Contribution of solar radiation to decadal temperature variability over land.

Global air temperature has become the primary metric for judging global climate change. The variability of global temperature on a decadal timescale is still poorly understood. This paper examines further one suggested hypothesis, that variations in solar radiation reaching the surface (Rs) have caused much of the observed decadal temperature variability. Because Rs only heats air during the day, its variability is plausibly related to the variability of diurnal temperature range (daily maximum temperature minus its minimum). We show that the variability of diurnal temperature range is consistent with the variability of Rs at timescales from monthly to decadal. This paper uses long comprehensive datasets for diurnal temperature range to establish what has been the contribution of Rs to decadal temperature variability. It shows that Rs over land globally peaked in the 1930s, substantially decreased from the 1940s to the 1970s, and changed little after that. Reduction of Rs caused a reduction of more than 0.2 °C in mean temperature during May to October from the 1940s through the 1970s, and a reduction of nearly 0.2 °C in mean air temperature during November to April from the 1960s through the 1970s. This cooling accounts in part for the near-constant temperature from the 1930s into the 1970s. Since then, neither the rapid increase in temperature from the 1970s through the 1990s nor the slowdown of warming in the early twenty-first century appear to be significantly related to changes of Rs.

Developed and developing world responsibilities for historical climate change and CO2 mitigation.

At the United Nations Framework Convention on Climate Change Conference in Cancun, in November 2010, the Heads of State reached an agreement on the aim of limiting the global temperature rise to 2 °C relative to preindustrial levels. They recognized that long-term future warming is primarily constrained by cumulative anthropogenic greenhouse gas emissions, that deep cuts in global emissions are required, and that action based on equity must be taken to meet this objective. However, negotiations on emission reduction among countries are increasingly fraught with difficulty, partly because of arguments about the responsibility for the ongoing temperature rise. Simulations with two earth-system models (NCAR/CESM and BNU-ESM) demonstrate that developed countries had contributed about 60-80%, developing countries about 20-40%, to the global temperature rise, upper ocean warming, and sea-ice reduction by 2005. Enacting pledges made at Cancun with continuation to 2100 leads to a reduction in global temperature rise relative to business as usual with a 1/3-2/3 (CESM 33-67%, BNU-ESM 35-65%) contribution from developed and developing countries, respectively. To prevent a temperature rise by 2 °C or more in 2100, it is necessary to fill the gap with more ambitious mitigation efforts.

Constrained future brightening of solar radiation and its implication for China's solar power.

As Earth's primary energy source, surface downward solar radiation (R s) determines the solar power potential and usage for climate change mitigation. Future projections of R s based on climate models have large uncertainties that interfere with the efficient deployment of solar energy to achieve China's carbon-neutrality goal. Here we assess 24 models in the latest Coupled Model Intercomparison Project Phase 6 with historical observations in China and find systematic biases in simulating historical R s values likely due to model biases in cloud cover and clear-sky radiation, resulting in largely uncertain projections for future changes in R s. Based on emergent constraints, we obtain credible R s with narrowed uncertainties by ∼56% in the mid-twenty-first century and show that the mean R s change during 2050-2069 relative to 1995-2014 is 30% more brightening than the raw projections. Particularly in North China and Southeast China with higher power demand, the constrained projections present more significant brightening, highlighting the importance of considering the spatial changes in future Rs when locating new solar energy infrastructures.

Carbon mitigation potential afforded by rooftop photovoltaic in China.

Rooftop photovoltaics (RPVs) are crucial in achieving energy transition and climate goals, especially in cities with high building density and substantial energy consumption. Estimating RPV carbon mitigation potential at the city level of an entire large country is challenging given difficulties in assessing rooftop area. Here, using multi-source heterogeneous geospatial data and machine learning regression, we identify a total of 65,962 km2 rooftop area in 2020 for 354 Chinese cities, which represents 4 billion tons of carbon mitigation under ideal assumptions. Considering urban land expansion and power mix transformation, the potential remains at 3-4 billion tons in 2030, when China plans to reach its carbon peak. However, most cities have exploited less than 1% of their potential. We provide analysis of geographical endowment to better support future practice. Our study provides critical insights for targeted RPV development in China and can serve as a foundation for similar work in other countries.

Mitigation of China's carbon neutrality to global warming.

Projecting mitigations of carbon neutrality from individual countries in relation to future global warming is of great importance for depicting national climate responsibility but is poorly quantified. Here, we show that China's carbon neutrality (CNCN) can individually mitigate global warming by 0.48 °C and 0.40 °C, which account for 14% and 9% of the global warming over the long term under the shared socioeconomic pathway (SSP) 3-7.0 and 5-8.5 scenarios, respectively. Further incorporating changes in CH4 and N2O emissions in association with CNCN together will alleviate global warming by 0.21 °C and 0.32 °C for SSP1-2.6 and SSP2-4.5 over the long term, and even by 0.18 °C for SSP2-4.5 over the mid-term, but no significant impacts are shown for all SSPs in the near term. Divergent responses in alleviated warming are seen at regional scales. The results provide a useful reference for the global stocktake, which assesses the collective progress towards the climate goals of the Paris Agreement.

Contrast patterns and trends of lapse rates calculated from near-surface air and land surface temperatures in China from 1961 to 2014.

The near-surface lapse rate reflects the atmospheric stability above the surface. Lapse rates calculated from land surface temperature (γTs) and near-surface air temperature (γTa) have been widely used. However, γTs and γTa have different sensitivity to local surface energy balance and large-scale energy transport and therefore they may have diverse spatial and temporal variability, which has not been clearly illustrated in existing studies. In this study, we calculated and compared γTa and γTs at ~ 2200 stations over China from 1961 to 2014. This study finds that γTa and γTs have a similar multiyear national average (0.53 °C/100 m) and seasonal cycle. Nevertheless, γTs shows steeper multiyear average than γTa at high latitudes, and γTs in summer is steeper than γTa, especially in Northwest China. The North China shows the shallowest γTa and γTs, then inhibiting the vertical diffusion of air pollutants and further reducing the lapse rates due to accumulation of pollutants. Moreover, the long-term trend signs for γTa and γTs are opposite in northern China. However, the trends in γTa and γTs are both negative in Southwest China and positive in Southeast China. Surface incident solar radiation, surface downward longwave radiation and precipitant frequency jointly can account for 80% and 75% of the long-term trends in γTa and γTs in China, respectively, which provides an explanation of trends of γTa and γTs from perspective of surface energy balance.

Provincial and gridded population projection for China under shared socioeconomic pathways from 2010 to 2100.

In response to a growing demand for subnational and spatially explicit data on China's future population, this study estimates China's provincial population from 2010 to 2100 by age (0-100+), sex (male and female) and educational levels (illiterate, primary school, junior-high school, senior-high school, college, bachelor's, and master's and above) under different shared socioeconomic pathways (SSPs). The provincial projection takes into account fertility promoting policies and population ceiling restrictions of megacities that have been implemented in China in recent years to reduce systematic biases in current studies. The predicted provincial population is allocated to spatially explicit population grids for each year at 30 arc-seconds resolution based on representative concentration pathway (RCP) urban grids and historical population grids. The provincial projection data were validated using population data in 2017 from China's Provincial Statistical Yearbook, and the accuracy of the population grids in 2015 was evaluated. These data have numerous potential uses and can serve as inputs in climate policy research with requirements for precise administrative or spatial population data in China.

Considering atmospheric N2O dynamic in SWAT model avoids the overestimation of N2O emissions in river networks.

Modeling studies have focused on N2O emissions in temperate rivers under static atmospheric N2O (N2Oairc), with cold temperate river networks under dynamic N2Oairc receiving less attention. To address this knowledge and methodological gap, the dissolved N2O concentration (N2Odisc) and N2Oairc algorithms were integrated with an air-water gas exchange model (FN2O) into the SWAT (Soil and Water Assessment Tool). This new model (SWAT-FN2O) allows users to simulate daily riverine N2O emissions under dynamic atmospheric N2O. The spatiotemporal fluctuations in the riverine N2O emissions was simulated and its response to the static and dynamic atmospheric N2O were analyzed in a middle-high latitude agricultural watershed in northeastern China. The results show that the SWAT-FN2O model is a useful method for capturing the hotspots in riverine N2O emissions. The model showed strong riverine N2O absorption and weak N2O emissions from September to February, which acted as a sink for atmospheric N2O in this cold temperate area. High N2O emissions occurred from April to July, which accounted for 83.34% of the yearly emissions. Spatial analysis indicated that the main stream and its tributary could contribute 302.3-1043.7 and 41.5-163.4 µg N2O/(m2·d) to the total riverine N2O emissions (15.02 t/a), respectively. The riverine N2O emissions rates in the subbasins dominated by forests and paddy fields were lower than those in the subbasins dominated by arable and residential land. Riverine N2O emissions can be overestimated under the static atmospheric N2O rather than under the increasing atmospheric N2O. This overestimation has increased from 1.52% to 23.97% from 1990 to 2016 under the static atmospheric N2O. The results of this study are valuable for water quality and future climate change assessments that aim to protect aquatic and atmospheric environments.

Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism.

Air pollution over China has attracted wide interest from public and academic community. PM2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM2.5 concentrations are essential to understand the variability of PM2.5 and seek methods to control PM2.5. Since 2013, the measurement of PM2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM2.5 concentrations. We start with an introduction of general meteorological conditions and PM2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM2.5 concentrations. Next, major methods used to quantify meteorological influences on PM2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM2.5. The feedback effects of PM2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM2.5 pollution are made finally.

Exploring the Holiday Effect on Air Temperatures.

Anthropogenic emissions are generally lower during holidays than they are on workdays, this pattern is expected to result in temperature variations. Variations in the daily maximum (Tmax), mean (Tmean) and minimum (Tmin) air temperatures and the diurnal temperature range (DTR) during the Chinese New Year holiday are evaluated with two methods using daily meteorological observations collected at 2200 stations in China from 1961 to 2015. These two methods yield nearly equivalent results that reflect strong variations in the defined holiday effects. During the period from 1961 to 1980, Tmean, Tmax, Tmin and the DTR all exhibit cooling holiday effects, this effect as measured by the DTR disappears during the period from 1981 to 2000. However, during the period from 2001 to 2015 warming holiday effects are observed for Tmax and the DTR. The evaluation shows that the holiday effect is neither unique nor statistically significant. These results indicate that the holiday effect is primarily caused by natural atmospheric oscillations, because ΔT oscillates noticeably with periods of approximately 7.1 days, 8.5 days and 16.2 days, and these oscillations can account for approximately 75.6% of the variance in ΔT. The oscillation identified here is consistent with the fundamental theory of Rossby wave in the atmosphere.

Coldest Temperature Extreme Monotonically Increased and Hottest Extreme Oscillated over Northern Hemisphere Land during Last 114 Years.

Most studies on global warming rely on global mean surface temperature, whose change is jointly determined by anthropogenic greenhouse gases (GHGs) and natural variability. This introduces a heated debate on whether there is a recent warming hiatus and what caused the hiatus. Here, we presented a novel method and applied it to a 5° × 5° grid of Northern Hemisphere land for the period 1900 to 2013. Our results show that the coldest 5% of minimum temperature anomalies (the coldest deviation) have increased monotonically by 0.22 °C/decade, which reflects well the elevated anthropogenic GHG effect. The warmest 5% of maximum temperature anomalies (the warmest deviation), however, display a significant oscillation following the Atlantic Multidecadal Oscillation (AMO), with a warming rate of 0.07 °C/decade from 1900 to 2013. The warmest (0.34 °C/decade) and coldest deviations (0.25 °C/decade) increased at much higher rates over the most recent decade than last century mean values, indicating the hiatus should not be interpreted as a general slowing of climate change. The significant oscillation of the warmest deviation provides an extension of previous study reporting no pause in the hottest temperature extremes since 1979, and first uncovers its increase from 1900 to 1939 and decrease from 1940 to 1969.

Spatiotemporal Divergence of the Warming Hiatus over Land Based on Different Definitions of Mean Temperature.

Existing studies of the recent warming hiatus over land are primarily based on the average of daily minimum and maximum temperatures (T2). This study compared regional warming rates of mean temperature based on T2 and T24 calculated from hourly observations available from 1998 to 2013. Both T2 and T24 show that the warming hiatus over land is apparent in the mid-latitudes of North America and Eurasia, especially in cold seasons, which is closely associated with the negative North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) and cold air propagation by the Arctic-original northerly wind anomaly into mid-latitudes. However, the warming rates of T2 and T24 are significantly different at regional and seasonal scales because T2 only samples air temperature twice daily and cannot accurately reflect land-atmosphere and incoming radiation variations in the temperature diurnal cycle. The trend has a standard deviation of 0.43 °C/decade for T2 and 0.41 °C/decade for T24, and 0.38 °C/decade for their trend difference in 5° × 5° grids. The use of T2 amplifies the regional contrasts of the warming rate, i.e., the trend underestimation in the US and overestimation at high latitudes by T2.

Contribution of Atmospheric Diffusion Conditions to the Recent Improvement in Air Quality in China.

This study analyzed hourly mass concentration observations of PM2.5 (particulate matters with diameter less than 2.5 µm) at 512 stations in China from December 2013 to May 2015. We found that the mean concentrations of PM2.5 during the winter and spring of 2015 Dec. 2014 to Feb. 2015 and Mar. 2015 to May 2015) decreased by 20% and 14% compared to the previous year, respectively. Hazardous air-quality days decreased by 11% in 2015 winter, with more frequent good to unhealthy days; and the good and moderate air-quality days in 2015 spring increased by 9% corresponding to the less occurrence of unhealthy conditions. We compared the atmospheric diffusion conditions during these two years and quantified its contribution to the improvement of air quality during the first half of 2015 over China. Our results show that during the 2015 winter and spring, 70% and 57% of the 512 stations experienced more favorable atmospheric diffusion conditions compared to those of previous year. Over central and northern China, approximately 40% of the total decrease in PM2.5 during the 2015 winter can be attributed to the favorable atmospheric diffusion conditions. The atmospheric diffusion conditions during the spring of 2015 were not as favorable as in winter; and the average contributions of the atmospheric conditions were slight.

Regional Contrasts of the Warming Rate over Land Significantly Depend on the Calculation Methods of Mean Air Temperature.

Global analyses of surface mean air temperature (T(m)) are key datasets for climate change studies and provide fundamental evidences for global warming. However, the causes of regional contrasts in the warming rate revealed by such datasets, i.e., enhanced warming rates over the northern high latitudes and the "warming hole" over the central U.S., are still under debate. Here we show these regional contrasts depend on the calculation methods of T(m). Existing global analyses calculate T(m) from daily minimum and maximum temperatures (T2). We found that T2 has a significant standard deviation error of 0.23 °C/decade in depicting the regional warming rate from 2000 to 2013 but can be reduced by two-thirds using T(m) calculated from observations at four specific times (T4), which samples diurnal cycle of land surface air temperature more often. From 1973 to 1997, compared with T4, T2 significantly underestimated the warming rate over the central U.S. and overestimated the warming rate over the northern high latitudes. The ratio of the warming rate over China to that over the U.S. reduces from 2.3 by T2 to 1.4 by T4. This study shows that the studies of regional warming can be substantially improved by T4 instead of T2.

Sampling biases in datasets of historical mean air temperature over land.

Global mean surface air temperature (Ta) has been reported to have risen by 0.74°C over the last 100 years. However, the definition of mean Ta is still a subject of debate. The most defensible definition might be the integral of the continuous temperature measurements over a day (Td0). However, for technological and historical reasons, mean Ta over land have been taken to be the average of the daily maximum and minimum temperature measurements (Td1). All existing principal global temperature analyses over land rely heavily on Td1. Here, I make a first quantitative assessment of the bias in the use of Td1 to estimate trends of mean Ta using hourly Ta observations at 5600 globally distributed weather stations from the 1970s to 2013. I find that the use of Td1 has a negligible impact on the global mean warming rate. However, the trend of Td1 has a substantial bias at regional and local scales, with a root mean square error of over 25% at 5° × 5° grids. Therefore, caution should be taken when using mean Ta datasets based on Td1 to examine high resolution details of warming trends.

Measurement biases explain discrepancies between the observed and simulated decadal variability of surface incident solar radiation.

Observations have reported a widespread dimming of surface incident solar radiation (Rs) from the 1950s to the 1980s and a brightening afterwards. However, none of the state-of-the-art earth system models, including those from the Coupled Model Intercomparison Project phase 5 (CMIP5), could successfully reproduce the dimming/brightening rates over China. We find that the decadal variability of observed Rs may have important errors due to instrument sensitivity drifting and instrument replacement. While sunshine duration (SunDu), which is a robust measurement related to Rs, is nearly free from these problems. We estimate Rs from SunDu with a method calibrated by the observed Rs at each station. SunDu-derived Rs declined over China by -2.8 (with a 95% confidence interval of -1.9 to -3.7) W m(-2) per decade from 1960 to 1989, while the observed Rs declined by -8.5 (with a 95% confidence interval of -7.3 to -9.8) W m(-2) per decade. The former trend was duplicated by some high-quality CMIP5 models, but none reproduced the latter trend.

Clear sky visibility has decreased over land globally from 1973 to 2007.

Visibility in the clear sky is reduced by the presence of aerosols, whose types and concentrations have a large impact on the amount of solar radiation that reaches Earth's surface. Here we establish a global climatology of inverse visibilities over land from 1973 to 2007 and interpret it in terms of changes in aerosol optical depth and the consequent impacts on incident solar radiation. The aerosol contribution to "global dimming," first reported in terms of strong decreases in measured incident solar radiation up to the mid-1980s, has monotonically increased over the period analyzed. Since that time, visibility has increased over Europe, consistent with reported European "brightening," but has decreased substantially over south and east Asia, South America, Australia, and Africa, resulting in net global dimming over land.