GCM intercomparison of global cloud regimes: present-day evaluation and climate change response

The radiative feedback from clouds remains the largest source of variation in climate sensitivity amongst general circulation models (GCMs). A cloud clustering methodology is applied to six contemporary GCMs in order to provide a detailed intercomparison and evaluation of the simulated cloud regimes. By analysing GCMs in the context of cloud regimes, processes related to particular cloud types are more likely to be evaluated. In this paper, the mean properties of the global cloud regimes are evaluated, and the cloud response to climate change is analysed in the cloud-regime framework. Most of the GCMs are able to simulate the principal cloud regimes, however none of the models analysed have a good representation of trade cumulus in the tropics. The models also share a difficulty in simulating those regimes with cloud tops at mid-levels, with only ECHAM5 producing a regime of tropical cumulus congestus. Optically thick, high top cloud in the extra-tropics, typically associated with the passage of frontal systems, is simulated considerably too frequently in the ECHAM5 model. This appears to be a result of the cloud type persisting in the model after the meteorological conditions associated with frontal systems have ceased. The simulation of stratocumulus in the MIROC GCMs is too extensive, resulting in the tropics being too reflective. Most of the global-mean cloud response to doubled CO₂ in the GCMs is found to be a result of changes in the cloud radiative properties of the regimes, rather than changes in the relative frequency of occurrence (RFO) of the regimes. Most of the variance in the global cloud response between the GCMs arises from differences in the radiative response of frontal cloud in the extra-tropics and from stratocumulus cloud in the tropics. This variance is largely the result of excessively high RFOs of specific regimes in particular GCMs. It is shown here that evaluation and subsequent improvement in the simulation of the present-day regime properties has the potential to reduce the variance of the global cloud response, and hence climate sensitivity, amongst GCMs. For the ensemble of models considered in this study, the use of observations of the mean present-day cloud regimes suggests a potential reduction in the range of climate sensitivity of almost a third. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

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.     

Climate sensitivity and cloud feedback processes imposed by two different external forcings in an aquaplanet GCM

The sensitivity of climate to an increase in sea surface temperature (SST) and CO₂, as well as cloud feedback processes, is analyzed through a series of aquaplanet experiments listed in the Coupled Model Intercomparison Project. Rainfall is strengthened in a +4K anomaly SST experiment due to the enhanced surface evaporation; while in a quadruple CO₂ experiment, precipitation and total cloud cover are appreciably weakened. In both the +4K and quadruple CO₂ (4xCO₂) experiments, the Hadley cell is impaired, with an increase in moderate subsidence and a decrease in the frequency of strong convective activity. Regarding cloud radiation forcing (CRF), the analysis technique of Bony et al. (Climate Dynamics, 22:71–86, 2004) is used to sort cloud variables by dynamic regimes using the 500-hPa vertical velocity in tropical areas (30°S–30°N). Results show that the tropically averaged CRF change is negative and is dominated mainly by the thermodynamic component. Within convective regimes, the behavior of longwave CRF is different in the +4K and 4xCO₂ experiments, with positive and negative changes, respectively. The globally averaged CRF also reveals a negative change in both aquaplanet and Earthlike experiments, implying that clouds may play a role in decelerating global warming. The calculated climate sensitivity demonstrates that our results are close to those obtained from other models, with 0.384 and 0.584?Km²?W^?1 for aquaplanet and Earthlike experiments, respectively.     

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.     

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.     

Coupling between subtropical cloud feedback and the local hydrological cycle in a climate model

In HadGEM2-A, AMIP experiments forced with observed sea surface temperatures respond to uniform and patterned +4 K SST perturbations with strong positive cloud feedbacks in the subtropical stratocumulus/trade cumulus transition regions. Over the subtropical Northeast Pacific at 137°W/26°N, the boundary layer cloud fraction reduces considerably in the AMIP +4 K patterned SST experiment. The near-surface wind speed and the air-sea temperature difference reduces, while the near-surface relative humidity increases. These changes limit the local increase in surface evaporation to just 3 W/m² or 0.6 %/K. Previous studies have suggested that increases in surface evaporation may be required to maintain maritime boundary layer cloud in a warmer climate. This suggests that the supply of water vapour from surface evaporation may not be increasing enough to maintain the low level cloud fraction in the warmer climate in HadGEM2-A. Sensitivity tests which force the surface evaporation to increase substantially in the +4 K patterned SST experiment result in smaller changes in boundary layer cloud and a weaker cloud feedback in HadGEM2-A, supporting this idea. Although global mean surface evaporation in climate models increases robustly with global temperature (and the resulting increase in atmospheric radiative cooling), local values may increase much less, having a significant impact on cloud feedback. These results suggest a coupling between cloud feedback and the hydrological cycle via changes in the patterns of surface evaporation. A better understanding of both the factors controlling local changes in surface evaporation and the sensitivity of clouds to such changes may be required to understand the reasons for inter-model differences in subtropical cloud feedback.     

Non-linear climate feedback analysis in an atmospheric general circulation model

 A method is described for evaluating the ‘partial derivatives’ of globally averaged top-of-atmosphere (TOA) radiation changes with respect to basic climate model physical parameters. This method is used to analyse feedbacks in the Australian Bureau of Meteorology Research Centre general circulation model. The parameters considered are surface temperature, water vapour, lapse rate and cloud cover. The climate forcing which produces the changes is a globally uniform sea surface temperature (SST) perturbation. The first and second order differentials of model parameters with respect to the forcing (i.e. SST changes) are estimated from quadratic least square fitting. Except for total cloud cover, variables are found to be strong functions of global SST. Strongly non-linear variations of lapse rate and high cloud amount and height appear to relate to the non-linear response in penetrative convection. Globally averaged TOA radiation differentials with respect to model parameters are also evaluated. With the exception of total cloud contributions, a high correlation is generally found to exist, on the global mean level, between TOA radiation and the respective parameter perturbations. The largest non-linear terms contributing to radiative changes are those due to lapse rate and high cloud. The contributions of linear and non-linear terms to the overall radiative response from a 4 K SST perturbation are assessed. Significant non-linear responses are found to be associated with lapse rate, water vapour and cloud changes. Although the exact magnitude of these responses is likely to be a function of the particular model as well as the imposed SST perturbation pattern, the present experiments flag these as processes which cannot properly be understood from linear theory in the evaluation of climate change sensitivity. Received: 16 January 1997/Accepted: 9 May 1997     

On dynamic and thermodynamic components of cloud changes

Clouds are sensitive to changes in both the large-scale circulation and the thermodynamic structure of the atmosphere. In the tropics, temperature changes that occur on seasonal to decadal time scales are often associated with circulation changes. Therefore, it is difficult to determine the part of cloud variations that results from a change in the dynamics from the part that may result from the temperature change itself. This study proposes a simple framework to unravel the dynamic and non-dynamic (referred to as thermodynamic) components of the cloud response to climate variations. It is used to analyze the contrasted response, to a prescribed ocean warming, of the tropically-averaged cloud radiative forcing (CRF) simulated by the ECMWF, LMD and UKMO climate models. In each model, the dynamic component largely dominates the CRF response at the regional scale, but this is the thermodynamic component that explains most of the average CRF response to the imposed perturbation. It is shown that this component strongly depends on the behaviour of the low-level clouds that occur in regions of moderate subsidence (e.g. in the trade wind regions). These clouds exhibit a moderate sensitivity to temperature changes, but this is mostly their huge statistical weight that explains their large influence on the tropical radiation budget. Several propositions are made for assessing the sensitivity of clouds to changes in temperature and in large-scale motions using satellite observations and meteorological analyses on the one hand, and mesoscale models on the other hand.     

Uncertainties linked to land-surface processes in climate change simulations

 The impact of climate change on the hydrology of continental surfaces is critical for human activities but the response of the surface to this perturbation may also affect the sensitivity of the climate. This complex feedback is simulated in general circulation models (GCMs) used for climate change predictions by their land-surface schemes. The present study attempts to quantify the uncertainty associated with these schemes and what impact it has on our confidence in the simulated climate anomalies. Four GCMs, each coupled to two different land-surface schemes, are used to explore the spectrum of uncertainties. It is shown that, in this sample, surface processes have a significant contribution to our ability to predict surface temperature changes and perturbations of the hydrological cycle in an environment with doubled greenhouse gas concentration. The results reveal that the uncertainty introduced by land-surface processes in the simulated climate is different from its impact on the sensitivity of GCMs to climate change, indeed an alteration of the surface parametrization with little impact on model climate can affect sensitivity significantly. This result leads us to believe that the validation of land-surface schemes should not be limited to the current climate but should also cover their sensitivity to variations in climatic forcing. Received: 24 June 1999 / Accepted: 20 April 2000     

Simulations of 20th and 21st century Arctic cloud amount in the global climate models assessed in the IPCC AR4

Simulations of late 20th and 21st century Arctic cloud amount from 20 global climate models (GCMs) in the Coupled Model Intercomparison Project phase 3 (CMIP3) dataset are synthesized and assessed. Under recent climatic conditions, GCMs realistically simulate the spatial distribution of Arctic clouds, the magnitude of cloudiness during the warmest seasons (summer–autumn), and the prevalence of low clouds as the predominant type. The greatest intermodel spread and most pronounced model error of excessive cloudiness coincides with the coldest seasons (winter–spring) and locations (perennial ice pack, Greenland, and the Canadian Archipelago). Under greenhouse forcing (SRES A1B emissions scenario) the Arctic is expected to become cloudier, especially during autumn and over sea ice, in tandem with cloud decreases in middle latitudes. Projected cloud changes for the late 21st century depend strongly on the simulated modern (late 20th century) annual cycle of Arctic cloud amount: GCMs that correctly simulate more clouds during summer than winter at present also tend to simulate more clouds in the future. The simulated Arctic cloud changes display a tripole structure aloft, with largest increases concentrated at low levels (below 700 hPa) and high levels (above 400 hPa) but little change in the middle troposphere. The changes in cloud radiative forcing suggest that the cloud changes are a positive feedback annually but negative during summer. Of potential explanations for the simulated Arctic cloud response, local evaporation is the leading candidate based on its high correlation with the cloud changes. The polar cloud changes are also significantly correlated with model resolution: GCMs with higher spatial resolution tend to produce larger future cloud increases.     

Limitations of time-slice experiments for predicting regional climate change over South Asia

While time-slice simulations with atmospheric general circulation models (GCMs) have been used for many years to regionalize climate projections and/or assess their uncertainties, there is still no consensus about the method used to prescribe sea surface temperature (SST) in such experiments. In the present study, the response of the Indian summer monsoon to increasing amounts of greenhouse gases and sulfate aerosols is compared between a reference climate scenario and three sets of time-slice experiments, consisting of parallel integrations for present-day and future climates. Different monthly mean SST boundary conditions have been tested in the present-day integrations: raw climatological SST derived from the reference scenario, observed climatological SST, and observed SST with interannual variability. For future climate, the SST forcing has been obtained by superimposing climatological monthly mean SST anomalies derived from the reference scenario onto the present-day SST boundary conditions. None of these sets of time-slice experiments is able to capture accurately the response of the Indian summer monsoon simulated in the transient scenario. This finding suggests that the ocean–atmosphere coupling is a fundamental feature of the climate system. Neglecting the SST feedback and variability at the intraseasonal to interannual time scales has a significant impact on the projected monsoon response to global warming. Adding interannual variability in the prescribed SST boundary conditions does not mitigate the problem, but can on the contrary reinforce the discrepancies between the forced and coupled experiments. The monsoon response is also shown to depend on the simulated control climate, and can therefore be sensitive to the use of observed rather than model-derived SSTs to drive the present-day atmospheric simulation, as well as to any approximation in the prescribed radiative forcing. While such results do not challenge the use of time-slice experiments for assessing uncertainties and understanding mechanisms in transient scenarios, they emphasize the need for high-resolution coupled atmosphere-ocean GCMs for dynamical downscaling, or at least for high-resolution atmospheric GCMs coupled with a slab or a regional ocean model.     

Effect of tropospheric temperature change on the zonal mean circulation and SH winter extratropical cyclones

This study aims to understand the mechanisms which cause an overall reduction of SH extratropical cyclone activity with a slight increase in the high latitudes in a warmer climate simulated in general circulation models (GCMs) with increasing CO₂. For this purpose, we conducted idealized model experiments by forcing warm temperature anomalies to the areas where climate change models exhibit local maximum warming—the tropics in the upper troposphere and the polar regions in the lower troposphere—simultaneously and separately. The Melbourne University atmospheric GCM (R21) coupled with prescribed SST was utilized for the experiments. Our results demonstrate that the reduction of SH extratropical cyclone frequency and depth in the midlatitudes but the slight increase in the high latitudes suggested in climate change models result essentially from the tropical upper tropospheric warming. With this tropical warming, the enhanced static stability which decreases baroclinicity in the low and midlatitudes turns out to be a major contributor to the decrease of cyclone activity equatorward of 45°S whereas the increased meridional temperature gradient in the high latitudes seems an important mechanism for the increase of cyclone activity over 50°–60°S.     

Tropical convective responses to microphysical and radiative processes: a sensitivity study with a 2-D cloud resolving model

Summary Prognostic cloud schemes are increasingly used in weather and climate models in order to better treat cloud-radiation processes. Simplifications are often made in such schemes for computational efficiency, like the scheme being used in the National Centers for Environment Prediction models that excludes some microphysical processes and precipitation-radiation interaction. In this study, sensitivity tests with a 2-D cloud resolving model are carried out to examine effects of the excluded microphysical processes and precipitation-radiation interaction on tropical thermodynamics and cloud properties. The model is integrated for 10 days with the imposed vertical velocity derived from the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment. The experiment excluding the depositional growth of snow from cloud ice shows anomalous growth of cloud ice and more than 20% increase of fractional cloud cover, indicating that the lack of the depositional snow growth causes unrealistically large mixing ratio of cloud ice. The experiment excluding the precipitation-radiation interaction displays a significant cooling and drying bias. The analysis of heat and moisture budgets shows that the simulation without the interaction produces more stable upper troposphere and more unstable mid and lower troposphere than does the simulation with the interaction. Thus, the suppressed growth of ice clouds in upper troposphere and stronger radiative cooling in mid and lower troposphere are responsible for the cooling bias, and less evaporation of rain associated with the large-scale subsidence induces the drying in mid and lower troposphere.     

Interpretation of the positive low-cloud feedback predicted by a climate model under global warming

The response of low-level clouds to climate change has been identified as a major contributor to the uncertainty in climate sensitivity estimates among climate models. By analyzing the behaviour of low-level clouds in a hierarchy of models (coupled ocean-atmosphere model, atmospheric general circulation model, aqua-planet model, single-column model) using the same physical parameterizations, this study proposes an interpretation of the strong positive low-cloud feedback predicted by the IPSL-CM5A climate model under climate change. In a warmer climate, the model predicts an enhanced clear-sky radiative cooling, stronger surface turbulent fluxes, a deepening and a drying of the planetary boundary layer, and a decrease of tropical low-clouds in regimes of weak subsidence. We show that the decrease of low-level clouds critically depends on the change in the vertical advection of moist static energy from the free troposphere to the boundary-layer. This change is dominated by variations in the vertical gradient of moist static energy between the surface and the free troposphere just above the boundary-layer. In a warmer climate, the thermodynamical relationship of Clausius-Clapeyron increases this vertical gradient, and then the import by large-scale subsidence of low moist static energy and dry air into the boundary layer. This results in a decrease of the low-level cloudiness and in a weakening of the radiative cooling of the boundary layer by low-level clouds. The energetic framework proposed in this study might help to interpret inter-model differences in low-cloud feedbacks under climate change.     

The impact of vertical resolution upon GCM simulations of marine stratocumulus

 The impact of increased vertical resolution in the Hadley Centre Climate Model upon the simulation of stratocumulus is investigated in experiments using single column (SCM) and general circulation (GCM) model configurations. A threefold enhancement of vertical resolution in the boundary layer leads to improvements in the vertical structure of the cloud-topped boundary layer produced by the SCM and GCM in both well-mixed and decoupled situations. However, single and decoupled mixed layers in the marine stratocumulus subsidence regions are still too shallow and, despite increasing, layer cloud amounts remain generally too low. Moreover, closer examination of GCM data and SCM timeseries reveals an underlying sensitivity to vertical resolution in model interactions between boundary layer and convection processes which appears unrealistic. Stratocumulus simulation is thus unlikely to improve significantly as a result of enhanced resolution alone and further work is being undertaken to improve the Hadley Centre model’s boundary layer scheme and, in particular, its interaction with the convection scheme. Nevertheless, this study shows that the full benefit of an improved boundary layer scheme will not be realized if the boundary layer structure is constrained by the rather poor lower troposphere resolution of the standard 19-level climate model. Future Hadley Centre model versions will seek to combine the added flexibility of a better resolved structure with improvements to the subgrid boundary layer parametrizations. Received: 14 April 1998 / Accepted: 5 November 1998     

On the vertical extent of atmospheric feedbacks

This study addresses the question: what vertical regions contribute the most to water vapor, surface temperature, lapse rate and cloud fraction feedback strengths in a general circulation model? Multi-level offline radiation perturbation calculations are used to diagnose the feedback contribution from each model level. As a first step, to locate regions of maximum radiative sensitivity to climate changes, the top of atmosphere radiative impact for each feedback is explored for each process by means of idealized parameter perturbations on top of a control (1?×?CO₂) model climate. As a second step, the actual feedbacks themselves are calculated using the changes modelled from a 2?×?CO₂ experiment. The impact of clouds on water vapor and lapse rate feedbacks is also isolated using `clear sky' calculations. Considering the idealized changes, it is found that the radiative sensitivity to water vapor changes is a maximum in the tropical lower troposphere. The sensitivity to temperature changes has both upper and lower tropospheric maxima. The sensitivity to idealized cloud changes is positive (warming) for upper level cloud increases but negative (cooling) for lower level increases, due to competing long and shortwave effects. Considering the actual feedbacks, it is found that water vapor feedback is a maximum in the tropical upper troposphere, due to the large relative increases in specific humidity which occur there. The actual lapse rate feedback changes sign with latitude and is a maximum (negative) again in the tropical upper troposphere. Cloud feedbacks reflect the general decrease in low- to mid-level low-latitude cloud, with an increase in the very highest cloud. This produces a net positive (negative) shortwave (longwave) cloud feedback. The role of clouds in the strength of the water vapor and lapse rate feedbacks is also discussed.     

On the spread of changes in marine low cloud cover in climate model simulations of the 21st century

In 36 climate change simulations associated with phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5), changes in marine low cloud cover (LCC) exhibit a large spread, and may be either positive or negative. Here we develop a heuristic model to understand the source of the spread. The model’s premise is that simulated LCC changes can be interpreted as a linear combination of contributions from factors shaping the clouds’ large-scale environment. We focus primarily on two factors—the strength of the inversion capping the atmospheric boundary layer (measured by the estimated inversion strength, EIS) and sea surface temperature (SST). For a given global model, the respective contributions of EIS and SST are computed. This is done by multiplying (1) the current-climate’s sensitivity of LCC to EIS or SST variations, by (2) the climate-change signal in EIS or SST. The remaining LCC changes are then attributed to changes in greenhouse gas and aerosol concentrations, and other environmental factors. The heuristic model is remarkably skillful. Its SST term dominates, accounting for nearly two-thirds of the intermodel variance of LCC changes in CMIP3 models, and about half in CMIP5 models. Of the two factors governing the SST term (the SST increase and the sensitivity of LCC to SST perturbations), the SST sensitivity drives the spread in the SST term and hence the spread in the overall LCC changes. This sensitivity varies a great deal from model to model and is strongly linked to the types of cloud and boundary layer parameterizations used in the models. EIS and SST sensitivities are also estimated using observational cloud and meteorological data. The observed sensitivities are generally consistent with the majority of models as well as expectations from prior research. Based on the observed sensitivities and the relative magnitudes of simulated EIS and SST changes (which we argue are also physically reasonable), the heuristic model predicts LCC will decrease over the 21st-century. However, to place a strong constraint, for example on the magnitude of the LCC decrease, will require longer observational records and a careful assessment of other environmental factors producing LCC changes. Meanwhile, addressing biases in simulated EIS and SST sensitivities will clearly be an important step towards reducing intermodel spread in simulated LCC changes.     

The radiative influence of aerosol effects on liquid-phase cumulus and stratiform clouds based on sensitivity studies with two climate models

Aerosol effects on warm (liquid-phase) cumulus cloud systems may have a strong radiative influence via suppression of precipitation in convective systems. A consequence of this suppression of precipitation is increased liquid water available for large-scale stratiform clouds, through detrainment, that in turn affect their precipitation efficiency. The nature of this influence on radiation, however, is dependent on both the treatment of convective condensate and the aerosol distribution. Here, we examine these issues with two climate models—CSIRO and GISS, which treat detrained condensate differently. Aerosol–cloud interactions in warm stratiform and cumulus clouds (via cloud droplet formation and autoconversion) are treated similarly in both models. The influence of aerosol–cumulus cloud interactions on precipitation and radiation are examined via simulations with present-day and pre-industrial aerosol emissions. Sensitivity tests are also conducted to examine changes to climate due to changes in cumulus cloud droplet number (N _c); the main connection between aerosols and cumulus cloud microphysics. Results indicate that the CSIRO GCM is quite sensitive to changes in aerosol concentrations such that an increase in aerosols increases N _c, cloud cover, total liquid water path (LWP) and reduces total precipitation and net cloud radiative forcings. On the other hand, the radiative fluxes in the GISS GCM appear to have minimal changes despite an increase in aerosols and N _c. These differences between the two models—reduced total LWP in the GISS GCM for increased aerosols, opposite to that seen in CSIRO—appear to be more sensitive to the detrainment of convective condensate, rather than to changes in N _c. If aerosols suppress convective precipitation as noted in some observationally based studies (but not currently treated in most climate models), the consequence of this change in LWP suggests that: (1) the aerosol indirect effect (calculated as changes to net cloud radiative forcing from anthropogenic aerosols) may be higher than previously calculated or (2) lower than previously calculated. Observational constrains on these results are difficult to obtain and hence, until realistic cumulus-scale updrafts are implemented in models, the logic of detraining non-precipitating condensate at appropriate levels based on updrafts and its effects on radiation, will remain an uncertainty.     

Why radiative forcing might fail as a predictor of climate change

Radiative forcing has been widely used as a metric of climate change, i.e. as a measure by which various contributors to a net surface temperature change can be quantitatively compared. The extent to which this concept is valid for spatially inhomogeneous perturbations to the climate system is tested. A series of climate model simulations involving ozone changes of different spatial structure reveals that the climate sensitivity parameter is highly variable: for an ozone increase in the northern hemisphere lower stratosphere, it is more than twice as large as for a homogeneous CO₂ perturbation. A global ozone perturbation in the upper troposphere, however, causes a significantly smaller surface temperature response than CO₂. The variability of the climate sensitivity parameter is shown to be mostly due to the varying strength of the stratospheric water vapour feedback. The variability of the sea-ice albedo feedback modifies climate sensitivity of perturbations with the same vertical structure but a different horizontal structure. This feedback is also the origin of the comparatively larger climate sensitivity to perturbations restricted to the northern hemisphere extratropics. As cloud feedback does not operate independently from the other feedbacks, quantifying its effect is rather difficult. However, its effect on the variability of for horizontally and vertically inhomogeneous perturbations within one model framework seems to be comparatively small.This revised version was published online March 2005 with corrections to table 5.     

Hydrologic Sensitivity of Global Rivers to Climate Change

Climate predictions from four state-of-the-art general circulation models (GCMs) were used to assess the hydrologic sensitivity to climate change of nine large, continental river basins (Amazon, Amur, Mackenzie, Mekong, Mississippi, Severnaya Dvina, Xi, Yellow, Yenisei). The four climate models (HCCPR-CM2, HCCPR-CM3, MPI-ECHAM4, and DOE-PCM3) all predicted transient climate response to changing greenhouse gas concentrations, and incorporated modern land surface parameterizations. Model-predicted monthly average precipitation and temperature changes were downscaled to the river basin level using model increments (transient minus control) to adjust for GCM bias. The variable infiltration capacity (VIC) macroscale hydrological model (MHM) was used to calculate the corresponding changes in hydrologic fluxes (especially streamflow and evapotranspiration) and moisture storages. Hydrologic model simulations were performed for decades centered on 2025 and 2045. In addition, a sensitivity study was performed in which temperature and precipitation were increased independently by 2 °C and 10%, respectively, during each of four seasons. All GCMs predict a warming for all nine basins, with the greatest warming predicted to occur during the winter months in the highest latitudes. Precipitation generally increases, but the monthly precipitation signal varies more between the models than does temperature. The largest changes in the hydrological cycle are predicted for the snow-dominated basins of mid to higher latitudes. This results in part from the greater amount of warming predicted for these regions, but more importantly, because of the important role of snow in the water balance. Because the snow pack integrates the effects of climate change over a period of months, the largest changes occur in early to mid spring when snow melt occurs. The climate change responses are somewhat different for the coldest snow dominated basins than for those with more transitional snow regimes. In the coldest basins, the response to warming is an increase of the spring streamflow peak, whereas for the transitional basins spring runoff decreases. Instead, the transitional basins have large increases in winter streamflows. The hydrological response of most tropical and mid-latitude basins to the warmer and somewhat wetter conditions predicted by the GCMs is a reduction in annual streamflow, although again, considerable disagreement exists among the different GCMs. In contrast, for the high-latitude basins increases in annual flow volume are predicted in most cases.