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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.

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At least 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Relating Convection to GCM Grid-Scale Fields Using Cloud-Resolving Model Simulation of a Squall Line Observed during MC3E Field Experiment

In this study, a WRF (Weather Research and Forecasting) model is used as a cloud-resolving model to simulate a squall line observed on 20 May 2011 in the Southern Great Plains (SGP) of the United States. The model output is then used to examine the relationships between convective precipitation and coarse-grained variables averaged over a range of subdomain sizes equivalent to various global climate model horizontal resolutions. The objective is to determine to what extent convection within the subdomains can be related to these “large-scale” variables, thus that they can potentially serve as closure in convective parameterization. Results show that convective precipitation is well correlated with the vertical velocity at 500 hPa, column integrated moisture convergence and CAPE change due to large-scale advective forcing (dCAPE) for various subdomain sizes, but the correlation decreases with decreasing subdomain size. dCAPE leads convective precipitation for all subdomain sizes examined; however, the lead time decreases with decreasing subdomain size. Moisture convergence leads convective precipitation for subdomain sizes greater than 32 km but has no lead time for smaller subdomain sizes. Mid-tropospheric vertical velocity has no lead time or slightly lags convective precipitation. The lead/lag composite analysis with respect to maximum precipitation time indicates that peaks of large-scale variables increase with decreasing subdomain size. The peaks of 500 hPa vertical velocity and column integrated moisture convergence occur at the same time as maximum precipitation, but maximum dCAPE leads maximum precipitation by twelve minutes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Lignin Forcefield Source Datafiles

Three archives are provided here comprising the development of a CHARMM36-compatible lignin forcefield suitable for use in biomolecular simulations. One archive parameters.zip contains the output topology and parameter files in CHARMM format suitable for use in constructing simulation systems. The remaining two hold the logic of the parameterization featuring scripts that do the optimization including the source for the GPU-accelerated objective function evaluation and target data creation scripts.zip as well as minimal outputs needed to recreate the work datafiles.zip. Due to licensing restrictions Gaussian log files that created the target data are not provided here. Instead only binary-formatted parsed target data as well as the input decks used to create the target data are provided in datafiles.zip. Readme files for individual directories created within the directory structure are contained within scripts.zip.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

A Hierarchical Evaluation of Regional Climate Simulations

Global climate models (GCMs) are the primary tools for predicting the evolution of the climate system. Through decades of development, GCMs have demonstrated useful skill in simulating climate at continental to global scales. However, large uncertainties remain in projecting climate change at regional scales, which limit our ability to inform decisions on climate change adaptation and mitigation. To bridge this gap, different modeling approaches including nested regional climate models (RCMs), global stretch-grid models, and global high-resolution atmospheric models have been used to provide regional climate simulations (Leung et al. 2003). In previous efforts to evaluate these approaches, isolating their relative merits was not possible because factors such as dynamical frameworks, physics parameterizations, and model resolutions were not systematically constrained. With advances in high performance computing, it is now feasible to run coupled atmosphere-ocean GCMs at horizontal resolution comparable to what RCMs use today. Global models with local refinement using unstructured grids have become available for modeling regional climate (e.g., Rauscher et al. 2012; Ringler et al. 2013). While they offer opportunities to improve climate simulations, significant efforts are needed to test their veracity for regional-scale climate simulations.

climate↗
DOE OSTI2014

Scalability of grid- and subbasin-based land surface modeling approaches for hydrologic simulations

This paper investigates the relative merits of grid- and subbasin-based land surface modeling approaches for hydrologic simulations, with a focus on their scalability (i.e., abilities to perform consistently across a range of spatial resolutions) in simulating runoff generation. Simulations produced by the grid- and subbasin-based configurations of the Community Land Model (CLM) are compared at four spatial resolutions (0.125o, 0.25o, 0.5o and 1o) over the topographically diverse region of the U.S. Pacific Northwest. Using the 0.125o resolution simulation as the “reference”, statistical skill metrics are calculated and compared across simulations at 0.25o, 0.5o and 1o spatial resolutions of each modeling approach at basin and topographic region levels. Results suggest significant scalability advantage for the subbasin-based approach compared to the grid-based approach for runoff generation. Basin level annual average relative errors of surface runoff at 0.25o, 0.5o, and 1o compared to 0.125o are 3%, 4%, and 6% for the subbasin-based configuration and 4%, 7%, and 11% for the grid-based configuration, respectively. The scalability advantages of the subbasin-based approach are more pronounced during winter/spring and over mountainous regions. The source of runoff scalability is found to be related to the scalability of major meteorological and land surface parameters of runoff generation. More specifically, the subbasin-based approach is more consistent across spatial scales than the grid-based approach in snowfall/rainfall partitioning, which is related to air temperature and surface elevation. Scalability of a topographic parameter used in the runoff parameterization also contributes to improved scalability of the rain driven saturated surface runoff component, particularly during winter. Hence this study demonstrates the importance of spatial structure for multi-scale modeling of hydrological processes, with implications to surface heat fluxes in coupled land-atmosphere modeling.

Tesfa, Teklu K.↗
DOE OSTI2014

Intercomparison of Large-eddy Simulations of Arctic Mixed-phase Clouds: Importance of Ice Size Distribution Assumptions

Large-eddy simulations of mixed-phase Arctic clouds by 11 different models are analyzed with the goal of improving understanding and model representation of processes controlling the evolution of these clouds. In a case based on observations from the Indirect and Semi-Direct Aerosol Campaign (ISDAC), it is found that ice number concentration, Ni, exerts significant influence on the cloud structure. Increasing Ni leads to a substantial reduction in liquid water path (LWP) and potential cloud dissipation, in agreement with earlier studies. By comparing simulations with the same microphysics coupled to different dynamical cores as well as the same dynamics coupled to different microphysics schemes, it is found that the ice water path (IWP) is mainly controlled by ice microphysics, while the inter-model differences in LWP are largely driven by physics and numerics of the dynamical cores. In contrast to previous intercomparisons, all models here use the same ice particle properties (i.e., mass-size, mass-fall speed, and mass-capacitance relationships) and a common radiation parameterization. The constrained setup exposes the importance of ice particle size distributions (PSD) in influencing cloud evolution. A clear separation in LWP and IWP predicted by models with bin and bulk microphysical treatments is documented and attributed primarily to the assumed shape of ice PSD used in bulk schemes. Compared to the bin schemes that explicitly predict the PSD, schemes assuming exponential ice PSD underestimate ice growth by vapor deposition and overestimate mass-weighted fall speed leading to an underprediction of IWP by a factor of two in the considered case.

Arctic↗
DOE OSTI2014

Extrapolating active layer thickness measurements across Arctic polygonal terrain using LiDAR and NDVI data sets

Landscape attributes that vary with microtopography, such as active layer thickness (ALT), are labor intensive and difficult to document effectively through in situ methods at kilometer spatial extents, thus rendering remotely sensed methods desirable. Spatially explicit estimates of ALT can provide critically needed data for parameterization, initialization, and evaluation of Arctic terrestrial models. In this work, we demonstrate a new approach using high-resolution remotely sensed data for estimating centimeter-scale ALT in a 5 km 2 area of ice-wedge polygon terrain in Barrow, Alaska. We use a simple regression-based, machine learning data-fusion algorithm that uses topographic and spectral metrics derived from multisensor data (LiDAR and WorldView-2) to estimate ALT (2 m spatial resolution) across the study area. Comparison of the ALT estimates with ground-based measurements, indicates the accuracy (r 2 = 0.76, RMSE ±4.4 cm) of the approach. While it is generally accepted that broad climatic variability associated with increasing air temperature will govern the regional averages of ALT, consistent with prior studies, our findings using high-resolution LiDAR and WorldView-2 data, show that smaller-scale variability in ALT is controlled by local eco-hydro-geomorphic factors. This work demonstrates a path forward for mapping ALT at high spatial resolution and across sufficiently large regions for improved understanding and predictions of coupled dynamics among permafrost, hydrology, and land-surface processes from readily available remote sensing data.

58 GEOSCIENCES↗
DOE OSTI2014

Downscaling with a nested regional climate model in near-surface fields over the contiguous United States: WRF dynamical downscaling

The Weather Research and Forecasting (WRF) model is used for dynamic downscaling of 2.5 degree National Centers for Environmental Prediction-U.S. Department of Energy Reanalysis II (NCEP-R2) data for 1980-2010 at 12 km resolution over most of North America. The model's performance for surface air temperature and precipitation is evaluated by comparison with high-resolution observational data sets. The model's ability to add value is investigated by comparison with NCEP-R2 data and a 50 km regional climate simulation. The causes for major model bias are studied through additional sensitivity experiments with various model setup/integration approaches and physics representations. The WRF captures the main features of the spatial patterns and annual cycles of air temperature and precipitation over most of the contiguous United States. However, simulated air temperatures over the south central region and precipitation over the Great Plains and the Southwest have significant biases. Allowing longer spin-up time, reducing the nudging strength, or replacing the WRF Single-Moment 6-class microphysics with Morrison microphysics reduces the bias over some subregions. However, replacing the Grell-Devenyi cumulus parameterization with Kain-Fritsch shows no improvement. The 12 km simulation does add value above the NCEP-R2 data and the 50 km simulation over mountainous and coastal zones.

Dynamical Downscaling↗
DOE OSTI2014

Evaluating Clouds, Aerosols, and their Interactions in Three Global Climate Models using COSP and Satellite Observations

Accurately representing aerosol-cloud interactions in global climate models is challenging. As parameterizations evolve, it is important to evaluate their performance with appropriate use of observations. In this work we compare aerosols, clouds, and their interactions in three climate models (AM3, CAM5, ModelE) to MODIS satellite observations. Modeled cloud properties were diagnosed using the CFMIP Observations Simulator Package (COSP). Cloud droplet number concentrations (N) were derived using the same algorithm for both satellite-simulated model values and observations. We find that aerosol optical depth tau simulated by models is similar to observations. For N, AM3 and CAM5 capture the observed spatial pattern of higher values in near-coast versus remote ocean regions, though modeled values in general are higher than observed. In contrast, ModelE simulates lower N in most near-coast versus remote regions. Aerosol- cloud interactions were computed as the sensitivity of N to tau for marine liquid clouds off the coasts of South Africa and Eastern Asia where aerosol pollution varies in time. AM3 and CAM5 are in most cases more sensitive than observations, while the sensitivity for ModelE is statistically insignificant. This widely used sensitivity could be subject to misinterpretation due to the confounding influence of meteorology on both aerosols and clouds. A simple framework for assessing the N – tau sensitivity at constant meteorology illustrates that observed sensitivity can change from positive to statistically insignificant when including the confounding influence of relative humidity. Satellite simulated values of N were compared to standard model output and found to be higher with a bias of 83 cm-3.

aerosols↗
DOE OSTI2014

Scale dependence of entrainment-mixing mechanisms in cumulus clouds

This work empirically examines the dependence of entrainment-mixing mechanisms on the averaging scale in cumulus clouds using in situ aircraft observations during the Routine Atmospheric Radiation Measurement Aerial Facility Clouds with Low Optical Water Depths Optical Radiative Observations (RACORO) field campaign. A new measure of homogeneous mixing degree is defined that can encompass all types of mixing mechanisms. Analysis of the dependence of the homogenous mixing degree on the averaging scale shows that, on average, the homogenous mixing degree decreases with increasing averaging scales, suggesting that apparent mixing mechanisms gradually approach from homogeneous mixing to extreme inhomogeneous mixing with increasing scales. The scale dependence can be well quantified by an exponential function, providing first attempt at developing a scale-dependent parameterization for the entrainment-mixing mechanism. The influences of three factors on the scale dependence are further examined: droplet-free filament properties (size and fraction), microphysical properties (mean volume radius and liquid water content of cloud droplet size distributions adjacent to droplet-free filaments), and relative humidity of entrained dry air. It is found that the decreasing rate of homogeneous mixing degree with increasing averaging scales becomes larger with larger droplet-free filament size and fraction, larger mean volume radius and liquid water content, or higher relative humidity. The results underscore the necessity and possibility of considering averaging scale in representation of entrainment-mixing processes in atmospheric models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

The parametric sensitivity of CAM5's MJO

Abstract We systematically explore the ability of the Community Atmospheric Model version 5 (CAM5) to simulate the Madden‐Julian Oscillation (MJO), through an analysis of MJO metrics calculated from a 1100‐member perturbed parameter ensemble of 5 year simulations with observed sea surface temperatures. Parameters from the deep convection scheme make the greatest contribution to the variance in MJO simulation quality with a much smaller contribution from parameters in the large‐scale cloud, shallow convection, and boundary layer turbulence schemes. Improved MJO variability results from a larger lateral entrainment rate and a reduction in the precipitation efficiency of deep convection that was achieved by a smaller autoconversion of cloud to rainwater and a larger evaporation of convective precipitation. Unfortunately, simulations with an improved MJO also have a significant negative impact on the climatological values of low‐level cloud and absorbed shortwave radiation, suggesting that structural in addition to parametric modifications to CAM5's parameterization suite are needed in order to simultaneously well simulate the MJO and mean‐state climate.

Boyle, J. S.↗
DOE OSTI2015

A variance-based decomposition and global sensitivity index method for uncertainty quantification: Application to retrieved ice cloud properties

This study develops a novel uncertainty quantification (UQ) method for cloud microphysical property retrievals using variance-based decomposition and global sensitivity index. In this UQ framework, empirical orthogonal function (EOF) analysis is applied to the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) ground-based observations, which are the inputs for the cloud retrieval studied here. The principal components (PCs) in the EOF expansion are parameterized as random input variables, and hence, the input dimension is greatly reduced (up to a factor of 50), allowing large ensemble of random samplings. The EOF expansion improves the accuracy of the uncertainty estimation by taking into account the cross correlations in the input data profiles. Here, this method enables a probabilistic representation of a retrieval process by adding normally distributed perturbations into PCs of sample means of input data profiles within a time window. Therefore, it effectively facilitates objective validation of climate models against cloud retrievals under a probabilistic framework for rigorous statistical inferences. Moreover, the variance-based global sensitivity index analysis, part of this method, attributes the output uncertainties to each individual source, thus providing directions for improving retrieval algorithms and observation strategies. For demonstration, we apply this method to quantify the uncertainties of the ARM program's baseline cloud retrieval algorithm for an ice cloud case observed at the Southern Great Plains site on 9 March 2000.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Environmental drivers of methane fluxes from an urban temperate wetland park

Abstract Methane (CH 4 ) emissions were measured at the Wilma H. Schiermeier Olentangy River Wetland Research Park (ORWRP) over three summers and two winters using an eddy covariance system. We used an empirical model to determine the main environmental drivers of methane emissions. Methane emissions covary strongly with water vapor fluxes, CO 2 fluxes, and soil temperature. We adjust our models to account for the heterogeneous environment of the wetland by including the flux footprint distribution among different microsites as a predictive variable in the methane model. We used a forward linear stepwise model in combination with an Akaike information criteria‐based model selection process and neural network modeling to determine which environmental variables are most effective in modeling methane emissions in our site. Different models and environmental variables best represented methane fluxes in the winter and summer and also during the day or night within each season. We parameterized an optimal empirical model for methane emissions from the ORWRP that is used for gap filling of site‐level methane fluxes over 2 years. Some of the most effective variables for modeling methane were carbon, water vapor, and heat fluxes, all of which typically have the same data gaps as the time series of methane flux. In order to determine if these variables were useful for modeling methane despite the additional gap‐filling error, we determined through an error propagation experiment that eddy covariance gap‐filling models for methane may be best developed by including other gap‐filled fluxes as predictors, despite the high level of shared gaps and subsequent gap‐fill error propagation.

Morin, T. H.↗
DOE OSTI2015

Identifying multiscale zonation and assessing the relative importance of polygon geomorphology on carbon fluxes in an Arctic tundra ecosystem

Abstract We develop a multiscale zonation approach to characterize the spatial variability of Arctic polygonal ground geomorphology and to assess the relative controls of these elements on land surface and subsurface properties and carbon fluxes. Working within an ice wedge polygonal region near Barrow, Alaska, we consider two scales of zonation: polygon features (troughs, centers, and rims of polygons) that are nested within different polygon types (high, flat, and low centered). In this study, we first delineated polygons using a digital elevation map and clustered the polygons into four types along two transects, using geophysical and kite‐based landscape‐imaging data sets. We extrapolated those data‐defined polygon types to all the polygons over the study site, using the polygon statistics extracted from the digital elevation map. Based on the point measurements, we characterized the distribution of vegetation, hydrological, thermal, and geochemical properties, as well as carbon fluxes, all as a function of polygon types and polygon features. Results show that nested polygon geomorphic zonation—polygon types and polygon features—can be used to represent distinct distributions of carbon fluxes and associated properties, as well as covariability among those properties. Importantly, the results indicate that polygon types have more power to explain the variations in those properties than polygon features. The approach is expected to be useful for improved system understanding, site characterization, and parameterization of numerical models aimed at predicting ecosystem feedbacks to the climate.

Wainwright, Haruko M.↗
DOE OSTI2015

Modeling methane emissions from arctic lakes: Model development and site‐level study

Abstract To date, methane emissions from lakes in the pan‐arctic region are poorly quantified. In order to investigate the response of methane emissions from this region to global warming, a process‐based climate‐sensitive lake biogeochemical model was developed. The processes of methane production, oxidation, and transport were modeled within a one‐dimensional sediment and water column. The sizes of 14 C‐enriched and 14 C‐depleted carbon pools were explicitly parameterized. The model was validated using observational data from five lakes located in Siberia and Alaska, representing a large variety of environmental conditions in the arctic. The model simulations agreed well with the measured water temperature and dissolved CH 4 concentration (mean error less than 1°C and 0.2 μM, respectively). The modeled CH 4 fluxes were consistent with observations in these lakes. We found that bubbling‐rate‐controlling nitrogen (N 2 ) stripping was the most important factor in determining CH 4 fraction in bubbles. Lake depth and ice cover thickness in shallow waters were also controlling factors. This study demonstrated that the thawing of Pleistocene‐aged organic‐rich yedoma can fuel sediment methanogenesis by supplying a large quantity of labile organic carbon. Observations and modeling results both confirmed that methane emission rate at thermokarst margins of yedoma lakes was much larger (up to 538 mg CH 4 m −2 d −1 ) than that at nonthermokarst zones in the same lakes and a nonyedoma, nonthermokarst lake (less than 42 mg CH 4 m −2 d −1 ). The seasonal variability of methane emissions can be explained primarily by energy input and organic carbon availability.

Tan, Zeli↗
DOE OSTI2014

Cloud feedbacks on greenhouse warming in the superparameterized climate model SP‐CCSM 4

Abstract Cloud feedbacks on greenhouse warming are studied in a superparameterized version of the Community Climate System Model (SP‐CCSM4) in an atmospheric component SP‐CAM4 that explicitly simulates cumulus convection. A 150 year simulation in an abrupt quadrupling of CO 2 is branched from a control run. It develops moderate positive global cloud feedback and an implied climate sensitivity of 2.8 K comparable to the conventionally parameterized CCSM4 and the median of other modern climate models. All of SP‐CCSM4's positive shortwave cloud feedback is due to a striking decrease in low cloud over land, which is much more pronounced than in most other climate models, including CCSM4. Four other cloud responses – decreased midlevel cloud, more Arctic water and ice cloud, a slight poleward shift of midlatitude storm track cloud, and an upward shift of high clouds – are also typical of conventional global climate models. SP‐CCSM4 does not simulate the large warming‐induced decrease in Southern Ocean cloud found in CCSM4. Two companion uncoupled SP‐CAM4 simulations, one with a uniform 4 K sea‐surface temperature increase and one with quadrupled CO 2 but fixed SST, suggest that SP‐CCSM4's global‐scale cloud changes are primarily mediated by the warming, rather than by rapid adjustments to increased CO 2 . SP‐CAM4 show spatial patterns of cloud response qualitatively similar to the previous‐generation superparameterized SP‐CAM3, but with systematically more positive low cloud feedbacks over low‐latitude land and ocean.

Bretherton, Christopher S.↗
DOE OSTI2015

A multiscale modeling framework model (superparameterized CAM5) with a higher‐order turbulence closure: Model description and low‐cloud simulations

Abstract In this study, a higher‐order turbulence closure scheme, called Cloud Layers Unified By Binormals (CLUBB), is implemented into a Multiscale Modeling Framework (MMF) model to improve low‐cloud simulations. The performance of CLUBB in MMF simulations with two different microphysics configurations (one‐moment cloud microphysics without aerosol treatment and two‐moment cloud microphysics coupled with aerosol treatment) is evaluated against observations and further compared with results from the Community Atmosphere Model, Version 5 (CAM5) with conventional cloud parameterizations. CLUBB is found to improve low‐cloud simulations in the MMF, and the improvement is particularly evident in the stratocumulus‐to‐cumulus transition regions. Compared to the single‐moment cloud microphysics, CLUBB with two‐moment microphysics produces clouds that are closer to the coast and agrees better with observations. In the stratocumulus‐to‐cumulus transition regions, CLUBB with two‐moment cloud microphysics produces short‐wave cloud forcing in better agreement with observations, while CLUBB with single‐moment cloud microphysics overestimates short‐wave cloud forcing. CLUBB is further found to produce quantitatively similar improvements in the MMF and CAM5, with slightly better performance in the MMF simulations (e.g., MMF with CLUBB generally produces low clouds that are closer to the coast than CAM5 with CLUBB). Improved low‐cloud simulations in MMF make it an even more attractive tool for studying aerosol‐cloud‐precipitation interactions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Mechanisms of convective cloud organization by cold pools over tropical warm ocean during the AMIE/DYNAMO field campaign

Abstract This paper investigates the mechanisms of convective cloud organization by precipitation‐driven cold pools over the warm tropical Indian Ocean during the 2011 Atmospheric Radiation Measurement (ARM) Madden‐Julian Oscillation (MJO) Investigation Experiment/Dynamics of the MJO (AMIE/DYNAMO) field campaign. A high‐resolution regional model simulation is performed using the Weather Research and Forecasting model during the transition from suppressed to active phases of the November 2011 MJO. The simulated cold pool lifetimes, spatial extent, and thermodynamic properties agree well with the radar and ship‐borne observations from the field campaign. The thermodynamic and dynamic structures of the outflow boundaries of isolated and intersecting cold pools in the simulation and the associated secondary cloud populations are examined. Intersecting cold pools last more than twice as long, are twice as large, 41% more intense (measured with buoyancy), and 62% deeper than isolated cold pools. Consequently, intersecting cold pools trigger 73% more convection than do isolated ones. This is due to stronger outflows that enhance secondary updraft velocities by up to 45%. However, cold pool‐triggered convective clouds grow into deep convection not because of the stronger secondary updrafts at cloud base, but rather due to closer spacing (aggregation) between clouds and larger cloud clusters that form along the cold pool boundaries when they intersect. The close spacing of large clouds moistens the local environment and reduces entrainment drying, increasing the probability that the clouds further develop into deep convection. Implications for the design of future convective parameterization with cold pool‐modulated entrainment rates are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Future climate change under RCP emission scenarios with GISS ModelE2

We examine the anthropogenically forced climate response for the 21st century representative concentration pathway (RCP) emission scenarios and their extensions for the period 2101–2500. The experiments were performed with ModelE2, a new version of the NASA Goddard Institute for Space Sciences (GISS) coupled general circulation model that includes three different versions for the atmospheric composition components: a noninteractive version (NINT) with prescribed composition and a tuned aerosol indirect effect (AIE), the TCAD version with fully interactive aerosols, whole-atmosphere chemistry, and the tuned AIE, and the TCADI version which further includes a parameterized first indirect aerosol effect on clouds. Each atmospheric version is coupled to two different ocean general circulation models: the Russell ocean model (GISS-E2-R) and HYCOM (GISS-E2-H). By 2100, global mean warming in the RCP scenarios ranges from 1.0 to 4.5° C relative to 1850–1860 mean temperature in the historical simulations. In the RCP2.6 scenario, the surface warming in all simulations stays below a 2 °C threshold at the end of the 21st century. For RCP8.5, the range is 3.5–4.5° C at 2100. Decadally averaged sea ice area changes are highly correlated to global mean surface air temperature anomalies and show steep declines in both hemispheres, with a larger sensitivity during winter months. By the year 2500, there are complete recoveries of the globally averaged surface air temperature for all versions of the GISS climate model in the low-forcing scenario RCP2.6. TCADI simulations show enhanced warming due to greater sensitivity to CO₂, aerosol effects, and greater methane feedbacks, and recovery is much slower in RCP2.6 than with the NINT and TCAD versions. All coupled models have decreases in the Atlantic overturning stream function by 2100. In RCP2.6, there is a complete recovery of the Atlantic overturning stream function by the year 2500 while with scenario RCP8.5, the E2-R climate model produces a complete shutdown of deep water formation in the North Atlantic.

Nazarenko, L.↗