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- Description:
- Future projections suggest an increase in drought globally with climate change. Current vegetation models typically regulate the plant photosynthetic response to soil moisture stress through an empirical function, rather than a mechanistic response where plant water potentials respond to changes in soil water. This representation of soil moisture stress may introduce significant uncertainty into projections for the terrestrial carbon cycle. We examined the use of the soil moisture limitation function in historical and future emissions scenarios in nine Earth system models. We found that soil moisture-limited productivity across models represented a large and uncertain component of the simulated carbon cycle, comparable to 3-286% of current global productivity. Approximately 40-80% of the intermodel variability was due to the functional form of the limitation equation alone. Our results highlight the importance of implementing mechanistic water limitation schemes in models and illuminate several avenues for improving projections of the land carbon sink.
- Creator:
- Medvigy, David, Trugman, Anna T., Mankin, Justin S., and Anderegg, William R.L.
- Owner:
- BRIAN MCBRIDE
- Language:
- English
- Date Uploaded:
- 07/11/2019
- Date Modified:
- 12/09/2021
- Date Created:
- Spring 2016
- Resource Type:
- Dataset
- Identifier:
- https://doi.org/10.7278/S5707ZMS
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- Description:
- Thin boundary layer Arctic mixed-phase clouds are generally thought to precipitate pristine and aggregate ice crystals. Here we present automated surface photographic measurements showing that only 35\% of precipitation particles exhibit negligible riming and that graupel particles $\geq1\,\rm{mm}$ in diameter commonly fall from clouds with liquid water paths less than $50\,\rm{g\,m^{-2}}$. A simple analytical formulation predicts that significant riming enhancement can occur in updrafts with speeds typical of Arctic clouds, and observations show that such conditions are favored by weak temperature inversions and strong radiative cooling at cloud top. However, numerical simulations suggest that a mean updraft speed of $0.75\,\rm{m\,s^{-1}}$ would need to be sustained for over one hour. Graupel can efficiently remove moisture and aerosols from the boundary layer. The causes and impacts of Arctic riming enhancement remain to be determined.
- Subject:
- Atmospheric sciences, Computational research, and Arctic research
- Creator:
- Garrett, Timothy J. and Fitch, Kyle E.
- Contributor:
- Shkurko, Konstantin , Talaei, Ahmad, Gaustad, Krista, and Maahn, Maximilian
- Owner:
- BRIAN MCBRIDE
- Based Near Label Tesim:
- Oliktok Point, Alaska, United States
- Language:
- English
- Date Uploaded:
- 06/04/2020
- Date Modified:
- 07/21/2020
- Date Created:
- Code creation dates from 20161208 to 20180609 and Processed between 20170627 and 20190320
- Resource Type:
- Software or Program Code and Dataset
- Identifier:
- https://doi.org/10.7278/s50dva5jk2pd
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- Description:
- Weather-related research often requires synthesizing vast amounts of data that need archival solutions that are both economical and viable during and past the lifetime of the project. Public cloud computing services (e.g., from Amazon, Microsoft, or Google) or private clouds managed by research institutions are providing object data storage systems appropriate for long-term archives of such large geophysical data sets. , Current Status: Our research group no longer needs to maintain archives of High Resolution Rapid Refresh (HRRR) model output at the University of Utah since complete publicly-accessible archives of HRRR model output are now available from the Google Cloud Platform and Amazon Web Services (AWS) as part of the NOAA Open Data Program. Google and AWS store the HRRR model output in GRIB2 format, a file type that efficiently stores hundreds of two-dimensional variable fields for a single valid time. Despite the highly compressible nature of GRIB2 files, they are often on the order of several hundred MB each, making high-volume input/output applications challenging due to the memory and compute resources needed to parse these files. With support from the Amazon Sustainability Data Initiative, our group is now creating and maintaining HRRR model output in an optimized format, Zarr, in a publicly-accessible S3 bucket- hrrrzarr. HRRR-Zarr contains sets for each model run of analysis and forecast files sectioned into 96 small chunks for every variable. The structure of the HRRR-Zarr files are designed to allow users the flexibility to access only the data they need through selecting subdomains and parameters of interest without the overhead that comes from accessing numerous GRIB2 files. , and History: This effort began in 2015 to illustrate the use of a private cloud object store developed by the Center for High Performance Computing (CHPC) at the University of Utah. We began archiving thousands of two-dimensional gridded fields (each one containing over 1.9 million values over the contiguous United States) from the High-Resolution Rapid Refresh (HRRR) data assimilation and forecast modeling system. The archive has been used for retrospective analyses of meteorological conditions during high-impact weather events, assessing the accuracy of the HRRR forecasts, and providing initial and boundary conditions for research simulations. The archive has been accessible interactively and through automated download procedures for researchers at other institutions that can be tailored by the user to extract individual two-dimensional grids from within the highly compressed files. Over a thousand users have voluntarily registered to use the HRRR archive at the University of Utah. Our archive has grown to over 130 Tbytes of model output but we no longer need to continue that effort since the GRIB2 files are available now via Google and AWS. As mentioned above, we now provide much of the same information in an alternative format that is appropriate particularly for machine-learning applications.
- Keyword:
- data assimilation, Zarr, weather, forecasts, high resolution rapid refresh, and numerical weather prediction
- Subject:
- atmospheric science
- Creator:
- Horel, John and Blaylock, Brian
- Contributor:
- University of Utah Center for High Performance Computing, NOAA Earth Systems Research Laboratory, Amazon Open Data Program, and NOAA Environmental Modeling Center
- Depositor:
- BRIAN MCBRIDE
- Owner:
- JOHN HOREL
- Based Near Label Tesim:
- Alaska, Alaska, United States and United States, , United States
- Language:
- binary and English
- Date Uploaded:
- 07/10/2019
- Date Modified:
- 04/18/2024
- Date Created:
- 2015-04-18 to 2019-07-10
- License:
- CC BY – Allows others to use and share your data, even commercially, with attribution.
- Resource Type:
- Dataset
- Identifier:
- https://dx.doi.org/10.7278/S5JQ0Z5B