Remote impacts of 2009 and 2015 El Niño on oceanic and biological processes in a marginal sea of the Northwestern Pacific

The significance of long-term teleconnections derived from the anomalous climatic conditions of El Niño has been a highly debated topic, where the remote response of coastal hydrodynamics and marine ecosystems to El Niño conditions is not completely understood. The 14-year long data from a ship-borne acoustic Doppler current profiler was used to examine the El Niño’s impact, in particular, 2009 and 2015 El Niño events, on oceanic and biological processes in coastal regions across the Korea/Tsushima Strait. Here, it was revealed that the summer volume transport could be decreased by 8.7% (from 2.46 ± 0.39 to 2.24 ± 0.26 Sv) due to the anomalous northerly winds in the developing year of El Niño. Furthermore, the fall mean volume backscattering strength could be decreased by 1.8% (from − 97.09 ± 2.14 to − 98.84 ± 2.10 dB) due to the decreased surface solar radiation after the El Niño events. Overall, 2009 and 2015 El Niño events remotely affected volume transport and zooplankton abundance across the Korea/Tsushima Strait through climatic teleconnections.

Solar radiation in the cloud-free atmosphere

<p>The quantification of Earth’s solar radiation budget and its temporal changes is essential for the understanding of the genesis and evolution of climate on our planet. While the solar radiative fluxes in and out of the climate system can be accurately tracked and quantified from space by satellite programs such as CERES or SORCE, the disposition of solar energy within in the climate system is afflicted with larger uncertainties. A better quantification of the solar radiative fluxes not only under cloudy, but also under cloud-free conditions can help to reduce these uncertainties and is essential for example for the determination of cloud radiative effects or for the understanding of  temporal changes in the solar radiative components of the climate system.</p> <p>We combined satellite observations of Top of Atmosphere fluxes with the information contained in surface flux observations and climate models to infer the absorption of solar radiation in the atmosphere, which we estimated at 73 Wm<sup>-2</sup> globally under cloud-free conditions (Wild et al. 2019 Clim Dyn). The latest generation of climate models participating in CMIP6 is now able to reproduce this magnitude surprisingly well, whereas in previous climate model  generations the cloud-free atmosphere was typically too transparent for solar radiation, which stated a long-standing modelling issue (Wild 2020 Clim Dyn, Wild et al. 1995 JClim).</p> <p>With respect to changes in solar fluxes, there is increasing evidence that the substantial long-term decadal variations in surface solar radiation known as dimming and brightening occur not only under all-sky, but similarly also under clear-sky conditions (Manara et al. 2016 ACP, Yang et al. 2019 JClim; Wild et al. 2021 GRL). This points to aerosol radiative effects as major factor for the explanation of this phenomenon.</p>

SARAH-3 – a new satellite-based Climate Data Record of Surface Solar Radiation parameters

<p class="western"><span lang="en-US">The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) generates and distributes high quality long-term climate data records (CDR) of energy and water cycle parameters, which are freely available.</span></p> <p class="western"><span lang="en-US">In 2022, a new version of the “Surface Solar Radiation data set – Heliosat” will be released: SARAH-3. As the previous editions, the SARAH-3 climate data record is based on satellite observations from the first and second METEOSAT generations and provides various surface radiation parameters, including global radiation, direct radiation, sunshine duration, photosynthetic active radiation and others. SARAH-3 covers the time period 1983 to 2020 and offers 30-minute instantaneous data as well as daily and monthly means on a regular 0.05° x 0.05° lon/lat grid.</span></p> <p class="western" align="left"><span lang="en-US">In this presentation, an overview of the SARAH climate data record and their applications will be given. A focus will be on the SARAH-3 developments and validation with surface reference observations. Further, SARAH-3 will be used for a first analysis of the climate variability and potential trends of global radiation in Europe during the last decades. </span><span lang="en-US">The data record reveals that there is an increasing trend of surface solar radiation in Europe during the last decades, which is superimposed by decadal and regional variability.</span></p>

Evaluation of subseasonal to seasonal forecasts over India for renewable energy applications

This study evaluates subseasonal to seasonal scale (S2S) forecasts of meteorological variables relevant for the renewable energy (RE) sector of India from six ocean-atmosphere coupled models: ECMWF SEAS5, DWD GCFS 2.0, Météo-France's System 6, NCEP CFSv2, UKMO GloSea5 GC2-LI, and CMCC SPS3. The variables include 10 m wind speed, incoming solar radiation, 2 m temperature, and 2 m relative humidity because they are critical for estimating the supply and demand of renewable energy. The study is conducted over seven homogenous regions of India for 1994–2016. The target months are April and May when the electricity demand is the highest and June–September when the renewable resources outstrip the demand. The evaluation is done by comparing the forecasts at 1, 2, 3, 4, and 5-months lead-times with the ERA5 reanalysis spatially averaged over each region. The fair continuous ranked probability skill score (FCRPSS) is used to quantitatively assess the forecast skill. Results show that incoming surface solar radiation predictions are the best, while 2 m relative humidity is the worst. Overall SEAS5 is the best performing model for all variables, for all target months in all regions at all lead times while GCFS 2.0 performs the worst. Predictability is higher over the southern regions of the country compared to the north and north-eastern parts. Overall, the quality of the raw S2S forecasts from numerical models over India are not good. These forecasts require calibration for further skill improvement before being deployed for applications in the RE sector.

Evaluation of ECMWF model to predict daily and monthly solar radiation over Indonesia region

Solar radiation forecast is a pivotal information needed in the operational activity of large-scale solar energy production. In this study, the reliability of SSRD (surface solar radiation downward) forecast from the 51 ensemble members in the ECMWF (European Centre for Medium Range Forecast) long-range forecast to predict daily and monthly radiation in 5 climatological stations in Indonesia is evaluated. The global horizontal irradiance (GHI) data from the solar radiation observation network from January 2018 – December 2020 are used in the quantitative evaluation of the SSRD forecast. Post-processing methods are applied to the model output, namely the bilinear interpolation method and the empirical quantile mapping to reduce consistent biases in the model output. The evaluation was carried out for different cloud covers based on the calculation of clearness index (k_t). The cloud condition affects the performance of the model, where the highest correlation value is achieved during sunny days (0.18 – 0.65) and the lowest correlation happens in overcast days (0.05 – 0.35). Models also tend to underestimate radiation when the sky is clear and overestimate it in cloudy days, based on negative MBE values during clear days (-0.47 kWh/m2 – -1.29 kWh/m2). The spatial averaging method did not necessarily improve the accuracy of the forecast, but the empirical quantile mapping method provides better accuracy, which is indicated by a values (mean error ratio) lower than 1 in most stations. Information about the influence of cloud cover on model performance can be used in future application of the model output and the bias correction process carried out in this study can be applied to reduce bias in the model.

Meteorology and climatology of historical weekly wind and solar power resource droughts over western North America in ERA5

Wind and solar electricity generation is projected to expand substantially over the next several decades due both to rapid cost declines as well as regulation designed to achieve climate targets. With increasing reliance on wind and solar generation, future energy systems may be vulnerable to previously underappreciated synoptic-scale variations characterized by low wind and/or surface solar radiation. Here we use western North America as a case study region to investigate the historical meteorology of weekly-scale “droughts” in potential wind power, potential solar power and their compound occurrence. We also investigate the covariability between wind and solar droughts with potential stresses on energy demand due to temperature deviations away human comfort levels. We find that wind power drought weeks tend to occur in late summer and are characterized by a mid-level atmospheric ridge centered over British Columbia and high sea level pressure on the lee side of the Rockies. Solar power drought weeks tend to occur near winter solstice when the seasonal minimum in incoming solar radiation co-occurs with the tendency for mid-level troughs and low pressure systems over the U.S. southwest. Compound wind and solar power drought weeks consist of the aforementioned synoptic pattern associated with wind droughts occurring near winter solstice when the solar resource is at its seasonal minimum. We find that wind drought weeks are associated with high solar power (and vice versa) both seasonally and in terms of synoptic meteorology, which supports the notion that wind and solar power generation can play complementary roles in a diversified energy portfolio at synoptic spatiotemporal scales over western North America.

Global trends in downward surface solar radiation from spatial interpolated ground observations during 1961-2019

Downward surface solar radiation (SSR) is a crucial component of the Global Energy Balance, affecting temperature and the hydrological cycle profoundly, and it provides crucial information about climate change. Many studies have examined SSR trends, however they are often concentrated on specific regions due to limited spatial coverage of ground based observation stations. To overcome this spatial limitation, this study performs a spatial interpolation based on a machine learning method, Random Forest, to interpolate monthly SSR anomalies using a number of climatic variables (various temperature indices, cloud coverage, etc.), time point indicators (years and months of SSR observations), and geographical characteristics of locations (latitudes, longitudes, etc). The predictors that provide the largest explanatory power for interannual variability are diurnal temperature range and cloud coverage. The output of the spatial interpolation is a 0:5° ×0:5° monthly gridded dataset of SSR anomalies with complete land coverage over the period 1961-2019, which is used afterwards in a comprehensive trend analysis for i) each continent separately, and ii) the entire globe.The continental level analysis reveals the major contributors to the global dimming and brightening. In particular, the global dimming before the 1980s is primarily dominated by negative trends in Asia and North America, while Europe and Oceania have been the two largest contributors to the brightening after 1982 and up until 2019.