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Climate model emulators have a crucial role in assessing warming levels of many emission scenarios from probabilistic climate projections, based on new insights into Earth system response to CO2 and other forcing factors. This article describes one such tool, MCE, from model formulation to application examples associated with a recent model intercomparison study. The MCE is based on impulse response functions and parameterized physics of effective radiative forcing and carbon uptake over ocean and land. Perturbed model parameters for probabilistic projections are generated from statistical models and constrained with a Metropolis-Hastings independence sampler. A part of the model parameters associated with CO2-induced warming have a covariance structure, as diagnosed from complex climate models of the Coupled Model Intercomparison Project (CMIP). Although perturbed ensembles can cover the diversity of CMIP models effectively, they need to be constrained toward substantially lower climate sensitivity for the resulting historical warming to agree with the observed trends over recent decades. The model's simplicity and resulting successful calibration imply that a method with less complicated structures and fewer control parameters offers advantages when building reasonable perturbed ensembles in a transparent way. Experimental results for future scenarios show distinct differences between CMIP- and observation-consistent ensembles, suggesting that perturbed ensembles for scenario assessment need to be properly constrained with new insights into forced response over historical periods.

The role of advanced end-use technologies has been investigated in multiple series of scenarios using an integrated assessment model BET-GLUE, which comprises an energy-economic module (BET) and a bioenergy-land-use module (GLUE). The scenarios consider different technology assumptions on the availability of bioenergy with carbon capture and storage (BECCS) and end-use efficiencies featuring electrification under a wide range of carbon price trajectories, which start at 1–690 USD/tCO2 in 2030, increase at 4.5%/year, and level off in 2100. This scenario design allows close examination of energy, economic, and environmental implications of different levels of policy stringency and carbon budgets. While improving end-use efficiencies consistently decrease policy costs for a wide range of carbon budgets, the value of BECCS availability in terms of cost reduction is crucial only in a limited range toward lower budgets. Constraints on BECCS, including those indirectly imposed by the limited bioenergy supply, also tend to narrow the lower range of attainable budget levels, indicating technological and economic challenges, although they may have an impact on reducing the total budget including land-use emissions. Overall, the advanced end-use efficiency has a significant effect on the required level of policy stringency for a given climate goal, so that it can compensate for the biomass constraints.

One of the key applications of simple climate models is probabilistic climate projections to assess a variety of emission scenarios in terms of their compatibility with global warming mitigation goals. The second phase of the Reduced Complexity Model Intercomparison Project (RCMIP) compares nine participating models for their probabilistic projection methods through scenario experiments, focusing on consistency with given constraints for climate indicators including radiative forcing, carbon budget, warming trends, and climate sensitivity. The MCE is one of the nine models, recently developed by the author, and has produced results that well match the ranges of the constraints. The model is based on impulse response functions and parameterized physics of effective radiative forcing and carbon uptake over ocean and land. Perturbed model parameters are generated from statistical models and constrained with a Metropolis-Hastings independence sampler. A parameter subset associated with CO2-induced warming is assured to have a covariance structure as diagnosed from complex climate models of the Coupled Model Intercomparison Project (CMIP). The model's simplicity and the successful results imply that a method with less complicated structures and fewer control parameters has an advantage when building reasonable perturbed ensembles in a transparent way despite less capacity to emulate detailed Earth system components. Experimental results for future scenarios show that the climate sensitivity of CMIP models is overestimated overall, suggesting that probabilistic climate projections need to be constrained with observed warming trends.

Bioenergy is expected to play an important role in long-run climate change mitigation strategies as highlighted by many integrated assessment model (IAM) scenarios. These scenarios, however, also show a very wide range of results, with uncertainty about bioenergy conversion technology deployment and biomass feedstock supply. To date, the underlying differences in model assumptions and parameters for the range of results have not been conveyed. Here we explore the models and results of the 33rd study of the Stanford Energy Modeling Forum to elucidate and explore bioenergy technology specifications and constraints that underlie projected bioenergy outcomes. We first develop and report consistent bioenergy technology characterizations and modeling details. We evaluate the bioenergy technology specifications through a series of analyses—comparison with the literature, model intercomparison, and an assessment of bioenergy technology projected deployments. We find that bioenergy technology coverage and characterization varies substantially across models, spanning different conversion routes, carbon capture and storage opportunities, and technology deployment constraints. Still, the range of technology specification assumptions is largely in line with bottom-up engineering estimates. We then find that variation in bioenergy deployment across models cannot be understood from technology costs alone. Important additional determinants include biomass feedstock costs, the availability and costs of alternative mitigation options in and across end-uses, the availability of carbon dioxide removal possibilities, the speed with which large scale changes in the makeup of energy conversion facilities and integration can take place, and the relative demand for different energy services.

Here we present an update to the FaIR model for use in probabilistic future climate and scenario exploration, integrated assessment, policy analysis and education. In this update we have focussed on identifying a minimum level of structural complexity in the model. The result is a set of six equations, five of which correspond to the standard Impulse Response model used for greenhouse gas (GHG) metric calculations in the IPCC's fifth assessment report, plus one additional physically-motivated additional equation to represent state-dependent feedbacks on the response timescales of each greenhouse gas cycle. This additional equation is necessary to reproduce non-linearities in the carbon cycle apparent in both Earth System Models and observations. These six equations are transparent and sufficiently simple that the model is able to be written in standard tabular data analysis packages, such as Excel; increasing the potential user base considerably. However, we demonstrate that the equations are flexible enough to be tuned to emulate the behaviour of several key processes within more complex models from CMIP6. The model is exceptionally quick to run, making it ideal for integrating large probabilistic ensembles. We apply a constraint based on the current estimates of the global warming trend to a one million member ensemble, using the constrained ensemble to make scenario dependent projections and infer ranges for properties of the climate system. Through these analyses, we reaffirm that simple climate models (unlike more complex models) are not themselves intrinsically biased hot or cold: it is the choice of parameters and how those are selected that determines the model response, something that appears to have been misunderstood in the past. This updated FaIR model is able to reproduce the global climate system response to GHG and aerosol emissions with sufficient accuracy to be useful in a wide range of applications; and therefore could be used as a lowest common denominator model to provide consistency in different contexts. The fact that FaIR can be written down in just six equations greatly aids transparency in such contexts.

Here we present a Generalised Impulse Response (GIR) model for use in probabilistic future climate and scenario exploration, integrated assessment, policy analysis and teaching. This model is based on a set of only six equations, which correspond to the standard Impulse Response model used for greenhouse gas metric calculations by the IPCC, plus one physically-motivated additional equation to represent state-dependent feedbacks on the response timescales of each greenhouse gas cycle. These six equations are simple and transparent enough to be easily understood and implemented in other models without reliance on the original source code, but flexible enough to reproduce observed well-mixed greenhouse gas (GHG) concentrations and atmospheric lifetimes, best-estimate effective radiative forcing, and temperature response. We describe the assumptions and methods used in selecting the default parameters, but emphasize that other methods would be equally valid: our focus here is on identifying a minimum level of structural complexity. The tunable nature of the model lends it to use as a fully transparent emulator of complex Earth System Models, such as those participating in CMIP6, while also reproducing the behaviour of other simple climate models. We argue that this GIR model is adequate to reproduce the global temperature response to global emissions and effective radiative forcing, and that it should be used as a lowest-common denominator to provide consistency and continuity between different climate assessments. The model design is such that it can be written in tabular data analysis software, such as Excel, increasing the potential user base considerably.

Here we present results from the first phase of the Reduced Complexity Model Intercomparison Project (RCMIP). RCMIP is a systematic examination of reduced complexity climate models (RCMs), which are used to complement and extend the insights from more complex Earth System Models (ESMs), in particular those participating in the Sixth Coupled Model Intercomparison Project (CMIP6). In Phase 1 of RCMIP, with 14 participating models namely ACC2, AR5IR (2 and 3 box versions), CICERO-SCM, ESCIMO, FaIR, GIR, GREB, Hector, Held et al. two layer model, MAGICC, MCE, OSCAR and WASP, we highlight the structural differences across various RCMs and show that RCMs are capable of reproducing global-mean surface air temperature (GSAT) changes of ESMs and historical observations. We find that some RCMs are capable of emulating the GSAT response of CMIP6 models to within a root-mean square error of 0.2 °C (of the same order of magnitude as ESM internal variability) over a range of scenarios. Running the same model configurations for both RCP and SSP scenarios, we see that the SSPs exhibit higher effective radiative forcing throughout the second half of the 21st Century. Comparing our results to the difference between CMIP5 and CMIP6 output, we find that the change in scenario explains approximately 46 % of the increase in higher end projected warming between CMIP5 and CMIP6. This suggests that changes in ESMs from CMIP5 to CMIP6 explain the rest of the increase, hence the higher climate sensitivities of available CMIP6 models may not be having as large an impact on GSAT projections as first anticipated. A second phase of RCMIP will complement RCMIP Phase 1 by exploring probabilistic results and emulation in more depth to provide results available for the IPCC's Sixth Assessment Report author teams.

The role of advanced end-use technologies has been investigated in multiple series of scenarios using an integrated assessment model BET-GLUE, which comprises an energy-economic module (BET) and a bioenergy-land-use module (GLUE). The scenarios consider different technology assumptions on the availability of bioenergy with carbon capture and storage (BECCS) and end-use efficiencies featuring electrification under a wide range of carbon price trajectories, which start at 1–690 USD/tCO2 in 2030, increase at 4.5%/year, and level off in 2100. This scenario design allows close examination of energy, economic, and environmental implications of different levels of policy stringency and carbon budgets. While improving end-use efficiencies consistently decrease policy costs for a wide range of carbon budgets, the value of BECCS availability in terms of cost reduction is crucial only in a limited range toward lower budgets. Constraints on BECCS, including those indirectly imposed by the limited bioenergy supply, also tend to narrow the lower range of attainable budget levels, indicating technological and economic challenges, although they may have an impact on reducing the total budget including land-use emissions. Overall, the advanced end-use efficiency has a significant effect on the required level of policy stringency for a given climate goal, so that it can compensate for the biomass constraints.

The present study establishes a general formulation to represent the behavior and variation of an ensemble of complex climate models in terms of the global mean surface temperature response to atmospheric CO2 increase. The response parameters of this formulation provide a set of metrics that extends the conventional concept of climate sensitivity and quantifies transient temperature changes with sufficient simplicity and transparency to serve studies on climate change mitigation. Two commonly used metrics for transient and equilibrium climate sensitivity are analytically derived from the formulation, such that conventional estimates of equilibrium climate sensitivity based on standard numerical experiments for quadrupling CO2 increase are properly scaled down to the reference level of doubling CO2. The characteristics and variations of a specific ensemble of complex climate models can be simulated with a statistical model built using the principal components (PCs) of the response parameters. This approach is applied to the probabilistic assessment of temperature changes as well as to the diagnosis of the base ensemble. In current complex climate models, the ratio of transient-to-equilibrium sensitivity decreases with an increase of equilibrium sensitivity, as identified in variations associated with two specific PCs that characterize coherence between transient temperature response and properties of heat uptake by the ocean.

The degree of reversibility in the behavior of El Niño–Southern Oscillation (ENSO) in response to CO2 concentration changes is examined using the air–sea coupled Community Climate System Model, version 4. The model is run under simple and idealized climate change scenarios where atmospheric CO2 concentration is gradually increased to four times the preindustrial level and then gradually reduced at a similar rate along this trajectory. While the simulated ENSO amplitude is reduced when CO2 concentration increases, the amplitude is enhanced when CO2 decreases. This enhancement occurs with a relative El Niño–like warming of mean sea surface temperature. Most of the El Niño–like warming is attributed to a weakened cold water upwelling in the equatorial eastern Pacific. This weakened cooling is a result of a vertical ocean temperature gradient that is reduced in relation to a lag between subsurface temperature warming and surface temperature change. From these findings, a statistical parameterization expressing ENSO variability is developed in terms of the global-mean surface temperature and the vertical gradient of the global-mean temperature in the upper ocean. Plugging this parameterization into a statistical model of ENSO and combining the latter with a statistical spatial pattern of the ENSO-related temperature anomaly enables the reconstruction of surface temperature variability over North America. This approach can be useful when one assesses future climate changes under various CO2 emission scenarios using very simple models whose outputs are limited to a small number of global-mean variables.

In this study, we develop a new integrated assessment model called the BET model (Basic Energy systems, Economy, Environment, and End-use Technology Model). It is a multi-regional, global model based on Ramsey’s optimal growth theory and includes not only traditional end-use technologies but also advanced end-use technologies such as heat-pump water heaters and electric vehicles. Using the BET model, we conduct simulations and obtain the following results. (1) Advanced end-use technologies have an important role in containing carbon prices as well as GDP losses when GHG (greenhouse gas) constraints are stringent. (2) Electrification based on energy services progresses rapidly in scenarios with stringent GHG constraints. This is because electricity can be supplied by various methods of non-fossil power generation, and advanced end-use technologies can drastically improve energy-to-service efficiencies. The BET’s results indicate the importance of analyses that systematically combine environmental constraints, end-use technologies, supply energy technologies, and economic development.

Following Part 1, a comparison of CO(2)-emissions pathways between "zero-emissions stabilization (Z-stabilization)" and traditional stabilization is made under more realistic conditions that take into account the radiative forcings of other greenhouse gases and aerosols with the constraint that the temperature rise must not exceed 2 °C above the preindustrial level. It is shown that the findings in Part 1 on the merits of Z-stabilization hold under the more realistic conditions. The results clarify the scientific basis of the policy claim of 50% reduction of the world CO(2) emissions by 2050. Since the highest greenhouse gas (GHG) concentration and temperature occur only temporarily in Z-stabilization pathways, we may slightly relax the upper limit of the temperature rise. We can then search for a scenario with larger emissions in the 21st century; such a scenario may have potential for practical application. It is suggested that in this Z-stabilization pathway, larger emissions in the near future may be important from a socioeconomic viewpoint.

The concept of "stabilization" of atmospheric CO(2) concentration is re-examined in connection with climate-change mitigation strategies. A new "zero-emissions stabilization (Z-stabilization)" is proposed, where CO(2) emissions are reduced to zero at some time and thereafter the concentration is decreased by natural removal processes, eventually reaching an equilibrated stable state. Simplified climate experiments show that, under Z-stabilization, considerably larger emissions are permissible in the near future compared with traditional stabilization, with the same constraint on temperature rise. Over longer time scales, the concentration and temperature decrease close to their equilibrium values, much lower than those under traditional stabilization. The smaller temperature rise at final state is essential to avoid longer-term risk of sea level rise, a significant concern under traditional stabilization. Because of these advantages a Z-stabilization pathway can be a candidate of practical mitigation strategies as treated in Part 2.

A web application for a simple climate model - SEEPLUS (a Simple climate model to Examine Emission Pathways Leading to Updated Scenarios) - has been developed. SEEPLUS consists of carbon-cycle and climate-change modules, through which it provides the information infrastructure required to perform climate-change experiments, even on a millennial-timescale. The main objective of this application is to share the latest scientific knowledge acquired from climate modeling studies among the different stakeholders involved in climate-change issues. Both the carbon-cycle and climate-change modules employ impulse response functions (IRFs) for their key processes, thereby enabling the model to integrate the outcome from an ensemble of complex climate models. The current IRF parameters and forcing manipulation are basically consistent with, or within an uncertainty range of, the understanding of certain key aspects such as the equilibrium climate sensitivity and ocean CO2 uptake data documented in representative literature. The carbon-cycle module enables inverse calculation to determine the emission pathway required in order to attain a given concentration pathway, thereby providing a flexible way to compare the module with more advanced modeling studies. The module also enables analytical evaluation of its equilibrium states, thereby facilitating the long-term planning of global warming mitigation.

The intensities of the tropical cyclones (TCs) that approach or make landfall in Japan and the possible changes in these intensities due to global warming have been investigated on the basis of the theoretical model of Holland (1997). This theoretical model calculates the maximum potential intensity (MPI) of a TC as a lower limit of central sea level pressure for a given upper-air temperature profile and underlying sea surface temperature (SST). This study uses the climatology of the JRA-25 long-term reanalysis for the present environment, along with global warming anomalies derived from CMIP3 multi model climate experiments, which are added to the reanalysis climatology to form a warmed environment. A characteristic of the tropical atmosphere is that warming anomalies are amplified toward the upper troposphere, so that TC intensification due to a rise in SST is suppressed to some extent by greater warming aloft. This study evaluates the uncertainty ranges for the MPI changes by considering inter model differences in the warming structure.The MPI-SST relationship in the present climate is consistent with the historical minimum surface pressure in the western North Pacific when the eyewall relative humidity (RH), one of the model’s key parameters, is set at 88%. The average change in the MPI due to a 1°C rise in the SST is -6.7 hPa [-0.6 to -12.0 hPa], where a negative change indicates intensification and the estimated range in brackets reflects the uncertainty in the warming structure. The error of these MPI changes is estimated to be about 15%, assuming that the likely range of the RH parameter is 88 ± 2%. The fractional change in the surface pressure drop is 3.6%, 8.4%, and 19%, in response to a 0.5, 1.0, and 2.0C rise, respectively, in SST, which is basically comparable to existing studies based on observations and numerical simulations. While TC intensification due to global warming tends to be greater for intense TCs at low latitudes, the tendencies at relatively high latitudes near the main islands of Japan suggest that TC development occurs in a broader region over a more prolonged season in a warmed environment.

There is a theory for estimating the maximum potential intensity (MPI) of tropical cyclones (TCs) from large-scale thermodynamic environment. The present study investigates sea surface temperature (SST) and the vertical profile of upper-air temperature simulated using the Community Climate System Model (CCSM3) to assess possible near-future MPI changes focusing on the western North Pacific.The MPI derived from the simulated thermodynamic environment for the present climate is generally reasonable in terms of geographical distribution and its relation to SST. Significant MPI changes in 2010 to 2029 relative to 1980 to 1999 are found in the tropics, where the central pressure fall increases by 5-7 hPa, or 6-8%, on average. Associated thermodynamic changes include SST warming of 0.6-0.7AC and amplification of upper tropospheric warming at a factor of two to three. This greater warming in the upper troposphere is important in suppressing a rapid increase in the MPI. Although the northward expansion of TC development area is marginal, greater thermodynamic change in the middle latitudes suggests an impact on the lifecycle of a TC moving into this region.

A long-term global atmospheric reanalysis, named “Japanese 25-year Reanalysis (JRA-25)” was completed using the Japan Meteorological Agency (JMA) numerical assimilation and forecast system. The analysis covers the period from 1979 to 2004. This is the first long-term reanalysis undertaken in Asia. JMA's latest numerical assimilation system, and specially collected observational data, were used to generate a consistent and high-quality reanalysis dataset designed for climate research and operational monitoring and forecasts. One of the many purposes of JRA-25 is to enhance the analysis to a high quality in the Asian region.Six-hourly data assimilation cycles were performed, producing 6-hourly atmospheric analysis and forecast fields of various physical variables. The global model used in JRA-25 has a spectral resolution of T106 (equivalent to a horizontal grid size of around 120 km) and 40 vertical layers with the top level at 0.4 hPa. In addition to conventional surface and upper air observations, atmospheric motion vector (AMV) wind retrieved from geostationary satellites, brightness temperature from TIROS Operational Vertical Sounder (TOVS), precipitable water retrieved from orbital satellite microwave radiometer radiance and other satellite data are assimilated with three-dimensional variational method (3D-Var). JMA produced daily sea surface temperature (SST), sea ice and three-dimensional ozone profiles for JRA-25. A new quality control method for TOVS data was developed and applied in advance.Many advantages have been found in the JRA-25 reanalysis. Predicted 6-hour global total precipitation distribution and amount are well reproduced both in space and time. The performance of the long time series of the global precipitation is the best among the other reanalyses, with few unrealistic variations from degraded satellite data contaminated by volcanic eruptions. Secondly, JRA-25 is the first reanalysis to assimilate wind profiles around tropical cyclones reconstructed from historical best track information; tropical cyclones were analyzed properly in all the global regions. Additionally, low-level cloud along the subtropical western coast of continents is well simulated and snow depth analysis is also of a good quality. The article also covers material which requires attention when using JRA-25.

From multi-ensembles of climate simulations using the Community Climate System Model version 3, global climate changes have been investigated focusing on long-term responses to stabilized anthropogenic forcings. In addition to the standard forcing scenarios for the current international assessment, an overshoot scenario, where radiative forcings are decreased from one stabilized level to another, is also considered. The globally-averaged annual surface air temperature increases during the twenty-first century by 2.58 and 1.56°C for increased forcings under two future scenarios denoted by A1B and B1, respectively. These changes continue but at much slower rates in later centuries under forcings stabilized at year 2100. The overshoot scenario provides a different pathway to the lower B1 level by way of the greater A1B level. This scenario results in a surface climate similar to that in the B1 scenario within 100 years after the forcing reaches the B1 level. Contrasting to the surface changes, responses in the ocean are significantly delayed. It is estimated from the linear response theory that temperature changes under stabilized forcings to a final equilibrium state in the A1B (B1) scenario are factors of 0.3–0.4, 0.9, and 17 (0.3, 0.6, and 11) to changes during the twenty-first century, respectively, for three ocean layers of the surface to 100, 100–500, and 500 m to the bottom. Although responses in the lower ocean layers imply a nonlinear behavior, the ocean temperatures in the overshoot and B1 scenarios are likely to converge in their final equilibrium states.

The response of the North Atlantic thermohaline circulation to idealized climate forcing of 1% per year compound increase in CO2 is examined in three configurations of the Community Climate System Model version 3 that differ in their component model resolutions. The strength of the Atlantic overturning circulation declines at a rate of 22%–26% of the corresponding control experiment maximum overturning per century in response to the increase in CO2. The mean meridional overturning and its variability on decadal time scales in the control experiments, the rate of decrease in the transient forcing experiments, and the rate of recovery in periods of CO2 stabilization all increase with increasing component model resolution. By examining the changes in ocean surface forcing with increasing CO2 in the framework of the water-mass transformation function, we show that the decline in the overturning is driven by decreasing density of the subpolar North Atlantic due to increasing surface heat fluxes. While there is an intensification of the hydrologic cycle in response to increasing CO2, the net effect of changes in surface freshwater fluxes on those density classes that are involved in deep-water formation is to increase their density; that is, changes in surface freshwater fluxes act to maintain a stronger overturning circulation. The differences in the control experiment overturning strength and the response to increasing CO2 are well predicted by the corresponding differences in the water-mass transformation rate. Reduction of meridional heat transport and enhancement of meridional salt transport from mid- to high latitudes with increasing CO2 also act to strengthen the overturning circulation. Analysis of the trends in an ideal age tracer provides a direct measure of changes in ocean ventilation time scale in response to increasing CO2. In the subpolar North Atlantic south of the Greenland–Scotland ridge system, there is a significant increase in subsurface ages as open-ocean deep convection is diminished and ventilation switches to a predominance of overflow waters. In middle and low latitudes there is a decrease in age within and just below the thermocline in response to a decrease in the upwelling of old deep waters. However, when considering ventilation within isopycnal layers, age increases for layers in and below the thermocline due to the deepening of isopycnals in response to global warming.

The progress and status of a new atmospheric re-analysis, JRA-25 the Japanese 25-year Re-analysis which covers the 26 years from 1979 to 2004, are introduced. Observational data include some newly produced for JRA-25 and advantages and drawbacks of performance are briefly described. JRA-25 has many advantages such as its handling of precipitation amounts, tropical cyclone analysis, and the extent of low-level cloud along western continents that are among the best compared to other re-analyses. The snow analysis is also good and stable. JRA-25 outputs analysis products every 6 hours. Copyright © 2005 Royal Meteorological Society

To investigate the impacts of anthropogenic global warming on tropical cyclone (TC) activity, climate simulations were conducted under the present, and CO2-warmed conditions, using the National Center for Atmospheric Research Community Climate Model version 2. The CO2-warmed condition includes doubled atmospheric CO2 concentration, and about 1°C of tropical sea surface temperature (SST) warming. Simulated TCs were objectively selected from twice daily instantaneous outputs during an eight-year time integration period of each simulation. The changes associated with global warming were examined in terms of the frequency of occurrence, and mean intensity of TCs. The frequency of global TC occurrence remains unchanged in response to the CO2-induced warming. Although the hydrologic cycle is generally enhanced in the warmed climate, increased precipitation does not necessarily make a great impact on TC activity. This unchanged global TC frequency seems to coincide with almost neutral variations in the zonally-averaged moist instability in the tropics. However, there is some uncertainty in the model regarding the treatment of physical processes that control moisture distributions in the middle to lower troposphere. On the regional scale, the CO2-induced changes in TC occurrence were generally not statistically significant. TC intensities were enhanced over warmed SST regions in the western Pacific, which contribute to the significantly increased mean intensity of global TCs.

To investigate the impacts of anthropogenic global warming on tropical cyclone (TC) activity, climate simulations were conducted under the present and CO2-warmed conditions using the National Center for Atmospheric Research Community Climate Model version 2. The CO2-warmed condition includes doubled atmospheric CO2 concentration and about 1°C of tropical sea surface temperature (SST) warming. Simulated TCs were objectively selected from twice daily instantaneous outputs during an eight-year time integration period of each simulation, and the changes associated with global warming were examined in terms of frequency of occurrence and mean intensity.The frequency of global TC occurrence remains unchanged in response to the CO2-induced warming. This fairly constant global TC frequency is in agreement with the almost neutral tendency in the zonally-averaged moist instability in the tropics. On the other hand, regional TC frequencies indicate relatively large tendencies depending on TC basins, including increased tendency in the western North Pacific and decreased tendency in the Western Hemisphere. These regional variations are connected with large-scale circulation and similar to the natural variability of observed TCs. Simulated changes in the mean TC intensity highlight increased tendencies over the warm SST regions in the western Pacific, which contribute to the significantly increased mean intensity of global TCs.

Numerical experiments were conducted with a regional atmospheric model to investigate the suitability of the model for simulation/prediction of tropical cyclones (TCs). The model uses primitive equations and is similar in construction to a regional atmospheric model, called RegCM, which has been used extensively for regional climate modeling. The suitability of the model for TC study was tested in the mode of short-range forecasts to find out how well the model can reproduce TC behaviors with a special attention on their genesis and development. Objective analysis data for typhoons Ed (9018) and Flo (9019) in September 1990 were used as the input data. In addition, observed precipitation (PR) rates estimated from hourly infrared satellite measurements were used. In the prediction experiments, we focused on two aspects: One was to study the impact of data initialization. The other was to examine the sensitivity of forecast depending on the choice of two cumulus parameterizations, Kuo and relaxed Arakawa-Schubert (RAS) schemes. The initialization to the input data was done through the combination of diabatic nonlinear normal mode initialization for dynamical fields and cumulus initialization for moisture. The diabatic heating rate was determined from radiative and condensation heating rates using the input data, including observed PR rates. The initialization of moisture was done through the inversion of cumulus scheme for a specified PR rate. Positive impacts of the initialization on reproducing the behaviors of both typhoons are clearly demonstrated in the use of both cumulus schemes. In particular, the initialization results in successful simulations of initial PR field without spinup problems. Thus, the positive impact is greatest in the case of TC genesis. The reproducibility of PR by the model with RAS scheme is superior, and the RAS scheme appears more suitable than the Kuo scheme for the intensity prediction of developing typhoons. However, the use of the Kuo scheme produces better scores in mean displacement errors.

To generate convective precipitation consistent with observations at the beginning of a forecast with an atmospheric prediction model, the irrotational circulation and moisture fields must be initialized properly for dynamical balance with the input rotational and mass fields. This article describes a diabatic initialization procedure for a tropical cyclone forecast model, including the adjustment of the input moisture field with the use of “observed” precipitation data from satellites. The condensation heating rate estimated from “observed” precipitation data and the radiative heating rate calculated from the input data are used as the diabatic heat source to calculate the balanced irrotational wind field using a diabatic nonlinear normal mode initialization algorithm. The adjustment of the moisture field is performed by the inversion of cumulus parameterization, knowing the input precipitation rate. The inversion methods of three cumulus parameterization schemes are presented. The three cumulus...

The possibility of simulating tropical cyclones (TCs) using the National Center for Atmospheric Research community climate model (CCM2) is explored. Daily outputs from two long-term simulation runs using the standard T42 resolution CCM2 are examined to identify simulated tropical cyclones (STCs) using a search scheme that selects qualified STCs resembling observed TCs. The two simulation cases are a 20-year run driven by climatological sea surface temperatures (SSTs) and a 10-year run, corresponding to the decade from 1979 to 1988, with the same model configuration except for the use of observed SSTs. A composite technique is adopted to reveal the horizontal and vertical structures of well-developed STCs, and a comparison with those of observed TCs is presented. Then, the climatologies of STCs from the two simulation cases are discussed in terms of their genesis, movements, and seasonal and interannual variations through the comparisons with observed TC statistics. Despite obvious shortcomings of the standard CCM2, such as a coarse horizontal resolution, the structure and climatology of STCs identified in both climate runs are in reasonably good agreement with those of observed TCs. The annual STC frequency shows a better agreement with the observed SST run than the climatological SST run, while many other aspects of STCs in the two climate runs are comparable.

[1] A long-term numerical experiment has been conducted using the Whole Atmosphere Community Climate Model (WACCM) to investigate the response of the middle atmosphere to time-varying spectral solar irradiance over multiple 11-year cycles, modeled on the basis of observed 10.7-cm radio flux (F10.7). The model domain covers from the Earth's surface to the lower thermosphere with approximately two-degree horizontal resolution and 66 vertical layers. Sea surface temperatures are prescribed by a climatological annual cycle, and boundary data for chemical compositions are held constant. The experiment does not include spontaneous nor imposed quasi-biennial oscillation. Temperature and ozone differences near the stratopause between solar max and min, typically 0.8 K and 1.6% corresponding to approximately 100 units of F10.7 variation, have general agreement with the current scientific understanding. The model's dynamical responses as an indirect solar effect are substantially weak during winter against evidences from past empirical studies. The indirect solar signal tends to appear when the polar vortex becomes weak. The most striking signal is more frequent stratospheric sudden warmings during solar max in the Northern Hemisphere late winter through early spring, supporting observed tendencies. This modulation has an aspect of the annular mode and results in a major impact on the troposphere in early spring. Such a signal, however, does not appear in the Southern Hemisphere where the model has a westerly bias. There is no marked response in the equatorial lower stratosphere.

Long-term climate experiments up to the year 2300 have been conducted using two full-scale complex Earth system models (ESMs), CESM1(BGC) and MIROC-ESM, for a CO2 emissions reduction pathway, termed Z650, where annual CO2 emissions peak at 11 PgC in 2020, decline by 50% every 30 years, and reach zero in 2160. The results have been examined by focusing on the approximate linear relationship between the temperature increase and cumulative CO2 emissions. Although the temperature increase is nearly proportional to the cumulative CO2 emissions in both models, this relationship does not necessarily provide a robust basis for the restriction of CO2 emissions because it is substantially modulated by non-CO2 forcing. CO2-induced warming, estimated from the atmospheric CO2 concentrations in the models, indicates an approximate compensation of nonlinear changes between fast-mode responses to concentration changes at less than 10 years and slow-mode response at more than 100 years due to the thermal inertia of the ocean. In this estimate, CESM1(BGC) closely approximates a linear trend of 1.7 °C per 1000 PgC, whereas MIROC-ESM shows a deviation toward higher temperatures after the emissions peak, from 1.8 °C to 2.4 °C per 1000 PgC over the range of 400–850 PgC cumulative emissions corresponding to years 2000–2050. The evolution of temperature under zero emissions, 2160–2300, shows a slight decrease of about 0.1 °C per century in CESM1(BGC), but remains almost constant in MIROC-ESM. The fast-mode response toward the equilibrium state decreases with a decrease in the airborne fraction owing to continued CO2 uptake (carbon cycle inertia), whereas the slow-mode response results in more warming owing to continued heat uptake (thermal inertia). Several specific differences are noted between the two models regarding the degree of this compensation and in some key regional aspects associated with sustained warming and long-term climate risks. Overall, elevated temperatures continue for at least a few hundred years under zero emissions.

This chapter deals with the impacts of global warming and climate change on the human society. Infrastructures and socio-economic activities are considered to be sensitive to rises in atmospheric temperature and mean sea level, and changes in rainfall and typhoon caused by global warming. Since Japan has concentrated its population and industries to low-lying coastal plains, the effects of sea-level rise and intensified typhoons and storm surges will be severe. In addition, the effects of global warming spread in a wide area of highly industrialized society, such as human life, traffic systems and industries, through the adverse effects on the supply systems of fresh water and energy. Though the entire picture of such effects has not been studied yet, it was tried to review existing studies to extract as quantitative information as possible. When quantitative information was not available, possible impacts were estimated on the qualitative basis.

Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state‐of‐the‐art knowledge in an internally self‐consistent modeling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain‐specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low‐emissions SSP1‐1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850–1900, using an observationally based historical warming estimate of 0.8°C between 1850–1900 and 1995–2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above preindustrial in the long‐term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections., Key Points Probabilistic global‐mean temperature projections often use reduced complexity climate models (RCMs) because of their low computational costWe evaluate how well RCMs’ probabilistic setups can synthesize knowledge from multiple research domains for policy relevant projectionsNo RCM is able to capture all forcing, warming, heat uptake and carbon cycle metrics we evaluate, however some come close across a range