Spatial Inhomogeneity of Atmospheric CO_(2) Concentration and Its Uncertainty in CMIP6 Earth System Models

This paper provides a systematic evaluation of the ability of 12 Earth System Models(ESMs)participating in the Coupled Model Intercomparison Project Phase 6(CMIP6)to simulate the spatial inhomogeneity of the atmospheric carbon dioxide(CO_(2))concentration.The multi-model ensemble mean(MME)can reasonably simulate the increasing trend of CO_(2) concentration from 1850 to 2014,compared with the observation data from the Scripps CO_(2) Program and CMIP6 prescribed data,and improves upon the CMIP5 MME CO_(2) concentration(which is overestimated after 1950).The growth rate of CO_(2) concentration in the northern hemisphere(NH)is higher than that in the southern hemisphere(SH),with the highest growth rate in the mid-latitudes of the NH.The MME can also reasonably simulate the seasonal amplitude of CO_(2) concentration,which is larger in the NH than in the SH and grows in amplitude after the 1950s(especially in the NH).Although the results of the MME are reasonable,there is a large spread among ESMs,and the difference between the ESMs increases with time.The MME results show that regions with relatively large CO_(2) concentrations(such as northern Russia,eastern China,Southeast Asia,the eastern United States,northern South America,and southern Africa)have greater seasonal variability and also exhibit a larger inter-model spread.Compared with CMIP5,the CMIP6 MME simulates an average spatial distribution of CO_(2) concentration that is much closer to the site observations,but the CMIP6-inter-model spread is larger.The inter-model differences of the annual means and seasonal cycles of atmospheric CO_(2) concentration are both attributed to the differences in natural sources and sinks of CO_(2) between the simulations.

Global and regional soil organic carbon estimates:Magnitudes and uncertainties

Globally,soil is the largest terrestrial carbon(C)reservoir.Robust quantification of soil organic C(SOC)stocks in existing global observation-based estimates avails accurate predictions in carbon-climate feedbacks and future climate trends.We investigated the magnitudes and distributions of global and regional SOC estimates(i.e.,density and stocks)based on five widely used global gridded SOC datasets,a regional permafrost dataset developed in 2021(UM2021),and a global-scale soil profile database(World Soil Information Service)reporting measurements of a series of physical and chemical edaphic attributes.The five global gridded SOC datasets were the Harmonized World Soil Database(HWSD),World Inventory of Soil Emission Potentials at 30 arc-second resolution(WISE30sec),Global Soil Dataset for Earth System Models(GSDE),Global Gridded Soil Information at 250-m resolution(SoilGrids250m),and Global Soil Organic Carbon Map(GSOCmap).Our analyses showed that the magnitude and distribution of SOC varied widely among datasets,with certain datasets showing region-specific robustness.At the global scale,SOC stocks at the top 30 and 100 cm were estimated to be 828(range:577–1171)and 1873(range:1086–2678)Pg C,respectively.The estimates from GSDE,GSOCmap,and WISE30sec were comparable,and those of SoilGrids250m and HWSD were at the upper and lower ends.The spatial SOC distribution varied greatly among datasets,especially in the northern circumpolar and Tibetan Plateau permafrost regions.Regionally,UM2021 and WISE30sec performed well in the northern circumpolar permafrost regions,and GSDE performed well in China.The estimates of SOC by different datasets also showed large variabilities across different soil layers and biomes.The discrepancies were generally smaller for the 0–30 cm soil than the 0–100 cm soil.The datasets demonstrated relatively higher agreement in grasslands,croplands,and shrublands/savannas than in other biomes(e.g.,wetlands).The users should be mindful of the gaps between regions and biomes while choosing the most appropriate SOC dataset for specific uses.Large uncertainties in existing global gridded SOC estimates were generally derived from soil sampling density,different sources,and various mapping methods for soil datasets.We call for future efforts for standardizing soil sampling efforts,cross-dataset comparison,proper validation,and overall global collaboration to improve SOC estimates.

Variation of Dust in Northern China and Its Reproduction in BCC-ESM1 since 1980

In this paper,we explore the possible causes and mechanisms for the variation of dust in northern China from 1980to 2014 using the Modern-Era Retrospective analysis for Research and Applications version 2(MERRA-2)data,observational data,and BCC-ESM1(Beijing Climate Center Earth System Model version 1)simulation data.Two important dust centers are identified in China:one in the Taklamakan Desert in southern Xinjiang Region and the other in the Badain Jaran Desert in western Inner Mongolia Plateau.Both centers display distinct seasonal variations,with high dust concentration in spring and summer and low in autumn and winter.BCC-ESM1 is able to generally capture the main spatial and temporal characteristics of dust in northern China.Both the MERRA-2 reanalysis data and BCCESM1 simulation data show a decreasing trend in spring dust,which is evident during 1980–2000 and 2001–2014.The analysis based on daily mean dust loads and wind fields from MERRA-2 and BCC-ESM1 indicates that dusty weather in North China may be mainly caused by transport of the dust,especially that from the central and western Inner Mongolia Plateau during the prior 0–2 days,through the westerly winds from the upstream“dust core”region(38°–45°N,90°–105°E).This is one of the important paths for dust to move into North China.The weakened westerly wind in the lower troposphere in this“dust core”region may be responsible for the reduction of spring dust in North China.

Country-level land carbon sink and its causing components by the middle of the twenty-first century

Background:Countries have long been making efforts by reducing greenhouse-gas emissions to mitigate climate change.In the agreements of the United Nations Framework Convention on Climate Change,involved countries have committed to reduction targets.However,carbon(C)sink and its involving processes by natural ecosystems remain difficult to quantify.Methods:Using a transient traceability framework,we estimated country-level land C sink and its causing components by 2050 simulated by 12 Earth System Models involved in the Coupled Model Intercomparison Project Phase 5(CMIP5)under RCP8.5.Results:The top 20 countries with highest C sink have the potential to sequester 62 Pg C in total,among which,Russia,Canada,USA,China,and Brazil sequester the most.This C sink consists of four components:productiondriven change,turnover-driven change,change in instantaneous C storage potential,and interaction between production-driven change and turnover-driven change.The four components account for 49.5%,28.1%,14.5%,and 7.9%of the land C sink,respectively.Conclusion:The model-based estimates highlight that land C sink potentially offsets a substantial proportion of greenhouse-gas emissions,especially for countries where net primary production(NPP)likely increases substantially and inherent residence time elongates.

Present-Day PM_(2.5)over Asia:Simulation and Uncertainty in CMIP6 ESMs

This study assesses the ability of 10 Earth System Models(ESMs)that participated in the Coupled Model Intercomparison Project Phase 6(CMIP6)to reproduce the present-day inhalable particles with diameters less than 2.5 micrometers(PM_(2.5))over Asia and discusses the uncertainty.PM_(2.5)accounts for more than 30%of the surface total aerosol(fine and coarse)concentration over Asia,except for central Asia.The simulated spatial distributions of PM_(2.5)and its components,averaged from 2005 to 2020,are consistent with the Modern-Era Retrospective Analysis for Research and Applications version 2(MERRA-2)reanalysis.They are characterized by the high PM_(2.5)concentrations in eastern China and northern India where anthropogenic components such as sulfate and organic aerosol dominate,and in northwestern China where the mineral dust in PM_(2.5)fine particles(PM_(2.5)DU)dominates.The present-day multimodel mean(MME)PM_(2.5)concentrations slightly underestimate ground-based observations in the same period of 2014–2019,although observations are affected by the limited coverage of observation sites and the urban areas.Those model biases partly come from other aerosols(such as nitrate and ammonium)not involved in our analyses,and also are contributed by large uncertainty in PM_(2.5)simulations on local scale among ESMs.The model uncertainties over East Asia are mainly attributed to sulfate and PM_(2.5)DU;over South Asia,they are attributed to sulfate,organic aerosol,and PM_(2.5)DU;over Southeast Asia,they are attributed to sea salt in PM_(2.5)fine particles(PM_(2.5)SS);and over central Asia,they are attributed to PM_(2.5)DU.They are mainly caused by the different representations of aerosols within individual ESMs including the representation of aerosol size distributions,dynamic transport,and physical and chemistry mechanisms.