Incomegetting and Environmental Degradation

Drawing on Alfred Schütz’s thought, as well as on a number of modern pragmatists and practice theorists, we theorize incomegetting—referring to practices of getting income, typically salaried work—as the paramount structurer of everyday life and, therefore, also the chief mediator of the human–nature metabolism. Even though the pragmatics of everyday life as an aggregate underlie the bulk of environmental impacts, these insidious impacts impose little immediate influence on everyday life, in particular in the urban Global North. In other words, the pragmatic dimension of everyday activities—principally, work—that takes place within a vastly complex and globally interlinked productive world system, has most often no immediate connection to the “natural” environment. While parts of the populations are directly dependent in terms of livelihoods on the “natural” environment, these populations are typically pushed to the margins of the global productive system. The understanding formulated in this essay suggests that in environmental social sciences there is a reason to shift the epicenter of the analysis from consumption to everyday life, to the varied practices of incomegetting. Against the backdrop of this paper, universal basic income schemes ought to have radical impacts on the way we relate also to the “natural” environment and such schemes necessitate understanding the essence of money in our contemporary realities

Incomegetting and Environmental Degradation

Drawing on Alfred Schütz’s thought, as well as on a number of modern pragmatists and practice theorists, we theorize incomegetting—referring to practices of getting income, typically salaried work—as the paramount structurer of everyday life and, therefore, also the chief mediator of the human–nature metabolism. Even though the pragmatics of everyday life as an aggregate underlie the bulk of environmental impacts, these insidious impacts impose little immediate influence on everyday life, in particular in the urban Global North. In other words, the pragmatic dimension of everyday activities—principally, work—that takes place within a vastly complex and globally interlinked productive world system, has most often no immediate connection to the “natural” environment. While parts of the populations are directly dependent in terms of livelihoods on the “natural” environment, these populations are typically pushed to the margins of the global productive system. The understanding formulated in this essay suggests that in environmental social sciences there is a reason to shift the epicenter of the analysis from consumption to everyday life, to the varied practices of incomegetting. Against the backdrop of this paper, universal basic income schemes ought to have radical impacts on the way we relate also to the “natural” environment and such schemes necessitate understanding the essence of money in our contemporary realities

Necessary conditions for algorithmic tuning of weather prediction models using OpenIFS as an example

Algorithmic model tuning is a promising approach to yield the best possible forecast performance of multi-scale multi-phase atmospheric models once the model structure is fixed. The problem is to what degree we can trust algorithmic model tuning. We approach the problem by studying the convergence of this process in a semi-realistic case. Let M (x, theta) denote the time evolution model, where x and theta are the initial state and the default model parameter vectors, respectively. A necessary condition for an algorithmic tuning process to converge is that theta is recovered when the tuning process is initialised with perturbed model parameters theta' and the default model forecasts are used as pseudo-observations. The aim here is to gauge which conditions are sufficient in a semi-realistic test setting to obtain reliable results and thus build confidence on the tuning in fully realistic cases. A large set of convergence tests is carried in semi-realistic cases by applying two different ensemble-based parameter estimation methods and the atmospheric forecast model of the Integrated Forecasting System (OpenIFS) model. The results are interpreted as general guidance for algorithmic model tuning, which we successfully tested in a more demanding case of simultaneous estimation of eight OpenIFS model parameters.Peer reviewe

Parameter variations in prediction skill optimization at ECMWF

Peer reviewe

From Extractivism to Global Extractivism : The Evolution of an Organizing Concept

All the named authors were members of the Helsinki Research Working Group on Global Extractivisms and Alternatives, who jointly constructed this article. Equal authorship by all authors is recognised.Research on extractivism has rapidly proliferated, expanding into new empirical and conceptual spaces. We examine the origins, evolution, and conceptual expansion of the concept. Extractivism is useful to analyze resource extraction practices around the world. ‘Global Extractivism’ is a new conceptual tool for assessing global phenomena. We situate extractivism within an ensemble of concepts, and explore its relation to development, the state, and value. Extractivism as an organizing concept addresses many fields of research. Extractivism forms a complex of self-reinforcing practices, mentalities, and power differentials underwriting and rationalizing socio-ecologically destructive modes of organizing life-through subjugation, depletion, and non-reciprocity.Peer reviewe

EC-Earth3-AerChem: A global climate model with interactive aerosols and atmospheric chemistry participating in CMIP6

This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). EC-Earth3-AerChem has interactive aerosols and atmospheric chemistry and contributes to the Aerosols and Chemistry Model Intercomparison Project (AerChemMIP). In this paper, we give an overview of the model, describe in detail how it differs from the other EC-Earth3 configurations, and outline the new features compared with the previously documented version of the model (EC-Earth 2.4). We explain how the model was tuned and spun up under preindustrial conditions and characterize the model's general performance on the basis of a selection of coupled simulations conducted for CMIP6. The net energy imbalance at the top of the atmosphere in the preindustrial control simulation is on average -0.09 W m-2 with a standard deviation due to interannual variability of 0.25 W m-2, showing no significant drift. The global surface air temperature in the simulation is on average 14.08 ∼ C with an interannual standard deviation of 0.17 ∼ C, exhibiting a small drift of 0.015 ± 0.005 ∼ C per century. The model's effective equilibrium climate sensitivity is estimated at 3.9 ∼ C, and its transient climate response is estimated at 2.1 ∼ C. The CMIP6 historical simulation displays spurious interdecadal variability in Northern Hemisphere temperatures, resulting in a large spread across ensemble members and a tendency to underestimate observed annual surface temperature anomalies from the early 20th century onwards. The observed warming of the Southern Hemisphere is well reproduced by the model. Compared with the ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis version 5 (ERA5), the surface air temperature climatology for 1995-2014 has an average bias of -0.86 ± 0.05 ∼ C with a standard deviation across ensemble members of 0.35 ∼ C in the Northern Hemisphere and 1.29 ± 0.02 ∼ C with a corresponding standard deviation of 0.05 ∼ C in the Southern Hemisphere. The Southern Hemisphere warm bias is largely caused by errors in shortwave cloud radiative effects over the Southern Ocean, a deficiency of many climate models. Changes in the emissions of near-term climate forcers (NTCFs) have significant effects on the global climate from the second half of the 20th century onwards. For the SSP3-7.0 Shared Socioeconomic Pathway, the model gives a global warming at the end of the 21st century (2091-2100) of 4.9 ∼ C above the preindustrial mean. A 0.5 ∼ C stronger warming is obtained for the AerChemMIP scenario with reduced emissions of NTCFs. With concurrent reductions of future methane concentrations, the warming is projected to be reduced by 0.5 ∼ C

OpenIFS@home version 1: a citizen science project for ensemble weather and climate forecasting

Weather forecasts rely heavily on general circulation models of the atmosphere and other components of the Earth system. National meteorological and hydrological services and intergovernmental organizations, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), provide routine operational forecasts on a range of spatio-temporal scales by running these models at high resolution on state-of-the-art high-performance computing systems. Such operational forecasts are very demanding in terms of computing resources. To facilitate the use of a weather forecast model for research and training purposes outside the operational environment, ECMWF provides a portable version of its numerical weather forecast model, OpenIFS, for use by universities and other research institutes on their own computing systems. In this paper, we describe a new project (OpenIFS@home) that combines OpenIFS with a citizen science approach to involve the general public in helping conduct scientific experiments. Volunteers from across the world can run OpenIFS@home on their computers at home, and the results of these simulations can be combined into large forecast ensembles. The infrastructure of such distributed computing experiments is based on our experience and expertise with the climateprediction.net (https://www.climateprediction.net/, last access: 1 June 2021) and weather@home systems. In order to validate this first use of OpenIFS in a volunteer computing framework, we present results from ensembles of forecast simulations of Tropical Cyclone Karl from September 2016 studied during the NAWDEX field campaign. This cyclone underwent extratropical transition and intensified in mid-latitudes to give rise to an intense jet streak near Scotland and heavy rainfall over Norway. For the validation we use a 2000-member ensemble of OpenIFS run on the OpenIFS@home volunteer framework and a smaller ensemble of the size of operational forecasts using ECMWF's forecast model in 2016 run on the ECMWF supercomputer with the same horizontal resolution as OpenIFS@home. We present ensemble statistics that illustrate the reliability and accuracy of the OpenIFS@home forecasts and discuss the use of large ensembles in the context of forecasting extreme events.</p

The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6

The Earth system model EC-Earth3 for contributions to CMIP6 is documented here, with its flexible coupling framework, major model configurations, a methodology for ensuring the simulations are comparable across different high-performance computing (HPC) systems, and with the physical performance of base configurations over the historical period. The variety of possible configurations and sub-models reflects the broad interests in the EC-Earth community. EC-Earth3 key performance metrics demonstrate physical behavior and biases well within the frame known from recent CMIP models. With improved physical and dynamic features, new Earth system model (ESM) components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond

OpenIFS@home version 1: a citizen science project for ensembleweather and climate forecasting

Weather forecasts rely heavily on general circulation models of the atmosphere and other components of the Earth system. National meteorological and hydrological services and intergovernmental organisations, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), provide routine operational forecasts on a range of spatio-temporal scales, by running these models in high resolution on state-of-the-art high-performance computing systems. Such operational forecasts are very demanding in terms of computing resources. To facilitate the use of a weather forecast model for research and training purposes outside the operational environment, ECMWF provides a portable version of its numerical weather forecast model, OpenIFS, for use by universities and other research institutes on their own computing systems. In this paper, we describe a new project (OpenIFS@home) that combines OpenIFS with a citizen science approach to involve the general public in helping conduct scientific experiments. Volunteers from across the world can run OpenIFS@home on their computers at home and the results of these simulations can be combined into large forecast ensembles. The infrastructure of such distributed computing experiments is based on our experience and expertise with the climateprediction.net and weather@home systems. In order to validate this first use of OpenIFS in a volunteer computing framework, we present results from ensembles of forecast simulations of tropical cyclone Karl from September 2016, studied during the NAWDEX field campaign. This cyclone underwent extratropical transition and intensified in mid-latitudes to give rise to an intense jet-streak near Scotland and heavy rainfall over Norway. For the validation we use a two thousand member ensemble of OpenIFS run on the OpenIFS@home volunteer framework and a smaller ensemble of the size of operational forecasts using ECMWF’s forecast model in 2016 run on the ECMWF supercomputer with the same horizontal resolution as OpenIFS@home. We present ensemble statistics that illustrate the reliability and accuracy of the OpenIFS@home forecasts as well as discussing the use of large ensembles in the context of forecasting extreme events.</p