ITCZ, with a northward shift over eastern Africa and the Indian Ocean, and a southward shift in the eastern Pacific and Atlantic Ocean by 2100, for the SSP3-7.0 scenario. Using a two-dimensional energetics framework, we find that the revealed ITCZ response is consistent with future changes in the divergent atmospheric energy transport over the tropics, and sector-mean shifts of the energy flux equator (EFE). The changes in the EFE appear to be the result of zonally opposing imbalances in the hemispheric atmospheric heating over the two sectors, consisting of increases in atmospheric heating over Eurasia and cooling over the Southern Ocean, which contrast with atmospheric cooling over the North Atlantic Ocean due to a model-projected weakening of the Atlantic meridional overturning circulation

Approximate Bayesian Computation: a nonparametric perspective

Approximate Bayesian Computation is a family of likelihood-free inference techniques that are well-suited to models defined in terms of a stochastic generating mechanism. In a nutshell, Approximate Bayesian Computation proceeds by computing summary statistics s_obs from the data and simulating summary statistics for different values of the parameter theta. The posterior distribution is then approximated by an estimator of the conditional density g(theta|s_obs). In this paper, we derive the asymptotic bias and variance of the standard estimators of the posterior distribution which are based on rejection sampling and linear adjustment. Additionally, we introduce an original estimator of the posterior distribution based on quadratic adjustment and we show that its bias contains a fewer number of terms than the estimator with linear adjustment. Although we find that the estimators with adjustment are not universally superior to the estimator based on rejection sampling, we find that they can achieve better performance when there is a nearly homoscedastic relationship between the summary statistics and the parameter of interest. To make this relationship as homoscedastic as possible, we propose to use transformations of the summary statistics. In different examples borrowed from the population genetics and epidemiological literature, we show the potential of the methods with adjustment and of the transformations of the summary statistics. Supplemental materials containing the details of the proofs are available online

Understanding and Visualizing Droplet Distributions in Simulations of Shallow Clouds

Thorough analysis of local droplet-level interactions is crucial to better understand the microphysical processes in clouds and their effect on the global climate. High-accuracy simulations of relevant droplet size distributions from Large Eddy Simulations (LES) of bin microphysics challenge current analysis techniques due to their high dimensionality involving three spatial dimensions, time, and a continuous range of droplet sizes. Utilizing the compact latent representations from Variational Autoencoders (VAEs), we produce novel and intuitive visualizations for the organization of droplet sizes and their evolution over time beyond what is possible with clustering techniques. This greatly improves interpretation and allows us to examine aerosol-cloud interactions by contrasting simulations with different aerosol concentrations. We find that the evolution of the droplet spectrum is similar across aerosol levels but occurs at different paces. This similarity suggests that precipitation initiation processes are alike despite variations in onset times.Comment: 4 pages, 3 figures, accepted at NeurIPS 2023 (Machine Learning and the Physical Sciences Workshop