Twentieth-century Simulations Research Articles

Revisiting the Indian summer monsoon–ENSO links in the IPCC AR4 projections: A cautionary outlook

The climate change experiments under the fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), namely the twentieth century simulations (20C3M) and Special Report on Emissions Scenarios (SRES) A1B, are revisited to study whether these models can reproduce the ENSO and ENSO Modoki patterns as the two important modes from statistical linear analysis as observed. The capability of the models in simulating realistic ENSO/ENSO Modoki teleconnections with the Indian summer monsoon, and also the implications for the future are also explored. Results from the study indicate that only ~1/4th of the models from 20C3M capture either ENSO or ENSO Modoki pattern in JJAS. Of this 1/4th, only two models simulate both ENSO and ENSO Modoki as important modes. Again, out of these two, only one model simulates both ENSO and ENSO Modoki as important modes during both summer and winter.It is also shown that the two models that demonstrate ENSO Modoki as well as ENSO associated variance in both 20C3M and SRESA1B represent the links of the ISMR with ENSO reasonably in 20C3M, but indicate opposite type of impacts in SREA1B. With the limited skills of the models in reproducing the monsoon, the ENSO and ENSO Modoki, it is difficult to reconcile that the teleconnections of a tropical driver can change like that. All these indicate the challenges associated with the limitations of the models in reproducing the variability of the monsoons and ENSO flavors, not to speak of failing in capturing the potential impacts of global warming as they are expected to. More research in improving the current day simulations, improving model capacity to simulate better by improving the Green House Gases (GHG) and aerosols in the models are some of the important and immediate steps that are necessary.

Validating climate models for computing evapotranspiration in hydrologic studies: how relevant are climate model simulations over Florida?

A non-stationary climate requires a new paradigm in the current practice of using past observations for future planning. Next to precipitation, evapotranspiration is the most important variable in water budgets of regions such as Florida, the location of this study. Dynamical simulations using Regional Climate Models (RCMs) provide all the variables necessary to compute potential or reference crop evapotranspiration (RET) for hydrologic modeling under climate change scenarios. Data sets for the variables necessary to compute RET using the Penman–Monteith model were obtained from RCMs provided by the North American Regional Climate Change Assessment Program to evaluate their skills in estimating RET during the late twentieth century simulations. Comparison of RET values simulated from RCMs with those computed from a high-resolution weather-monitoring network and satellite data show significant biases in spatial patterns and seasonality. In particular, seasonal patterns of RET computed from RCMs show a phase shift with peak values occurring during mid-summer months, whereas observed data show earlier peaks in May and June. The phase shift reflects similar behavior in model predictions of incoming solar radiation. In addition, relative humidity values estimated from RCMs are significantly higher than observed values and the wind speed estimates from many RCMs have a significant bias during dry season months. Further improvements to RCMs may be needed before they can be used effectively for hydrologic modeling under future climate change scenarios.

Behaviour of the winter North Atlantic eddy-driven jet stream in the CMIP3 integrations

A systematic analysis of the winter North Atlantic eddy-driven jet stream latitude and wind speed from 52 model integrations, taken from the coupled model intercomparison project phase 3, is carried out and compared to results obtained from the ERA-40 reanalyses. We consider here a control simulation, twentieth century simulation, and two time periods (2046–2065 and 2081–2100) from a twenty-first century, high-emission A2 forced simulation. The jet wind speed seasonality is found to be similar between the twentieth century simulations and the ERA-40 reanalyses and also between the control and forced simulations although nearly half of the models overestimate the amplitude of the seasonal cycle. A systematic equatorward bias of the models jet latitude seasonality, by up to 7°, is observed, and models additionally overestimate the seasonal cycle of jet latitude about the mean, with the majority of the models showing equatorward and poleward biases during the cold and warm seasons respectively. A main finding of this work is that no GCM under any forcing scenario considered here is able to simulate the trimodal behaviour of the observed jet latitude distribution. The models suffer from serious problems in the structure of jet variability, rather than just quantitiative errors in the statistical moments.

Atlantic Warm-Pool Variability in the IPCC AR4 CGCM Simulations

Abstract This study investigates Atlantic warm pool (AWP) variability in the twentieth century and preindustrial simulations of coupled GCMs submitted to the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). In the twentieth-century simulations, most coupled models show very weak AWP variability, represented by an AWP area index, because of the cold SST bias in the AWP. Among the IPCC models, a higher AWP SST index corresponds to increased net downward shortwave radiation and decreased low-level cloud fraction during the AWP peak season. This suggests that the cold SST bias in the AWP region is at least partly caused by an excessive amount of simulated low-level cloud, which blocks shortwave radiation from reaching the sea surface. AWP natural variability is examined in preindustrial simulations. Spectral analysis reveals that only multidecadal band variability of the AWP is significant in observations. All models successfully capture the multidecadal band, but they show that interannual and/or decadal variability is also significant. On the multidecadal time scale, the global SST difference pattern between large AWP years and small AWP years resembles the geographic pattern of the AMO for most coupled models. Observational analysis indicates that both positive ENSO phase and negative NAO phase in winter correspond to reduced trade winds in the AWP region. The westerly anomalies induced by positive ENSO and negative NAO lead to local heating and warm SST from March to May and February to April, respectively. This behavior as a known feature of anomalous AWP growth is well captured by only five models.

Quantitative assessment of the climate components driving the pacific decadal oscillation in climate models

The Pacific decadal oscillation (PDO) is defined as the first empirical orthogonal function (EOF) mode of the North Pacific sea surface temperature anomalies. In this study, we reconstructed the PDO using the first-order autoregressive model from various climate indices representing the El Nino-Southern oscillation (ENSO), Aleutian Low (AL), sea surface height (SSH), and thermocline depth over the Kuroshio–Oyashio extension (KOE) region. The climate indices were obtained from observation and twentieth-century simulations of the eight coupled general circulation models (CGCMs) participating in the Climate Model Intercomparison Project Phase III (CMIP3). In this manner, we quantitatively assessed the major climate components generating the PDO using observation and models. Based on observations, the PDO pattern in the central to eastern North Pacific was accurately reconstructed by the AL and ENSO indices, and that in the western North Pacific was best reconstructed by the SSH and thermocline indices. In the CMIP3 CGCMs, the relative contribution of each component to the generation of the PDO varied greatly from model to model, and observations, although the PDO patterns from most of the models were similar to the pattern observed. In the models, the PDO pattern in the eastern and western North Pacific were well reconstructed using the AL and SSH indices, respectively. However, the PDO pattern reconstructed by the ENSO index was quite different from the observed pattern, which was possibly due to the model's common deficiency in simulating the amplitude and location of the ENSO. Furthermore, the differences in the contribution of the KOE thermocline index between the observed pattern and most of the models indicated that the PDO pattern associated with ocean wave dynamics is not properly simulated by most models. Therefore, the virtually well simulated PDO pattern by models is a result of physically inconsistent processes.

North Australian Sea Surface Temperatures and the El Niño–Southern Oscillation in Observations and Models

Abstract Interannual variations in the sea surface temperature (SST) to the north of Australia are strongly linked to variations in Australian climate, including winter rainfall and tropical cyclone numbers. The north Australian SSTs are also closely linked to ENSO and tropical Pacific SSTs, with the relationship exhibiting a strong seasonal cycle. Credible predictions of Australian climate change therefore depend on climate models being able to represent ENSO and its connection to north Australian SSTs, the topic of this study. First, the observational datasets of the Met Office Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) and the NOAA Extended Reconstructed Sea Surface Temperature (ERSST) are used to document the links between the Niño-3.4 index and a north Australian SST index, and the temporal evolution of north Australian SSTs during ENSO events. During austral autumn, the correlation between Niño-3.4 SST and north Australian SST is positive, while in austral spring it is strongly negative. During El Niño events, the north Australian SST anomalies become negative in the austral spring preceding the development of the positive Niño-3.4 SST anomalies. The coupled models participating in the Coupled Model Intercomparison Project phase 3 (CMIP3) are evaluated in terms of this temporal evolution of Niño-3.4 SST and the relationship to north Australian SST for the twentieth-century simulations. Some of the models perform very well, while some do not capture the seasonal cycle of correlations at all. The way in which these relationships may change in the future is examined using the A2 emissions scenario in those models that do a reasonable job of capturing the present-day observed relationship, and very little change is found.

Climate Drift in the CMIP3 Models

Abstract Even in the absence of external forcing, climate models often exhibit long-term trends that cannot be attributed to natural variability. This so-called climate drift arises for various reasons including the following: perturbations to the climate system on coupling component models together and deficiencies in model physics and numerics. When examining trends in historical or future climate simulations, it is important to know the error introduced by drift so that action can be taken where necessary. This study assesses the importance of drift for a number of climate properties at global and local scales. To illustrate this, the present paper focuses on simulated trends over the second half of the twentieth century. While drift in globally averaged surface properties is generally considerably smaller than observed and simulated twentieth-century trends, it can still introduce nontrivial errors in some models. Furthermore, errors become increasingly important at smaller scales. The direction of drift is not systematic across different models or variables, as such drift is considerably reduced in the multimodel mean. Despite drift being primarily associated with ocean adjustment, it is also apparent in atmospheric variables. For example, most models have local drift magnitudes in surface air and ocean temperatures that are typically between 15% and 35% of the twentieth-century simulation trend magnitudes for 1950–2000. Below depths of 1000–2000 m, drift dominates over any forced trend in most regions. As such steric sea level is strongly affected and for some models and regions the sea level trend direction is reversed. Thus depending on the application, drift may be negligible or may make up an important part of the simulated trend.

Optimal Determination of Time-Varying Climate Change Signals

Abstract This paper proposes an optimal method for estimating time-dependent climate change signals from general circulation models. The basic idea is to identify vectors that maximize the mean-square detection statistic derived from optimal fingerprinting techniques. The method also provides an objective and systematic procedure for identifying the limit to which a signal can be restricted in space and time without losing detectability. As an illustration, the method is applied to the Coupled Model Intercomparison Project, phase 3 multimodel dataset to determine the continental seasonal-mean anomaly in surface air temperature and precipitation that is most detectable, on average, in these models. Anomalies in seasonal-mean surface air temperature are detectable in all seasons by almost all models on all continents but Europe; seasonal-mean anomalies over Europe are undetectable for some models, though this does not preclude other expressions of the signal, such as those that include longer time averages or time-lag information, from being detectable. Detectability in seasonal-mean temperature is found not only for multidecadal warming trends but also for cooling after major volcanic eruptions. In contrast, seasonal-mean precipitation anomalies are detectable in only a few models for averages over 5 yr or more, suggesting that the signal should include more spatiotemporal detail to be detectable across more models. Nevertheless, of the precipitation anomalies that are detectable, the signal appears to be of two characters: a systematic trend and enhanced frequency of extreme values. These results derived from twentieth-century simulations appear to be consistent with previous studies based on twenty-first-century simulations with larger signal-to-noise ratios.

Understanding Simulated Extreme Precipitation Events in Madison, Wisconsin, and the Role of Moisture Flux Convergence during the Late Twentieth and Twenty-First Centuries*

AbstractUnderstanding extreme precipitation events in the current and future climate system is an important aspect of climate change for adaptation and mitigation purposes. The current study investigates extreme precipitation events over Madison, Wisconsin, during the late twentieth and late twenty-first centuries using 18 coupled ocean–atmosphere general circulation models that participated in the Coupled Model Intercomparison Project (CMIP3). An increase of ~10% is found in the multimodel average of annual precipitation received in Madison by the end of the twenty-first century, with the largest increases projected to occur during winter [December–February (DJF)] and spring [March–May (MAM)]. It is also found that the observed seasonal cycle of precipitation in Madison is not accurately captured by the models. The multimodel average shows a strong seasonal peak in May, whereas observations peak during midsummer. Model simulations also do not accurately capture the annual cycle of extreme precipitation events in Madison, which also peak in summer. Instead, the timing of model-simulated extreme events exhibits a bimodal distribution that peaks during spring and fall. However, spatial composites of average daily precipitation simulated by GCMs during Madison’s wettest 1% of precipitation events during the twentieth century strongly resemble the spatial pattern produced in observations. The role of specific humidity and vertically integrated moisture flux convergence (MFC) during extreme precipitation events in Madison is investigated in twentieth- and twenty-first-century simulations. Spatial composites of MFC during the wettest 1% of days during the twentieth-century simulations agree well with results from the North American Regional Reanalysis dataset (NARR), suggesting that synoptic-scale dynamics are vital to extreme precipitation events.

Temporal- and Spatial-Scale Dependence of Three CMIP3 Climate Models in Simulating the Surface Temperature Trend in the Twentieth Century

Abstract Motivated by increasing interests in regional- and decadal-scale climate predictions, this study systematically analyzed the spatial- and temporal-scale dependence of the prediction skill of global climate models in surface air temperature (SAT) change in the twentieth century. The linear trends of annual mean SAT over moving time windows (running linear trends) from two observational datasets and simulations by three global climate models [Community Climate System Model, version 3.0 (CCSM3.0), Climate Model, version 2.0 (CM2.0), and Model E-H] that participated in CMIP3 are compared over several temporal (10-, 20-, 30-, 40-, and 50-yr trends) and spatial (5° × 5°, 10° × 10°, 15° × 15°, 20° × 20°, 30° × 30°, 30° latitudinal bands, hemispheric, and global) scales. The distribution of root-mean-square error is improved with increasing spatial and temporal scales, approaching the observational uncertainty range at the largest scales. Linear correlation shows a similar tendency, but the limited observational length does not provide statistical significance over the longer temporal scales. The comparison of RMSE to climatology and a Monte Carlo test using preindustrial control simulations suggest that the multimodel ensemble mean is able to reproduce robust climate signals at 30° zonal mean or larger spatial scales, while correlation requires hemispherical or global mean for the twentieth-century simulations. Persistent lower performance is observed over the northern high latitudes and the North Atlantic southeast of Greenland. Although several caveats exist for the metrics used in this study, the analyses across scales and/or over running time windows can be taken as one of the approaches for climate system model evaluations.

Mid-Twenty-First-Century Changes in Extreme Events over Northern and Tropical Africa

Abstract Changes in rainfall and temperature extremes are predicted by many global climate models as a response to greenhouse gas increases, and such changes will have significant environmental and social impacts. A regional climate model is used to predict changes in extremes across tropical and northern Africa for 2041–60 under a midline emissions forcing scenario. Six indicators are examined, including annual extreme and daily diurnal temperature ranges, heat wave days, number of dry days, number of extreme wet days, and extreme wet day rainfall intensity. Confidence in the projections is evaluated by examining the ensemble spread and the validation of extreme events in the twentieth-century simulation. Despite an increase in both daily minimum and maximum temperatures, diurnal temperature ranges decrease from West Africa to Ethiopia during spring and fall, over the Sahel during summer, and over the Congo basin during winter and spring. Diurnal temperature ranges increase over the Horn of Africa during boreal winter and over Kenya and Tanzania during boreal summer. The number of heat wave days increases north of 8°N with the largest increase (60–120 days) over the western Sahel. The number of dry days decreases over the Congo and the central Sahel but increases over East Africa, the latter associated with a reduction in the springtime long rains. The number of extreme wet rainfall days is projected to increase over West Africa, the Sahel, and the Ethiopian Highlands but decrease over the Congo. The predicted changes in extreme wet rainfall intensity are highly regional.

The CCSM4 Ocean Component

The ocean component of the Community Climate System Model version 4 (CCSM4) is described, and its solutions from the twentieth-century (20C) simulations are documented in comparison with observations and those of CCSM3. The improvements to the ocean model physical processes include new parameterizations to represent previously missing physics and modifications of existing parameterizations to incorporate recent new developments. In comparison with CCSM3, the new solutions show some significant improvements that can be attributed to these model changes. These include a better equatorial current structure, a sharper thermocline, and elimination of the cold bias of the equatorial cold tongue all in the Pacific Ocean; reduced sea surface temperature (SST) and salinity biases along the North Atlantic Current path; and much smaller potential temperature and salinity biases in the near-surface Pacific Ocean. Other improvements include a global-mean SST that is more consistent with the present-day observations due to a different spinup procedure from that used in CCSM3. Despite these improvements, many of the biases present in CCSM3 still exist in CCSM4. A major concern continues to be the substantial heat content loss in the ocean during the preindustrial control simulation from which the 20C cases start. This heat loss largely reflects the top of the atmospheric model heat loss rate in the coupled system, and it essentially determines the abyssal ocean potential temperature biases in the 20C simulations. There is also a deep salty bias in all basins. As a result of this latter bias in the deep North Atlantic, the parameterized overflow waters cannot penetrate much deeper than in CCSM3.

Improved Sea Ice Shortwave Radiation Physics in CCSM4: The Impact of Melt Ponds and Aerosols on Arctic Sea Ice

The Community Climate System Model, version 4 has revisions across all components. For sea ice, the most notable improvements are the incorporation of a new shortwave radiative transfer scheme and the capabilities that this enables. This scheme uses inherent optical properties to define scattering and absorption characteristics of snow, ice, and included shortwave absorbers and explicitly allows for melt ponds and aerosols. The deposition and cycling of aerosols in sea ice is now included, and a new parameterization derives ponded water from the surface meltwater flux. Taken together, this provides a more sophisticated, accurate, and complete treatment of sea ice radiative transfer. In preindustrial CO2 simulations, the radiative impact of ponds and aerosols on Arctic sea ice is 1.1 W m−2 annually, with aerosols accounting for up to 8 W m−2 of enhanced June shortwave absorption in the Barents and Kara Seas and with ponds accounting for over 10 W m−2 in shelf regions in July. In double CO2 (2XCO2) simulations with the same aerosol deposition, ponds have a larger effect, whereas aerosol effects are reduced, thereby modifying the surface albedo feedback. Although the direct forcing is modest, because aerosols and ponds influence the albedo, the response is amplified. In simulations with no ponds or aerosols in sea ice, the Arctic ice is over 1 m thicker and retains more summer ice cover. Diagnosis of a twentieth-century simulation indicates an increased radiative forcing from aerosols and melt ponds, which could play a role in twentieth-century Arctic sea ice reductions. In contrast, ponds and aerosol deposition have little effect on Antarctic sea ice for all climates considered.

Reproducibility by Climate Models of Cloud Radiative Forcing Associated with Tropical Convection

Abstract In this study, cloud radiative forcing (CRF) associated with convective activity over tropical oceans is analyzed for monthly mean data from twentieth-century simulations of 18 climate models participating in phase 3 of the Coupled Model Intercomparison Project (CMIP3) in comparison with observational and reanalysis data. The analysis is focused on the warm oceanic regions with sea surface temperatures (SSTs) above 27°C to exclude the regions with cold SSTs typically covered by low stratus clouds. CRF is evaluated for different regimes sorted by pressure-coordinated vertical motion at 500 hPa (ω500) as an index of large-scale circulation. The warm oceanic regions cover the regime of vertical motion ranging from strong ascent to weak descent. The most notable feature found in this study is a systematic underestimation by most models of the ratio of longwave cloud radiative forcing (LWCRF) to shortwave cloud radiative forcing (SWCRF) over the weak vertical motion regime defined as −10 < ω500 < 20 hPa day−1. The underestimation of the ratio corresponds to the underestimation of LWCRF and the overestimation of SWCRF. Clouds in models seem to be lower in the amount of high clouds but more reflective than those in the observations in this regime. In the weak vertical motion regime, the lower free troposphere is dry. In the large-scale environment condition, the reproducibility of LWCRF is high in models adopting the scheme where the relative humidity–based suppression for deep convection occurrence is implemented. Models adopting the Zhang and McFarlane scheme show good performance without such a suppression mechanism.

Sea-Ice in Twentieth-Century Simulations by New MIROC Coupled Models: A Comparison between Models with High Resolution and with Ice Thickness Distribution

Sea-Ice in Twentieth-Century Simulations by New MIROC Coupled Models: A Comparison between Models with High Resolution and with Ice Thickness Distribution

Comparative Study of Performance of CMIP3 GCMs in Simulating the East Asian Monsoon Variability

This study evaluates variability of East Asian Monsoon simulated by 24 coupled general circulation models (GCMs) participating in Coupled Model Inter-comparison Project 3 (CMIP3). Fifty years (1950 - 1999) of each model's twentieth-century climate simulation are analyzed and compared with observed data. Both East Asian Summer Monsoon (EASM) and East Asian Winter Monsoon (EAWM) are considered. Suitable indices are selected to analyze EASM and EAWM. The Wang-Fan index, associated with 850 hPa circulation pattern and tripole rainfall pattern are considered to analyze EASM. ICHENW, associated with 850 hPa circulation pattern, IGONGDY, associated with Siberian High, and ISUNBM, associated with 500 hPa East Asian trough are considered for analysis of EAWM. Inter-decadal and inter-annual variability of the EASM and EAWM are major focus of this study.The results indicate that, amplitude of inter-annual component EASM becomes larger after 1980's. The decadal component shows, weakening trend and switching from positive monsoon phase to negative monsoon phase in mid-80s for both EASM and EAWM. The simulated composite differences between weak and strong monsoon decades by models with above mentioned characteristics are compared with observation for further evaluation. The EASM weakening is associated with weaker southwesterly and stronger anti-cyclonic pattern over Western North Pacific and results in more rainfall in south and north China, Korea, Japan and less rainfall in central China. The EAWM weakening is associated with weakening of Siberian High and temperature rise over East Asia. EASM and EAWM matrices are constructed according to the relative merits of GCMs.

Tropical Atlantic Biases in CCSM4

This paper focuses on diagnosing biases in the seasonal climate of the tropical Atlantic in the twentieth-century simulation of the Community Climate System Model, version 4 (CCSM4). The biases appear in both atmospheric and oceanic components. Mean sea level pressure is erroneously high by a few millibars in the subtropical highs and erroneously low in the polar lows (similar to CCSM3). As a result, surface winds in the tropics are ~1 m s−1 too strong. Excess winds cause excess cooling and depressed SSTs north of the equator. However, south of the equator SST is erroneously high due to the presence of additional warming effects. The region of highest SST bias is close to southern Africa near the mean latitude of the Angola–Benguela Front (ABF). Comparison of CCSM4 to ocean simulations of various resolutions suggests that insufficient horizontal resolution leads to the insufficient northward transport of cool water along this coast and an erroneous southward stretching of the ABF. A similar problem arises in the coupled model if the atmospheric component produces alongshore winds that are too weak. Erroneously warm coastal SSTs spread westward through a combination of advection and positive air–sea feedback involving marine stratocumulus clouds. This study thus highlights three aspects to improve to reduce bias in coupled simulations of the tropical Atlantic: 1) large-scale atmospheric pressure fields; 2) the parameterization of stratocumulus clouds; and 3) the processes, including winds and ocean model resolution, that lead to errors in seasonal SST along southwestern Africa. Improvements of the latter require horizontal resolution much finer than the 1° currently used in many climate models.

The Community Climate System Model Version 4

The fourth version of the Community Climate System Model (CCSM4) was recently completed and released to the climate community. This paper describes developments to all CCSM components, and documents fully coupled preindustrial control runs compared to the previous version, CCSM3. Using the standard atmosphere and land resolution of 1° results in the sea surface temperature biases in the major upwelling regions being comparable to the 1.4°-resolution CCSM3. Two changes to the deep convection scheme in the atmosphere component result in CCSM4 producing El Niño–Southern Oscillation variability with a much more realistic frequency distribution than in CCSM3, although the amplitude is too large compared to observations. These changes also improve the Madden–Julian oscillation and the frequency distribution of tropical precipitation. A new overflow parameterization in the ocean component leads to an improved simulation of the Gulf Stream path and the North Atlantic Ocean meridional overturning circulation. Changes to the CCSM4 land component lead to a much improved annual cycle of water storage, especially in the tropics. The CCSM4 sea ice component uses much more realistic albedos than CCSM3, and for several reasons the Arctic sea ice concentration is improved in CCSM4. An ensemble of twentieth-century simulations produces a good match to the observed September Arctic sea ice extent from 1979 to 2005. The CCSM4 ensemble mean increase in globally averaged surface temperature between 1850 and 2005 is larger than the observed increase by about 0.4°C. This is consistent with the fact that CCSM4 does not include a representation of the indirect effects of aerosols, although other factors may come into play. The CCSM4 still has significant biases, such as the mean precipitation distribution in the tropical Pacific Ocean, too much low cloud in the Arctic, and the latitudinal distributions of shortwave and longwave cloud forcings.

How weather impacts the forced climate response

The new interactive ensemble modeling strategy is used to diagnose how noise due to internal atmospheric dynamics impacts the forced climate response during the twentieth century (i.e., 1870–1999). The interactive ensemble uses multiple realizations of the atmospheric component model coupled to a single realization of the land, ocean and ice component models in order to reduce the noise due to internal atmospheric dynamics in the flux exchange at the interface of the component models. A control ensemble of so-called climate of the twentieth century simulations of the Community Climate Simulation Model version 3 (CCSM3) are compared with a similar simulation with the interactive ensemble version of CCSM3. Despite substantial differences in the overall mean climate, the global mean trends in surface temperature, 500 mb geopotential and precipitation are largely indistinguishable between the control ensemble and the interactive ensemble. Large differences in the forced response; however, are detected particularly in the surface temperature of the North Atlantic. Associated with the forced North Atlantic surface temperature differences are local differences in the forced precipitation and a substantial remote rainfall response in the deep tropical Pacific. We also introduce a simple variance analysis to separately compare the variance due to noise and the forced response. We find that the noise variance is decreased when external forcing is included. In terms of the forced variance, we find that the interactive ensemble increases this variance relative to the control.

The Quasi-Biennial Oscillation in a Double CO2 Climate

AbstractThe effects of anticipated twenty-first-century global climate change on the stratospheric quasi-biennial oscillation (QBO) have been studied using a high-resolution version of the Model for Interdisciplinary Research on Climate (MIROC) atmospheric GCM. This version of the model is notable for being able to simulate a fairly realistic QBO for present-day conditions including only explicitly resolved nonstationary waves. A long control integration of the model was run with observed climatological sea surface temperatures (SSTs) appropriate for the late twentieth century, followed by another integration with increased atmospheric CO2 concentration and SSTs incremented by the projected twenty-first-century warming in a multimodel ensemble of coupled ocean–atmosphere runs that were forced by the Special Report on Emissions Scenarios (SRES) A1B scenario of future atmospheric composition. In the experiment for late twenty-first-century conditions the QBO period becomes longer and QBO amplitude weaker than in the late twentieth-century simulation. The downward penetration of the QBO into the lowermost stratosphere is also curtailed in the late twenty-first-century run. These changes are driven by a significant (30%–40%) increase of the mean upwelling in the equatorial stratosphere, and the effect of this enhanced mean circulation overwhelms counteracting influences from strengthened wave fluxes in the warmer climate. The momentum fluxes associated with waves propagating upward into the equatorial stratosphere do strengthen overall by ∼(10%–15%) in the warm simulation, but the increases are almost entirely in zonal phase speed ranges that have little effect on the stratospheric QBO but that would be expected to have important influences in the mesosphere and lower thermosphere.