Surface Downwelling Longwave Research Articles

An assessment of ECMWF analyses and model forecasts over the North Slope of Alaska using observations from the ARM Mixed‐Phase Arctic Cloud Experiment

European Centre for Medium‐Range Weather Forecasts (ECMWF) analysis and model forecast data are evaluated using observations collected during the Atmospheric Radiation Measurement (ARM) October 2004 Mixed‐Phase Arctic Cloud Experiment (M‐PACE) at its North Slope of Alaska (NSA) site. It is shown that the ECMWF analysis reasonably represents the dynamic and thermodynamic structures of the large‐scale systems that affected the NSA during M‐PACE. The model‐analyzed near‐surface horizontal winds, temperature, and relative humidity also agree well with the M‐PACE surface measurements. Given the well‐represented large‐scale fields, the model shows overall good skill in predicting various cloud types observed during M‐PACE; however, the physical properties of single‐layer boundary layer clouds are in substantial error. At these times, the model substantially underestimates the liquid water path in these clouds, with the concomitant result that the model largely underpredicts the downwelling longwave radiation at the surface and overpredicts the outgoing longwave radiation at the top of the atmosphere. The model also overestimates the net surface shortwave radiation, mainly because of the underestimation of the surface albedo. The problem in the surface albedo is primarily associated with errors in the surface snow prediction. Principally because of the underestimation of the surface downwelling longwave radiation at the times of single‐layer boundary layer clouds, the model shows a much larger energy loss (−20.9 W m−2) than the observation (−9.6 W m−2) at the surface during the M‐PACE period.

Comparison of surface radiative flux data sets over the Arctic Ocean

Recent satellite data analysis and reanalysis projects have provided an unprecedented wealth of data sets relevant to surface energy budget in the Arctic Ocean. To assess how well we can reconstruct the variations of surface radiative fluxes used as boundary conditions to force sea ice models, the surface temperature, surface downwelling shortwave and longwave radiative fluxes of the (1) Cloud and Surface Parameter Retrieval (CASPR), (2) International Satellite Cloud Climatology Project (ISCCP‐FD), (3) National Centers for Environmental Prediction Reanalysis II (NCEP‐R2), and (4) European Centre for Medium‐Range Weather Forecast 40‐Year Reanalysis (ERA‐40) were evaluated over the period November 1997 to September 1998. At the local scale the accuracy of these surface parameters was compared to the high‐quality in situ measurements from the Surface Heat Budget of the Arctic Ocean (SHEBA) over the seasonal cycle and during particular storm events. The CASPR and NCEP‐R2 are closer to the SHEBA for the surface temperature. The CASPR and ISCCP‐FD are more accurate for the surface downward shortwave radiative fluxes. The ERA‐40 agrees well with the SHEBA for the surface downward longwave radiative fluxes. At the basin scale the consistency of dominant spatial/temporal variability of these surface parameters across different data sets was examined. All data sets reproduce the patterns associated with the seasonal cycle, but they vary in their ability to capture patterns linked to synoptic variability.

Radiation Budget of the Tropical Intraseasonal Oscillation

This study examines the relationship between precipitation and radiative heating on intraseasonal time scales in the Tropics using collocated top-of-atmosphere (TOA) and surface radiative flux measurements from special field program data [Atmospheric Radiation Measurement (ARM) Program and Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) experiments] as well as long-term TOA flux data [from Earth Radiation Budget Experiment (ERBE) and Advanced Very High Resolution Radiometer (AVHRR) satellite data]. All the different datasets consistently show that the atmosphere-integrated radiative heating is nearly in phase with the precipitation and enhances the net condensation heating by about 10%–15%. The dominant contribution to this radiative warming during wet periods is the reduction of TOA outgoing longwave radiation (OLR), primarily by clouds but with a small contribution by water vapor. This radiative heating is reduced slightly by enhanced surface downwelling longwave radiation, attributable to low cloud bases and reduced atmospheric shortwave absorption attributable to shadowing by high cloud tops. The intraseasonal budget of TOA radiation, reflecting heating of the whole ocean plus atmosphere column, is characterized by shortwave cloud forcing anomalies that are substantially larger than the longwave cloud forcing anomaly. This imbalance is in contrast with the near cancellation between these two terms at the seasonal time scale.

Evaluation of Methods to Estimate the Surface Downwelling Longwave Flux during Arctic Winter

Surface longwave radiation fluxes dominate the energy budget of nighttime polar regions, yet little is known about the relative accuracy of existing satellite-based techniques to estimate this parameter. We compare eight methods to estimate the downwelling longwave radiation flux and to validate their performance with measurements from two field programs in the Arctic: the Coordinated Eastern Arctic Experiment (CEAREX) conducted in the Barents Sea during the autumn and winter of 1988, and the Lead Experiment performed in the Beaufort Sea in the spring of 1992. Five of the eight methods were developed for satellite-derived quantities, and three are simple parameterizations based on surface observations. All of the algorithms require information about cloud fraction, which is provided from the NASA–NOAA Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) polar pathfinder dataset (Path-P); some techniques ingest temperature and moisture profiles (also from Path-P); one-half of the methods assume that clouds are opaque and have a constant geometric thickness of 50 hPa, and three include no thickness information whatsoever. With a somewhat limited validation dataset, the following primary conclusions result: 1) all methods exhibit approximately the same correlations with measurements and rms differences, but the biases range from −34 W m−2 (16% of the mean) to nearly 0; 2) the error analysis described here indicates that the assumption of a 50-hPa cloud thickness is too thin by a factor of 2 on average in polar nighttime conditions; 3) cloud-overlap techniques, which effectively increase mean cloud thickness, significantly improve the results; 4) simple Arctic-specific parameterizations performed poorly, probably because they were developed with surface-observed cloud fractions whereas the tests discussed here used satellite-derived effective cloud fractions; and 5) the single algorithm that includes an estimate of cloud thickness exhibits the smallest differences from observations.

Algorithm development strategies for retrieving the downwelling longwave flux at the Earth's surface

Algorithm development strategies for retrieving the surface downwelling longwave flux (SDLW) have been formulated on the basis of detailed studies with radiative transfer models and observational data. The model sensitivity studies were conducted with the column radiation model from the National Center for Atmospheric Research Community Climate Model Version 3 and the Moderate‐Resolution Transmittance radiation model. The studies show the clear‐sky SDLW can be largely determined from only two parameters: the surface upwelling longwave flux and the column precipitable water vapor. Cloudy‐sky sensitivity tests show that, as would be expected, cloud base height is an important factor in determining the SDLW, especially for low clouds. However, when considering broken clouds as occur in reality, there is no way of logically defining an average cloud base height. Instead, cloud liquid water path is shown to be a preferable parameter for use in an all‐sky algorithm, not because it serves as a direct cloud input parameter, but rather that it serves as a useful surrogate for cloud base height. Observational data from the Atmospheric Radiation Measurements Program at the U.S. Southern Great Plains (SGP) Oklahoma Central Facility and the Tropical Western Pacific (TWP) Manus Island were used in deriving and validating an illustrative algorithm. The observations show similar relations as found in the model sensitivity tests and suggest that a single algorithm could be applicable for both clear and cloudy conditions as well as for diverse geographical locations. For example, when applied to the TWP data, an algorithm based on a regression of SGP all‐sky data produces a relative bias error in SDLW of only 1.4% under all‐sky conditions and −0.2% for clear skies.

Validation of longwave atmospheric radiation models using Atmospheric Radiation Measurement data

Data taken at the Atmospheric Radiation Measurement Program's central facility in Oklahoma and processed as part of the Clouds and the Earth's Radiant Energy System‐Atmospheric Radiation Measurement‐Global Energy and Water Cycle Experiment (CAGEX) project have been used to validate the top‐of‐the‐atmosphere and surface longwave radiative fluxes for two widely used radiation models: the Column Radiation Model from the National Center for Atmospheric Research Community Climate Model (CCM), and the Moderate Resolution Transmittance (MODTRAN3) radiation code. The results show that for clear skies the models slightly overestimate outgoing longwave radiation at the top of the atmosphere (OLR) and underestimate the surface downwelling longwave flux (SDLW). The accuracy of the radiation models is quite consistent with their respective levels of complexity. For MODTRAN3, for example, the OLR overestimate is 7.1 Wm−2 while the SDLW underestimate is 4.2 Wm−2. For cloudy skies it is emphasized that the cloud input parameters, as determined from measurements by various instruments, require careful examination and preprocessing. Spatial and temporal averaging could result in the parameters representing different volumes of the atmosphere. The discrepancy between model calculations and observations is shown to be significantly reduced through the proper choice of input parameters.

Evaluation of Simulated Clear-Sky Longwave Radiation Using Ground-Based Observations

Surface observations from a tropical ocean and a subarctic land-based site are employed to evaluate the clearsky surface downwelling longwave irradiance (SDL) simulated using the European Centre for Medium-Range Weather Forecasts reanalysis (ERA). Comparison of simulated clear-sky and observed all-sky SDL highlights coincident periods of irradiance variability on various timescales in both datasets. Measurements during cloudy conditions are subsequently removed using a combination of the measured shortwave and longwave surface irradiances and recorded rainfall. The most reasonable filtering specifications are determined experimentally; clear-sky filtered observations of SDL are compared with corresponding simulated values. The root-mean-square differences between simulated and observed clear-sky SDL are within the observational uncertainty of 610 W m22. Simulated clear-sky SDL is about 8 W m22 more than the measured tropical values. In the subarctic, the simulated clear-sky SDL is less than observed values in the winter and greater than observed values in the summer. The clear-sky SDL differences are explained partially by the differences in ERA moisture profiles and near-surface temperature in comparison with radiosonde ascents. A primary limitation of the radiometric measurements is the lack of information regarding cloud amount; if model-simulated clear-sky fluxes and cloud radiative forcing are to be fully evaluated, it is highly desirable that such information should accompany surfacebased radiation data.