Downwelling surface shortwave radiation, or surface solar radiation (hereafter referred to as SSR), is a fundamental determinant of the global energy balance, and a crucial driving force for temperature change and hydrological cycle variation (Budyko, 1969;Obryk et al., 2018). Furthermore, the impact of SSR on various aspects of a country's economy is widespread, including, but not limited to agricultural productivity, energy use, food security, and human health risk from increased poverty risk (

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.

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<p>Global warming has slowed economic growth and aggravated global economic inequality, affecting individual wellbeing in a wide-ranging aspects. Quantifying these historical impacts is critical for informing climate change mitigation and adaptation and achieving a more equitable economic development. This paper extends existing literature by exploring the effects of precipitation on economic growth. Based on a panel of 169 countries over the period 1961-2019, we demonstrate a statistically significant non-linear effect of precipitation on economic growth, such that output is maximized at around 2.03 metres of annual total precipitation. Despite of the significant sensitivity of precipitation, we find its impacts are relatively small and are completely overwhelmed by the effects of temperature. We examine the historical marginal effects of climate change and find realized temperature has lowered the annual global growth rate by 0.31 percentage points per year on average, whereas realized precipitation has increased the annual economic growth by roughly 0.01 percentage points. Furthermore, we highlight that countries endowed with different climate conditions exhibit substantially different reactions to historical climate change. For example, Europe and Central Asia countries have benefited both from temperature rising and precipitation fluctuations; while adverse impacts are observed for both factors in African countries. These findings suggest the importance of precipitation for countries with vulnerable ecosystems and inform the possibility of incorporating precipitation in economic development projections under future climate trajectories.</p>

<div> <div> <div> <p>Downward surface solar radiation (SSR) is a crucial component of the Global Energy Balance. 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 <em>diurnal temperature range</em> and <em>cloud coverage</em>. The output of the spatial interpolation is a 0.5<sup>◦</sup> × 0.5<sup>◦ </sup>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.</p> <div> <div> <div> <p>The out-of-sample cross-validation shows that the applied machine learning method is able to capture 49% of the interannual long-term variations in observed SSR, which demonstrates the robustness of the method and shows that the interpolated dataset could serve as a foundation for further SSR research.</p> <div> <div> <div> <p>The current research was published in<em> Journal of Climate</em> (Yuan, Leirvik, and Wild, 2021). Based on the established work, we propose to carry out more extensions:</p> <div> <div> <div> <ul><li>We will evaluate the model’s forecasting accuracy. Yuan, Leirvik, and Wild (2021) validated the model against the Global Energy Balance Archive (GEBA) over the period from the 1950s until 2013. The recent update of GEBA until 2019 makes possible the forecast validation over the more recent period 2014-2019. Not only is the validation an out-of-sample verification, but it will also test the model’s ability in predicting future values.</li> </ul><div> <div> <div> <ul><li>We further propose to use external SSR data to cross validate our interpolated dataset. By external, we mean these data are not included in the GEBA and therefore not used in training the model. This validation will provide further proof for the robustness of our method and the reliability of our dataset. We aim to use World Radiation Data Center (WRDC) and Baseline Surface Radiation Network (BSRN) in this application. In particular, we will conduct a correlation analysis and calculate spatial sampling errors that arise from estimating the temporal variability of SSR for a grid box (0.5<sup>◦</sup>×0.5<sup>◦</sup>) from a point observation.</li> <li>Following the aforementioned in-depth validation of our interpolated dataset, we aim to use it as a reference to assess the performance of the global climate models in CMIP6. Based on our constructed dataset, we aim to implement a comprehensive evaluation of the extent of the discrepancy between CMIP6 model simulations and our synthetic observations. A weighted-average ensemble series could be further developed by giving the better performing models larger weights and less competent models lower weights.</li> </ul></div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>