Assessing the robustness of projected precipitation changes over central Africa on the basis of a multitude of global and regional climate projections

It is well accepted within the scientific community that a large ensemble of different projections is required to achieve robust climate change information for a specific region. For this purpose we have compiled a state-of-the-art multi-model multi-scenario ensemble of global and regional precipitation projections. This ensemble combines several global projections from the CMIP3 and CMIP5 databases, along with some recently downscaled regional CORDEX-Africa projections. Altogether daily precipitation data from 77 different climate change projections is analysed; separated into 31 projections for a high and 46 for a low emission scenario. We find a robust indication that, independent of the underlying emission scenario, annual total precipitation amounts over the central African region are not likely to change severely in the future. However some robust changes in precipitation characteristics, like the intensification of heavy rainfall events as well as an increase in the number of dry spells during the rainy season are projected for the future. Further analysis shows that over some regions the results of the climate change assessment clearly depend on the size of the analyzed ensemble. This indicates the need of a “large-enough” ensemble of independent climate projections to allow for a reliable climate change assessment.     

Climate change simulated by full and mixed-layer ocean versions of CSIRO Mk3.5 and Mk3.0: The Asia-Pacific region

The patterns of climate change in the Asia-Pacific region simulated by versions of the CSIRO Mk3.5 and Mk3.0 climate models are examined and compared with those from 23 CMIP3 models. Using fields standardized by global warming, it is seen that both CSIRO coupled models simulate larger surface warming in the tropical western Pacific Ocean, and smaller warming in the eastern Indian Ocean, than the CMIP3 average, and also model versions with a mixed-layer ocean. Corresponding differences in the changes in the pressure, winds, rainfall and other quantities were simulated. Introducing the coupled Mk3.5’s sea surface temperature field for the present climate, which has a warm bias, as the base climate for the MLO version had only a minor effect on the MLO version’s pattern of climate change. A Pacific-Indian Dipole index quantifying the amplitude of the warming pattern explains much of the variation in rainfall change simulated by the CMIP3 models over Australia and the Indonesian and Melanesian regions. It relates more strongly to Australian average rainfall than several other indices representing southern hemispheric circulation changes. The decline in Australian rainfall produced by the full ocean coupling is largest in summer, but occurs in each season, and extends across the continent. Further assessment of the importance of the dipole change pattern in new simulations is warranted. Analyses aimed at reducing the uncertainty in its potential amplitude could help narrow the range of projections for change in the Australasian region.     

Consistency of simulated and observed regional changes in temperature, sea level pressure and precipitation

There is increasing pressure from stakeholders for highly localised climate change projections. A comprehensive assessment of climate model performance at the grid box scale in simulating recent change, however, is not available at present. Therefore, we compare observed changes in near-surface temperature, sea level pressure (SLP) and precipitation with simulations available from the Coupled Model Intercomparison Projects 3 and 5 (CMIP3 and CMIP5). In both multi-model datasets we find coherent areas of inconsistency between observed and simulated local trends per degree global warming in both temperature and SLP in the majority of models. Localised projections should thus take into account the possibility of regional biases shared across models. In contrast, simulated changes in precipitation are not significantly different from observations due to low signal-to-noise ratio of local precipitation changes. Therefore, recent regional rainfall change is likely not providing useful constraints for future projections as of yet. Comparing the two most recent sets of internationally coordinated climate model experiments, we find no indication of improvement in the models’ ability to reproduce local trends in temperature, SLP and precipitation.     

Understanding and partitioning future climates for Australian regions from CMIP3 using ocean warming indices

The patterns of large-scale climate change over the 21st century simulated by 23 CMIP3 global climate models are analyzed to provide understanding of the range of projected temperature T and precipitation P changes for Australia published in 2007. Means of change, standardized by the global warming, within each of 11 regions are calculated for each model. Correlations between regions across the 23 models indicate that the changes are rather coherent across much of the mainland. The all-Australian average changes are also well correlated with a pattern of tropical sea surface temperatures. A Pacific-Indian Dipole index, representing this pattern, correlates strongly with Australian P. It also correlates well with variables in Southeast Asia. The global warming itself correlates well with Australian warming. These two indices of large-scale ocean warming are used to partition the 23 models into four representative future climates. For Australia overall, these can be described as much warmer and drier, much warmer, warmer and drier, and warmer. The four climates span much of the range of the earlier Australian projections over most of the continent. Further, they may be reproduced by a downscaling model forced with the SST anomalies. An assessment of the realism of the ocean pattern changes has the potential to reduce the uncertainty of projections, both for Australia and beyond.     

Why ensemble mean projection of south Asian monsoon rainfall by CMIP5 models is not reliable?

Future projections of the Indian summer monsoon rainfall (ISMR) and its large-scale thermodynamic driver are studied by using CMIP5 model outputs. While all models project an increasing precipitation in the future warming scenario, most of them project a weakening large-scale thermodynamic driver arising from a weakening of the upper tropospheric temperature (UTT) gradient over south Asian summer monsoon region. The weakening of the UTT gradient under global warming scenarios is related to the increase in sea surface temperature (SST) over the equatorial Indian Ocean (EIO) leading to a stronger increase of UTT over the EIO region relative to the northern Indian region, a hypothesis supported by a series of Atmospheric General Circulation Model (AGCM) experiments forced by projected SSTs. To diagnose the inconsistency between the model projections of precipitation and the large-scale thermodynamic driver, we have examined the rate of total precipitation explained by convective and stratiform precipitations in observations and in CMIP5 models. It is found that most models produce too much (little) convective (stratiform) precipitation compared to observations. In addition, we also find stronger precipitable water—precipitation relationship in most CMIP5 models as compared to observations. Hence, the atmospheric moisture content produced by the model immediately gets converted to precipitation even though the large-scale thermodynamics in models weaken. Therefore, under global warming scenarios, due to increased temperature and resultant increased atmospheric moisture supply, these models tend to produce unrealistic local convective precipitation often not in tune with other large-scale variables. Our results questions the reliability of the ISMR projections in CMIP5 models and highlight the need to improve the convective parameterization schemes in coupled models for the reliable projections of the ISMR.     

Future evolution of the Sahel precipitation zonal contrast in CESM1

The main focus of this study is the zonal contrast of the Sahel precipitation shown in the CMIP5 climate projections: precipitation decreases over the western Sahel (i.e., Senegal and western Mali) and increases over the central Sahel (i.e., eastern Mali, Burkina Faso and Niger). This zonal contrast in future precipitation change is a robust model response to climate change but suffers from a lack of an explanation. To this aim, we study the impact of current and future climate change on Sahel precipitation by using the Large Ensemble of the Community Earth System Model version 1 (CESM1). In CESM1, global warming leads to a strengthening of the zonal contrast, as shown by the difference between the 2060–2099 period (under a high emission scenario) and the 1960–1999 period (under the historical forcing). The zonal contrast is associated with dynamic shifts in the atmospheric circulation. We show that, in absence of a forced response, that is, when only accounting for internal climate variability, the zonal contrast is associated with the Pacific and the tropical Atlantic oceans variability. However, future patterns in sea surface temperature (SST) anomalies are not necessary to explaining the projected strengthening of the zonal contrast. The mechanisms underlying the simulated changes are elucidated by analysing a set of CMIP5 idealised simulations. We show the increase in precipitation over the central Sahel to be mostly associated with the surface warming over northern Africa, which favour the displacement of the monsoon cell northwards. Over the western Sahel, the decrease in Sahel precipitation is associated with a southward shift of the monsoon circulation, and is mostly due to the warming of the SST. These two mechanisms allow explaining the zonal contrast in precipitation change.

     

Past and future polar amplification of climate change: climate model intercomparisons and ice-core constraints

Climate model simulations available from the PMIP1, PMIP2 and CMIP (IPCC-AR4) intercomparison projects for past and future climate change simulations are examined in terms of polar temperature changes in comparison to global temperature changes and with respect to pre-industrial reference simulations. For the mid-Holocene (MH, 6,000 years ago), the models are forced by changes in the Earth’s orbital parameters. The MH PMIP1 atmosphere-only simulations conducted with sea surface temperatures fixed to modern conditions show no MH consistent response for the poles, whereas the new PMIP2 coupled atmosphere–ocean climate models systematically simulate a significant MH warming both for Greenland (but smaller than ice-core based estimates) and Antarctica (consistent with the range of ice-core based range). In both PMIP1 and PMIP2, the MH annual mean changes in global temperature are negligible, consistent with the MH orbital forcing. The simulated last glacial maximum (LGM, 21,000 years ago) to pre-industrial change in global mean temperature ranges between 3 and 7°C in PMIP1 and PMIP2 model runs, similar to the range of temperature change expected from a quadrupling of atmospheric CO₂ concentrations in the CMIP simulations. Both LGM and future climate simulations are associated with a polar amplification of climate change. The range of glacial polar amplification in Greenland is strongly dependent on the ice sheet elevation changes prescribed to the climate models. All PMIP2 simulations systematically underestimate the reconstructed glacial–interglacial Greenland temperature change, while some of the simulations do capture the reconstructed glacial–interglacial Antarctic temperature change. Uncertainties in the prescribed central ice cap elevation cannot account for the temperature change underestimation by climate models. The variety of climate model sensitivities enables the exploration of the relative changes in polar temperature with respect to changes in global temperatures. Simulated changes of polar temperatures are strongly related to changes in simulated global temperatures for both future and LGM climates, confirming that ice-core-based reconstructions provide quantitative insights on global climate changes. An erratum to this article can be found at     

How Frequently Will the Persistent Heavy Rainfall over the Middle and Lower Yangtze River Basin in Summer 2020 Happen under Global Warming?

The middle and lower Yangtze River basin (MLYRB) suffered persistent heavy rainfall in summer 2020, with nearly continuous rainfall for about six consecutive weeks. How the likelihood of persistent heavy rainfall resembling that which occurred over the MLYRB in summer 2020 (hereafter 2020PHR-like event) would change under global warming is investigated. An index that reflects maximum accumulated precipitation during a consecutive five-week period in summer (Rx35day) is introduced. This accumulated precipitation index in summer 2020 is 60% stronger than the climatology, and a statistical analysis further shows that the 2020 event is a 1-in-70-year event. The model projection results derived from the 50-member ensemble of CanESM2 and the multimodel ensemble (MME) of the CMIP5 and CMIP6 models show that the occurrence probability of the 2020PHR-like event will dramatically increase under global warming. Based on the Kolmogorov–Smirnoff test, one-third of the CMIP5 and CMIP6 models that have reasonable performance in reproducing the 2020PHR-like event in their historical simulations are selected for the future projection study. The CMIP5 and CMIP6 MME results show that the occurrence probability of the 2020PHR-like event under the present-day climate will be double under lower-emission scenarios (CMIP5 RCP4.5, CMIP6 SSP1-2.6, and SSP2-4.5) and 3–5 times greater under higher-emission scenarios (3.0 times for CMIP5 RCP8.5, 2.9 times for CMIP6 SSP3-7.0, and 4.8 times for CMIP6 SSP5-8.5). The inter-model spread of the probability change is small, lending confidence to the projection results. The results provide a scientific reference for mitigation of and adaptation to future climate change.     

Pacific variability reconciles observed and modelled global mean temperature increase since 1950

Global mean temperature change simulated by climate models deviates from the observed temperature increase during decadal-scale periods in the past. In particular, warming during the ‘global warming hiatus’ in the early twenty-first century appears overestimated in CMIP5 and CMIP6 multi-model means. We examine the role of equatorial Pacific variability in these divergences since 1950 by comparing 18 studies that quantify the Pacific contribution to the ‘hiatus’ and earlier periods and by investigating the reasons for differing results. During the ‘global warming hiatus’ from 1992 to 2012, the estimated contributions differ by a factor of five, with multiple linear regression approaches generally indicating a smaller contribution of Pacific variability to global temperature than climate model experiments where the simulated tropical Pacific sea surface temperature (SST) or wind stress anomalies are nudged towards observations. These so-called pacemaker experiments suggest that the ‘hiatus’ is fully explained and possibly over-explained by Pacific variability. Most of the spread across the studies can be attributed to two factors: neglecting the forced signal in tropical Pacific SST, which is often the case in multiple regression studies but not in pacemaker experiments, underestimates the Pacific contribution to global temperature change by a factor of two during the ‘hiatus’; the sensitivity with which the global temperature responds to Pacific variability varies by a factor of two between models on a decadal time scale, questioning the robustness of single model pacemaker experiments. Once we have accounted for these factors, the CMIP5 mean warming adjusted for Pacific variability reproduces the observed annual global mean temperature closely, with a correlation coefficient of 0.985 from 1950 to 2018. The CMIP6 ensemble performs less favourably but improves if the models with the highest transient climate response are omitted from the ensemble mean.

     

Sensitivity of climate change projections to uncertainties in the estimates of observed changes in deep-ocean heat content

The MIT 2D climate model is used to make probabilistic projections for changes in global mean surface temperature and for thermosteric sea level rise under a variety of forcing scenarios. The uncertainties in climate sensitivity and rate of heat uptake by the deep ocean are quantified by using the probability distributions derived from observed twentieth century temperature changes. The impact on climate change projections of using the smallest and largest estimates of twentieth century deep ocean warming is explored. The impact is large in the case of global mean thermosteric sea level rise. In the MIT reference (“business as usual”) scenario the median rise by 2100 is 27 and 43 cm in the respective cases. The impact on increases in global mean surface air temperature is more modest, 4.9 and 3.9 C in the two respective cases, because of the correlation between climate sensitivity and ocean heat uptake required by twentieth century surface and upper air temperature changes. The results are also compared with the projections made by the IPCC AR4’s multi-model ensemble for several of the SRES scenarios. The multi-model projections are more consistent with the MIT projections based on the largest estimate of ocean warming. However, the range for the rate of heat uptake by the ocean suggested by the lowest estimate of ocean warming is more consistent with the range suggested by the twentieth century changes in surface and upper air temperatures, combined with the expert prior for climate sensitivity.     

Drivers of mean climate change around the Netherlands derived from CMIP5

For the construction of regional climate change scenarios spanning a relevant fraction of the spread in climate model projections, an inventory of major drivers of regional climate change is needed. For the Netherlands, a previous set of regional climate change scenarios was based on the decomposition of local temperature/precipitation changes into components directly linked to the level of global warming, and components related to changes in the regional atmospheric circulation. In this study this decomposition is revisited utilizing the extensive modelling results from the CMIP5 model ensemble in support for the 5th IPCC assessment. Rather than selecting a number of GCMs based on performance metrics or relevant response features, a regression technique was developed to utilize all available model projections. The large number of projections allows a quantification of the separate contributions of emission scenarios, systematic model responses and natural variability to the total likelihood range. Natural variability plays a minor role in modelled differences in the global mean temperature response, but contributes for up to 50 % to the range of mean sea level pressure responses and local precipitation. Using key indicators (“steering variables”) for the temperature and circulation response, the range in local seasonal mean temperature and precipitation responses can be fairly well reproduced.     

Impact of projected SST changes on summer rainfall in southeastern South America

Recent studies have shown that global warming and associated sea-surface temperature (SST) changes may trigger an important rainfall increase in southeastern South America (SESA) during the austral summer (December–January–February, DJF). The goal of this paper is to provide some insight into processes which may link global and SESA changes. For this purpose, a “two-way nesting” system coupling interactively the regional and global versions of the LMDZ4 atmospheric model is used to study the response to prescribed SST changes. The regional model is a variable-grid version of the global model, with a zoom focused over South America. An ensemble of simulations forced by distinct patterns of DJF SST changes has been carried out using a decomposition of full SST changes into their longitudinal and latitudinal components. The full SST changes are based on projections for the end of the twenty-first century from a multi-model ensemble of WCRP/CMIP3. Results confirm the presence of a major rainfall dipole structure, characterized by an increase in SESA and a decrease in the South Atlantic Convergence Zone region. Rainfall changes found in the WCRP/CMIP3 models are largely explained as a response of this dipole structure to the zonally-asymmetric (or longitudinal) component of SST changes. The rainfall response to the zonal-mean (or latitudinal) SST changes (including the global warming signal itself) shows an opposite contribution. The processes explaining the role of zonally-asymmetric SST changes involve remote effects of SST warming over the equatorial Indian and Pacific oceans inducing an atmospheric wave-train extended across the South Pacific into South America.     

A transition from CMIP3 to CMIP5 for climate information providers: the case of surface temperature over eastern North America

The release of new data constituting the Coupled Model Intercomparison Project—Phase 5 (CMIP5) database is an important event in both climate science and climate services issues. Although users’ eagerness for a fast transition from CMIP3 to CMIP5 is expected, this change implies some challenges for climate information providers. The main reason is that the two sets of experiments were performed in different ways regarding radiative forcing and hence continuity between both datasets is partially lost. The objective of this research is to evaluate a metric that is independent of the amount and the evolution of radiative forcing, hence facilitating comparison between the two sets for surface temperature over eastern North America. The link between CMIP3 and CMIP5 data sets is explored spatially and locally (using the ratio of local to global temperatures) through the use of regional warming patterns, a relationship between the grid-box and the global mean temperature change for a certain time frame. Here, we show that local to global ratios are effective tools in making climate change information between the two sets comparable. As a response to the global mean temperature change, both CMIP experiments show very similar warming patterns, trends, and climate change uncertainty for both winter and summer. Sensitivity of the models to radiative forcing is not assessed. Real inter-model differences remain the largest source of uncertainty when calculating warming patterns as well as spatially-based patterns for the pattern scaling approach. This relationship between the datasets, which may escape users when they are provided with a single radiative forcing pathway, needs to be stressed by climate information providers.     

Present-day and future Amazonian precipitation in global climate models: CMIP5 versus CMIP3

The present study aims at evaluating and comparing precipitation over the Amazon in two sets of historical and future climate simulations based on phase 3 (CMIP3) and 5 (CMIP5) of the Coupled Model Intercomparison Project. Thirteen models have been selected in order to discuss (1) potential improvements in the simulation of present-day climate and (2) the potential reduction in the uncertainties of the model response to increasing concentrations of greenhouse gases. While several features of present-day precipitation—including annual cycle, spatial distribution and co variability with tropical sea surface temperature (SST)—have been improved, strong uncertainties remain in the climate projections. A closer comparison between CMIP5 and CMIP3 highlights a weaker consensus on increased precipitation during the wet season, but a stronger consensus on a drying and lengthening of the dry season. The latter response is related to a northward shift of the boreal summer intertropical convergence zone in CMIP5, in line with a more asymmetric warming between the northern and southern hemispheres. The large uncertainties that persist in the rainfall response arise from contrasted anomalies in both moisture convergence and evapotranspiration. They might be related to the diverse response of tropical SST and ENSO (El Niño Southern Oscillation) variability, as well as to spurious behaviours among the models that show the most extreme response. Model improvements of present-day climate do not necessarily translate into more reliable projections and further efforts are needed for constraining the pattern of the SST response and the soil moisture feedback in global climate scenarios.     

Climate of Russia in the 21st century. Part 1. New evidence of anthropogenic climate change and the state of the art of its simulation

This is the first of three papers devoted to the study of climate change in Russia in the 20th and 21st centuries using ensembles of CMIP3 Atmosphere-Ocean General Circulation Models. Current studies of observed global and regional climate changes are briefly reviewed based on the analysis of the Intergovernmental Panel on Climate Change (IPCC). The anthropogenic nature of climate change is emphasized. It is also noted that the observed warming in Russia is significantly larger than global warming. Alternative hypotheses on causes of global climate changes discussed in some Russian publications are reviewed and their groundlessness is shown. The paper discusses some characteristics of ensembles of CMIP3 models that participated in the preparation of the IPCC Fourth Assessment Report. A model quality index is introduced. The dependence of simulated climate change in Russia on the choice of model ensembles and emission scenarios is considered. It is shown that the climate change in Russia does not depend significantly on the emission scenario choice until the middle of the 21st century.     

Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models

There is a long-standing debate on how the El Niño/Southern Oscillation (ENSO) amplitude may change during the twenty-first century in response to global warming. Here we identify the sources of uncertainty in the ENSO amplitude projections in models participating in the Coupled Model Intercomparison Phase 5 (CMIP5) and Phase 6 (CMIP6), and quantify scenario uncertainty, model uncertainty and uncertainty due to internal variability. The model projections exhibit a large spread, ranging from increasing standard deviation of up to 0.6 °C to diminishing standard deviation of up to − 0.4 °C by the end of the twenty-first century. The ensemble-mean ENSO amplitude change is close to zero. Internal variability is the main contributor to the uncertainty during the first three decades; model uncertainty dominates thereafter, while scenario uncertainty is relatively small throughout the twenty-first century. The total uncertainty increases from CMIP5 to CMIP6: while model uncertainty is reduced, scenario uncertainty is considerably increased. The models with “realistic” ENSO dynamics have been analyzed separately and categorized into models with too small, moderate and too large ENSO amplitude in comparison to instrumental observations. The smallest uncertainties are observed in the sub-ensemble exhibiting realistic ENSO dynamics and moderate ENSO amplitude. However, the global warming signal in ENSO-amplitude change is undetectable in all sub-ensembles. The zonal wind-SST feedback is identified as an important factor determining ENSO amplitude change: global warming signal in ENSO amplitude and zonal wind-SST feedback strength are highly correlated across the CMIP5 and CMIP6 models.

     

Sea-ice and its response to CO2 forcing as simulated by global climate models

The simulation of sea-ice in global climate models participating in the Coupled Model Intercomparison Project (CMIP1 and CMIP2) is analyzed. CMIP1 simulations are of the unpertubed control climate whereas in CMIP2, all models have been forced with the same 1% yr^–1 increase in CO₂ concentration, starting from a near equilibrium initial condition. These simulations are not intended as forecasts of climate change, but rather provide a means of evaluating the response of current climate models to the same forcing. The difference in modeled response therefore indicates the range (or uncertainty) in model sensitivity to greenhouse gas and other climatic perturbations. The results illustrate a wide range in the ability of climate models to reproduce contemporary sea-ice extent and thickness; however, the errors are not obviously related to the manner in which sea-ice processes are represented in the models (e.g. the inclusion or neglect of sea-ice motion). The implication is that errors in the ocean and atmosphere components of the climate model are at least as important. There is also a large range in the simulated sea-ice response to CO₂ change, again with no obvious stratification in terms of model attributes. In contrast to results obtained earlier with a particular model, the CMIP ensemble yields rather mixed results in terms of the dependence of high-latitude warming on sea-ice initial conditions. There is an indication that, in the Arctic, models that produce thick ice in their control integration exhibit less warming than those with thin ice. The opposite tendency appears in the Antarctic (albeit with low statistical significance). There is a tendency for models with more extensive ice coverage in the Southern Hemisphere to exhibit greater Antarctic warming. Results for the Arctic indicate the opposite tendency (though with low statistical significance).A list of the CMIP modeling groups is included in the Acknowledgements section.     

CMIP5气候模式对中国未来气候变化的预估和应用

气候模式是研究气候系统和气候变化的有力工具,其模拟结果是进行气候预测和气候变化风险评估的重要数据基础。随着全球气候变暖速度加快,地表生态环境、水文动态循环过程、社会经济发展等都受到其影响,进而影响到人类的生产和生活。利用气候模式对未来气候变化特征进行评估和预测,可为人类调整发展策略以适应气候变化提供科学依据。通过汇总CMIP5(Coupled Model Intercomparison Project Phase 5)模式在气候变化方面的相关研究,综述了CMIP5气候模式在农业生产、水文动态监控以及其他领域中的应用,最后指出了CMIP5气候模式在模拟预估未来气候变化上存在的不足,并展望了CMIP5气候模式在未来的应用。     

Evaluating the performance of CMIP3 and CMIP5 global climate models over the north-east Atlantic region

One of the main sources of uncertainty in estimating climate projections affected by global warming is the choice of the global climate model (GCM). The aim of this study is to evaluate the skill of GCMs from CMIP3 and CMIP5 databases in the north-east Atlantic Ocean region. It is well known that the seasonal and interannual variability of surface inland variables (e.g. precipitation and snow) and ocean variables (e.g. wave height and storm surge) are linked to the atmospheric circulation patterns. Thus, an automatic synoptic classification, based on weather types, has been used to assess whether GCMs are able to reproduce spatial patterns and climate variability. Three important factors have been analyzed: the skill of GCMs to reproduce the synoptic situations, the skill of GCMs to reproduce the historical inter-annual variability and the consistency of GCMs experiments during twenty-first century projections. The results of this analysis indicate that the most skilled GCMs in the study region are UKMO-HadGEM2, ECHAM5/MPI-OM and MIROC3.2(hires) for CMIP3 scenarios and ACCESS1.0, EC-EARTH, HadGEM2-CC, HadGEM2-ES and CMCC-CM for CMIP5 scenarios. These models are therefore recommended for the estimation of future regional multi-model projections of surface variables driven by the atmospheric circulation in the north-east Atlantic Ocean region.     

气候变化的归因与预估模拟研究

本文总结了近五年来中国科学院大气物理研究所在气候变暖的归因模拟与预估研究上的主要进展。研究表明,利用海温、太阳辐射和温室气体等实际强迫因子驱动大气环流模式,能够较为合理地模拟全球平均地表气温在20世纪的演变,但是难以模拟出包括北大西洋涛动／北极涛动和南极涛动在内的高纬度环流的长期变化趋势。利用温室气体和硫酸盐气溶胶等“历史资料”驱动气候系统模式,能够较好地模拟出20世纪后期的全球增暖,但如果要再现20世纪前期(1940年代)的变暖,还需同时考虑太阳辐射等自然外强迫因子。20世纪中国气温演变的耦合模式模拟技巧,较之全球平均情况要低;中国气候在1920年代的变暖机理目前尚不清楚。对于近50年中国东部地区“南冷北暖”、“南涝北旱”的气候变化,基于大气环流模式特别是区域气候模式的数值试验表明,夏季硫酸盐气溶胶的负辐射效应超过了温室气体的增暖效应,从而对变冷产生贡献。但现有的数值模拟证据,不足以说明气溶胶增加对“南涝北旱”型降水异常有贡献。20世纪中期以来,青藏高原主体存在明显增温趋势,温室气体浓度的增加对这种增暖有显著贡献。多模式集合预估的未来气候变化表明,21世纪全球平均温度将继续增暖,增温幅度因不同排放情景而异;中国大陆年均表面气温的增暖与全球同步,但增幅在东北、西部和华中地区较大,冬季升温幅度高于夏季、日最低温度升幅要强于日最高温度;全球增暖有可能对我国中东部植被的地理分布产生影响。伴随温室气体增加所导致的夏季平均温度升高,极端温度事件增多;在更暖的气候背景下,中国大部分地区总降水将增多,极端降水强度加大且更频繁发生,极端降水占总降水的比例也将增大。全球增暖有可能令大洋热盐环流减弱,但是减弱的幅度因模式而异。全球增暖可能不是导致北太平洋副热带－热带经圈环流自20世纪70年代以来变弱的原因。文章同时指出了模式预估结果中存在的不确定性。