Assessing the Performance of CMIP5 Global Climate Models for Simulating Future Precipitation Change in the Tibetan Plateau

Kang, Jia; Ruan, Yao; Yang, Yanzhao; Zhang, Chao

doi:10.3390/w11091771

Cited by 47 publications

(28 citation statements)

References 62 publications

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“…Based on a similar coupling design, Strandberg et al (2011) found that the impact of a different land cover on LGM climate simulations is small compared to the uncertainties in the proxy reconstructions. Even though this is also true in our study, our results and discussion suggest that modifications in land cover like deforestation could play an important role when other forcing agents marginally change, as is observed in some climate change scenarios such as RCP 2.6 and 4.5 (Strandberg and Kjellström, 2019;Davin et al, 2020;Jia et al, 2020).…”

Section: Influence Of External Forcing and Land Cover On Climatesupporting

confidence: 49%

“…Global climate models (GCMs) show little agreement in LGM simulations for Europe (Braconnot et al, 2012;Kageyama et al, 2017;Ludwig et al, 2019;Kageyama et al, 2021). It has been suggested that a reason for the large uncertainty could be related to the spatial resolution in the climate models (Walsh et al, 2008;Jia et al, 2019b;Ludwig et al, 2019;Raible et al, 2020). Advances in regional climate models have led to the application of such models to the glacial climate of Europe on a high spatial resolution (e.g.…”

Section: Introductionmentioning

confidence: 99%

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The role of land cover in the climate of glacial Europe

et al. 2021

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Abstract. Earth system models show wide disagreement when simulating the climate of the continents at the Last Glacial Maximum (LGM). This disagreement may be related to a variety of factors, including model resolution and an incomplete representation of Earth system processes. To assess the importance of resolution and land–atmosphere feedbacks on the climate of Europe, we performed an iterative asynchronously coupled land–atmosphere modelling experiment that combined a global climate model, a regional climate model, and a dynamic vegetation model. The regional climate and land cover models were run at high (18 km) resolution over a domain covering the ice-free regions of Europe. Asynchronous coupling between the regional climate model and the vegetation model showed that the land–atmosphere coupling achieves quasi-equilibrium after four iterations. Modelled climate and land cover agree reasonably well with independent reconstructions based on pollen and other paleoenvironmental proxies. To assess the importance of land cover on the LGM climate of Europe, we performed a sensitivity simulation where we used LGM climate but present-day (PD) land cover. Using LGM climate and land cover leads to colder and drier summer conditions around the Alps and warmer and drier climate in southeastern Europe compared to LGM climate determined by PD land cover. This finding demonstrates that LGM land cover plays an important role in regulating the regional climate. Therefore, realistic glacial land cover estimates are needed to accurately simulate regional glacial climate states in areas with interplays between complex topography, large ice sheets, and diverse land cover, as observed in Europe.

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Section: Influence Of External Forcing and Land Cover On Climatesupporting

confidence: 49%

Section: Introductionmentioning

confidence: 99%

The role of land cover in the climate of glacial Europe

et al. 2021

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“…3a-3d)] was employed to examine trends in monthly time series of the temperature datasets. This is a nonparametric statistic that has been used in earlier studies for trend analysis in climate time series data due to its robustness (Akinsanola et al, 2017;Abatan et al, 2016;Agyekum et al, 2018;Shiru et al, 2018;Jia et al, 2019;Mondal et al, 2018;Fu et al, 2013;Akande et al, 2017). The method was therefore utilized in this study to assess the trends and the magnitude of monthly time series data at 5 % significant level ( ).…”

Section: Methodsmentioning

confidence: 99%

Validating Gauge-based Spatial Surface Atmospheric Temperature Datasets for Upper Benue River Basin, Nigeria

Salaudeen

Ismail

Adeogun

et al. 2021

Nig. J. Env. Sci. & Tech.

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Like most other countries of Africa, one of the main problems threatening effective impact modelling in Nigeria including Upper Benue river basin, dwells in lack of high-quality in-situ observation datasets at appropriate spatiotemporal scales. Gridded meteorological variables can serve as promising alternatives to in-situ measurements in data sparse regions, but then, require validations to assess quantitatively their level of accuracies and reliabilities. As a consequence, this study makes comparative analysis of two gauge-based, spatially interpolated surface atmospheric temperature datasets with in-situ measurements in seven distinct meteorological stations covering the period of 1982-2006. Correspondingly, spatial analysis and statistical measures were used to assess the performances of the gridded datasets from the Climate Research Unit (CRU) and the Climate Prediction Centre (CPC). Results from spatial distributions depict 8, 11 and 10 °C as observed minimum temperatures and 33, 36, 42 °C as observed maximum temperatures over the Cameroon highland (Gembu), the Jos plateau and at the northern fringes of the basin respectively. Consequently, both the CRU and CPC datasets captured remarkably well the observed temperature gradients along the varying topography, though with differing margins. The interannual variabilities indicate CRU dataset to better capture the signs and magnitudes of the observed anomalies as compared to the CPC data. Moreover, the CRU data was noted to be more outstanding in representing the observed features in seasonal temperature variations over most stations. Also, the shapes of the probability density function (PDF) for both datasets in minimum and maximum temperatures measured closely the shapes of the observed PDF. Trend analysis suggests CRU datasets to better represent the warming and the cooling trends than the CPC. Overall, the CRU datasets are the most outstanding in this study and is therefore preferred for water resource application over the study area.

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“…For comparison purposes, the CMIP5 model data and Asfazari data were regridded to a uniform horizontal resolution 0.5 × 0.5 grid using a bilinear interpolation scheme before further processing. This technique is a resampling F I G U R E 3 The climatological area-averaged bias in (a) the annual frost days, (b) TNn, (c) TXn, (d) Tn90p, (e) Tx90p and (f) the warm spell duration index, for each model relative to the observations method that uses the distance-weighted average of the four nearest pixel values to estimate a new pixel value (Zhou and Zhao, 2015;Jia et al, 2019) which has little "smoothing" effect on extreme climatic values (Kopparla et al, 2013).…”

Section: Methodsmentioning

confidence: 99%

Future changes in temperature extremes in climate variability over Iran

Darand

2020

Meteorological Applications

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The objective of the current study was to project changes in extreme temperature indices over Iran by the end of the 21st Century based on the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations. We used six extreme temperature indices recommended and defined by the Expert Team on Climate Change Detection and Indices from a set of 18 CMIP5 model simulations from different modelling centres. The extreme temperature indices simulation results of the models were compared with the gridded extreme temperature indices observations of the Asfazari National Database with a spatial resolution of 15 × 15 km during the period 1962–2005. For comparison purposes, the CMIP5 model data and the Asfazari data were regridded to a uniform horizontal resolution 0.5° × 0.5° grid using a bilinear interpolation scheme before further processing. After first validating the model performance by computing bias, future changes in the period 2006–2100 were projected. The modified Mann Kendall trend test and the Sen slope estimator test were used for evaluating the trend of indices and estimating the change rate, respectively, at a 95% confidence level. The results show that the minimum of the daily minimum temperature (TNn) and the minimum of the daily maximum temperature (TXn) will increase in the future. Warming of night time temperature indices (TNn and TN90p) are projected to be stronger than those of daytime temperature indices (TXn and TX90p). Warming is not uniform across the country, with considerably more warming in the western and southern parts. In terms of the frequency of frost days, a decrease is generally projected in the 21st Century. The largest decrease is projected for the Zagros mountain ranges in the west and the mountainous region in the northeastern part of the country with a magnitude of around 8–10 days⋅decade−1. The number of heat wave days is expected to increase sharply from about 13 days in the reference period (1963–2005) to 100 days by mid‐century to more than 235 days by the end of the century under Representative Concentration Pathway 8.5.

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Assessing the Performance of CMIP5 Global Climate Models for Simulating Future Precipitation Change in the Tibetan Plateau

Cited by 47 publications

References 62 publications

The role of land cover in the climate of glacial Europe

The role of land cover in the climate of glacial Europe

Validating Gauge-based Spatial Surface Atmospheric Temperature Datasets for Upper Benue River Basin, Nigeria

Future changes in temperature extremes in climate variability over Iran

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