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- ItemPerformance evaluation of global hydrological models in six large Pan-Arctic watersheds(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2020) Gädeke, Anne; Krysanova, Valentina; Aryal, Aashutosh; Chang, Jinfeng; Grillakis, Manolis; Hanasaki, Naota; Koutroulis, Aristeidis; Pokhrel, Yadu; Satoh, Yusuke; Schaphoff, Sibyll; Müller Schmied, Hannes; Stacke, Tobias; Tang, Qiuhong; Wada, Yoshihide; Thonicke, KirstenGlobal Water Models (GWMs), which include Global Hydrological, Land Surface, and Dynamic Global Vegetation Models, present valuable tools for quantifying climate change impacts on hydrological processes in the data scarce high latitudes. Here we performed a systematic model performance evaluation in six major Pan-Arctic watersheds for different hydrological indicators (monthly and seasonal discharge, extremes, trends (or lack of), and snow water equivalent (SWE)) via a novel Aggregated Performance Index (API) that is based on commonly used statistical evaluation metrics. The machine learning Boruta feature selection algorithm was used to evaluate the explanatory power of the API attributes. Our results show that the majority of the nine GWMs included in the study exhibit considerable difficulties in realistically representing Pan-Arctic hydrological processes. Average APIdischarge (monthly and seasonal discharge) over nine GWMs is > 50% only in the Kolyma basin (55%), as low as 30% in the Yukon basin and averaged over all watersheds APIdischarge is 43%. WATERGAP2 and MATSIRO present the highest (APIdischarge > 55%) while ORCHIDEE and JULES-W1 the lowest (APIdischarge ≤ 25%) performing GWMs over all watersheds. For the high and low flows, average APIextreme is 35% and 26%, respectively, and over six GWMs APISWE is 57%. The Boruta algorithm suggests that using different observation-based climate data sets does not influence the total score of the APIs in all watersheds. Ultimately, only satisfactory to good performing GWMs that effectively represent cold-region hydrological processes (including snow-related processes, permafrost) should be included in multi-model climate change impact assessments in Pan-Arctic watersheds. © 2020, The Author(s).
- ItemHow evaluation of global hydrological models can help to improve credibility of river discharge projections under climate change(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2020) Krysanova, Valentina; Zaherpour, Jamal; Didovets, Iulii; Gosling, Simon N.; Gerten, Dieter; Hanasaki, Naota; Müller Schmied, Hannes; Pokhrel, Yadu; Satoh, Yusuke; Tang, Qiuhong; Wada, YoshihideImportance of evaluation of global hydrological models (gHMs) before doing climate impact assessment was underlined in several studies. The main objective of this study is to evaluate the performance of six gHMs in simulating observed discharge for a set of 57 large catchments applying common metrics with thresholds for the monthly and seasonal dynamics and summarize them estimating an aggregated index of model performance for each model in each basin. One model showed a good performance, and other five showed a weak or poor performance in most of the basins. In 15 catchments, evaluation results of all models were poor. The model evaluation was supplemented by climate impact assessment for a subset of 12 representative catchments using (1) usual ensemble mean approach and (2) weighted mean approach based on model performance, and the outcomes were compared. The comparison of impacts in terms of mean monthly and mean annual discharges using two approaches has shown that in four basins, differences were negligible or small, and in eight catchments, differences in mean monthly, mean annual discharge or both were moderate to large. The spreads were notably decreased in most cases when the second method was applied. It can be concluded that for improving credibility of projections, the model evaluation and application of the weighted mean approach could be recommended, especially if the mean monthly (seasonal) impacts are of interest, whereas the ensemble mean approach could be applied for projecting the mean annual changes. The calibration of gHMs could improve their performance and, consequently, the credibility of projections. © 2020, The Author(s).
- ItemAssessment of the MACC reanalysis and its influence as chemical boundary conditions for regional air quality modeling in AQMEII-2(Amsterdam : Elsevier, 2015) Giordano, L.; Brunner, D.; Flemming, J.; Hogrefe, C.; Im, U.; Bianconi, R.; Badia, A.; Balzarini, A.; Baró, R.; Hirtl, M.; Honzak, L.; Jorba, O.; Knote, C.; Kuenen, J.J.P.; Makar, P.A.; Manders-Groot, A.; Neal, L.; Pérez, J.L.; Pirovano, G.; Pouliot, G.; San José, R.; Savage, N.; Schröder, W.; Sokhi, R.S.; Syrakov, D.; Torian, A.; Tuccella, P.; Werhahn, J.; Wolke, R.; Yahya, K.; Žabkar, R.; Zhang, Y.; Galmarini, S.The Air Quality Model Evaluation International Initiative (AQMEII) has now reached its second phase which is dedicated to the evaluation of online coupled chemistry-meteorology models. Sixteen modeling groups from Europe and five from North America have run regional air quality models to simulate the year 2010 over one European and one North American domain. The MACC re-analysis has been used as chemical initial (IC) and boundary conditions (BC) by all participating regional models in AQMEII-2. The aim of the present work is to evaluate the MACC re-analysis along with the participating regional models against a set of ground-based measurements (O3, CO, NO, NO2, SO2, SO42−) and vertical profiles (O3 and CO). Results indicate different degrees of agreement between the measurements and the MACC re-analysis, with an overall better performance over the North American domain. The influence of BC on regional air quality simulations is analyzed in a qualitative way by contrasting model performance for the MACC re-analysis with that for the regional models. This approach complements more quantitative approaches documented in the literature that often have involved sensitivity simulations but typically were limited to only one or only a few regional scale models. Results suggest an important influence of the BC on ozone for which the underestimation in winter in the MACC re-analysis is mimicked by the regional models. For CO, it is found that background concentrations near the domain boundaries are rather close to observations while those over the interior of the two continents are underpredicted by both MACC and the regional models over Europe but only by MACC over North America. This indicates that emission differences between the MACC re-analysis and the regional models can have a profound impact on model performance and points to the need for harmonization of inputs in future linked global/regional modeling studies.
- ItemComparative analysis of meteorological performance of coupled chemistry-meteorology models in the context of AQMEII phase 2(Amsterdam : Elsevier, 2014) Brunner, Dominik; Savage, Nicholas; Jorba, Oriol; Eder, Brian; Giordano, Lea; Badia, Alba; Balzarini, Alessandra; Baró, Rocío; Bianconi, Roberto; Chemel, Charles; Curci, Gabriele; Forkel, Renate; Jiménez-Guerrero, Pedro; Hirtl, Marcus; Hodzic, Alma; Honzak, Luka; Im, Ulas; Knote, Christoph; Makar, Paul; Manders-Groot, Astrid; van Meijgaard, Erik; Neal, Lucy; Pérez, Juan L.; Pirovano, Guido; San Jose, Roberto; Schröder, Wolfram; Sokhi, Ranjeet S.; Syrakov, Dimiter; Torian, Alfreida; Tuccella, Paolo; Werhahn, Johannes; Wolke, Ralf; Yahya, Khairunnisa; Zabkar, Rahela; Zhang, Yang; Hogrefe, Christian; Galmarini, StefanoAir pollution simulations critically depend on the quality of the underlying meteorology. In phase 2 of the Air Quality Model Evaluation International Initiative (AQMEII-2), thirteen modeling groups from Europe and four groups from North America operating eight different regional coupled chemistry and meteorology models participated in a coordinated model evaluation exercise. Each group simulated the year 2010 for a domain covering either Europe or North America or both. Here were present an operational analysis of model performance with respect to key meteorological variables relevant for atmospheric chemistry processes and air quality. These parameters include temperature and wind speed at the surface and in the vertical profile, incoming solar radiation at the ground, precipitation, and planetary boundary layer heights. A similar analysis was performed during AQMEII phase 1 (Vautard et al., 2012) for offline air quality models not directly coupled to the meteorological model core as the model systems investigated here. Similar to phase 1, we found significant overpredictions of 10-m wind speeds by most models, more pronounced during night than during daytime. The seasonal evolution of temperature was well captured with monthly mean biases below 2 K over all domains. Solar incoming radiation, precipitation and PBL heights, on the other hand, showed significant spread between models and observations suggesting that major challenges still remain in the simulation of meteorological parameters relevant for air quality and for chemistry–climate interactions at the regional scale.