Evaluation of CMIP5 retrospective simulations of temperature and precipitation in northeastern Argentina

Lovino, Miguel A.; Berbery, Ernesto Hugo; Müller, Gabriela V.

doi:10.1002/joc.5441

Cited by 55 publications

(90 citation statements)

References 59 publications

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“…The resulting ensemble showed an improved correlation coefficient of 0.5 with both RMSE and Bias being less than 1 °C. The 0.5 coefficient observed herein is lower than what has previously been reported in other areas, e.g., Argentina were coefficients as high as 0.9 were found (Lovino et al 2018). This shows that generally, over Africa models do not perform as well as they do in other regions.…”

Section: Model Performancecontrasting

confidence: 90%

“…Generally, all models accurately mimic the trend of temperature over Zambia with only CESM-1-CAM5 being negatively correlated to CRU TS v4.01. 5 Models that scored ≤ 1 °C RMSE and bias were selected and included in (Lovino et al 2018). The models that were selected include HaDGEM-2-ES of the Met Office Hadley Centre, MIROC-5 developed by the University of Tokyo, MRI-CGCM3 a product of the Meteorological Research Institute (Japan), CSIRO-MK360 developed by Commonwealth Scientific and Industrial Research Organization and GFDL-ESM-2G of the Geophysical Fluid Dynamics Laboratory, USA.…”

Section: Model Performancementioning

confidence: 99%

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Multi-model synthesis of future extreme temperature indices over Zambia

Libanda

2020

Model. Earth Syst. Environ.

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Although temperature is one of the most important climate variables to be considered in adapting systems to climate change, its study over Zambia has until recently been largely ignored. A dearth of the literature on future temperature extremes is especially apparent. For the first time, future extreme temperature variability is analysed in Zambia for the period 1961-2100. The nonparametric Mann-Kendall test statistic is used at 5% significant level to compute trends. Sen's slope estimator is used to give the magnitude of the observed trends. A two-tailed Kolmogorov-Smirnov statistical test is used to calculate the significance of the observed changes. What stands out from the findings is that warm days and nights become warmer while cool days and nights are projected to become non-existent by 2100. Additionally, an increase in radiative forcing and time is seen to intensify extreme temperature events. Notably, much of southern Zambia is projected to experience ~ 30% increment in TX90p while the northern half will experience ~ 40% under RCP4.5 by the middle of the century (2021-2050). If the business-as-usual trajectory (RCP8.5) is followed, an intensification of TX90p is observed with ~ 40% in the southern half of the country and ~ 50% in the northern half. These projections are observed to almost double towards the end of the century (2071-2100). The projected increase in warm nights (TN90p) is more over Luapula and Northwestern province while the lowest is observed over the Livingstone/Magoye region. These results provide a foundation for strategic planning purposes to avert losses related to extreme temperature.

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Section: Model Performancecontrasting

confidence: 90%

Section: Model Performancementioning

confidence: 99%

Multi-model synthesis of future extreme temperature indices over Zambia

Libanda

2020

Model. Earth Syst. Environ.

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“…Only one model was selected from each modelling centre. We used the simulations identified as “r1i1p1,” where r indicates the realization number, i the indicator of the initialization method, and p the perturbed physics from each experiment (Taylor et al, ; Lovino et al, ). The observations and model simulations were all re‐gridded to a common 2 × 2° resolution through a bilinear interpolation method (https://uvcdat.llnl.gov) and were used to investigate the performance of these models in simulating the P–T dependence and its changes under global warming.…”

Section: Data and Resultsmentioning

confidence: 99%

“…A list of these models (and corresponding institutes and spatial resolutions) is shown in selected from each modelling centre. We used the simulations identified as "r1i1p1," where r indicates the realization number, i the indicator of the initialization method, and p the perturbed physics from each experiment (Taylor et al, 2012b;Lovino et al, 2018). The observations and model simulations were all re-gridded to a common 2 × 2 resolution through a bilinear interpolation method (https://uvcdat.…”

Section: Datamentioning

confidence: 99%

Changes in the dependence between global precipitation and temperature from observations and model simulations

Hao

Phillips

Hao

et al. 2019

Intl Journal of Climatology

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Precipitation and temperature are physically related to each other with significant dependences that vary with seasons at regional and global scales. The dependence between precipitation and temperature (P–T dependence) plays a central role in characterizing the joint behaviour of the two variables. Along with extensive studies on the variation in the mean, variance, or extremes of precipitation and temperature under global warming, it is of particular interest to understand the climate‐change impacts on the covariability of precipitation and temperature. The aim of this study therefore is to assess climate change impacts on the P–T dependence over global land areas from observations, and the performance of the current‐generation Coupled Model Intercomparison Project (CMIP5) models in simulating these patterns. In general, the CMIP5 models correctly simulate the overall pattern of the P–T dependence (around 87–94% of global land areas). However, they capture both the sign and change of P–T dependence for relatively small regions (around 41–47% of global land areas). Results from this study may provide useful insights for the future development of climate models.

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“…Numerous scholars have assessed the performance of the CMIP5 GCMs in simulating precipitation at the global scale [14][15][16][17][18], regional scale [11,[19][20][21], and subregional scale [12,[22][23][24]. Thus far, the multimodel ensembles of CMIP5 for projection of climate variables have been effectively used [20], and some authors consider these ensembles to be better than individual GCM [25].…”

Section: Introductionmentioning

confidence: 99%

Ranking of CMIP5 GCM Skills in Simulating Observed Precipitation over the Lower Mekong Basin, Using an Improved Score-Based Method

Ruan

Yao

Wang

et al. 2018

Water

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This study assessed the performances of 34 Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) in reproducing observed precipitation over the Lower Mekong Basin (LMB). Observations from gauge-based data of the Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) precipitation data were obtained from 1975 to 2004. An improved score-based method was used to rank the performance of the GCMs in reproducing the observed precipitation over the LMB. The results revealed that most GCMs effectively reproduced precipitation patterns for the mean annual cycle, but they generally overestimated the observed precipitation. The GCMs showed good ability in reproducing the time series characteristics of precipitation for the annual period compared to those for the wet and dry seasons. Meanwhile, the GCMs obviously reproduced the spatial characteristics of precipitation for the dry season better than those for annual time and the wet season. More than 50% of the GCMs failed to reproduce the positive trend of the observed precipitation for the wet season and the dry season (approximately 52.9% and 64.7%, respectively), and approximately 44.1% of the GCMs failed to reproduce positive trend for annual time over the LMB. Furthermore, it was also revealed that there existed different robust criteria for assessing the GCMs’ performances at a seasonal scale, and using multiple criteria was superior to a single criterion in assessing the GCMs’ performances. Overall, the better-performed GCMs were obtained, which can provide useful information for future precipitation projection and policy-making over the LMB.

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Evaluation of CMIP5 retrospective simulations of temperature and precipitation in northeastern Argentina

Cited by 55 publications

References 59 publications

Multi-model synthesis of future extreme temperature indices over Zambia

Multi-model synthesis of future extreme temperature indices over Zambia

Changes in the dependence between global precipitation and temperature from observations and model simulations

Ranking of CMIP5 GCM Skills in Simulating Observed Precipitation over the Lower Mekong Basin, Using an Improved Score-Based Method

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