Surface Downwelling Shortwave Radiation Research Articles

Are Northern Hemisphere boreal forest fires more sensitive to future aerosol mitigation than to greenhouse gas-driven warming?

Considerable interest exists in understanding how climate change affects wildfire activity. Here, we use the Community Earth System Model version 2 to show that future anthropogenic aerosol mitigation yields larger increases in fire activity in the Northern Hemisphere boreal forests, relative to a base simulation that lacks climate policy and has large increases in greenhouse gases. The enhanced fire response is related to a deeper layer of summertime soil drying, consistent with increased downwelling surface shortwave radiation and enhanced surface evapotranspiration. In contrast, soil column drying is muted under increasing greenhouse gases due to plant physiological responses to increased carbon dioxide and by enhanced melting of soil ice at a depth that increases soil liquid water. Although considerable uncertainty remains in the representation of fire processes in models, our results suggest that boreal forest fires may be more sensitive to future aerosol mitigation than to greenhouse gas-driven warming.

Assessing the cloud radiative bias at Macquarie Island in the ACCESS-AM2 model

Abstract. As a long-standing problem in climate models, large positive shortwave radiation biases exist at the surface over the Southern Ocean, impacting the accurate simulation of sea surface temperature, atmospheric circulation, and precipitation. Underestimations of low-level cloud fraction and liquid water content are suggested to predominantly contribute to these radiation biases. Most model evaluations for radiation focus on summer and rely on satellite products, which have their own limitations. In this work, we use surface-based observations at Macquarie Island to provide the first long-term, seasonal evaluation of both downwelling surface shortwave and longwave radiation in the Australian Community Climate and Earth System Simulator Atmosphere-only Model version 2 (ACCESS-AM2) over the Southern Ocean. The capacity of the Clouds and the Earth’s Radiant Energy System (CERES) product to simulate radiation is also investigated. We utilize the novel lidar simulator, the Automatic Lidar and Ceilometer Framework (ALCF), and all-sky cloud camera observations of cloud fraction to investigate how radiation biases are influenced by cloud properties. Overall, we find an overestimation of +9.5±33.5 W m−2 for downwelling surface shortwave radiation fluxes and an underestimation of -2.3±13.5 W m−2 for downwelling surface longwave radiation in ACCESS-AM2 in all-sky conditions, with more pronounced shortwave biases of +25.0±48.0 W m−2 occurring in summer. CERES presents an overestimation of +8.0±18.0 W m−2 for the shortwave and an underestimation of -12.1±12.2 W m−2 for the longwave in all-sky conditions. For the cloud radiative effect (CRE) biases, there is an overestimation of +4.8±28.0 W m−2 in ACCESS-AM2 and an underestimation of -7.9±20.9 W m−2 in CERES. An overestimation of downwelling surface shortwave radiation is associated with an underestimated cloud fraction and low-level cloud occurrence. We suggest that modeled cloud phase is also having an impact on the radiation biases. Our results show that the ACCESS-AM2 model and CERES product require further development to reduce these radiation biases not just in shortwave and in all-sky conditions, but also in longwave and in clear-sky conditions.

Determination of the Availability and Variation of Surface Downwelling Shortwave Radiation in Saudi Arabia Using EUMETSAT Satellite Imagery

Solar energy is expected to be a viable alternative sustainable energy source in the near future due to diminishing fossil fuel resources and escalating changes in the climate. Surface Downwelling Shortwave (SDS) radiation is an important component for the characterization of energy deposition of the earth’s surface. The monthly means of SDS radiation for Saudi Arabia were extracted from EUMETSAT (SARAH-2.1 climate data record) between 1983 and 2022. The country experienced the lowest level (188 ± 40 W m-2) of SDS radiation in the winter season with a range of 67-271 W m-2. The summer season in Saudi Arabia has the highest level of radiation with a mean value of 304± 27 W m-2, which is 1.62 times the mean of the winter season. The region experienced the highest (10.6 W m-2/10y) and lowest (-10.80 W m-2/10y) levels of a statistically significant upward and downward trend in SDS radiation during the spring and summer seasons respectively in the first decade of the 21st century (2001-2010). During the spring season, there was an upward trend in the availability of SDS radiation across the region, with a magnitude of 3.8 W m-2/10y with a 99% confidence level. In contrast, there was a downward trend in SDS radiation in the summer with a magnitude of 1.0 W m-2/10y with a 90% confidence level followed by the autumn season when the trend was lowest (-0.45 W m-2/10y) and statistically non-significant.

The potential global distribution of an emerging forest pathogen, Lecanosticta acicola, under a changing climate

Brown spot needle blight (BSNB), caused by Lecanosticta acicola (Thüm.) Syd., is an emerging forest disease of Pinus species originating from North America and introduced to Europe and Asia. Severity and spread of the disease has increased in the last two decades in North America and Europe as a response to climate change. No modeling work on spread, severity, climatic suitability, or potential distribution has been done for this important emerging pathogen. This study utilizes a global dataset of 2,970 independent observations of L. acicola presence and absence from the geodatabase, together with Pinus spp. distribution data and 44 independent climatic and environmental variables. The objectives were to (1) identify which bioclimatic and environmental variables are most influential in the distribution of L. acicola; (2) compare four modeling approaches to determine which modeling method best fits the data; (3) examine the realized distribution of the pathogen under climatic conditions in the reference period (1971–2000); and (4) predict the potential future global distribution of the pathogen under various climate change scenarios. These objectives were achieved using a species distribution modeling. Four modeling approaches were tested: regression-based model, individual classification trees, bagging with three different base learners, and random forest. Altogether, eight models were developed. An ensemble of the three best models was used to make predictions for the potential distribution of L. acicola: bagging with random tree, bagging with logistic model trees, and random forest. Performance of the model ensemble was very good, with high precision (0.87) and very high AUC (0.94). The potential distribution of L. acicola was computed for five global climate models (GCM) and three combined pathways of Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (SSP-RCP): SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5. The results of the five GCMs were averaged on combined SSP-RCP (median) per 30-year period. Eight of 44 studied factors determined as most important in explaining L. acicola distribution were included in the models: mean diurnal temperature range, mean temperature of wettest quarter, precipitation of warmest quarter, precipitation seasonality, moisture in upper portion of soil column of wettest quarter, surface downwelling longwave radiation of driest quarter, surface downwelling shortwave radiation of warmest quarter and elevation. The actual distribution of L. acicola in the reference period 1971–2000 covered 5.9% of Pinus spp. area globally. However, the model ensemble predicted potential distribution of L. acicola to cover an average of 58.2% of Pinus species global cover in the reference period. Different climate change scenarios (five GCMs, three SSP-RCPs) showed a positive trend in possible range expansion of L. acicola for the period 1971–2100. The average model predictions toward the end of the century showed the potential distribution of L. acicola rising to 62.2, 61.9, 60.3% of Pinus spp. area for SSP1-RCP2.6, SSP2-RCP4.5, SSP5-RCP8.5, respectively. However, the 95% confidence interval encompassed 35.7–82.3% of global Pinus spp. area in the period 1971–2000 and 33.6–85.8% in the period 2071–2100. It was found that SSP-RCPs had a little effect on variability of BSNB potential distribution (60.3–62.2% in the period 2071–2100 for medium prediction). In contrast, GCMs had vast impact on the potential distribution of L. acicola (33.6–85.8% of global pines area). The maps of potential distribution of BSNB will assist forest managers in considering the risk of BSNB. The results will allow practitioners and policymakers to focus surveillance methods and implement appropriate management plans.

BESSv2.0: A satellite-based and coupled-process model for quantifying long-term global land–atmosphere fluxes

Recent remote-sensing-based global carbon, water and energy budgets over land still include considerable uncertainties. Most existing flux products of terrestrial carbon, water and energy components were developed individually, despite the inherently coupled processes among them. In this study, we present a new set of global daily surface downwelling shortwave radiation (SW), net radiation (Rnet), evapotranspiration (ET), gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) datasets at 0.05° resolutions from 1982 to 2019, by improving a satellite-based and coupled-process model—the Breathing Earth System Simulator (BESS). The new version of BESS (v2.0) integrated a newly developed ecosystem respiration module, an optimality-based maximum carboxylation rate (Vcmax) model, and extended the temporal coverage of flux datasets from 1982 to 2019. We evaluated BESS products against the FLUXNET2015 dataset at the site scale, and against several remote sensing and/or machine learning products on a global scale. At the site scale, BESS products agreed well with FLUXNET measurements, capturing 84%, 53%, 65%, 51% and 31% of daily variation in Rnet, ET, GPP, TER and NEE, respectively. Interannual variation in BESS NEE showed relatively low consistency with the FLUXNET measurements, while the rest fluxes explained approximately half of the interannual variation. On a global scale, we found marked discrepancies in spatio-temporal patterns between BESS and several benchmark products. Over the period 1982–2019, BESSv2.0 estimated the mean annual global Rnet, ET, GPP, TER and NEE to be 340.97 ± 5.22 ZJ yr−1 (mean ± 1 SD), 67.67 ± 0.71 103 km3 yr−1, 125.74 ± 5.95 Pg C yr−1, 109.30 ± 3.16 Pg C yr−1, and − 16.28 ± 2.95 Pg C yr−1, respectively, with significant annual linear trends (P < 0.01) by −0.05 103 km3 yr−2 for ET, by 0.52 Pg C yr−2 for GPP, by 0.28 Pg C yr−2 for TER, and by −0.25 Pg C yr−2 for NEE. We further evaluated various coupled processes derived by BESS in terms of functional properties (i.e., Budyko relation, carbon-use efficiency, water-use efficiency, and light-use efficiency), which agreed well with FLUXNET observations, unlike the benchmark products. Overall, BESSv2.0 can serve as a set of reliable and independent products from other global satellite products, facilitating studies related to global carbon, water and energy budgets in a coupled and comprehensive manner.

Future projections of wind and solar energy resources over China from regional climate models based on bias correction

In this study, the wind and solar energy resources over China are projected during 2031–2060 under the Representative Concentration Pathway (RCP) 8.5 scenario with the Weather Research and Forecasting (WRF) model and Regional Climate Model version 4 (RegCM4). The bias correction method based on probability distribution is applied to correct the simulated 100-m wind speed and surface down-welling shortwave radiation. The results show that the bias correction method can remarkably improve the skill of the individual simulations and the ensemble mean in capturing the features of wind speed and shortwave radiation from the aspects of spatial distribution, annual cycle, quantile distribution, probability distribution and cumulative probability distribution. The future projections from the bias-corrected ensemble show that both the wind and solar energy resources are abundant over northwestern China, with higher wind power density reaching up to 130 W m−2 in winter and richer surface down-welling shortwave radiation above 240 W m−2 in summer. Generally, the wind and solar energy resources will decrease in the future, by around 6% over the western regions and 4% over southeastern China in winter, respectively. The interannual variability of the two resources is projected to increase over most areas of northern China, indicating a decrease in the stability of resources, while tends to decrease over Southeastern China.

Global climate-related predictors at kilometer resolution for the past and future

Abstract. A multitude of physical and biological processes on which ecosystems and human societies depend are governed by the climate, and understanding how these processes are altered by climate change is central to mitigation efforts. We developed a set of climate-related variables at as yet unprecedented spatiotemporal detail as a basis for environmental and ecological analyses. We downscaled time series of near-surface relative humidity (hurs) and cloud area fraction (clt) under the consideration of orography and wind as well as near-surface wind speed (sfcWind) using the delta-change method. Combining these grids with mechanistically downscaled information on temperature, precipitation, and solar radiation, we then calculated vapor pressure deficit (vpd), surface downwelling shortwave radiation (rsds), potential evapotranspiration (pet), the climate moisture index (cmi), and site water balance (swb) at a monthly temporal and 30 arcsec spatial resolution globally from 1980 until 2018 (time-series variables). At the same spatial resolution, we further estimated climatological normals of frost change frequency (fcf), snow cover days (scd), potential net primary productivity (npp), growing degree days (gdd), and growing season characteristics for the periods 1981–2010, 2011–2040, 2041–2070, and 2071–2100, considering three shared socioeconomic pathways (SSP126, SSP370, SSP585) and five Earth system models (projected variables). Time-series variables showed high accuracy when validated against observations from meteorological stations and when compared to alternative products. Projected variables were also highly correlated with observations, although some variables showed notable biases, e.g., snow cover days. Together, the CHELSA-BIOCLIM+ dataset presented here (https://doi.org/10.16904/envidat.332, Brun et al., 2022) allows improvement to our understanding of patterns and processes that are governed by climate, including the impact of recent and future climate changes on the world's ecosystems and the associated services on societies.

The Role of Anthropogenic Aerosol Forcing in the 1850–1985 Strengthening of the AMOC in CMIP6 Historical Simulations

Abstract Previous work has shown that anthropogenic aerosol (AA) forcing drives a strengthening in the Atlantic meridional overturning circulation (AMOC) in CMIP6 historical simulations over 1850–1985, but the mechanisms have not been fully understood. Across CMIP6 models, it is shown that there is a strong correlation between surface heat loss over the subpolar North Atlantic (SPNA) and the forced strengthening of the AMOC. Despite the link to AA forcing, the AMOC response is not strongly related to the contribution of anomalous downwelling surface shortwave radiation to SPNA heat loss. Rather, the spread in AMOC response is primarily due to the spread in turbulent heat loss. We hypothesize that turbulent heat loss is larger in models with strong AA forcing because the air advected over the ocean is colder and drier, in turn because of greater AA-forced cooling over the continents upwind, especially North America. The strengthening of the AMOC also feeds back on itself positively in two distinct ways: by raising the sea surface temperature and hence further increasing turbulent heat loss in the SPNA, and by increasing the sea surface density across the SPNA due to increased northward transport of saline water. A comparison of key indices suggests that the AMOC response in models with strong AA forcing is not likely to be consistent with observations.

The Seasonal Cycle of Surface Soil Moisture

Abstract The seasonal cycle contributes substantially to soil moisture temporal variability in many parts of the world, with important implications for seasonal forecasting relevant to agriculture and the health of humans and ecosystems. There is considerable spatial variability in the seasonal cycle of soil moisture, yet a lack of global observations has hindered the development of parsimonious theories explaining that variability. Here, we use 6 years of global satellite observations to describe and explain the seasonal cycle of surface soil moisture globally. An unsupervised clustering algorithm is used to identify five distinct seasonal cycle regimes. Each seasonal cycle regime typically arises in both hemispheres, on multiple continents, and across substantially different local climates. To explain this spatial variability, we then show that the observed seasonal cycle regimes are reproduced very well by a simple but physically based water balance model, which only uses precipitation and downwelling surface shortwave radiation as inputs, and includes no free parameters. Surprisingly, no information on vegetation or land cover is required. To our knowledge, this is the first characterization of the seasonal cycle of surface soil moisture based on global observations.

An energy budget approach to understand the Arctic warming during the Last Interglacial

Abstract. The Last Interglacial period (129–116 ka) is characterised by a strong orbital forcing which leads to a different seasonal and latitudinal distribution of insolation compared to the pre-industrial period. In particular, these changes amplify the seasonality of the insolation in the high latitudes of the Northern Hemisphere. Here, we investigate the Arctic climate response to this forcing by comparing the CMIP6 lig127k and piControl simulations performed with the IPSL-CM6A-LR (the global climate model developed at Institut Pierre-Simon Laplace) model. Using an energy budget framework, we analyse the interactions between the atmosphere, ocean, sea ice and continents. In summer, the insolation anomaly reaches its maximum and causes a rise in near-surface air temperature of 3.1 ∘C over the Arctic region. This warming is primarily due to a strong positive anomaly of surface downwelling shortwave radiation over continental surfaces, followed by large heat transfer from the continents to the atmosphere. The surface layers of the Arctic Ocean also receive more energy but in smaller quantity than the continents due to a cloud negative feedback. Furthermore, while heat exchange from the continental surfaces towards the atmosphere is strengthened, the ocean absorbs and stores the heat excess due to a decline in sea ice cover. However, the maximum near-surface air temperature anomaly does not peak in summer like insolation but occurs in autumn with a temperature increase of 4.2 ∘C relative to the pre-industrial period. This strong warming is driven by a positive anomaly of longwave radiation over the Arctic Ocean enhanced by a positive cloud feedback. It is also favoured by the summer and autumn Arctic sea ice retreat (-1.9×106 and -3.4×106 km2, respectively), which exposes the warm oceanic surface and thus allows oceanic heat storage and release of water vapour in summer. This study highlights the crucial role of sea ice cover variations, Arctic Ocean, as well as changes in polar cloud optical properties on the Last Interglacial Arctic warming.

Dominant Modes of Tibetan Plateau Summer Surface Sensible Heating and Associated Atmospheric Circulation Anomalies

Based on empirical orthogonal function (EOF) analysis, the dominant modes of variations in summer surface sensible heating (SH) over the Tibetan Plateau (TP), as well as the associated atmospheric circulation anomalies, were investigated in this study. The results show that the first dominant mode of summer SH presents a feature of decadal reduction over the whole TP on an interdecadal time scale, and the second dominant mode is characterized by a zonally asymmetric pattern with positive (negative) SH anomalies in the western (eastern) TP on an interannual time scale. The variations of summer SH are dominated by anomalies in downwelling surface shortwave radiation (DSWR), which are associated with atmospheric circulation changes. The first dominant mode of variation in SH is connected to the interdecadal variation of the Silk Road Pattern (SRP). Further analysis reveals that the interdecadal phase shift of the SRP induces anticyclone circulation to the northeast of the TP, leading to enhanced water vapor supply and convergence over the TP. This can lead to an increase in the total cloud cover, and a reduction in DSWR, contributing to the decadal reduction in SH over the TP. The second dominant mode of variation in SH is related to a stationary teleconnection pattern over the Eurasian continent named the North Atlantic-East and North Asia pattern (NAENA). Corresponding to the positive phase of the NAENA, there is a cyclone anomaly to the west TP, leading to anomalous water vapor convergence (divergence) over the eastern (western) TP. This can result in enhanced (decreased) cloud cover, reduced (increased) DSWR, and therefore, an anomalous decrease (enhancement) in SH over the east (west) of the TP. Furthermore, the southwesterly wind anomaly, which is accompanied by the anomalous cyclone to the west TP, leads to positive SH in the western TP.

DSRC: An Improved Topographic Correction Method for Optical Remote-Sensing Observations Based on Surface Downwelling Shortwave Radiation

The complex terrain in mountainous areas distorts solar illumination, which brings a strong topographic effect on optical remote-sensing observations. Although many efforts have been done to correct this effect via normalizing solar illumination induced differences, there are still high uncertainty, especially for poor illuminated surfaces. In this study, a downwelling shortwave radiation (DSR)-based correction (DSRC) method was proposed. The topographic effects were accounted by normalizing DSR differences at different topographic conditions, and a stratified correction strategy was applied by separating the image into different groups according to normalized difference vegetation index (NDVI) to consider the spectral differences of different land-cover types. The DSRC method was applied to nine Landsat 8 scenes with high-resolution DSR data acquired by downscaling the Meteosat Second Generation (MSG) DSR product. The performance analysis indicates that the correlation coefficient between the corrected surface reflectance and illumination conditions notably decreased. Compared with SCS + C, empirical rotation, Statistical-Empirical, and Modified Minnaert methods, the DSRC method well retains inherent spectral pattern and provides good advantages in normalizing the aspect differences of surface reflectance. Furthermore, the comparison of NDVI values before and after correction indicated that DSRC preserved the original values and successfully corrected the overestimated NDVI values of poor illuminated surfaces. The corrected NDVI time series provide more reasonable cycle of the phenology of vegetated surfaces than the original series. In summary, the DSRC method has a strong potential for reducing topographic effects that currently limit the applications of remotely sensed data in mountainous areas.

The influence of radiation flux in Northwest Pacific on the Western Pacific warm pools and typhoons over the past 170 years

Based on various statistical methods and empirical orthogonal function (EOF) analysis, this study analyzes the correlation of radiation flux of Northwest Pacific in the 100 years scale with the western Pacific warm pool and typhoon development. The key results are as follows. First, the surface downwelling longwave radiation (SDLR) received by key areas in Northwest Pacific significantly increased over the past 170 years. The surface downwelling shortwave radiation (SDSR) decreased, and TOA (Top of Atmosphere) incident shortwave radiation (TISR) slightly fluctuated and increased in the 11a (11 years) period. Second, there was the strongest correlation between the Western Pacific warm pool and SDLR, and both increased continuously. Third, since 1945, there has been a tendency of increasing after decreasing in the annual frequency and the share of severe typhoons, and the formation area distribution of typhoons has turned more even. Taking 1998 as a cut-off point, before 1998, there was no obvious correlation between the strong typhoon frequency and SDLR. However, such correction became stronger after 1998. They were affected by the changes of SDLR, SDSR, TISR, vapor, vorticity, vertical velocity, SST and h 100. Forth, the SDLR and TISR are major factors influencing the Western Pacific warm pool, typhoon motion and other varieties. While SDLR mainly increases in the tropical areas, TISR tends to fluctuate and increase slightly. Their changes are consistent with the change general characteristics of strengthening of typhoon.

Surface downwelling shortwave radiation flux projections for 2021‒2050 in Morocco according to CORDEX-Africa regional climate models

The article considers the results of an estimation of the shortwave radiation flux at the surface in Morocco from 2021-2050. Average monthly values of shortwave radiation coming to the underlying surface (Surface Downwelling Shortwave Radiation, RSDS), calculated using 11 regional climate models of the CORDEX-Africa project, were used as input data. The model calculation was performed assuming a greenhouse gas concentration scenario of RCP4.5. The aim of the work is to determine the characteristics of the distribution of shortwave radiation over the territory of Morocco from 2021-2050 and to identify areas with favourable conditions for developing solar energy in the near future. The results of an analysis of annual RSDS values in the vicinity of the solar power station in Ouarzazate in 2021-2050 are also presented.

Climatic, vegetative, and disturbance predictors of lichen species’ height in Arctic Alaska, USA

Lichens cover approximately 8% of land surface, primarily in alpine and polar habitats. Lichen biomass is often correlated with cover and thallus height, but quick and effective methods of lichen biomass estimation have not been tested across large areas. We utilize a dataset of over 7800 height measurements made throughout the Arctic Network of National Parks in the US to model mat-forming macrolichen height by species as a function of morphological, climatic, and habitat factors. Our goal was to understand how lichen height, as proxy for biomass, varied by species in different areas, and determine which factors best predict lichen height. We found that the single best predictor of lichen height was surface downwelling shortwave radiation. Climatic, vegetative, and other physiographic variables were moderate predictors of lichen height. We determined that differences in height observed between species were not explained by the degree of branching nor thallus width. Our findings highlight that the key factors predicting lichen height may not be easy to mitigate through management (e.g., radiation, vapor pressure deficit, length of growing season), yet may be altered in future climate change scenarios. Further, rapid height field measurements, like those used in our study, could improve our understanding of the drivers of lichen biomass, underscore its variability across species, and could be used for other applications. Accurate biomass measurements are necessary for landscape-scale monitoring and predictions of future lichen biomass in the face of climate perturbations.

Bias in CMIP6 models as compared to observed regional dimming and brightening

Abstract. Anthropogenic aerosol emissions have increased considerably over the last century, but climate effects and quantification of the emissions are highly uncertain as one goes back in time. This uncertainty is partly due to a lack of observations in the pre-satellite era, making the observations we do have before 1990 additionally valuable. Aerosols suspended in the atmosphere scatter and absorb incoming solar radiation and thereby alter the Earth's surface energy balance. Previous studies show that Earth system models (ESMs) do not adequately represent surface energy fluxes over the historical era. We investigated global and regional aerosol effects over the time period 1961–2014 by looking at surface downwelling shortwave radiation (SDSR). We used observations from ground stations as well as multiple experiments from eight ESMs participating in the Coupled Model Intercomparison Project Version 6 (CMIP6). Our results show that this subset of models reproduces the observed transient SDSR well in Europe but poorly in China. We suggest that this may be attributed to missing emissions of sulfur dioxide in China, sulfur dioxide being a precursor to sulfate, which is a highly reflective aerosol and responsible for more reflective clouds. The emissions of sulfur dioxide used in the models do not show a temporal pattern that could explain observed SDSR evolution over China. The results from various aerosol emission perturbation experiments from DAMIP, RFMIP and AerChemMIP show that only simulations containing anthropogenic aerosol emissions show dimming, even if the dimming is underestimated. Simulated clear-sky and all-sky SDSR do not differ greatly, suggesting that cloud cover changes are not a dominant cause of the biased SDSR evolution in the simulations. Therefore we suggest that the discrepancy between modeled and observed SDSR evolution is partly caused by erroneous aerosol and aerosol precursor emission inventories. This is an important finding as it may help interpret whether ESMs reproduce the historical climate evolution for the right or wrong reason.

Daytime low-level clouds in West Africa – occurrence, associated drivers, and shortwave radiation attenuation

Abstract. This study focuses on daytime low-level clouds (LLCs) that occur within the first 2 km of the atmosphere over West Africa (WA). These daytime LLCs play a major role in the earth's radiative balance, yet their understanding is still relatively low in WA. We use the state-of-the-art ERA5 dataset to understand their occurrence and associated drivers as well as their impact on the incoming surface solar radiation in the two contrasting Guinean and Sahelian regions of WA. The diurnal cycle of the daytime occurrence of three LLC classes namely No LCC, LLC Class-1 (LLCs with lower fraction), and LLC Class-2 (LLCs with higher fraction) is first studied. The monthly evolutions of hourly and long-lasting LLC (for at least 6 consecutive hours) events are then analyzed as well as the synoptic-scale moisture flux associated with the long-lasting LLC events. Finally, the impact of LLC on the surface heat fluxes and the incoming solar irradiance is investigated. During the summer months in the Guinean region, LLC Class-1 occurrence is low, while LLC Class-2 is frequent (occurrence frequency around 75 % in August). In the Sahel, LLC Class-1 is dominant in the summer months (occurrence frequency more than 80 % from June to October); however the peak occurrence frequency of Class-2 is also in the summer. In both regions, events with No LLC do not present any specific correlation with the time of the day. However, a diurnal evolution that appears to be strongly different from one region to the other is noted for the occurrence of LLC Class-2. LLC occurrence in both regions is associated with high moisture flux driven by strong southwesterly winds from the Gulf of Guinea and significant background moisture levels. LLC Class-2 in particular leads to a significant reduction in the upward transfer of energy and a net downward energy transfer caused by the release of large amounts of energy in the atmosphere during the cloud formation. In July, August, and September (JAS), most of the LLC Class-2 events may likely be the low-level stratiform clouds that occur frequently over the Guinean region, while they may be deep convective clouds in the Sahel. Additionally, LLC Class-2 causes high attenuation of the incoming solar radiation, especially during JAS, where about 49 % and 44 % of the downwelling surface shortwave radiation is lost on average in Guinea and the Sahel, respectively.

Simulation of factors affecting &amp;lt;i&amp;gt;Emiliania huxleyi&amp;lt;/i&amp;gt; blooms in Arctic and sub-Arctic seas by CMIP5 climate models: model validation and selection

Abstract. The observed warming in the Arctic is more than double the global average, and this enhanced Arctic warming is projected to continue throughout the 21st century. This rapid warming has a wide range of impacts on polar and sub-polar marine ecosystems. One of the examples of such an impact on ecosystems is that of coccolithophores, particularly Emiliania huxleyi, which have expanded their range poleward during recent decades. The coccolithophore E. huxleyi plays an essential role in the global carbon cycle. Therefore, the assessment of future changes in coccolithophore blooms is very important. Currently, there are a large number of climate models that give projections for various oceanographic, meteorological, and biochemical variables in the Arctic. However, individual climate models can have large biases when compared to historical observations. The main goal of this research was to select an ensemble of climate models that most accurately reproduces the state of environmental variables that influence the coccolithophore E. huxleyi bloom over the historical period when compared to reanalysis data. We developed a novel approach for model selection to include a diverse set of measures of model skill including the spatial pattern of some variables, which had not previously been included in a model selection procedure. We applied this method to each of the Arctic and sub-Arctic seas in which E. huxleyi blooms have been observed. Once we have selected an optimal combination of climate models that most skilfully reproduce the factors which affect E. huxleyi, the projections of the future conditions in the Arctic from these models can be used to predict how E. huxleyi blooms will change in the future. Here, we present the validation of 34 CMIP5 (fifth phase of the Coupled Model Intercomparison Project) atmosphere–ocean general circulation models (GCMs) over the historical period 1979–2005. Furthermore, we propose a procedure of ranking and selecting these models based on the model's skill in reproducing 10 important oceanographic, meteorological, and biochemical variables in the Arctic and sub-Arctic seas. These factors include the concentration of nutrients (NO3, PO4, and SI), dissolved CO2 partial pressure (pCO2), pH, sea surface temperature (SST), salinity averaged over the top 30 m (SS30 m), 10 m wind speed (WS), ocean surface current speed (OCS), and surface downwelling shortwave radiation (SDSR). The validation of the GCMs' outputs against reanalysis data includes analysis of the interannual variability, seasonal cycle, spatial biases, and temporal trends of the simulated variables. In total, 60 combinations of models were selected for 10 variables over six study regions using the selection procedure we present here. The results show that there is neither a combination of models nor one model that has high skill in reproducing the regional climatic-relevant features of all combinations of the considered variables in target seas. Thereby, an individual subset of models was selected according to our model selection procedure for each combination of variable and Arctic or sub-Arctic sea. Following our selection procedure, the number of selected models in the individual subsets varied from 3 to 11. The paper presents a comparison of the selected model subsets and the full-model ensemble of all available CMIP5 models to reanalysis data. The selected subsets of models generally show a better performance than the full-model ensemble. Therefore, we conclude that within the task addressed in this study it is preferable to employ the model subsets determined through application of our procedure than the full-model ensemble.

Satellite Retrieval of Downwelling Shortwave Surface Flux and Diffuse Fraction under All Sky Conditions in the Framework of the LSA SAF Program (Part 2: Evaluation)

High frequency knowledge of the spatio-temporal distribution of the downwelling surface shortwave flux (DSSF) and its diffuse fraction (fd) at the surface is nowadays essential for understanding climate processes at the surface–atmosphere interface, plant photosynthesis and carbon cycle, and for the solar energy sector. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Land Surface Analysis operationally delivers estimation of the MDSSFTD (MSG Downwelling Surface Short-wave radiation Fluxes—Total and Diffuse fraction) product with an operational status since the year 2019. The method for retrieval was presented in a companion paper. Part 2 now focuses on the evaluation of the MDSSFTD algorithm and presents a comparison of the corresponding outputs, i.e., total DSSF and diffuse fraction (fd) components, against in situ measurements acquired at four Baseline Surface Radiation Network (BSRN) stations over a seven-month period. The validation is performed on an instantaneous basis. We show that the satellite estimates of DSSF and fd meet the target requirements defined by the user community for all-sky (clear and cloudy) conditions. For DSSF, the requirements are 20 Wm−2 for DSSF &lt; 200 Wm−2, and 10% for DSSF ≥ 200 Wm−2. The mean bias error (MBE) and relative mean bias error (rMBE) compared to the ground measurements are 3.618 Wm−2 and 0.252%, respectively. For fd, the requirements are 0.1 for fd &lt; 0.5, and 20% for fd ≥ 0.5. The MBE and rMBE compared to the ground measurements are −0.044% and −17.699%, respectively. The study also provides a separate analysis of the product performances for clear sky and cloudy sky conditions. The importance of representing the cloud–aerosol radiative coupling in the MDSSFTD method is discussed. Finally, it is concluded that the quality of the aerosol optical depth (AOD) forecasts currently available is accurate enough to obtain reliable diffuse solar flux estimates. This quality of AOD forecasts was still a limitation a few years ago.

Evaluation of four different climate sources on pavement mechanistic-empirical design and impact of surface shortwave radiation

ABSTRACT This paper compares the predicted distresses (via in the AASHTOWare Pavement ME Design software (PMED)) of asphalt concrete (AC) and jointed plain concrete pavement (JPCP) using four different climate data sources. These sources also include the Modern Era Retrospective Analysis for Research and Application (MERRA) Versions 1.0 and 2.0 (MERRA-1 and MERRA-2). Pavement performance predictions generated using these data showed disagreement among some of the climate data sources, especially for MERRA-2. Comprehensive diurnal and other time-series analyses of the raw climate data found significant disagreements in the percent sunshine estimates. Percent sunshine is used in the PMED environmental effects model to semi-empirically estimate the downwelling shortwave radiation reaching the pavement surface. Both MERRA data (MERRA -1 and MERRA-2) independently provide direct predictions of downwelling surface shortwave radiation (SSR). The direct model predictions of SSR were used to back calculate ‘synthetic’ per cent sunshine for input into the PMED. The use of the synthetic per cent sunshine derived from the predicted SSR eliminated nearly all discrepancies in the predicted pavement performance using MERRA-1 versus MERRA-2 data. Based on these results, the authors recommend that SSR rather than per cent sunshine be used as a direct input to the PMED.