The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data

We study the influence of station network density on the distributions and trends in indices of area-average daily precipitation and temperature in the E-OBS high resolution gridded dataset of daily climate over Europe, which was produced with the primary purpose of Regional Climate Model evaluation. Area averages can only be determined with reasonable accuracy from a sufficiently large number of stations within a grid-box. However, the station network on which E-OBS is based comprises only 2,316 stations, spread unevenly across approximately 18,000 0.22° grid-boxes. Consequently, grid-box data in E-OBS are derived through interpolation of stations up to 500 km distant, with the distance of stations that contribute significantly to any grid-box value increasing in areas with lower station density. Since more dispersed stations have less shared variance, the resultant interpolated values are likely to be over-smoothed, and extreme daily values even more so. We perform an experiment over five E-OBS grid boxes for precipitation and temperature that have a sufficiently dense local station network to enable a reasonable estimate of the area-average. We then create a series of randomly selected station sub-networks ranging in size from four to all stations within the E-OBS interpolation search radii. For each sub-network realisation, we estimate the grid-box average applying the same interpolation methodology as used for E-OBS, and then evaluate the effect of network density on the distribution of daily values, as well as trends in extremes indices. The results show that when fewer stations have been used for the interpolation, both precipitation and temperature are over-smoothed, leading to a strong tendency for interpolated daily values to be reduced relative to the “true” area-average. The smoothing is greatest for higher percentiles, and therefore has a disproportionate effect on extremes and any derived extremes indices. For many regions of the E-OBS dataset, the station density is sufficiently low to expect this smoothing effect to be significant and this should be borne in mind by any users of the E-OBS dataset.     

Trends of Temperature Extremes in China and its Relationship with Global temperature anomalies Relationship with Global Temperature Anomalies

Changes of temperature extremes over China were evaluated using daily maximum and minimum temperature data from 591 stations for the period 1961--2002. A set of indices of warm extremes, cold extremes and daily temperature range (DTR) extremes was studied with a focus on trends. The results showed that the frequency of warm extremes (F_WE) increased obviously in most parts of China, and the intensity of warm extremes (I_WE) increased significantly in northern China. The opposite distribution was found in the frequency and intensity of cold extremes. The frequency of high DTR extremes was relatively uniform with that of intensity: an obvious increasing trend was located over western China and the east coast, while significant decreases occurred in central, southeastern and northeastern China; the opposite distribution was found for low DTR extreme days. Seasonal trends illustrated that both F_WE and I_WE showed significant increasing trends, especially over northeastern China and along the Yangtze Valley basin in spring and winter. A correlation technique was used to link extreme temperature anomalies over China with global temperature anomalies. Three key regions were identified, as follows: northeastern China and its coastal areas, the high-latitude regions above 40^oN, and southwestern China and the equatorial eastern Pacific.     

Changes in Seasonal Cycle and Extremes in China during the Period 1960–2008

Recent trends in seasonal cycles in China are analyzed, based on a homogenized dataset of daily temperatures at 541 stations during the period 1960–2008. Several indices are defined for describing the key features of a seasonal cycle, including local winter/summer (LW/LS) periods and local spring/autumn phase (LSP/LAP). The Ensemble Empirical Mode Decomposition method is applied to determine the indices for each year. The LW period was found to have shortened by 2–6 d (10 yr)-1, mainly due to an earlier end to winter conditions, with the LW mean temperature having increased by 0.2°C–0.4°C (10 yr)?1, over almost all of China. Records of the most severe climate extremes changed less than more typical winter conditions did. The LS period was found to have lengthened by 2–4 d (10 yr)?1, due to progressively earlier onsets and delayed end dates of the locally defined hot period. The LS mean temperature increased by 0.1°C–0.2°C (10 yr)-1 in most of China, except for a region in southern China centered on the mid-lower reaches of the Yangtze River. In contrast to the winter cases, the warming trend in summer was more prominent in the most extreme records than in those of more typical summer conditions. The LSP was found to have advanced significantly by about 2 d (10 yr)-1 in most of China. Changes in the autumn phase were less prominent. Relatively rapid changes happened in the 1980s for most of the regional mean indices dealing with winter and in the 1990s for those dealing with summer.     

High-resolution spatiotemporal distribution of precipitation in Iran: a comparative study with three global-precipitation datasets

High-resolution precipitation datasets are used for numerous applications. However, depending on the procedures for obtaining these products, such as number of observations, quality checking, error-correction procedures, and interpolation techniques, they include many uncertainties. Therefore, the accuracy of these products needs to be evaluated over different regions. In this study, the Iranian National Dataset (INDS), a new 1?×?1 km precipitation dataset based on precipitation data of 1,441 quality-controlled stations for the climatic period from 1961 to 2005, was constructed using the digital elevation model, correlation method, and Kriging interpolation procedure. Iran's annual precipitation values at grids and stations were extracted from Climatic Research Unit (CRU) CL 2.0, CRU TS 3.10.01, and WorldClim datasets, and differences between corresponding values in each of the three datasets and INDS were calculated and analyzed. The coefficient of determination (R ²) between the national network stations' data and the CRU CL 2.0, CRU TS 3.10.01, and WorldClim datasets were 0.50, 0.13, and 0.62, respectively. Moreover, R ² values between the grids of each dataset and INDS were 0.51, 0.40, and 0.60, respectively. To determine the global datasets' efficiency for displaying temporal patterns of precipitation, the monthly values gathered from them at 11 stations (as representative of Iran's various precipitation regimes) were compared with the real values at these stations. The results showed that in term of temporal patterns, the concurrences among the three global datasets and the INDS was more acceptable, especially in the case of CRU CL 2.0. In general, it is concluded that the global datasets could be deployed for the primary assessment of the annual precipitation distribution; however, for more precise studies, use of local data is highly recommended.     

Long-Term Trends in Extreme Temperatures in Hong Kong and Southern China

The observed long-term trends in extreme temperatures in Hong Kong were studied based on the meteorological data recorded at the Hong Kong Observatory Headquarters from 1885-2008. Results show that, over the past 124 years, the extreme daily minimum and maximum temperatures, as well as the length of the warm spell in Hong Kong, exhibit statistically significant long-term rising trends, while the length of the cold spell shows a statistically significant decreasing trend. The time-dependent return period analysis also indicated that the return period for daily minimum temperature at 4°C or lower lengthened considerably from 6 years in 1900 to over 150 years in 2000, while the return periods for daily maximum temperature reaching 35°C or above shortened drastically from 32 years in 1900 to 4.5 years in 2000. Past trends in extreme temperatures from selected weather stations in southern China from 1951-2004 were also assessed. Over 70% of the stations studied yielded a statistically significant rising trend in extreme daily minimum temperature, while the trend for extreme maximum temperatures was found to vary, with no significant trend established for the majority of stations.     

Spatial Interpolation of Daily Precipitation in China: 1951--2005

Climate research relies heavily on good quality instrumental data; for modeling efforts gridded data are needed. So far, relatively little effort has been made to create gridded climate data for China. This is especially true for high-resolution daily data. This work, focuses on identifying an accurate method to produce gridded daily precipitation in China based on the observed data at 753 stations for the period 1951--2005. Five interpolation methods, including ordinary nearest neighbor, local polynomial, radial basis function, inverse distance weighting, and ordinary kriging, have been used and compared. Cross-validation shows that the ordinary kriging based on seasonal semi-variograms gives the best performance, closely followed by the inverse distance weighting with a power of 2. Finally the ordinary kriging is chosen to interpolate the station data to a 18 km×18 km grid system covering the whole country. Precipitation for each 0.5o×0.5o latitude-longitude block is then obtained by averaging the values at the grid nodes within the block. Owing to the higher station density in the eastern part of the country, the interpolation errors are much smaller than those in the west (west of 100oE). Excluding 145 stations in the western region, the daily, monthly, and annual relative mean absolute errors of the interpolation for the remaining 608 stations are 74%, 29%, and 16%, respectively. The interpolated daily precipitation has been made available on the internet for the scientific community.     

Does CMIP6 Inspire More Confidence in Simulating Climate Extremes over China?

Based on climate extreme indices calculated from a high-resolution daily observational dataset in China during1961–2005, the performance of 12 climate models from phase 6 of the Coupled Model Intercomparison Project(CMIP6),and 30 models from phase 5 of CMIP(CMIP5), are assessed in terms of spatial distribution and interannual variability. The CMIP6 multi-model ensemble mean(CMIP6-MME) can simulate well the spatial pattern of annual mean temperature,maximum daily maximum temperature, and minimum daily minimum temperature. However, CMIP6-MME has difficulties in reproducing cold nights and warm days, and has large cold biases over the Tibetan Plateau. Its performance in simulating extreme precipitation indices is generally lower than in simulating temperature indices. Compared to CMIP5, CMIP6 models show improvements in the simulation of climate indices over China. This is particularly true for precipitation indices for both the climatological pattern and the interannual variation, except for the consecutive dry days. The arealmean bias for total precipitation has been reduced from 127%(CMIP5-MME) to 79%(CMIP6-MME). The most striking feature is that the dry biases in southern China, very persistent and general in CMIP5-MME, are largely reduced in CMIP6-MME. Stronger ascent together with more abundant moisture can explain this reduction in dry biases. Wet biases for total precipitation, heavy precipitation, and precipitation intensity in the eastern Tibetan Plateau are still present in CMIP6-MME, but smaller, compared to CMIP5-MME.     

Future change in the frequency of warm and cold spells over Pakistan simulated by the PRECIS regional climate model

Climate change caused by anthropogenic activities has generated a variety of research focusing on investigating the past climate, predicting the future climate and quantifying the change in climate extreme events by using different climate models. Climate extreme events are valuable to evaluate the potential impact of climate change on human activities, agriculture and economy and are also useful to monitor the climate change on global scale. Here, a Regional Climate Model (RCM) simulation is used to study the future variations in the temperature extreme indices, particularly change in frequency of warm and cold spells duration over Pakistan. The analyses are done on the basis of simulating two 30 years simulations with the Hadley Center’s RCM PRECIS, at a horizontal resolution of 50 km. Simulation for the period 1961–1990 represents the recent climate and simulation for the period 2071–2100 represents the future climate. These simulations are driven by lateral boundary conditions from HadAM3P GCM of Hadley centre UK. For the validation of model, observed mean, maximum and minimum temperatures for the period 1961–1990 at all the available stations in Pakistan are first averaged and are then compared with the PRECIS averaged grid-box data. Also the observed monthly gridded data set of Climate Research Unit (UK) data is used to validate the model. Temperature indices in the base period as well as in future are then calculated and the corresponding change is observed. Percentile based spatial change of temperature shows that in summer, increase in daily minimum temperature is more as compared to the increase of daily maximum temperature whereas in winter, the change in maximum temperature is high. The occurrence of annual cold spells shows significantly decreasing trend while for warm spells there is slight increasing trend over Pakistan.     

基于ETCCDI指数2017年中国极端温度和降水特征分析

利用中国1961—2017年2419站均一化逐日气候数据,计算了气候变化检测和指数联合专家组定义的26个极端气候指数,分析2017年中国极端温度和降水特征。结果表明：2017年中国区域平均的所有极端高温指数均高于1961—1990年30年平均,所有极端低温指数均低于1961—1990年30年平均。中国区域平均的多个极端温度指数达到或者接近历史极值,其中年最小日最高气温(TXn)和年最小日最低气温(TNn)均达到历史最高值,冷夜(TN10p)、冷昼(TX10p)和持续冷日日数(CSDI)达到历史最低值。年最大日最高气温(TXx)、年最大日最低气温(TNx)、暖夜(TN90p)、霜冻(FD)、冰冻(ID)、热夜(TR)、生长期长度(GSL)排在1961年以来的第2或第3位,其余极端温度指数全部排在了1961年以来前10位。2017年中国区域平均的10个极端降水指数中,有7个指数值处于1961—2017年1个标准差范围内,指示2017年的极端降水接近正常年。     

Evaluation and projection of daily maximum and minimum temperatures over China using the high-resolution NEX-GDDP dataset

A new bias-corrected, statistically downscaled product, the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, has been developed and released to help in understanding climate change at local to regional scales. Here, we evaluate the performance of the NEX-GDDP data in simulating daily maximum temperature (TX) and daily minimum temperature (TN) in the historical period 1961–2005 over China at national and regional scales. Projected future changes in TX and TN are assessed under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 emissions scenarios. Results show that the NEX-GDDP data can capture the basic spatial patterns of TX and TN, but these results underestimate the warming trends of TX and TN from 1961 to 2005 over China. The largest biases are found in western China due to its complex terrain conditions; these biases are 2.33 and 2.21 times larger than those found in eastern China for TX and TN, respectively. The climate projections show that the difference in uncertainties is small between the east and the west, and higher warming changes correspond to greater uncertainties. The increasing trends under the RCP8.5 are 2.22 and 2.31 times the size found under the RCP4.5 by the end of the twenty-first century for TX and TN, respectively. The Tibetan plateau has the fastest warming trend under the two scenarios.

     

中国近40年极端气温和降水的分布特征及年代际差异

利用全国119（154）站1961-2000年逐日平均气温（降水）资料，采用世界气象组织（WMO）最近公布的极端气候指数，分析了中国近40a来极端气温和降水的分布及变化特征。结果表明：40a气温极端冷指数整体呈下降趋势，极端暖指数整体呈上升趋势，表现为气温变暖，与全球变暖一致，北方地区极端气温指数变化最大；比较了1961-1975年和1976-2000年2个子时段各极端气温指数的变化趋势，第一时段表现为气温变冷趋势，第二时段为气温变暖趋势。全国年降水量、中等雨日指数（R75％）、强降水日指数（R95％）和强降水比率指数（R95％tot）的整体线性变化为上升趋势，前2个指数地理差异明显，后2个指数地理差异不明显。在上述2个时段中，第二时段较第一时段的年雨日数减少，但强降水日数和平均降水强度增大，且极端降水正线性变化趋势范围比第一时段也增大。     

Application of Multiple Analysis of Series for Homogenization to Beijing Daily Temperature Series （1960-2006）

Homogenization of climate observations remains a challenge to climate change researchers, especially in cases where metadata (e.g., probable dates of break points) are not always available. To examine the influence of metadata on homogenizing climate data, the authors applied the recently developed Multiple Analysis of Series for Homogenization (MASH) method to the Beijing (BJ) daily temperature series for 1960--2006 in three cases with different references: (1) 13M---considering metadata at BJ and 12 nearby stations; (2) 13NOM---considering the same 13 stations without metadata; and (3) 21NOM---considering 20 further stations and BJ without metadata. The estimated mean annual, seasonal, and monthly inhomogeneities are similar between the 13M and 13NOM cases, while those in the 21NOM case are slightly different. The detected biases in the BJ series corresponding to the documented relocation dates are as low as -0.71^oC, -0.79^oC, and -0.5^oC for the annual mean in the 3 cases, respectively. Other biases, including those undocumented in metadata, are minor. The results suggest that any major inhomogeneity could be detected via MASH, albeit with minor differences in estimating inhomogeneities based on the different references. The adjusted annual series showed a warming trend of 0.337, 0.316, and 0.365^oC (10 yr)^-1 for the three cases, respectively, smaller than the estimate of 0.453^oC (10 yr)^-1 in the original series, mainly due to the relocation-induced biases. The impact of the MASH-type homogenization on estimates of climate extremes in the daily temperature series is also discussed.     

1901~2013年GPCC和CRU降水资料在中国大陆的适用性评估

利用1901~2013年中国大陆地区的气象台站实测降水资料,对东英吉利（East Anglia）大学气候研究中心（Climatic Research Unit,CRU）和全球降水气候中心（Global Precipitation Climatology Centre,GPCC）的降水资料分别从季节、年际和年代际尺度上进行了评估。结果表明：1961~2013年CRU与GPCC降水资料均能较准确地描述中国大陆地区的降水特征,且在东部较西部地区、夏季较冬季与站点实测降水情况更为一致。将中国大陆划分为不同区域并在其季节、年际和年代际时间尺度上通过比较降水偏差绝对值的百分比、均方根误差和相关系数等统计量后发现：CRU在青藏高原和其它较大的山脉附近与站点实测降水的差别较大,且年均降水趋势在西北一带的阿尔金山脉、黄土高原、东南地区和长江下游地区,比实测降水的年均趋势小、甚至出现趋势相反的情况。此外,CRU降水的年代际变化趋势也偏小。而GPCC数据不论是降水量还是降水趋势都更接近实际情况。在1901~1961年,通过与65个长期气象观测站点的降水时间序列比较发现,CRU在110°E以西地区与站点观测的降水资料间的差别较大,而GPCC与站点观测资料的吻合较好。最后,利用1961~2013年两套降水资料和站点实测资料分别计算了标准化降水指数（SPI）,简单分析了中国大陆地区的干旱变化,发现GPCC对旱涝的时空变化特征的描述比CRU更接近站点实际观测;并且CRU也没有反映出1997年夏季中国地区出现的严重干旱情况,而GPCC较为准确地反映出了这一干旱事件特征。因此,本文的研究结果认为,就中国大陆地区长时期降水资料而言,GPCC的适用性优于CRU。     

Spatial and temporal variabilities of rainfall data using functional data analysis

The paper analyzes equivalent data for a low density meteorological station network (spatially discontinuous data) and poor temporal homogeneity of thunderstorm observational data. Due to that, a Regional Climate Model (RegCM) dataset was tested. The Most Unstable Convective Available Potential Energy index value (MUCAPE) above the 200 J kg^?1 threshold was selected as a predictor describing favorable conditions for the occurrence of thunderstorms. The quality of the dataset was examined through a comparison between model results and soundings from several aerological stations in Central Europe. Good, statistically significant (0.05 significance level) results were obtained through correlation analysis; the value of Pearson’s correlation coefficient was above 0.8 in every single case. Then, using methods associated with gridded climatology, data series for 44 weather stations were derived and an analysis of correlation between RegCM modeled data and in situ thunderstorm observations was conducted with coefficients in the range of 0.75–0.90. The possibility of employing the dataset in thunderstorm climatology analysis was checked via a few examples by mapping monthly, seasonal, and annual means. Moreover, long-term variability and trend analysis along with modeled MUCAPE data were tested. As a result, the RegCM modeled MUCAPE gridded dataset was proposed as an easily available, suitable, and valuable predictor for thunderstorm climatology analysis and mapping. Finally, some limitations are discussed and recommendations for further improvements are given.     

Changes in temperature extremes based on a 6-hourly dataset in China from 1961–2005

Changes in Chinese temperature extremes are presented based on a six-hourly surface air temperature dataset for the period 1961--2005. These temperature series are manually observed at 0200, 0800, 1400, and 2000 Beijing Time (LST), and percentile based extreme indices of these time series are chosen for analysis. Although there is a difference in time among the different time zones across China, as more than 80% of the stations are located in two adjacent time zones, these indices for all the stations are called warm (cold) nights (0200 LST), warm (cold) mornings (0800 LST), warm (cold) days (1400 LST), and warm (cold) evenings (2000 LST), respectively for convenience. The frequency of the annual warm extremes has generally increased, while the frequency of the annual cold extremes has decreased, and significant changes are mainly observed in northern China, the Tibetan Plateau, and the southernmost part of China. Based on the national average, annual warm (cold) nights increase (decrease) at a rate of 5.66 (-5.92) d (10 yr)^-1, annual warm (cold) days increase (decrease) at a rate of 3.97 (-2.98) d (10 yr)^-1, and the trends for the annual warm (cold) mornings and evenings are 4.35 (-4.96) and 5.95 (-4.35) d (10 yr)^-1, respectively. For China as a whole, the increasing rates for the occurrence of seasonal warm extremes are larger in the nighttime (0200, 2000 LST) than these in the daytime (0800, 1400 LST), the maximal increase occurs at 2000 LST except in the summer and the minimal increase occurs at 1400 LST except in autumn; the maximal decrease in the occurrence of seasonal cold extremes occurs at 0200 LST and the minimal decrease occurs at 1400 LST.     

Trends in temperature extremes over nine integrated agricultural regions in China, 1961–2011

By characterizing the patterns of temperature extremes over nine integrated agricultural regions (IARs) in China from 1961 to 2011, this study performed trend analyses on 16 extreme temperature indices using a high-resolution (0.5° × 0.5°) daily gridded dataset and the Mann-Kendall method. The results show that annually, at both daytime and nighttime, cold extremes significantly decreased but warm extremes significantly increased across all IARs. Overall, nighttimes tended to warm faster than daytimes. Diurnal temperature ranges (DTR) diminished, apart from the mid-northern Southwest China Region and the mid-Loess Plateau Region. Seasonally, DTR widely diminished across all IARs during the four seasons except for spring. Higher minimum daily minimum temperature (TNn) and maximum daily maximum temperature (TXx), in both summer and winter, were recorded for most IARs except for the Huang-Huai-Hai Region; in autumn, all IARs generally encountered higher TNn and TXx. In all seasons, warming was observed at daytime and nighttime but, again, nighttimes warmed faster than daytimes. The results also indicate a more rapid warming trend in Northern and Western China than in Southern and Eastern China, with accelerated warming at high elevations. The increases in TNn and TXx might cause a reduction in agriculture yield in spring over Northern China, while such negative impact might occur in Southern China during summer. In autumn and winter, however, the negative impact possibly occurred in most of the IARs. Moreover, increased TXx in the Pearl River Delta and Yangtze River Delta is possibly related to rapid local urbanization. Climatically, the general increase in temperature extremes across Chinese IARs may be induced by strengthened Northern Hemisphere Subtropical High or weakened Northern Hemisphere Polar Vortex.

     

Assessment of indices of temperature extremes simulated by multiple CMIP5 models over China

Given that climate extremes in China might have serious regional and global consequences, an increasing number of studies are examining temperature extremes in China using the Coupled Model Intercomparison Project Phase 5 (CMIP5) models. This paper investigates recent changes in temperature extremes in China using 25 state-of-the-art global climate models participating in CMIP5. Thirteen indices that represent extreme temperature events were chosen and derived by daily maximum and minimum temperatures, including those representing the intensity (absolute indices and threshold indices), duration (duration indices), and frequency (percentile indices) of extreme temperature. The overall performance of each model is summarized by a "portrait" diagram based on relative root-mean-square error, which is the RMSE relative to the median RMSE of all models, revealing the multi-model ensemble simulation to be better than individual model for most indices. Compared with observations, the models are able to capture the main features of the spatial distribution of extreme temperature during 1986-2005. Overall, the CMIP5 models are able to depict the observed indices well, and the spatial structure of the ensemble result is better for threshold indices than frequency indices. The spread amongst the CMIP5 models in different subregions for intensity indices is small and the median CMIP5 is close to observations; however, for the duration and frequency indices there can be wide disagreement regarding the change between models and observations in some regions. The model ensemble also performs well in reproducing the observational trend of temperature extremes. All absolute indices increase over China during 1961-2005.     

Mapping near-surface air temperature,pressure, relative humidity and wind speed over Mainland China with high spatiotemporal resolution

As part of a joint effort to construct an atmospheric forcing dataset for mainland China with high spatiotemporal reso- lution, a new approach is proposed to construct gridded near-surface temperature, relative humidity, wind speed and surface pressure with a resolution of 1 km× 1 km. The approach comprises two steps： （1） fit a partial thin-plate smoothing spline with orography and reanalysis data as explanatory variables to ground-based observations for estimating a trend surface; （2） apply a simple kriging procedure to the residual for trend surface correction. The proposed approach is applied to observations collected at approximately 700 stations over mainland China. The generated forcing fields are compared with the corresponding components of the National Centers for Environmental Predic- tion （NCEP） Climate Forecast System Reanalysis dataset and the Princeton meteorological forcing dataset. The comparison shows that, both within the station network and within the resolutions of the two gridded datasets, the interpolation errors of the proposed approach are markedly smaller than the two gridded datasets.     

土壤湿度年际变化对中国区域极端气候事件模拟的影响研究 I. 基于CAM3.1的模式评估

利用NCAR大气模式CAM3.1对中国区域近40年的极端气候事件进行了模拟试验;在此基础上, 利用1961～2000年中国区域452站的逐日最高、最低气温和降水资料, 从气候平均、年际变化和长期变化趋势等方面全面评估了该模式对中国极端气候事件的模拟能力。结果表明:(1)模式对中国区域极端气候指数气候平均态的大尺度空间分布特征具有一定的模拟能力;模式对极端降水指标空间分布的模拟能力较好, 而对极端气温指标的模拟较差;模式对极端气候指标的模拟存在系统性的偏差, 模拟的极端降水的系统性偏差要远大于对极端温度的模拟。(2)模式对极端气温指数的年际变化特征具有较强的模拟能力, 而对极端降水指数的年际变化基本没有模拟能力;模式模拟的各极端降水指标的年际变幅与观测存在较大的偏差。(3)模式较好地模拟出了暖夜和暖昼指数在中国大部分区域的增加趋势, 但变幅较实测偏小;模式对热浪持续指数长期趋势的模拟则相对略差。模式对极端气温指标长期趋势的模拟能力总体优于对极端降水指标的模拟。模式对极端降水频次和中雨日数长期趋势的模拟尚可, 但对持续湿期长期趋势的空间分布模拟较差。研究结果可为该模式用于极端气候的模拟研究提供一定参考。     

Projection of heat waves over China for eight different global warming targets using 12 CMIP5 models

Simulation and projection of the characteristics of heat waves over China were investigated using 12 CMIP5 global climate models and the CN05.1 observational gridded dataset. Four heat wave indices (heat wave frequency, longest heat wave duration, heat wave days, and high temperature days) were adopted in the analysis. Evaluations of the 12 CMIP5 models and their ensemble indicated that the multi-model ensemble could capture the spatiotemporal characteristics of heat wave variation over China. The inter-decadal variations of heat waves during 1961–2005 can be well simulated by multi-model ensemble. Based on model projections, the features of heat waves over China for eight different global warming targets (1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0 °C) were explored. The results showed that the frequency and intensity of heat waves would increase more dramatically as the global mean temperature rise attained higher warming targets. Under the RCP8.5 scenario, the four China-averaged heat wave indices would increase from about 1.0 times/year, 2.5, 5.4, and 13.8 days/year to about 3.2 times/year, 14.0, 32.0, and 31.9 days/year for 1.5 and 5.0 °C warming targets, respectively. Those regions that suffer severe heat waves in the base climate would experience the heat waves with greater frequency and severity following global temperature rise. It is also noteworthy that the areas in which a greater number of severe heat waves occur displayed considerable expansion. Moreover, the model uncertainties exhibit a gradual enhancement with projected time extending from 2006 to 2099.