Dynamical Downscaling Simulations Research Articles

Assessing Climate Extremes in Dynamical Downscaling Simulations Driven by a Novel Bias‐Corrected CMIP6 Data

AbstractDynamical downscaling is a widely‐used approach for generating regional projections of climate extremes at a finer scale. However, the systematic bias of the global climate model (GCM) generally degrades the reliability of projections. Recently, novel bias‐corrected CMIP6 data was generated using a mean‐variance‐trend (MVT) bias correction method for dynamical downscaling simulation. To validate the effectiveness of this data in the dynamical downscaling simulation of climate extremes, we carry out three Weather Research and Forecasting (WRF) simulations over Asia‐western North Pacific with a 25 km grid spacing from 1980 to 2014. The dynamical downscaling simulations driven by the raw GCM data set (hereafter WRF_GCM) and the bias‐corrected GCM (hereafter WRF_GCMbc) were assessed against the simulation driven by the European Center for Medium‐Range Weather Forecasts Reanalysis 5 data set. The results indicate that the MVT bias correction significantly improves the climatological mean and inter‐annual variability of downscaled climate extreme indices. In terms of the climatological mean, the WRF_GCMbc shows a 25%–82% decrease in root mean square errors (RMSEs) against the WRF_GCM. As for the inter‐annual variability, the MVT bias correction can improve the downscaling simulation of almost all precipitation extreme indices and 70% of the temperature extreme indices, characterized by the RMSEs' reduction of 1%–58%. The improvements of the climate extremes in terms of the climatological mean in the WRF_GCMbc primarily stem from the improved large‐scale circulation and ocean evaporation in the WRF, which in turn improves the downscaled precipitation and 2 m temperature through the advection, radiation, and surface energy exchange process.

Impact of historical and future warming on heavy rainfall over eastern Japan induced by Typhoon Hagibis (2019) in dynamical downscaling simulations

Impact of historical and future warming on heavy rainfall over eastern Japan induced by Typhoon Hagibis (2019) in dynamical downscaling simulations

Evaluation of dynamical downscaling in a fully coupled regional earth system model

A set of decadal simulations has been completed and evaluated for gains using the Regional Arctic System Model (RASM) to dynamically downscale data from a global Earth system model and two atmospheric reanalyses. RASM is a fully coupled atmosphere–land–ocean–sea ice regional Earth system model. Nudging to the forcing data is applied to approximately the top half of the atmospheric domain. RASM simulations were also completed with a modification to the atmospheric physics for evaluating changes to the modeling system. The results show that for the top half of the atmosphere, the RASM simulations follow closely to that of the forcing data, regardless of the forcing data. The results for the lower half of the atmosphere, as well as the surface, show a clustering of atmospheric state and surface fluxes based on the modeling system. At all levels of the atmosphere the imprint of the weather from the forcing data is present as indicated in the pattern of the annual means. Biases, in comparison to reanalyses, are evident in the Earth system model forced simulations for the top half of the atmosphere but are not present in the lower atmosphere. This suggests that bias correction is not needed for fully coupled dynamical downscaling simulations. While the RASM simulations tended to go to the same mean state for the lower atmosphere, there are a differences in the variability and changes of weather patterns across the ensemble of simulations. These differences in the weather result in variances in the sea ice and oceanic states.

Assessing the Performance of a Dynamical Downscaling Simulation Driven by a Bias-Corrected CMIP6 Dataset for Asian Climate

Assessing the Performance of a Dynamical Downscaling Simulation Driven by a Bias-Corrected CMIP6 Dataset for Asian Climate

Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM–MPAS variable-resolution model

Abstract. Comprehensive assessment of climate datasets is important for communicating model projections and associated uncertainties to stakeholders. Uncertainties can arise not only from assumptions and biases within the model but also from external factors such as computational constraint and data processing. To understand sources of uncertainties in global variable-resolution (VR) dynamical downscaling, we produced a regional climate dataset using the Model for Prediction Across Scales (MPAS; dynamical core version 4.0) coupled to the Community Atmosphere Model (CAM; version 5.4), which we refer to as CAM–MPAS hereafter. This document provides technical details of the model configuration, simulations, computational requirements, post-processing, and data archive of the experimental CAM–MPAS downscaling data. The CAM–MPAS model is configured with VR meshes featuring higher resolutions over North America as well as quasi-uniform-resolution meshes across the globe. The dataset includes multiple uniform- (240 and 120 km) and variable-resolution (50–200, 25–100, and 12–46 km) simulations for both the present-day (1990–2010) and future (2080–2100) periods, closely following the protocol of the North American Coordinated Regional Climate Downscaling Experiment. A deviation from the protocol is the pseudo-warming experiment for the future period, using the ocean boundary conditions produced by adding the sea surface temperature and sea-ice changes from the low-resolution version of the Max Planck Institute Earth System Model (MPI-ESM-LR) in the Coupled Model Intercomparison Project Phase 5 to the present-day ocean state from a reanalysis product. Some unique aspects of global VR models are evaluated to provide background knowledge to data users and to explore good practices for modelers who use VR models for regional downscaling. In the coarse-resolution domain, strong resolution sensitivity of the hydrological cycles exists over the tropics but does not appear to affect the midlatitude circulations in the Northern Hemisphere, including the downscaling target of North America. The pseudo-warming experiment leads to similar responses of large-scale circulations to the imposed radiative and boundary forcings in the CAM–MPAS and MPI-ESM-LR models, but their climatological states in the historical period differ over various regions, including North America. Such differences are carried to the future period, suggesting the importance of the base state climatology. Within the refined domain, precipitation statistics improve with higher resolutions, and such statistical inference is verified to be negligibly influenced by horizontal remapping during post-processing. Limited (≈50 % slower) throughput of the current code is found on a recent many-core/wide-vector high-performance computing system, which limits the lengths of the 12–46 km simulations and indirectly affects sampling uncertainty. Our experience shows that global and technical aspects of the VR downscaling framework require further investigations to reduce uncertainties for regional climate projection.

Changes in convective and stratiform precipitation over the Tibetan Plateau projected by global and regional climate models

AbstractPrecipitation is a crucial influencing factor in the global hydrological cycle and terrestrial ecosystems. Previous studies have mostly focused on the historical precipitation variations over the Tibetan Plateau (TP). However, little attention has been given to the differences between convective and stratiform precipitation under future climate change scenarios. In order to acquire the high‐resolution precipitation information over the TP, the dynamical downscaling simulation was conducted utilizing a regional climate model (Weather Research and Forecasting [WRF] model) forced with a global climate model (Community Climate System Model [CCSM]). In this study, we focus on projections of future changes in convective and stratiform precipitation over the TP. The downscaling results during a 26‐year period 1980–2005 are compared for dry season, wet season and annual mean precipitation against the gridded observation dataset. Then, the changes in the wet season convective and stratiform precipitation with warming between CCSM and WRF‐CCSM were compared in the future period 2070–2099 under two scenarios (RCP4.5 and RCP8.5). Compared with the coarse‐resolution forcing, the historical precipitation of dry season, wet season and annual mean over the TP were found to better reproduce in WRF‐CCSM simulation. WRF‐CCSM projects an opposite spatial pattern of total precipitation change between the northern and southern TP in the future under different scenarios, which were attributed to the decreased stratiform precipitation in the southern TP and the increased convective precipitation in the northern TP, contrasting with the uniform increase of total precipitation in its forcing. The surface air temperature projected in WRF‐CCSM is larger increase than in the CCSM forcing. Compared to the CCSM forcing, WRF‐CCSM projects more pronounced increase in convective precipitation in response to rapid warming, and increasingly dominates changes of total precipitation with temperature exceeds the Clausius–Clapeyron rate over the northern TP.

Relative contributions of internal variability and external forcing to the inter-decadal transition of climate patterns in East Asia

The annual precipitation in North China and South China shows a dipole pattern with a clear inter-decadal transition around the late 1970s. However, the relative contribution of internal variability and external forcing to this inter-decadal transition is still unclear. Here, we separate internal variability from the externally forced climate response through a set of dynamical downscaling simulations with lateral boundary conditions derived from reanalysis data and a large ensemble mean of the CMIP5 historical simulations. We find that internal variability accounts for about 65 and 55% of the inter-decadal transition of the annual precipitation in South and North China, respectively. By contrast, external forcing accounts for about 70% of the warming trend in eastern China over the second half of the 20th century. This study highlights the differential response of regional precipitation and air temperature to internal variability and external forcing over eastern China on an inter-decadal timescale.

A Projection of Future JPCZs by WRF Dynamical Downscaling Simulations based on MIROC6 ScenarioMIP ssp585

A Projection of Future JPCZs by WRF Dynamical Downscaling Simulations based on MIROC6 ScenarioMIP ssp585

Earlier Onset and Shortened Meiyu Season During the Last Interglacial Based on Dynamical Downscaling Simulations

AbstractDeciphering the Meiyu behavior during the Last Interglacial (∼127 ka; LIG) may deepen our understanding of drought and flooding events over East Asia in a warmer‐than‐present world induced by Earth's orbit. Based on dynamical downscaling simulations, we suggested a 15‐ and 30‐day earlier onset and retreat of the Meiyu during the LIG relative to preindustrial, respectively, leading to a shortened Meiyu season. Meantime, extreme precipitation in the Meiyu season became stronger and occurred more frequently. Intensified monsoonal circulation, poleward‐located western North Pacific subtropical high, and poleward shift of East Asian westerly jet during the LIG provided favorable moisture conditions for earlier onset and retreat of the Meiyu. The variations of atmospheric circulations were further regulated by the joint effect of the three oceans and diabatic heating over Tibetan Plateau. Our results highlight an important role of Earth's orbit in the Meiyu and imply more flooding events in a warmer world.

WRF gray-zone dynamical downscaling over the Tibetan Plateau during 1999–2019: model performance and added value

The Tibetan Plateau (TP) is an important component of the global climate system, while the characteristics of its climate are poorly represented in most regional climate models at coarse resolutions. In this study, a 20-year (2000–2019) dynamical downscaling simulation at the gray-zone resolution (9 km) using the WRF model driven by the ERA5 reanalysis is conducted over the TP. Based on comparisons against in-situ observations and the Integrated Multi-satellite Retrievals for GPM (IMERG) version 6 satellite precipitation product, the assessment of basic climate variables, such as near-surface air temperature (T2m) and precipitation, is performed to evaluate the model’s performance and understand its added value better. Results show that both WRF and ERA5 can successfully reproduce the spatial patterns of annual mean and seasonal mean surface air temperature. However, significant cold and wet biases are found especially over the southeastern TP in ERA5, which are greatly improved in WRF with reduced RMSEs. Not only the climatological characteristics, but also the inter-annual variability and seasonal variation of T2m and precipitation are well captured by WRF which reduces the cold and wet biases especially in winter and summer compared to ERA5, respectively. Besides, at daily scale, the overestimation of precipitation in WRF and ERA5 is mainly caused by the overestimated precipitation frequency when precipitation intensity changed slightly. Furthermore, WRF outperforms ERA5 in capturing the diurnal variation of precipitation with more realistic peak time in all sub-regions over the TP. Further investigation into the mechanism of model bias reveals that less simulated snow cover fraction plays a crucial role in increasing the surface net energy by affecting surface albedo over the southeastern TP in WRF, leading to higher T2m. In addition, less water vapor transport from the southern boundary of TP leads to reduced wet bias in WRF, indicating that the added value in dynamical downscaling at gray-zone resolution is obtained by representing water vapor transport more realistically.

Increasing risk from landfalling tropical cyclone-heatwave compound events to coastal and inland China

Tropical cyclones (TCs) and heatwaves are amongst the most deadly and costly natural hazards. Despite considerable advances in understanding each of them, their occurrences in rapid sequence (e.g. in a week) that introduce disproportionately large impacts to infrastructure and human health have received far less attention. Based on dynamical downscaling simulations, we project that currently rare landfalling TC-heatwave compound events would be five to ten times more frequent in coastal Southeast China, and migrate northward and westward to the intact interior. It is the substantial increase in heatwaves that contributes most to the projected increase in frequency and novel emergence of compound events. There would be higher fraction of severer compound events composed of either intense TCs (in the top 10% historically) or exceptional heatwaves (above the historical 99.9th percentile), with coastal Southeast China even bracing for out-of-ordinary combinations of the two. On top of the unprecedented frequency, intensity and land exposure, future emergence of unseasonal compound events in South and Southeast China would further overwhelm local adaptive capacities.

Precipitation extremes over the tropical Americas under RCP4.5 and RCP8.5 climate change scenarios: Results from dynamical downscaling simulations

AbstractThe Regional Atmospheric Modelling System (RAMS) driven by data from the CMIP5 Earth System Model HadGEM2‐ES was used to simulate daily precipitation over the tropical Americas for both current and future climate, including distinct scenarios (representative concentration pathways 4.5 and 8.5) and different time horizons (short‐term, mid‐term and long‐term changes). The major objective was to evaluate possible future changes in extreme events, with emphasis on the intensity of precipitation events and the duration of wet and dry spells. According to RAMS, longer dry spells are expected over most regions of the tropical Americas in the future, with indications for Northeast Brazil, Caribbean, Northern Amazon, and shorter wet spells over Central America and Amazon. With the exception of the Caribbean, there is a general tendency towards the increased frequency of intense precipitation in the tropical Americas.

Future Projection of Solar Energy Over China Based on Multi‐Regional Climate Model Simulations

AbstractTo support future solar energy deployment in China, long‐term changes in solar energy resources over China were investigated based on high‐resolution dynamical downscaling simulations under three emission scenarios. First, an evaluation of model performance was conducted through comparison with station and ERA5 data, which indicated that although consistent overestimation was found, the spatial distribution and intra‐annual variation of solar radiation were captured well by ensemble simulation, showing added value over regions with complex topography as well. Then, the trends of the solar power output from photovoltaic (PV) systems during 2020–2099 were projected, characterized by an increase in east and central China, and a consistent decrease in the solar‐energy‐abundant regions (e.g., northeast China, the Tibetan Plateau, and northwest China) under the three scenarios. Except for under the low emission scenario, the regional averaged trends of PV were projected to decrease consistently over China in all four seasons, with significant magnitude apparent in both spring and winter. In addition, a more intense magnitude of decline was found under the higher emission scenario, with more subregions with a significant trend and higher intra‐annual and inter‐annual variability. However, it seems that the emission scenarios impact the spatial pattern only remarkably in the late century, however strengthened the decreasing magnitude in all future periods. In addition, results indicate decline in the future PV potential over most of subregions of China due to reduced radiation and increased temperature, but for the decrease over Tibetan Plateau the reduced wind speed also act as an important factor.

Snowfall climatology in the Tianshan Mountains based on 36 cold seasons of WRF dynamical downscaling simulation

Snowfall is a critical component of the hydrological cycle and water resources supply in mountainous areas. The snowfall regime varies according to time and space, and its uncertainties exist under climate change conditions. It is a necessary process to monitor the moisture characteristics of snowfall so as to investigate the information on snowfall, but a separate study on the snow moisture characteristics did not sufficiently describe over the Tianshan Mountains (TS). This study investigated the spatiotemporal variability of snowfall in the TS during the cold season from 1982 to 2018 based on the Weather Research and Forecasting (WRF) model simulations and analyzed moisture characteristics of snowfall using the regional moisture budget analysis. The results demonstrated that the snowfall amounted to 84.53 mm in the TS during the cold season and experienced a slight increase (P > 0.05). A significant increase in snowfall was noticed in the Eastern TS and the high-altitude regions. Western Eurasia and the Arabian Sea were primary moisture sources of snowfall, and the abundant moisture was transported by the mid-latitude westerly and the southwesterly to the interior. The anomalous cyclone over Europe transported more moisture flux to Central Asia. Meanwhile, the anomalous cyclone of Mongolia and the anticyclone in southwestern Central Asia caused a declining moisture divergence in the TS and an increase of the net moisture budget, which resulted in a snowfall and snow mass augmentation. The obtained study outcomes might improve understanding of snowfall information and provide a scientific reference for water resource management under a warming climate.

Impacts of Bias‐Corrected ERA5 Initial Snow Depth on Dynamical Downscaling Simulations for the Tibetan Plateau

AbstractThe Tibetan Plateau (TP) is often called the “Water Tower of Asia,” which contains the largest amount of snow and glaciers outside the polar regions. As an important and variable feature of the land surface, snow coverage on the TP has great impacts on regional climate. However, the commonly used ERA5 reanalysis in dynamical downscaling largely overestimates the snow depth for the TP. To improve the representation of snow cover in ERA5, a new ERA5‐driven downscaling data set (High Asia Refined analysis version 2, HAR v2) was generated by the Weather Research and Forecasting (WRF) model with the bias‐corrected snow depth. This study aims to identify and better understand the impact of bias‐corrected initial snow conditions on simulated regional climate, by comparing the HAR v2 with a 5‐year ERA5 forced WRF simulation without bias correction of initial snow depth (referred to as WRF_ERA5). The results show that the bias correction significantly improves the simulation of 2 m air temperature (T2), with regional mean cold bias reduced by 0.2°C–2.4°C, but no significant improvement in precipitation simulation is found. Further comparative analysis reveals that higher snow depth in WRF_ERA5 leads to T2, mean daily precipitation, summer extreme precipitation, and contributions of convective precipitation to summer mean daily precipitation decrease by 0°C–4°C, 0%–60%, 0%–40%, and 0%–10%, respectively, in most areas of the TP. In addition, the bias‐corrected initial snow depth also has impacts on simulated diurnal cycles of precipitation and T2 and leads to peak hours one hour earlier. Overall, this study confirms the importance of snow cover for the climate in the TP.

Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979\u20132100)

Dynamical downscaling is an important approach to obtaining fine-scale weather and climate information. However, dynamical downscaling simulations are often degraded by biases in the large-scale forcing itself. We constructed a bias-corrected global dataset based on 18 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset. The bias-corrected data have an ERA5-based mean climate and interannual variance, but with a non-linear trend from the ensemble mean of the 18 CMIP6 models. The dataset spans the historical time period 1979–2014 and future scenarios (SSP245 and SSP585) for 2015–2100 with a horizontal grid spacing of (1.25° × 1.25°) at six-hourly intervals. Our evaluation suggests that the bias-corrected data are of better quality than the individual CMIP6 models in terms of the climatological mean, interannual variance and extreme events. This dataset will be useful for dynamical downscaling projections of the Earth’s future climate, atmospheric environment, hydrology, agriculture, wind power, etc.

The Impact of the Variation in Weather and Season on WRF Dynamical Downscaling in the Pearl River Delta Region

In this study, National Centers for Environmental Prediction (NCEP) Final (FNL) operational global analysis data and meteorological observation data from 2013 to 2017 were used to evaluate the impact of seasonal changes and different circulation classifications on the dynamical downscaling simulation results of Weather Research and Forecasting (WRF) in the Pearl River Delta (PRD) region. The results show that the dynamical downscaling method can accurately simulate the time variation characteristics of the near-surface meteorological field and the hit rates of a 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind direction are 92.66%, 93.98%, 26.78%, and 76.78%, respectively. The WRF model slightly underestimates the temperature and relative humidity, and overestimates the wind speed and precipitation. For precipitation, the WRF model can better simulate the variation characteristics of light rain and heavy rain, with the probability of detection are 0.59 and 0.69, respectively. For seasonal factors, the WRF model can conduct a perfect simulation in autumn and winter, followed by spring, while summer is vulnerable to extreme weather, so the result of the simulation is relatively poor. The circulation type is an important parameter of downscaling assessment. When the PRD is controlled by high pressure, the simulated results of WRF are good, and when the PRD is affected by low pressure or extreme weather, the simulation results are relatively poor.

3D-var assimilation of GTS observation with the gravity wave drag scheme improves summer high resolution climate simulation over the Tibetan Plateau

The Tibetan Plateau (TP) is one of the most complicated orographic regions worldwide, and due to the lack of quantitative observations, the different simulation biases are still existing via various climate models over the TP. In this study, a one-summer-month 6-km dynamical downscaling simulation is conducted to evaluate the improvement of the new gravity wave drag (GWD) scheme and cycled assimilation of observations from the Global Telecommunications System (GTS) by the three-dimensional variational data assimilation (3D-Var) method. The updated GWD scheme provides better simulation results for surface and vertical winds, temperature, and humidity. 3D-Var cycled assimilation of GTS observations with the GWD scheme further improves the wind forecasting at the upper atmosphere levels but enhances temperature cold bias over the TP, and the latter may be partly related to less surface sensible and latent heat flux after assimilation. Notably, it obviously performs better at the spatial distribution and temporal variation of daily precipitation, thus effectively reduces the precipitation wet bias, especially for the eastern TP. This benefits from the more accurate simulation of different precipitation categories by data assimilation, especially for the light rain (1–10 mm/day). The mechanism of less precipitation wet bias is that assimilation of GTS observation results in weaker monsoon flow water vapor transport from low-latitude oceans, weaker Tibetan High with more accurate circulation fields and less upward vertical velocity. This research may provide guidance for establishing a downscaling dataset of higher spatiotemporal resolution for the TP.

General Circulation Model Selection Technique for Downscaling: Exemplary Application to East Africa

AbstractDownscaling is widely used in studies of local and/or regional climate as it yields a greater spatial resolution than general circulation models (GCMs) can provide. It utilizes GCM output or reanalysis data, which is transformed using mathematical relationships or used to force the lateral boundaries of a regional climate model. However, there is no set selection technique to determine which GCM realization(s) to employ. Here, a comprehensive yet easily applicable model selection technique for studies requiring GCM data as a constraint was developed. The technique evaluates, with respect to a reanalysis product and/or observational data, the ability of GCM realizations to reconstruct the mean state of the climate and the space‐time climatic anomalies for the atmospheric state variables at three distinct pressure levels. It was applied to the region of East Africa, where GISS‐E2‐H r6i1p3 was found to perform the strongest. The top ranked realizations were found to better capture processes when evaluated for the example of the Indian Ocean Dipole. Furthermore, the surface air temperature and precipitation from three 10‐year regional climate model simulations, one forced by the Modern‐Era Retrospective Analysis for Research and Applications version 2 reanalysis, one forced by the top ranked GCM, and one by the lowest ranked one, were compared to gridded observations. Results show that using a top ranked GCM for the boundary conditions leads to a better dynamical downscaling simulation than a low‐ranked GCM, suggesting the potential of the proposed technique for future downscaling techniques.

Impact of a Dense Surface Network on High-Resolution Dynamical Downscaling via Observation Nudging

AbstractHigh-density surface networks have become available in recent years in a number of regions throughout the world, but their utility in high-resolution dynamic downscaling has not been examined. As an attempt to fill such a gap, a suite of high-resolution (4 km) dynamical downscaling simulations is developed in this study with the Weather Research and Forecasting (WRF) Model and observation nudging over Liaoning in northeastern China. Three experiments, including no nudging (CTL), analysis nudging (AN), and combined analysis nudging and observation nudging with surface observations (AON), are conducted to downscale the CFSv2 reanalysis with the WRF Model for the year 2015. The three 1-yr regional climate simulations were compared with the independent surface observations. The results show that observational nudging can improve the simulation of surface variables, including temperature, wind speed, humidity, and pressure, more than nudging large-scale driving data with AN alone. The two nudging simulations can improve the cold bias for the temperature of the WRF Model. For precipitation, both the simulations with AN and observation nudging can capture the pattern of precipitation; however, with the introduction of small-scale information at the surface, AON cannot further improve the simulation of precipitation.