The Maximum temperature in midsummer over Southern China and its extended-range prediction based on the dynamical downscaling method

The traditional dynamical downscaling (TDD) method employs continuous integration of regional climate models (RCM) with the general circulation model (GCM) providing the initial and lateral boundary conditions. Dynamical downscaling simulations are constrained by physical principles and can generate a full set of climate information, providing one of the important approaches to projecting fine spatial-scale future climate information. However, the systematic biases of climate models often degrade the TDD simulations and hinder the application of dynamical downscaling in the climate-change related studies. New methods developed over past decades improve the performance of dynamical downscaling simulations. These methods can be divided into four groups: the TDD method, the pseudo global warming method, dynamical downscaling with GCM bias corrections, and dynamical downscaling with both GCM and RCM bias corrections. These dynamical downscaling methods are reviewed and compared in this paper. The merits and limitations of each dynamical downscaling method are also discussed. In addition, the challenges and potential directions in progressing dynamical downscaling methods are stated.

Projected heat wave increasing trends over China based on combined dynamical and multiple statistical downscaling methods

The dynamical downscaling method with a regional climate model (RCM) is widely used to assess the spatially detailed information about regional climate. However, the RCM result is considerably influenced by the systematic errors inherent to a general circulation model (GCM), which provides the initial and boundary conditions to the RCM. Such systematic errors sometimes lead to meaningless downscaled results. Many modified boundary dynamical downscaling (MBDDS) methods have been proposed to reduce the influences of the systematic errors of a GCM and extract meaningful signals for regional climate change. This study comprehensively reviews the MBDDS methods. The MBDDS methods partially modify the climate information projected by a GCM and use them as the boundary conditions of an RCM. The objectives of the methods are organized into two main objectives, that is, to obtain more reliable projections by correcting the biases in boundary conditions and to better understand the regional climate change mechanisms. To ensure comprehensive understanding of the MBDDS methods, this study attempts to interpret the errors included in the downscaled results using mathematical expressions, separating the GCM‐originated bias and RCM's own bias. Using this analysis, the MBDDS methods are classified based on the following questions: What effect is expected from the bias correction? Which of the climate change components projected by a GCM is considered when assessing the future climate change? The direction and issues that need to be addressed in the future for better understanding the regional climate change are also discussed.

General circulation models (GCMs) are important tools for the study of climate change, but their resolutions are too coarse for station-scale impact assessments. Statistical and dynamical downscaling methods are widely used to translate the predictions of GCMs to the finer spatial scale and it is important to understand the difference between statistical and dynamical downscaling methods in different climatic zones and time periods. Moreover, statistical downscaling can be used on both GCM and regional climate model (RCM) outputs. In this study, two sets of GCM precipitations were dynamically and statistically downscaled and their performances were evaluated against the observed precipitation from 308 stations distributed throughout the Yellow, Yangtze, and Pearl River basins. These stations have distinct climatic characteristics from the historical period (1961–2000) and future period (2031–2050). Results suggest dynamically downscaled GCM precipitation does not present lower biases when comparing observed site-specific precipitation to GCM outputs, and biases of the initial dynamically downscaled GCM outputs decreased in areas with higher humidity. This demonstrates that statistical downscaling can improve GCM and RCM outputs, and the statistical downscaling method can reproduce local-scale precipitation satisfactorily without dynamical downscaling. However, statistical downscaling reduced spatial regularity of the biases that exist in GCM and RCM outputs between the observations and simulation. Additionally, the spatial discrepancy between statistically downscaled GCM and RCM precipitations was very small. In the future period, discrepancies between statistically downscaled RCM and GCM precipitations in the two climate scenarios were larger than the historical period for all climate zones.

Abstract. The study of extreme events has become of great interest in recent years due to their direct impact on society. Extremes are usually evaluated by using extreme indicators, based on order statistics on the tail of the probability distribution function (typically percentiles). In this study, we focus on the tail of the distribution of daily maximum and minimum temperatures. For this purpose, we analyse high (95th) and low (5th) percentiles in daily maximum and minimum temperatures on the Iberian Peninsula, respectively, derived from different downscaling methods (statistical and dynamical). First, we analyse the performance of reanalysis-driven downscaling methods in present climate conditions. The comparison among the different methods is performed in terms of the bias of seasonal percentiles, considering as observations the public gridded data sets E-OBS and Spain02, and obtaining an estimation of both the mean and spatial percentile errors. Secondly, we analyse the increments of future percentile projections under the SRES A1B scenario and compare them with those corresponding to the mean temperature, showing that their relative importance depends on the method, and stressing the need to consider an ensemble of methodologies.

This study assesses the midsummer precipitation prediction over eastern China by the dynamic downscaling method. Based on the Climate Forecast System version 2 of the National Centers for Environmental Prediction and the Weather Research and Forecasting Model, the prediction performance of global and regional models on the July precipitation over eastern China is further analyzed by hindcast experiments from 1982 to 2010 and prediction experiments from 2011 to 2021. The results suggest that the regional model forced by the global model can noticeably improve the prediction skill for precipitation in eastern China, especially in the region from the South of North China to the Yangtze River Basin, referred as the northern China in this paper. In addition, we perform a diagnostic analysis of the reason for the improvement of the model prediction skill. The results indicate that the high resolution of the regional model and the refinement of physical process parameterizations contribute to improving the simulation ability for the East Asian atmospheric circulation pattern, heat flux, especially for the meridional teleconnection pattern in East Asia and the sensible heat flux in the northern China, thus further improving precipitation prediction.

This study aims to assess the performance of different dynamical downscaling methods using updated land surface information. Particular attention is given to obtaining high-resolution climate information over China by the combination of an appropriate dynamical downscaling method and updated land surface information. Two group experiments using two land surface datasets are performed, including default Weather Research and Forecasting (WRF) land surface data (OLD) and accurate dynamically accordant MODIS data (NEW). Each group consists of three types of experiments for the summer of 2014, including traditional continuous integration (CT), spectral nudging (SN), and re-initialization (Re) experiments. The Weather Research and Forecasting (WRF) model is used to dynamically downscale ERA-Interim (reanalysis of the European Centre for Medium-Range Weather Forecast, ECMWF) data with a grid spacing of 30 km over China. The simulations are evaluated via comparison with observed conventional meteorological variables, showing that the CT method, which notably overestimates 2 m temperature and underestimates 2 m relative humidity across China, performs the worst; the SN and Re runs outperform the CT method, and the Re shows the smallest RMSE (root means square error). A comparison of observed and simulated precipitation shows that the SN simulation is closest to the observed data, while the CT and Re simulations overestimate precipitation south of the Yangtze River. Compared with the OLD group, the RMSE values of temperature and relative humidity are significantly improved in CT and SN, and there is smaller improved in Re. However, obvious improvements in precipitation are not evident.

Background: High-fidelity meteorological data is a prerequisite for the realistic simulation of atmospheric dispersion of radioactive materials near nuclear power plants (NPPs). However, many meteorological models frequently overestimate near-surface wind speeds, failing to represent local meteorological conditions near NPPs. This study presents a new high-resolution (approximately 1 km) meteorological downscaling method for modeling short-range (< 100 km) atmospheric dispersion of accidental NPP plumes.Materials and Methods: Six considerations from literature reviews have been suggested for a new dynamic downscaling method. The dynamic downscaling method is developed based on the Weather Research and Forecasting (WRF) model version 3.6.1, applying high-resolution land-use and topography data. In addition, a new subgrid-scale topographic drag parameterization has been implemented for a realistic representation of the atmospheric surface-layer momentum transfer. Finally, a year-long simulation for the Kori and Wolsong NPPs, located in southeastern coastal areas, has been made for 2016 and evaluated against operational surface meteorological measurements and the NPPs’ on-site weather stations.Results and Discussion: The new dynamic downscaling method can represent multiscale atmospheric motions from the synoptic to the boundary-layer scales and produce three-dimensional local meteorological fields near the NPPs with a 1.2 km grid resolution. Comparing the year-long simulation against the measurements showed a salient improvement in simulating near-surface wind fields by reducing the root mean square error of approximately 1 m/s. Furthermore, the improved wind field simulation led to a better agreement in the Eulerian estimate of the local atmospheric dispersion. The new subgrid-scale topographic drag parameterization was essential for improved performance, suggesting the importance of the subgrid-scale momentum interactions in the atmospheric surface layer.Conclusion: A new dynamic downscaling method has been developed to produce high-resolution local meteorological fields around the Kori and Wolsong NPPs, which can be used in short-range atmospheric dispersion modeling near the NPPs.

Climate change impacts are accelerating and intensifying, as observed over the past years, especially in the past year 2023.The current CMIP6 global climate simulations (GCMs) show higher climate sensitivity resulting in stronger warming and related impacts than previous simulations. Mountain regions are especially vulnerable as the warming climate relates to thawing of permafrost destabilising slopes and the emerging risk of heat and altered precipitation pattern that cause (extreme) flooding. Furthermore, the occurrence of compound events has gained increased attention as those pose substantial threat to the prevailing settlements and infrastructure.Nevertheless, the available GCM simulations are spatially too coarse to investigate the mentioned extreme events in complex terrain. Therefore, statistical and dynamical downscaling is performed within the ICARIA project (Russo et al., 2023) to better analyse future climate impacts for the mountain regions of Salzburg. For the dynamical downscaling two regional climate models (RCMs), the WRF and COSMO-CLM (CCLM) are used to simulate the future climate conditions for the SSP126, SSP585 at spatial resolution of 2-5 km2 until 2100.The verification of the two RCMs with respect to CHELSA (Karger et al., 2017) display that the 5km² WRF model simulations overestimate the precipitation intensities, especially in mountainous regions, the same goes for CCLM. With respect to temperature, WRF and CCLM display an underestimation of temperature in higher altitudes (above 600m) and a good representation below.Additionally, statistical downscaling has also been performed within ICARIA following the FICLIMA method. For this procedure, a set of 59 weather observations were used together with 10 CMIP6 GCMs. ERA5-Land and statistics such as MAE, Bias or Kolmogorov-Smirnov test were used for verification purposes of the methodology for each spot and model. Those that passed filters of quality and performance in the representation of past climate produced local downscaled climate projections at daily resolution for each location for the Tier 1 SSPs (1.26, 2.45, 3.70 and 5.85). Both the statistical and dynamical downscaling methods' outputs will serve to compare results and better assess the inherent uncertainties of climate projections.Since the focus is on extreme events, the prevailing simulations are analysed with respect to the global warming levels (1.5°C, 2°C, 3°C and 4°C) and their related local impacts. To investigate extreme events related to precipitation and wind, as well as their compound occurrence, suitable indicators are investigated, such as precipitation intensity estimates through future IDF curves and wind gust events with return periods of 1, 2, 5, 10, 20, 50, 100, 500 years. Further, consecutive events, that have a compound impact on the system, are considered through investigating the region and hazard specific time period before and after the occurrence of the extreme event. Russo, B., de la Cruz Coronas, À., Leone, M., Evans, B., Brito, R. S., Havlik, D., ... & Sfetsos, A. (2023). Improving Climate Resilience of Critical Assets: The ICARIA Project. Sustainability, 15(19), 14090Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., ... & Kessler, M. (2017). Climatologies at high resolution for the earth’s land surface areas. Scientific data, 4(1), 1-20.

This study investigates the capability of the dynamic downscaling method (DDM) in an East Asian climate study for June 1998 using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research non-hydrostatic Mesoscale Model (MM5). Sensitivity experiments show that MM5 results at upper atmospheric levels cannot match reanalyses data, but the results show consistent improvement in simulating moisture transport at low levels. The downscaling ability for precipitation is regionally dependent. During the monsoon season over the Yangtze River basin and the pre-monsoon season over North China, the DDM cannot match observed precipitation. Over Northwest China and the Tibetan Plateau (TP), where there is high topography, the DDM shows better performance than reanalyses. Simulated monsoon evolution processes over East Asia, however, are much closer to observational data than reanalyses. The convection scheme has a substantial impact on extreme rainfall over the Yangtze River basin and the pre-monsoon over North China, but only a marginal contribution for Northwest China and the TP. Land surface parameterizations affect the locations and pattern of rainfall bands. The 10-day re-initialization in this study shows some improvement in simulated precipitation over some sub-regions but with no obvious improvement in circulation. The setting of the location of lateral boundaries (LLB) westward improves performance of the DDM. Including the entire TP in the western model domain improves the DDM performance in simulating precipitation in most sub-regions. In addition, a seasonal simulation demonstrates that the DDM can also obtain consistent results, as in the June case, even when another two months consist of no strong climate/weather events.

Statistical and dynamical downscaling methods are tested and compared for downscaling seasonal precipitation forecasts over Spain from two DEMETER models: the European Centre for Medium-Range Weather Forecasts (ECMWF) and theUKMeteorological Office (UKMO). The statistical method considered is a particular implementation of the standard analogue technique, based on close neighbours of the predicted atmospheric geopotential and humidity fields. Dynamical downscaling is performed using the Rossby Centre Climate Atmospheric model, which has been nested to the ECMWF model output, and run in climate mode for six months. We first check the performance of the direct output models in the period 1986–1997 and compare it with the results obtained applying the analogue method. We have found that the direct outputs underestimate the precipitation amount and that the statistical downscaling method improves the results as the skill of the direct forecast increases. The highest skills — relative operating characteristic skill areas (RSAs) above 0.6 — are associated with early and late spring, summer and autumn seasons at zero- and one-month lead times. On the other hand, models have poor skill during winter with the exception of the El Niño period (1986–1988), especially in the south of Spain. In this case, high RSAs and economic values have been found. We also compare statistical and dynamical downscaling during four seasons, obtaining no concluding result. Both methods outperform direct output from DEMETER models, but depending on the season and on the region of Spain one method is better than the other. Moreover, we have seen that dynamical and statistical methods can be used in combination, yielding the best skill scores in some cases of the study.

Statistical and dynamical downscaling methods are tested and compared for downscaling seasonal precipitation forecasts over Spain from two DEMETER models: the European Centre for Medium-Range Weather Forecasts (ECMWF) and the UK Meteorological Office (UKMO). The statistical method considered is a particular implementation of the standard analogue technique, based on close neighbours of the predicted atmospheric geopotential and humidity fields. Dynamical downscaling is performed using the Rossby Centre Climate Atmospheric model, which has been nested to the ECMWF model output, and run in climate mode for six months. We first check the performance of the direct output models in the period 1986–1997 and compare it with the results obtained applying the analogue method. We have found that the direct outputs underestimate the precipitation amount and that the statistical downscaling method improves the results as the skill of the direct forecast increases. The highest skills – relative operating characteristic skill areas (RSAs) above 0.6 – are associated with early and late spring, summer and autumn seasons at zero- and one-month lead times. On the other hand, models have poor skill during winter with the exception of the El Niño period (1986–1988), especially in the south of Spain. In this case, high RSAs and economic values have been found. We also compare statistical and dynamical downscaling during four seasons, obtaining no concluding result. Both methods outperform direct output from DEMETER models, but depending on the season and on the region of Spain one method is better than the other. Moreover, we have seen that dynamical and statistical methods can be used in combination, yielding the best skill scores in some cases of the study.

Extreme surface winds and temperatures were estimated by the dynamical downscaling method combined with the generalized extreme value theory for the construction of Hardanger Suspension Bridge and the maintenance of Sotra Bridge in southwestern Norway. The Weather Research and Forecasting Model was used to downscale the Norwegian Earth System Model data from 2.5° × 1.8° to 1 km × 1 km horizontal grids. Simulations were performed for the control period, the 1990s, and the projection period, the 2050s, under the RCP8.5 radiative forcing scenario. Monthly maximum winds were compared with observations at three nearby observation stations for the warm and the cold seasons as well as the annual period. The simulated extreme wind distributions are in good agreement with the observed distributions at the coastal area, but have systematic positive deviations on the mountain. An extrapolation method was used to project extreme winds in the early and the late this century. Comparison of the simulated extreme winds between the 1990s and the 2050s shows that future extreme winds are unlikely to change with statistical significance during the cold season, but tend to decrease at mountainous and coastal areas with statistical significance during the warm season. They are possibly the reflections of the shift in the regional storm activities associated with the changes of the North Atlantic Oscillations and the effects of the local mountain topography. For surface maximum and minimum temperatures, the model can well reproduce the spreads of the pdf distributions. Both distributions shift towards higher temperatures in the 2050s.

TM4-O-05 Introduction: The impact of climate change on food-borne diseases is far from clear. Few relevant studies have been conducted in China. Using disease surveillance data, this paper examines the relationship between climate variables and bacillary dysentery in different climatic areas in China. Methods: Jinan city of Shandong Province in northern China, with a temperate climate, and Baoan district of Shenzhen in southern China, with a subtropical climate, were chosen as study areas. Monthly notified bacillary dysentery cases were obtained from local Centers for Diseases Prevention and Control. Monthly meteorologic data, including maximum temperature, minimum temperature, rainfall, relative humidity, and air pressure, were collected from the Chinese Meteorologic Bureau. Spearman correlation analysis and seasonal Autoregressive Integrated Moving Average (ARIMA) model were performed to investigate the association between climatic variables and the incidence of bacillary dysentery in the 2 study areas. The effect of seasonality was taken into account. The hockey stick model was used to explore the threshold of the effect of temperatures. Results: The incidence of dysentery positively correlated with maximum temperature, minimum temperature, rainfall, humidity, and related negatively to air pressure in both cities with the lag effects from zero to 2 months. In the ARIMA models, the only significantly included climatic variables are maximum and minimum temperatures. Additionally, an order-2 autoregressive variable and a seasonal variable were significant in the northern city, whereas an order-1 autoregressive variable and no seasonal variable were significant in the southern city. The thresholds for the effects of maximum and minimum temperatures were 17°C and 8°C, respectively, in the northern city. No thresholds were detected in the southern city. Conclusions: Although there are different seasonal patterns in the distribution of bacillary dysentery, maximum and minimum temperatures affect the transmission of bacillary dysentery in both temperate and subtropical areas in China. Threshold temperatures in the northern city were detected, which may have policy implications for the disease control and prevention. However, no threshold was identified in the southern city, which could be due to its subtropical climate with relevant higher temperatures throughout the year.

Dynamical and statistical downscaling of seasonal temperature forecasts in Europe: Added value for user applications