Comparison of GCM Precipitation Predictions with Their RMSEs and Pattern Correlation Coefficients

Yoo, Chulsang; Cho, Eunsaem

doi:10.3390/w10010028

Cited by 19 publications

(17 citation statements)

References 52 publications

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“…We chose five GCMs (IPSL-CM5A-MR, MIROC5, MIROC-ESM-CHEM, MRI-CGCM3, and NorESM1-M) from CMIP5 by comparing the root-mean-square errors (RMSEs) between the historical and observed climate data [23,41]. The smaller RMSEs indicated that the corresponding model performed better [22]. Thus, we considered that the five GCMs performed relatively well [21].…”

Section: Climate Data and Climate Change Scenariosmentioning

confidence: 99%

“…Nicholls [19] concluded that the coastal wetlands would be lost with 5-20% losses by the 2080s in the A1F1 world downscaled from the HadCM3 model. However, previous climate warming scenarios (e.g., SRES [18][19][20]), did not include socioeconomic drivers [21], whereas the RCPs were able to account for the effects of various combinations of economies, technology developments, and demographics [17,22,23]. Additionally, the IPCC 5th Assessment Report pointed out that the simulations of climate change from RCP scenarios would lead to precipitation changes and ice as well as snow melting and the geographic distribution of land and water would be altered to adapt to climate change [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

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Predicting Wetland Distribution Changes under Climate Change and Human Activities in a Mid- and High-Latitude Region

Zhao

Wang

et al. 2018

Sustainability

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Abstract:Wetlands in the mid-and high-latitudes are particularly vulnerable to environmental changes and have declined dramatically in recent decades. Climate change and human activities are arguably the most important factors driving wetland distribution changes which will have important implications for wetland ecological functions and services. We analyzed the importance of driving variables for wetland distribution and investigated the relative importance of climatic factors and human activity factors in driving historical wetland distribution changes. We predicted wetland distribution changes under climate change and human activities over the 21st century using the Random Forest model in a mid-and high-latitude region of Northeast China. Climate change scenarios included three Representative Concentration Pathways (RCPs) based on five general circulation models (GCMs) downloaded from the Coupled Model Intercomparison Project, Phase 5 (CMIP5). The three scenarios (RCP 2.6, RCP 4.5, and RCP 8.5) predicted radiative forcing to peak at 2.6, 4.5, and 8.5 W/m 2 by the 2100s, respectively. Our results showed that the variables with high importance scores were agricultural population proportion, warmness index, distance to water body, coldness index, and annual mean precipitation; climatic variables were given higher importance scores than human activity variables on average. Average predicted wetland area among three emission scenarios were 340,000 ha, 123,000 ha, and 113,000 ha for the 2040s, 2070s, and 2100s, respectively. Average change percent in predicted wetland area among three periods was greatest under the RCP 8.5 emission scenario followed by RCP 4.5 and RCP 2.6 emission scenarios, which were 78%, 64%, and 55%, respectively. Losses in predicted wetland distribution were generally around agricultural lands and expanded continually from the north to the whole region over time, while the gains were mostly associated with grasslands and water in the most southern region. In conclusion, climatic factors had larger effects than human activity factors on historical wetland distribution changes and wetland distributions were predicted to decline remarkably over time under climate change scenarios. Our findings have important implications for wetland resource management and restoration because predictions of future wetland changes are needed for wetlands management planning.

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Section: Climate Data and Climate Change Scenariosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Wetland Distribution Changes under Climate Change and Human Activities in a Mid- and High-Latitude Region

Zhao

Wang

et al. 2018

Sustainability

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“…Description of Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) used in our study and their spatial resolution. The spatial correlation coefficient was generally used to compare the major Empirical Orthogonal Functions (EOFs), which are derived from GCMs data and observation [18][19][20][21]. Kioutsioukis et al applied the root mean square error (RMSE) to compare the differences between models and observation [22].…”

Section: Datamentioning

confidence: 99%

Assessment of Precipitation Simulations in Central Asia by CMIP5 Climate Models

Sun

et al. 2018

Water

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The Coupled Model Intercomparison Project Phase 5 (CMIP5) provides data, which is widely used to assess global and regional climate change. In this study, we evaluated the ability of 37 global climate models (GCMs) of CMIP5 to simulate historical precipitation in Central Asia (CA). The relative root mean square error (RRMSE), spatial correlation coefficient, and Kling-Gupta efficiency (KGE) were used as criteria for evaluation. The precipitation simulation results of GCMs were compared with the Climatic Research Unit (CRU) precipitation in 1986–2005. Most models show a variety of precipitation simulation capabilities both spatially and temporally, whereas the top six models were identified as having good performance in CA, including HadCM3, MIROC5, MPI-ESM-LR, MPI-ESM-P, CMCC-CM, and CMCC-CMS. As the GCMs have large uncertainties in the prediction of future precipitation, it is difficult to find the best model to predict future precipitation in CA. Multi-Model Ensemble (MME) results can give a good simulation of precipitation, and are superior to individual models.

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“…The increase in greenhouse gas (GHGs) emissions from anthropogenic activities is undoubtedly admitted as the major cause of global warming and climate change [2][3][4][5]. The complex inter-relations between the climate system and several natural processes of the earth such as the hydrological cycle, biodiversity, and health of the ecosystems [6], etc., extends the potential impacts of climate change to the existence of several endeavours of humans in a very diverse way [1]. Unfortunately, Africa is identified as one of the most vulnerable regions to impacts of climate change [1, 7,8] and its terrible consequence such as flooding, prolonged drought, and heat wave phenomena have been witnessed frequently over the last few decades.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the coarse resolution of GCMs restricts their capability to capture important climate phenomena such as the influence of orographic and vegetation heterogeneity on climate systems at a regional to local scale. Furthermore, due to the differences in theories, initial boundary conditions, and overall algorithms applied, different climate models provide different climate projections [6,16] which are often reported as significant sources of uncertainty in the climate projections, particularly referring to rainfall projection [33]. Hence, no single global climate model is perfect, and all climate models exhibit different levels of uncertainties accompanying their climate modelling and climate projections.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Climate Models Performance and Associated Uncertainties in Rainfall Projection from CORDEX over the Eastern Nile Basin, Ethiopia

Seid

Bouanani

Kumar

et al. 2022

Climate

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The adverse impact of climate change on different regionally important sectors such as agriculture and hydropower is a serious concern and is currently at the epicentre of global interest. Despite the extensive efforts to project the future climate and assess its potential impact, it is surrounded by uncertainties. This study aimed to assess climate models’ performance and associated uncertainties in rainfall projection over the eastern Nile basin, Ethiopia. Seventeen climate models from Coordinated Regional Climate Downscaling Experiment (CORDEX) and their four ensemble models were evaluated in terms of their historical prediction performance (1986–2005) and future simulation skill (2006–2016) at rainfall station (point location), grid-scale (0.44° × 0.44°) and basin scale. Station-based and spatially interpolated observed rainfall data were used as a reference during climate model performance evaluation. In addition, CRU data was used as an alternative reference data to check the effect of the reference data source on the climate models evaluation process. As the results showed, climate models have a large discrepancy in their projected rainfall and hence prior evaluation of their performance is necessary. For instance, the bias in historical mean annual rainfall averaged over the basin ranges from +760 mm (wet bias) to −582 mm (dry bias). The spatial pattern correlation (r) of climate models output and observed rainfall ranges from −0.1 to 0.7. The ensemble formed with selected (performance-based) member models outperforms the widely used multi-model ensemble in most of the evaluation metrics. This showed the need for reconsidering the widely used multi-model approach in most climate model-based studies. The use of CRU data as a reference resulted in a change in the magnitude of climate model bias. To conclude, each climate model has a certain degree of uncertainty in the rainfall projection, which potentially affects the studies on climate change and its impact (e.g., on water resources). Therefore, climate-related studies have to consider uncertainties in climate projections, which will help end-users (decision-makers) at least to be aware of the potential range of deviation in the future projected outcomes of interest.

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Comparison of GCM Precipitation Predictions with Their RMSEs and Pattern Correlation Coefficients

Cited by 19 publications

References 52 publications

Predicting Wetland Distribution Changes under Climate Change and Human Activities in a Mid- and High-Latitude Region

Predicting Wetland Distribution Changes under Climate Change and Human Activities in a Mid- and High-Latitude Region

Assessment of Precipitation Simulations in Central Asia by CMIP5 Climate Models

Assessment of Climate Models Performance and Associated Uncertainties in Rainfall Projection from CORDEX over the Eastern Nile Basin, Ethiopia

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