The impact of perturbations to ocean-model parameters on climate and climate change in a coupled model

Assessments of the impacts of uncertainties in parameters on mean climate and climate change in complex climate models have, to date, largely focussed on perturbations to parameters in the atmosphere component of the model. Here we expand on a previously published study which found the global impacts of perturbed ocean parameters on the rate of transient climate change to be small compared to perturbed atmosphere parameters. By separating the climate-change-induced ocean vertical heat transport in each perturbed member into components associated with the resolved flow and each parameterisation scheme, we show that variations in global mean heat uptake in different perturbed versions are an order of magnitude smaller than the average heat uptake. The lack of impact of the perturbations is attributed to (1) the relatively small impact of the perturbation on the direct vertical heat transport associated with the perturbed process and (2) a compensation between those direct changes and indirect changes in heat transport from other processes. Interactions between processes and changes appear to combine in complex ways to limit ensemble spread and uncertainty in the rate of warming. We also investigate regional impacts of the perturbations that may be important for climate change predictions. We find variations across the ensemble that are significant when measured against natural variability. In terms of the experimental set-up used here (models without flux adjustments) we conclude that perturbed physics ensembles with ocean parameter perturbations are an important component of any probabilistic estimate of future climate change, despite the low spread in global mean quantities. Hence, careful consideration should be given to assessing uncertainty in ocean processes in future probabilistic assessments of regional climate change.     

A simple model perturbed physics study of the simulated climate sensitivity uncertainty and its relation to control climate biases

In this study the relationship between climate model biases in the control climate and the simulated climate sensitivity are discussed on the basis of perturbed physics ensemble simulations with a globally resolved energy balance (GREB) model. It is illustrated that the uncertainties in the simulated climate sensitivity (estimated by the transient response to CO₂ forcing scenarios in the twenty first century or idealized 2 × CO₂ forcing experiments) can be conceptually split into two parts: a direct effect of the perturbed physics on the climate sensitivity independent of the control mean climate and an indirect effect of the perturbed physics by changing the control mean climate, which in turn changes the climate sensitivity, as the climate sensitivity itself is depending on the control climate. Biases in the control climate are negatively correlated with the climate sensitivity (colder climates have larger sensitivities), if no physics are perturbed. Perturbed physics that lead to warmer control climate, would in average also lead to larger climate sensitivities, if the control climate is held at the observed reference climate by flux corrections. Thus the effects of control biases and perturbed physics are opposing each other and are partially cancelling each other out. In the GREB model the biases in the control climate are the more important effect for the regional climate sensitivity uncertainties, but for the global mean climate sensitivity both, the biases in the control climate and the perturbed physics, are equally important.     

Global seasonal climate predictability in a two tiered forecast system: part I: boreal summer and fall seasons

This paper shows demonstrable improvement in the global seasonal climate predictability of boreal summer (at zero lead) and fall (at one season lead) seasonal mean precipitation and surface temperature from a two-tiered seasonal hindcast forced with forecasted SST relative to two other contemporary operational coupled ocean–atmosphere climate models. The results from an extensive set of seasonal hindcasts are analyzed to come to this conclusion. This improvement is attributed to: (1) The multi-model bias corrected SST used to force the atmospheric model. (2) The global atmospheric model which is run at a relatively high resolution of 50 km grid resolution compared to the two other coupled ocean–atmosphere models. (3) The physics of the atmospheric model, especially that related to the convective parameterization scheme. The results of the seasonal hindcast are analyzed for both deterministic and probabilistic skill. The probabilistic skill analysis shows that significant forecast skill can be harvested from these seasonal hindcasts relative to the deterministic skill analysis. The paper concludes that the coupled ocean–atmosphere seasonal hindcasts have reached a reasonable fidelity to exploit their SST anomaly forecasts to force such relatively higher resolution two tier prediction experiments to glean further boreal summer and fall seasonal prediction skill.     

Climate model errors, feedbacks and forcings: a comparison of perturbed physics and multi-model ensembles

Ensembles of climate model simulations are required for input into probabilistic assessments of the risk of future climate change in which uncertainties are quantified. Here we document and compare aspects of climate model ensembles from the multi-model archive and from perturbed physics ensembles generated using the third version of the Hadley Centre climate model (HadCM3). Model-error characteristics derived from time-averaged two-dimensional fields of observed climate variables indicate that the perturbed physics approach is capable of sampling a relatively wide range of different mean climate states, consistent with simple estimates of observational uncertainty and comparable to the range of mean states sampled by the multi-model ensemble. The perturbed physics approach is also capable of sampling a relatively wide range of climate forcings and climate feedbacks under enhanced levels of greenhouse gases, again comparable with the multi-model ensemble. By examining correlations between global time-averaged measures of model error and global measures of climate change feedback strengths, we conclude that there are no simple emergent relationships between climate model errors and the magnitude of future global temperature change. Algorithms for quantifying uncertainty require the use of complex multivariate metrics for constraining projections.     

Future changes in tropical cyclone genesis in fully dynamic ocean- and mixed layer ocean-coupled climate models: a low-resolution model study

The global and regional projected changes in tropical cyclone (TC) genesis due to increased CO₂ concentrations has been investigated through a large-scale TC genesis parameter (convective seasonal genesis parameter, ConvGP) in two perturbed physics ensembles. The ensembles are based on the third generation Hadley Centre atmosphere?Cocean general circulation model with the first ensemble using a coupled fully dynamic ocean (HadCM3) and the second coupled to a simplified mixed layer thermodynamic ocean (HadSM3) both consisting of 17 members. In each ensemble, parameters are identically perturbed to provide a wide range of climate sensitivity whilst retaining a credible present-day climate simulation. It is found, by comparing the ConvGP climatology from reanalysis data with the best track genesis, that it is possible to reproduce the observed genesis distribution. Future changes in the spatial ConvGP distribution are explored with respect to each tropical ocean basin. Whilst there is a similarity in the gross pattern of the ensemble-mean projected ConvGP change between HadCM3 and HadSM3, there is a non-trivial difference in the tropical Pacific Ocean, arising from different patterns of tropical Pacific sea surface temperature change. This indicates that ocean representation can be important for regional scale projections. The quantitative contribution of individual constituent parameters (i.e. vorticity parameter, shear parameter and convective potential) to the projected ConvGP change is estimated. It is found that all three large-scale parameters generally contribute constructively, but with different magnitude, in the regions where a large doubled CO₂ response is found.     

The variation of ENSO characteristics associated with atmospheric parameter perturbations in a coupled model

We analyse the differences in the properties of the El Niño Southern Oscillation (ENSO) in a set of 17 coupled integrations with the flux-adjusted, 19-level HadCM3 model with perturbed atmospheric parameters. Within this ensemble, the standard deviation of the NINO3.4 deseasonalised SSTs ranges from 0.6 to 1.3 K. The systematic changes in the properties of the ENSO with increasing amplitude confirm that ENSO in HadCM3 is prevalently a surface (or SST) mode. The tropical-Pacific SST variability in the ensemble of coupled integrations correlates positively with the SST variability in the corresponding ensemble of atmosphere models coupled with a static mixed-layer ocean (“slab” models) perturbed with the same changes in atmospheric parameters. Comparison with the respective coupled ENSO-neutral climatologies and with the slab-model climatologies indicates low-cloud cover to be an important controlling factor of the strength of the ENSO within the ensemble. Our analysis suggests that, in the HadCM3 model, increased SST variability localised in the south-east tropical Pacific, not originating from ENSO and associated with increased amounts of tropical stratocumulus cloud, causes increased ENSO variability via an atmospheric bridge mechanism. The relationship with cloud cover also results in a negative correlation between the ENSO activity and the model’s climate sensitivity to doubling CO₂.     

The sensitivity of the meridional overturning circulation to modelling uncertainty in a perturbed physics ensemble without flux adjustment

Ocean dynamics play a key role in the climate system, by redistributing heat and freshwater. The uncertainty of how these processes are represented in climate models, and how this uncertainty affects future climate projections can be investigated using perturbed physics ensembles of global circulation models (GCMs). Techniques such as flux adjustments should be avoided since they can impact the sensitivity of the ensemble to the imposed forcing. In this study a method for developing an coupled ensemble with a GCM that does not use flux adjustment is presented. The ensemble is constrained by using information from a prior ensemble with a mixed layer ocean coupled to an atmosphere GCM, to reduce drifts in the coupled ensemble. Constraints on parameter perturbations are derived by using observational constraints on surface temperature, and top of the atmosphere radiative fluxes. As an example of such an ensemble developed with this methodology, uncertainty in response of the meridional overturning circulation (MOC) to increased CO₂ concentrations is investigated. The ensemble mean MOC strength is 17.1?Sv and decreases by 2.1?Sv when greenhouse gas concentrations are doubled. No rapid changes or shutdown of the MOC are seen in any of the ensemble members. There is a strong negative relationship between global mean temperature and MOC strength across the ensemble which is not seen in a multimodel ensemble. A positive relationship between climate sensitivity and the decrease of MOC strength is also seen.     

大气环流模式（SAMIL）海气耦合前后性能的比较

基于耦合器框架，中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室大气环流谱模式 （SAMIL）最近成功地实现了与海洋、海冰等气候分量模式的耦合，形成了“非通量调整”的海－陆－气－冰直接耦合的气候模式系统（FGOALS-s）。在耦合系统中，由于海温、海冰等的分布由预报模式驱动，大气与海洋、海冰之间引入了相互作用过程，这样大气环流的模拟特征与耦合前会有不同。为分析耦合系统的性能，作者对耦合前后的模拟结果进行了分析比较，重点是大气模拟特征的差异。结果表明，耦合前、后大气环流的基本特征相似，都能成功地模拟出主要的环流系统分布及季节变化，但是由于海温和海冰的模拟存在系统性的偏差，使得耦合后的大气环流受到明显影响。例如耦合后热带海温偏冷，南大洋、北太平洋和北大西洋等中纬度地区的海温偏高，导致海温等值线向高纬海域的伸展较弱，海温经向梯度减小。耦合后海冰在北极区域范围偏大，在南极周边地区则偏小。海温、海冰分布模拟的偏差影响到中、高纬低层大气的温度。热带海温偏低，使得赤道地区降水偏弱，凝结潜热减少，热带对流层中高层温度比耦合前要低，大气温度的经向梯度减小。经向温度梯度的改变，直接影响到对平均经圈环流及西风急流强度的模拟。尽管耦合系统中海温、海冰的模拟存在偏差，但在亚洲季风区，耦合后季风环流及降水等的分布都比耦合前单独大气模式的结果合理，表明通过海[CD*2]气相互作用可减少耦合前季风区的模拟误差，改善季风模拟效果。比较发现，海温、海冰模拟的偏差，除与海洋模式中经向热输送偏弱、海冰模式中海冰处理等有关外，也与大气模式中总云量模拟偏低有关。大气模式本身的误差，特别是云、辐射过程带来的误差，对耦合结果具有极为重要的影响。完全耦合后，这些误差通过与海洋、海冰的反馈作用而放大。因此，对于FGOALS-s而言，要提高耦合系统的整体性能，除改进各气候分量模式的模拟性能外，需要重点改进大气模式中的云、辐射过程。     

Influence of SST biases on future climate change projections

We use a quantile-based bias correction technique and a multi-member ensemble of the atmospheric component of NCAR CCSM3 (CAM3) simulations to investigate the influence of sea surface temperature (SST) biases on future climate change projections. The simulations, which cover 1977?C1999 in the historical period and 2077?C2099 in the future (A1B) period, use the CCSM3-generated SSTs as prescribed boundary conditions. Bias correction is applied to the monthly time-series of SSTs so that the simulated changes in SST mean and variability are preserved. Our comparison of CAM3 simulations with and without SST correction shows that the SST biases affect the precipitation distribution in CAM3 over many regions by introducing errors in atmospheric moisture content and upper-level (lower-level) divergence (convergence). Also, bias correction leads to significantly different precipitation and surface temperature changes over many oceanic and terrestrial regions (predominantly in the tropics) in response to the future anthropogenic increases in greenhouse forcing. The differences in the precipitation response from SST bias correction occur both in the mean and the percent change, and are independent of the ocean?Catmosphere coupling. Many of these differences are comparable to or larger than the spread of future precipitation changes across the CMIP3 ensemble. Such biases can affect the simulated terrestrial feedbacks and thermohaline circulations in coupled climate model integrations through changes in the hydrological cycle and ocean salinity. Moreover, biases in CCSM3-generated SSTs are generally similar to the biases in CMIP3 ensemble mean SSTs, suggesting that other GCMs may display a similar sensitivity of projected climate change to SST errors. These results help to quantify the influence of climate model biases on the simulated climate change, and therefore should inform the effort to further develop approaches for reliable climate change projection.     

SST and circulation trend biases cause an underestimation of European precipitation trends

Clear precipitation trends have been observed in Europe over the past century. In winter, precipitation has increased in north-western Europe. In summer, there has been an increase along many coasts in the same area. Over the second half of the past century precipitation also decreased in southern Europe in winter. An investigation of precipitation trends in two multi-model ensembles including both global and regional climate models shows that these models fail to reproduce the observed trends. In many regions the model spread does not cover the trend in the observations. In contrast, regional climate model (RCM) experiments with observed boundary conditions reproduce the observed precipitation trends much better. The observed trends are largely compatible with the range of uncertainties spanned by the ensemble, indicating that the boundary conditions of RCMs are responsible for large parts of the trend biases. We find that the main factor in setting the trend in winter is atmospheric circulation, for summer sea surface temperature (SST) is important in setting precipitation trends along the North Sea and Atlantic coasts. The causes of the large trends in atmospheric circulation and summer SST are not known. For SST there may be a connection with the well-known ocean circulation biases in low-resolution ocean models. A quantitative understanding of the causes of these trends is needed so that climate model based projections of future climate can be corrected for these precipitation trend biases.     

Transient climate changes in a perturbed parameter ensemble of emissions-driven earth system model simulations

We describe results from a 57-member ensemble of transient climate change simulations, featuring simultaneous perturbations to 54 parameters in the atmosphere, ocean, sulphur cycle and terrestrial ecosystem components of an earth system model (ESM). These emissions-driven simulations are compared against the CMIP3 multi-model ensemble of physical climate system models, used extensively to inform previous assessments of regional climate change, and also against emissions-driven simulations from ESMs contributed to the CMIP5 archive. Members of our earth system perturbed parameter ensemble (ESPPE) are competitive with CMIP3 and CMIP5 models in their simulations of historical climate. In particular, they perform reasonably well in comparison with HadGEM2-ES, a more sophisticated and expensive earth system model contributed to CMIP5. The ESPPE therefore provides a computationally cost-effective tool to explore interactions between earth system processes. In response to a non-intervention emissions scenario, the ESPPE simulates distributions of future regional temperature change characterised by wide ranges, and warm shifts, compared to those of CMIP3 models. These differences partly reflect the uncertain influence of global carbon cycle feedbacks in the ESPPE. In addition, the regional effects of interactions between different earth system feedbacks, particularly involving physical and ecosystem processes, shift and widen the ESPPE spread in normalised patterns of surface temperature and precipitation change in many regions. Significant differences from CMIP3 also arise from the use of parametric perturbations (rather than a multimodel ensemble) to represent model uncertainties, and this is also the case when ESPPE results are compared against parallel emissions-driven simulations from CMIP5 ESMs. When driven by an aggressive mitigation scenario, the ESPPE and HadGEM2-ES reveal significant but uncertain impacts in limiting temperature increases during the second half of the twenty-first century. Emissions-driven simulations create scope for development of errors in properties that were previously prescribed in coupled ocean–atmosphere models, such as historical CO₂ concentrations and vegetation distributions. In this context, historical intra-ensemble variations in the airborne fraction of CO₂ emissions, and in summer soil moisture in northern hemisphere continental regions, are shown to be potentially useful constraints, subject to uncertainties in the relevant observations. Our results suggest that future climate-related risks can be assessed more comprehensively by updating projection methodologies to support formal combination of emissions-driven perturbed parameter and multi-model earth system model simulations with suitable observational constraints. This would provide scenarios underpinned by a more complete representation of the chain of uncertainties from anthropogenic emissions to future climate outcomes.     

An efficient climate forecasting method using an intermediate complexity Earth System Model and the ensemble Kalman filter

We present the implementation and results of a model tuning and ensemble forecasting experiment using an ensemble Kalman filter for the simultaneous estimation of 12 parameters in a low resolution coupled atmosphere-ocean Earth System Model by tuning it to realistic data sets consisting of Levitus ocean temperature/salinity climatology, and NCEP/NCAR atmospheric temperature/humidity reanalysis data. The resulting ensemble of tuned model states is validated by comparing various diagnostics, such as mass and heat transports, to observational estimates and other model results. We show that this ensemble has a very reasonable climatology, with the 3-D ocean in particular having comparable realism to much more expensive coupled numerical models, at least in respect of these averaged indicators. A simple global warming experiment is performed to investigate the response and predictability of the climate to a change in radiative forcing, due to 100 years of 1% per annum atmospheric CO₂ increase. The equilibrium surface air temperature rise for this CO₂ increase is 4.2±0.1°C, which is approached on a time scale of 1,000 years. The simple atmosphere in this version of the model is missing several factors which, if included, would substantially increase the uncertainty of this estimate. However, even within this ensemble, there is substantial regional variability due to the possibility of collapse of the North Atlantic thermohaline circulation (THC), which switches off in more than one third of the ensemble members. For these cases, the regional temperature is not only 3–5°C colder than in the warmed worlds where the THC remains switched on, but is also 1–2°C colder than the current climate. Our results, which illustrate how objective probabilistic projections of future climate change can be efficiently generated, indicate a substantial uncertainty in the long-term future of the THC, and therefore the regional climate of western Europe. However, this uncertainty is only apparent in long-term integrations, with the initial transient response being similar across the entire ensemble. Application of this ensemble Kalman filtering technique to more complete climate models would improve the objectivity of probabilistic forecasts and hence should lead to significantly increased understanding of the uncertainty of our future climate.     

Evolution of model systematic errors in the Tropical Atlantic Basin from coupled climate hindcasts

Significant systematic errors in the tropical Atlantic Ocean are common in state-of-the-art coupled ocean–atmosphere general circulation models. In this study, a set of ensemble hindcasts from the NCEP coupled forecast system (CFS) is used to examine the initial growth of the coupled model bias. These CFS hindcasts are 9-month integrations starting from perturbed real-time oceanic and atmospheric analyses for 1981–2003. The large number of integrations from a variety of initial states covering all months provides a good opportunity to examine how the model systematic errors grow. The monthly climatologies of ensemble hindcasts from various initial months are compared with both observed and analyzed oceanic and atmospheric datasets. Our analyses show that two error patterns are dominant in the hindcasts. One is the warming of the sea surface temperature (SST) in the southeastern tropical Atlantic Ocean. This error grows faster in boreal summer and fall and peaks in November–December at round 2°C in the open ocean. It is caused by an excessive model surface shortwave radiative flux in this region, especially from boreal summer to fall. The excessive radiative forcing is in turn caused by the CFS inability to reproduce the observed amount of low cloud cover in the southeastern ocean and its seasonal increase. According to a comparison between the seasonal climatologies from the CFS hindcasts and a long-term simulation of the atmospheric model forced with observed SST, the CFS low cloud and radiation errors are inherent to its atmospheric component. On the other hand, the SST error in CFS is a major cause of the model’s southward bias of the intertropical convergence zone (ITCZ) in boreal winter and spring. An analysis of the SST errors of the 6-month ensemble hindcasts by seven coupled models in the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction project shows that this SST error pattern is common in coupled climate hindcasts. The second error pattern is an excessive deepening of the model thermocline depth to the north of the equator from the western coast toward the central ocean. This error grows fastest in boreal summer. It is forced by an overly strong local anticyclonic surface wind stress curl and is in turn related to the weakened northeast trade winds in summer and fall. The thermocline error in the northwest delays the annual shoaling of the equatorial thermocline in the Gulf of Guinea remotely through the equatorial waveguide.     

Diagnosing southeast tropical Atlantic SST and ocean circulation biases in the CMIP5 ensemble

Warm sea-surface temperature (SST) biases in the southeastern tropical Atlantic (SETA), which is defined by a region from 5°E to the west coast of southern Africa and from 10°S to 30°S, are a common problem in many current and previous generation climate models. The Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble provides a useful framework to tackle the complex issues concerning causes of the SST bias. In this study, we tested a number of previously proposed mechanisms responsible for the SETA SST bias and found the following results. First, the multi-model ensemble mean shows a positive shortwave radiation bias of ~20 W m^?2, consistent with models’ deficiency in simulating low-level clouds. This shortwave radiation error, however, is overwhelmed by larger errors in the simulated surface turbulent heat and longwave radiation fluxes, resulting in excessive heat loss from the ocean. The result holds for atmosphere-only model simulations from the same multi-model ensemble, where the effect of SST biases on surface heat fluxes is removed, and is not sensitive to whether the analysis region is chosen to coincide with the maximum warm SST bias along the coast or with the main SETA stratocumulus deck away from the coast. This combined with the fact that there is no statistically significant relationship between simulated SST biases and surface heat flux biases among CMIP5 models suggests that the shortwave radiation bias caused by poorly simulated low-level clouds is not the leading cause of the warm SST bias. Second, the majority of CMIP5 models underestimate upwelling strength along the Benguela coast, which is linked to the unrealistically weak alongshore wind stress simulated by the models. However, a correlation analysis between the model simulated vertical velocities and SST biases does not reveal a statistically significant relationship between the two, suggesting that the deficient coastal upwelling in the models is not simply related to the warm SST bias via vertical heat advection. Third, SETA SST biases in CMIP5 models are correlated with surface and subsurface ocean temperature biases in the equatorial region, suggesting that the equatorial temperature bias remotely contributes to the SETA SST bias. Finally, we found that all CMIP5 models simulate a southward displaced Angola–Benguela front (ABF), which in many models is more than 10° south of its observed location. Furthermore, SETA SST biases are most significantly correlated with ABF latitude, which suggests that the inability of CMIP5 models to accurately simulate the ABF is a leading cause of the SETA SST bias. This is supported by simulations with the oceanic component of one of the CMIP5 models, which is forced with observationally derived surface fluxes. The results show that even with the observationally derived surface atmospheric forcing, the ocean model generates a significant warm SST bias near the ABF, underlining the important role of ocean dynamics in SETA SST bias problem. Further model simulations were conducted to address the impact of the SETA SST biases. The results indicate a significant remote influence of the SETA SST bias on global model simulations of tropical climate, underscoring the importance and urgency to reduce the SETA SST bias in global climate models.     

An assessment of oceanic variability for 1960–2010 from the GFDL ensemble coupled data assimilation

The Geophysical Fluid Dynamics Laboratory has developed an ensemble coupled data assimilation (ECDA) system based on the fully coupled climate model, CM2.1, in order to provide reanalyzed coupled initial conditions that are balanced with the climate prediction model. Here, we conduct a comprehensive assessment for the oceanic variability from the latest version of the ECDA analyzed for 51 years, 1960–2010. Meridional oceanic heat transport, net ocean surface heat flux, wind stress, sea surface height, top 300 m heat content, tropical temperature, salinity and currents are compared with various in situ observations and reanalyses by employing similar configurations with the assessment of the NCEP’s climate forecast system reanalysis (Xue et al. in Clim Dyn 37(11):2511–2539, 2011). Results show that the ECDA agrees well with observations in both climatology and variability for 51 years. For the simulation of the Tropical Atlantic Ocean and global salinity variability, the ECDA shows a good performance compared to existing reanalyses. The ECDA also shows no significant drift in the deep ocean temperature and salinity. While systematic model biases are mostly corrected with the coupled data assimilation, some biases (e.g., strong trade winds, weak westerly winds and warm SST in the southern oceans, subsurface temperature and salinity biases along the equatorial western Pacific boundary, overestimating the mixed layer depth around the subpolar Atlantic and high-latitude southern oceans in the winter seasons) are not completely eliminated. Mean biases such as strong South Equatorial Current, weak Equatorial Under Current, and weak Atlantic overturning transport are generated during the assimilation procedure, but their variabilities are well simulated. In terms of climate variability, the ECDA provides good simulations of the dominant oceanic signals associated with El Nino and Southern Oscillation, Indian Ocean Dipole, Pacific Decadal Oscillation, and Atlantic Meridional Overturning Circulation during the whole analyzed period, 1960–2010.     

The variance of sea surface temperature and projected changes with global warming

Based on the surface energy budget, the sea surface temperature (SST) variance is related to the product of three factors: the sum of the variances of surface radiative and turbulent energy fluxes and of ocean heat transport, an efficiency factor depending on the covariances among them, and a transfer factor involving the persistence of surface temperature via its lagged autocorrelation. These quantities are analyzed for current climate conditions based on results from the NCEP/NCAR reanalyses and a simulation with the CCCma coupled climate model. Potential changes with climate change are considered based on two quasi-equilibrium climate change integrations for which the forcing has been stabilized at years 2050 and 2100 values of the IS92a forcing scenario. The surface energy fluxes, which contribute to the variance of SST, are similar in the modelled and reanalyzed atmosphere but modelled temperature variance is conditioned on the thickness of the upper ocean model layer. Changes of SST variance with global warming show broad scale patterns with decreases in the tropical central-eastern Pacific and the northern extra-tropical Pacific, and increases in both the sub-tropical Pacific and mid-latitudes of the North Atlantic. The changes in SST variance are not associated only with changes in the variances of surface energy fluxes/transports but also with changes in the covariances among them and by changes in the temperature autocorrelation structure.     

Climate feedbacks determined using radiative kernels in a multi-thousand member ensemble of AOGCMs

The use of radiative kernels to diagnose climate feedbacks is a recent development that may be applied to existing climate change simulations. We apply the radiative kernel technique to transient simulations from a multi-thousand member perturbed physics ensemble of coupled atmosphere-ocean general circulation models, comparing distributions of model feedbacks with those taken from the CMIP-3 multi GCM ensemble. Although the range of clear sky longwave feedbacks in the perturbed physics ensemble is similar to that seen in the multi-GCM ensemble, the kernel technique underestimates the net clear-sky feedbacks (or the radiative forcing) in some perturbed models with significantly altered humidity distributions. In addition, the compensating relationship between global mean atmospheric lapse rate feedback and water vapor feedback is found to hold in the perturbed physics ensemble, but large differences in relative humidity distributions in the ensemble prevent the compensation from holding at a regional scale. Both ensembles show a similar range of response of global mean net cloud feedback, but the mean of the perturbed physics ensemble is shifted towards more positive values such that none of the perturbed models exhibit a net negative cloud feedback. The perturbed physics ensemble contains fewer models with strong negative shortwave cloud feedbacks and has stronger compensating positive longwave feedbacks. A principal component analysis used to identify dominant modes of feedback variation reveals that the perturbed physics ensemble produces very different modes of climate response to the multi-model ensemble, suggesting that one may not be used as an analog for the other in estimates of uncertainty in future response. Whereas in the multi-model ensemble, the first order variation in cloud feedbacks shows compensation between longwave and shortwave components, in the perturbed physics ensemble the shortwave feedbacks are uncompensated, possibly explaining the larger range of climate sensitivities observed in the perturbed simulations. Regression analysis suggests that the parameters governing cloud formation, convection strength and ice fall speed are the most significant in altering climate feedbacks. Perturbations of oceanic and sulfur cycle parameters have relatively little effect on the atmospheric feedbacks diagnosed by the kernel technique.     

Mechanisms of the atmospheric response to North Atlantic multidecadal variability: a model study

The atmospheric circulation response to decadal fluctuations of the Atlantic meridional overturning circulation (MOC) in the IPSL climate model is investigated using the associated sea surface temperature signature. A SST anomaly is prescribed in sensitivity experiments with the atmospheric component of the IPSL model coupled to a slab ocean. The prescribed SST anomaly in the North Atlantic is the surface signature of the MOC influence on the atmosphere detected in the coupled simulation. It follows a maximum of the MOC by a few years and resembles the model Atlantic multidecadal oscillation. It is mainly characterized by a warming of the North Atlantic south of Iceland, and a cooling of the Nordic Seas. There are substantial seasonal variations in the geopotential height response to the prescribed SST anomaly, with an East Atlantic Pattern-like response in summer and a North Atlantic oscillation-like signal in winter. In summer, the response of the atmosphere is global in scale, resembling the climatic impact detected in the coupled simulation, albeit with a weaker amplitude. The zonally asymmetric or eddy part of the response is characterized by a trough over warm SST associated with changes in the stationary waves. A diagnostic analysis with daily data emphasizes the role of transient-eddy forcing in shaping and maintaining the equilibrium response. We show that in response to an intensified MOC, the North Atlantic storm tracks are enhanced and shifted northward during summer, consistent with a strengthening of the westerlies. However the anomalous response is weak, which suggests a statistically significant but rather modest influence of the extratropical SST on the atmosphere. The winter response to the MOC-induced North Atlantic warming is an intensification of the subtropical jet and a southward shift of the Atlantic storm track activity, resulting in an equatorward shift of the polar jet. Although the SST anomaly is only prescribed in the Atlantic ocean, significant impacts are found globally, indicating that the Atlantic ocean can drive a large scale atmospheric variability at decadal timescales. The atmospheric response is highly non-linear in both seasons and is consistent with the strong interaction between transient eddies and the mean flow. This study emphasizes that decadal fluctuations of the MOC can affect the storm tracks in both seasons and lead to weak but significant dynamical changes in the atmosphere.     

Quantifying uncertainties in projections of extremes��a perturbed land surface parameter experiment

Uncertainties in the climate response to a doubling of atmospheric CO₂ concentrations are quantified in a perturbed land surface parameter experiment. The ensemble of 108 members is constructed by systematically perturbing five poorly constrained land surface parameters of global climate model individually and in all possible combinations. The land surface parameters induce small uncertainties at global scale, substantial uncertainties at regional and seasonal scale and very large uncertainties in the tails of the distribution, the climate extremes. Climate sensitivity varies across the ensemble mainly due to the perturbation of the snow albedo parameterization, which controls the snow albedo feedback strength. The uncertainty range in the global response is small relative to perturbed physics experiments focusing on atmospheric parameters. However, land surface parameters are revealed to control the response not only of the mean but also of the variability of temperature. Major uncertainties are identified in the response of climate extremes to a doubling of CO₂. During winter the response both of temperature mean and daily variability relates to fractional snow cover. Cold extremes over high latitudes warm disproportionately in ensemble members with strong snow albedo feedback and large snow cover reduction. Reduced snow cover leads to more winter warming and stronger variability decrease. As a result uncertainties in mean and variability response line up, with some members showing weak and others very strong warming of the cold tail of the distribution, depending on the snow albedo parametrization. The uncertainty across the ensemble regionally exceeds the CMIP3 multi-model range. Regarding summer hot extremes, the uncertainties are larger than for mean summer warming but smaller than in multi-model experiments. The summer precipitation response to a doubling of CO₂ is not robust over many regions. Land surface parameter perturbations and natural variability alter the sign of the response even over subtropical regions.     

Coupled atmosphere–ocean data assimilation experiments with a low-order climate model

A simple idealized atmosphere–ocean climate model and an ensemble Kalman filter are used to explore different coupled ensemble data assimilation strategies. The model is a low-dimensional analogue of the North Atlantic climate system, involving interactions between large-scale atmospheric circulation and ocean states driven by the variability of the Atlantic meridional overturning circulation (MOC). Initialization of the MOC is assessed in a range of experiments, from the simplest configuration consisting of forcing the ocean with a known atmosphere to performing fully coupled ensemble data assimilation. “Daily” assimilation (that is, at the temporal frequency of the atmospheric observations) is contrasted with less frequent assimilation of time-averaged observations. Performance is also evaluated under scenarios in which ocean observations are limited to the upper ocean or are non-existent. Results show that forcing the idealized ocean model with atmospheric analyses is inefficient at recovering the slowly evolving MOC. On the other hand, daily assimilation rapidly leads to accurate MOC analyses, provided a comprehensive set of oceanic observations is available for assimilation. In the absence of sufficient observations in the ocean, the assimilation of time-averaged atmospheric observations proves to be more effective for MOC initialization, including the case where only atmospheric observations are available.