Comparison of GCM Derived Rainfall for Bharathapuzha River Basin

Future climate change has emerged as a national and a global security threat. To carry out the needed adaptation and mitigation steps, a quantification of the expected level of climate change is needed, both at the global and the regional scale; in the end, the impact of climate change is felt at the local/regional level. An important part of such climate change assessment is uncertainty quantification. Decision and policy makers are not only interested in 'best guesses' of expected climate change, but rather probabilistic quantification (e.g., Rougier, 2007). For example, consider the following question: What is the probability that the average summer temperature will increase by at least 4 C in region R if global CO{sub 2} emission increases by P% from current levels by time T? It is a simple question, but one that remains very difficult to answer. It is answering these kind of questions that is the focus of this effort. The uncertainty associated with future climate change can be attributed to three major factors: (1) Uncertainty about future emission of green house gasses (GHG). (2) Given a future GHG emission scenario, what is its impact on the global climate? (3) Given a particular evolution of the global climate, what does it mean for a particular location/region? In what follows, we assume a particular GHG emission scenario has been selected. Given the GHG emission scenario, the current batch of the state-of-the-art global climate models (GCMs) is used to simulate future climate under this scenario, yielding an ensemble of future climate projections (which reflect, to some degree our uncertainty of being able to simulate future climate give a particular GHG scenario). Due to the coarse-resolution nature of the GCM projections, they need to be spatially downscaled for regional impact assessments. To downscale a given GCM projection, two methods have emerged: dynamical downscaling and statistical (empirical) downscaling (SDS). Dynamic downscaling involves configuring and running a regional climate model (RCM) nested within a given GCM projection (i.e., the GCM provides bounder conditions for the RCM). On the other hand, statistical downscaling aims at establishing a statistical relationship between observed local/regional climate variables of interest and synoptic (GCM-scale) climate predictors. The resulting empirical relationship is then applied to future GCM projections. A comparison of the pros and cons of dynamical versus statistical downscaling is outside the scope of this effort, but has been extensively studied and the reader is referred to Wilby et al. (1998); Murphy (1999); Wood et al. (2004); Benestad et al. (2007); Fowler et al. (2007), and references within those. The scope of this effort is to study methodology, a statistical framework, to propagate and account for GCM uncertainty in regional statistical downscaling assessment. In particular, we will explore how to leverage an ensemble of GCM projections to quantify the impact of the GCM uncertainty in such an assessment. There are three main component to this effort: (1) gather the necessary climate-related data for a regional SDS study, including multiple GCM projections, (2) carry out SDS, and (3) assess the uncertainty. The first step is carried out using tools written in the Python programming language, while analysis tools were developed in the statistical programming language R; see Figure 1.

Climate change (CC) affects millions of people directly or indirectly. Especially, the effect of CC on the hydrological regime is extensive. Hence, understanding its impact is highly essential. In this study, the Bharathapuzha river basin (BRB) lying in the Western Ghats region of southern India is considered for CC impact assessment, as it is a highly complex and challenging watershed, due to its varying topographical features, such as soil texture, land use/land cover types, slope, and climatology, including rainfall and temperature patterns. To understand the CC impact on the hydrological variables at BRB in the future, five downscaled global circulation models (GCMs) were used, namely BNU-ESM, Can-ESM, CNRM, MPI-ESM MR, and MPI-ESM LR. These GCMs were obtained for two representative concentration pathway (RCP) scenarios: 4.5 representing normal condition and 8.5 representing the worst condition of projected carbon and greenhouse gases concentration on the lower atmosphere. To obtain the continuous simulation of hydrological variables, the SWAT hydrological model was adopted in this study. Results showed that rainfall pattern, evapotranspiration, and soil moisture will increase at moderate to significant levels in the future. This is especially seen during the far future period (i.e., 2071 to 2100). Similar results were obtained for surface runoff. For instance, surface runoff will increase up to 19.2% (RCP 4.5) and 36% (RCP 8.5) during 2100, as compared to the average historical condition (1981–2010). The results from this study will be useful for various water resources management and adaptation measures in the future, and the methodology can be adopted for similar regions.

Global climate models (GCMs) represent invaluable instrument for various purposes, most prominently studying climate system dynamics, evolution of past climates and climate change projections. The newest set of GCM simulations has been produced under CMIP6 initiative coordinated by the the World Climate Research Programme’s (WCRP) Working Group on Coupled Modelling. Due to their coarse spatial resolution, some kind of downscaling is necessary for applications of GCM outputs on regional and local scales, with dynamical downscaling using regional climate models (RCMs) being a common solution of this issue. Clearly, the outputs of RCMs are influenced by the boundary conditions provided at the lateral boundaries of the integration domain. The magnitude of this influence is subject of ongoing research and depends on various aspects including the geographical region, temporal scale, climatic variable etc. Nevertheless, previous studies proved that an analysis of boundary conditions is needed for for proper RCM evaluation. Especially with regard to potential error propagation.              In present study we evaluate the simulation of CNRM-ESM2-1, one of the CMIP6 GCMs, and compare it to other CMIP6 ensemble members. The CNRM-ESM2-1 is being used as driving model for convective-permitting simulation of Aladin-CLIMATE RCM within Czech national project PERUN aimed at creation of updated climate change scenarios for the Czech Republic. We concentrate on historical GCM simulations in the period of 1990-2014, and use reanalysis ERA5 as the reference. The analysis is conducted over the boundaries of Aladin-CLIMATE/CZ intergration domain, which covers Central Europe. The deviations of the driving GCM (CNRM-ESM2-1) from reanalysis are compared to other CMIP6 GCMs. We also evaluate uncertainty arising from natural climate variability using perturbed initial conditions ensemble of CNRM-ESM2-1.

Abstract. Projections of coastal sea level (SL) changes are of great interest for coastal risk assessment and decision making. SL projections are typically produced using global climate models (GCMs), which cannot fully resolve SL changes at the coast due to their coarse resolution and lack of representation of some relevant processes (tides, atmospheric surface pressure forcing, waves). To overcome these limitations and refine projections at regional scales, GCMs can be dynamically downscaled through the implementation of a high-resolution regional climate model (RCM). In this study, we developed the IBI-CCS (Iberian–Biscay–Ireland Climate Change Scenarios) regional ocean model based on a 1/12∘ northeastern Atlantic Nucleus for European Modelling of the Ocean (NEMO) model configuration to dynamically downscale CNRM-CM6-1-HR, a GCM with a 1/4∘ resolution ocean model component participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) by the Centre National de Recherches Météorologiques (CNRM). For a more complete representation of the processes driving coastal SL changes, tides and atmospheric surface pressure forcing are explicitly resolved in IBI-CCS in addition to the ocean general circulation. To limit the propagation of climate drifts and biases from the GCM into the regional simulations, several corrections are applied to the GCM fields used to force the RCM. The regional simulations are performed over the 1950 to 2100 period for two climate change scenarios (SSP1-2.6 and SSP5-8.5). To validate the dynamical downscaling method, the RCM and GCM simulations are compared to reanalyses and observations over the 1993–2014 period for a selection of ocean variables including SL. Results indicate that large-scale performance of IBI-CCS is better than that of the GCM thanks to the corrections applied to the RCM. Extreme SLs are also satisfactorily represented in the IBI-CCS historical simulation. Comparison of the RCM and GCM 21st century projections shows a limited impact of increased resolution (1/4 to 1/12∘) on SL changes. Overall, bias corrections have a moderate impact on projected coastal SL changes, except in the Mediterranean Sea, where GCM biases were substantial.

California’s Sierra Nevada is a high-elevation mountain range with significant seasonal snow cover. Under anthropogenic climate change, amplification of the warming is expected to occur at elevations near snow margins due to snow albedo feedback. However, climate change projections for the Sierra Nevada made by global climate models (GCMs) and statistical downscaling methods miss this key process. Dynamical downscaling simulates the additional warming due to snow albedo feedback. Ideally, dynamical downscaling would be applied to a large ensemble of 30 or more GCMs to project ensemble-mean outcomes and intermodel spread, but this is far too computationally expensive. To approximate the results that would occur if the entire GCM ensemble were dynamically downscaled, a hybrid dynamical–statistical downscaling approach is used. First, dynamical downscaling is used to reconstruct the historical climate of the 1981–2000 period and then to project the future climate of the 2081–2100 period based on climate changes from five GCMs. Next, a statistical model is built to emulate the dynamically downscaled warming and snow cover changes for any GCM. This statistical model is used to produce warming and snow cover loss projections for all available CMIP5 GCMs. These projections incorporate snow albedo feedback, so they capture the local warming enhancement (up to 3°C) from snow cover loss that other statistical methods miss. Capturing these details may be important for accurately projecting impacts on surface hydrology, water resources, and ecosystems.

Global climate models are our main tool to generate quantitative climate projections, but these models do not resolve the effects of complex topography, regional scale atmospheric processes and small-scale extreme events. To understand potential regional climatic changes, and to provide information for regional-scale impact modeling and adaptation planning, downscaling approaches have been developed. Regional climate change modeling, even though it is still a matter of basic research and questioned by many researchers, is urged to provide operational results. One major downscaling class is statistical downscaling, which exploits empirical relationships between larger-scale and local weather. The main statistical downscaling approaches are perfect prog (often referred to as empirical statistical downscaling), model output statistics (which is typically some sort of bias correction), and weather generators. Statistical downscaling complements or adds to dynamical downscaling and is useful to generate user-tailored local-scale information, or to efficiently generate regional scale information about mean climatic changes from large global climate model ensembles. Further research is needed to assess to what extent the assumptions underlying statistical downscaling are met in typical applications, and to develop new methods for generating spatially coherent projections, and for including process-understanding in bias correction. The increasing resolution of global climate models will improve the representation of downscaling predictors and will, therefore, make downscaling an even more feasible approach that will still be required to tailor information for users.

Empirical-statistical downscaling (ESD) models use statistical relationships to infer local climate information from large-scale climate information produced by global climate models (GCMs), as an alternative to the dynamical downscaling provided by regional climate models (RCMs). Among various statistical downscaling approaches, the nonlinear methods are mainly used to construct downscaling models for local variables that strongly deviate from linearity and normality, such as daily precipitation. These approaches are also appropriate to handle downscaling of extreme rainfall. There are nonlinear downscaling techniques of various complexities. The simplest one is represented by the analog method that originated in the late 1960s from the need to obtain local details of short-term weather forecasting for various variables (air temperature, precipitation, wind, etc.). Its first application as a statistical downscaling approach in climate science was carried out in the late 1990s. More sophisticated statistical downscaling models have been developed based on a wide range of nonlinear functions. Among them, the artificial neural network (ANN) was the first nonlinear regression–type method used as a statistical downscaling technique in climate science in the late 1990s. The ANN was inspired by the human brain, and it was used early in artificial intelligence and robotics. The impressive development of machine learning algorithms that can automatically extract information from a vast amount of data, usually through nonlinear multivariate models, contributed to improvements of ANN downscaling models and the development of other new, machine learning-based downscaling models to overcome some ANN drawbacks, such as support vector machine and random forest techniques. The mixed models combining various machine learning downscaling approaches maximize the downscaling skill in local climate change applications, especially for extreme rainfall indices. Other nonlinear statistical downscaling approaches refer to conditional weather generators, combining a standard weather generator (WG) with a separate statistical downscaling model by conditioning the WG parameters on large-scale predictors via a nonlinear approach. The most popular ways to condition the WG parameters are the weather-type approach and generalized linear models. This article discusses various aspects of nonlinear statistical downscaling approaches, their strengths and weaknesses, as well as comparison with linear statistical downscaling models. A proper validation of the nonlinear statistical downscaling models is an important issue, allowing selection of an appropriate model to obtain credible information on local climate change. Selection of large-scale predictors, the model’s ability to reproduce historical trends, extreme events, and the uncertainty related to future downscaled changes are important issues to be addressed. A better estimation of the uncertainty related to downscaled climate change projections can be achieved by using ensembles of more GCMs as drivers, including their ability to simulate the input in downscaling models. Comparison between more future statistical downscaled climate change signals and those derived from dynamical downscaling driven by the same global model, including a complex validation of the RCMs, gives a measure of the reliability of downscaled regional climate changes.

Climate change is one of the most significant challenges of the 21st century, particularly its impact on surface water availability in arid and hyper-arid regions within the Euphrates River Basin. This study aims to analyze the impacts of climate change using five global climate models (GCMs) within the Coupled Model Intercomparison Project Phase 6 (CMIP6, IPCC 2021). Model outputs were statistically downscaled using Statistical Downscaling Model (SDSM 6.1) under three greenhouse gas emission scenarios, and bias correction was performed using Climate Model Data for Hydrologic Modeling (CMhyd 1.02). Performance evaluation was based on data from meteorological stations distributed across five climatic zones within the basin, covering the period from 1982 to 2023. The study results showed that the uncertainty-related biases in CMIP6 models limit the accuracy of climate projections in the basin. Using individual GCM models instead of relying solely on linear trends can reduce bias and uncertainty in estimating climate variables across different regions. Although the SDSM model provides a good fit with GCM outputs, the variability in spatial patterns and emission scenarios is reflected in the limited accuracy of some climate indices. Applying bias correction using the linear scaling method has been shown to improve the accuracy of statistical modeling. Projections indicate that rising temperatures due to increased greenhouse gas emissions may decrease rainfall in arid regions while increasing in humid regions. These results contribute to a better understanding of climate change’s impacts on water resources in river basins and provide a scientific basis for developing effective adaptation strategies and future planning.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-15483-x.

Abstract: Climate change like relative humidity can cause various negative impacts on water resources system, ecosystem, etc. To deal with these effects, it is necessary to study the climate change. There are various ways to study climate change in which one of the ways is the study of downscaling. Downscaling is the procedure in which prediction of information is done for local scale area from the available information of a large-scale area. In the downscaling of climatic variables, General Circulation Model (GCM) plays an important role. GCM gives larger scale climatic variables. With the help of this downscaling, we can predict different climatic variables such as temperature, for future time period over the selected area. To perform this downscaling there are different ways, we can classify it as statistical downscaling and dynamical downscaling. In statistical downscaling, we can find relation between predictand and predictors and this statistical relation we use for the future prediction of the selected climatic variable. In dynamical downscaling, we use Regional Climatic Model (RCM), and with the help of this, we carry out downscaling procedure. In this study, statistical downscaling has studied for temperature parameter such as relative humidity by considering the basic equation given by Wilby in (Inter-research). The study area selected for this research is jalna district Maharashtra State, India (Latitude: 19° .8347r, Longitude: 75° .8816r). In this study, in the first step, statistical downscaling has been done with the help of statistical downscaling model (SDSM) software by using HadCM3 GCM with A2a and B2a scenarios for temperature parameter for the future time period up to 2099. In second step, the Statistical downscaling performed by using basic equation given by Wilby Temperature values predicted up to 2099. These results are considered with three different series such as 2020s, 2050s, and 2080s. Downscaled results of temperature parameter by “SDSM” model were compared for future series. After study of these results, it is concluded that SDSM gives higher value of temperature changes in jalna district marathwada region. Key Words: Climate change Impact, possible future series, SDSM, HadCM3, A2a, B2a, relative humidity

Assessing information on climate change over a regional scale is made possible through general circulation models (GCMs). However, developers generally have a dilemma in selecting suitable GCM for regional scale downscaling to reduce the computational burden. Ranking of GCMs based on various conditions will help these purposes, and the present study evaluates the performance of GCMs using various performance evaluation parameters for ranking. Performance of twenty-six Coupled Model Intercomparison Project Phase 5 (CMIP5) GCMs was assessed in the present study to evaluate and rank the predictability of near-surface air temperature (tas) and precipitation (pr). The non-parametric trend existing in observed data from 35 stations is compared with GCM projected trends using Mann-Kendall trend analysis to assess the model reliability. Performance evaluation parameters such as percentage BIAS (PBIAS %), normalized root mean squared error (NRMSE %) and coefficient of determination (R2). Neither of the CMIP5 GCM performed consistently well throughout all four seasons. Also, models performing better in projecting temperature statistics are poor in capturing the precipitation trends and vice versa. The seasonal ranking of GCMs based on their ability to reproduce the regional weather condition would help in selecting suitable GCM for regional climate studies.

<p>The present study analyses the various uncertainties and nonstationarity in the streamflow projections of Subarnarekha river basin in Eastern India using two widely used hydrological climate models: 1) general circulation model (GCM), and 2) forcing climate change scenarios. These two climate models are used to force the ArcSWAT model. Subsequently this model is calibrated using SUFI-2 optimization technique. The downscaled and bias-corrected data from an ensemble of 10 climate projections with representative concentration pathways (RCP) 4.5 and 8.5 scenarios (five each) were used in first model, whereas in second model a total of 63 (7 perturbed precipitations and 9 perturbed temperatures) combinations of hypothetical climate change scenarios were used. The results show very good correlation during monthly calibration time steps and relatively good agreement between the observed and simulated streamflows in daily calibration time steps. The uncertainties are expressed in probabilistic terms using probability density function (PDF) and cumulative distribution function (CDF) as they provide significant information for decision process in climate change adaptation in the river basin. The uncertainties associated with climate models, return periods and streamflow extremes are also analysed in the present work. The RCP 8.5 scenarios seem more appropriate than RCP 4.5 scenarios in quantifying the uncertainties under nonstationarity assumptions. The mean values of water balance components and their percentage variation for both historic and future periods reveal that the water balance components get affected significantly due to climate change in a future period. Consequently, the streamflows are likely to decline in the river basin. The present study also highlights the comprehensive approaches that are being planned to facilitate adaptation to climate change as well as those that are specific to the water resources management in the study region. The findings in this work are useful for overall well-being of people in the study area.</p>

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 28:109-122 (2005) - doi:10.3354/cr028109 Validation of a coupled GCM and projection of summer rainfall change over South Africa, using a statistical downscaling method Yan Zhao1,2,*, Pierre Camberlin1, Yves Richard1 1Centre de recherches de climatologie, CNRS UMR 5080, Université de Bourgogne, Sciences Gabriel, 6 Bd Gabriel, 21000 Dijon, France2Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG),Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing PO Box 9804, 100029 Beijing, China *Email: yan.zhao@cea.fr ABSTRACT: In this study, the southern African climate response to increasing amounts of greenhouse gases is investigated, based on the dataset of a 150-yr climate change experiment following the IPCC Special Report on Emissions Scenarios marker scenario B2 (SRES-B2) performed with the coupled ARPEGE/OPA/GELATO general circulation model (GCM). The method of canonical correlation analysis (CCA) is adopted to validate the ability of the GCM to simulate the present-day climate over the southern African region and project the late-summer rainfall change over South Africa at the end of the 21st century. The model validation shows that the ARPEGE/OPA/GELATO GCM is able to capture the observed link between rainfall over South Africa and adjacent sea-level pressure (SLP), despite the existence of some systematic errors. The structure and variability of SLP are reproduced by the GCM in a realistic way. The major controlling mechanism of rainfall over South Africa can be identified in the GCM. The projection of rainfall indicates a drying trend during the 21st century over most parts of South Africa, in particular the central interior. Compared to present-day climatology, the overall late-summer rainfall will decrease by 8.2% by the end of 21st century as derived from GCM grid-point output, and by 16.1% from the downscaling model. KEY WORDS: Climate change · Model evaluation · Statistical downscaling · Canonical correlation analysis · South Africa Full article in pdf format PreviousNextExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 28, No. 2. Online publication date: March 16, 2005 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2005 Inter-Research.

Changes in ocean properties and circulation lead to a spatially non-uniform pattern of ocean dynamic sea-level change (DSLC). The projections of ocean dynamic sea level presented in the IPCC AR5 were constructed with global climate models (GCMs) from the Coupled Model Intercomparison Project 5 (CMIP5). Since CMIP5 GCMs have a relatively coarse resolution and exclude tides and surges it is unclear whether they are suitable for providing DSLC projections in shallow coastal regions such as the Northwestern European Shelf (NWES). One approach to addressing these shortcomings is dynamical downscaling – i.e. using a high-resolution regional model forced with output from GCMs. Here we use the regional shelf seas model AMM7 to show that, depending on the driving CMIP5 GCM, dynamical downscaling can have a large impact on DSLC simulations in the NWES region. For a business-as-usual greenhouse gas concentration scenario, we find that downscaled simulations of twenty-first century DSLC can be up to 15.5 cm smaller than DSLC in the GCM simulations along the North Sea coastline owing to unresolved processes in the GCM. Furthermore, dynamical downscaling affects the simulated time of emergence of sea-level change (SLC) above sea-level variability, and can result in differences in the projected change of the amplitude of the seasonal cycle of sea level of over 0.3 mm/yr. We find that the difference between GCM and downscaled results is of similar magnitude to the uncertainty of CMIP5 ensembles used for previous DSLC projections. Our results support a role for dynamical downscaling in future regional sea-level projections to aid coastal decision makers.

This study investigates the potential of projecting regional precipitation over Yunlin County using a singular‐value‐decomposition (SVD) based statistical downscaling scheme to cope with climate change; the change rate of local precipitation and its connection with the variability of large‐scale circulation are also discussed. It is found that rainfall over Yunlin County is closely tied to the large‐scale circulation over the East Asian monsoon region, and that general circulation models (GCMs) perform reasonably in simulating the mean states of sea level pressure (SLP) and meridional wind field at 850 hPa (V850) over this region. Consequently, the two large‐scale variables, SLP and V850, both of which are taken from seven GCMs involving the 20C3M, A1B, and B1 scenarios, are used as predictors for downscaling. The result shows that the downscaling schemes based on these predictors show a skillful and stable performance in reproducing historical seasonal rainfall anomalies over Yunlin County, especially when the strategies of using two predictors and multiple model ensemble (MME) are considered. It also shows that comparing the mean states of 20C3M‐downscaled and scenario‐projected seasonal local rainfall in the coming 36 years with respect to the period of 1975–2000 reveals a suppressive pattern in the wet season and an increasing pattern in the dry season; the finding may be associated with the strength of large‐scale circulation under different scenarios. Overall, this study demonstrates that useful information concerning the impact of climate change on regional precipitation can be obtained by downscaling schemes using outputs of GCMs as predictors.

<p>Climate change impact and adaptation studies make use of future climate simulations by Global Climate Models (GCMs). Nevertheless, their coarse resolution makes it necessary to apply some sort of downscaling on them. There are two common approaches for this purpose, dynamical and statistical downscaling (SD), with their particular strengths and limitations. The computational cheapness of SD compared to dynamical downscaling, which allows to explore uncertainties through the generation of large ensembles, as well as its capability to downscale to single point scale, makes this option commonly used for impact and adaptation studies. SD has been extensively evaluated and applied in the extra tropics, but few experiences exist in tropical regions. In this study four state-of-the-art methods belonging to different families (Model Output Statistics, Analog, Transfer Function and Weather Generators) have been evaluated for maximum/minimum daily temperature and daily accumulated precipitation in two regions with very different climates: Spain (Mid-latitudes) and Central America (Tropics). Some key assumptions of SD have been tested: the strength of the predictors/predictand links, the skill of different approaches, the extrapolation capability of each method, the reliability of the GCMs themselves in each region, etc. Although SD has been found to be less skilful in the Tropics, it still adds important value over the raw projections by GCMs. No significant evidence of different reliability by GCMs in both regions has been detected, although this specific question might need a more detailed analysis. Relevant predictors in each region have been found to differ from one to another region, which was expected due to the different climate drivers in both regions. And finally, some methods have been found to behave significantly differently in each region.</p>