Validation of the nesting technique in a regional climate model and sensitivity tests to the resolution of the lateral boundary conditions during summer

The ability of a regional climate model (RCM) to successfully reproduce the fine-scale features of a regional climate during summer is evaluated using an approach nick-named the “Big-Brother Experiment” (BBE). The BBE establishes a reference virtual-reality climate with a RCM applied on a large and high-resolution domain: this simulation is called the Big-Brother (BB) simulation. This reference simulation is then downgraded by filtering small-scale features that are unresolved in today’s global objective analyses. The resulting fields are then used as nesting data to drive the same RCM, which is integrated, at the same high resolution as the BB, only over a sub-area of the larger BB domain, hence, producing the Little-Brother simulation (LB). With the BBE approach, differences between the two simulated climates (BB and LB) can be unambiguously attributed to errors associated with the dynamical downscaling technique, and not to model errors or observational limitations. The current study focuses on the summer over the West Coast of North America. Results of the stationary and transient parts of the fields, decomposed by horizontal scales, are presented for the month of July, for 5 consecutive years (1990–1994). Three degrees of spatial filtering (roughly equivalent to the global spectral resolution of T30, T60 and T360) as well as two update intervals (3 and 6 h) of the lateral boundary conditions (LBC) have been employed. This study establishes that the maximum acceptable resolution of driving data for summer is T30, with improved results employing the T60 resolution of LBC. There is little improvement by reducing the time interval from 6 h to 3 h. These results are generally in agreement with previous studies carried out for winter. The good correlation between LB and BB simulations is more difficult to achieve during the summer season, mostly due to weaker control exerted by LBC. Poor correlations are more pronounced for the transient parts than they are for the stationary parts of the fields. This is especially true for the precipitation field, where differences can be attributed to higher temporal variability during the summer due to the presence of convection.     

Sensitivity to domain size of mid-latitude summer simulations with a regional climate model

The issue of Regional Climate Model (RCM) domain size is studied here by using a perfect-model approach, also known as the Big-Brother experiment. It is known that the control exerted by the lateral boundary conditions (LBC) on nested simulations increases when reducing the domain size. The large-scale component of the simulation that is forced by the LBC influences the small-scale features that develop along the large-scale flow. Small-scale transient eddies need space and time to develop sufficiently however, and small domains can impede their development. Our tests performed over eastern North America in summer reveal that the small-scale features are systematically underestimated over the entire domain, even for domain as large as 140 by 140 grid points. This result differs from that obtained in winter where the small scales were mainly underestimated on the west (inflow) side of the domain. This difference is due to the circulation regime over Eastern Canada, which is characterized by weak and variable flow in summer, but strong and westerly flow in winter. For both seasons, the small-scale transient-eddy amplitudes are systematically underestimated at higher levels, but this problem is less severe in summer. Overall the model is more skilful in regenerating the small scales in summer than in winter for comparable domain sizes, which can be related to the weaker summer flow and stronger physical processes occurring in this season.     

Evaluation of the internal variability and estimation of the downscaling ability of the Canadian Regional Climate Model for different domain sizes over the north Atlantic region using the Big-Brother experimental approach

The ability of a nested model to accurately simulate the subarctic climate is studied here. Two issues have been investigated: Model??s internal variability (IV) and the impact of domain size (DS). For this purpose we combine the ??perfect model?? approach, Big-Brother Experiment (BBE) (Denis et al. in Clim Dyn 18:627?C646, 2002) with the ensemble of simulations. The advantage of this framework is the possibility to study small-scale climate features that constitute the main added value of RCM. The effects of the DS on result were studied by employing two Little-Brother (LB) domain sizes. IV has been evaluated by introducing small differences in initial conditions in an ensemble of 20 simulations over each LB. Results confirm previous findings that the IV is more important over the larger domain of integration. The temporal evolution over two domain sizes is rather different and depends strongly on the synoptic situation. Small-scales solution over the larger domain diverges freely from the boundary forcing in some periods. Over the smaller domain, the amplitude of small-scale transient eddies is systematically underestimated, especially at higher altitude characterized by the strongest winds along the storm tracks. Over the larger domain, the amplitude of small-scale transient eddies is better represented. However, the weaker control by the lateral boundaries over the larger domain results in solutions with large internal variability. As a result, the ensemble average strongly underestimates the transient-eddy variance due to partial destructive interference of individual ensemble member solutions.     

The impact of lateral boundary data errors on the simulated climate of a nested regional climate model

In this study, we investigate the response of a Regional Climate Model (RCM) to errors in the atmospheric data used as lateral boundary conditions (LBCs) using a perfect-model framework nick-named the “Big-Brother Experiment” (BBE). The BBE has been designed to evaluate the errors due to the nesting process excluding other model errors. First, a high-resolution (45 km) RCM simulation is made over a large domain. This simulation, called the Perfect Big Brother (PBB), is driven by the National Centres for Environmental Prediction (NCEP) reanalyses; it serves as reference virtual-reality climate to which other RCM runs will be compared. Next, errors of adjustable magnitude are introduced by performing RCM simulations with increasingly larger domains at lower horizontal resolution (90 km mesh). Such simulations with errors typical of today’s Coupled General Circulation Models (CGCM) are called the Imperfect Big-Brother (IBB) simulations. After removing small scales in order to achieve low-resolution typical of today’s CGCMs, they are used as LBCs for driving smaller domain high-resolution RCM runs; these small-domain high-resolution simulations are called Little-Brother (LB) simulations. The difference between the climate statistics of the IBB and those of PBB simulations mimic errors of the driving model. The comparison of climate statistics of the LB to those of the PBB provides an estimate of the errors resulting solely from nesting with imperfect LBCs. The simulations are performed over the East Coast of North America using the Canadian RCM, for five consecutive February months (from 1990 to 1994). It is found that the errors contained in the large scales of the IBB driving data are transmitted to and reproduced with little changes by the LB. In general, the LB restores a great part of the IBB small-scale errors, even if they do not take part in the nesting process. The small scales are seen to improve slightly in regions with important orographic forcing due to the finer resolution of the RCM. However, when the large scales of the driving model have errors, the small scales developed by the LB have errors as well, suggesting that the large scales precondition the small scales. In order to obtain correct small scales, it is necessary to provide the accurate large-scale circulation at the lateral boundary of the RCM.     

Can added value be expected in RCM-simulated large scales?

Nested Limited-Area Models require driving data to define their lateral boundary conditions (LBC). The optimal choice of domain size and the repercussions of LBC errors on Regional Climate Model (RCM) simulations are important issues in dynamical downscaling work. The main objective of this paper is to investigate the effect of domain size, particularly on the larger scales, and to question whether an RCM, when run over very large domains, can actually improve the large scales compared to those of the driving data. This study is performed with a detailed atmospheric model in its global and regional configurations, using the “Imperfect Big-Brother” (IBB) protocol. The ERA-Interim reanalyses and five global simulations are used to drive RCM simulations for five winter seasons, on four domain sizes centred over the North American continent. Three variables are investigated: precipitation, specific humidity and zonal wind component. The results following the IBB protocol show that, when an RCM is driven by perfect LBC, its skill at reproducing the large scales decreases with increasing the domain of integration, but the errors remain small even for very large domains. On the other hand, when driven by LBC that contain errors, RCMs can bring some reduction of errors in large scales when very large domains are used. The improvement is found especially in the amplitude of patterns of both the stationary and the intra-seasonal transient components. When large errors are present in the LBC, however, these are only partly corrected by the RCM. Although results showed that an RCM can have some skill at improving imperfect large scales supplied as driving LBC, the main added value of an RCM is provided by its small scales and its skill to simulate extreme events, particularly for precipitation. Under the IBB protocol all RCM simulations were fairly skilful at reproducing small scales statistics, although the skill decreased with increasing LBC errors. Coarse-resolution model simulations have difficulties in simulating heavy precipitation events, and as a result their precipitation distributions are systematically shifted toward smaller intensity. Under the IBB protocol, all RCM simulations have distributions very similar to the reference field, being little affected by LBC errors, and no significant differences were found between the small scales statistics and the precipitation distributions obtained over different RCM domains.     

Investigation of regional climate models’ internal variability with a ten-member ensemble of 10-year simulations over a large domain

Previous investigations on regional climate models’ (RCM) internal variability (IV) were limited owing to small ensembles, short simulations and small domains. The present work extends previous studies with a ten-member ensemble of 10-year simulations performed with the Canadian Regional Climate Model over a large domain covering North America. The results show that the IV has no long-term tendency but rather fluctuates in time following the synoptic situation within the domain. The IV of mean-sea-level pressure (MSLP) and screen temperature (ST) show a small annual cycle with larger values in spring, which differs from previous studies. For precipitation (PCP), the IV shows a clear annual cycle with larger values in summer, as previously reported. The 10-year climatology of the IV for MSLP and ST shows a well-defined spatial distribution with larger values in the northeast of the domain, near the outflow boundary. A comparison of the IV of MSLP and ST in summer with the transient-eddy variance reveals that the IV is close to its maximum in a small region near the outflow boundary. Same analysis for PCP in summer shows that the IV reaches its maximum in most parts of the domain, except for a small region on the western side near the inflow boundary. Finally, a comparison of the 10-year climate of each simulation of the ensemble showed that the IV may have a significant impact on the climatology of some variables.     

Dependence of the agcm climatology on the method of prescribing surface boundary conditions and its climatological implication

By using IAP 9L AGCM, two sets of long-term climatological integration have been per-formed with the two different interpolation procedures for generating the daily surface boundary conditions. One interpolation procedure is the so-called “traditional” scheme, for which the daily surface boundary conditions are obtained by linearly interpolating between the observed monthly mean values, however the observed monthly means cannot be preserved after interpolation. The other one is the “new” scheme, for which the daily surface boundary conditions are obtained by linearly interpolating between the "artificial" monthly mean values which are based on, but are dif-ferent from the observed ones, after interpolating with this new scheme, not only the observed monthly mean values are preserved, the time series of the new generated daily values is also more consistent with the observation. Comparison of the model results shows that the differences of the globally or zonally averaged fields between these two integrations are quite small, and this is due to the compensating effect between the different regions. However, the differences of the two patterns (the global or regional geographical distributions), are quite significant, for example, the magni-tude of the difference in the JJA mean rainfall between these two integrations can exceed 2 mm/day over Asian monsoon regions, and the difference in DJF mean surface air temperature can also exceed 2oC over this region. The fact that the model climatology depends quite strongly on the method of prescribing the daily surface boundary conditions suggests that in order to validate the climate model or to predict the short-term climate anomalies, either the " new* interpolation scheme or the high frequency surface boundary conditions (e.g., daily or weekly data instead of the monthly data) should be introduced. Meanwhile, as for the coupled model, the daily coupling scheme between the different component cli?mate models (e.g., atmospheric and oceanic general circulation models) is preferred in order to partly eliminate the “climate drift” problem which may appear during the course of direct coupling.     

Sensitivity of the thermohaline circulation and climate to ocean exchanges in a simple coupled model

 A new simple, coupled climate model is presented and used to investigate the sensitivity of the thermohaline circulation and climate to ocean vertical and horizontal exchange. As formulated, the model highlights the role of thin, ocean surface layers in the communication between the atmosphere and the subsurface ocean. Model vertical exchange is considered to be an analogue to small-scale, diapycnal mixing and convection (when present) in the ocean. Model horizontal exchange is considered to be an analogue to the effects of the wind-driven circulation. For small vertical exchange in the ocean, the model exhibits only one steady-state solution: a relatively cold, mid-high-latitude climate associated with a weak, salinity-driven circulation (“off ” mode). For large vertical and horizontal exchange in the ocean, the model also exhibits only one steady-state solution: a relatively warm, mid-high-latitude climate associated with a strong, thermally-driven circulation (“on” mode). For sufficiently weak horizontal exchange but large enough vertical exchange, both modes are possible stable, steady-state solutions. When model parameters are calibrated to fit tracer distributions of the modern ocean-atmosphere system, only the “on” mode is possible in this standard case. This suggests that the wind-driven circulation in consort with diapycnal mixing suppresses the “off ” mode in the modern ocean-atmosphere system. Since both diapycnal mixing and the wind-driven circulation would be expected to increase in a cold climate with greater meridional temperature gradients and enhanced winds, vertical and horizontal exchange in the ocean are probably associated with strong negative feedbacks which tend to stabilize climate. These results point to the need to resolve ocean wind-driven circulation and to greatly improve the treatment of ocean diapycnal mixing in more complete models of the climate system. Received: 16 November 1999 / Accepted: 19 June 2000     

Time mean and variability of the scale-decomposed atmospheric water budget in a 25-year simulation of the Canadian Regional Climate Model over North America

The scaled-decomposed atmospheric water budget over North America is investigated through the analysis of 25 years of simulation by the Canadian Regional Climate Model (CRCM) driven by the NCEP–NCAR reanalyses for the period 1975–1999. The time average and time variability of the atmospheric water budget for the winter and summer seasons are decomposed into their large-scale and small-scale components to identify the added value of the regional model. For the winter season, the intra-seasonal transient-eddy variance is the main temporal variability. The large- and small-scale terms are of the same order of magnitude, and are large over both coasts and weak over the continent. For the summer season, the time–mean atmospheric water budget is rather different to that of winter, with maximum values over the south-eastern part of the continent. The summer intra-seasonal variance is about twice stronger than in winter and also dominates the variability, but the inter-monthly variance is non-negligible and can be in part associated to North American Monsoon System. Over the continent, the intra-seasonal climatological variance is dominated by the variability of the small scales. The small scales, that is those scales that are only resolved in the regional model but not in the reanalyses, contribute to the added value in a regional climate simulation. In the winter season, the added value of the CRCM is large and dominated by oceanic forcing, while in summer, it is dominant (larger than the large scales) and controlled mainly by convective processes.     

The coupling of optimal economic growth and climate dynamics

In this paper, we study optimal economic growth programs coupled with climate change dynamics. The study is based on models derived from MERGE, a well established integrated assessment model (IAM). We discuss first the introduction in MERGE of a set of “tolerable window” constraints which limit both the temperature change and the rate of temperature change. These constraints, obtained from ensemble simulations performed with the Bern 2.5-D climate model, allow us to identity a domain intended to preserve the Atlantic thermohaline circulation. Next, we report on experiments where a two-way coupling is realized between the economic module of MERGE and an intermediate complexity “3-D-” climate model (C-GOLDSTEIN) which computes the changes in climate and mean temperature. The coupling is achieved through the implementation of an advanced “oracle based optimization technique” which permits the integration of information coming from the climate model during the search for the optimal economic growth path. Both cost-effectiveness and cost-benefit analysis modes are explored with this combined “meta-model” which we refer to as GOLDMERGE. Some perspectives on future implementations of these approaches in the context of “collaborative” or “community” integrated assessment modules are derived from the comparison of the different approaches.     

The Big Brother Experiment and seasonal predictability in the NCEP regional spectral model

The Big Brother Experiment methodology of Denis et al. (Clim Dyn 18:627-646, 2002) is applied to test the downscaling ability of a one-way nested regional climate model. This methodology consists of first obtaining a reference climate by performing a large domain, high resolution regional climate model simulation—the Big Brother. The small scales are then filtered out from the Big Brother’s output to produce a data set whose effective resolution is comparable to those of the data sets typically used to drive regional climate models. This filtered data set is then used to drive the same nested regional climate model, integrated over a smaller domain, but at the same high resolution as the Big Brother - the Little Brother. Any differences can only be attributed either to errors associated with the nesting strategy and downscaling technique, or to inherent unpredictability of the system, but not to model errors. This methodology was applied to the National Center for Environmental Prediction Regional Spectral Model over a tropical domain for a 1-month simulation period. The Little Brother reproduced most fields of the Big Brother quite well, with the important exception of the small-scale component of the precipitation field, which was poorly reproduced. Sensitivity experiments indicated that the poor agreement of the precipitation at these scales in a tropical domain was due primarily to the behavior of convective processes, and is specific to the Big Brother Experiment on the tropical domain. Much better agreement for the small-scale precipitation component was obtained in an extratropical winter case, suggesting that one factor explaining the tropical result is the importance of convective processes in controlling precipitation, versus the greater importance of large-scale dynamics in the winter extratropics. In the tropical case, results from two ensembles of five 3-month seasonal simulations forced by GCM output suggest a considerably greater predictability for the small-scale stationary component of tropical precipitation than did the Big Brother Experiment.     

A rose by any other name ...?: What members of the general public prefer to call “climate change”

Unlike many other environmental problems, the terms used to describe the phenomenon of increasing atmospheric concentrations of anthropogenic greenhouse gases are many, with multiple and sometimes conflicting meanings. Whether there are meaningful distinctions in public perceptions of “global warming,” “climate change,” and “global climate change” has been a topic of research over the past decade. This study examines public preferences for these terms based on respondent characteristics, including climate change beliefs, political affiliation, and audience segment status derived from the “Global Warming’s Six Americas” classification. Certainty of belief in global warming, political affiliation and audience segment status were found to be the strongest predictors of preference, although “I have no preference” was the modal response. Global warming appears to be a more polarizing term than climate change, preferred most by people already concerned about the issue, and least by people who don’t believe climate change is occurring. Further research is needed to identify which of these two names promotes the engagement of people across the spectrum of climate change beliefs in constructive dialogue about the issue.     

Present climate simulation over Korea with a regional climate model using a one-way double-nested system

Summary This study investigates the capability of the regional climate model RegCM3 to simulate surface air temperature and precipitation over the Korean Peninsula. The model is run in one-way double nested mode, with a 60 km grid point spacing “mother” domain encompassing the eastern regions of Asia and a 20 km grid point spacing nested domain covering the Korean Peninsula. The simulation spans the three-year period of 1 October 2000 through 30 September 2003 and the boundary conditions needed to run the mother domain experiment are provided from the NCEP reanalysis of observations. The model results are compared with a high density station observation dataset to examine the fine scale structure of the surface climate signal. The model shows a good performance in capturing both the sign and magnitude of the seasonal and inter-annual variations of the surface variables both over East Asia as a whole and over the Korean Peninsula in the nested system. Some persistent biases are however present. Surface temperature is systematically underestimated, especially over mountainous regions in the warm season. This feature may be due to the relatively coarse representation of the Korean topography. The simulated precipitation over the mother domain successfully reproduces the broad spatial pattern of observed precipitation over East Asia along with its seasonal evolution. On the other hand, fine scale details from the nested results show a varying level of quality for the different individual years. Because of the better resolved topographic forcing, the increased resolution of the nested model improves the spatial agreement with the fine scale observation fields for temperature and cold season precipitation. For summer monsoon precipitation the simulation of individual monsoon convective events and tropical storms is however more important than the topographic forcing, and therefore the performance of the nested system is more case-dependent.     

The effect of mountainous topography on moisture exchange between the “surface” and the free atmosphere

Typical numerical weather and climate prediction models apply parameterizations to describe the subgrid-scale exchange of moisture, heat and momentum between the surface and the free atmosphere. To a large degree, the underlying assumptions are based on empirical knowledge obtained from measurements in the atmospheric boundary layer over flat and homogeneous topography. It is, however, still unclear what happens if the topography is complex and steep. Not only is the applicability of classical turbulence schemes questionable in principle over such terrain, but mountains additionally induce vertical fluxes on the meso-γ scale. Examples are thermally or mechanically driven valley winds, which are neither resolved nor parameterized by climate models but nevertheless contribute to vertical exchange. Attempts to quantify these processes and to evaluate their impact on climate simulations have so far been scarce. Here, results from a case study in the Riviera Valley in southern Switzerland are presented. In previous work, measurements from the MAP-Riviera field campaign have been used to evaluate and configure a high-resolution large-eddy simulation code (ARPS). This model is here applied with a horizontal grid spacing of 350 m to detect and quantify the relevant exchange processes between the valley atmosphere (i.e. the ground “surface” in a coarse model) and the free atmosphere aloft. As an example, vertical export of moisture is evaluated for three fair-weather summer days. The simulations show that moisture exchange with the free atmosphere is indeed no longer governed by turbulent motions alone. Other mechanisms become important, such as mass export due to topographic narrowing or the interaction of thermally driven cross-valley circulations. Under certain atmospheric conditions, these topographical-related mechanisms exceed the “classical” turbulent contributions a coarse model would see by several times. The study shows that conventional subgrid-scale parameterizations can indeed be far off from reality if applied over complex topography, and that large-eddy simulations could provide a helpful tool for their improvement.     

The Last Glacial Maximum climate over Europe and western Siberia: a PMIP comparison between models and data

 Under the framework of the Palaeoclimate Modelling Intercomparison Project (PMIP), 17 climate models, 16 of which are atmospheric general circulation models, have been run to simulate the climate of the Last Glacial Maximum (21 000 years ago) using the same set of boundary conditions. Parallel to these numerical experiments, new, consistent, data bases have been developed on a continental scale. The present work compares the range of the model responses to the large perturbation corresponding to the conditions of the Last Glacial Maximum with consistently derived climate reconstructions from pollen records over Europe and western Siberia. It accounts for the differences in the model results due to the models themselves and directly compares this “error bar” due to the models to the uncertainties in the climate reconstructions from the pollen records. Overall the Last Glacial Maximum climate simulated by the models over western Europe is warmer, especially in winter, and wetter than the one depicted by the reconstructions. This is the region where the reconstructed increase in temperature, precipitation and moisture index from the Last Glacial Maximum to the present conditions is largest. The same disagreement, but of smaller amplitude, is found over Central Europe and the eastern Mediterranean Basin, while models and data are in broad agreement over western Siberia. The numerous modelling results allow a study of the link between the changes in atmospheric circulation and those in temperature, and an interpretation of the discrepancies in precipitation in terms of those in temperature. Received: 1 February 2000 / Accepted: 9 May 2000     

Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal

Realizing the error characteristics of regional climate models (RCMs) and the consequent limitations in their direct utilization in climate change impact research, this study analyzes a quantile-based empirical-statistical error correction method (quantile mapping, QM) for RCMs in the context of climate change. In particular the success of QM in mitigating systematic RCM errors, its ability to generate “new extremes” (values outside the calibration range), and its impact on the climate change signal (CCS) are investigated. In a cross-validation framework based on a RCM control simulation over Europe, QM reduces the bias of daily mean, minimum, and maximum temperature, precipitation amount, and derived indices of extremes by about one order of magnitude and strongly improves the shapes of the related frequency distributions. In addition, a simple extrapolation of the error correction function enables QM to reproduce “new extremes” without deterioration and mostly with improvement of the original RCM quality. QM only moderately modifies the CCS of the corrected parameters. The changes are related to trends in the scenarios and magnitude-dependent error characteristics. Additionally, QM has a large impact on CCSs of non-linearly derived indices of extremes, such as threshold indices.     

Agroclimatic study of mountainous regions and its progress in China

In recent years, the agroclimatic investigations have been made at various temporal and spatial scales in moun-tainous and hilly regions, and so have the adaptability tests on the crop ecoclimate. A large quantity of reliable and representative data are obtained. Through the synthetic studies on “bell”, “layer”, “topography” and “ecological-type” in mountainy and hilly regions, climate with agriculture and zonality with non-zonality are closely combined to show the similarities and differences of agroclimatic resources at various layers and with different topography types in mountainy and hilly regions. A general review is given in this paper.     

On the northward shift of the West African monsoon

The regional climate model (MAR) is used to perform a simulation of the year 1992 over West Africa. It is shown that MAR is able to simulate the main features of the rainy regime over West Africa and especially the discontinuous seasonal progression of the West African Monsoon along the year. One particular feature that is reasonably well reproduced is the abrupt shift of the rain band from 5° to 10°N at the end of June (also called “monsoon jump”). This study suggests that such a phenomenon is associated with the shift of the Saharan heat low between two favourite positions: one being over the Sahelian area (10–15°N) and the other over the Saharan area (20–25°N). These two favourite locations of the heat low are linked to the spatial distribution of surface albedo over West Africa that drives the spatio-temporal location of the surface temperature maxima. A detailed analysis of this “monsoon jump” is performed and the causes of the strong decrease in precipitation that precedes the northward shift of the rain band are also investigated.     

The energy cycle in atmospheric models

The energy cycle characterizes basic aspects of the physical behaviour of the climate system. Terms in the energy cycle involve first and second order climate statistics (means, variances, covariances) and the intercomparison of energetic quantities offers physically motivated “second order” insight into model and system behaviour. The energy cycle components of 12 models participating in AMIP2 are calculated, intercompared and assessed against results based on NCEP and ERA reanalyses. In general, models simulate a modestly too vigorous energy cycle and the contributions to and reasons for this are investigated. The results suggest that excessive generation of zonal available potential energy is an important driver of the overactive energy cycle through “generation push” while excessive dissipation of eddy kinetic energy in models is implicated through “dissipation pull‘’. The study shows that “ensemble model” results are best or among the best in the comparison of energy cycle quantities with reanalysis-based values. Thus ensemble approaches are apparently “best” not only for the simulation of 1st order climate statistics as in Lambert and Boer (Clim Dyn 17:83–106, 2001) but also for the higher order climate quantities entering the energy cycle.