CMIP5 General Circulation Models Research Articles

A regional scale impact and uncertainty assessment of climate change in the Western Ghats in India.

The general circulation models (GCMs) and emission scenarios (RCP 4.5 and 8.5) have proven to be significantly functional in evaluating the impacts of climate change (CC) on hydrology, although their performance and accuracy varies on a regional scale. The objective of the present study is to evaluate the performance of five CMIP5 GCMs (CanESM2, BNU-ESM, CNRM-CM5, MPI-ESM-LR and MPI-ESM-MR) on a regional scale in the West Flowing River Basins-2 (WFRB-2) in India to model the impact of CC and its scenario uncertainty using reliability ensemble average (REA) method. For quantifying the results, the upper, middle and lower regions of WFRB-2 are separately analysed. The MPIMR and MPILR GCM model shows highest reliability factor range (0.3-0.6) in predicting the annual mean and annual maximum rainfall for most of the grids in the region. The GCM-simulated runoff using VIC (variable infiltration capacity) model is evaluated using statistical parameters such as root mean square error (RMSE), percentage bias (Pbias) and standard deviation (Std). The annual mean (maximum) runoff obtained using REA ensemble shows least RMSE, Pbias and Std values, i.e. 21.08%, 9.10mm and 8.9mm (6%, 39.1mm, 39.1mm), respectively for the middle region, which demonstrates higher reliability of GCM outputs in the flood-prone regions of WFRB-2. Furthermore, the future projection of annual maximum rainfall/runoff shows an increase of 50mm/15mm in the near future (2011-2040) for lower and 20mm/6mm for middle regions, which may cause flooding activities in the lower and middle region of WFRB-2.

Future Climate Projections Using SDSM and LARS-WG Downscaling Methods for CMIP5 GCMs over the Transboundary Jhelum River Basin of the Himalayas Region

Climate change is one of the leading issues affecting river basins due to its direct impacts on the cryosphere and hydrosphere. General circulation models (GCMs) are widely applied tools to assess climate change but the coarse spatial resolution of GCMs limit their direct application for local studies. This study selected five CMIP5 GCMs (CCSM4, HadCM3, GFDL-CM3, MRI-CGCM3 and CanESM2) for performance evaluation ranked by Nash–Sutcliffe coefficient (NSE) and Kling–Gupta Efficiency (KGE). CCSM4 and HadCM3 large-scale predictors were favored based on ranks (0.71 and 0.68, respectively) for statistical downscaling techniques to downscale the climatic indicators Tmax, Tmin and precipitation. The performance of two downscaling techniques, Statistical Downscaling Methods (SDSM) and Long Ashton Research Station Weather Generator (LARS-WG), were examined using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), bias, NSE and KGE with weights (Wi) for the validation period. The results of statistical measures proved SDSM more efficient (0.67) in comparison to the LARS-WG (0.51) for the validation time for the Jhelum River basin. The findings revealed that the SDSM simulation for Tmax and Tmin are more comparable to the reference data for the validation period except simulation of extreme events by precipitation. The 21st century climatic projections exhibited a significant rise in Tmax (2.37–4.66 °C), Tmin (2.47–4.52 °C) and precipitation (7.4–11.54%) for RCP-4.5 and RCP-8.5, respectively. Overall, the results depicted that winter and pre-monsoon seasons were potentially most affected in terms of warming and precipitation, which has the potential to alter the cryosphere and runoff of the Jhelum River basin.

How Do Regional Distributions of Daily Precipitation Change under Warming?

AbstractGlobal warming is changing the intensity distribution of daily precipitation, with an increased frequency of heavy precipitation and reduced frequency of light/moderate precipitation in general circulation model (GCM) projections. Projected future CMIP5 GCM changes in regional daily precipitation distribution can be described by a combination of two idealized modes: a frequency decrease mode, representing a reduction in the frequency of precipitation at all rain rates; and a frequency shift mode, where the distribution shifts toward heavier rain rates. A decrease in daily precipitation frequency and an increase in intensity are projected in most regions, but the magnitude of change shows large regional variations. The two modes generally capture the projected shift from light/moderate to heavy rain rates but do not recreate GCM changes at the very highest and lowest rain rates. We propose a simple framework for deep convective precipitation change based on the dry static energy (DSE) budget, which provides a physical explanation of these idealized modes in regions and seasons where deep convection dominates precipitation. One possibility is that a frequency decrease mode is driven by increased convective inhibition (CIN). In this DSE framework, increased moisture under warming could influence the shape of the precipitation intensity distribution, particularly at the highest rain rates, but does not govern the overall magnitude of the shift to heavier rain rates, which is not well described by the Clausius–Clapeyron relationship. Changes in daily regional precipitation are not free to respond only to local changes (in e.g., moisture) but are also constrained by the DSE budget, particularly by DSE transport associated with the large-scale circulation.

Climate change impacts on the shrinkage of Lake Urmia

Abstract The purpose of this study was to investigate the impact of climate change on the water level and shrinkage of Lake Urmia. To achieve this, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm was used to select the top 10 general circulation models (GCMs) among 23 CMIP5 GCMs in the baseline period (1951–2005). Based on the K-nearest neighbors (KNN) method, 10 GCMs were combined and their uncertainties were quantified. Also, the future period (2028–2079) data were generated by using the LARS-WG model. According to the results, the temperature increased in all seasons of the future period. Under the RCP4.5 scenario, the precipitation decreases by 10.4 and 27.8% in spring and autumn, respectively, while it increases by 18.2 and 3.4% in summer and winter, respectively. Moreover, the RCP8.5 scenario lowers the precipitation by 11.4, 22.7, and 4.8% in spring, autumn, and winter, respectively, while it rises by 26.5% in summer. Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were used to calculate the short-, medium- and long-term meteorological droughts of the baseline and future periods. The occurrence number and peaks of droughts increase, while their durations decrease, in the future period. In general, the SPEI has a robust relationship than the SPI with changes in the water level of Lake Urmia.

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.

Evaluation of Surface Upward Longwave Radiation in the CMIP6 Models with Ground and Satellite Observations

Surface upward longwave radiation (SULR) is an indicator of thermal conditions over the Earth’s surface. In this study, we validated the simulated SULR from 51 Coupled Model Intercomparison Project (CMIP6) general circulation models (GCMs) through a comparison with ground measurements and satellite-retrieved SULR from the Clouds and the Earth’s Radiant Energy System, Energy Balanced and Filled (CERES EBAF). Moreover, we improved the SULR estimations by a fusion of multiple CMIP6 GCMs using multimodel ensemble (MME) methods. Large variations were found in the monthly mean SULR among the 51 CMIP6 GCMs; the bias and root mean squared error (RMSE) of the individual CMIP6 GCMs at 133 sites ranged from −3 to 24 W m−2 and 22 to 38 W m−2, respectively, which were higher than those found between the CERES EBAF and GCMs. The CMIP6 GCMs did not improve the overestimation of SULR compared to the CMIP5 GCMs. The Bayesian model averaging (BMA) method showed better performance in simulating SULR than the individual GCMs and simple model averaging (SMA) method, with a bias of 0 W m−2 and an RMSE of 19.29 W m−2 for the 133 sites. In terms of the global annual mean SULR, our best estimation for the CMIP6 GCMs using the BMA method was 392 W m−2 during 2000–2014. We found that the SULR varied between 386 and 393 W m−2 from 1850 to 2014, exhibiting an increasing tendency of 0.2 W m−2 per decade (p &lt; 0.05).

A potential tipping point in the thermal regime of a warm monomictic reservoir under climate change using three-dimensional hydrodynamic modeling

ABSTRACT The response of the Maroon reservoir in southwest Iran is modeled to evaluate possible impacts of 21st century climate change. Projections of 3 CMIP5 General Circulation Models (GCMs) were investigated using 2 Representative Concentration Pathways (RCP4.5 and RCP8.5), which respectively represent low/medium and high greenhouse gas emission scenarios. The raw GCM projections are statistically bias-corrected to provide boundary conditions for the 3-dimensional Aquatic Ecosystem Model (AEM3D). A new overturn bias analysis method was developed to evaluate possible bias in the GCM predictors during the observational (historic) period and determined it to be negligible. The modeling results, quantified by the analysis of various limnological parameters (onset and length of mixing and stratification periods, hypolimnion and epilimnion thickness and temperature, Brunt-Väisälä frequency, and Schmidt stability) indicate for RCP4.5 a continuous reduction in winter mixing and complete suppression of mixing by the end of the 21st century, implying a switch from monomictic to weakly oligomictic behavior. Under RCP8.5, the behavior transition occurs abruptly in the late 2050s in the form of a tipping point, followed by intermittent oligomictic behavior and transition to permanent stratification (thermal meromixis) within a decade. The change in behavior occurs because the surface and mixing temperatures significantly increase due to climate warming, whereas the hypolimnion is less affected, partly because of milder warming of cold winter river inflows (underflow) and strengthening stratification. The climate change-induced tipping point for the thermal regime of Maroon reservoir might serve as an indicator of changes in other warm monomictic systems.

Uncertainty of gridded precipitation and temperature reference datasets in climate change impact studies

Abstract. Climate change impact studies require a reference climatological dataset providing a baseline period to assess future changes and post-process climate model biases. High-resolution gridded precipitation and temperature datasets interpolated from weather stations are available in regions of high-density networks of weather stations, as is the case in most parts of Europe and the United States. In many of the world's regions, however, the low density of observational networks renders gauge-based datasets highly uncertain. Satellite, reanalysis and merged product datasets have been used to overcome this deficiency. However, it is not known how much uncertainty the choice of a reference dataset may bring to impact studies. To tackle this issue, this study compares nine precipitation and two temperature datasets over 1145 African catchments to evaluate the dataset uncertainty contribution to the results of climate change studies. These deterministic datasets all cover a common 30-year period needed to define the reference period climate. The precipitation datasets include two gauge-only products (GPCC and CPC Unified), two satellite products (CHIRPS and PERSIANN-CDR) corrected using ground-based observations, four reanalysis products (JRA55, NCEP-CFSR, ERA-I and ERA5) and one merged gauged, satellite and reanalysis product (MSWEP). The temperature datasets include one gauged-only (CPC Unified) product and one reanalysis (ERA5) product. All combinations of these precipitation and temperature datasets were used to assess changes in future streamflows. To assess dataset uncertainty against that of other sources of uncertainty, the climate change impact study used a top-down hydroclimatic modeling chain using 10 CMIP5 (fifth Coupled Model Intercomparison Project) general circulation models (GCMs) under RCP8.5 and two lumped hydrological models (HMETS and GR4J) to generate future streamflows over the 2071–2100 period. Variance decomposition was performed to compare how much the different uncertainty sources contribute to actual uncertainty. Results show that all precipitation and temperature datasets provide good streamflow simulations over the reference period, but four precipitation datasets outperformed the others for most catchments. They are, in order, MSWEP, CHIRPS, PERSIANN and ERA5. For the present study, the two-member ensemble of temperature datasets provided negligible levels of uncertainty. However, the ensemble of nine precipitation datasets provided uncertainty that was equal to or larger than that related to GCMs for most of the streamflow metrics and over most of the catchments. A selection of the four best-performing reference datasets (credibility ensemble) significantly reduced the uncertainty attributed to precipitation for most metrics but still remained the main source of uncertainty for some streamflow metrics. The choice of a reference dataset can therefore be critical to climate change impact studies as apparently small differences between datasets over a common reference period can propagate to generate large amounts of uncertainty in future climate streamflows.

Uncertainty assessment of drought characteristics projections in humid subtropical basins in China based on multiple CMIP5 models and different index definitions

This study presents an assessment of projection and uncertainty of drought characteristics (frequency DF, drought area Da) using three drought indices (Palmer Drought Severity Index, PDSI; Standardized Precipitation Index, SPI; Standardized Precipitation Evapotranspiration Index, SPEI) in the humid subtropical Pearl River basin in southern China during the period 2021–2050. The projection is based on 13 CMIP5 general circulation models (GCMs) under three Representative Concentration Pathway scenarios (RCP2.6, RCP4.5 and RCP8.5). Specifically, the SPI is derived by the precipitation simulations of 13 GCMs, whereas the PDSI and SPEI are computed based on the simulations from the Variable Infiltration Capacity (VIC) model forced by 13 GCMs. The uncertainty of projected drought indices (PDSI, SPI and SPEI) due to various GCMs and RCPs is quantified by the variance-based sensitivity analysis approach. The results indicate that the sign and magnitude of the projected changes in DF and Da are highly dependent on the index definition at the regional scale, and the SPI tends to underestimate the projected changes in DF compared with PDSI and SPEI. There is a large model spread in the projected DF changes (especially for SPEI) under all RCP scenarios, with larger model spread for more extreme drought events. Uncertainty analysis shows that GCM contributes more than 90% of total uncertainty in drought indices projections, while the RCP uncertainty is rather limited (< 10%) compared with GCM. The GCM uncertainty is spatially unevenly distributed and shows large variability at the interannual scale. This study highlights the sensitivity of drought projections to the index definition as well as the large spatial–temporal variability of general sources of uncertainty in drought projections.

Spatiotemporal differences and uncertainties in projections of precipitation and temperature in South Korea from CMIP6 and CMIP5 general circulation models

AbstractThis study compared the historical simulations and future projections of precipitation and temperature of Coupled Model Intercomparison Project (CMIP)5 and CMIP6 general circulation models (GCMs) to quantify the differences in the projections due to differences in scenarios. Five performance indicators were used to quantify the model reproducibility of the observed precipitation levels at 22 stations for the historical period of 1970–2005. The percentages of change in precipitation and temperature were estimated for the near (2025–2060) and far future (2065–2100) for two Representative Concentration Pathway (RCP)4.5 and RCP8.5 scenarios of CMIP5 and two Shared Socioeconomic Pathway (SSP)2–4.5 and SSP5‐8.5 scenarios of CMIP6. The uncertainty in the projection in each case was calculated using the reliability ensemble average (REA) method. As a result, the CMIP6 GCMs showed an improvement compared with the CMIP5 GCMs with regard to the ability to simulate the historical climate. The uncertainty in the precipitation projections was higher for SSPs than that in RCPs. With regard to the temperature, the uncertainty was higher for RCPs than for SSPs. The ensemble means of the precipitation and temperature showed higher changes in the far future compared with the near future for both RCPs and SSPs. This study contributes to improvement in the confidence of future projections using CMIP6 GCMs and bolsters our understanding of the relative uncertainty in SSPs and RCPs.

First assessment of the earth heat inventory within CMIP5 historical simulations

Abstract. The energy imbalance at the top of the atmosphere over the last century has caused an accumulation of heat within the ocean, the continental subsurface, the atmosphere and the cryosphere. Although ∼90 % of the energy gained by the climate system has been stored in the ocean, the other components of the Earth heat inventory cannot be neglected due to their influence on associated climate processes dependent on heat storage, such as sea level rise and permafrost stability. However, there has not been a comprehensive assessment of the heat inventory within global climate simulations yet. Here, we explore the ability of 30 advanced general circulation models (GCMs) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) to simulate the distribution of heat within the Earth's energy reservoirs for the period 1972–2005 of the Common Era. CMIP5 GCMs simulate an average heat storage of 247±172 ZJ (96±4 % of total heat content) in the ocean, 5±9 ZJ (2±3 %) in the continental subsurface, 2±3 ZJ (1±1 %) in the cryosphere and 2±2 ZJ (1±1 %) in the atmosphere. However, the CMIP5 ensemble overestimates the ocean heat content by 83 ZJ and underestimates the continental heat storage by 9 ZJ and the cryosphere heat content by 5 ZJ, in comparison with recent observations. The representation of terrestrial ice masses and the continental subsurface, as well as the response of each model to the external forcing, should be improved in order to obtain better representations of the Earth heat inventory and the partition of heat among climate subsystems in global transient climate simulations.

Future projections in tropical cyclone activity over multiple CORDEX domains from RegCM4 CORDEX-CORE simulations

The characteristics of tropical cyclone (TC) activity over five TC basins lying within four Coordinated Regional Downscaling Experiment (CORDEX) domains are examined for present and future climate conditions using a new ensemble of simulations completed as part of the CORDEX-CORE initiative with the regional climate model RegCM4. The simulations are conducted at a 25 km horizontal grid spacing and are driven by three CMIP5 general circulation models (GCMs) under two Representative Concentration Pathways (RCP2.6 and RCP8.5). The RegCM4 captures most features of the observed TC climatology, except for the TC intensity, which is thus statistically adjusted using a bias correction procedure to account for the effect of the coarse model resolution. The RegCM4 exhibits an improved simulation of several TC statistics compared to the driving GCMs, over most basins analyzed. In future climate conditions we find significant increases in TC frequency over the North Indian Ocean, the Northwest Pacific and Eastern Pacific regions, which are consistent with an increase in mid-tropospheric relative humidity. The North Atlantic and Australasia regions show a decrease in TC frequency, mostly associated with an increase in wind shear. We also find a consistent increase in future storm rainfall rates associated with TCs and in the frequency of the most intense TCs over most domains. Our study shows robust responses often, but not always, in line with previous studies, still implying the presence of significant uncertainties in the projection of TC characteristics, which need to be addressed using large ensembles of simulations with high-resolution models.

Comparison of Projection in Meteorological and Hydrological Droughts in the Cheongmicheon Watershed for RCP4.5 and SSP2-4.5

Due to the recent appearance of shares socioeconomic pathway (SSP) scenarios, there have been many studies that compare the results between Coupled Model Intercomparison Project (CMIP)5 and CMIP6 general circulation models (GCMs). This study attempted to project future drought characteristics in the Cheongmicheon watershed using SSP2-4.5 of Australian Community Climate and Earth System Simulator-coupled model (ACCESS-CM2) in addition to Representative Concentration Pathway (RCP) 4.5 of ACCESS 1-3 of the same institute. The historical precipitation and temperature data of ACCESS-CM2 were generated better than those of ACCESS 1-3. Two meteorological drought indices, namely, Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were used to project meteorological drought while a hydrological drought index, Standardized Streamflow Index (SDI), was used to project the hydrological drought characteristics. The metrological data of GCMs were bias-corrected using quantile mapping method and the streamflow was obtained using Soil and Water Assessment Tool (SWAT) and bias-corrected meteorological data. As a result, there were large differences of drought occurrences and severities between RCP4.5 and SSP2-4.5 for the values of SPI, SPEI, and SDI. The differences in the minimum values of drought index between near (2021–2060) and far futures (2061–2100) were very small in SSP2-4.5, while those in RCP4.5 were very large. In addition, the longest drought period from SDI was the largest because the variation in precipitation usually affects the streamflow with a lag. Therefore, it was concluded that it is important to consider both CMIP5 and CMIP6 GCMs in establishing the drought countermeasures for the future period.

Climate change reduces winter overland travel across the Pan-Arctic even under low-end global warming scenarios

Amplified climate warming has led to permafrost degradation and a shortening of the winter season, both impacting cost-effective overland travel across the Arctic. Here we use, for the first time, four state-of-the-art Land Surface Models that explicitly consider ground freezing states, forced by a subset of bias-adjusted CMIP5 General Circulation Models to estimate the impact of different global warming scenarios (RCP2.6, 6.0, 8.5) on two modes of winter travel: overland travel days (OTDs) and ice road construction days (IRCDs). We show that OTDs decrease by on average −13% in the near future (2021–2050) and between −15% (RCP2.6) and −40% (RCP8.5) in the far future (2070–2099) compared to the reference period (1971–2000) when 173 d yr−1 are simulated across the Pan-Arctic. Regionally, we identified Eastern Siberia (Sakha (Yakutia), Khabarovsk Krai, Magadan Oblast) to be most resilient to climate change, while Alaska (USA), the Northwestern Russian regions (Yamalo, Arkhangelsk Oblast, Nenets, Komi, Khanty-Mansiy), Northern Europe and Chukotka are highly vulnerable. The change in OTDs is most pronounced during the shoulder season, particularly in autumn. The IRCDs reduce on average twice as much as the OTDs under all climate scenarios resulting in shorter operational duration. The results of the low-end global warming scenario (RCP2.6) emphasize that stringent climate mitigation policies have the potential to reduce the impact of climate change on winter mobility in the second half of the 21st century. Nevertheless, even under RCP2.6, our results suggest substantially reduced winter overland travel implying a severe threat to livelihoods of remote communities and increasing costs for resource exploration and transport across the Arctic.

EVALUATION AND BIAS CORRECTION OF MARINE SURFACE WINDS IN THE WESTERN NORTH PACIFIC FROM CMIP5 AND CMIP6 GCMS FOR WAVE CLIMATE MODELLING

For over years, fundamental component and dataset in climate projection had been covered by general circulation models (GCMs) output mainly from the Coupled Model Inter-comparison Project (CMIP). Marine surface winds are an important output of GCMs and they provide input to marine forecasts and warning systems. Their accuracy have direct implications for marine safety, air-sea fluxes, and wave and ocean modellings. Western North Pacific (WNP) is known as a highly vulnerable region to oceanic and atmospheric hazards, such as storm surges, waves and typhoons. Therefore, this study aims to examine the quality of marine surface winds from CMIP5 and CMIP6 GCMs in the WNP and its sub-regions with respect to a reference data, and presents bias correction of marine surface winds for contributing to wave and ocean modelling communities.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/750mqrERbS8

Extreme value analysis dilemma for climate change impact assessment on global flood and extreme precipitation

A reliable estimation of hydrological extremes with potentially severe socio-economic impacts is of crucial importance for efficient planning and design of hydraulic structures. Extreme value theory provides a firm theoretical foundation for the statistical modelling of extreme hydrological events. The dilemma in the modelling is on whether to use block maxima (BM) or peak-over-threshold (POT) method, each with its own cons and pros. It remains unexplored to what extent future projected changes in extreme hydrological events are influenced by the method choice, especially when some simplifications are made to lessen the computational burden for large-scale studies. This study addresses this research question by a comparative analysis between the BM and POT methods on future climate change impact on global flood and extreme precipitation. The extreme precipitation analysis is performed using 24 CMIP5 general circulation models (GCMs), while the flood analysis is based on a multi-model ensemble of 20 members including five global impact models forced by four CMIP5 GCMs. The results reveal that the BM and POT methods agree on the sign of changes in flood and extreme precipitation intensities, but disagree on the magnitude. The discrepancy between the BM and POT results increases with event extremity. The difference also varies with season, where the difference in the global land area with increasing signals peaks in winter at the rates of 11% for extreme precipitation and in summer at the rates of 3.5% for flood.

Evaluation of Historical CMIP5 GCM Simulation Results Based on Detected Atmospheric Teleconnections

Atmospheric teleconnections are characteristic to the climate system and exert major impacts on the global and regional climate. Accurate representation of teleconnections by general circulation models (GCMs) is indispensable given their fundamental role in the large scale circulation patterns. In this study a statistical method is introduced to evaluate historical GCM outputs of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) with respect to teleconnection patterns. The introduced method is based on the calculation of correlations between gridded time series of the 500 hPa geopotential height fields in the Northern Hemisphere. GCMs are quantified by a simple diversity index. Additionally, potential action centers of the teleconnection patterns are identified on which the local polynomial regression model is fitted. Diversity fields and regression curves obtained from the GCMs are compared against the NCEP/NCAR Reanalysis 1 and the ERA-20C reanalysis datasets. The introduced method is objective, reproducible, and reduces the number of arbitrary decisions during the analysis. We conclude that major teleconnection patterns are positioned in the GCMs and in the reanalysis datasets similarly, however, spatial differences in their intensities can be severe in some cases that could hamper the applicability of the GCM results for some regions. Based on the evaluation method, best-performing GCMs can be clearly distinguished. Evaluation of the GCMs based on the introduced method might help the modeling community to choose GCMs that are the most applicable for impact studies and for regional downscaling exercises.

Selection of CMIP5 general circulation model outputs of precipitation for peninsular Malaysia

AbstractReduction of uncertainty in climate change projections is a major challenge in impact assessment and adaptation planning. General circulation models (GCMs) along with projection scenarios are the major sources of uncertainty in climate change projections. Therefore, the selection of appropriate GCMs for a region can significantly reduce uncertainty in climate projections. In this study, 20 GCMs were statistically evaluated in replicating the spatial pattern of monsoon propagation towards Peninsular Malaysia at annual and seasonal time frames against the 20th Century Reanalysis dataset. The performance evaluation metrics of the GCMs for different time frames were compromised using a state-of-art multi-criteria decision-making approach, compromise programming, for the selection of GCMs. Finally, the selected GCMs were interpolated to 0.25° × 0.25° spatial resolution and bias-corrected using the Asian Precipitation – Highly-Resolved Observational Integration Towards Evaluation (APHRODITE) rainfall as reference data. The results revealed the better performance of BCC-CSM1-1 and HadGEM2-ES in replicating the historical rainfall in Peninsular Malaysia. The bias-corrected projections of selected GCMs revealed a large variation of the mean, standard deviation and 95% percentile of daily rainfall in the study area for two futures, 2020–2059 and 2060–2099 compared to base climate.

Uncertainty assessment in river flow projections for Ethiopia’s Upper Awash Basin using multiple GCMs and hydrological models

ABSTRACT Uncertainty in climate change impacts on river discharge in the Upper Awash Basin, Ethiopia, is assessed using five MIKE SHE hydrological models, six CMIP5 general circulation models (GCMs) and two representative concentration pathways (RCP) scenarios for the period 2071–2100. Hydrological models vary in their spatial distribution and process representations of unsaturated and saturated zones. Very good performance is achieved for 1975–1999 (NSE: 0.65–0.8; r: 0.79–0.93). GCM-related uncertainty dominates variability in projections of high and mean discharges (mean: –34% to +55% for RCP4.5, – 2% to +195% for RCP8.5). Although GCMs dominate uncertainty in projected low flows, inter-hydrological model uncertainty is considerable (RCP4.5: –60% to +228%, RCP8.5: –86% to +337%). Analysis of variance uncertainty attribution reveals that GCM-related uncertainty occupies, on average, 68% of total uncertainty for median and high flows and hydrological models no more than 1%. For low flows, hydrological model uncertainty occupies, on average, 18% of total uncertainty; GCM-related uncertainty remains substantial (average: 28%).

Climate change projections of temperature and precipitation in Chile based on statistical downscaling

General circulation models (GCMs) allow the analysis of potential changes in the climate system under different emissions scenarios. However, their spatial resolution is too coarse to produce useful climate information for impact/adaptation assessments. This is especially relevant for regions with complex orography and coastlines, such as in Chile. Downscaling techniques attempt to reduce the gap between global and regional/local scales; for instance, statistical downscaling methods establish empirical relationships between large-scale predictors and local predictands. Here, statistical downscaling was employed to generate climate change projections of daily maximum/minimum temperatures and precipitation in more than 400 locations in Chile using the analog method, which identifies the most similar or analog day based on similarities of large-scale patterns from a pool of historical records. A cross-validation framework was applied using different sets of potential predictors from the NCEP/NCAR reanalysis following the perfect prognosis approach. The best-performing set was used to downscale six different CMIP5 GCMs (forced by three representative concentration pathways, RCPs). As a result, minimum and maximum temperatures are projected to increase in the entire Chilean territory throughout all seasons. Specifically, the minimum (maximum) temperature is projected to increase by more than 2 °C (6 °C) under the RCP8.5 scenario in the austral winter by the end of the twenty-first century. Precipitation changes exhibit a larger spatial variability. By the end of the twenty-first century, a winter precipitation decrease exceeding 40% is projected under RCP8.5 in the central-southern zone, while an increase of over 60% is projected in the northern Andes.