Cooperative Institute for Research in the Atmosphere

About: Cooperative Institute for Research in the Atmosphere is a based out in . It is known for research contribution in the topics: Snow & Data assimilation. The organization has 332 authors who have published 997 publications receiving 38835 citations. The organization is also known as: CIRA.

Simulating estimation of California fossil fuel and biosphere carbon dioxide exchanges combining in situ tower and satellite column observations

Abstract: Author(s): Fischer, Marc L.; Parazoo, Nicholas; Brophy, Kieran; Cui, Xinguang; Jeong, Seongeun; Liu, Junjie; Kelling, Ralph; Taylor, Thomas E.; Gurney, Kevin; Oda, Tomohiro; Graven, Heather | Abstract: We report simulation experiments estimating the uncertainties in California regional fossil fuel 36 and biosphere CO2 exchanges that might be obtained using an atmospheric inverse modeling 37 system driven by the combination of ground-based observations of radiocarbon and total CO2, 38 together with column-mean CO2 observations from NASA’s Orbiting Carbon Observatory 39 (OCO-2). The work includes an initial examination of statistical uncertainties in prior models for 40 CO2 exchange, in radiocarbon-based fossil fuel CO2 measurements, in OCO-2 measurements, 41 and in a regional atmospheric transport modeling system. Using these nominal assumptions for 42 measurement and model uncertainties, we find that flask measurements of radiocarbon and total 43 CO2 at 10 towers can be used to distinguish between different fossil fuel emissions data products 44 for major urban regions of California. We then show that the combination of flask and OCO-2 45 observations yield posterior uncertainties in monthly-mean fossil fuel emissions of ~ 5-10%, 46 levels likely useful for policy relevant evaluation of bottom-up fossil fuel emission estimates. 47 Similarly, we find that inversions yield uncertainties in monthly biosphere CO2 exchange of ~ 48 6%-12%, depending on season, providing useful information on net carbon uptake in 49 California’s forests and agricultural lands. Finally, initial sensitivity analysis suggests that 50 obtaining the above results requires control of systematic biases below approximately 0.5 ppm, 51 placing requirements on accuracy of the atmospheric measurements, background subtraction, and 52 atmospheric transport modeling.

The Andes Cordillera. Part III: glacier surface mass balance and contribution to sea level rise (1979–2014)

Abstract: Glacier surface mass balance (SMB) observations for the Andes Cordillera are limited and therefore estimates of the SMB contribution to sea-level rise are highly uncertain Here (in Part 3), we simulate glacier surface meteorological and hydrological conditions and trends for the Andes Cordillera (1979/80–2013/14; 35 years), covering the tropical latitudes in the north down to the sub-polar latitudes in the far south, including the Northern Patagonia Icefield (NPI) and Southern Patagonia Icefield (SPI) Surface meteorological conditions and heat- and mass-transfer processes were simulated for all glaciers having an area equal to or greater than 05 km2 SnowModel – a fully integrated energy balance, blowing-snow distribution, multi-layer snowpack, and runoff routing model – was used to simulate glacier SMBs for the Andes Cordillera The Randolph Glacier Inventory (RGI; v 40) and NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) products, downscaled in SnowModel, allowed us to conduct relatively high-resolution (1-km horizontal grid; 3-h time step) simulations of glacier air temperature, precipitation, sublimation, evaporation, runoff, and SMB These simulated glacier SMBs were verified against both independent direct observed annual glacier SMB and satellite gravimetry and altimetry derived SMB, indicating a good agreement For Andes glaciers, the 35-year mean annual SMB was found to be −113 m water equivalent (we), while the cumulative SMB was −396 m we, which is equal to a cumulative SMB contribution of 34 mm sea-level equivalent (SLE) (∼01 mm SLE per year) However, for both NPI and SPI, the mean SMB was positive (where likely calving explains why geodetic estimates are negative) For the Andes Cordillera, the simulated mean glacier-specific runoff was 41 L s−1 km−2, while for NPI and SPI it was 213 and 198 L s−1 km−2, respectively, indicating available water resources from NPI and SPI

Assessing the impact of pre-GPM microwave precipitation observations in the Goddard WRF ensemble data assimilation system

Abstract: The forthcoming Global Precipitation Measurement (GPM) Mission will provide next generation precipitation observations from a constellation of satellites. Since precipitation by nature has large variability and low predictability at cloud-resolving scales, the impact of precipitation data on the skills of mesoscale numerical weather prediction (NWP) is largely affected by the characterization of background and observation errors and the representation of nonlinear cloud/precipitation physics in an NWP data assimilation system. We present a data impact study on the assimilation of precipitation-affected microwave (MW) radiances from a pre-GPM satellite constellation using the Goddard WRF Ensemble Data Assimilation System (Goddard WRF-EDAS). A series of assimilation experiments are carried out in a Weather Research Forecast (WRF) model domain of 9 km resolution in western Europe. Sensitivities to observation error specifications, background error covariance estimated from ensemble forecasts with different ensemble sizes, and MW channel selections are examined through single-observation assimilation experiments. An empirical bias correction for precipitation-affected MW radiances is developed based on the statistics of radiance innovations in rainy areas. The data impact is assessed by full data assimilation cycling experiments for a storm event that occurred in France in September 2010. Results show that the assimilation of MW precipitation observations from a satellite constellation mimicking GPM has a positive impact on the accumulated rain forecasts verified with surface radar rain estimates. The case-study on a convective storm also reveals that the accuracy of ensemble-based background error covariance is limited by sampling errors and model errors such as precipitation displacement and unresolved convective scale instability.

Winter snow and spring temperature have differential effects on vegetation phenology and productivity across Arctic plant communities

Abstract: Tundra dominates two-thirds of the unglaciated, terrestrial Arctic. Although this region has experienced rapid and widespread changes in vegetation phenology and productivity over the last several decades, the specific climatic drivers responsible for this change remain poorly understood. Here we quantified the effect of winter snowpack and early spring temperature conditions on growing season vegetation phenology (timing of the start, peak, and end of the growing season) and productivity of the dominant tundra vegetation communities of Arctic Alaska. We used daily remotely sensed normalized difference vegetation index (NDVI), and daily snowpack and temperature variables produced by SnowModel and MicroMet, coupled physically based snow and meteorological modeling tools, to (1) determine the most important snowpack and thermal controls on tundra vegetation phenology and productivity and (2) describe the direction of these relationships within each vegetation community. Our results show that soil temperature under the snowpack, snowmelt timing, and air temperature following snowmelt are the most important drivers of growing season timing and productivity among Arctic vegetation communities. Air temperature after snowmelt was the most important control on timing of season start and end, with warmer conditions contributing to earlier phenology in all vegetation communities. In contrast, the controls on the timing of peak season and productivity also included snowmelt timing and soil temperature under the snowpack, dictated in part by the snow insulating capacity. The results of this novel analysis suggest that while future warming effects on phenology may be consistent across communities of the tundra biome, warming may result in divergent, community-specific productivity responses if coupled with reduced snow insulating capacity lowers winter soil temperature and potential nutrient cycling in the soil.