Qiaohong Sun

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

Assessing the performance of climate models in surface air temperature (SAT) simulation andprojection have received increasing attention during the recent decades. This paper assesses theperformance of the Coupled Model Intercomparison Project phase 5 (CMIP5) in simulatingintra-annual, annual and decadal temperature over Northern Eurasia from 1901 to 2005. Weevaluate the skill of different multi-model ensemble techniques and use the best technique toproject the future SAT changes under different emission scenarios. The results show that most ofthe general circulation models (GCMs) overestimate the annual mean SAT in Northern Eurasiaand the difference between the observation and the simulations primarily comes from the winterseason. Most of the GCMs can approximately capture the decadal SAT trend; however, theaccuracy of annual SAT simulation is relatively low. The correlation coefficient R between eachGCM simulation and the annual observation is in the range of 0.20 to 0.56. The Taylor diagramshows that the ensemble results generated by the simple model averaging (SMA), reliabilityensemble averaging (REA) and Bayesian model averaging (BMA) methods are superior to anysingle GCM output; and the decadal SAT change generated by SMA, REA and BMA are almostidentical during 1901–2005. Heuristically, the uncertainty of BMA simulation is the smallestamong the three multi-model ensemble simulations. The future SAT projection generated by theBMA shows that the SAT in Northern Eurasia will increase in the 21st century by around1.03°C/100yr, 3.11°C/100yr and 7.14°C/100yr under the RCP 2.6, RCP 4.5 and RCP 8.5scenarios, respectively; and the warming accelerates with the increasing latitude. In addition, thespring season contributes most to the decadal warming occurring under the RCP 2.6 and RCP4.5 scenarios, while the winter season contributes most to the decadal warming occurring underthe RCP 8.5 scenario. Generally, the uncertainty of the SAT projections increases with time inthe 21st century.S Online supplementary data available from stacks.iop.org/ERL/9/055007/mmediaKeywords: CMIP5, multi-model ensembles, Northern Eurasia, temperature

Would the ‘real’ observed dataset stand up? A critical examination of eight observed gridded climate datasets for China

This research compared and evaluated the spatio-temporal similarities and differences of eight widely used gridded datasets. The datasets include daily precipitation over East Asia (EA), the Climate Research Unit (CRU) product, the Global Precipitation Climatology Centre (GPCC) product, the University of Delaware (UDEL) product, Precipitation Reconstruction over Land (PREC/L), the Asian Precipitation Highly Resolved Observational (APHRO) product, the Institute of Atmospheric Physics (IAP) dataset from the Chinese Academy of Sciences, and the National Meteorological Information Center dataset from the China Meteorological Administration (CN05). The meteorological variables focus on surface air temperature (SAT) or precipitation (PR) in China. All datasets presented general agreement on the whole spatio-temporal scale, but some differences appeared for specific periods and regions. On a temporal scale, EA shows the highest amount of PR, while APHRO shows the lowest. CRU and UDEL show higher SAT than IAP or CN05. On a spatial scale, the most significant differences occur in western China for PR and SAT. For PR, the difference between EA and CRU is the largest. When compared with CN05, CRU shows higher SAT in the central and southern Northwest river drainage basin, UDEL exhibits higher SAT over the Southwest river drainage system, and IAP has lower SAT in the Tibetan Plateau. The differences in annual mean PR and SAT primarily come from summer and winter, respectively. Finally, potential factors impacting agreement among gridded climate datasets are discussed, including raw data sources, quality control (QC) schemes, orographic correction, and interpolation techniques. The implications and challenges of these results for climate research are also briefly addressed.

Comparative analysis of CMIP3 and CMIP5 global climate models for simulating the daily mean, maximum, and minimum temperatures and daily precipitation over China

This study assesses the simulations of the daily mean, maximum, and minimum temperatures and daily precipitation over China during the period 1990–1999, based on phase 3 and phase 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5). Fourteen CMIP3 models and 14 CMIP5 models were investigated over eight regions across China. Skill scores quantifying the match between the simulated and observed probability density functions (PDFs) were applied to evaluate the performance of the models. For daily mean, maximum, and minimum temperatures, the results revealed that CMIP3 and CMIP5 models captured the basic pattern of the observed PDFs in all regions. However, the probabilities at lower values were overestimated in most models. In all regions except the west of Northwest China (region 7), all CMIP5 models captured more than 80% of the observed PDFs. Compared with performance at the annual time scale, the models tended to perform relatively worse over the period June to August. The performances of the CMIP5 and CMIP3 models were not as good for daily precipitation as for daily temperature, and the skill scores for precipitation were generally lower than 0.7 in all regions. The amount of drizzle (daily precipitation < 5 mm) was overestimated notably in all regions. The amount of very heavy precipitation (daily precipitation ≥ 20 mm) tended to be underestimated in humid regions but overestimated in arid regions. Compared with CMIP3, CMIP5 models showed some improvements in the simulation of daily mean, maximum, and minimum temperatures, but there was a lack of apparent improvement for simulation of daily precipitation.

Evaluation and application of Bayesian multi-model estimation in temperature simulations

Use of multi-model ensembles from global climate models to simulate the current and future climate change has flourished as a research topic during recent decades. This paper assesses the performance of multi-model ensembles in simulating global land temperature from 1960 to 1999, using Nash-Sutcliffe model efficiency and Taylor diagrams. The future trends of temperature for different scales and emission scenarios are projected based on the posterior model probabilities estimated by Bayesian methods. The results show that ensemble prediction can improve the accuracy of simulations of the spatiotemporal distribution of global temperature. The performance of Bayesian model averaging (BMA) at simulating the annual temperature dynamic is significantly better than single climate models and their simple model averaging (SMA). However, BMA simulation can demonstrate the temperature trend on the decadal scale, but its annual assessment of accuracy is relatively weak. The ensemble prediction presents dissimilarly accurate descriptions in different regions, and the best performance appears in Australia. The results also indicate that future temperatures in northern Asia rise with the greatest speed in some scenarios, and Australia is the most sensitive region for the effects of greenhouse gas emissions. In addition to the uncertainty of ensemble prediction, the impacts of climate change on agriculture production and water resources are discussed as an extension of this research.

Century-scale causal relationships between global dry/wet conditions and the state of the Pacific and Atlantic Oceans

The Granger causality test is used to examine the effects of the El Nino–Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), and the North Atlantic Oscillation (NAO) on global dry/wet conditions. The results show robust relationships between dry/wet conditions and the ocean states, as assessed through a multi-index (standardized precipitation evapotranspiration index and standardized precipitation index) and multiscale (3 months and 12 months) evaluation. The influence of ENSO events is widespread, dominating about 38% of the global land surface (excluding Antarctica). Southern and western North America, northern South America, and eastern Russia are influenced by the PDO. The NAO influences not only dry/wet conditions in Europe but also dry/wet conditions in northern Africa. Similarly, climate variability in southern Europe and northern Africa may be due to the concurrence of the ENSO and the NAO. Knowledge of the spatial influence of ocean states on global dry/wet conditions is valuable for improving drought and flood forecasting.

Linkage Between Hourly Precipitation Events and Atmospheric Temperature Changes over China during the Warm Season

We investigated changes in the temporospatial features of hourly precipitation during the warm season over mainland China. The frequency and amount of hourly precipitation displayed latitudinal zonation, especially for light and moderate precipitation, which showed successive downward change over time in northeastern and southern China. Changes in the precipitation amount resulted mainly from changes in frequency rather than changes in intensity. We also evaluated the linkage between hourly precipitation and temperature variations and found that hourly precipitation extreme was more sensitive to temperature than other categories of precipitation. A strong dependency of hourly precipitation on temperature occurred at temperatures colder than the median daily temperature; in such cases, regression slopes were greater than the Clausius-Clapeyron (C-C) relation of 7% per degree Celsius. Regression slopes for 31.6%, 59.8%, 96.9%, and 99.1% of all stations were greater than 7% per degree Celsius for the 75th, 90th, 99th, and 99.9th percentiles for precipitation, respectively. The mean regression slopes within the 99.9th percentile of precipitation were three times the C-C rate. Hourly precipitation showed a strong negative relationship with daily maximum temperature and the diurnal temperature range at most stations, whereas the equivalent correlation for daily minimum temperature was weak.

A nonstationary bias-correction technique to remove bias in GCM simulations

We developed an updated nonstationary bias-correction method for a monthly global climate model of temperature and precipitation. The proposed method combines two widely used quantile mapping bias-correction methods to eliminate potential illogical values of the variable. Instead of empirical parameter estimation in the more-common quantile mapping method, our study compared bias-correction performance when parametric or nonparametric procedures were used to estimate the probability distribution. The results showed our proposed bias-correction method to be very effective in reducing the model bias: it removed over 80% and 83% of model bias for surface air temperature and precipitation, respectively, during the validation period. Compared with a widely used method of bias correction (delta change), our proposed technique demonstrates improved correction of the distribution of variables. In addition, nonparametric estimation procedures further reduced the mean absolute errors in temperature and precipitation during the validation period by approximately 2% and 0.4%, respectively, compared with parametric procedures. The proposed method can remove over 40% and 60% of the uncertainty from model temperature and precipitation projections, respectively, at the global land scale.

Unraveling anthropogenic influence on the changing risk of heat waves in China

Heat waves trigger substantial social and environmental impacts and even cause massive civilian casualties in extreme cases. Observations show the areas affected by heat waves have increased over China, with the most extreme heat wave occurring during the past five decades. Here we show that both trends can be attributed to anthropogenic influences. We report that under the moderate Representative Concentration Pathways 4.5 scenario, anthropogenic influences will increase the risk of occurrence of the observed maximum Heat Wave Magnitude Index in the late 21st century and will cause a more than tenfold increase in the likelihood of the strongest events on record recurring across more than half China. More than 50% of land area in China is projected to be affected by intense heat waves. Our results show that over eastern China, the extremes in heat distribution are more sensitive to precipitation deficits, indicating stronger heat wave amplification trends to occur under drier conditions. The likelihood of concurrent droughts and heat waves is expected to increase in large parts of China in the late 21st century.

Record-Breaking Heat in Northwest China in July 2015: Analysis of the Severity and Underlying Causes

Introduction. In July 2015, northwest China experienced an unusually long and intense heat wave, especially in Xinjiang Autonomous Region (Fig. 19.1a). Maximum daily temperatures (TMX) exceeded 40°C on a record-breaking number of July days in 50 out of 88 counties in Xinjiang, and historical TMX records were broken in 28 counties. The highest TMX was 47.7°C in Turpan. This year also smashed the historical records of heat wave duration in 51 counties. Our paper poses three questions: How extreme was the heat in Northwest China in July 2015 in a historical context? What factors led to the record-breaking heat? Did human-induced climate change increase the odds of abnormally high July heat in Xinjiang?

The nonstationary impact of local temperature changes and ENSO on extreme precipitation at the global scale

The El Niño–Southern Oscillation (ENSO) and local temperature are important drivers of extreme precipitation. Understanding the impact of ENSO and temperature on the risk of extreme precipitation over global land will provide a foundation for risk assessment and climate-adaptive design of infrastructure in a changing climate. In this study, nonstationary generalized extreme value distributions were used to model extreme precipitation over global land for the period 1979–2015, with ENSO indicator and temperature as covariates. Risk factors were estimated to quantify the contrast between the influence of different ENSO phases and temperature. The results show that extreme precipitation is dominated by ENSO over 22% of global land and by temperature over 26% of global land. With a warming climate, the risk of high-intensity daily extreme precipitation increases at high latitudes but decreases in tropical regions. For ENSO, large parts of North America, southern South America, and southeastern and northeastern China are shown to suffer greater risk in El Niño years, with more than double the chance of intense extreme precipitation in El Niño years compared with La Niña years. Moreover, regions with more intense precipitation are more sensitive to ENSO. Global climate models were used to investigate the changing relationship between extreme precipitation and the covariates. The risk of extreme, high-intensity precipitation increases across high latitudes of the Northern Hemisphere but decreases in middle and lower latitudes under a warming climate scenario, and will likely trigger increases in severe flooding and droughts across the globe. However, there is some uncertainties associated with the influence of ENSO on predictions of future extreme precipitation, with the spatial extent and risk varying among the different models.