Climate Zoning Under Climate Change Scenarios In The Basin Of Lake Urmia And Vicinity Basins

A study of climate change scenarios is presented in this paper by projecting a set of recorded precipitation data into three future periods by statistical downscaling methods by employing LARS-WG using data from 7 synoptic stations. The study area covers the basin of Lake Urmia and its overlaps with two of its surrounding basins flowing to the Caspian Sea. The modelling is at two stages: Downscaling comprises: (i) use large-scale GCM models to provide climate variables (predictors); and (ii) downscale them to the local climatic variables for correlating with the observed timeseries (e.g. rainfall) for the period of T0: 1961-2001 - 40 years; Projecting comprises the derivation of precipitation values during the time periods of ; T1: 2011-2030), T2: 2046-2065 and T3: 2080-2099 at synoptic stations using three of standard scenarios: A1B, A2 and B1. These values are then used to map the climate zoning, which show: (i) climates at T1 are still similar to T0 and if any difference, precipitation increases; but changes are likely at T2 and T3 periods; (ii) the climate is moving toward a peakier regime at the northern region but drier towards the central region; and (iii) precipitation is likely to decrease in some of the zones. Thus, the results underpin the need for more responsive policymaking and should this not be realised in the next 5 to 10 years, the future seems bleak, as the loss of Lake Urmia and the depletion of aquifers are likely to be permanent, inflicting immigration from the region.

How Do Nepotism and Favouritism Affect Organisational Climate?

This study seeks to determine the effect of nepotism and favouritism on organisational climate. Using the method of random sampling, 269 persons working in Lithuanian organisations were surveyed. The received data was analysed via the application of the methods of correlation and linear regression. It was determined that organisational climate is influenced significantly by variables such as the manager’s behaviour, safety and relationships with employees, values and traditions, communication, sharing of information, behaviour of employees, and interrelationships and tolerance of one another. Meanwhile, nepotism and favouritism are influenced by the lower number of climate variables (fear related to the absence of concreteness and security, such as joining an organisation, union and tolerance of individuals who have shared interests). This work fills the void in the knowledge of connections that nepotism and favouritism have with organisational climate, drawing attention to the mutual interaction between these phenomena. The article presents a discussion and the research limitations, and provides guidelines for further research.

KrigR – A tool for downloading and statistically downscaling climate reanalysis data

Advances in climate science have rendered obsolete the gridded observation data widely used in downstream applications. Novel climate reanalysis products outperform legacy data products in accuracy, temporal resolution, and provision of uncertainty metrics. Consequently, there is an urgent need to develop a workflow through which to integrate these improved data into biological analyses. The ERA5 product family (ERA5 and ERA5-Land) are the latest and most advanced global reanalysis products created by the European Center for Medium-range Weather Forecasting (ECMWF). These data products offer up to 83 essential climate variables (ECVs) at hourly intervals for the time-period of 1981 to today with preliminary back-extensions being available for 1950-1981. Spatial resolutions range from 30x30km (ERA5) to 11x11km (ERA5-Land) and can be statistically downscaled to study-requirements at finer spatial resolutions. Kriging is one such method to interpolate data to finer resolutions and has the advantages that one can leverage additional covariate information and obtain the uncertainty associated with the downscaling. The KrigR R-package enables users to (1) download ERA5(-Land) climate reanalysis data for a user-specified region, and time-period, (2) aggregate these climate products to desired temporal resolutions and metrics, (3) acquire topographical co-variates, and (4) statistically downscale spatial data to a user-specified resolution using co-variate data via kriging. KrigR can execute all these tasks in a single function call, thus enabling the user to obtain any of 83 (ERA5) / 50 (ERA5-Land) climate variables at high spatial and temporal resolution with a single R-command. Additionally, KrigR contains functionality for computation of bioclimatic variables and aggregate metrics from the variables offered by ERA5(-Land). This R-package provides an easy-to-implement workflow for implementation of state-of-the-art climate data while avoiding issues of storage limitations at high temporal and spatial resolutions by providing data according to user-needs rather than in global data sets. Consequently, KrigR provides a toolbox to obtain a wide range of tailored climate data at unprecedented combinations of high temporal and spatial resolutions thus enabling the use of world-leading climate data through the R-interface and beyond.

Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation

Analyzing the impact of climate variables into the operational planning processes is essential for the robust implementation of a sustainable power system. This paper deals with the modeling of the run-of-river hydropower production based on climate variables on the European scale. A better understanding of future run-of-river generation patterns has important implications for power systems with increasing shares of solar and wind power. Run-of-river plants are less intermittent than solar or wind but also less dispatchable than dams with storage capacity. However, translating time series of climate data (precipitation and air temperature) into time series of run-of-river-based hydropower generation is not an easy task as it is necessary to capture the complex relationship between the availability of water and the generation of electricity. This task is also more complex when performed for a large interconnected area. In this work, a model is built for several European countries by using machine learning techniques. In particular, we compare the accuracy of models based on the Random Forest algorithm and show that a more accurate model is obtained when a finer spatial resolution of climate data is introduced. We then discuss the practical applicability of a machine learning model for the medium term forecasts and show that some very context specific but influential events are hard to capture.

Satellitenbasierte Klimadatensätze und Produkte vom EUMETSAT’s CM SAF als Bestandteil einer nachhaltigen Infrastruktur zur Beobachtung der GCOS ECV‘s

<p>Das CM SAF (EUMETSAT Satellite Application Facility on Climate Monitoring) produziert, archiviert und stellt unter https://www.cmsaf.eu langjährige satellitenbasierte Klimadatensätze von vielen GCOS Essential Climate Variables (ECVs, essentielle Klimavariablen) bereit, die inzwischen auch die komplette aktuelle WMO Klimareferenzperiode 1990-2020 abdecken und damit eine gute Grundlage für die Analyse von Klimavariabilität und Klimawandel liefern. Seit 1999 hat das CM SAF kontinuierlich eine nachhaltige Infrastruktur zur Erzeugung von Klimadatensätzen aufgebaut, mit der Zeitreihen in hoher Qualität in einer operationellen Umgebung erzeugt werden, die auch aktuelle wissenschaftliche Entwicklungen berücksichtigen.</p> <p>Der inhaltliche Fokus des CM SAF liegt auf ECVs, wie Wolken, Wasserdampf, Niederschlag, Landoberflächentemperatur oder der Strahlungskomponenten (langwellig/kurzwellig) am Erdboden und am Oberrand der Atmosphäre, die durch GCOS (Global Climate Observing System) definiert wurden und im Zusammenhang mit dem globalen Energie und Wasser Kreislauf stehen. Einerseits nutzt das CM SAF dazu polarumlaufende Satelliten mit einer globalen räumlichen Abdeckung. Andererseits werden vom CM SAF für Afrika und Europa, Klimadatensätze für Wolken und Strahlung  basierend auf den zeitlich hochaufgelösten Messungen der METEOSAT-Instrumente erzeugt.</p> <p>Alle Daten des CM SAF werden kostenlos abgebeben, sind umfangreich dokumentiert und unabhängig extern begutachtet, um eine hohe Qualität zu gewährleisten. Dies wird unterstützt durch einen umfassenden Service für Kunden, indem beispielsweise Trainingsworkshops und andere Aktivitäten angeboten werden.</p> <p>Diese Präsentation wird einen Überblick über die aktuellen und geplanten Aktivitäten des CM SAF geben und soll interessierten Nutzern durch beispielhafte Anwendungen den Umgang mit CM SAF Produkten verdeutlichen. Zudem werden zukünftige mögliche Anwendungen der Datensätze aufgezeigt. </p>

Long-Term Lake Area Change and Its Relationship with Climate in the Endorheic Basins of the Tibetan Plateau

Lakes are sensitive indicators of climate change in the Tibetan Plateau (TP), which have shown high temporal and spatial variability in recent decades. The driving forces for the change are still not entirely clear. This study examined the area change of the lakes greater than 1 km2 in the endorheic basins of the Tibetan Plateau (EBTP) using Landsat images from 1990 to 2019, and analysed the relationships between lake area and local and large-scale climate variables at different geographic scales. The results show that lake area in the EBTP has increased significantly from 1990 to 2019 at a rate of 432.52 km2·year−1. In the past 30 years, lake area changes in the EBTP have mainly been affected by local climate variables such as precipitation and temperature. At a large scale, Indian Summer Monsoon (ISM) has correlations with lake area in western sub-regions in the Inner Basin (IB). While Atlantic Multidecadal Oscillation (AMO) has a significant connection with lake area, the North Atlantic Oscillation (NAO) does not. We also found that abnormal drought (rainfall) brought by the El Niño/La Niña events are significantly correlated with the lake area change in most sub-regions in the IB.