Based on observations and historical simulations from the fifth phase of the Coupled Model Intercomparison Project(CMIP5) archive, the contributions of human activities(including greenhouse gases(GHGs), anthropogenic aerosols(AAs), and land use(LU)) and external natural forcings(Nat) to climate changes in China over the past 50 years were quantified. Both anthropogenic and external natural forcings account for 95%–99% of the observed temperature change from 1951–1975 to 1981–2005. In particular, the temperature changes induced by GHGs are approximately 2–3 times stronger than the observed changes, and AAs impose a significant cooling effect. The total external forcings can explain 65%–78% of the observed precipitation changes over the past 50 years, in which AAs and GHGs are the primary external forcings leading to the precipitation changes; in particular, AAs dominate the main spatial features of precipitation changes in eastern China. Human activities also dominate the long-term non-linear trends in observed temperature during the past several decades, and, in particular, GHGs, the primary warming contributor, have produced significant warming since the 1960 s. Compared to the long-term non-linear trends in observed precipitation, GHGs have largely caused the wetting changes in the arid-semiarid region since the 1970 s, whereas AAs have led to the drying changes in the humid-semihumid region; both LU and Nat can impose certain impacts on the long-term non-linear trends in precipitation. Using the optimal fingerprinting detection approach, the effects of human activities on the temperature changes can be detected and attributed in China, and the effect of GHGs can be clearly detected from the observations in humid-semihumid areas. However, the anthropogenic effects cannot be detected in the observed precipitation changes, which may be due to the uncertainties in the model simulations and to other issues. Although some results in this paper still need improvement due to uncertainties in the coupled mode
Diurnal temperature range(DTR) is an important measure in studies of climate change and variability. The changes of DTR in different regions are affected by many different factors. In this study, the degree of correlation between the DTR and atmospheric precipitable water(PW) over China is explored using newly homogenized surface weather and sounding observations. The results show that PW changes broadly reflect the geographic patterns of DTR long-term trends over most of China during the period 1970–2012, with significant anticorrelations of trend patterns between the DTR and PW, especially over those regions with higher magnitude DTR trends. PW can largely explain about 40% or more(r2 ≥0.40) of the DTR changes, with a d(PW)/d(DTR) slope of-2% to-10% K-1 over most of northwestern and southeastern China, despite certain seasonal dependencies. For China as whole, the significant anticorrelations between the DTR and PW anomalies range from-0.42 to-0.75, with a d(PW)/d(DTR) slope of-6% to-11% K-1. This implies that long-term DTR changes are likely to be associated with opposite PW changes, approximately following the Clausius-Clapeyron equation. Furthermore, the relationship is more significant in the warm season than in the cold season. Thus, it is possible that PW can be considered as one potential factor when exploring long-term DTR changes over China. It should be noted that the present study has a largely statistical focus and that the underlying physical processes should therefore be examined in future work.
Abstract The authors evaluate the performance of models from Coupled Model Intercomparison Project Phase 5(CMIP5)in simulating the historical(1951-2000)modes of interannual variability in the seasonal mean Northern Hemisphere(NH)500 hPa geopotential height during winter(December-January-February,DJF).The analysis is done by using a variance decomposition method,which is suitable for studying patterns of interannual variability arising from intraseasonal variability and slow variability(time scales of a season or longer).Overall,compared with reanalysis data,the spatial structure and variance of the leading modes in the intraseasonal component are generally well reproduced by the CMIP5 models,with few clear differences between the models.However,there are systematic discrepancies among the models in their reproduction of the leading modes in the slow component.These modes include the dominant slow patterns,which can be seen as features of the Pacific-North American pattern,the North Atlantic Oscillation/Arctic Oscillation,and the Western Pacific pattern.An overall score is calculated to quantify how well models reproduce the three leading slow modes of variability.Ten models that reproduce the slow modes of variability relatively well are identified.
Based on Climatic Research Unit Time Series3.1 temperature and Global Precipitation Climatology Center full data reanalysis version 6 precipitation data,the abilities of climate models from the fifth phase of the Coupled Model Intercomparison Project to simulate climate changes over arid and semiarid areas were assessed.Simulations of future climate changes under different representative concentration pathways(RCPs)were also examined.The key findings were that most of the models are able to capture the dominant features of the spatiotemporal changes in temperature,especially the geographic distribution,during the past 60 years,both globally as well as over arid and semiarid areas.In addition,the models can reproduce the observed warming trends,but with magnitudes generally less than the observations of around0.1–0.3°C/50a.Compared to temperature,the models perform worse in simulating the annual evolution of observed precipitation,underestimating both the variability and tendency,and there is a huge spread among the models in terms of their simulated precipitation results.The multimodel ensemble mean is overall superior to any individual model in reproducing the observed climate changes.In terms of future climate change,an ongoing warming projected by the multi-model ensemble over arid and semiarid areas can clearly be seen under different RCPs,especially under the high emissions scenario(RCP8.5),which is twice that of the moderate scenario(RCP4.5).Unlike the increasing temperature,precipitation changes vary across areas and are more significant under high-emission RCPs,with more precipitation over wet areas but less precipitation over dry areas.In particular,northern China is projected to be one of the typical areas experiencing significantly increased temperature and precipitation in the future.
Quantile regression(QR) is proposed to examine the relationships between large-scale atmospheric variables and all parts of the distribution of daily precipitation amount at Beijing Station from 1960 to 2008. QR is also applied to evaluate the relationship between large-scale predictors and extreme precipitation(90th quantile) at 238 stations in northern China.Finally, QR is used to fit observed daily precipitation amounts for wet days at four sample stations. Results show that meridional wind and specific humidity at both 850 h Pa and 500 h Pa(V850, SH850, V500, and SH500) strongly affect all parts of the Beijing precipitation distribution during the wet season(April–September). Meridional wind, zonal wind, and specific humidity at only 850 h Pa(V850, U850, SH850) are significantly related to the precipitation distribution in the dry season(October–March). Impacts of these large-scale predictors on the daily precipitation amount with higher quantile become stronger, whereas their impact on light precipitation is negligible. In addition, SH850 has a strong relationship with wet-season extreme precipitation across the entire region, whereas the impacts of V850, V500, and SH500 are mainly in semi-arid and semi-humid areas. For the dry season, both SH850 and V850 are the major predictors of extreme precipitation in the entire region. Moreover, QR can satisfactorily simulate the daily precipitation amount at each station and for each season, if an optimum distribution family is selected. Therefore, QR is valuable for detecting the relationship between the large-scale predictors and the daily precipitation amount.
为了探究温室气体(greenhouse gas,GHG)和土地利用/覆盖变化(land use and land cover change,LULCC)对于地面气温日较差(diurnal temperature range,DTR)的影响及相对贡献作用,本文采用耦合地球系统模式(Community Earth System Model)进行了模拟研究。模拟结果表明:GHG浓度的增加导致北半球中高纬度地区年平均DTR显著降低,但GHG引起DTR变化存在显著的季节差异,在暖季和冷季,北美地区和西伯利亚地区呈现出相反的变化特征,GHG增加对于中高纬度地区年平均DTR的降低作用主要是由冷季贡献的。LULCC通过影响叶面积指数和地面反照率显著降低东亚、南亚、欧洲和北美东部地区的DTR。通过创建一种新的分析方式,本文研究了GHG和LULCC对DTR的相对贡献作用,在北半球高纬度地区,GHG在DTR的变化中扮演着主导作用,但在中纬度地区和南亚地区,无论是DTR变化数值的正负符号还是大小,LULCC都起着显著的影响作用。
A number of recent studies have examined trends in extreme temperature indices using a linear regression model based on ordinary least-squares. In this study, quantile regression was, for the first time, applied to examine the trends not only in the mean but also in all parts of the distribution of several extreme temperature indices in China for the period 1960–2008. For China as a whole, the slopes in almost all the quantiles of the distribution showed a notable increase in the numbers of warm days and warm nights, and a significant decrease in the number of cool nights. These changes became much faster as the quantile increased. However, although the number of cool days exhibited a significant decrease in the mean trend estimated by classical linear regression, there was no obvious trend in the upper and lower quantiles. This finding suggests that examining the trends in different parts of the distribution of the time-series is of great importance. The spatial distribution of the trend in the 90 th quantile indicated that there was a pronounced increase in the numbers of warm days and warm nights, and a decrease in the number of cool nights for most of China, but especially in the northern and western parts of China, while there was no significant change for the number of cool days at almost all the stations.
Two land surface models, Community Land Model (CLM3.5) and NOAH model, have been coupled to the Weather Research and Forecasting (WRF) model and been used to simulate the precipitation, temperature, and circulation fields, respectively, over eastern China in a typical flood year (1998). The purpose of this study is to reveal the effects of land surface changes on regional climate modeling. Comparisons of simulated results and observation data indicate that changes in land surface processes have significant impact on spatial and temporal distribution of precipitation and temperature patterns in eastern China. Coupling of the CLM3.5 to the WRF model (experiment WRF-C) substantially improves the simulation results over eastern China relative to an older version of WRF coupled to the NOAH-LSM (experiment WRF-N). It is found that the simulation of the spatial pattern of summer precipitation in WRF-C is better than in WRF-N. WRF-C also significantly reduces the summer positive bias of surface air temperature, and its simulated surface air temperature matches more closely to observations than WRF-N does, which is associated with lower sensible heat fluxes and higher latent heat fluxes in WRF-C.
Two numerical experiments were performed by using the Community Atmosphere Model version 3 (CAM3) with different sea ice datasets to assess the contribution of the decline of Arctic sea ice to warming in the Northern Hemisphere. One experiment was driven by observed sea ice cover data; for the other one, the authors used the sea ice data of the 4×CO2 scenario simulated by the fourth-generation European Centre Hamburg atmospheric general circulation Model of Istituto Nazionale di Geofisica e Vulcanologia, Italy (INGV_ECHAM4). The comparison of the two experiments indicates that the decline of the Arctic sea ice leads to a dramatic warming over the high latitudes of the Northern Hemisphere, characterized by a maximum warming of more than 26℃ over the Arctic region. The significant warming is closely related to the enhanced atmospheric heat source. A 40-60 Wm-2 increase in the apparent heat source was simulated in winter due to the decline of Arctic sea ice. In contrast, no significant change was found in the atmospheric apparent heat source in summer. As a result, the summer temperature change induced by the decline of Arctic sea ice appears to be weak. This study suggests that accurate sea ice cover data is crucial for future climate projection of air temperature in high latitudes.
