Surface Downwelling Longwave Radiation Research Articles

Estimation of Downwelling Surface Longwave Radiation under Heavy Dust Aerosol Sky

The variation of aerosols, especially dust aerosol, in time and space plays an important role in climate forcing studies. Aerosols can effectively reduce land surface longwave emission and re-emit energy at a colder temperature, which makes it difficult to estimate downwelling surface longwave radiation (DSLR) with satellite data. Using the latest atmospheric radiative transfer code (MODTRAN 5.0), we have simulated the outgoing longwave radiation (OLR) and DSLR under different land surface types and atmospheric profile conditions. The results show that dust aerosol has an obvious “warming” effect to longwave radiation compared with other aerosols; that aerosol longwave radiative forcing (ALRF) increased with the increasing of aerosol optical depth (AOD); and that the atmospheric water vapor content (WVC) is critical to the understanding of ALRF. A method is proposed to improve the accuracy of DSLR estimation from satellite data for the skies under heavy dust aerosols. The AOD and atmospheric WVC under cloud-free conditions with a relatively simple satellite-based radiation model yielding the high accurate DSLR under heavy dust aerosol are used explicitly as model input to reduce the effects of dust aerosol on the estimation of DSLR. Validations of the proposed model with satellites data and field measurements show that it can estimate the DSLR accurately under heavy dust aerosol skies. The root mean square errors (RMSEs) are 20.4 W/m2 and 24.2 W/m2 for Terra and Aqua satellites, respectively, at the Yingke site, and the biases are 2.7 W/m2 and 9.6 W/m2, respectively. For the Arvaikheer site, the RMSEs are 23.2 W/m2 and 19.8 W/m2 for Terra and Aqua, respectively, and the biases are 7.8 W/m2 and 10.5 W/m2, respectively. The proposed method is especially applicable to acquire relatively high accurate DSLR under heavy dust aerosol using MODIS data with available WVC and AOD data.

Dynamical and Thermodynamical Impacts of High- and Low-Frequency Atmospheric Eddies on the Initial Melt of Arctic Sea Ice

Abstract The role of high-frequency and low-frequency eddies in the melt onset of Arctic sea ice is investigated through an examination of eddy effects on lower-tropospheric (1000–500 hPa) meridional heat transport into the Arctic and local surface downwelling shortwave and longwave radiation. Total and eddy components of the meridional heat transport into the Arctic from 1979 to 2012 are calculated from reanalysis data, and surface radiation data are acquired from the NASA Clouds and the Earth’s Radiant Energy System (CERES) project dataset. There is a significant positive correlation between the mean initial melt date and the September sea ice minimum extent, with each quantity characterized by a negative trend. Spatially, the earlier mean melt onset date is primarily found in a region bounded by 90°E and 130°W. The decline in this region is steplike and not associated with an increase in meridional heat transport but with an earlier appearance of above-freezing temperatures in the troposphere. In most years, discrete short-duration episodes of melt onset over a large area occur. In an investigation of two of these melt episodes, a positive total meridional heat transport is associated with the peak melt, with the product of high-frequency eddy wind and mean temperature fields being the most important contributor. Additionally, there is a key positive anomaly in surface downwelling longwave radiation immediately preceding the peak melt that is associated with increased cloud cover and precipitable water. These results suggest the importance of carefully considering and properly representing atmospheric eddies when modeling the melt onset of Arctic sea ice.

Dark Warming

Abstract As the Arctic sea ice thins and ultimately disappears in a warming climate, its insulating power decreases. This causes the surface air temperature to approach the temperature of the relatively warm ocean water below the ice. The resulting increases in air temperature, water vapor, and cloudiness lead to an increase in the surface downwelling longwave radiation (DLR), which enables a further thinning of the ice. This positive ice–insulation feedback operates mainly in the autumn and winter. A climate change simulation with the Community Earth System Model shows that, averaged over the year, the increase in Arctic DLR is 3 times stronger than the increase in Arctic absorbed solar radiation at the surface. The warming of the surface air over the Arctic Ocean during fall and winter creates a strong thermal contrast with the colder surrounding continents. Sea level pressure falls over the Arctic Ocean, and the high-latitude circulation reorganizes into a shallow “winter monsoon.” The resulting increase in surface wind speed promotes stronger surface evaporation and higher humidity over portions of the Arctic Ocean, thus reinforcing the ice–insulation feedback.

The Importance of Representing Mixed-Phase Clouds for Simulating Distinctive Atmospheric States in the Arctic*

Abstract Observations from the Surface Heat Budget of the Arctic Ocean experiment (SHEBA) suggest that the Arctic Basin is characterized by two distinctly different preferred atmospheric states during wintertime. These states appear as two peaks in the frequency distribution of surface downwelling longwave radiation (LWD), representing radiatively clear and opaque conditions. Here, the authors have investigated the occurrence and representation of these states in the widely used ECMWF Interim Re-Analysis (ERA-Interim) dataset. An interannually recurring bimodal distribution of LWD values is not a clearly observable feature in the reanalysis data. However, large differences in the simulated liquid water content of clouds in ERA-Interim compared to observations are identified and these are linked to the lack of a radiatively opaque peak in the reanalysis. Using a single-column model, dynamically controlled by data from ERA-Interim, the authors show that, by tuning the glaciation speed of supercooled liquid clouds, it is possible to reach a very good agreement between the model and observations from the SHEBA campaign in terms of LWD. The results suggest that the presence of two preferred Arctic states, as observed during SHEBA, is a recurring feature of the Arctic climate system during winter [December–March (DJFM)]. The mean increase in LWD during the Arctic winter compared to ERA-Interim is 15 W m−2. This has a substantial bearing on climate model evaluation in the Arctic as it indicates the importance of representing Arctic states in climate models and reanalysis data and that doing so could have a significant impact on winter ice thickness and surface temperatures in the Arctic.

A Simulated Climatology of Spectrally Decomposed Atmospheric Infrared Radiation

Abstract A simulation experiment is conducted to inquire into the mean climate state and likely trends in atmospheric infrared radiation spectra. Upwelling and downwelling spectra at five vertical levels from the surface to the top of the atmosphere (TOA) are rigorously calculated from a climate-model-simulated atmosphere for a 25-yr period. Tracing the longwave radiation flux vertically and spectrally renders a dissection of the greenhouse effect of the earth atmosphere and its change due to climate forcings and feedbacks. The results show that the total outgoing longwave radiation (OLR) at the TOA may be conserved due to 1) compensating temperature and opacity effects and 2) contrasting temperature changes in troposphere and stratosphere. The tightly coupled tropospheric temperature and opacity effects reduce the overall tropospheric contribution to OLR change to be comparable to the overall stratospheric contribution, which suggests that transient OLR change is constrained by the relative strengths of stratospheric and tropospheric temperature changes. The total OLR energy, however, is redistributed across its spectrum. The earliest detectable global climate change signal lies in the CO2 absorption bands, which results from stratospheric cooling and the CO2 opacity effect. This signal can be detected much sooner than surface temperature change and is little affected by achievable instrument accuracy. In contrast, both tropospheric temperature and opacity effects increase downwelling longwave radiation (DLR), which makes DLR a verifiable aspect of global warming. The time it takes to detect surface DLR change roughly equals that of surface temperature change. Measuring downwelling radiances at strong water vapor lines at the tropopause can particularly help monitor stratospheric water vapor.

Estimation of high-spatial resolution clear-sky longwave downward and net radiation over land surfaces from MODIS data

Surface downwelling longwave radiation (LWDN) and surface net longwave radiation (LWNT) are two components in the surface radiation budget. In this study, we developed new linear and nonlinear models using a hybrid method to derive instantaneous clear-sky LWDN over land from the Moderate Resolution Imaging Spectroradiometer (MODIS) TOA radiance at 1 km spatial resolution. The hybrid method is based on extensive radiative transfer simulation (physical) and statistical analysis (statistical). Linear and nonlinear models were derived at 5 sensor view zenith angles (0°, 15°, 30°, 45°, and 60°) to estimated LWDN using channels 27–29 and 31–34. Separate models were developed for daytime and nighttime observations. Surface pressure effect was considered by incorporating elevation in the models. The linear LWDN models account for more than 92% of variations of the simulated data sets, with standard errors less than 16.27 W/m 2 for all sensor view zenith angles. The nonlinear LWDN models explain more than 93% of variations, with standard errors less than 15.20 W/m 2. The linear and nonlinear LWDN models were applied to both Terra and Aqua TOA radiance and validated using ground data from six SURFRAD sites. The nonlinear models outperform the linear models at five sites. The averaged root mean squared errors (RMSE) of the nonlinear models are 17.60 W/m 2 (Terra) and 16.17 W/m 2 (Aqua), with averaged RMSE ~ 2.5 W/m 2 smaller than that of the linear models. LWNT was estimated using the nonlinear LWDN models and the artificial neural network (ANN) model method that predicts surface upwelling longwave radiation. LWNT was also validated using the same six SURFRAD sites. The averaged RMSEs are 17.72 (Terra) and 16.88 (Aqua) W/m 2; the averaged biases are − 2.08 (Terra) and 1.99 (Aqua) W/m 2. The LWNT RMSEs are less than 20 W/m 2 for both Terra and Aqua observations at all sites.

Estimation of instantaneous net surface longwave radiation from MODIS cloud-free data

This paper develops a statistical regression method to estimate the instantaneous Downwelling Surface Longwave Radiation (DSLR) for cloud-free skies using only the satellite-based radiances measured at the Top Of the Atmosphere (TOA), and subsequently combines the DSLR with the MODIS land surface temperature/emissivity products (MOD11_L2) to estimate the instantaneous Net Surface Longwave Radiation (NSLR). The proposed method relates the DSLR directly to the TOA radiances in the MODIS Thermal InfraRed (TIR) channels provided that the terrain altitude and the satellite Viewing Zenith Angle (VZA) are known. The simulation analysis shows that the instantaneous DSLR could be estimated by the proposed method with the Root Mean Square Error (RMSE) of 12.4 W/m 2 for VZA = 0 and terrain altitude z = 0 km. Similar results are obtained for the other VZAs and altitudes. Considering the MODIS instrumental errors of 0.25 K for the TOA brightness temperatures in channels 28, 33 and 34, and of 0.05 K for channels 29 and 31, and of 0.35 K for channel 36, the overall retrieval accuracy in terms of the RMSE is decreased to 13.1 W/m 2 for the instantaneous DSLR. Moreover, a comparison of MODIS derived DSLR and NSLR are done with the field measurements made at six sites of the Surface Radiation Budget Network (SURFRAD) in the United States for days with cloud-free conditions at the moment of MODIS overpass in 2006. The results show that the bias, RMSE and the square of the correlation coefficient ( R 2) between the MODIS derived DSLR with the proposed method and the field measured DSLR are 20.3 W/m 2, 30.1 W/m 2 and 0.91 respectively, and bias = 11.7 W/m 2, RMSE = 26.1 W/m 2 and R 2 = 0.94 for NSLR. In addition, the scheme proposed by Bisht et al. [Bisht, G., Venturini, V., Islam, S., & Jiang, L. (2005). Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear-sky days. Remote Sensing of Environment, 97, 52–67], which requires the MODIS atmospheric profile product (MOD07) and also the MODIS land surface temperature/emissivity products (MOD11_L2) as inputs, is used to estimate the instantaneous DSLR and NSLR for comparison with the field measurements as well as the MODIS derived DSLR and NSLR using our proposed method. The results of the comparisons show that, at least for our cases, our proposed method for estimating DSLR from the MODIS radiances at the TOA and the resultant NSLR gives results comparable to those estimated with Bisht et al.'s scheme [Bisht, G., Venturini, V., Islam, S., & Jiang, L. (2005). Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear-sky days. Remote Sensing of Environment, 97, 52–67].

An improved algorithm for retrieving surface downwelling longwave radiation from satellite measurements

Zhou and Cess (2001) developed an algorithm for retrieving surface downwelling longwave radiation (SDLW) based upon detailed studies using radiative transfer model calculations and surface radiometric measurements. The algorithm links clear sky SDLW with surface upwelling longwave (LW) flux and column precipitable water vapor. For cloudy sky cases, the cloud liquid water path is used as an additional parameter to account for the effects of clouds. Despite the simplicity of the algorithm, it performs very well for most geographical regions except for those regions where the atmospheric conditions near the surface tend to be extremely cold and dry. Systematic errors are also found for scenes that are covered with ice clouds. An improved version of the algorithm prevents the large errors in the SDLW at low water vapor amounts by taking into account that, under such conditions, the SDLW and water vapor amount are nearly linear in their relationship. The new algorithm also utilizes cloud fraction and cloud liquid and ice water paths available from the Cloud and the Earth's Radiant Energy System (CERES) single‐scanner footprint (SSF) product to separately compute the clear and cloudy portions of the fluxes. The new algorithm has been validated against surface measurements at 29 stations around the globe for Terra and Aqua satellites. The results show significant improvement over the original version. Preliminary tests also suggest that the new algorithm works quite well for high elevation locations such as Tibet site where current satellite products exhibit large biases. The revised Zhou‐Cess algorithm is also slightly better or comparable to more sophisticated algorithms currently implemented in the CERES processing and will be incorporated as one of the CERES empirical surface radiation algorithms.

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.

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.