Coupled Model Intercomparison Project Phase 5 Global Climate Models Research Articles

Future precipitation changes in the Central Ethiopian Main Rift under CMIP5 GCMs

Abstract The purpose of this study is to predict future changes in precipitation in the Central Ethiopian Main Rift, which is vulnerable to climate change. The Long Ashton Research Station Weather Generator (LARS-WG) model was applied to project precipitation based on five global climate models (GCMs) (EC-EARTH, MIROC-ESM, HadGEM2-ES, INM-CM4, and CCSM4) from Coupled Model Intercomparison Project phase 5 (CMIP5) under two representative concentration pathways (RCP4.5 and RCP8.5) in the periods of 2041–2060 compared to the baseline period of 1976–2005. The model's calibration and validation results showed that it could predict future precipitation. According to the analysis, the mean rainfall is expected to increase in January (up to 14.2%) and December (up to 27.8%) under the RCP 4.5 and RCP 8.5 scenarios, respectively. However, a drop is anticipated in June (up to 8.2%) and May (up to 7%) under the RCP 4.5 and RCP 8.5 scenarios, respectively. In both scenarios, summer precipitation (usually the rainy season) is predicted to fall, while winter precipitation (usually the dry season) is expected to climb. Furthermore, annual and spring precipitation forecasts are anticipated to decrease in most locations. The findings of this research will be utilized to guide future water resource management in the study region.

Potential influence of sea surface temperature representation in climate model simulations over CORDEX‐SEA domain

AbstractRegional climate simulations from the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment – Southeast Asia (SEA) indicated model biases in temperature and rainfall over SEA. Given the influence of sea surface temperature (SST) variability on SEA climate, this study examines SST representation in climate models to investigate its potential contribution to the resulting model biases over the Philippines. Observed SST over SEA is first characterized by its spatial patterns and temporal variability. An analysis of the SST representation over SEA and its potential influence on modelled climate over the Philippines in Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) is then conducted, followed by an assessment of the potential influence of SST representation in CMIP5 GCMs on downscaled regional climate output. Our results show that GCMs with well represented SSTs (i.e., low bias, well captured variability, and pattern) can produce climate simulations well over the Philippines. Whether or not the GCMs with poor SST representation can perform well is inconclusive. During boreal winter (summer), climate variables with high (low) spatial correlation with model SST get poor (better) spatial correlation with observed climate. Over west of the Philippines, where model SST seasonal variability is captured well, models also adequately simulate climate variables. Results suggest that the negative temperature biases, and positive precipitation and wind speed biases, in both GCMs and downscaled simulations, are associated with negative model SST biases. These findings give a better understanding on how SST potentially influences modelled climatology over the Philippines.

Influence of the Coupling South Atlantic Convergence Zone-El Niño-Southern Oscillation (SACZ-ENSO) on the Projected Precipitation Changes over the Central Andes

There are no studies related to the influence of the coupling between the South Atlantic Convergence Zone (SACZ) and El Niño-Southern Oscillation (ENSO) pattern variability on future changes in the austral summer (December-February, DJF) precipitation over the central Andes. Therefore, we evaluated the historical simulations (1980–2005) and projections (2070–2099) for the Representative Concentration Pathway 8.5 (RCP 8.5) scenario of 25 global climate models (GCMs) from the Coupled Model Intercomparison Project phase 5 (CMIP5). Moreover, we also consider the Regional Climate Model version 4 (RegCM4) projections nested in three CMIP5 GCMs (GFDL-ESM2M, MPI-ESM-MR, and HadGEM2-ES) under RCP 8.5. We separate the CMIP5 GCMs according to their abilities to simulate the nonlinear characteristics of ENSO and the SACZ for the historical period. We found that only three out of 25 CMIP5 GCMs (hereafter group A) simulate the nonlinear characteristics of ENSO and the SACZ during the historical period. Although most CMIP5 GCM project DJF precipitation decreases over the central Andes, group A project precipitation increases related to the projected increase in deep convection over the central Peruvian Amazon. On the regional scale, only RegGFDL (nested in a group A CMIP5 GCM) projects a statistically significant increase in DJF precipitation (~5–15%) over the northern central Andes and the central Peruvian Amazon. Conversely, all RegCM4 simulations project a decrease in DJF precipitation (~−10%) over the southern central Andes.

Future rainfall and temperature changes in Brazil under global warming levels of 1.5ºC, 2ºC and 4ºC

The present study analyzes the impacts of global warming of 1.5ºC, 2ºC, and 4ºC above pre-industrial levels in the Brazilian territory. Climate change projected among the different global warming levels has been analyzed for rainfall, temperature and extreme climate indices. The projections are derived from the global climate model HadGEM3-A, from the High-End cLimate Impacts and eXtremes (HELIX) international project, from the United Kingdom, forced by sea surface temperature and sea ice concentration of a subset of six CMIP5 (Coupled Model Intercomparison Project phase 5) global climate models and considering the RCP 8.5 (Representative Concentration Pathways) emissions scenario throughout the 21st century. Projections indicate robust differences in regional climate characteristics. These differences include changes: in the minimum and maximum air temperature close to the surface to all the country’s regions, in extremes of heat, particularly in northern Brazil, in the occurrence of heavy rainfall (Southern and Southeastern regions), and in the probability of droughts and rain deficits in some regions (Northern and Northeastern Brazil).

Evaluation of CMIP5 Global Climate Models for Simulating Climatological Temperature and Precipitation for Southeast Asia

This study evaluates the performances of all forty different global climate models (GCMs) that participate in the Coupled Model Intercomparison Project Phase 5 (CMIP5) for simulating climatological temperature and precipitation for Southeast Asia. Historical simulations of climatological temperature and precipitation of the 40 GCMs for the 40-year period of 1960–1999 for both land and sea and those for the century of 1901–1999 for land are evaluated using observation and reanalysis datasets. Nineteen different performance metrics are employed. The results show that the performances of different GCMs vary greatly. CNRM-CM5-2 performs best among the 40 GCMs, where its total error is 3.25 times less than that of GCM performing worst. The performance of CNRM-CM5-2 is compared with those of the ensemble average of all 40 GCMs (40-GCM-Ensemble) and the ensemble average of the 6 best GCMs (6-GCM-Ensemble) for four categories, i.e., temperature only, precipitation only, land only, and sea only. While 40-GCM-Ensemble performs best for temperature, 6-GCM-Ensemble performs best for precipitation. 6-GCM-Ensemble performs best for temperature and precipitation simulations over sea, whereas CNRM-CM5-2 performs best over land. Overall results show that 6-GCM-Ensemble performs best and is followed by CNRM-CM5-2 and 40-GCM-Ensemble, respectively. The total errors of 6-GCM-Ensemble, CNRM-CM5-2, and 40-GCM-Ensemble are 11.84, 13.69, and 14.09, respectively. 6-GCM-Ensemble and CNRM-CM5-2 agree well with observations and can provide useful climate simulations for Southeast Asia. This suggests the use of 6-GCM-Ensemble and CNRM-CM5-2 for climate studies and projections for Southeast Asia.

Assessment of CMIP5 global climate models and projected changes in surface air temperature over the Arabian Peninsula in the twenty-first century

This study examines the surface air temperature projections and associated uncertainties over the Arabian Peninsula by using data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under the Representative Concentration Pathways RCP4.5 and RCP8.5 scenarios. The 30-member CMIP5 mean multi-model ensemble (MME) reveals a significant (at the 99% level) increase in temperature by the end of the twenty-first century of around 2.28 (5.53) °C/100 years under the RCP4.5 (RCP8.5) scenarios. The warming as well as the uncertainty associated with the temperature projections over the Arabian Peninsula will increase with time up to the end of the twenty-first century. In the near future (2021–2050), both RCPs (RCP4.5 and RCP8.5) reveal similar increases (~ 2 to 2.5 °C) in temperature compared to the present climate (1976–2005). For the period 2070–2099, the temperatures are projected to be from 2.55 ± 1.58 (4.89 ± 1.58) °C relative to the present climate under RCP4.5 (RCP8.5), while the associated uncertainty ranges from − 1.38 to 5.95 (0.57 to 7.9) °C. Under RCP4.5 (RCP8.5), all models reach 2 °C in the Arabian Peninsula in the twenty-first century relative to 1976–2005 from 2023 (2030) to 2062 (2049). The warming over the Arabian Peninsula is projected to be asymmetric that will vary both spatially and seasonally. A strong increase in the annual mean temperature over the central and northwestern parts of the Arabian Peninsula comes mainly from the summer and autumn season. The projected warming rate is higher in the autumn compared to the other seasons during the twenty-first century. The decadal and centurial warming rates are lower in winter and higher in autumn.

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

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

CMIP5 vs. CORDEX over the Indian region: how much do we benefit from dynamical downscaling?

Given the general notion that dynamical downscaling leads to added accuracy in both historical simulations as well as climate change projections, this paper investigates its validity over India using historical data (1975–2005) from the CORDEX models and their driving global climate models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5), and comparing them against observed temperature and rainfall. We find that downscaling invariably leads to an improvement in the spatial pattern of surface air temperature, but compared to the driving GCMs, the errors in magnitude after downscaling are even worse in some cases. In regard to JJAS rainfall simulations, the CMIP5 driving GCMs are found to be superior to their dynamically downscaled counterparts both in terms of spatial patterns as well as magnitude of errors. Both CMIP5 driving GCMs as well as the CORDEX models underestimate rainfall during JJAS; however, negative bias in CORDEX models is worse. Unlike the driving CMIP5 GCMs, their dynamically downscaled counterparts simulate an early onset followed by a slow and late withdrawal of the Indian summer monsoon rainfall. The frequency of occurrence of rainfall intensities is simulated well by both sets of models in the lower intensity regime (0–20 mm/day); however, for higher intensities, the driving CMIP5 GCMs underestimate whereas the CORDEX models overestimate.

Potential impact of climate change on streamflow of major Ethiopian rivers

In this study, HSPF (Hydrologic Simulation Program-FORTRAN) was used to analyze the potential impact of climate change on the streamflow of four major river basins in Ethiopia: Awash, Baro, Genale, and Tekeze. The calibrated and validated HSPF model was forced with daily climate data of 10 CMIP5 (Coupled Model Intercomparison Project phase 5) Global Climate Models (GCMs) for the 1971–2000 control period and the RCP4.5 and RCP8.5 climate projections of 2041–2070 (2050s) and 2071–2100 (2080s). The ensemble median of these 10 GCMs projects the temperature in the four study areas to increase by about 2.3 °C (3.3 °C) in 2050s (2080s), whereas the mean annual precipitation is projected to increase by about 6% (9%) in 2050s (2080s). This results in about 3% (6%) increase in the projected annual streamflow in Awash, Baro, and Tekeze rivers whereas the annual streamflow of Genale river is projected to increase by about 18% (33%) in the 2050s (2080s). However, such projected increase in the mean annual streamflow due to increasing precipitation over Ethiopia contradicts the decreasing trends in mean annual precipitation observed in recent decades. Regional climate models of high resolutions could provide more realistic climate projections for Ethiopia’s complex topography, thus reducing the uncertainties in future streamflow projections.

Evaluating biases in simulated land surface albedo from CMIP5 global climate models

AbstractLand surface albedo is a key parameter affecting energy balance and near‐surface climate. In this study, we used satellite data to evaluate simulated surface albedo in 37 models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). There was a systematic overestimation in the simulated seasonal cycle of albedo with the highest bias occurring during the Northern Hemisphere's winter months. The bias in surface albedo during the snow‐covered season was classified into that in snow cover fraction (SCF) and albedo contrast (β1). There was a general overestimation of β1 due to the simulated snow‐covered albedo being brighter than the observed value; negative biases in SCF were not always related to negative albedo biases, highlighting the need for realistic representation of snow‐covered albedo in models. In addition, models with a lower leaf area index (LAI) tend to produce a higher surface albedo over the boreal forests during the winter, which emphasizes the necessity of improving LAI simulations in CMIP5 models. Insolation weighting showed that spring albedo biases were of greater importance for climate. The removal of albedo biases is expected to improve temperature simulations particularly over high‐elevation regions.

Future changes in autumn atmospheric river events in British Columbia, Canada, as projected by CMIP5 global climate models

AbstractAtmospheric rivers (ARs) often trigger extreme precipitation events in British Columbia (BC), Canada. Here we analyze how well the autumn AR events with the highest probability for extreme precipitation over BC, henceforth called AR‐extreme events, are simulated in five Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) and how these AR‐extreme events are projected to change by the end of the century. We examine the daily synoptic patterns of integrated water vapor transport (IVT) over the Pacific Ocean that favor the formation of AR‐extreme events. Our analysis and comparison with AR‐extreme events in four reanalysis products for the period 1979–2010 reveal that the GCMs more successfully resolve their seasonality and interannual variability than their frequencies and amount of precipitation brought to BC. For the CMIP5 scenario's Representative Concentration Pathway (RCP) 4.5 and RCP8.5, the frequency of AR‐extreme events will increase for the period 2070–2100 with the largest increase in December. All models project an increase in total precipitation over BC, due to the increase in frequency and intensity of the AR‐extreme events; however, the dominant factor is the increase in frequency, especially of those events with precipitation exceeding 20 mmd−1. The path of the ARs during the AR‐extreme events is projected to move northward, bringing stronger IVT and more precipitation to the north coast of BC, while the south coast may become drier than at the present day. The shift in the ARs is driven by the northward shift in the Aleutian Low pressure system, especially in RCP8.5.

Changes in global ocean wave heights as projected using multimodel CMIP5 simulations

Ocean surface waves can be major hazards in coastal and offshore activities. However, there exists very limited information on ocean wave behavior in response to climate change, because such information is not simulated in current global climate models. This study made statistical projections of changes in ocean wave heights using sea level pressure (SLP) information from 20 CMIP5 (Coupled Model Intercomparison Project Phase 5) global climate models for the 21st century. The results show significant wave height increases in the tropics (especially in the eastern tropical Pacific) and in Southern Hemisphere high latitudes (south of 45°S). Under the projected 2070–2099 climate condition of the rising high concentration pathway—the RCP8.5 scenario, the occurrence frequency of the present‐day one in 10 year extreme wave heights is likely to double or triple in several coastal regions around the world. These wave height increases are primarily driven by increased SLP gradients and hence increased surface wind energy.

Evaluation of global climate models in simulating extreme precipitation in China

Variations in extreme precipitation can be described by various indices. In order to evaluate a climate model's ability to simulate extreme precipitation, gridded extreme precipitation indices from observations are needed. There are two ways to obtain gridded extreme precipitation indices from station-based observations: either through interpolation of station-based extreme indices (EISTA) or estimated from gridded precipitation datasets (EIGRID). In this work, we evaluated these two methods and compared observational extreme precipitation indices in China to those obtained from a set of widely used global climate models. Results show that the difference between the two methods is quite large; and in some cases it is even larger than the difference between model simulations and observed gridded EISTA. Based on the sensitivity of the indices to horizontal resolution, it was suggested that EIGRID is more appropriate for evaluating extreme indices simulated by models. Subsequently, historic simulations of extreme precipitation from 21 CMIP5 (Coupled Model Intercomparison Project Phase 5) global climate models were evaluated against two reanalysis datasets during 1961–2000. It was found that most models overestimate extreme precipitation in the mountain regions in western China and northern China and underestimate extreme precipitation in southern China. In eastern China, these models simulate mean extreme precipitation fairly well. Despite this bias, the temporal trend in extreme precipitation for western China is well captured by most models. However, in eastern China, the trend of extreme precipitation is poorly captured by most models, especially for the so-called southern flood and northern drought pattern. Overall, our results suggest that the dynamics of inter-decadal summer monsoon variability should be improved for better prediction of extreme precipitation by the global climate models.

A case study of subdaily simulated and observed continental convective precipitation: CMIP5 and multiscale global climate models comparison

[1] We analyze subdaily continental convective precipitation data relative to the Southeastern U.S. from gridded rain gauge measurements, conventional global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive, and a multiscale GCM. GCMs react too quickly to local convective instability and, therefore, overestimate the incidence of middle rainfall events and underestimate the incidence of no, little, and heavy rainfall events. Moreover, GCMs overestimate the persistence of heavy precipitation and underestimate the persistence of no and light precipitation. In general, GCMs with suppression mechanisms in the treatments of convective precipitation compare best with rain gauge derived data and should be trusted more than the others when assessing the risk from extreme precipitation events. The multiscale GCM has the best estimate of the diurnal cycle and a good estimate of heavy rainfall persistence.