skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset
Abstract Diffuse solar radiation is an important, but understudied, component of the Earth’s surface radiation budget, with most global climate models not archiving this variable and a dearth of ground-based observations. Here, we describe the development of a global 40-year (1980–2019) monthly database of total shortwave radiation, including its diffuse and direct beam components, called BaRAD (Bias-adjusted RADiation dataset). The dataset is based on a random forest algorithm trained using Global Energy Balance Archive (GEBA) observations and applied to the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) dataset at the native MERRA-2 resolution (0.5° by 0.625°). The dataset preserves seasonal, latitudinal, and long-term trends in the MERRA-2 data, but with reduced biases than MERRA-2. The mean bias error is close to 0 (root mean square error = 10.1 W m −2 ) for diffuse radiation and −0.2 W m −2 (root mean square error = 19.2 W m −2 ) for the total incoming shortwave radiation at the surface. Studies on atmosphere-biosphere interactions, especially those on the diffuse radiation fertilization effect, can benefit from this dataset.  more » « less
Award ID(s):
1933630
PAR ID:
10314616
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Scientific Data
Volume:
8
Issue:
1
ISSN:
2052-4463
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ; ; (, Journal of Geophysical Research: Atmospheres)
    Abstract Clouds and radiation play an important role in warming events over the Southern Ocean (SO). Here we evaluate European Center for Medium‐Range Weather Forecasts Reanalysis version 5 (ERA5) and Polar Weather Research Forecast (PWRF) output through comparison to surface‐based measurements of clouds, radiation, and the atmospheric state over the SO during 2017–2023 at Escudero Station (62.2°S, 58.97°W) on King George Island. ERA5 mean monthly downward shortwave (DSW) radiative fluxes are found to be 38–50 W m−2higher than observations in summer, whereas ERA5 mean monthly downward longwave (DLW) is biased by −18 to −22 W m−2in summer and −16 W m−2on average over the year. Comparisons of temperature, humidity, and lowest‐cloud base heights between ERA5 and observations rule these factors out as large contributors to the DLW flux biases. The similarity between observed DLW cloud forcing distributions for atmospheric columns containing low‐level liquid and ice‐only clouds suggests limited influence of cloud phase errors on DLW biases. Thus the most likely explanation for DLW flux biases in ERA5 is underestimated cloud optical depth, which is also consistent with DSW flux biases. Similar biases in ERA5 are found during atmospheric river (AR) events. By contrast, PWRF flux bias magnitudes are much smaller during AR events (−12 W m−2for DSW and −2 W m−2for DLW). After bias correction, ERA5 monthly average net cloud forcing over 2017–2023 is found to be a minimum of −107 W m−2in January and a maximum of 65 W m−2in June. 
    more » « less
  2. ; (, Journal of Climate)
    Abstract Though the partitioning of shortwave radiation ( K ↓ ) at the surface into its diffuse ( K ↓,d ) and direct beam ( K ↓,b ) components is relevant for, among other things, the terrestrial energy and carbon budgets, there is a dearth of large-scale comparisons of this partitioning across reanalysis and satellite-derived products. Here we evaluate K ↓ , K ↓,d , and K ↓,b , as well as the diffuse fraction ( k d ) of solar radiation in four current-generation reanalysis (NOAA-CIRES-DOE, NCEP/NCAR, MERRA-2, ERA5) datasets and one satellite-derived product (CERES) using ≈1400 site years of observations. Although the systematic positive biases in K ↓ is consistent with previous studies, the biases in gridded K ↓,d and K ↓,b vary in direction and magnitude, both annually and across seasons. The inter-model variability in cloud cover strongly explains the biases in both K ↓,d and K ↓,b . Over Europe and China, the long-term (10-year plus) trends in K ↓,d in the gridded products are noticeably differ from corresponding observations and the grid-averaged 35-year trends show an order of magnitude variability. In the MERRA-2 reanalysis, which includes both clouds and assimilated aerosols, the reduction in both clouds and aerosols reinforce each other to establish brightening trends over Europe, while the effect of increasing aerosols overwhelm the effect of decreasing cloud cover over China. The inter-model variability in k d seen here (0.27 to 0.50 from CERES to MERRA-2) suggests substantial differences in shortwave parameterization schemes and their inputs in climate models and can contribute to inter-model variability in coupled simulations. Based on these results, we call for systematic evaluations of K ↓,d and K ↓,b in CMIP6 models. 
    more » « less
  3. ; ; ; (, Journal of Geophysical Research: Oceans)
    Surface heat flux estimates from widely used atmospheric reanalyses differ locally by 10–30 W m−2 even in time mean. Here a method is presented to help identify the errors causing these differences and to reduce these errors by exploiting hydrographic observations and the resulting temperature increments produced by an ocean reanalysis. The method is applied to improve the climatological monthly net surface heat fluxes from three atmospheric reanalyses: MERRA‐2, ERA‐Interim, and JRA‐55, during an 8 year test period 2007–2014. The results show that the time mean error, as evaluated by consistency with the ocean heat budget, is reduced to less than ±5 W m−2 over much of the subtropical and midlatitude ocean. For the global ocean, after all the corrections have been made, the 8 year mean global net surface heat imbalance has been reduced to 3.4 W m−2. A method is also presented to quantify the uncertainty in the heat flux estimates by repeating the procedure with many different atmospheric reanalyses and then examining the resulting spread in estimates. This reevaluation of net surface flux reveals, among other results, that the Southern Ocean is a source of heat to the atmosphere. 
    more » « less
  4. ; ; ; (, Atmospheric Chemistry and Physics)
    Abstract. Mineral aerosols (i.e., dust) can affect climate and weather by absorbing and scattering shortwave and longwave radiation in the Earth's atmosphere, the direct radiative effect. Yet understanding of the direct effect is so poor that the sign of the net direct effect at top of the atmosphere (TOA) is unconstrained, and thus it is unknown if dust cools or warms Earth's climate. Here we develop methods to estimate the instantaneous shortwave direct effect via observations of aerosols and radiation made over a 3-year period in a desert region of the southwestern US, obtaining a direct effect of -14±1 and -9±6 W m−2 at the surface and TOA, respectively. We also generate region-specific dust optical properties via a novel dataset of soil mineralogy from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), which are then used to model the dust direct radiative effect in the shortwave and longwave. Using this modeling method, we obtain an instantaneous shortwave direct effect of -21±7 and -1±7 W m−2. The discrepancy between the model and observational direct effect is due to stronger absorption in the model, which we interpret as an AVIRIS soil iron oxide content that is too large. Combining the shortwave observational direct effect with a modeled longwave TOA direct effect of 1±1 W m−2, we obtain an instantaneous TOA net effect of -8±6 W m−2, implying a cooling effect of dust. These findings provide a useful constraint on the dust direct effect in the southwestern United States. 
    more » « less
  5. ; ; ; (, Weather and Forecasting)
    Abstract Global Forecast System (GFS), North American Mesoscale Forecast System (NAM), and High-Resolution Rapid Refresh (HRRR) 2-m temperature, 10-m wind speed, and precipitation accumulation forecasts initialized at 1200 UTC are verified against New York State Mesonet (NYSM) observations from 1 January 2018 through 31 December 2021. NYSM observations at 126 site locations are used to calculate standard error statistics (e.g., forecast error, root-mean-square error) for temperature and wind speed and contingency table statistics for precipitation across forecast hours, meteorological seasons, and regions. The majority of the focus is placed on the first 18 forecast hours to allow for comparison among all three models. A daily NYSM station-mean temperature error analysis identified a slight cold bias at temperatures below 25°C in the GFS, a cool-to-warm bias as forecast temperatures warm in the HRRR, and a warm bias at temperatures above 30°C in each model. Differences arise when considering temperature biases with respect to lead times and seasons. Wind speeds are overforecast at all ranges in each season, and forecast wind speeds ≥ 18 m s−1are rarely observed. Performance diagrams indicate overall good forecast performance at precipitation thresholds of 0.1–1.5 mm, but with a high frequency bias in the GFS and NAM. This paper provides an overview of deterministic forecast performance across New York State, with the aim of sharing common biases associated with temperature, wind speed, and precipitation with operational forecasters and is the first step in developing a real-time model forecast uncertainty prediction tool. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Text Encoded Conversion
  • XML
  • Save / Share this Record
  • Send to Email