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.     

Diagnostic budget study of the internal variability in ensemble simulations of the Canadian RCM

Due to the chaotic and nonlinear nature of the atmospheric dynamics, it is known that small differences in the initial conditions (IC) of models can grow and affect the simulation evolution. In this study, we perform a quantitative diagnostic budget calculation of the various diabatic and dynamical contributions to the time evolution and spatial distribution of internal variability (IV) in simulations with the nested Canadian Regional Climate Model. We establish prognostic budget equations of the IV for the potential temperature and the relative vorticity fields. For both of these variables, the IV equations present similar terms, notably terms relating to the transport of IV by ensemble-mean flow and to the covariance of fluctuations acting on the gradient of the ensemble-mean state. We show the skill of these equations to diagnose the IV that took place in an ensemble of 20 3-month (summer season) simulations that differed only in their IC. Our study suggests that the dominant terms responsible for the large increase of IV are either the covariance term involving the potential temperature fluctuations and diabatic heating fluctuations, or the covariance of inter-member fluctuations acting upon ensemble-mean gradients. Our results also show that, on average, the third-order terms are negligible, but they can become important when the IV is large.     

An approximate energy cycle for inter-member variability in ensemble simulations of a regional climate model

The presence of internal variability (IV) in ensembles of nested regional climate model (RCM) simulations is now widely acknowledged in the community working on dynamical downscaling. IV is defined as the inter-member spread between members in an ensemble of simulations performed by a given RCM driven by identical lateral boundary conditions (LBC), where different members are being initialised at different times. The physical mechanisms responsible for the time variations and structure of such IV have only recently begun to receive attention. Recent studies have shown empirical evidence of a close parallel between the energy conversions associated with the time fluctuations of IV in ensemble simulations of RCM and the energy conversions taking place in weather systems. Inspired by the classical work on global energetics of weather systems, we sought a formulation of an energy cycle for IV that would be applicable for limited-area domain. We develop here a novel formalism based on local energetics that can be applied to further our understanding IV. Prognostic equations for ensemble-mean kinetic energy and available enthalpy are decomposed into contributions due to ensemble-mean variables (EM) and those due to deviations from the ensemble mean (IV). Together these equations constitute an energy cycle for IV in ensemble simulations of RCM. Although the energy cycle for IV was developed in a context entirely different from that of energetics of weather systems, the exchange terms between the various reservoirs have a rather similar mathematical form, which facilitates some interpretations of their physical meaning.     

Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian climate centre general circulation model

Abstract

A simplified cumulus parameterization scheme, suitable for use in GCMs, is presented. This parameterization is based on a plume ensemble concept similar to that originally proposed by Arakawa and Schubert (1974). However, it employs three assumptions which significantly simplify the formulation and implementation of the scheme. It is assumed that an ensemble of convective‐scale updrafts with associated saturated downdrafts may exist when the atmosphere is locally conditionally unstable in the lower troposphere. However, the updraft ensemble is comprised only of those plumes which are sufficiently buoyant to penetrate through this unstable layer. It is assumed that all such plumes have the same upward mass flux at the base of the convective layer. The third assumption is that moist convection, which occurs only when there is convective available potential energy (CAPE) for reversible ascent of an undiluted parcel from the sub‐cloud layer, acts to remove CAPE at an exponential rate with a specified adjustment time scale.

The performance of the scheme and its sensitivity to choices of disposable parameters is illustrated by presenting results from a series of idealized single‐column model tests. These tests demonstrate that the scheme permits establishment of a quasi‐equilibrium between large‐scale forcing and convective response. However, it is also shown that the strength of convective downdrafts is an important factor in determining the nature of the equilibrium state. Relatively strong down‐drafts give rise to an unsteady irregularly fluctuating state characterized by alternate periods of deep and shallow convection.

The effect of using the scheme for GCM climate simulations is illustrated by presenting selected results of a multi‐year simulation carried out with the Canadian Climate Centre GCM using the new parameterization (the CONV simulation). Comparison of these results with those for a climate simulation made with the standard model (the CONTROL simulation, as documented by McFarlane et al., 1992) reveals the importance of other parameterized processes in determining the ultimate effect of introducing the new convective scheme. The radiative response to changes in the cloudiness regime is particularly important in this regard.     

Rain-on-Snow events in Japan as projected by a large ensemble of regional climate simulations

Climate Dynamics - Rain-on-Snow (ROS) events can cause severe snowmelt hazards such as river flooding, avalanches, and landslides that have significant impacts on various sectors. The influence of...     

Interannual variability and predictability in an ensemble of AMIP climate simulations conducted with the CCC GCM2

This study considers an ensemble of six 10-year climate simulations conducted with the Canadian Climate Centre 2nd generation General Circulation Model (CCC GCM2). Each simulation was forced according to the Atmospheric Model Intercomparison Project (AMIP) experimental protocol using monthly mean sea surface temperatures and sea-ice extents based on observations for January, 1979 to December 1988. One simulation, conducted on a CRAY computer, was initiated from analysed 1 January 1979 conditions while the remaining 5 simulations, conducted on a NEC computer, were initiated from previously simulated model states obtained from a long control integration. The interannual variability and potential predictability of simulated and observed 500 hPa geopotential, 850 hPa temperature and 300 hPa stream function are examined and inter-compared using statistical analysis of variance techniques to partition variance into a number of components. The boundary conditions specified by AMIP are found to induce statistically significant amounts of predictable variance on the interannual time scale in the tropics and, to a lesser extent, at extratropical latitudes. In addition, local interactions between the atmosphere and the land surface apparently induce significant amounts of potentially predictable interannual variance in the tropical lower atmosphere and also at some locations in the temperate lower atmosphere. No evidence was found that the atmosphere's internal dynamics on their own generate potentially predictable variations on the interannual time scale. The sensitivity of the statistical methods used is demonstrated by the fact that we are able to detect differences between the climates simulated on the two computers used. The causes of these physically insignificant changes are traced. The statistical procedures are checked by confirming that the choice of initial conditions does not lead to significant inter-simulation variation. The simulations are also interpreted as an ensemble of climate forecasts that rely only on the specified boundary conditions for their predictive skill. The forecasts are verified against observations and against themselves. In agreement with other studies it was found that the forecasts have very high skill in the tropics and moderate skill in the extratropics. Received: 18 December 1995 / Accepted: 4 April 1996     

ENSO forcing of the Northern Hemisphere climate in a large ensemble of model simulations based on a very long SST record

The January–March (JFM) climate response of the Northern Hemisphere atmosphere to observed sea surface temperature (SST) anomalies for the period 1855–2002 is analysed from a 35-member ensemble made with SPEEDY, an atmospheric general circulation model (AGCM) of intermediate complexity. The model was run at the T30-L8 resolution, and initial conditions and the early stage of model runs differ among ensemble members in the definition of tropical diabatic heating. SST anomalies in the Niño3.4 region were categorised into five classes extending from strong cold to strong warm. Composites based on such a categorisation enabled an analysis of the influence of the tropical Pacific SST on the Northern Hemisphere atmospheric circulation with an emphasis on the Pacific-North America (PNA) and the North Atlantic-Europe (NAE) regions. As expected, the strongest signal was detected over the PNA region. An “asymmetry” in the model response was found for the opposite polarity of the Niño3.4 index; however, this asymmetry stems mainly from the difference in the amplitude of model response rather than from the phase shift between responses to warm and cold El Niño-Southern Oscillation (ENSO) events. The extratropical signal associated with warm ENSO events was found to be stronger than that related to cold events. The results also reveal that, for the PNA region, the amplitude of the response is positively correlated with the strength of ENSO, irrespective of the sign of ENSO. With almost no phase shift between model responses to El Niño and La Niña, the linear component of the response is much stronger than the non-linear component. Although the model climate response over the NAE region is much weaker than that over the PNA region, some striking similarities with the PNA are found. Both sea level pressure and precipitation responses are positively correlated with the strength of ENSO. This is not true for the 200-hPa geopotential heights, and no plausible explanation for such a result could be offered. An appreciable linear component in model response over the NAE was also found. The model results over the NAE region agree reasonably well with observational studies. An additional analysis of the remote atmospheric response to very weak ENSO forcing (defined from the interval between 0.5σ and 1.0σ of the interannual variance) was also carried out. A discernible model response in the Northern Hemisphere to such a weak SST forcing was found.     

Convective and stratiform precipitation characteristics in an ensemble of regional climate model simulations

We apply a recently proposed algorithm for disaggregating observed precipitation data into predominantly convective and stratiform, and evaluate biases in characteristics of parameterized convective (subgrid) and stratiform (large-scale) precipitation in an ensemble of 11 RCM simulations for recent climate in Central Europe. All RCMs have a resolution of 25 km and are driven by the ERA-40 reanalysis. We focus on mean annual cycle, proportion of convective precipitation, dependence on altitude, and extremes. The results show that characteristics of total precipitation are often better simulated than are those of convective and stratiform precipitation evaluated separately. While annual cycles of convective and stratiform precipitation are reproduced reasonably well in most RCMs, some of them consistently and substantially overestimate or underestimate the proportion of convective precipitation throughout the year. Intensity of convective precipitation is underestimated in all RCMs. Dependence on altitude is also simulated better for stratiform and total precipitation than for convective precipitation, for which several RCMs produce unrealistic slopes. Extremes are underestimated for convective precipitation while they tend to be slightly overestimated for stratiform precipitation, thus resulting in a relatively good reproduction of extremes in total precipitation amounts. The results suggest that the examined ensemble of RCMs suffers from substantial deficiencies in reproducing precipitation processes and support previous findings that climate models’ errors in precipitation characteristics are mainly related to deficiencies in the representation of convection.     

The seasonal footprinting mechanism in CFSv2: simulation and impact on ENSO prediction

The seasonal footprinting mechanism (SFM) is thought to be a pre-cursor to the El Nino Southern Oscillation (ENSO). Fluctuations in the North Pacific Oscillation (NPO) impact the ocean via surface heat fluxes during winter, leaving a sea-surface temperature (SST) “footprint” in the subtropics. This footprint persists through the spring, impacting the tropical Pacific atmosphere–ocean circulation throughout the following year. The simulation of the SFM in the National Centers for Environmental Prediction (NCEP)/Climate Forecast System, version 2 (CFSv2) is likely to have an impact on operational predictions of ENSO and potentially seasonal predictions in the United States associated with ENSO teleconnection patterns. The ability of the CFSv2 to simulate the SFM and the relationship between the SFM and ENSO prediction skill in the NCEP/CFSv2 are investigated. Results indicate that the CFSv2 is able to simulate the basic characteristics of the SFM and its relationship with ENSO, including extratropical sea level pressure anomalies associated with the NPO in the winter, corresponding wind and SST anomalies that impact the tropics, and the development of ENSO-related SST anomalies the following winter. Although the model is able to predict the correct sign of ENSO associated with the SFM in a composite sense, probabilistic predictions of ENSO following a positive or negative NPO event are generally less reliable than when the NPO is not active.     

The reluctance of resource-users to adopt seasonal climate forecasts to enhance resilience to climate variability on the rangelands

As the impacts of climate-change on resource-dependent industries manifest, there is a commensurate effort to identify and implement strategies to reduce them. Yet, even when useful knowledge and tools exist, there can be poor adoption of adaptation strategies. We examine the reasons behind sub-optimal adoption of seasonal climate forecasts by graziers for managing climate variability. We surveyed 100 graziers in north-east Queensland, Australia and examined the influence of adaptive capacity, resource-dependency and forecast-perception on uptake. Technical perceptions were not important. Strategic skills, environmental awareness and social capital were. Results suggest that social factors (but not technical factors) are significant. These insights are important for adaptation planning and for maximising the resilience of communities and industries dependent on climate-sensitive resources.     

Summertime inter-annual temperature variability in an ensemble of regional model simulations: analysis of the surface energy budget

The inter-annual variability in monthly mean summer temperatures derived from nine different regional climate model (RCM) integrations is investigated for both the control climate (1961–1990) and a future climate (2071–2100) based on A2 emissions. All regional model integrations, carried out in the PRUDENCE project, use the same boundaries of the HadAM3H global atmospheric model. Compared to the CRU TS 2.0 observational data set most RCMs (but not all) overpredict the temperature variability significantly in their control simulation. The behaviour of the different regional climate models is analysed in terms of the surface energy budget, and the contributions of the different terms in the surface energy budget to the temperature variability are estimated. This analysis shows a clear relation in the model ensemble between temperature variability and the combined effects of downward long wave, net short wave radiation and evaporation (defined as F). However, it appears that the overestimation of the temperature variability has no unique cause. The effect of short-wave radiation dominates in some RCMs, whereas in others the effect of evaporation dominates. In all models the temperature variability and F increase when imposing future climate boundary conditions, with particularly high values in central Europe.     

Sea ice in the Barents Sea: seasonal to interannual variability and climate feedbacks in a global coupled model

Sea ice variability in the Barents Sea and its impact on climate are analyzed using a 465-year control integration of a global coupled atmosphere–ocean–sea ice model. Sensitivity simulations are performed to investigate the response to an isolated sea ice anomaly in the Barents Sea. The interannual variability of sea ice volume in the Barents Sea is mainly determined by variations in sea ice import into Barents Sea from the Central Arctic. This import is primarily driven by the local wind field. Horizontal oceanic heat transport into the Barents Sea is of minor importance for interannual sea ice variations but is important on longer time scales. Events with strong positive sea ice anomalies in the Barents Sea are due to accumulation of sea ice by enhanced sea ice imports and related NAO-like pressure conditions in the years before the event. Sea ice volume and concentration stay above normal in the Barents Sea for about 2 years after an event. This strongly increases the albedo and reduces the ocean heat release to the atmosphere. Consequently, air temperature is much colder than usual in the Barents Sea and surrounding areas. Precipitation is decreased and sea level pressure in the Barents Sea is anomalously high. The large-scale atmospheric response is limited with the main impact being a reduced pressure over Scandinavia in the year after a large ice volume occurs in the Barents Sea. Furthermore, high sea ice volume in the Barents Sea leads to increased sea ice melting and hence reduced surface salinity. Generally, the climate response is smallest in summer and largest in winter and spring.     

Review of simulations of climate variability and change with the GFDL R30 coupled climate model

A review is presented of the development and simulation characteristics of the most recent version of a global coupled model for climate variability and change studies at the Geophysical Fluid Dynamics Laboratory, as well as a review of the climate change experiments performed with the model. The atmospheric portion of the coupled model uses a spectral technique with rhomboidal 30 truncation, which corresponds to a transform grid with a resolution of approximately 3.75° longitude by 2.25° latitude. The ocean component has a resolution of approximately 1.875° longitude by 2.25° latitude. Relatively simple formulations of river routing, sea ice, and land surface processes are included. Two primary versions of the coupled model are described, differing in their initialization techniques and in the specification of sub-grid scale oceanic mixing of heat and salt. For each model a stable control integration of near millennial scale duration has been conducted, and the characteristics of both the time-mean and variability are described and compared to observations. A review is presented of a suite of climate change experiments conducted with these models using both idealized and realistic estimates of time-varying radiative forcing. Some experiments include estimates of forcing from past changes in volcanic aerosols and solar irradiance. The experiments performed are described, and some of the central findings are highlighted. In particular, the observed increase in global mean surface temperature is largely contained within the spread of simulated global mean temperatures from an ensemble of experiments using observationally-derived estimates of the changes in radiative forcing from increasing greenhouse gases and sulfate aerosols.     

Quantifying uncertainty in changes in extreme event frequency in response to doubled CO2 using a large ensemble of GCM simulations

We discuss equilibrium changes in daily extreme surface air temperature and precipitation events in response to doubled atmospheric CO₂, simulated in an ensemble of 53 versions of HadSM3, consisting of the HadAM3 atmospheric general circulation model (GCM) coupled to a mixed layer ocean. By virtue of its size and design, the ensemble, which samples uncertainty arising from the parameterisation of atmospheric physical processes and the effects of natural variability, provides a first opportunity to quantify the robustness of predictions of changes in extremes obtained from GCM simulations. Changes in extremes are quantified by calculating the frequency of exceedance of a fixed threshold in the 2 × CO₂ simulation relative to the 1 × CO₂ simulation. The ensemble-mean value of this relative frequency provides a best estimate of the expected change while the range of values across the ensemble provides a measure of the associated uncertainty. For example, when the extreme threshold is defined as the 99th percentile of the 1 × CO₂ distribution, the global-mean ensemble-mean relative frequency of extremely warm days is found to be 20 in January, and 28 in July, implying that events occurring on one day per hundred under present day conditions would typically occur on 20–30 days per hundred under 2 × CO₂ conditons. However the ensemble range in the relative frequency is of similar magnitude to the ensemble-mean value, indicating considerable uncertainty in the magnitude of the increase. The relative frequencies in response to doubled CO₂ become smaller as the threshold used to define the extreme event is reduced. For one variable (July maximum daily temperature) we investigate this simulated variation with threshold, showing that it can be quite well reproduced by assuming the response to doubling CO₂ to be characterised simply as a uniform shift of a Gaussian distribution. Nevertheless, doubling CO₂ does lead to changes in the shape of the daily distributions for both temperature and precipitation, but the effect of these changes on the relative frequency of extreme events is generally larger for precipitation. For example, around one-fifth of the globe exhibits ensemble-mean decreases in time-averaged precipitation accompanied by increases in the frequency of extremely wet days. The ensemble range of changes in precipitation extremes (relative to the ensemble mean of the changes) is typically larger than for temperature extremes, indicating greater uncertainty in the precipitation changes. In the global average, extremely wet days are predicted to become twice as common under 2 × CO₂ conditions. We also consider changes in extreme seasons, finding that simulated increases in the frequency of extremely warm or wet seasons under 2 × CO₂ are almost everywhere greater than the corresponding increase in daily extremes. The smaller increases in the frequency of daily extremes is explained by the influence of day-to-day weather variability which inflates the variance of daily distributions compared to their seasonal counterparts.     

Analysis of precipitation extremes in an ensemble of transient regional climate model simulations for the Rhine basin

A non-stationary index-flood model was used to analyse the 1-day summer and 5-day winter precipitation maxima in the Rhine basin in an ensemble of 15 transient regional climate model (RCM) simulations. It is assumed that the seasonal precipitation maxima follow a generalized extreme value (GEV) distribution with time varying parameters. The index-flood assumption implies that the dispersion coefficient (the ratio of the scale and the location parameters) and the shape parameter are constant over predefined regions, while the location parameter varies within these regions. A comparison with the estimates from gridded observations shows that these GEV parameters are too large in the summer season, while there is a large overestimation of the location parameter and underestimation of the dispersion coefficient in winter. However, a large part of the biases in the summer season might be due to the low number of stations used for gridding the observations. Though there is considerable variation in the changes of the extreme value distributions among the RCM simulations, common tendencies can be identified. In summer, large quantiles increase as a consequence of an increase of the dispersion coefficient, while there is almost no change of low quantiles. In winter, low quantiles increase because of an increase of the location parameter. This effect is, however, counterbalanced by a decrease of the shape parameter in most RCM simulations, resulting in only a slight increase of large quantiles. Departures from the assumed index-flood model were observed in the Alpine region in the south of the basin. This is due to the strong spatial heterogeneity in the dispersion coefficient in a number of RCM simulations and a significant altitude dependence of the trend in the location parameter in winter in five RCM simulations.     

Uncertainty in climate change projections: the role of internal variability

Uncertainty in future climate change presents a key challenge for adaptation planning. In this study, uncertainty arising from internal climate variability is investigated using a new 40-member ensemble conducted with the National Center for Atmospheric Research Community Climate System Model Version 3 (CCSM3) under the SRES A1B greenhouse gas and ozone recovery forcing scenarios during 2000–2060. The contribution of intrinsic atmospheric variability to the total uncertainty is further examined using a 10,000-year control integration of the atmospheric model component of CCSM3 under fixed boundary conditions. The global climate response is characterized in terms of air temperature, precipitation, and sea level pressure during winter and summer. The dominant source of uncertainty in the simulated climate response at middle and high latitudes is internal atmospheric variability associated with the annular modes of circulation variability. Coupled ocean-atmosphere variability plays a dominant role in the tropics, with attendant effects at higher latitudes via atmospheric teleconnections. Uncertainties in the forced response are generally larger for sea level pressure than precipitation, and smallest for air temperature. Accordingly, forced changes in air temperature can be detected earlier and with fewer ensemble members than those in atmospheric circulation and precipitation. Implications of the results for detection and attribution of observed climate change and for multi-model climate assessments are discussed. Internal variability is estimated to account for at least half of the inter-model spread in projected climate trends during 2005–2060 in the CMIP3 multi-model ensemble.     

Assessing the role of deep rooted vegetation in the climate system with model simulations: mechanism, comparison to observations and implications for Amazonian deforestation

Deep rooted vegetation (of up to 68?m) has been found in many parts of the tropics. However, models of the general atmospheric circulation (GCMs) typically use rooting depths of less than 2?m in their land surface parametrizations. How does the incorporation of deep roots into such a model affect the simulated climate? We assess this question by using a GCM and find that deeper roots lead to a pronounced seasonal response. During the dry season, evapotranspiration and the associated latent heat flux are considerably increased over large regions leading to a cooling of up to 8?K. The enhanced atmospheric moisture is transported towards the main convection areas in the inner tropical convergence zone where it supplies more energy to convection thus intensifying the tropical circulation patterns. Comparison to different kinds of data reveals that the simulation with deeper roots is much closer to observations. The inclusion of deep roots also leads to a general increased climatic sensitivity to rooting depth change. We investigate this aspect in the context of the climatic effects of large-scale deforestation in Amazonia. Most of the regional and remote changes can be attributed to the removal of deep roots. We conclude that deep rooted vegetation is an important part of the tropical climate system. Without the consideration of deep roots, the present-day surface climate cannot adequately be simulated.     

从检验CMIP5气候模式看CMIP6地球系统模式的发展

世界气候研究项目（WCRP）正在组织第六次气候模式对比计划（CMIP6）,全球各个气候模式组正在抓紧发展新模式和做多种相关的数值试验。此时,有必要回顾大量CMIP5气候模式模拟气候变化的效果,以便展望CMIP6的地球系统模式的可能发展前景。目前IPCC第六次评估报告正在启动和进行中,其中所用的主要工具是CMIP6的地球系统模式。