Engineering topics
Ahmed, Fiaz
Publications and source records attributed to Ahmed, Fiaz.
The Physics behind Precipitation Onset Bias in CMIP6 Models: The Pseudo-Entrainment Diagnostic and Trade-Offs between Lapse Rate and Humidity
Conditional instability and the buoyancy of plumes drive moist convection but have a variety of representations in model convective schemes. Vertical thermodynamic structure information from Atmospheric Radiation Measurement (ARM) sites and reanalysis (ERA5), satellite-derived precipitation (TRMM3b42), and diagnostics relevant for plume buoyancy are used to assess climate models. Previous work has shown that CMIP6 models represent moist convective processes more accurately than their CMIP5 counterparts. However, certain biases in convective onset remain pervasive among generations of CMIP modeling efforts. Here we diagnose these biases in a cohort of nine CMIP6 models with sub-daily output, assessing conditional instability in profiles of equivalent potential temperature θ e and saturation equivalent potential temperature θ es in comparison to a plume model with different mixing assumptions. Most models capture qualitative aspects of the θ es vertical structure, including a substantial decrease with height in the lower free troposphere associated with the entrainment of subsaturated air. We define a “pseudo-entrainment” diagnostic that combines subsaturation and a θ es measure of conditional instability similar to what entrainment would produce under the small-buoyancy approximation. This captures the trade-off between larger θ es lapse rates (entrainment of dry air) and small subsaturation (permits positive buoyancy despite high entrainment). This pseudo-entrainment diagnostic is also a reasonable indicator of the critical value of integrated buoyancy for precipitation onset. Models with poor θ e /θ es structure (those using variants of the Tiedtke Scheme) or low entrainment runs of CAM5, and models with low subsaturation, such as NASA-GISS, lie outside the observational range in this diagnostic.
Climate-invariant machine learning
Projecting climate change is a generalization problem: We extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML) algorithms hold promise to improve such process representations but tend to extrapolate poorly to climate regimes that they were not trained on. To get the best of the physical and statistical worlds, we propose a framework, termed “climate-invariant” ML, incorporating knowledge of climate processes into ML algorithms, and show that it can maintain high offline accuracy across a wide range of climate conditions and configurations in three distinct atmospheric models. Our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency, data efficiency, and generalizability across climate regimes.
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore’s Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to MLexperts because of lack of training data and relevant, easy-to-use workflows. Wepresent ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator’s macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res2) and code(https://leap-stc.github.io/ClimSim)arereleasedopenlytosupport the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.
Atmosphere–Ocean Coupled Energy Budgets of Tropical Convective Discharge–Recharge Cycles
An energy budget combining atmospheric moist static energy (MSE) and upper ocean heat content (OHC) is used to examine the processes impacting day-to-day convective variability in the tropical Indian and western Pacific Oceans. Feedbacks arising from atmospheric and oceanic transport processes, surface fluxes, and radiation drive the cyclical amplification and decay of convection around suppressed and enhanced convective equilibrium states, referred to as shallow and deep convective discharge–recharge (D–R) cycles, respectively. The shallow convective D–R cycle is characterized by alternating enhancements of shallow cumulus and stratocumulus, often in the presence of extensive cirrus clouds. The deep convective D–R cycle is characterized by sequential increases in shallow cumulus, congestus, narrow deep precipitation, wide deep precipitation, a mix of detached anvil and altostratus and altocumulus, and once again shallow cumulus cloud types. Transitions from the shallow to deep D–R cycle are favored by a positive “column process” feedback, while discharge of convective instability and OHC by mesoscale convective systems (MCSs) contributes to transitions from the deep to shallow D–R cycle. Variability in the processes impacting MSE is comparable in magnitude to, but considerably more balanced than, variability in the processes impacting OHC. Variations in the quantity of atmosphere–ocean coupled static energy (MSE + OHC) result primarily from atmospheric and oceanic transport processes, but are mainly realized as changes in OHC. MCSs are unique in their ability to rapidly discharge both lower-tropospheric convective instability and OHC.
Process-Oriented Diagnostics: Principles, Practice, Community Development, and Common Standards
Process-oriented diagnostics (PODs) aim to provide feedback for model developers through model analysis based on physical hypotheses. However, the step from a diagnostic based on relationships among variables, even when hypothesis driven, to specific guidance for revising model formulation or parameterizations can be substantial. The POD may provide more information than a purely performance-based metric, but a gap between POD principles and providing actionable information for specific model revisions can remain. Furthermore, in coordinating diagnostics development, there is a trade-off between freedom for the developer, aiming to capture innovation, and near-term utility to the modeling center. Best practices that allow for the former, while conforming to specifications that aid the latter, are important for community diagnostics development that leads to tangible model improvements. Promising directions to close the gap between principles and practice include the interaction of PODs with perturbed physics experiments and with more quantitative process models as well as the inclusion of personnel from modeling centers in diagnostics development groups for immediate feedback during climate model revisions. Examples are provided, along with best-practice recommendations, based on practical experience from the NOAA Model Diagnostics Task Force (MDTF). Common standards for metrics and diagnostics that have arisen from a collaboration between the MDTF and the Department of Energy’s Coordinated Model Evaluation Capability are advocated as a means of uniting community diagnostics efforts.
A Process Model for ITCZ Narrowing under Warming Highlights Clear-Sky Water Vapor Feedbacks and Gross Moist Stability Changes in AMIP Models
Tropical areas with mean upward motion—and as such the zonal-mean intertropical convergence zone (ITCZ)—are projected to contract under global warming. To understand this process, a simple model based on dry static energy and moisture equations is introduced for zonally symmetric overturning driven by sea surface temperature (SST). Processes governing ascent area fraction and zonal mean precipitation are examined for insight into Atmospheric Model Intercomparison Project (AMIP) simulations. Bulk parameters governing radiative feedbacks and moist static energy transport in the simple model are estimated from the AMIP ensemble. Uniform warming in the simple model produces ascent area contraction and precipitation intensification—similar to observations and climate models. Contributing effects include stronger water vapor radiative feedbacks, weaker cloud-radiative feedbacks, stronger convection-circulation feedbacks, and greater poleward moisture export. The simple model identifies parameters consequential for the inter-AMIP-model spread; an ensemble generated by perturbing parameters governing shortwave water vapor feedbacks and gross moist stability changes under warming tracks inter-AMIP-model variations with a correlation coefficient ~0.46. Here, the simple model also predicts the multimodel mean changes in tropical ascent area and precipitation with reasonable accuracy. Furthermore, the simple model reproduces relationships among ascent area precipitation, ascent strength, and ascent area fraction observed in AMIP models. A substantial portion of the inter-AMIP-model spread is traced to the spread in how moist static energy and vertical velocity profiles change under warming, which in turn impact the gross moist stability in deep convective regions—highlighting the need for observational constraints on these quantities.
Model spread in tropical low cloud feedback tied to overturning circulation response to warming
Among models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6), here we show that the magnitude of the tropical low cloud feedback, which contributes considerably to uncertainty in estimates of climate sensitivity, is intimately linked to tropical deep convection and its effects on the tropical atmospheric overturning circulation. First, a reduction in tropical ascent area and an increased frequency of heavy precipitation result in high cloud reduction and upper-tropospheric drying, which increases longwave cooling and reduces subsidence weakening, favoring low cloud reduction (Radiation-Subsidence Pathway). Second, increased longwave cooling decreases tropospheric stability, which also reduces subsidence weakening and low cloudiness (Stability-Subsidence Pathway). In summary, greater high cloud reduction and upper-tropospheric drying (negative longwave feedback) lead to a more positive cloud feedback among CMIP6 models by contributing to a greater reduction in low cloudiness (positive shortwave feedback). Varying strengths of the two pathways contribute considerably to the intermodel spread in climate sensitivity.
Structure–property investigations in urea tethered iodinated triphenylamines
A methylene urea bridged di-iodo triphenylamine dimer and its corresponding methylene di-iodo triphenylamine monomer are crystallized to correlate their structures with properties. In addition, their conductivity is compared against Spiro-OMeTAD.
Westward-Propagating Moisture Mode Over the Tropical Western Hemisphere
A westward-propagating Rossby-like wave signal is found to explain a large fraction of the intraseasonal variance in cloud brightness over the Western Hemisphere. A series of diagnostic criteria suggest that this wave is a moisture mode: its moisture anomalies dominate the distribution of moist static energy (MSE) and are in phase with the precipitation anomalies; and the thermodynamic equation obeys the weak temperature gradient approximation. Further, the wave propagates westward due to zonal moisture advection by the mean flow and is maintained by radiative heating and meridional moisture advection. These properties compare favorably with the westward propagating Rossby mode in an equatorial beta-plane model with prognostic moisture, mean meridional moisture gradient, and mean zonal wind. These results underscore the importance of water vapor in the dynamics of slowly evolving tropical systems, and the limitations of dry shallow water theory that rely on a “reduced equivalent depth” to represent moist dynamics.
Heterometallic Actinide‐Containing Photoresponsive Metal‐Organic Frameworks: Dynamic and Static Tuning of Electronic Properties
Abstract Acquiring fundamental knowledge of properties of actinide‐based materials is a necessary step to create new possibilities for addressing the current challenges in the nuclear energy and nuclear waste sectors. In this report, we established a photophysics–electronics correlation for actinide‐containing metal‐organic frameworks (An‐MOFs) as a function of excitation wavelength, for the first time. A stepwise approach for dynamically modulating electronic properties was applied for the first time towards actinide‐based heterometallic MOFs through integration of photochromic linkers. Optical cycling, modeling of density of states near the Fermi edge, conductivity measurements, and photoisomerization kinetics were employed to shed light on the process of tailoring optoelectronic properties of An‐MOFs. Furthermore, the first photochromic MOF‐based field‐effect transistor, in which the field‐effect response could be changed through light exposure, was constructed. As a demonstration, the change in current upon light exposure was sufficient to operate a two‐LED fail‐safe indicator circuit.
Heterometallic Actinide-Containing Photoresponsive Metal-Organic Frameworks: Dynamic and Static Tuning of Electronic Properties
Acquiring fundamental knowledge of properties of actinide-based materials is a necessary step to create new possibilities for addressing the current challenges in the nuclear energy and nuclear waste sectors. Here in this report, we established a photophysics–electronics correlation for actinide-containing metal-organic frameworks (An-MOFs) as a function of excitation wavelength, for the first time. A stepwise approach for dynamically modulating electronic properties was applied for the first time towards actinide-based heterometallic MOFs through integration of photochromic linkers. Optical cycling, modeling of density of states near the Fermi edge, conductivity measurements, and photoisomerization kinetics were employed to shed light on the process of tailoring optoelectronic properties of An-MOFs. Furthermore, the first photochromic MOF-based field-effect transistor, in which the field-effect response could be changed through light exposure, was constructed. As a demonstration, the change in current upon light exposure was sufficient to operate a two-LED fail-safe indicator circuit.
Spectral Signatures of Moisture–Convection Feedbacks over the Indian Ocean
Abstract Positive feedbacks between the cloud population and the environmental moisture field are central to theoretical expositions on the Madden–Julian oscillation (MJO). This study investigates the statistical incidence of positive moisture–convection feedbacks across multiple space and time scales over the tropical Indian Ocean. This work uses vertically integrated moisture budget terms from the ECMWF interim reanalysis [ERA-Interim (ERA-I)] in a framework proposed by Hannah et al. Positive moisture–convection feedbacks are primarily a low-frequency, low-wavenumber phenomenon with significant spectral signatures in the 32–48-day time scale. The efficacy of these feedbacks, however, is subject to horizontal moisture advection variations, whose relative importance varies with scale. Wave-filtered Tropical Rainfall Measuring Mission (TRMM) satellite precipitation is used to show that these moisture–convection feedbacks contribute more to moisture increases in the MJO than in other equatorial waves. A moving-window correlation analysis suggests that instances of moisture–convection feedbacks are more frequent in drier conditions, when column water vapor (CWV) is below its climatological mean value, with the implication that positive moisture–convection feedbacks shape the mean CWV field by moistening drier air columns, but that they are less effective in moistening already moist environments. Ground radar observations show that stratiform rain damps local CWV increases on short time scales (<2 days) and therefore precludes positive moisture–convection feedbacks in high-CWV environments. Vertical coherence structures from ERA-I confirm that relatively bottom-heavy cloud ensembles (i.e., peaks between 700 and 850 hPa) are more effective in inducing low-frequency positive moisture–convection feedbacks than ensembles with other vertical structures. Low-frequency horizontal advective drying damps moisture increases and is strongly coherent with upper-level rising motion.
GoAmazon2014/5 campaign points to deep-inflow approach to deep convection across scales
Significance Representations of strongly precipitating deep-convective systems in climate models are among the most important factors in their simulation. Parameterizations of these motions face the dual challenge of unclear pathways to including mesoscale organization and high sensitivity of convection to approximations of turbulent entrainment of environmental air. Ill-constrained entrainment processes can even affect global average climate sensitivity under global warming. Multiinstrument observations from the Department of Energy GoAmazon2014/5 field campaign suggest that an alternative formulation from radar-derived dominant updraft structure yields a strong relationship of precipitation to buoyancy in both mesoscale and smaller-scale convective systems. This simultaneously provides a key step toward representing the influence of mesoscale convection in climate models and sidesteps a problematic dependence on traditional entrainment rates.
A Retrieval of Tropical Latent Heating Using the 3D Structure of Precipitation Features
Radar-based latent heating retrievals typically apply a lookup table (LUT) derived from model output to surface rain amounts and rain type to determine the vertical structure of heating. In this study, a method has been developed that uses the size characteristics of precipitating systems (i.e., area and mean echo-top height) instead of rain amount to estimate latent heating profiles from radar observations. This technique [named the convective–stratiform area (CSA) algorithm] leverages the relationship between the organization of convective systems and the structure of latent heating profiles and avoids pitfalls associated with retrieving accurate rainfall information from radars and models. The CSA LUTs are based on a high-resolution regional model simulation over the equatorial Indian Ocean. The CSA LUTs show that convective latent heating increases in magnitude and height as area and echo-top heights grow, with a congestus signature of midlevel cooling for less vertically extensive convective systems. Stratiform latent heating varies weakly in vertical structure, but its magnitude is strongly linked to area and mean echo-top heights. The CSA LUT was applied to radar observations collected during the DYNAMO/Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY2011)/ARM MJO Investigation Experiment (AMIE) field campaign, and the CSA heating retrieval was generally consistent with other measures of heating profiles. Finally, the impact of resolution and spatial mismatch between the model and radar grids is addressed, and unrealistic latent heating profiles in the stratiform LUT, namely, a low-level heating peak, an elevated melting layer, and net column cooling, were identified. These issues highlight the need for accurate convective–stratiform separations and improvement in PBL and microphysical parameterizations.