Atmospheric and Environmental Research Inc.

Lexington, MA, United States

Atmospheric and Environmental Research Inc.

Lexington, MA, United States
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Ponte R.M.,Atmospheric and Environmental Research Inc.
Geophysical Research Letters | Year: 2012

An ocean state estimate constrained by most available data is explored to assess characteristics of variability in deep steric height-a mostly unobserved quantity, yet important for understanding the relation between sea level, heat content and other ocean climate parameters. Results are based on monthly-averaged steric height anomalies, vertically integrated over the "unobserved" deep ocean (below ∼1700 m). Excluding linear trends, variability in deep steric height is typically 10-20% of that in the upper ocean, with larger values seen in extensive regions. Enhanced deep variability, at monthly to interannual time scales, occurs in areas of strong eddy energy. Deep signals are mostly thermosteric in nature, with halosteric contributions tightly correlated and generally compensating in the Atlantic and Indian oceans and adding in the Pacific. Potential inference of deep signals from knowledge of the upper ocean is hampered by poor correlations, and regressions based on upper ocean steric height fail to represent the estimated deep variability. Monthly sampling at ∼2° scales would allow for best determination of deep variability and long term trends. Copyright 2012 by the American Geophysical Union.

Agency: NSF | Branch: Standard Grant | Program: | Phase: PHYSICAL & DYNAMIC METEOROLOGY | Award Amount: 88.87K | Year: 2016

The terrestrial climate system is sensitive to the radiation budget. Thus, accurate knowledge about the solar and thermal infrared radiation in the coupled atmosphere-ocean system is critical to robust climate study. An example of this sensitivity is the suggestion that a 1% decrease in the solar constant could lead to an ice age. The effect of doubling CO2 on radiative forcing is approximately 4 Wm-2, whereas uncertainties in radiation simulations due to, for example, insufficient knowledge about the optical properties of clouds, may be larger than this value. During the 1980s and 1990s, many researchers made substantial progress in developing and improving radiative transfer schemes used in general climate models (GCMs), and various intercomparisons of GCM radiation codes were published. Since that time, significant progress has been made in light scattering computational methods, in-situ measurements and laboratory studies of the optical and microphysical properties of clouds and aerosols, gaseous absorption line parameters and the water vapor continuum absorption, optical properties of various oceanic constituents, and the efficiency of numerical schemes for solving radiative transfer equations. There is a pressing need to incorporate the aforesaid progress into radiative transfer modeling capabilities. Moreover, the ocean and atmosphere are not coupled in many existing radiative transfer models. The scattering and absorption of radiation by oceanic water, dissolved organic matter (the so-called yellow substance), and phytoplankton have an influence in heating the uppermost water layers, and consequently affect thermal and dynamic properties such as the sea surface temperature and depth of the mixed atmosphere-ocean layer. The reflection of radiation by the oceans, including the effects of a wavy air-water interface and whitecap, can also affect the spectral characteristics and magnitude of radiation and, thus, the radiative heating and cooling rates in the atmosphere. The overarching goal is to systematically evaluate and further improve current radiative transfer modeling capabilities.

Intellectual Merit:
The outcomes of the study will include 1) systematic quantification of the potential errors/inaccuracies of the aforesaid radiative transfer models, 2) extension of the current radiative transfer modeling capabilities to an atmosphere-ocean coupled system, 3) implementation of spectrally consistent parameterizations of ice clouds and dust aerosols, and 4) development and implementation of a computationally efficient radiative transfer solver.

Broader Impacts:
The research effort will improve the radiative transfer package currently used in climate models, and be a valuable contribution to the atmospheric radiative transfer and climate study communities. Furthermore, the light scattering modeling and parameterization capabilities can find potential applications in other areas such as remote sensing of dust aerosol and ice cloud properties. The associated educational pursuits will focus on mentoring a postdoc researcher, training a graduate student, and developing teaching materials. This effort will contribute to training young researchers in the discipline of radiative transfer and light scattering that is a quite unique branch of atmospheric physics. Furthermore, the integration of RRTMG into classroom teaching will directly benefit the educational program in atmospheric sciences, particularly, in hands-on experience in atmospheric radiative transfer simulation.

Agency: NSF | Branch: Standard Grant | Program: | Phase: CR, Earth System Models | Award Amount: 450.67K | Year: 2015

Devastating storm surges result from a combination of the characteristics of the storm itself; e.g. wind strength, direction of storm approach to the coast, storm duration; and from preconditioning due to rising sea level, such that the storm waves can overtop protective barriers that provided adequate defense when sea level was lower. Water added to the oceans from melting glacier, ice caps, and ice sheets is a significant cause of sea level rise. In particular, the Greenland Ice Sheet is projected to be a major contributor to sea level rise during the present century. Much of the recently observed contribution is a response to warming ocean temperatures around Greenland, which cause marine-terminating glaciers to melt and calve icebergs into the ocean. Models that are used to predict this anticipated sea level rise exhibit a broad spread in ocean temperatures around the Greenland Ice Sheet, for reasons that are not well understood. This project is designed to improve understanding of the physical processes responsible for this spread in projected ocean temperatures amongst models.

The lead principal investigator for this project, through his ongoing work with local and state governments, will ensure that the results are relevant to and transferred to planners and policy-makers. His parent company will assist in a similar information transfer to the private sector. The project will also contribute to workforce development through support for the training of a graduate student in state-of-the-art interdisciplinary science and through support of three early-career scientists during their formative years.

The detailed mechanistic understanding provided by this work will reveal: the physical processes underlying the spread in CMIP5 projections of near-Greenland ocean warming; the nature and location of the surface fluxes driving warming; and the linkages between warming at different depths and different locations around the ice sheet. It will also provide a physical basis for linkages between near-Greenland ocean warming and other related Arctic climate system processes (e.g. Northern Hemisphere sea ice and the Atlantic Meridional Overturning Circulation). These linkages are vital to understanding how climate-driven changes in Greenland?s mass balance are coupled to other processes such as the loss of sea ice and more general polar surface warming. A two-part strategy will be used to evaluate causal physical mechanisms underlying the spread in CMIP5 projections of ocean warming in an efficient and detailed manner. Statistical analysis of ocean temperature will cluster AOGCMs by their ocean warming patterns and their co-variability (across space and models) with surface fluxes and other climate processes. Numerical simulations, forced by surface fluxes from a representative subset of CMIP5 models, will then be used to develop detailed oceanic heat budgets. Targeted perturbation experiments will isolate the role of atmospheric and Greenland meltwater flux in the context of widely varying CMIP5 representations of the Arctic freshwater budget.

Agency: NSF | Branch: Standard Grant | Program: | Phase: CLIMATE & LARGE-SCALE DYNAMICS | Award Amount: 243.45K | Year: 2013

This project considers the generation of North African and Mediterranean climate anomalies by El Nino/Southern Oscillation (ENSO) events and Eurasian snow cover fluctuations. Two causal mechanisms are examined, one involving tropospheric teleconnections produced by Rossby wave generation and propagation (a horizontal mechanism), and the other involving stratosphere-troposphere interactions (a vertical mechanism). The horizontal mechanism is hypothesized to consist of 1) a canonical northeastward propagating wave train that emanates from the eastern equatorial Pacific, and 2) an ultra-low frequency Rossby wave that is excited over the Pacific, trapped within the North African-Asian (NAA) jet and propagates upstream to the Mediterranean region. The vertical mechanism begins with an expansion of Eurasian snow cover, which leads to colder surface conditions and lowering of mid-tropospheric geopotential height. This in turn amplifies the wave-1 stationary wave pattern, which propagates into the stratosphere and reduces the strength of the polar vortex, resulting in a negative anomaly of the Arctic or North Atlantic Oscillation, which shifts the jet stream and storm track southward. The southward shift of the jet and storm track allows colder temperatures to invade the middle latitudes, including the Mediterranean region. These influences will be examined through statistical analysis combined with a suite of models including a Rossby wave ray tracing calculation, a linearized barotropic model, a state-of-the-art global atmospheric model (CAM), and an atmospheric model with a well-resolved stratosphere (WACCM). In addition, an empirical prediction scheme will be developed and tested using Eurasian snow cover and ENSO as predictors for seasonal Mediterranean precipitation and surface temperature.

The work has societal broader impacts due to the agricultural and other consequences of climate variability in the Mediterranean region. More specifically, the empirical prediction scheme will be provided to operational centers including the International Research Institute at Columbia University. In addition, a website will be developed to provide public information regarding the role of atmospheric teleconnections in producing climate anomalies. The project will also support and train a graduate student, thereby providing for the future scientific workforce in this research area.

Agency: NSF | Branch: Standard Grant | Program: | Phase: PHYSICAL & DYNAMIC METEOROLOGY | Award Amount: 311.85K | Year: 2016

Gravity waves, generated by thunderstorms, are commonly associated with nighttime thunderstorm clusters called Mesoscale Convective Systems (MCSs); storm systems that are frequent producers of heavy rainfall and damaging winds. These gravity waves modify both the environment surrounding the MCS and its internal circulation. Considering that even small changes in the surrounding environment or internal circulation have been shown to affect MCS development, improved understanding of these wave processes is important to successful MCS prediction. Gravity waves have been linked to storm intensification and potential severe weather, so improved understanding of these features would lead to improved immediate, short-term forecasts of severe weather risks. This research supports an early career female investigator and one graduate student, and also involves a collaboration between a university and private industry involved in atmospheric research.

Knowledge of the MCS gravity wave generation-feedback process, including the influence of microphysical perturbations on this process, will lead to better understanding of MCS development, as well as the sensitivity of convection to these environmental changes. Understanding the extent to which changes in the environment due to convectively generated gravity waves need to be captured to correctly simulate MCSs is a significant step to understanding MCS predictability. Furthermore, knowledge of the gravity wave characteristics produced by an MCS allows inferences to be made about the phase, depth, and intensity of the latent heating profile without direct microphysical observations that can be resource-intensive to obtain. Understanding of the influence of microphysical perturbations upon the generation and impacts of gravity waves will be useful in microphysical parameterization development, leading to better numerical model prediction of convection in general. Model simulations will be compared against actual data from the Plains Elevated Convection at Night (PECAN) Field Campaign.

Agency: NSF | Branch: Standard Grant | Program: | Phase: ATMOSPHERIC CHEMISTRY | Award Amount: 227.82K | Year: 2016

This research is focused on studying the aging of emissions from biomass burning. Small particles emitted from biomass combustion can react in the atmosphere, changing their size, number, and composition. These aging processes will be modeled and the model results will be tested against actual data from field campaigns.

The following questions will be investigated: (1) What are the chemical processes that, when combined with the dispersion and coagulation of biomass-burning emissions, capture the evolution of aerosol size and number concentrations seen in the laboratory and the field? (2) Are the secondary organic aerosol (SOA) formation rates and size-distribution changes measured in lab experiments consistent with the field measurements of aerosol aging? If they are not, can we determine why (e.g. lack of continuous dilution or wall losses in chamber experiments)? (3) What properties most strongly determine the aged biomass-burning aerosol size and number? E.g. total mass emission flux, fresh particle size, fuel type, modified combustion efficiency, wind speed, fire area, vertical mixing depth, sunlight. (4) Can the variability in aged biomass-burning aerosol size and number be captured by a simple parameterization that is a function of the most important of the above properties?

Agency: NSF | Branch: Standard Grant | Program: | Phase: PHYSICAL OCEANOGRAPHY | Award Amount: 302.55K | Year: 2016

Global sea levels have risen steadily over the last century and there is concern that sea level rise will accelerate over the next century. Early detection of sea level acceleration, necessary for adaptation efforts, depends on an improved understanding of multidecadal sea level changes. This study consists of a detailed investigation of multidecadal sea level changes using sea level data, ocean and climate models, atmospheric reanalyses, vertical land motion, and a hierarchical Bayesian data-assimilation approach. An outcome of this study will be an enhanced, value-added sea level dataset based on the hierarchical Bayesian model approach, which will complement the available sea level estimates from altimetry, tide gauges, and ocean circulation models. These sea level fields from the Bayesian model solution will be made freely and publicly accessible and available. With their estimates of uncertainty, these fields would be suited for use in state estimation efforts on century time scales. This project is a partnership between academia and industry and would also support an early career scientist.

The main objectives of this project include: reconstructing regional maps and global time series of sea level going back two centuries; elucidating roles of internal versus external forcing of global mean multidecadal sea level change; diagnosing the impacts of static and dynamic processes on multidecadal regional sea level changes; and evaluating the veracity of climate models. The outcomes will fill basic knowledge gaps, improving understanding of ocean circulation and climate change. To increase basic knowledge of ocean circulation and climate change, and to cope with difficulties related to the sparseness of sea level data in space and time, a hierarchical Bayesian model will be brought to bear on extant datasets (altimetry, tide gauge, vertical land motion), giving observation-based constraints on sea level behavior over the last two centuries. Exploration of the underlying ocean dynamics based on hierarchical Bayesian model solutions will elucidate the physical drivers and space-time scales of multidecadal sea level changes and provide a basis to evaluate climate models.

Agency: NSF | Branch: Continuing grant | Program: | Phase: ATMOSPHERIC CHEMISTRY | Award Amount: 303.97K | Year: 2012

Biomass burning is a major source of trace gases and particles to the atmosphere, but current models have not yet captured the processes that lead to the significant formation of ozone and secondary organic aerosol (SOA) that is often observed in the first few hours after emission. This project will develop a new state-of-the-art modeling framework that incorporates recent advances in modeling gas- and aerosol-phase photochemistry into a high resolution Lagrangian dispersion model. The new modeling framework will be evaluated against detailed measurements of the chemical evolution of two North American smoke plumes. This will allow the following science questions to be addressed:

* What causes the high ozone and hydroxyl radical (OH) concentrations observed in fresh biomass smoke plumes?
* What are the sources of SOA within the smoke plumes?
* What is the impact of smoke aerosols on photolysis rates, and hence photochemistry, within the smoke plumes?

The aircraft measurements of young biomass burning smoke plumes from the 2006 MILAGRO (Megacity Initiative Local and Global Research Observations) campaign in Mexico and the 2009 San Luis Obispo Biomass Burning (SLOBB) experiment in California provide comprehensive trace gas and aerosol evolution data, including the first in situ observations of OH in biomass burning plumes, and are thus ideal to evaluate the model development. The chemical evolution of these plumes will be modeled using a new version of the Aerosol Simulation Program (ASP) that will incorporate the semi-empirical two-dimensional Volatility Basis Set (2D-VBS) scheme that has been used successfully to model SOA formation in Mexico City. This updated version of ASP will be incorporated into an enhanced version of the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) dispersion model driven by high-resolution output from the Weather Research and Forecasting (WRF) model. The combined models, constrained by aircraft and GOES satellite observations, will then be used to study the formation of O3 and SOA within the smoke plumes.

This research will lead to a better fundamental understanding of the climate, air quality, and human health impacts of biomass burning. Use of the WRF-HYSPLIT framework for modeling atmospheric dispersion in this work will facilitate the future inclusion of the model into the existing forecasting system models. This project will establish collaboration between an academic scientist specializing in the measurement of trace gases in biomass burning smoke plumes and two private sector scientists, one specializing in the modeling of atmospheric chemistry within smoke plumes and one specializing in modeling the dispersion of atmospheric plumes. The work will also stimulate and support the continued development of the WRF and ASP models, which were developed with previous NSF support. Two graduate students will be trained to use the developed models, which will give them the opportunity to work with scientists in a non-academic environment.

Agency: NSF | Branch: Standard Grant | Program: | Phase: ARCTIC NATURAL SCIENCES | Award Amount: 299.94K | Year: 2015

Arctic snow and ice form an integral part of the climate system. They have undergone unprecedented changes within the past decade. Initial studies on the potential remote and larger scale influences of their variability have often been inconclusive and even contradictory. In a recent review article, the principal investigators (PIs) of this proposal hypothesized that sea ice and snow cover can combine to force large-scale atmospheric variability. This project focuses on analyzing reanalysis datasets and model output from targeted numerical modeling experiments in order to understand the physical pathways linking sea ice and snow cover variability with atmospheric climate variability. This variability may, in turn, influence mid-latitude weather on seasonal time scales. Understanding such processes is anticipated to improve weather prediction on similar time scales, with consequent benefits to the energy, farming, and reinsurance industries, amongst others. The project will contribute to STEM manpower development through providing support for the training of a graduate student, entrainment of undergraduate students into scientific research, and development of a short course of climate prediction. Finally, the project will promote international collaboration with a German research institute.

Prescribed sea ice and snow cover perturbation experiments with the Whole Atmosphere Community Climate Model (WACCM) will lead to a quantitative assessment of the physical pathways between sea ice, snow cover and the initiation and maintenance of atmospheric variability, particularly in winter, that cannot be accomplished using statistical analysis alone. The use of a high-top model, which has only recently become available, to study the influence of sea ice and snow cover on the hemispheric winter circulation is novel to this proposal. In parallel, the PIs will collaborate with colleagues running similar experiments with ECHAM6. They will further analyze reanalysis atmospheric data, to test hypotheses about the combined role of sea ice and snow cover in climate variability learned from the model output. The proposed project will analyze the combined impact of sea ice and snow cover anomalies on atmospheric climate variability. This will improve understanding of the atmospheric response associated with changes in sea ice and snow cover, and lead to a quantitative assessment of the links between high-latitude and lower-latitude climates as well as enable improved climate predictions. The proposed research focuses specifically on the combined role of sea ice and snow cover in the initiation and maintenance of the dominant mode of high-latitude atmospheric variability, i.e. the annular mode. Modulation of the annular mode is hypothesized to be a key physical mechanism for climate feedback in high latitudes and is the dominant mode of variability in the mid-latitudes including the industrial centers of the United States, Europe and Asia. The conceptual framework developed in this project will be applied in an operational seasonal forecast model at the end of the project.

Agency: NSF | Branch: Standard Grant | Program: | Phase: ATMOSPHERIC CHEMISTRY | Award Amount: 250.00K | Year: 2013


Long lived greenhouse gas (GHG; CO2, CH4, N2O, some CFCs) emissions play a critical role in regulating the Earths climate. Urban environments increasingly lie at the core of many major environmental issues including climate change. Much of the human induced greenhouse gases (GHGs) emitted globally originate in or nearby cities. How do you measure the carbon balance of a mega-city? Society must be able to understand and predict the distribution of emitted GHGs within the urban atmosphere in order to assess the validity of emissions inventories, and the efficacy of any emission reduction programs. Uncertainties in self-reported bottom up GHG emission inventories, derived from activity use data and generalized conversion factors, are often larger than emission reduction goals [IPCC, 2006; NRC, 2010]. This shortcoming can potentially be addressed by top-down (inverse) modeling that uses GHG measurements in the atmosphere, coupled with transport modeling and a priori flux constraints, to quantify source emissions.

This project, centered on observation and modeling of carbon fluxes in the Boston urban dome, has potentially broad impact, including: 1) use and refinement of the WRF-STILT model (which couples a research weather forecasting model with a Lagrangian particle dispersion model) to connect atmospheric (observation) with surface fluxes in large cities; 2) Regional and potentially worldwide applications of the model to verify regulatory, cap and trade and even global treaty needs; 3) Education of students and the public. Project data, results, and software will continue to be made publicly available during the proposed research.

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