Assessment of CMIP5 climate models and projected temperature changes over Northern Eurasia

Abstract

Assessing the performance of climate models in surface air temperature (SAT) simulation and projection have received increasing attention during the recent decades. This paper assesses the performance of the Coupled Model Intercomparison Project phase 5 (CMIP5) in simulating intra-annual, annual and decadal temperature over Northern Eurasia from 1901 to 2005. We evaluate the skill of different multi-model ensemble techniques and use the best technique to project the future SAT changes under different emission scenarios. The results show that most of the general circulation models (GCMs) overestimate the annual mean SAT in Northern Eurasia and the difference between the observation and the simulations primarily comes from the winter season. Most of the GCMs can approximately capture the decadal SAT trend; however, the accuracy of annual SAT simulation is relatively low. The correlation coefficient R between each GCM simulation and the annual observation is in the range of 0.20 to 0.56. The Taylor diagram shows that the ensemble results generated by the simple model averaging (SMA), reliability ensemble averaging (REA) and Bayesian model averaging (BMA) methods are superior to any single GCM output; and the decadal SAT change generated by SMA, REA and BMA are almost identical during 1901-2005. Heuristically, the uncertainty of BMA simulation is the smallest among the three multi-model ensemble simulations. The future SAT projection generated by the BMA shows that the SAT in Northern Eurasia will increase in the 21st century by around 1.03°C/100 yr, 3.11°C/100 yr and 7.14°C/100 yr under the RCP 2.6, RCP 4.5 and RCP 8.5 scenarios, respectively; and the warming accelerates with the increasing latitude. In addition, the spring season contributes most to the decadal warming occurring under the RCP 2.6 and RCP 4.5 scenarios, while the winter season contributes most to the decadal warming occurring under the RCP 8.5 scenario. Generally, the uncertainty of the SAT projections increases with time in the 21st century.

Future Climate Impact on the Desertification in the Dry Land Asia Using AVHRR GIMMS NDVI3g Data

“…Historical data extends from 1850 to 2005, while the future dataset runs from 2006 to 2100. The three RCP pathways represent low-(RCP 26), medium-(RCP 45) and high-(RCP 85) emission scenarios with radiative forcings of 2.6, 4.5 and 8.5 Wm [20][21][22]. We compare these two methods to obtain the ensemble mean of the 22 climate models.…”

“…The BMA generates a probability density function (PDF), which is a weighted average of the PDFs centered on the forecasts. Thus, BMA weights reflect the relative contributions of the component models to the predictive skill over a training sample [21]. Then, a Taylor diagram is used to summarize multiple aspects of model performance in a single diagram that maps the correlation, centered root mean-square-error (RMSE) and the amplitude of the standard deviations of each model.…”

Optimizing hotspot areas for ecological planning and management based on biodiversity and ecosystem services

“…In addition, climate change (Miao et al, 2014) highlighted the role of ecosystems related to the functions of mitigation and adaption. However, there are few studies on the quantitative estimation of the biodiversity and ecosystem services, as well as the priority areas for planning and management.…”

Evaluation of CMIP5 Global Climate Models over the Volta Basin: Precipitation

“…ey also indicated the high performance of the multimodel ensemble average compared to the individual models. Other studies have evaluated the performance of CMIP5 models over other regions including North Pacific [21], Europe [22][23][24], and Asia (e.g., [25][26][27][28]) and reported high variations in the performances of the GCMs over the different regions.…”