Differences in global data sets of atmospheric and surface parameters and their impact on outgoing longwave radiation and surface downward flux calculations
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Correlation statistics are used to investigate some of the characteristic temporal and spatial scales of the day-to-day changes in cloudiness which tend to dominate the variance of outgoing LW radiation and SW radiation fields. The temporal decorrelation time of the radiation field as measured by the lag time required for the autocorrelation of a region to fall to 0.4 is about 12 hours in the midlatitude storm track. Decorrelation time approaches one to two days when the averaging area of the region is increased significantly. Decorrelation times are also longer in the equatorial and tropical regions. The short decorrelation times are a manifestation of the creation, breakup, and movement of cloud systems. Cross-correlation analysis of the radiation field in the midlatitude storm track region and the subtropical dry zone shows advection speeds that range from about 25 m/sec in the storm tracks to about 8 m/sec in the subtropical dry zone.
A vertically pointing 3.2-cm radar is used to observe altostratus and cirrus clouds as they pass overhead. Radar reflectivities are used in combination with an empirical Z(sub i)-IWC (ice water content) relationship developed by Sassen (1987) to parameterize IWC, which is then integrated to obtain estimates of ice water path (IWP). The observed dataset is segregated into all-ice and mixed-phase periods using measurements of integrated liquid water paths (LWP) detected by a collocated, dual-channel microwave radiometer. The IWP values for the all ice periods are compared to measurements of infrared (IR) downward fluxes measured by a collocated narrowband (9.95-11.43 microns) IR radiometer, which results in scattergrams representing the observed dependence of IR fluxes on IWP. A two-stream model is used to calculate the infrared fluxes expected from ice clouds with boundary conditions specified by the actual clouds, and similar curves relating IWP and infrared fluxes are obtained. The model and observational results suggest that IWP is one of the primary controls on infrared thermal fluxes for ice clouds.
This paper presents a brief description of the procedure used to produce global surface emissivity maps for the broadband LW, the 8-12 micrometer window, and 12 narrow LW bands. For a detailed description of the methodology and the input data, the reader is referred to Wilber et al. (1999). These maps are based on a time-independent surface type map published by the IGBP, and laboratory measurements of spectral reflectances of surface materials. These maps represent a first attempt to characterize emissivity based on surface types, and many improvements to the methodology presented here are already underway. Effects of viewing zenith angle and sea state on the emissivity of ocean surface (Smith et al. 1996, Wu and Smith 1997, Masuda et al. 1988) will be taken into account. Measurements form ASTER and MODIS will be incorporated as they become available. Seasonal variation of emissivity based on changes in the characteristics of vegetation will be considered, and the variability of emissivity of barren land areas will be accounted for with the use of Zobler World Soil Maps (Zobler 1986). The current maps have been made available to the scientific community from the web site: http://tanalo.larc.nasa.gov:8080/surf_htmls/ SARB_surf.html
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Although greenhouse gases absorb primarily long-wave radiation, they also absorb short-wave radiation. Recent studies have highlighted the importance of methane short-wave absorption, which enhances its stratospherically adjusted radiative forcing by up to ~ 15%. The corresponding climate impacts, however, have been only indirectly evaluated and thus remain largely unquantified. Here we present a systematic, unambiguous analysis using one model and separate simulations with and without methane short-wave absorption. We find that methane short-wave absorption counteracts ~30% of the surface warming associated with its long-wave radiative effects. An even larger impact occurs for precipitation as methane short-wave absorption offsets ~60% of the precipitation increase relative to its long-wave radiative effects. The methane short-wave-induced cooling is due largely to cloud rapid adjustments, including increased low-level clouds, which enhance the reflection of incoming short-wave radiation, and decreased high-level clouds, which enhance outgoing long-wave radiation. The cloud responses, in turn, are related to the profile of atmospheric solar heating and corresponding changes in temperature and relative humidity. Despite our findings, methane remains a potent contributor to global warming, and efforts to reduce methane emissions are vital for keeping global warming well below 2 °C above preindustrial values.
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This data contains output from the pumphouse eddy covariance tower that includes shortwave radiation, longwave radiation, net radiation, air temperature, relative humidity, as well as sensible, latent, and ground heat fluxes. Also included is calculated evapotranspiration from the latent heat flux and the latent heat of vaporization. All data are on a daily timestep and displayed in Mountain Time. The data has been processed, and Quality Assurance / Quality Control (QA/QC) was done, but any daily gaps in the data have not been filled in. This research was funded by the Department of Energy and performed as part of the Watershed Function Scientific Focus Area. This research aimed to constrain evapotranspiration in a high-elevation catchment.The dataset includes one comma-separated values (CSV) data file (EddyCovariance_MeteorlogicalVariables_CrestedButtePumphouse.csv). Additionally, three metadata CSV files are included: (1) location metadata file (locations.csv), which contains location metadata and coordinates; (2) a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and (3) a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.
This data package includes a time series of meteorological data (air temperature, relative humidity, barometric pressure, precipitation, wind speed, soil heat flux, incoming shortwave radiation, incoming longwave radiation, outgoing shortwave radiation and outgoing longwave radiation) from the Slate River floodplain outside Crested Butte, Colorado, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The package also includes reference evapotranspiration calculated via the Penman-Monteith equation and an estimate of willow (Salix spp.) evapotranspiration using the method of Irmak et al., J. Hydrology, 2013. Due to two malfunctioning sensors (incoming shortwave radiation from Oct 2020-May 2021 and incoming longwave radiation from May 2021-March 2022), observed net radiation and calculated evapotranspiration are only included from July-October 2020. However, users may be able to extend calculated evapotranspiration using estimates of these two parameters from other sources. Both sensors have been replaced and data beyond 2021 be uploaded to this repository when it is available.Within the data package, "FLMD.csv" describes file-level metadata and "dd.csv" defines column headers and universal terms across the dataset. The data package includes 3 "*data.csv" files, two for meteorological data for 2020 and 2021 and one for reference evapotranspiration and transpiration data from 2020 ("SLT_OBJ2_PenmanMonteith_2020_data.csv"). Each "*data.csv" file of meteorological data has a corresponding "*_InstallationMethods.csv" file that describes the location, sensor model, sensor serial number and other metadata corresponding to each measured parameter. We also include a Jupyter notebook ("PenmanMonteithET_Calculations.ipynb") that details the assumptions and equations used to calculate daily and hourly reference evapotranspiration and daily willow transpiration from raw meteorological data.
This dataset contains 40 years (1980-2019) of historical hourly meteorology and 80 years (2020-2099) of projected hourly meteorology for 54 Balancing Authorities (BAs) in the conterminous United States. Details about the scenarios and variables included in this dataset are in the readme.pdf file. This dataset is derived from the IM3/HyperFACETS Thermodynamic Global Warming (TGW) simulations (https://doi.org/10.57931/1885756). More details on the TGW approach can be found at: https://tgw-data.msdlive.org/. If you use this dataset please also cite the raw TGW dataset (Jones, A. D., Rastogi, D., Vahmani, P., Stansfield, A., Reed, K., Thurber, T., Ullrich, P., & Rice, J. S. (2022). IM3/HyperFACETS Thermodynamic Global Warming (TGW) Simulation Datasets (v1.0.0) [Data set]. MSD-LIVE Data Repository. https://doi.org/10.57931/1885756). To go from the TGW data to these BA-level aggregated data we first averaged the raw gridded data by county in the United States. That intermediate data step is also stored in MSD-LIVE (https://doi.org/10.57931/1960548). We then population-weight the county-level hourly data in order to create population-weighted meteorology time series for each BA. Historical populations are from the United States Census Bureau and the evolving future populations are based on Shared Socioeconomic Pathways (SSPs) 3 and 5. The four climate scenarios crossed with the two SSPs yield eight different future projections for each BA: rcp45cooler_ssp3, rcp45cooler_ssp5, rcp45hotter_ssp3, rcp45hotter_ssp5, rcp85cooler_ssp3, rcp85cooler_ssp5, rcp85hotterssp3, rcp85hotterssp5. For the historical period and each of the eight future scenarios the dataset has hourly estimates of the population-weighted average of five meteorological variables: Temperature, specific humidity, shortwave radiation, longwave radiation, and wind speed. All times are in Coordinated Universal Time (UTC). The code to go from the raw TGW data to county-level and then BA-level projections is available at: https://github.com/IMMM-SFA/im3components/tree/main/im3components/wrf_to_tell.
This dataset contains 40 years (1980-2019) of historical hourly meteorology and 80 years (2020-2099) of projected hourly meteorology for each county in the conterminous United States. Details about the scenarios and variables included in this dataset are in the readme.pdf file. This dataset is derived from the IM3/HyperFACETS Thermodynamic Global Warming (TGW) simulations (https://doi.org/10.57931/1885756). More details on the TGW approach can be found at: https://tgw-data.msdlive.org/. If you use this dataset please also cite the raw TGW dataset (Jones, A. D., Rastogi, D., Vahmani, P., Stansfield, A., Reed, K., Thurber, T., Ullrich, P., & Rice, J. S. (2022). IM3/HyperFACETS Thermodynamic Global Warming (TGW) Simulation Datasets (v1.0.0) [Data set]. MSD-LIVE Data Repository. https://doi.org/10.57931/1885756). The four future climate scenarios in the TGW data are: rcp45cooler, rcp45hotter, rcp85cooler, and rcp85hotter. For the historical period and each of the four future scenarios the dataset has hourly estimates of the spatial-average of six meteorological variables for each county: Temperature, specific humidity, shortwave radiation, longwave radiation, and the U (east-west) and V (north-south) components of the wind speed. Times for each file are in the filename and all times are in Coordinated Universal Time (UTC). Counties are identified by their Federal Information Processing Standard (FIPS) code. The mapping between counties and FIPS codes is provided in the state_and_county_fips_codes.csv file. The code to go from the raw TGW data to county-level projections is available at: https://github.com/IMMM-SFA/im3components/tree/main/im3components/wrf_to_tell.
Global diabatic heating is estimated using fields of directly computed heating components, in particular those due to shortwave radiation, longwave radiation, sensible heating, and latent heating produced every 6 hours. The role of average fields of diabatic heating in the generation of available potential energy is examined. It is observed that latent heating is most significant in generating available potential energy.
Black carbon aerosols (BC) influence precipitation through a range of processes. The climate response to the presence of BC is however highly dependent on its vertical distribution. Here, we analyze the changes in the energy budget and precipitation impacts of adding a layer of BC at a range of altitudes in two independent global climate models. The models are run with atmosphere‐only and slab ocean model setup to analyze both fast and slow responses, respectively. Globally, precipitation changes are tightly coupled to the energy budget. We decompose the precipitation change into contributions from absorption of solar radiation, atmospheric longwave radiative cooling, and sensible heat flux at the surface. We find that for atmosphere‐only simulations, BC rapidly suppress precipitation, independent of altitude, mainly because of strong atmospheric absorption. This reduction is offset by increased atmospheric radiative longwave cooling and reduced sensible heat flux at the surface, but not of sufficient magnitude to prevent reduced precipitation. On longer timescales, when the surface temperature is allowed to respond, we find that the precipitation increase associated with surface warming can compensate for the initial reduction, particularly for BC in the lower atmosphere. Even though the underlying processes are strikingly similar in the two models, the resulting change in precipitation and temperature by BC differ quite substantially.