Effects of Aerosols and Clouds on Climate Change Forcing
2011-BNL-EE630EECA-Budg [KP1205030]
P.I.: Robert McGraw

Brookhaven National Laboratory (BNL) research focuses on determining the effects of aerosols and clouds on climate change forcing and on accurately and efficiently including these effects in models. Aerosol direct and indirect contributions to global-average forcing offset contributions from greenhouse gases and dominate uncertainty of anthropogenic influences on climate. This uncertainty translates to an inability to reliably predict the amount of incremental atmospheric CO₂ that would result in a given increase in global mean temperature and, more generally, the climate response to potential perturbations from changes in future energy needs. BNL research is focused on describing aerosol and cloud microphysical properties and their interactions (aerosol direct and indirect effects) and on the chemical/physical/meteorological processes that govern their evolution and impact on the Earth’s energy balance. Aerosol research emphasizes development of process-level understanding by laboratory and field measurements that follow the life cycle of aerosols, their radiative impact, and their effects on cloud properties. Comprehensive cloud studies integrating theory, modeling, and multiple observations from in situ (e.g., aircraft), remote sensing (radar), and coordinated analysis of long-term measurements collected at Atmospheric Radiation Measurement (ARM) sites are conducted to investigate the microphysical, radiative, and dynamical properties of clouds and precipitation. These are strongly integrated activities all of which contribute to process-level understanding and provide the science foundation to meet Department of Energy (DOE) objectives by improving overall understanding of how aerosol-cloud direct and indirect effects affect the Earth's radiant-energy balance and by quantifying the degree to which changes in aerosol and cloud properties offset the positive forcing from greenhouse gases.

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Fast-Physics System Testbed and Research (FASTER) Project
2011-BNL-EE631EECA-Budg [KP1703020]
P.I.: Yangang Liu

The Fast-Physics System Testbed and Research (FASTER) project arises from the proposal “Continuous Evaluation of Fast Processes in Climate Models Using ARM Measurements” funded by the Department of Energy's (DOE’s) Earth System Modeling program. The overarching goal of this project is to narrow uncertainty and biases in general circulation models (GCMs) by utilizing continuous Atmospheric Radiation Measurements (ARM) to enhance and accelerate evaluation and improvement of parameterizations of fast processes in GCMs involving clouds, precipitation, and aerosols, with six primary objectives: 1) Construction of a Fast-Physics Testbed to rapidly evaluate fast physics in GCMs by comparing model results against continuous long-term cloud observations made by the ARM program; 2) Execution of a suite of cloud resolving model (CRM) simulations for selected periods/cases to augment the Fast-Physics Testbed. Weather research and forecasts (WRFs) will be run with different parameterizations as CRMs, CRMs with bin-microphysics, and multi-scale modeling framework; 3) Continuous evaluation of model performance to identify and determine model errors by comparing the numerical weather prediction (NWP) and single column model (SCM) results against continuous ARM observations, and to each other. The long-time data record at the ARM sites (e.g., Southern Great Plains (SGP)) permits evaluation of various statistical properties (e.g., probability density functions (PDFs)) and recurring cloud regimes; 4) Examination and improvement of parameterizations of key cloud processes/properties (e.g., convection, microphysics, and aerosol-cloud interactions), thus, narrowing the range of treatments of fast processes that exert strong influences on model sensitivity so as to better constrain climate sensitivity; 5) Assessment and development of metrics of model performance. Different metrics will be applied and tested in the evaluation, and new metrics will be explored. Special care will be taken to address the issue of scale-mismatch between observations and models; and, 6) Incorporation of newly acquired knowledge on parameterizations into the full participating GCMs to evaluate the impact of the refined parameterizations on GCM and ascertain the improvement in the representation of fast physics in the GCMs.

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