scholarly journals Response of Summer Precipitation over Eastern China to Large Volcanic Eruptions

2010 ◽  
Vol 23 (3) ◽  
pp. 818-824 ◽  
Author(s):  
Youbing Peng ◽  
Caiming Shen ◽  
Wei-Chyung Wang ◽  
Ying Xu
Keyword(s):  
Climate Model ◽  
Model Simulation ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Eastern China ◽  
Summer Precipitation ◽  
Volcanic Eruptions ◽  
Global Climate Model Simulation ◽  
Large Volcanic Eruptions

Abstract Studies of the effects of large volcanic eruptions on regional climate so far have focused mostly on temperature responses. Previous studies using proxy data suggested that coherent droughts over eastern China are associated with explosive low-latitude volcanic eruptions. Here, the authors present an investigation of the responses of summer precipitation over eastern China to large volcanic eruptions through analyzing a 1000-yr global climate model simulation driven by natural and anthropogenic forcing. Superposed epoch analyses of 18 cases of large volcanic eruption indicate that summer precipitation over eastern China significantly decreases in the eruption year and the year after. Model simulation suggests that this reduction of summer precipitation over eastern China can be attributed to a weakening of summer monsoon and a decrease of moisture vapor over tropical oceans caused by large volcanic eruptions.

scholarly journals Validation of a transient dynamical downscaling simulation approach with multi-model ensemble-based GCM bias corrections

2021 ◽  
Author(s):  
Zhongfeng Xu ◽  
Ying Han ◽  
Chi-Yung Tam ◽  
Zong-Liang Yang ◽  
Congbin Fu
Keyword(s):  
Large Scale ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Eastern China ◽  
Summer Precipitation ◽  
Precipitation Pattern ◽  
Improved Performance

Abstract Dynamical downscaling is the most widely used physics-based approach to obtaining fine-scale weather and climate information. However, traditional dynamical downscaling approaches are often degraded by biases in the large-scale forcing. To improve the confidence in future projection of regional climate, we used a novel bias-corrected global climate model (GCM) dataset to drive a regional climate model (RCM) over the period for 1980–2014. The dynamical downscaling simulations driven by the original GCM dataset (MPI-ESM1-2-HR model) (hereafter WRF_GCM), the bias-corrected GCM (hereafter WRF_GCMbc) are validated against that driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5 dataset (hereafter WRF_ERA5), respectively. The results suggest that, compared with the WRF_GCM, the WRF_GCMbc shows a 50–90% reduction in RMSEs of the climatological mean of downscaled variables (e.g. temperature, precipitation, wind, relative humidity). Similarly, the WRF_GCMbc also shows improved performance in simulating the interannual variability of downscaled variables. The RMSEs of interannual variances of downscaled variables are reduced by 30–60%. An EOF analysis suggests that the WRF_GCMbc can successfully reproduce the dominant tri-pole mode in the interannual summer precipitation variations observed over eastern China as opposed to the mono-pole precipitation pattern simulated by the WRF_GCM. Such improvements are primarily caused by the correct simulation of the location of the western North Pacific subtropical high by the WRF_GCMbc due to the GCM bias correction.

scholarly journals Internal Variability of the Canadian RCM’s Hydrological Variables at the Basin Scale in Quebec and Labrador

2012 ◽  
Vol 13 (2) ◽  
pp. 443-462 ◽  
Author(s):  
Marco Braun ◽  
Daniel Caya ◽  
Anne Frigon ◽  
Michel Slivitzky
Keyword(s):  
Climate Model ◽  
Model Simulation ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Internal Variability ◽  
Spectral Nudging ◽  
Model Driven ◽  
Basin Scale ◽  
Hydrological Variables

Abstract The effect of a regional climate model’s (RCM’s) internal variability (IV) on climate statistics of annual series of hydrological variables is investigated at the scale of 21 eastern Canada watersheds in Quebec and Labrador. The analysis is carried out on 30-yr pairs of simulations (twins), performed with the Canadian Regional Climate Model (CRCM) for present (reanalysis and global climate model driven) and future (global climate model driven) climates. The twins differ only by the starting date of the regional simulation—a standard procedure used to trigger internal variability in RCMs. Two different domain sizes are considered: one comparable to domains used for RCM simulations over Europe and the other comparable to domains used for North America. Results for the larger North American domain indicate that mean relative differences between twin pairs of 30-yr climates reach ±5% when spectral nudging is used. Larger differences are found for extreme annual events, reaching about ±10% for 10% and 90% quantiles (Q10 and Q90). IV is smaller by about one order of magnitude in the smaller domain. Internal variability is unaffected by the period (past versus future climate) and by the type of driving data (reanalysis versus global climate model simulation) but shows a dependence on watershed size. When spectral nudging is deactivated in the large domain, the relative difference between pairs of 30-yr climate means almost doubles and approaches the magnitude of a global climate model’s internal variability. This IV at the level of the natural climate variability has a profound impact on the interpretation, analysis, and validation of RCM simulations over large domains.

scholarly journals APPLICABILITY OF D4PDF DATASET TO GLOBAL STOCHASTIC TROPICAL CYCLONE MODEL

2020 ◽  
pp. 26
Author(s):  
Sota Nakajo ◽  
Jinji Umeda ◽  
Nobuhito Mori
Keyword(s):  
Tropical Cyclone ◽  
Climate Model ◽  
Model Simulation ◽  
Global Climate ◽  
Global Climate Model ◽  
Observation Data ◽  
Change Rate ◽  
Regional Area ◽  
Global Climate Model Simulation ◽  
Disaster Event

Disaster damage caused by tropical cyclone has grown every year. However, our experience of tropical cyclone is not enough to evaluate very low frequent and catastrophic disaster event. Stochastic tropical cyclone model has been used for assessment of tropical cyclone disaster. Global stochastic model was improved by using a lot of ensemble Global Climate Model simulation data (d4PDF) instead of limited number of observation data. The model bias included d4PDF was corrected by each regional grid by simple statistical method and interpolation. The accuracy of new model was verified at representative regional area in different basins. Generally, the improvement is remarkable where tropical cyclones rarely passed. The variation of joint PDF of tropical cyclone change rate between previous model and present model agree with model improvement. As an example of application, the frequencies of strong tropical cyclone events of two cases were estimated.

scholarly journals A new bias-correction method for precipitation over complex terrain suitable for different climate states: a case study using WRF (version 3.8.1)

2020 ◽  
Vol 13 (10) ◽  
pp. 5007-5027
Author(s):  
Patricio Velasquez ◽  
Martina Messmer ◽  
Christoph C. Raible
Keyword(s):  
Regional Climate Model ◽  
Bias Correction ◽  
Complex Terrain ◽  
Climate Model ◽  
Model Simulation ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Correction Method ◽  
Bias Correction Method

Abstract. This work presents a new bias-correction method for precipitation over complex terrain that explicitly considers orographic characteristics. This consideration offers a good alternative to the standard empirical quantile mapping (EQM) method during colder climate states in which the orography strongly deviates from the present-day state, e.g. during glacial conditions such as the Last Glacial Maximum (LGM). Such a method is needed in the event that absolute precipitation fields are used, e.g. as input for glacier modelling or to assess potential human occupation and according migration routes in past climate states. The new bias correction and its performance are presented for Switzerland using regional climate model simulations at 2 km resolution driven by global climate model outputs obtained under perpetual 1990 and LGM conditions. Comparing the present-day regional climate model simulation with observations, we find a strong seasonality and, especially during colder months, a height dependence of the bias in precipitation. Thus, we suggest a three-step correction method consisting of (i) a separation into different orographic characteristics, (ii) correction of very low intensity precipitation, and (iii) the application of an EQM, which is applied to each month separately. We find that separating the orography into 400 m height intervals provides the overall most reasonable correction of the biases in precipitation. The new method is able to fully correct the seasonal precipitation bias induced by the global climate model. At the same time, some regional biases remain, in particular positive biases over high elevated areas in winter and negative biases in deep valleys and Ticino in winter and summer. A rigorous temporal and spatial cross-validation with independent data exhibits robust results. The new bias-correction method certainly leaves some drawbacks under present-day conditions. However, the application to the LGM demonstrates that it is a more appropriate correction compared to the standard EQM under highly different climate conditions as the latter imprints present-day orographic features into the LGM climate.

Revisiting the Krakatau 1883 Volcanic Aerosol Dispersal 

2021 ◽  
Author(s):  
Rafael Castro ◽  
Tushar Mittal ◽  
Stephen Self
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Volcanic Eruptions ◽  
Climate Impact ◽  
Volcanic Plume ◽  
Quasi Biennial Oscillation ◽  
Aerosol Cloud ◽  
Pinatubo Eruption ◽  
The Impact

<p>The 1883 Krakatau eruption is one of the most well-known historical volcanic eruptions due to its significant global climate impact as well as first recorded observations of various aerosol associated optical and physical phenomena. Although much work has been done on the former by comparison of global climate model predictions/ simulations with instrumental and proxy climate records, the latter has surprisingly not been studied in similar detail. In particular, there is a wealth of observations of vivid red sunsets, blue suns, and other similar features, that can be used to analyze the spatio-temporal dispersal of volcanic aerosols in summer to winter 1883. Thus, aerosol cloud dispersal after the Krakatau eruption can be estimated, bolstered by aerosol cloud behavior as monitored by satellite-based instrument observations after the 1991 Pinatubo eruption. This is one of a handful of large historic eruptions where this analysis can be done (using non-climate proxy methods). In this study, we model particle trajectories of the Krakatau eruption cloud using the Hysplit trajectory model and compare our results with our compiled observational dataset (principally using Verbeek 1884, the Royal Society report, and Kiessling 1884).</p><p>In particular, we explore the effect of different atmospheric states - the quasi-biennial oscillation (QBO) which impacts zonal movement of the stratospheric volcanic plume - to estimate the phase of the QBO in 1883 required for a fast-moving westward cloud. Since this alone is unable to match the observed latitudinal spread of the aerosols, we then explore the impact of an  umbrella cloud (2000 km diameter) that almost certainly formed during such a large eruption. A large umbrella cloud, spreading over ~18 degrees within the duration of the climax of the eruption (6-8 hours), can lead to much quicker latitudinal spread than a point source (vent). We will discuss the results of the combined model (umbrella cloud and correct QBO phase) with historical accounts and observations, as well as previous work on the 1991 Pinatubo eruption. We also consider the likely impacts of water on aerosol concentrations and the relevance of this process for eruptions with possible significant seawater interactions, like Krakatau. We posit that the role of umbrella clouds is an under-appreciated, but significant, process for beginning to model the climatic impacts of large volcanic eruptions.</p>

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