Total Cloud Cover Research Articles

Diurnal changes in cloud cover in eastern Gabon and their impacts on energy balance, light availability and water demand: a case study of the 2022 dry season

Abstract Western Central Africa is atypical of the equatorial domain as the main dry season is cloudier than the rainy seasons. To understand this cloud cover's diurnal evolution, we set-up an infrared camera and acquired measurements of the total cloud cover fraction (TCF) and cloud optical depth at Bambidie, Gabon (0°44’30.5” S,12°58’12.4” O) from May to October 2022. Diurnal variations in TCF can be summarized into four types, mostly discretized through the timing and duration of clouds clearing in the afternoon (Early afternoon Clearing: EaC, Late afternoon Clearing: LaC and Clear Night: CNi) while one type (No Clearing: NoC) shows overcast conditions all day long. Meteorological measurements show that NoC days record 50W/m2 less shortwave incoming surface radiation resulting in daytime temperatures 1°C lower than the seasonal norm, but 20% more diffuse light and 0.5mm/day less ETo. Conversely, EaC days record 50W/m2 more shortwave incoming surface radiation leading to temperatures 1.5°C higher than the seasonal norm, but 40% more direct light. The larger water demand (0.5mm/day more ETo) is partly compensated by more frequent rainfall at night-time. The SAFNWC satellite estimates well capture the TCF variations for most of the 4 types. They confirm that TCF is dominated by very low and low clouds whose dissipation in the afternoon and evolution into fractional and cumuliform convective clouds explains the clearings on EaC and LaC days. Satellite estimates also show that the 4 types of days extracted at Bambidie are representative of a larger-scale cloud cover evolution in Western Central Africa, with a W-E gradient in the timing of afternoon cloud dissipation.

Performance of advanced-deep learning algorithm for modeling and prediction of rain height over some selected locations in the tropics and sub-tropics zone for radio propagation applications

As demand for high-frequency broadband communication services keeps rising, rain-induced attenuation remains the predominant threat to radiowave propagation. Accurate prediction of attenuation requires continuous measurement and monitoring of rain-induced meteorological parameters, specifically rain rate and rain height, due to their spatio-temporal variations. Rain height is an upper dataset mostly computed from Zero- degree Isotherm Heights (ZDIH) measured by radar. This research proposes a novel approach for predicting rain height from earth surface data such as surface temperature, pressure, total cloud cover, dew point temperature, surface solar radiation, water vapor amount in the air, and humidity. This research investigates the relationship between meteorological surface data and rain height. Subsequently, six machine learning models were employed for predicting rain height using the ten years surface data as input variables. The models were applied to six sub-tropical (Polokwane, Pretoria. and Cape Town) and tropical (Sokoto, Akure, and Port Harcourt (PH)) stations in South Africa and Nigeria, respectively. Analysis of the results shows that the Gradient Boosting Algorithm (GBA) performed best with determination coefficients greater than 0.80 and RMSE less than 350 in all three stations in South Africa. However, all the models failed to produce good result for the Nigeria stations. The Random Forest model has the fairest performance metrics with r2 of 0.40, 0.46 and 0.46 in Sokoto, Akure and PH. respectively. GBA is recommended for predicting rain height in South Africa. The research outcome would assist radio engineers in improving the prediction of rain-induced attenuation and determining appropriate fade mitigation techniques.

Statistics of precipitable water vapour, optical thickness and cloud cover within the Northern part of Eurasia

One of the most important tasks in astroclimatic studies of possible locations for the Eurasian Submillimeter Telescopes is estimating statistics of precipitable water vapour, optical thickness and cloud cover. In this paper, the statistics of precipitable water vapour and total cloud cover within Northern part of Eurasia are studied using ERA-5 reanalysis. Optical thickness statistics at a wavelength of 3 mm were obtained using the Liebe model from the ERA-5 reanalysis for the region where the BTA is located. The most favorable astroclimatic zones of Eurasia include Tibet and the Eastern Pamirs, certain regions of the Sayan Mountains, Altai and Mts within Dagestan. Also we verified the ERA-5 reanalysis data using radiosonde data, GNSS measurement data and radiometric measurements for 2021.

Runoff concentration decline for Tarim river due to a dramatic increasing of runoff in cold season and hydro-junction regulation: Past and future

Study regionThe three headwaters of Tarim river, south of the Xinjiang Province, China. Study focusTemporal runoff concentration variations are crucial for water resources utilization and management, particularly in arid regions. In this study, the runoff concentration in the Tarim River Basin is studied based on analysis of the trend, abrupt changes, and periodicity, identifying key extreme climate indices influencing the Gini-runoff coefficient and predicting its future changes under three Shared Socioeconomic Pathways (SSPs). New hydrological insights for the regionFrom 1962–2020, the Gini-runoff coefficient decreased by 8.3 %, while annual runoff increased by 14.2 %. This is attributed to temperature changes, escalating hydrological risk uncertainty. The anomaly of low geopotential high fields and Central Asian anticyclones have caused an increase in total cloud cover (TCC) and precipitation, and a decrease in the Daily Temperature Range (DTR). Increasing of Warm nights (TN90), Maximum Tmax (TXx), and Hot nights (TR) have enhanced the warm-season runoff. However, the climatic impact on warm-season runoff is less pronounced than that in the cold season, which led to the runoff concentration decline. Changes in hydro-junction regulation led to decrease of Gini-runoff coefficient by 12.11 % from 2001 to 2020 along the Hotan river. Under the three SSP scenarios, the average Gini-runoff coefficient for the Aksu and Hotan rivers are expected to decrease by 3.3 % and 2.6 %, respectively.

Sensitivity of Arctic Clouds to Ice Microphysical Processes in the NorESM2 Climate Model

Abstract Ice formation remains one of the most poorly represented microphysical processes in climate models. While primary ice production (PIP) parameterizations are known to have a large influence on the modeled cloud properties, the representation of secondary ice production (SIP) is incomplete and its corresponding impact is therefore largely unquantified. Furthermore, ice aggregation is another important process for the total cloud ice budget, which also remains largely unconstrained. In this study, we examine the impact of PIP, SIP, and ice aggregation on Arctic clouds, using the Norwegian Earth System Model, version 2 (NorESM2). Simulations with both prognostic and diagnostic PIP show that heterogeneous freezing alone cannot reproduce the observed cloud ice content. The implementation of missing SIP mechanisms (collisional breakup, drop shattering, and sublimation breakup) in NorESM2 improves the modeled ice properties, while improvements in liquid content occur only in simulations with prognostic PIP. However, results are sensitive to the description of collisional breakup. This mechanism, which dominates SIP in the examined conditions, is very sensitive to the treatment of the sublimation correction factor, a poorly constrained parameter that is included in the utilized parameterization. Finally, variations in ice aggregation treatment can also significantly impact cloud properties, mainly through their impact on collisional breakup efficiency. Overall, enhancement in ice production through the addition of SIP mechanisms and the reduction in ice aggregation (in line with radar observations of shallow Arctic clouds) result in enhanced cloud cover and decreased TOA radiation biases, compared to satellite measurements, especially during the cold months. Significance Statement Arctic clouds remain a large source of uncertainty in projections of the future climate due to the poor representation of the microphysical processes that govern their life cycle. Ice formation is among the least understood processes. While it is widely recognized that better constraints on primary ice production (PIP) are needed to improve existing parameterizations, we show that secondary ice production (SIP) and ice aggregation can have also a significant impact on ice number concentrations. Constraining ice formation through the addition of missing SIP mechanisms and reducing ice aggregation can improve the representation of the cloud macrophysical properties and enhance total cloud cover in the Arctic region, which in turn contributes to decreased TOA radiation biases in the cold months.

Seasonal Variation in Total Cloud Cover and Cloud Type Characteristics in Xinjiang, China Based on FY-4A

In order to deepen the knowledge of the seasonal variation in total cloud cover (TCC) in Xinjiang, China (XJ), a typical arid region, and to broaden the understanding of the seasonal variation in cloud type (CLT) in the region, we used TCC and CLT datasets from the latest generation of the geostationary satellite Fengyun 4A (FY-4A) from 2018 to 2022 to investigate the seasonal variation characteristics of TCC and CLT in XJ. Meanwhile, to verify the accuracy of TCC from FY-4A, ground observation (GROB) TCC datasets from 105 national meteorological stations (NMSs) in XJ and TCC datasets from ERA5 during the same period were used. In addition, the correlation between TCC from FY-4A and meteorological factors from ERA5 was also analyzed in this study. The TCC from FY-4A, GROB, and ERA5 can all well reflect the significant seasonal variation in TCC in XJ, with the highest (lowest) mean TCC and a distribution pattern of high in the southwest (northwest) and low in the northeast (southeast) in spring (fall) in XJ. Although the mean TCC from FY-4A in all four seasons was lower than that from GROB, the two were comparable in spring (44.09% and 47.32%) and summer (42.88% and 43.17%), while there was a significant difference between the two in fall (27.86% and 40.19%) and winter (30.58% and 46.93%) for 105 NMSs in XJ. The TCC from FY-4A was lower (higher) than that from GROB in spring and summer at most NMSs in northern (southern) XJ, while the TCC from FY-4A was lower than that from GROB for the vast majority of NMSs in fall and winter, especially in northern XJ. The seasonal variation in the spatial distribution of different CLTs (clear, water-type, supercooled-type, mixed-type, ice-type, cirrus-type, and overlap-type) from FY-4A exhibited diverse variation characteristics. Water-type (supercooled-water-type) had a high-frequency center of over 30% in the Tarim Basin (Kunlun Mountains) during summer. Mixed-type (ice-type and cirrus-type) had the highest frequency in winter (spring), while overlap-type had the highest frequency in summer. The correlation between TCC and water vapor conditions (total column vertically integrated water vapor, specific humidity at 250 hPa, 500 hPa, and 700 hPa) was positive in XJ.

Climatic characteristics and meteorology-sensitivity of surface solar radiation in reanalysis products compared to observations and satellite data over China

Due to the significant role of the surface solar radiation (SSR) in climate, hydrological, and biogeochemical cycles, this study evaluates the climatology, annual cycle, and interannual variability and meteorological sensitivity (total cloud cover (TCC), aerosol optical depth (AOD), and precipitable water (PW)) of six reanalysis SSR datasets (GLDAS, ERA5, NCEP1, NCEP2, JRA55, and MERRA2) using a satellite dataset (CERES SYN), and 130 radiation stations in China during 2001–2020.The results show that the climatology (R = 0.88–0.93) and annual cycle (R = 0.92–0.98) of the SSR datasets exhibit better spatial characteristics in these products compared to interannual variability (EOF1: R = 0.03–0.88; EOF2: R = 0.27–0.83). The GLDAS has relatively small estimation SSR error in the climatology but is limited to reproduce interannual variability (EOF1: R = 0.08). As the concentration of meteorological elements increases, the sensitivity of most SSRs errors to TCC increases, while the trend of sensitivity to AOD and PW decreases due to the mitigating effect of the corresponding tendency of TCC error reduction. The importance of PW for estimating SSRs increased significantly in high altitude, ranging from 29.5 to 53%. Overall, ERA5 is the most consistent in quality of the three time-scales and high-meteorological variability.

Meteorological Influences on Annual Variation and Trend of Autumn Surface Ozone in the Pearl River Delta

Based on the ozone （O3） monitoring data of the Pearl River Delta （PRD） from 2015 to 2022 and the reanalysis of meteorological data, the impact of meteorological conditions on the annual variation and trends of the maximum daily 8-hour average O3 concentration （MDA8-O3） were quantified using multiple linear regression （MLR） and LMG methods. The results indicated that the MLR model constructed using meteorological parameters from individual months in autumn better simulated the variation in MDA8-O3 compared to that in the model built using meteorological parameters from the entire autumn season. The combined influence of total cloud cover, relative humidity, 2 m maximum temperature, and 850 hPa zonal wind led to a reduction of 34.1 μg·m-3 in MAD8-O3 in 2020 compared to that in 2019, with contributions of 31.3%, 45.2%, 15.8%, and 6.7%, respectively. The observed trends of MDA8-O3 in the PRD for September, October, November, and the autumn season during 2015-2022 were 7.3, 5.2, 4.8, and 5.8 μg·（m3·a）-1, respectively. Among these, the trends driven by meteorological factors were 3.6, 2.4, 2.4, and 3.1 μg·（m3·a）-1. Overall, meteorological conditions contributed 53.4% to the variations in autumn MDA8-O3 in the PRD from 2015 to 2022.

Seasonal variations of Arctic cloud in recent 14 years using CALIPSO-GOCCP

This study documents the three-dimensional structure and long-term trend of Arctic cloud cover with 14-year Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations from a seasonal perspective. Results show that Arctic Ocean presents larger low-level cloudiness and more evident vertical contrast between low and high-level cloud cover than Arctic land. Furthermore, Arctic Atlantic experiences the largest total and low-level cloud cover all year long, with relatively weak seasonal variation. In contrast, the low-level cloud cover over Arctic Pacific displays a pronounced bimodal annual cycle, as well as the low-level cloud cover over Arctic land. Arctic Atlantic with the maximum cloud fraction has the highest altitude of about 1.2 km above open sea in winter, which is near the surface in summer. The cloudiness in the lower troposphere over open water is significantly larger than that over ice except in winter, whereas, the liquid-containing cloud over open water is significantly larger than that over ice in the lower troposphere in four seasons. Moreover, though the liquid-containing cloud dominates the low-level cloud cover, its increasing trend is driven by liquid-containing and ice-containing cloud together. Instead, the ice-containing cloud dominates the high-level cloud cover and its increasing trend in all seasons over most Arctic regions, except a negative trend over Greenland in winter. Our work presents a more comprehensive picture of the Arctic cloud cover, which provides an insight into understanding of Arctic cloud and Arctic climate system.

On the characterization of Cloud occurrence and its impact on solar radiation in Mbour, Senegal

The objective of this study is to evaluate the clouds seasonal occurrence characteristics, and to estimate their impact on solar radiation in Mbour, Senegal, West Africa. Here, we use datasets from various sources including: i) observations from the Clouds and Earth's Radiant Energy System satellite sensors, ii) in situ shortwave radiation measurement obtained from the Mbour station, and iii) the outgoing longwave radiation (OLR) obtained from the National Centers for Environmental Prediction reanalysis data. Results show a marked seasonality, associated with high spatial variation in terms of cloud occurrence over Senegal. The maximum cloud occurrences are observed during the wet summer season (June–October), whilst the minimum cloud occurrences are recorded during the long-dry season from November to May. During the monsoon season the cloud activity becomes more intense with a total cloud cover of about 80%, a cloud optical depth of around 7, and a high convective activity illustrated by a low OLR (below 240 W/m2). Likewise, across Senegal a strong north-south gradient of the cloud characteristics is observed. Based on quantitative comparison between cloud occurrence and radiation measurement, results show an important seasonal impact on available solar potential in Mbour. Conversely to the cloud occurrence, the maximum of both direct normal and global solar potentials is recorded during the dry season, coinciding with the period with clean sky. An investigation of the cloud influence on solar radiation on selected study cases indicates a decrease of 60% (80%) for the total (direct normal) radiation during the peak of the summer monsoon season.

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.

An effort to distinguish the effects of cloud cover and aerosols on the decadal variations of surface solar radiation in the Northern Hemisphere

Surface solar radiation (SSR) serves as the primary energy source on Earth. However, a relative lack of research systematically quantifies long-term SSR variations and their driving factors based on complete and reliable baseline data. This paper presents a new assessment of the Northern Hemisphere/regional SSR variations and the influence of total cloud cover (TCC) on these variations, based on the latest reconstructed SSR gridded dataset. We also address multicollinearity among multiple aerosol types and quantify the effects of multiple aerosol/precursors on SSR variability using a partial least squares regression model. The results indicate that TCC is not the predominant driver of longer-term SSR variations, known as ‘dimming’ and ‘brightening’. The variations of NH3 and SO2 primarily drive inter-decadal SSR variations in North America, while the variations of SO2 and NO X mainly influence inter-decadal SSR variations in Europe.

Evaluation of the simulation performance of WRF-Solar for a summer month in China using ground observation network data

Solar energy is a pivotal clean energy source in the transition to carbon neutrality from fossil fuels. However, the intermittent and stochastic characteristics of solar radiation pose challenges for accurate simulation and prediction. Accurately simulating and predicting solar radiation and its variability are crucial for optimizing solar energy utilization. This study conducted simulation experiments using the WRF-Solar model from 25 June to 25 July 2022, to evaluate the accuracy and performance of the simulated solar radiation across China. The simulations covered the whole country with a grid spacing of 27 km and were compared with ground observation network data from the Chinese Ecosystem Research Network. The results indicated that WRF-Solar can accurately capture the spatiotemporal patterns of global horizontal irradiance over China, but there is still an overestimation of solar radiation, and the model underestimates the total cloud cover. The root-mean-square error ranged from 92.83 to 188.13 Wm−2 and the mean bias (MB) ranged from 21.05 to 56.22 Wm−2. The simulation showed the smallest MB at Lhasa on the Qinghai–Tibet Plateau, while the largest MB was observed in Southeast China. To enhance the accuracy of solar radiation simulation, the authors compared the Fast All-sky Radiation Model for Solar with the Rapid Radiative Transfer Model for General Circulation Models and found that the former provides better simulation.摘要准确模拟和预测太阳辐射及其变化对于优化太阳能利用至关重要. 本研究使用WRF-Solar模式对中国2022 年 6 月 25 日至 7 月 25 日的太阳辐射情况进行了深入模拟, 模式网格水平分辨率为27 km, 通过与中国生态系统研究网络 (CERN) 的地面观测网络数据的37个观测站点进行对比, 以评估模式性能. 结果表明: WRF-Solar模式能较好地捕捉到中国上空的辐照度GHI (Global Horizontal Irradiance) 的时空分布特征, 但存在高估太阳辐射量, 以及低估总云量的情况. 全国的总辐射模拟均方根误差范围为92.83–188.13 W m−2, 平均偏差范围为21.05–56.22 W m−2. 青藏高原拉萨站的偏差最小, 在中国东南部的偏差最大. 为了进一步提升太阳辐射模拟的精确度, 本研究还对比了FARMS与RRTMG辐射方案, 发现FARMS方案的模拟精度更高.

On the characteristics and long-term trend of total cloud cover in Iran

On the characteristics and long-term trend of total cloud cover in Iran

Analysis of the Interaction of Oceanic Cloudiness with the Upper Oceanic Stratum

Objective: This paper addressed the relationship between two physical quantities of interest in climatology: the thermal energy present in the upper ocean stratum (UOS) and the oceanic cloudiness (OC), i.e., the one located above oceans. Methods: The interplay between these physical quantities was pointed out by analyzing the time series of seasonal and diurnal anomalies of ocean's total cloud cover, and the anomalies of the thermal energy present in the UOS. We examined these time series to identify signals indicative of interactions between the UOS and OC. We then aimed to explain these interactions at a climatological level. Finally, for validation, we demonstrated that our explanations are consistent with a global climatological model that we developed. Results: It was demonstrated that in both cases, the time series could be described as a Markov-1 alternating type, exhibiting similar structures. This led to the conclusion that the OC served as a natural thermostat in relation to the thermal energy contained in the UOS. By using a simple mathematical model previously introduced in a recent paper by the author (2023) to account for the thermal exchanges between the troposphere and the UOS, we confirmed that the OC acted as a natural thermostat for the thermal energy in the UOS. This "natural thermostat" effect, incorporated in the model, resulted in simulations projecting significantly less warming by 2095 compared to those from most existing global climate models (GCMs). Conclusion: Methodologically, this paper confirmed the interest of using Markov chains to identify climatological interactions. The developed GCM, utilized for validating hypotheses explaining these interactions, proved simple and efficient for simulating key climatological parameters in the evolution of the global climate. On a strictly scientific level, the work's main contribution lies in providing a definitive answer about cloud cover feedback in global warming, establishing it as significantly negative.

Comparing FY-2F/CTA products to ground-based manual total cloud cover observations in Xinjiang under complex underlying surfaces and different weather conditions

Abstract. Clouds are an important parameter of artificial water augmentation, which is of substantial significance to judge the precipitation capacity. Xinjiang is an arid region in northwestern China, where weather stations are sparsely distributed, the types of underlying surface are complex, and the climate between the southern and northern region varies greatly. However, the retrieval of the total cloud cover (TCC) from satellite in arid areas is a challenging task. Based on the TCC data observed by ground observation stations (GOSs) from June 2015 to May 2016 considering the complex underlying surfaces and different weather conditions, the precision, consistency, and error between the cloud total amount from the FengYun-2F stationary satellite (FY-2F/CTA) and manually observed TCC are compared and evaluated in the Xinjiang region. The findings of this study are as follows: (1) the precision rate (PR) of FY-2F/CTA in the Xinjiang region is 75.6 %, which gradually decreases from north to south, demonstrating a high false rate (FR) and a low missing rate (MR). The consistency rate (CR) is 51.5 %, with little difference among three subregions of Xinjiang, all showing a high weak rate (WR) and low strong rate (SR), which means that the TCC values inverted from FY-2 satellite data are generally lower than those observed by GOSs, especially in southern Xinjiang. The bias is −20 %, and all the error indexes (EIs) including bias, MAE, and RMSE increase from central to the north and south of Xinjiang; that means the EIs are the lowest in Tianshan and the highest in southern Xinjiang. FY-2F/CTA exhibits higher PR and CR in the underlying surface of vegetation compared to non-vegetation; that is to say that FY-2F/CTA performs best in the underlying surfaces of forest and plowland, while their performance is relatively poorer in the underlying surface of snow and ice. (2) With rising temperature the PR and CR of FY-2F/CTA increase, while the EIs decrease. Under various temperature conditions, FY-2F/CTA has always exhibited high MR, low FR (on the contrary in January), high WR, and low SR. From low elevation to high elevation, the PR and CR of FY-2F/CTA decrease, but the PR increases significantly when the altitude is higher than 2000 m. (3) Dust reduces the CR of FY-2F/CTA and increases their WR and MR but has a relatively minor impact on the identification of cloud and non-cloud. (4) Under different cloud cover levels, the PR and EIs of FY-2F/CTA are directly proportional to the amount of TCC, while the CR is inversely proportional to it: that is, the CR is higher and the PR and EIs are lower under clear-sky and partly cloudy conditions, and the CR is lower and the PR and EIs are higher under cloudy and overcast conditions. This study assessed the FY-2F/CTA under various conditions in arid areas of Xinjiang, including complex underlying surface, various temperature and altitude, dust effects, and different cloud cover levels. Thus, the research finding could serve as a valuable reference for satellite-based retrieval and applications related to TCC in arid regions.

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.

Assessment of the Sensitivity of the Mean Climate Simulation over West Africa to Planetary Boundary Layer Parameterization Using RegCM5 Regional Climate Model

This study evaluates the performance of two planetary boundary parameterizations in simulating the mean climate of West Africa using the Regional Climate Model version 5 (RegCM5). These planetary boundary parameterizations are the Holtslag scheme and the University of Washington scheme. Two sets of three one-year simulations were carried out at 25 km horizontal resolution with three different initial conditions. The first set of simulations used the Holtslag scheme (hereafter referred to as Hol), while the second used the University of Washington (UW) scheme (hereafter referred to as UW). The results displayed in this study are an average of the three simulations. During the JJAS rainy season, with respect to GPCP, both models overestimated total rainfall in the orographic regions. The UW experiment represented total rainfall fairly well compared to its counterpart, Hol. Both models reproduced convective rainfall well, with a relatively weak dry bias over the Guinean coast subregion. Globally, UW performed better than Hol in simulating precipitation. The pattern of near-surface temperature in both models was well reproduced with a higher bias with Hol than with UW. Indeed, the UW scheme led to a cooling effect owing to the reduction in eddy heat diffusivity in the lower troposphere contributing to reduce the bias. As a consequence, the height of the planetary boundary layer (PBL) was best simulated using the UW scheme but was underestimated compared to ERA5, while using the Hol scheme failed to capture the height of the PBL. This is coherent with the distribution of total cloud cover, which was better simulated with the UW scheme compared to the Hol scheme. This study shows that use of both planetary boundary parameterizations leads to a good simulation of most of the climatological characteristics of the West African region. Nevertheless, use of the UW scheme contributes to a better performance than use of the Hol scheme, and the differentiation between the two schemes is significant along the Guinea Coast and in orographic regions. In these topographically complex regions, UW appears to be more appropriate than Hol. This study emphasizes the importance of planetary boundary parameterizations for accurately simulating climate variables and for improving climate forecasts and projections in West Africa.

Interannual variations in ozone pollution with a dipole structure over Eastern China associated with springtime thermal forcing over the Tibetan Plateau

The Tibetan Plateau (TP) is essential in modulating climate change in downstream Eastern China (EC). As a meteorology-sensitive pollutant, changes in ozone (O3) in connection with the TP have received limited attention. In this study, using climate analysis of the China High Air Pollutants O3 product and ERA5 reanalysis data of meteorology for 1980–2020, the effect of springtime TP thermal forcing on the warm season (April–September) O3 pollution over EC was investigated. The strong TP thermal effect significantly modulates the interannual variations in O3 pollution with a dipole pattern over EC, inducing more O3 pollution in northern EC regions and alleviating O3 pollution in the southern regions. In northern (southern) EC, strong TP thermal forcing triggers a significant anomalous high (low) pressure center accompanied by anticyclonic (cyclonic) anomalies, resulting in decreased (increased) total cloud cover, increased (reduced) surface downward solar radiation and air temperature, which are conducive to the anomalous increase (decrease) in surface O3 concentrations. Moreover, the key sources of springtime thermal forcing over the TP influence the major O3 pollution regions over southern and northern EC with an inverse pattern, depending on their locations and orientations to the large topography of the TP. This research reveals an important driving factor for the dipole interannual variation in O3 pollution over EC, providing a new prospect for the effect of the TP on atmospheric environmental change.

Climatology and trends of cloudiness in a productive rice and vegetable region of South‐Eastern South America

AbstractClouds are a key element of the climate system. They play a crucial role in modulating solar radiative flux reaching the Earth's surface and influencing environmental conditions. In particular, total cloud cover (TCC) has both direct and indirect influence on agricultural production. Through dialogues with vegetable and rice producers of Northeastern Argentina (NEA), an important productive region in South‐Eastern South America, the key role of cloud cover on crop yields was brought to the forefront. In this study, we present the climatology and observed long‐term changes in TCC in NEA. The analyses are performed based on two independent datasets: TCC ground‐based (GB) observations and satellite‐based estimates from the International Satellite Cloud Climatology Project (ISCCP). The datasets cover a common period, from December 1983 to November 2016 (Satellite Period), while GB TCC observations extend further, from March 1961 to February 2021 (GB Period). To facilitate a more comprehensive examination of GB TCC, we introduce a novel Cloud Index, expressed in a familiar unit (%), which allows the study of its temporal variations. TCC exhibits a distinct annual cycle and substantial spatial variability over NEA, consistently evident in both satellite and GB datasets. Over the Satellite Period (GB Period), we observed a decrease in NEA's TCC in both datasets (GB dataset), with seasonal variations and spatial heterogeneity. We ensured the reliability of our results by comparing the two datasets, which showed similar temporal variability, although the ISCCP Cloud Amount values were larger than the GB TCC. The described TCC climatology and observed changes have considerable implications for NEA's agricultural production and provide a robust foundation for future research. This article generates robust methodological basis for the analysis of this complex variable, a necessary step to explore the forcing of climate variability that modulates cloudiness in future research, and that could facilitate future studies in other regions of South America and the world.