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.     

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.     

Simulation of Asian Monsoon Seasonal Variations with Climate Model R42L9／LASG

The seasonal variations of the Asian monsoon were explored by applying the atmospheric general circulation model R42L9 that was developed recently at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences (LASG/IAP/CAS). The 20-yr (1979-1998) simulation was done using the prescribed 20-yr monthly SST and sea-ice data as required by Atmospheric Model Intercomparison Project (AMIP)Ⅱ in the model. The monthly precipitation and monsoon circulations were analyzed and compared with the observations to validate the model‘s performance in simulating the climatological mean and seasonal variations of the Asian monsoon. The results show that the model can capture the main features of the spatial distribution and the temporal evolution of precipitation in the Indian and East Asian monsoon areas. The model also reproduced the basic patterns of monsoon circulation. However, some biases exis tin this model. The simulation of the heating over the Tibetan Plateau in summer was too strong. The overestimated heating caused a stronger East Asian monsoon and a weaker Indian monsoon than the observations. In the circulation fields, the South Asia high was stronger and located over the Tibetan Plateau. The western Pacific subtropical high was extended westward, which is in accordance with the observational results when the heating over the Tibetan Plateau is stronger. Consequently, the simulated rainfall around this area and in northwest China was heavier than in observations, but in the Indian monsoon area and west Pacific the rainfall was somewhat deficient.     

A study of long-term climate change in a simple seasonal nonlinear climate model

Solar radiation cycles, earth-orbital changes, and continental drift drive long to very long term (10³–10⁶ years) climatic changes. Lin and North used the stationary solutions of a simple energy balance model (EBM) to study the equilibrium climatic stages. In this paper, we study time dependent solutions and, in particular, transition processes. We make use of two time scales: a seasonal cycle (fast variation) and a long term time change (slow variation). Variations over short time scales are solved using a Fourier transform in time and long term variations are studied using a 4th order Runge-Kutta method. The energy balance equation is a parabolic type equation and it is well posed. Climate changes depend mainly on external forcing and the state of the climate is determined by the slow time scale forcing. In other words, transitions from one climate stage (snow-covered) to another (snow-free) at bifurcation points are monotonic, despite 20% to 50% shortperiod random fluctuations in the solar energy. This smooth transition is especially noticeable when the land bands lie close to the north pole (70° N to 90° N) or at high latitudes (50° N to 75° N).Now at Applied Physics Laboratory, The Johns Hopkins University, Laurel, MD 20723, USA     

Evaluation of tropical cloud regimes in observations and a general circulation model

Tropical cloud regimes defined by cluster analysis of International Satellite Cloud Climatology Project (ISCCP) cloud top pressure (CTP)–optical thickness distributions and ISCCP-like Goddard Institute for Space Studies (GISS) general circulation model (GCM) output are analyzed in this study. The observations are evaluated against radar–lidar cloud-top profiles from the atmospheric radiation measurement (ARM) Program active remote sensing of cloud layers (ARSCL) product at two tropical locations and by placing them in the dynamical context of the Madden–Julian oscillation (MJO). ARSCL highest cloud-top profiles indicate that differences among some of the six ISCCP regimes may not be as prominent as suggested by ISCCP at the ARM tropical sites. An experimental adjustment of the ISCCP CTPs to produce cloud-top height profiles consistent with ARSCL eliminates the independence between those regimes. Despite these ambiguities, the ISCCP regime evolution over different phases of the MJO is consistent with existing MJO mechanisms, but with a greater mix of cloud types in each phase than is usually envisioned. The GISS Model E GCM produces two disturbed and two suppressed regimes when vertical convective condensate transport is included in the model’s cumulus parameterization. The primary model deficiencies are the absence of an isolated cirrus regime, a lack of mid-level cloud relative to ARSCL, and a tendency for occurrences of specific parameterized processes such as deep and shallow convection and stratiform low cloud formation to not be associated preferentially with any single cloud regime.     

Understanding variations and seasonal characteristics of net primary production under two types of climate change scenarios in China using the LPJ model

The approach of conditional nonlinear optimal perturbation related to parameter (CNOP-P) is employed to provide a possible climate scenario and to study the impact of climate change on the simulated net primary production (NPP) in China within a state-of-the-art Lund-Potsdam-Jena dynamic global vegetation model (LPJ DGVM). The CNOP-P, as a type of climate perturbation to bring variation in climatology and climate variability of the reference climate condition, causes the maximal impact on the simulated NPP in China. A linear climate perturbation that induces variation in climatology, as another possible climate scenario, is also applied to explore the role of variation in climate variability in the simulated NPP. It is shown that NPP decreases in northern China and increases in northeastern and southern China when the temperature changes as a result of a CNOP-P-type temperature change scenario. A similar magnitude of change in the spatial pattern variations of NPP is caused by the CNOP-P-type and the linear temperature change scenarios in northern and northeastern China, but not in southern China. The impact of the CNOP-P-type temperature change scenario on magnitude of change of NPP is more intense than that of the linear temperature change scenario. The numerical results also show that in southern China, the change in NPP caused by the CNOP-P-type temperature change scenario compared with the reference simulated NPP is sensitive. However, this sensitivity is not observed under the linear temperature change scenario. The seasonal simulations indicate that the differences between the variations in NPP due to the two types of temperature change scenarios principally stem from the variations in summer and autumn in southern China under the LPJ model. These numerical results imply that NPP is sensitive to the variation in temperature variability. The results influenced by the CNOP-P-type precipitation change scenario are similar to those under the linear precipitation change scenario, which cause the increasing NPP in arid and semi-arid regions of the northern China. The above findings indicate that the CNOP-P approach is a useful tool for exploring the nonlinear response of NPP to climate variability.     

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.     

Resolution effects on regional climate model simulations of seasonal precipitation over Europe

We analyze a set of nine regional climate model simulations for the period 1961–2000 performed at 25 and 50 km horizontal grid spacing over a European domain in order to determine the effects of horizontal resolution on the simulation of precipitation. All of the models represent the seasonal mean spatial patterns and amount of precipitation fairly well. Most models exhibit a tendency to over-predict precipitation, resulting in a domain-average total bias for the ensemble mean of about 20% in winter (DJF) and less than 10% in summer (JJA) at both resolutions, although this bias could be artificially enhanced by the lack of a gauge correction in the observations. A majority of the models show increased precipitation at 25 km relative to 50 km over the oceans and inland seas in DJF, JJA, and ANN (annual average), although the response is strongest during JJA. The ratio of convective precipitation to total precipitation decreases over land for most models at 25 km. In addition, there is an increase in interannual variability in many of the models at 25 km grid spacing. Comparison with gridded observations indicates that a majority of models show improved skill in simulating both the spatial pattern and temporal evolution of precipitation at 25 km compared to 50 km during the summer months, but not in winter or on an annual mean basis. Model skill at higher resolution in simulating the spatial and temporal character of seasonal precipitation is found especially for Great Britain. This geographic dependence of the increased skill suggests that observed data of sufficient density are necessary to capture fine-scale climate signals. As climate models increase their horizontal resolution, it is thus a key priority to produce high quality fine scale observations for model evaluation.     

Impact of improved representation of horizontal and vertical cloud structure in a climate model

Many studies have investigated the effects that misrepresentation of sub-grid cloud structure can have on the radiation budget. In this study, we perform 20-year simulations of the current climate using an atmosphere-only version of the Met Office Unified Model to investigate the effects of cloud approximation on model climate. We apply the “Tripleclouds” scheme for representing horizontal cloud inhomogeneity and “exponential-random” overlap, both separately and in combination, in place of a traditional plane-parallel representation with maximum-random overlap, to the clouds within the radiation scheme. The resulting changes to both the radiation budget and other meteorological variables, averaged over the 20?years, are compared. The combined global effect of the parameterizations on top-of-atmosphere short-wave and long-wave radiation budget is less than 1?W?m^?2, but changes of up to 10?W?m^?2 are identified in marine stratocumulus regions. A cooling near the surface over the winter polar regions of up to 3°C is also identified when horizontal cloud inhomogeneity is represented, and a warming of similar magnitude is found when exponential-random overlap is implemented. Corresponding changes of the same sign are also found in zonally averaged temperature, with maximum changes in the upper tropical troposphere of up to 0.5°C. Changes in zonally averaged cloud fraction in this location were of opposite sign and up to 0.02. The individual effects on tropospheric temperature of improving the two components of cloud structure are of similar magnitudes to about 2% of the warming created by a quadrupling of carbon dioxide.     

Diurnal and seasonal variations of CO2 fluxes and their climate controlling factors for a subtropical forest in Ningxiang

In this study, the diurnal and seasonal variations of CO2 fluxes in a subtropical mixed evergreen forest in Ningxiang of Hunan Province, part of the East Asian monsoon region, were quantified for the first time. The fluxes were based on eddy covariance measurements from a newly initiated flux tower. The relationship between the CO2 fluxes and climate factors was also analyzed. The results showed that the target ecosystem appeared to be a clear carbon sink in 2013, with integrated net ecosystem CO2exchange(NEE), ecosystem respiration(RE), and gross ecosystem productivity(GEP) of-428.8, 1534.8 and1963.6 g C m-2yr-1, respectively. The net carbon uptake(i.e. the-NEE), RE and GEP showed obvious seasonal variability,and were lower in winter and under drought conditions and higher in the growing season. The minimum NEE occurred on12 June(-7.4 g C m-2d-1), due mainly to strong radiation, adequate moisture, and moderate temperature; while a very low net CO2 uptake occurred in August(9 g C m-2month-1), attributable to extreme summer drought. In addition, the NEE and GEP showed obvious diurnal variability that changed with the seasons. In winter, solar radiation and temperature were the main controlling factors for GEP, while the soil water content and vapor pressure deficit were the controlling factors in summer. Furthermore, the daytime NEE was mainly limited by the water-stress effect under dry and warm atmospheric conditions, rather than by the direct temperature-stress effect.     

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.     

Potential use of a regional climate model in seasonal tropical cyclone activity predictions in the western North Pacific

This study investigates the potential use of a regional climate model in forecasting seasonal tropical cyclone (TC) activity. A modified version of Regional Climate Model Version 3 (RegCM3) is used to examine the ability of the model to simulate TC genesis and landfalling TC tracks for the active TC season in the western North Pacific. In the model, a TC is identified as a vortex satisfying several conditions, including local maximum relative vorticity at 850?hPa with a value?≥450?×?10^?6?s^?1, and the temperature at 300?hPa being 1°C higher than the average temperature within 15° latitude radius from the TC center. Tracks are traced by following these found vortices. Six-month ensemble (8 members each) simulations are performed for each year from 1982 to 2001 so that the climatology of the model can be compared to the Joint Typhoon Warning Center (JTWC) observed best-track dataset. The 20-year ensemble experiments show that the RegCM3 can be used to simulate vortices with a wind structure and temperature profile similar to those of real TCs. The model also reproduces tracks very similar to those observed with features like genesis in the tropics, recurvature at higher latitudes and landfall/decay. The similarity of the 500-hPa geopotential height patterns between RegCM3 and the European Centre for Medium-Range Weather Forecasts 40 Year Re-analysis (ERA-40) shows that the model can simulate the subtropical high to a large extent. The simulated climatological monthly spatial distributions as well as the interannual variability of TC occurrence are also similar to the JTWC data. These results imply the possibility of producing seasonal forecasts of tropical cyclones using real-time global climate model predictions as boundary conditions for the RegCM3.     

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.