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The study of Antarctic precipitation has attracted a lot of attention recently. The reliability of climate models in simulating Antarctic precipitation, however, is still debatable. This work assess the precipitation and surface air temperature (SAT) of Antarctica (90 oS to 60 oS) using 49 Coupled Model Intercomparison Project phase 5 (CMIP5) global climate models and the European Centre for Medium-range Weather Forecasts “Interim” reanalysis (ERA-Interim); the National Centers for Environmental Prediction Climate Forecast System Reanalysis (CFSR); the Japan Meteorological Agency 55-year Reanalysis (JRA-55); and the Modern Era Retrospective-analysis for Research and Applications (MERRA) datasets for 1979–2005 (27 years). For precipitation, the time series show that the MERRA and JRA-55 have significantly increased from 1979 to 2005, while the ERA-Int and CFSR have insignificant changes. The reanalyses also have low correlation with one another (generally less than +0.69). 37 CMIP5 models show increasing trend, 18 of which are significant. The resulting CMIP5 MMM also has a significant increasing trend of 0.29 ± 0.06 mm year−1. For SAT, the reanalyses show insignificant changes and have high correlation with one another, while the CMIP5 MMM shows a significant increasing trend. Nonetheless, the variability of precipitation and SAT of MMM could affect the significance of its trend. One of the many reasons for the large differences of precipitation is the CMIP5 models' resolution., 近年、南極の降水に関する研究は注目を浴びている。気候モデルによる南極の降水量シミュレーションについての信頼性は、依然、議論のあるところである。本研究は、南極の降水量と地上気温を、結合気候モデル相互比較計画フェーズ５（CMIP5）の49の全球気候モデルと、ヨーロッパ中期予報センター・インテリム再解析（ERA-Interim）、アメリカ環境予測気候予報センター・システム再解析（CFSR）、気象庁55年再解析（JRA-55）、MERRA再解析データ1979—2005年を使って評価した。降水に関しては、時系列データからMERRA とJRA-55 では1979—2005年に増加しているのに対し、ERA-IntとCFSRでは変化していない。再解析データ同士は相関が低かった（通常0.69以下）。CMIP5モデルでは37モデルが増加傾向で、そのうち18は有意であった。CMIP5多モデル平均値（MMM）も0.29±0.06 mm/年という有意な増加傾向を示した。地上気温については、再解析データは有意な変化は示さなかったが、相互には有意な相関があり、CMIP5のMMMでは有意な増加傾向がみられた。しかしながら、降水と地上気温MMMの変動度は変化傾向の優位性を左右するものである。モデル毎の降水の大きな違いの原因の一つはCMIP5モデルの分解能の違いである。

Climate models predict that the Hadley circulation will expand poleward in a warmer climate, a trend which may cause significant changes in global precipitation patterns. However, recent studies have disagreed as to how strongly changes in the Hadley circulation and changes in the hydrological cycle (specifically the latitude at which precipitation balances evaporation) are related. Here we analyze dynamical and hydrological measures of the Southern Hemisphere edge of the tropics using simulations from the fifth Coupled Model Intercomparison Project (CMIP5) and four reanalysis data sets. In simulations with an abrupt quadrupling of atmospheric CO2 concentrations, all models show a poleward expansion in both metrics. However, there is a large spread among models; the ratio of the hydrological to dynamical expansions varies from 0.6 to 1.4. We show that this model spread can be largely explained by differences in internal variability, which in turn is related to the mean state of models. Differences in mean states among reanalyses are similar to those of models, and so reanalyses do not help constrain uncertainty in model trends.

This study is conducted over the data-poor Limpopo basin centered over southern Africa using reanalysis downscaled to useful resolution. Reanalysis products are of limited value in hydrological applications due to the coarse spatial scales they are available at. Dynamical downscaling of these products over a domain of interest offers a means to convert them to finer spatial scales in a dynamically consistent manner. Additionally, this downscaling also offers a way to resolve dominantatmospheric processes, leading to improved accuracy in the atmospheric variables derived. This study thus evaluates high-resolution downscaling of an objectively chosen reanalysis (ERA-I) over the Limpopo basin using Weather Research and Forecasting (WRF) as a regional climate model. The model generally under-estimates temperature and over-estimates precipitation over the basin, although reasonably consistent with observations. The model does well in simulating observed sustained hydrological extremes as assessed using the Standardized Precipitation Index (SPI) although it consistently under-estimates the severity ofmoisture deficit for the wettest part of the year during the dry years. The basin's aridity index (I) is above the severe drought threshold during summer and is more severe in autumn. This practically restricts rain-fed agriculture to around 3 months in a year over the basin. This study presents possible beneficial use of the downscaled simulations foroptimal hydrologic design and water resources planning in data scarce parts of the world.

International audience; The Arctic region has experienced significant warming during the past two decades with major implications on the cryosphere. The causes of Arctic amplification are still an open question within the scientific community, attracting recent interest. The goal of this study is to quantify the contribution of atmospheric circulation on temperature variability in the Atlantic–Arctic region at decadal to intra‐annual timescales from 1951 to 2014. Daily 20th Century reanalyses geopotential height anomalies at 500 hPa were clustered into different weather regimes to assess their contribution to observed temperature variability. The results show that in winter, 25% of the warming (cooling) in the North Atlantic Ocean (northeastern Canada) is due to temporal decreases of high geopotential anomalies in Greenland. This regime influences air mass migration patterns, bringing less cold (warm) air masses into these regions. Additionally, atmospheric warming or cooling has been attributed to a change in nearby oceanic basin surface conditions because of sea ice decline. In summer, about 15% of the warming observed in Norwegian/Greenland Seas is related to an increase in temporal anticyclonic patterns. This ratio reaches 37% in Norway due to an amplification from downwards solar radiation. This study allows for better understanding how natural climate variability modulates the regional signature of climate change and estimating the uncertainties in climate projections.

Major sudden stratospheric warmings (SSWs) represent one of the most abrupt phenomena of the boreal wintertime stratospheric variability, and constitute the clearest example of coupling between the stratosphere and the troposphere. A good representation of SSWs in climate models is required to reduce their biases and uncertainties in future projections of stratospheric variability. The ability of models to reproduce these phenomena is usually assessed with just one reanalysis. However, the number of reanalyses has increased in the last decade and their own biases may affect the model evaluation. Here we compare the representation of the main aspects of SSWs across reanalyses. The examination of their main characteristics in the pre- and post-satellite periods reveals that reanalyses behave very similarly in both periods. However, discrepancies are larger in the pre-satellite period compared to afterwards, particularly for the NCEP-NCAR reanalysis. All datasets reproduce similarly the specific features of wavenumber-1 and wavenumber-2 SSWs. A good agreement among reanalyses is also found for triggering mechanisms, tropospheric precursors, and surface response. In particular, differences in blocking precursor activity of SSWs across reanalyses are much smaller than between blocking definitions., This research has been supported by the FP7 Environment (STRATOCLIM 603557 project under program FP7- ENV.2013.6.1-2), the Spanish Ministry of Economy and Competitiveness (PALEOSTRAT project, grant no. CGL2015-69699-R), and Blanca Ayarzagüena was funded by the Universidad Complutense de Madrid (Ayudas para la contratación de personal postdoctoral de formación en docencia e investigación en los departamentos de la UCM project).

The ocean–sea ice reanalyses are one of the main sources of Arctic sea ice thickness data both in terms of spatial and temporal resolution, since observations are still sparse in time and space. In this work, we first aim at comparing how the sea ice thickness from an ensemble of 14 reanalyses compares with different sources of observations, such as moored upward-looking sonars, submarines, airbornes, satellites, and ice boreholes. Second, based on the same reanalyses, we intend to characterize the timescales (persistence) and length scales of sea ice thickness anomalies. We investigate whether data assimilation of sea ice concentration by the reanalyses impacts the realism of sea ice thickness as well as its respective timescales and length scales. The results suggest that reanalyses with sea ice data assimilation do not necessarily perform better in terms of sea ice thickness compared with the reanalyses which do not assimilate sea ice concentration. However, data assimilation has a clear impact on the timescales and length scales: reanalyses built with sea ice data assimilation present shorter timescales and length scales. The mean timescales and length scales for reanalyses with data assimilation vary from 2.5 to 5.0 months and 337.0 to 732.5 km, respectively, while reanalyses with no data assimilation are characterized by values from 4.9 to 7.8 months and 846.7 to 935.7 km, respectively.

A range of in situ, satellite and reanalysis products on a common daily 1° × 1° latitude/longitude grid were extracted from the Frequent Rainfall Observations on Grids database to help facilitate intercomparison and analysis of precipitation extremes on a global scale. 22 products met the criteria for this analysis, namely that daily data were available over global land areas from 50°S to 50°N since at least 2001. From these daily gridded data, 10 annual indices that represent aspects of extreme precipitation frequency, duration and intensity were calculated. Results were analysed for individual products and also for four cluster types: (i) in situ, (ii) corrected satellite, (iii) uncorrected satellite and (iv) reanalyses. Climatologies based on a common 13-year period (2001–2013) showed substantial differences between some products. Timeseries (which ranged from 13 years to 67 years) also highlighted some substantial differences between products. A coefficient of variation showed that the in situ products were most similar to each other while reanalysis products had the largest variations. Reanalyses however agreed better with in situ observations over extra-tropical land areas compared to the satellite clusters, although reanalysis products tended to fall into 'wet' and 'dry' camps overall. Some indices were more robust than others across products with daily precipitation intensity showing the least variation between products and days above 20 mm showing the largest variation. In general, the results of this study show that global space-based precipitation products show the potential for climate scale analyses of extremes. While we recommend caution for all products dependent on their intended application, this particularly applies to reanalyses which show the most divergence across results. LVA, MB and PLN are supported by the Australian Research Council (ARC) grants DP160103439 and CE170100023. MGD was supported by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 grant reference RYC-2017-22964. Indices were calculated using the climpactv2 software (https://climpact-sci.org/get-started) and all analyses and graphics were produced using the NCAR Command Language (NCL 2013).

The stratosphere and its dynamics are a very important part of atmospheric circulation. We need to analyze its climatology, as well as long-term trends. A long-term trend study needs homogenous datasets without significant artificial discontinuities. The analysis is based on the two newest released reanalyses, Modern Era-Retrospective Analysis (MERRA2) and European Center for Medium-Range Weather Forecast Reanalysis (ERA5). The aim of this study is to detect discontinuities in the temperature time series from the above reanalyses with the help of the Pettitt homogeneity test for pressure layers above 500 hPa up to 1 hPa in January and February, and show a comparison of temperature trends from the studied reanalyses and GPS radio occultation (GPS RO). We search for individual grid points where these discontinuities occur, and also for the years when they occur (geographical and temporal distribution). As expected, the study confirms better results for the Northern Hemisphere due to the denser data coverage. A high number of grid points with jumps on the Southern Hemisphere is found, especially at higher pressure levels (from 50 hPa). The spatial and vertical distribution of discontinuities is also presented. The vertical distribution reveals the reduction of the number of jumps around 10 hPa, especially for ERA5 reanalysis. The results show that ERA5 has significantly less jumps than MERRA2. We also study temperature trends from reanalyses and GPS RO and our analysis shows that the agreement between the reanalyses and observations are very good for the period 2006&ndash, 2018.

Changes in climate extremes affect socioeconomics and natural systems in northeastern Argentina (NEA) and may increase its vulnerability leading to unprecedented disasters. This study investigates the long-term changes and interannual variability of daily temperature and precipitation climate extremes and assesses to what extent global reanalyses reproduce the observed variability in the recent past. Datasets include quality-controlled observations (1963–2013) and ERA-Interim and NCEP2 reanalyses (1979–2011). Climate extremes are characterized spatially and temporally by 15 indices proposed by the Expert Team on Climate Change Detection and Indices. The leading modes of the area-averaged index time series were obtained by means of a Singular Spectrum Analysis, while the spatial distribution of mean changes was estimated by fitting nonparametric linear trends to each index time series. The results show that temperature extremes are changing towards warmer conditions. The number of warm days has been increasing since 1990 while the number of cold days has been decreasing. Warm and cold nights show a significant signal of warming that seems to be stabilizing in recent decades. Heat waves almost double the frequency and duration of cold waves, and the duration of heat waves increased while cold spells decreased in last decades. Longer heat waves are related to longer dry spells. On the other hand, the number of frost days remained stable although they exhibit high interannual and decadal variability. As well, intense precipitation events in most of the region increased steadily since 1970. The annual maximum amount of 1-day and 5-day precipitation events increased from the 1970s to the 2000s, stabilizing in recent years. The ERA-Interim and NCEP2 reanalyses represent climate extremes with different success. ERA-Interim can recognize temperature extremes in time and space, while the older NCEP2 presents systematic positive errors and has some difficult to replicate the interannual variability of the number of summer days. Both reanalyses reproduce dry spells and the annual maximum 5-day precipitation with large biases, which are particularly noticeable at each observation station. Although reanalyses would be expected to add information for climate extremes in areas of scarce observations like northeastern Argentina, they still need to be used with great caution and only as a complement to observations, especially in studies focusing on precipitation extremes. Fil: Lovino, Miguel Angel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina Fil: Müller, Omar Vicente. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina Fil: Berbery, Ernesto H.. University of Maryland; Estados Unidos Fil: Muller, Gabriela Viviana. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina

The major sources of uncertainty in short-term assessment of global horizontal radiation (G) are the pyranometer type and their operation conditions for measurements, whereas the modeling approach and the geographic location are critical for estimations. The influence of all these factors in the uncertainty of the data has rarely been compared. Conversely, solar radiation data users are increasingly demanding more accurate uncertainty estimations. Here we compare the annual bias and uncertainty of all the mentioned factors using 732 weather stations located in Spain, two satellite-based products and three reanalyses. The largest uncertainties were associated to operational errors such as shading (bias = −8.0%) or soiling (bias = −9.4%), which occurred frequently in low-quality monitoring networks but are rarely detected because they pass conventional QC tests. Uncertainty in estimations greatly changed from reanalysis to satellite-based products, ranging from the gross accuracy of ERA-Interim ( + 6.1 - 6.7 + 18.8 %) to the high quality and spatial homogeneity of SARAH-1 ( + 1.4 - 5.3 + 5.6 %). Finally, photodiodes from the Spanish agricultural network SIAR showed an uncertainty of - 5 . 4 + 6 . 9 %, which is far greater than that of secondary standards ( ± 1.5%) and similar to SARAH-1. This is probably caused by the presence of undetectable operational errors and the use of uncorrected photodiodes. Photodiode measurements from low-quality monitoring networks such as SIAR should be used with caution, because the chances of adding extra uncertainties due to poor maintenance or inadequate calibration considerably increase.

International audience; Most chemistry-climate models show an intensification of the Brewer-Dobson circulation (BDC) in the stratosphere associated with increasing greenhouse gas emissions and ozone depletion in the last decades, but this trend remains to be confirmed in observational data. In this work the evolution of the advective BDC for the period 1979-2012 is evaluated and compared in three modern reanalyses (ERA-Interim, MERRA, and JRA-55). Three different estimates of the BDC are computed for each reanalysis, one based on the definition of the residual circulation and two indirect estimates derived from momentum and thermodynamic balances. The comparison among the nine estimates shows substantial uncertainty in the mean magnitude (∼40%) but significant common variability. The tropical upwelling series show variability linked to the stratospheric quasi-biennial oscillation and to El Niño-Southern Oscillation (ENSO) and also reflect extreme events such as major sudden stratospheric warmings and volcanic eruptions. The trend analysis suggests a strengthening of tropical upwelling of around 2-5%/decade throughout the layer 100-10 hPa. The global spatial structure of the BDC trends provides evidence of an overall acceleration of the circulation in both hemispheres, with qualitative agreement among the estimates. The global BDC trends are mainly linked to changes in the boreal winter season and can be tracked to long-term increases in the resolved wave drag in both hemispheres.

Surface incident solar radiation (Rs) is a key parameter in many climatic and ecological processes. The data from satellites and reanalysis have been widely used. However, for reanalysis, Rs data has been shown to have substantial spatial bias, and the time span of reliable satellite Rs is too short for climatic and ecological studies. Combining reanalysis and satellite data would be an effective method for generating long-term and consistent Rs datasets. Here, we apply a cumulative probability density function-based (CPDF) method to merge eight reanalyses with the latest available satellite Rs data from Clouds and Earth’s Radiant Energy System Energy Balanced and Filled (CERES EBAF) surface retrievals. The CPDF method not only reduces the spatial bias of the reanalysis Rs data, but also makes the Rs datasets in a global, long-term and consistent way. The observed Rs data collected at 54 Baseline Surface Radiation Network (BSRN) stations from 1992 to 2016 are used to evaluate the method. Results show that the CPDF method could reduce the mean absolute biases (MAB) of the reanalysis Rs effectively by 21.24–64.36%. The European Centre for Medium-Range Weather Forecasts Re-Analysis interim (ERA-interim) reanalysis Rs data, which are available for 1979 onward, perform the best before MAB = 13.20 W·m−2 and after MAB = 10.40 W·m−2 merging. This small post-merging MAB of the ERA-interim reanalysis is caused by the MAB of 9.90 W·m−2 in the satellite Rs retrievals. The Japanese 55-year reanalysis provides Rs values back to 1958, and CPDF can reduce its MAB by 32.87%, to 11.17 W·m−2. The National Oceanic and Atmospheric Administration (NOAA)-CIRES twentieth-century reanalysis (CIRES) and the ECMWF twentieth-century reanalysis (ERA20CM) provide century-long Rs estimates. CIRES performs better after merging. The MAB of CIRES can be reduced by 32.10%, to 12.99 W·m−2, while ERA20CM’s can be reduced by 12.51%, to 16.40 W·m−2.

The Regional Ocean Modeling System ocean model is used to simulate the decadal evolution of the regional waters in offshore Iberia in response to atmospheric fields given by ECMWF ERA-40 (1961–2001) and ERA-Interim (1989–2008) reanalyses. The simulated sea surface temperature (SST) fields are verified against satellite AVHRR SST, and they are analysed to characterise the variability and trends of coastal upwelling in the region. Opposing trends in upwelling frequency are found at the northern limit, where upwelling has been decreasing in recent decades, and at its southern edge, where there is some evidence of increased upwelling. These results confirm previous observational studies and, more importantly, indicate that observed SST trends are not only due to changes in radiative or atmospheric heat fluxes alone but also due to changes in upwelling dynamics, suggesting that such a process may be relevant in climate change scenarios. Keywords: climate change, upwelling, ROMS, reanalyses (Published: 22 May 2013) Citation: Tellus A 2013, 65 , 19245, http://dx.doi.org/10.3402/tellusa.v65i0.19245

For the Greenland Ice Sheet (GrIS), large-scale melt area has increased in recent years and is detectable via remote sensing, but its relation to runoff is not known. Historical, modeled melt area and runoff from Modern-Era Retrospective Analysis for Research and Applications (MERRA-Replay), the Interim Re-Analysis of the European Centre for Medium Range Weather Forecasts (ERA-I), the Climate Forecast System Reanalysis (CFSR), the Modèle Atmosphérique Régional (MAR), and the Arctic System Reanalysis (ASR) are examined. These sources compare favorably with satellite-derived estimates of surface melt area for the period 2000-2012. Spatially, the models markedly disagree on the number of melt days in the interior of the southern part of the ice sheet, and on the extent of persistent melt areas in the northeastern GrIS. Temporally, the models agree on the mean seasonality of daily surface melt and on the timing of large-scale melt events in 2012. In contrast, the models disagree on the amount, seasonality, spatial distribution, and temporal variability of runoff. As compared to global reanalyses, time series from MAR indicate a lower correlation between runoff and melt area (r2 = 0.805). Runoff in MAR is much larger in the second half of the melt season for all drainage basins, while the ASR indicates larger runoff in the first half of the year. This difference in seasonality for the MAR and to an extent for the ASR provide a hysteresis in the relation between runoff and melt area, which is not found in the other models. The comparison points to a need for reliable observations of surface runoff.

Decreasing sea ice and snow cover are reducing the surface albedo and changing the Arctic surface energy balance. How these surface albedo changes influence the planetary albedo is a more complex question, though, that depends critically on the modulating effects of the intervening atmosphere. To answer this question, we partition the observed top of atmosphere (TOA) albedo into contributions from the surface and atmosphere, the latter being heavily dependent on clouds. While the surface albedo predictably declines with lower sea ice and snow cover, the TOA albedo decreases approximately half as much. This weaker response can be directly attributed to the fact that the atmosphere contributes more than 70% of the TOA albedo in the annual mean and is less dependent on surface cover. The surface accounts for a maximum of 30% of the TOA albedo in spring and less than 10% by the end of summer. Reanalyses (ASR versions 1 and 2, ERA-Interim, MERRA-2, and NCEP R2) represent the annual means of surface albedo fairly well, but biases are found in magnitudes of the TOA albedo and its contributions, likely due to their representations of clouds. Reanalyses show a wide range of TOA albedo sensitivity to changing sea ice concentration, 0.04&ndash, 0.18 in September, compared to 0.11 in observations.

Some of the natural variability in climate is understood to come from changes in the Sun. A key route whereby the Sun may influence surface climate is initiated in the tropical stratosphere by the absorption of solar ultraviolet (UV) radiation by ozone, leading to a modification of the temperature and wind structures and consequently to the surface through changes in wave propagation and circulation. While changes in total, spectrally-integrated, solar irradiance lead to small variations in global mean surface temperature, the `top-down' UV effect preferentially influences on regional scales at mid-to-high latitudes with, in particular, a solar signal noted in the North Atlantic Oscillation (NAO). The amplitude of the UV variability is fundamental in determining the magnitude of the climate response but understanding of the UV variations has been challenged recently by measurements from the SOlar Radiation and Climate Experiment (SORCE) satellite, which show UV solar cycle changes up to 10 times larger than previously thought. Indeed, climate models using these larger UV variations show a much greater response, similar to NAO observations. Here we present estimates of the ozone solar cycle response using a chemistry-climate model (CCM) in which the effects of transport are constrained by observations. Thus the photolytic response to different spectral solar irradiance (SSI) datasets can be isolated. Comparison of the results with the solar signal in ozone extracted from observational datasets yields significantly discriminable responses. According to our evaluation the SORCE UV dataset is not consistent with the observed ozone response whereas the smaller variations suggested by earlier satellite datasets, and by UV data from empirical solar models, are in closer agreement with the measured stratospheric variations. Determining the most appropriate SSI variability to apply in models..., Comment: This is the original version submitted to Nature Geoscience in July 2015 with the title "Ozone observations reveal lower solar cycle spectral variations", this has changed to the one given above. 4 Figures, Nature Geoscience, 2016, http://www.nature.com/ngeo/journal/vaop/ncurrent/full/ngeo2640.html

The presence of clouds, and their characteristics, has a strong impact on the radiative balance of the Earth and on the amount of solar radiation reaching the Earth's surface. Many applications require accurate forecasts of surface radiation on weather timescales, for example, solar energy and UV radiation forecasts. Here we investigate how operational forecasts of low and mid-level clouds affect the accuracy of solar radiation forecasts. Four years of cloud and solar radiation observations from one site – Helsinki, Finland, are analysed. Cloud observations are obtained from a ceilometer and therefore, we first develop algorithms to reliably detect cloud base, precipitation and fog. These new algorithms are widely applicable for both operational use and research, such as in-cloud icing detection for the wind energy industry and for aviation. The cloud and radiation observations are compared to forecasts from the Integrated Forecast System (IFS) run operationally and developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). We develop methods to evaluate the skill of the cloud and radiation forecasts. These methods can potentially be extended to hundreds of sites globally. Over Helsinki, the measured Global Horizontal Irradiance (GHI) is strongly influenced by its northerly location and the annual variation in cloudiness. Solar radiation forecast error is therefore larger in summer than in winter, but the relative error in the solar radiation forecast is more or less constant throughout the year. The mean overall bias in the GHI forecast is positive (8 W m−2). The observed and forecast distributions in cloud cover, at the spatial scales we are considering, are strongly skewed towards clear-sky and overcast situations. Cloud cover forecasts show more skill in winter when the cloud cover is predominantly overcast; in summer there are more clear-sky and broken cloud situations. A negative bias was found in forecast GHI for correctly forecast clear-sky cases and a positive bias in correctly forecast overcast cases. Temporal averaging improved the cloud cover forecast and hence decreased the solar radiation forecast error, but made little impact on the overall bias. The positive bias seen in overcast situations occurs when the model cloud has low values of liquid water path (LWP). We attribute this bias to the model having LWP values that are too low or that the model optical properties for clouds with low LWP are incorrect.