Climate sensitivities of two versions of FGOALS model to idealized radiative forcing

Projections of future climate change by climate system models depend on the sensitivities of models to specified greenhouse gases.To reveal and understand the different climate sensitivities of two versions of LASG/IAP climate system model FGOALS-g2 and FGOALS-s2,we investigate the global mean surface air temperature responses to idealized CO2 forcing by using the output of abruptly quadrupling CO2 experiments.The Gregory-style regression method is used to estimate the"radiative forcing"of quadrupled CO2 and equilibrium sensitivity.The model response is separated into a fast-response stage associated with the CO2 forcing during the first 20 years,and a slow-response stage post the first 20 years.The results show that the radiative forcing of CO2 is overestimated due to the positive water-vapor feedback and underestimated due to the fast cloud processes.The rapid response of water vapor in FGOALS-s2 is responsible for the stronger radiative forcing of CO2.The climate sensitivity,defined as the equilibrium temperature change under doubled CO2 forcing,is about 3.7 K in FGOALS-g2 and4.5 K in FGOALS-s2.The larger sensitivity of FGOALS-s2 is due mainly to the weaker negative longwave clear-sky feedback and stronger positive shortwave clear-sky feedback at the fast-response stage,because of the more rapid response of water vapor increase and sea-ice decrease in FGOALS-s2 than in FGOALS-g2.At the slow-response stage,similar to the fast-response stage,net negative clear-sky feedback is weaker in FGOALS-s2.Nevertheless,the total negative feedback is larger in FGOALS-s2 due to a larger negative shortwave cloud feedback that involves a larger response of total cloud fraction and condensed water path increase.The uncertainties of estimated forcing and net feedback mainly come from the shortwave cloud processes.     

THE INFLUENCE OF GREENHOUSE WARMING ON THE ATMOSPHERIC COMPONENT OF THE HYDROLOGICAL CYCLE

An atmosphere–ocean climate box model is used to examine the influence of cloud feedback on the equilibria of the climate system. The model consists of three non-linear ordinary differential equations, which are simplified forms of the first law of thermodynamics for the atmosphere and ocean and the continuity equation for the atmospheric component of the hydrological cycle. The mass continuity equation expresses the cloud liquid water content as a function of the evaporation rate from the ocean surface and the precipitation rate. Cloud formation releases latent heat. The model clouds also absorb solar energy at a rate consistent with recent findings. The model simulates snow–ice albedo feedback, water vapour feedback and cloud feedback. The global mean precipitation and surface temperature are analysed as they respond to enhanced greenhouse warming. Model results show that cloud feedback can lead to the occurrence of multiple climate equilibria. Some of these are warmer than the present equilibrium, with increased precipitation, while others are colder, with reduced precipitation. If the cloud feedback is weak, enhanced greenhouse forcing leads to a small alteration of the present equilibrium. If the cloud feedback is strong enough, the climate system can be forced into a warmer and wetter equilibrium.     

Effects of alternative cloud radiation parameterizations in a general circulation model

Using the National Center for Atmospheric Research (NCAR) general circulation model (CCM2), a suite of alternative cloud radiation parameterizations has been tested. Our methodology relies on perpetual July integrations driven by ±2 K sea surface temperature forcing. The tested parameterizations include relative humidity based clouds and versions of schemes involving a prognostic cloud water budget. We are especially interested in testing the effect of cloud optical thickness feedbacks on global climate sensitivity. All schemes exhibit negative cloud radiation feedbacks, i.e., cloud moderates the global warming. However, these negative net cloud radiation feedbacks consist of quite different shortwave and longwave components between a scheme with interactive cloud radiative properties and several schemes with specified cloud water paths. An increase in cloud water content in the warmer climate leads to optically thicker middle- and low-level clouds and in turn negative shortwave feedbacks for the interactive radiative scheme, while a decrease in cloud amount leads to a positive shortwave feedback for the other schemes. For the longwave feedbacks, a decrease in high effective cloudiness for the schemes without interactive radiative properties leads to a negative feedback, while no distinct changes in effective high cloudiness and the resulting feedback are exhibited for the scheme with interactive radiative properties. The resulting magnitude of negative net cloud radiation feed-back is largest for the scheme with interactive radiative properties. Even though the simulated values of cloud radiative forcing for the present climate using this method differ most from the observational data, the approach shows great promise for the future.     

Observational Constraints on Cloud Feedbacks: The Role of Active Satellite Sensors

Cloud profiling from active lidar and radar in the A-train satellite constellation has significantly advanced our understanding of clouds and their role in the climate system. Nevertheless, the response of clouds to a warming climate remains one of the largest uncertainties in predicting climate change and for the development of adaptions to change. Both observation of long-term changes and observational constraints on the processes responsible for those changes are necessary. We review recent progress in our understanding of the cloud feedback problem. Capabilities and advantages of active sensors for observing clouds are discussed, along with the importance of active sensors for deriving constraints on cloud feedbacks as an essential component of a global climate observing system.     

Low-Cloud Feedbacks from Cloud-Controlling Factors: A Review

The response to warming of tropical low-level clouds including both marine stratocumulus and trade cumulus is a major source of uncertainty in projections of future climate. Climate model simulations of the response vary widely, reflecting the difficulty the models have in simulating these clouds. These inadequacies have led to alternative approaches to predict low-cloud feedbacks. Here, we review an observational approach that relies on the assumption that observed relationships between low clouds and the “cloud-controlling factors” of the large-scale environment are invariant across time-scales. With this assumption, and given predictions of how the cloud-controlling factors change with climate warming, one can predict low-cloud feedbacks without using any model simulation of low clouds. We discuss both fundamental and implementation issues with this approach and suggest steps that could reduce uncertainty in the predicted low-cloud feedback. Recent studies using this approach predict that the tropical low-cloud feedback is positive mainly due to the observation that reflection of solar radiation by low clouds decreases as temperature increases, holding all other cloud-controlling factors fixed. The positive feedback from temperature is partially offset by a negative feedback from the tendency for the inversion strength to increase in a warming world, with other cloud-controlling factors playing a smaller role. A consensus estimate from these studies for the contribution of tropical low clouds to the global mean cloud feedback is 0.25 ± 0.18 W m^?2 K^?1 (90% confidence interval), suggesting it is very unlikely that tropical low clouds reduce total global cloud feedback. Because the prediction of positive tropical low-cloud feedback with this approach is consistent with independent evidence from low-cloud feedback studies using high-resolution cloud models, progress is being made in reducing this key climate uncertainty.     

Cosmic Rays and Climate

Among the most puzzling questions in climate change is that of solar-climate variability, which has attracted the attention of scientists for more than two centuries. Until recently, even the existence of solar-climate variability has been controversial—perhaps because the observations had largely involved correlations between climate and the sunspot cycle that had persisted for only a few decades. Over the last few years, however, diverse reconstructions of past climate change have revealed clear associations with cosmic ray variations recorded in cosmogenic isotope archives, providing persuasive evidence for solar or cosmic ray forcing of the climate. However, despite the increasing evidence of its importance, solar-climate variability is likely to remain controversial until a physical mechanism is established. Although this remains a mystery, observations suggest that cloud cover may be influenced by cosmic rays, which are modulated by the solar wind and, on longer time scales, by the geomagnetic field and by the galactic environment of Earth. Two different classes of microphysical mechanisms have been proposed to connect cosmic rays with clouds: firstly, an influence of cosmic rays on the production of cloud condensation nuclei and, secondly, an influence of cosmic rays on the global electrical circuit in the atmosphere and, in turn, on ice nucleation and other cloud microphysical processes. Considerable progress on understanding ion–aerosol–cloud processes has been made in recent years, and the results are suggestive of a physically-plausible link between cosmic rays, clouds and climate. However, a concerted effort is now required to carry out definitive laboratory measurements of the fundamental physical and chemical processes involved, and to evaluate their climatic significance with dedicated field observations and modelling studies.     

Convective Self-Aggregation in Numerical Simulations: A Review

Organized convection in the tropics occurs across a range of spatial and temporal scales and strongly influences cloud cover and humidity. One mode of organization found is “self-aggregation,” in which moist convection spontaneously organizes into one or several isolated clusters despite spatially homogeneous boundary conditions and forcing. Self-aggregation is driven by interactions between clouds, moisture, radiation, surface fluxes, and circulation, and occurs in a wide variety of idealized simulations of radiative–convective equilibrium. Here we provide a review of convective self-aggregation in numerical simulations, including its character, causes, and effects. We describe the evolution of self-aggregation including its time and length scales and the physical mechanisms leading to its triggering and maintenance, and we also discuss possible links to climate and climate change.     

Climate hypersensitivity to solar forcing?

We compare the equilibrium climate responses of a quasi-dynamical energy balance model to radiative forcing by equivalent changes in CO₂, solar total irradiance (S_tot) and solar UV (S_UV). The response is largest in the S_UV case, in which the imposed UV radiative forcing is preferentially absorbed in the layer above 250 mb, in contrast to the weak response from global-columnar radiative loading by increases in CO₂ or S_tot. The hypersensitive response of the climate system to solar UV forcing is caused by strongly coupled feedback involving vertical static stability, tropical thick cirrus ice clouds and stratospheric ozone. This mechanism offers a plausible explanation of the apparent hypersensitivity of climate to solar forcing, as suggested by analyses of recent climatic records. The model hypersensitivity strongly depends on climate parameters, especially cloud radiative properties, but is effective for arguably realistic values of these parameters. The proposed solar forcing mechanism should be further confirmed using other models (e.g., general circulation models) that may better capture radiative and dynamical couplings of the troposphere and stratosphere.     

Air temperature feedback and its contribution to global warming

Air temperature feedback results from the thermal-radiative coupling between the atmosphere and the surface and plays an important role in surface energy balance. This paper reveals the contribution of air temperature feedback to the global warming from 1980 to 2000. The air temperature feedback kernel, evaluated using the ERA-Interim reanalysis data, is used to discuss the physical mechanism for air temperature feedback, the dependency of the strength of air temperature feedback on the climatological spatial distributions of air temperature, water vapor and cloud content, and the contributions of air temperature feedback to rapid global warming. The coupling between temperature feedback and each of the external forcings and individual feedback processes will amplify the anomaly of direct energy flux convergence at the surface induced by the external forcings and individual processes. The air temperature feedback amplifies the initial surface warming due to the increase in CO₂ concentration, ice and snow melting, increase in water vapor, and change in ocean heat storage. It also amplifies the surface warming due to the longwave radiaitve forcing associated with the increase in cloud cover, which acts to suppress the cooling of the shortwave effect of cloud forcing. Overall, temperature feedback plays an important role in the global warming from 1980 to 2000, as the net positive contribution to the perturbation of global mean energy flux at the surface from the air temperature feedback is larger than the net negative contribution from external forcing and all non-temperature feedbacks.     

On the regional climatic impact of contrails: microphysical and radiative properties of contrails and natural cirrus clouds

The impact of contrail-induced cirrus clouds on regional climate is estimated for mean atmospheric conditions of southern Germany in the months of July and October. This is done by use of a regionalized one-dimensional radiative convective model (RCM). The influence of an increased ice cloud cover is studied by comparing RCM results representing climatological values with a modified case. In order to study the sensitivity of this effect on the radiative characteristics of the ice cloud, two types of additional ice clouds were modelled: cirrus and contrails, the latter cloud type containing a higher number of smaller and less of the larger cloud particles. Ice cloud parameters are calculated on the basis of a particle size distribution which covers the range from 2 to 2000 m, taking into consideration recent measurements which show a remarkable amount of particles smaller than 20 m. It turns out that a 10% increase in ice cloud cover leads to a surface temperature increase in the order of 1K, ranging from 1.1 to 1.2K in July and from 0.8 to 0.9K in October depending on the radiative characteristics of the air-traffic-induced ice clouds. Modelling the current contrail cloud cover which is near 0.5% over Europe yields a surface temperature increase in the order of 0.05 K.     

The Role of Clouds: An Introduction and Rapporteur Report

This paper presents an overview of discussions during the Cloud’s Role session at the Observing and Modelling Earth’s Energy Flows Workshop. N. Loeb and B. Soden convened this session including 10 presentations by B. Stevens, B. Wielicki, G. Stephens, A. Clement, K. Sassen, D. Hartmann, T. Andrews, A. Del Genio, H. Barker, and M. Sugi addressing critical aspects of the role of clouds in modulating Earth energy flows. Presentation topics covered a diverse range of areas from cloud microphysics and dynamics, cloud radiative transfer, and the role of clouds in large-scale atmospheric circulations patterns in both observations and atmospheric models. The presentations and discussions, summarized below, are organized around several key questions raised during the session. (1) What is the best way to evaluate clouds in climate models? (2) How well do models need to represent clouds to be acceptable for making climate predictions? (3) What are the largest uncertainties in clouds? (4) How can these uncertainties be reduced? (5) What new observations are needed to address these problems? Answers to these critical questions are the topics of ongoing research and will guide the future direction of this area of research.     

Quantifying anthropogenic influence on recent near-surface temperature change

We assess the extent to which observed large-scale changes in near-surface temperatures over the latter half of the twentieth century can be attributed to anthropogenic climate change as simulated by a range of climate models. The hypothesis that observed changes are entirely due to internal climate variability is rejected at a high confidence level independent of the climate model used to simulate either the anthropogenic signal or the internal variability. Where the relevant simulations are available, we also consider the alternative hypothesis that observed changes are due entirely to natural external influences, including solar variability and explosive volcanic activity. We allow for the possibility that feedback processes, other than those simulated by the models considered, may be amplifying the observed response to these natural influences by an unknown amount. Even allowing for this possibility, the hypothesis of no anthropogenic influence can be rejected at the 5% level in almost all cases. The influence of anthropogenic greenhouse gases emerges as a substantial contributor to recent observed climate change, with the estimated trend attributable to greenhouse forcing similar in magnitude to the total observed warming over the 20th century. Much greater uncertainty remains in the response to other external influences on climate, particularly the response to anthropogenic sulphate aerosols and to solar and volcanic forcing. Our results remain dependent on model-simulated signal patterns and internal variability, and would benefit considerably from a wider range of simulations, particularly of the responses to natural external forcing.     

Reducing the biases in shortwave cloud radiative forcing in tropical and subtropical regions from the perspective of boundary layer processes

Biases in shortwave cloud radiative forcing (SWCF), which cause overestimates in tropical regions and underestimates in subtropical marine stratocumulus regions, are common in many climate models. Here, two boundary layer processes are investigated in the atmospheric model GAMIL2, entrainment at the top of the boundary layer and longwave radiative cooling at the top of stratocumulus clouds, in order to reduce biases and reveal the mechanisms underlying these processes. Our results show that including the entrainment process in the model can reduce negative SWCF biases in most tropical regions but increases positive SWCF biases in subtropical marine stratocumulus regions. This occurs because entrainment reduces the low-level cloud fraction and its cloud liquid water content by suppressing the vertical turbulent diffusion in the boundary layer and decreasing the relative humidity when warm and dry free atmosphere is entrained in the boundary layer. Longwave radiative cooling at the top of stratocumulus clouds can enhance turbulent diffusion within the stratocumulus-topped boundary layer. When combined with the entrainment process, longwave radiative cooling reduces the positive SWCF biases in subtropical marine stratocumulus regions that are observed using the entrainment process alone. The incorporation of these two boundary layer processes improves the simulated SWCF in tropical and subtropical regions in GAMIL2.     

Representing the Sensitivity of Convective Cloud Systems to Tropospheric Humidity in General Circulation Models

Convective cloud variability on many times scales can be viewed as having three major components: a suppressed phase of shallow and congestus clouds, a disturbed phase of deep convective clouds, and a mature phase of transition to stratiform upper-level clouds. Cumulus parameterization development has focused primarily on the second phase until recently. Consequently, many parameterizations are not sufficiently sensitive to variations in tropospheric humidity. This shortcoming may affect global climate model simulations of climate sensitivity to external forcings, the continental diurnal cycle of clouds and precipitation, and intraseasonal precipitation variability. The lack of sensitivity can be traced in part to underestimated entrainment of environmental air into rising convective clouds and insufficient evaporation of rain into the environment. As a result, the parameterizations produce deep convection too easily while stabilizing the environment too quickly to allow the effects of convective mesoscale organization to occur. Recent versions of some models have increased their sensitivity to tropospheric humidity and improved some aspects of their variability, but a parameterization of mesoscale organization is still absent from most models. Evidence about the effect of these uncertainties on climate change projections suggests that climate modelers should make improved simulation of high and convective clouds as high a priority as better representations of low clouds.     

Mechanisms and Model Diversity of Trade-Wind Shallow Cumulus Cloud Feedbacks: A Review

Shallow cumulus clouds in the trade-wind regions are at the heart of the long standing uncertainty in climate sensitivity estimates. In current climate models, cloud feedbacks are strongly influenced by cloud-base cloud amount in the trades. Therefore, understanding the key factors controlling cloudiness near cloud-base in shallow convective regimes has emerged as an important topic of investigation. We review physical understanding of these key controlling factors and discuss the value of the different approaches that have been developed so far, based on global and high-resolution model experimentations and process-oriented analyses across a range of models and for observations. The trade-wind cloud feedbacks appear to depend on two important aspects: (1) how cloudiness near cloud-base is controlled by the local interplay between turbulent, convective and radiative processes; (2) how these processes interact with their surrounding environment and are influenced by mesoscale organization. Our synthesis of studies that have explored these aspects suggests that the large diversity of model responses is related to fundamental differences in how the processes controlling trade cumulus operate in models, notably, whether they are parameterized or resolved. In models with parameterized convection, cloudiness near cloud-base is very sensitive to the vigor of convective mixing in response to changes in environmental conditions. This is in contrast with results from high-resolution models, which suggest that cloudiness near cloud-base is nearly invariant with warming and independent of large-scale environmental changes. Uncertainties are difficult to narrow using current observations, as the trade cumulus variability and its relation to large-scale environmental factors strongly depend on the time and/or spatial scales at which the mechanisms are evaluated. New opportunities for testing physical understanding of the factors controlling shallow cumulus cloud responses using observations and high-resolution modeling on large domains are discussed.     

Observing and Modeling Earth’s Energy Flows

This article reviews, from the authors’ perspective, progress in observing and modeling energy flows in Earth’s climate system. Emphasis is placed on the state of understanding of Earth’s energy flows and their susceptibility to perturbations, with particular emphasis on the roles of clouds and aerosols. More accurate measurements of the total solar irradiance and the rate of change of ocean enthalpy help constrain individual components of the energy budget at the top of the atmosphere to within ±2 W m^?2. The measurements demonstrate that Earth reflects substantially less solar radiation and emits more terrestrial radiation than was believed even a decade ago. Active remote sensing is helping to constrain the surface energy budget, but new estimates of downwelling surface irradiance that benefit from such methods are proving difficult to reconcile with existing precipitation climatologies. Overall, the energy budget at the surface is much more uncertain than at the top of the atmosphere. A decade of high-precision measurements of the energy budget at the top of the atmosphere is providing new opportunities to track Earth’s energy flows on timescales ranging from days to years, and at very high spatial resolution. The measurements show that the principal limitation in the estimate of secular trends now lies in the natural variability of the Earth system itself. The forcing-feedback-response framework, which has developed to understand how changes in Earth’s energy flows affect surface temperature, is reviewed in light of recent work that shows fast responses (adjustments) of the system are central to the definition of the effective forcing that results from a change in atmospheric composition. In many cases, the adjustment, rather than the characterization of the compositional perturbation (associated, for instance, with changing greenhouse gas concentrations, or aerosol burdens), limits accurate determination of the radiative forcing. Changes in clouds contribute importantly to this adjustment and thus contribute both to uncertainty in estimates of radiative forcing and to uncertainty in the response. Models are indispensable to calculation of the adjustment of the system to a compositional change but are known to be flawed in their representation of clouds. Advances in tracking Earth’s energy flows and compositional changes on daily through decadal timescales are shown to provide both a critical and constructive framework for advancing model development and evaluation.     

Diagnosing Climate Feedbacks in Coupled Ocean–Atmosphere Models

We review the methodologies used to quantify climate feedbacks in coupled models. The method of radiative kernels is outlined and used to illustrate the dependence of lapse rate, water vapor, surface albedo, and cloud feedbacks on (1) the length of the time average used to define two projected climate states and (2) the time separation between the two climate states. Except for the shortwave component of water vapor feedback, all feedback processes exhibit significant high-frequency variations and intermodel variability of feedback strengths for sub-decadal time averages. It is also found that the uncertainty of lapse rate, water vapor, and cloud feedback decreases with the increase in the time separation. The results suggest that one can substantially reduce the uncertainty of cloud and other feedbacks with the accumulation of accurate, long-term records of satellite observations; however, several decades may be required.     

Solar Activity,Lightning and Climate

The physics of solar forcing of the climate and long term climate change is summarized, and the role of energetic charged particles (including cosmic rays) on cloud formation and their effect on climate is examined. It is considered that the cosmic ray-cloud cover hypothesis is not supported by presently available data and further investigations (during Forbush decreases and at other times) should be analyzed to further examine the hypothesis. Another player in climate is lightning through the production of NO_x; this greenhouse gas, water vapour in the troposphere (and stratosphere) and carbon dioxide influence the global temperature through different processes. The enhancement of aerosol concentrations and their distribution in the troposphere also affect the climate and may result in enhanced lightning activity. Finally, the roles of atmospheric conductivity on the electrical activity of thunderstorms and lightning discharges in relation to climate are discussed.     

Modelling and analysis of the impact of irrigation on local arid climate over northwest China

This study focuses on how irrigation processes affect local climate over arid areas. The chosen study area is northwest China, a typical arid region where three dominant land‐use types are irrigated cropland, grassland, and desert. Observational analysis indicates that the highest precipitation, the coolest surface temperatures, and the slowest warming trend are seen over irrigated cropland from 1979 to 2005. The single column atmospheric model (SCAM), developed by the National Center for Atmospheric Research (NCAR), was used to investigate and better understand the differences in long‐term climate conditions and change over the above three land‐use types. The results indicate that local climate conditions are predominantly controlled by large‐scale forcing in this arid region and that local land surface forcing related to vegetation cover change and irrigation processes also has a significant impact. This study strongly suggests that a realistic climate forecast for this region can be achieved only with both accurate large‐scale and local climate forcing. The irrigated cropland of the region generates stronger evaporation that cools the surface and slows the warming trend more than does the evaporation from the natural grassland and desert. Stronger evaporation also significantly increases precipitation, potentially alleviating the stress of irrigation demands in arid regions. A series of sensitivity SCAM simulations indicate that a drier and warmer climate occurs with decreasing vegetation cover in the irrigated cropland region. Copyright © 2011 John Wiley & Sons, Ltd.     

River sensitivity: a lost foundation concept in fluvial geomorphology

In the twenty‐first century, fluvial geomorphologists are ideally placed to use their science in an applied manner, and provide guidance on environmental issues of concern. Understanding the impact of floods and droughts, land use and climate change, water use, etc. on river forms, processes and evolution requires that we understand interactions between water, sediment and vegetation, and how climate and anthropogenic impacts shape those interactions. More frequently, fluvial geomorphologists are asked to provide answers to a range of river issues, make forecasts about how systems might adjust in the future, and work with managers to implement strategies on‐the‐ground. To some, the field of fluvial geomorphology is underprepared for this task as several principles of landscape form, process and evolution are yet to be fully explored. Others however, see that geomorphologists have a suite of principles and tools at their disposal, and sufficient understanding to make forecasts about future river adjustments with some level of confidence. One concept that has been lost in recent years, but should lie at the heart of such analyses is that of river sensitivity. In this paper I draw on foundation literature to review the concept of river sensitivity. I provide examples that demonstrate how this concept could be reshaped and used for analyses at landform, reach and catchment scales. At the landform scale, morphological sensitivity is a function of textural and geometric sensitivity. At the reach scale, analyses consider inherent behavioural and change sensitivity. At the catchment scale river response and recovery are a function of locational, transmission and filter sensitivity. I then discuss how some temporal concepts can be used to consider how sensitivity in itself adjusts over time. Finally, I discuss future challenges for analysis of river sensitivity and consider how it could be used to improve geomorphological forecasting for use in river management. Copyright © 2016 John Wiley & Sons, Ltd.