Parametrizations of global thermal emissions for simple climate models

 A new, observationally based, parametrization of the thermal emissions by the Earth-atmosphere system for use in simple climate models is presented and discussed. The parametrization, allowing for the modelling of outgoing longwave fluxes in both clear and cloudy sky conditions, introduces temperature-dependent emissivities in a grey-body formulation, whose expressions are based on data from by the International Satellite Cloud Climatology Program (ISCCP). The proposed parametrization is compared to traditional ones and is used in a simple energy-balance model. It is shown how, unlike the traditional parametrizations, the proposed one correctly reproduces the runaway greenhouse phenomenon, i.e. the impossibility of an equilibrium for values of the solar constant larger than a critical one, which is known to occur in more complex radiative-convective models. It is also seen that the new parametrization induces the existence of a varying number of equilibria, depending on the value of the solar forcing, in agreement with previous studies. Further, the new parametrization is used to explore the effects of clouds on the Earth-atmosphere energy balance. It is found that for certain values of the model parameters the radiative effect of clouds might be either net cooling or net warming, depending on the magnitude of the solar forcing. Received: 12 January 1998 / Accepted: 23 July 1998     

An AOGCM simulation of the climate response to a volcanic super-eruption

Volcanic ‘super-eruptions’ have been suggested to have significantly influenced the Earth’s climate, perhaps causing glaciations and impacting on the human population. Climatic changes following a hypothetical ‘super-eruption’ are simulated using a coupled atmosphere ocean general circulation model, incorporating scaled volcanic stratospheric aerosols. Assumptions are made about the stratospheric sulphate aerosol loading, size distribution, lifetime, chemical make up and spatial distribution. As this study is concentrating on the physical climatological impacts over long timescales, microphysics and chemical interactive processes are not simulated. Near-surface temperatures fall by as much as 10 K globally for a few months and a considerable deviation from normal temperatures continues for several decades. A warming pattern is evident over northern land masses during the winter due to increased longwave forcing and a positive AO mode. The overturning rate of the North Atlantic thermohaline circulation doubles in intensity. Snow and ice increases in extent to a maximum coverage of 35% of the Earth. Despite these and other impacts longer term climatic changes that could lead to a transition to a glaciation do not occur, for present day boundary conditions and one possible plausible aerosol loading.     

A simple parameterization of sub-grid scale open water for climate models

The effects of small fractions ( < 30%) of open water covering a grid element are currently neglected even in atmospheric general circulation models (AGCMs) which incorporate complex land surface parameterization schemes. Here, a method for simulating sub-grid scale open water is proposed which permits any existing land surface model to be modified to account for open water. This new parameterization is tested as an addition to an advanced land surface scheme and, as expected, is shown to produce general increases in the surface latent heat flux at the expense of the surface sensible heat flux. Small changes in temperature are associated with this change in the partitioning of available energy which is driven by an increase in the wetness of the grid element. The sensitivity of the land surface to increasing amounts of open water is dependent upon the type of vegetation represented. Dense vegetation (with a high leaf area index) is shown to complicate the apparently simple model sensitivity and indicates that more advanced methods of incorporating open water into AGCMs need to be considered and compared against the parameterization suggested here. However, the sensitivity of one land surface model to incorporating open water is large enough to warrant consideration of its incorporation into climate models.     

Communicating global climate change using simple indices: an update

Previous studies have shown that there are several indices of global-scale temperature variations, in addition to global-mean surface air temperature, that are useful for distinguishing natural internal climate variations from anthropogenic climate change. Appropriately defined, such indices have the ability to capture spatio-temporal information in a similar manner to optimal fingerprints of climate change. These indices include the contrast between the average temperatures over land and over oceans, the Northern Hemisphere meridional temperature gradient, the temperature contrast between the Northern and Southern Hemisphere and the magnitude of the annual cycle of average temperatures over land. They contain information independent of the global-mean temperature for internal climate variations at decadal time scales and represent different aspects of the climate system, yet they show common responses to anthropogenic climate change. In addition, the ratio of average temperature changes over land to those over the oceans should be nearly constant for transient climate change. Hence, supplementing analysis of global-mean surface temperature with analyses of these indices can strengthen results of attribution studies of causes of observed climate variations. In this study, we extend the previous work by including the last 10 years of observational data and the CMIP3 climate model simulations analysed for the IPCC AR4. We show that observed changes in these indices over the last 10 years provide increased evidence of an anthropogenic influence on climate. We also show the usefulness of these indices for evaluating the performance of climate models in simulating large-scale variability of surface temperature.     

A simple hydrologic framework for simulating wetlands in climate and earth system models

Wetlands are ecosystems of important functions in the earth??s climate system. Through relatively high evapotranspiration, they affect surface water and energy exchange with the atmosphere directly influencing the physical climate. Through CH₄, CO₂ and N₂O fluxes, they regulate the biogeochemical cycles, indirectly influencing the physical climate. However, current models do not explicitly include the water table, present under all large and stable wetlands; model wetlands are identified as flat land with wet soil resulting from precipitation events. That is, the wetlands are only ??wetted?? from above but not from below by the high water table. Furthermore, without the knowledge of the water table position, estimates of CH₄ and other gases (e.g., CO₂ and N₂O) are poorly constrained. We present a simple hydrologic framework for simulating wetlands based on water table depth. A synthesis of hydrologic controls on wetlands highlights the key role that groundwater plays. It directly feeds wetlands, supports surface-water fed wetlands by maintaining a saturated substrate, and links land drainage to sea level by impeding drainage in lowlands. Forced by routine climate model output (precipitation?Cevapotranspiration-surface runoff), land topography, and sea level, we simulate the present-day water table in North America at the 1?km scale. We validate the simulation with water table observations and compare regions of shallow water table to mapped wetlands. Our results show that the framework captures the salient features of wetland distribution and extent at regional and continental scales, a direct result of large-scale groundwater convergence that nourishes the lowlands even in arid climates. The low requirement of forcing and computation make the framework easy to adopt in climate and earth system models for simulating wetland responses to climate and sea level change for the present, paleo reconstructions, and future projections.     

Comparison of the last interglacial climate simulated by a coupled global model of intermediate complexity and an AOGCM

The climate at the Last Interglacial Maximum (125 000 years before present) is investigated with the atmosphere-ocean general circulation model ECHAM-1/LSG and with the climate system model of intermediate complexity CLIMBER-2. Comparison of the results of the two models reveals broad agreement in most large-scale features, but also some discrepancies. The fast turnaround time of CLIMBER-2 permits one to perform a number of sensitivity experiments to (1) investigate the possible reasons for these differences, in particular the impact of different freshwater fluxes to the ocean, (2) analyze the sensitivity of the results to changes in the definition of the modern reference run concerning CO₂ levels (preindustrial versus “present”), and (3) estimate the role of vegetation in the changed climate. Interactive vegetation turns out to be capable of modifying the initial climate signals significantly, leading especially to warmer winters in large parts of the Northern Hemisphere, as indicated by various paleodata. Differences due to changes in the atmospheric CO₂ content and due to interactive vegetation are shown to be at least of the same order of magnitude as differences between the two completely different models, demonstrating the importance of careful experimental design.     

Climate-related uncertainties in projections of the twenty-first century terrestrial carbon budget: off-line model experiments using IPCC greenhouse-gas scenarios and AOGCM climate projections

A terrestrial ecosystem model (Sim-CYCLE) was driven by multiple climate projections to investigate uncertainties in predicting the interactions between global environmental change and the terrestrial carbon cycle. Sim-CYCLE has a spatial resolution of 0.5°, and mechanistically evaluates photosynthetic and respiratory CO₂ exchange. Six scenarios for atmospheric-CO₂ concentrations in the twenty-first century, proposed by the Intergovernmental Panel on Climate Change, were considered. For each scenario, climate projections by a coupled atmosphere–ocean general circulation model (AOGCM) were used to assess the uncertainty due to socio-economic predictions. Under a single CO₂ scenario, climate projections with seven AOGCMs were used to investigate the uncertainty stemming from uncertainty in the climate simulations. Increases in global photosynthesis and carbon storage differed considerably among scenarios, ranging from 23 to 37% and from 24 to 81 Pg C, respectively. Among the AOGCM projections, increases ranged from 26 to 33% and from 48 to 289 Pg C, respectively. There were regional heterogeneities in both climatic change and carbon budget response, and different carbon-cycle components often responded differently to a given environmental change. Photosynthetic CO₂ fixation was more sensitive to atmospheric CO₂, whereas soil carbon storage was more sensitive to temperature. Consequently, uncertainties in the CO₂ scenarios and climatic projections may create additional uncertainties in projecting atmospheric-CO₂ concentrations and climates through the interactive feedbacks between the atmosphere and the terrestrial ecosystem.     

不同分辨率下全球气候模式的集合结果检验

利用参与IPCC AR4的25个全球气候模式的模拟结果与观测资料,通过分类集合检验气候模式对东亚大陆气温和降水的模拟能力.结果表明:(1)高、中、低分辨率以及所有模式的集合(分别记为Ⅰ、Ⅱ、Ⅲ、Ⅳ类)结果均能较好地再现1901-1999年温度的增温趋势,其中Ⅱ类的线性趋势与观测值最为接近,但年际变率均小于观测值;(2)Ⅰ、Ⅱ、Ⅲ、Ⅳ类均能很好地模拟出气候态温度的月变化特征以及空间分布型,但模拟值偏小,其中Ⅰ类的冷偏差最小;(3)对于降水的时间演变特征,模拟效果明显偏差、不确定性显著;(4)4种集合方式均能较好模拟出气候态降水的空间分布型,但模拟值普遍偏高,其中Ⅰ类的误差最小.可见,在温度和降水的时间变化方面,高分辨率的集合结果并没有体现出相应的优势,但在空间分布上模拟效果明显要好于其他3种方式.     

Examination of multi-model ensemble seasonal prediction methods using a simple climate system

A simple climate model was designed as a proxy for the real climate system, and a number of prediction models were generated by slightly perturbing the physical parameters of the simple model. A set of long (240 years) historical hindcast predictions were performed with various prediction models, which are used to examine various issues of multi-model ensemble seasonal prediction, such as the best ways of blending multi-models and the selection of models. Based on these results, we suggest a feasible way of maximizing the benefit of using multi models in seasonal prediction. In particular, three types of multi-model ensemble prediction systems, i.e., the simple composite, superensemble, and the composite after statistically correcting individual predictions (corrected composite), are examined and compared to each other. The superensemble has more of an overfitting problem than the others, especially for the case of small training samples and/or weak external forcing, and the corrected composite produces the best prediction skill among the multi-model systems.     

Validation of parametrisations for the meridional energy and moisture transport used in simple climate models

Parametrisations of meridional energy and moisture transport used in zonally averaged climate models are validated using reanalysis data and results from a doubling CO₂-experiment from a general circulation model. Global meridional fluxes of moisture and sensible heat are calculated by integrating surface and top-of-the-atmosphere vertical fluxes from one pole to the other. The parametrisations include an eddy-diffusion term, representing down-gradient transport of specific humidity and temperature due to the transient atmospheric eddies at mid- and high latitudes, and simple representations of the mean meridional circulation. Qualitative and quantitative agreement between the increased hydrological cycle in the 2×CO₂-run from the GCM and the parametrisation is found. The performance for the sensible heat flux shows larger differences to the GCM results, particularly at low latitudes. Seasonal variations of the moisture and sensible heat transport are well captured by parametrisations including the influence of the mean meridional circulation. Interannual variability cannot be simulated. An examination of the parametrisations on different spatial scales suggests that they should not be used for small scales. Furthermore, two closures for the zonal distribution of precipitation were examined. They are used in zonally averaged atmosphere models coupled to an ocean model with different ocean basins at one latitudinal belt. An assessment of both the reanalysis data and the GCM results shows that both closures exhibit very similar behaviour and are valid in the long-term mean and seasonal cycle. Interannual variability is not captured well. They become invalid for spatial scales smaller than 10^∘. Received: 30 November 1998 / Accepted: 4 July 1999     

Probabilistic prediction of Indian summer monsoon rainfall using global climate models

Probabilistic seasonal predictions of rainfall that incorporate proper uncertainties are essential for climate risk management. In this study, three different multi-model ensemble (MME) approaches are used to generate probabilistic seasonal hindcasts of the Indian summer monsoon rainfall based on a set of eight global climate models for the 1982–2009 period. The three MME approaches differ in their calculation of spread of the forecast distribution, treated as a Gaussian, while all three use the simple multi-model subdivision average to define the mean of the forecast distribution. The first two approaches use the within-ensemble spread and error residuals of ensemble mean hindcasts, respectively, to compute the variance of the forecast distribution. The third approach makes use of the correlation between the ensemble mean hindcasts and the observations to define the spread using a signal-to-noise ratio. Hindcasts are verified against high-resolution gridded rainfall data from India Meteorological Department in terms of meteorological subdivision spatial averages. The use of correlation for calculating the spread provides better skill than the other two methods in terms of rank probability skill score. In order to further improve the skill, an additional method has been used to generate multi-model probabilistic predictions based on simple averaging of tercile category probabilities from individual models. It is also noted that when such a method is used, skill of probabilistic forecasts is improved as compared with using the multi-model ensemble mean to define the mean of the forecast distribution and then probabilities are estimated. However, skill of the probabilistic predictions of the Indian monsoon rainfall is too low.     

Simulation of glacial-interglacial cycles by simple relaxation models: consistency with observational results

The model of Paillard and Parrenin (Earth Planet Sci Lett 227:263–271, 2004) was modified to obtain a closer fit to δ¹⁸O and CO₂ time series for the last 800 kyr. The model performance can be improved if its CO₂ sensitivity to I₆₅ insolation is eliminated and if different response times are assumed for ablation/accumulation of ice. Correlations between simulated and experimental time series for CO₂ and ice volume V increase from 0.59 and 0.63 to 0.79 and 0.88, respectively. According to these models, terminations are produced by I₆₅ amplification through CO₂-T and T-CO₂ feedbacks, in synergy with an extra CO₂ contribution from the deep ocean. This contribution is strongly dependent on ice-sheet extent and ice volume (or alternatively, CO₂ concentration, which is a good proxy of Antarctic temperature) but is insensitive to Southern Ocean (SO) insolation on 21 February (I₆₀). Change of deep SO state may be the “order parameter” for nonlinear deglacial changes. According to these models, 100 kyr periodicity of glacial cycles arises from the characteristic time of Antarctic ice sheet advance to the continental slope.     

Variability and trends of sub-continental scale surface climate in the twentieth century. Part II: AOGCM simulations

This work presents an analysis of simulated temperature and precipitation variability and trends throughout the twentieth century over 22 land regions of sub-continental scale in the HADCM3 and HADCM2 (two realizations) coupled models. Regional temperature biases in the HADCM3 and HADCM2 are mostly in the range of -5 K to +3 K for the seasonal averages and -3 K to +2 K for the annual average. Seasonal precipitation biases are mostly in the range of -50% to 75% of present day precipitation, with a tendency in both models to overpredict cold season precipitation. Except for cold season temperature in mid- and high-latitude Northern Hemisphere regions, the average climatology of the HADCM2 and HADCM3 is of comparable quality despite the lack of an ocean flux adjustment in the HADCM3. Both models show warming trends of magnitude in line with observations, although the observed inter-regional patterns of warming trend are not well reproduced. Measures of temperature and precipitation interannual to interdecadal variability in the models are in general agreement with observations except for Northern Hemisphere summer temperature variability, which is overestimated. The models somewhat underestimate the inter-decadal variations in interannual variability measures observed during the century and overestimate the range of anomalies. Both models tend to overpredict the occurrences of short persistences (1-3 years) and underpredict the occurrence and maximum length of long persistences (greater than three years), which is an indication of a deficiency in the simulation of long-lived anomaly regimes. Compared to observations, the models produce a higher magnitude of temporal anomaly correlation across regions and correlation between temperature and precipitation anomalies for a given region. This suggests that local processes that may be effective in decoupling the observed regional anomalies are not captured well. Overall, the variability measures in the HADCM2 and HADCM3 are of similar quality, indicating that the use of a flux correction in the HADCM2 does not strongly affect the regional variability characteristics of the model.     

Agricultural drought in a future climate: results from 15 global climate models participating in the IPCC 4th assessment

This study examines the impact of greenhouse gas warming on soil moisture based on predictions of 15 global climate models by comparing the after-stabilization climate in the SRESA1b experiment with the pre-industrial control climate. The models are consistent in predicting summer dryness and winter wetness in only part of the northern middle and high latitudes. Slightly over half of the models predict year-round wetness in central Eurasia and/or year-round dryness in Siberia and mid-latitude Northeast Asia. One explanation is offered that relates such lack of seasonality to the carryover effect of soil moisture storage from season to season. In the tropics and subtropics, a decrease of soil moisture is the dominant response. The models are especially consistent in predicting drier soil over the southwest North America, Central America, the Mediterranean, Australia, and the South Africa in all seasons, and over much of the Amazon and West Africa in the June–July–August (JJA) season and the Asian monsoon region in the December–January–February (DJF) season. Since the only major areas of future wetness predicted with a high level of model consistency are part of the northern middle and high latitudes during the non-growing season, it is suggested that greenhouse gas warming will cause a worldwide agricultural drought. Over regions where there is considerable consistency among the analyzed models in predicting the sign of soil moisture changes, there is a wide range of magnitudes of the soil moisture response, indicating a high degree of model dependency in terrestrial hydrological sensitivity. A major part of the inter-model differences in the sensitivity of soil moisture response are attributable to differences in land surface parameterization.     

Uncertainties in solar radiation assessment in the United States using climate models

Climate Dynamics - Solar energy is abundant and offers significant potential for future climate change mitigation. This study investigates the impacts of climate change on surface solar radiation...     

Performance of ENSEMBLES regional climate models over Central Europe using various metrics

We show the evaluation of ENSEMBLES regional climate models (RCMs) driven by reanalysis ERA40 over a region centered at the Czech Republic. Attention is paid especially to the model ALADIN-CLIMATE/CZ, being used as the basis of the new climate change scenarios simulation for the Czech Republic. The validation criteria used here are based on monthly or seasonal mean air temperature and precipitation. We concentrate not only on spatiotemporal mean values but also on temporal standard deviation, inter-annual variability, the mean annual cycle, and the skill of the models to represent the observed spatial patterns of these quantities. Model ALADIN-CLIMATE/CZ performs quite well in comparison to the other RCMs; we find its performance satisfactory for further use for impact studies. However, it is also shown that the results of evaluation of the RCMs’ skill in simulating observed climate strongly depend on the criteria incorporated for the evaluation.     

Moisture-transfer coefficient for climate models

Recent parameterizations of the moisture-transfer coefficient from measurements in the field and from tuning of the ECMWF model are reviewed. A formula for the neutral transfer coefficient varying continuously with the wind velocity is proposed for climate models.     

Uncertainties in regional climate change prediction: a regional analysis of ensemble simulations with the HADCM2 coupled AOGCM

We analyze ensembles (four realizations) of historical and future climate transient experiments carried out with the coupled atmosphere-ocean general circulation model (AOGCM) of the Hadley Centre for Climate Prediction and Research, version HADCM2, with four scenarios of greenhouse gas (GHG) and sulfate forcing. The analysis focuses on the regional scale, and in particular on 21 regions covering all land areas in the World (except Antarctica). We examine seasonally averaged surface air temperature and precipitation for the historical period of 1961–1990 and the future climate period of 2046–2075. Compared to previous AOGCM simulations, the HADCM2 model shows a good performance in reproducing observed regional averages of summer and winter temperature and precipitation. The model, however, does not reproduce well observed interannual variability. We find that the uncertainty in regional climate change predictions associated with the spread of different realizations in an ensemble (i.e. the uncertainty related to the internal model variability) is relatively low for all scenarios and regions. In particular, this uncertainty is lower than the uncertainty due to inter-scenario variability and (by comparison with previous regional analyses of AOGCMs) with inter-model variability. The climate biases and sensitivities found for different realizations of the same ensemble were similar to the corresponding ensemble averages and the averages associated with individual realizations of the same ensemble did not differ from each other at the 5% confidence level in the vast majority of cases. These results indicate that a relatively small number of realizations (3 or 4) is sufficient to characterize an AOGCM transient climate change prediction at the regional scale. Received: 12 January 1998 / Accepted: 7 July 1999