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The existence and magnitude of the recently suggested global warming hiatus, or slowdown, have been strongly debated1–3. Although various physical processes4–8 have been examined to elucidate this phenomenon, the accuracy and completeness of observational data that comprise global average surface air temperature (SAT) datasets is a concern9,10. In particular, these datasets lack either complete geographic coverage or in situ observations over the Arctic, owing to the sparse observational network in this area 9 . As a consequence, the contribution of Arctic warming to global SAT changes may have been underestimated, leading to an uncertainty in the hiatus debate. Here, we constructed a new Arctic SAT dataset using the most recently updated global SATs 2 and a drifting buoys based Arctic SAT dataset 11 through employing the ‘data interpolating empirical orthogonal functions’ method 12 . Our estimate of global SAT rate of increase is around 0.112 °C per decade, instead of 0.05 °C per decade from IPCC AR5 1 , for 1998–2012. Analysis of this dataset shows that the amplified Arctic warming over the past decade has significantly contributed to a continual global warming trend, rather than a hiatus or slowdown. The Arctic is under-represented in surface temperature datasets and this could affect estimates of global warming. A new dataset with greater coverage of the Arctic shows a higher warming rate of 0.112 °C per decade compared to 0.005 °C from IPCC AR5.

Decadal prediction experiments of Beijing Climate Center climate system model version 1.1 (BCC-CSM1.1) participated in Coupled Model Intercomparison Project Phase 5 (CMIP5) had poor skill in extratropics of the North Atlantic, the initialization of which was done by relaxing modeled ocean temperature to the Simple Ocean Data Assimilation (SODA) reanalysis data. This study aims to improve the prediction skill of this model by using the assimilation technique in the initialization. New ocean data are firstly generated by assimilating the sea surface temperature (SST) of the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) dataset to the ocean model of BCC-CSM1.1 via Ensemble Optimum Interpolation (EnOI). Then a suite of decadal re-forecasts launched annually over the period 1961-2005 is carried out with simulated ocean temperature restored to the assimilated ocean data. Comparisons between the re-forecasts and previous CMIP5 forecasts show that the re-forecasts are more skillful in mid-to-high latitude SST of the North Atlantic. Improved prediction skill is also found for the Atlantic multi-decadal oscillation (AMO), which is consistent with the better skill of Atlantic meridional overturning circulation (AMOC) predicted by the re-forecasts. We conclude that the EnOI assimilation generates better ocean data than the SODA reanalysis for initializing decadal climate prediction of BCC-CSM1.1 model.

To highlight the compatibility of climate model simulation and proxy reconstruction at different timescales, a timescale separation merging method combining proxy records and climate model simulations is presented. Annual mean surface temperature anomalies for the last millennium (851–2005 AD) at various scales over the land of the Northern Hemisphere were reconstructed with 2° × 2° spatial resolution, using an optimal interpolation (OI) algorithm. All target series were decomposed using an ensemble empirical mode decomposition method followed by power spectral analysis. Four typical components were obtained at inter-annual, decadal, multidecadal, and centennial timescales. A total of 323 temperature-sensitive proxy chronologies were incorporated after screening for each component. By scaling the proxy components using variance matching and applying a localized OI algorithm to all four components point by point, we obtained merged surface temperatures. Independent validation indicates that the most significant improvement was for components at the inter-annual scale, but this became less evident with increasing timescales. In mid-latitude land areas, 10–30% of grids were significantly corrected at the inter-annual scale. By assimilating the proxy records, the merged results reduced the gap in response to volcanic forcing between a pure reconstruction and simulation. Difficulty remained in verifying the centennial information and quantifying corresponding uncertainties, so additional effort should be devoted to this aspect in future research.

Sea ice plays an important role in the air–ice–ocean interaction, but it is often represented simply in many regional atmospheric models. The Noah sea ice scheme, which is the only option in the current Weather Research and Forecasting (WRF) model (version 3.6.1), has a problem of energy imbalance due to its simplification in snow processes and lack of ablation and accretion processes in ice. Validated against the Surface Heat Budget of the Arctic Ocean (SHEBA) in situ observations, Noah underestimates the sea ice temperature which can reach −10 °C in winter. Sensitivity tests show that this bias is mainly attributed to the simulation within the ice when a time-dependent ice thickness is specified. Compared with the Noah sea ice model, the high-resolution thermodynamic snow and ice model (HIGHTSI) uses more realistic thermodynamics for snow and ice. Most importantly, HIGHTSI includes the ablation and accretion processes of sea ice and uses an interpolation method which can ensure the heat conservation during its integration. These allow the HIGHTSI to better resolve the energy balance in the sea ice, and the bias in sea ice temperature is reduced considerably. When HIGHTSI is coupled with the WRF model, the simulation of sea ice temperature by the original Polar WRF is greatly improved. Considering the bias with reference to SHEBA observations, WRF-HIGHTSI improves the simulation of surface temperature, 2 m air temperature and surface upward long-wave radiation flux in winter by 6, 5 °C and 20 W m−2, respectively. A discussion on the impact of specifying sea ice thickness in the WRF model is presented. Consistent with previous research, prescribing the sea ice thickness with observational information results in the best simulation among the available methods. If no observational information is available, we present a new method in which the sea ice thickness is initialized from empirical estimation and its further change is predicted by a complex thermodynamic sea ice model. The ice thickness simulated by this method depends much on the quality of the initial guess of the ice thickness and the role of the ice dynamic processes.

Regional climate models often lack detailed description of ice sheet surface and, as a result, are limited in their capability to provide useful information for Antarctic climate research and field campaigns. In this study, an upgraded scheme of surface physics for Antarctic ice sheet (IST) is developed to improve the surface temperature simulations in Antarctica. Through stand-alone simulations, IST shows advantages over the Noah glacial module, a commonly utilized scheme in the widely used Weather Research and Forecasting (WRF) model. These improvements are mainly attributed to the incorporation of detailed snow physics and optimized surface layer parameterization, which results in better simulations of both the surface albedo in summer and the turbulent sensible heat flux in winter. When coupled with IST instead of Noah, WRF models show improved simulation of surface temperatures throughout the year. The bias and root-mean-square-error of annual mean surface temperatures are reduced from 5.7 and 6.0 to 0.2 and 2.7 K.

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.

A new dataset of surface temperature over North America has been constructed by merging climate model results and empirical tree-ring data through the application of an optimal interpolation algorithm. Errors of both the Community Climate System Model version 4 (CCSM4) simulation and the tree-ring reconstruction were considered to optimize the combination of the two elements. Variance matching was used to reconstruct the surface temperature series. The model simulation provided the background field, and the error covariance matrix was estimated statistically using samples from the simulation results with a running 31-year window for each grid. Thus, the merging process could continue with a time-varying gain matrix. This merging method (MM) was tested using two types of experiment, and the results indicated that the standard deviation of errors was about 0.4 °C lower than the tree-ring reconstructions and about 0.5 °C lower than the model simulation. Because of internal variabilities and uncertainties in the external forcing data, the simulated decadal warm–cool periods were readjusted by the MM such that the decadal variability was more reliable (e.g., the 1940–1960s cooling). During the two centuries (1601–1800 AD) of the preindustrial period, the MM results revealed a compromised spatial pattern of the linear trend of surface temperature, which is in accordance with the phase transition of the Pacific decadal oscillation and Atlantic multidecadal oscillation. Compared with pure CCSM4 simulations, it was demonstrated that the MM brought a significant improvement to the decadal variability of the gridded temperature via the merging of temperature-sensitive tree-ring records.

In this paper, we analyze the two dominant modes of winter surface air temperature (SAT) variations over China and their relationships with large-scale circulation anomalies. We then examine the fidelities of 20 individual models participating in the Coupled Model Inter-comparison Project Phase 5 in reproducing these two perspectives. Results showed that the winter SAT variations over China are dominated by two modes, a homogeneous warming pattern and a tripole pattern with warm departure in Northwest and Northeast China and cold departure in central and southern China. Consistent with the previous studies which documented the variations of the two modes are associated with the Siberian high and Arctic Oscillation (AO) anomalies, respectively, it is newly found that the variation of Empirical Orthogonal Function 2 (EOF2) mode is associated with the Northwest Pacific south–north dipole sea surface temperature anomaly in addition to the AO anomaly. Through comparisons with the observations, we identified that eight models outperform the others in simulating the two dominant modes and their relationships with large-scale circulation anomalies. These high-performing models were then selected to project future winter SAT changes over China under the Representative Concentration Pathway 4.5 (RCP4.5) scenario. Based on the multi-model ensemble mean, a nationwide warming was projected relative to the present climatology (1970–1999), with the largest increase in the Tibetan Plateau of 1.45 ± 0.62 °C by the period 2010–2039 and 2.87 ± 0.82 °C by the period 2050–2079; followed by Northeast China, Northwest China, North China, East China, Southwest China, and, finally, Southeast China.

To mitigate global warming, the call for usingcleanenergy,developinglow-carboneconomyandini-tiatinggreenenvironmentalprotectionhasneverbeenlouder. One of the hot topics, which has receivedwidespreadattentionsintheworld,isthedevelopmentandutilizationofwindenergy.Atthesametime,someoftheclimatechangestudiesfocusonthechangesinglobalwindspeedsandstrongwindswhicharerelatedtowindenergyutilization. Theissues,whicharethesubjects of these studies, can be summarized in thefollowing.

Scientific Reports 6: Article number: 37548; published online: 28 November 2016; updated: 10 April 2017. The Acknowledgements section in this Article is incomplete. “We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP. We thank the climate groups (listed in Table 1) for providing the model output.

Large spread appears in the projection of air-sea CO2 fluxes using the latest simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Here, two methods are applied to narrow this spread in 13 CMIP5 models. One method involves model selection based on the ability of models to reproduce the observed air-sea CO2 fluxes from 1980 to 2005. The other method involves constrained estimation based on the strong relationship between the historical and future air-sea CO2 fluxes. The estimated spread of the projected air-sea CO2 fluxes is effectively reduced by using these two approaches. These two approaches also show great agreement in the global ocean and three regional oceans of the equatorial Pacific Ocean, the North Atlantic Ocean and the Southern Ocean, including the average state and evolution characteristics. Based on the projections of the two approaches, the global ocean carbon uptake will increase in the first half of the 21st century then remain relatively stable and is projected to be 3.68–4.57 PgC/yr at the end of 21st century. The projections indicate that the increase in the CO2 uptake by the oceans will cease at the year of approximately 2070.

The extreme monthly-mean temperatures simulated by 28 models in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are evaluated and compared with those from 24 models in the third phase of the Coupled Model Intercomparison Project (CMIP3). Comparisons with observations and reanalyses indicate that the models from both CMIP3 and CMIP5 perform well in simulating temperature extremes, which are expressed as 20-yr return values. When the climatological annual cycle is removed, the ensemble spread in CMIP5 is smaller than that in CMIP3. Benefitting from a higher resolution, the CMIP5 models perform better at simulating extreme temperatures on the local gridcell scale. The CMIP5 representative concentration pathway (RCP4.5) and CMIP3 B1 experiments project a similar change pattern in the near future for both warm and cold extremes, and the pattern is in agreement with that of the seasonal extremes. By the late twenty-first century, the changes in monthly temperature extremes projected under the three CMIP3 (B1, A1B, and A2) and two CMIP5 (RCP4.5 and RCP8.5) scenarios generally follow the changes in climatological annual cycles, which is consistent with previous studies on daily extremes. Compared with the CMIP3 ensemble, the CMIP5 ensemble shows a larger intermodel uncertainty with regard to the change in cold extremes in snow-covered regions. Enhanced changes in extreme temperatures that exceed the global mean warming are found in regions where the retreat of snow (or the soil moisture feedback effect) plays an important role, confirming the findings for daily temperature extremes.

In this study, the maximum wind speed (WSmax) changes across China from 1956 to 2004 were analyzed based on observed station data, and the changes of WSmax for 2046–2065 and 2080–2099 are projected using three global climate models (GFDL_CM2_0, CCCMA_CGCM3, and MRI_CGCM2) that have participated in the IPCC Fourth Assessment Report (AR4). The observed annual and seasonal WSmax and the frequency of gale days showed obvious declining trends. The annual WSmax decreased by approximately 1.46 m s−1 per decade, and the number of gale days decreased by 3.0 days per decade from 1956 to 2004. The amplitudes of the annual and seasonal WSmax decreases are larger than those of the annual and seasonal average wind speeds (WSavg). The weakening of the East Asian winter and summer monsoons is the cause for the distinct decreases of both WSmax and WSavg over the whole China. The decrease of WSmax in the southeast coastal areas of China is related to the reduced intensity of cold waves in China and the decreasing number (and decreasing intensity) of land-falling typhoons originated in the Northwest Pacific Ocean.

An overview of basic research on climate change in recent years in China is presented. In the past 100 years in China, average annual mean surface air temperature (SAT) has increased at a rate ranging from 0.03°C (10 yr)−1 to 0.12°C (10 yr)−1. This warming is more evident in northern China and is more significant in winter and spring. In the past 50 years in China, at least 27% of the average annual warming has been caused by urbanization. Overall, no significant trends have been detected in annual and/or summer precipitation in China on a whole for the past 100 years or 50 years. Both increases and decreases in frequencies of major extreme climate events have been observed for the past 50 years. The frequencies of extreme temperature events have generally displayed a consistent pattern of change across the country, while the frequencies of extreme precipitation events have shown only regionally and seasonally significant trends. The frequency of tropical cyclone landfall decreased slightly, but the frequency of sand/dust storms decreased significantly. Proxy records indicate that the annual mean SAT in the past a few decades is the highest in the past 400–500 years in China, but it may not have exceeded the highest level of the Medieval Warm Period (1000–1300 AD). Proxy records also indicate that droughts and floods in eastern China have been characterized by continuously abnormal rainfall periods, with the frequencies of extreme droughts and floods in the 20th century most likely being near the average levels of the past 2000 years. The attribution studies suggest that increasing greenhouse gas (GHG) concentrations in the atmosphere are likely to be a main factor for the observed surface warming nationwide. The Yangtze River and Huaihe River basins underwent a cooling trend in summer over the past 50 years, which might have been caused by increased aerosol concentrations and cloud cover. However, natural climate variability might have been a main driver for the mean and extreme precipitation variations observed over the past century. Climate models generally perform well in simulating the variations of annual mean SAT in China. They have also been used to project future changes in SAT under varied GHG emission scenarios. Large uncertainties have remained in these model-based projections, however, especially for the projected trends of regional precipitation and extreme climate events.

This paper examines the capability of three regional climate models (RCMs), i.e., RegCM3 (the International Centre for Theoretical Physics Regional Climate Model), PRECIS (Providing Regional Climates for Impacts Studies) and CMM5 (the fifth-generation Pennsylvania State University-the National Center for Atmospheric Research of USA, NCAR Mesoscale Model) to simulate the near-surface-layer winds (10 m above surface) all over China in the late 20th century. Results suggest that like global climate models (GCMs), these RCMs have the certain capability of imitating the distribution of mean wind speed and fail to simulate the greatly weakening wind trends for the past 50 years in the country. However, RCMs especially RegCM3 have the better capability than that of GCMs to simulate the distribution and change feature of mean wind speed. In view of their merits, these RCMs were used to project the variability of near-surface-layer winds over China for the 21st century. The results show that 1) summer mean wind speed for 2020–2029 will be lower compared to those in 1990–1999 in most area of China; 2) annual and winter mean wind speed for 2081–2100 will be lower than those of 1971–1990 in the whole China; and 3) the changes of summer mean wind speed for 2081–2100 are uncertain. As a result, although climate models are absolutely necessary for projecting climate change to come, there are great uncertainties in projections, especially for wind speed, and these issues need to be further explored.

In the version of this Letter originally published, the increments on the y axis of Fig. 3 were incorrectly labelled as ‘0.0; 0.2; 0.2; 0.3’; they should have read ‘0.0; 0.1; 0.2; 0.3’. This has now been corrected in all versions of the Letter.

Based on two observational data sets in China from 1956 to 2004, wind speed changes are analyzed. The annual mean wind speed (MWS), days of strong wind (SWDs), and maximum wind (MW) all show declining trends over broad areas of China. Only in the southeastern Tibetan Plateau and the regions from the Great Bend of the Yellow River southward to Yunnan and Guangxi Provinces wind speeds are not significantly reduced, but rather, in partial, these regions’ winds speeds are slightly increased. The regions with declining trends match the areas with relatively strong observed winds and the regions without significant declining trends match the areas with light observed winds. In the meantime, the regions with relatively strong winds correspond to areas of reduced days of SWDs. Trends for both increasing intensities and for the number of days of light winds both impact the installation of wind energy facilities. These may be advantageous to the development of wind energy in different regions. Urbanization, the change of anemometers, or relocation of stations are factors that are mildly responsible for the decreasing trend of MWS. The main reason for the decreasing trend is that under the background of global warming, the contrasts of the sea level pressure, and near-surface temperature between the Asian continent and the Pacific Ocean have become significantly smaller, and the east Asian trough has shifted eastward and northward, and has weakened as well. Both East Asian winter and summer monsoons are decreasing, and all of these impacts have resulted in declines of MWS in China.

This article summarizes the main results and findings of studies conducted by Chinese scientists in the past five years. It is shown that observed climate change in China bears a strong similarity with the global average. The country-averaged annual mean surface air temperature has increased by 1.1°C over the past 50 years and 0.5–0.8°C over the past 100 years, slightly higher than the global temperature increase for the same periods. Northern China and winter have experienced the greatest increases in surface air temperature. Although no significant trend has been found in country-averaged annual precipitation, interdecadal variability and obvious trends on regional scales are detectable, with northwestern China and the mid and lower Yangtze River basin having undergone an obvious increase, and North China a severe drought. Some analyses show that frequency and magnitude of extreme weather and climate events have also undergone significant changes in the past 50 years or so. Studies of the causes of regional climate change through the use of climate models and consideration of various forcings, show that the warming of the last 50 years could possibly be attributed to an increased atmospheric concentration of greenhouse gases, while the temperature change of the first half of the 20th century may be due to solar activity, volcanic eruptions and sea surface temperature change. A significant decline in sunshine duration and solar radiation at the surface in eastern China has been attributed to the increased emission of pollutants. Projections of future climate by models of the NCC (National Climate Center, China Meteorological Administration) and the IAP (Institute of Atmospheric Physics, Chinese Academy of Sciences), as well as 40 models developed overseas, indicate a potential significant warming in China in the 21st century, with the largest warming set to occur in winter months and in northern China. Under varied emission scenarios, the country-averaged annual mean temperature is projected to increase by 1.5–2.1°C by 2020, 2.3–3.3°C by 2050, and by 3.9–6.0°C by 2100, in comparison to the 30-year average of 1961–1990. Most models project a 10%–12% increase in annual precipitation in China by 2100, with the trend being particularly evident in Northeast and Northwest China, but with parts of central China probably undergoing a drying trend. Large uncertainty exists in the projection of precipitation, and further studies are needed. Furthermore, anthropogenic climate change will probably lead to a weaker winter monsoon and a stronger summer monsoon in eastern Asia.

Summer temperature extremes over the global land area were investigated by comparing 26 models of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) with observations from the Goddard Institute for Space Studies (GISS) and the Climate Research Unit (CRU). Monthly data of the observations and models were averaged for each season, and statistics were calculated for individual models before averaging them to obtain ensemble means. The summers with temperature anomalies (relative to 1951–1980) exceeding 3σ (σ is based on the local internal variability) are defined as “extremely hot”. The models well reproduced the statistical characteristics evolution, and partly captured the spatial distributions of historical summer temperature extremes. If the global mean temperature increases 2°C relative to the pre-industrial level, “extremely hot” summers are projected to occur over nearly 40% of the land area (multi-model ensemble mean projection). Summers that exceed 5σ warming are projected to occur over approximately 10% of the global land area, which were rarely observed during the reference period. Scenarios reaching warming levels of 3°C to 5°C were also analyzed. After exceeding the 5°C warming target, “extremely hot” summers are projected to occur throughout the entire global land area, and summers that exceed 5σ warming would become common over 70% of the land area. In addition, the areas affected by “extremely hot” summers are expected to rapidly expand by more than 25%/°C as the global mean temperature increases by up to 3°C before slowing to less than 16%/°C as the temperature continues to increase by more than 3°C. The area that experiences summers with warming of 5σ or more above the warming target of 2°C is likely to maintain rapid expansion of greater than 17%/°C. To reduce the impacts and damage from severely hot summers, the global mean temperature increase should remain low.

In this paper, we investigate the role that horizontal resolution plays in the simulation of East Asia precipitation. Two sets of numerical experiments are performed using the Regional Climate Model (RegCM2) nested in one-way mode within the CSIRO global coupled atmosphere-ocean model. In the first set we use the actual RegCM2 topography at the selected model resolutions, which are 45, 60, 90, 120, 180, 240 and 360 km. In the second set of the experiments, the same coarse CSIRO model topography is used in all simulations using the different resolutions of the first set. The results demonstrate that the simulation of East Asian precipitation improves as the horizontal resolution is increased. Moreover, it is shown that the simulations using a higher resolution along with the coarse CSIRO topography perform better than the simulations using a coarser model resolution with corresponding model topography. This suggests that over East Asia adequate spatial resolution to resolve the physical and dynamical processes is more important than topography. Lastly, the results indicate that model resolutions of 60 km or higher are needed to accurately simulate the distribution of precipitation over China and East Asia.

This paper compares the temperature change patterns and the signals of regional aerosol-induced forcing in eastern China and the southeastern US during the latter half of the twentieth century. Both regions show decreasing trends in the mean maximum temperature over recent decades and the cooling effect of Pinatubo volcanic aerosols. In contrast to the southeastern US, we found a slight overall warming trend in eastern China. Our analysis suggests that in addition to greenhouse warming effect, observed high concentrations of absorbing aerosols over eastern China during winter and spring might be one of the major reasons for the observed warming trend.

Regional climate models have become a common research tool for downscaling global climate simulations. To further examine their usefulness for climate studies and the impacts that different physical parameterizations have on the simulations, an intercomparison experiment has been performed where three regional climate models are used to simulate an extreme flood event. Although the dynamical components of the models are almost identical, the physical parameterizations used to represent clouds, radiative transfer, turbulence transport, and surface processes are very different. The models were used to simulate the heavy precipitation during the 1991 summer which caused severe flooding over the Yangtze River in China. This extreme event is selected to highlight the differences among regional climate models. Results from the intercomparison show that all models simulated the gross flood conditions reasonably well, although each model reproduced the observed rainband to varying degrees, and significant differences are found in the simulated energy and hydrological cycles, especially over cloudy areas. Through detailed analyses of model simulations at different spatial and temporal scales, several reasons are found to cause the departure of model simulations from each other. These include the simulation of the amount and vertical distribution of clouds, the treatment of cloud-radiative feedbacks, and the representation of land surface processes. This study suggests that aspects other than surface temperature and precipitation of the regional climate simulations need to be more carefully evaluated. One specially important evaluation criterion is the radiation balance which has serious implications for long-term climate simulations.

Large-scale climate history of the past millennium reconstructed solely from tree-ring data is prone to underestimate the amplitude of low-frequency variability. In this paper, we aimed at solving this problem by utilizing a novel method termed "MDVM", which was a combination of the ensemble empirical mode decomposition (EEMD) and variance matching techniques. We compiled a set of 211 tree-ring records from the extratropical Northern Hemisphere (30-90°N) in an effort to develop a new reconstruction of the annual mean temperature by the MDVM method. Among these dataset, a number of 126 records were screened out to reconstruct temperature variability longer than decadal scale for the period 850-2000 AD. The MDVM reconstruction depicted significant low-frequency variability in the past millennium with evident Medieval Warm Period (MWP) over the interval 950-1150 AD and pronounced Little Ice Age (LIA) cumulating in 1450-1850 AD. In the context of 1150-year reconstruction, the accelerating warming in 20th century was likely unprecedented, and the coldest decades appeared in the 1640s, 1600s and 1580s, whereas the warmest decades occurred in the 1990s, 1940s and 1930s. Additionally, the MDVM reconstruction covaried broadly with changes in natural radiative forcing, and especially showed distinct footprints of multiple volcanic eruptions in the last millennium. Comparisons of our results with previous reconstructions and model simulations showed the efficiency of the MDVM method on capturing low-frequency variability, particularly much colder signals of the LIA relative to the reference period. Our results demonstrated that the MDVM method has advantages in studying large-scale and low-frequency climate signals using pure tree-ring data.

The signing of the UN Framework Convention on Climate Change in Rio de Janeiro in June 1992 by 160 nations has firmly identified global climate change due to human pollution as a pressing global environmental concern. Among the responsibilities that the nations which ratify the Convention will have are the drawing up of inventories of greenhouse gas sources and sinks and the formulation of national strategies to respond to climate change through adaptive and or preventive measures. One requirement for identifying appropriate response strategies will be the undertaking of regional assessments of climate change and its associated impacts. This paper is concerned with climate change in the East Asian region, both over the last 100 years (using instrumental data) and also for the next 100 years (using results from climate model experiments). The juxtaposing of these two analyses, historical and future, enables a better interpretation of the significance of regional climate change to be made. Instrumental temperature and precipitation data for the East Asian region are analysed and compared with the observed global-scale trends in these two variables. Although the region has undoubtedly warmed over the last century, understanding the exact causes of the complex seasonal, diurnal, and spatial dimensions of this warming is difficult. We examine the role of increasing urbanization in inducing rising temperatures and suggest that, although substantial, urban warming cannot account for all of the observed temperature change. The paper also illustrates a flexible composite-model approach to regional climate change scenario construction that avoids the need for multiple transient GCM experiments, which can explicitly incorporate the effects of intermodel uncertainty, and is flexible enough to incorporate new scientific findings and results from new GCM experiments. The scenario presented here suggests that by 2050, mean conditions are expected to be warmer than the extremely warm seasonal anomalies that occurred during the most recent decade in East Asia. Precipitation is estimated to rise over most of the region in all seasons, although the uncertainty range attached to this estimate is much wider than for temperature.

As we know, the projections of future climate change including impacts and strategies in the IPCC Assessment Reports were based on global climate models with scenarios on various human activities. Global climate model simulations provide key inputs for climate change assessments. In this study, the main objective is to analyze if the projections of future climate change by global climate models are reliable. Several workshops have been held on this issue, such as the IPCC expert meeting on assessing and combining multi-model climate projections in January of 2010 (presided by the co-chairs of the IPCC WGI and WGII AR5), and the workshop of the combined global climate model group held by NCAR in June of 2010. Many modeling experts and users took part in those workshops. These series of workshops and conferences on climate models investigated several key issues on advances in determining the reliability of future climate change projections. The description and analysis of the workshop results are intended as guidance for future climate scientists and for impact and adaptation studies by using the results from model inter-comparison projects on global and regional climate change.

Based on the historical and future outputs of 17 coupled model intercomparison project phase 5 (CMIP5) models, simulation of the precipitation extremes in China was evaluated under baseline climate condition compared to a gridded daily observation dataset CN05.1. The variations in precipitation extremes for eight global warming targets were also projected. The 17 individual models and the multi-model ensemble accurately reproduced the spatial distribution of precipitation extremes, although they were limited in their ability to capture the temporal characteristics. A notable dry bias existed in Southeast China, while a wet bias was present in North and Northwest China. The precipitation extremes in China were projected to be more frequent and more intense as global temperature rise reached the 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0 °C warming targets. The projected percentage changes in the annual number of days with precipitation >50 mm (R50) and total precipitation during days in which the daily precipitation exceeds the 99th percentile (R99p) are projected to increase by 25.81 and 69.14 % relative to the baseline climate for a 1.5 °C warming target, and by 95.52 and 162.00 % for a 4.0 °C warming target, respectively. As the global mean temperature rise increased from 1.5 to 5 °C, the subregions considerably affected by the East Asian summer monsoon (e.g., Southwest China, South China, and the Yangtze-Huai River Valley) were projected to experience a more dramatic increase in extreme precipitation events, in both number of days and intensity, while North and Northwest China were projected to suffer from relatively slight increases. The model uncertainties in the projected precipitation extremes in China by 17 CMIP5 models increase as global temperature rise increases.