A Bayesian stochastic generator to complement existing climate change scenarios: supporting uncertainty quantification in marine and coastal ecosystems

Available climate change projections, which can be used for quantifying future changes in marine and coastal ecosystems, usually consist of a few scenarios. Studies addressing ecological impacts of climate change often make use of a low- (RCP2.6), moderate- (RCP4.5) or high climate scenario (RCP8.5), without taking into account further uncertainties in these scenarios. In this research a methodology is proposed to generate further synthetic scenarios, based on existing datasets, for a better representation of climate change induced uncertainties. The methodology builds on Regional Climate Model scenarios provided by the EURO-CORDEX experiment. In order to generate new realizations of climate variables, such as radiation or temperature, a hierarchical Bayesian model is developed. In addition, a parameterized time series model is introduced, which includes a linear trend component, a seasonal shape with varying amplitude and time shift, and an additive residual term. The seasonal shape is derived with the non-parametric locally weighted scatterplot smoothing, and the residual term includes the smoothed variance of residuals and independent and identically distributed noise. The distributions of the time series model parameters are estimated through Bayesian parameter inference with Markov chain Monte Carlo sampling (Gibbs sampler). By sampling from the predictive distribution numerous new statistically representative synthetic scenarios can be generated including uncertainty estimates. As a demonstration case, utilizing these generated synthetic scenarios and a physically based ecological model (Delft3D-WAQ) that relates climate variables to ecosystem variables, a probabilistic simulation is conducted to further propagate the climate change induced uncertainties to marine and coastal ecosystem indicators.

Seeing the "Changing Nature of Work" through a Precarity Lens

This article reviews the concept of precarity and offers critical reflections on its contribution to thestudy of contemporary labour and livelihoods. A stock-take of key and recent literature suggeststhat, despite conceptual ambiguity and overstretching, “thinking with precarity” continues to provea valuable and worthwhile exercise – so long as that thinking is carefully articulated. This involvesunderstanding precarity as: 1) rooted in concrete labour market experiences but also connected tobroader anxieties over social and political life; 2) a process-focused concept rather than end-statedescriptor; and 3) speaking to longer histories and wider geographies than its commonplace statusas a residual term or category implies. The analytical advantages of thinking in such a way areillustrated through a critical analysis of the World Bank’s World Development Report 2019 on the“changing nature of work”, and in particular its handling of digital labour.KEYWORDS: precarious work; politics of precarity; livelihoods; digital labour; gig economy

Towards the closure of momentum budget analyses in the WRF (v3.8.1) model

Budget analysis of a tendency equation is widely utilized in numerical studies to quantify different physical processes in a simulated system. While such analysis is often post-processed when the output is made available, it is well acknowledged that the closure of a budget is difficult to achieve without temporal and/or spatial averaging. Nevertheless, the development of errors in such calculations has not been systematically investigated. In this study, an inline budget retrieval method is first developed in the WRF v3.8.1 model and tested on a 2D idealized slantwise convection case with a focus on the momentum equations. This method extracts all the budget terms following the model solver, which gives a high accuracy, with a residual term always less than 0.1 % of the tendency term. Then, taking the inline values as truth, several offline budget analyses with different commonly used simplifications are performed to investigate how they may affect the accuracy of the estimation of individual terms and the resultant residual. These assumptions include using a lower-order advection operator than the one used in the model, neglecting grid staggering, or following a mathematically equivalent but transformed format of the governing equations. Errors in these post-processed analyses are found mostly over the area where the dynamics are the most active, thus impairing the subsequent physical interpretation. A maximum 99th percentile residual can reach >50 % of the concurrent tendency term, indicating the danger of neglecting the residual term as done in many budget studies. This work provides general guidance not only for budget diagnoses with the WRF model but also for minimizing the errors in post-processed budget calculations.

N2O-based climatology of the Brewer-Dobson Circulation in WACCM, a chemical reanalysis and a CTM driven by four dynamical reanalyses

<p>The Brewer-Dobson Circulation (BDC) plays a major role in the stratospheric dynamics in terms of tracer transport through the mean residual meridional advection and the isentropic 2-way mixing. <br>The climatological BDC in the Whole Atmosphere Community Climate Model (WACCM) is separated in its components and evaluated through a comparison with a chemical reanalysis of the Aura Microwave Limb Sounder version 2 (BRAM2) and with a chemistry-transport model driven by four modern reanalyses (ERA-Interim, JRA-55, MERRA and MERRA2). The BDC seasonal means and climatological annual cycle are addressed using the Transformed Eulerian Mean (TEM) analysis of the long-lived tracer N2O. The N2O TEM budget terms considered in this study are the vertical residual advection and the horizontal two-way mixing terms.<br>WACCM presents a general underestimation of the horizontal mixing term in the wintertime Northern Hemisphere with respect to the reanalyses throughout the stratosphere.In the wintertime antarctic region the mid-low stratospheric horizontal mixing term in WACCM does not agree with the reanalyses: it shows near-zero positive values, while all the reanalyses show a consistent negative contribution. This disagreement between WACCM and the reanalyses is located in the region and period of the polar vortex development, and can be related to a different representation of the polar jet. In this region the reanalyses are nevertheless affected by large uncertanties of the TEM analysis: the residual term of the budget has the same magnitude as the horizontal mixing term.Even though the residual term can be interpreted as the effect of sub-grid mixing processes, caution must be exerted when considering these regions because the N2O TEM budget is not completetely closed.<br>The mid-stratospheric arctic region are characterized by smaller uncertanties of the TEM budget together with large differences among the datasets during winter: the WACCM realizations, characterized by a large internal variability, show a smaller horizontal mixing contribution with respect to the reanalyses. <br>The agreement among datasets is generally improved when considering the middle and low latitudes, especially in the Northern Hemisphere: those regions are characterized by smaller differences among datasets and a well-closed TEM budget.<br>The inter-annual variability of the horizontal mixing term and the vertical advection term is highly latitude-dependent: the horizontal mixing term presents a large variability, together with a large dataset spread, in the antarctic region in the austral fall and during boreal winter in the Arctic; the vertical advection shows large variability in the arctic region and large model spread in the Tropical regions.</p>

A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM2.5 Concentration Forecasting

In this study, we focus our attention on the forecasting of daily PM2.5 concentrations. According to the principle of “divide and conquer,” we propose a novel decomposition ensemble learning approach by integrating ensemble empirical mode decomposition (EEMD), artificial neural networks (ANNs), and adaptive particle swarm optimization (APSO) for forecasting PM2.5 concentrations. Our proposed decomposition ensemble learning approach is formulated exclusively to deal with difficulties in quantitating meteorological information with high volatility, irregularity, and complicacy. This decomposition ensemble learning approach mainly consists of three steps. First, we utilize EEMD to decompose original time series of PM2.5 concentrations into a specific amount of independent intrinsic mode functions (IMFs) and residual term. Second, the ANN, whose connection parameters are optimized by APSO algorithm, is employed to model IMFs and residual terms, respectively. Finally, another APSO-ANN is applied to aggregate the forecast IMFs and residual term into a collection as the final forecasting results. The empirical results show that the forecasting of our decomposition ensemble learning approach outperforms other benchmark models in terms of level accuracy and directional accuracy.

Construction of EMD-SVR-QGA Model for Electricity Consumption: Case of University Dormitory

In the context of the nationwide call for “energy savings” in China, it is desirable to establish a more accurate forecasting model to manage the electricity consumption from the university dormitory, to provide a suitable management approach, and eventually, to achieve the “green campus” policy. This paper applies the empirical mode decomposition (EMD) method and the quantum genetic algorithm (QGA) hybridizing with the support vector regression (SVR) model to forecast the daily electricity consumption. Among the decomposed intrinsic mode functions (IMFs), define three meaningful items: item A contains the terms but the residual term; item B contains the terms but without the top two IMFs (with high randomness); and item C contains the terms without the first two IMFs and the residual term, where the first two terms imply the first two high-frequency part of the electricity consumption data, and the residual term is the low-frequency part. These three items are separately modeled by the employed SVR-QGA model, and the final forecasting values would be computed as A + B − C. Therefore, this paper proposes an effective electricity consumption forecasting model, namely EMD-SVR-QGA model, with these three items to forecast the electricity consumption of a university dormitory, China. The forecasting results indicate that the proposed model outperforms other compared models.

Towards the closure of momentum budget analyses in the WRF (v3.8.1) model

Budget analysis of a tendency equation is widely utilized in numerical studies to quantify different physical processes in a simulated system. While such analysis is often post-processed when the output is made available, it is well-acknowledged that the closure of a budget is difficult to achieve without averaging. Nevertheless, the potential rise of the errors in such calculation has not been systematically investigated. In this study, an inline budget retrieval method is first developed in the WRF v3.8.1 model and tested on a 2D idealized slantwise convection case with a focus on the momentum equations. This method extracts all the budget terms following the model solver, which gives a high accuracy with a residual term always less than 0.02 % of the tendency term. Then, taking the inline values as truth, several post-processing budget analyses with different commonly-used simplifications are performed to investigate how they may affect the accuracy of the estimation of individual terms and the resultant residual. These assumptions include using a lower order advection operator than the one used in the model, neglecting the C staggering grids, or following a mathematically-equivalent but transformed format of equation. Errors in these post-processed analyses are found mostly over the area where the dynamics are the most active, impairing the subsequent physical interpretation. A maximum 99th percentile residual can reach 800 % of the concurrent tendency term, indicating the danger of neglecting the residual term as done in many budget studies. This work provides general guidance not only for applying an inline budget retrieval to the WRF model but for minimizing the errors in post-processing budget calculations.

On Stone’s renewal theorem for arithmetic distributions

The well-known Stone’s renewal theorem is refined for the case of arithmetic distributions having at least one exponentially decreasing tail. A very general version of the renewal theorem for arithmetic distributions with a semi-multiplicative bound of the residual term is proved.