Impacts of Multi-Scale Solar Activity on Climate.Part Ⅰ:Atmospheric Circulation Patterns and Climate Extremes

The impacts of solar activity on climate are explored in this two-part study. Based on the principles of atmospheric dynamics, Part I propose an amplifying mechanism of solar impacts on winter climate extremes through changing the atmospheric circulation patterns. This mechanism is supported by data analysis of the sunspot number up to the predicted Solar Cycle 24, the historical surface temperature data, and atmospheric variables of NCEP/NCAR Reanalysis up to the February 2011 for the Northern Hemisphere winters. For low solar activity, the thermal contrast between the low- and high-latitudes is enhanced, so as the mid-latitude baroclinic ultra-long wave activity. The land-ocean thermal contrast is also enhanced, which amplifies the topographic waves. The enhanced mid-latitude waves in turn enhance the meridional heat transport from the low to high latitudes, making the atmospheric ＂heat engine＂ more efficient than normal. The jets shift southward and the polar vortex is weakened. The Northern Annular Mode （NAM） index tends to be negative. The mid-latitude surface exhibits large-scale convergence and updrafts, which favor extreme weather/climate events to occur. The thermally driven Siberian high is enhanced, which enhances the East Asian winter monsoon （EAWM）. For high solar activity, the mid-latitude circulation patterns are less wavy with less meridional transport. The NAM tends to be positive, and the Siberian high and the EAWM tend to be weaker than normal. Thus the extreme weather/climate events for high solar activity occur in different regions with different severity from those for low solar activity. The solar influence on the mid- to high-latitude surface temperature and circulations can stand out after removing the influence from the E1 Nifio-Southern Oscillation. The atmospheric amplifying mechanism indicates that the solar impacts on climate should not be simply estimated by the magnitude of the change in the solar radiation over solar cycles when it is compared with other external radiative forcings that do not influence the climate in the same way as the sun does.

Impacts of Multi-Scale Solar Activity on Climate.Part Ⅱ:Dominant Timescales in Decadal-Centennial Climate Variability

Part II of this study detects the dominant decadal-centennial timescales in four SST indices up to the 2010/2011 winter and tries to relate them to the observed 11-yr and 88-yr solar activity with the sunspot number up to Solar Cycle 24. To explore plausible solar origins of the observed decadal-centennial timescales in the SSTs and climate variability in general, we design a simple one-dimensional dynamical system forced by an annual cycle modulated by a small-amplitude single- or multi-scale ＂solar activity.＂ Results suggest that nonlinear harmonic and subharmonic resonance of the system to the forcing and period-doubling bifurcations are responsible for the dominant timescales in the system, including the 60-yr timescale that dominates the Atlantic Multidecadal Oscillation. The dominant timescales in the forced system depend on the system＇s parameter setting. Scale enhancement among the dominant response timescales may result in dramatic amplifications over a few decades and extreme values of the time series on various timescales. Three possible energy sources for such amplifications and extremes are proposed. Dynamical model results suggest that solar activity may play an important yet not well recognized role in the observed decadal-centennial climate variability. The atmospheric dynamical amplifying mechanism shown in Part I and the nonlinear resonant and bifurcation mechanisms shown in Part II help us to understand the solar source of the multi-scale climate change in the 20th century and the fact that different solar influenced dominant timescales for recurrent climate extremes for a given region or a parameter setting. Part II also indicates that solar influences on climate cannot be linearly compared with non-cyclic or sporadic thermal forcings because they cannot exert their influences on climate in the same way as the sun does.

A Fair Plan to Safeguard Earth’s Climate. 3: Outlook for Global Temperature Change throughout the 21st Century

We apply Singular Spectrum Analysis to four datasets of observed global-mean near-surface temperature from start year to through 2012: HadCRU (to = 1850), NOAA (to = 1880), NASA (to = 1880), and JMA (to = 1891). For each dataset, SSA reveals a trend of increasing temperature and several quasi-periodic oscillations (QPOs). QPOs 1, 2 and 3 are predictable on a year-by-year basis by sine waves with periods/amplitudes of: 1) 62.4 years/0.11°C;2) 20.1 to 21.4 years/0.04°C to 0.05°C;and 3) 9.1 to 9.2 years/0.03°C to 0.04°C. The remainder of the natur°l variability is not predictable on a year-by-year basis. We represent this noise by its 90 percent confidence interval. We combine the predictable and unpredictable natural variability with the temperature changes caused by the 11-year solar cycle and humanity, the latter for both the Reference and Revised-Fair-Plan scenarios for future emissions of greenhouse gases. The resulting temperature departures show that we have moved from the first phase of learning—Ignorance—through the second phase—Uncertainty—and are now entering the third phase—Resolution—when the human-caused signal is much larger than the natural variability. Accordingly, it is now time to transition to the post-fossil-fuel age by phasing out fossil-fuel emissions from 2020 through 2100.

Toward a Learnable Climate Model in the Artificial Intelligence Era

Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of physics in climate science has occasionally been overlooked.Our perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics,rather than an“either/or”scenario.Scrutinizing controversies around current physical inconsistencies in large AI models,we stress the critical need for detailed dynamic diagnostics and physical constraints.Furthermore,we provide illustrative examples to guide future assessments and constraints for AI models.Regarding AI integration with numerical models,we argue that offline AI parameterization schemes may fall short of achieving global optimality,emphasizing the importance of constructing online schemes.Additionally,we highlight the significance of fostering a community culture and propose the OCR(Open,Comparable,Reproducible)principles.Through a better community culture and a deep integration of physics and AI,we contend that developing a learnable climate model,balancing AI and physics,is an achievable goal.

New Record Ocean Temperatures and Related Climate Indicators in 2023

The global physical and biogeochemical environment has been substantially altered in response to increased atmospheric greenhouse gases from human activities.In 2023,the sea surface temperature(SST)and upper 2000 m ocean heat content(OHC)reached record highs.The 0–2000 m OHC in 2023 exceeded that of 2022 by 15±10 ZJ(1 Zetta Joules=1021 Joules)(updated IAP/CAS data);9±5 ZJ(NCEI/NOAA data).The Tropical Atlantic Ocean,the Mediterranean Sea,and southern oceans recorded their highest OHC observed since the 1950s.Associated with the onset of a strong El Niño,the global SST reached its record high in 2023 with an annual mean of~0.23℃ higher than 2022 and an astounding>0.3℃ above 2022 values for the second half of 2023.The density stratification and spatial temperature inhomogeneity indexes reached their highest values in 2023.

Climate-growth relationships of Pinus tabuliformis along an altitudinal gradient on Baiyunshan Mountain,Central China

A set of standard chronologies for tree-ring width(TRW),earlywood width(EWW)and latewood width(LWW)in Pinus tabuliformis Carr.along an altitudi-nal gradient(1450,1400,and 1350 m a.s.l.)on Baiyunshan Mountain,Central China to analyze the effect of varying temperature and precipitation on growth along the gradi-ent.Correlation analyses showed that at all three altitudes and the TRW and EWW chronologies generally had signifi-cant negative correlations with mean and maximum tem-peratures in the current April and May and with minimum temperatures in the prior July and August,but significant positive correlations with precipitation in the current May.Correlations were generally significantly negative between LWW chronologies and all temperatures in the prior July and August,indicating that the prior summer temperature had a strong lag effect on the growth of P.tabuliformis that increased with altitude.The correlation with the standard-ized precipitation evapotranspiration index(SPEI)confirmed that wet conditions in the current May promoted growth of TR and EW at all altitudes.Significant altitudinal differences were also found;at 1400 m,there were significant positive correlations between EWW chronologies and SPEI in the current April and significant negative correlations between LWW chronologies and SPEI in the current September,but these correlations were not significant at 1450 m.At 1350 m,there were also significant negative correlations between the TRW and the EWW chronologies and SPEI in the prior October and the current July and between LWW chronology and SPEI in the current August,but these cor-relations were not significant at 1400 m.Moving correlation results showed a stable response of EWW in relation to the SPEI in the current May at all three altitudes and of LWW to maximum temperature in the prior July-August at 1400 m from 2002 to 2018.The EWW chronology at 1400 m and the LWW chronology at 1450 m were identified as more suitable for climate reconstruction.These results provide a strong scientific basis for forest management decisions and climate reconstructions in Central China.

Spatiotemporal changes of gross primary productivity and its response to drought in the Mongolian Plateau under climate change

Gross primary productivity(GPP)of vegetation is an important constituent of the terrestrial carbon sinks and is significantly influenced by drought.Understanding the impact of droughts on different types of vegetation GPP provides insight into the spatiotemporal variation of terrestrial carbon sinks,aiding efforts to mitigate the detrimental effects of climate change.In this study,we utilized the precipitation and temperature data from the Climatic Research Unit,the standardized precipitation evapotranspiration index(SPEI),the standardized precipitation index(SPI),and the simulated vegetation GPP using the eddy covariance-light use efficiency(EC-LUE)model to analyze the spatiotemporal change of GPP and its response to different drought indices in the Mongolian Plateau during 1982-2018.The main findings indicated that vegetation GPP decreased in 50.53% of the plateau,mainly in its northern and northeastern parts,while it increased in the remaining 49.47%area.Specifically,meadow steppe(78.92%)and deciduous forest(79.46%)witnessed a significant decrease in vegetation GPP,while alpine steppe(75.08%),cropland(76.27%),and sandy vegetation(87.88%)recovered well.Warming aridification areas accounted for 71.39% of the affected areas,while 28.53% of the areas underwent severe aridification,mainly located in the south and central regions.Notably,the warming aridification areas of desert steppe(92.68%)and sandy vegetation(90.24%)were significant.Climate warming was found to amplify the sensitivity of coniferous forest,deciduous forest,meadow steppe,and alpine steppe GPP to drought.Additionally,the drought sensitivity of vegetation GPP in the Mongolian Plateau gradually decreased as altitude increased.The cumulative effect of drought on vegetation GPP persisted for 3.00-8.00 months.The findings of this study will improve the understanding of how drought influences vegetation in arid and semi-arid areas.

A study on the simulation of carbon and water fluxes of Dangxiong alpine meadow and its response to climate change

The alpine meadow ecosystem accounts for 27%of the total area of the Tibetan Plateau and is also one of the most important vegetation types.The Dangxiong alpine meadow ecosystem,located in the south-central part of the Tibetan Plateau,is a typical example.To understand the carbon and water fluxes,water use efficiency(WUE),and their responses to future climate change for the alpine meadow ecosystem in the Dangxiong area,two parameter estimation methods,the Model-independent Parameter Estimation(PEST)and the Dynamic Dimensions Search(DDS),were used to optimize the Biome-BGC model.Then,the gross primary productivity(GPP)and evapotranspiration(ET)were simulated.The results show that the DDS parameter calibration method has a better performance.The annual GPP and ET show an increasing trend,while the WUE shows a decreasing trend.Meanwhile,ET and GPP reach their peaks in July and August,respectively,and WUE shows a“dual-peak”pattern,reaching peaks in May and November.Furthermore,according to the simulation results for the next nearly 100 years,the ensemble average GPP and ET exhibit a significant increasing trend,and the growth rate under the SSP5–8.5 scenario is greater than that under the SSP2–4.5 scenario.WUE shows an increasing trend under the SSP2–4.5 scenario and a significant increasing trend under the SSP5–8.5 scenario.This study has important scientific significance for carbon and water cycle prediction and vegetation ecological protection on the Tibetan Plateau.

Future changes in precipitation and water availability over the Tibetan Plateau projected by CMIP6 models constrained by climate sensitivity

Precipitation projections over the Tibetan Plateau(TP)show diversity among existing studies,partly due to model uncertainty.How to develop a reliable projection remains inconclusive.Here,based on the IPCC AR6–assessed likely range of equilibrium climate sensitivity(ECS)and the climatological precipitation performance,the authors constrain the CMIP6(phase 6 of the Coupled Model Intercomparison Project)model projection of summer precipitation and water availability over the TP.The best estimates of precipitation changes are 0.24,0.25,and 0.45 mm d^(−1)(5.9%,6.1%,and 11.2%)under the Shared Socioeconomic Pathway(SSP)scenarios of SSP1–2.6,SSP2–4.5,and SSP5–8.5 from 2050–2099 relative to 1965–2014,respectively.The corresponding constrained projections of water availability measured by precipitation minus evaporation(P–E)are 0.10,0.09,and 0.22 mm d^(−1)(5.7%,4.9%,and 13.2%),respectively.The increase of precipitation and P–E projected by the high-ECS models,whose ECS values are higher than the upper limit of the likely range,are about 1.7 times larger than those estimated by constrained projections.Spatially,there is a larger increase in precipitation and P–E over the eastern TP,while the western part shows a relatively weak difference in precipitation and a drier trend in P–E.The wetter TP projected by the high-ECS models resulted from both an approximately 1.2–1.4 times stronger hydrological sensitivity and additional warming of 0.6℃–1.2℃ under all three scenarios during 2050–2099.This study emphasizes that selecting climate models with climate sensitivity within the likely range is crucial to reducing the uncertainty in the projection of TP precipitation and water availability changes.

Climate change drives flooding risk increases in the Yellow River Basin

The Yellow River Basin(YRB)has experienced severe floods and continuous riverbed elevation throughout history.Global climate change has been suggested to be driving a worldwide increase in flooding risk.However,owing to insufficient evidence,the quantitative correlation between flooding and climate change remains illdefined.We present a long time series of maximum flood discharge in the YRB dating back to 1843 compiled from historical documents and instrument measurements.Variations in yearly maximum flood discharge show distinct periods:a dramatic decreasing period from 1843 to 1950,and an oscillating gentle decreasing from 1950 to 2021,with the latter period also showing increasing more extreme floods.A Mann-Kendall test analysis suggests that the latter period can be further split into two distinct sub-periods:an oscillating gentle decreasing period from 1950 to 2000,and a clear recent increasing period from 2000 to 2021.We further predict that climate change will cause an ongoing remarkable increase in future flooding risk and an∼44.4 billion US dollars loss of floods in the YRB in 2100.

Prevalence of vegetation browning in China’s drylands under climate change

Vegetation greening has long been acknowledged,but recent studies have pointed out that vegetation greening is possibly stalled or even reversed.However,detailed analyses about greening reversal or increased browning of vegetation remain scarce.In this study,we utilized the normalized difference vegetation index(NDVI)as an indicator of vegetation to investigate the trends of vegetation greening and browning(monotonic,interruption,and reversal)through the breaks for the additive season and trend(BFAST)method across China’s drylands from 1982 to 2022.It also reveals the impacts of ecological restoration programs(ERPs)and climate change on these vegetation trends.We find that the vegetation displays an obvious pattern of east-greening and west-browning in China’s drylands.Greening trends mainly exhibits monotonic greening(29.8%)and greening with setback(36.8%),whereas browning shows a greening to browning reversal(19.2%).The increase rate of greening to browning reversal is 0.0342/yr,which is apparently greater than that of greening with setback,0.0078/yr.This research highlights that,under the background of widespread vegetation greening,vegetation browning is pro-gressively increasing due to the effects of climate change.Furthermore,the ERPs have significantly increased vegetation coverage,with the increase rate in 2000-2022 being twice as much as that of 1982-1999 in reveg-etation regions.Vegetation browning in southwestern Qingzang Plateau is primarily driven by adverse climatic factors and anthropogenic disturbances,which offset the efforts of ERPs.

Differential response of radial growth and δ^(13)C in Qinghai spruce(Picea crassifolia) to climate change on the southern and northern slopes of the Qilian Mountains in Northwest China

Tree radial growth can have significantly differ-ent responses to climate change depending on the environ-ment.To elucidate the effects of climate on radial growth and stable carbon isotope(δ^(13)C)fractionation of Qing-hai spruce(Picea crassifolia),a widely distributed native conifer in northwestern China in different environments,we developed chronologies for tree-ring widths and δ^(13)C in trees on the southern and northern slopes of the Qilian Mountains,and analysed the relationship between these tree-ring variables and major climatic factors.Tree-ring widths were strongly influenced by climatic factors early in the growing season,and the radial growth in trees on the northern slopes was more sensitive to climate than in trees on the southern.Tree-ring δ^(13)C was more sensitive to climate than radial growth.δ^(13)C fractionation was mainly influenced by summer temperature and precipitation early in the growing season.Stomatal conductance more strongly limited stable carbon isotope fractionation in tree rings than photosynthetic rate did.The response between tree rings and climate in mountains gradually weakened as climate warmed.Changes in radial growth and stable carbon isotope fractionation of P.crassifolia in response to climate in the Qilian Mountains may be further complicated by continued climate change.

A progress review of black carbon deposition on Arctic snow and ice and its impact on climate change

The rapid warming of the Arctic,accompanied by glacier and sea ice melt,has significant consequences for the Earth’s climate,ecosystems,and economy.Black carbon(BC)deposition on snow and ice can trigger a significant reduction in snow albedo and accelerate melting of snow and ice in the Arctic.By reviewing the published literatures over the past decades,this work provides an overview of the progress in both the measurement and modeling of BC deposition and its impact on Arctic climate change.In summary,the maximum value of BC deposition appears in the western Russian Arctic(26 ng·g^(–1)),and the minimum value appears in Greenland(3 ng·g^(–1)).BC records in the Arctic ice core already peaked in 1920s and 1970s,and shows a regional difference between Greenland and Canadian Arctic.The different temporal variations of Arctic BC ice core records in different regions are closely related to the large variability of BC emissions and transportation processes across the Arctic region.Model simulations usually underestimate the concentration of BC in snow and ice by 2–3 times,and cannot accurately reflect the seasonal and regional changes in BC deposition.Wet deposition is the main removal mechanism of BC in the Arctic,and observations show different seasonal variations in BC wet deposition in Ny-Ålesund and Barrow.This discrepancy may result from varying contributions of anthropogenic and biomass burning(BB)emissions,given the strong influence by BC from BB emissions at Barrow.Arctic BC deposition significantly influences regional climate change in the Arctic,increasing fire activities in the Arctic have made BB source of Arctic BC more crucial.On average,BC in Arctic snow and ice causes an increase of+0.17 W·m^(–2)in radiative forcing and 8 Gt·a^(–1)in runoff in Greenland.As stressed in the latest Arctic Monitoring and Assessment Programme report,reliable source information and long-term and high-resolution observations on Arctic BC deposition will be crucial for a more comprehensive understanding and a better mitigation strategy of Arctic BC.In the future,it is necessary to collect more observations on BC deposition and the corresponding physical processes(e.g.,snow/ice melting,surface energy balance)in the Arctic to provide reliable data for understanding and clarifying the mechanism of the climatic impacts of BC deposition on Arctic snow and ice.

基于Consultant Climate导向与斯维尔验证的烟台某售楼处节能改造研究

以烟台某售楼处节能改造项目为例,通过Consultant Climate分析帮助设计者快速找到主要能耗矛盾并进行节能改造,再利用斯维尔进行改造后的评价。结果显示:Consultant Climate能够快速找到地区气候缺陷并提出量化策略,斯维尔能够准确合理给出节能率。结合两者可以帮助设计者快速准确地找到适应气候的设计策略,提高工作效率,避免因气候调节能力不佳导致重新改造所带来的资源浪费。

Response of drought to climate extremes in a semi-arid inland river basin in China

Against the backdrop of global warming,climate extremes and drought events have become more severe,especially in arid and semi-arid areas.This study forecasted the characteristics of climate extremes in the Xilin River Basin(a semi-arid inland river basin)of China for the period of 2021–2100 by employing a multi-model ensemble approach based on three climate Shared Socioeconomic Pathway(SSP)scenarios(SSP1-2.6,SSP2-4.5,and SSP5-8.5)from the latest Coupled Model Intercomparison Project Phase 6(CMIP6).Furthermore,a linear regression,a wavelet analysis,and the correlation analysis were conducted to explore the response of climate extremes to the Standardized Precipitation Evapotranspiration Index(SPEI)and Streamflow Drought Index(SDI),as well as their respective trends during the historical period from 1970 to 2020 and during the future period from 2021 to 2070.The results indicated that extreme high temperatures and extreme precipitation will further intensify under the higher forcing scenarios(SSP5-8.5>SSP2-4.5>SSP1-2.6)in the future.The SPEI trends under the SSP1-2.6,SSP2-4.5,and SSP5-8.5 scenarios were estimated as–0.003/a,–0.004/a,and–0.008/a,respectively,indicating a drier future climate.During the historical period(1970–2020),the SPEI and SDI trends were–0.003/a and–0.016/a,respectively,with significant cycles of 15 and 22 a,and abrupt changes occurring in 1995 and 1996,respectively.The next abrupt change in the SPEI was projected to occur in the 2040s.The SPEI had a significant positive correlation with both summer days(SU)and heavy precipitation days(R10mm),while the SDI was only significantly positively correlated with R10mm.Additionally,the SPEI and SDI exhibited a strong and consistent positive correlation at a cycle of 4–6 a,indicating a robust interdependence between the two indices.These findings have important implications for policy makers,enabling them to improve water resource management of inland river basins in arid and semi-arid areas under future climate uncertainty.

Climate and topography regulate the spatial pattern of soil salinization and its effects on shrub community structure in Northwest China

Soil salinization may affect biodiversity and species composition,leading to changes in the plant community structure.However,few studies have explored the spatial pattern of soil salinization and its effects on shrub community structure at the ecosystem scale.Therefore,we conducted a transect sampling of desert shrublands in Northwest China during the growing season(June–September)in 2021.Soil salinization(both the degree and type),shrub community structure(e.g.,shrub density and height),and biodiversity parameters(e.g.,Simpson diversity,Margalf abundance,Shannon-Wiener diversity,and Pielou evenness indices)were used to assess the effects of soil salinization on shrub community structure.The results showed that the primary degree of soil salinization in the study area was light salinization,with the area proportion of 69.8%.Whereas the main type of soil salinization was characterized as sulfate saline soil,also accounting for 69.8%of the total area.Notably,there was a significant reduction in the degree of soil salinization and a shift in the type of soil salinization from chloride saline soil to sulfate saline soil,with an increase in longitude.Regional mean annual precipitation(MAP),mean annual evapotranspiration(MAE),elevation,and slope significantly contributed to soil salinization and its geochemical differentiation.As soil salinization intensified,shrub community structure displayed increased diversity and evenness,as indicated by the increases in the Simpson diversity,Shannon-Wiener diversity,and Pielou evenness indices.Moreover,the succulent stems and leaves of Chenopodiaceae and Tamaricaceae exhibited clear advantages under these conditions.Furthermore,regional climate and topography,such as MAP,MAE,and elevation,had greater effects on the distribution of shrub plants than soil salinization.These results provide a reference for the origin and pattern of soil salinization in drylands and their effects on the community structure of halophyte shrub species.

Discovery of the Significant Impacts of Swell Propagation on Global Wave Climate Change

This study compared the differences in the wave climate in the South China Sea and North Indian Ocean under these two datasets:ERA-40 wave reanalysis and Mei’s hindcast wave data.In the numerical calculation of regional ocean waves,the wave climate characteristics exhibited significant bias if the influence of external swells(swells from afar)was not fully considered,which may provide an incorrect basis for global climate change analysis.1)The trends of the significant wave height(SWH)obtained from the two datasets showed significant differences,such as those of the Bay of Bengal and the Java Sea in June-July-August.For the past 45 years,SWH from ERA-40(SWH-ERA)exhibited a significant annual increase in low-latitude waters of the North Indian Ocean(0.2-0.6 cm yr^(-1))and South China Sea(0.2-0.8 cm yr^(-1)).2)In the Bay of Bengal,the SWH-ERA in each month was generally 0.5 m higher than the SWH from Mei’s hindcast wave data(SWH-Mei)and can reach 1.0 m higher in some months.3)In the Bay of Bengal,SWH-ERA and SWH-Mei increased significantly at annual rates of 0.13 and 0.27 cm yr^(-1),respectively.This increasing trend was mainly reflected after 1978.SWH-ERA showed a trough in 1975(1.33 m)and a crest in 1992(1.83 m),which were not reflected in SWH-Mei.

Machine learning ensemble model prediction of northward shift in potato cyst nematodes(Globodera rostochiensis and G.pallida)distribution under climate change conditions

Potato cyst nematodes(PCNs)are a significant threat to potato production,having caused substantial damage in many countries.Predicting the future distribution of PCN species is crucial to implementing effective biosecurity strategies,especially given the impact of climate change on pest species invasion and distribution.Machine learning(ML),specifically ensemble models,has emerged as a powerful tool in predicting species distributions due to its ability to learn and make predictions based on complex data sets.Thus,this research utilised advanced machine learning techniques to predict the distribution of PCN species under climate change conditions,providing the initial element for invasion risk assessment.We first used Global Climate Models to generate homogeneous climate predictors to mitigate the variation among predictors.Then,five machine learning models were employed to build two groups of ensembles,single-algorithm ensembles(ESA)and multi-algorithm ensembles(EMA),and compared their performances.In this research,the EMA did not always perform better than the ESA,and the ESA of Artificial Neural Network gave the highest performance while being cost-effective.Prediction results indicated that the distribution range of PCNs would shift northward with a decrease in tropical zones and an increase in northern latitudes.However,the total area of suitable regions will not change significantly,occupying 16-20%of the total land surface(18%under current conditions).This research alerts policymakers and practitioners to the risk of PCNs’incursion into new regions.Additionally,this ML process offers the capability to track changes in the distribution of various species and provides scientifically grounded evidence for formulating long-term biosecurity plans for their control.

Designated critical habitats for U.S.imperiled species are not protected from climate and land-use change

Designation of critical habitat is an important conservation tool for species listed as threatened or endangered under the United States(U.S.)Endangered Species Act(ESA).While this is an important protective mechanism,lands designated as critical habitat could still be subject to degradation and fragmentation if they are not also in a protected status that prioritizes biodiversity conservation.Additionally,most designations of critical habitat do not explicitly take climate change into account.The objective of our study was to determine whether and to what extent critical habitats for species listed under the ESA are located within protected areas and areas previously identified as climate refugia or climate corridors,to inform management strategies to better conserve and recover these species.We mapped the designated critical habitats of 153 ESA-listed species and measured their overlap with previously-identified areas of climate refugia and corridors(CRC),and also with lands designated as nature-protected by U.S.Geological Survey’s Gap Analysis Project(GAP Status 1 or 2)and working lands with wildlife habitat potential(GAP Status 3).Only 18%of all designated critical habitat is located on lands that are both in CRC and nature-protected,and only 9%of species had over half of their designated critical habitats in such lands.84%of species had<25%overlap of their critical habitats with these areas.Critical habitats may therefore not fulfill their essential role of helping imperiled species persist and recover.

Potential reduction in carbon fixation capacity under climate change in a Pinus koraiensis forest

There has been an increasing recognition of the crucial role of forests, responsible for sequestering atmospheric CO_(2), as a moral imperative for mitigating the pace of climate change. The complexity of evaluating climate change impacts on forest carbon and water dynamics lies in the diverse acclimations of forests to changing environments. In this study, we assessed two of the most common acclimation traits, namely leaf area index and the maximum rate of carboxylation(V_(cmax)), to explore the potential acclimation pathways of Pinus koraiensis under climate change. We used a mechanistic and process-based ecohydrological model applied to a P. koraiensis forest in Mt. Taehwa, South Korea. We conducted numerical investigations into the impacts of(i) Shared Socioeconomic Pathways 2–4.5(SSP2-4.5) and 5–8.5(SSP5-8.5),(ii) elevated atmospheric CO_(2) and temperature, and(iii) acclimations of leaf area index and V_(cmax)on the carbon and water dynamics of P. koraiensis. We found that there was a reduction in net primary productivity(NPP) under the SSP2-4.5 scenario, but not under SSP5-8.5, compared to the baseline, due to an imbalance between increases in atmospheric CO_(2) and temperature. A decrease in leaf area index and an increase in V_(cmax)of P. koraiensis were expected if acclimations were made to reduce its leaf temperature. Under such acclimation pathways, it would be expected that the well-known CO_(2) fertilizer effects on NPP would be attenuated.