Net Surface Shortwave Radiation Research Articles

The emission, transport, and impacts of the extreme Saharan dust storm of 2015

Abstract. ​​​​​​​Each summer, the Saharan Air Layer (SAL) transports massive amounts of mineral dust across the Atlantic Ocean, affecting weather, climate, and public health over large areas. Despite the considerable impacts of African dust, the causes and impacts of extreme trans-Atlantic African dust events are not fully understood. The “Godzilla” trans-Atlantic dust event of 2020 has been extensively studied, but little is known about other similar events. Here, we examine the June 2015 event, the second strongest trans-Atlantic African dust event that occurred during the summers from 2003–2022. This event was characterized by moderately high dust emissions over western North Africa and an extremely high aerosol optical depth (AOD) over the tropical North Atlantic. The high dust loading over the Atlantic is associated with atmospheric circulation extremes similar to the Godzilla event. Both the African easterly jet (AEJ) and Caribbean low-level jet (CLLJ) have greatly intensified, along with a westward extension of the North Atlantic subtropical high (NASH), all of which favor the westward transport of African dust. The enhanced dust emissions are related to anomalously strong surface winds in dust source regions and reduced vegetation density and soil moisture across the northern Sahel. The dust plume reduced net surface shortwave radiation over the eastern tropical North Atlantic by about 25 W m−2 but increased net longwave flux by about 3 W m−2. In contrast to the Godzilla event, the 2015 event had minor air quality impacts on the US, partially due to the extremely intensified CLLJ that dispersed the dust plume towards the Pacific.

The Inter‐Model Uncertainty of Projected Precipitation Change in Northern China: The Modulating Role of North Atlantic Sea Surface Temperature

AbstractPrecipitation changes in northern China are projected to increase in the Coupled Model Inter‐comparison Project Phase 6 (CMIP6) multi‐model ensemble. However, these projections are accompanied by notable inter‐model uncertainty, and the sources of this uncertainty remain largely unexplored. By analyzing 30 CMIP6 models, this research explores the source of inter‐model uncertainty in projected precipitation change and reveals the fundamental mechanism driving uncertainty spread. Following the empirical orthogonal function of inter‐model projected precipitation change, the leading mode displays a seesaw spatial pattern between northwest and north China. This phenomenon predominantly stems from the inter‐model divergence of projected sea surface temperature (SST) warming in the North Atlantic. Further scrutinizing the ocean mixed layer heat budget, we discover that the combined effect of surface sensible heat flux, net surface shortwave radiation flux, and ocean heat transport convergence influences heat flux and SST change of North Atlantic. The multi‐model projections indicate that localized increases in solar radiation and heat convergence warm sea surface, raising SST and initiating convective motion. This convective motion subsequently transforms the 200 hPa teleconnection wave train, leading to an anti‐phase pattern over northern China. This wave pattern modulates total cloud cover percentage, influences surface upward latent heat flux, and adjusts the top of atmosphere outgoing longwave radiation, collectively resulting in the seesaw pattern. Our study underscores the pivotal role of inter‐model disparities in North Atlantic SST warming projection, which is a primary driver of precipitation uncertainty in northern China. These insights offer an essential foundation for refining and diminishing inter‐model uncertainty.

Rapid high-latitude cooling in the southeastern Pacific sector driven by North Atlantic warming during 1979–2013 in CESM1

During the 1979–2013 satellite observation period, the sea surface temperature (SST) has cooled substantially in the high-latitude Southern Ocean, with the most pronounced cooling tendency centered in the southeastern Pacific domain. Previous hypotheses have commonly ascribed the recent Southern Ocean cooling to either the tropical eastern Pacific cooling or North Atlantic and tropical Indian Ocean SST warming. However, the mechanisms underpinning significant cooling in the southeastern Pacific sector remain debatable. By diagnosing three pacemaker experiments with a state-of-the-art global climate model in which SSTs in the North Atlantic, tropical central-eastern Pacific, and tropical Indian Ocean-western Pacific are individually nudged to mimic the observed trajectory, we show that the North Atlantic dominates in the cold SST response of the southeastern Pacific sector during 1979–2013. Anomalous North Atlantic warming initiates a quasi-stationary Rossby wave response to a south-to-north cross-equatorial Hadley circulation strengthening, leading to an enhanced Amundsen Sea Low. As a result, due primarily to the increased low-level marine cloud cover, the net surface shortwave radiation reduction triggers rapid SST cooling in the southeastern Pacific domain. The southeastern Pacific cold SST anomalies are further maintained via the shortwave radiation-low-cloud-SST positive feedback on decadal timescales. Our results suggest that the shortwave radiation-low-cloud SST feedback is fundamental to the observed long-term cooling of the high-latitude Southern Ocean, with profound climate consequences worldwide.

Influence of the Pacific Decadal Oscillation on Winter Temperatures and Precipitation Over the Southern Tibetan Plateau

AbstractWe used observational data and a long‐term piControl simulation from the Community Earth System Model Version 2 to investigate the influence of the Pacific Decadal Oscillation (PDO) on the winter climate over the Tibetan Plateau. The results showed that changes in the phase of the PDO have a significant effect on winter temperatures and precipitation over the southern Tibetan Plateau. Changes in the sea surface temperature (SST) during the positive PDO can weaken the Walker circulation and increase the SST in the Indian Ocean. Our analyses of the moist static energy showed that warming of the tropical troposphere over the Indian Ocean caused by the increased SST has resulted in the horizontal advection of anomalous moist enthalpy by the climatological zonal winds, which was responsible for anomalous ascending motion over the Tibetan Plateau. The additional moisture budget suggests that enhanced vertical motion contributes to the increase in winter precipitation and related total cloud cover over the Tibetan Plateau, leading to the increase of snow depth. The increased total cloud cover and snow depth, in turn, reduces net surface shortwave radiation. The surface air temperature of the Tibetan Plateau is then decreased as a result of the reduction in the net surface shortwave radiation. The PDO therefore has an important modulating role in the interdecadal variability of the winter climate over the Tibetan Plateau. We therefore need to focus on changes in the PDO in research related to the decadal prediction of the climate over the Tibetan Plateau.

Zonally asymmetric component of summer surface temperature trends caused by intraseasonal time-scale processes

Recent years have witnessed extreme heatwaves in Europe and western North America. This study shows that these regions stand out in the zonally asymmetric component of the long-term trend of boreal summer surface temperature, and that intraseasonal timescale processes play an important role in shaping the zonally asymmetric trend pattern. However, these two regions have warmed by different mechanisms. Over Europe, the warming is mostly caused by the positive trend of the net (downward minus upward) surface shortwave radiation weighted by its intraseasonal timescale connection with the skin temperature. The long-term warming in western North America has been caused by the declining surface latent heat flux (weakened evaporative cooling) weighted by its intraseasonal connection with the skin temperature. These mechanisms are consistent with those identified in earlier studies of individual extreme events in the two regions, indicating that part of the long trends are a manifestation of extreme events. The overall findings indicate that to make accurate projections of regional climate change using climate model simulations, it is critical to ensure that the models also accurately simulate intraseasonal variability.

Enhanced seasonality of surface air temperature over China during the mid-Holocene

Using all available simulations performed by climate models participating in PMIP4 (Paleoclimate Modelling Intercomparison Project – Phase 4), the authors quantify the seasonality change of surface air temperature over China during the mid-Holocene (6000 years ago) and the associated physical mechanisms. Relative to the preindustrial period, all 16 models consistently show an enhanced temperature seasonality (i.e., summer minus winter temperature) across China during that interglacial period, with a nationally averaged enhancement of 2.44 °C or 9% for the multimodel mean. The temperature seasonality change is closely related with the seasonal contrast variation of surface energy fluxes mainly due to the mid-Holocene orbital forcing. Specifically, the summer–winter increase in surface net shortwave radiation dominates the intensified temperature seasonality at the large scale of China during the mid-Holocene; the surface net longwave radiation has a minor positive contribution in most of the Tibetan Plateau and eastern China; and both the surface latent and sensible heat fluxes show partial offset effects in most of the country. There are uncertainties in the reconstructed temperature seasonality over China during the mid-Holocene based on the proxy data that can reflect seasonal signals.摘要利用PMIP4多模式试验数据, 作者量化了中全新世 (距今约6000年) 中国温度季节性变化. 结果表明: 相对于工业革命前期, 所有16个模式一致模拟显示中全新世我国温度季节性 (即夏季与冬季温差) 增强, 平均增幅9%; 这与该时期轨道强迫引起的地表能量通量的季节对比变化密切相关, 其中净短波辐射起主导作用, 净长波辐射作用次之, 感热和潜热为负贡献; 与模拟不同, 重建结果存在不确定性.

Understanding and Reducing Warm and Dry Summer Biases in the Central United States: Analytical Modeling to Identify the Mechanisms for CMIP Ensemble Error Spread

Abstract Most climate models in phase 6 of the Coupled Model Intercomparison Project (CMIP6) still suffer pronounced warm and dry summer biases in the central United States (CUS), even in high-resolution simulations. We found that the cloud base definition in the cumulus parameterization was the dominant factor determining the spread of the biases among models and those defining cloud base at the lifting condensation level (LCL) performed the best. To identify the underlying mechanisms, we developed a physically based analytical bias model (ABM) to capture the key feedback processes of land–atmosphere coupling. The ABM has significant explanatory power, capturing 80% variance of temperature and precipitation biases among all models. Our ABM analysis via counterfactual experiments indicated that the biases are attributed mostly by surface downwelling longwave radiation errors and second by surface net shortwave radiation errors, with the former 2–5 times larger. The effective radiative forcing from these two errors as weighted by their relative contributions induces runaway temperature and precipitation feedbacks, which collaborate to cause CUS summer warm and dry biases. The LCL cumulus reduces the biases through two key mechanisms: it produces more clouds and less precipitable water, which reduce radiative energy input for both surface heating and evapotranspiration to cause a cooler and wetter soil; it produces more rainfall and wetter soil conditions, which suppress the positive evapotranspiration–precipitation feedback to damp the warm and dry bias coupling. Most models using non-LCL schemes underestimate both precipitation and cloud amounts, which amplify the positive feedback to cause significant biases.

Biophysical Impact of Land-Use and Land-Cover Change on Subgrid Temperature in CMIP6 Models

Abstract In this study, we investigate the air temperature response to land-use and land-cover change (LULCC; cropland expansion and deforestation) using subgrid land model output generated by a set of CMIP6 model simulations. Our study is motivated by the fact that ongoing land-use activities are occurring at local scales, typically significantly smaller than the resolvable scale of a grid cell in Earth system models. It aims to explore the potential for a multimodel approach to better characterize LULCC local climatic effects. On an annual scale, the CMIP6 models are in general agreement that croplands are warmer than primary and secondary land (psl; mainly forests, grasslands, and bare ground) in the tropics and cooler in the mid–high latitudes, except for one model. The transition from warming to cooling occurs at approximately 40°N. Although the surface heating potential, which combines albedo and latent heat flux effects, can explain reasonably well the zonal mean latitudinal subgrid temperature variations between crop and psl tiles in the historical simulations, it does not provide a good prediction on subgrid temperature for other land tile configurations (crop vs forest; grass vs forest) under Shared Socioeconomic Pathway 5–8.5 (SSP5–8.5) forcing scenarios. A subset of simulations with the CESM2 model reveals that latitudinal subgrid temperature variation is positively related to variation in net surface shortwave radiation and negatively related to variation in the surface energy redistribution factor, with a dominant role from the latter south of 30°N. We suggest that this emergent relationship can be used to benchmark the performance of land surface parameterizations and for prediction of local temperature response to LULCC.

A Method for Estimating Daytime Average Evapotranspiration From Diurnal Land Surface Temperature Measurements

Evapotranspiration (ET) is an essential parameter in the water cycle and surface energy balance. Accurate estimation of daytime or daily ET is of great significance for many activities in human economic and social development. This study aims to propose a novel method for estimating daytime average ET by using temporal measurements of land surface temperature (LST) and net surface shortwave radiation (NSSR), following a previous developed elliptical relationship between diurnal cycles of LST and NSSR under cloud-free days. The method was primarily developed from the simulated data of a physics-based Atmosphere–Land Exchange (ALEX) model under different underlying surfaces and atmospheric conditions. Based on the simulated data, the proposed method showed considerable accuracy with the overall coefficient of determination ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) of 0.958 and the root mean square error (RMSE) of 25.3 Wm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> . In addition, ground ET measurements at four Ameriflux sites (US-ARM, US-SRM, US-Whs, and US-Wkg) during the 2018 growing season were collected to assess the estimated daytime average ET. Results show an overall RMSE of 64.7 Wm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> for the estimated ET at the four sites, and the US-Whs site reveals a best accuracy ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.825, RMSE=44.4 Wm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> ). These results indicated a potential for generating daytime ET with geostationary satellite observations at regional scales in future development.

Estimation of fine spatial resolution all-sky surface net shortwave radiation over mountainous terrain from Landsat 8 and Sentinel-2 data

Net shortwave radiation (NSR) plays an essential role in surface energy budget and has been reported to exhibit low estimation accuracy when ignoring topographic effects. Several algorithms have been developed to estimate clear-sky NSR in mountains, but none have estimated NSR in mountainous terrain under all-sky (i.e., both clear and cloudy) conditions. Moreover, surface heterogeneity is considerable in mountains, and coarse-spatial-resolution satellites may not provide sufficient details for fine-scale applications. In this study, to address these challenges, we developed a method for fine-spatial-resolution (i.e., from 20 m to 30 m resolution) all-sky NSR estimation in mountains directly from Landsat 8 and Sentinel-2 top-of-atmosphere (TOA) observations with mountainous radiative transfer scheme (herein denoted ‘TOPO’). For comparison, we also created the model neglecting topography (herein called ‘FLAT’). The validation of the TOPO model in complex terrain against Chengde ground-based pyrometers in China was good, with an R2 of 0.91 and a root-mean-square error (RMSE) of 80.9 W/m2, which was an improvement of 46.1% (reduction of RMSE) against the FLAT model. Meanwhile, the consistency of NSR generated from Landsat 8 and Sentinel-2 using TOPO model was satisfactory. Time-series comparisons of NSR derived from TOPO and FLAT models showed that topographic effects were substantial both in clear sky (maximum deviation of 643.4 W/m2) and cloudy sky (270.2 W/m2) when NSR estimates from the two models were compared. The evaluation of the FLAT model with a dataset simulated from the mountainous radiative transfer (created for the TOPO model) showed that neglecting topographic effects over mountainous terrain could lead to RMSEs of 541.7 W/m2 (relative RMSE = 159.5%) and 201.4 W/m2 (relative RMSE = 217.1%) for NSR estimation under clear and cloudy skies, respectively. This study provides a straightforward method of deriving fine-spatial-resolution all-sky NSR with topographic consideration, which can be further extended to the global mapping of NSR over mountainous terrain.

Trapezoid-based surface soil moisture retrieval using a pixel-to-pixel scheme: A preliminary result over the North China Plain

• A pixel-to-pixel scheme of the trapezoid-based methods has been developed. • Satellite data was used to obtain atmospheric variables required by the trapezoid. • Estimated surface soil moisture were evaluated with in-situ and microwave data. The trapezoid configuration constituted by spatially distributed land surface temperature (LST) and fraction vegetation cover (FVC) has been widely used for estimating surface soil moisture (SSM) with satellite data. However, the trapezoid-based methods still suffer from a major challenge that the determination of the trapezoid pixel window is always subjective, because it requires quasi-identical atmospheric conditions to make SSM retrievals for each pixel spatially comparable. Meanwhile, the trapezoid pixel window should also contain full vegetation coverages and SSM dynamics to fulfill the inscape of the trapezoid. Due to the heterogeneity of atmospheric variables and underlying surfaces, it is commonly difficult to determine a proper trapezoid pixel window in practices. The development of the pixel-to-pixel scheme of the trapezoid aims to decrease the dependence of trapezoid-based SSM retrieval on the subjective requirements of quasi-identical atmospheric conditions, full vegetation coverages and SSM dynamics. In the present study, for each pixel over the North China Plain, a unique virtual trapezoid was created and the theoretical boundaries were determined following the principle of surface energy balance where satellite-derived essential atmospheric variables of air temperature, net surface shortwave radiation and relative humidity were provided as inputs. Based on the proposed approach, a daily/5-km SSM dataset during the growing season from April to October in 2016 was produced over the North China Plain. A preliminary evaluation reveals an overall acceptable accuracy of the estimated SSM with root mean square error of ∼ 0.06 m 3 /m 3 and ∼ 0.08 m 3 /m 3 when comparing to in-situ measurements and microwave SSM product, respectively.

Arctic sea ice modulation of summertime heatwaves over western North America in recent decades

A catastrophic heatwave struck North America (NA) in the summer of 2021, the underlying cause of which currently remains unclear. The reanalysis data (1980–2021) is analyzed to elucidate the mechanism modulating the summer heatwaves. We find the heatwaves over western NA tend to occur concurrently with quasi-barotropic ridges (QBTRs). The 2021 record-breaking heatwave, in particular, coincides with an extended eight-day QBTR event. The frequency of QBTRs is modulated by large-scale forcing. During the period of 1980–2000, it is correlated with the Arctic Oscillation. After 2000, however, the QBTR frequency is highly associated with sea ice variations. Specifically, the negative sea ice anomalies in the Chukchi Sea are usually associated with stronger net surface shortwave radiation and low cloud cover, triggering upward motion and a low-pressure center in the low- and mid-troposphere. The low pressure strengthens a stationary wave response, concomitant with two alternately high- and low-pressure centers, inducing more frequent QBTRs over western NA. These findings indicate that further Arctic sea ice loss under a warming climate will likely lead to more devastating heatwaves over western NA.

Interactions Between a Marine Heatwave and Tropical Cyclone Amphan in the Bay of Bengal in 2020

Interactions are diagnosed between a marine heatwave (MHW) event and tropical super cyclone Amphan in the Bay of Bengal. In May 2020, an MHW developed in the Bay of Bengal driven by coupled ocean-atmosphere processes which included shoaling of the mixed layer depth due to reduced wind speed, increased net surface shortwave radiation flux into the ocean, increased upper ocean stratification, and increased sub-surface warming. Ocean temperature, rather than salinity, dominated the stratification that contributed to the MHW development and the subsurface ocean warming that also increased tropical cyclone heat potential. The presence of this strong MHW with sea surface temperature anomalies &amp;gt;2.5°C in the western Bay of Bengal coincided with the cyclone track and facilitated the rapid intensification of tropical cyclone Amphan to a super cyclone in just 24 h. This rapid intensification of a short-lived tropical cyclone, with a lifespan of 5 days over the ocean, is unprecedented in the Bay of Bengal during the pre-monsoon period (March-May). As the cyclone approached landfall in northern India, the wind-induced mixing deepened the mixed layer, cooled the ocean's surface, and reduced sub-surface warming in the bay, resulting in the demise of the MHW. This study provides new perspectives on the interactions between MHWs and tropical cyclones that could aid in improving the current understanding of compound extreme events that have severe socio-economic consequences in affected countries.

Understanding Sea Surface Temperature Cooling in the Central‐East Pacific Sector of the Southern Ocean During 1982–2020

AbstractSea surface temperature (SST) in the Southern Ocean plays an important role in air‐sea interactions, forcing atmospheric variability. Using various observational and reanalysis data sets, we show that the SST cooling trend in the Southern Ocean (50°S–70°S) continues (∼0.3°C from 1982 to 2020). The largest SST cooling trend is in the central‐east Pacific sector, which can extend to 250 m. A detailed trend analysis for the mixed layer heat budget equation suggests that changes in the net surface shortwave radiation and Ekman horizontal advection are the two key contributors, though the other processes contribute to the SST cooling trend in winter and spring. The reduced downward solar radiation is largely due to the increase of cloud cover, which enhances shortwave‐cloud‐radiative feedback and decreases SST. Changes in Ekman advection associated with increased zonal winds is in favor of the colder water remaining in upper ocean layer.

Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016: 2. The Impact of Assimilating Satellite‐Based Snow Cover and Freeze/Thaw Observations Into a Land Surface Model

AbstractThis second paper of the two‐part series focuses on demonstrating the impact of assimilating satellite‐based snow cover and freeze/thaw observations into the hyper‐resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region from 2003 to 2016. To this end, this study systematically evaluates a total of six sets of 0.01° (∼1 km) model simulations forced by different precipitation forcings, with and without the dual assimilation scheme enabled, at point‐scale, basin‐scale, and domain‐scale. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near‐surface soil temperature, snow depth, snow water equivalent (SWE), and total runoff. First, the point‐scale assessment is mainly conducted via evaluating against ground‐based measurements. In general, the assimilation enabled estimates are better than no‐assimilation counterparts. Second, the basin‐scale runoff assessment demonstrates that across three snow‐dominated basins, the assimilation enabled experiment yields systematic improvements in all goodness‐of‐fit statistics through mitigating the negative effects brought by the fixed long‐term precipitation correction factors. For example, when forced by the bias‐corrected precipitation, the assimilation‐enabled experiment improves the bias by 69%, the root‐mean‐squared error by 30%, and the unbiased root‐mean‐squared error by 18% (relative to the no‐assimilation counterpart). Finally, the domain‐scale assessment is conducted via evaluating against satellite‐based SWE and skin temperature products. Both sets of domain‐scale analysis further corroborate the findings in the point‐scale evaluations. Overall, this study suggests the benefits of the proposed multi‐variate assimilation system in improving the cryospheric‐hydrological process within a land surface model for use in HMA.

Earth system model parameter adjustment using a Green's functions approach

Abstract. We demonstrate the practicality and effectiveness of using a Green's functions estimation approach for adjusting uncertain parameters in an Earth system model (ESM). This estimation approach has previously been applied to an intermediate-complexity climate model and to individual ESM components, e.g., ocean, sea ice, or carbon cycle components. Here, the Green's functions approach is applied to a state-of-the-art ESM that comprises a global atmosphere/land configuration of the Goddard Earth Observing System (GEOS) coupled to an ocean and sea ice configuration of the Massachusetts Institute of Technology general circulation model (MITgcm). Horizontal grid spacing is approximately 110 km for GEOS and 37–110 km for MITgcm. In addition to the reference GEOS-MITgcm simulation, we carried out a series of model sensitivity experiments, in which 20 uncertain parameters are perturbed. These “control” parameters can be used to adjust sea ice, microphysics, turbulence, radiation, and surface schemes in the coupled simulation. We defined eight observational targets: sea ice fraction, net surface shortwave radiation, downward longwave radiation, near-surface temperature, sea surface temperature, sea surface salinity, and ocean temperature and salinity at 300 m. We applied the Green's functions approach to optimize the values of the 20 control parameters so as to minimize a weighted least-squares distance between the model and the eight observational targets. The new experiment with the optimized parameters resulted in a total cost reduction of 9 % relative to a simulation that had already been adjusted using other methods. The optimized experiment attained a balanced cost reduction over most of the observational targets. We also report on results from a set of sensitivity experiments that are not used in the final optimized simulation but helped explore options and guided the optimization process. These experiments include an assessment of sensitivity to the number of control parameters and to the selection of observational targets and weights in the cost function. Based on these sensitivity experiments, we selected a specific definition for the cost function. The sensitivity experiments also revealed a decreasing overall cost as the number of control variables was increased. In summary, we recommend using the Green's functions estimation approach as an additional fine-tuning step in the model development process. The method is not a replacement for modelers' experience in choosing and adjusting sensitive model parameters. Instead, it is an additional practical and effective tool for carrying out final adjustments of uncertain ESM parameters.

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.

Anthropogenic aerosol forcing of the Atlantic meridional overturning circulation and the associated mechanisms in CMIP6 models

Abstract. By regulating the global transport of heat, freshwater, and carbon, the Atlantic meridional overturning circulation (AMOC) serves as an important component of the climate system. During the late 20th and early 21st centuries, indirect observations and models suggest a weakening of the AMOC. Direct AMOC observations also suggest a weakening during the early 21st century but with substantial interannual variability. Long-term weakening of the AMOC has been associated with increasing greenhouse gases (GHGs), but some modeling studies suggest the build up of anthropogenic aerosols (AAs) may have offset part of the GHG-induced weakening. Here, we quantify 1900–2020 AMOC variations and assess the driving mechanisms in state-of-the-art climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6). The CMIP6 forcing (GHGs, anthropogenic and volcanic aerosols, solar variability, and land use and land change) multi-model mean shows negligible AMOC changes up to ∼ 1950, followed by robust AMOC strengthening during the second half of the 20th century (∼ 1950–1990) and weakening afterwards (1990–2020). These multi-decadal AMOC variations are related to changes in North Atlantic atmospheric circulation, including an altered sea level pressure gradient, storm track activity, surface winds, and heat fluxes, which drive changes in the subpolar North Atlantic surface density flux. To further investigate these AMOC relationships, we perform a regression analysis and decompose these North Atlantic climate responses into an anthropogenic aerosol-forced component and a subsequent AMOC-related feedback. Similar to previous studies, CMIP6 GHG simulations yield robust AMOC weakening, particularly during the second half of the 20th century. Changes in natural forcings, including solar variability and volcanic aerosols, yield negligible AMOC changes. In contrast, CMIP6 AA simulations yield robust AMOC strengthening (weakening) in response to increasing (decreasing) anthropogenic aerosols. Moreover, the CMIP6 all-forcing AMOC variations and atmospheric circulation responses also occur in the CMIP6 AA simulations, which suggests these are largely driven by changes in anthropogenic aerosol emissions. More specifically, our results suggest that AMOC multi-decadal variability is initiated by North Atlantic aerosol optical thickness perturbations to net surface shortwave radiation and sea surface temperature (and hence sea surface density), which in turn affect sea level pressure gradient and surface wind and – via latent and sensible heat fluxes – sea surface density flux through its thermal component. AMOC-related feedbacks act to reinforce this aerosol-forced AMOC response, largely due to changes in sea surface salinity (and hence sea surface density), with temperature-related (and cloud-related) feedbacks acting to mute the initial response. Although aspects of the CMIP6 all-forcing multi-model mean response resembles observations, notable differences exist. This includes CMIP6 AMOC strengthening from ∼ 1950 to 1990, when the indirect estimates suggest AMOC weakening. The CMIP6 multi-model mean also underestimates the observed increase in North Atlantic ocean heat content, and although the CMIP6 North Atlantic atmospheric circulation responses – particularly the overall patterns – are similar to observations, the simulated responses are weaker than those observed, implying they are only partially externally forced. The possible causes of these differences include internal climate variability, observational uncertainties, and model shortcomings, including excessive aerosol forcing. A handful of CMIP6 realizations yield AMOC evolution since 1900 similar to the indirect observations, implying the inferred AMOC weakening from 1950 to 1990 (and even from 1930 to 1990) may have a significant contribution from internal (i.e., unforced) climate variability. Nonetheless, CMIP6 models yield robust, externally forced AMOC changes, the bulk of which are due to anthropogenic aerosols.

Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016, Part I: A Hyper‐Resolution Terrestrial Modeling System

AbstractThis first paper of the two‐part series focuses on demonstrating the accuracy of a hyper‐resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region. To this end, this study systematically evaluates four sets of model simulations at point scale, basin scale, and domain scale obtained from different spatial resolutions including 0.01° (∼1‐km) and 0.25° (∼25‐km). The assessment is conducted via comparisons against ground‐based observations and satellite‐derived reference products. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near‐surface soil temperature, snow depth, snow water equivalent, and total runoff. In the evaluation against ground‐based measurements, the superiority of the 0.01° estimates are mostly demonstrated across relatively complex terrain. Specifically, hyper‐resolution modeling improves the skill in meteorological forcing estimates (except precipitation) by 9% relative to coarse‐resolution estimates. The model forced by downscaled forcings in its entirety yields the highest skill in model output states as well as precipitation, which improves the skill obtained by coarse‐resolution estimates by 7%. These findings, on one hand, corroborate the importance of employing the hyper‐resolution versus coarse‐resolution modeling in areas characterized by complex terrain. On the other hand, by evaluating four sets of model simulations forced with different precipitation products, this study emphasizes the importance of accurate hyper‐resolution precipitation products to drive model simulations.

A Method for Downscaling Satellite Soil Moisture Based on Land Surface Temperature and Net Surface Shortwave Radiation

Due to the coarse spatial resolution of currently available microwave (mostly passive) soil moisture (SM) products, it is difficult to apply these SM data in watersheds or at local scales. To this end, a number of downscaling approaches have been developed to improve the spatial resolution of microwave SM products. Specifically, the optical-/thermal-based downscaling methods are most widely used in recent decades. However, such methods normally rely on instantaneous optical/thermal land surface parameters, which are commonly inapplicable under cloudy conditions. The purpose of this study is to develop a new downscaling method based on the temporal variation in geostationary satellite-derived land surface temperature and net surface shortwave radiation. The proposed method has a certain potential to improve data availability under cloudy conditions, because geostationary satellites are capable of providing land surface parameters at high temporal resolution. The proposed method was tested over the REMEDHUS network in Spain. The scaling strategy of cumulative distribution function matching was used to remove systematic differences in spatial mismatch between satellite pixels and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> SM measurements. Results indicate that the downscaled SM agrees well with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements and has comparable accuracy with the original microwave SM product. The overall root mean square errors with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements for the original microwave SM and the downscaled SM are 0.054 and 0.057 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{3}/\text{m}^{3}$ </tex-math></inline-formula> , respectively. This method not only has a successful attempt to downscale microwave SM data using temporal information but also has the potential to avoid the failure of traditional instantaneous observations-based downscaling procedure due to clouds.