The global energy balance as represented in CMIP6 climate models

Factors determining tropical upper-level cloud radiative effect in the radiative-convective equilibrium framework

Investigation of the major factors determining tropical upper-level cloud radiative effect (TUCRE) is crucial for understanding cloud feedback mechanisms. We examined the TUCRE inferred from the outputs of historical runs and AMIP runs from CMIP6 models employing a radiative-convective equilibrium (RCE). In this study, we incorporated the RCE model configurations of atmospheric dynamics and thermodynamics from the climate models, while simplifying the intricate systems. Using the RCE model, we adjusted the global mean surface temperature to achieve energy balance, considering variations in tropical cloud fraction, regional reflectivity, and emission temperature corresponding to each climate model. Subsequently, TUCRE was calculated as a unit of K/%, representing the change in global mean surface temperature (K) in response to an increment in the tropical upper-level clouds (%). Our RCE model simulation indicates that the major factors determining the TUCRE are the emission temperatures of tropical moist-cloudy and moist-clear regions, as well as the fraction of tropical upper-level clouds. The higher determination coefficients between TUCRE and both the emission temperature of tropical moist regions and the upper-level cloud fraction are attributable to their contribution to the trapping effect on the outgoing longwave radiations, which predominantly determines TUCRE. Consequently, the results of this study underscore the importance of accurately representing the upper-level cloud fraction and emission temperature in tropical moist regions to enhance the representation of TUCRE in climate models.

Natural short-lived halogens exert an indirect cooling effect on climate

Observational evidence shows the ubiquitous presence of ocean-emitted short-lived halogens in the global atmosphere1-3. Natural emissions of these chemical compounds have been anthropogenically amplified since pre-industrial times4-6, while, in addition, anthropogenic short-lived halocarbons are currently being emitted to the atmosphere7,8. Despite their widespread distribution in the atmosphere, the combined impact of these species on Earth's radiative balance remains unknown. Here we show that short-lived halogens exert a substantial indirect cooling effect at present (-0.13 ± 0.03 watts per square metre) that arises from halogen-mediated radiative perturbations of ozone (-0.24 ± 0.02 watts per square metre), compensated by those from methane (+0.09 ± 0.01 watts per square metre), aerosols (+0.03 ± 0.01 watts per square metre) and stratospheric water vapour (+0.011 ± 0.001 watts per square metre). Importantly, this substantial cooling effect has increased since 1750 by -0.05 ± 0.03 watts per square metre (61 per cent), driven by the anthropogenic amplification of natural halogen emissions, and is projected to change further (18-31 per cent by 2100) depending on climate warming projections and socioeconomic development. We conclude that the indirect radiative effect due to short-lived halogens should now be incorporated into climate models to provide a more realistic natural baseline of Earth's climate system.

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 R_s when locating new solar energy infrastructures.

20% of uncertainty in yield estimates could be caused by the radiation source

Solar radiation is the energy for all biological, physical, and chemical processes of the earth's surface system, and affects the growth and development of crops at all stages. But the diverse data sources and fusion algorithms lead to large differences in the radiation values in various climate datasets. Accurate estimates of the radiation data is not an easy task, the uncertainty of which and the impact on crop yield simulation remains unknown. In this study, the total solar radiation amounts from four independent global radiation datasets were shown considerable heterogeneity across regions and cropping seasons. Forcing the dynamic crop models with the four radiation inputs produced similarly great uncertainties of simulated yield in most regions, with the greatest uncertainty up to 30% of average yield for wheat in Europe. The global-scale uncertainty of simulated yield is increasing during the past three decades and would reach up to 20% of its averages in the future, equivalent to 300 million tons when converting to the global crop production. The results of this study suggest that the previously projected crop yield changes with climate change have large uncertainties propagated from solar radiation data sources used for projections. These uncertainties may mislead the assessment of future food security.

Projected Meteorological Drought over Asian Drylands under Different CMIP6 Scenarios

Asia currently has the world’s largest arid and semi-arid zones, so a timely assessment of future droughts in the Asian drylands is prudent, particularly in the context of recent frequent sandstorms. This paper assesses the duration, frequency, and intensity of drought events in the Asian drylands based on nine climate models of the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results show that a high percentage of land area is experiencing significant drought intensification of 65.1%, 89.9%, and 99.8% under Shared Socioeconomic Pathways (SSP)126, SSP245, and SSP585 scenarios, respectively. Furthermore, the data indicate that future droughts will become less frequent but longer in duration and more intense, with even more severe future droughts predicted for northwest China and western parts of Uzbekistan and Kazakhstan. Drought durations of 10.8 months and 13.4 months are anticipated for the future periods of 2021–2060 and 2061–2100, respectively, compared to the duration of 6.6 months for the historical period (1960–2000). Meanwhile, drought intensity is expected to reach 1.37 and 1.66, respectively, for future events compared to 0.97 for the historical period. However, drought severity under SSP245 will be weaker than that under SSP126 due to the mitigating effect of precipitation. The results of this study can provide a basis for the development of adaptation measures in Asian dryland nations.

Increased high-temperature extremes and associated population exposure in Africa by the mid-21st century

Previous studies warned that heat extremes are likely to intensify and frequently occur in the future due to climate change. Apart from changing climate, the population's size and distribution contribute to the total changes in the population exposed to heat extremes. The present study uses the ensemble mean of global climate models from the Coupled Model Inter-comparison Project Phase six (CMIP6) and population projection to assess the future changes in high-temperature extremes and exposure to the population by the middle of this century (2041-2060) in Africa compared to the recent climate taken from 1991 to 2010. Two Shared Socioeconomic Pathways (SSPs), namely SSP2-4.5 and SSP5-8.5, are used. Changes in population exposure and its contributors are quantified at continental and for various sub-regions. The intensity of high-temperature extremes is anticipated to escalate between 0.25 to 1.8 °C and 0.6 to 4 °C under SSP2-4.5 and SSP5-8.5, respectively, with Sahara and West Southern Africa projected to warm faster than the rest of the regions. On average, warm days' frequency is also expected to upsurge under SSP2-4.5 (26-59%) and SSP5-8.5 (30-69%) relative to the recent climate. By the mid-21st century, continental population exposure is expected to upsurge by ~25% (28%) of the reference period under SSP2-4.5|SSP2 (SSP5-8.5|SSP5). The highest increase in exposure is expected in most parts of West Africa (WAF), followed by East Africa. The projected changes in continental exposure (~353.6 million person-days under SSP2-4.5|SSP2 and ~401.4 million person-days under SSP5-8.5|SSP5) are mainly due to the interaction effect. However, the climate's influence is more than the population, especially for WAF, South-East Africa and East Southern Africa. The study findings are vital for climate change adaptation.

Estimation of Long-Term Surface Downward Longwave Radiation over the Global Land from 2000 to 2018

It is of great importance for climate change studies to construct a worldwide, long-term surface downward longwave radiation (Ld, 4–100 μm) dataset. Although a number of global Ld datasets are available, their low accuracies and coarse spatial resolutions limit their applications. This study generated a daily Ld dataset with a 5-km spatial resolution over the global land surface from 2000 to 2018 using atmospheric parameters, which include 2-m air temperature (Ta), relative humidity (RH) at 1000 hPa, total column water vapor (TCWV), surface downward shortwave radiation (Sd), and elevation, based on the gradient boosting regression tree (GBRT) method. The generated Ld dataset was evaluated using ground measurements collected from AmeriFlux, AsiaFlux, baseline surface radiation network (BSRN), surface radiation budget network (SURFRAD), and FLUXNET networks. The validation results showed that the root mean square error (RMSE), mean bias error (MBE), and correlation coefficient (R) values of the generated daily Ld dataset were 17.78 W m−2, 0.99 W m−2, and 0.96 (p < 0.01). Comparisons with other global land surface radiation products indicated that the generated Ld dataset performed better than the clouds and earth’s radiant energy system synoptic (CERES-SYN) edition 4.1 dataset and ERA5 reanalysis product at the selected sites. In addition, the analysis of the spatiotemporal characteristics for the generated Ld dataset showed an increasing trend of 1.8 W m−2 per decade (p < 0.01) from 2003 to 2018, which was closely related to Ta and water vapor pressure. In general, the generated Ld dataset has a higher spatial resolution and accuracy, which can contribute to perfect the existing radiation products.

Potential Driving Factors on Surface Solar Radiation Trends over China in Recent Years

The annual mean surface solar radiation (SSR) trends under all-sky, clear-sky, all-sky-no-aerosol, and clear-sky-no-aerosol conditions as well as their possible causes are analyzed during 2005–2018 across China based on different satellite-retrieved datasets to determine the major drivers of the trends. The results confirm clouds and aerosols as the major contributors to such all-sky SSR trends over China but play differing roles over sub-regions. Aerosol variations during this period result in a widespread brightening, while cloud effects show opposite trends from south to north. Moreover, aerosols contribute more to the increasing all-sky SSR trends over northern China, while clouds dominate the SSR decline over southern China. A radiative transfer model is used to explore the relative contributions of cloud cover from different cloud types to the all-types-of-cloud-cover-induced (ACC-induced) SSR trends during this period in four typical sub-regions over China. The simulations point out that the decreases in low-cloud-cover (LCC) over the North China Plain are the largest positive contributor of all cloud types to the marked annual and seasonal ACC-induced SSR increases, and the positive contributions from both high-cloud-cover (HCC) and LCC declines in summer and winter greatly contribute to the ACC-induced SSR increases over East China. The contributions from medium-low-cloud-cover (mid-LCC) and LCC variations dominate the ACC-caused SSR trends over southwestern and South China all year round, except for the larger HCC contribution in summer.