SEARCH · Engineering Papers

Results for “climate 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.

SourceResearch connectionFrom yearThrough year

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

At least 145 records · Page 8

All sources NASA NTRS DOE OSTI

NASA NTRS2011

AIRS-Observed Interrelationships of Anomaly Time-Series of Moist Process-Related Parameters and Inferred Feedback Values on Various Spatial Scales

In the beginning, a good measure of a GMCs performance was their ability to simulate the observed mean seasonal cycle. That is, a reasonable simulation of the means (i.e., small biases) and standard deviations of TODAY?S climate would suffice. Here, we argue that coupled GCM (CG CM for short) simulations of FUTURE climates should be evaluated in much more detail, both spatially and temporally. Arguably, it is not the bias, but rather the reliability of the model-generated anomaly time-series, even down to the [C]GCM grid-scale, which really matter. This statement is underlined by the social need to address potential REGIONAL climate variability, and climate drifts/changes in a manner suitable for policy decisions.

Molnar, Gyula I.↗

NASA NTRS2014

Climate Forcing Datasets for Agricultural Modeling: Merged Products for Gap-Filling and Historical Climate Series Estimation

The AgMERRA and AgCFSR climate forcing datasets provide daily, high-resolution, continuous, meteorological series over the 1980-2010 period designed for applications examining the agricultural impacts of climate variability and climate change. These datasets combine daily resolution data from retrospective analyses (the Modern-Era Retrospective Analysis for Research and Applications, MERRA, and the Climate Forecast System Reanalysis, CFSR) with in situ and remotely-sensed observational datasets for temperature, precipitation, and solar radiation, leading to substantial reductions in bias in comparison to a network of 2324 agricultural-region stations from the Hadley Integrated Surface Dataset (HadISD). Results compare favorably against the original reanalyses as well as the leading climate forcing datasets (Princeton, WFD, WFD-EI, and GRASP), and AgMERRA distinguishes itself with substantially improved representation of daily precipitation distributions and extreme events owing to its use of the MERRA-Land dataset. These datasets also peg relative humidity to the maximum temperature time of day, allowing for more accurate representation of the diurnal cycle of near-surface moisture in agricultural models. AgMERRA and AgCFSR enable a number of ongoing investigations in the Agricultural Model Intercomparison and Improvement Project (AgMIP) and related research networks, and may be used to fill gaps in historical observations as well as a basis for the generation of future climate scenarios.

Climate Forcing Data↗

NASA NTRS2020

GISS Model E2.2: A Climate Model Optimized for the Middle Atmosphere. Part 1: Model Structure, Climatology, Variability and Climate Sensitivity

We introduce a new climate model (GISS E2.2) that has been specially optimized for the Middle Atmosphere, and whose output is being contributed to the CMIP6 archive. The top of the model is at a geopotential altitude of 89 km, and parameterizations of moist convection and various forms of gravity wave drag based on tropospheric processes are chosen specifically for this optimization. We first evaluate the model in its configuration as a coupled atmosphere-chemistry model with respect to its simulation of the mean state of the Middle Atmosphere, from the mesosphere down through the Upper Troposphere/Lower Stratosphere. Then we assess its use as a coupled atmosphere-ocean climate model by exploring its mean ocean climatology. To evaluate its variability, we report on its simulation of the primary modes in the troposphere, stratosphere and ocean. Two climate change simulations are presented, the responses to instantaneous increases of 2xCO2 and 4xCO2, run with two different ocean models. Sensitivity studies are performed to illustrate the effect of parameterizations on the model results. We compare these results to the lower vertical resolution/top GISS Model E2.1, whose output has also been submitted to CMIP6. The different choices made for these models are explored. It is shown that important improvements in the circulation above and below the tropopause can be obtained when attention is paid to representation of Middle Atmosphere processes in climate model development.

D. Rind↗

NASA NTRS2023

Importance of Internal Variability for Climate Model Assessment

Benchmarking climate model simulations against observations of the climate is core to the process of building realistic climate models and developing accurate future projections. However, in many cases, models do not match historical observations, particularly on regional scales. If there is a mismatch between modeled and observed climate features, should we necessarily conclude that our models are deficient? Using several illustrative examples, we emphasize that internal variability can easily lead to marked differences between the basic features of the model and observed climate, even when decades of model and observed data are available. This can appear as an apparent failure of models to capture regional trends or changes in global teleconnections, or simulation of extreme events. Despite a large body of literature on the impact of internal variability on climate, this acknowledgment has not yet penetrated many model evaluation activities, particularly for regional climate. We emphasize that using a single or small ensemble of simulations to conclude that a climate model is in error can lead to premature conclusions on model fidelity. A large ensemble of multidecadal simulations is therefore needed to properly sample internal climate variability in order to robustly identify model deficiencies and convincingly demonstrate progress between generations of climate models.

Climate and Earth system modelling↗

DOE OSTI2025

AMOC Variability in Climate Models and Its Dependence on the Mean State

Abstract Understanding internal variability of the climate system is critical when isolating internal and anthropogenically forced signals. Here, we investigate the modes of Atlantic Meridional Overturning Circulation ( AMOC ) variability using perturbation experiments with the Institut Pierre‐Simon Laplace's (IPSL) coupled model and compare them to Coupled Model Intercomparison Project Phase 6 (CMIP6) pre‐industrial control simulations. We identify two characteristic modes of variability—decadal‐to‐multidecadal ( DMD var ) and centennial ( CEN var ). The former is driven largely by temperature anomalies in the subpolar North Atlantic, while the latter is driven by salinity in the western subpolar North Atlantic. The amplitude of each mode scales linearly with the mean AMOC strength in the IPSL experiments. The DMD var amplitude correlates well with the AMOC mean strength across CMIP6 models, while the CEN var mode does not. These findings suggest that the strength of DMD var depends robustly on the North Atlantic mean state, while the CEN var mode may be model‐dependent.

Ferster, Brady S. [LOCEAN‐IPSL Sorbonne Université↗

NASA NTRS2012

Temporal Variability of Observed and Simulated Hyperspectral Earth Reflectance

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a climate observation system designed to study Earth's climate variability with unprecedented absolute radiometric accuracy and SI traceability. Observation System Simulation Experiments (OSSEs) were developed using GCM output and MODTRAN to simulate CLARREO reflectance measurements during the 21st century as a design tool for the CLARREO hyperspectral shortwave imager. With OSSE simulations of hyperspectral reflectance, Feldman et al. [2011a,b] found that shortwave reflectance is able to detect changes in climate variables during the 21st century and improve time-to-detection compared to broadband measurements. The OSSE has been a powerful tool in the design of the CLARREO imager and for understanding the effect of climate change on the spectral variability of reflectance, but it is important to evaluate how well the OSSE simulates the Earth's present-day spectral variability. For this evaluation we have used hyperspectral reflectance measurements from the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY), a shortwave spectrometer that was operational between March 2002 and April 2012. To study the spectral variability of SCIAMACHY-measured and OSSE-simulated reflectance, we used principal component analysis (PCA), a spectral decomposition technique that identifies dominant modes of variability in a multivariate data set. Using quantitative comparisons of the OSSE and SCIAMACHY PCs, we have quantified how well the OSSE captures the spectral variability of Earth?s climate system at the beginning of the 21st century relative to SCIAMACHY measurements. These results showed that the OSSE and SCIAMACHY data sets share over 99% of their total variance in 2004. Using the PCs and the temporally distributed reflectance spectra projected onto the PCs (PC scores), we can study the temporal variability of the observed and simulated reflectance spectra. Multivariate time series analysis of the PC scores using techniques such as Singular Spectrum Analysis (SSA) and Multichannel SSA will provide information about the temporal variability of the dominant variables. Quantitative comparison techniques can evaluate how well the OSSE reproduces the temporal variability observed by SCIAMACHY spectral reflectance measurements during the first decade of the 21st century. PCA of OSSE-simulated reflectance can also be used to study how the dominant spectral variables change on centennial scales for forced and unforced climate change scenarios. To have confidence in OSSE predictions of the spectral variability of hyperspectral reflectance, it is first necessary for us to evaluate the degree to which the OSSE simulations are able to reproduce the Earth?s present-day spectral variability.

Roberts, Yolanda↗

NASA NTRS2012

The Effect of ENSO on Phytoplankton Composition in the Pacific Ocean

The effect of climate variability on phytoplankton communities was assessed for the tropical and sub-tropical Pacific Ocean between 1998 and 2005 using an established biogeochemical assimilation model. The phytoplankton communities exhibited wide range of responses to climate variability, from radical shifts in the Equatorial Pacific, to changes of only a couple of phytoplankton groups in the North Central Pacific, to no significant changes in the South Pacific. In the Equatorial Pacific, climate variability dominated the variability of phytoplankton. Here, nitrate, chlorophyll and all but one of the 4 phytoplankton types (diatoms, cyanobacteria and coccolithophores) were strongly correlated (p less than 0.01) with the Multivariate El Nino Southern Oscillation Index (MEI). In the North Central Pacific, MEI and chlorophyll were significantly (p<0.01) correlated along with two of the phytoplankton groups (chlorophytes and coccolithophores). Ocean biology in the South Pacific was not significantly correlated with MEI. During La Ni a events, diatoms increased and expanded westward along the cold tongue (correlation with MEI, r=-0.81), while cyanobacteria concentrations decreased significantly (r=0.78). El Nino produced the reverse pattern, with cyanobacteria populations increasing while diatoms plummeted. The diverse response of phytoplankton in the different major basins of the Pacific suggests the different roles climate variability can play in ocean biology.

Rousseaux, Cecile↗

NASA NTRS2007

Effects of Global Change on U.S. Urban Areas: Vulnerabilities, Impacts, and Adaptation

Human settlements, both large and small, are where the vast majority of people on the Earth live. Expansion of cities both in population and areal extent, is a relentless process that will accelerate in the 21st century. As a consequence of urban growth both in the United States and around the globe, it is important to develop an understanding of how urbanization will affect the local and regional environment. Of equal importance, however, is the assessment of how cities will be impacted by the looming prospects of global climate change and climate variability. The potential impacts of climate change and variability has recently been annunciated by the IPCC's "Climate Change 2007" report. Moreover, the U.S. Climate Change Science Program (CCSP) is preparing a series of "Synthesis and Assessment Products" (SAPs) reports to support informed discussion and decision making regarding climate change and variability by policy matters, resource managers, stakeholders, the media, and the general public. We are authors on a SAP describing the effects of global climate change on human settlements. This paper will present the elements of our SAP report that relate to what vulnerabilities and impacts will occur, what adaptation responses may take place, and what possible effects on settlement patterns and characteristics will potentially arise, on human settlements in the U.S. as a result of climate change and climate variability. We will also present some recommendations about what should be done to further research on how climate change and variability will impact human settlements in the U.S., as well as how to engage government officials, policy and decision makers, and the general public in understanding the implications of climate change and variability on the local and regional levels. Additionally, we wish to explore how technology such as remote sensing data coupled with modeling, can be employed as synthesis tools for deriving insight across a spectrum of impacts (e.g. public health, urban planning for mitigation strategies) on how cities can cope and adapt to climate change and variability. This latter point parallels the concepts and ideas presented in the U.S. National Academy of Sciences, Decadal Survey report on "Earth Science Applications from Space: National Imperatives for the Next Decade and Beyond" wherein the analysis of the impacts of climate change and variability, human health, and land use change are listed as key areas for development of future Earth observing remote sensing systems.

Quattrochi, Dale A.↗

NASA NTRS1999

Climate Change and a Global City: An Assessment of the Metropolitan East Coast Region

The objective of the research is to derive an assessment of the potential climate change impacts on a global city - in this case the 31 county region that comprises the New York City metropolitan area. This study comprises one of the regional components that contribute to the ongoing U.S. National Assessment: The Potential Consequences of Climate Variability and Change and is an application of state-of-the-art climate change science to a set of linked sectoral assessment analyses for the Metro East Coast (MEC) region. We illustrate how three interacting elements of global cities react and respond to climate variability and change with a broad conceptual model. These elements include: people (e.g., socio- demographic conditions), place (e.g., physical systems), and pulse (e.g., decision-making and economic activities). The model assumes that a comprehensive assessment of potential climate change can be derived from examining the impacts within each of these elements and at their intersections. Thus, the assessment attempts to determine the within-element and the inter-element effects. Five interacting sector studies representing the three intersecting elements are evaluated. They include the Coastal Zone, Infrastructure, Water Supply, Public Health, and Institutional Decision-making. Each study assesses potential climate change impacts on the sector and on the intersecting elements, through the analysis of the following parts: 1. Current conditions of sector in the region; 2. Lessons and evidence derived from past climate variability; 3. Scenario predictions affecting sector; potential impacts of scenario predictions; 4. Knowledge/information gaps and critical issues including identification of additional research questions, effectiveness of modeling efforts, equity of impacts, potential non-local interactions, and policy recommendations; and 5. Identification of coping strategies - i.e., resilience building, mitigation strategies, new technologies, education that affects decision-making, and better preparedness for contingencies.

Rosenzweig, Cynthia↗

NASA NTRS2015

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects.

climate↗

NASA NTRS2005

The Calibration and Characterization of Earth Remote Sensing and Environmental Monitoring Instruments

The use of remote sensing instruments on orbiting satellite platforms in the study of Earth Science and environmental monitoring was officially inaugurated with the April 1, 1960 launch of the Television Infrared Observation Satellite (TIROS) [1]. The first TIROS accommodated two television cameras and operated for only 78 days. However, the TIROS program, in providing in excess of 22,000 pictures of the Earth, achieved its primary goal of providing Earth images from a satellite platform to aid in identifying and monitoring meteorological processes. This marked the beginning of what is now over four decades of Earth observations from satellite platforms. reflected and emitted radiation from the Earth using instruments on satellite platforms. These measurements are input to climate models, and the model results are analyzed in an effort to detect short and long-term changes and trends in the Earth's climate and environment, to identify the cause of those changes, and to predict or influence future changes. Examples of short-term climate change events include the periodic appearance of the El Nino-Southern Oscillation (ENSO) in the tropical Pacific Ocean [2] and the spectacular eruption of Mount Pinatubo on the Philippine island of Luzon in 1991. Examples of long term climate change events, which are more subtle to detect, include the destruction of coral reefs, the disappearance of glaciers, and global warming. Climatic variability can be both large and small scale and can be caused by natural or anthropogenic processes. The periodic El Nino event is an example of a natural process which induces significant climatic variability over a wide range of the Earth. A classic example of a large scale anthropogenic influence on climate is the well-documented rapid increase of atmospheric carbon dioxide occurring since the beginning of the Industrial Revolution [3]. An example of the study of a small-scale anthropogenic influence in climate variability is the Atlanta Land-use Analysis Temperature and Air-quality (ATLANTA) project [4]. This project has found that the replacement of trees and vegetation with concrete and asphalt in Atlanta, Georgia, and its environs has created a microclimate capable of producing wind and thunderstorms. A key objective of climate research is to be able to distinguish the natural versus human roles in climate change and to clearly communicate those findings to those who shape and direct environmental policy.

Butler, James J.↗

NASA NTRS1999

Ocean-Atmosphere Interaction in Climate Changes

The diagram, which attests the El Nino teleconnection observed by the NASA Scatterometer (NSCAT) in 1997, is an example of the results of our research in air-sea interaction - the core component of our three-part contribution to the Climate Variability Program. We have established an interplay among scientific research, which turns spacebased data into knowledge, a push in instrument technology, which improves observations of climate variability, and an information system, which produces and disseminates new data to support our scientific research. Timothy Liu led the proposal for advanced technology, in response to the NASA Post-2002 Request for Information. The sensor was identified as a possible mission for continuous ocean surface wind measurement at higher spatial resolution, and with the unique capability to measure ocean surface salinity. He is participating in the Instrument Incubator Program to improve the antenna technology, and is initiating a study to integrate the concept on Japanese missions. He and his collaborators have set up a system to produce and disseminate high level (gridded) ocean surface wind/stress data from NSCAT and European missions. The data system is being expanded to produce real-time gridded ocean surface winds from Quikscat, and precipitation and evaporation from the Tropical Rain Measuring Mission. It will form the basis for a spacebased data analysis system which will include momentum, heat and water fluxes. The study on 1997 El Nino teleconnection illustrates our interdisciplinary and multisensor approach to study climate variability. The diagram shows that the collapse of trade wind and the westerly wind anomalies in the central equatorial Pacific led to the equatorial ocean warming. The equatorial wind anomalies are connected to the anomalous cyclonic wind pattern in the northeast Pacific. The anomalous warming along the west coast of the United States is the result of the movement of the pre-existing warm sea surface temperature anomalies with the cyclonic wind anomalies toward the coast. The results led to a new study which identifies decadal ocean variations in the Northeast Pacific. Three studies of oceanic responses to wind forcing caused by the seasonal change of monsoons, the passage of a typhoon, and the 1997 El Nino, were successfully conducted. Besides wind forcing, we continue to examine new techniques for estimating thermal and hydrologic fluxes, through the inverse ocean mixed-layer model, through divergence of atmospheric water transport, and by direct retrieval from radiances observed by microwave radiometers. Greenhouse warming has been linked to water vapor measured by two spaceborne sensors in two studies. In the first study, strong baroclinicity and deep convection were found to transport water vapor to the upper atmosphere and increase greenhouse trapping over the storm tracks of the North Pacific and Atlantic. In another study, the annual cycle of greenhouse warming were related to sea surface temperature (SST) and integrated water vapor, and the latitudinal dependence of the magnitudes and phases of the annual cycles were compared.

Liu, W. Timothy↗

NASA NTRS2015

Solar Signals in CMIP-5 Simulations: The Stratospheric Pathway

The 11 year solar-cycle component of climate variability is assessed in historical simulations of models taken from the Coupled Model Intercomparison Project, phase 5 (CMIP-5). Multiple linear regression is applied to estimate the zonal temperature, wind and annular mode responses to a typical solar cycle, with a focus on both the stratosphere and the stratospheric influence on the surface over the period approximately 1850-2005. The analysis is performed on all CMIP-5 models but focuses on the 13 CMIP-5 models that resolve the stratosphere (high-top models) and compares the simulated solar cycle signature with reanalysis data. The 11 year solar cycle component of climate variability is found to be weaker in terms of magnitude and latitudinal gradient around the stratopause in the models than in the reanalysis. The peak in temperature in the lower equatorial stratosphere (approximately 70 hPa) reported in some studies is found in the models to depend on the length of the analysis period, with the last 30 years yielding the strongest response. A modification of the Polar Jet Oscillation (PJO) in response to the 11 year solar cycle is not robust across all models, but is more apparent in models with high spectral resolution in the short-wave region. The PJO evolution is slower in these models, leading to a stronger response during February, whereas observations indicate it to be weaker. In early winter, the magnitude of the modeled response is more consistent with observations when only data from 1979-2005 are considered. The observed North Pacific high-pressure surface response during the solar maximum is only simulated in some models, for which there are no distinguishing model characteristics. The lagged North Atlantic surface response is reproduced in both high- and low-top models, but is more prevalent in the former. In both cases, the magnitude of the response is generally lower than in observations.

stratosphere↗

NASA NTRS2013

Consistency of Estimated Global Water Cycle Variations Over the Satellite Era

Motivated by the question of whether recent indications of decadal climate variability and a possible "climate shift" may have affected the global water balance, we examine evaporation minus precipitation (E-P) variability integrated over the global oceans and global land from three points of view-remotely sensed retrievals / objective analyses over the oceans, reanalysis vertically-integrated moisture convergence (MFC) over land, and land surface models forced with observations-based precipitation, radiation and near-surface meteorology. Because monthly variations in area-averaged atmospheric moisture storage are small and the global integral of moisture convergence must approach zero, area-integrated E-P over ocean should essentially equal precipitation minus evapotranspiration (P-ET) over land (after adjusting for ocean and land areas). Our analysis reveals considerable uncertainty in the decadal variations of ocean evaporation when integrated to global scales. This is due to differences among datasets in 10m wind speed and near-surface atmospheric specific humidity (2m qa) used in bulk aerodynamic retrievals. Precipitation variations, all relying substantially on passive microwave retrievals over ocean, still have uncertainties in decadal variability, but not to the degree present with ocean evaporation estimates. Reanalysis MFC and P-ET over land from several observationally forced diagnostic and land surface models agree best on interannual variations. However, upward MFC (i.e. P-ET) reanalysis trends are likely related in part to observing system changes affecting atmospheric assimilation models. While some evidence for a low-frequency E-P maximum near 2000 is found, consistent with a recent apparent pause in sea-surface temperature (SST) rise, uncertainties in the datasets used here remain significant. Prospects for further reducing uncertainties are discussed. The results are interpreted in the context of recent climate variability (Pacific Decadal Oscillation, Atlantic Meridional Overturning), and efforts to distinguish these modes from longer-term trends.

Robertson, F. R.↗

NASA NTRS2013

Consistency of Estimated Global Water Cycle Variations Over the Satellite Era

Robertson, F. R.↗

DOE OSTI2024

Changing effects of external forcing on Atlantic–Pacific interactions

Recent studies have highlighted the increasingly dominant role of external forcing in driving Atlantic and Pacific Ocean variability during the second half of the 20th century. This paper provides insights into the underlying mechanisms driving interactions between modes of variability over the two basins. We define a set of possible drivers of these interactions and apply causal discovery to reanalysis data, two ensembles of pacemaker simulations where sea surface temperatures in either the tropical Pacific or the North Atlantic are nudged to observations, and a pre-industrial control run. We also utilize large-ensemble means of historical simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to quantify the effect of external forcing and improve the understanding of its impact. A causal analysis of the historical time series between 1950 and 2014 identifies a regime switch in the interactions between major modes of Atlantic and Pacific climate variability in both reanalysis and pacemaker simulations. A sliding window causal analysis reveals a decaying El Niño–Southern Oscillation (ENSO) effect on the Atlantic as the North Atlantic fluctuates towards an anomalously warm state. The causal networks also demonstrate that external forcing contributed to strengthening the Atlantic's negative-sign effect on ENSO since the mid-1980s, where warming tropical Atlantic sea surface temperatures induce a La Niña-like cooling in the equatorial Pacific during the following season through an intensification of the Pacific Walker circulation. The strengthening of this effect is not detected when the historical external forcing signal is removed in the Pacific pacemaker ensemble. The analysis of the pre-industrial control run supports the notion that the Atlantic and Pacific modes of natural climate variability exert contrasting impacts on each other even in the absence of anthropogenic forcing. The interactions are shown to be modulated by the (multi)decadal states of temperature anomalies of both basins with stronger connections when these states are “out of phase”. We show that causal discovery can detect previously documented connections and provides important potential for a deeper understanding of the mechanisms driving changes in regional and global climate variability.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2013

Detection and Attribution of Anthropogenic Climate Change Impacts

Human-influenced climate change is an observed phenomenon affecting physical and biological systems across the globe. The majority of observed impacts are related to temperature changes and are located in the northern high- and midlatitudes. However, new evidence is emerging that demonstrates that impacts are related to precipitation changes as well as temperature, and that climate change is impacting systems and sectors beyond the Northern Hemisphere. In this paper, we highlight some of this new evidence-focusing on regions and sectors that the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) noted as under-represented-in the context of observed climate change impacts, direct and indirect drivers of change (including carbon dioxide itself), and methods of detection. We also present methods and studies attributing observed impacts to anthropogenic forcing. We argue that the expansion of methods of detection (in terms of a broader array of climate variables and data sources, inclusion of the major modes of climate variability, and incorporation of other drivers of change) is key to discerning the climate sensitivities of sectors and systems in regions where the impacts of climate change currently remain elusive. Attributing such changes to human forcing of the climate system, where possible, is important for development of effective mitigation and adaptation. Current challenges in documenting adaptation and the role of indigenous knowledge in detection and attribution are described.

detection↗

DOE OSTI2025

Using multiple high-resolution datasets to benchmark the energy exascale earth system model (E3SM) for renewable resource assessment

The United States is accelerating its shift toward a renewable energy system. However, renewable resources, which harness energy from the Earth system, are susceptible to both present-day climate variability and future climate change. For example, variations in regional climate can alter renewable energy production patterns and site viability. The use of high-resolution climate model projections can therefore facilitate and may be critical to long-term planning of renewable energy investments. However, climate models must first be validated for renewable resource assessment. This research employs multiple high-spatiotemporal-resolution datasets to assess the capability of the Department of Energy’s (DOE) Energy Exascale Earth System Model version 2 North American Regionally Refined Model (E3SMv2-NARRM) for predicting multi-year climatological values of solar and wind energy capacity factors in the continental U.S., with a focus on regional and seasonal variability. Present-day E3SMv2-NARRM simulations are compared with reported utility-scale production data obtained from the Energy Information Administration (EIA). In addition, E3SMv2-NARRM data are evaluated against non-climate benchmark models from the National Renewable Energy Laboratory, including the Wind Integration National Dataset Toolkit and the National Solar Radiation Database (NSRDB), as well as three wind energy datasets from PLUSWIND. Our analysis indicates that solar capacity factors from E3SM closely match those from the NSRDB dataset. However, both datasets tend to overestimate values by 10% in comparison to EIA data. Furthermore, biases in wind capacity factors within E3SM are notably pronounced in the West Coast regions, where the seasonal cycle diverges from EIA data.

Energy forecasting, Capacity factor, Renewable ene↗