Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Tropical variability”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Satellite Observations of the Tropical Terrestrial Carbon Balance and Interactions With the Water Cycle During the 21st Century

A constellation of satellites are now in orbit providing information about terrestrial carbon and water storage and fluxes. These combined observations show that the tropical biosphere has changed significantly in the last two decades from the combined effects of climate variability and land use. Large areas of forest have been cleared in both wet and dry forests, increasing the source of carbon to the atmosphere. Concomitantly, tropical fire emissions have declined, at least until 2016, from changes in land-use practices and rainfall, increasing the net carbon sink. Measurements of carbon stocks and fluxes from disturbance and recovery and of vegetation photosynthesis show significant regional variability of net biosphere exchange (NBE) and gross primary productivity (GPP) across the tropics and are tied to seasonal and interannual changes in water fluxes and storage. Comparison of satellite based estimates of evapotranspiration (ET), photosynthesis, and the deuterium content of water vapor with patterns of total water storage and rainfall demonstrate the presence of vegetation-atmosphere interactions and feedback mechanisms across tropical forests. However, these observations of stocks, fluxes and inferred interactions between them do not point unambiguously to either positive or negative feedbacks in carbon and water exchanges. These ambiguities highlight the need for assimilation of these new measurements with Earth System models for a consistent assessment of process interactions, along with focused field campaigns that integrate ground, aircraft and satellite measurements, to quantify the controlling carbon and water processes and their feedback mechanisms.

58 GEOSCIENCES↗
DOE OSTI2024

Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3

Systematic, routine, and comprehensive evaluation of Earth system models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize the thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (PMP) is an open-source Python software package that provides quick-look objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including its extremes. The PMP comparison results are produced using all model simulations contributed to CMIP6 and earlier CMIP phases. An important objective of the PMP is to document the performance of ESMs participating in the recent phases of CMIP, together with providing version-controlled information for all datasets, software packages, and analysis codes being used in the evaluation process. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. Quantitative model evaluation provided by the PMP can assist modelers in their development priorities. In this paper, we provide an overview of the PMP, including its latest capabilities, and discuss its future direction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Diagnosis of convective organization and cold pools using ARM datasets and evaluation of a unified convection parameterization (UNICON)

Tropical thunderstorms often cluster together. Studies have suggested that the degree to which the tropical thunderstorms are clustered impacts Earth's energy balance and water cycle, as well as extreme precipitation events. However, the processes controlling the spatial distribution of the thunderstorms are poorly understood and are not properly represented in most computer models for weather and climate prediction. Under the goals of better understanding how convection organizes at the mesoscale and advancing the representation of mesoscale convective organization in global climate models, we i) objectively quantified the degrees of convective organization and diagnosed cold pool processes using ARM field campaign observations, ii) examined the organization processes in storm-resolving model simulations, and iii) evaluated the impacts of mesoscale convective organization in global model simulations. The project yielded a firm reference against which the global model representation of mesoscale convective organization and cold pools can be evaluated against and shed new light into the role of parameterized convective organization in the global model simulation of the basic state and variability. Our results revealed two distinct phases of convective clustering during the two-day rain episodes (Cheng et al. 2018) and a new mechanism through which vertical wind shear in the low-troposphere can aid convective organization over tropical oceans (Cheng et al. 2020). It was demonstrated that the interactive representation of cold pools and mesoscale convective organization is key for global models to successfully simulate both the mean state and intraseasonal variability in the tropics (Ahn et al. 2019; 2020).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quantifying the Role of Ocean Dynamics in SST Variability across GCMs and Observations

Abstract Midlatitude SSTs forced by mesoscale oceanic processes can affect the large-scale atmosphere, pointing to the ocean’s crucial role outside the tropics. Previous studies have shown oceanic mesoscale processes’ effect on global and regional climate variability. This study quantifies the local contribution of ocean dynamics to mixed-layer temperature across the globe by directly estimating the ocean heat flux divergence resolved by state-of-the-art ocean reanalysis, eddy-resolving, and eddy-parameterized versions of two U.S. national climate models and indirectly from air–sea flux satellite-based estimates. Our results show that the eddy-resolving climate simulations resolve mixed-layer temperature variances that are larger and closer to those inferred from observations than both their eddy-parameterized counterparts and ECCO over much of the extratropics. The observations and the eddy-resolving models indicate a more significant role of ocean dynamics in the mixed-layer temperature variability than the surface fluxes over most extratropics compared to their eddy-parameterized versions. A frequency domain analysis shows that the better-resolved ocean mesoscale and thermal gradients enhance the variance over a time scale from 2 months to 30 years. Results show agreement in the ocean’s contribution among satellite-based estimates, ocean reanalysis products, and ocean eddy-resolving simulations. At the same time, differences emerge for ECCO and the eddy-parameterized models, suggesting that surface fluxes account for a larger fraction of the mixed-layer temperature variability in most of the extratropics.

Siqueira, L.↗
DOE OSTI2021

The Phase-Locking of Tropical North Atlantic and the Contribution of ENSO

The Tropical North Atlantic (TNA) is characterized by significant interannual variability in sea surface temperature (SST), which is phase-locked to the boreal spring. In this study, the phase-locking of TNA is investigated by adopting a linear stochastic-dynamical model (SDM) using seasonally modulated TNA feedbacks together with the seasonal modulation of ENSO forcing. In the observations, the role of local TNA feedbacks and ENSO forcing in TNA phase-locking are equivalently important with both preferring the peak of TNA variability to appear in the boreal spring. Besides, the seasonal modulation of TNA feedbacks and ENSO forcing strength are both mainly controlled by thermodynamic processes. In most climate models, the contribution of ENSO on TNA phase-locking is weaker than that in observations. The strength of ENSO-related TNA phase-locking is highly correlated with the relationship between ENSO and TNA, which is mainly determined by the amplitude of ENSO and its teleconnection patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

How Well Can CMIP6 Models Represent the Observed Influence of the Pacific and Indian Oceans on the Indian Summer Monsoon Rainfall?

This study evaluates the ability of CMIP6 climate models to simulate the observed effects of tropical Pacific and Indian Ocean sea surface temperature anomalies (SSTAs) on Indian summer monsoon rainfall (ISMR) variability. Using observational data and the large ensemble historical simulations of seven CMIP6 models from 1950 to 2014, we applied a cyclostationary linear inverse model (CS-LIM) to isolate the impacts of tropical Pacific SSTAs, Indian Ocean SSTAs and their interaction on the interannual variability of ISMR. Overall, CMIP6 models well reproduced the observed enhanced (reduced) ISMR variability from Pacific SSTAs (Indian Ocean SSTAs and the Indo-Pacific interaction), but with varying spatial patterns and magnitudes. While CESM2 and E3SM-2-0 showed the best agreement with observations for the effects of Pacific SSTAs and the Indo-Pacific interaction, respectively, CMIP6 models showed mixed results for the impacts from Indian Ocean SSTAs. Composite analysis of ISMR anomalies during the developing phases of pure and co-occurring El Niño-Southern Oscillation (ENSO) and Indian Ocean dipole (IOD) events revealed that the impacts from Pacific SSTAs were captured reasonably well by E3SM-2-0, CESM2, MIROC6, and MPI-ESM1-2-LR, while E3SM-2-0 also showed the best agreement with observations for the effects from the Indo-Pacific interaction. However, all models showed substantial biases in simulating the Indian Ocean SSTA impacts on ISMR, especially for pure El Niño events. Overall, this study provides new insights into how individual CMIP6 models simulate the isolated impacts from the tropical Pacific and Indian Oceans, which has important applications for improving ISMR predictions and interpreting ISMR future projections.

monsoon↗
DOE OSTI2025

Large‐Scale Statistically Meaningful Patterns (LSMPs) Associated With Precipitation Extremes Over Northern California

Abstract We analyze large‐scale statistically meaningful patterns (LSMPs) that precede extreme precipitation (PEx) events over Northern California (NorCal). We find LSMPs by applying k‐means clustering to the two leading principal components of daily 500 hPa geopotential height anomalies two days before the onset, from October to March during 1948–2015. Statistical significance testing based on Monte Carlo simulations suggests a minimum of four statistically distinguished LSMP clusters. The four LSMP clusters are characterized as Northwest continental negative height anomaly, Eastward positive “Pacific‐North American Pattern (PNA),” Westward negative “PNA,” and Prominent Alaskan ridge. These four clusters, shown in multiple variables, evolve very differently and have differing links to the Arctic and tropical Pacific regions. Using binary forecast skill measures and a new copula‐based framework for predicting PEx events, we find LSMP indices that are useful predictors of NorCal PEx events, with moisture‐based variables being the best predictors of PEx events at least 6 days before the onset, and the lower atmospheric variables being better than their upper atmospheric counterparts any day in advance tested. To ensure statistical rigor, the LSMPs analyzed here (with the modified acronym) include local tests of both significance and consistency, which are not always featured in the literature on large‐scale meteorological patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Diurnal cycle of precipitation over the tropics and central United States: intercomparison of general circulation models

Diurnal precipitation is a fundamental mode of variability that climate models have difficulty in accurately simulating. Here, in this work, the diurnal cycle of precipitation (DCP) in participating climate models from the Global Energy and Water Exchanges' DCP project is evaluated over the tropics and central United States. Common model biases such as excessive precipitation over the tropics, too frequent light-to-moderate rain, and the failure to capture propagating convection in the central United States still exist. Over the central United States, the issues of too weak rainfall intensity in climate runs is well improved in their hindcast runs with initial conditions from numerical weather prediction analyses. But the improvement is minimal over the central Amazon. Incorporating the role of the large-scale environment in convective triggering processes helps resolve the phase-locking issue in many models where precipitation often incorrectly peaks near noon due to maximum insolation over land. Allowing air parcels to be lifted above the boundary layer improves the simulation of nocturnal precipitation which is often associated with the propagation of mesoscale systems. Including convective memory in cumulus parameterizations acts to suppress light-to-moderate rain and promote intense rainfall; however, it also weakens the diurnal variability. Simply increasing model resolution (with cumulus parameterizations still used) cannot fully resolve the biases of low-resolution climate models in DCP. The hierarchy modeling framework from this study is useful for identifying the missing physics in models and testing new development of model convective processes over different convective regimes.

58 GEOSCIENCES↗
DOE OSTI2020

The Impacts of Horizontal Grid Spacing and Cumulus Parameterization on Subseasonal Prediction in a Global Convection-Permitting Model

Monthlong simulations targeting four Madden–Julian oscillation events made with several global model configurations are verified against observations to assess the roles of grid spacing and convective parameterization on the representation of tropical convection and midlatitude forecast skill. Specifically, the performance of a global convection-permitting model (CPM) configuration with a uniform 3-km mesh is compared to that of a global 15-km mesh with and without convective parameterization, and of a variable-resolution “channel” simulation using 3-km grid spacing only in the tropics with a scale-aware convection scheme. It is shown that global 3-km simulations produce realistic tropical precipitation statistics, except for an overall wet bias and delayed diurnal cycle. The channel simulation performs similarly, although with an unrealistically higher frequency of heavy rain. The 15-km simulations with and without cumulus schemes produce too much light and heavy tropical precipitation, respectively. Without convection parameterization, the 15-km global model produces unrealistically abundant, short-lived, and intense convection throughout the tropics. Only the global CPM configuration is able to capture eastward-propagating Madden–Julian oscillation events, and the 15-km runs favor stationary or westward-propagating convection organized at the planetary scale. The global 3-km CPM exhibits the highest extratropical forecast skill aloft and at the surface, particularly during week 3 of each hindcast. Although more cases are needed to confirm these results, this study highlights many potential benefits of using global CPMs for subseasonal forecasting. Furthermore, results show that alternatives to global convection-permitting resolution—using coarser or spatially variable resolution—feature compromises that may reduce their predictive performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Information Entropy as Quantifier of Potential Predictability in the Tropical Indo-Pacific Basin

Global warming is posed to modify the modes of variability that control much of the climate predictability at seasonal to interannual scales. The quantification of changes in climate predictability over any given amount of time, however, remains challenging. Here we build upon recent advances in non-linear dynamical systems theory and introduce the climate community to an information entropy quantifier based on recurrence. The entropy, or complexity of a system is associated with microstates that recur over time in the time-series that define the system, and therefore to its predictability potential. A computationally fast method to evaluate the entropy is applied to the investigation of the information entropy of sea surface temperature in the tropical Pacific and Indian Oceans, focusing on boreal fall. In this season the predictability of the basins is controlled by two regularly varying non-linear oscillations, the El Niño-Southern Oscillation and the Indian Ocean Dipole. We compute and compare the entropy in simulations from the CMIP5 catalog from the historical period and RCP8.5 scenario, and in reanalysis datasets. Discrepancies are found between the models and the reanalysis, and no robust changes in predictability can be identified in future projections. The Indian Ocean and the equatorial Pacific emerge as troublesome areas where the modeled entropy differs the most from that of the reanalysis in many models. A brief investigation of the source of the bias points to a poor representation of the ocean mean state and basins' connectivity at the Indonesian Throughflow.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Satellite-Based Estimate of Convective Vertical Velocity and Convective Mass Flux: Global Survey and Comparison with Radar Wind Profiler Observations

Convective vertical velocity (w c ) and convective mass flux (M c ) lie at the heart of GCM cumulus parameterizations, but few observations of these critical parameters are available. In this paper, we develop and evaluate a novel, satellite-based method for estimating profiles of w c and M c . Here, comparisons with collocated ground-based radar wind profiler (RWP) observations show that satellite estimated median w c is slightly greater than the RWP estimates, but they show solid agreement when compared at the 95th percentiles (intense updrafts). RWP-derived and satellite estimated M c are broadly comparable in the lower and middle troposphere, with some differences in the upper troposphere due to differences in convective core sampling. A k-means cluster analysis of multiple years of w c data shows that convective characteristics are distinctly different among extratropical convection, tropical land convection, and tropical oceanic convection. Tropical land convection is significantly more intense and more variable than the oceanic counterpart.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Explainable deep learning for insights in El Niño and river flows

The El Niño Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

National CO 2 budgets (2015–2020) inferred from atmospheric CO 2 observations in support of the global stocktake

Accurate accounting of emissions and removals of CO 2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries' carbon budgets. These estimates are based on “top-down” NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-averaged dry-air mole fraction (X CO 2 ) retrievals (ACOS v10), in situ CO 2 measurements or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with “bottom-up” estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers. These flux and stock change estimates are reported annually (2015–2020) as both a global 1° x 1° gridded dataset and a country-level dataset and are available for download from the Committee on Earth Observation Satellites' (CEOS) website. Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29–4.58 Pg CO2 yr -1 (0.90–1.25 Pg C yr -1 ). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Enhanced predictability of Eastern North Pacific Tropical cyclone activity using the ENSO Longitude Index

Past studies have indicated that El Nino-Southern Oscillation (ENSO) plays a major role in the interannual variability of Eastern Pacific hurricane activity. The primary mechanism being the eastward displacement of the warm pool during an El Nino, which carries warm water into that basin thereby creating favorable oceanic conditions. Despite this, the question of whether an accurate knowledge of ENSO enhances seasonal predictabiity of Eastern Pacific hurricanes has not been addressed specifically. In this study, we show that unlike traditional indices of ENSO, the ENSO Longitude Index (ELI) is able to predict Eastern Pacific hurricane activity at significant lead times. By capturing changes in the location of deep convection and associated thermocline processes more accurately, ELI explains the most variability in the upper-ocean heat content in the main development region of the Eastern Pacific basin compared to other ENSO indices. These results have substantial implications for operational seasonal forecasts of Eastern Pacific hurricanes.

Balaguru, Karthik↗
DOE OSTI2023

Understanding uncertainties in projections of western North Pacific tropical cyclogenesis

Reliable projections of tropical cyclone (TC) activities in the western North Pacific (WNP) are crucial for climate policy-making in densely-populated coastal Asia. Existing projections, however, exhibit considerable uncertainties with unclear sources. Here, based on future projections by the latest Coupled Model Intercomparison Project Phase 6 climate models, we identify a new and prevailing source of uncertainty arising from different TC identification schemes. Notable differences in projections of detected TCs and empirical genesis potential indices are found to be caused by inconsistent changes in dynamic and thermodynamic environmental factors affecting TC formations. While model uncertainty holds the secondary importance, we show large potential in reducing it through improved model simulations of present-day TC characteristics. Internal variability noticeably impacts near-term projections of the WNP tropical cyclogenesis, while the relative contribution of scenario uncertainty remains small. Our findings provide valuable insights into model development and TC projections, thereby aiding in adaptation decisions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Acceleration of U.S. Southeast and Gulf coast sea-level rise amplified by internal climate variability

While there is evidence for an acceleration in global mean sea level (MSL) since the 1960s, its detection at local levels has been hampered by the considerable influence of natural variability on the rate of MSL change. Here we report a MSL acceleration in tide gauge records along the U.S. Southeast and Gulf coasts that has led to rates (>10 mm yr -1 since 2010) that are unprecedented in at least 120 years. We show that this acceleration is primarily induced by an ocean dynamic signal exceeding the externally forced response from historical climate model simulations. However, when the simulated forced response is removed from observations, the residuals are neither historically unprecedented nor inconsistent with internal variability in simulations. A large fraction of the residuals is consistent with wind driven Rossby waves in the tropical North Atlantic. This indicates that this ongoing acceleration represents the compounding effects of external forcing and internal climate variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Evaluating uncertainty in aerosol forcing of tropical precipitation shifts

Abstract. An observed southward shift in tropical rainfall over land between 1950 and 1985, followed by a weaker recovery post-1985, has been attributed to anthropogenic aerosol radiative forcing and cooling of the Northern Hemisphere relative to the Southern Hemisphere. We might therefore expect models that have a strong historic hemispheric contrast in aerosol forcing to simulate a further northward tropical rainfall shift in the near-term future when anthropogenic aerosol emission reductions will predominantly warm the Northern Hemisphere. We investigate this paradigm using a perturbed parameter ensemble (PPE) of transient coupled ocean–atmosphere climate simulations that span a range of aerosol radiative forcing comparable to multi-model studies. In the 20th century, in our single-model ensemble, we find no relationship between the magnitude of pre-industrial to 1975 inter-hemispheric anthropogenic aerosol radiative forcing and tropical precipitation shifts. Instead, tropical precipitation shifts are associated with major volcanic eruptions and are strongly affected by internal variability. However, we do find a relationship between the magnitude of pre-industrial to 2005 inter-hemispheric anthropogenic aerosol radiative forcing and future tropical precipitation shifts over 2006 to 2060 under scenario RCP8.5. Our results suggest that projections of tropical precipitation shifts will be improved by reducing aerosol radiative forcing uncertainty, but predictive gains may be offset by temporary shifts in tropical precipitation caused by future major volcanic eruptions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Southwest Pacific tropical cyclone development classification utilizing machine learning and synoptic composites

This study evaluates the ability of machine learning algorithms to classify tropical depressions (TDs) and tropical storms (TSs) in the western region of the southwest Pacific Ocean (SWPO). Decision rules are generated to predict the environment required for a depression to fully develop into a mature storm, and the most influential predictors in the classification decision are ranked. TD and TS are discriminated based on a maximum sustained wind speed threshold (≥17 ms -1 ). Various aerosol, thermodynamic, and dynamic parameters are extracted closest to the initiation point of each non-developing and developing sample. The covariates associated with each labelled sample are used to train a decision tree and random forest model. Results using a testing dataset suggest the random forest approach more accurately distinguishes between non-developing and developing samples. The classification accuracy of the decision tree and random forest are 72% and 91%, respectively. Random forest outperformed the decision tree by providing higher accuracy in test data. The most important variables for binary classification are sea salt aerosol optical depth (AOD), 1,000 mb relative humidity, and sea surface temperature. AOD is a quantitative estimate of the aerosols presents in the air through the extinction of a ray of light as it passes through the atmosphere. Mean composite maps constructed in an unsupervised manner have been created for the most important variables identified by the random forest classifier during TD and TS events to highlight the difference in geophysical and aerosol variables' climatology during the two different classifications. This work will advance the risk management strategies for northeastern Australia and other SWPO basin islands to control their tropical cyclone related losses through prioritizing forecasting variables that are the strongest predictors of the strengthening of tropical depressions into tropical cyclones.

54 ENVIRONMENTAL SCIENCES↗