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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Low Frequency Modulation of Extreme Temperature Regimes in a Changing Climate

The project examines long-term changes in extreme temperature episodes (ETE) associated with planetary climate modes (PCMs) in both the real atmospheric and climate model simulations. The focus is on cold air outbreaks (CAOs) and warm waves (WWs) occurring over the continental US during the past 60 winters. No significant long-term trends in either WWs or CAOs are observed over the US. The annual frequency of CAOs is affected by the (i) North Atlantic Oscillation (NAO) over the Southeast US and (ii) Pacific–North American (PNA) pattern over the Northwest US. WW frequency is influenced by the (i) NAO over the eastern US and (ii) combined influence of PNA, Pacific decadal oscillation (PDO), and ENSO over the southern US. The collective influence of PCMs accounts for as much as 50% of the regional variability in ETE frequency. During CAO (WW) events occurring over the southeast US, there are low (high) pressure anomalies at higher atmospheric levels over the southeast US with oppositely-signed pressure anomalies in the lower atmosphere over the central US. These patterns lead to anomalous northerly (for CAOs) or southerly (for WWs) flow into the southeast leading to cold or warm surface air temperature anomalies, respectively. One distinction is that CAOs involve substantial air mass transport while WW formation is more local in nature. The primary differences among event categories are in the origin and nature of the pressure anomaly features linked to ETE onset. In some cases, PCMs help to provide a favorable environment for event onset. Heat budget analyses indicate that latitudinal transport in the lower atmosphere is the main contributor to regional cooling during CAO onset. This is partly offset by adiabatic warming associated with subsiding air. Additional diagnoses reveal that this latitudinal transport is partly due to the remote physical influence of a shallow cold pool of air trapped along the east side of the Rocky Mountains. ETE and PCM behavior is also studied in (CMIP5) climate model simulations. Although the climate models considered are able to represent the overall behavior of ETEs, the frequency of WWs (CAOs) is too high (low) in many models. While all models qualitatively replicate the overall structure of the PNA pattern, a small minority of models fails to properly simulate the NAO pattern. Model shortcomings in representing the NAO and PNA patterns have important consequences for simulating associated regional variability in surface air temperature and storm track behavior. The influence of PCMs on ETEs is underestimated in most CMIP5 models. In particular, none of the models are able to accurately simulate observed linkages between ETEs and the PDO, due to a gross misrepresentation of the PDO pattern in most models. Our results indicate that predictions of future CAO and WW behavior are currently limited by the ability of climate models to accurately represent PCM characteristics. Our study also considers the behavior of PCMs known as annular modes. It is determined that north-south movements in the stratospheric jet stream (related to the Polar Annular Mode) result in long-lasting impacts upon surface weather conditions including regional air temperature anomalies. The structure and dynamics of the stratospheric northern annular mode (or SNAM, related to changes in the strength of the stratospheric jet stream) was studied in CMIP5 models. In models with poorly-resolved stratospheres, the amplitude of SNAM at stratospheric altitudes is typically too weak, consistent with weaker stratospheric jet variability. However, this distinction does not carry over to the associated tropospheric signature of SNAM. A regional analysis illustrates that most CMIP5 models (regardless of whether the stratosphere is well-resolved) have anomalously weak and eastward shifted (compared to observed SNAM events) storm track and sea level pressure anomaly patterns during SNAM events. Analyses of stratosphere–troposphere coupling reveal that large-scale wave activity in the stratosphere is anomalously weak in CMIP5 models having poorly-resolved stratospheres, due to correspondingly weak upward propagation of wave activity from the troposphere. This suggests that such models have anomalously weak stratosphere-troposphere coupling in association with SNAM events. Paradoxically, however, there is little apparent impact upon the attendant tropospheric variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Using ARM Measurements to Understand and Reduce the Double ITCZ Biases in the Community Atmospheric Model

1. Understanding of the observed variability of ITCZ in the equatorial eastern Pacific. The annual mean precipitation in the eastern Pacific has a maximum zonal band north of the equator in the ITCZ where the maximum SST is located. During the boreal spring (referring to February, March, and April throughout the present paper), because of the accumulated solar radiation heating and oceanic heat transport, a secondary maximum of SST exists in the southeastern equatorial Pacific. Associated with this warm SST is also a seasonal transitional maximum of precipitation in the same region in boreal spring, exhibited as a weak double ITCZ pattern in the equatorial eastern Pacific. This climatological seasonal variation, however, varies greatly from year to year: double ITCZ in the boreal spring occurs in some years but not in other years; when there a single ITCZ, it can appear either north, south or at the equator. Understanding this observed variability is critical to find the ultimate cause of the double ITCZ in climate models. Seasonal variation of ITCZ south of the eastern equatorial Pacific: By analyzing data from satellites, field measurements and atmospheric reanalysis, we have found that in the region where spurious ITCZ in models occurs, there is a “seasonal cloud transition” — from stratocumulus to shallow cumulus and eventually to deep convection —in the South Equatorial Pacific (SEP) from September to April that is similar to the spatial cloud transition from the California coast to the equator. This seasonal transition is associated with increasing sea surface temperature (SST), decreasing lower tropospheric stability and large-scale subsidence. This finding of seasonal cloud transition points to the same source of model errors in the ITCZ simulations as in simulation of stratocumulus-cumulus-deep convection transition. It provides a test for climate models to simulate the relationships between clouds and large-scale atmospheric fields in a region that features a spurious double Inter-tropical Convergence Zone (ITCZ) in most models. This work is recently published in Yu et al. (2016). Interannual variation of ITCZ south of the eastern equatorial Pacific: By analyzing data from satellites, field measurements and atmospheric reanalysis, we have characterized the interannual variation of boreal spring precipitation in the eastern tropical Pacific and found the cause of the observed interannual variability. We have shown that ITCZ in this region can occur as a single ITCZ along the Equator, single ITCZ north of the Equator, single ITCZ south of the Equator, and double ITCZ on both sides of the Equator. We have found that convective instability only plays a secondary role in the ITCZ interannual variability. Instead, the remote impact of the Pacific basin-wide SST on the horizontal gradient of surface pressure and wind convergence is the primary driver of this interannual variability. Results point to the need to include moisture convergence in convection schemes to improve the simulation of precipitation in the eastern tropical Pacific. This result has been recently submitted for publication (Yu and Zhang 2016). 2. Improvement of model parameterizations to reduce the double ITCZ bias We analyzed the current status of climate model performance in simulating precipitation in the equatorial Pacific. We have found that the double ITCZ bias has not been reduced in CMIP5 models relative to CMIP4 models. We have characterized the dynamic structure of the common bias by using precipitation, sea surface temperature, surface winds and sea-level. Results are published in Zhang et al. (2015): Since cumulus convection plays a significant role in the double ITCZ behavior in models, we have used measurements from ARM and other sources to carry out a systematic analysis of the roles of shallow and deep convection in the CAM. We found that in both CAM4 and CAM5, when the intensity of deep convection decreases as a result of parameterization change, the intensity of shallow convection increases, leading to very different changes in precipitation partitions but little change in the total precipitation. The different precipitation partition however can manifest themselves in other measures of model performances including temperature and humidity. This study points to the need to treat model physical parameterizations as integrated system rather than individual components. Results from this study are published in Wang and Zhang (2013). Since shallow convection interacts with the deep convection scheme and surface turbulence to trigger the double ITCZ, we studied methods to improve the shallow convection scheme in climate models. We investigated the bulk budgets of the vertical velocity and its parameterization in convective cores, convective updrafts, and clouds by using large-eddy simulation (LES) of four shallow convection cases including one from ARM. We proposed optimal forms of the Simpson and Wiggert equation to calculate the vertical velocity in bulk mass flux convection schemes for convective cores, convective updrafts, and convective clouds as parameterization schemes. The new scheme is published in Wang and Zhang (2014). By using long-term radar-based ground measurements from ARM, we derived a scale-aware inhomogeneity parameterization of cloud liquid water in climate models. We found a relationship between the inhomogeneity parameter and the model grid size as well as atmospheric stability. This relationship is implemented in the CESM to describe the subgrid-scale cloud inhomogeneity. Relative to the default CESM with the finite-volume dynamic core at 2-degree resolution, the new parameterization leads to smaller cloud inhomogeneity and larger cloud liquid-water path in high latitudes, and the opposite effect in low latitudes, with the regional impact on shortwave cloud radiation effect of up to 10 W/m 2 . This is due to both the smaller (larger) grid size in high (low) latitudes in the longitude-latitude grid setting of CESM and the more stable (unstable) atmosphere. This parameterization is expected lead to more realistic simulation of tropical precipitation in high-resolution models. Results from this study are reported in Xie and Zhang (2015).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Scaling Global Warming Impacts on Ocean Ecosystems: Lessons From a Suite of Earth System Models

An important technique used by climate modelers to isolate the impacts of increasing greenhouse gasses on Earth System processes is to simulate the impact of an abrupt increase in carbon dioxide. The spatial pattern of change provides a “fingerprint” that is generally much larger than natural variability. Insofar as the response to radiative forcing is linear (the impact of quadrupling CO 2 is twice the impact of doubling CO 2 ) this fingerprint can then be used to estimate the impact of historical greenhouse gas forcing. However, the degree to which biogeochemical cycles respond linearly to radiative forcing has rarely been tested. In this paper, we evaluate which ocean biogeochemical fields are likely to respond linearly to changing radiative forcing, which ones do not, and where linearity breaks down. We also demonstrate that the representation of lateral mixing by mesoscale eddies, which varies significantly across climate models, plays an important role in modulating the breakdown of linearity. Globally integrated surface rates of biogeochemical cycling (primary productivity, particulate export) respond in a relatively linear fashion and are only moderately sensitive to mixing. By contrast, the habitability of the interior ocean (as determined by hypoxia and calcite supersaturation) behaves non-linearly and is very sensitive to mixing. This is because the deep ocean, as well as certain regions in the surface ocean, are very sensitive to the magnitude of deep wintertime convection. The cessation of convection under global warming is strongly modulated by the representation of eddy mixing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Mid-latitude clouds contribute to Arctic amplification via interactions with other climate feedbacks

Abstract Traditional feedback analyses, which assume that individual climate feedback mechanisms act independently and add linearly, suggest that clouds do not contribute to Arctic amplification. However, feedback locking experiments, in which the cloud feedback is disabled, suggest that clouds, particularly outside of the Arctic, do contribute to Arctic amplification. Here, we reconcile these two perspectives by introducing a framework that quantifies the interactions between radiative feedbacks, radiative forcing, ocean heat uptake, and atmospheric heat transport. We show that including the cloud feedback in a comprehensive climate model can result in Arctic amplification because of interactions with other radiative feedbacks. The surface temperature change associated with including the cloud feedback is amplified in the Arctic by the surface-albedo, Planck, and lapse-rate feedbacks. A moist energy balance model with a locked cloud feedback exhibits similar behavior as the comprehensive climate model with a disabled cloud feedback and further indicates that the mid-latitude cloud feedback contributes to Arctic amplification via feedback interactions. Feedback locking in the moist energy balance model also suggests that the mid-latitude cloud feedback contributes substantially to the intermodel spread in Arctic amplification across comprehensive climate models. These results imply that constraining the mid-latitude cloud feedback will greatly reduce the intermodel spread in Arctic amplification. Furthermore, these results highlight a previously unrecognized non-local pathway for Arctic amplification.

58 GEOSCIENCES↗
DOE OSTI2021

Climate impacts on global agriculture emerge earlier in new generation of climate and crop models

Potential climate-related impacts on future crop yield are a major societal concern. Previous projections of the Agricultural Model Intercomparison and Improvement Project’s Global Gridded Crop Model Intercomparison based on the Coupled Model Intercomparison Project Phase 5 identified substantial climate impacts on all major crops, but associated uncertainties were substantial. Here we report new twenty-first-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5% to -6% (SSP126) and from +1% to -24% (SSP585)—explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9% shifted to +18%, SSP585), linked to higher CO2 concentrations and expanded high-latitude gains. The ‘emergence’ of climate impacts consistently occurs earlier in the new projections—before 2040 for several main producing regions. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

Jagermeyr, Jonas↗
DOE OSTI2015

Cluster Analysis of Downscaled and Explicitly Simulated North Atlantic Tropical Cyclone Tracks

A realistic representation of the North Atlantic tropical cyclone tracks is crucial as it allows, for example, explaining potential changes in U.S. landfalling systems. Here, the authors present a tentative study that examines the ability of recent climate models to represent North Atlantic tropical cyclone tracks. Tracks from two types of climate models are evaluated: explicit tracks are obtained from tropical cyclones simulated in regional or global climate models with moderate to high horizontal resolution (1°–0.25°), and downscaled tracks are obtained using a downscaling technique with large-scale environmental fields from a subset of these models. Here, for both configurations, tracks are objectively separated into four groups using a cluster technique, leading to a zonal and a meridional separation of the tracks. The meridional separation largely captures the separation between deep tropical and subtropical, hybrid or baroclinic cyclones, while the zonal separation segregates Gulf of Mexico and Cape Verde storms. The properties of the tracks’ seasonality, intensity, and power dissipation index in each cluster are documented for both configurations. The authors’ results show that, except for the seasonality, the downscaled tracks better capture the observed characteristics of the clusters. The authors also use three different idealized scenarios to examine the possible future changes of tropical cyclone tracks under 1) warming sea surface temperature, 2) increasing carbon dioxide, and 3) a combination of the two. The response to each scenario is highly variable depending on the simulation considered. Lastly, the authors examine the role of each cluster in these future changes and find no preponderant contribution of any single cluster over the others.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Subpolar North Atlantic Mean State Affects the Response of the Atlantic Meridional Overturning Circulation to the North Atlantic Oscillation in CMIP6 Models

Abstract The Atlantic meridional overturning circulation (AMOC) plays an important role in climate, transporting heat and salt to the subpolar North Atlantic. The AMOC’s variability is sensitive to atmospheric forcing, especially the North Atlantic Oscillation (NAO). Because AMOC observations are short, climate models are a valuable tool to study the AMOC’s variability. Yet, there are known issues with climate models, like uncertainties and systematic biases. To investigate this, preindustrial control experiments from models participating in the phase 6 of Coupled Model Intercomparison Project (CMIP6) are evaluated. There is a large, but correlated, spread in the models’ subpolar gyre mean surface temperature and salinity. By splitting models into groups of either a warm–salty or cold–fresh subpolar gyre, it is shown that warm–salty models have a lower sea ice cover in the Labrador Sea and, hence, enable a larger heat loss during a positive NAO. Stratification in the Labrador Sea is also weaker in warm–salty models, such that the larger NAO-related heat loss can also affect greater depths. As a result, subsurface density anomalies are much stronger in the warm–salty models than in those that tend to be cold and fresh. As these anomalies propagate southward along the western boundary, they establish a zonal density gradient anomaly that promotes a stronger delayed AMOC response to the NAO in the warm–salty models. These findings demonstrate how model mean state errors are linked across variables and affect variability, emphasizing the need for improvement of the subpolar North Atlantic mean states in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Climate Change Projections of the North American Regional Climate Change Assessment Program (NARCCAP)

We investigate major results of the NARCCAP multiple regional climate model (RCM) experiments driven by multiple global climate models (GCMs) regarding climate change for seasonal temperature and precipitation over North America. We focus on two major questions: How do the RCM simulated climate changes differ from those of the parent GCMs and thus affect our perception of climate change over North America, and how important are the relative contributions of RCMs and GCMs to the uncertainty (variance explained) for different seasons and variables? The RCMs tend to produce stronger climate changes for precipitation: larger increases in the northern part of the domain in winter and greater decreases across a swath of the central part in summer, compared to the four GCMs driving the regional models as well as to the full set of CMIP3 GCM results. We pose some possible process-level mechanisms for the difference in intensity of change, particularly for summer. Detailed process-level studies will be necessary to establish mechanisms and credibility of these results. The GCMs explain more variance for winter temperature and the RCMs for summer temperature. The same is true for precipitation patterns. Thus, we recommend that future RCM-GCM experiments over this region include a balanced number of GCMs and RCMs.

climatic change↗
DOE OSTI2020

Multi-Frequency Analysis of Simulated versus Observed Variability in Tropospheric Temperature

Studies seeking to identify a human-caused global warming signal generally rely on climate model estimates of the “noise” of intrinsic natural variability. Assessing the reliability of these noise estimates is of critical importance. We evaluate here the statistical significance of differences between climate model and observational natural variability spectra for global-mean mid- to upper-tropospheric temperature (TMT). We use TMT information from satellites and large multimodel ensembles of forced and unforced simulations. Our main goal is to explore the sensitivity of model-versus-data spectral comparisons to a wide range of subjective decisions. These include the choice of satellite and climate model TMT datasets, the method for separating signal and noise, the frequency range considered, and the statistical model used to represent observed natural variability. Of particular interest is the amplitude of the interdecadal noise against which an anthropogenic tropospheric warming signal must be detected. We find that on time scales of 5–20 years, observed TMT variability is (on average) overestimated by the last two generations of climate models participating in the Coupled Model Intercomparison Project. This result is relatively insensitive to different plausible analyst choices, enhancing confidence in previous claims of detectable anthropogenic warming of the troposphere and indicating that these claims may be conservative. A further key finding is that two commonly used statistical models of short-term and long-term memory have deficiencies in their ability to capture the complex shape of observed TMT spectra.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Advancing Measurements and Understanding of the Rate and Structure of Atmospheric Warming

The rate and geographic pattern of atmospheric warming are key indicators of historical climate change and play a role in modulating future changes in Earth’s climate. The past and future evolution of atmospheric temperature is simulated using global climate models while observational estimates of past warming are derived from satellite microwave measurements. Individual climate models simulate widely varying rates of past and future atmospheric warming and most model simulations exhibit greater tropospheric (lowest ~10 km of atmosphere) temperature change than satellite observations between 1979 and 2020. This project examined intermodel differences in the pattern of atmospheric warming and how these differences influence climate feedbacks that can amplify or damp the rate of global surface warming. A key result is that climate model representation of the current climate can influence climate feedbacks and the simulation of future changes in climate. Another focus of this project was to analyze how models respond to different input datasets, such as volcanic aerosols or sea surface temperature (in atmosphere-only simulations). We find that different prescribed inputs can affect simulated changes in atmospheric temperature, even though “input uncertainty” is often unconsidered in model-observational comparisons. A final focus of this research project was to consider the influence of natural internal climate variability on satellite era changes in climate. Although the Earth is warming substantially due to anthropogenic emissions of greenhouse gases, the observed warming rate can be modulated by natural variations in the Earth’s climate. We find that natural climate variability has slowed the rate of tropical tropospheric warming, which explains model-satellite differences in the rate of tropospheric warming.

58 GEOSCIENCES↗
DOE OSTI2019

Regionally refined test bed in E3SM atmosphere model version 1 (EAMv1) and applications for high-resolution modelling

Climate simulation with more accurate process-level representation at finer resolutions is a pressing necessity in order to provide actionable information to policy-makers regarding extreme events in a changing climate. Computational limitation is a major obstacle for building, and running high-resolution (HR, here 0.25° average grid spacing at the equator) models (HRM). A more affordable path to HRM is to use a global regionally refined model (RRM), which only simulates a portion of the globe at HR while the remaining is at low-resolution (LR, 1°). In this study, we compare the Energy Exascale Earth System Model (E3SM) atmosphere model version 1 (EAMv1) RRM with the HR mesh over the contiguous United States (CONUS) to its corresponding globally uniform LR and HR configurations, as well as to observations and reanalysis data. The RRM has a significantly reduced computational cost (roughly proportional to the HR mesh size) relative to the globally uniform HRM. Over the CONUS, we evaluate the simulation of important dynamical and physical quantities as well as various precipitation measures. Differences between the RRM and HRM over the HR region are predominantly small, demonstrating that the RRM reproduces both well- and poorly simulated behaviours of the HRM over the CONUS. Further analysis based on RRM simulations with the LR vs. HR model parameters reveals that RRM performance is greatly influenced by the different parameter choices used in the LR and HR EAMv1. This is a result of the poor scale-aware behaviour of physical parameterizations, especially for variables influencing sub-grid scale physical processes. RRM can serve as a useful framework to test physics schemes across a range of scales, leading to improved consistency in future E3SM versions. Applying nudging-to-observations techniques within the RRM framework also demonstrates significant advantages over a free-running configuration for use as a testbed, and as such represents an efficient and more robust physics testbed capability. Our conclusions provide additional confirmatory evidence that the RRM is an efficient and effective approach for HRM development and hydrologic research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Achieving Realistic Runoff in the Western United States with a Land Surface Model Forced by Dynamically Downscaled Meteorology

Abstract In this study, we calibrate a regional climate model’s (RCM) underlying land surface model (LSM). In addition to providing a realistic representation of runoff across the hydroclimatically diverse western United States, this is done to take advantage of the RCM’s ability to physically resolve meteorological forcing data in ungauged regions, and to prepare the calibrated hydrologic model for tight coupling, or the ability to represent land surface–atmosphere interactions, with the RCM. Specifically, we use a 9-km resolution meteorological forcing dataset across the western United States, from the fifth generation ECMWF Reanalysis (ERA5) downscaled by the Weather Research Forecasting (WRF) regional climate model, as an offline forcing for Noah-Multiparameterization (Noah-MP). We detail the steps involved in producing an LSM capable of accurately representing runoff, including physical parameterization selection, parameter calibration, and regionalization to ungauged basins. Based on our model evaluation from 1954 to 2021 for 586 basins with daily natural streamflow, the streamflow bias is reduced from 24.2% to 4.4%, and the median daily Nash–Sutcliffe efficiency (NSE) is improved from 0.12 to 0.36. When validating against basins with monthly natural streamflow data, we obtain a similar reduction in bias and a median monthly NSE improvement from 0.18 to 0.56. In this study, we also discover the optimal setup when using a donor-basin method to regionalize parameters to ungauged basins, which can vary by 0.06 NSE for unique designs of this regionalization method. Significance Statement This study provides useful guidance for improving a land surface model to accurately represent runoff across a spatially extensive and hydroclimatically diverse region (the western United States). The land surface model is updated to represent runoff more accurately at gauged basins, and then additionally updated for basins without observational data using a mathematical approach called the donor-basin method. We make use of a regional climate model’s reanalysis-derived meteorological data and its underlying land surface model to achieve realistic runoff. The calibrated land surface model can thus be tightly coupled in subsequent studies in a manner that should more accurately reflect runoff conditions. Findings from this study will serve as a useful reference for the atmospheric (and hydrologic) modeling communities and their ability to represent large-scale hydrology accurately.

Meteorology & Atmospheric Sciences↗
DOE OSTI2018

The Impact of Surface Heterogeneities and Land‐Atmosphere Interactions on Shallow Clouds Over ARM SGP Site

Abstract Continental shallow clouds are an important component of the land‐atmosphere coupled climate system because of their role in modulating energy and water budgets. The parameterization of these clouds in climate models presents a significant challenge. We evaluate the potential impact of subgrid‐scale (for climate models) land‐atmosphere interactions on shallow clouds using nested large‐eddy simulations (LESs). We compare LESs that allow for land‐atmosphere interactions to ones that artificially suppress all or part of them by smoothing out surface heterogeneities in surface heat, moisture and radiation fluxes within the domain. The LES domains are coupled to an interactive land surface model and nested inside a mesoscale domain. Three summertime shallow convection cases over the Department of Energy Atmospheric Radiation Measurement Facility's Southern Great Plains site are examined. A consistent increase in cloud water content is observed in all cases when surface heterogeneities and the land‐atmosphere interactions they induce in the LES domain are removed. By comparing experiments where only surface flux heterogeneities induced by cloud shadows are removed to ones where all surface flux heterogeneities are removed, we find that cloud shading‐induced surface heterogeneities can have a larger impact on shallow convection than static land surface heterogeneities in our cases. Furthermore, the impact of cloud shading on cloud water content and cloud size is found to vary significantly with the solar incidence angle. Our results suggest that the impact of cloud shadows needs to be considered when parameterizing land‐atmosphere interactions in the presence of shallow clouds for regional or global climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Plant water potential improves prediction of empirical stomatal models

Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Climate-Weather Modeling Studies Using a Prototype Global Cloud-System Resolving Model: ALCF-2 Early Science Program

Clouds remain the largest source of uncertainty in our understanding of global climate change. Different aspects of the planetary cloud field can provide positive and negative feedback to the Earth’s energy balance, and clouds of course are directly implicated in changes to the planetary distribution of precipitation. A fundamental problem in our current understanding of the role of clouds in the dynamics of climate are that current resolutions do not resolve the fundamental length scales associated with clouds. We expect our understanding of the role of clouds in climate to undergo a qualitative change as the resolutions of global models begin to encompass clouds. At these resolutions (which roughly scale with the tropopause height of 10km) non-hydrostatic dynamics become significant and deep convective processes are resolved. We are poised at the threshold of being able to run global scale simulations that include direct, nonparameterized, simulations of deep convective clouds. The goal of this research is to use the Argonne Leadership Computing Facility to explore the frontier of weather prediction and climate modeling with the newly developed Geophysical Fluid Dynamics (GFDL) global cloud-resolving model. A single unified atmospheric modeling system with a cubed-sphere dynamical core and bulk cloud micro-physics running at 3.5km resolutions was run with the goal of capturing the climatology of clouds and severe storms in a warming world. The ability to reproduce historical tropical storm statistics will be used as a test of this ground-breaking model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Relative Controls of Vapor Pressure Deficit and Soil Water Stress on Canopy Conductance in Global Simulations by an Earth System Model

Climate change impacts drought and vapor pressure deficit (VPD) that critically influence land-atmosphere water vapor and CO 2 exchange. There is a lack of model-based global evaluation of the relative roles of the two types of water stresses in limiting canopy conductance (G c ). Using the Energy Exascale Earth System Model (E3SM), we conducted four land-atmosphere coupled simulations for the historical period with and without a plant hydraulics scheme (PHS) and under present-day and quadrupling of CO 2 concentrations. By fitting an empirical model of G c to the E3SM simulations, we evaluated the sensitivity of G c to water stresses caused by VPD and soil water (SW) in the growing season. The empirical model is based on a multiplicative algorithm that adjusts G c according to VPD, soil water saturation, and their interaction. Our results show positive trends of VPD over land during the historical period, with large differences between simulations with and without PHS. Despite the differences, a majority of land pixels that experience both SW and VPD stresses are in semi-arid and tropical savanna ecosystems. G c is more sensitive to VPD than SW in less than 20% of land pixels where the empirical model provides a good fit for the simulated G c . Ignoring SW and VPD interaction may underestimate the relative role of VPD on G c by as much as 28%. Including PHS accentuates SW stress limitation on G c by simulating wetter soil and higher ET that lower VPD. Elevated CO 2 reduces G c by increasing water use efficiency and ameliorates SW stress on G c .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Recent advances in understanding secondary organic aerosol: Implications for global climate forcing: Advances in Secondary Organic Aerosol

Anthropogenic emissions and land use changes have modified atmospheric aerosol concentrations and size distributions over time. Understanding preindustrial conditions and changes in organic aerosol due to anthropogenic activities is important because these features (1) influence estimates of aerosol radiative forcing and (2) can confound estimates of the historical response of climate to increases in greenhouse gases. Secondary organic aerosol (SOA), formed in the atmosphere by oxidation of organic gases, represents a major fraction of global submicron-sized atmospheric organic aerosol. Over the past decade, significant advances in understanding SOA properties and formation mechanisms have occurred through measurements, yet current climate models typically do not comprehensively include all important processes. Our review summarizes some of the important developments during the past decade in understanding SOA formation. We also highlight the importance of some processes that influence the growth of SOA particles to sizes relevant for clouds and radiative forcing, including formation of extremely low volatility organics in the gas phase, acid-catalyzed multiphase chemistry of isoprene epoxydiols, particle-phase oligomerization, and physical properties such as volatility and viscosity. Several SOA processes highlighted in this review are complex and interdependent and have nonlinear effects on the properties, formation, and evolution of SOA. Current global models neglect this complexity and nonlinearity and thus are less likely to accurately predict the climate forcing of SOA and project future climate sensitivity to greenhouse gases. Efforts are also needed to rank the most influential processes and nonlinear process-related interactions, so that these processes can be accurately represented in atmospheric chemistry-climate models.

54 ENVIRONMENTAL SCIENCES↗