Assessment of climate damage in China based on integrated assessment framework

Developing a localized and consistent model framework for climate loss and damage assessment is crucial for the policy-making of climate change mitigation and adaptation.This study introduces a comprehensive,multidisciplinary Integrated Assessment Model(IAM)framework for evaluating climate damage in China,utilizing BCC-SESM climate model and FUND sectoral climate damage model under the SSP2-RCPs scenario.Employing a bottom-up approach,the research estimates climate damage across eight major sectors,recalibrates sectoral climate damage functions and parameters for China,and elucidates distinctions among direct climate loss,market climate loss,and aggregate climate loss.The findings reveal that the total climate damage function for China follows a quadratic pattern in response to temperature rise.By 2050,the estimated climate damage is projected to be 5.4%,5.7%,and 8.2%of GDP under RCP2.6,RCP4.5,and RCP8.5,respectively.Additionally,both direct and market climate losses are projected to remain below 2%of GDP by 2050,while the aggregate climate loss could reach as high as 8.2%,which is predominantly attributed to non-market sectors.From a sectoral perspective,under the RCP8.5 scenario,human health damage constitutes the largest share(61.9%)of the total climate loss by 2050,followed by sea-level rise damage(18.6%).This study sheds lights on the adaptation policy that should attach importance to the non-market sectors,particularly focusing on human health and sea-level rise.

Spatial and temporal variations of gross primary production simulated by land surface model BCC_AVIM2.0

Gross primary production(GPP)is the largest flux and a crucial player in the terrestrial carbon cycle and has been studied extensively,yet large uncertainties remain in the spatiotemporal patterns of GPP in both observations and simulations.This study evaluates the performance of the second version of the Beijing Climate Center Atmosphere−Vegetation Interaction Model(BCC_AVIM2.0)in simulating GPP on multiple spatial and temporal scales in the Coupled Model Intercomparison Project Phase 6(CMIP6)experiments.Model simulations driven by two meteorological datasets were compared with two observation-based GPP products covering 1982–2008.Spatial patterns of annual GPP show a significant latitudinal gradient in each dataset,increasing from cold(tundra)and dry(desert)biomes to warm(temperate)and humid(tropical rainforest)biomes.BCC_AVIM2.0 overestimates GPP in most parts of the globe,especially in boreal forest regions and Southeast China,while underestimating GPP in subhumid regions in eastern South America and tropical Africa.The four datasets broadly agree on the GPP seasonal cycle,but BCC_AVIM2.0 predicts an earlier beginning of spring growth and a larger amplitude of seasonal variations than those in the observations.The observation-based datasets exhibit slight interannual variability(IAV)and weak GPP linear trends,while the BCC_AVIM2.0 simulations demonstrate relatively large year-to-year variability and significant trends in the low-latitudes and temperate monsoon regions in North America and East Asia.Regarding the possible relationships between annual means of GPP and climate factors,BCC_AVIM2.0 predicts more extensive regions of the globe where the IAV of annual GPP is dominated by precipitation,especially in mid-to-high latitudes of the Northern Hemisphere and tropical Africa,while the observed GPP in the above regions is temperature-or radiation-dominant.The positive GPP biases due to earlier spring growth in boreal forest regions and negative GPP biases in off-equator tropical areas in the BCC_AVIM2.0 simulations imply that cold stress on biomes in boreal mid-to-high latitudes should be strengthened to restrain plant growth,while drought stress in low-latitude regions might be eased to enhance plant production in the future version of BCC_AVIM.

Revisiting BCC-SESM parameters sensitivity with BCC-CSM1.1 co_(2)-concentration-driven simulations

Based on the results of the complex climate model BCC-CSM,the Beijing Climate Center Simple Earth System Model(BCC-SESM)was developed for climate system simulations in Integrated Assessment Models(IAMs).The first version of the BCC-SESM model was based on a high-emissions scenario(ESMRCP8.5)and tends to overestimate the temperatures in low and medium emissions scenarios.To address this problem,this study uses three CO_(2)-concentration-driven simulations under different RCP scenarios of complex climate models to evaluate parameters sensitivity and their impacts on projection efficacy.The results show that the new version of the BCC-SESM(denoted as BCC-SESM1.1)model based on a medium-emissions scenario experiment(RCP4.5)is more suitable for temperature projections for various climate scenarios.It can well reproduce the original value of complex climate model.At the same time,it also has high predictive efficacies for medium(RCP4.5)and low(RCP2.6)emissions scenarios,although it tends to underestimate for high emissions scenario(RCP8.5).The sensitivity tests for different RCP scenarios shows that the BCC-SESM1.1 has higher efficacy in projections of future climate change than those model versions based on the other scenarios.The projection deviations for the global average temperature by the BCC-SESM1.1(<2%)are better than the previous BCC-SESM(<5%).In light of recent progress in climate policy,the BCC-SESM1.1 is hence more suitable for coupling with IAMs for the purposes of assessing climate outcomes.