Search NASA⌕ Search

SEARCH · Search NASA

Results for “climate model ensemble”

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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

What CMIP6 Models Tell Us About the Impact of AMOC Variability on the Arctic

In this paper we address the question whether variability in the Atlantic Meridional Overturning Circulation (AMOC) and its associated heat transport at mid-latitudes impacts the Arctic Earth system. To that end, we perform coherence analysis on time series of ocean heat transport, AMOC strength, and Arctic climate metrics across a large number of models from the CMIP6 ensemble. We find that, on multidecadal timescales, the majority of CMIP6 models indeed display a statistically significant relationship between AMOC at mid-latitudes (45 °N) and metrics like Arctic surface air temperatures and sea ice. However, our results do not support the narrative that heat transport anomalies at lower latitudes propagate northward toward the Arctic. Instead, our results confirm that variability in meridional ocean heat transport arises in the subpolar North Atlantic and propagates southward and northward.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1999

The Tropical Rainfall Measuring Mission (TRMM)

Recognizing the importance of rain in the tropics and the accompanying latent heat release, NASA for the U.S. and NASDA for Japan have partnered in the design, construction and flight of an Earth Probe satellite to measure tropical rainfall and calculate the associated heating. Primary mission goals are: 1) the understanding of crucial links in climate variability by the hydrological cycle, 2) improvement in the large-scale models of weather and climate, and 3) improvement in understanding cloud ensembles and their impacts on larger scale circulations. The linkage with the tropical oceans and landmasses are also emphasized. The Tropical Rainfall Measuring Mission (TRMM) satellite was launched in November 1997 with fuel enough to obtain a four to five year data set of rainfall over the global tropics from 37 deg N to 37 deg S. This paper reports progress from launch date through the spring of 1999. The data system and its products and their access is described, as are the algorithms used to obtain the data. Some exciting early results from TRMM are described. Some important algorithm improvements are shown. These will be used in the first total data reprocessing, scheduled to be complete in early 2000. The reader is given information on how to access and use the data.

Simpson, Joanne↗
NASA NTRS2020

Projecting exposure to extreme climate impact events across six event categories and three spatial scales

The extent and impact of climate‐related extreme events depend on the underlying meteorological, hydrological, or climatological drivers as well as on human factors such as land use or population density. Here we quantify the pure effect of historical and future climate change on the exposure of land and population to extreme climate impact events using an unprecedentedly large ensemble of harmonized climate impact simulations from the Inter‐Sectoral Impact Model Intercomparison Project phase 2b. Our results indicate that global warming has already more than doubled both the global land area and the global population annually exposed to all six categories of extreme events considered: river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves. Global warming of 2°C relative to preindustrial conditions is projected to lead to a more than five‐fold increase in cross‐category aggregate exposure globally. Changes in exposure are unevenly distributed, with tropical and subtropical regions facing larger increases than higher latitudes. The largest increases in overall exposure are projected for the population of South Asia.

climate‐related extreme events↗
DOE OSTI2022

Process-based climate change assessment for European winds using EURO-CORDEX and global models

Wind energy is an important pillar of decarbonization strategies and potentially vulnerable to climate change. Existing wind climate change assessments rely on climate models but a systematic investigation of the global-to-regional climate modeling chain is missing. In this study, I highlight key limitations, namely (a) the differing representation of land use change in global and regional climate models which compromises comparability, and (b) the consistency of large-scale features along the global-to-regional climate modeling chain. To this end, I analyze the large European Coordinated Downscaling Experiment (EURO-CORDEX) ensemble (rcp85: N = 49; rcp45: N= 18; rcp26: N = 22) along with the driving global models (rcp85: N = 7; rcp45: N = 5; rcp26: N = 7), finding evidence that climate change reduces mean wind speeds by up to -0.8 ms -1 (offshore) and -0.3 ms -1 (onshore). I provide physical explanations for these changes by identifying two key drivers. First, onshore wind speeds drop in the driving global models in regions and scenarios with strong land use change but show no drop in EURO-CORDEX where land use is held constant. Second, offshore wind reductions follow decreases in the equator-to-pole temperature gradient remarkably well with correlations reaching around 0.9 in resource-rich European countries like Ireland, the United Kingdom and Norway, implying that arctic amplification is a severe risk for European offshore wind energy. My results suggest that earlier conclusions of negligible climate change impacts on wind energy might be premature if either land use changes strongly or polar amplification is at or above the range sampled in global climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Changing Trends in Drought Patterns over the Northeastern United States Using Multiple Large Ensemble Datasets

Since the infamous extreme drought of the 1960s, the climate of the northeastern United States (NEUS) has generally trended toward warmer and wetter conditions. Nonetheless, there is mounting evidence that short-term droughts will continue to pose a significant risk for this region. To better explore the processes governing events such as these, climate models have adopted more complex representations of the fully coupled atmosphere–land–ocean–sea ice system; however, large uncertainties in future projections still persist, with internal variability necessitating large ensembles to understand trends in both rare and high-impact extreme events such as rapidly developing droughts (a term here that includes flash droughts developing on monthly scales). In this study, seven large ensemble (LE) models are employed to answer the outstanding question: How are the frequency and character of drought in the NEUS changing under a warming climate? We find that most LE models indicate the NEUS will experience a long-term wetting trend with more “extremely wet” months, but also more frequent short-term extreme droughts. These changes are associated with increasing precipitation, atmospheric water demand, and climate variability. We also conclude that discrepant trends in precipitation and evapotranspiration variability will lead to increasing anticorrelation of these variables, which is relevant to the intensification of rapidly developing drought, particularly in the spring season. These changes are associated with an increase in evapotranspiration from plants, brought by an earlier emergence of the growing season and denser vegetation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Improving Seasonal Forecast Using Probabilistic Deep Learning

The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits relies on improving general circulation model (GCM) based dynamical forecast systems. To improve dynamical seasonal forecasts, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge costs in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. Here, we develop a probabilistic deep learning-based statistical forecast methodology, drawing on a wealth of climate simulations to enhance seasonal forecast capability and forecast diagnosis. By explicitly modeling the internal climate variability and GCM formulation differences, the proposed Conditional Generative Forecasting (CGF) methodology enables bypassing crucial barriers in dynamical forecast, and offers a top-down viewpoint to examine how complicated GCMs encode the seasonal predictability information. We apply the CGF methodology for global seasonal forecast of precipitation and 2 m air temperature, based on a unique data set consisting 52,201 years of climate simulation. Results show that the CGF methodology can faithfully represent the seasonal predictability information encoded in GCMs. We successfully apply this learned relationship in real-world seasonal forecast, achieving competitive performance compared to dynamical forecasts. Using this CGF as benchmark, we reveal the impact of insufficient forecast spread sampling that limits the skill of the considered dynamical forecast system. Finally, we introduce different strategies for composing ensembles using the CGF methodology, highlighting the potential for leveraging the strengths of multiple GCMs to achieve advantgeous seasonal forecast.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Peatland fires in Alaska will double by the end of the century

During recent summers, warm and dry conditions have increased the occurrence of wildfires and potentially peat-fires across Alaska. Limitations in resolving the fine-scale distribution of peatlands and climate observations have constrained our ability to accurately predict peat-fire dynamics. Using a new high-resolution peatland map of Alaska, we evaluated the climate and environmental controls of past and future peat-fire activity. Ensemble machine learning models identified reduced soil moisture, higher temperatures, and evapotranspiration as key predictors of annual total burned peatland area (tenfold CV R 2 = 0.62, RMSE = 221.1 km 2 ). By the end of the twenty-first century, models forced with climate datasets from representative concentration pathways (RCPs) 4.5, 6.0, and 8.5 emission scenarios project a statewide doubling of burned peatlands (increasing 61–121%), with regional increases ranging from 25–165% in polar, 61–95% in boreal, and 102–106% in maritime ecoregions. These projections indicate that wildfires will progressively encroach further into organic-rich moist and wet peaty soils, potentially amplifying soil carbon release across Alaska.

climate-change ecology↗
NASA NTRS2021

Evaluating the Large-Scale Hydrological Cycle Response within the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2) Ensemble

The mid-Pliocene (∼3 Ma) is one of the most recent warm periods with high CO2 concentrations in the atmosphere and resulting high temperatures, and it is often cited as an analog for near-term future climate change. Here, we apply a moisture budget analysis to investigate the response of the large-scale hydrological cycle at low latitudes within a 13-model ensemble from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2). The results show that increased atmospheric moisture content within the mid-Pliocene ensemble (due to the thermodynamic effect) results in wetter conditions over the deep tropics, i.e., the Pacific intertropical convergence zone (ITCZ) and the Maritime Continent, and drier conditions over the subtropics. Note that the dynamic effect plays a more important role than the thermodynamic effect in regional precipitation minus evaporation (PmE) changes (i.e., northward ITCZ shift and wetter northern Indian Ocean). The thermodynamic effect is offset to some extent by a dynamic effect involving a northward shift of the Hadley circulation that dries the deep tropics and moistens the subtropics in the Northern Hemisphere (i.e., the subtropical Pacific). From the perspective of Earth's energy budget, the enhanced southward cross-equatorial atmospheric transport (0.22 PW), induced by the hemispheric asymmetries of the atmospheric energy, favors an approximately 1∘ northward shift of the ITCZ. The shift of the ITCZ reorganizes atmospheric circulation, favoring a northward shift of the Hadley circulation. In addition, the Walker circulation consistently shifts westward within PlioMIP2 models, leading to wetter conditions over the northern Indian Ocean. The PlioMIP2 ensemble highlights that an imbalance of interhemispheric atmospheric energy during the mid-Pliocene could have led to changes in the dynamic effect, offsetting the thermodynamic effect and, hence, altering mid-Pliocene hydroclimate.

mid-Pliocene↗
DOE OSTI2024

Enhancing Extreme Precipitation Predictions With Dynamical Downscaling: A Convection‐Permitting Modeling Study in Texas and Oklahoma

Precipitation in the Southern Plains of the United States is relatively well depicted by the Community Earth System Model (CESM). However, despite its ability to capture seasonal mean precipitation anomalies, CESM consistently underestimates extreme pluvial and drought events, rendering it an insufficient tool for extending simulation lead times for exceptional events, such as the abnormally dry May 2011, which helped drive Texas into its worst period of drought in more than a century, and the abnormally wet May 2015, which led to widespread flooding in that state. Ensemble‐based regional climate experiments are completed for the two extreme years using Weather Research and Forecasting model (WRF) and downscaled from CESM. WRF simulations are at convection‐permitting grid resolution for improved physical representation of simulated precipitation over the Southern Great Plains. By integrating convection‐permitting models (CPMs) into each individual member of a CESM ocean data assimilation ensemble, this study demonstrates that high‐resolution dynamical downscaling can improve model skillfulness at capturing these two events and is thus a potentially useful tool for forecasting extremely high and extremely low precipitation events at subseasonal or even seasonal lead times.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Region and cloud regime dependence of parametric sensitivity in E3SM atmosphere model

Abstract The Department of Energy (DOE)’s Energy Exascale Earth System Model (E3SM), including its atmosphere model (EAM), has many relatively new features. In a previous study we conducted a systematic parametric sensitivity analysis for EAM based on short, perturbed parameter ensemble (PPE) simulations, mainly focusing on global mean climate features and metrics. While parameter values in global climate models are generally invariant in space and time, model response to parameters perturbation may vary by regions and climate regimes, which motivates the need to better understand the EAM model behaviors and physics at regional scale and process level. In this study, using the same set of PPE simulations and a similar sensitivity analysis framework, we identify parameters that cause largest sensitivities over different regions and compare model responses in fast atmospheric processes to the parameters across different cloud regimes for several important cloud-related fidelity metrics. We find that cloud forcing has opposite response to some parameters over mid-latitude vs. tropical land. We also analyze how the parametric sensitivity varies as stratocumulus transitions to shallow convection and to deep convection over ocean. Low cloud forcing and shortwave cloud forcing in the subtropical eastern Pacific are most sensitive to the parameters controlling the width of the probability density function (PDF) of the subgrid vertical velocity ( w’ ) ( gamma ) and the damping of the w’ skewness ( c8 ) near the coast but become more sensitive to the parameter affecting the damping of the w’ variance ( c1 ) further offshore. Detailed interpretation of the spatial dependence of parametric sensitivity is provided. We also investigate how the parametric sensitivity evolves with prediction duration. This study improves our process-level understanding of cloud physics and parameterization and provides insights for developing more advanced regime-aware parameterization schemes in global climate model.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Quantifying the Aerosol Semi-Direct Effect in the NASA GEOS-5 AGCM

Aerosols such as black carbon, dust, and some organic carbon species both scatter and absorb incoming solar radiation. This direct aerosol radiative forcing (DARF) redistributes solar energy both by cooling the surface and warming the atmosphere. As a result, these aerosols affect atmospheric stability and cloud cover (the semi-direct effect, or SDE). Furthermore, in regions with persistent high loadings of absorbing aerosols (e.g. Asia), regional circulation patterns may be altered, potentially resulting in changes in precipitation patterns. Here we investigate aerosol-climate coupling using the NASA Goddard Earth Observing System model version 5 (GEOS-5) atmospheric general circulation model (AGCM), in which we have implemented an online version of the Goddard Chemistry, Aerosol, Radiation and Transport (GOCART) model. GOCART includes representations of the sources, sinks, and chemical transformation of externally mixed dust, sea salt, sulfate, and carbonaceous aerosols. We examine a series of free-running ensemble climate simulations of the present-day period (2000-2009) forced by observed sea surface temperatures to determine the impact of aerosols on the model climate. The SDE and response of each simulation is determined by differencing with respect to the control simulation (no aerosol forcing). In a free-running model, any estimate of the SDE includes changes in clouds due both to atmospheric heating from aerosols and changes in circulation. To try and quantify the SDE without these circulation changes we then examine the DARF and SDE in GEOS-5 with prescribed meteorological analyses introduced by the MERRA analysis. By doing so, we are able to examine changes in model clouds that occur on shorter scales (six hours). In the GEOS-5 data assimilation system (DAS), the analysis is defined as the best estimate of the atmospheric state at any given time, and it is determined by optimally combining a first-guess short-term GCM forecast with all available observations. The Incremental Analysis Update (IAU) is added to the model forecast tendencies to align them with the analysis every six hours, thus preventing longer timescale feedbacks due to the aerosol forcing. We calculate the SDE by comparing model runs with and without aerosols, and the difference in the IAU between these runs is a useful metric with which to evaluate the impact of the SDE on the model atmosphere and clouds. Decreasing the IAU indicates that the aerosol direct and semi-direct effects act to reduce the bias between the model and observations and vice versa.

Randles, Cynthia A.↗
DOE OSTI2021

Sources of Subseasonal-To-Seasonal Predictability of Atmospheric Rivers and Precipitation in the Western United States

Atmospheric rivers (ARs) account for a large portion of winter precipitation in the western US. To evaluate the sources of AR and precipitation predictability at subseasonal-to-seasonal timescales, we examine the relationships between two climate modes, the El Niño/Southern Oscillation (ENSO) and Madden-Julian oscillation (MJO), and winter hydroclimate in the western US. Our analysis uses a large ensemble of the Weather Research and Forecast (WRF) model simulations from 1981 to 2017, facilitating the assessment of uncertainty in climate mode-AR relationship due to climate variability and short length of observations. Over the North Pacific basin, we find ENSO-related latitudinal shifts of ARs, but ENSO has little effect on basin-wide averaged AR frequency. Over the western US, there is some uncertainty in the ENSO-AR connection due to the impacts of ENSO indices and datasets. However, extreme El Niño events defined by the ENSO longitude index are consistently linked to increased landfalling AR activity. The MJO can lead to significantly enhanced or suppressed landfalling AR activity in the large-ensembles, depending on the phase of MJO and time lag, while observations are too short in length to robustly show this signal. ENSO substantially modulates the MJO-AR relationship, triggering variegated responses of landfalling ARs and AR precipitation in La Niña and El Niño years. Our findings highlight the need to evaluate concurrent effects of different climate modes on ARs and precipitation, and may shed light on a path toward more accurate subseasonal-to-seasonal prediction of ARs and precipitation over the western US.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1981

A stochastic-dynamical approach to the study of the natural variability of the climate

A method, suggested by Leith (1975), which employed stochastic-dynamic forecasts obtained from a general circulation model in such a way as to satisfy the definition of climatic noise, was used to validate assumptions accounting for the effects of external influences in estimating the climatic noise. Two assumptions were investigated: (1) that the weather fluctuations can be represented as a Markov process, and (2) that changing external conditions do not influence the atmosphere's statistical properties on short time scales. The general circulation model's simulation of the daily weather fluctuations was generated by performing integrations with prescribed climatological boundary conditions for random initial atmospheric states, with resulting dynamical forecasts providing an ensemble of simulated data for the autoregressive modeling of weather fluctuations. To estimate the climatic noise from the observational data (consisting of hourly values of sea level pressure and surface temperature at 54 U.S. stations for the month of January for the years 1949-1975) use of the short time-scale assumption is made. The simulated and observed data were found not to be consistent with either white noise or a Markov process of weather fluctuations. Good agreement was found between the results of the hypothetical testing of the simulated and the observed surface temperatures; and only partial support was found for the short time-scale assumption, i.e., for sea level pressure.

Straus, D. M.↗
DOE OSTI2022

Prescreening-Based Subset Selection for Improving Predictions of Earth System Models With Application to Regional Prediction of Red Tide

We present the ensemble method of prescreening-based subset selection to improve ensemble predictions of Earth system models (ESMs). In the prescreening step, the independent ensemble members are categorized based on their ability to reproduce physically-interpretable features of interest that are regional and problem-specific. The ensemble size is then updated by selecting the subsets that improve the performance of the ensemble prediction using decision relevant metrics. We apply the method to improve the prediction of red tide along the West Florida Shelf in the Gulf of Mexico, which affects coastal water quality and has substantial environmental and socioeconomic impacts on the State of Florida. Red tide is a common name for harmful algal blooms that occur worldwide, which result from large concentrations of aquatic microorganisms, such as dinoflagellate Karenia brevis, a toxic single celled protist. We present ensemble method for improving red tide prediction using the high resolution ESMs of the Coupled Model Intercomparison Project Phase 6 (CMIP6) and reanalysis data. The study results highlight the importance of prescreening-based subset selection with decision relevant metrics in identifying non-representative models, understanding their impact on ensemble prediction, and improving the ensemble prediction. These findings are pertinent to other regional environmental management applications and climate services. Additionally, our analysis follows the FAIR Guiding Principles for scientific data management and stewardship such that data and analysis tools are findable, accessible, interoperable, and reusable. As such, the interactive Colab notebooks developed for data analysis are annotated in the paper. This allows for efficient and transparent testing of the results’ sensitivity to different modeling assumptions. Moreover, this research serves as a starting point to build upon for red tide management, using the publicly available CMIP, Coordinated Regional Downscaling Experiment (CORDEX), and reanalysis data.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Enhanced Weathering Strategies for Stabilizing Climate and Averting Ocean Acidification

Chemical breakdown of rocks, weathering, is an important but very slow part of the carbon cycle that ultimately leads to CO2 being locked up in carbonates on the ocean floor. Artificial acceleration of this carbon sink via distribution of pulverized silicate rocks across terrestrial landscapes may help offset anthropogenic CO2 emissions. We show that idealized enhanced weathering scenarios over less than a third of tropical land could cause significant drawdown of atmospheric CO2 and ameliorate ocean acidification by 2100. Global carbon cycle modelling driven by ensemble Representative Concentration Pathway (RCP) projections of twenty-first-century climate change (RCP8.5, business-as-usual; RCP4.5, medium-level mitigation) indicates that enhanced weathering could lower atmospheric CO2 by 30-300 ppm by 2100, depending mainly on silicate rock application rate (1 kg or 5 kg m(exp -2) yr (exp -1)) and composition. At the higher application rate, end-of-century ocean acidification is reversed under RCP4.5 and reduced by about two-thirds under RCP8.5. Additionally, surface ocean aragonite saturation state, a key control on coral calcification rates, is maintained above 3.5 throughout the low latitudes, thereby helping maintain the viability of tropical coral reef ecosystems. However, we highlight major issues of cost, social acceptability, and potential unanticipated consequences that will limit utilization and emphasize the need for urgent efforts to phase down fossil fuel emissions.

Taylor, Lyla L.↗
NASA NTRS1999

Timescales of Soil Moisture Anomalies: Results from Two GCMs

Soil moisture anomalies dissipate over timescales that may span weeks to months. Characterizing the geographical and seasonal variations in these timescales can have important practical benefit; significant soil moisture "memory" allows long-lead forecasts of soil moisture, which have been found in recent studies to be essential for useful Ion--lead forecasts of precipitation in many regions. In this talk, we will present and compare the soil moisture timescales derived in two separate general circulation model (GCM) studies. Both studies employ multiple ensembles of short-term climate simulations. Timescales at a given point are effectively estimated by determining how quickly the soil moisture distribution generated in one ensemble of simulations (characterized by a unique set of initial soil moisture conditions) approaches that produced by another ensemble (characterized by a different set of initial soil moisture conditions). The talk will include a discussion of why the timescales produced by the two GCMs differ in some regions, and it will also describe the impact of soil moisture memory on simulated precipitation.

Koster, Randal D.↗
NASA NTRS2021

Elevation Dependent Warming in the Eastern Siberian Arctic

There is evidence for elevation-dependent warming (EDW) in many mountainous regions, including the Alps, Rockies, and Tibetan Plateau, all of which are in mid-latitudes. Most studies finding evidence of EDW indicate that both recent decadal and future projected warming rates are greater at higher elevations. In this study, we examine the roles of Arctic amplification and elevation on future warming rates in winter and summer in eastern Siberia (50-70°N; 80-180°E). This region includes four major river basins that flow into the Arctic Ocean (the Yenisei, Lena, Indigirka, and Kolyma) and intersects with mountain ranges in northern Mongolia and eastern Siberia. We analyse projected 21st century temperature projections using a six-member ensemble of the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM4) with a radiative forcing of 8.5 W m-2. Projected warming rates in winter for the 21st century are dominated by Arctic amplification, which leads to significantly larger warming rates at higher latitudes, with latitudinal gradients of about 0.16°C per degree latitude. In summer, the latitudinal gradient is near zero (0.02°C per degree of latitude). Within specific latitude bands, we also find EDW. However, unlike most mid-latitude locations where warming rates are greater at higher elevations, we find that future warming rates are smaller at higher elevations for this high-latitude region, particularly during winter, with statistically significant rates varying between -0.70 and -2.46 °C/km for different 5° latitude bands. The decrease in warming rates with elevation in winter at the highest latitudes is primarily attributed to strong inversions and changes in the lapse rate as free-air temperatures warm at slower rates than surface temperatures. In summer, the elevation dependence is much weaker than in winter but still statistically significant and negative in all but the most northern latitude band with values ranging between -0.10 and -0.56 °C/km.

elevation-dependent warming (EDW)↗