Von P. Walden

A comparison of the two Arctic atmospheric winter states observed during N‐ICE2015 and SHEBA

Winter time atmospheric observations from the 2015 Norwegian young sea-ICE campaign (N-ICE2015) are compared with data from the 1997-1998 Surface Heat Budget of the Arctic (SHEBA) campaign. Both datasets have a bimodal distribution of the net longwave radiative flux for January-February, with modal values of -40 W m-2 and 0 W m-2. These values correspond to the radiatively clear and opaquely cloudy states, respectively, and are likely to be representative of the wider Arctic. The new N-ICE2015 observations demonstrate that the two winter states operate in the Atlantic sector of the Arctic and regions of thin sea ice. We compare the N-ICE2015 and SHEBA data with ERA-Interim and output from the coupled Arctic regional climate model HIRHAM-NAOSIM. ERA-Interim simulates two Arctic winter states well and captures the timing of transitions from one state to the other, despite underestimating the cloud liquid water path. HIRHAM-NAOSIM has more cloud liquid water compared with ERA-Interim, but simulates the two states poorly. Our results demonstrate that models must simulate realistic synoptic forcing and temperature profiles to accurately capture the two Arctic winter states, and not only the presence of mixed-phase clouds. Using ERA-Interim, we find a positive trend in the number of opaquely cloudy days in the western Atlantic sector of the Arctic, and a strong correlation with the mean winter temperature over much of the Arctic Basin. Hence, the two Arctic winter states are important for understanding inter-annual variability in the Arctic. The N-ICE2015 dataset will help improve our understanding of these relationships.

BioEarth: Envisioning and developing a new regional earth system model to inform natural and agricultural resource management

As managers of agricultural and natural resources are confronted with uncertainties in global change impacts, the complexities associated with the interconnected cycling of nitrogen, carbon, and water present daunting management challenges. Existing models provide detailed information on specific sub-systems (e.g., land, air, water, and economics). An increasing awareness of the unintended consequences of management decisions resulting from interconnectedness of these sub-systems, however, necessitates coupled regional earth system models (EaSMs). Decision makers’ needs and priorities can be integrated into the model design and development processes to enhance decision-making relevance and “usability” of EaSMs. BioEarth is a research initiative currently under development with a focus on the U.S. Pacific Northwest region that explores the coupling of multiple stand-alone EaSMs to generate usable information for resource decision-making. Direct engagement between model developers and non-academic stakeholders involved in resource and environmental management decisions throughout the model development process is a critical component of this effort. BioEarth utilizes a bottom-up approach for its land surface model that preserves fine spatial-scale sensitivities and lateral hydrologic connectivity, which makes it unique among many regional EaSMs. This paper describes the BioEarth initiative and highlights opportunities and challenges associated with coupling multiple stand-alone models to generate usable information for agricultural and natural resource decision-making.

Downwelling longwave flux over Summit, Greenland, 2010–2012: Analysis of surface‐based observations and evaluation of ERA‐Interim using wavelets

This study analyzes the downwelling longwave radiation (DLW) over the Greenland Ice Sheet (GrIS) using surface-based observations from Summit Station (72°N, 38°W; 3210 m) and the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim) DLW fields. Since surface-based observations are sparse in the Arctic, the accuracy of including reanalyses for spatial context is assessed. First, the DLW at Summit is reported, including the significant time scales of variability using time-frequency decomposition (wavelet analysis). A new method for evaluating reanalyses is then introduced that also uses wavelet analysis. ERA-Interim DLW performs reasonably well at Summit, but because it includes too many thin clouds and too few thick clouds, it is biased low overall. The correlation between the observations and ERA-Interim drops from r2 > 0.8 to near 0 for time series reconstructed from time scales less than ~4 days. These low correlations and additional analyses suggest that the spatial resolution of the data sets is a factor in representing variability on short time scales. The bias is low across all time scales and is thus likely tied to cloud generation processes in the model rather than the spatial representation of the atmosphere across the GrIS. The exception is autumn, when ERA-Interim overestimates the influence of clouds at time scales of 1 and 4 weeks. The spatial distribution of cloud influence on the DLW across the GrIS indicates that Summit is located in a transition zone with respect to cloud properties. The gradient across this transition zone is steepest near Summit in autumn, so the spatial characteristics of the atmosphere near Summit may contribute to the ERA-Interim bias during this time.

Variability in AIRS‐retrieved cloud amount and thermodynamic phase over west versus east Antarctica influenced by the SAM

In a sample of summertime cloud retrievals from the NASA Atmospheric Infrared Sounder (AIRS), a positive Southern Annular Mode (SAM) index polarity is associated with greater cloud frequency and larger effective cloud fraction over West Antarctica compared with a negative SAM index polarity. The opposite result appears over the high East Antarctic Plateau. Comparing AIRS-retrieved cloud fraction with Antarctic Automatic Weather Station 2 m air temperature data, a positive and significant correlation is found over most of West Antarctica, signifying a longwave heating effect of clouds. Over East Antarctica correlations between Sun elevation and 2 m air temperature are strongest, consistent with lower cloud amount.

Analyzing the Relationship between Human Behavior and Indoor Air Quality

In the coming decades, as we experience global population growth and global aging issues, there will be corresponding concerns about the quality of the air we experience inside and outside buildings. Because we can anticipate that there will be behavioral changes that accompany population growth and aging, we examine the relationship between home occupant behavior and indoor air quality. To do this, we collect both sensor-based behavior data and chemical indoor air quality measurements in smart home environments. We introduce a novel machine learning-based approach to quantify the correlation between smart home features and chemical measurements of air quality, and evaluate the approach using two smart homes. The findings may help us understand the types of behavior that measurably impact indoor air quality. This information could help us plan for the future by developing an automated building system that would be used as part of a smart city.

Indoor air quality and wildfire smoke impacts in the Pacific Northwest

Efforts to improve energy efficiency in homes and buildings have led to tighter structures. However, these changes can also produce negative consequences for indoor air quality and human health. One of the dramatic effects of climate change and weather is the increase in destructive wildfires, such as those experienced in the Pacific Northwest during the summer of 2015. The current article presents data for measurements at two houses during periods with and without high levels of wildfire smoke outdoors. For each house, indoor and outdoor pollutant measurements were obtained for ozone (O3), fine particulate matter (PM2.5), and volatile organic compounds along with outdoor weather conditions and occupant activities including the use of windows and doors. The volatile organic compound measurements were obtained using a Proton Transfer Reaction Mass Spectrometer. Compounds monitored included acetonitrile (a biomass burning tracer), formaldehyde, acetaldehyde, methanol, acetone, benzene, toluene, and C2-alkylbenzenes (i.e., sum of xylenes and ethylbenzene), C3-alkylbenzenes (i.e., sum of trimethylbenzene, ethyltoluene, and propylbenzene isomers), and C4-alkylbenzenes (i.e., sum of tetramethylbenzene and its isomers). A carbon dioxide tracer method was used to measure in situ ventilation rates, and blower door tests were also completed to determine standard ventilation rates. For smoky periods with elevated outdoor pollutant levels, penetration factors, defined as the ratio of indoor/outdoor concentrations were quite low. Penetration factors for PM2.5 were 11% for H2 and 15% for H3, except when windows or doors were open. The penetration factors for O3 were also low at 24% for H2 and 5% for H3. Elevated indoor volatile organic compound levels were not typically associated with outdoor levels, but reflected significant indoor sources. During smoke events, acetonitrile, a biomass burning tracer compound, was elevated outdoors and indoors in both houses, and benzene was elevated outdoors and indoors in H3.

Super-cooled liquid fogs over the central Greenland ice sheet

Radiation fogs at Summit, Greenland (72.58°N, 38.48°W, 3210 masl) are frequently reported by observers. The fogs are often accompanied by fogbows, indicating the particles are composed of liquid and because of the low temperatures at Summit, this liquid is super-cooled. Here we analyse the formation of these fogs as well as their physical and radiative properties. In situ observations of particle size and droplet number concentration were made using scattering spectrometers 15 near 2 m and 10 m height from 2012 to 2014. These data are complemented by co-located observations of meteorology, turbulent and radiative fluxes, and remote sensing. We find that liquid fogs occur in all seasons with the highest frequency in September and a minimum in April. Due to the characteristics of the boundary-layer meteorology, the fogs are elevated, forming between 2 m and 10 m and the particles then fall toward the surface. The diameter of mature particles is typically 2025 μm in summer. Number concentrations are higher at warmer temperatures and, thus, higher in summer compared to winter. 20 The fogs form at temperatures as warm as warm as -5 °C, while the coldest form at temperatures approaching -40 °C. Facilitated by the elevated condensation, in winter 2/3 of fogs occurred within a relatively warm layer above the surface when the nearsurface air is below -40 °C, as cold as -57 °C, which is well below that which can support liquid water. This implies that fog particles settling through this layer of cold air freeze in the air column before contacting the surface, thereby accumulating at the surface as ice without riming. Liquid fogs under otherwise clear skies impart annually 1.5 W m-2 of cloud radiative forcing 25 (CRF). While this is a relatively small contribution to the surface radiation climatology, individual events are influential. The mean CRF during liquid fog events is 26 W m-2, but can sometimes be much higher. An extreme case study was observed to radiatively force 5 °C of surface warming during the coldest part of the day, effectively damping the diurnal cycle. At lower elevations of the ice sheet where melting is more common, such damping could signal a role for fogs in preconditioning the surface for melting later in the day. 30 Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-819 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 17 October 2018 c

Adventure learning as a curricular approach that transcends geographies and connects people to place

Effectively communicating scientific research has taken on greater importance as climate change impacts the world we live in. It is increasingly incumbent upon the science and education communities to produce and deliver curriculum that is timely, accessible, and scientifically accurate. In the summer of 2012, scientists and educators worked together to develop and conduct the Adventure Learning @ Greenland (AL@GL) project, which explored the capacity of hands-on and web-based climate science education experiences that occurred in Greenland and the US. The Adventure Learning approach and associated framework was used to design the learning experience during AL@GL activities. Participating students were from Greenland, Denmark, and the US; these students included participants who were diverse, rural, and traditionally underrepresented. Participating students worked closely with educators and scientists to learn about an atmospheric observatory at Summit Station, located on the Greenland Ice Sheet. The purpose of this article is to inform readers in how they may use Adventure Learning and the newly developed curriculum model called Content, Transition, Inquiry, and Synthesis for the education and outreach of research projects.