Global Climate Models Model Research Articles

Machine learning‐based streamflow forecasting using CMIP6 scenarios: Assessing performance and improving hydrological projections and climate change

AbstractWater is essential for humans as well as for all living organisms to sustain their lives. Therefore, any climate‐driven change in available resources has significant impacts on the environment and life. Global climate models (GCMs) are one of the most practical methods to evaluate climate change. Based on this, this research evaluated the capability of GCMs from the Coupled Model Intercomparison Project 6 (CMIP6) to reproduce the historical flow of climate prediction centre data for the Konya Closed basin and to project the climate of the basin using the selected GCMs. Global climate models based on the CMIP6 under the scenario of common socioeconomic pathways (SSP245 and SSP 585) were used to analyse the climate change effect on streamflow of the study area by Bias Correction of GCM Models using Long Short‐Term Memory (LSTM), Bidirectional LSTM (BiLSTM), AdaBoost, Gradient Boosting, Regression Tree, and Random Forest methods. The coefficient of determination (R2), mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) were used to assess the performance of the methods. Findings show that the Random Forest Model consistently outperformed other models in both the testing and training phases. A significant downward in the volume of water flowing through the region's rivers and streams in the next decades. It is critical to enhance climate‐resilient water infrastructure financing, establish an early warning system for drought, introduce best management practices, implement integrated water resource management, public awareness, and support water research to alleviate the negative consequences of drought and increase resilience against the effects of climate change on Turkey's water resources.

A Data-Driven Probabilistic Network Approach to Assess Model Similarity in CMIP Ensembles

Abstract The different phases of the Coupled Model Intercomparison Project (CMIP) provide ensembles of past, present, and future climate simulations crucial for climate change impact and adaptation activities. These ensembles are produced using multiple global climate models (GCMs) from different modeling centers with some shared building blocks and interdependencies. Applications typically follow the “model democracy” approach which might have significant implications in the resulting products (e.g., large bias and low spread). Thus, quantifying model similarity within ensembles is crucial for interpreting model agreement and multimodel uncertainty in climate change studies. The classical methods used for assessing GCM similarity can be classified into two groups. The a priori approach relies on expert knowledge about the components of these models, while the a posteriori approach seeks similarity in the GCMs’ output variables and is thus data-driven. In this study, we apply probabilistic network models (PNMs), a well-established machine learning technique, as a new a posteriori method to measure intermodel similarities. The proposed methodology is applied to surface temperature fields of the historical experiments from the CMIP5 multimodel ensemble and different reanalysis gridded datasets. PNMs are able to learn the complex spatial dependency structures present in climate data, including teleconnections operating on multiple spatial scales, characteristic of the underlying GCM. A distance metric building on the resulting PNMs is applied to characterize GCM model dependencies. The results of this approach are in line with those obtained with more traditional methods but have further explanatory potential building on probabilistic model querying. Significance Statement The present study proposes the use of probabilistic network models (PNMs) to quantify model similarity within ensembles of global climate models (GCMs). This is crucial for interpreting model agreement and multimodel uncertainty in climate change studies. When applied to climate data (gridded global surface temperature in this study), PNMs encode the relevant spatial dependencies (local and remote connections). Similarities among the PNMs resulting from different GCMs can be quantified and are shown to capture similar GCM formulations reported in previous studies. Differently to other machine learning methods previously applied to this problem, PNMs are fully explainable (allowing probabilistic querying) and are applicable to high-dimensional gridded raw data.

Forecasting changes in precipitation and temperatures of a regional watershed in Northern Iraq using LARS-WG model

Abstract Regions characterized by an arid or semi-arid climate are highly susceptible to prospective climate change impacts worldwide. Therefore, evaluating the effects of global warming on water availability in such regions must be accurately addressed to identify the optimal operation policy of water management facilities. This study used the weather generator model LARS-WG6.0 to forecast possible variations in precipitation and temperature of the Mosul Dam Reservoir in northern Iraq. Future climate change was predicted using three greenhouse gas emission scenarios (i.e., RCP2.6, RCP4.5, and RCP8.5) for four time intervals (2021–2040, 2041–2060, 2061–2080, and 2081–2100) using five Global climate models (GCMs): CSIRO-Mk3.6.0, HadGEM2-ES, CanESM2, BCC-CSM1-1, and NorESM1-M. The model’s calibration and validation were conducted using data from 2001 to 2020 from eight meteorological stations in the study area. The results showed that the weather generator model’s performance was outstanding in predicting daily climate variables. The results also showed that the highest increase in maximum and minimum temperatures was 5.70°C in July and 5.30°C in September, respectively, for the future period 2081–2100 under RCP8.5. The highly forecasted minimum and maximum temperatures were extracted from the CanESM2 and HadGEM2-ES GCM models. It was demonstrated that the study region would experience different patterns of precipitation change during the wet seasons in the evaluated periods. Finally, the variations in precipitation and temperatures in the Mosul dam region would significantly impact the amount of freshwater obtained in these areas due to rising loss rates of evaporation. This could lead to a water shortage and mismanagement of the sustainable operations of the dam.

Comparison of CMIP5 models for drought predictions and trend analysis over Mojo catchment, Awash Basin, Ethiopia

Climate change's effects on water, such as floods, droughts, ocean acidification, and increasing sea levels, are expected to worsen in the future years. In the context of the development the subject of mitigating the anticipated effects of drought has become highly important. Droughts in the African mainland are immediately in danger of hunger, particularly in Ethiopia. The creation of Global Climate Models (GCMs) and analysis of impending catastrophic events are two of the finest ways to handle this issue. This study analyzes the future Meteorological and Hydrological Drought with a comparison of GCM models on the Mojo Catchment using the Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), and Streamflow Drought Index (SDI) methodologies, under three slice periods at near (2006-2030), mid (2031-2055) and far (2056-2080) under the Representative Concentration Pathways (RCPs) including RCP4.5 and RCP8.5 scenario data. Overall Meteorological drought results show that at yearly time scale drought incidence is lower than biannual time scale in both indices. MIROC-MIROC5 and MPI-M-MPI-ESM-LR models are conducted for drought analysis. Under SPI6 (biannual period), 22% of the highest drought observed at far future periods at a scenario of RCP4.5 and for time scale SPI12 the frequency of drought decreases. On the catchment, there is no significant trend increase but both the meteorological and hydrological drought indices show the catchment to be highly susceptible to moderate range of drought and then severely drought takes place. Model MPI-M-MPI-ESM-LR is the most significant contributor to the analysis of the size and frequency of droughts and station Ejere is more venerable to drought as compared with other stations under the MPI-M-MPI-ESM-LR model. The occurrence of SDI12 drought on the MPI-M-MPI-ESM-LR model has a direct relation with the anomaly's climate variables (temperature and Precipitation). the maximum driest drought variability occurred at the same GCM model (anomalies up to -3.7mm) in the year 2041. Additionally, the frequency occurrence of drought at the annual timescale (SDI12) under RCP8.5 at the far future period of the catchment is suspected to 22% probability of drought occurrence. Overall, the Mojo catchment is moderately vulnerable to drought, and SPI-6 is more suspected to high frequency on drought under RCP4.5 especially near and mid periods. It is crucial for an organization, individuals, academics, water resource professionals, and disaster risk management in the catchment on alerting and planning water projects.

Projections of heatwave-attributable mortality under climate change and future population scenarios in China

SummaryBackgroundIn China, most previous projections of heat-related mortality have been based on modeling studies using global climate models (GCMs), which can help to elucidate the risks of extreme heat events in a changing climate. However, spatiotemporal changes in the health effects of climate change considering specific regional characteristics remain poorly understood. We aimed to use credible climate and population projections to estimate future heatwave-attributable deaths under different emission scenarios and to explore the drivers underlying these patterns of changes.MethodsWe derived climate data from a regional climate model driven by three CMIP5 GCM models and calculated future heatwaves in China under Representative Concentration Pathway (RCP) 2.6, RCP4.5, and RCP8.5. The future gridded population data were based on Shared Socioeconomic Pathway 2 assumption with different fertility rates. By applying climate zone-specific exposure-response functions to mortality during heatwave events, we projected the scale of heatwave-attributable deaths under each RCP scenario. We further analyzed the factors driving changes in heatwave-related deaths and main sources of uncertainty using a decomposition method. We compared differences in death burden under the 1.5°C target, which is closely related to achieving carbon neutrality by mid-century.FindingsThe number of heatwave-related deaths will increase continuously to the mid-century even under RCP2.6 and RCP4.5 scenarios, and will continue increasing throughout the century under RCP8.5. There will be 20,303 deaths caused by heatwaves in 2090 under RCP2.6, 35,025 under RCP4.5, and 72,260 under RCP8.5, with half of all heatwave-related deaths in any scenario concentrated in east and central China. Climate effects are the main driver for the increase in attributable deaths in the near future till 2060, explaining 78% of the total change. Subsequent population decline cannot offset the losses caused by higher incidence of heatwaves and an aging population under RCP8.5. Although health loss under the 1.5°C warming scenario is 1.6-fold higher than the baseline period 1986–2005, limiting the temperature rise to 1.5°C can reduce the annual mortality burden in China by 3,534 deaths in 2090 compared with RCP2.6 scenarios.InterpretationWith accelerating climate change and population aging, the effects of future heatwaves on human health in China are likely to increase continuously even under a low emission scenario. Significant health benefits are expected if the optimistic 1.5°C goal is achieved, suggesting that carbon neutrality by mid-century is a critical target for China's sustainable development. Policymakers need to tighten climate mitigation policies tailored to local conditions while enhancing climate resilience technically and infrastructurally, especially for vulnerable elderly people.FundingNational Key R&D Program of China (2018YFA0606200), Wellcome Trust (209734/Z/17/Z), Natural Science Foundation of China (41790471), and Guangdong Major Project of Basic and Applied Basic Research (2020B0301030004).

Ranking of CMIP6 based High-resolution Global Climate Models for India using TOPSIS

ABSTRACT Changing temperature and precipitation are the prominent candle bearers of climate change. Global Climate Models (GCM) simulate and predict historical, present, and future climate scenarios. This study focused on selecting the best performing GCM under High-resolution Model Intercomparison Project (HighResMIP) suitable for the Indian subcontinent for predicting daily maximum (Tasmax) and minimum (Tasmin) temperature. Entropy-TOPSIS Multicriteria Decision-Making (MCDM) approach was employed for the relative ranking of GCM models. This study compared 64 years (1951–2014) historical data of 11 high-resolution GCMs under three different nominal resolutions viz., 25 km, 50 km, and 100 km. India Meteorological Department (IMD) gridded data was used as observed data for the performance measure. The data were evaluated using three factors, i.e. Intra- and inter-annual variation of temperature, tempo-spatial extreme events study, and using selected performance indicators. The predictive ability of the GCM models spatially in different Koppen climate zones of India is also reported in this study. This study highlighted Hadgem3-Gc31 and ECMWF-IFS models for Tasmax; NICAM16-8S and MPI-ESM1-2 models for Tasmin are suitable for simulating Indian temperature. The outcome of this study will be helpful to the climate researchers and decision-makers to choose the best suitable CMIP6 GCM model at a regional level.

Tropical Cyclone Characteristics Represented by the Ocean Wave-Coupled Atmospheric Global Climate Model Incorporating Wave-Dependent Momentum Flux

Abstract Understanding the systematic characteristics of tropical cyclones (TCs) represented in global climate models (GCMs) is important for reliable climate change impact assessments. The atmospheric GCM (AGCM) and ocean wave models were coupled by incorporating the wave-dependent momentum flux. Systematic impacts of wave-dependent momentum flux on TC characteristics were estimated by analyzing 100 historical TCs that occurred in the western North Pacific Ocean. Wave-dependent momentum flux parameterization considering wind and wave direction misalignment was used for assessing the wave–atmosphere interaction. The larger the wave age and misalignment are, the larger the drag coefficient is. The drag coefficient at the left-hand side of the TC was enhanced by the wave condition. It was found that the wave-dependent momentum flux did not have any impact on peak TC intensity. On the other hand, the wave-dependent momentum flux showed a significant impact on TC development during the early development stage. Although systematic differences in TC intensity at most developed stages were not detected, systematic differences in TC tracks between experiments were observed. The TC tracks of the wave-coupled AGCM tend to pass in a relatively eastward direction in comparison with those from the uncoupled AGCM. This is because the wave-dependent momentum flux in the coupled AGCM altered the environmental steering flow and the smaller beta effect of smaller TC at the early developing stage. Systematic differences in TC tracks have significant impacts on climate change assessments, such as extreme sea level changes in coastal regions due to climate change.

Evaluation of climate change impacts and effectiveness of adaptation options on nitrate loss, microbial respiration, and soil organic carbon in the Southeastern USA

CONTEXTClimate change presents an agricultural challenge in the Southeastern USA with implications for maintaining environmental quality. OBJECTIVEThe objective of this study was to assess climate change impacts and adaptation practices (biochar and irrigation) simulated with the Environmental Policy Integrated Climate (EPIC) model on nitrate-N (NO3-N) losses, microbial respiration (MR) and soil organic carbon (SOC) in the Southeastern USA. METHODSThe EPIC model was used to assess the impacts of climate change and adaptations on NO3-N losses in leachate and runoff from the soil profile (0–100 cm), loss of soil C via MR (0–100 cm), and impacts on SOC stocks (0–10 cm) for representative farms growing C3 and C4 crops within ten Southeastern USA states. The adaptations explored were annual biochar applications and irrigation. Historical baseline (1979–2009) and future (2041–2070) climate scenarios were used for simulations with CO2 concentrations of 360 ppm and 500 ppm, respectively. Four regional climate models (RCMs), nested within global climate models (GCMs) for their boundary conditions, simulated changes in air temperatures, precipitation, and solar radiation. RESULTS AND CONCLUSIONSClimate change increased simulated NO3-N losses in leaching and runoff by 40–80%, compared to historical baseline scenarios that was attributed to overall increased annual precipitation under three of the four RCM_GCM models. For the C4 crops, NO3-N leaching and runoff losses were 16–47% and 31–45% lower than for the C3 crops, respectively. Biochar applications reduced NO3-N leaching in the West region during 2066–2070 under the RCM3_CGCM3 model. The differences in MR between the C4 and C3 crops ranged from 3 to 75%. SOC increased under C4 crops and when biochar was applied. We concluded that inclusion of C4 crops in crop rotations and the applications of biochar under wetter climate scenarios may be a promising adaptation strategy to reduce NO3-N losses and increase SOC content in the soils of the Southeastern USA. SIGNIFICANCEThis study represents one of the first attempts to assess the effectiveness of climate change adaptations such as the agricultural use of biochar and irrigation. The findings from this study strongly contribute to our understanding of potential climate change impacts on a region's agriculture and resulting environmental footprint. This information may be used by the scientific community along with decision and policy makers working on conceptual and practical technologies to mitigate the impacts of climate change on agriculture and the environment.

Greater Greenland Ice Sheet contribution to global sea level rise in CMIP6

Future climate projections show a marked increase in Greenland Ice Sheet (GrIS) runoff during the 21st century, a direct consequence of the Polar Amplification signal. Regional climate models (RCMs) are a widely used tool to downscale ensembles of projections from global climate models (GCMs) to assess the impact of global warming on GrIS melt and sea level rise contribution. Initial results of the CMIP6 GCM model intercomparison project have revealed a greater 21st century temperature rise than in CMIP5 models. However, so far very little is known about the subsequent impacts on the future GrIS surface melt and therefore sea level rise contribution. Here, we show that the total GrIS sea level rise contribution from surface mass loss in our high-resolution (15 km) regional climate projections is 17.8 ± 7.8 cm in SSP585, 7.9 cm more than in our RCP8.5 simulations using CMIP5 input. We identify a +1.3 °C greater Arctic Amplification and associated cloud and sea ice feedbacks in the CMIP6 SSP585 scenario as the main drivers. Additionally, an assessment of the GrIS sea level contribution across all emission scenarios highlights, that the GrIS mass loss in CMIP6 is equivalent to a CMIP5 scenario with twice the global radiative forcing.

The impacts of climate change based on regional and global climate models (RCMs and GCMs) projections (case study: Ilam province)

The climate change is one of the most important challenges facing the environment. In this study, climate change by air temperature and precipitation parameters is evaluated and compared for observational and future data. For the baseline period (1981–2016) and for future (2020–2080) detemined. To estimate future data, utilized the CNRM-CM5 and HadGEM2-ES models output the global climate models (GCMs) from the MarksimGCM database and the CNRM-CERFACS and MIROC-ESM models as regional climate models (RCMs) from the CORDEX project for the domain of south Asia is based on the representation concentration pathway RCP8.5 and RCP4.5 scenarios were considered. The results showed that is an increasing and decreasing trend for air temperature and precipitation in the baseline period, respectively. The validation of the models showed that the CNRM-CM5 model as a GCM model and the CNRM-CERFACS model as an RCM model have a higher performance in simulating climate change for study area. In fact, output of these models have close relation to the observed data. Evaluation of the temperature components (minimum and maximum temperatures) showed these components had an increasing trend in the baseline period and would have an upward deviation in the future. With respect to the changes in air temperature, precipitation and reference evapotranspiration (Eto) for the future, the status of water resources in the study area will decline significantly. Therefore, the effects of climate change are significant for the study area, which need to consider adaptive strategies such as; optimal water management, cropping pattern and resource management.

Climate projections of a multivariate heat stress index: the role of downscaling and bias correction

Abstract. Along with the higher demand for bias-corrected data for climate impact studies, the number of available data sets has largely increased in recent years. For instance, the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) constitutes a framework for consistently projecting the impacts of climate change across affected sectors and spatial scales. These data are very attractive for any impact application since they offer worldwide bias-corrected data based on global climate models (GCMs). In a complementary way, the CORDEX initiative has incorporated experiments based on regionally downscaled bias-corrected data by means of debiasing and quantile mapping (QM) methods. In light of this situation, it is challenging to distil the most accurate and useful information for climate services, but at the same time it creates a perfect framework for intercomparison and sensitivity analyses. In the present study, the trend-preserving ISIMIP method and empirical QM are applied to climate model simulations that were carried out at different spatial resolutions (CMIP5 GCM and EURO-CORDEX regional climate models (RCMs), at approximately 150, 50 and 12 km horizontal resolution) in order to assess the role of downscaling and bias correction in a multivariate framework. The analysis is carried out for the wet-bulb globe temperature (WBGT), a heat stress index that is commonly used in the context of working people and labour productivity. WBGT for shaded conditions depends on air temperature and dew-point temperature, which in this work are individually bias corrected prior to the index calculation. Our results show that the added value of RCMs with respect to the driving GCM is limited after bias correction. The two bias correction methods are able to adjust the central part of the WBGT distribution, but some added value of QM is found in WBGT percentiles and in the inter-variable relationships. The evaluation in present climate of such multivariate indices should be performed with caution since biases in the individual variables might compensate, thus leading to better performance for the wrong reason. Climate change projections of WBGT reveal a larger increase in summer mean heat stress for the GCM than for the RCMs, related to the well-known reduced summer warming of the EURO-CORDEX RCMs. These differences are lowered after QM, since this bias correction method modifies the change signals and brings the results for the GCM and RCMs closer to each other. We also highlight the need for large ensembles of simulations to assess the feasibility of the derived projections.

Application of a Disaggregation Method for the Generation of Climate Changed Intensity-Duration-Frequency Curves for Predicting Future Extreme Rainfall Impacts on Transportation Infrastructure

Potential consequences of climate change are the increase in the magnitude and frequency of extreme rainfall storm events. In order to assess what are the potential impacts of climate change in the transportation infrastructure, new intensity-duration-frequency curves are needed. In this study, projected IDF curves were created based on three Global Climate Models (GCM) for the representative concentration pathways (RCP) 4.5 and 8.5. The selected GCMs are: ACCESS1-0, CSIRO-MK3-0-6 and GFDL-ESM2M. Projected IDFs for the near (2025-2049), mid (2050-2074) and far future (2075-2099) were created after disaggregating the project rainfall time series using the Bartlett-Lewis Rectangular Pulses Stochastic Model. The projected IDFs were compared with the IDF currently used and generated based on historical data. The results indicate that climate change is likely to decrease rainfall intensities in all the future horizons in the tested area of San Antonio, Texas. Further analysis is recommended, including the use of bias correction of those GCM models and use of a broader range of models that can better quantify uncertainty of the future rainfall regime.

Higher contributions of uncertainty from global climate models than crop models in maize‐yield simulations under climate change

Quantifying and separating different sources of uncertainty helps to improve the understanding of the projected effects of climate change and can inform decision‐making in adaptation planning. This paper (1) evaluated four process‐based crop models; (2) assessed the effects of climate change on maize yield using climate change outputs from seven global climate models (GCMs) under three representative concentration pathways (RCPs); and (3) disaggregated the contributions of multiple crop models, GCMs and RCPs to overall uncertainty. All four models captured more than 80% of the variation in days to silking, maturity and yield, indicating reasonably reproduced observations. Similarly, the root mean square errors were moderate for days to silking and maturity (fewer than 4 days) and yield (0.5–0.7 t/ha). Overall, the results indicate that the models could assess grain yield at the study sites reasonably well. The results of the multiple models ensemble indicate that the maize yield will decrease by 9–11% with a probability of 72–80% on average during the period 2010–2039 relative to the baseline (1976–2005). The uncertainty in the maize‐yield simulations might arise mostly from the GCM models, followed by the crop models and RCPs, the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments.

Drought Persistence Errors in Global Climate Models.

The persistence of drought events largely determines the severity of socioeconomic and ecological impacts, but the capability of current global climate models (GCMs) to simulate such events is subject to large uncertainties. In this study, the representation of drought persistence in GCMs is assessed by comparing state‐of‐the‐art GCM model simulations to observation‐based data sets. For doing so, we consider dry‐to‐dry transition probabilities at monthly and annual scales as estimates for drought persistence, where a dry status is defined as negative precipitation anomaly. Though there is a substantial spread in the drought persistence bias, most of the simulations show systematic underestimation of drought persistence at global scale. Subsequently, we analyzed to which degree (i) inaccurate observations, (ii) differences among models, (iii) internal climate variability, and (iv) uncertainty of the employed statistical methods contribute to the spread in drought persistence errors using an analysis of variance approach. The results show that at monthly scale, model uncertainty and observational uncertainty dominate, while the contribution from internal variability is small in most cases. At annual scale, the spread of the drought persistence error is dominated by the statistical estimation error of drought persistence, indicating that the partitioning of the error is impaired by the limited number of considered time steps. These findings reveal systematic errors in the representation of drought persistence in current GCMs and suggest directions for further model improvement.

Evaluation of climate change impacts and effectiveness of adaptation options on crop yield in the Southeastern United States

The Environmental Policy Integrated Climate (EPIC) model was used to assess the impacts of climate change and proposed adaptation measures on yields of corn (Zea mays L.) and soybean (Glycine max L.) as well as aggregated yields of C3 [soybean, alfalfa (Medicago sativa L.), winter wheat (Triticum aestivum L.)] and C4 [corn, sorghum (Sorghum bicolor L.), pearl millet (Pennisetum glaucum L.)] crop types from representative farms in ten Southeastern US states. Adaptations included annual biochar applications and irrigation. Historical baseline (1979–2009) and future (2041–2070) climate scenarios were used for simulations with baseline and future CO2 concentrations of 360ppmv and 500ppmv, respectively. Four regional climate models (RCMs) nested within global climate models (GCMs) were used to run future simulations. The experiment was analyzed as randomized complete block design with split-plots in time for baseline vs. future comparisons, and as a randomized complete block design with repeated measures for comparisons between future periods within each RCM_GCM model. Compared to historical baseline scenario, increases in future corn yield ranged between 36–83%, but yields decreased by 5–13% towards 2066–2070 due to temperature stress. Future soybean yields decreased by 1–13% due to temperature and moisture stresses. Future aggregated C4 crops produced higher yields compared to historical C4 yields. There were no differences between future aggregated and historical C3 crop yields. Both crop types were negatively affected by progressing climate change impacts towards the end of 2066–2070 simulation period. Reductions in future aggregated C3 crop yields ranged between 10–22%, and between 6–10% for C4 crops. We explained lower reductions in C4 compared to C3 crops due to a lesser degree of photorespiration, better water use efficiency, and better heat tolerance under conditions of high light intensities and increased temperatures in C4 crops. Irrigation resulted in increased future corn yields between 29–33%, and 3–38% of aggregated C4 crop yields, with no effect on soybean or aggregated C3 crop yields. In some regions, biochar applications caused significant yield reductions of 9.5–20% for corn, 5–7% for aggregated C3, and 3–5% for aggregated C4 crops, depending on the model. Yield reductions were ascribed to alterations in plant nutrient availability. It was concluded that under drier weather scenarios, irrigation may be a promising adaptation strategy for agriculture in the Southeastern US.

On the climate model simulation of Indian monsoon low pressure systems and the effect of remote disturbances and systematic biases

Monsoon low pressure systems (LPS) are synoptic-scale systems forming over the Indian monsoon trough region, contributing substantially to seasonal mean summer monsoon rainfall there. Many current global climate models (GCMs), including the Met Office Unified Model (MetUM), show deficient rainfall in this region, much of which has previously been attributed to remote systematic biases such as excessive equatorial Indian Ocean (EIO) convection, while also substantially under-representing LPS and associated rainfall as they travel westwards across India. Here the sources and sensitivities of LPS to local, remote and short-timescale forcing are examined, in order to understand the poor representation in GCMs. An LPS tracking method is presented using TRACK feature tracking software for comparison between re-analysis data-sets, MetUM GCM and regional climate model (RCM) simulations. RCM simulations, at similar horizontal resolution to the GCM and forced with re-analysis data at the lateral boundaries, are carried out with different domains to examine the effects of remote biases. The results suggest that remote biases contribute significantly to the poor simulation of LPS in the GCM. As these remote systematic biases are common amongst many current GCMs, it is likely that GCMs are intrinsically capable of representing LPS, even at relatively low resolution. The main problem areas are time-mean excessive EIO convection and poor representation of precursor disturbances transmitted from the Western Pacific. The important contribution of the latter is established using RCM simulations forced by climatological 6-hourly lateral boundary conditions, which also highlight the role of LPS in moving rainfall from steep orography towards Central India.

Projection of hydro-climatological changes over eastern Himalayan catchment by the evaluation of RegCM4 RCM and CMIP5 GCM models

AbstractHere, a regional climate model (RCM) RegCM4 and Coupled Model Intercomparison Project phase 5 (CMIP5) global climate models (GCMs) such as Coupled Physical Model (CM3), Coupled Climate Model phase 1 (CM2P1) and Earth System Model (ESM-2M) with their representative concentration pathway (RCP) datasets were utilized in projecting hydro-climatological variables such as precipitation, temperature, and streamflow in Teesta River basin in north Sikkim, eastern Himalaya, India. For downscaling, a ‘predictor selection analysis’ was performed utilizing a statistical downscaling model. The precision and applicability of RCM and GCM datasets were assessed using several statistical evaluation functions. The downscaled temperature and precipitation datasets were used in the Soil and Water Assessment Tool (SWAT) model for projecting the water yield and streamflow. A Sequential Uncertainty Parameter Fitting 2 optimization algorithm was used for optimizing the coefficient parameter values. The Mann–Kendall test results showed increasing trend in projected temperature and precipitation for future time. A significant increase in minimum temperature was found for the projected scenarios. The SWAT model-based projected outcomes showed a substantial increase in the streamflow and water yield. The results provide an understanding about the hydro-climatological data uncertainties and future changes associated with hydrological components that could be expected because of climate change.

Impacts of weighting climate models for hydro-meteorological climate change studies

Weighting climate models is controversial in climate change impact studies using an ensemble of climate simulations from different climate models. In climate science, there is a general consensus that all climate models should be considered as having equal performance or in other words that all projections are equiprobable. On the other hand, in the impacts and adaptation community, many believe that climate models should be weighted based on their ability to better represent various metrics over a reference period. The debate appears to be partly philosophical in nature as few studies have investigated the impact of using weights in projecting future climate changes. The present study focuses on the impact of assigning weights to climate models for hydrological climate change studies. Five methods are used to determine weights on an ensemble of 28 global climate models (GCMs) adapted from the Coupled Model Intercomparison Project Phase 5 (CMIP5) database. Using a hydrological model, streamflows are computed over a reference (1961–1990) and future (2061–2090) periods, with and without post-processing climate model outputs. The impacts of using different weighting schemes for GCM simulations are then analyzed in terms of ensemble mean and uncertainty. The results show that weighting GCMs has a limited impact on both projected future climate in term of precipitation and temperature changes and hydrology in terms of nine different streamflow criteria. These results apply to both raw and post-processed GCM model outputs, thus supporting the view that climate models should be considered equiprobable.

Seasonal Precipitation Bias Correction of GCM Outputs for Thailand: Rayong Province Region

Global Climate Models (GCMs) are sources of future simulated climate grid data. Their coarse resolution outputs are restricted to use for climate change impact analysis, though they require downscaling before application at fine scale. Using dynamical downscaling for coarse resolution GCM outputs requires large scale computation that may not be practicable for the daily climate data and spatial grid data of various GCM models; however the effectiveness of downscaling can be improved by statistical downscaling of GCM at fine scale. This study performed bias correction based on seasonal climate characteristics using statistical downscaling of daily precipitation data of six GCM models for Rayong Province in eastern coastal Thailand, selected from a total of 23 GCM models, and compared to GPCP past data covers Thailand region from 1981 to 2000 using score criteria of spatial correlation and RMSE method. Since the climate of Thailand is directly affected by Southern Local Wind, Tropical Monsoon, and Southern West Monsoon, the climate of Rayong Province can be divided into 3 seasons: dry season (Dec to Feb), first rainy season (Mar to Jul) and second rainy season (Aug to Nov) respectively. Seasonal bias correction was performed on monthly precipitation data classified by daily precipitation data based on seasonal types, i.e. rainless day, normal rainy day, and extreme rainy day. The seasonal bias correction of monthly downscaled precipitation data from the selected GCM outputs was expressed as best fit for the realistic parameter of monthly observed rainfall data at the considered station. The results of this study reveal that seasonal bias correction can effectively compensate for unrealistic precipitation data and increase the reliability of precipitation data from GCM outputs to accurately reflect the precipitation characteristics of Thailand.

Equidistance Quantile Matching Method for Updating IDFCurves under Climate Change

Recent increase in intensity and frequency of catastrophic hydrologic events is shown to be a major threat to the global economy. These major events have been strongly linked to climate change and are expected to become worse in the future. The intensity-duration-frequency (IDF) curves quantify the extreme precipitations which are commonly used in planning and design of hydraulic structures. Since it is expected that the trends in extreme precipitation events will alter in the future, this will impact the IDF curves and they will have to be updated. In this study we present a methodology based on equidistance quantile matching (EQM) for updating the IDF curves under climate change. The two main steps in the proposed methodology are: (i) spatial downscaling of the maximum daily precipitation values from the global climate model/s (GCM) data to each of the sub-daily maximums observed at a station under consideration; (ii) explicit description of the changes in the GCM data between the baseline period and the future period (temporal downscaling). The main advantage of the proposed method compared to the existing methods which only use the spatial downscaling/disaggregation methods at baseline period, is that the proposed methodology additionally incorporates the changes in the distributional characteristics of the GCM model between the baseline period and the projection period. In addition, the method is simple to adopt and computationally efficient. To demonstrate the utility of the proposed methodology we use: (i) Canadian GCM model CanESM2 and (ii) its Representative Concentration Pathways (RCPs) for greenhouse gas concentration trajectories adopted by the IPCC for its Fifth Assessment Report (AR5) for the description of future conditions. The sub-daily annual maximum intensities are obtained for four stations in Canada located at London (Ontario), Hamilton (Ontario), Calgary (Alberta) and Vancouver (British Columbia). It is observed that the proposed approach is skillful in capturing and replicating the historical intensities and frequencies. The results indicated that for all RCP simulations considered in this study there is increase in precipitation intensities for all return periods. The relative increase in precipitation extremes is consistent with the RCP scenarios, i.e., the intensity of RCP-26 is lower than the RCP-45 which in-turn is lower than RCP-85. The quantile-based modeling without the temporal downscaling consistently underestimates the precipitation intensity when compared to the proposed method. The proposed method offers a valuable contribution to water resources planning and management of future extreme conditions.