Performance Of General Circulation Models Research Articles

Skill of isotope-enabled climate models for daily surface water vapour in East Asia

The isotope-enabled general circulation models (GCM) have been widely applied to simulate the variability of stable isotopes in meteoric water at various time scales. The in-situ observations of water vapour isotopes are an important basis for assessing the performance of isotope-enabled GCMs, although they are still limited. Here we compiled the observations of near-surface water vapour isotopes on a daily scale at 17 stations in East Asia, and assessed the skill and the association between isotope error and meteorological errors on a daily scale. Generally, the spatial pattern and seasonal variability can be well simulated in the isotope-enabled GCMs. The models show better skill for warm and humid backgrounds, which also corresponds to the monsoonal regions with lower latitudes in East Asia. As spatial resolution is finer, the skill of models is better, which can be seen from the two GCMs. According to the correlation coefficient, the improvement of resolution is more obvious in summer than in winter, especially for IsoGSM. In addition, the correlation coefficient in winter is usually larger than that in summer. The daily modelling has good potential to investigate the daily or synoptic climate information in water isotopes. The findings are useful for understanding the applicability of isotope-enabled models in East Asia and the climate factors influencing the skill of isotope-enabled models on a daily basis.

Enhanced performance of CMIP6 climate models in simulating historical precipitation in the Florida Peninsula

AbstractAssessing the performance of General Circulation Models (GCMs) in simulating historical regional precipitation is essential for climate assessments. This study analysed 18 GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 27 GCMs from Phase 6 (CMIP6) for the Florida Peninsula. Naïve combinations formed the CMIP5‐Ensemble and CMIP6‐Ensemble. The reference dataset consisted of global gridded precipitation data from National Oceanic and Atmospheric Administration. GCM‐simulated precipitation data were compared to the reference dataset across multiple time scales between 1951 and 2005. Most CMIP5 and CMIP6 GCMs exhibited a negative bias at the daily time scale, with an absolute value ranging between 0 and 2 mm day−1 at the median level, except for INM‐CM4 and MRI‐CGCM3 (CMIP5) and CNRM‐ESM2‐1 (CMIP6). Two performance metrics, that is, Pearson correlation and mean absolute error (MAE), were used to evaluate the GCMs. A correlation value above 0.3 is statistically significant at the significance level (α = 0.05). For the Pearson correlation between simulated and observed monthly precipitation, only 15% of CMIP5 GCMs (3 out of 19) had a median value above 0.3, whilst this increased to approximately 39% (11 out of 28) for CMIP6 GCMs. For monthly climatological precipitation, only 21% (4 out of 21) of GCMs in CMIP5 showed a correlation with a median value of 0.8 or higher. This percentage rose to 50% (14 out of 28) for CMIP6. A notable difference was observed in the MAE between CMIP5 and CMIP6 climate models. Lastly, GCM raw outputs were evaluated based on rainy season characteristics (onset, demise and duration). Significant improvements from the CMIP5 to the CMIP6 models were observed in capturing the rainy season in the Florida Peninsula. This study thoroughly compares CMIP5 and CMIP6 climate models in simulating historical precipitation at various time scales for the study region. Although the results are specific to the study area, the methods can be applied more broadly.

Evaluation of the Horizontal Winds Simulated by IAP-HAGCM through Comparison with Beijing MST Radar Observations

The performance of general circulation models (GCMs) in simulating horizontal winds is important because the distribution and variation in horizontal winds are central to investigating atmospheric dynamic characteristics and processes. Also, horizontal wind data can be used to extract some of the required information on gravity waves, tides, and planetary waves. In this context, the present paper evaluates the capability of the Institute of Atmospheric Physics atmospheric general circulation model high-top version (IAP-HAGCM) in simulating the horizontal winds and tides of the troposphere and lower stratosphere by presenting a climatological and statistical comparison against observations of the powerful Beijing mesosphere–stratosphere–troposphere (MST) radar (39.78°N, 116.95°E) during 2012–2014. The results illustrated that the IAP-HAGCM can successfully reproduce the time–altitude distribution of the monthly mean zonal wind and diurnal tide amplitude, albeit with some underestimation. The mean correlation coefficients and root-mean-square error for the zonal (meridional) winds were 0.94 (0.73) and 6.60 m s−1 (2.90 m s–1), respectively. Additionally, the IAP-HAGCM can capture the temporal variation in both the zonal and meridional winds. It is worth noting that, compared with the seven coupled model intercomparison project phase 6 (CMIP6) models, the IAP-HAGCM performs better in meridional wind simulations below 15 km. However, there are discrepancies in altitudinal ranges with large wind velocities, such as the westerly jet, in the transition region of the troposphere and stratosphere, and in February, April, July, and September. It is suggested that model users should take advantage of the model’s simulation ability by combining this information regarding when and where it is optimal with their own research purposes. Moreover, the evaluation results in this paper can also serve as a reference for guiding improvements of the IAP-HAGCM.

General circulation models for rainfall simulations: Performance assessment using complex networks

Reliable assessment of the impact of climate change on hydrology in a region requires proper selection of General Circulation Models (GCMs). The present study introduces the concepts of complex networks for evaluating the performance of GCMs in simulating rainfall and selecting an ensemble of the best-performing GCMs. The performance of 49 GCMs from the Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating monthly rainfall over India is assessed. The observed (interpolated and gridded rainfall provided by the India Meteorological Department) rainfall data and GCM-simulated rainfall data over the period 1961–2014 at a spatial resolution of 1° x 1° (a total of 288 grids) are studied. The GCMs are evaluated for two cases: (1) Case 1 – whole year rainfall; and (2) Case 2 – summer monsoon rainfall (June–September). The clustering coefficient of the rainfall network is used as a network measure to evaluate the ability of the GCMs in simulating rainfall. For rainfall network construction, each grid is considered as a network. The phase space reconstruction concept is used to reconstruct the single-variable rainfall time series in a multi-dimensional phase space to represent the rainfall dynamics. The optimal dimension for reconstruction is determined using the false nearest neighbor (FNN) algorithm. For network construction, each reconstructed vector is considered as a node, and the connections between them, identified using a distance threshold for the reconstructed vectors in the phase space, serve as the links. For each of the 288 grids, the GCMs are ranked based on the difference in the clustering coefficient between the observed and GCM-simulated rainfall networks. The group decision-making (GDM) approach is employed to identify the ensemble of the best-performing GCMs for the entire study area considering all the grids. The results suggest different models perform well for the whole-year rainfall and summer monsoon rainfall. For the whole-year rainfall, the models CMCC-CM2-HR4, GFDL-ESM4, CMCC-ESM2, EC-Earth3-AerChem, and CMCC-CM2-SR5 rank as the top five. For the summer monsoon rainfall, FIO-ESM-2-0, E3SM-1-1, CESM2-FV2, CMCC-CM2-HR4, and CMCC-CM2-SR5 perform the best. Considering the rank of each model for both the cases, the best-performing models are identified to be CMCC-CM2-HR4, CMCC-CM2-SR5, CMCC-ESM2, FIOESM-2-0, and E3SM-1-1. The rainfall simulated from these models also show close resemblance to the observed rainfall, especially in terms of monthly mean rainfall. Therefore, even among the many GCMs that show good agreement with the monthly mean observed rainfall, the clustering coefficient-based analysis helps to narrow down the models for the ensemble of best-performing GCMs.

Comparison of CMIP5 and CMIP6 GCM performance for flood projections in the Mekong River Basin

Study regionMekong River Basin. Study focusThe Coupled Model Intercomparison Project Phase 6 (CMIP6) recently announced an updated version of general circulation models (GCMs). This study investigated the performance of improved CMIP6 over those of CMIP5 with respect to precipitation and flood representations in the Mekong River Basin (MRB). The correlation and error comparison from the referenced precipitation exhibited a significant improvement in the peak value representation. Hence, the impacts of climate change on future floods in the MRB were simulated and assessed using a distributed rainfall–runoff–inundation model. New hydrological insights for the regionThe results indicated that precipitation from CMIP6 had a higher correlation and a lower error coefficient than CMIP5. Similarly, the simulation of GCM ensembles of monthly discharge from CMIP6 exhibited a comparable average value to the observations, whereas CMIP5 underestimated the discharge simulations. The performance of the mean annual peak discharge improved from 37,220 m3/s (CMIP5) to 45,423 m3/s (CMIP6) compared to 43,521 m3/s (observation). The projections of future floods in the MRB from CMIP6 exhibited an increase of annual peak discharge at Chiang Saen, Vientiane, Pakse, and Kratie stations by 10–15%, 20–22%, and 24–29% for the SSP2-4.5 scenario, and 10–18%, 24–29%, and 41–54% for the SSP5-8.5 scenario in the near future (2026–2050), mid-future (2051–2075), and far future (2076–2100), respectively. The statistical K-S test showed significant changes in all stations and projected periods with a p-value < 0.01.

Receiver Operating Characteristic Curve Analysis-Based Evaluation of GCMs Concerning Atmospheric Teleconnections

The evaluation of general circulation models (GCM) is a fundamental step in climate research in terms of both quality assurance/quality control and realistic representation of the dynamics of the atmospheric flows in the future projections. In this paper, a statistical method is introduced to evaluate GCMs with respect to teleconnection patterns in the winter 500 hPa geopotential height field over the Northern Hemisphere (NH). The procedure uses the combination of negative extrema method and receiver operating characteristic (ROC) curve analysis. The proposed method is demonstrated using selected general circulation models (GCMs) disseminated by the CMIP5 project. The ERA-20C reanalysis was used as a reference, supported by the NCEP/NCAR R1 reanalysis. The proposed method enables us to track changes in the geographical positions of the action centers (ACs); therefore, to detect improvement/deterioration in the GCM performance with time. It was found that the majority of the GCMs reproduce prominent teleconnections of the NH but fail to capture the eastward shift of the ACs over the Pacific Ocean in the last decades of the 20th century. The GCMs reproduce teleconnections with stronger correlations over the north-western part of the Atlantic Ocean compared to the reanalyses. The construction of mobile teleconnection indices is proposed to gain further insight into the performance of the models and to support a regional-scale analysis. The method can be easily applied to the recent CMIP6 simulations.

Unravelling the influence of subjectivity on ranking of CMIP6 based climate models: A case study

AbstractThe skill of General Circulation Models (GCMs) in mimicking the observed climate is assessed through various procedures and are ranked. The performance of a GCM is site‐specific and the ranking pattern varies spatially. In general, a set of best performing GCMs is extracted to study the impact of climate change. As, there is no universally accepted ranking procedure, the ranking of GCMs is prone to subjectivity. In this study, it is aimed to address the effect of this subjectivity on the GCM rankings. The past performance of GCMs from Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating the maximum and minimum temperature across India are evaluated and ranked by different ranking procedures. These ranking procedures involve combinations of various components present in the ranking procedure such as model evaluation criteria, criteria weightage allocation methods, Multicriteria Decision Making methods (MCDM) and reference gridded datasets. Different criteria and methods involved in the ranking procedures are carefully selected to address the subjectivity involved in ranking of GCMs. The effect of each individual component on the ranking pattern is systematically analysed and the spatial distribution of grids with same ranking patterns across all the combinations are considered as grids with same ranking. The performance of best performing GCM is attributed to homogenous climatic zones and its corresponding topological features. An ensemble of frequently performing top five GCMs among 16 different ranking procedures are extracted for each climate zone as the most suitable set of GCMs.

Colombian climatology in CMIP5/CMIP6 models: Persistent biases and improvements

Northern South America is among the regions with the highest vulnerability to climate change. General Circulation Models (GCMs) are among the different tools considered to analyze the impacts of climate change. In particular, GCMs have been proved to provide useful information, although they exhibit systematic biases and fail in reproducing regional climate, particularly in terrains with complex topography. This work evaluates the performance of GCMs included in the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP), representing the annual cycle of precipitation and air surface temperature in Colombia. To evaluate this, we consider different observational and reanalysis datasets, including in situ gauges from the Colombian Meteorological Institute. Our results indicate that although the most recent generation of GCMs (CMIP6) show improvements with respect to the previous generation (CMIP5), they still have systematic biases in representing the Intertropical Convergence Zone and elevation-dependent processes, which highly determine intra-annual precipitation and air surface temperature in Colombia. In addition, CMIP6 models have larger biases in temperature over the Andes than CMIP5. We also analyze climate projections by the end of the 21st century according to the CMIP5/CMIP6 simulations under the highest greenhouse gases emission scenarios. Models show projections toward warmer air surface temperatures and mixed changes of precipitation, with decreases of precipitation over the Orinoco and Colombian Amazon in September-November and increases over the eastern equatorial Pacific during the entire year.

Future trend and sensitivity analysis of evapotranspiration in the Senegal River Basin

Study regionSenegal River Basin in West Africa. Study focusThis work aimed to assess reference evapotranspiration (ET0) trends and its sensitivity to climate variables on the period 2036–2065 in the Senegal River basin. Seven General Circulation Models (GCMs) and seven Regional Climate Models (RCMs) of the CMIP5 project were used under the scenarios RCP4.5 and RCP8.5. The performance of GCMs and RCMs was first evaluated by comparing their outputs with the reanalyses data. The change of ET0 is determined between the periods 1971–2000 and 2036–2065. A sensitivity coefficient was calculated to analyze the influence of climatic variables on ET0. Finally, the Mann Kendall test and Sen slope were used to detect future trends in ET0 and climate variables. New hydrological insights for the regionIt was found that RCMs were here more robust than GCMs in estimating reference evapotranspiration over the period 1984–2000. Compared to the period 1971–2000, the RCMs show that ET0 will increase by 14–293 mm under RCP4.5 and by 55–387 mm under RCP8.5 according to the climatic zones. The maximum values are observed in Sahelian zone and the minimum one in Guinean area. The sensitivity analysis shows that ET0 is more sensitive to relative humidity, maximum temperature and solar radiation. The trend analysis reveals, generally, a significant increase in ET0 and in maximum and minimum temperatures in the period 2036–2065 under the RCP4.5 and RCP8.5 scenarios. This means that ET0 will not be stationary and may continue to increase after 2065 because of the increase of temperature.

A new method for assessing the performance of general circulation models based on their ability to simulate the response to observed forcing

AbstractThe reliability of General Circulation Models (GCMs) is commonly associated with their ability to reproduce relevant aspects of observed climate and thus, the evaluation of GCMs performance has become a standard practice for climate change studies. As such, there is an ever-growing literature that focuses on developing and evaluating metrics to assess GCMs performance. In this paper it is shown that some commonly applied metrics provide little information for discriminating GCMs based on their performance, once uncertainty is included. A new methodology is proposed that differs from common approaches in that it focuses on evaluating GCMs ability to reproduce the observed response of surface temperature to changes in external radiative forcing (RF), while controlling for observed and simulated variability. It uses formal statistical tests to evaluate two aspects of the warming trend that are central for climate change studies: 1) if the response to RF produced by a particular GCM is compatible with observations and 2) if the magnitudes of the observed and simulated rates of warming are statistically similar. We illustrate the proposed methodology by evaluating the ability of 21 GCMs to reproduce the observed warming trend at the global scale and eight sub-continental land domains. Results show that most of the GCMs provide an adequate representation of the observed warming trend for the global scale and for domains located in the southern hemisphere. However, GCMs tend to overestimate the warming rate for domains in the northern hemisphere, particularly since the mid-1990s.

Assessment and ranking of CMIP5 GCMs performance based on observed statistics over Cauvery river basin – Peninsular India

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.

Evaluating the effects of climate change on precipitation and temperature for Iran using RCP scenarios

AbstractClimate change has caused many changes in hydrologic processes and climatic conditions globally, while extreme events are likely to occur more frequently at a global scale with continued warming. Given the importance of general circulation models (GCMs) as an essential tool for climate studies at global/regional scales, together with the wide range of GCMs available, selecting appropriate models is of great importance. In this study, six synoptic weather stations were selected as representative of different climatic zones over Iran. Utilizing monthly data for 20 years (1981–2000), the outputs of 25 GCMs for surface air temperature (SAT) and precipitation were evaluated for the historical period. The root-mean-square error and skill score were chosen to evaluate the performance of GCMs in capturing observed seasonal climate. Finally, the outputs of selected GCMs for the three Representative Concentration Pathways emission scenarios (RCPs), namely RCP2.6, RCP4.5, and RCP8.5, were downscaled using the change factor method for each station for the period 2046–2065. Results indicate that SAT in all months is likely to increase for each region, while for precipitation, large uncertainties emerge, despite the selection of climate models that best capture the observed seasonal cycle. These results highlight the importance of selecting a representative ensemble of GCMs for assessing future hydro-climatic changes for Iran.

Disentangling and quantifying contributions of distinct forcing factors to the observed global sea level pressure field

Variations of the global sea level pressure (SLP) field reflect atmospheric and oceanic influences and have a profound influence on temperature, precipitation and the global carbon cycle. The impact of various forcing factors on this field was investigated mainly based on numerical simulations. Alternatively, here we identify and quantify the influences of various forcing factors on observational, reanalysis and simulated SLP fields. By applying canonical correlation analysis (CCA) on the aforementioned data sets, we separated and quantified the impact of increase CO2 concentration, El Nino–Southern Oscillation (ENSO), Atlantic Multidecadal Oscillation (AMO), Arctic Oscillation (AO) and solar forcing on the global SLP field, based on their associations with known footprints on the sea surface temperature (SST). Together, their corresponding SLP spatial structures explain ~ 60% of the observed variance. Whereas the atmospheric CO2 concentration has the most prominent impact on the global SLP field, explaining 28% of variance, ENSO and AO account for 9% each. The solar forcing and AMO explain 7%, respectively 6% of global SLP variance. Similar spatial structures corresponding to the same forcing factors are identified based on the reanalysis SLP data. CCA applied on simulated SLP fields derived from six CMIP5 model simulations captures only the spatial structures of atmospheric CO2 concentration, ENSO, AAO and AO. Such a decomposition of the global pressure field based on a linear combination of coupled SST-SLP pairs provide a reference against which one could validate the performance of general circulation models in simulating the lower atmosphere dynamics.

Evaluation of Bayesian Multimodel Estimation in Surface Incident Shortwave Radiation Simulation over High Latitude Areas

Surface incident shortwave radiation (SSR) is crucial for understanding the Earth’s climate change issues. Simulations from general circulation models (GCMs) are one of the most practical ways to produce long-term global SSR products. Although previous studies have comprehensively assessed the performance of the GCMs in simulating SSR globally or regionally, studies assessing the performance of these models over high-latitude areas are sparse. This study evaluated and intercompared the SSR simulations of 48 GCMs participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) using quality-controlled SSR surface measurements at 44 radiation sites from three observation networks (GC-NET, BSRN, and GEBA) and the SSR retrievals from the Clouds and the Earth’s Radiant Energy System, Energy Balanced and Filled (CERES EBAF) data set over high-latitude areas from 2000 to 2005. Furthermore, this study evaluated the performance of the SSR estimations of two multimodel ensemble methods, i.e., the simple model averaging (SMA) and the Bayesian model averaging (BMA) methods. The seasonal performance of the SSR estimations of individual GCMs, the SMA method, and the BMA method were also intercompared. The evaluation results indicated that there were large deficiencies in the performance of the individual GCMs in simulating SSR, and these GCM SSR simulations did not show a tendency to overestimate the SSR over high-latitude areas. Moreover, the ensemble SSR estimations generated by the SMA and BMA methods were superior to all individual GCM SSR simulations over high-latitude areas, and the estimations of the BMA method were the best compared to individual GCM simulations and the SMA method-based estimations. Compared to the CERES EBAF SSR retrievals, the uncertainties of the SSR estimations of the GCMs, the SMA method, and the BMA method are relatively large during summer.

Heuristic estimation of low-level cloud fraction over the globe based on a decoupling parameterization

Abstract. Based on the decoupling parameterization of the cloud-topped planetary boundary layer, a simple equation is derived to compute the inversion height. In combination with the lifting condensation level and the amount of water vapor in near-surface air, we propose a low-level cloud suppression parameter (LCS) and estimated low-level cloud fraction (ELF), as new proxies for the analysis of the spatiotemporal variation of the global low-level cloud amount (LCA). Individual surface and upper-air observations are used to compute LCS and ELF as well as lower-tropospheric stability (LTS), estimated inversion strength (EIS), and estimated cloud-top entrainment index (ECTEI), three proxies for LCA that have been widely used in previous studies. The spatiotemporal correlations between these proxies and surface-observed LCA were analyzed. Over the subtropical marine stratocumulus deck, both LTS and EIS diagnose seasonal–interannual variations of LCA well. However, their use as a global proxy for LCA is limited due to their weaker and inconsistent relationship with LCA over land. EIS is anti-correlated with the decoupling strength more strongly than it is correlated with the inversion strength. Compared with LTS and EIS, ELF and LCS better diagnose temporal variations of LCA, not only over the marine stratocumulus deck but also in other regions. However, all proxies have a weakness in diagnosing interannual variations of LCA in several subtropical stratocumulus decks. In the analysis using all data, ELF achieves the best performance in diagnosing spatiotemporal variation of LCA, explaining about 60 % of the spatial–seasonal–interannual variance of the seasonal LCA over the globe, which is a much larger percentage than those explained by LTS (2 %) and EIS (4 %). Our study implies that accurate prediction of inversion base height and lifting condensation level is a key factor necessary for successful simulation of global low-level clouds in general circulation models (GCMs). Strong spatiotemporal correlation between ELF (or LCS) and LCA identified in our study can be used to evaluate the performance of GCMs, identify the source of inaccurate simulation of LCA, and better understand climate sensitivity.

Fidelity assessment of general circulation model simulated precipitation and temperature over Pakistan using a feature selection method

General Circulation Models (GCMs) provide vital information on the likely future climate, much needed for the effective planning and management of water resources. The performance assessment of GCMs has received significant attention in recent years for reliable estimation of future climate. Even though many approaches have been trialled in the ranking of GCMs for the selection of an appropriate ensemble, there is still a need to explore the potential of the state-of-the-art ranking approaches for a more dependable selection of GCMs suitable for a given task, over a region of interest. The present study assessed the potential of a state-of-the-art feature selection method known as Symmetrical Uncertainty (SU) in ranking 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCMs based on their ability to simulate monthly precipitation and the monthly average of daily maximum and minimum temperature for annual, monsoon and winter seasons. The performance of GCMs was assessed using gridded climate data obtained from Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU) and Princeton Global Meteorological Forcing dataset (Prin) over the period 1961–2005 considering Pakistan as the study area. The ranks obtained with SU were compared with those obtained using two well-established ranking approaches; (1) Compromise Programming (CP) and (2) Wavelet-based Skill Score (WSS). According to the results of this study, for the simulation of all the three climate variables in all seasons; CESM1-CAM5, HadGEM2-AO, NorESM1-M and HadGEM2-ES were identified as the best GCMs by SU, whereas CESM1-CAM5, HadGEM2-AO, NorESM1-M and GFDL-CM3 were identified as the best GCMs by CP, and CCSM4, CESM1-CAM5, GFDL-ESM2G and HadGEM2-ES by WSS. The comparison of ranks of GCMs obtained using the same ranking approach but based on different gridded data products showed more or less similar ranks for a given GCM. However, the differences were noticeable when the ranking was conducted with the same gridded data but employing different ranking approaches. The approach presented in this study can be extended to any number of GCMs and can be applied over any region, for the identification of the best performing ensemble of GCMs for a set of climate variables.

Performance Assessment of General Circulation Model in Simulating Daily Precipitation and Temperature Using Multiple Gridded Datasets

The performance of general circulation models (GCMs) in a region are generally assessed according to their capability to simulate historical temperature and precipitation of the region. The performance of 31 GCMs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) is evaluated in this study to identify a suitable ensemble for daily maximum, minimum temperature and precipitation for Pakistan using multiple sets of gridded data, namely: Asian Precipitation–Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE), Berkeley Earth Surface Temperature (BEST), Princeton Global Meteorological Forcing (PGF) and Climate Prediction Centre (CPC) data. An entropy-based robust feature selection approach known as symmetrical uncertainty (SU) is used for the ranking of GCM. It is known from the results of this study that the spatial distribution of best-ranked GCMs varies for different sets of gridded data. The performance of GCMs is also found to vary for both temperatures and precipitation. The Commonwealth Scientific and Industrial Research Organization, Australia (CSIRO)-Mk3-6-0 and Max Planck Institute (MPI)-ESM-LR perform well for temperature while EC-Earth and MIROC5 perform well for precipitation. A trade-off is formulated to select the common GCMs for different climatic variables and gridded data sets, which identify six GCMs, namely: ACCESS1-3, CESM1-BGC, CMCC-CM, HadGEM2-CC, HadGEM2-ES and MIROC5 for the reliable projection of temperature and precipitation of Pakistan.

An Assessment of GCM Performance at a Regional Scale Using a Score-Based Method

A multicriteria score-based method was developed to assess the performances of 18 general circulation models (GCMs) in the study region from 1970 to 2005. The results indicate the following. (1) GCMs simulate temperature better than rainfall. The temporal and spatial distributions of simulated temperature performed well compared with those from the observations. In comparison to temperature, the spatial distribution of simulated precipitation performed poorly. Most of the GCMs underestimated temperature and overestimated precipitation. (2) The Grubbs test was used to detect anomalous moving changes in the rank score (RS) results; the inm-cm4 and ipsl-cm5b-lr models were rejected when simulating temperature, while the bnu-esm and canesm2 models performed poorly when simulating precipitation. (3) Adding or removing any criterion does not significantly influence the RS result, which indicates that the multicriteria score-based method is robust. The advantages of using multicriteria score-based method to assess GCMs performance were demonstrated, and this method also provides a more comprehensive assessment when compared with the single-criterion method. The multicriteria method could replace other criteria as the research requirements and could be easily extended to different study regions; the results could be used for better informed regional climate change impact analyses.

Hydrologic Impacts of Climate Change : Quantification of Uncertainties

General Circulation Models (GCMs), which are mathematical models based on principles of fluid dynamics, thermodynamics and radiative transfer, are the most reliable tools available for projecting climate change. However, the spatial scale on which typical GCMs operate is very coarse as compared to that of a hydrologic process and hence, the output from a GCM cannot be directly used in hydrologic models. Statistical Downscaling (SD) derives a statistical or empirical relationship between the variables simulated by the GCM (predictors) and a point-scale meteorological series (predictand). In this work, a new downscaling model called CRF-downscaling model, is developed where the conditional distribution of the hydrologic predictand sequence, given atmospheric predictor variables, is represented as a conditional random field (CRF) to downscale the predictand in a probabilistic framework. Features defined in the downscaling model capture information about various factors influencing precipitation such as circulation patterns, temperature and pressure gradients and specific humidity levels. Uncertainty in prediction is addressed by projecting future cumulative distribution functions (CDFs) for a number of most likely precipitation sequences. Direct classification of dry/wet days as well as precipitation amount is achieved within a single modeling framework, and changes in the non-parametric distribution of precipitation and dry and wet spell lengths are projected. Application of the method is demonstrated with the case study of downscaling to daily precipitation in the Mahanadi basin in Orissa, with the A1B scenario of the MIROC3.2 GCM from the Center for Climate System Research (CCSR), Japan. An uncertainty modeling framework is presented in this work, which combines GCM, scenario and downscaling uncertainty using the Dempster-Shafer (D-S) evidence theory for representing and combining uncertainty. The methodology for combining uncertainties is applied to projections of hydrologic drought in terms of monsoon standardized streamflow index (SSFI-4) from streamflow projections for the Mahanadi river at Hirakud. The results from the work indicate an increasing probability of extreme, severe and moderate drought and decreasing probability of normal to wet conditions, as a result of a decrease in monsoon streamflow in the Mahanadi river due to climate change. In most studies to date, the nature of the downscaling relationship is assumed stationary, or remaining unchanged in a future climate. In this work, an uncertainty modeling framework is presented in which, in addition to GCM and scenario uncertainty, uncertainty in the downscaling relationship itself is explored by linking downscaling with changes in frequencies of modes of natural variability. Downscaling relationships are derived for each natural variability cluster and used for projections of hydrologic drought. Each projection is weighted with the future projected frequency of occurrence of that cluster, called ‘cluster-linking’, and scaled by the GCM performance…

Evaluation of CMIP5 and CORDEX derived wave climate in Indian Ocean

The effects of climate change are currently a widely investigated issue. However, little attention has been paid to the effects of climate change on regional wave climate. With the growing need towards developing future projections of waves to assess climate-driven impacts on coastal processes, it is required to evaluate the performance of General Circulation Models (GCMs) and Regional Climate Models (RCMs) in simulating wave climate independent of their ability to simulate other standard variables. Near-surface winds, derived from GCMs participating in the Coupled Model Intercomparison Project (CMIP5) and RCMs from Coordinated Regional Climate Downscaling Experiment (CORDEX), are used to force a spectral wave model to simulate hindcast waves over Indian Ocean (IO) region. The GCM and RCM forced climatological wave characteristics (significant wave height, mean wave period, and mean wave direction) are compared with reanalysis data derived from ERA-Interim for performance evaluation. RCMs work at fine resolution and are assumed to simulate regional climate better than GCMs. However, we identified that there is no added value in simulating wave climate using RCMs. We also identified that there is no improvement in wave simulation upon choosing a fine resolution GCM (~ 1.4°) over a coarse GCM (~ 2.5°). Seasonality in wave characteristics is an important aspect in the IO. The skill of climate models in capturing seasonality was also evaluated. It is observed that ensemble GCM forced wave simulations capture seasonality better than other models. Finally, it is recommended to use ensemble GCM wind forcing for better wave simulation in the IO region.