Assessing the value meteorological ensembles add to dispersion modelling using hypothetical releases

Atmospheric dispersion model output is frequently used to provide advice to decision makers, for example, about the likely location of volcanic ash erupted from a volcano or the location of deposits of radioactive material released during a nuclear accident. Increasingly, scientists and decision makers are requesting information on the uncertainty of these dispersion model predictions. One source of uncertainty is in the meteorology used to drive the dispersion model, and in this study ensemble meteorology from the Met Office ensemble prediction system is used to provide meteorological uncertainty to dispersion model predictions. Two hypothetical scenarios, one volcanological and one radiological, are repeated every 12 h over a period of 4 months. The scenarios are simulated using ensemble meteorology and deterministic forecast meteorology and compared to output from simulations using analysis meteorology using the Brier skill score. Adopting the practice commonly used in evaluating numerical weather prediction (NWP) models where observations are sparse or non-existent, we consider output from simulations using analysis NWP data to be truth. The results show that on average the ensemble simulations perform better than the deterministic simulations, although not all individual ensemble simulations outperform their deterministic counterpart. The results also show that greater skill scores are achieved by the ensemble simulation for later time steps rather than earlier time steps. In addition there is a greater increase in skill score over time for deposition than for air concentration. For the volcanic ash scenarios it is shown that the performance of the ensemble at one flight level can be different to that at a different flight level; e.g. a negative skill score might be obtained for FL350-550 and a positive skill score for FL200-350. This study does not take into account any source term uncertainty, but it does take the first steps towards demonstrating the value of ensemble dispersion model predictions.

Interne Modellvariabilität von Ensemblesimulationen des gekoppelten Wellen- und Atmosphärenmodells GCOAST

<p>Ziel dieser Studie (Wiese et al., 2020) ist, die Signifikanz des Einflusses des Wellenmodells auf das regionale Atmosphärenmodell und die interne Modellvariabilität sowohl des Atmosphärenmodells, als auch des gekoppelten Systems bestehend aus Wellen- und Atmosphärenmodell zu bestimmen. In einer vorhergehenden Studie wurde gezeigt, dass die Rauigkeit, die im Wellenmodell berechnet wird, größer ist, als die Rauigkeit, die im Atmosphärenmodell approximiert wird, was zu Unterschieden im Atmosphärenmodell führt (Wiese et al. 2019). Hier soll nun untersucht werden, ob diese Unterschiede im Atmosphärenmodell signifikant sind.  Dazu werden Ensemblesimulation mit einem Referenz Setup (das Atmosphärenmodell sendet den Wind an das Wellenmodell) und dem gekoppelten Setup (zusätzlich zum Windaustausch, sendet das Wellenmodell die Rauigkeitslänge über dem Meer zurück an das Atmosphärenmodell) durchgeführt. Bei der Analyse der internen Modellvariabilität zwischen beiden Ensembles zeigt sich, dass die interne Modellvariabilität im gekoppelten Ensemble gegenüber dem Referenzensemble reduziert ist. Dieser Effekt tritt während Extremereignissen am stärksten auf, ist aber auch bei einer generellen Analyse der internen Modellvariabilität über den gesamten Zeitraum sichtbar. Außerdem können die Effekte der Kopplung von der internen Modellvariabilität unterschieden werden, da die Effekte der Kopplung größer sind, als die interne Modellvariabilität. Diese Studie zeigt daher das Potential sowohl in operationellen Systemen als auch Systemen für Klimastudien die Unsicherheit zu reduzieren, wenn das Wellenmodell mit dem Atmosphärenmodell gekoppelt wird. Hinzu kommt, dass die Effekte der Kopplung klar von der internen Modellvariabilität unterschieden werden können, wodurch außerdem eine verbesserte Übereinstimmung des gekoppelten Systems gegenüber dem Referenzensemble mit Beobachtungsdaten erzielt werden kann. In einem nächsten Schritt soll nun zusätzlich der Ozean gekoppelt und die Auswirkungen auf das gesamte System untersucht werden.</p> <p> </p> <p>Literatur:</p> <p>Wiese A, Stanev E, Koch W, Behrens A, Geyer B and Staneva J (2019) The Impact of the Two-Way Coupling between Wind Wave and Atmospheric Models on the Lower Atmosphere over the North Sea. Atmosphere. 10(7):386. doi: 10.3390/atmos10070386</p> <p>Wiese A, Staneva J, Ho-Hagemann HTM, Grayek S, Koch W and Schrum C (2020) Internal Model Variability of Ensemble Simulations With a Regional Coupled Wave-Atmosphere Model GCOAST. Front. Mar. Sci. 7:596843. doi: 10.3389/fmars.2020.596843</p>

WESTPA 2.0: High-performance upgrades for weighted ensemble simulations and analysis of longer-timescale applications

ABSTRACTThe weighted ensemble (WE) family of methods is one of several statistical-mechanics based path sampling strategies that can provide estimates of key observables (rate constants, pathways) using a fraction of the time required by direct simulation methods such as molecular dynamics or discrete-state stochastic algorithms. WE methods oversee numerous parallel trajectories using intermittent overhead operations at fixed time intervals, enabling facile interoperability with any dynamics engine. Here, we report on major upgrades to the WESTPA software package, an open-source, high-performance framework that implements both basic and recently developed WE methods. These upgrades offer substantial improvements over traditional WE. Key features of the new WESTPA 2.0 software enhance efficiency and ease of use: an adaptive binning scheme for more efficient surmounting of large free energy barriers, streamlined handling of large simulation datasets, exponentially improved analysis of kinetics, and developer-friendly tools for creating new WE methods, including a Python API and resampler module for implementing both binned and “binless” WE strategies.

Process-based analysis of terrestrial carbon flux predictability

Despite efforts to decrease the discrepancy between simulated and observed terrestrial carbon fluxes, the uncertainty in trends and patterns of the land carbon fluxes remains high. This difficulty raises the question of the extent to which the terrestrial carbon cycle is predictable and which processes explain the predictability. Here, the perfect model approach is used to assess the potential predictability of net primary production (NPPpred) and heterotrophic respiration (Rhpred) by using ensemble simulations conducted with the Max Planck Institute Earth system model. In order to assess the role of local carbon flux predictability (CFpred) in the predictability of the global carbon cycle, we suggest a new predictability metric weighted by the amplitude of the flux anomalies. Regression analysis is used to determine the contribution of the predictability of different environmental drivers to NPPpred and Rhpred (soil moisture, air temperature, and radiation for NPP, and soil organic carbon, air temperature, and precipitation for Rh). Global NPPpred is driven to 62 % and 30 % by the predictability of soil moisture and temperature, respectively. Global Rhpred is driven to 52 % and 27 % by the predictability of soil organic carbon and temperature, respectively. The decomposition of predictability shows that the relatively high Rhpred compared to NPPpred is due to the generally high predictability of soil organic carbon. The seasonality in NPPpred and Rhpred patterns can be explained by the change in limiting factors over the wet and dry months. Consequently, CFpred is controlled by the predictability of the currently limiting environmental factor. Differences in CFpred between ensemble simulations can be attributed to the occurrence of wet and dry years, which influences the predictability of soil moisture and temperature. This variability of predictability is caused by the state dependency of ecosystem processes. Our results reveal the crucial regions and ecosystem processes to be considered when initializing a carbon prediction system.

Variability of Tropical Cyclone Frequency Over the Western North Pacific in 2018–2020

In this study, we examine the tropical cyclone (TC) activity over the western North Pacific (WNP) in 2018–2020 and its relationship with planetary scale convection and circulation anomalies, which play an important role for TC genesis. To determine the sea surface temperature (SST)-forced atmospheric variability, atmospheric general circulation model (AGCM) ensemble simulations are executed along with the observed SST. For AGCM experiments, we use two different convection schemes to examine uncertainty in convective parameterization and robustness of simulated atmospheric response. The observed TC activity and genesis potential demonstrated consistent features. In our AGCM ensemble simulations, the updated convection scheme improves the simulation ability of observed genesis potential as well as planetary scale convection and circulation features, e.g., in September–October–November (SON), a considerable increase in the genesis potential index over the WNP in SON 2018, WNP in SON 2019, and South China Sea (SCS) in SON 2020, which were not captured in the Emanuel scheme, have been simulated in the updated convection scheme.

A very likely weakening of Pacific Walker Circulation in constrained near-future projections

The observational records have shown a strengthening of the Pacific Walker circulation (PWC) since 1979. However, whether the observed change is forced by external forcing or internal variability remains inconclusive, a solid answer to more societal relevantly question of how the PWC will change in the near future is still a challenge. Here we perform a quantitative estimation on the contributions of external forcing and internal variability to the recent observed PWC strengthening using large ensemble simulations from six state-of-the-art Earth system models. We find the phase transition of the Interdecadal Pacific Oscillation (IPO), which is an internal variability mode related to the Pacific, accounts for approximately 63% (~51–72%) of the observed PWC strengthening. Models with sufficient ensemble members can reasonably capture the observed PWC and IPO changes. We further constrain the projection of PWC change by using climate models’ credit in reproducing the historical phase of IPO. The result shows a high probability of a weakened PWC in the near future.

Can we assess the value meteorological ensembles add to dispersion modelling using hypothetical releases?

Atmospheric dispersion model output is frequently used to provide advice to decision makers, for example, about the likely location of volcanic ash erupted from a volcano or the location of deposits of radioactive material released during a nuclear accident. Increasingly scientists and decision makers are requesting information on the uncertainty of these dispersion model predictions. One source of uncertainty is in the meteorology used to drive the dispersion model and in this study ensemble meteorology from the Met Office ensemble prediction system is used to provide meteorological uncertainty to dispersion model predictions. Two hypothetical scenarios, one volcanological and one radiological, are repeated every 12 hours over a period of 4 months. The scenarios are simulated using ensemble meteorology and deterministic forecast meteorology and compared to output from simulations using analysis meteorology using the Brier skill score. Adopting the practice commonly used in evaluating numerical weather prediction models (NWP) where observations are sparse or non-existent we consider output from simulations using analysis NWP data to be truth. The results show that on average the ensemble simulations perform better than the deterministic simulations although not all individual ensemble simulations outperform their deterministic counterpart. The results also show that greater skill scores are achieved by the ensemble simulation for later time steps rather than earlier time steps and at those later time steps for deposition than for air concentration. For the volcanic ash scenarios it is shown that the performance of the ensemble at one flight level can be different to that at a different flight level, e.g. a negative skill score might be obtained for FL350-550 and a positive skill score for FL200-350. This study does not take into account any source term uncertainty but it does take the first steps towards demonstrating the value of ensemble dispersion model predictions.