Modeling Dust in East Asia by CESM and Sources of Biases.

East Asian dust has a significant impact on regional and global climate. In this study, we evaluate the spatial distributions and temporal variations of dust extinction profiles and dust optical depth (DOD) over East Asia simulated from the Community Earth System Model (CESM) with satellite retrievals from Luo et al. (2015a, 2015b) (L15), Yu et al. (2015) (Y15), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) level 3 (CAL-L3) products. Both L15 and Y15 are based on CALIPSO products but use different algorithms to separate dust from non-dust aerosols. We find high model biases of dust extinction in the upper troposphere over the Taklamakan Desert, Gobi Desert, and Tibetan Plateau, especially in the summer (June-July-August, JJA). CESM with dust emission scheme of Kok et al. (2014a, 2014b) has the best agreement with dust extinction profiles and DOD from L15 in the Taklamakan Desert and Tibetan Plateau. CESM with the default dust emission scheme of Zender et al. (2003a) underpredicts DOD in the Tibetan Plateau compared with observations from L15 due to the underestimation of local dust emission. Large uncertainties exist in observations from L15, Y15, and CAL-L3 and have significant impacts on the model evaluation of dust spatial distributions. We also assess dust surface concentrations and 10 m wind speed with meteorological records from weather stations in the Taklamakan and Gobi Deserts during dust events. CESM underestimates dust surface concentrations at most weather stations due to the inability of CESM to capture strong surface wind events.

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2).

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) is the latest atmospheric reanalysis of the modern satellite era produced by NASA's Global Modeling and Assimilation Office (GMAO). MERRA-2 assimilates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme so as to provide a viable ongoing climate analysis beyond MERRA's terminus. While addressing known limitations of MERRA, MERRA-2 is also intended to be a development milestone for a future integrated Earth system analysis (IESA) currently under development at GMAO. This paper provides an overview of the MERRA-2 system and various performance metrics. Among the advances in MERRA-2 relevant to IESA are the assimilation of aerosol observations, several improvements to the representation of the stratosphere including ozone, and improved representations of cryospheric processes. Other improvements in the quality of MERRA-2 compared with MERRA include the reduction of some spurious trends and jumps related to changes in the observing system, and reduced biases and imbalances in aspects of the water cycle. Remaining deficiencies are also identified. Production of MERRA-2 began in June 2014 in four processing streams, and converged to a single near-real time stream in mid 2015. MERRA-2 products are accessible online through the NASA Goddard Earth Sciences Data Information Services Center (GES DISC).

A global perspective of atmospheric carbon dioxide concentrations.

A high-resolution (7 km) non-hydrostatic global mesoscale simulation using the Goddard Earth Observing System (GEOS-5) model is used to visualize the flow and fluxes of carbon dioxide throughout the year. Carbon dioxide (CO2) is the most important greenhouse gas affected by human activity. About half of the CO2 emitted from fossil fuel combustion remains in the atmosphere, contributing to rising temperatures, while the other half is absorbed by natural land and ocean carbon reservoirs. Despite the importance of CO2, many questions remain regarding the processes that control these fluxes and how they may change in response to a changing climate. This visualization shows how column CO2 mixing ratios are strongly affected by local emissions and large-scale weather systems. In order to fully understand carbon flux processes, observations and atmospheric models must work closely together to determine when and where observed CO2 came from. Together, the combination of high-resolution data and models will guide climate models towards more reliable predictions of future conditions.