Evaluation of CMIP6 precipitation simulations across different climatic zones: Uncertainty and model intercomparison

Yazdandoost, Farhad; Moradian, Sogol; Izadi, Ardalan; AghaKouchak, Amir

doi:10.1016/j.atmosres.2020.105369

Cited by 137 publications

(82 citation statements)

References 67 publications

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“…Comparing the predicted and recorded CSLs, it is observed that all scenarios of CanESM5 models are higher than the observed values of CSL in 2015 and 2016. It might be related to the overall performance of CMIP6 models that differs across different climatic zones (Yazdandoost et al, 2020). The outputs of different scenarios reveal that CSL predictions by SSP1-1-2.6 and SSP4-60 scenarios have minimum mean errors (see Table 2).…”

Section: Resultsmentioning

confidence: 98%

Long-Term Prediction of Caspian Sea Level Under Cmip6 Scenarios Using Artificial Neural Networks

Hoseini¹,

Soltanpour²

2020

Int. Conf. Coastal. Eng.

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Artificial Neural Network (ANN) is employed to predict the long-term Caspian Sea level (CSL). 114-year observed CSL data (1900-2014) and the precipitation and temperature of historical and future scenarios of Coupled Model Intercomparison Phase 6 (CMIP6) are used to predict the future fluctuations of CSL (2015-2050). The values of the statistical indices in training, validating and testing periods (1900-2014) indicate the efficiency of the ANN in reconstruction of the CSL. Considering the outputs of different climate change projections (CMIP6) and excluding the human interventions, the study predicts the CSL fluctuation range of -28 m to -26m until 2050.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/Kfj-gr65TR8

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Section: Resultsmentioning

confidence: 98%

Long-Term Prediction of Caspian Sea Level Under Cmip6 Scenarios Using Artificial Neural Networks

Hoseini¹,

Soltanpour²

2020

Int. Conf. Coastal. Eng.

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“…The number of regions considered for the case study is limited and may summarize the complexity of the different biomes somewhat simplistically. We estimated future climate based on downscaled projections of several state-of-the-art climate models used by the IPCC (CMIP6) but still these projections are associated with substantial spatial and temporal uncertainty (Yazdandoost et al 2021) and do not account for extreme events such floods, fires, and droughts or floods which can cause large tree mortality (Brun, Psomas, et al 2020). While it was possible to validate the map with other data for Europe, the evaluation of the quality of the projections for other regions is more difficult, making the quality of the maps in the corresponding regions more uncertain.…”

Section: Discussionmentioning

confidence: 99%

Mapping tree species for restoration potential resilient to climate change

Tiel

Lyu

Fopp

et al. 2021

Preprint

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The restoration of forest ecosystems is associated with key benefits for biodiversity and ecosystem services. Where possible, ecosystem restoration efforts should be guided by a detailed knowledge of the native flora to regenerate ecosystems in a way that benefits natural biodiversity, ecosystem services, and nature's contribution to people. Machine learning can map the ecological suitability of tree species globally, which then can guide restoration efforts, especially in regions where knowledge about the native tree flora is still insufficient. We developed an algorithm that combines ecological niche modelling and geographic distributions that allows for the high resolution (1km) global mapping of the native range and suitability of 3,987 tree species under current and future climatic conditions. We show that in most regions where forest cover could be potentially increased, heterogeneity in ecological conditions and narrow species niche width limit species occupancy, so that in several areas with reforestation potential, a large amount of potentially suitable species would be required for successful reforestation. Local tree planting efforts should consider a wide variety of species to ensure that the equally large variety of ecological conditions can be covered. Under climate change, a large fraction of the surface for restoration will suffer significant turnover in suitability, so that areas that are suitable for many species under current conditions will not be suitable in the future anymore. Such a turnover due to shifting climate is less pronounced in regions containing species with broader geographical distributions. This indicates that if restoration decisions are solely based on current climatic conditions, a large fraction of the restored area will become unsuitable in the future. Decisions on forest restoration should therefore take the niche width of a tree species into account to mitigate the risk of climate-driven ecosystem degradation.

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“…The CMIP6 GCMs' resolutions are not different from CMIP5. Therefore, the performance of CMIP6 GCMs is not much different from CMIP5 GCMs in most of the globe (Rivera and Arnould 2020; Chen et al 2021; Yazdandoost et al 2021). The improved performance of some of the CMIP6 GCMs may be due to enhanced parameterization.…”

Section: Discussionmentioning

confidence: 99%

Intercomparison of Historical Simulation and Future Projection of Rainfall and Temperature by CMIP5 and CMIP6 GCMs Over Egypt

Hamed¹,

Nashwan²,

Shahid

2021

Preprint

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The performances of the Global Climate Models (GCMs) of recently released Coupled Model Intercomparison Project phase 6 (CMIP6) compared to its predecessor, CMIP5 are evaluated to anticipate the expected changes in climate over Egypt, globally one of the most environmentally fragile countries due to water insecurity and climate change. Thirteen common GCMs and their multi-model ensemble (MME) of both CMIPs were used for this purpose. The future projections were compared for two radiative concentration pathways (RCP 4.5 and 8.5), and two shared socioeconomic pathways (SSP 2-4.5 and 5-8.5) scenarios. The results revealed improvement in most CMIP6 models in replicating historical rainfall, maximum temperature (Tmax) and minimum temperature (Tmin) climatology over Egypt. The MME of the CMIPs revealed that both could reproduce the spatial distribution and seasonal variability of climate in Egypt. However, the bias in CMIP6 is much less than that for CMIP5. The uncertainty in simulating seasonal variability of rainfall and temperature was lower for CMIP6 compared to CMIP5. The future projection of rainfall using CMIP6 MME revealed a higher reduction of precipitation (4 to 10 mm) in the economically crucial northern region compared to that estimated using CMIP5 (10 to >15 mm). CMIP6 also projected a 1.5 to 2.5ºC more rise in Tmax and Tmin compared to CMIP5. The study indicates more aggravated scenarios of climate changes in Egypt than anticipated earlier, using the CMIP5 model. Therefore, Egypt needs to streamline the existing adaptation measures formulated based on CMIP5 projections.

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Evaluation of CMIP6 precipitation simulations across different climatic zones: Uncertainty and model intercomparison

Cited by 137 publications

References 67 publications

Long-Term Prediction of Caspian Sea Level Under Cmip6 Scenarios Using Artificial Neural Networks

Long-Term Prediction of Caspian Sea Level Under Cmip6 Scenarios Using Artificial Neural Networks

Mapping tree species for restoration potential resilient to climate change

Intercomparison of Historical Simulation and Future Projection of Rainfall and Temperature by CMIP5 and CMIP6 GCMs Over Egypt

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