Translators serve a unique role in the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) user facility, offering scientific input through various leadership and service roles. The Translators direct the creation of value-added products (VAPs) and analysis tools that make ARM measurements more accessible to the scientific community. Translators also serve as liaisons between users and the ARM infrastructure, collecting information about priorities and communicating ARM data and services. A key group focus is supporting the DOE Atmospheric System Research (ASR) program scientists and ASR’s efforts towards a process-level understanding of cloud-aerosol interactions, and in reducing uncertainty in global climate model projections. The ARM Translator Group (Table 1) consists of the five Translators, a representative of software development, and one from the Data Quality Office (DQO). Additionally, the ARM Engineering and Process Manager participates in this group and provides input and direction from ARM and its programmatic priorities.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

humidogram.c1

fRH equivalent hygroscopicity parameter calculated by a nonlinear least squares fit method

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Aerosol and Cloud Optical Properties from the ARM Raman Lidars: The Feature Detection and Extinction (RLPROF-FEX) Value-Added Product

Aerosols and their interactions and influence on clouds are among the main sources of uncertainties in radiative direct and indirect forcing (IPCC 2013). Continuous height-resolved measurements of cloud and aerosol optical properties are needed to reduce these uncertainties. Here we describe the Raman Lidar Profiles – Feature detection and Extinction (RLPROF-FEX) Value-Added Product (VAP) derived using Raman lidar data at multiple U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility sites. RLPROF-FEX provides estimates of extinction, backscatter, and depolarization using the algorithm described by Thorsen et al. 2015 and Thorsen and Fu 2015. This document provides a description of the FEX algorithm, its input and output data, and related details about the Raman lidar (RL) system.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

acsmtofcdce.c1: Aerosol Chemical Speciation Monitor Time-Of-Flight Composition-Dependent Collection Efficiency

This VAP corrects aerosol chemical speciation monitor (ACSM) time of flight (TOF) data for non-unity particle detection through the application of a composition-dependent collection efficiency (CDCE). The data will initially be released at GUC S2.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

AOS: aerosol chemical speciation monitor, time-of-flight species b1-level data

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Retrieved Number concentration of Cloud Condensation Nuclei (RNCCN) Value-Added Product (VAP)

The objective of Retrieved Number concentration of Cloud Condensation Nuclei (RNCCN) Value-Added Product (VAP) is to provide the vertical distribution of cloud condensation nuclei (CCN) number concentrations to better represent aerosol indirect effects in climate models. The RNCCN VAP allows to collect a large number of independent samples of vertical CCN measurements at various supersaturation values from the ground. This helps to avoid the high cost of aircraft and increases the temporal distribution of retrieved CCN in the planetary boundary layer. The VAP method is based on Ghan and Collins (2004) and Ghan et al. (2006) proposed parameterizations that retrieve the vertical profiles of CCN directly from the ground level CCN, humidification factor, and in-situ lidar measurements. Further, the VAP accuracy is improved by using the quality controlled data that ensures that the boundary layer is well mixed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

The response of the Amazon ecosystem to the photosynthetically active radiation fields: integrating impacts of biomass burning aerosol and clouds in the NASA GEOS Earth system model

The Amazon experiences fires every year, and the resulting biomass burning aerosols, together with cloud particles, influence the penetration of sunlight through the atmosphere, increasing the ratio of diffuse to direct photosynthetically active radiation (PAR) reaching the vegetation canopy and thereby potentially increasing ecosystem productivity. In this study, we use the NASA Goddard Earth Observing System (GEOS) model with coupled aerosol, cloud, radiation, and ecosystem modules to investigate the impact of Amazon biomass burning aerosols on ecosystem productivity, as well as the role of the Amazon's clouds in tempering this impact. The study focuses on a 7-year period (2010–2016) during which the Amazon experienced a variety of dynamic environments (e.g., La Niña, normal years, and El Niño). The direct radiative impact of biomass burning aerosols on ecosystem productivity – called here the aerosol diffuse radiation fertilization effect – is found to increase Amazonian gross primary production (GPP) by 2.6 % via a 3.8 % increase in diffuse PAR (DFPAR) despite a 5.4 % decrease in direct PAR (DRPAR) on multiyear average during burning seasons. On a monthly basis, this increase in GPP can be as large as 9.9 % (occurring in August 2010). Consequently, the net primary production (NPP) in the Amazon is increased by 1.5 %, or ~92 Tg C yr -1 – equivalent to ~37 % of the average carbon lost due to Amazon fires over the 7 years considered. Clouds, however, strongly regulate the effectiveness of the aerosol diffuse radiation fertilization effect. The efficiency of this fertilization effect is the highest in cloud-free conditions and linearly decreases with increasing cloud amount until the cloud fraction reaches ~0.8, at which point the aerosol-influenced light changes from being a stimulator to an inhibitor of plant growth. Nevertheless, interannual changes in the overall strength of the aerosol diffuse radiation fertilization effect are primarily controlled by the large interannual changes in biomass burning aerosols rather than by changes in cloudiness during the studied period.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

AOS: aerosol chemical speciation monitor, time-of-flight species data

54 ENVIRONMENTAL SCIENCES↗