Towards evaluating cloud response to climate change using clustering technique identification of cloud regimes

Most of the discrepancies in the climate sensitivity of general circulation models (GCMs) are believed to be due to differences in cloud radiative feedback. Analysis of cloud response to climate change in different ‘regimes’ may offer a more detailed understanding of how the cloud response differs between GCMs. In which case, evaluation of simulated cloud regimes against observations in terms of both their cloud properties and frequency of occurrence will assist in assessing confidence in the cloud response to climate change in a particular GCM. In this study, we use a clustering technique on International Satellite Cloud Climatology Project (ISCCP) data and on ISCCP-like diagnostics from two versions of the Hadley Centre GCM to identify cloud regimes over four different geographical regions. The two versions of the model are evaluated against observational data and their cloud response to climate change compared within the cloud regime framework. It is found that cloud clusters produced by the more recent GCM, HadSM4, compare more favourably with observations than HadSM3. In response to climate change, although the net cloud response over particular regions is often different in the two models, in several instances the same basic processes may be seen to be operating. Overall, both changes in the frequency of occurrence of cloud regimes and changes in the properties (optical depth and cloud top height) of the cloud regimes contribute to the cloud response to climate change.     

Evaluation of a component of the cloud response to climate change in an intercomparison of climate models

Most of the uncertainty in the climate sensitivity of contemporary general circulation models (GCMs) is believed to be connected with differences in the simulated radiative feedback from clouds. Traditional methods of evaluating clouds in GCMs compare time–mean geographical cloud fields or aspects of present-day cloud variability, with observational data. In both cases a hypothetical assumption is made that the quantity evaluated is relevant for the mean climate change response. Nine GCMs (atmosphere models coupled to mixed-layer ocean models) from the CFMIP and CMIP model comparison projects are used in this study to demonstrate a common relationship between the mean cloud response to climate change and present-day variability. Although atmosphere–mixed-layer ocean models are used here, the results are found to be equally applicable to transient coupled model simulations. When changes in cloud radiative forcing (CRF) are composited by changes in vertical velocity and saturated lower tropospheric stability, a component of the local mean climate change response can be related to present-day variability in all of the GCMs. This suggests that the relationship is not model specific and might be relevant in the real world. In this case, evaluation within the proposed compositing framework is a direct evaluation of a component of the cloud response to climate change. None of the models studied are found to be clearly superior or deficient when evaluated, but a couple appear to perform well on several relevant metrics. Whilst some broad similarities can be identified between the 60°N–60°S mean change in CRF to increased CO₂ and that predicted from present-day variability, the two cannot be quantitatively constrained based on changes in vertical velocity and stability alone. Hence other processes also contribute to the global mean cloud response to climate change.     

Quantitative evaluation of the seasonal variations in climate model cloud regimes

An extended cloud-clustering method to assess the seasonal variation of clouds is applied to five CMIP5 models. The seasonal variation of the total cloud radiative effect (CRE) is dominated by variations in the relative frequency of occurrence of the different cloud regimes. Seasonal variations of the CRE within the individual regimes contribute much less. This is the case for both observations, models and model errors. The error in the seasonal variation of cloud regimes, and its breakdown into mean amplitude and time varying components, are quantified with a new metric. The seasonal variation of the CRE of the cloud regimes is relatively well simulated by the models in the tropics, but less well in the extra-tropics. The stratocumulus regime has the largest seasonal variation of shortwave CRE in the tropics, despite having a small magnitude in the climatological mean. Most of the models capture the temporal variation of the CRE reasonably well, with the main differences between models coming from the variation in amplitude. In the extra-tropics, most models fail to correctly represent both the amplitude and time variation of the CRE of congestus, frontal and stratocumulus regimes. The annual mean climatology of the CRE and its amplitude in the seasonal variation are both underestimated for the anvil regime in the tropics, the cirrus regime and the congestus regime in the extra-tropics. The models in this study that best capture the seasonal variation of the cloud regimes tend to have higher climate sensitivities.     

Radiative forcing and response of a GCM to ice age boundary conditions: cloud feedback and climate sensitivity

 A general circulation model is used to examine the effects of reduced atmospheric CO₂, insolation changes and an updated reconstruction of the continental ice sheets at the Last Glacial Maximum (LGM). A set of experiments is performed to estimate the radiative forcing from each of the boundary conditions. These calculations are used to estimate a total radiative forcing for the climate of the LGM. The response of the general circulation model to the forcing from each of the changed boundary conditions is then investigated. About two-thirds of the simulated glacial cooling is due to the presence of the continental ice sheets. The effect of the cloud feedback is substantially modified where there are large changes to surface albedo. Finally, the climate sensitivity is estimated based on the global mean LGM radiative forcing and temperature response, and is compared to the climate sensitivity calculated from equilibrium experiments with atmospheric CO₂ doubled from present day concentration. The calculations here using the model and palaeodata support a climate sensitivity of about 1 Wm^-2 K^-1 which is within the conventional range. Received: 8 February 1997 / Accepted: 4 June 1997     

Recent intense hurricane response to global climate change

An Anthropogenic Climate Change Index (ACCI) is developed and used to investigate the potential global warming contribution to current tropical cyclone activity. The ACCI is defined as the difference between the means of ensembles of climate simulations with and without anthropogenic gases and aerosols. This index indicates that the bulk of the current anthropogenic warming has occurred in the past four decades, which enables improved confidence in assessing hurricane changes as it removes many of the data issues from previous eras. We find no anthropogenic signal in annual global tropical cyclone or hurricane frequencies. But a strong signal is found in proportions of both weaker and stronger hurricanes: the proportion of Category 4 and 5 hurricanes has increased at a rate of ~25–30 % per °C of global warming after accounting for analysis and observing system changes. This has been balanced by a similar decrease in Category 1 and 2 hurricane proportions, leading to development of a distinctly bimodal intensity distribution, with the secondary maximum at Category 4 hurricanes. This global signal is reproduced in all ocean basins. The observed increase in Category 4–5 hurricanes may not continue at the same rate with future global warming. The analysis suggests that following an initial climate increase in intense hurricane proportions a saturation level will be reached beyond which any further global warming will have little effect.     

CMIP1 evaluation and intercomparison of coupled climate models

 The climates simulated by 15 coupled atmosphere/ocean climate models participating in the first phase of the Coupled Model Intercomparison Project (CMIP1) are intercompared and evaluated. Results for global means, zonal averages, and geographical distributions of basic climate variables are assembled and compared with observations. The current generation of climate models reproduce the major features of the observed distribution of the basic climate parameters, but there is, nevertheless, a considerable scatter among model results and between simulated and observed values. This is particularly true for oceanic variables. Flux adjusted models generally produce simulated climates which are in better accord with observations than do non-flux adjusted models; however, some non-flux adjusted model results are closer to observations than some flux adjusted model results. Other model differences, such as resolution, do not appear to provide a clear distinction among model results in this generation of models. Many of the systematic differences (those differences common to most models), evident in previous intercomparison studies are exhibited also by the CMIP1 group of models although often with reduced magnitudes. As is characteristic of intercomparison results, different climate variables are simulated with different levels of success by different models and no one model is “best” for all variables. There is some evidence that the “mean model” result, obtained by averaging over the ensemble of models, provides an overall best comparison to observations for climatological mean fields. The model deficiencies identified here do not suggest immediate remedies and the overall success of the models in simulating the behaviour of the complex non-linear climate system apparently depends on the slow improvement in the balance of approximations that characterize a coupled climate model. Of course, the results of this and similar studies provide only an indication, at a particular time, of the current state and the moderate but steady evolution and improvement of coupled climate models. Received: 26 January 2000 / Accepted: 9 June 2000     

A comparison of low-latitude cloud properties and their response to climate change in three AGCMs sorted into regimes using mid-tropospheric vertical velocity

Low-latitude cloud distributions and cloud responses to climate perturbations are compared in near-current versions of three leading U.S. AGCMs, the NCAR CAM 3.0, the GFDL AM2.12b, and the NASA GMAO NSIPP-2 model. The analysis technique of Bony et al. (Clim Dyn 22:71–86, 2004) is used to sort cloud variables by dynamical regime using the monthly mean pressure velocity ω at 500 hPa from 30S to 30N. All models simulate the climatological monthly mean top-of-atmosphere longwave and shortwave cloud radiative forcing (CRF) adequately in all ω-regimes. However, they disagree with each other and with ISCCP satellite observations in regime-sorted cloud fraction, condensate amount, and cloud-top height. All models have too little cloud with tops in the middle troposphere and too much thin cirrus in ascent regimes. In subsidence regimes one model simulates cloud condensate to be too near the surface, while another generates condensate over an excessively deep layer of the lower troposphere. Standardized climate perturbation experiments of the three models are also compared, including uniform SST increase, patterned SST increase, and doubled CO₂ over a mixed layer ocean. The regime-sorted cloud and CRF perturbations are very different between models, and show lesser, but still significant, differences between the same model simulating different types of imposed climate perturbation. There is a negative correlation across all general circulation models (GCMs) and climate perturbations between changes in tropical low cloud cover and changes in net CRF, suggesting a dominant role for boundary layer cloud in these changes. For some of the cases presented, upper-level clouds in deep convection regimes are also important, and changes in such regimes can either reinforce or partially cancel the net CRF response from the boundary layer cloud in subsidence regimes. This study highlights the continuing uncertainty in both low and high cloud feedbacks simulated by GCMs.     

Detection of anthropogenic climate change using an atmospheric GCM

 Atmosphere-only general circulation models are shown to be a useful tool for detecting an anthropogenic effect on climate and understanding recent climate change. Ensembles of atmospheric runs are all forced with the same observed changes in sea surface temperatures and sea-ice extents but differ in terms of the combinations of anthropogenic effects included. Therefore, our approach aims to detect the `immediate' anthropogenic impact on the atmosphere as opposed to that which has arisen via oceanic feedbacks. We have adapted two well-used detection techniques, pattern correlations and fingerprints, and both show that near-decadal changes in the patterns of zonal mean upper air temperature are well simulated, and that it is highly unlikely that the observed changes could be accounted for by sea surface temperature variations and internal variability alone. Furthermore, we show that for zonally averaged upper air temperature, internal `noise' in the atmospheric model is small enough that a signal emerges from the data even on interannual time scales; this would not be possible in a coupled ocean-atmosphere general circulation model. Finally, although anthropogenic forcings have had a significant impact on global mean land surface temperature, we find that their influence on the pattern of local deviations about this mean is so far undetectable. In order to achieve this in the future, as the signal grows, it will also be important that the response of the Northern Hemisphere mid-latitude westerly flow to changing sea surface temperatures is well simulated in climate model detection studies. Received: 3 December 1999 / Accepted: 30 October 2000     

Racing climate change: Collaboration and conflict in California's global climate change policy arena

Media accounts routinely refer to California's Assembly Bill 32 (AB 32), the Global Warming Solutions Act of 2006, as “landmark” climate change legislation. On its surface, this label is an accurate reflection of the state's forward-thinking stance across many environmental issues including pesticides, toxic substances, solid waste, and air quality. For all its promise, however, AB 32 can also be considered a low point in the landscape of conflict between state environmental regulators and California's environmental justice movement. While the legislation included several provisions to address the procedural and distributive dimensions of environmental justice, the implementation of AB 32 has been marked by heated conflict. The most intense conflicts over AB 32 revolve around the primacy of market mechanisms such as “cap and trade.” This article examines the drivers and the manifestations of these dynamics of collaboration and conflict between environmental justice advocates and state regulators, and pays particular attention to the scalar and racialized quality of the neoliberal discourse. The contentiousness of climate change politics in California offers scholars and practitioners around the world a cautionary tale of how the best intentions for integrating environmental justice principles into climate change policy do not necessarily translate into implementation and how underlying racialized fractures can upend collaboration between state and social movement actors.     

A quantitative performance assessment of cloud regimes in climate models

Differences in the radiative feedback from clouds account for much of the variation in climate sensitivity amongst General Circulation Models (GCMs). Therefore metrics of model performance which are demonstrated to be relevant to the cloud response to climate change form an important contribution to the overall evaluation of GCMs. In this paper we demonstrate an alternative method for assigning model data to observed cloud regimes obtained from clustering histograms of cloud amount in joint cloud optical depth—cloud top pressure classes. The method removes some of the subjectivity that exists in previous GCM cloud clustering studies. We apply the method to ten GCMs submitted to the Cloud Feedback Model Intercomparison Project (CFMIP), evaluate the simulated cloud regimes and analyse the climate change response in the context of these regimes. We also propose two cloud regime metrics, one of which is specifically targeted at assessing GCMs for the purpose of obtaining the global cloud radiative response to climate change. Most of the global variance in the cloud radiative response between GCMs is due to low clouds, with 47% arising from the stratocumulus regime and 18% due to the regime characterised by clouds undergoing transition from stratocumulus to cumulus. This result is found to be dominated by two structurally similar GCMs. The shallow cumulus regime, though widespread, has a smaller contribution and reduces the variance. For the stratocumulus and transition regimes, part of the variance results from a large model spread in the radiative properties of the regime in the control simulation. Comparison with observations reveals a systematic bias for both the stratocumulus and transition regimes to be overly reflective. If this bias was corrected with all other aspects of the response unchanged, the variance in the low cloud response would reduce. The response of some regimes with high cloud tops differ between the GCMs. These regimes are simulated too infrequently in a few of the models. If the frequency in the control simulation were more realistic and changes within the regimes were unaltered, the variance in the cloud radiative response from high-top clouds would increase. As a result, use of observations of the mean present-day cloud regimes suggests that whilst improvements in the simulation of the cloud regimes would impact the climate sensitivity, the inter-model variance may not reduce. When the cloud regime metric is calculated for the GCMs analysed here, only one model is on average consistent with observations within their uncertainty (and even this model is not consistent with the observations for all regimes), indicating scope for improvement in the simulation of cloud regimes. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

A comparison of a GCM response to historical anthropogenic land cover change and model sensitivity to uncertainty in present-day land cover representations

This study assesses the sensitivity of the fully coupled NCAR-DOE PCM to three different representations of present-day land cover, based on IPCC SRES land cover information. We conclude that there is significant model sensitivity to current land cover characterization, with an observed average global temperature range of 0.21 K between the simulations. Much larger contrasts (up to 5 K) are found on the regional scale; however, these changes are largely offsetting on the global scale. These results show that significant biases can be introduced when outside data sources are used to conduct anthropogenic land cover change experiments in GCMs that have been calibrated to their own representation of present-day land cover. We conclude that hybrid systems that combine the natural vegetation from the native GCM datasets combined with human land cover information from other sources are best for simulating such impacts. We also performed a prehuman simulation, which had a 0.39 K ~higher average global temperature and, perhaps of greater importance, temperature changes regionally of about 2 K. In this study, the larger regional changes coincide with large-scale agricultural areas. The initial cooling from energy balance changes appear to create feedbacks that intensify mid-latitude circulation features and weaken the summer monsoon circulation over Asia, leading to further cooling. From these results, we conclude that land cover change plays a significant role in anthropogenically forced climate change. Because these changes coincide with regions of the highest human population this climate impact could have a disproportionate impact on human systems. Therefore, it is important that land cover change be included in past and future climate change simulations.     

湖南气候对全球气候变化的响应

利用湖南省96个台站1960—2010年逐日气象观测资料,在进行均一性检验和订正的基础上对湖南省气候变化事实进行检测分析。结果表明:湖南气候与全球气候变化一致,呈现以变暖为主要特征的变化,且变暖存在季节、地域上的差异,冬、春、秋气温变暖趋势显著,增暖幅度最大的区域在湘北地区;对气候变暖响应敏感的要素主要是与平均气温、冬季气温相关密切的要素,如季平均气温、年平均最低气温、活动积温等;湖南气温在突变时间上具有较好的时间逻辑关系;湖南降水量无显著趋势变化,但极端降水增加,地域性差异明显,湖南东部地区降水量呈现明显增加趋势,日降水量大于等于100 mm的日数呈显著增加趋势;湖南日照时数、风速、相对湿度均呈现显著减少的变化趋势。     

Misperceptions of global climate change: information policies

Previous experimental studies have found that people generally misperceive the basic dynamics of renewable resources, and in particular the accumulation of greenhouse gases (GHGs) in the atmosphere. The purpose of the present laboratory experiment is to find out why people misperceive the dynamics of CO₂ accumulation and how misperceptions could be avoided. Using a simulator, 242 subjects were each asked to control total global emissions of CO₂ to reach a given target for the stock of CO₂ in the atmosphere. Consistent with previous investigations we find a strong tendency for people to overshoot the stated goal. Furthermore, our results point out that people need help to develop proper mental models of CO₂ accumulation and they need motivation to reconsider inappropriate decision heuristics. Based on these results and the literature on conceptual change a new information strategy is designed. To motivate, it imposes cognitive conflict; and to facilitate new understanding, it provides simple analogies. A new test shows promising learning effects. The results have important implications for the Intergovernmental Panel on Climate Change (IPCC), governments, and media covering the climatic change issue as well as for general education.     

Fast versus slow response in climate change: implications for the global hydrological cycle

Recent studies have shown that changes in global mean precipitation are larger for solar forcing than for CO₂ forcing of similar magnitude. In this paper, we use an atmospheric general circulation model to show that the differences originate from differing fast responses of the climate system. We estimate the adjusted radiative forcing and fast response using Hansen’s “fixed-SST forcing” method. Total climate system response is calculated using mixed layer simulations using the same model. Our analysis shows that the fast response is almost 40% of the total response for few key variables like precipitation and evaporation. We further demonstrate that the hydrologic sensitivity, defined as the change in global mean precipitation per unit warming, is the same for the two forcings when the fast responses are excluded from the definition of hydrologic sensitivity, suggesting that the slow response (feedback) of the hydrological cycle is independent of the forcing mechanism. Based on our results, we recommend that the fast and slow response be compared separately in multi-model intercomparisons to discover and understand robust responses in hydrologic cycle. The significance of this study to geoengineering is discussed.     

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.     

Biogeophysical impacts of land use on present-day climate: near-surface temperature change and radiative forcing

Changes in land cover affect climate through the surface energy and moisture budgets, but these biogeophysical impacts of land use have not yet been included in General Circulation Model (GCM) simulations of 20th century climate change. Here, the importance of these effects was assessed by comparing climate simulations performed with current and potential natural vegetation. The northern mid-latitude agricultural regions were simulated to be approximately 1–2 K cooler in winter and spring in comparison with their previously forested state, due to deforestation increasing the surface albedo by approximately 0.1 during periods of snow cover. Some other regions such as the Sahel and India experienced a small warming due to land use. Although the annual mean global temperature is only 0.02 K lower in the simulation with present-day land use, the more local temperature changes in some regions are of a similar magnitude to those observed since 1860. The global mean radiative forcing by anthropogenic surface albedo change relative to the natural state is simulated to be −0.2 Wm², which is comparable with the estimated forcings relative to pre-industrial times by changes in stratospheric and tropospheric ozone, N₂O, halocarbons, and the direct effect of anthropogenic aerosols. Since over half of global deforestation has occurred since 1860, simulations of climate since that date should include the biogeophysical effects of land use.     

ENSO variability and atmospheric response in a global coupled atmosphere-ocean GCM

The interannual variability associated with the El Ni?o/Southern Oscillation (ENSO) cycle is investigated using a relatively high-resolution (T42) coupled general circulation model (CGCM) of the atmosphere and ocean. Although the flux correction is restricted to annual means of heat and freshwater, the annual as well as the seasonal climate of the CGCM is in good agreement with that of the atmospheric model component forced with observed sea surface temperatures (SSTs). During a 100-year simulation of the present-day climate, the model is able to capture many features of the observed interannual SST variability in the tropical Pacific. This includes amplitude, lifetime and frequency of occurrence of El Ni?o events and also the phase locking of the SST anomalies to the annual cycle. Although the SST warming during the evolution of El Ni?os is too confined spatially, and the warming along the Peruvian coast is much too weak, the patterns and magnitudes of key atmospheric anomalies such as westerly wind stress and precipitation, and also their eastward migration from the western to the central equatorial Pacific is in accord with observations. There is also a qualitative agreement with the results obtained from the atmospheric model forced with observed SSTs from 1979 through 1994. The large-scale dynamic response during the mature phase of ENSO (December through February) is characterized by an eastward displacement and weakening of the Walker cell in the Pacific while the Hadley cell intensifies and moves equatorward. Similar to the observations, there is a positive correlation between tropical Pacific SST and the winter circulation in the North Pacific. The deepening of the Aleutian low during the ENSO winters is well captured by the model as well as the cooling in the central North Pacific and the warming over Canada and Alaska. However, there are indications that the anomalies of both SST and atmospheric circulation are overemphasized in the North Pacific. Finally, there is evidence of a coherent downstream effect over the North Atlantic as indicated by negative correlations between the PNA index and the NAO index, for example. The weakening of the westerlies across the North Atlantic in ENSO winters which is related to a weakening and southwestward displacement of the Icelandic low, is in broad agreement with the observations, as well as the weak tendency for colder than normal winters in Europe. Received: 31 October 1995 / Accepted: 29 May 1996     

从全球到区域气候变化

尽管气候变化是全球性的现象,但其表现和结果随区域不同而不同,因此区域气候信息对于气候变化的作用和风险评估很重要.基于此,IPCC第六次评估报告(AR6)第一工作组(WGI)报告第十章对如何从全球链接到区域气候变化方面进行了评估.区域气候变化是对自然强迫和人类活动的区域响应、对大尺度气候系统内部变率的响应和区域气候本身反...