Coordinated Regional Downscaling Experiment Research Articles

Modelling the potential of land use change to mitigate the impacts of climate change on future drought in the Western Cape, South Africa

AbstractSeveral studies have shown that climate change may enhance the severity of droughts over the Western Cape (South Africa) in the future, but there is a dearth of information on how to reduce the impacts of climate change on water yields. This study investigates the extent to which land-use changes can reduce the projected impacts of climate change on hydrological droughts in the Western Cape catchments. For the study, the Soil Water Assessment Tool (SWAT +) model was calibrated and evaluated over several river catchments, and the climate simulation dataset from the COordinated Regional Downscaling EXperiment (CORDEX) was bias-corrected. Using the bias-corrected climate data as a forcing, the SWAT + was used to project the impacts of future climate change on water yield in the catchments and to quantify the sensitivity of the projection to four feasible land-use change scenarios in the catchments. The land-use scenarios are the spread of forest (FOMI), the restoration of shrubland (SHRB), the expansion of cropland (CRDY), and the restoration of grassland (GRSL).The model evaluation shows a good agreement between the simulated and observed monthly streamflows at four stations, and the bias correction of the CORDEX dataset improved the hydrological simulations. The climate change projection features an increase in temperature and potential evaporation, but a decrease in precipitation and all the hydrological variables. The drying occurs across the Western Cape, with the magnitude increasing with higher global warming levels (GWLs). The land-use changes alter these climate change impacts through changes in the hydrological water balance. FOMI increases streamflow and decreases runoff, while SHRB decreases streamflow and runoff. The influence of CRDY and GRSL are more complex. However, all the impacts of land-use changes are negligible compared to the impacts of climate change. Hence, land-use changes in the Western Cape may not be the most efficient strategies for mitigating the impacts of climate change on hydrological droughts over the region. The results of the study have application towards improving water security in the Western Cape river catchments.

Predicting climate-driven changes in reservoir inflows and hydropower in Côte d'Ivoire using machine learning modeling

This study investigates the impact of climate change and variability on reservoir inflow and hydropower generation at three key hydropower plants in Côte d'Ivoire including Buyo, Kossou, and Taboo. To simulate inflow to reservoir and energy generation, the Random Forest (RF), a machine-learning algorithm allowing fewer input variables was applied. In three-step, RF k-fold cross validation (with k = 5) was used; (i) 12 and 6 multiple lags of precipitation and temperature at monthly increments were used as predictors, respectively; (ii) the five most important variables were used in addition to the current month's precipitation and temperature; and (iii) a residual RF was built. The bias-adjusted ensemble mean of eleven climate models output of the COordinated Regional Downscaling Experiment was used for the representative concentration pathways (RCP4.5 and RCP8.5). The model output was highly correlated with the observations, with Pearson correlations >0.90 for inflow and >0.85 for energy for the three hydropower plants. The temperature in the selected sub-catchments may increase significantly from 0.9 to 3 °C in the near (2040–2069) and from 1.7 to 4.2 °C in far (2070–2099) future periods relative to the reference period (1981–2010). A time series of precipitation showed a change in range −7 and 15 % in the near and −8 to 20 % in the far future and more years are with increasing change. Depending on the sub-catchment, the magnitude of temperature and precipitation changes will increase as greenhouse gas emissions (GHG)(greater in RCP8.5 than RCP4.5) rise. At all time scales (monthly, seasonal, and annual), the simulated inflow and energy changes were related to climate variables such as temperature and precipitation. At the annual time scale, the inflow is projected to change between −10 and 37 % and variability may depend on the reservoir. However, the energy change is promised to change between −10 and 25 %, −30 to 15 %, and 5–40 % relative to the historical (1981–2010) period for Taabo, Kossou, and Buyo dams, respectively at an annual scale. The changes may vary according to the year, the RCPs, and the dam. Consequently, decision-makers are recommended to take into consideration an energy mix plan to meet the energy demand in these seasons.

Impact of the climate change on the site suitability for solar farms: Case study of Cameroon

It is widely agreed that in the coming decade the Earth's climate will undergo significant changes, which will considerably affect the ecosystems and populations in various ways. In this study, the climate change impact on the suitability of the recommended sustainable locations for siting solar PV facilities in Cameroon is assessed based on the downscaled climate projections and the geographic information system integrated with a multi-criteria decision-making technique. Spatial tools are used to identify the potential locations by extracting the constraint areas. The analytical hierarchy process is applied to weigh the criteria (climate, orography and vegetation) and calculate a land suitability index (LSI) to evaluate potential sites. Based on the Coordinated Regional Downscaling Experiment, the change in LSI towards the end of century is assessed considering two climate change scenarios, one optimistic and the other pessimistic. Results show that 13.7% of the study area is suitable for installing utility-size PV power plants with the highly suitable areas located mainly in the Extreme-North region of Cameroon. Under the RCP2.6 and RCP8.5 climate change scenarios, a very slight change in LSI (−5% ≤ LSI ≤ 5%) is projected for most of the potential sites across the country.

An Index for Snowmelt-Induced Landslide Prediction for Zavoj Lake, Serbia

In February 1963, a huge landslide (ca. 1,950,000 m3) blocked the Visočica River and, thus, formed Zavoj Lake. The primary objective of this research was to investigate the importance of snowmelt in relation to landslide occurrence and to define the critical climatic conditions that may trigger massive winter landslides. We used monthly precipitation and average monthly maximum temperature data from meteorological and precipitation stations in the Visočica River basin (Dojkinci) and in the immediate proximity of Lake Zavoj (Pirot, Dimitrovgrad and Topli Do) as data inputs to the Snow-Melt Landslide (SML) index. It considers the summed monthly precipitation for previous months that continuously have an average maximum temperature below 0 °C. According to this method, the event at Zavoj Lake stands out among all other precipitation and snowmelt values for the past 72 years. After applying the SML index, all stations showed values of >300 mm for February 1963, which we consider as the threshold value for potential landslides appearance. In addition to meteorological data, we applied the SML index to data from the Coordinated Regional Downscaling Experiment (CORDEX) regional climate model outputs for the region from 2022 to 2100. As expected, climate change will have influenced the temperature values, especially during the winter. Conversely, the study area is experiencing drastic changes in land use caused by depopulation, leading to a reduced risk of winter landslides in the Visočica basin. We suggest that future climatic conditions in the area will make it more likely to experience extreme summer precipitation events, which might trigger large landslides. The SML method can be implemented for all landscapes that experience snowy winters, providing information in a timely manner so that local residents can react properly when the probability of landslide occurrence rises. The SML index, grounded in essential meteorological principles, provides a tailor-made, data-driven methodology applicable across varied geographical settings. Its utility extends to mitigating hydro-meteorological hazards on scales ranging from local to national scales, offering diverse and effective early warning solutions.

Towards an intelligent malaria outbreak warning model based intelligent malaria outbreak warning in the northern part of Benin, West Africa

BackgroundMalaria is one of the major vector-borne diseases most sensitive to climatic change in West Africa. The prevention and reduction of malaria are very difficult in Benin due to poverty, economic insatiability and the non control of environmental determinants. This study aims to develop an intelligent outbreak malaria early warning model driven by monthly time series climatic variables in the northern part of Benin.MethodsClimate data from nine rain gauge stations and malaria incidence data from 2009 to 2021 were extracted from the National Meteorological Agency (METEO) and the Ministry of Health of Benin, respectively. Projected relative humidity and temperature were obtained from the coordinated regional downscaling experiment (CORDEX) simulations of the Rossby Centre Regional Atmospheric regional climate model (RCA4).A structural equation model was employed to determine the effects of climatic variables on malaria incidence. We developed an intelligent malaria early warning model to predict the prevalence of malaria using machine learning by applying three machine learning algorithms, including linear regression (LiR), support vector machine (SVM), and negative binomial regression (NBiR).ResultsTwo ecological factors such as factor 1 (related to average mean relative humidity, average maximum relative humidity, and average maximal temperature) and factor 2 (related to average minimal temperature) affect the incidence of malaria. Support vector machine regression is the best-performing algorithm, predicting 82% of malaria incidence in the northern part of Benin.The projection reveals an increase in malaria incidence under RCP4.5 and RCP8.5 over the studied period.ConclusionThese results reveal that the northern part of Benin is at high risk of malaria, and specific malaria control programs are urged to reduce the risk of malaria.

Impact of Climate Change on the Water Balance of the Akaki Catchment

Climate change has an impact on water resources. Estimations of the variations in water balance under climate change variables are essential for managing and developing the water resource of a catchment. The current investigation evaluated the magnitude of the change in the water balance component of the Akaki catchment, Ethiopia, using the semi-distributed hydrological model, the Soil and Water Assessment Tool (SWAT), with the integration of the Coordinated Regional Downscaling Experiment of Africa under RCP4.5 and 8.5. The SWAT model was developed using spatial and temporal data; it was calibrated (1991–2001) and validated (2002–2004) using SWAT-CUP. The statistical monthly SWAT model performance values of the NSE, PBIAS (%), and R2 showed good agreement between calibration and validation. On an annual basis, projected rainfall is expected to increase by 14.96%, 4.13%, 8.39%, and 10.39% in the 2040s under RCP4.5 and 8.5 and in the 2060s under RCP4.5 and 8.5, respectively, with inconsistent change on a monthly projections basis for each scenario. The projected monthly and yearly temperatures are expected to increase under different climate change scenarios. Annual evapotranspiration and potential evapotranspiration increased under both RCPs, whereas surface runoff, lateral flow, and water yield declined under the climate scenarios of each RCP. Monthly projected water yield showed a non-uniform change in the first 30 years and in the second years under the RCP4.5 and RCP8.5 scenarios. These results show that the catchment is highly vulnerable to hydrological and agricultural drought due to water availability. These research findings provide valuable evidence on the role of climate change in water balance, which will help decision makers to achieve better water resource management.

Benefits assessment of cool skin and ventilated cavity skin: Saving energy and mitigating heat and grid stress

This study assessed the energy-saving and climate-adaptive potential of cool skin and ventilated cavity skin facade technologies in Seoul's high-rise apartment buildings. We created weather scenarios for historical, mid-term future, and long-term future conditions using Coordinated Regional Downscaling EXperiment (CORDEX) method. Building energy simulations were conducted on a South Korean high-rise apartment model to evaluate their performance under different weather conditions. The results indicate that cool skin and ventilated cavity skin technologies can save cooling energy during summers but lead to heating energy penalties in winters. Ventilated cavity skin outperforms cool skin, offering better cooling energy savings and reduced heating penalties. Combining both technologies yields the highest overall energy savings, with 7 %, 9 %, and 10 % cooling energy savings for cool skin, ventilated cavity skin, and the combined package, respectively. However, cool skin increases heating energy consumption by 5 %, while ventilated cavity skin has minimal impact on heating energy. These envelope technologies also reduce peak electricity demand by at least 5 %, 8 %, and 9 %, respectively. They contribute to heat stress reduction, enhance resilience, and decrease extreme heat risks for occupants during power outages by at least 18 % under various weather conditions. Considering the prevalence of aging high-rise apartments in South Korea, adopting these envelope renovation strategies can effectively reduce cooling loads, enhance thermal comfort, and boost resilience under future climates, while avoiding costly reconstruction.

Projecting the Impact of Climate Change on Runoff in the Tarim River Simulated by the Soil and Water Assessment Tool Glacier Model

Analyzing the future changes in runoff is crucial for efficient water resources management and planning in arid regions with large river systems. This paper investigates the future runoffs of the headwaters of the Tarim River Basin under different emission scenarios by forcing the hydrological model SWAT-Glacier using six regional climate models from the Coordinated Regional Downscaling Experiment (CORDEX) project. Results indicate that compared to the period of 1976~2005, temperatures are projected to increase by 1.22 ± 0.72 °C during 2036~2065 under RCP8.5 scenarios, with a larger increment in the south Tianshan mountains and a lower increment in the north Kunlun Mountains. Precipitation is expected to increase by 3.81 ± 14.72 mm and 20.53 ± 27.65 mm during 2036–2065 and 2066–2095, respectively, under the RCP8.5 scenario. The mountainous runoffs of the four headwaters that directly recharge the mainstream of the Tarim River demonstrate an overall increasing trend in the 21st century. Under the RCP4.5 and RCP8.5 scenarios, the runoff is projected to increase by 3.2% and 3.9% (amounting to 7.84 × 108 m3 and 9.56 × 108 m3) in 2006–2035. Among them, the runoff of the Kaidu River, which is dominated by rainfall and snowmelt, is projected to present slightly decreasing trends of 3~8% under RCP4.5 and RCP8.5 scenarios. For catchments located in the north Kunlun Mountains (e.g., the Yarkant and Hotan Rivers which are mix-recharged by glacier melt, snowmelt, and rainfall), the runoff will increase significantly, especially in summer due to increased glacier melt and precipitation. Seasonally, the Kaidu River shows a forward shift in peak flow. The summer streamflow in the Yarkant and Hotan rivers is expected to increase significantly, which poses challenges in flood risk management.

Climate change impact on photovoltaic power potential in South America

Abstract This paper presents the first study of the long-term impact of climate change on photovoltaic potential in South America. This region has great potential for implementing renewable energy, mainly solar energy solutions, due to its high solar irradiance levels. Based on the Coordinated Regional Downscaling Experiment (CORDEX) for the South American region, we estimate how climate change could affect photovoltaic power potential by the end of the century. The evidence suggests that photovoltaic potential could have a maximum decrease of around 15%, and a maximum increase of approximately 7%, primarily due to changes in solar irradiance of different zones. Furthermore, it is observed that regions with increased temperature also show increased solar irradiance levels, which could, to some extent, compensate for the losses caused by the rise in temperature. Therefore, photovoltaic production in most of the territory will not be negatively affected.

Quantification and allocation of uncertainties of climate change impacts on hydropower potential under 1.5 °C and 2.0 °C global warming levels in the headwaters of the Benue River Basin, Cameroon

Hydropower is the world's largest producer of renewable energy and represents more than 43% of the low-carbon energy. However, it is sensitive to climate variability and change. This study evaluates the climate change impacts on hydropower potential in the headwaters of the Benue River Basin (HBRB) under 1.5 °C and 2.0 °C global warming levels (GWLs) and quantifies the main sources of uncertainty in the modeling chain. Precipitation and temperature from 17 members of the Coordinated Regional Downscaling Experiment over the Africa domain (CORDEX-Africa) under two representative concentration pathways (RCPs 4.5 and 8.5) were used to run two calibrated Lumped-conceptual hydrological models (HMs) (Hydrologiska Byrans Vattenavdelning (HBV-Light) and HYdrological MODel (HYMOD)). An analysis of variance (ANOVA) decomposition was used to quantify the uncertainties related to each impact modeling chain step in the hydropower potential calculation process. Results reveal a high uncertainty in both climatic and hydrologic parameters. The change in precipitation associated with an increase in potential evapotranspiration (PET) causes a significant decrease in hydropower generation associated with a large uncertainty range. The ANOVA sensitivity test reveals that the dominant contributing source to hydropower projections uncertainty varies with GWL. Given the likely breach of GWL 1.5 by the early 2030s, these findings contribute information for consideration in water and energy planning in the region over the next decade, and stresses that these considerations are urgent for the socioeconomic well-being of the region.

Performance evaluation of multiple regional climate models to simulate rainfall in the Central Rift Valley Lakes Basin of Ethiopia and their selection criteria for the best climate model.

The historical datasets of five regional climate models (RCMs) available in the Coordinated Regional Downscaling Experiment (CORDEX)-Africa database are evaluated against ground-based observed rainfall in the Central Rift Valley Lakes Basin of Ethiopia. The evaluation is aimed at determining how well the RCMs reproduce monthly, seasonal, and annual cycles of rainfall and quantify the uncertainty between the RCMs in downscaling the same global climate model outputs. Root mean square, bias, and correlation coefficient are used to evaluate the ability of the RCM output. The multicriteria decision method of compromise programming was used to choose the best climate models for the climate condition of the Central Rift Valley Lakes subbasin. The Rossby Center Regional Atmospheric Model (RCA4) has downscaled ten global climate models (GCMs) and reproduces the monthly rainfall with a complex spatial distribution of bias and root mean square errors. The monthly bias varies in the range of - 35.8 to 189%. The summer (wet), spring, winter (dry), and annual rainfall varied within the range of 1.44 to 23.66%, - 7.08 to 20.04%, - 7.35 to 57%, and - 3.11 to 16.5%, respectively. To find the source of uncertainty, the same GCMs but downscaled by different RCMs were analyzed. The test results showed that each RCM differently downscaled the same GCM, and there was no single RCM model that consistently simulated the climate conditions over the stations in the study regions. However, the evaluation finds reasonable model skill in representing the temporal cycles of rainfall and suggests the use of RCMs where climate data is scarce after bias correction.

Exploring the Future of Rainfall Extremes Over CONUS: The Effects of High Emission Climate Change Trajectories on the Intensity and Frequency of Rare Precipitation Events

AbstractThe impacts of climate change on extreme rainfall characteristics at fine spatiotemporal scales are governed by substantial uncertainty, primarily due to the systematic error components inherited from conventional numerical prediction systems, and/or the intrinsic assumptions of the selected modeling schemes. Here, we attempt to robustly evaluate the effects of future climate scenarios on intensity‐duration‐frequency (IDF) curves over the entire Contiguous United States, while accounting for the nonstationary nature of the rainfall process across adequately fine spatiotemporal resolutions. To do so, we apply a parametric approach to statistically downscaled climate model outputs that reflect the Representative Concentration Pathway 8.5, which are offered by the North American Coordinated Regional Downscaling Experiment. Compared to traditional IDF estimation techniques, the employed framework is based on multifractal (MF) scaling arguments and assumes that the statistical structure of rainfall at interannual scales can be approximated by sequential realizations of a stationary MF process with parameters that vary slowly across (not within) realizations. The obtained results show that return period estimates exhibit significant downward trends over most of the domain, which slowly dampen with time, as the effects of climate change are more pronounced at lower exceedance probability levels. Given the observed rate of changes in the frequency and intensity of extreme rainfall for the remainder of the century, we argue that future infrastructure design should be strategically tailored to account for a wide range of potential outcomes.

Evolution and copula modelling of drought duration and severity over Africa using CORDEX‐CORE regional climate models

AbstractThis study assessed the dependence of drought severity and duration across four subregions (Southern, Western, Eastern and Central) in Africa using copula modelling. The analysis was carried out for the reference period 1991–2020 and the future period 2071–2099. Simulated daily precipitation at a horizontal resolution of 0.22° were obtained from three regional climate models (RCMs) participating in the Coordinated Output for Regional Evaluations within the Coordinated Regional Downscaling Experiment (CORDEX‐CORE). The RCMs were downscaled by three global climate models and validated using three high‐resolution gridded daily precipitation products obtained from The Climate Hazards Group InfraRed Precipitation with Stations data (CHIRPS), Climate Prediction Center Africa Rainfall Climatology Version 2.0 (CPC‐ARC2) and Tropical Applications of Meteorology using SATellite data and ground‐based observations (TAMSAT). Comparison of precipitation from the RCMs with the three gridded reference observations shows relatively good performance across the different regions with median value within the range of 3–5 mm·day−1 in Central Africa, 1.5–4.5 mm·day−1 in Western Africa, 1.8–2.8 mm·day−1 in Eastern Africa and 0.5–2.2 mm·day−1 in Southern Africa. On the other hand, the correlation values of drought duration–severity from CORDEX‐CORE models in the different regions of Africa exhibit predominantly strong values greater than 0.8 for both historical and projected climate. The analysis also considered two families of copulas: Archimedean (Frank, Clayton, Gumbel) and Elliptical (Gaussian, Student's t). The performance of the copula functions were estimated using the Akaike information criteria (AIC). Generally, there is a good agreement in the distribution of observed precipitation among the three observational data products across the subregion of Africa, with slight differences attributed to the different processing algorithms of the products. Across West Africa, mean precipitation values from CPC, CHIRPS and TAMSAT was 2.86, 3.16 and 3.07 mm·day−1, respectively. The CCLM‐NCC underestimated the mean values of precipitation reported in the observation data, while CCLM‐MPI and CCLM‐HAD overestimated the mean values of precipitation in the West Africa region.

Future Changes in Thermal Bioclimate Conditions over West Bengal, India, Based on a Climate Model

Changes in extreme human bioclimate conditions are accepted evidence for and serve as a broad measure of anthropogenic climate change. The essential objective of the current study was to investigate past and future thermal bioclimate conditions across West Bengal (WB), India. The daily physiologically equivalent temperature (PET) was calculated by considering definite climate variables as inputs. These meteorological variables were captured from the Coordinated Regional Downscaling Experiment (CORDEX)-South Asia. The initial results from this research work present the mean monthly distribution of each PET class over the considered stations of WB during the period (1986–2005) and three future time periods: (i) near future (2016–2035), (ii) mid-future (2046–2065), and (iii) far future (2080–2099). It was observed that the months from April to June comprise heat stress months in terms of human thermal perception, whereas thermally acceptable conditions begin in November and continue until March for most stations. Results from future PET changes over WB in the context of the reference period (1986–2005) reveal a prominent increase in warm and hot PETs for all future time periods in two different greenhouse gas emission scenarios. During the far-future time period, stations within a kilometer of the Bay of Bengal such as Digha, Diamond Harbour, Canning, and Baruipur account for the highest percentage in the warm PET class (35.7–43.8 °C) in high-end emission scenarios. Simultaneously, during the period from 2080 to 2099, Kolkata, Dum Dum, Kharagpur, and Siliguri will experience a PET greater than 43.8 °C for close to 10% of the days in the year and more than 10% in Sriniketan, Malda, Asansol, and Birbhum. During the far-future period, a negative change in the very cool PET class (<3.3 °C) indicating a decrease in cold days was the largest for Darjeeling.

Multiscale flood risk assessment under climate change: the case of the Miño River in the city of Ourense, Spain

Abstract. River floods, which are one of the most dangerous natural hazards worldwide, have increased in intensity and frequency in recent decades as a result of climate change, and the future scenario is expected to be even worse. Therefore, their knowledge, predictability, and mitigation represent a key challenge for the scientific community in the coming decades, especially in those local areas that are most vulnerable to these extreme events. In this sense, a multiscale analysis is essential to obtain detailed maps of the future evolution of floods. In the multiscale analysis, the historical and future precipitation data from the CORDEX (Coordinated Regional Downscaling Experiment) project are used as input in a hydrological model (HEC-HMS) which, in turn, feeds a 2D hydraulic model (Iber+). This integration allows knowing the projected future changes in the flow pattern of the river, as well as analyzing the impact of floods in vulnerable areas through the flood hazard maps obtained with hydraulic simulations. The multiscale analysis is applied to the case of the Miño-Sil basin (NW Spain), specifically to the city of Ourense. The results show a delay in the flood season and an increase in the frequency and intensity of extreme river flows in the Miño-Sil basin, which will cause more situations of flooding in many areas frequented by pedestrians and in important infrastructure of the city of Ourense. In addition, an increase in water depths associated with future floods was also detected, confirming the trend for future floods to be not only more frequent but also more intense. Detailed maps of the future evolution of floods also provide key information to decision-makers to take effective measures in advance in those areas most vulnerable to flooding in the coming decades. Although the methodology presented is applied to a particular area, its strength lies in the fact that its implementation in other basins and cities is simple, also taking into account that all the models used are freely accessible.

Use of expert elicitation to assign weights to climate and hydrological models in climate impact studies

Abstract. Various methods are available for assessing uncertainties in climate impact studies. Among such methods, model weighting by expert elicitation is a practical way to provide a weighted ensemble of models for specific real-world impacts. The aim is to decrease the influence of improbable models in the results and easing the decision-making process. In this study both climate and hydrological models are analysed, and the result of a research experiment is presented using model weighting with the participation of six climate model experts and six hydrological model experts. For the experiment, seven climate models are a priori selected from a larger EURO-CORDEX (Coordinated Regional Downscaling Experiment – European Domain) ensemble of climate models, and three different hydrological models are chosen for each of the three European river basins. The model weighting is based on qualitative evaluation by the experts for each of the selected models based on a training material that describes the overall model structure and literature about climate models and the performance of hydrological models for the present period. The expert elicitation process follows a three-stage approach, with two individual rounds of elicitation of probabilities and a final group consensus, where the experts are separated into two different community groups: a climate and a hydrological modeller group. The dialogue reveals that under the conditions of the study, most climate modellers prefer the equal weighting of ensemble members, whereas hydrological-impact modellers in general are more open for assigning weights to different models in a multi-model ensemble, based on model performance and model structure. Climate experts are more open to exclude models, if obviously flawed, than to put weights on selected models in a relatively small ensemble. The study shows that expert elicitation can be an efficient way to assign weights to different hydrological models and thereby reduce the uncertainty in climate impact. However, for the climate model ensemble, comprising seven models, the elicitation in the format of this study could only re-establish a uniform weight between climate models.

Towards Ensemble-Based Kilometer-Scale Climate Simulations over the Third Pole Region

The Tibetan Plateau and its surrounding mountains have an average elevation of 4,400 m and a glaciated area of $$\sim $$ 100,000 $$\hbox {km}^{2}$$ giving it the name “Third Pole (TP) region”. The TP is the headwater of many major rivers in Asia that provide fresh water to hundreds of millions of people. Climate change is altering the energy and water cycle of the TP at a record pace but the future of this region is highly uncertain due to major challenges in simulating weather and climate processes in this complex area. The Convection-Permitting Third Pole (CPTP) project is a Coordinated Regional Downscaling Experiment (CORDEX) Flagship Pilot Study (FPS) that aims to revolutionize our understanding of climate change impacts on the TP through ensemble-based, kilometer-scale climate modeling. Here we present the experimental design and first results from multi-model, multi-physics ensemble simulations of three case studies. The five participating modeling systems show high performance across a range of meteorological situations and are close to having ”observational quality” in simulating precipitation and near-surface temperature. This is partly due to the large differences between observational datasets in this region, which are the leading source of uncertainty in model evaluations. However, a systematic cold bias above 2000 m exists in most modeling systems. Model physics sensitivity tests performed with the Weather Research and Forecasting (WRF) model show that planetary boundary layer (PBL) physics and microphysics contribute equally to model uncertainties. Additionally, larger domains result in better model performance. We conclude by describing high-priority research needs and the next steps in the CPTP project.

Future Projections of Precipitation from CMIP5 Models in the Western Ghats Region, India - A Case Study

The analysis of meteorological variables, particularly in countries where rainfall dependent agriculture ispredominant, is vital to assess climate-induced changes and suggest feasible adaptation strategies. To thatend, statistical analysis has been employed to inspect the changes in rainfall of Kokkarne catchment belongingto Seetha River basin using CORDEX (Coordinated Regional Downscaling Experiment) data for SouthAsia from 2021 to 2050. Rainfall data have been analyzed using the coefficientof variation, and standarddeviation. The outcome of the study revealed intra-annual and inter-annual variability of rainfall. Nearly87 % of annual rainfall is contributed by monsoon season only with standard deviation of 424.4mm andCV of 12%.

The impact of climate change on photovoltaic power potential in Southwestern Colombia

In this paper, we present the first study of the long-term climate-change impact on photovoltaic power potential in Nariño, Colombia. In this region, more than half of the territory does not have a constant electricity supply, but it has great potential for solutions with renewable energy sources. Based on the Coordinated Regional Downscaling Experiment (CORDEX), we assess the change in photovoltaic power potential towards the end of this century, considering two climate change scenarios, one optimistic and the other pessimistic. Our results suggest that changes in photovoltaic power potential, by the end of the century, will have a maximum decrease of around 2.49% in the central zone of Nariño, with some non-affected areas, and a maximum increase of 2.52% on the southeastern side with respect to the pessimistic climate change scenario.

Bias correction of surface air temperature and precipitation in CORDEX East Asia simulation: What should we do when applying bias correction?

Three groups of bias correction (BC) methods, which are linear scaling, distributional-based quantile mapping (QM) and empirical-based QM method, have been applied to correct surface air temperature and precipitation from Weather Research and Forecasting model (WRF) simulation within the second phase of Coordinated Regional Downscaling Experiment East Asia (CORDEX-EA-II) framework. WRF simulation and bias-corrected results are evaluated with gridded observations from CN05.1 and APHRODITE. The evaluation is conducted in terms of climate mean, seasonal cycle and extreme indices. Results show that WRF exhibits large biases in simulating surface air temperature and precipitation, which can be significantly reduced by bias correction. Among the three groups of BC approaches, empirical-based QM is the most comprehensive method in adjusting WRF simulation. However, when considering different time scales and regions, distributional-based QM can better correct precipitation higher than 25 mm/day than empirical-based QM, and a simple linear scaling can also show comparable skills in correcting seasonal cycles. Furthermore, based solely on statistical relationships between simulations and observations, bias correction cannot adjust circulation controlled and consecutive events. These results emphasize the importance of BC validation before climate change application. In addition, BC methods should be used carefully, considering regional climate and research subjects.