Food systems are deeply affected by climate change and air pollution,while being key contributors to these environmental challenges.Understanding the complex interactions among food systems,climate change,and air poll...Food systems are deeply affected by climate change and air pollution,while being key contributors to these environmental challenges.Understanding the complex interactions among food systems,climate change,and air pollution is crucial for mitigating climate change,improving air quality,and promoting the sustainable development of food systems.However,the literature lacks a comprehensive review of these interactions,particularly in the current phase of rapid development in the field.To address this gap,this study systematically reviews recent research on the impacts of climate change and air pollution on food systems,as well as the greenhouse gas and air pollutant emissions from agri-food systems and their contribution to global climate change and air pollution.In addition,this study summarizes various strategies for mitigation and adaptation,including adjustments in agricultural practices and food supply chains.Profound changes in food systems are urgently needed to enhance adaptability and reduce emissions.This review offers a critical overview of current research on the interactions among food systems,climate change,and air pollution and highlights future research directions to support the transition to sustainable food systems.展开更多
Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable energy....Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable energy.Although deeplearning-based downscaling methods effectively capture the complex nonlinear mapping between meteorological data of varying scales,the supervised deep-learning-based downscaling methods suffer from insufficient high-resolution data in practice,and unsupervised methods struggle with accurately inferring small-scale specifics from limited large-scale inputs due to small-scale uncertainty.This article presents DualDS,a dual-learning framework utilizing a Generative Adversarial Network–based neural network and subgrid-scale auxiliary information for climate downscaling.Such a learning method is unified in a two-stream framework through up-and downsamplers,where the downsampler is used to simulate the information loss process during the upscaling,and the upsampler is used to reconstruct lost details and correct errors incurred during the upscaling.This dual learning strategy can eliminate the dependence on high-resolution ground truth data in the training process and refine the downscaling results by constraining the mapping process.Experimental findings demonstrate that DualDS is comparable to several state-of-the-art deep learning downscaling approaches,both qualitatively and quantitatively.Specifically,for a single surface-temperature data downscaling task,our method is comparable with other unsupervised algorithms with the same dataset,and we can achieve a 0.469 dB higher peak signal-to-noise ratio,0.017 higher structural similarity,0.08 lower RMSE,and the best correlation coefficient.In summary,this paper presents a novel approach to addressing small-scale uncertainty issues in unsupervised downscaling processes.展开更多
This study mainly introduces the development of the Flexible Global Ocean-Atmosphere-Land System Model： Grid-point Version 2 （FGOALS-g2） and the preliminary evaluations of its performances based on re- sults from t...This study mainly introduces the development of the Flexible Global Ocean-Atmosphere-Land System Model： Grid-point Version 2 （FGOALS-g2） and the preliminary evaluations of its performances based on re- sults from the pre-industrial control run and four members of historical runs according to the fifth phase of the Coupled Model Intercomparison Project （CMIP5） experiment design. The results suggest that many obvi- ous improvements have been achieved by the FGOALS-g2 compared with the previous version, FGOALS-gl, including its climatological mean states, climate variability, and 20th century surface temperature evolution. For example, FGOALS-g2 better simulates the frequency of tropical land precipitation, East Asian Monsoon precipitation and its seasonal cycle, MJO and ENSO, which are closely related to the updated cumulus parameterization scheme, as well as the alleviation of uncertainties in some key parameters in shallow and deep convection schemes, cloud fraction, cloud macro/microphysical processes and the boundary layer scheme in its atmospheric model. The annual cycle of sea surface temperature along the equator in the Pacific is significantly improved in the new version. The sea ice salinity simulation is one of the unique characteristics of FGOALS-g2, although it is somehow inconsistent with empirical observations in the Antarctic.展开更多
The second-generation Global Ocean Data Assimilation System of the Beijing Climate Center （BCC_GODAS2.0） has been run daily in a pre-operational mode. It spans the period 1990 to the present day. The goal of this pa...The second-generation Global Ocean Data Assimilation System of the Beijing Climate Center （BCC_GODAS2.0） has been run daily in a pre-operational mode. It spans the period 1990 to the present day. The goal of this paper is to introduce the main components and to evaluate BCC_GODAS2.0 for the user community. BCC_GODAS2.0 consists of an observational data preprocess, ocean data quality control system, a three-dimensional variational （3DVAR） data assimilation, and global ocean circulation model [Modular Ocean Model 4 （MOM4）]. MOM4 is driven by six-hourly fluxes from the National Centers for Environmental Prediction. Satellite altimetry data, SST, and in-situ temperature and salinity data are assimilated in real time. The monthly results from the BCC_GODAS2.0 reanalysis are compared and assessed with observations for 1990-201 I. The climatology of the mixed layer depth of BCC_GODAS2.0 is generally in agreement with that of World Ocean Atlas 2001. The modeled sea level variations in the tropical Pacific are consistent with observations from satellite altimetry on interannual to decadal time scales. Performances in predicting variations in the SST using BCC_GODAS2.0 are evaluated. The standard deviation of the SST in BCC_GODAS2.0 agrees well with observations in the tropical Pacific. BCC_GODAS2.0 is able to capture the main features of E1 Nifio Modoki I and Modoki II, which have different impacts on rainfall in southern China. In addition, the relationships between the Indian Ocean and the two types of E1 Nino Modoki are also reproduced.展开更多
The Flexible Global Ocean-Atmosphere-Land System model, Grid-point Version 2 （FGOALS-g2） for decadal predictions, is evaluated preliminarily, based on sets of ensemble 10-year hindcasts that it has produced. The res...The Flexible Global Ocean-Atmosphere-Land System model, Grid-point Version 2 （FGOALS-g2） for decadal predictions, is evaluated preliminarily, based on sets of ensemble 10-year hindcasts that it has produced. The results show that the hindcasts were more accurate in decadal variability of SST and surface air temperature （SAT）, particularly in that of Nifio3.4 SST and China regional SAT, than the second sample of the historical runs for 20th-century climate （the control） by the same model. Both the control and the hindcasts represented the global warming well using the same external forcings, but the control overestimated the warming. The hindcasts produced the warming closer to the observations. Performance of FGOALS-g2 in hindcasts benefits from more realistic initial conditions provided by the initialization run and a smaller model bias resulting from the use of a dynamic bias correction scheme newly developed in this study. The initialization consists of a 61-year nudging-based assimilation cycle, which follows on the control run on 01 January 1945 with the incorporation of observation data of upper-ocean temperature and salinity at each integration step in the ocean component model, the LASG IAP Climate System Ocean Model, Version 2 （LICOM2）. The dynamic bias correction is implemented at each step of LICOM2 during the hindcasts to reduce the systematic biases existing in upper-ocean temperature and salinity by incorporating multi-year monthly mean increments produced in the assimilation cycle. The effectiveness of the assimilation cycle and the role of the correction scheme were assessed prior to the hindcasts.展开更多
As an important secondary photochemical pollutant,peroxyacetyl nitrate(PAN)has been studied over decades,yet its simulations usually underestimate the corresponding observations,especially in polluted areas.Recent obs...As an important secondary photochemical pollutant,peroxyacetyl nitrate(PAN)has been studied over decades,yet its simulations usually underestimate the corresponding observations,especially in polluted areas.Recent observations in north China found unusually high concentrations of PAN during wintertime heavy haze events,but the current model still cannot reproduce the observations,and researchers speculated that nitrous acid(HONO)played a key role in PAN formation.For the first time we systematically assessed the impact of potential HONO sources on PAN formation mechanisms in eastern China using the Weather Research and Forecasting/Chemistry(WRF-Chem)model in February of 2017.The results showed that the potential HONO sources significantly improved the PAN simulations,remarkably accelerated the RO x(sum of hydroxyl,hydroperoxyl,and organic peroxy radicals)cycles,and resulted in 80%–150%enhancements of PAN near the ground in the coastal areas of eastern China and 10%–50%enhancements in the areas around 35–40°N within 3 km during a heavy haze period.The direct precursors of PAN were aldehyde and methylglyoxal,and the primary precursors of PAN were alkenes with C>3,xylenes,propene and toluene.The above results suggest that the potential HONO sources should be considered in regional and global chemical transport models when conducting PAN studies.展开更多
Based on historical runs,one of the core experiments of the fifth phase of the Coupled Model Intercomparison Project （CMIP5）,the snow depth （SD） and snow cover fraction （SCF） simulated by two versions of the Fle...Based on historical runs,one of the core experiments of the fifth phase of the Coupled Model Intercomparison Project （CMIP5）,the snow depth （SD） and snow cover fraction （SCF） simulated by two versions of the Flexible Global OceanAtmosphere-Land System （FGOALS） model,Grid-point Version 2 （g2） and Spectral Version 2 （s2）,were validated against observational data.The results revealed that the spatial pattern of SD and SCF over the Northern Hemisphere （NH） are simulated well by both models,except over the Tibetan Plateau,with the average spatial correlation coefficient over all months being around 0.7 and 0.8 for SD and SCF,respectively.Although the onset of snow accumulation is captured wellby the two models in terms of the annual cycle of SD and SCF,g2 overestimates SD/SCF over most mid-and high-latitude areas of the NH.Analysis showed that g2 produces lower temperatures than s2 because it considers the indirect effects of aerosols in its atmospheric component,which is the primary driver for the SD/SCF difference between the two models.In addition,both models simulate the significant decreasing trend of SCF well over （30°-70°N） in winter during the period 1971-94.However,as g2 has a weak response to an increase in the concentration of CO2 and lower climate sensitivity,it presents weaker interannual variation compared to s2.展开更多
Various ensemble-based schemes are employed in data assimilation because they can use the ensemble to estimate the flow-dependent background error covariance. The most common way to generate the real-time ensemble is ...Various ensemble-based schemes are employed in data assimilation because they can use the ensemble to estimate the flow-dependent background error covariance. The most common way to generate the real-time ensemble is to use an ensemble forecast; however, this is very time-consuming. The historical sampling approach is an alternative way to generate the ensemble,by picking some snapshots from historical forecast series.With this approach, many ensemble-based assimilation schemes can be used in a deterministic forecast environment. Furthermore, considering the time that it saves, the method has the potential for operational application.However, the historical sampling approach carries with it a special kind of sampling error because, in a historical forecast, the way to integrate the ensemble members is different from the way to integrate the initial conditions at the analysis time(i.e., forcing and lateral boundary condition differences, and ‘warm start' or ‘cold start' differences). This study analyzes the results of an experiment with the Global Regional Assimilation Prediction System-Global Forecast System(GRAPES-GFS), to evaluate how the different integration configurations influence the historical sampling error for global models. The results show that the sampling error is dominated by diurnal cycle patterns as a result of the radiance forcing difference.Although the RMSEs of the sampling error are small, in view of the correlation coefficients of the perturbed ensemble, the sampling error for some variables on some levels(e.g., low-level temperature and humidity, stratospheric temperature and geopotential height and humidity), is non-negligible. The results suggest some caution must be applied, and advice taken, when using the historical sampling approach.展开更多
Accurate wetland delineation is the basis of wetland definition and mapping, and is of great importance for wetland management and research. The Zoige Plateau on the Qinghai-Tibet Plateau was used as a research site f...Accurate wetland delineation is the basis of wetland definition and mapping, and is of great importance for wetland management and research. The Zoige Plateau on the Qinghai-Tibet Plateau was used as a research site for research on alpine wetland delineation. Several studies have analyzed the spatiotemporal pattern and dynamics of these alpine wetlands, but none have addressed the issues of wetland boundaries. The objective of this work was to discriminate the upper boundaries of alpine wetlands by coupling ecological methods and satellite observations. The combination of Landsat 8 images and supervised classification was an effective method for rapid identification of alpine wetlands in the Zoig6 Plateau. Wet meadow was relatively stable compared with hydric soils and wetland hydrology and could be used as a primary indicator for discriminating the upper boundaries of alpine wetlands. A slope of less than 4.5° could be used as the threshold value for wetland delineation. The normalized difference vegetation index （NDVI） in 434 field sites showed that a threshold value of 0.3 could distinguish grasslands from emergent marsh and wet meadow in September. The median normalized difference water index （NDWI） of emergent marsh remained more stable than that of wet meadow and grasslands during the period from September until July of the following year. The index of mean density in wet meadow zones was higher than the emergent and upland zones. Over twice the number of species occurred in the wet meadow zone compared with the emergent zone, and close to the value of upland zone. Alpine wetlands in the three reserves in 2014 covered 1175.19 kin2 with a classification accuracy of 75.6%. The combination of ecological methods and remote sensing technology will play an important role in wetland delineation at medium and small scales. The correct differentiation between wet meadow and grasslands is the key to improving the accuracy of future wetland delineation.展开更多
A database of real-world diesel vehicle emission factors, based on type and technology, has been developed following tests on more than 300 diesel vehicles in China using a portable emission measurement system. The da...A database of real-world diesel vehicle emission factors, based on type and technology, has been developed following tests on more than 300 diesel vehicles in China using a portable emission measurement system. The database provides better understanding of diesel vehicle emissions under actual driving conditions. We found that although new regulations have reduced real-world emission levels of diesel trucks and buses significantly for most pollutants in China, NOx emissions have been inadequately controlled by the current standards, especially for diesel buses, because of bad driving conditions in the real world. We also compared the emission factors in the database with those calculated by emission factor models and used in inventory studies. The emission factors derived from COPERT（Computer Programmer to calculate Emissions from Road Transport） and MOBILE may both underestimate real emission factors, whereas the updated COPERT and PART5（Highway Vehicle Particulate Emission Modeling Software） models may overestimate emission factors in China. Real-world measurement results and emission factors used in recent emission inventory studies are inconsistent,which has led to inaccurate estimates of emissions from diesel trucks and buses over recent years. This suggests that emission factors derived from European or US-based models will not truly represent real-world emissions in China. Therefore, it is useful and necessary to conduct systematic real-world measurements of vehicle emissions in China in order to obtain the optimum inputs for emission inventory models.展开更多
An ensemble optimal interpolation(EnOI)data assimilation method is applied in the BCCCSM1.1 to investigate the impact of ocean data assimilations on seasonal forecasts in an idealized twin experiment framework.Pseudoo...An ensemble optimal interpolation(EnOI)data assimilation method is applied in the BCCCSM1.1 to investigate the impact of ocean data assimilations on seasonal forecasts in an idealized twin experiment framework.Pseudoobservations of sea surface temperature(SST),sea surface height(SSH),sea surface salinity(SSS),temperature and salinity(T/S)profiles were first generated in a free model run.Then,a series of sensitivity tests initialized with predefined bias were conducted for a one-year period;this involved a free run(CTR)and seven assimilation runs.These tests allowed us to check the analysis field accuracy against the"truth".As expected,data assimilation improved all investigated quantities;the joint assimilation of all variables gave more improved results than assimilating them separately.One-year predictions initialized from the seven runs and CTR were then conducted and compared.The forecasts initialized from joint assimilation of surface data produced comparable SST root mean square errors to that from assimilation of T/S profiles,but the assimilation of T/S profiles is crucial to reduce subsurface deficiencies.The ocean surface currents in the tropics were better predicted when initial conditions produced by assimilating T/S profiles,while surface data assimilation became more important at higher latitudes,particularly near the western boundary currents.The predictions of ocean heat content and mixed layer depth are significantly improved initialized from the joint assimilation of all the variables.Finally,a central Pacific El Ni?o was well predicted from the joint assimilation of surface data,indicating the importance of joint assimilation of SST,SSH,and SSS for ENSO predictions.展开更多
Initial errors in the tropical Indian Ocean(IO-related initial errors) that are most likely to yield the Spring Prediction Barrier(SPB) for La Ni?a forecasts are explored by using the CESM model.These initial errors c...Initial errors in the tropical Indian Ocean(IO-related initial errors) that are most likely to yield the Spring Prediction Barrier(SPB) for La Ni?a forecasts are explored by using the CESM model.These initial errors can be classified into two types.Type-1 initial error consists of positive sea temperature errors in the western Indian Ocean and negative sea temperature errors in the eastern Indian Ocean,while the spatial structure of Type-2 initial error is nearly opposite.Both kinds of IO-related initial errors induce positive prediction errors of sea temperature in the Pacific Ocean,leading to underprediction of La Nina events.Type-1 initial error in the tropical Indian Ocean mainly influences the SSTA in the tropical Pacific Ocean via atmospheric bridge,leading to the development of localized sea temperature errors in the eastern Pacific Ocean.However,for Type-2 initial error,its positive sea temperature errors in the eastern Indian Ocean can induce downwelling error and influence La Ni?a predictions through an oceanic channel called Indonesian Throughflow.Based on the location of largest SPB-related initial errors,the sensitive area in the tropical Indian Ocean for La Nina predictions is identified.Furthermore,sensitivity experiments show that applying targeted observations in this sensitive area is very useful in decreasing prediction errors of La Nina.Therefore,adopting a targeted observation strategy in the tropical Indian Ocean is a promising approach toward increasing ENSO prediction skill.展开更多
A terrain-following coordinate （a-coordinate） in which the computational form of pressure gradient force （PGF） is two-term （the so-called classic method） has significant PGF errors near steep terrain. Using the ...A terrain-following coordinate （a-coordinate） in which the computational form of pressure gradient force （PGF） is two-term （the so-called classic method） has significant PGF errors near steep terrain. Using the covariant equations of the a-coordinate to create a one-term PGF （the covariant method） can reduce the PGF errors. This study investigates the factors inducing the PGF errors of these two methods, through geometric analysis and idealized experiments. The geometric analysis first demonstrates that the terrain slope and the vertical pressure gradient can induce the PGF errors of the classic method, and then generalize the effect of the terrain slope to the effect of the slope of each vertical layer （φ）. More importantly, a new factor, the direction of PGF （a）, is proposed by the geometric analysis, and the effects of φ and a are quantified by tan φ.tan a. When tan φ.tan a is greater than 1/9 or smaller than -10/9, the two terms of PGF of the classic method are of the same order but opposite in sign, and then the PGF errors of the classic method are large. Finally, the effects of three factors on inducing the PGF errors of the classic method are validated by a series of idealized experiments using various terrain types and pressure fields. The experimental results also demonstrate that the PGF errors of the covariant method are affected little by the three factors.展开更多
In this paper, we present a set of best practices for workflow design and implementation for numerical weather prediction models and meteorological data service, which have been in operation in China Meteorological Ad...In this paper, we present a set of best practices for workflow design and implementation for numerical weather prediction models and meteorological data service, which have been in operation in China Meteorological Administration (CMA) for years and have been proven effective in reliably managing the complexities of large-scale meteorological related workflows. Based on the previous work on the platforms, we argue that a minimum set of guidelines including workflow scheme, module design, implementation standards and maintenance consideration during the whole establishment of the platform are highly recommended, serving to reduce the need for future maintenance and adjustment. A significant gain in performance can be achieved through the workflow-based projects. We believe that a good workflow system plays an important role in the weather forecast service, providing a useful tool for monitoring the whole process, fixing the errors, repairing a workflow, or redesigning an equivalent workflow pattern with new components.展开更多
In 2018,a total of US$166 billion global economic losses and a new high of 55.3 Gt of CO_(2)equivalent emission were generated by 831 climate-related extreme events.As the world’s largest CO_(2)emitter,we reported Ch...In 2018,a total of US$166 billion global economic losses and a new high of 55.3 Gt of CO_(2)equivalent emission were generated by 831 climate-related extreme events.As the world’s largest CO_(2)emitter,we reported China’s recent progresses and pitfalls in climate actions to achieve climate mitigation targets(i.e.,limit warming to 1.5-2°C above the pre-industrial level).We first summarized China’s integrated actions(2015 onwards)that benefit both climate change mitigation and Sustainable Development Goals(SDGs).These projects include re-structuring organizations,establishing working goals and actions,amending laws and regulations at national level,as well as increasing social awareness at community level.We then pointed out the shortcomings in different regions and sectors.Based on these analyses,we proposed five recommendations to help China improving its climate policy strategies,which include:1)restructuring the economy to balance short-term and long-term conflicts;2)developing circular economy with recycling mechanism and infrastructure;3)building up unified national standards and more accurate indicators;4)completing market mechanism for green economy and encouraging green consumption;and 5)enhancing technology innovations and local incentives via bottom-up actions.展开更多
The compilation of technology lists addressing climate change has a guiding effect on promoting technological research and development,demonstration,and popularization.It is also crucial for China to strengthen ecolog...The compilation of technology lists addressing climate change has a guiding effect on promoting technological research and development,demonstration,and popularization.It is also crucial for China to strengthen ecological civilization construction,achieve the carbon emission peak and carbon neutrality target,and enhance global climate governance capabilities.This study first proposes the existing classification outline of the technology promotion lists,technology demand lists,and future technology lists.Then,different methodologies are integrated on the basis of the existing outline of four technology lists:China’s existing technological promotion list for addressing climate change,China’s demand list for climate change mitigation technology,China’s key technology list for addressing climate change,and China’s future technology list for addressing climate change.What’s more,core technologies are analyzed in the aspects of technology maturity,carbon reduction cost,carbon reduction potential,economic benefits,social influence,uncertainty,etc.The results show that:key industries and sectors in China already have relatively mature mitigation/adaptation technologies to support the achievement of climate change targets.The multi-sectoral system of promoting climate friendly technologies has been established,which has played an active role in tackling climate change.Currently,climate technology needs are concentrated in the traditional technology and equipment upgrading,renewable energy technology,and management decision-making support technology.The key technologies are concentrated in 3 major areas and 12 technological directions that urgently need a breakthrough.For carbon emmission peak and nentrality,carbon depth reduction and zero carbon emissions and geoengineering technology(CDR and SRM)have played an important role in forming the structure of global emissions and achieving carbon neutrality in the future.Thus,the uncertainty assessment for the comprehensive technology cost effectiveness,technology integration direction,technical maturity,ethics and ecological impacts is supportive to the national technology strategy.Finally,the presented study proposes several policy implications for medium-and long-term technology deployment,improving technology conversion rate,promoting the research and development of core technologies,and forming a technology list collaborative update and release mechanism.展开更多
Tropical cyclone(TC) rainfall forecast has remained a challenge. To create initial conditions with high quality for simulation, the present study implemented a data assimilation scheme based on the EnKF method to inge...Tropical cyclone(TC) rainfall forecast has remained a challenge. To create initial conditions with high quality for simulation, the present study implemented a data assimilation scheme based on the EnKF method to ingest the satellite-retrieved cloud water path(C_(w)) and tested it in WRF. The scheme uses the vertical integration of forecasted cloud water content to transform control variables to the observation space, and creates the correlations between C_(w) and control variables in the flow-dependent background error covariance based on all the ensemble members, so that the observed cloud information can affect the background temperature and humidity. For two typhoons in 2018(Yagi and Rumiba), assimilating C_(w) significantly increases the simulated rainfalls and TC intensities. In terms of the average equitable threat score of daily moderate to heavy rainfall(5-120 mm), the improvements are over 130%, and the dry biases are cut by about 30%. Such improvements are traced down to the fact that C_(w) assimilation increases the moisture content, especially that further away from the TC center, which provides more precipitable water for the rainfall,strengthens the TC and broadens the TC size via latent heat release and internal wind field adjustment.展开更多
Fire season affects the dynamic changes of post-fire vegetation communities and carbon emissions.Analyzing its global patterns supports understanding of the ecological impacts of fires and responses of fires to climat...Fire season affects the dynamic changes of post-fire vegetation communities and carbon emissions.Analyzing its global patterns supports understanding of the ecological impacts of fires and responses of fires to climate change.Meteorological variables have been widely used to quantify fire season in current studies.However,their results can not be used to assess climate impacts on the seasonality of fire activities.Here we utilized satellite-based Moderate Resolution Imaging Spectroradiometer(MODIS)burned area data from 2001 to 2022 to identify global fire season types based on the number of peaks within a year.Using satellite data and innovatively processing the data to obtain a more accurate length of the fire season.We divided fire season types and examined the spatial distribution of fire season types across the Koppen-Geiger climate(KGC)zones.At a global scale,we identified three major fire season types,including unimodal(31.25%),bimodal(52.07%),and random(16.69%).The unimodal fire season primarily occurs in boreal and tropical regions lasting about 2.7 mon.In comparison,temperate ecosystems tend to have a longer fire season(3 mon)with two peaks throughout the year.The KGC zones show divergent contributions from the fire season types,indicating potential impacts of the climatic conditions on fire seasonality in these regions.展开更多
Based on the C-Coupler platform,the semi-unstructured Climate System Model,Synthesis Community Integrated Model version 2(SYCIM2.0),has been developed at the School of Atmospheric Sciences,Sun Yat-sen University.SYCIM...Based on the C-Coupler platform,the semi-unstructured Climate System Model,Synthesis Community Integrated Model version 2(SYCIM2.0),has been developed at the School of Atmospheric Sciences,Sun Yat-sen University.SYCIM2.0 aims to meet the demand for seamless climate prediction through accurate climate simulations and projections.This paper provides an overview of SYCIM2.0 and highlights its key features,especially the coupling of an unstructured ocean model and the tuning process.An extensive evaluation of its performance,focusing on the East Asian Summer Monsoon(EASM),is presented based on long-term simulations with fixed external forcing.The results suggest that after nearly 240 years of integration,SYCIM2.0 achieves a quasi-equilibrium state,albeit with small trends in the net radiation flux at the top-of-atmosphere(TOA)and Earth’s surface,as well as with global mean near-surface temperatures.Compared to observational and reanalysis data,the model realistically simulates spatial patterns of sea surface temperature(SST)and precipitation centers to include their annual cycles,in addition to the lower-level wind fields in the EASM region.However,it exhibits a weakened and eastward-shifted Western Pacific Subtropical High(WPSH),resulting in an associated precipitation bias.SYCIM2.0 robustly captures the dominant mode of the EASM and its close relationship with the El Niño-Southern Oscillation(ENSO)but exhibits relatively poor performance in simulating the second leading mode and the associated air–sea interaction processes.Further comprehensive evaluations of SYCIM2.0 will be conducted in future studies.展开更多
基金supported by the National Natural Science Foundation of China(42277087,42130708,42471021,42277482,and 42361144876)the Natural Science Foundation of Guangdong Province(2024A1515012550)+3 种基金the Hainan Institute of National Park grant(KY-23ZK01)the Tsinghua Shenzhen International Graduate School Cross-disciplinary Research and Innovation Fund Research Plan(JC2022011)the Shenzhen Science and Technology Program(JCYJ20240813112106009 and ZDSYS20220606100806014)the Scientific Research Start-up Funds(QD2021030C)from Tsinghua Shenzhen International Graduate School。
文摘Food systems are deeply affected by climate change and air pollution,while being key contributors to these environmental challenges.Understanding the complex interactions among food systems,climate change,and air pollution is crucial for mitigating climate change,improving air quality,and promoting the sustainable development of food systems.However,the literature lacks a comprehensive review of these interactions,particularly in the current phase of rapid development in the field.To address this gap,this study systematically reviews recent research on the impacts of climate change and air pollution on food systems,as well as the greenhouse gas and air pollutant emissions from agri-food systems and their contribution to global climate change and air pollution.In addition,this study summarizes various strategies for mitigation and adaptation,including adjustments in agricultural practices and food supply chains.Profound changes in food systems are urgently needed to enhance adaptability and reduce emissions.This review offers a critical overview of current research on the interactions among food systems,climate change,and air pollution and highlights future research directions to support the transition to sustainable food systems.
基金supported by the following funding bodies:the National Key Research and Development Program of China(Grant No.2020YFA0608000)National Science Foundation of China(Grant Nos.42075142,42375148,42125503+2 种基金42130608)FY-APP-2022.0609,Sichuan Province Key Tech nology Research and Development project(Grant Nos.2024ZHCG0168,2024ZHCG0176,2023YFG0305,2023YFG-0124,and 23ZDYF0091)the CUIT Science and Technology Innovation Capacity Enhancement Program project(Grant No.KYQN202305)。
文摘Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable energy.Although deeplearning-based downscaling methods effectively capture the complex nonlinear mapping between meteorological data of varying scales,the supervised deep-learning-based downscaling methods suffer from insufficient high-resolution data in practice,and unsupervised methods struggle with accurately inferring small-scale specifics from limited large-scale inputs due to small-scale uncertainty.This article presents DualDS,a dual-learning framework utilizing a Generative Adversarial Network–based neural network and subgrid-scale auxiliary information for climate downscaling.Such a learning method is unified in a two-stream framework through up-and downsamplers,where the downsampler is used to simulate the information loss process during the upscaling,and the upsampler is used to reconstruct lost details and correct errors incurred during the upscaling.This dual learning strategy can eliminate the dependence on high-resolution ground truth data in the training process and refine the downscaling results by constraining the mapping process.Experimental findings demonstrate that DualDS is comparable to several state-of-the-art deep learning downscaling approaches,both qualitatively and quantitatively.Specifically,for a single surface-temperature data downscaling task,our method is comparable with other unsupervised algorithms with the same dataset,and we can achieve a 0.469 dB higher peak signal-to-noise ratio,0.017 higher structural similarity,0.08 lower RMSE,and the best correlation coefficient.In summary,this paper presents a novel approach to addressing small-scale uncertainty issues in unsupervised downscaling processes.
基金supported by the National"863"Project(Grant No.2010AA012304)the"973"Project(Grant No.2010CB951904)+1 种基金the China Meteorological Administration R&D Special Fund for Public Welfare(meteorology)(Grant No.GYHY201006014)the National Natural Science Foundation of China(Grant Nos.40923002 and 41005053)
文摘This study mainly introduces the development of the Flexible Global Ocean-Atmosphere-Land System Model： Grid-point Version 2 （FGOALS-g2） and the preliminary evaluations of its performances based on re- sults from the pre-industrial control run and four members of historical runs according to the fifth phase of the Coupled Model Intercomparison Project （CMIP5） experiment design. The results suggest that many obvi- ous improvements have been achieved by the FGOALS-g2 compared with the previous version, FGOALS-gl, including its climatological mean states, climate variability, and 20th century surface temperature evolution. For example, FGOALS-g2 better simulates the frequency of tropical land precipitation, East Asian Monsoon precipitation and its seasonal cycle, MJO and ENSO, which are closely related to the updated cumulus parameterization scheme, as well as the alleviation of uncertainties in some key parameters in shallow and deep convection schemes, cloud fraction, cloud macro/microphysical processes and the boundary layer scheme in its atmospheric model. The annual cycle of sea surface temperature along the equator in the Pacific is significantly improved in the new version. The sea ice salinity simulation is one of the unique characteristics of FGOALS-g2, although it is somehow inconsistent with empirical observations in the Antarctic.
基金supported by the National Natural Science Foundation of China (Grant No. 41306005)the National Basic Research Program of China (Grant No. 2012CB955903)the CAS/SAFEA International Partnership Program for Creative Research Teams
文摘The second-generation Global Ocean Data Assimilation System of the Beijing Climate Center （BCC_GODAS2.0） has been run daily in a pre-operational mode. It spans the period 1990 to the present day. The goal of this paper is to introduce the main components and to evaluate BCC_GODAS2.0 for the user community. BCC_GODAS2.0 consists of an observational data preprocess, ocean data quality control system, a three-dimensional variational （3DVAR） data assimilation, and global ocean circulation model [Modular Ocean Model 4 （MOM4）]. MOM4 is driven by six-hourly fluxes from the National Centers for Environmental Prediction. Satellite altimetry data, SST, and in-situ temperature and salinity data are assimilated in real time. The monthly results from the BCC_GODAS2.0 reanalysis are compared and assessed with observations for 1990-201 I. The climatology of the mixed layer depth of BCC_GODAS2.0 is generally in agreement with that of World Ocean Atlas 2001. The modeled sea level variations in the tropical Pacific are consistent with observations from satellite altimetry on interannual to decadal time scales. Performances in predicting variations in the SST using BCC_GODAS2.0 are evaluated. The standard deviation of the SST in BCC_GODAS2.0 agrees well with observations in the tropical Pacific. BCC_GODAS2.0 is able to capture the main features of E1 Nifio Modoki I and Modoki II, which have different impacts on rainfall in southern China. In addition, the relationships between the Indian Ocean and the two types of E1 Nino Modoki are also reproduced.
基金the Ministry of Science and Technology of China for the National High-tech R&D Program(863 Program:Grant No.2010AA012304)the National Basic Research Program of China(973 Program:Grant No.2011CB309704)
文摘The Flexible Global Ocean-Atmosphere-Land System model, Grid-point Version 2 （FGOALS-g2） for decadal predictions, is evaluated preliminarily, based on sets of ensemble 10-year hindcasts that it has produced. The results show that the hindcasts were more accurate in decadal variability of SST and surface air temperature （SAT）, particularly in that of Nifio3.4 SST and China regional SAT, than the second sample of the historical runs for 20th-century climate （the control） by the same model. Both the control and the hindcasts represented the global warming well using the same external forcings, but the control overestimated the warming. The hindcasts produced the warming closer to the observations. Performance of FGOALS-g2 in hindcasts benefits from more realistic initial conditions provided by the initialization run and a smaller model bias resulting from the use of a dynamic bias correction scheme newly developed in this study. The initialization consists of a 61-year nudging-based assimilation cycle, which follows on the control run on 01 January 1945 with the incorporation of observation data of upper-ocean temperature and salinity at each integration step in the ocean component model, the LASG IAP Climate System Ocean Model, Version 2 （LICOM2）. The dynamic bias correction is implemented at each step of LICOM2 during the hindcasts to reduce the systematic biases existing in upper-ocean temperature and salinity by incorporating multi-year monthly mean increments produced in the assimilation cycle. The effectiveness of the assimilation cycle and the role of the correction scheme were assessed prior to the hindcasts.
基金This work was partially supported by the National Key Research and Development Program of China(No.2017YFC0209801)the National Natural Science Foundation of China(Nos.91544221,41575124)the National Research Program for Key Issues in Air Pollution Control(Nos.DQGG0102,DQGG0103).
文摘As an important secondary photochemical pollutant,peroxyacetyl nitrate(PAN)has been studied over decades,yet its simulations usually underestimate the corresponding observations,especially in polluted areas.Recent observations in north China found unusually high concentrations of PAN during wintertime heavy haze events,but the current model still cannot reproduce the observations,and researchers speculated that nitrous acid(HONO)played a key role in PAN formation.For the first time we systematically assessed the impact of potential HONO sources on PAN formation mechanisms in eastern China using the Weather Research and Forecasting/Chemistry(WRF-Chem)model in February of 2017.The results showed that the potential HONO sources significantly improved the PAN simulations,remarkably accelerated the RO x(sum of hydroxyl,hydroperoxyl,and organic peroxy radicals)cycles,and resulted in 80%–150%enhancements of PAN near the ground in the coastal areas of eastern China and 10%–50%enhancements in the areas around 35–40°N within 3 km during a heavy haze period.The direct precursors of PAN were aldehyde and methylglyoxal,and the primary precursors of PAN were alkenes with C>3,xylenes,propene and toluene.The above results suggest that the potential HONO sources should be considered in regional and global chemical transport models when conducting PAN studies.
基金supported by the Key Projects in the National Science & Technology Pillar Program during the Twelfth Five-Year Plan Period (Grant No. 2012BAC22B02)the National Key Basic Research Program of China (Grant No. 2013CB956603)the Ministry of Science and Technology of China (Grant No. 2013CBA01805)
文摘Based on historical runs,one of the core experiments of the fifth phase of the Coupled Model Intercomparison Project （CMIP5）,the snow depth （SD） and snow cover fraction （SCF） simulated by two versions of the Flexible Global OceanAtmosphere-Land System （FGOALS） model,Grid-point Version 2 （g2） and Spectral Version 2 （s2）,were validated against observational data.The results revealed that the spatial pattern of SD and SCF over the Northern Hemisphere （NH） are simulated well by both models,except over the Tibetan Plateau,with the average spatial correlation coefficient over all months being around 0.7 and 0.8 for SD and SCF,respectively.Although the onset of snow accumulation is captured wellby the two models in terms of the annual cycle of SD and SCF,g2 overestimates SD/SCF over most mid-and high-latitude areas of the NH.Analysis showed that g2 produces lower temperatures than s2 because it considers the indirect effects of aerosols in its atmospheric component,which is the primary driver for the SD/SCF difference between the two models.In addition,both models simulate the significant decreasing trend of SCF well over （30°-70°N） in winter during the period 1971-94.However,as g2 has a weak response to an increase in the concentration of CO2 and lower climate sensitivity,it presents weaker interannual variation compared to s2.
基金supported by the China Meteorological Administration for the R&D Special Fund for Public Welfare Industry (Meteorology) (Grant No. GYHY(QX)201406015)the Southern China Monsoon Rainfall Experiment (SCMREX)
文摘Various ensemble-based schemes are employed in data assimilation because they can use the ensemble to estimate the flow-dependent background error covariance. The most common way to generate the real-time ensemble is to use an ensemble forecast; however, this is very time-consuming. The historical sampling approach is an alternative way to generate the ensemble,by picking some snapshots from historical forecast series.With this approach, many ensemble-based assimilation schemes can be used in a deterministic forecast environment. Furthermore, considering the time that it saves, the method has the potential for operational application.However, the historical sampling approach carries with it a special kind of sampling error because, in a historical forecast, the way to integrate the ensemble members is different from the way to integrate the initial conditions at the analysis time(i.e., forcing and lateral boundary condition differences, and ‘warm start' or ‘cold start' differences). This study analyzes the results of an experiment with the Global Regional Assimilation Prediction System-Global Forecast System(GRAPES-GFS), to evaluate how the different integration configurations influence the historical sampling error for global models. The results show that the sampling error is dominated by diurnal cycle patterns as a result of the radiance forcing difference.Although the RMSEs of the sampling error are small, in view of the correlation coefficients of the perturbed ensemble, the sampling error for some variables on some levels(e.g., low-level temperature and humidity, stratospheric temperature and geopotential height and humidity), is non-negligible. The results suggest some caution must be applied, and advice taken, when using the historical sampling approach.
基金Under the auspices of National Natural Science Foundation of China(No.41201445,41103041)National Science and Technology Support Program(No.2012BAJ24B01)National High Technology Research and Development Program of China(No.2009AA12200307)
文摘Accurate wetland delineation is the basis of wetland definition and mapping, and is of great importance for wetland management and research. The Zoige Plateau on the Qinghai-Tibet Plateau was used as a research site for research on alpine wetland delineation. Several studies have analyzed the spatiotemporal pattern and dynamics of these alpine wetlands, but none have addressed the issues of wetland boundaries. The objective of this work was to discriminate the upper boundaries of alpine wetlands by coupling ecological methods and satellite observations. The combination of Landsat 8 images and supervised classification was an effective method for rapid identification of alpine wetlands in the Zoig6 Plateau. Wet meadow was relatively stable compared with hydric soils and wetland hydrology and could be used as a primary indicator for discriminating the upper boundaries of alpine wetlands. A slope of less than 4.5° could be used as the threshold value for wetland delineation. The normalized difference vegetation index （NDVI） in 434 field sites showed that a threshold value of 0.3 could distinguish grasslands from emergent marsh and wet meadow in September. The median normalized difference water index （NDWI） of emergent marsh remained more stable than that of wet meadow and grasslands during the period from September until July of the following year. The index of mean density in wet meadow zones was higher than the emergent and upland zones. Over twice the number of species occurred in the wet meadow zone compared with the emergent zone, and close to the value of upland zone. Alpine wetlands in the three reserves in 2014 covered 1175.19 kin2 with a classification accuracy of 75.6%. The combination of ecological methods and remote sensing technology will play an important role in wetland delineation at medium and small scales. The correct differentiation between wet meadow and grasslands is the key to improving the accuracy of future wetland delineation.
基金supported by the National Science Foundation of China (Nos. 41275124, 51278272)the Beijing Natural Science Foundation (8142011)+1 种基金the Ministry of Environmental Protection of China (No. 201209007)the International Council on Clean Transportation (ICCT) research program
文摘A database of real-world diesel vehicle emission factors, based on type and technology, has been developed following tests on more than 300 diesel vehicles in China using a portable emission measurement system. The database provides better understanding of diesel vehicle emissions under actual driving conditions. We found that although new regulations have reduced real-world emission levels of diesel trucks and buses significantly for most pollutants in China, NOx emissions have been inadequately controlled by the current standards, especially for diesel buses, because of bad driving conditions in the real world. We also compared the emission factors in the database with those calculated by emission factor models and used in inventory studies. The emission factors derived from COPERT（Computer Programmer to calculate Emissions from Road Transport） and MOBILE may both underestimate real emission factors, whereas the updated COPERT and PART5（Highway Vehicle Particulate Emission Modeling Software） models may overestimate emission factors in China. Real-world measurement results and emission factors used in recent emission inventory studies are inconsistent,which has led to inaccurate estimates of emissions from diesel trucks and buses over recent years. This suggests that emission factors derived from European or US-based models will not truly represent real-world emissions in China. Therefore, it is useful and necessary to conduct systematic real-world measurements of vehicle emissions in China in order to obtain the optimum inputs for emission inventory models.
基金The National Key Research and Development Program of China under contract Nos 2016YFA0602102 and2016YFC1401702the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou)under contract No.GML2019ZD0306+1 种基金the National Natural Science Foundation of China under contract No.41306005CAS Pioneer Hundred Talents Program Startup Fund by South China Sea Institute of Oceanology under contract No.Y9SL011001。
文摘An ensemble optimal interpolation(EnOI)data assimilation method is applied in the BCCCSM1.1 to investigate the impact of ocean data assimilations on seasonal forecasts in an idealized twin experiment framework.Pseudoobservations of sea surface temperature(SST),sea surface height(SSH),sea surface salinity(SSS),temperature and salinity(T/S)profiles were first generated in a free model run.Then,a series of sensitivity tests initialized with predefined bias were conducted for a one-year period;this involved a free run(CTR)and seven assimilation runs.These tests allowed us to check the analysis field accuracy against the"truth".As expected,data assimilation improved all investigated quantities;the joint assimilation of all variables gave more improved results than assimilating them separately.One-year predictions initialized from the seven runs and CTR were then conducted and compared.The forecasts initialized from joint assimilation of surface data produced comparable SST root mean square errors to that from assimilation of T/S profiles,but the assimilation of T/S profiles is crucial to reduce subsurface deficiencies.The ocean surface currents in the tropics were better predicted when initial conditions produced by assimilating T/S profiles,while surface data assimilation became more important at higher latitudes,particularly near the western boundary currents.The predictions of ocean heat content and mixed layer depth are significantly improved initialized from the joint assimilation of all the variables.Finally,a central Pacific El Ni?o was well predicted from the joint assimilation of surface data,indicating the importance of joint assimilation of SST,SSH,and SSS for ENSO predictions.
基金supported by the National Key R&D Program of China (Grant No.2019YFC1408004)together with the National Natural Science Foundation of China (Grant Nos.41930971,41805069,41606031)the Office of China Postdoctoral Council (OCPC) under Award Number 20190003。
文摘Initial errors in the tropical Indian Ocean(IO-related initial errors) that are most likely to yield the Spring Prediction Barrier(SPB) for La Ni?a forecasts are explored by using the CESM model.These initial errors can be classified into two types.Type-1 initial error consists of positive sea temperature errors in the western Indian Ocean and negative sea temperature errors in the eastern Indian Ocean,while the spatial structure of Type-2 initial error is nearly opposite.Both kinds of IO-related initial errors induce positive prediction errors of sea temperature in the Pacific Ocean,leading to underprediction of La Nina events.Type-1 initial error in the tropical Indian Ocean mainly influences the SSTA in the tropical Pacific Ocean via atmospheric bridge,leading to the development of localized sea temperature errors in the eastern Pacific Ocean.However,for Type-2 initial error,its positive sea temperature errors in the eastern Indian Ocean can induce downwelling error and influence La Ni?a predictions through an oceanic channel called Indonesian Throughflow.Based on the location of largest SPB-related initial errors,the sensitive area in the tropical Indian Ocean for La Nina predictions is identified.Furthermore,sensitivity experiments show that applying targeted observations in this sensitive area is very useful in decreasing prediction errors of La Nina.Therefore,adopting a targeted observation strategy in the tropical Indian Ocean is a promising approach toward increasing ENSO prediction skill.
基金jointly supported by the National Basic Research Program of China[973 Program,grant number 2015CB954102]National Natural Science Foundation of China[grant numbers41305095 and 41175064]
文摘A terrain-following coordinate （a-coordinate） in which the computational form of pressure gradient force （PGF） is two-term （the so-called classic method） has significant PGF errors near steep terrain. Using the covariant equations of the a-coordinate to create a one-term PGF （the covariant method） can reduce the PGF errors. This study investigates the factors inducing the PGF errors of these two methods, through geometric analysis and idealized experiments. The geometric analysis first demonstrates that the terrain slope and the vertical pressure gradient can induce the PGF errors of the classic method, and then generalize the effect of the terrain slope to the effect of the slope of each vertical layer （φ）. More importantly, a new factor, the direction of PGF （a）, is proposed by the geometric analysis, and the effects of φ and a are quantified by tan φ.tan a. When tan φ.tan a is greater than 1/9 or smaller than -10/9, the two terms of PGF of the classic method are of the same order but opposite in sign, and then the PGF errors of the classic method are large. Finally, the effects of three factors on inducing the PGF errors of the classic method are validated by a series of idealized experiments using various terrain types and pressure fields. The experimental results also demonstrate that the PGF errors of the covariant method are affected little by the three factors.
文摘In this paper, we present a set of best practices for workflow design and implementation for numerical weather prediction models and meteorological data service, which have been in operation in China Meteorological Administration (CMA) for years and have been proven effective in reliably managing the complexities of large-scale meteorological related workflows. Based on the previous work on the platforms, we argue that a minimum set of guidelines including workflow scheme, module design, implementation standards and maintenance consideration during the whole establishment of the platform are highly recommended, serving to reduce the need for future maintenance and adjustment. A significant gain in performance can be achieved through the workflow-based projects. We believe that a good workflow system plays an important role in the weather forecast service, providing a useful tool for monitoring the whole process, fixing the errors, repairing a workflow, or redesigning an equivalent workflow pattern with new components.
文摘In 2018,a total of US$166 billion global economic losses and a new high of 55.3 Gt of CO_(2)equivalent emission were generated by 831 climate-related extreme events.As the world’s largest CO_(2)emitter,we reported China’s recent progresses and pitfalls in climate actions to achieve climate mitigation targets(i.e.,limit warming to 1.5-2°C above the pre-industrial level).We first summarized China’s integrated actions(2015 onwards)that benefit both climate change mitigation and Sustainable Development Goals(SDGs).These projects include re-structuring organizations,establishing working goals and actions,amending laws and regulations at national level,as well as increasing social awareness at community level.We then pointed out the shortcomings in different regions and sectors.Based on these analyses,we proposed five recommendations to help China improving its climate policy strategies,which include:1)restructuring the economy to balance short-term and long-term conflicts;2)developing circular economy with recycling mechanism and infrastructure;3)building up unified national standards and more accurate indicators;4)completing market mechanism for green economy and encouraging green consumption;and 5)enhancing technology innovations and local incentives via bottom-up actions.
基金Special Programm for Compiling the Fourth National Assessment Report on Climate Change of the Ministry of Science and Technology.
文摘The compilation of technology lists addressing climate change has a guiding effect on promoting technological research and development,demonstration,and popularization.It is also crucial for China to strengthen ecological civilization construction,achieve the carbon emission peak and carbon neutrality target,and enhance global climate governance capabilities.This study first proposes the existing classification outline of the technology promotion lists,technology demand lists,and future technology lists.Then,different methodologies are integrated on the basis of the existing outline of four technology lists:China’s existing technological promotion list for addressing climate change,China’s demand list for climate change mitigation technology,China’s key technology list for addressing climate change,and China’s future technology list for addressing climate change.What’s more,core technologies are analyzed in the aspects of technology maturity,carbon reduction cost,carbon reduction potential,economic benefits,social influence,uncertainty,etc.The results show that:key industries and sectors in China already have relatively mature mitigation/adaptation technologies to support the achievement of climate change targets.The multi-sectoral system of promoting climate friendly technologies has been established,which has played an active role in tackling climate change.Currently,climate technology needs are concentrated in the traditional technology and equipment upgrading,renewable energy technology,and management decision-making support technology.The key technologies are concentrated in 3 major areas and 12 technological directions that urgently need a breakthrough.For carbon emmission peak and nentrality,carbon depth reduction and zero carbon emissions and geoengineering technology(CDR and SRM)have played an important role in forming the structure of global emissions and achieving carbon neutrality in the future.Thus,the uncertainty assessment for the comprehensive technology cost effectiveness,technology integration direction,technical maturity,ethics and ecological impacts is supportive to the national technology strategy.Finally,the presented study proposes several policy implications for medium-and long-term technology deployment,improving technology conversion rate,promoting the research and development of core technologies,and forming a technology list collaborative update and release mechanism.
基金National Key R&D Project of China(2018YFC1507001)。
文摘Tropical cyclone(TC) rainfall forecast has remained a challenge. To create initial conditions with high quality for simulation, the present study implemented a data assimilation scheme based on the EnKF method to ingest the satellite-retrieved cloud water path(C_(w)) and tested it in WRF. The scheme uses the vertical integration of forecasted cloud water content to transform control variables to the observation space, and creates the correlations between C_(w) and control variables in the flow-dependent background error covariance based on all the ensemble members, so that the observed cloud information can affect the background temperature and humidity. For two typhoons in 2018(Yagi and Rumiba), assimilating C_(w) significantly increases the simulated rainfalls and TC intensities. In terms of the average equitable threat score of daily moderate to heavy rainfall(5-120 mm), the improvements are over 130%, and the dry biases are cut by about 30%. Such improvements are traced down to the fact that C_(w) assimilation increases the moisture content, especially that further away from the TC center, which provides more precipitable water for the rainfall,strengthens the TC and broadens the TC size via latent heat release and internal wind field adjustment.
基金Under the auspices of the National Key Research and Development Program of China(No.2019YFA0606603)。
文摘Fire season affects the dynamic changes of post-fire vegetation communities and carbon emissions.Analyzing its global patterns supports understanding of the ecological impacts of fires and responses of fires to climate change.Meteorological variables have been widely used to quantify fire season in current studies.However,their results can not be used to assess climate impacts on the seasonality of fire activities.Here we utilized satellite-based Moderate Resolution Imaging Spectroradiometer(MODIS)burned area data from 2001 to 2022 to identify global fire season types based on the number of peaks within a year.Using satellite data and innovatively processing the data to obtain a more accurate length of the fire season.We divided fire season types and examined the spatial distribution of fire season types across the Koppen-Geiger climate(KGC)zones.At a global scale,we identified three major fire season types,including unimodal(31.25%),bimodal(52.07%),and random(16.69%).The unimodal fire season primarily occurs in boreal and tropical regions lasting about 2.7 mon.In comparison,temperate ecosystems tend to have a longer fire season(3 mon)with two peaks throughout the year.The KGC zones show divergent contributions from the fire season types,indicating potential impacts of the climatic conditions on fire seasonality in these regions.
基金funded by the National Natural Science Foundation of China(Grant Nos.U21A6001,42261144687,42175173)the Project supported by Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(Grant No.SML2023SP208)the GuangDong Basic and Applied Basic Research Foundation(2023A1515240036).
文摘Based on the C-Coupler platform,the semi-unstructured Climate System Model,Synthesis Community Integrated Model version 2(SYCIM2.0),has been developed at the School of Atmospheric Sciences,Sun Yat-sen University.SYCIM2.0 aims to meet the demand for seamless climate prediction through accurate climate simulations and projections.This paper provides an overview of SYCIM2.0 and highlights its key features,especially the coupling of an unstructured ocean model and the tuning process.An extensive evaluation of its performance,focusing on the East Asian Summer Monsoon(EASM),is presented based on long-term simulations with fixed external forcing.The results suggest that after nearly 240 years of integration,SYCIM2.0 achieves a quasi-equilibrium state,albeit with small trends in the net radiation flux at the top-of-atmosphere(TOA)and Earth’s surface,as well as with global mean near-surface temperatures.Compared to observational and reanalysis data,the model realistically simulates spatial patterns of sea surface temperature(SST)and precipitation centers to include their annual cycles,in addition to the lower-level wind fields in the EASM region.However,it exhibits a weakened and eastward-shifted Western Pacific Subtropical High(WPSH),resulting in an associated precipitation bias.SYCIM2.0 robustly captures the dominant mode of the EASM and its close relationship with the El Niño-Southern Oscillation(ENSO)but exhibits relatively poor performance in simulating the second leading mode and the associated air–sea interaction processes.Further comprehensive evaluations of SYCIM2.0 will be conducted in future studies.
