Surface Downward Longwave Radiation Research Articles

Sensitivity of surface downward longwave radiation to aerosol optical depth over the Lake Taihu region, China

Downward longwave radiation (DLR) is an important component of the global radiation budget, significantly influencing surface energy dynamics. Accurate quantification of aerosol impacts on surface DLR is crucial for refining numerical weather predictions and comprehending surface energy balance. While the impact of aerosols on downward shortwave radiation has garnered substantial attention, investigation into the longwave radiation effect of aerosols remains relatively limited. In this study, the impact of aerosols on surface DLR is quantified by using continuous radiation data, conventional meteorological observations, and aerosol optical depth (AOD) retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. These datasets span the period from 2011 to 2018 and are obtained from six stations of the Lake Taihu Flux Observational Network situated in Eastern China. The sensitivity of DLR to AOD (dDLR/dAOD) is determined through multiple linear regressions using both observational data and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. Additionally, a radiative transfer model is employed to derive the dDLR/dAOD. A three-variable linear regression of the observational data shows that DLR increases by 4.66 ± 1.99 W m−2 per unit increase in AOD (mean ± 1 standard deviation) under clear-sky conditions. The radiative transfer model and the three-variable linear regression of MERRA-2 reanalysis yield similar sensitivity values of 4.69 ± 1.23 W m−2 AOD−1 and 4.10 ± 0.25 W m−2 AOD−1, respectively. However, these sensitivity values are considerably smaller than those reported in previous studies. Plausible factors contributing to the observed variance in sensitivity values are discussed. These results help to improve our understanding on the aerosol longwave radiation effect over the Lake Taihu region in Eastern China.

A Cloud Water Path-Based Model for Cloudy-Sky Downward Longwave Radiation Estimation from FY-4A Data

Clouds are a critical factor in regulating the climate system, and estimating cloudy-sky Surface Downward Longwave Radiation (SDLR) from satellite data is significant for global climate change research. The models based on cloud water path (CWP) are less affected by cloud parameter uncertainties and have superior accuracy in SDLR satellite estimation when compared to those empirical and parameterized models relying mainly on cloud fraction or cloud-base temperature. However, existing CWP-based models tend to overestimate the low SDLR values and underestimate the larger SDLR. This study found that this phenomenon was caused by the fact that the models do not account for the varying relationships between cloud radiative effects and key parameters under different Liquid Water Path (LWP) and Precipitable Water Vapor (PWV) ranges. Based upon this observation, this study utilized Fengyun-4A (FY-4A) cloud parameters and ERA5 data as data sources to develop a new CWP-based model where the model coefficients depend on the cloud phase and cloud water path range. The accuracy of the new model’s estimated SDLR is 20.8 W/m2 for cloudy pixels, with accuracies of 19.4 W/m2 and 23.5 W/m2 for overcast and partly cloudy conditions, respectively. In contrast, the accuracy of the old CWP-based model was 22.4, 21.2, and 24.8 W/m2, respectively. The underestimation and overestimation present in the old CWP-based model are effectively corrected by the new model. The new model exhibited higher accuracy under various station locations, cloud cover scenarios, and cloud phase conditions compared to the old one. Comparatively, the new model showcased its most remarkable improvements in situations involving overcast conditions, water clouds with low PWV and low LWP values, ice clouds with large PWV, and conditions with PWV ≥ 5 cm. Over a temporal scale, the new model effectively captured the seasonal variations in SDLR.

Estimation of Surface Downward Longwave Radiation and Cloud Base Height Based on Infrared Multichannel Data of Himawari-8

Surface downward longwave radiation (SDLR) is significant with regard to surface energy budgets and climate research. The uncertainty of cloud base height (CBH) retrieval by remote sensing induces the vast majority of SDLR estimation errors under cloudy conditions; reliable CBH observation and estimation are crucial for determining the cloud radiative effect. This study presents a CBH retrieval methodology built from 10 thermal spectral data from Himawari-8 (H-8) observations, utilizing the random forest (RF) algorithm to fully account for each band’s contribution to CBH. The algorithm utilizes only infrared band data, making it possible to obtain CBH 24 h a day. Considering some factors that can significantly affect the CBH estimation, RF models are trained for different clouds using inputs from multiple H-8 channels together with geolocation information to target CBH derived from CloudSat/CALIPSO combined measurements. The validation results reveal that the new methodology performs well, with a root-mean-square error (RMSE) of only 1.17 km for all clouds. To evaluate the effect of CBH on SDLR estimation, an all-sky SDLR estimation algorithm based on previous CBH predictions is proposed. The new SDLR product not only has a resolution that is noticeably higher than that of benchmark products of the SDLR, such as the Clouds and the Earth’s Radiant Energy System (CERES) and the next-generation reanalysis (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF), but it also has greater accuracy, with an RMSE of 21.8 W m−2 for hourly surface downward longwave irradiance (SDLI).

Estimating All-Weather Surface Longwave Radiation from Satellite Passive Microwave Data

Surface longwave radiation (SLR) is an essential geophysical parameter of Earth’s energy balance, and its estimation based on thermal infrared (TIR) remote sensing data has been extensively studied. However, it is difficult to estimate cloudy SLR from TIR measurements. Satellite passive microwave (PMW) radiometers measure microwave radiation under the clouds and therefore can estimate SLR in all weather conditions. We constructed SLR retrieval models using brightness temperature (BT) data from an Advanced Microwave Scanning Radiometer 2 (AMSR2) based on a neural network (NN) algorithm. SLR from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) product was used as the reference. NN-based models were able to reproduce well the spatial variability of SLR from ERA5 at the global scale. Validations indicate a reasonably good performance was found for land sites, with a bias of 1.32 W/m2, root mean squared error (RMSE) of 35.37 W/m2, and coefficient of determination (R2) of 0.89 for AMSR2 surface upward longwave radiation (SULR) data, and a bias of −2.26 W/m2, RMSE of 32.94 W/m2, and R2 of 0.82 for AMSR2 surface downward longwave radiation (SDLR) data. AMSR2 SULR and SDLR retrieval accuracies were higher for oceanic sites, with biases of −2.98 and −4.04 W/m2, RMSEs of 6.50 and 13.42 W/m2, and R2 values of 0.83 and 0.66, respectively. This study provides a solid foundation for the development of a PMW SLR retrieval model applicable at the global scale to generate long-term continuous SLR products using multi-year satellite PMW data and for future research with a higher spatiotemporal resolution.

Assessment of Three Satellite-Derived Surface Downward Longwave Radiation Products in Polar Regions

The radiation budget in polar regions plays an important role in global climate change study. This study investigates the performance of downward longwave radiation (DLR) of three satellite radiation products in polar regions, including GEWEX-SRB, ISCCP-FD, and CERES-SYN. The RMSEs are 35.8, 40.5, and 26.9 W/m2 at all polar sites for GEWEX-SRB, ISCCP-FD, and CERES-SYN. The results in the Arctic are much better than those in the Antarctic, RMSEs of the three products are 34.7 W/m2, 36.0 W/m2, and 26.2 W/m2 in the Arctic and are 38.8 W/m2 and 54.8 W/m2, and 28.6 W/m2 in the Antarctic. Both GEWEX-SRB and CERES-SYN underestimate DLRs at most sites, while ISCCP-FD overestimates DLRs at most sites. CERES-SYN and GEWEX-SRB DLR products can capture most of the DLR seasonal variation in both the Antarctic and Arctic. Though CERES-SYN has the best results that RMSE within 30 W/m2 in most polar sites, the accuracy of satellite products in polar regions still cannot meet the requirement of climate research. The improvement of satellite DLR products in polar regions mainly depends on the quality of improving input atmospheric parameters, the accuracy of improving cloud detection over the snow and ice surface and cloud parameters, and better consideration of spatial resolution and heterogeneity.

Surface downward longwave radiation estimation from new generation geostationary satellite data

Surface downward longwave radiation (SDLR) is a key component of the Earth's energy budget. To use new-generation geostationary satellite data with high spatial and spectral resolution for SDLR estimation, we developed a clear-sky SDLR algorithm based on multi-channel brightness temperature (BT) and precipitable water vapor (PWV), named as MBTW, for strictly clear conditions, and a clear-sky SDLR algorithm based on atmospheric parameters (named as NewAtmos) for probably clear conditions. Then, we developed a framework to estimate all-sky SDLR; the framework combined the two clear-sky algorithms and an existing cloudy-sky algorithm. Finally, the clear-sky and all-sky SDLRs were calculated using the proposed algorithms and framework for the FY-4A and GOES-16 data. The validation results against field measurements indicated that the MBTW and NewAtmos algorithms performed well under the expected clear-sky conditions. The clear-sky SDLRs combined using MBTW and NewAtmos algorithms had RMSEs of 24.8 W/m2 and 18.3 W/m2 for the FY-4A and GOES-16 data, which is comparable to those derived using a single algorithm based on atmospheric parameters; the former portrayed better consistency with field measurements than the latter. The RMSE of instantaneous all-sky SDLR calculated using the proposed method were 24.6 W/m2 and 20.1 W/m2 for FY-4A and GOES-16 data, and they were 23.7 W/m2 and 18.3 W/m2 for the hourly SDLR. The hourly SDLR from the combined algorithms showed a better accuracy than those of the ERA5 SDLR at the same resolution, and also yielded more details of spatial variation than those from ERA5 and from single atmospheric-parameter-based algorithm, because high-resolution thermal infrared (TIR) BT was used in our method. Furthermore, using ERA5 derived PWV, cloud liquid water path and ice water path produced very similar SDLR to those produced using satellite parameters; therefore, for the conditions missing satellite parameters, the ERA5 parameters can be an alternative to achieve continuous SDLR estimation.

Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave Radiation

The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth’s radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m−2 are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model.

Assessment of surface downward longwave radiation in CMIP6 with comparison to observations and CMIP5

Surface downward longwave radiation (SDLR) plays an important role in understanding the greenhouse effect and global warming. The simulated SDLR from 47 coupled models in the Coupled Model Intercomparison Project (CMIP6) general circulation models (GCMs) was evaluated by comparing them with ground measurements and CMIP5 results. The estimated SDLR using all CMIP6 GCMs based on the multimodel ensemble (MME) methods was validated as well. The bias values of the SDLR simulations from individual CMIP6 GCMs averaged over the selected 183 sites around the world varied from −10 to 10 W m−2, while the root mean squared error (RMSE) values ranged from 20 to 26 W m−2. Compared to CMIP5 models, the CMIP6 GCMs did not show a significant tendency to underestimate SDLR. However, the SDLR from CMIP6 GCMs exhibited the relatively better precision at low altitude and low latitude sites compared to that at high altitude and high latitude sites. Moreover, the Bayesian model averaging (BMA) method increased the correlation coefficient (R) by approximately 0.02 and reduced the RMSE by approximately 5 W m−2 on average compared to the individual CMIP6 GCMs. The trend in SDLR was also investigated in this study, which has been related to the changes in air temperature (SAT), and water vapor pressure (WVP).

Global validation of clear-sky models for retrieving land-surface downward longwave radiation from MODIS data

Surface longwave radiation (SLR) plays an important role in the energy budget of the Earth's climate system. Remote sensing provides various data sources to retrieve SLR on a large scale and with high spatial resolution (e.g., 1 km). Multiple retrieval methods of surface downward longwave radiation (SDLR) based on satellite thermal infrared data produce different retrieval results for the same scenario. Therefore, validating these models is necessary to understand their characteristics and limitations. To this end, ground-based measurements were used to provide independent validation of six widely used SDLR models for clear sky conditions over 41 Baseline Surface Radiation Network (BSRN) stations worldwide. The Wang2020 model had the best overall performance (bias of −5.480 W/m2, root-mean-square errors [RMSE] of 23.226 W/m2, R2 of 0.879), and Tang2008 model had similar retrieval capability. The errors of LST had limited influence on the retrieval accuracy of SDLR models. When using the near-surface air temperature, the retrieval accuracy of the Zhou2007 model was significantly improved with a range of ~9.5 W/m2 for the RMSE. The uncertainty of TCWV had significant effect on all model performances, wherein the Zhou2007 model had stronger error resilience of TCWV. Moreover, MODIS TIR TCWV data provided better performance than NIR TCWV in most situations, and thereby are preferred to use in the SDLR retrieval. Surface altitude had a lesser impact on SDLR retrieval than terrain effects. All models overestimated SDLR for peak stations in mountainous areas, with biases reaching 56.614 W/m2 and RMSE reaching 63.909 W/m2. Land cover type also had a significant effect on retrieval accuracy; model performances were poorer in the desert and barren where atmospheric conditions are extremely dry and hot. Remote sensing SDLR data with high accuracy are needed for hydrological, agricultural, and climate change applications. The results of this study provide a reference for the SDLR retrieval accuracy based on clear-sky models.

Estimation of Long-Term Surface Downward Longwave Radiation over the Global Land from 2000 to 2018

It is of great importance for climate change studies to construct a worldwide, long-term surface downward longwave radiation (Ld, 4–100 μm) dataset. Although a number of global Ld datasets are available, their low accuracies and coarse spatial resolutions limit their applications. This study generated a daily Ld dataset with a 5-km spatial resolution over the global land surface from 2000 to 2018 using atmospheric parameters, which include 2-m air temperature (Ta), relative humidity (RH) at 1000 hPa, total column water vapor (TCWV), surface downward shortwave radiation (Sd), and elevation, based on the gradient boosting regression tree (GBRT) method. The generated Ld dataset was evaluated using ground measurements collected from AmeriFlux, AsiaFlux, baseline surface radiation network (BSRN), surface radiation budget network (SURFRAD), and FLUXNET networks. The validation results showed that the root mean square error (RMSE), mean bias error (MBE), and correlation coefficient (R) values of the generated daily Ld dataset were 17.78 W m−2, 0.99 W m−2, and 0.96 (p < 0.01). Comparisons with other global land surface radiation products indicated that the generated Ld dataset performed better than the clouds and earth’s radiant energy system synoptic (CERES-SYN) edition 4.1 dataset and ERA5 reanalysis product at the selected sites. In addition, the analysis of the spatiotemporal characteristics for the generated Ld dataset showed an increasing trend of 1.8 W m−2 per decade (p < 0.01) from 2003 to 2018, which was closely related to Ta and water vapor pressure. In general, the generated Ld dataset has a higher spatial resolution and accuracy, which can contribute to perfect the existing radiation products.

Estimation of Long-term Surface Downward Longwave Radiation over the Global Land from 2000 to 2007

Estimation of Long-term Surface Downward Longwave Radiation over the Global Land from 2000 to 2007

Impact of Air Temperature Inversion on the Clear-Sky Surface Downward Longwave Radiation Estimation

Parameterization schemes for estimating clear-sky surface downward longwave radiation (SDLR) are well recognized for their simplicity and acceptable accuracy, especially at the local scale. The near-surface temperature and/or water vapor are usually used to predict the clear-sky SDLR in a parameterization scheme. Air temperature inversion (ATI) alters the atmospheric state at the near-surface boundary layer and affects the accuracy of the clear-sky SDLR estimation. However, few studies have investigated the impact of ATI on the estimate of the clear-sky SDLR. This article investigated the impact of ATI on the estimate of the clear-sky SDLR using six widely used parameterization schemes. According to the evaluation results using ATI profiles from the Thermodynamic Initial Guess Retrieval (TIGR) database and the Surface Radiation Budget Network (SURFRAD) sites, all the parameterization schemes are sensitive to ATI, and their accuracy is degraded greatly as a whole. The SDLR is underestimated for the ATI profile both in the TIGR database and SURFRAD sites. The best three schemes can achieve the accuracy with bias values of approximately -10 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and root-mean-square errors (RMSEs) less than 20 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for the ATI profiles in the TIGR database. The reason the SDLR is underestimated for the ATI profiles is provided by a simulation study. An empirical method is proposed to correct the impact of ATI. The accuracy of all the parameterization schemes is remarkably improved at SURFRAD sites after correcting the impact of ATI, with the absolute values of bias and RMSEs less than 10 and 20 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> at SURFRAD sites.

What Drives the North Atlantic Oscillation’s Temperature Anomaly Pattern? Part II: A Decomposition of the Surface Downward Longwave Radiation Anomalies

AbstractRadiative transfer calculations are conducted to determine the contribution of temperature and water vapor anomalies toward the surface clear-sky downward longwave radiation (DLR) anomalies of the NAO. These calculations are motivated by the finding that the NAO’s skin temperature anomalies are driven primarily by changes in surface DLR. The clear-sky radiative transfer calculations follow the result that the clear-sky surface DLR anomalies can account for most of the all-sky surface DLR anomalies of the NAO. The results of the radiative transfer calculations prompt an analysis of the thermodynamic energy and total column water (TCW) budget equations, as water vapor and temperature anomalies are found to be equally important drivers of the surface DLR anomalies of the NAO. Composite analysis of the thermodynamic energy equation reveals that the temperature anomalies of the NAO are wind driven: the advection of climatological temperature by the anomalous wind drives the NAO’s temperature anomalies at all levels except for those in the upper troposphere–lower stratosphere where the advection of anomalous temperature by the climatological wind becomes dominant. A similar analysis of the TCW budget reveals that changes in TCW are driven by water flux convergence. In addition to determining the drivers of the temperature and TCW anomalies, the thermodynamic energy and water budget analyses reveal that the decay of the temperature anomalies occurs primarily through vertical mixing, and that of the water anomalies mostly by evaporation minus precipitation.

A Comparative Study of Bulk Parameterization Schemes for Estimating Cloudy-Sky Surface Downward Longwave Radiation

Parameterization schemes (bulk formulae) are widely used to estimate all-sky surface downward longwave radiation (SDLR) due to the simple, readily available inputs and acceptable accuracy from local to regional scales. Seven widely used bulk formulae are evaluated using the ground measurements collected from 44 globally distributed flux measurement sites of five networks. The Bayesian model averaging (BMA) method is introduced to integrate multiple bulk formulae to obtain an estimate of cloudy-sky SDLR for the first time. The second multiple linear regression model of Carmona et al. (2014) performs the best, with BIAS, RMSE, and R2 of zero, 20.13 W·m−2 and 0.87, respectively. The BMA method can achieve balanced results that are close to the accuracy of the second multiple linear regression model of Carmona et al. (2014) and better than the average accuracy of seven bulk formulae, with BIAS, RMSE, and R2 of −1.08 W·m−2, 21.99 W·m−2 and 0.87, respectively. In addition, the bulk formula of Crawford and Duchon (1999) is preferred if there is insufficient data to calibrate the bulk formulae because it does not need local calibration and has an acceptable accuracy, with BIAS, RMSE, and R2 of 0.96 W·m−2, 26.58 W·m−2 and 0.82, respectively. The effects of climate type, land cover type, and surface elevation are also investigated to fully assess the applicability of each bulk formula and BMA. In general, there is no cloudy-sky bulk parametrization scheme that can be successfully applied everywhere.

Correction to: Surface Downward Longwave Radiation Retrieval Algorithm for GEO-KOMPSAT-2A/AMI

In the original version of this article the necessary acknowledgements were unfortunately missing. The correct acknowledgements are given below.

Surface Downward Longwave Radiation Retrieval Algorithm for GEO-KOMPSAT-2A/AMI

This study contributes to the development of an algorithm to retrieve the Earth’s surface downward longwave radiation (DLR) for 2nd Geostationary Earth Orbit KOrea Multi-Purpose SATellite (GEO-KOMPSAT-2A; GK-2A)/Advanced Meteorological Imager (AMI). Regarding simulation data for algorithm development, we referred to Clouds and the Earth’s Radiant Energy System (CERES), and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-interim reanalysis data. The clear sky DLR calculations were in good agreement with the Gangneung-Wonju National University (GWNU) Line-By-Line (LBL) model. Compared with CERES data, the Root Mean Square Error (RMSE) was 10.14Wm−2. In the case of cloudy sky DLR, we estimated the cloud base temperature empirically by utilizing cloud liquid water content (LWC) according to the cloud type. As a result, the correlation coefficients with CERES all sky DLRs were greater than 0.99. However, the RMSE between calculated DLR and CERES data was about 16.67Wm−2, due to ice clouds and problems of mismatched spatial and temporal resolutions for input data. This error may be reduced when GK-2A is launched and its products can be used as input data. Accordingly, further study is needed to improve the accuracy of DLR calculation by using high-resolution input data. In addition, when compared with BSRN surface-based observational data and retrieved DLR for all sky, the correlation coefficient was 0.86 and the RMSE was 31.55 Wm−2, which indicates relatively high accuracy. It is expected that increasing the number of experimental Cases will reduce the error.

Comprehensive assessment of parameterization methods for estimating clear-sky surface downward longwave radiation

Surface downward longwave radiation (SDLR) is a key variable for calculating the earth’s surface radiation budget. In this study, we evaluated seven widely used clear-sky parameterization methods using ground measurements collected from 71 globally distributed fluxnet sites. The Bayesian model averaging (BMA) method was also introduced to obtain a multi-model ensemble estimate. As a whole, the parameterization method of Carmona et al. (2014) performs the best, with an average BIAS, RMSE, and R2 of − 0.11 W/m2, 20.35 W/m2, and 0.92, respectively, followed by the parameterization methods of Idso (1981), Prata (Q J R Meteorol Soc 122:1127-1151, 1996), Brunt and Sc (Q J R Meteorol Soc 58:389-420, 1932), and Brutsaert (Water Resour Res 11:742-744, 1975). The accuracy of the BMA is close to that of the parameterization method of Carmona et al. (2014) and comparable to that of the parameterization method of Idso (1981). The advantage of the BMA is that it achieves balanced results compared to the integrated single parameterization methods. To fully assess the performance of the parameterization methods, the effects of climate type, land cover, and surface elevation were also investigated. The five parameterization methods and BMA all failed over land with the tropical climate type, with high water vapor, and had poor results over forest, wetland, and ice. These methods achieved better results over desert, bare land, cropland, and grass and had acceptable accuracies for sites at different elevations, except for the parameterization method of Carmona et al. (2014) over high elevation sites. Thus, a method that can be successfully applied everywhere does not exist.

Effect of retreating sea ice on Arctic cloud cover in simulated recent global warming

Abstract. This study investigates the effect of sea ice reduction on Arctic cloud cover in historical simulations with the coupled atmosphere–ocean general circulation model MIROC5. Arctic sea ice has been substantially retreating since the 1980s, particularly in September, under simulated global warming conditions. The simulated sea ice reduction is consistent with satellite observations. On the other hand, Arctic cloud cover has been increasing in October, with about a 1-month lag behind the sea ice reduction. The delayed response leads to extensive sea ice reductions because the heat and moisture fluxes from the underlying open ocean into the atmosphere are enhanced. Sensitivity experiments with the atmospheric part of MIROC5 clearly show that sea ice reduction causes increases in cloud cover. Arctic cloud cover increases primarily in the lower troposphere, but it decreases in the near-surface layers just above the ocean; predominant temperature rises in these near-surface layers cause drying (i.e., decreases in relative humidity), despite increasing moisture flux. Cloud radiative forcing due to increases in cloud cover in autumn brings an increase in the surface downward longwave radiation (DLR) by approximately 40–60 % compared to changes in clear-sky surface DLR in fall. These results suggest that an increase in Arctic cloud cover as a result of reduced sea ice coverage may bring further sea ice retreat and enhance the feedback processes of Arctic warming.

The Surface Downward Longwave Radiation in the ECMWF Forecast System

Abstract The surface downward longwave radiation, computed by the European Centre for Medium-Range Weather Forecasts (ECMWF) forecast system used for the ECMWF 40-yr reanalysis, is compared with surface radiation measurements for the April–May 1999 period, available as part of the Baseline Surface Radiation Network (BSRN), Surface Radiation (SURFRAD), and Atmospheric Radiation Measurement (ARM) programs. Emphasis is put on comparisons on a 1-h basis, as this allows discrepancies to be more easily linked to differences between model description and observations of temperature, humidity, and clouds. It is also possible to compare the model and observed temporal variability in the surface radiation fluxes. Comparisons are first carried out at locations for which the spectral model orography differs from the actual station height. Sensitivity of the model fluxes to various algorithms to correct for this discrepancy is explored. A simple interpolation–extrapolation scheme for pressure, temperature, and specifi...