Engineering Papers⌕ Search

Engineering topics

Clara Orbe

Publications and source records attributed to Clara Orbe.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

CMIP6 Historical Simulations (1850‐2014) with GISS‐E2.1

Simulations of the CMIP6 historical period 1850‐‐2014, characterized by the emergence of anthropogenic climate drivers like greenhouse gases, are presented for different configurations of the NASA Goddard Institute for Space Studies (GISS) Earth System ModelE2.1. The GISS‐E2.1 ensembles are more sensitive to forcing than their CMIP5 predecessors (GISS‐E2), but warm less during recent decades due to reduced total forcing. This forcing reduction is attributed to an increase in longwave opacity in pre‐industrial simulations, resulting in an atmosphere less sensitive to further increases in opacity that result from rising greenhouse gas concentrations. This demonstrates the importance of the base climatology to forcing and forced climate trends. Most model versions match observed temperature trends since 1979 from the ocean to the stratosphere. The choice of ocean model is important to the transient climate response, as found in CMIP5 GISS‐E2: the model that more efficiently exports heat to the deep ocean shows a smaller rise in tropospheric temperature. Model sea level rise over the historical period is traced to excessive drawdown of aquifers to meet irrigation demand with a smaller contribution from thermal expansion. This shows how fully coupled models can provide indirect observational constraints upon forcing, in this case, constraining irrigation rates with observed sea level changes. The overall agreement of GISS‐E2.1 with observed trends is familiar from evaluation of its predecessors, as is the conclusion that these trends are almost entirely anthropogenic in origin.

NASA Goddard Institute for Space Studies (GISS) Ea↗
NASA NTRS2020

GISS Model E2.2: A Climate Model Optimized for the Middle Atmosphere. Part 2: Validation of Large-Scale Transport and Evaluation of Climate Response

Here we examine the large‐scale transport characteristics of the new ``Middle Atmosphere" NASA Goddard Institute for Space Studies climate model (E2.2). First we evaluate the stratospheric transport circulation in historical atmosphere‐only simulations integrated with interactive trace gas and aerosol chemistry. Compared to lower vertical resolution model versions, E2.2 exhibits improved tropical ascent and older stratospheric mean ages that are more consistent with observed values. In the troposphere, poleward transport to the Arctic and interhemispheric mean ages in E2.2 are comparable to models participating in the Chemistry Climate Modeling Initiative. In addition to validating E2.2 we also assess its ``transport sensitivity" using the coupled atmosphere‐ocean abrupt 4xCO2 and transient 1%CO2 simulations submitted to the Coupled Model Intercomparison Project, Phase 6, along with a 2xCO2 simulation used to evaluate the linearity of the transport circulation's response to increased CO2. We show that decreases (increases) in a stratospheric mean age (idealized surface loss) tracer scale linearly with increased lower stratospheric upwelling, which also increases linearly with warming tropical sea surface temperatures (SSTs). Abrupt 2xCO2 and 4xCO2 experiments constrained with (fixed) pre‐industrial SSTs are also used to quantify the relative importance of rapid adjustments versus SST feedbacks to the transport circulation responses in the model. Finally, sensitivity experiments are presented to illustrate the impact of changes in the convective parameterization on stratospheric transport.

GISS Model E2.2↗
NASA NTRS2020

Dependence of Atmospheric Transport into the Arctic on the Extent of the Hadley Cell

Recent studies have shown a large spread in the transport of atmospheric tracers into the Arctic among a suite of chemistry climate models, and have suggested that this is related to the multi–model spread in the meridional extent of the Hadley Cell (HC). Here we examine the HC–transport relationship using an idealized model, where we can vary the mean circulation and isolate its impact on transport to the Arctic. It is shown that the poleward transport depends on the relative position between the northern edge of the HC and the tracer source, with maximum poleward transport occurring when the HC edge lies near the middle of the source region. Such dependence highlights the critical role of near–surface transport by Eulerian mean circulation rather than eddy mixing in the free troposphere. These results suggest that variations in the HC extent and the tracer source region are both important for modeling Arctic composition.

Atmospheric transport↗
NASA NTRS2020

Representation of Modes of Variability in 6 U.S. Climate Models

We compare the performance of several modes of variability across six US climate modeling groups, with a focus on identifying robust improvements in recent models (including those participating in the Coupled Model Intercomparison Project (CMIP) Phase 6) compared to previous versions. In particular, we examine the representation of the Madden-Julian Oscillation (MJO), the El Ni˜no/Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the Quasi-Biennial Oscillation (QBO) in the tropical stratosphere and the dominant modes of extra-tropical variability, including the Southern Annular Mode (SAM), the Northern Annular Mode (NAM) (and the closely related North Atlantic Oscillation (NAO)), and the Pacific-North American Pattern (PNA). Where feasible, we explore the processes driving these improvements through the use of “intermediary” experiments that utilize model versions between CMIP3/5 and CMIP6 as well as targeted sensitivity experiments in which individual modeling parameters are altered. We find clear and systematic improvements in the MJO and QBO and in the teleconnection patterns associated with the PDO and ENSO. Some gains arise from better process representation, while others (e.g. the QBO) from higher resolution that allows for a greater range of interactions. Our results demonstrate that the incremental development processes in multiple climate model groups lead to more realistic simulations over time.

Modes of variability↗
NASA NTRS2020

GISS‐E2.1: Configurations and Climatology

This paper describes the GISS‐E2.1 contribution to the Coupled Model Intercomparison Project, Phase 6 (CMIP6). This model version differs from the predecessor model (GISS‐E2) chiefly due to parameterization improvements to the atmospheric and ocean model components, while keeping atmospheric resolution the same. Model skill when compared to modern era climatologies is significantly higher than in previous versions. Additionally, updates in forcings have a material impact on the results. In particular, there have been specific improvements in representations of modes of variability (such as the Madden‐Julian Oscillation and other modes in the Pacific) and significant improvements in the simulation of the climate of the Southern Oceans, including sea ice. The effective climate sensitivity to 2xCO2 is slightly higher than previously at 2.7‐‐3.1°C (depending on version), and is a result of lower CO2 radiative forcing and stronger positive feedbacks.

General Circulation Model↗
NASA NTRS2020

Mechanisms Linked to Recent Ozone Decreases in the Northern Hemisphere Lower Stratosphere

Previous studies have robustly identified a decrease since 1998 in lower stratospheric (LS) ozone in the Northern Hemisphere (NH). While this ozone decrease is qualitatively explained as resulting from changes in the large‐scale circulation, there is not yet a quantitative mechanistic explanation of these changes. Here we explore the drivers of recent ozone changes using two different configurations of the Goddard Earth Observing System (GEOS) general circulation model. The first configuration of GEOS includes a full chemistry module and is constrained with meteorological fields from the Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2). This configuration (M2GMI) is used to analyze an idealized tracer that covaries closely with ozone on interannual and decadal timescales, revealing that recent ozone decreases in the NH subtropics are associated with a poleward expansion of upwelling in the NH LS, with reduced (enhanced) downwelling over northern subtropics (midlatitudes). The second configuration of GEOS is a free‐running version of the GEOS Chemistry‐Climate Model (CCM) that is used to perform a ten‐member ensemble of free‐running simulations. Comparisons of the two configurations reveal that, while the free‐running model can produce negative ozone changes in the NH LS, the magnitude of these changes is significantly weaker, relative to both M2GMI and MERRA‐2; moreover, these weaker ozone decreases are consistent with weaker simulated changes in the residual circulation. Finally, we examine the GEOS model results in the broader context of the hindcast simulations performed as part of Phase 1 of the Chemistry Climate Modeling Initiative. We show that the majority of the free‐running simulations considered here also exhibit weaker long‐term residual circulation changes, compared to reanalyses.

Ozone↗
NASA NTRS2020

Description and Evaluation of the Specified-Dynamics Experiment in the Chemistry-Climate Model Initiative

We provide an overview of the REF-C1SD specified-dynamics experiment that was conducted as part of phase 1 of the Chemistry-Climate Model Initiative (CCMI). The REF-C1SD experiment, which consisted of mainly nudged general circulation models (GCMs) constrained with (re)analysis fields, was designed to examine the influence of the large-scale circulation on past trends in atmospheric composition. The REF-C1SD simulations were produced across various model frameworks and are evaluated in terms of how well they represent different measures of the dynamical and transport circulations. In the troposphere there are large (~40 %) differences in the climatological mean distributions, seasonal cycle amplitude, and trends of the meridional and vertical winds. In the stratosphere there are similarly large (~50 %) differences in the magnitude, trends and seasonal cycle amplitude of the transformed Eulerian mean circulation and among various chemical and idealized tracers. At the same time, interannual variations in nearly all quantities are very well represented, compared to the underlying reanalyses. We show that the differences in magnitude, trends and seasonal cycle are not related to the use of different reanalysis products; rather, we show they are associated with how the simulations were implemented, by which we refer both to how the large-scale flow was prescribed and to biases in the underlying free-running models. In most cases these differences are shown to be as large or even larger than the differences exhibited by free-running simulations produced using the exact same models, which are also shown to be more dynamically consistent. Overall, our results suggest that care must be taken when using specified-dynamics simulations to examine the influence of large-scale dynamics on composition.

Clara Orbe↗
NASA NTRSDate not supplied

Response of the QBO to Abrupt Increases in CO2 Using Three Atmospheric Chemistry Configurations

Long-term projections of the Quasi-Biennial Oscillation (QBO) remain highly uncertain. This is partly due to the paucity of models which are able to properly simulate that phenomenon. Only 5 of the 47 CMIP5 models are capable of spontaneously generating a realistic QBO (Butchart et al., 2018), and even those models exhibit large biases in key QBO characteristics (e.g. amplitude, period, vertical extent) when compared with observations. Furthermore, only 1 of these 5 employed interactive atmospheric chemistry, which is known to modulate QBO dynamics. We here investigate the QBO response to increased greenhouse gases using the NASA Goddard Institute for Space Studies Middle Atmosphere Model E2.2. Compared to lower vertical resolution versions of Model E, version 2.2 has a higher model top (0.002 hPa), and additional interactive non-orographic gravity wave drag sources from convection and shear, which produce a sufficiently realistic QBO, thus rendering it suitable for use in climate change studies. Steady-state responses to doubled and quadrupled CO2 from a pre-industrial control are analyzed, as well as the transient response to a 1% per year CO2 increase. In addition, we systematically explore the impact of interactive chemistry in modulating the QBO response to increased CO2 by contrasting interactive, prescribed, and linearized ozone chemistry configurations of the model. Overall, in response to increase CO2 concentrations the QBO is seen to increase in frequency and weaken in amplitude, consistent with previous results, but the memory of the tropical stratosphere may complicate assessments of trends in chemistry and surface impacts. We also discuss implications for the trade-off between ensemble size and the complexity of the chemistry scheme in the model.

Quasi-Biennial Oscillation (QBO)↗
NASA NTRSDate not supplied

21st century trends in stratosphere-to-troposphere transport

One of the key questions in the air quality and climate sciences is how will tropospheric ozone concentrations change in the future. This will depend on two factors: changes in stratosphere-to-troposphere transport (STT) and changes in tropospheric chemistry. Here we aim to identify robust changes in STT using simulations from the Chemistry Climate Model Initiative (CCMI) under a common climate change scenario (RCP6.0). We use two idealized stratospheric tracers implemented in the models to examine changes in transport. We find that the strengthening of the shallow branch of the Brewer-Dobson circulation (BDC) in the lower stratosphere and of the upper part of the Hadley cell in the upper troposphere lead to enhanced STT in the subtropics. The acceleration of the deep branch of the BDC in the NH and changes in eddy transport contribute to increase STT at high latitudes. In the SH, the deep branch does not accelerate due to the dynamical effects of the ozone hole recovery.

stratosphere-to-troposphere transport↗
NASA NTRSDate not supplied

QBO Responses to Increased CO2: The Roles of Convection and SSTs

Long-term changes to the Quasi-Biennial Oscillation (QBO) remain uncertain. Recent multimodel investigations (Richter et al. 2020a, Butchart et al. 2020) suggest that QBO amplitude will weaken in the future, but trends in QBO period and stability are unclear. Additionally, the parameterizations that generate the QBO in CMIP6 models vary greatly, and about half of CMIP6 models do not represent the QBO at all (Richter et al. 2020b). Tropical convection is likely to play a role in QBO trends, yet a majority of gravity wave parameterizations in QBO-resolving models are fixed and cannot respond to changes in convection. We consider future changes of the QBO using the NASA Goddard Institute for Space Studies (GISS) Model E2.2, which has a realistic QBO produced partly through use of a non-orographic gravity wave drag scheme that explicitly links lower stratospheric momentum fluxes to convection (Rind et al., 2014, 2020; Orbe et al. 2020). The experiments include historical integrations and abrupt increases in CO which we use to probe the roles of convection and SST feedbacks. Overall, both the QBO period and amplitude decrease in response to CO2, and our results link the period response to changes in convection and the amplitude response to changes in upwelling.

Quasi-Biennial Oscillation (QBO)↗
NASA NTRSDate not supplied

Simulated Disruptions of the Quasi-Biennial Oscillation

The Quasi-Biennial Oscillation has exhibited remarkable stability over the observational record—until a well-documented 2015/16 disruption and an emerging disruption in 2020/21. The possibility that disruptions are more frequent in a changing climate is important to consider, as the QBO affects predictability, stratospheric composition, and surface weather. However, this possibility is challenging to assess for a variety of reasons. For instance, the 2015/16 disruption has been attributed to anomalous easterly momentum flux from extratropical waves. By comparison, the 2020/21 disruption involves anomalous westerly forcing, less likely to originate from the same mechanism. We present a rich variety of QBO disruptions that spontaneously arise in integrations of the hightop NASA GISS Model E2.2. The disruptions loosely fall into several categories, some of which are analogous to the 2015/16 disruption and the 2020 disruption, as well as a previously undocumented possible disruption in 1988. Several factors appear to influence QBO disruptions in the model: natural variability, climate change, tropical SSTs, volcanic eruptions, and model physics/tuning. Although QBO representation is an ongoing challenge for models, the results point to a model-independent framework for assessment of disruptions.

Quasi-Biennial Oscillation↗
NASA NTRSDate not supplied

The Response of the Ozone Layer Under Abrupt 4xCO2 in CMIP6

Previous studies indicate a possible role of stratospheric ozone chemistry feedbacks in the climate response to 4xCO2, either via a reduction in equilibrium climate sensitivity (ECS) (Nowack et al., 2015), or via changes in the tropospheric circulation (Chiodo and Polvani, 2017). However, these effects are subject to uncertainty. Part of the uncertainty may stem from the dependency of the feedback on the pattern of the ozone response, as the radiative efficiency of ozone largely depends on its vertical distribution (Lacis et al., 1990). Here, an analysis is presented of the ozone layer response to 4xCO2 in chemistry–climate models (CCMs) which participated to CMIP intercomparisons. In a previous study using CMIP5 models, it has been shown that under 4xCO2, ozone decreases in the tropical lower stratosphere, and increases over the high latitudes and throughout the upper stratosphere. It was also found that a substantial portion of the spread in the tropical column ozone is tied to inter-model spread in tropical upwelling, which is in turn partly tied to ECS (Chiodo et al., 2018). Here, we revisit this connection using4xCO2 data from CMIP6, thereby exploiting the larger number of CCMs than in CMIP5. In addition, we explore the linearity of the ozone response, by complementing the analysis with simulations using transient CO2 forcing (1pc/year). We show that the pattern of the ozone response in is similar to CMIP5. In some models (e.g. WACCM), we find larger ozone responses in CMIP6 than in CMIP5, partly because of the larger ECS and thus larger upwelling response in the tropical pipe. In this presentation, we will discuss the relationship between radiative forcing, transport and ozone, as well as further implications for CMIP6 models.

Ozone layer↗
NASA NTRSDate not supplied

Dynamical and Trace Gas Responses of the Quasi-Biennial Oscillation to Historical and Future Climate Change

The Quasi-Biennial Oscillation (QBO) dominates the variability of the tropical stratosphere. The QBO can be seen in stratospheric zonal wind, temperature, and composition, but it also has a number of teleconnections—including to the polar vortices, extratropical surface winter climate, and the Madden-Julian Oscillation. Thus, its future trends will have a signature on the surface. Recent multi-model investigations (Richter et al. 2019, Butchart et al. 2020) suggest that a weakening of the QBO amplitude is very likely, but changes to QBO period and stability remain unclear, due in part to its highly parameterized nature. We investigate these aspects using multiple configurations of the NASA Goddard Institute for Space Studies Model E2.2 (Rind et al., 2020; Orbe et al. 2020). As a high-top model with gravity wave drag partly sourced from (parameterized) convection, Model E2.2 includes key pathways for climate forcings to influence the QBO. Overall, both the period and amplitude decrease in response to increased CO2, with convection feedbacks critical for the period response, and ozone feedbacks enhancing the amplitude response. We use these results to interpret QBO trends in the historical and SSP integrations. Lastly, we examine the effect of volcanic eruptions on the QBO

Quasi-Biennial Oscillation↗