Opaque Cloud Cover Research Articles

The surface longwave cloud radiative effect derived from space lidar observations

Abstract. Clouds warm the surface in the longwave (LW), and this warming effect can be quantified through the surface LW cloud radiative effect (CRE). The global surface LW CRE has been estimated over more than 2 decades using space-based radiometers (2000–2021) and over the 5-year period ending in 2011 using the combination of radar, lidar and space-based radiometers. Previous work comparing these two types of retrievals has shown that the radiometer-based cloud amount has some bias over icy surfaces. Here we propose new estimates of the global surface LW CRE from space-based lidar observations over the 2008–2020 time period. We show from 1D atmospheric column radiative transfer calculations that surface LW CRE linearly decreases with increasing cloud altitude. These computations allow us to establish simple parameterizations between surface LW CRE and five cloud properties that are well observed by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) space-based lidar: opaque cloud cover and altitude and thin cloud cover, altitude, and emissivity. We evaluate this new surface LWCRE–LIDAR product by comparing it to existing satellite-derived products globally on instantaneous collocated data at footprint scale and on global averages as well as to ground-based observations at specific locations. This evaluation shows good correlations between this new product and other datasets. Our estimate appears to be an improvement over others as it appropriately captures the annual variability of the surface LW CRE over bright polar surfaces and it provides a dataset more than 13 years long.

Satellite Observed Sensitivity of Tropical Clouds and Moisture to Sea Surface Temperature on Various Time and Space Scales: 2. Focus on Marine Low Level Clouds

AbstractThis study examines how satellite observed relative humidity (RH) profiles, cloud covers, and cloud altitudes vary with sea surface temperature (SST) in low cloud regions on different temporal and spatial scales over the tropical oceans (30°N–30°S). We split low clouds into optically opaque and optically thin clouds and characterize for the first time their altitude variations with SST using space lidar observations. On the process scale, the median instantaneous observations at high spatial resolution show simultaneously decreasing low opaque cloud top altitude, decreasing low opaque cloud cover, increasing middle‐tropospheric RH, and decreasing lower‐tropospheric RH with SST. Collectively, these observational results suggest decreasing opaque marine boundary layer cloud cover and enhanced mixing between the middle and lower troposphere with SST on the process scale, consistent with previous Large‐Eddy Simulation studies. The covariations between opaque cloud cover and RH with SST are quantified on all time and space scales (from the process scale to the annual tropical mean scale). Meanwhile, on the process scale, low optically thin clouds, coexisting with opaque clouds, rise with SST (consistent with the literature mechanism), while the low optically thin cloud cover is insensitive to SST (not consistent with the literature mechanism). These results are then compared to a general circulation model, which captures the observed sign of change of opaque cloud cover and middle‐tropospheric RH with SST but fails to reproduce the observed vertical shrinkage of low opaque clouds with SST and the variations of low thin clouds with SST.

Satellite Observed Sensitivity of Tropical Clouds and Moisture to Sea Surface Temperature on Various Time and Space Scales: 1. Focus on High Level Cloud Situations Over Ocean

AbstractThis study examines variations in high cloud properties and relative humidity (RH) with sea surface temperature (SST) over tropical oceans (30°N–30°S) using spaceborne lidar and microwave radiometer observations. The mean values over the tropics indicate that middle‐tropospheric RH increases, high cloud covers decrease and cloud altitudes rise with SST. These signatures are consistent with the hypotheses proposed in the literature. The analysis of this same data set but at the scale of local processes shows different behaviors for SSTs <302 K and SSTs >302 K. Between 299 and 302 K, middle‐tropospheric RH, opaque cloud cover, and cloud top altitude increase together with SST, while optically thin cloud cover decreases. Over SSTs >302 K, middle‐tropospheric RH, opaque cloud cover, and opaque cloud top altitude decrease with SST, while the cover of optically thin clouds increases. Interestingly, the altitude of high clouds (not the cloud top) increases monotonically with SST from 299 to 305 K on a range of space and time scales, and the altitude of optically thin clouds remains higher than that of opaque clouds. The observed relationships on different time and space scales are compared to simulations of a global atmospheric model. Despite systematic biases, the model reproduces the sensitivity of the middle‐tropospheric RH and cloud altitude to SST rather well but fails to reproduce the variations of the balance between high opaque cloud cover and high optically thin cloud cover.

Link Between Opaque Cloud Properties and Atmospheric Dynamics in Observations and Simulations of Current Climate in the Tropics, and Impact on Future Predictions

AbstractUsing spaceborne lidar observations and reanalyzes (2008–2014), we relate the vertical wind speed at 500 hPa (ω500), indicator of atmospheric circulation, to properties of opaque clouds (altitude and cover) and to the Cloud Radiative Effect (CRE) in the Tropics. We confront those observations with simulations by IPSL‐CM6 and CESM1 climate models using early 21st century emissions. Both models overestimate the average opaque cloud cover. IPSL‐CM6 puts high opaque clouds too high (+2 km), especially in ascendance. CESM1 overestimates the intermediate opaque cloud cover and underestimates small and large opaque cloud covers. Both models agree that cloud properties behave differently at wind speed above (strong subsidence) or below (weak subsidence and ascendance) 20 hPa/day. In future climate (2089–2095), variables affected by biases in current climate are affected by notable changes: IPSL‐CM6 puts high opaque clouds even higher (+2 km) while opaque cloud cover above 30% decreases and below 30% increases in CESM1. Both models predict very little change in the average net CRE in the future. We find that predicted changes of cloud properties can be regionally driven by dynamic or thermodynamic changes, depending on the relationship between opaque cloud altitude and ω500 in the model. Overall, most changes are due to thermodynamic changes in the relationship between cloud property and atmospheric dynamics.

Investigating ESA Sentinel-2 products' systematic cloud cover overestimation in very high altitude areas

Cloud detection in optical remote sensing imagery is crucial because undetected clouds can produce misleading results in analyses. Almost all optical remote sensing data access portals rely to some degree on a cloud cover filter. Here we show that cirrus as well as opaque cloud cover in Sentinel-2 Level-1C (L1C) and Level-2A (L2A) imagery is systematically and significantly overestimated in very high altitude areas (e.g. Himalayas, Andes). We argue that this systematic bias is created by applying simple thresholds to single bands instead of using a multi-band spectral signature in the cloud detection process. This results in a lot of “hidden” data for very high altitude areas when each image's estimated cloud cover is used as an automated selection criterion for analysis (e.g. global analyses, cloud-free mosaic production). We show geographic locations exemplifying this overestimation, and compare the L1C and L2A cloud masks produced by ESA to cloud masks generated by an expert system that uses comprehensive spectral signatures, showing that reliable cloud estimations are possible in very high altitudes. Based on this comparison, we argue for changes to L1C and L2A cloud detection algorithms in order to improve initial querying and selection of big EO data, where reliable yet automated quality indicators are necessary to handle an overwhelming data volume and velocity. Our contribution raises awareness of potential bias when pre-selecting images based on reported cloud cover in very high altitude areas for researchers and users of Sentinel-2 imagery in the environmental domain.

Warm Temperature Extremes Across Greenland Connected to Clouds

AbstractGreenland ice sheet melt is a large contributor to rising global sea level and melt is dependent on surface air temperature. Arctic temperatures are strongly coupled to clouds but spatial connections between clouds and temperature have yet to be established across Greenland. By mapping spaceborne lidar measurements and surface observations, it is shown that radiatively opaque clouds generally coincide with anomalously warm near‐surface temperatures at Greenland sites. These results indicate that both temperatures over 0 °C as well as positive daily temperature anomalies relate to spatially extensive opaque cloud cover. While prior studies indicate that clouds enhance extreme melt events, this research shows that opaque cloud cover and surface warming are closely related across the Greenland ice sheet, particularly in the ablation region. These findings establish broadly the spatial relationships between opaque clouds and temperatures and demonstrate the importance of direct observations across Greenland.

Observed Evolution of the Tropical Atmospheric Water Cycle with Sea Surface Temperature

AbstractBetter understanding of how moisture, clouds, and precipitation covary under climate warming lacks a comprehensive observational view. This paper analyzes the tropical atmospheric water cycle’s evolution with sea surface temperature (SST), using for the first time, the synergistic dataset of instantaneous observations of the relative humidity profile from the Megha-Tropiques satellite, clouds from the CALIPSO satellite, and near-surface precipitation from the CloudSat satellite, and quantifies their rates of change with SST warming. The dataset is partitioned into three vertical velocity regimes, with cloudy grid boxes categorized by phase (ice or liquid), opacity (opaque or thin), and the presence of near-surface precipitation. Opaque cloud cover is always larger in the presence of near-surface precipitation (high ice clouds especially). Low liquid water clouds in the descending regime dominate for SSTs < 299.25 K, where the free troposphere is dry (~20%), and opaque liquid water cloud cover decreases with SST warming (−8% K−1) and thin liquid water cloud cover stays constant (~20%). High ice clouds dominate the ascending regime in which, for 299.25 < SST < 301.75 K, humidity increases with SST in the lower free troposphere and peaks around 302 K. Over the warm SST range (>301.75 K), in the ascending regime, opaque high ice cloud cover decreases with SST (−13% K−1), while thin ice cloud cover increases (+6% K−1). Over the warm SST range, total cloudiness decreases with warming in all regimes. This paper characterizes fundamental relationships between aspects of the tropical atmospheric water cycle and SST.

Understanding Land–Atmosphere–Climate Coupling from the Canadian Prairie Dataset

Analysis of the hourly Canadian Prairie data for the past 60 years has transformed our quantitative understanding of land–atmosphere–cloud coupling. The key reason is that trained observers made hourly estimates of the opaque cloud fraction that obscures the sun, moon, or stars, following the same protocol for 60 years at all stations. These 24 daily estimates of opaque cloud data are of sufficient quality such that they can be calibrated against Baseline Surface Radiation Network data to yield the climatology of the daily short-wave, long-wave, and total cloud forcing (SWCF, LWCF and CF, respectively). This key radiative forcing has not been available previously for climate datasets. Net cloud radiative forcing changes sign from negative in the warm season, to positive in the cold season, when reflective snow reduces the negative SWCF below the positive LWCF. This in turn leads to a large climate discontinuity with snow cover, with a systematic cooling of 10 °C or more with snow cover. In addition, snow cover transforms the coupling between cloud cover and the diurnal range of temperature. In the warm season, maximum temperature increases with decreasing cloud, while minimum temperature barely changes; while in the cold season with snow cover, maximum temperature decreases with decreasing cloud, and minimum temperature decreases even more. In the warm season, the diurnal ranges of temperature, relative humidity, equivalent potential temperature, and the pressure height of the lifting condensation level are all tightly coupled to the opaque cloud cover. Given over 600 station-years of hourly data, we are able to extract, perhaps for the first time, the coupling between the cloud forcing and the warm season imbalance of the diurnal cycle, which changes monotonically from a warming and drying under clear skies to a cooling and moistening under cloudy skies with precipitation. Because we have the daily cloud radiative forcing, which is large, we are able to show that the memory of water storage anomalies, from precipitation and the snowpack, goes back many months. The spring climatology shows the memory of snowfall back through the entire winter, and the memory in summer, goes back to the months of snowmelt. Lagged precipitation anomalies modify the thermodynamic coupling of the diurnal cycle to the cloud forcing, and shift the diurnal cycle of the mixing ratio, which has a double peak. The seasonal extraction of the surface total water storage is a large damping of the interannual variability of precipitation anomalies in the growing season. The large land-use change from summer fallow to intensive cropping, which peaked in the early 1990s, has led to a coupled climate response that has cooled and moistened the growing season, lowering cloud-base, increasing equivalent potential temperature, and increasing precipitation. We show a simplified energy balance of the Prairies during the growing season, and its dependence on reflective cloud.

Cloud removal for hyperspectral remotely sensed images based on hyperspectral information fusion

ABSTRACTHyperspectral remote sensing plays an important role in a wide variety of fields. However, its specific application for land surface analysis has been constrained due to the different shapes of thick, opaque cloud cover. The reconstruction of missing information obscured by clouds in remote-sensing images is an area of active research. However, most of the available cloud-removal methods are not suitable for hyperspectral images, because they lose the spectral information which is very important for hyperspectral analysis. In this article, we developed a new spectral resolution enhancement method for cloud removal (SREM-CR) from hyperspectral images, with the help of an auxiliary cloud-free multispectral image acquired at different times. In the fixed hyperspectral image, spectra of the cloud cover pixels are reconstructed depending on the relationship between the original hyperspectral and multispectral images. The final resulting image has the same spectral resolution as the original hyperspectral image but without clouds. This approach was tested on two experiments, in which the results were compared by visual interpretation and statistical indices. Our method demonstrated good performance.

Using Space Lidar Observations to Decompose Longwave Cloud Radiative Effect Variations Over the Last Decade

AbstractMeasurements of the longwave cloud radiative effect (LWCRE) at the top of the atmosphere assess the contribution of clouds to the Earth warming but do not quantify the cloud property variations that are responsible for the LWCRE variations. The CALIPSO space lidar observes directly the detailed profile of cloud, cloud opacity, and cloud cover. Here we use these observations to quantify the influence of cloud properties on the variations of the LWCRE observed between 2008 and 2015 in the tropics and at global scale. At global scale, the method proposed here gives good results except over the Southern Ocean. We find that the global LWCRE variations observed over ocean are mostly due to variations in the opaque cloud properties (82%); transparent cloud columns contributed 18%. Variation of opaque cloud cover is the first contributor to the LWCRE evolution (58%); opaque cloud temperature is the second contributor (28%).

Analysis of near‐surface biases in ERA‐Interim over the Canadian Prairies

AbstractWe quantify the biases in the diurnal cycle of temperature in ERA‐Interim for both warm and cold season using hourly climate station data for four stations in Saskatchewan from 1979 to 2006. The warm season biases increase as opaque cloud cover decreases, and change substantially from April to October. The bias in mean temperature increases almost monotonically from small negative values in April to small positive values in the fall. Under clear skies, the bias in maximum temperature is of the order of −1°C in June and July, and −2°C in spring and fall; while the bias in minimum temperature increases almost monotonically from +1°C in spring to +2.5°C in October. The bias in the diurnal temperature range falls under clear skies from −2.5°C in spring to −5°C in fall. The cold season biases with surface snow have a different structure. The biases in maximum, mean and minimum temperature with a stable BL reach +1°C, +2.6°C and +3°C respectively in January under clear skies. The cold season bias in diurnal range increases from about −1.8°C in the fall to positive values in March. These diurnal biases in 2 m temperature and their seasonal trends are consistent with a high bias in both the diurnal and seasonal amplitude of the model ground heat flux, and a warm season daytime bias resulting from the model fixed leaf area index. Our results can be used as bias corrections in agricultural modeling that use these reanalysis data, and also as a framework for understanding model biases.

Revisiting Hydrometeorology Using Cloud and Climate Observations

Abstract This paper uses 620 station years of hourly Canadian Prairie climate data to analyze the coupling of monthly near-surface climate with opaque cloud, a surrogate for radiation, and precipitation anomalies. While the cloud–climate coupling is strong, precipitation anomalies impact monthly climate for as long as 5 months. The April climate has memory of precipitation anomalies back to freeze-up in November, mostly stored in the snowpack. The summer climate has memory of precipitation anomalies back to the beginning of snowmelt in March. In the warm season, mean temperature is strongly correlated to opaque cloud anomalies, but only weakly to precipitation anomalies. Mixing ratio anomalies are correlated to precipitation, but only weakly to cloud. The diurnal cycle of mixing ratio shifts upward with increasing precipitation anomalies. Positive precipitation anomalies are coupled to a lower afternoon lifting condensation level and a higher afternoon equivalent potential temperature; both favor increased convection and precipitation. Regression coefficients on precipitation increase from wet to dry conditions. This is consistent with increased uptake of soil water when monthly precipitation is low, until drought conditions are reached, and also consistent with gravity satellite observations. Regression analysis shows monthly opaque cloud cover is tightly correlated to three climate variables that are routinely observed: diurnal temperature range, mean temperature, and mean relative humidity. The set of correlation coefficients, derived from cloud and climate observations, could be used to evaluate the representation of the land–cloud–atmosphere system in both forecast and climate models.

Direct atmosphere opacity observations from CALIPSO provide new constraints on cloud‐radiation interactions

AbstractThe spaceborne lidar CALIPSO (Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation) directly measures atmospheric opacity. In 8 years of CALIPSO observations, we find that 69% of vertical profiles penetrate through the complete atmosphere. The remaining 31% do not reach the surface, due to opaque clouds. The global mean altitude of full attenuation of the lidar beam (z_opaque) is 3.2 km, but there are large regional variations in this altitude. Of relevance to cloud‐climate studies, the annual zonal mean longwave cloud radiative effect and annual zonal mean z_opaque weighted by opaque cloud cover are highly correlated (0.94). The annual zonal mean shortwave cloud radiative effect and annual zonal mean opaque cloud cover are also correlated (−0.95). The new diagnostics introduced here are implemented within a simulator framework to enable scale‐aware and definition‐aware evaluation of the LMDZ5B global climate model. The evaluation shows that the model overestimates opaque cloud cover (31% obs. versus 38% model) and z_opaque (3.2 km obs. versus 5.1 km model). In contrast, the model underestimates thin cloud cover (35% obs. versus 14% model). Further assessment shows that reasonable agreement between modeled and observed longwave cloud radiative effects results from compensating errors between insufficient warming by thin clouds and excessive warming due to overestimating both z_opaque and opaque cloud cover. This work shows the power of spaceborne lidar observations to directly constrain cloud‐radiation interactions in both observations and models.

Annual Climatology of the Diurnal Cycle on the Canadian Prairies

We show the annual climatology of the diurnal cycle, stratified by opaque cloud, using the full hourly resolution of the Canadian Prairie data. The opaque cloud field itself has distinct cold and warm season diurnal climatologies; with a near-sunrise peak of cloud in the cold season and an early afternoon peak in the warm season. There are two primary climate states on the Canadian Prairies, separated by the freezing point of water, because a reflective surface snow cover acts as a climate switch. Both cold and warm season climatologies can be seen in the transition months of November, March and April with a large difference in mean temperature. In the cold season with snow, the diurnal ranges of temperature and relative humidity increase quasi-linearly with decreasing cloud, and increase from December to March with increased solar forcing. The warm season months, April to September, show a homogeneous coupling to the cloud cover, and a diurnal cycle of temperature and humidity that depends only on net longwave. Our improved representation of the diurnal cycle shows that the warm season coupling between diurnal temperature range and net longwave is weakly quadratic through the origin, rather than the linear coupling shown in earlier papers. We calculate the conceptually important 24-h imbalances of temperature and relative humidity (and other thermodynamic variables) as a function of opaque cloud cover. In the warm season under nearly clear skies, there is a warming of +2oC and a drying of -6% over the 24-h cycle, which is about 12% of their diurnal ranges. We summarize results on conserved variable diagrams and explore the impact of surface windspeed on the diurnal cycle in the cold and warm seasons. In all months, the fall in minimum temperature is reduced with increasing windspeed, which reduces the diurnal temperature range. In July and August, there is an increase of afternoon maximum temperature and humidity at low windspeeds, and a corresponding rise in equivalent potential temperature of 4.4K that appears coupled to increased precipitation. However overcast skies are associated with the major rain events and higher windspeeds.

Observational study of land-surface-cloud-atmosphere coupling on daily timescales

Our aim is to provide an observational reference for the evaluation of the surface and boundary layer parameterizations used in large-scale models using the remarkable long-term Canadian Prairie hourly dataset. First we use shortwave and longwave data from the Baseline Surface Radiation Network (BSRN) station at Bratt’s Lake, Saskatchewan, and clear sky radiative fluxes from ERA-Interim, to show the coupling between the diurnal cycle of temperature and relative humidity and effective cloud albedo and net longwave flux. Then we calibrate the nearby opaque cloud observations at Regina, Saskatchewan in terms of the BSRN radiation fluxes. We find that in the warm season, we can determine effective cloud albedo to ±0.08 from daytime opaque cloud, and net long-wave radiation to ±8 W/m2 from daily mean opaque cloud and relative humidity. This enables us to extend our analysis to the 55 years of hourly observations of opaque cloud cover, temperature, relative humidity, and daily precipitation from 11 climate stations across the Canadian Prairies. We show the land-surface-atmosphere coupling on daily timescales in summer by stratifying the Prairie data by opaque cloud, relative humidity, surface wind, day-night cloud asymmetry and monthly weighted precipitation anomalies. The multiple linear regression fits relating key diurnal climate variables, the diurnal temperature range, afternoon relative humidity and lifting condensation level, to daily mean net longwave flux, wind-speed and precipitation anomalies have R2 values between 0.61 and 0.69. These fits will be a useful guide for evaluating the fully coupled system in models.

Climate coupling between temperature, humidity, precipitation, and cloud cover over the Canadian Prairies

AbstractThis analysis uses over 50 years of hourly observations of temperature, relative humidity, and opaque cloud cover and daily precipitation from 11 climate stations across the Canadian Prairies to analyze the monthly, seasonal, and long‐term climate coupling in the warm season. On climate time scales, temperature depends on cloud forcing, while relative humidity depends on precipitation. The monthly climate depends on both opaque cloud cover for the current month and precipitation for both the present and past 2 months in summer. Multiple linear regression shows that anomalies of opaque cloud and precipitation explain 60–80% of the variance in the diurnal temperature range, afternoon relative humidity, and lifting condensation level on monthly time scales. We analyze the internal coupling of diurnal climate observables as a further guide to evaluating models. We couple the statistics to simplified energy and water budgets for the Prairies in the growing season. The opaque cloud observations have been calibrated against the incoming shortwave and longwave fluxes. We estimate that the drydown of total water storage on the landscape damps 56% of precipitation anomalies for the growing season on large spatial scales, although this drydown increases evapotranspiration. This couples the climatological surface fluxes to four key observables: cloud forcing, precipitation, temperature, and humidity. We estimate a climatological evaporative fraction of 0.61 for the Prairies. The observational relationships of the coupled Prairie climate system across time scale will be useful for evaluating these coupled processes in models for weather and seasonal forecasting and climate simulation.

Coupling of winter climate transitions to snow and clouds over the Prairies

AbstractUsing data from 13 climate stations on the Canadian Prairies, together with opaque cloud cover and daily snow depth, to analyze the winter climate transitions with snow, we find that a snow cover acts as a fast climate switch. Surface temperature falls by about 10 K with fresh snowfall and rises by a similar amount with snowmelt, while the daily range of relative humidity falls to around 5–15% with snow cover. These are robust climate signals. For every 10% decrease in days with snow cover over the Canadian Prairies, the mean October to April climate is warmer by about 1.4 K. Stratifying by daily mean opaque cloud cover across snow transitions shows the rapid shift within 5 days from a diurnal cycle dominated by shortwave cloud forcing to one dominated by longwave cloud forcing. We calculate the change in the surface radiative budget with snow using surface albedo data from the Moderate Resolution Imaging Spectroradiometer and station longwave data. We find that with the fall‐winter snow transitions, the surface radiative heating is reduced by 50 Wm−2, with 69% coming from the reduced net shortwave flux, resulting from the increased surface albedo and a small increase in effective cloud albedo, and 31% from a reduced incoming longwave flux. This drop in surface radiative heating is sufficient to produce a drop in the surface radiometric skin temperature of 11 K. We find that in winter, the monthly mean diurnal climate is more closely coupled to the diurnal shortwave forcing than the mean diurnal climate.

Impact of land use change on the diurnal cycle climate of the Canadian Prairies

AbstractThis paper uses hourly observations from 1953 to 2011 of temperature, relative humidity, and opaque cloud cover from 14 climate stations across the Canadian Prairies to analyze the impact of agricultural land use change on the diurnal cycle climate, represented by the mean temperature and relative humidity and their diurnal ranges. We show the difference between the years 1953–1991 and 1992–2011. The land use changes have been largest in Saskatchewan where 15–20% of the land area has been converted in the past four decades from summer fallow (where the land was left bare for 1 year) to annual cropping. During the growing season from 20 May to 28 August, relative humidity has increased by about 7%. During the first 2 months, 20 May to 19 July, maximum temperatures and the diurnal range of temperature have fallen by 1.2°C and 0.6°C, respectively, cloud cover has increased by about 4%, reducing surface net radiation by 6 W m−2, and precipitation has increased. We use the dry‐downs after precipitation to separate the impact of cloud cover and show the coupling between evapotranspiration and relative humidity. We estimate, using reanalysis data from ERA‐Interim, that increased transpiration from the larger area of cropland has reduced the surface Bowen ratio by 0.14–0.2. For the month on either side of the growing season, cloud cover has fallen slightly; maximum temperatures have increased, increasing the diurnal temperature range and the diurnal range of humidity.

Cloud radiative forcing of the diurnal cycle climate of the Canadian Prairies

This analysis uses 40 years of hourly observations of temperature (T), relative humidity (RH), and opaque cloud cover from 14 climate stations across the Canadian Prairies to analyze the diurnal cycle climate, represented by the mean T and RH and their diurnal ranges. From April to October, when incoming shortwave radiation dominates over longwave cooling, maximum temperature and the diurnal ranges of T and RH increase with decreasing opaque cloud cover, while minimum temperature is almost independent of cloud. During the winter period, both maximum and minimum temperatures fall with decreasing cloud, as longwave cooling dominates over the net shortwave flux, which is reduced by the high solar zenith angle and surface reflection by snow. We relate the daily mean opaque cloud cover to the longwave and shortwave cloud forcing and the effective cloud albedo, using multiyear measurements of downward shortwave and longwave fluxes, and longwave fluxes under clear skies from historical weather reanalysis. We provide quadratic fits to compute effective cloud albedo and net longwave fluxes from opaque cloud cover. During the warm season, the daytime rise of temperature is related to the net radiation, and the nighttime fall is related to the net longwave cooling. The diurnal range of T, RH, and all the net radiative fluxes have a quasi‐linear dependence on the effective cloud albedo. This gives a seasonal climate perspective on the coupled land‐surface system of T, RH, and cloud cover over the Canadian Prairies, and the winter transitions in snowy climates.

Calculation of solar radiation using a methodology with worldwide potential

Surface meteorological observations from the DATSAV2 database provide the capability to use the METSTAT (meteorological/statistical) model to calculate hourly values of direct normal, diffuse horizontal, and global horizontal solar radiation for locations throughout the world. Opaque cloud cover, a key input parameter to the METSTAT model, is derived from the DATSAV2 layered cloud cover information. Resulting multiyear data sets include solar radiation and other meteorological data such as dry bulb temperature, dew point temperature, wind speed, and atmospheric pressure. Data filling procedures ensure that the multiyear data sets are serially complete. A minor revision to METSTAT improved solar radiation estimates for conditions of high cloud amounts and low ceiling heights. The methodology was applied to regions of Southern Africa and Saudi Arabia.