Effect of climate and geography on worldwide fine resolution economic activity

Geography, including climatic factors, have long been considered potentially important elements in shaping socio-economic activities, alongside other determinants, such as institutions. Here we demonstrate that geography and climate variables satisfactorily explain the worldwide economic activity as measured by the per capita Gross Cell Product (GCP-PC) at a fine geographical resolution, typically much higher than country average. A 1° by 1° GCPPC dataset has been key for establishing and testing a direct relationship between 'local' geography/climate and GCP-PC. Not only have we tested the geography and climate hypothesis using many possible explanatory variables, importantly we have also predicted and reconstructed GCP-PC worldwide by retaining the most significant predictors. While this study confirms that latitude is the most important predictor for GCP-PC when taken in isolation, the accuracy of the GCP-PC prediction is greatly improved when other factors mainly related to variations in climatic variables, rather than average climatic conditions as typically used, are considered. However, latitude diminishes in importance when only the wealthier parts of the globe are considered. This work points to specific features of the climate system which explain economic activity, such as the variability in air pressure. Implications of these findings range from an improved understanding of why socio-economically better-off societies are geographically placed where they are in the present, past and future to informing where new economic activities could be established in order to yield favourable economic outcomes based on geography and climate conditions

Weather & Climate Services for the Energy Industry

Climate Change; Climate Forecasting; Energy Industry; Climate Risk Management; Meteorolog

Reconstruction of Multidecadal Country-Aggregated Hydro Power Generation in Europe Based on a Random Forest Model

Hydro power can provide a source of dispatchable low-carbon electricity and a storage solution in a climate-dependent energy mix with high shares of wind and solar production. Therefore, understanding the effect climate has on hydro power generation is critical to ensure a stable energy supply, particularly at a continental scale. Here, we introduce a framework using climate data to model hydro power generation at the country level based on a machine learning method, the random forest model, to produce a publicly accessible hydro power dataset from 1979 to present for twelve European countries. In addition to producing a consistent European hydro power generation dataset covering the past 40 years, the specific novelty of this approach is to focus on the lagged effect of climate variability on hydro power. Specifically, multiple lagged values of temperature and precipitation are used. Overall, the model shows promising results, with the correlation values ranging between 0.85 and 0.98 for run-of-river and between 0.73 and 0.90 for reservoir-based generation. Compared to the more standard optimal lag approach the normalised mean absolute error reduces by an average of 10.23% and 5.99%, respectively. The model was also implemented over six Italian bidding zones to also test its skill at the sub-country scale. The model performance is only slightly degraded at the bidding zone level, but this also depends on the actual installed capacity, with higher capacities displaying higher performance. The framework and results presented could provide a useful reference for applications such as pan-European (continental) hydro power planning and for system adequacy and extreme events assessments

Major drivers of East African Monsoon variability and improved prediction for Onset dates

Monsoon rain and its year-to-year variability have a profound influence on Africa’s socio-economic structure by heavily impacting agricultural and energy sectors. The current study focuses on major drivers of the east African Monsoon during October-November-December (OND) which is a common onset window for various rainfall patterns, unimodal or bimodal. Major drivers of monsoon rain in the East African sector, covering Tanzania, Malawi, Kenya and Somalia could be different in early or extended boreal winter, due to the relative positioning of the Intertropical convergence zone and its seasonal migration -hence the location and season is the focus here. Two drivers viz. Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO) both separately indicate very strong positive connections with monsoon(OND) rain. Not only is a strong significant correlation present in OND season with zero seasonal lag, but the signal is also present even a season ahead (before four months too). This is also confirmed using various data sources, detrending the data, using regression technique and covering even earlier as well as later periods. To further strengthen results, a compositing technique is applied that can additionally identify strong signals when different combinations of ENSO and IOD phases act as confounding factors. Results of precipitation anomaly suggest that when IOD and ENSO are both on the same phase in July-August-September (JAS), a significant OND rainfall anomaly is noticed around the east African sector: a deficit (excess) of monsoon rain when both drivers are in the negative (positive) phase. Walker circulation seems to play a major part in transporting signals, via reversing its ascending or descending branch over the regions, when IOD and ENSO are in the same phase. These results can be used for prediction purposes and interestingly, that criterion of IOD and ENSO being of same phase in JAS was again matched in 2022 (both negative) and hence it was possible to deliver early warnings for a deficit in the rain, a season ahead. Methods to compute the Monsoon Onset as determined by meteorological services such as the Tanzania Meteorological Authority rely on various thresholds (these can vary according to the country). To overcome some of the biases with such methods, other definitions of ‘Onset’ take into account cumulative rainfall amount: these have also been tested. Late (early) Onsets dominate years when ENSO and IOD are both in their negative (positive) phases during the JAS season. The cumulative rainfall and Onset days are correlated such that early Onsets are usually associated with more seasonal rainfall and vice versa. Uncertainty in cumulative rain as well as the Onset date of the OND Monsoon is reduced to a large degree when years are categorised based on ENSO and IOD phases of the previous season. Such results have implications for future planning in optimizing agricultural and energy outputs, mitigating severe consequences and losses, alongside taking advantage of favourable weather scenarios. It will impact the livelihoods of millions of Africans

Important drivers of East African monsoon variability and improving rainy season onset prediction

Monsoon rain and its year-to-year variability have a profound influence on Africa’s socio-economic structure by heavily impacting sectors such as agricultural and energy. This study focuses on major drivers of the east African monsoon during October-November-December (OND) which is the standard time window for the onset of the rainy season, be it unimodal or bimodal. Two drivers viz. Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO) both separately indicate very strong positive connections with monsoon (OND) rain not only in the OND season with zero seasonal lag, but the signal is also present even taking IOD and ENSO a season ahead. A compositing approach is applied that can additionally identify strong signals when different combinations of ENSO and IOD phases act as confounding factors. Results of precipitation anomaly suggest that when IOD and ENSO are both on the same phase in July-August-September (JAS), a significant OND rainfall anomaly occurs around the east African sector: A deficit (excess) of OND monsoon rain occurs when both drivers are in a negative (positive) phase during JAS. A location Kibaha in Tanzania, for which station data are available, is considered for a more in-depth analysis. The uncertainty range in cumulative OND rainfall is also reduced to a large degree when IOD and ENSO phases are both negative in JAS. These results can be used for prediction purposes and interestingly, that criterion of IOD and ENSO being of same phase in JAS was again matched in 2022 (both negative) and hence it was possible to deliver early warnings for a deficit in rainfall a season ahead. Techniques to compute the monsoon onset as determined by meteorological services such as the Tanzania Meteorological Authority rely on various thresholds, which may also vary by country. To overcome some of the issues with thresholds-based techniques, other definitions of ‘onset’ take into account cumulative rainfall amount and such technique has also been tested and compared. In both approaches, late (early) onsets dominate in years when ENSO and IOD are both negative (positive) during JAS. In these cases, it is therefore possible to provide an estimation of cumulative rainfall and onset for OND in terms of average, median value, range and distribution of rainfall one season in advance. Such results have implications for optimizing agricultural, water and energy management, also mitigating possible severe production losses, which would impact the livelihoods of millions of Africans

Introduction

Humanity recognised millennia ago the importance of climate variability to the sustenance of life, whether that variability was expressed in the form of droughts, floods, heat, cold, or wind. Coping strategies, developed to handle the consequences of climate variability, helped ensure mankind’s survival, although the historic record indicates that not all societies successfully overcame past challenges imposed by long-term droughts, extensive flooding, and the like. Early coping strategies included migration, invasion, appropriation and storage. In addition many, probably most, perhaps all, societies developed indigenous knowledge or belief systems that they felt enabled them to foresee or control those elements of the climate that are so critical for maintaining water and food supplies

Comparing monthly statistical distributions of wind speed measured at wind towers and estimated from ERA-Interim

International audienceThe energy sector is undergoing a major transformation with an increasing share of power supply from variable renewable energy sources and an increasing variability in energy demand in a variable and changing climate. The European Climatic Energy Mixes (ECEM) project will develop a demonstrator to assess how well different energy supply mixes in Europe will meet demand, over seasonal to long-term decadal time horizons, focusing on the role climate has on the mixes. ECEM is funded under the Copernicus Climate Change Service, operated by ECMWF on behalf of the European Union. Many surface climate variables needed to develop energy profiles are provided by the ERA-Interim Reanalysis. Among these profiles, are wind power supply with wind speed at different heights as main inputs to determine periods when the wind power plants are expected to produce more or less than expected. In this view, a preliminary assessment of the monthly statistical distribution of wind speed at the standard height for wind power plants (80 m) has been performed. Time series of wind speed were obtained for the towers at Cabauw in The Netherlands and offshore at Docking Shoal in the North Sea. Reference statistical distributions were built for each month. Similarly, estimated statistical distributions were built using ERA-Interim estimates of wind speed at different levels. One series was built with a power approach and a second with a log approach. The estimated statistical distributions are then compared to the reference for each month. The log approach produces stronger winds than the power approach for both sites. At Cabauw, both approaches do not produce enough large wind speed for all months. At Docking Shoal, the power approach exhibits statistical distributions very close to the reference ones. Those from the log approach are biased towards higher wind speeds

Trace-Metal Concentrations in Coastal Marshes of the Lower Parana River and the Rio-de-La-Plata Estuary

Fil: Villar, Carlos Alberto. Instituto de Limnología Dr. Raúl A. Ringuelet (ILPLA). Facultad de Ciencias Naturales y Museo. Universidad Nacional de La Plata; ArgentinaFil: Stripeikis, J.. INQUlMAE. Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires; ArgentinaFil: Tudino, M.. INQUlMAE. Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires; ArgentinaFil: Dhuicque, L.. INQUlMAE. Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires; ArgentinaFil: Troccoli, O.. INQUlMAE. Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires; ArgentinaFil: Bonetto, Carlos Alberto. Instituto de Limnología Dr. Raúl A. Ringuelet (ILPLA). Facultad de Ciencias Naturales y Museo. Universidad Nacional de La Plata; Argentin