Surface Longwave Radiation Research Articles

Contribution of warm and moist atmospheric flow to a record minimum July sea ice extent of the Arctic in 2020

Abstract. The satellite observations unveiled that the July sea ice extent of the Arctic shrank to the lowest value, since 1979, in 2020 with a major ice retreat in the Eurasian shelf seas including Kara, Laptev, and East Siberian seas. Based on the ERA-5 reanalysis products, we explored the impacts of warm and moist air-mass transport on this extreme event. The results revealed that anomalously high energy and moisture converged into these regions in the spring months (April to June) of 2020, leading to a burst of high moisture content and warming within the atmospheric column. The convergence is accompanied by local enhanced downward longwave surface radiation and turbulent fluxes, which is favorable for initiating an early melt onset in the region with severe ice loss. Once the melt begins, solar radiation plays a decisive role in leading to further sea ice depletion due to ice–albedo positive feedback. The typical trajectories of the synoptic cyclones that occurred on the Eurasian side in spring 2020 agree well with the path of atmospheric flow. Assessments suggest that variations in characteristics of the spring cyclones are conducive to the severe melt of sea ice. We argue that large-scale atmospheric circulation and synoptic cyclones acted in concert to trigger the exceptional poleward transport of total energy and moisture from April to June to cause this record minimum of sea ice extent in the following July.

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.

Direct impact of solar farm deployment on surface longwave radiation

Motivated by a previous study of using the Moderate Resolution Imaging Spectroradiometers (MODIS) observations to quantify changes in surface shortwave spectral reflectances caused by six solar farms in the southwest United States, here we used a similar method to study the longwave effects of the same six solar farms, with emphases on surface emissivities and land surface temperature (LST). Two MODIS surface products were examined: one relying on generalized split-window algorithm while assuming emissivities from land cover classifications (MYD11A2), the other based on Temperature Emissivity Separation algorithm capable of dynamically retrieving emissivities (MYD21A2). Both products suggest that, compared to adjacent regions without changes before and after solar farm constructions, the solar farm sites have reduced outgoing radiances in three MODIS infrared window channels. Such reduction in upward longwave radiation is consistent with previous in situ measurements. The MYD11A2 results show constant emissivities before and after solar farm constructions because its land type classification algorithm is not aware of the presence of solar farms. The estimated daytime and nighttime LST reduction due to solar farm deployment are ∼1–4K and ∼0.2–0.9K, respectively. The MYD21A2 results indicate a decrease in Band 31 (10.78–11.28 μm) emissivity up to −0.01 and little change in Band 32 (11.77–12.27 μm) emissivity. The LST decreases in the MYD21A2 is slightly smaller than its counterpart in the MYD11A2. Laboratory and in situ measurements indicate the longwave emissivity of solar panels can be as low as 0.83, considerably smaller than MODIS retrieved surface emissivity over the solar farm sites. The contribution of exposed and shaded ground within the solar farm to the upward longwave radiation needs to be considered to fully explain the results. A synthesis of MODIS observations and published in situ measurements is presented. Implication for parameterizing such solar farm longwave effect in the climate models is also discussed.

Validation and comparison of surface shortwave and longwave radiation products over the three poles

Global warming has currently become a great concern to the international community, among which the three poles (the Arctic, Antarctic, and Qinghai-Tibet Plateau) are the most serious. In this paper, in order to improve the understanding of the matter and energy cycle in the three poles and even the world, eleven shortwave products, (namely, CERES-SYN, ERA5, MERRA-2, NCEP-CFSR, JRA-55, GLDAS, BESS_Rad, MCD18A1, ISCCP-HXG-SSR, GLASS and APP-x), and seven longwave products, (CERES-SYN, ERA5, MERRA-2, NCEP-CFSR, JRA-55, GLDAS, and APP-x) are evaluated and inter-compared in terms of accuracy. During the assessment, the ground measurements collected from four independent ground observation networks (BSRN, CEOP, TPDC, and NMC) are used as reference, and the being compared products are aggregated to the same spatial and temporal scales to make them comparable. To better examine their performance, the eleven radiation products are comprehensively compared in multiple spatial (original scale and 1°×1°) and temporal scales (1-hourly, 3-hourly, daily, and monthly means, and instantaneous). The results show that in the three poles, CERES-SYN and ERA5 show overall better accuracy, at daily, 1°×1° resolutions. The (r)bias and (r)RMSE are less than (3%)5 W/m2 and (23%)40 W/m2 for SWDR, and less than (3%)7 W/m2 and (15%)25 W/m2 for LWDR, respectively, over the polar regions (the Arctic and Antarctic), which are generally better than that of the Qinghai-Tibet Plateau for most products. The remote sensing products and reanalysis products have their own advantages and disadvantages at different regions. In addition, with the spatio-temporal resolution decreasing, the accuracy of radiation products will gradually increase, except for the products of MCD18A1, ISCCP-HXG-SSR, NCEP-CFSR and GLDAS.

Continuous evaluation of the spatial representativeness of land surface temperature validation sites

A reliable accuracy is essential for the application of land surface temperature (LST) products. Current satellite retrieved LSTs are mainly validated over a few homogeneous sites. However, most of the existing ground sites are located in inhomogeneous areas: thus, their spatial representativeness on satellite pixel scales is unknown. In this situation, how to evaluate the spatial representativeness of these inhomogeneous sites, quantify the influence introduced by the spatial representativeness and describe the variation of the site's spatial representativeness are critical questions for satellite LST validation. In an attempt to answer those questions, a so-called temporal variation method (TVM) is proposed for evaluating a ground site's spatial representativeness. The method defines a spatial representativeness indicator (SRI), which is the LST difference between a ground radiometer's field-of-view (FOV) and a satellite pixel, and describes a site's spatial representativeness. Based on the temporal variation of LST, the SRI time series consists of three temporal components: ∆ATC, ∆DTCF-P, and ∆USC, which describe the annual, diurnal, and instantaneous variations of SRI, respectively. Associated with the Landsat TM/ETM+ data and weather parameters, the method is implemented and tested at 16 Chinese ground sites for the validation of MODIS and AATSR LST products. Results show that the temporally continuous SRI (SRITPR) shows high correlations with the original SRI (SRIORI). The variation of SRITPR is mainly determined by changes in surface coverage (i.e. NDVI difference on the two scales) and affected by weather conditions (e.g. near-surface air temperature, accumulative downward solar radiation, and wind speed). Since the SRI is defined as the LST difference between the two scales, it can be used as a bridge to convert the in-situ LST to pixel scale to address the spatial scale mismatch in LST validation. With this idea, the in-situ LST at daytime was converted to pixel scale associated with the SRITPR, and the corresponding MODIS and AATSR LST were validated at the 16 ground sites. Results for MODIS and AATSR LST show that the effect of spatial representativeness on the validation results over the sites is large, with mean biases between −1.95 K and 5.60 K and standard deviations between 0.07 K and 3.72 K. Since the TVM method does not rely on a specific satellite or land surface product, it is readily applied to other LST products (e.g. Sentinel-3 SLSTR LST, NOAA VIIRS LST) and surface parameters (e.g. surface longwave radiation).

Seasonal Trends in Clouds and Radiation over the Arctic Seas from Satellite Observations during 1982 to 2019

A long-term dataset of 38 years (1982–2019) from the Advanced Very High Resolution Radiometer (AVHRR) satellite observations is applied to investigate the spatio-temporal seasonal trends in cloud fraction, surface downwelling longwave flux, and surface upwelling longwave flux over the Arctic seas (60~90° N) by the non-parametric methods. The results presented here provide a further contribution to understand the cloud cover and longwave surface radiation trends over the Arctic seas, and their correlations to the shrinking sea ice. Our results suggest that the cloud fraction shows a positive trend for all seasons since 2008. Both surface downwelling and upwelling longwave fluxes present significant positive trends since 1982 with higher magnitudes in autumn and winter. The spatial distribution of the trends is nearly consistent between the cloud fraction and the surface longwave radiation, except for spring over the Chukchi and Beaufort Seas. We further obtained a significant negative correlation between cloud fraction (surface downwelling/upwelling longwave fluxes) and sea-ice concentration during autumn, which is largest in magnitude for regions with substantial sea ice retreat. We found that the negative correlation between cloud fraction and sea-ice concentration is not as strong as that for the surface downwelling longwave flux. It indicates the increase in cloudiness may result in positive anomalies in surface downwelling longwave flux which is highly correlated with the sea-ice retreat in autumn.

Developing machine learning‐based snow depletion curves and analysing their sensitivity over complex mountainous areas

AbstractA snow depletion curve (SDC), the relationship between snow mass (e.g., snow depth [SD]) and fractional snow cover area (SCF), is essential to parameterize the effect of snowpack within a physically based snow model. Existing SDCs are constructed using traditional statistic methods may not be applicable in complex mountainous areas. In this study, we developed an information fusion framework to define the relationship between SCF and SD as well as 12 auxiliary factors by using a traditional statistical method and four prevailing machine learning (ML) algorithms, which have comprehensively considered the variable conditions that cause spatiotemporal heterogeneity of snow cover. We also performed a single‐dimensional sensitivity analysis to investigate the physical rationality of the newly developed SDCs. The Northern Xinjiang, Northwest China, is selected as the study area, and the data from 46 meteorological stations covering five snow seasons from 2010 to 2015 are used. The results illustrated that ML techniques can be used to establish high‐accuracy and robust SDCs for complex mountainous areas. Compared with SDCs constructed by traditional statistical, the performance of the four ML‐based SDCs is significantly improved, the RMSE values can be reduced by 50%, R2 above 0.75, and an average relative variance close to 0. ML‐based SDCs predicted SCF values showed a range of sensitivities to different input variables (e.g., Land surface temperature, aspect, longwave radiation and land cover type), in addition to SD, that were physically representative of effects that snow cover is sensitive to. Moreover, the complexity of SDCs can be reduced by removing insensitive input variables.

A simple method for surface radiation estimating using FY-4A data

AbstractFY-4A is a geostationary meteorological satellite with four advanced payloads, which can be used to quantitatively detect the earth's atmospheric system with multi spectral and high spatial-temporal resolution. However, the applicable model limits the application of the FY-4A satellite data. In this paper, the empirical statistical model developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor is extended for FY-4A Advanced Geosynchronous Radiation Imager (AGRI), and it is applied to observed data to evaluate the applicability of the model for AGRI measurements. To improve the accuracy of radiation estimation, the artificial intelligent particle swarm optimization (PSO) algorithm was used for model optimizing. Results show that the estimated radiation has diurnal variation, which accords with the characteristics of radiation variation. The estimated net surface shortwave radiation (Sn) and observed values show good correlation. However, large deviations from observations are found in the estimated values when the empirical model based on MODIS is directly used to process AGRI data. Thus, the empirical statistical model based on MODIS can be applied to AGRI data, but the empirical parameters need to be revised. Optimization of the empirical statistical model by the PSO algorithm can effectively improve the accuracy of radiation estimate. The Mean absolute percentage error (MAPE) of Sn estimated by optimized models is reduced to 15%. The MAPE of the net surface long-wave radiation (Ln) estimated by optimized models is reduced to 31%, and the MAPE of the net radiation (Rn) estimated by optimized models is reduced to 27%. However, for the uncertainty caused by error accumulation effect, the influence of PSO optimization on Rn is not as obvious as that of Ln. However, from the analysis of error distribution, it shows that PSO optimization does improve the estimation results of Rn. Based on AGRI data, the surface radiation can be estimated simply, and the regional or larger scale surface radiation retrieval can quickly realize by this method which has large application potential and popularization value.

Projected changes in the mean and intra-seasonal variability of the Indian summer monsoon in the RegCM CORDEX-CORE simulations under higher warming conditions

The present study employed the latest high-resolution regional climate model (RegCM4), driven by MPI-ESM-MR boundary conditions from the CORDEX-CORE South Asia framework to investigate the possible projected changes in the mean and intra-seasonal variability of the Indian summer monsoon (ISM) precipitation and their associated dynamics during near future (NF; 2041–2060) and far future (FF; 2080–2099) with respect to the historical period (1995–2014) under RCP8.5 scenario. Extensive evaluation analysis indicates that the RegCM4 is fairly able to simulate the spatial–temporal distribution of the observed mean and extreme precipitation, low-level jet, and intra-seasonal variability i.e. active and break composite patterns of the precipitation anomalies over India during the historical period. A substantial decline in the projected precipitation during ISM is estimated over central and northwest India in NF (about 10–30%) as well as in FF (upto 50%), which may be attributed to the weakening and northward shift of low-level winds. The occurrences as well as the intensity of the extreme precipitation events are expected to increase over India in the future. The precipitation during the projected active spells will escalate over the monsoon core region. This is supported by the decrease in sea level pressure over land, which favors the winds to transport more moisture from the adjoining seas for the formation of convective clouds, which is partly indicated through the decline in net surface longwave radiation. On the other hand, the precipitation intensity during the projected break spells is expected to further decrease in the future.

Global Land Surface Temperature Change (2003–2017) and Its Relationship with Climate Drivers: AIRS, MODIS, and ERA5-Land Based Analysis

Land surface temperature (LST) plays a critical role in the water cycle and energy balance at global and regional scales. Large-scale LST estimates can be obtained from satellite observations and reanalysis data. In this study, we first investigate the long-term changes of LST during 2003–2017 on a per-pixel basis using three different datasets derived from (i) the Atmospheric Infrared Sounder (AIRS) onboard Aqua satellite, (ii) the Moderate Resolution Imaging Spectroradiometer (MODIS) also aboard Aqua, and (iii) the recently released ERA5-Land reanalysis data. It was found that the spatio-temporal patterns of these data agree very well. All three products globally showed an uptrend in the annual average LST during 2003–2017 but with considerable spatial variations. The strongest increase was found over the region north of 45° N, particularly over Asian Russia, whereas a slight decrease was observed over Australia. The regression analysis indicated that precipitation (P), incoming surface solar radiation (SW↓), and incoming surface longwave radiation (LW↓) can together explain the inter-annual LST variations over most regions, except over tropical forests, where the inter-annual LST variation is low. Spatially, the LST changes during 2003–2017 over the region north of 45° N were mainly influenced by LW↓, while P and SW↓ played a more important role over other regions. A detailed look at Asian Russia and the Amazon rainforest at a monthly time scale showed that warming in Asian Russia is dominated by LST increases in February–April, which are closely related with the simultaneously increasing LW↓ and clouds. Over the southern Amazon, the most apparent LST increase is found in the dry season (August–September), primarily affected by decreasing P. In addition, increasing SW↓ associated with decreasing atmospheric aerosols was another factor found to cause LST increases. This study shows a high level of consistency among LST trends derived from satellite and reanalysis products, thus providing more robust characteristics of the spatio-temporal LST changes during 2003–2017. Furthermore, the major climatic drivers of LST changes during 2003–2017 were identified over different regions, which might help us predict the LST in response to changing climate in the future.

Summer Regional Pentad Heat Wave in Eastern China and Their Possible Causes

Identification and understanding of persistent regional heat wave are essential for adaption and alleviation of its severe effects on human health and ecological environment, particularly under the situation of rapid global warming. Based on Climate Prediction Center (CPC) global daily maximum temperature from 1979 to 2019, considering extreme, persistence and regionality of heat wave, summer regional pentad heat wave (RPHW) is defined by summer daily maximum temperature above 35 ℃ with no less than 3 days in a pentad, which is also required to cover more than 2% grids over the eastern China. It breaks down the continuity constraints in both time and space in previous regional heat wave definitions that mainly indicate the synoptic phenomena. Besides, our RPHW is much useful in examining climatic features of regional heat wave, since it filters out the weather noises to some extend in terms of a pentad window. Three categories of RPHW are identified by K-means cluster analysis over the eastern China, i.e., North-Central China (NCC), Central China (CC) and South China (SC). Generally, intensity and area of RPHW are positively proportional to each other, and more RPHW appears in SC but with weaker intensity, whereas less RPHW locates in NCC and CC with stronger intensity. While RPHW in CC and SC mostly appears in middle-late summer, RPHW in NCC mainly occurs in early-middle summer which is transferred from the middle-late summer before the late 1990s. Most RPHW persists for 1 pentad and occasionally for 5 or 6 pentads at most. And both frequency and accumulated days of RPHW have been significantly increased since the late 1990s, particularly in CC and SC. Significant high-pressure anomalies are accompanied with the RPHW in China, which favor more incoming net solar radiation, increasing surface soil temperature, and more upward surface longwave radiation and sensible heat flux as well, eventually forming the RPHW. Specifically, the RPHW in NCC and SC are mainly caused by diabatic heating from the land surface and adiabatic heating due to the anomalous subsidence movement, respectively; however, both the diabatic heating and the adiabatic heating are responsible for the RPHW in CC.

Investigation of black carbon climate effects in the Arctic in winter and spring

Black carbon (BC) exerts a potential influence on climate, especially in the Arctic, where the environment is very sensitive to climate change. Therefore, the study of climate effects of BC in this region is particularly important. In this study, numerical simulations were performed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) in the Arctic in winter and spring for two years to investigate the atmospheric BC causing changes in surface radiation, meteorology, and atmospheric stability. Generally, WRF-Chem well reproduced the temporal variations of meteorological variables and BC concentration. Numerical simulations showed that BC concentrations in the Arctic in winter were mostly higher than those in spring, and the BC-induced near-surface temperature changes were also stronger. The effects of BC on near-surface water vapor mixing ratio were consistent with the spatial pattern of near-surface temperature changes. That was probably the result of the regional circulation anomaly due to the temperature changes. Additionally, the distributions of near-surface temperature changes and horizontal wind changes also reflected in the distribution of planetary boundary layer height. Ultimately, this study revealed that the downward longwave radiation related to cloudiness changes played an important role for driving near-surface temperature in the Arctic in winter. While in spring, the relatively less changes in near-surface temperature may be the result of the mutual compensation between the surface longwave and shortwave radiation effects.

A Scheme to Estimate Diurnal Cycle of Evapotranspiration from Geostationary Meteorological Satellite Observations

The diurnal cycle of evapotranspiration (ET) is significant in studying the dynamics of land–atmosphere interactions. The diurnal ET cycle can be considered as an indicator of dry/wet surface conditions. However, the accuracy of current models in estimating the diurnal ET cycle is generally low. This study developed an improved scheme to estimate the diurnal cycle of ET by solving the surface energy balance equation combined with simplified parameterization, with daily ET as the constraint. Meteosat Second Generation (MSG) land surface temperature, and longwave and shortwave radiation products were the primary inputs. Daily ET was from the remote sensing-based ETMonitor model. The estimated instantaneous (30 min) ET from the improved scheme outperformed the official MSG instantaneous ET product when compared with in situ half-hourly measurements at 35 flux sites from the FLUXNET2015 dataset, and was also comparable with European Center for Medium-Range Weather Forecasts (ECMWF) ERA5 ET data, with an R2 of 0.617 and root mean square error (RMSE) of 65.8 W/m2 for the improved scheme. Results were largely improved compared with those without daily ET as the constraint. The improved method was stable for the estimation of ET’s diurnal cycle at the similar atmospheric conditions and the accuracy was comparative at different land cover surfaces. Errors in the input variables and the simplification of surface heat flux parameterization affected surface energy balance closure, which can lead to instability of the solution of constants in the simplified parameterization and further to the uncertainty of ET’s diurnal cycle estimation. Measurement errors, different source areas in measured variables, and inconsistent spatial representativeness between remote sensing and site measurements also impacted the evaluation.

Supercooled liquid water cloud observed, analysed, and modelled at the top of the planetary boundary layer above Dome C, Antarctica

Abstract. A comprehensive analysis of the water budget over the Dome C (Concordia, Antarctica) station has been performed during the austral summer 2018–2019 as part of the Year of Polar Prediction (YOPP) international campaign. Thin (∼100 m deep) supercooled liquid water (SLW) clouds have been detected and analysed using remotely sensed observations at the station (tropospheric depolarization lidar, the H2O Antarctica Microwave Stratospheric and Tropospheric Radiometer (HAMSTRAD), net surface radiation from the Baseline Surface Radiation Network (BSRN)), radiosondes, and satellite observations (CALIOP, Cloud-Aerosol LIdar with Orthogonal Polarization/CALIPSO, Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations) combined with a specific configuration of the numerical weather prediction model: ARPEGE-SH (Action de Recherche Petite Echelle Grande Echelle – Southern Hemisphere). The analysis shows that SLW clouds were present from November to March, with the greatest frequency occurring in December and January when ∼50 % of the days in summer time exhibited SLW clouds for at least 1 h. Two case studies are used to illustrate this phenomenon. On 24 December 2018, the atmospheric planetary boundary layer (PBL) evolved following a typical diurnal variation, which is to say with a warm and dry mixing layer at local noon thicker than the cold and dry stable layer at local midnight. Our study showed that the SLW clouds were observed at Dome C within the entrainment and the capping inversion zones at the top of the PBL. ARPEGE-SH was not able to correctly estimate the ratio between liquid and solid water inside the clouds with the liquid water path (LWP) strongly underestimated by a factor of 1000 compared to observations. The lack of simulated SLW in the model impacted the net surface radiation that was 20–30 W m−2 higher in the BSRN observations than in the ARPEGE-SH calculations, mainly attributable to the BSRN longwave downward surface radiation being 50 W m−2 greater than that of ARPEGE-SH. The second case study took place on 20 December 2018, when a warm and wet episode impacted the PBL with no clear diurnal cycle of the PBL top. SLW cloud appearance within the entrainment and capping inversion zones coincided with the warm and wet event. The amount of liquid water measured by HAMSTRAD was ∼20 times greater in this perturbed PBL than in the typical PBL. Since ARPEGE-SH was not able to accurately reproduce these SLW clouds, the discrepancy between the observed and calculated net surface radiation was even greater than in the typical PBL case, reaching +50 W m−2, mainly attributable to the downwelling longwave surface radiation from BSRN being 100 W m−2 greater than that of ARPEGE-SH. The model was then run with a new partition function favouring liquid water for temperatures below −20 down to −40 ∘C. In this test mode, ARPEGE-SH has been able to generate SLW clouds with modelled LWP and net surface radiation consistent with observations during the typical case, whereas, during the perturbed case, the modelled LWP was 10 times less than the observations and the modelled net surface radiation remained lower than the observations by ∼50 W m−2. Accurately modelling the presence of SLW clouds appears crucial to correctly simulate the surface energy budget over the Antarctic Plateau.

Sensitivity studies and comprehensive evaluation of RegCM4.6.1 high-resolution climate simulations over the Tibetan Plateau

This study provides the first comprehensive assessment of Regional Climate Model version 4.6.1 (RegCM4) for the Tibetan Plateau (TP) region. A wide range of model configurations were analyzed with different parameterizations employed to represent cumulus convection (Kuo, Grell, Emanuel, Kain, and Tiedtke), land surface processes (BATS and CLM), planetary boundary layer turbulence (Holtslag and UW PBL), and radiation (CCM3 and RRTM). In addition to the above experiments at a 30-km horizontal resolution, another experiment was conducted based upon the use of a double-nested dynamic downscaling method to construct a simulation at a 10-km resolution to study the sensitivity to the model resolution. We evaluated a 20-year simulation for precipitation, cloud cover, surface radiation budget, 2-m air temperature, and the surface atmospheric circulation against ground and satellite-based observations during the period 1989–2008. Among the factors analyzed regarding sensitivity, precipitation was unsurprisingly found to be sensitive to the cumulus parameterization scheme, and the CLM is found to reduce rainfall compared with BATS, which is satisfactory for both the Emanuel and Tiedtke schemes. Compared with the cumulus convection schemes, the cloud cover and surface radiation budget are sensitive to the land surface, PBL, and radiation schemes. Generally, the CLM is characterized by reduced mean cloud cover and enhanced surface longwave and shortwave radiation compared with BATS. Conversely, the UW PBL and RRTM radiation schemes result in increased cloud cover and less surface radiation compared with the default options in RegCM4. All experiments, except those employing the Kuo scheme, represent the mean 2-m air temperature and regional circulation patterns reasonably well. At the basin scale, the seasonal cycle and interannual variations of precipitation are found to be not well depicted by most model configurations, although the temperature field was well reproduced. Considering all the analyzed variables collectively, the Tiedtke scheme combined with the CLM land surface model is demonstrated to provide the best performance over the TP. However, the higher-resolution version of the model improves the precipitation simulation significantly, particularly in the Brahmaputra river basin, which is located in the north of the Himalayas.

How can 30\u201360-day ISO move from the South China Sea to Southern China?

In the present study, the northward-propagating intraseasonal oscillations (ISOs) over the South China Sea (SCS) during the summer monsoon season are divided into two types. One is the northward propagation of ISOs confined in the SCS (defined as P1-ISO), another for ISOs moving from the SCS to Southern China (SC) defined as P2-ISO. The spatial and temporal structures of the two types of ISOs are compared based on the analysis of the NCEP–NCAR reanalysis datasets. It is found that a significant difference appears in the heat flux anomalies over SC that is attributed to the cloud-radiation effect. A mechanism associated with the changes in the convective instability is proposed to understand the cause of the northward propagation of the ISO from the SCS to SC. For the P1-ISO, a more precipitation over SC leads to a less surface solar radiation and then a cold land surface temperature (LST), which induces an increase of static stability via surface longwave radiation and sensible heat flux. Whereas the total contribution to the convective instability changes does create an unfavorable for ISOs propagating northward, the moisture convergence, mainly controlled by the baroclinic vorticity advection effect, may contribute to an increase of convective instability that favors the ISOs moving northward. For the P2-ISO, a less precipitation over SC results in a more shortwave radiation reaching the ground that induces a warm LST, and thus leading to a warm low-level air temperature and destabilizing the atmosphere ahead of the convection. Moreover, the moisture convergence, dominated by the warm low-level air temperature, also contributes to the increase of convective instability. Thus the convective instability increment should favor the ISOs moving northward. It is worth noting that the baroclinic vorticity advection and moisture advection are not essential for ISOs propagating from the SCS to SC.

Assessment of the Long-Term High-Spatial-Resolution Global LAnd Surface Satellite (GLASS) Surface Longwave Radiation Product Using Ground Measurements

In this article, we comprehensively assessed the newly released long-term high-spatial-resolution Global LAnd Surface Satellite (GLASS) surface longwave (LW) radiation product using site measurements of LW fluxes. In total, three years of ground-measured LW fluxes (surface longwave upward radiation (LWUP), surface longwave downward radiation (LWDN), and surface longwave net radiation (LWNR) collected from 141 sites in six independent networks (AmeriFlux, AsiaFlux, BSRN, CEOP, HiWATER-MUSOEXE, and TIPEX-III) are used to evaluate the GLASS LW radiation product. These sites cover various land cover types, surface elevations, and climatic types. According to the evaluation results, the biases are -4.33, -3.77, and 0.70 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and the RMSEs are 18.15, 26.94, and 26.70 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for clear-sky LWUP, LWDN, and LWNR, respectively. The GLASS LW radiation product performs well in climate-change-sensitive areas such as poleward areas, semiarid areas, and the “third pole”, namely, the Tibetan Plateau. The accuracy of the GLASS LW product is higher or comparable to that of available LW products and studies but has a high-spatial-resolution of 1 km and a time span of 19 years. In conclusion, the overall accuracy of the clear-sky GLASS LW radiation product can satisfy the requirements of the hydrological, meteorological, and agricultural research communities on a global scale. We will continue to improve the retrieval algorithms and update the products accordingly.

Asymmetry of Cloud Vertical Structures and Associated Radiative Effects in Typhoon over the Northwest Pacific Based on CloudSat Tropical Cyclone Dataset

The clouds’ macro-, microphysical vertical structures and radiative effects in 4 shear-relative quadrants of typhoon over the northwest Pacific during development, maturity and extinction stages are studied based on CloudSat Tropical Cyclone dataset and China Meteorological Administration tropical cyclone dataset from 2nd June 2006 to 31th December 2015. The typhoon cloud is in an asymmetric “mushroom” shape, with the downshear quadrants (in particular of the downshear left quadrant (DL)) have denser clouds than the upshear quadrants. Cloud ice water content mainly distributes near typhoon center with wide vertical range (6–17 km). A large number of ice particles with small sizes are gathering in high levels, while small amount of ice particles with large sizes are gathering in low levels. As typhoon matures, the number concentration and size of cloud ice particles in inner ring increases, especially in the DL quadrant; while in the upshear left (UL) quadrant, a larger amount of ice particles with bigger sizes are transport to high levels (above 16 km) by deeper convection near storm center. The shortwave (longwave) cloud radiative effects (CRE) is mainly heating (cooling) upper layer atmosphere between 10 km and 17 km (between 14 km and 17 km), and the net CRE on atmosphere is heating almost at any levels in typhoon. The strongest heating of shortwave CRE and net CRE, as well as the strongest cooling of longwave CRE are in the DL quadrant at development stage and in the UL quadrant at maturity stage in inner core of storms. The existences of typhoon clouds mainly decrease solar radiation penetrating to the earth surface and increase longwave radiation absorbed by the whole atmosphere in typhoon’s inner core, and they are generally stronger in downshear (especially in DL) quadrants, except the maturity stage when the UL quadrant performs the strongest shortwave CRE on the surface and longwave CRE on the atmosphere in typhoon’s inner core.

Emergent Relationships Among Sea Ice, Longwave Radiation, and the Beaufort High Circulation Exposed Through Parameter Uncertainty Analysis

AbstractUsing a perturbed parameter ensemble of a coupled climate model, emerging relationships are identified between sea ice area, net surface longwave radiation, and the atmospheric circulation over the Beaufort gyre. There is a strong positive correlation between sea ice area and the net longwave radiation over the ocean‐ice surface during the melting season and a negative correlation during the freezing season. The mechanisms responsible for the longwave radiation balance at the surface are mainly driven by sea ice variations in the freezing season and by clouds in the melting season. A strong positive (negative) correlation is also found between the fall (summer) total sea ice area in the Arctic and the sea level pressure over the Beaufort High region. It is argued that as sea ice coverage is lost, static stability losses are severe in fall, resulting in enhanced evaporation, vertical motions, and weakening of the general large‐scale anticyclonic circulation of the Beaufort High.

Modeling surface longwave radiation over high-relief terrain

Thermal anisotropy is an important phenomenon in thermal infrared remote sensing as it restricts the retrieval accuracy of surface longwave radiation (SLR). Topography is an essential controlling factor for the directionality of SLR for high-relief regions (e.g., mountain regions) where there is land surface heterogeneity and non-isothermal properties at pixel scales. However, satellite sensors can only receive radiance from a specific surface object at a small number of simultaneous viewing angles, which makes the quantitative modeling of thermal anisotropy challenging. Therefore, we developed the topographic longwave radiation model (TLRM) to describe the directionality of SLR components taking into account the variability of both subpixel topography and thermal anisotropy in high-relief regions. The reliability of TLRM was validated using the Discrete Anisotropic Radiative Transfer (DART) model over two typical geomorphic areas: a valley scene and a peak scene. The preliminary validation shows good agreement in terms of surface upward longwave radiance, which confirms the potential of TLRM for capturing the anisotropic patterns of land surfaces. The radiance values simulated by the DART model were first revised for the spectral mismatch. Then, they are used to correct residual deviation for TLRM using linear regressions. The root mean square error (RMSE) and coefficient of determination (R2) were 0.830 W/(m2 ∙ sr) and 0.746 for the valley scene, respectively, and 0.239 W/(m2 ∙ sr) and 0.711 for the peak scene, respectively. Compared with TLRM, models that do not consider terrain effects generate significant discrepancies in high relief SLR components. The differences in downward longwave radiation can reach −60 W/m2 in valleys without considering terrain effects. Based on the reference of hemispherical upward longwave radiation, surface upward longwave radiation estimated by the direct estimation method had a bias of 11.41 W/m2 and standard deviation (STD) of 7.30 W/m2, while the directional upward longwave radiation had a bias of 5.99 W/m2 and STD of 4.08 W/m2, showing lower estimation variation. The discrepancy between surface net longwave radiation (NLR) and terrain-corrected NLR ranged between 50 and −130 W/m2 with clear negative biases mainly occurring in valleys. With higher spatial resolutions of remotely sensed imagery, the influence of complex terrain on land surface radiative flux has become more significant. This parameterization scheme is expected to better represent the topographic effects on SLR, enhance understanding of thermal anisotropy in non-isothermal mixed pixel areas of high relief, and improve the inversion accuracy of SLR.