Role of the warming trend in global land surface air temperature variations

Global warming and its climatic and environmental effects have mainly been investigated in terms of the absolute warming rate.Little attention has been paid to the contribution of absolute warming rate to variability on various time scales of surface air temperature(SAT),which may be a more direct index for measuring the ecoclimatic effect of warming trend.The present study analyzed the role of secular warming trend in the variations of global land SAT for 1901–2016.Less than one-third of annual SAT variations were contributed by the warming trend over large parts of the globe generally.The ratios were up to two-thirds over eastern South America,parts of South Africa and the regions around the southwestern Mediterranean and Sunda islands where the absolute warming rate was moderate but the endemic species were undergoing exceptional loss of habitat.The ratios also exhibited smallest seasonal difference over these regions.Therefore,the ratio of the warming trend to the SAT variations may be a better measure compared to the absolute warming rate for the local ecoclimate.We should also pay more attention to the regions with high ratio,not only the regions with the high absolute warming rate.

Assessment of Sea Surface Temperature Warming in the Tropical Indian Ocean Simulated by CMIP Models

The tropical Indian Ocean is an important region that affects local and remote climate systems,and the simulation of longterm trends in sea surface temperature(SST)is a major focus of climate research.This study presents a preliminary assessment of multiple model simulations of tropical Indian Ocean SST warming from 1950 to 1999 based on outputs from the 20 Coupled Model Intercomparison Project(CMIP)Phase 5(CMIP5)models and the 36 CMIP 6(CMIP6)models to analyze and compare the warming patterns in historical simulations.Results indicate large discrepancies in the simulations of tropical Indian Ocean SST warming,especially for the eastern equatorial Indian Ocean.The multimodel ensemble mean and most of the individual models generally perform well in reproducing basin-wide SST warming.However,the strength of the SST warming trends simulated by the CMIP5 and CMIP6 models are weaker than those observed,especially for the CMIP6 models.In addition to the general warming trend analysis,decadal trends are also assessed,and a statistical method is introduced to measure the near-term variability in an SST time series.The simulations indicate large decadal variability over the entire tropical Indian Ocean,differing from observations in which significant decadal trend variability is observed only in the southeastern Indian Ocean.In the CMIP model simulations,maximum decadal variability occurs in boreal autumn,but the observations display the minimum and maximum variability in boreal autumn and spring,respectively.

Response of Plant Climatic Productivity to the Warming and Drying Trend in Huanren in the Past 58 Years

[Objective]The research aimed to study the response of plant climatic productivity to warming and drying tendency in Huanren in the past 58 years.[Method]Based on the temperature and precipitation data in Huanren from 1953 to 2010,using trend analysis,Thornthwaite Memorial model and Mann-Kendall detection method,change characteristics of climate and plant climatic productivity in Huanren were analyzed,and the regression evaluation model between plant climatic productivity and temperature and precipitation was established.[Result]Annual average temperature in Huanren presented a significant upward trend,and its linear tendency rate was 0.29℃/10 a;annual precipitation presented a decreasing trend,and its linear tendency rate was-13.29 mm/10 a;dryness presented a declining trend.The warming and drying trend was obvious in Huanren.Plant climatic productivity presented a significant increasing trend,and its linear tendency rate was 8.39 g/（m2·10 a）.Plant climatic productivity was closely related to precipitation and temperature.[Conclusion]The research could provide basis and reference for the adjustment of agricultural structure and sufficiently playing the advantages of climate resources in Huanren.

The 2022 Extreme Heatwave in Shanghai,Lower Reaches of the Yangtze River Valley:Combined Influences of Multiscale Variabilities

In the summer of 2022,China(especially the Yangtze River Valley,YRV)suffered its strongest heatwave(HW)event since 1961.In this study,we examined the influences of multiscale variabilities on the 2022 extreme HW in the lower reaches of the YRV,focusing on the city of Shanghai.We found that about 1/3 of the 2022 HW days in Shanghai can be attributed to the long-term warming trend of global warming.During mid-summer of 2022,an enhanced western Pacific subtropical high(WPSH)and anomalous double blockings over the Ural Mountains and Sea of Okhotsk,respectively,were associated with the persistently anomalous high pressure over the YRV,leading to the extreme HW.The Pacific Decadal Oscillation played a major role in the anomalous blocking pattern associated with the HW at the decadal time scale.Also,the positive phase of the Atlantic Multidecadal Oscillation may have contributed to regulating the formation of the double-blocking pattern.Anomalous warming of both the warm pool of the western Pacific and tropical North Atlantic at the interannual time scale may also have favored the persistency of the double blocking and the anomalously strong WPSH.At the subseasonal time scale,the anomalously frequent phases 2-5 of the canonical northward propagating variability of boreal summer intraseasonal oscillation associated with the anomalous propagation of a weak Madden-Julian Oscillation suppressed the convection over the YRV and also contributed to the HW.Therefore,the 2022 extreme HW originated from multiscale forcing including both the climate warming trend and air-sea interaction at multiple time scales.

Oceanic Origin of A Recent La Nin a-Like Trend in the Tropical Pacific

Global ocean temperature has been rising since the late 1970s at a speed unprecedented during the past century of recordkeeping.This accelerated warming has profound impacts not only on the marine ecosystem and oceanic carbon uptake but also on the global water cycle and climate.During this rapid warming period,the tropical Pacific displays a pronounced La Nin a-like trend,characterized by an intensification of west-east SST gradient and of atmospheric zonal overturning circulation,namely the Walker circulation.This La Nin a-like trend differs from the El Nin o-like trend in warm climate projected by most climate models,and cannot be explained by responses of the global water cycle to warm climate.The results of this study indicate that the intensification of the zonal SST gradient and the Walker circulation are associated with recent strengthening of the upper-ocean meridional overturning circulation.

Regional warming induced by urban surface expansion in Shanghai

To detect the impacts of urban surface expansion on surface air temperature at 2-m（SAT） in Shanghai, China, nested numerical integrations based on satellite-derived urban data between the 1980 s and 2010 s were performed using the Weather Research and Forecasting（WRF） model. Urban surface expansion induced an annual-averaged warming of 0.31 °C from 1980 to 2016 across the whole of Shanghai, showing the greatest intensity between 2010 and 2016. The values were 0.36, 0.78, and 0.75 °C over grids that were classified as urban in both time periods（U2 U）, landuse grids that changed from non-urban to urban（N2 U）, and urban areas（including U2 U and N2 U）, respectively, and revealed weak warming over the inner-ring areas because the urban surfaces had been there since the 1980 s, whereas warming areas were coincident with the outward expansion of the urban surface. Meanwhile, marked seasonal variations could be detected, which were greater in spring and summer but less in autumn and winter. The approximately homogenously distributed SAT maximum（weaker） and heterogeneously SAT minimum（stronger） contributed to the decreased diurnal temperature range. Regional warming induced by urban surface expansion was approximately 0.12 °C per decade, which accounted for 19% of the overall warming across the whole of Shanghai. The values were 0.11 °C per decade and 0.39 °C per decade over U2 U and N2 U, which accounted for approximately 17% and 42% of the overall warming, respectively, and resulted in approximately 41% of the overall warming over urban areas.

Historical Evolution of Global and Regional Surface Air Temperature Simulated by FGOALS-s2 and FGOALS-g2: How Reliable Are the Model Results?

In order to assess the performance of two versions of the IAP/LASG Flexible Global Ocean-Atmosphere- Land System （FGOALS） model, simulated changes in surface air temperature （SAT）, from natural and an- thropogenie forcings, were compared to observations for the period 1850-2005 at global, hemispheric, conti- nental and regional scales. The global and hemispheric averages of SAT and their land and ocean components during 1850-2005 were well reproduced by FGOALS-g2, as evidenced by significant correlation coefficients and small RMSEs. The significant positive correlations were firstly determined by the warming trends, and secondly by interdecadal fluctuations. The abilities of the models to reproduce interdecadal SAT variations were demonstrated by both wavelet analysis and significant positive correlations for detrended data. The observed land-sea thermal contrast change was poorly simulated. The major weakness of FGOALS-s2 was an exaggerated warming response to anthropogenic forcing, with the simulation showing results that were far removed from observations prior to the 1950s. The observations featured warming trends （1906-2005） of 0.71, 0.68 and 0.79℃ （100 yr）-1 for global, Northern and Southern Hemispheric averages, which were overestimated by FGOALS-s2 [1.42, 1.52 and 1.13~C （100 yr）-1] but underestimated by FGOALS-g2 [0.69, 0.68 and 0.73~C （100 yr）-l]. The polar amplification of the warming trend was exaggerated in FGOALS- s2 but weakly reproduced in FGOALS-g2. The stronger response of FGOALS-s2 to anthropogenic forcing was caused by strong sea-ice albedo feedback and water vapor feedback. Examination of model results in 15 selected subcontinental-scale regions showed reasonable performance for FGOALS-g2 over most regions. However, the observed warming trends were overestimated by FGOALS-s2 in most regions. Over East Asia, the meridional gradient of the warming trend simulated by FGOALS-s2 （FGOALS-g2） was stronger （weaker） than observed.

Toward Understanding the Extreme Floods over Yangtze River Valley in June−July 2020:Role of Tropical Oceans

The extreme floods in the Middle/Lower Yangtze River Valley(MLYRV)during June−July 2020 caused more than 170 billion Chinese Yuan direct economic losses.Here,we examine the key features related to this extreme event and explore relative contributions of SST anomalies in different tropical oceans.Our results reveal that the extreme floods over the MLYRV were tightly related to a strong anomalous anticyclone persisting over the western North Pacific,which brought tropical warm moisture northward that converged over the MLYRV.In addition,despite the absence of a strong El Niño in 2019/2020 winter,the mean SST anomaly in the tropical Indian Ocean during June−July 2020 reached its highest value over the last 40 years,and 43%(57%)of it is attributed to the multi-decadal warming trend(interannual variability).Based on the NUIST CFS1.0 model that successfully predicted the wet conditions over the MLYRV in summer 2020 initiated from 1 March 2020(albeit the magnitude of the predicted precipitation was only about one-seventh of the observed),sensitivity experiment results suggest that the warm SST condition in the Indian Ocean played a dominant role in generating the extreme floods,compared to the contributions of SST anomalies in the Maritime Continent,central and eastern equatorial Pacific,and North Atlantic.Furthermore,both the multi-decadal warming trend and the interannual variability of the Indian Ocean SSTs had positive impacts on the extreme floods.Our results imply that the strong multi-decadal warming trend in the Indian Ocean needs to be taken into consideration for the prediction/projection of summer extreme floods over the MLYRV in the future.

Causes and Predictability of the 2021 Spring Southwestern China Severe Drought

In the spring of 2021,southwestern China(SWC)experienced extreme drought,accompanied by the highest seasonal-mean temperature record since 1961.This drought event occurred in the decaying phase of a La Niña event with negative geopotential height anomalies over the Philippine Sea,which is distinct from the historical perspective.Historically,spring drought over SWC is often linked to El Niño and strong western North Pacific subtropical high.Here,we show that the extreme drought in the spring of 2021 may be mainly driven by the atmospheric internal variability and amplified by the warming trend.Specifically,the evaporation increase due to the high temperature accounts for about 30%of drought severity,with the contributions of its linear trend portion being nearly 20%and the interannual variability portion being about 10%.Since the sea surface temperature forcing from the tropical central and eastern Pacific played a minor role in the occurrence of drought,it is a challenge for a climate model to capture the 2021 SWC drought beyond one-month lead times.

Why Does Extreme Rainfall Occur in Central China during the Summer of 2020 after a Weak El Niño?

In summer 2020,extreme rainfall occurred throughout the Yangtze River basin,Huaihe River basin,and southern Yellow River basin,which are defined here as the central China(CC)region.However,only a weak central Pacific(CP)El Niño happened during winter 2019/20,so the correlations between the El Niño–Southern Oscillation(ENSO)indices and ENSO-induced circulation anomalies were insufficient to explain this extreme precipitation event.In this study,reanalysis data and numerical experiments are employed to identify and verify the primary ENSO-related factors that cause this extreme rainfall event.During summer 2020,unusually strong anomalous southwesterlies on the northwest side of an extremely strong Northwest Pacific anticyclone anomaly(NWPAC)contributed excess moisture and convective instability to the CC region,and thus,triggered extreme precipitation in this area.The tropical Indian Ocean(TIO)has warmed in recent decades,and consequently,intensified TIO basinwide warming appears after a weak El Niño,which excites an extremely strong NWPAC via the pathway of the Indo-western Pacific Ocean capacitor(IPOC)effect.Additionally,the ENSO event of 2019/20 should be treated as a fast-decaying CP El Niño rather than a general CP El Niño,so that the circulation and precipitation anomalies in summer 2020 can be better understood.Last,the increasing trend of tropospheric temperature and moisture content in the CC region after 2000 is also conducive to producing heavy precipitation.

Evaluation of ERA-Interim Monthly Temperature Data over the Tibetan Plateau

In this study, surface air temperature from 75 meteorological stations above 3000 m on the Tibetan Plateau are applied for evaluation of the European Centre for Medium-Range Weather Forecasts(ECMWF) third-generation reanalysis product ERA-Interim in the period of 1979-2010. High correlations ranging from 0.973 to 0.999 indicate that ERA-Interim could capture the annual cycle very well. However, an average root-meansquare error(rmse) of 3.7°C for all stations reveals that ERA-Interim could not be applied directly for the individual sites. The biases can be mainly attributed to the altitude differences between ERA-Interim grid points and stations. An elevation correction method based on monthly lapse rates is limited to reduce the bias for all stations. Generally, ERA-Interim captured the Plateau-Wide annual and seasonal climatologies very well. The spatial variance is highly related to the topographic features of the TP. The temperature increases significantly(10°C- 15°C) from the western to the eastern Tibetan Plateau for all seasons, in particular during winter and summer. A significant warming trend(0.49°C/decade) is found over the entire Tibetan Plateau using station time series from 1979-2010. ERA-Interim captures the annual warming trend with an increase rate of 0.33°C /decade very well. The observation data and ERA-Interim data both showed the largest warming trends in winter with values of 0.67°C/decade and 0.41°C/decade, respectively. We conclude that in general ERA-Interim captures the temperature trends very well and ERA-Interim is reliable for climate change investigation over the Tibetan Plateau under the premise of cautious interpretation.

Skillful Seasonal Forecasts of Summer Surface Air Temperature in Western China by Global Seasonal Forecast System Version 5

Variations of surface air temperature （SAT） are key in affecting the hydrological cycle, ecosystems and agriculture in western China in summer. This study assesses the seasonal forecast skill and reliability of SAT in western China, using the GloSea5 operational forecast system from the UK Met Office. Useful predictions are demonstrated, with considerable skill over most regions of western China. The temporal correlation coefficients of SAT between model predictions and observations axe larger than 0.6, in both northwestern China and the Tibetan Plateau. There are two important sources of skill for these predictions in western China： interannual variation of SST in the western Pacific and the SST trend in the tropical Pacific. The tropical SST change in the recent two decades, with a warming in the western Pacific and cooling in the eastern Pacific, which is reproduced well by the forecast system, provides a large contribution to the skill of SAT in northwestern China. Additionally, the interannual variation of SST in the western Pacific gives rise to the reliable prediction of SAT around the Tibetan Plateau. It modulates convection around the Maritime Continent and further modulates the variation of SAT on the Tibetan Plateau via the surrounding circulation. This process is evident irrespective of detrending both in observations and the model predictions, and acts as a source of skill in predictions for the Tibetan Plateau. The predictability and reliability demonstrated in this study is potentially useful for climate services providing early warning of extreme climate events and could imply useful economic benefits.

Adaptive and local analysis of climate data

This paper focuses on how to extract physically meaningful information from climate data,with emphases placed on adaptive and local analysis. It is argued that many traditional statistical analysis methods with rigorous mathematical footing may not be efficient in extracting essential physical information from climate data;rather,adaptive and local analysis methods that agree well with fundamental physical principles are more capable of capturing key information of climate data. To illustrate the improved power of adaptive and local analysis of climate data,we also introduce briefly the empirical mode decomposition and its later developments.

Analysis on the Variation Characteristics of Temperature in Wengyuan from 1961 to 2018

Based on the monthly average temperature data from Wengyuan national meteorological observation station in Guangdong Province from 1961 to 2018, statistical methods were used for the trend analysis, mutation detection and wavelet analysis of annual average temperature and seasonal average temperature from 1961 to 2018. The results show that the annual, summer, autumn and winter average temperature in Wengyuan all showed a warming trend from 1961 to 2018, among which winter average temperature contributed the most to the increase of annual average temperature, and the increasing trend of average temperature in spring did not pass the significance test. The annual, summer, autumn and winter average temperature in Wengyuan changed significantly in 1994, 1981, 1996 and 1990, respectively. Wavelet analysis shows that the annual average temperature in Wengyuan had periodic oscillations on the time scales of 12-13, 24-25 and 6 years.

Could CMIP6 climate models reproduce the early-2000s global warming slowdown?

The unexpected global warming slowdown during 1998–2013 challenges the existing scientific understanding of global temperature change mechanisms,and thus the simulation and prediction ability of state-of-the-art climate models since most models participating in phase 5 of the Coupled Model Intercomparison Project(CMIP5)cannot simulate it.Here,we examine whether the new-generation climate models in CMIP6 can reproduce the recent global warming slowdown,and further evaluate their capacities for simulating key-scale natural variabilities which are the most likely causes of the slowdown.The results show that although the CMIP6 models present some encouraging improvements when compared with CMIP5,most of them still fail to reproduce the warming slowdown.They considerably overestimate the warming rate observed in 1998–2013,exhibiting an obvious warming acceleration rather than the observed deceleration.This is probably associated with their deficiencies in simulating the distinct temperature change signals from the human-induced long-term warming trend and/or the three crucial natural variabilities at interannual,interdecadal,and multidecadal scales.In contrast,the 4 models that can successfully reproduce the slowdown show relatively high skills in simulating the long-term warming trend and the three keyscale natural variabilities.Our work may provide important insight for the simulation and prediction of near-term climate changes.

PROCESS OF TRANSITION BETWEEN COLD AND WARM PERIODS AND ITS PREDICTION

The oscillation of multi-time scales and the process of transition between cold and warm periods over most parts of China and its 6 regions (the Northeast,North China,Changjiang River Valley,South China,the Southwest,the Northwest) were analyzed with wavelet transformation and by computing the variances of the wavelet components for the temperature grade series during January 191I to February 2001,The prediction model for cold and warm periods has been developed and the trend of cold and warm change in the coming 10 years is predicted.The results show that the oscillation with periods of around 30-40 years was the strongest in the last 100 years and the 3-year oscillation in both winter and summer was also stronger,especially in winter. The transition time of cold and warm periods in terms of winter mean did not coincide with that of annual mean,but the difference between summer mean and annual mean is less.The processes of transition of 6 regions are somewhat different,their main characteristics are that the beginning year of significant warming for 1980s to 1990s was very different for the southern and the northern part of China.It is found that the stronger oscillation with 3-year period causes cooling in Northeast China in recent several winters.The experimental predictions show that the models used in the paper can project the major transition between high and low temperature periods.