RCM Simulations Research Articles

Response of the Northeast China grain market to climate change based on the gravity model approach

Scientific evidence has revealed that climate change negatively affects agricultural crop production both regionally and globally. Previous studies have indicated that the role of climate change is significant in some parts of China. Thus, assessing the impact of the future climate on the grain market is vital for ensuring regional and national food security. In this study, regional climate model (RCM 4.5 and 8.5) simulations were employed to investigate the role of future climate change on a major grain-producing market in China (Northeast China). For this purpose, historical (2004–2017) and future (2020–2076) data were applied in the gravity model to examine the effects of climate change on the Northeast China grain market. The results revealed that the maximum temperature is a crucial climate factor that significantly affects the grain market. The analysis revealed that precipitation was positively related and that the temperature was significantly negatively related to domestic consumption and exports of rice, maize, and soybean. Moreover, the analysis of the RCM (4.5 and 8.5) simulations revealed a negative contribution of the maximum temperature to domestic consumption and export levels. Overall, the analysis enhances our understanding of the impacts of climate change on the Northeast China grain market.

Energetic Particle Injection During Short Isolated Bubble as Seen in RCM Simulation and Spacecraft Observations in the Flow Braking Region

AbstractAlthough energetic particle (EP) injections are commonly thought to be formed by the flow burst intrusion from the magnetotail, important details and quantitative aspects of their transport, acceleration and flow braking need further investigation and understanding. Motivated by frequent observations of short transient EP injections being not associated with substorms, we analyze high‐resolution Rice Convection Model simulations of a short (5‐min long) localized (∼3RE width) density depletion (evacuating 90% of flux tube content) initiated at the tailward simulation boundary (∼18RE) and allowed to evolve within an otherwise typical plasma sheet environment. We note that, driven by betatron‐like acceleration, the peak EP flux at fixed energy dramatically increases in a couple of minutes when the bubble head enters the inner magnetosphere at r < 8–10 RE giving rise to a localized injection of subsequently drifting EP clouds. Here the 50–200 keV electron flux reaches values as high as #105 (cm2 s sr keV)−1, and even higher energies (up to 1 MeV) may briefly appear. Surprisingly, at a later stage of bubble penetration, after termination of bubble jet from the tail, the injection boundary of high energy (HE) particles detaches from the bubble earthward boundary while the latter continues moving inward. Time History of Events and Macroscale Interactions during Substorms multi‐spacecraft mission observations of a short bubble‐like flow burst at the spacecraft cluster located near the flow stopping point, show much similarity with simulation results but also reveal important differences between responses of HE protons and electrons attributed to the finite gyroradius effect.

Characterizing the Madden–Julian Oscillation in the western Pacific Ocean from a regional coupled ocean–atmosphere model simulation

AbstractThis study reports on the analysis of the results from a 20 km grid spacing, Regional Coupled ocean–atmosphere Model (RCM) integration over the Western Pacific Warm Pool (WP2). The RCM was integrated over a 20‐year period (1986–2005) using reanalysis boundary conditions for the atmosphere and the ocean. This is a first‐of‐a‐kind study with an RCM at 20 km over the WP2. The RCM simulation shows reasonable fidelity of the mean state and of the Madden–Julian Oscillation (MJO). We utilize this successful integration of the RCM to understand a well‐known observed feature of MJOs in the WP2 to be of the strongest amplitude during the December–March period of the year. Our analysis of the model integration reveals that the recharge of moist static energy (MSE) prior to peak MJO convection and its discharge during and after the convection explains the MJO in the simulation. The recharge/discharge of the MSE is shown to be largely dictated by horizontal advection, which is stemmed to a small extent by column‐integrated radiative heating and surface evaporation. This balance of forces in the evolution of the MSE anomalies and their corresponding variations with sea‐surface temperature (SST) anomalies at MJO time‐scales in the WP2 is strongest in the December–March period in the RCM simulation.

Projected changes in extreme snowfall events over the Tibetan Plateau based on a set of RCM simulations

Extreme snowfall events over the Tibetan Plateau (TP) cause considerable damage to local society and natural ecosystems. In this study, the authors investigate the projected changes in such events over the TP and its surrounding areas based on an ensemble of a set of 21st century climate change projections using a regional climate model, RegCM4. The model is driven by five CMIP5 global climate models at a grid spacing of 25 km, under the RCP4.5 and RCP8.5 pathways. Four modified ETCCDI extreme indices—namely, SNOWTOT, S1mm, S10mm, and Sx5day—are employed to characterize the extreme snowfall events. RegCM4 generally reproduces the spatial distribution of the indices over the region, although with a tendency of overestimation. For the projected changes, a general decrease in SNOWTOT is found over most of the TP, with greater magnitude and better cross-simulation agreement over the eastern part. All the simulations project an overall decrease in S1mm, ranging from a 25% decrease in the west and to a 50% decrease in the east of the TP. Both S10mm and Sx5day are projected to decrease over the eastern part and increase over the central and western parts of the TP. Notably, S10mm shows a marked increase (more than double) with high cross-simulation agreement over the central TP. Significant increases in all four indices are found over the Tarim and Qaidam basins, and northwestern China north of the TP. The projected changes show topographic dependence over the TP in the latitudinal direction, and tend to decrease/increase in low-/high-altitude areas.摘要基于RegCM4区域气候模式的气候变化预估试验数据, 开展了青藏高原及其周边地区极端降雪事件的未来变化研究. 结果表明, 总降雪量在高原大部分地区呈减少趋势, 降雪日数在高原也将明显减少, 尤其是在东部. 大雪日数和五日最大降雪量在高原东部将减少, 而在中部和西部明显增加. 在高原周边的塔里木和柴达木盆地及中国西北地区, 极端降雪事件同样增加显著. 极端降雪事件在高原上呈现出东西方向上的地形依赖性, 在低/高海拔地区呈减少/增加趋势.

Deep Learning Regional Climate Model Emulators: A Comparison of Two Downscaling Training Frameworks

AbstractRegional climate models (RCMs) have a high computational cost due to their higher spatial resolution compared to global climate models (GCMs). Therefore, various downscaling approaches have been developed as a surrogate for the dynamical downscaling of GCMs. This study assesses the potential of using a cost‐efficient machine learning alternative to dynamical downscaling by using the example case study of emulating surface mass balance (SMB) over the Antarctic Peninsula. More specifically, we determine the impact of the training framework by comparing two training scenarios: (a) a perfect and (b) an imperfect model framework. In the perfect model framework, the RCM‐emulator learns only the downscaling function; therefore, it was trained with upscaled RCM (UPRCM) features at GCM resolution. This emulator accurately reproduced SMB when evaluated on UPRCM, but its predictions on GCM data conserved RCM‐GCM inconsistencies and led to underestimation. In the imperfect model framework, the RCM‐emulator was trained with GCM features and downscaled the GCM while exposed to RCM‐GCM inconsistencies. This emulator predicted SMB close to the truth, showing it learned the underlying inconsistencies and dynamics. Our results suggest that a deep learning RCM‐emulator can learn the proper GCM to RCM downscaling function while working directly with GCM data. Furthermore, the RCM‐emulator presents a significant computational gain compared to an RCM simulation. We conclude that machine learning emulators can be applied to produce fast and fine‐scaled predictions of RCM simulations from GCM data.

Temperature and precipitation biases in CORDEX RCM simulations over South America: possible origin and impacts on the regional climate change signal

Precipitation and temperature biases from a set of Regional Climate Models from the CORDEX initiative have been analysed to assess the extent to which the biases may impact the climate change signal. The analysis has been performed for the South American CORDEX domain. A large warm bias was found over central Argentina (CARG) for most models, mainly in the summer season. Results indicate that the possible origin of this bias is an overestimation of the incoming shortwave radiation, in agreement with an underestimation of the relative humidity at 850 hPa, a variable that could be used to diagnose cloudiness. Regarding precipitation, the largest biases were found during summertime over northeast of Brazil (NEB), where most models overestimate the precipitation, leading to wet biases over that region. This bias agrees with models’ underestimation of both the moisture flux convergence and the relative humidity at lower levels of the atmosphere. This outcome suggests that the generation of more clouds in the models may drive the wet bias over NEB. These systematic errors could affect the climate change signal, considering that these biases may not be stationary. For both CARG and NEB regions, models with higher warm biases project higher warming levels, mainly in the summer season. In addition, it was found that these relationships are statistically significant with a confidence level of 95%, pointing out that biases are linearly linked with the climate change signal. For precipitation, the relationship between the biases and the projected precipitation changes is only statistically significant for the NEB region, where models with the largest wet biases present the greatest positive precipitation changes during the warm season. As in the case of biases, the analysis of the temperature and precipitation projections over some regions of South America suggests that clouds could affect them. The results found in this study point out that the analysis of the bias behaviour could help in a better interpretation of the climate change signal.

A Case Study of Bias Correction and the Dynamical Downscaling of CFSv2 S2S Forecasts Using a WRF Model: Heatwave in 2018 over South Korea

Extreme weather events caused by climate change affect the growth of crops, requiring reliable weather forecasts. In order to provide day-to-season seamless forecasting data for the agricultural sector, improving the forecasting performance of the S2S period is necessary. A number of studies have been conducted to improve prediction performance based on the bias correction of systematic errors in GCM or by producing high-resolution data via dynamic detailing. In this study, a daily simple mean bias correction technique is applied on CFSv2 (∼100 km) data. We then use case studies to evaluate how beneficial the precision of the high-resolution RCM simulation is in improving S2S prediction performance using the bias-corrected lateral boundary. Based on our examination of 45-day sequences of WRF simulations with 27–9–3 km resolution, it can be concluded that a higher resolution is correlated with better prediction in the case of the extreme heatwave in Korea in 2018. However, the effect of bias correction in improving predictive performances is not significant, suggesting that further studies on more cases are necessary to obtain more solid conclusions in the future.

Climate change impact on water balance of quasi-closed lagoons in the North-Western Black Sea coast

The paper studies the impact of regional climate changes that occurred in the past and are expected to occur in the near future on the components of the annual fresh water balance of quasi-closed lagoons in the North-Western Black Sea coast. Currently these lagoons have no permanent connection with the sea. However, there are some episodic connections via artificially created channels, ducts and other waterworks. The value of insufficiency of the studied lagoons' annual fresh water balance is used as an indicator of their ecosystems' sensitivity to climate change. Action plans must be created and implemented for the lagoons that are the most sensitive to changes in regional climatic conditions in order to achieve and maintain their "good" environmental quality status.
 The climate-induced changes of meteorological parameters in the North-Western Black Sea coast that determine the components of the lagoons' water balance are estimated for two periods: the current one from 2000 to 2018 (based on the observations made at coastal meteorological stations) and the near-future one of 2021-2050 which is compared to the 1961-1990 period (based on the data of the Climate Cadastre of Ukraine). The expected climatic conditions in the near future (2021-2050) were determined by the RCM simulations of the Euro-CORDEX project for the RCP4.5 climate change scenario.
 The research evaluates the components of the lagoons' fresh water balance (precipitation reaching the lagoons' water surface, fresh water inflow from the catchments, evaporation from the lagoons' water surface) and identifies the residuals (shortages) of annual balance. It uses the "climate-runoff" hydrological model for the above climatic periods to determine the fresh water inflow into the lagoons through the watercourses of their catchment areas. The annual evaporation layer from the lagoons' water surface was evaluated using the long-term average monthly values of temperature and relative humidity for each of the periods.
 It was established that the climatic changes that occurred earlier and those expected in the current century caused and will cause the increasing insufficiency of the annual fresh water balance of quasi-closed lagoons in the North-Western Black Sea coast. This can threaten salinization, shallowing or even disappearance of some lagoons. Thus new strategies for water and environmental management of such lagoons must be developed. In terms of vulnerability to climate changes leading to significant annual insufficiency of fresh water balance (if there are no other water sources, e.g. water from the sea), lagoons are ranked in the following order: Kuialnytskyi Lagoon, Dofinivskyi Lagoon, Budatskyi Lagoon (if there is no water inflow from the Dnistrovskyi Lagoon), the Tuzla group of lagoons, Sasyk Lagoon, Tyligulskyi Lagoon, Hadzhybeisky Lagoon (if there is no anthropogenic runoff).

Multi-model ensemble of statistically downscaled GCMs over southeastern South America: historical evaluation and future projections of daily precipitation with focus on extremes

High-resolution rainfall information is of great value, particularly over southeastern South America (SESA) where the observed and projected climate changes pose a substantial threat to socio-economic activities and the hydrological sector. Consequently, this work focuses on the construction of an unprecedented multi-model ensemble of statistically downscaled (ESD) global climate models (GCMs) for daily precipitation. Different statistical techniques were employed - including analogs, stochastic versions of regression-based models involving neural networks and generalised linear models and linear regressions conditioned by weather types - and a variety of CMIP5 and CMIP6 models. In general, most of the models added value in the representation of the main features of daily precipitation, especially in the spatial and intra-annual variability of extremes. The statistical models were sensible to the driving GCMs, although the ESD family choice also introduced differences among the simulations. The ESD projections depicted increases in mean precipitation associated with a rising frequency of extreme events - mostly during the warm season - following the observed trends over SESA. Change rates were consistent among downscaled models up to mid-21st century, when model spread started to emerge. Furthermore, these projections were compared to the available CORDEX-CORE RCM simulations, evidencing a consistent agreement between statistical and dynamical downscaling procedures in terms of the sign of the changes, presenting the main differences in their intensity. Overall, this study evidences the potential of statistical downscaling in a changing climate and contributes to its undergoing development over SESA.

A high resolution coupled ocean-atmosphere simulation of the regional climate over Central America

This study analyzes a relatively high resolution (15 km grid spacing), regional coupled ocean–atmosphere simulation configured over Central America. The simulation is conducted with the Regional Spectral Model-Regional Ocean Model System (RSM-ROMS) forced with global atmospheric and oceanic reanalysis for a period of 25 years (1986–2010). The spatial resolution of the RSM-ROMS simulation is unprecedented for the region. The highlights of the RCM simulation include the verifiable seasonal cycle of mesoscale features like the Low-Level Jets (LLJs), the Mid-Summer Drought (MSD), and the seasonal Tropical Cyclone (TC) activity both in the Pacific and in the Atlantic Oceans. However, some of the biases of the RSM-ROMS simulation of the frequency and amplitude of the MSD, the track density of the TCs in the two ocean basins, and the seasonal bias of the LLJs are noted. The seasonal cycle of the robust surface ocean currents in the eastern Pacific and the Costa Rica Dome is also well captured in the RSM-ROMS simulation. The RSM-ROMS simulation also resolves the seasonal cycle of the cyclonic Panama-Colombia Gyre and the anti-cyclonic Gulf of Papagayo-Tehuantepec Gyre. In many instances we find the RSM-ROMS improves upon the global reanalysis forcing the simulation, indicating the potential value of dynamic downscaling. Furthermore, the co-evolving components of the atmosphere and ocean in the RCM is an added benefit to the atmosphere only and ocean only global reanalysis forcing the simulation. However, the RCM displays significant biases that manifest in precipitation, precipitable water, SST, and winds which in the cases of prognostic variables of RSM-ROMS are perpetrated by the biases in the lateral boundary forcing (e.g., precipitable water) and in other instances forced by biases within the RSM-ROMS (e.g., precipitation, SST).

Assessing Past Climate Biases and the Added Value of CORDEX-CORE Precipitation Simulations over Africa

The present study investigates the skills of CORDEX-CORE precipitation outputs in simulating Africa’s key seasonal climate features, emphasizing the added value (AV) of the dynamical downscaling approach from which they were derived. The results indicate the models’ good skills in capturing African rainfall patterns and dynamics at satellite-based observation resolutions, with up to 65.17% significant positive AV spatial coverage for the CCLM5 model and up to 55.47% significant positive AV spatial coverage for the REMO model. Unavoidable biases are however present in rainfall-abundant areas and are reflected in the AV results, but vary based on the season, the sub-area, and the Global Climate Model–Regional Climate Models (GCM-RCM) combination considered. The RCMs’ ensemble mean generally performs better than individual GCM–RCM simulations. A further analysis of the GCM–RCM model chain indicates a strong influence of the dynamical downscaling approach on the driving GCMs. However, exceptions are found in some seasons for specific RCMs’ outputs, where GCMs are influential. The findings also revealed that observational uncertainties can influence AV and contribute to a 6 to 34% difference in significant positive AV spatial coverage results. An analysis of these results suggests that the AV by CORDEX-CORE simulations over Africa depend on how well the GCM physics are integrated to those of the RCMs and how these features are accommodated in the high-resolution setting of the downscaling experiments. The deficiencies of the CORDEX-CORE simulations could be related to how well key processes are represented within the RCM models. For Africa, these results show that CORDEX-CORE products could be adequate for a wide range of high-resolution precipitation data applications.

Projection of future extreme precipitation: a robust assessment of downscaled daily precipitation

Statistical and dynamic downscaling approaches are commonly used to downscale large-scale climatic variables from global circulation (GCM) and regional circulation (RCM) model outputs to local precipitation. The performance of these two approaches may differ from each other for daily precipitation projections when applied in the same region. This is examined in this study based on the estimation of extreme precipitation. Daily precipitation series are generated from GCM HadCM3, CGCM3/T47 and RCM HadCM3 models for both historical hindcasts and future projections in accordance with the period from 1971 to 2070. The Waikato catchment of New Zealand is selected as a case study. Deterministic and probabilistic performances of the GCM and RCM simulations are evaluated using root-mean-square-error (RMSE) coefficient, percent bias (PBIAS) coefficient and equitable threat score (ETS). The value of RMSE, PBIAS and ETS is 2.89, − 2.16, 0.171 and 8.72, − 4.01, 0.442 for mean areal and at-site daily precipitation estimations, respectively. The study results reveal that the use of frequency analysis of partial duration series (FA/PDS) is very effective in evaluating the accuracy of downscaled daily precipitation series. Both the statistical and the dynamic downscaling perform well for simulating daily precipitation at station level for a return period equal to or less than 100 years. However, the latter outperforms the former for daily precipitation simulation at catchment level.

Ignition delay times of NH3 /DME blends at high pressure and low DME fraction: RCM experiments and simulations

Autoignition delay times of ammonia/dimethyl ether (NH3/DME) mixtures were measured in a rapid compression machine with DME fractions of 0, 2 and 5 and 100% in the fuel. The measurements were performed at equivalence ratios φ=0.5, 1.0 and 2.0 and pressures in the range 10–70 bar; depending on the fuel composition, the temperatures after compression varied from 610 K to 1180 K. Admixture of DME is seen to have a dramatic effect on the ignition delay time, effectively shifting the curves of ignition delay vs. temperature to lower temperatures, up to ~250 K compared to pure ammonia. Two-stage ignition is observed at φ=1.0 and 2.0 with 2% and 5% DME in the fuel, despite the pressure being higher than that at which pure DME shows two-stage ignition. At φ=0.5, a reproducible pre-ignition pressure rise is observed for both DME fractions, which is not observed in the pure fuel components. A novel NH3/DME mechanism was developed, including modifications in the NH3 subset and addition of the NH2+CH3OCH3 reaction, with rate coefficients calculated from ab initio theory. Simulations faithfully reproduce the observed pre-ignition pressure rise. While the mechanism also exhibits two-stage ignition for NH3/DME mixtures, both qualitative and quantitative improvement is recommended. The overall ignition delay times for ammonia/DME mixtures are predicted well, generally being within 50% of the experimental values, although reduced performance is observed for pure ammonia at φ=2.0. Simulating the ignition process, we observe that the DME is oxidized much more rapidly than ammonia. Analysis of the mechanism indicates that this ‘early DME oxidation’ generates reactive species that initiate the oxidation of ammonia, which in turn begins heat release that raises the temperature and accelerates the oxidation process towards ignition. The reaction path analysis shows that the low-temperature chain-branching reactions of DME are important in the early oxidation of the fuel, while the sensitivity analysis indicates that several reactions in the oxidation of DME, including cross reactions between DME and NH3 species presented here, are critical to ignition, even at fractions of 2% DME in the fuel.

High-Resolution Projections of Mean and Extreme Precipitation over China by Two Regional Climate Models

In this study, we employ two regional climate models (RCMs or RegCMs), which are RegCM4 and PRECIS (Providing Regional Climates for Impact Studies), with a horizontal grid spacing of 25 km, to simulate the precipitation dynamics across China for the baseline climate of 1981–2010 and two future climates of 2031–2060 and 2061–2090. The global climate model (GCM)—Hadley Centre Global Environment Model version 2-Earth Systems (HadGEM2-ES) is used to drive the two RCMs. The results of baseline simulations show that the two RCMs can correct the obvious underestimation of light rain below 5 mm day−1 and the overestimation of precipitation above 5 mm day−1 in Northwest China and the Qinghai-Tibetan Plateau, as being produced by the driving GCM. While PRECIS outperforms RegCM4 in simulating annual precipitation and wet days in several sub-regions of Northwest China, its underperformance shows up in eastern China. For extreme precipitation, the two RCMs provide a more accurate simulation of continuous wet days (CWD) with reduced biases and more realistic spatial patterns compared to their driving GCM. For other extreme precipitation indices, the RCM simulations show limited benefit except for an improved performance in some localized regions. The future projections of the two RCMs show an increase in the annual precipitation amount and the intensity of extreme precipitation events in most regions. Most areas of Southeast China will experience fewer number of wet days, especially in summer, but more precipitation per wet day (≥ 30 mm day−1). By contrast, number of wet days will increase in the Qinghai-Tibetan Plateau and some areas of northern China. The increase in both the maximum precipitation for five consecutive days and the regional extreme precipitation will lead to a higher risk of increased flooding. The findings of this study can facilitate the efforts of climate service institutions and government agencies to improve climate services and to make climate-smart decisions.

The Effect of Precipitation on Hydropower Generation Capacity: A Perspective of Climate Change

Hydropower, which is the most extensively used renewable energy, is sensitive to the change of streamflow under the great impact of precipitation. According to the relationship between the hydropower station generation and local precipitation, the impact of precipitation on hydropower can be analyzed. In this paper, the GCM and RCM simulations in precipitation are compared firstly, and the high-resolution precipitation data are then calculated by stepwise clustering analysis (SCA) statistical downscaling method. Secondly, based on the hydropower potential (HP), the hydropower response model driven by precipitation (HRMDP) is established. Finally, the simulated generation of a hydropower station in Dadu River basin is used as a case to validate this proposed model. The results show that precipitation will increase by around 42% from May to August in study region, while it will decrease by 40 % in other months in RCP4.5. For different periods of reservoir scheduling, the precipitation will increase by about 40% in the Neutral I and Wet period, while it will decrease by around 30% in other periods, which will lead to the shortening of the peak period of hydropower generation and the peak value will be decreased. Correspondingly, the results show power generation will decrease by around 12% from June to December and increase by around 4% in the rest months. On the other hand, owing to the changes in precipitation, the future power generation will increase by 25% in Neutral I and decrease by 13.5% in other periods, but the total hydropower generation will remain.

Bias correction of temperature and precipitation over China for RCM simulations using the QM and QDM methods

Two different bias correction methods, the quantile mapping (QM) and quantile delta mapping (QDM), are applied to simulated daily temperature and precipitation over China from a set of 21st century regional climate model (the ICTP RegCM4) projections. The RegCM4 is driven by five different general circulation models (GCMs) under the representative concentration pathway RCP4.5 at a grid spacing of 25 km using the CORDEX East Asia domain. The focus is on mean temperature and precipitation in December–January–February (DJF) and June–July–August (JJA). The impacts of the two methods on the present day biases and future change signals are investigated. Results show that both the QM and QDM methods are effective in removing the systematic model biases during the validation period. For the future changes, the QDM preserves the temperature change signals well, in both magnitude and spatial distribution, while the QM artificially modifies the change signal by decreasing the warming and modifying the patterns of change. For precipitation, both methods preserve the change signals well but they produce greater magnitude of the projected increase, especially the QDM. We also show that the effects of bias correction are variable- and season-dependent. Our results show that different bias correction methods can affect in different way the simulated change signals, and therefore care has to be taken in carrying out the bias correction process.

Evaluation and ensemble projection of extreme high and low temperature events in China from four dynamical downscaling simulations

AbstractExtreme climate events are often of great interest because of their close relationship with meteorological and hydrological disasters. In this study, four RCM simulations driven by different GCMs (CSIRO‐Mk3.6.0, EC‐EARTH, HadGEM2‐ES, and MPI‐ESM‐MR) are used as an ensemble to project changes in extreme high and low temperatures in China as represented by selected Expert Team on Climate Change Detection indices. Each simulation is performed at 25 km resolution from 1980 to 2099 using RegCM4.4 RCM. Results show the following: (a) All four reasonably reproduce climatological maximum daily maximum temperature (TX), warm spell duration, minimum daily minimum temperature (TN), and cold spell duration, but the majority shows relatively weak ability to capture climatological warm day and cold night frequencies. (b) Although the RCM homogenizes much of the variance originally shown between driving GCM simulations, marked dissimilarities are still shown in western China across the four downscaling simulations, implying a dependency on the individual GCM‐driven RCM simulation for this region. (c) All of China shows an increase in maximum TX, warm days, warm spell duration, minimum TN, and a decrease in cold nights and cold spell duration, during the near‐term, middle‐term, and long‐term. The Tibetan Plateau and its adjacent regions show more significant changes in extreme temperature, indicating stronger sensitivity to changes in extremes. RCM downscaling projects less significant change in extreme temperatures than the driving GCM. (d) The ensemble of RCM simulations projects that area‐averaged maximum TX, warm days, warm spell duration, and minimum TN increase over China as a whole by 2.5°C, 21.1%, 43.6 days, and 3.3°C, respectively, while cold nights and cold spell duration decrease by 6.8% and 2.0 days, respectively during the long‐term under the RCP4.5 scenario relative to 1986–2005. Future episodes of extreme high temperature are likely to require more research as they become more common.

Kilometer-Scale Climate Models: Prospects and Challenges

AbstractCurrently major efforts are underway toward refining the horizontal resolution (or grid spacing) of climate models to about 1 km, using both global and regional climate models (GCMs and RCMs). Several groups have succeeded in conducting kilometer-scale multiweek GCM simulations and decadelong continental-scale RCM simulations. There is the well-founded hope that this increase in resolution represents a quantum jump in climate modeling, as it enables replacing the parameterization of moist convection by an explicit treatment. It is expected that this will improve the simulation of the water cycle and extreme events and reduce uncertainties in climate change projections. While kilometer-scale resolution is commonly employed in limited-area numerical weather prediction, enabling it on global scales for extended climate simulations requires a concerted effort. In this paper, we exploit an RCM that runs entirely on graphics processing units (GPUs) and show examples that highlight the prospects of this approach. A particular challenge addressed in this paper relates to the growth in output volumes. It is argued that the data avalanche of high-resolution simulations will make it impractical or impossible to store the data. Rather, repeating the simulation and conducting online analysis will become more efficient. A prototype of this methodology is presented. It makes use of a bit-reproducible model version that ensures reproducible simulations across hardware architectures, in conjunction with a data virtualization layer as a common interface for output analyses. An assessment of the potential of these novel approaches will be provided.

Statistical bias correction of regional climate model simulations for climate change projection in the Jemma sub-basin, upper Blue Nile Basin of Ethiopia

This study evaluates bias correction methods and develops future climate scenarios using the output of a better bias correction technique at the Jemma sub-basin. The performance of different bias correction techniques was evaluated using several statistical metrics. The bias correction methods performance under climate condition different from the current climate was also evaluated using the differential split sample testing (DSST) and reveals that the distribution mapping technique is valid under climate condition different from the current climate. All bias correction methods were effective in adjusting mean monthly and annual RCM simulations of rainfall and temperature to the observed rainfall and temperature values. However, distribution mapping method was better in capturing the 90th percentile of observed rainfall and temperature and wet day probability of observed rainfall than other methods. As a result, we use the future (2021–2100) simulation of RCMs which are bias corrected using distribution mapping technique. The output of bias-adjusted RCMs unfolds a decline of rainfall, a persistent increase of temperature and an increase of extremes of rainfall and temperature in the future climate under emission scenarios of Representative Concentration Pathways 4.5, 8.5 and 2.6 (RCP4.5, RCP8.5 and RCP2.6). Thus, climate adaptation strategies that can provide optimal benefits under different climate scenarios should be developed to reduce the impact of future climate change.

Wind energy land distribution in Morocco in 2021–2050 according to RCM simulation of CORDEX-Africa project

According to the Ministry of Energy, more than 93% of Morocco’s energy resources are imported. In 2008, the Moroccan Government has developed the National Energy Strategy, which includes the Moroccan Integrated Wind Energy Program. Morocco is rich in wind energy resources. Morocco launched the renewable energy development program, which aims to achieve a total installed capacity 2000 MW from wind power by 2020. The aim of this study is to determine the characteristics of the distribution of wind power density in Morocco in 2021–2050 and determine the areas with favorable conditions for the development of wind energy in the near future. The article presents the calculation results of wind power density for the period 2021–2050 at 50 m height. As a base value of the wind speed was used value of wind speed calculated by the ensemble of the regional climate models (RСM) of the CORDEX-Africa project. The RCM modeling was carried out for the region of Africa, in a rectangular coordinate system with a spatial resolution of ~44 km. Model calculation was performed taking into account the greenhouse gas concentration trajectory of RCP 4.5. Eleven climate models were used for the calculation. The results of the regional climate modeling of the CORDEX-Africa project showed that in Morocco in 2021–2050, in terms of wind power density at 50 m height, the fair and good wind power class would be the Atlantic coast area between Cape Beddouza and Cape Ghir, where wind power farms in Jbel Lahdid, Essaouira, are located. The richest wind resources in the coming decades are projected on the Atlantic coast to the south of the city of Tarfaya, where the Akhfenir, Tiskrad, and Boujdour wind power farms are located. The Tanger-Tetouan-Al Hoceima region, where the Tanger and Koudia wind farms are located, will be characterized by fair wind power class during the period from February to April. The coast of the Atlantic Ocean, where the Jbel Lahdid, Essaouira, wind farms are located, will have a good and above wind power class over the period from April to September, and the coastal areas of the Laayoune-Sakia El Hamra region can be described by the RCM as a good and above wind power class throughout the year.