Search NASA⌕ Search

SEARCH · Search NASA

Results for “CLIMATE MODELING”

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 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Composition Changes After the "Halloween" Solar Proton Event: The High-Energy Particle Precipitation in the Atmosphere (HEPPA) Model Versus MIPAS Data Intercomparison Study

We have compared composition changes of NO, NO2, H2O2,O3, N2O, HNO3 , N2O5, HNO4, ClO, HOCl, and ClONO2 as observed by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat in the aftermath of the "Halloween" solar proton event (SPE) in October/November 2003 at 25-0.01 hPa in the Northern hemisphere (40-90 N) and simulations performed by the following atmospheric models: the Bremen 2D model (B2dM) and Bremen 3D Chemical Transport Model (B3dCTM), the Central Aerological Observatory (CAO) model, FinROSE, the Hamburg Model of the Neutral and Ionized Atmosphere (HAMMONIA), the Karlsruhe Simulation Model of the Middle Atmosphere (KASIMA), the ECHAM5/MESSY Atmospheric Chemistry (EMAC) model, the modeling tool for SO1ar Climate Ozone Links studies (SOCOL and SOCOLi), and the Whole Atmosphere Community Climate Model (WACCM4). The large number of participating models allowed for an evaluation of the overall ability of atmospheric models to reproduce observed atmospheric perturbations generated by SPEs, particularly with respect to NOS, and ozone changes. We have further assessed the meteorological conditions and their implications on the chemical response to the SPE in both the models and observations by comparing temperature and tracer (CH4 and CO) fields. Simulated SPE-induced ozone losses agree on average within 5% with the observations. Simulated NO(y) enhancements around 1 hPa, however, are typically 30% higher than indicated by the observations which can be partly attributed to an overestimation of simulated electron-induced ionization. The analysis of the observed and modeled NO(y) partitioning in the aftermath of the SPE has demonstrated the need to implement additional ion chemistry (HNO3 formation via ion-ion recombination and water cluster ions) into the chemical schemes. An overestimation of observed H2O2 enhancements by all models hints at an underestimation of the OH/HO2 ratio in the upper polar stratosphere during the SPE. The analysis of chlorine species perturbations has shown that the encountered differences between models and observations, particularly the underestimation of observed ClONO2 enhancements, are related to a smaller availability of ClO in the polar night region already before the SPE. In general, the intercomparison has demonstrated that differences in the meteorology and/or initial state of the atmosphere in the simulations causes a relevant variability of the model results, even on a short timescale of only a few days.

Funke, B.↗
NASA NTRS2014

Remote Sensing of Cloud Top Height from SEVIRI: Analysis of Eleven Current Retrieval Algorithms

The role of clouds remains the largest uncertainty in climate projections. They influence solar and thermal radiative transfer and the earth's water cycle. Therefore, there is an urgent need for accurate cloud observations to validate climate models and to monitor climate change. Passive satellite imagers measuring radiation at visible to thermal infrared (IR) wavelengths provide a wealth of information on cloud properties. Among others, the cloud top height (CTH) - a crucial parameter to estimate the thermal cloud radiative forcing - can be retrieved. In this paper we investigate the skill of ten current retrieval algorithms to estimate the CTH using observations from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG). In the first part we compare ten SEVIRI cloud top pressure (CTP) data sets with each other. The SEVIRI algorithms catch the latitudinal variation of the CTP in a similar way. The agreement is better in the extratropics than in the tropics. In the tropics multi-layer clouds and thin cirrus layers complicate the CTP retrieval, whereas a good agreement among the algorithms is found for trade wind cumulus, marine stratocumulus and the optically thick cores of the deep convective system. In the second part of the paper the SEVIRI retrievals are compared to CTH observations from the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) instruments. It is important to note that the different measurement techniques cause differences in the retrieved CTH data. SEVIRI measures a radiatively effective CTH, while the CTH of the active instruments is derived from the return time of the emitted radar or lidar signal. Therefore, some systematic differences are expected. On average the CTHs detected by the SEVIRI algorithms are 1.0 to 2.5 kilometers lower than CALIOP observations, and the correlation coefficients between the SEVIRI and the CALIOP data sets range between 0.77 and 0.90. The average CTHs derived by the SEVIRI algorithms are closer to the CPR measurements than to CALIOP measurements. The biases between SEVIRI and CPR retrievals range from −0.8 kilometers to 0.6 kilometers. The correlation coefficients of CPR and SEVIRI observations vary between 0.82 and 0.89. To discuss the origin of the CTH deviation, we investigate three cloud categories: optically thin and thick single layer as well as multi-layer clouds. For optically thick clouds the correlation coefficients between the SEVIRI and the reference data sets are usually above 0.95. For optically thin single layer clouds the correlation coefficients are still above 0.92. For this cloud category the SEVIRI algorithms yield CTHs that are lower than CALIOP and similar to CPR observations. Most challenging are the multi-layer clouds, where the correlation coefficients are for most algorithms between 0.6 and 0.8. Finally, we evaluate the performance of the SEVIRI retrievals for boundary layer clouds. While the CTH retrieval for this cloud type is relatively accurate, there are still considerable differences between the algorithms. These are related to the uncertainties and limited vertical resolution of the assumed temperature profiles in combination with the presence of temperature inversions, which lead to ambiguities in the CTH retrieval. Alternative approaches for the CTH retrieval of low clouds are discussed.

Hamann, U.↗
NASA NTRS2021

Evidence for the predictability of changes in the stratosphericaerosol size following volcanic eruptions of diverse magnitudesusing space-based instruments

An analysis of multiwavelength stratospheric aerosol extinction coefficient data from the Stratospheric Aerosol and Gas Experiment II and III/ISS instruments is used to demonstrate a coherent relationship between the perturbation in extinction coefficient in an eruption’s main aerosol layer and the wavelength dependence of that perturbation. This relationship spans multiple orders of magnitude in the aerosol extinction coefficient of stratospheric impact of volcanic events. The relationship is measurement-based and does not rely on assumptions about the aerosol size distribution. We note limitations on this analysis including that the presence of significant amounts of ash in the main sulfuric acid aerosol layer and other factors may significantly modulate these results. Despite these limitations, the findings suggest an avenue for improving aerosol extinction coefficient measurements from single-channel observations such as the Optical Spectrograph and Infrared Imager System as they rely on a prior assumptions about particle size. They may also represent a distinct avenue for the comparison of observations with interactive aerosol models used in global climate models and Earth system models.

Larry W Thomason↗
NASA NTRS1999

Climate Ocean Modeling on a Beowulf Class System

With the growing power and shrinking cost of personal computers, the availability of fast ethernet interconnections, and public domain software packages, it is now possible to combine them to build desktop parallel computers at a fraction of what it would cost to buy systems of comparable power from supercomputer companies.

Beowulf↗
DOE OSTI2016

Regional hydrologic response to climate change in the conterminous United States using high-resolution hydroclimate simulations

Despite the fact that Global Climate Model (GCM) outputs have been used to project hydrologic impacts of climate change using off-line hydrologic models for two decades, many of these efforts have been disjointed applications or at least calibrations have been focused on individual river basins and using a few of the available GCMs. This study improves upon earlier attempts by systematically projecting hydrologic impacts for the entire conterminous United States (US), using outputs from ten GCMs from the latest Coupled Model Intercomparison Project phase 5 (CMIP5) archive, with seamless hydrologic model calibration and validation techniques to produce a spatially and temporally consistent set of current hydrologic projections. The Variable Infiltration Capacity (VIC) model was forced with ten-member ensemble projections of precipitation and air temperature that were dynamically downscaled using a regional climate model (RegCM4) and bias-corrected to 1/24 (~4 km) grid resolution for the baseline (1966 2005) and future (2011 2050) periods under the Representative Concentration Pathway 8.5. Based on regional analysis, the VIC model projections indicate an increase in winter and spring total runoff due to increases in winter precipitation of up to 20% in most regions of the US. However, decreases in snow water equivalent (SWE) and snow-covered days will lead to significant decreases in summer runoff with more pronounced shifts in the time of occurrence of annual peak runoff projected over the eastern and western US. In contrast, the central US will experience year-round increases in total runoff, mostly associated with increases in both extreme high and low runoff. Furthermore, the projected hydrological changes described in this study have implications for various aspects of future water resource management, including water supply, flood and drought preparation, and reservoir operation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1978

Internally and externally caused climate change

A numerical climate model is used to simulate climate change forced only by random fluctuations of the atmospheric heat transport. This short-term natural variability of the atmosphere is shown to be a possible 'cause' not only of the variability of the annual world average temperature about its mean, but also long-term excursions from the mean. Various external causes of climate change are also tested with the model and the results compared with observations for the past 100 years. Volcanic dust is shown to have been an important cause of climate change, while the effects of sunspot-related solar constant variation and anthropogenic forcing are not evident.

Robock, A.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-33231-DND

It is well established that clouds have a profound influence on Earth's energy budget and that cloud feedbacks are responsible for much of the uncertainty in climate model projections of global warming in response to radiative forcing by greenhouse gases. While most model evaluation efforts focus on model representation of the observed mean state. For example, the regional mean climatology of key climate variables there is also tremendous value in testing how a model represents observed internal variations of the climate system. Here we use CERES observations during and after the so-called global warming hiatus to evaluate a subset of CMIP6 climate models. The CERES data show a marked 0.83 Wm-2 reduction in global mean reflected SW TOA flux during the three years following the hiatus that results in an increase in net energy into the climate system. The primary driver of the decrease in SW TOA flux is a reduction in low cloud cover over the eastern Pacific Ocean that occurs in response to an unprecedented increase in SSTs in that region following the hiatus. The CMIP6 simulations consist of AMIP-style experiments in which SSTs and sea-ice boundary conditions are constrained by observations. For each model, the AMIP runs were extended through the end of 2017 from the official CMIP6 AMIP end period in 2014. Results show that the models do a good job at capturing the pattern of observed changes in TOA flux observed by CERES, but tend to underestimate the magnitude.

Norman G Loeb↗
DOE OSTI2021

Causal Evaluations for Identifying Differences between Observations and Earth System Models

We use a nascent data-driven causal discovery method to find and compare causal relationships in observed data and climate model output. We consider ten different features in the Arctic climate collected from public databases on observational and Energy Exascale Earth System Model (E3SM) data. In identifying and analyzing the resulting causal networks, we make meaningful comparisons between observed and climate model interdependencies. This work demonstrates our ability to apply the PCMCI causal discovery algorithm to Arctic climate data, that there are noticeable similarities between observed and simulated Arctic climate dynamics, and that further work is needed to identify specific areas for improvement to better align models with natural observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Moving beyond the total sea ice extent in gauging model biases

Here, reproducing characteristics of observed sea ice extent remains an important climate modeling challenge. This study describes several approaches to improve how model biases in total sea ice distribution are quantified, and applies them to historically forced simulations contributed to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The quantity of hemispheric total sea ice area, or some measure of its equatorward extent, is often used to evaluate model performance. A new approach is introduced that investigates additional details about the structure of model errors, with an aim to reduce the potential impact of compensating errors when gauging differences between simulated and observed sea ice. Using multiple observational datasets, several new methods are applied to evaluate the climatological spatial distribution and the annual cycle of sea ice cover in 41 CMIP5 models. It is shown that in some models, error compensation can be substantial, for example resulting from too much sea ice in one region and too little in another. Error compensation tends to be larger in models that agree more closely with the observed total sea ice area, which may result from model tuning. The results herein suggest that consideration of only the total hemispheric sea ice area or extent can be misleading when quantitatively comparing how well models agree with observations. Further work is needed to fully develop robust methods to holistically evaluate the ability of models to capture the finescale structure of sea ice characteristics; however, the “sector scale” metric used here aids in reducing the impact of compensating errors in hemispheric integrals.

54 ENVIRONMENTAL SCIENCES↗