Spatial and temporal patterns of the sensitivity of radial growth response by Picea schrenkiana to regional climate change in the Tianshan Mountains

Climate change significantly impacts forest ecosystems in arid and semi-arid regions.However,spatiotemporal patterns of climate-sensitive changes in individual tree growth under increased climate warming and precipitation in north-west China is unclear.The dendrochronological method was used to study climate response sensitivity of radial growth of Picea schrenkiana from 158 trees at six sites during 1990-2020.The results show that climate warming and increased precipitation significantly promoted the growth of trees.The response to temperature first increased,then decreased.However,the response to increased precipitation and the self-calibrating Palmer Drought Severity Index(scPDSI)increased significantly.In most areas of the Tianshan Mountains,the proportion of trees under increased precipitation and scPDSI positive response was relatively high.Over time,small-diameter trees were strongly affected by drought stress.It is predicted that under continuous warming and increased precipitation,trees in most areas of the Tianshan Mountains,especially those with small diameters,will be more affected by precipitation.

Empirical Estimates of Global Climate Sensitivity:An Assessment of Strategies Using a Coupled GCM

A control integration with the normal solar constant and one with it increased by 2.5% in the National Center for Atmospheric Research （NCAR） coupled atmosphere-ocean Climate System Model were conducted to see how well the actual realized global warming could be predicted just by analysis of the control results. This is a test, within a model context, of proposals that have been advanced to use knowledge of the present day climate to make ＂empirical＂ estimates of global climate sensitivity. The scaling of the top-of-the-atmosphere infrared flux and the planetary albedo as functions of surface temperature was inferred by examining four different temporal and geographical variations of the control simulations. Each of these inferences greatly overestimates the climate sensitivity of the model, largely because of the behavior of the cloud albedo. In each inference the control results suggest that cloudiness and albedo decrease with increasing surface temperature. However, the experiment with the increased solar constant actually has higher albedo and more cloudiness at most latitudes. The increased albedo is a strong negative feedback, and this helps account for the rather weak sensitivity of the climate in the NCAR model. To the extent that these model results apply to the real world, they suggest empirical evaluation of the scaling of global-mean radiative properties with surface temperature in the present day climate provides little useful guidance for estimates of the actual climate sensitivity to global changes.

Reducing Uncertainties in Climate Projections with Emergent Constraints:Concepts, Examples and Prospects

Models disagree on a significant number of responses to climate change,such as climate feedback,regional changes,or the strength of equilibrium climate sensitivity.Emergent constraints aim to reduce these uncertainties by finding links between the inter-model spread in an observable predictor and climate projections.In this paper,the concepts underlying this framework are recalled with an emphasis on the statistical inference used for narrowing uncertainties,and a review of emergent constraints found in the last two decades.Potential links between highlighted predictors are explored,especially those targeting uncertainty reductions in climate sensitivity,cloud feedback,and changes of the hydrological cycle.Yet the disagreement across emergent constraints suggests that the spread in climate sensitivity can not be significantly narrowed.This calls for weighting the realism of emergent constraints by quantifying the level of physical understanding explaining the relationship.This would also permit more efficient model evaluation and better targeted model development.In the context of the upcoming CMIP6 model intercomparison a growing number of new predictors and uncertainty reductions is expected,which call for robust statistical inferences that allow cross-validation of more likely estimates.

Climate Sensitivity and Feedbacks of BCC-CSM to Idealized CO2 Forcing from CMIP5 to CMIP6

Climate sensitivity represents the response of climate system to doubled CO2 concentration relative to the preindustrial level, which is one of the sources of uncertainty in climate projections. It is unclear how the climate sensitivity and feedbacks will change as a model system is upgraded from the Coupled Model Intercomparison Project Phase 5(CMIP5) to CMIP6. In this paper, we address this issue by comparing two versions of the Beijing Climate Center Climate System Model(BCC-CSM) participating in CMIP6 and CMIP5, i.e., BCC-CSM2-MR and BCC-CSM1.1 m,which have the same horizontal resolution but different physical parameterizations. The results show that the equilibrium climate sensitivity(ECS) of BCC-CSM slightly increases from CMIP5(2.94 K) to CMIP6(3.04 K). The small changes in the ECS result from compensation between decreased effective radiative forcing(ERF) and the increased net feedback. In contrast, the transient climate response(TCR) evidently decreases from 2.19 to 1.40 K, nearly the lower bound of the CMIP6 multimodel spread. The low TCR in BCC-CSM2-MR is mainly caused by the small ERF overly even though the ocean heat uptake(OHU) efficiency is substantially improved from that in BCC-CSM1.1 m.Cloud shortwave feedback(λSWCL) is found to be the major cause of the increased net feedback in BCC-CSM2-MR,mainly over the Southern Ocean. The strong positive λSWCL in BCC-CSM2-MR is coincidently related to the weakened sea ice-albedo feedback in the same region. This result is caused by reduced sea ice coverage simulated during the preindustrial cold season, which leads to reduced melting per 1-K global warming. As a result, in BCCCSM2-MR, reduced surface heat flux and strengthened static stability of the planetary boundary layer cause a decrease in low-level clouds and an increase in incident shortwave radiation. This study reveals the important compensation between λSWCL and sea ice-albedo feedback in the Southern Ocean.

Why Does FGOALS-gl Reproduce a Weak Medieval Warm Period But a Reasonable Little Ice Age and 20th Century Warming?

To understand the strengths and limitations of a low-resolution version of Flexible Global Ocean Atmosphere-Land-Sea-ice （FGOALS-gl） to simulate the climate of the last millennium, the energy balance, climate sensitivity and absorption feedback of the model are analyzed. Simulation of last-millennium climate was carried out by driving the model with natural （solar radiation and volcanic eruptions） and anthropogenic （greenhouse gases and aerosols） forcing agents. The model feedback factors for （model sensitivity to） different forcings were calculated. The results show that the system feedback factor is about 2.5 （W m-2） K-1 in the pre-industrial period, while 1.9 （W m-2） K-1 in the industrial era. Thus, the model＇s sensitivity to natural forcing is weak, which explains why it reproduces a weak Medieval Warm Period. The relatively reasonable simulation of the Little Ice Age is caused by both the specified radiative forcing and unforced linear cold drift. The model sensitivity in the industrial era is higher than that of the pre-industrial period. A negative net cloud radiative feedback operates during whole-millennial simulation and reduces the model＇s sensitivity to specified forcing. The negative net cloud radiative forcing feedback under natural forcing in the period prior to 1850 is due to the underestimation （overestimation） of the response of cloudiness （in-cloud water path）. In the industrial era, the strong tropospheric temperature response enlarges the effective radius of ice clouds and reduces the fractional ice content within cloud, resulting in a weak negative net cloud feedback in the industrial period. The water vapor feedback in the industrial era is also stronger than that in the pre-industrial period. Both are in favor of higher model sensitivity and thus a reasonable simulation of the 20th century global warming.

BCC-ESM1 Model Datasets for the CMIP6 Aerosol Chemistry Model Intercomparison Project (AerChemMIP)

BCC-ESM1 is the first version of the Beijing Climate Center’s Earth System Model,and is participating in phase 6 of the Coupled Model Intercomparison Project(CMIP6).The Aerosol Chemistry Model Intercomparison Project(AerChemMIP)is the only CMIP6-endorsed MIP in which BCC-ESM1 is involved.All AerChemMIP experiments in priority 1 and seven experiments in priorities 2 and 3 have been conducted.The DECK(Diagnostic,Evaluation and Characterization of Klima)and CMIP historical simulations have also been run as the entry card of CMIP6.The AerChemMIP outputs from BCC-ESM1 have been widely used in recent atmospheric chemistry studies.To facilitate the use of the BCC-ESM1 datasets,this study describes the experiment settings and summarizes the model outputs in detail.Preliminary evaluations of BCC-ESM1 are also presented,revealing that:the climate sensitivities of BCC-ESM1 are well within the likely ranges suggested by IPCC AR5;the spatial structures of annual mean surface air temperature and precipitation can be reasonably captured,despite some common precipitation biases as in CMIP5 and CMIP6 models;a spurious cooling bias from the 1960s to 1990s is evident in BCC-ESM1,as in most other ESMs;and the mean states of surface sulfate concentrations can also be reasonably reproduced,as well as their temporal evolution at regional scales.These datasets have been archived on the Earth System Grid Federation(ESGF)node for atmospheric chemistry studies.

A Piecewise Modeling Approach for Climate Sensitivity Studies:Tests with a Shallow-Water Model

In model-based climate sensitivity studies, model errors may grow during continuous long-term inte- grations in both the ＂reference＂ and ＂perturbed＂ states and hence the climate sensitivity （defined as the difference between the two states）. To reduce the errors, we propose a piecewise modeling approach that splits the continuous long-term simulation into subintervals of sequential short-term simulations, and updates the modeled states through re-initialization at the end of each subinterval. In the re-initialization processes, this approach updates the reference state with analysis data and updates the perturbed states with the sum of analysis data and the difference between the perturbed and the reference states, thereby improving the credibility of the modeled climate sensitivity. We conducted a series of experiments with a shallow-water model to evaluate the advantages of the piecewise approach over the conventional continuous modeling approach. We then investigated the impacts of analysis data error and subinterval length used in the piecewise approach on the simulations of the reference and perturbed states as well as the resulting climate sensitivity. The experiments show that the piecewise approach reduces the errors produced by the conventional continuous modeling approach, more effectively when the analysis data error becomes smaller and the subinterval length is shorter. In addition, we employed a nudging assimilation technique to solve possible spin-up problems caused by re-initializations by using analysis data that contain inconsistent errors between mass and velocity. The nudging technique can effectively diminish the spin-up problem, resulting in a higher modeling skill.

Uncertainty in the 2℃ Warming Threshold Related to Climate Sensitivity and Climate Feedback

Climate sensitivity is an important index that measures the relationship between the increase in greenhouse gases and the magnitude of global warming.Uncertainties in climate change projection and climate modeling are mostly related to the climate sensitivity.The climate sensitivities of coupled climate models determine the magnitudes of the projected global warming.In this paper,the authors thoroughly review the literature on climate sensitivity,and discuss issues related to climate feedback processes and the methods used in estimating the equilibrium climate sensitivity and transient climate response（TCR）,including the TCR to cumulative CO2 emissions.After presenting a summary of the sources that affect the uncertainty of climate sensitivity,the impact of climate sensitivity on climate change projection is discussed by addressing the uncertainties in 2℃ warming.Challenges that call for further investigation in the research community,in particular the Chinese community,are discussed.

Empirical assessment of the role of the Sun in climate change using balanced multi-proxy solar records

The role of the Sun in climate change is hotly debated.Some studies suggest its impact is significant,while others suggest it is minimal.The Intergovernmental Panel on Climate Change(IPCC)supports the latter view and suggests that nearly 100%of the observed surface warming from 1850–1900 to 2020 is due to anthropogenic emissions.However,the IPCC’s conclusions are based solely on computer simulations made with global climate models(GCMs)forced with a total solar irradiance(TSI)record showing a low multi-decadal and secular variability.The same models also assume that the Sun affects the climate system only through radiative forcing–such as TSI–even though the climate could also be affected by other solar processes.In this paper I propose three“balanced”multi-proxy models of total solar activity(TSA)that consider all main solar proxies proposed in scientific literature.Their optimal signature on global and sea surface temperature records is assessed together with those produced by the anthropogenic and volcanic radiative forcing functions adopted by the CMIP6 GCMs.This is done by using a basic energy balance model calibrated with a differential multi-linear regression methodology,which allows the climate system to respond to the solar input differently than to radiative forcings alone,and to evaluate the climate’s characteristic time-response as well.The proposed methodology reproduces the results of the CMIP6 GCMs when their original forcing functions are applied under similar physical conditions,indicating that,in such a scenario,the likely range of the equilibrium climate sensitivity(ECS)could be 1.4℃to 2.8℃,with a mean of 2.1℃(using the HadCRUT5 temperature record),which is compatible with the low-ECS CMIP6 GCM group.However,if the proposed solar records are used as TSA proxies and the climatic sensitivity to them is allowed to differ from the climatic sensitivity to radiative forcings,a much greater solar impact on climate change is found,along with a significantly reduced radiative effect.In this case,the ECS is found to be 0.9–1.8℃,with a mean of around 1.3℃.Lower ECS ranges(up to 20%)are found using HadSST4,HadCRUT4,and HadSST3.The result also suggests that at least about 80%of the solar influence on the climate may not be induced by TSI forcing alone,but rather by other Sun-climate processes(e.g.,by a solar magnetic modulation of cosmic ray and other particle fluxes,and/or others),which must be thoroughly investigated and physically understood before trustworthy GCMs can be created.This result explains why empirical studies often found that the solar contribution to climate changes throughout the Holocene has been significant,whereas GCM-based studies,which only adopt radiative forcings,suggest that the Sun plays a relatively modest role.

Historical Change and Future Scenarios of Sea Level Rise in Macao and Adjacent Waters

Against a background of climate change, Macao is very exposed to sea level rise （SLR） because of its low elevation, small size, and ongoing land reclamation. Therefore, we evaluate sea level changes in Macao, both historical and, especially, possible future scenarios, aiming to provide knowledge and a framework to help accommodate and protect against future SLR. Sea level in Macao is now rising at an accelerated rate： 1.35 mm yr-1 over 1925-2010 and jumping to 4.2 mm yr I over 1970-2010, which outpaces the rise in global mean sea level. In addition, vertical land movement in Macao contributes little to local sea level change. In the future, the rate of SLR in Macao will be about 20% higher than the global average, as a consequence of a greater local warming tendency and strengthened northward winds. Specifically, the sea level is projected to rise 8-12, 22-51 and 35-118 cm by 2020, 2060 and 2100, respectively, depending on the emissions scenario and climate sensitivity. Under the --8.5 W m 2 Representative Concentration Pathway （RCP8.5） scenario the increase in sea level by 2100 will reach 65 118 cm--double that under RCP2.6. Moreover, the SLR will accelerate under RCP6.0 and RCP8.5, while remaining at a moderate and steady rate under RCP4.5 and RCP2.6. The key source of uncertainty stems from the emissions scenario and climate sensitivity, among which the discrepancies in SLR are small during the first half of the 21st century but begin to diverge thereafter.

Sustainability of Wind Energy under Changing Wind Regimes—A Case Study

A method was introduced to assess the sustainability of energy production over the lifetime (~20 y) of wind turbines. Community Earth System Model simulations were downscaled for the tourist seasons (mid-May to mid-September) of 2006 to 2012 (CESM-P1) and 2026 to 2032 (CESM-P2) to obtain a reference and projected wind-speed climatology, respectively. The wind speeds served to calculate the potential power output and capacity factors of seven turbine types. CESM-P1 wind-speed climatology, power output, and capacity factors were compared to those derived from wind speeds obtained by numerical weather forecasts for reference to known standard to wind-farm managers. Juneau, Alaska served as a virtual testbed as this region is known to experience changes in wind speeds in response to the Pacific Decadal Oscillation. CESM-P2 suggested about 2% decrease for wind speeds between the speeds at cut-in and rated power, and about 8% - 10% decrease in potential wind-power output. This means that in regions of decadal climate variations, the sustainability of wind-energy production should be part of the decision-making process. The study demonstrated that using mean values of wind-speeds can provide qualitative knowledge about decreases/increases in potential energy production, but not about the magnitude. Using the total individual wind-speed data of all seasons provided the same amount of total power output than summing up the power outputs of individual seasons. The main advantage of calculating individual seasonal wind-power outputs, however, is that it theoretically permits assessment of interannual variability in power output and capacity factors. Comparison to a known standard may help stakeholders in understanding of uncertainty and interpretation of projected changes.

Sensitivity of precipitation statistics to urban growth in a subtropical coastal megacity cluster

This short paper presents an investigation on how human activities may or may not affect precipitation based on numerical simulations of precipitation in a benchmark case with modified lower boundary conditions, representing different stages of urban development in the model. The results indicate that certain degrees of urbanization affect the likelihood of heavy precipitation significantly, while less urbanized or smaller cities are much less prone to these effects. Such a result can be explained based on our previous work where the sensitivity of precipitation statistics to surface anthropogenic heat sources lies in the generation of buoyancy and turbulence in the planetary boundary layer and dissipation through triggering of convection. Thus only mega cities of sufficient size, and hence human-activity-related anthropogenic heat emission, can expect to experience such effects. In other words, as cities grow, their effects upon precipitation appear to grow as well.

Impact of Climate Change on Maize Potential Productivity and the Potential Productivity Gap in Southwest China

The impact of climate change on maize potential productivity and the potential productivity gap in Southwest China（SWC） are investigated in this paper.We analyze the impact of climate change on the photosynthetic,light-temperature,and climatic potential productivity of maize and their gaps in SWC,by using a crop growth dynamics statistical method.During the maize growing season from 1961 to 2010,minimum temperature increased by 0.20℃ per decade（p 〈 0.01） across SWC.The largest increases in average and minimum temperatures were observed mostly in areas of Yunnan Province.Growing season average sunshine hours decreased by 0.2 h day^（-1） per decade（p 〈 0.01） and total precipitation showed an insignificant decreasing trend across SWC.Photosynthetic potential productivity decreased by 298 kg ha^（-1）per decade（p 〈 0.05）.Both light-temperature and climatic potential productivity decreased（p 〈 0.05） in the northeast of SWC,whereas they increased（p 〈 0.05） in the southwest of SWC.The gap between lighttemperature and climatic potential productivity varied from 12 to 2729 kg ha^（-1）,with the high value areas centered in northern and southwestern SWC.Climatic productivity of these areas reached only 10%-24%of the light-temperature potential productivity,suggesting that there is great potential to increase the maize potential yield by improving water management in these areas.In particular,the gap has become larger in the most recent 10 years.Sensitivity analysis shows that the climatic potential productivity of maize is most sensitive to changes in temperature in SWC.The findings of this study are helpful for quantification of irrigation water requirements so as to achieve maximum yield potentials in SWC.

Interannual variability and climatic sensitivity of global wildfire activity

Understanding historical wildfire variations and their environmental driving mechanisms is key to predicting and mitigating wildfires. However, current knowledge of climatic responses and regional contributions to the interannual variability (IAV) of global burned area remains limited. Using recent satellite-derived wildfire products and simulations from version v1.0 of the land component of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM land model [ELM] v1) driven by three different climate forcings, we investigated the burned area IAV and its climatic sensitivity globally and across nine biomes from 1997 to 2018. We found that 1) the ELM simulations generally agreed with the satellite observations in terms of the burned area IAV magnitudes, regional contributions, and covariations with climate factors, confirming the robustness of the ELM to the usage of different climate forcing sources;2) tropical savannas, tropical forests, and semi-arid grasslands near deserts were primary contributors to the global burned area IAV, collectively accounting for 71.7%–99.7% of the global wildfire IAV estimated by both the satellite observations and ELM simulations;3) precipitation was a major fire suppressing factor and dominated the global and regional burned area IAVs, and temperature and shortwave solar radiation were mostly positively related with burned area IAVs;and 4) noticeable local discrepancies between the ELM and remote-sensing results occurred in semi-arid grasslands, croplands, boreal forests, and wetlands, likely caused by uncertainties in the current ELM fire scheme and the imperfectly derived satellite observations. Our findings revealed the spatiotemporal diversity of wildfire variations, regional contributions and climatic responses, and provided new metrics for wildfire modeling, facilitating the wildfire prediction and management.

Chinese Contribution to CMIP5:An Overview of Five Chinese Models' Performances

An overview of Chinese contribution to Coupled Model Intercomparison Project-Phase 5 （CMIP5） is presented. The performances of five Chinese Climate/Earth System Models that participated in the CMIP5 pro ject are assessed in the context of climate mean states, seasonal cycle, intraseasonal oscillation, interan-nual variability, interdecadal variability, global monsoon, Asian-Australian monsoon, 20th-century historical climate simulation, climate change pro jection, and climate sensitivity. Both the strengths and weaknesses of the models are evaluated. The models generally show reasonable performances in simulating sea surface tem-perature （SST） mean state, seasonal cycle, spatial patterns of Madden-Julian oscillation （MJO） amplitude and tropical cyclone Genesis Potential Index （GPI）, global monsoon precipitation pattern, El Ni-no-Southern Oscillation （ENSO）, and Pacific Decadal Oscillation （PDO） related SST anomalies. However, the perfor-mances of the models in simulating the time periods, amplitude, and phase locking of ENSO, PDO time periods, GPI magnitude, MJO propagation, magnitude of SST seasonal cycle, northwestern Pacific mon-soon and North American monsoon domains, as well as the skill of large-scale Asian monsoon precipitation need to be improved. The model performances in simulating the time evolution and spatial pattern of the 20th-century global warming and the future change under representative concentration pathways pro jection are compared to the multimodel ensemble of CMIP5 models. The model discrepancies in terms of climate sensitivity are also discussed.

Deep learning projects future warming-induced vegetation growth changes under SSP scenarios

Climate warming has been projected to enhance vegetation growth more strongly in higher latitudes than in lower latitudes,but different projections show distinct regional differences.By employing big data analysis(deep learning),we established gridded,global-scale,climate-driven vegetation growth models to project future changes in vegetation growth under SSP scenarios.We projected no substantial trends of vegetation growth change under the sustainable development scenario(SSP1-1.9)by the end of the 21st century.However,the increase of vegetation growth driven by climate warming shows distinct regional variability under the scenario representing high carbon emissions and severe warming(SSP5-8.5),especially in Northeast Asia where growth could increase by(6.00%±4.21%).This may be attributed to the high temperature sensitivities of the deciduous needleleaf forests and permanent wetlands in these regions.When the temperature sensitivity that is defined as permutation importance in deep learning is greater than 0.05,the increase in vegetation growth will be more prominent.In addition,an extreme temperature increase across grasslands,as well as changing land-use management in northern China may also influence the vegetation growth in the future.The results suggest that the sustainable development scenario can maintain stable vegetation growth,and it may be a reliable way to mitigate global warming due to potential climate feedbacks driven by vegetation changes in boreal regions.Deciduous needleleaf forests will be a centre of greening in the future,and it should become the focus of future vegetation dynamics modelling studies and projections.