Bayesian multi-model projection of climate: bias assumptions and interannual variability

Current climate change projections are based on comprehensive multi-model ensembles of global and regional climate simulations. Application of this information to impact studies requires a combined probabilistic estimate taking into account the different models and their performance under current climatic conditions. Here we present a Bayesian statistical model for the distribution of seasonal mean surface temperatures for control and scenario periods. The model combines observational data for the control period with the output of regional climate models (RCMs) driven by different global climate models (GCMs). The proposed Bayesian methodology addresses seasonal mean temperatures and considers both changes in mean temperature and interannual variability. In addition, unlike previous studies, our methodology explicitly considers model biases that are allowed to be time-dependent (i.e. change between control and scenario period). More specifically, the model considers additive and multiplicative model biases for each RCM and introduces two plausible assumptions (“constant bias” and “constant relationship”) about extrapolating the biases from the control to the scenario period. The resulting identifiability problem is resolved by using informative priors for the bias changes. A sensitivity analysis illustrates the role of the informative prior. As an example, we present results for Alpine winter and summer temperatures for control (1961–1990) and scenario periods (2071–2100) under the SRES A2 greenhouse gas scenario. For winter, both bias assumptions yield a comparable mean warming of 3.5–3.6°C. For summer, the two different assumptions have a strong influence on the probabilistic prediction of mean warming, which amounts to 5.4°C and 3.4°C for the “constant bias” and “constant relation” assumptions, respectively. Analysis shows that the underlying reason for this large uncertainty is due to the overestimation of summer interannual variability in all models considered. Our results show the necessity to consider potential bias changes when projecting climate under an emission scenario. Further work is needed to determine how bias information can be exploited for this task.     

Estimating present climate in a warming world: a model-based approach

Weather services base their operational definitions of “present” climate on past observations, using a 30-year normal period such as 1961–1990 or 1971–2000. In a world with ongoing global warming, however, past data give a biased estimate of the actual present-day climate. Here we propose to correct this bias with a “delta change” method, in which model-simulated climate changes and observed global mean temperature changes are used to extrapolate past observations forward in time, to make them representative of present or future climate conditions. In a hindcast test for the years 1991–2002, the method works well for temperature, with a clear improvement in verification statistics compared to the case in which the hindcast is formed directly from the observations for 1961–1990. However, no improvement is found for precipitation, for which the signal-to-noise ratio between expected anthropogenic changes and interannual variability is much lower than for temperature. An application of the method to the present (around the year 2007) climate suggests that, as a geographical average over land areas excluding Antarctica, 8–9 months per year and 8–9 years per decade can be expected to be warmer than the median for 1971–2000. Along with the overall warming, a substantial increase in the frequency of warm extremes at the expense of cold extremes of monthly-to-annual temperature is expected.     

Past and future polar amplification of climate change: climate model intercomparisons and ice-core constraints

Climate model simulations available from the PMIP1, PMIP2 and CMIP (IPCC-AR4) intercomparison projects for past and future climate change simulations are examined in terms of polar temperature changes in comparison to global temperature changes and with respect to pre-industrial reference simulations. For the mid-Holocene (MH, 6,000 years ago), the models are forced by changes in the Earth’s orbital parameters. The MH PMIP1 atmosphere-only simulations conducted with sea surface temperatures fixed to modern conditions show no MH consistent response for the poles, whereas the new PMIP2 coupled atmosphere–ocean climate models systematically simulate a significant MH warming both for Greenland (but smaller than ice-core based estimates) and Antarctica (consistent with the range of ice-core based range). In both PMIP1 and PMIP2, the MH annual mean changes in global temperature are negligible, consistent with the MH orbital forcing. The simulated last glacial maximum (LGM, 21,000 years ago) to pre-industrial change in global mean temperature ranges between 3 and 7°C in PMIP1 and PMIP2 model runs, similar to the range of temperature change expected from a quadrupling of atmospheric CO₂ concentrations in the CMIP simulations. Both LGM and future climate simulations are associated with a polar amplification of climate change. The range of glacial polar amplification in Greenland is strongly dependent on the ice sheet elevation changes prescribed to the climate models. All PMIP2 simulations systematically underestimate the reconstructed glacial–interglacial Greenland temperature change, while some of the simulations do capture the reconstructed glacial–interglacial Antarctic temperature change. Uncertainties in the prescribed central ice cap elevation cannot account for the temperature change underestimation by climate models. The variety of climate model sensitivities enables the exploration of the relative changes in polar temperature with respect to changes in global temperatures. Simulated changes of polar temperatures are strongly related to changes in simulated global temperatures for both future and LGM climates, confirming that ice-core-based reconstructions provide quantitative insights on global climate changes. An erratum to this article can be found at     

Projection of Extreme Temperatures in Hong Kong in the 21st Century

The possible changes in the frequency of extreme temperature events in Hong Kong in the 21st century were investigated by statistically downscaling 26 sets of the daily global climate model projections (a combination of 11 models and 3 greenhouse gas emission scenarios, namely A2, A1B, and B1) of the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. The models’ performance in simulating the past climate during 1971–2000 has also been verified and discussed. The verification revealed that the models in general have an acceptable skill in reproducing past statistics of extreme temperature events. Moreover, the models are more skillful in simulating the past climate of the hot nights and cold days than that of the very hot days. The projection results suggested that, in the 21st century, the frequency of occurrence of extremely high temperature events in Hong Kong would increase significantly while that of the extremely low temperature events is expected to drop significantly. Based on the multi-model scenario ensemble mean, the average annual numbers of very hot days and hot nights in Hong Kong are expected to increase significantly from 9 days and 16 nights in 1980–1999 to 89 days and 137 nights respectively in 2090–2099. On the other hand, the average annual number of cold days will drop from 17 days in 1980–1999 to about 1 day in 2090–2099. About 65 percent of the model-scenario combinations indicate that there will be on average less than one cold day in 2090–2099. While all the model-emission scenarios in general have projected consistent trends in the change of temperature extremes in the 21st century, there is a large divergence in the projections between difierent model/emission scenarios. This reflects that there are still large uncertainties in the model simulation of the future climate of extreme temperature events.     

Comparison of regional climate model and statistical downscaling simulations of different winter precipitation change scenarios over Romania

Summary Regional climate model and statistical downscaling procedures are used to generate winter precipitation changes over Romania for the period 2071–2100 (compared to 1961–1990), under the IPCC A2 and B2 emission scenarios. For this purpose, the ICTP regional climate model RegCM is nested within the Hadley Centre global atmospheric model HadAM3H. The statistical downscaling method is based on the use of canonical correlation analysis (CCA) to construct climate change scenarios for winter precipitation over Romania from two predictors, sea level pressure and specific humidity (either used individually or together). A technique to select the most skillful model separately for each station is proposed to optimise the statistical downscaling signal. Climate fields from the A2 and B2 scenario simulations with the HadAM3H and RegCM models are used as input to the statistical downscaling model. First, the capability of the climate models to reproduce the observed link between winter precipitation over Romania and atmospheric circulation at the European scale is analysed, showing that the RegCM is more accurate than HadAM3H in the simulation of Romanian precipitation variability and its connection with large-scale circulations. Both models overestimate winter precipitation in the eastern regions of Romania due to an overestimation of the intensity and frequency of cyclonic systems over Europe. Climate changes derived directly from the RegCM and HadAM3H show an increase of precipitation during the 2071–2100 period compared to 1961–1990, especially over northwest and northeast Romania. Similar climate change patterns are obtained through the statistical downscaling method when the technique of optimum model selected separately for each station is used. This adds confidence to the simulated climate change signal over this region. The uncertainty of results is higher for the eastern and southeastern regions of Romania due to the lower HadAM3H and RegCM performance in simulating winter precipitation variability there as well as the reduced skill of the statistical downscaling model.     

Increased hurricane intensities with CO2-induced warming as simulated using the GFDL hurricane prediction system

 The impact of CO₂-induced global warming on the intensities of strong hurricanes is investigated using the GFDL regional high-resolution hurricane prediction system. The large-scale initial conditions and boundary conditions for the regional model experiments, including SSTs, are derived from control and transient CO₂ increase experiments with the GFDL R30-resolution global coupled climate model. In a case study approach, 51 northwest Pacific storm cases derived from the global model under present-day climate conditions are simulated with the regional model, along with 51 storm cases for high CO₂ conditions. For each case, the regional model is integrated forward for five days without ocean coupling. The high CO₂ storms, with SSTs warmer by about 2.2 °C on average and higher environmental convective available potential energy (CAPE), are more intense than the control storms by about 3–7 m/s (5%–11%) for surface wind speed and 7 to 24 hPa for central surface pressure. The simulated intensity increases are statistically significant according to most of the statistical tests conducted and are robust to changes in storm initialization methods. Near-storm precipitation is 28% greater in the high CO₂ sample. In terms of storm tracks, the high CO₂ sample is quite similar to the control. The mean radius of hurricane force winds is 2 to 3% greater for the composite high CO₂ storm than for the control, and the high CO₂ storms penetrate slightly higher into the upper troposphere. More idealized experiments were also performed in which an initial storm disturbance was embedded in highly simplified flow fields using time mean temperature and moisture conditions from the global climate model. These idealized experiments support the case study results and suggest that, in terms of thermodynamic influences, the results for the NW Pacific basin are qualitatively applicable to other tropical storm basins. Received: 20 July 1998/Accepted: 24 December 1998     

A study on combining global and regional climate model results for generating climate scenarios of temperature and precipitation for the Netherlands

Climate scenarios for the Netherlands are constructed by combining information from global and regional climate models employing a simplified, conceptual framework of three sources (levels) of uncertainty impacting on predictions of the local climate. In this framework, the first level of uncertainty is determined by the global radiation balance, resulting in a range of the projected changes in the global mean temperature. On the regional (1,000–5,000 km) scale, the response of the atmospheric circulation determines the second important level of uncertainty. The third level of uncertainty, acting mainly on a local scale of 10 (and less) to 1,000 km, is related to the small-scale processes, like for example those acting in atmospheric convection, clouds and atmospheric meso-scale circulations—processes that play an important role in extreme events which are highly relevant for society. Global climate models (GCMs) are the main tools to quantify the first two levels of uncertainty, while high resolution regional climate models (RCMs) are more suitable to quantify the third level. Along these lines, results of an ensemble of RCMs, driven by only two GCM boundaries and therefore spanning only a rather narrow range in future climate predictions, are rescaled to obtain a broader uncertainty range. The rescaling is done by first disentangling the climate change response in the RCM simulations into a part related to the circulation, and a residual part which is related to the global temperature rise. Second, these responses are rescaled using the range of the predictions of global temperature change and circulation change from five GCMs. These GCMs have been selected on their ability to simulate the present-day circulation, in particular over Europe. For the seasonal means, the rescaled RCM results obey the range in the GCM ensemble using a high and low emission scenario. Thus, the rescaled RCM results are consistent with the GCM results for the means, while adding information on the small scales and the extremes. The method can be interpreted as a combined statistical–dynamical downscaling approach, with the statistical relations based on regional model output.     

Improved regional scale processes reflected in projected hydrological changes over large European catchments

For the fourth assessment report of the Intergovernmental Panel on Climate Change (IPCC), the recent version of the coupled atmosphere/ocean general circulation model (GCM) of the Max Planck Institute for Meteorology has been used to conduct an ensemble of transient climate simulations These simulations comprise three control simulations for the past century covering the period 1860–2000, and nine simulations for the future climate (2001–2100) using greenhouse gas (GHG) and aerosol concentrations according to the three IPCC scenarios B1, A1B and A2. For each scenario three simulations were performed. The global simulations were dynamically downscaled over Europe using the regional climate model (RCM) REMO at 0.44° horizontal resolution (about 50 km), whereas the physics packages of the GCM and RCM largely agree. The regional simulations comprise the three control simulations (1950–2000), the three A1B simulations and one simulation for B1 as well as for A2 (2001–2100). In our study we concentrate on the climate change signals in the hydrological cycle and the 2 m temperature by comparing the mean projected climate at the end of the twenty-first century (2071–2100) to a control period representing current climate (1961–1990). The robustness of the climate change signal projected by the GCM and RCM is analysed focussing on the large European catchments of Baltic Sea (land only), Danube and Rhine. In this respect, a robust climate change signal designates a projected change that sticks out of the noise of natural climate variability. Catchments and seasons are identified where the climate change signal in the components of the hydrological cycle is robust, and where this signal has a larger uncertainty. Notable differences in the robustness of the climate change signals between the GCM and RCM simulations are related to a stronger warming projected by the GCM in the winter over the Baltic Sea catchment and in the summer over the Danube and Rhine catchments. Our results indicate that the main explanation for these differences is that the finer resolution of the RCM leads to a better representation of local scale processes at the surface that feed back to the atmosphere, i.e. an improved representation of the land sea contrast and related moisture transport processes over the Baltic Sea catchment, and an improved representation of soil moisture feedbacks to the atmosphere over the Danube and Rhine catchments.     

On the natural variability of the pre-industrial European climate

We suggest that climate variability in Europe for the “pre-industrial” period 1500–1900 is fundamentally a consequence of internal fluctuations of the climate system. This is because a model simulation, using fixed pre-industrial forcing, in several important aspects is consistent with recent observational reconstructions at high temporal resolution. This includes extreme warm and cold seasonal events as well as different measures of the decadal to multi-decadal variance. Significant trends of 50-year duration can be seen in the model simulation. While the global temperature is highly correlated with ENSO (El Nino- Southern Oscillation), European seasonal temperature is only weakly correlated with the global temperature broadly consistent with data from ERA-40 reanalyses. Seasonal temperature anomalies of the European land area are largely controlled by the position of the North Atlantic storm tracks. We believe the result is highly relevant for the interpretation of past observational records suggesting that the effect of external forcing appears to be of secondary importance. That variations in the solar irradiation could have been a credible cause of climate variations during the last centuries, as suggested in some previous studies, is presumably due to the fact that the models used in these studies may have underestimated the internal variability of the climate. The general interpretation from this study is that the past climate is just one of many possible realizations and thus in many respects not reproducible in its time evolution with a general circulation model but only reproducible in a statistical sense.     

基于气候系统模式FGOALS-g2的热带气旋活动及其影响的动力降尺度模拟

热带气旋是气候模拟关注的重要对象,但是,由于当前的气候系统模式分辨率较低,难以合理再现热带气旋分布特征,因此,动力降尺度就成为一种有效的手段。本文使用区域气候模式RegCM3,对中国科学院大气物理研究所气候系统模式FGOALS-g2的模拟结果进行动力降尺度,基于热带气旋路径追踪法,从热带气旋的路径、强度和降水三个方面,检验了动力降尺度在热带气旋模拟能力上的增值。结果表明,动力降尺度结果大幅提升了热带气旋路径频率的模拟,较之全球模式,其与观测的路径频率分布的空间相关系数从0.57提升至0.74;区域模式模拟的热带气旋强度与观测更为一致,全球模式难以模拟40 m s?1以上风速的热带气旋,区域模式能够模拟风速为60 m s?1的热带气旋;在热带气旋降水方面,降尺度后的热带气旋降水贡献率和平均热带气旋降水强度均有所改善,在西北太平洋区域较之全球模式,区域模式将热带气旋降水贡献率和降水强度提高了10%和4.7 mm d?1。动力降尺度后TC（tropical cyclone）的模拟技巧得到提升的区域为西北太平洋区域,但在中国南海区域,技巧提升的不显著甚至有所下降。关于动力降尺度结果在西北太平洋区域的技巧提升,分析表明能够更好体现CISK（Conditional Instability of the Second Kind）机制是主要原因,区域模式模拟的水汽增多、正涡度增强、上升运动增强而垂直风切变减弱都有显著贡献。     

Ability of an ensemble of regional climate models to reproduce weather regimes over Europe-Atlantic during the period 1961–2000

One of the main concerns in regional climate modeling is to which extent limited-area regional climate models (RCM) reproduce the large-scale atmospheric conditions of their driving general circulation model (GCM). In this work we investigate the ability of a multi-model ensemble of regional climate simulations to reproduce the large-scale weather regimes of the driving conditions. The ensemble consists of a set of 13 RCMs on a European domain, driven at their lateral boundaries by the ERA40 reanalysis for the time period 1961–2000. Two sets of experiments have been completed with horizontal resolutions of 50 and 25 km, respectively. The spectral nudging technique has been applied to one of the models within the ensemble. The RCMs reproduce the weather regimes behavior in terms of composite pattern, mean frequency of occurrence and persistence reasonably well. The models also simulate well the long-term trends and the inter-annual variability of the frequency of occurrence. However, there is a non-negligible spread among the models which is stronger in summer than in winter. This spread is due to two reasons: (1) we are dealing with different models and (2) each RCM produces an internal variability. As far as the day-to-day weather regime history is concerned, the ensemble shows large discrepancies. At daily time scale, the model spread has also a seasonal dependence, being stronger in summer than in winter. Results also show that the spectral nudging technique improves the model performance in reproducing the large-scale of the driving field. In addition, the impact of increasing the number of grid points has been addressed by comparing the 25 and 50 km experiments. We show that the horizontal resolution does not affect significantly the model performance for large-scale circulation.     

北京气候中心气候系统模式研发进展——在气候变化研究中的应用

较全面地介绍了北京气候中心气候系统模式(BCC_CSM)研发所取得的一些进展及其在气候变化研究中的应用,重点介绍了全球近280 km较低分辨率的全球海-陆-气-冰-生物多圈层耦合的气候系统模式BCC_CSM1.1和110 km中等大气分辨率的BCC_CSM1.1(m),以及大气、陆面、海洋、海冰各分量模式的发展。BCC_CSM1.1和BCC_CSM1.1(m)气候系统模式均包含了全球碳循环和动态植被过程。当给定全球人类活动导致的碳源排放后,就可以模拟和预估人类活动对气候变化的影响。BCC_CSM1.1和BCC_CSM1.1(m)已应用于IPCC AR5模式比较,为中外开展气候变化机理分析和未来气候变化预估提供了大量的试验数据。还介绍了BCC_CSM1.1和BCC_CSM1.1(m)参与国际耦合模式比较计划(CMIP5)的大量试验分析评估结果,BCC_CSM能够较好地模拟20世纪气温和降水等气候平均态和季节变化特征,以及近1000年的历史气候变化,所预估的未来100年气候变化与国际上其他模式的CMIP5试验预估结果相当。初步的分析表明,分辨率相对高的BCC_CSM1.1(m)在区域气候平均态的模拟上优于分辨率较低的BCC_CSM1.1。     

Intercomparison of the northern hemisphere winter mid-latitude atmospheric variability of the IPCC models

We compare, for the overlapping time frame 1962–2000, the estimate of the northern hemisphere mid-latitude winter atmospheric variability within the available 20th century simulations of 19 global climate models included in the Intergovernmental Panel on Climate Change—4th Assessment Report with the NCEP-NCAR and ECMWF reanalyses. We compute the Hayashi spectra of the 500 hPa geopotential height fields and introduce an ad hoc integral measure of the variability observed in the Northern Hemisphere on different spectral sub-domains. The total wave variability is taken as a global scalar metric describing the overall performance of each model, while the total variability pertaining to the eastward propagating baroclinic waves and to the planetary waves are taken as scalar metrics describing the performance of each model phenomenologically in connection with the corresponding specific physical process. Only two very high-resolution global climate models have a good agreement with reanalyses for both the global and the process-oriented metrics. Large biases, in several cases larger than 20%, are found in all the considered metrics between the wave climatologies of most IPCC models and the reanalyses, while the span of the climatologies of the various models is, in all cases, around 50%. In particular, the travelling baroclinic waves are typically overestimated by the climate models, while the planetary waves are usually underestimated, in agreement with what found is past analyses performed on global weather forecasting models. When comparing the results of similar models, it is apparent that in some cases the vertical resolution of the model atmosphere, the adopted ocean model, and the advection schemes seem to be critical in the bulk of the atmospheric variability. The models ensemble obtained by arithmetic averaging of the results of all models is biased with respect to the reanalyses but is comparable to the best five models. Nevertheless, the models results do not cluster around their ensemble mean. This study suggests caveats with respect to the ability of most of the presently available climate models in representing the statistical properties of the global scale atmospheric dynamics of the present climate and, a fortiori, in the perspective of modeling climate change.     

Past to future extreme events in Liverpool Bay: model projections from 1960–2100

Knowledge of the likely future wind, wave and surge climate in Liverpool Bay is of importance for coastal flood defence management. We examine a 140-year time series (1960–2100) of wind and wave model projections at the WaveNet buoy location in Liverpool Bay and also of surge model projection at two ports in Liverpool Bay, namely Liverpool and Heysham. To this end we use model projections from the UK Climate Projections 09 (UKCP09) programme. We use a medium emissions scenario ensemble from the HadCM3 climate model sensitivity tests. A continental shelf model (CS3) with ~12 km resolution was used to separately simulate the waves and the surge. The models are forced by hourly wind and pressure data from the Met Office (Hadley Centre) regional climate model (RCM). Swell wave boundary conditions are generated over the full Atlantic using global climate model (GCM) winds. Analysis of significant changes in the statistics over time shows that there is little change in extreme wave and surge conditions in Liverpool Bay. Although there is a slight increase in the severity of the most extreme events, the frequency of extreme wind and wave events is slightly reduced, while the frequency of extreme surge events slightly increases over the 140-year period. From the model projections, we find that the trends in the local wind are directly reflected in the wave field within Liverpool Bay. The trends in the skew surge projections deviate slightly from those in the wind patterns.     

Observed and projected climate change in Taiwan

Summary This study examined the secular climate change characteristics in Taiwan over the past 100 years and the relationship with the global climate change. Estimates for the likelihood of future climate changes in Taiwan were made based on the projection from the IPCC climate models. In the past 100 years, Taiwan experienced an island-wide warming trend (1.0–1.4 °C/100 years). Both the annual and daily temperature ranges have also increased. The warming in Taiwan is closely connected to a large-scale circulation and SAT fluctuations, such as the “cool ocean warm land” phenomenon. The water vapor pressure has increased significantly and could have resulted in a larger temperature increase in summer. The probability for the occurrence of high temperatures has increased and the result suggests that both the mean and variance in the SAT in Taiwan have changed significantly since the beginning of the 20th century. Although, as a whole, the precipitation in Taiwan has shown a tendency to increase in northern Taiwan and to decrease in southern Taiwan in the past 100 years, it exhibits a more complicated spatial pattern. The changes occur mainly in either the dry or rainy season and result in an enhanced seasonal cycle. The changes in temperature and precipitation are consistent with the weakening of the East Asian monsoon. Under consideration of both the warming effect from greenhouse gases and the cooling effect from aerosols, all projections from climate models indicated a warmer climate near Taiwan in the future. The projected increase in the area-mean temperature near Taiwan ranged from 0.9–2.7 °C relative to the 1961–1990 averaged temperature, when the CO₂ concentration increased to 1.9 times the 1961–1990 level. These simulated temperature increases were statistically significant and can be attributed to the radiative forcing associated with the increased concentration of greenhouse gases and aerosols. The projected changes in precipitation were within the range of natural variability for all five models. There is no evidence supporting the possibility of precipitation changes near Taiwan based on the simulations from five IPCC climate models. Received February 5, 2001 Revised July 30, 2001     

Extremely high temperatures in France at the end of the century

Power plant construction requires anticipation to achieve a liable dimensioning on the long functioning time of the installation. In the present climate change context, dimensioning towards extremely high temperature for installations intended to run until the 2070s or later implies an evaluation of plausible extreme values at this time scale. This study is devoted to such an estimation for France, using both observation series and climate model simulation results. The climate model results are taken from the European PRUDENCE (Prediction of Regional scenarios and Uncertainties for Defining European Climate change risks and Effects) project database of regional climate change scenarios for Europe. Comparison of high summer temperature distributions given by observations and climate models under current climate conditions, conducted using Generalized Extreme Value distribution, reveals that only a few models are able to correctly reproduce it. For these models, climate change under IPCC A2 and B2 scenarios leads to differences in the variability of high values, whose proportion has an important impact on future 100-year return levels. This study was first presented at the EGU General Assembly in Vienna, 2–7 April 2006.     

Climate change impact on snow and soil temperature in boreal Scots pine stands

Scenarios indicate that the air temperature will increase in high latitude regions in coming decades, causing the snow covered period to shorten, the growing season to lengthen and soil temperatures to change during the winter, spring and early summer. To evaluate how a warmer climate is likely to alter the snow cover and soil temperature in Scots pine stands of varying ages in northern Sweden, climate scenarios from the Swedish regional climate modelling programme SWECLIM were used to drive a Soil-Vegetation-Atmosphere Transfer (SVAT)-model (COUP). Using the two CO₂ emission scenarios A and B in the Hadley centres global climate model, HadleyA and HadleyB, SWECLIM predicts that the annual mean air temperature and precipitation will increase at most 4.8°C and 315 mm, respectively, within a century in the study region. The results of this analysis indicate that a warmer climate will shorten the period of persistent snow pack by 73–93 days, increase the average soil temperature by 0.9–1.5°C at 10 cm depth, advance soil warming by 15–19 days in spring and cause more soil freeze–thaw cycles by 31–38%. The results also predict that the large current variations in snow cover due to variations in tree interception and topography will be enhanced in the coming century, resulting in increased spatial variability in soil temperatures.     

Volcanic and solar impacts on climate since 1700

 Numerical experiments have been carried out with a two-dimensional sector averaged global climate model with a detailed radiative scheme in order to assess the possible impact of solar and volcanic activities on the Earth’s surface temperature at the secular time scale from 1700 to 1992. Our results indicate that while the general trend of the observed temperature variations on the century time scale can be generated in response to both the solar and volcanic forcings, these are clearly not sufficient to explain the observed 20th century warming and more specifically the warming trend which started at the beginning of the 1970s. However, the lack of volcanism during the period 1925–1960 could account, at least partly, for the observed warming trend in this period. Finally, while Schlesinger and Ramankutty (1994) assumed that random forcing could not be a possible source of the 65–70 year oscillation they detected in the global climate system, our results indicate that the volcanic forcing over the past 150 years could have introduced an oscillation of around 70 years in the Earth’s surface temperature. Received: 25 August 1997/Accepted: 27 November 1998     

Future change of climate in South America in the late twenty-first century: intercomparison of scenarios from three regional climate models

Regional climate change projections for the last half of the twenty-first century have been produced for South America, as part of the CREAS (Cenarios REgionalizados de Clima Futuro da America do Sul) regional project. Three regional climate models RCMs (Eta CCS, RegCM3 and HadRM3P) were nested within the HadAM3P global model. The simulations cover a 30-year period representing present climate (1961–1990) and projections for the IPCC A2 high emission scenario for 2071–2100. The focus was on the changes in the mean circulation and surface variables, in particular, surface air temperature and precipitation. There is a consistent pattern of changes in circulation, rainfall and temperatures as depicted by the three models. The HadRM3P shows intensification and a more southward position of the subtropical Pacific high, while a pattern of intensification/weakening during summer/winter is projected by the Eta CCS/RegCM3. There is a tendency for a weakening of the subtropical westerly jet from the Eta CCS and HadRM3P, consistent with other studies. There are indications that regions such of Northeast Brazil and central-eastern and southern Amazonia may experience rainfall deficiency in the future, while the Northwest coast of Peru-Ecuador and northern Argentina may experience rainfall excesses in a warmer future, and these changes may vary with the seasons. The three models show warming in the A2 scenario stronger in the tropical region, especially in the 5°N–15°S band, both in summer and especially in winter, reaching up to 6–8°C warmer than in the present. In southern South America, the warming in summer varies between 2 and 4°C and in winter between 3 and 5°C in the same region from the 3 models. These changes are consistent with changes in low level circulation from the models, and they are comparable with changes in rainfall and temperature extremes reported elsewhere. In summary, some aspects of projected future climate change are quite robust across this set of model runs for some regions, as the Northwest coast of Peru-Ecuador, northern Argentina, Eastern Amazonia and Northeast Brazil, whereas for other regions they are less robust as in Pantanal region of West Central and southeastern Brazil.     

Testing the Clausius–Clapeyron constraint on changes in extreme precipitation under CO<Subscript>2</Subscript> warming

Increases in extreme precipitation greater than in the mean under increased greenhouse gases have been reported in many climate models both on global and regional scales. It has been proposed in a previous study that whereas global-mean precipitation change is primarily constrained by the global energy budget, the heaviest events can be expected when effectively all the moisture in a volume of air is precipitated out, suggesting the intensity of these events increases with availability of moisture, and significantly faster than the global mean. Thus under conditions of constant relative humidity one might expect the Clausius–Clapeyron relation to give a constraint on changes in the uppermost quantiles of precipitation distributions. This study examines if the phenomenon manifests on regional and seasonal scales also. Zonal analysis of daily precipitation in the HadCM3 model under a transient CO₂ forcing scenario shows increased extreme precipitation in the tropics accompanied by increased drying at lower percentiles. At mid- to high-latitudes there is increased precipitation over all percentiles. The greatest agreement with Clausius–Clapeyron predicted change occurs at mid-latitudes. This pattern is consistent with other climate model projections, and suggests that regions in which the nature of the ambient flows change little give the greatest agreement with Clausius–Clapeyron prediction. This is borne out by repeating the analyses at gridbox level and over season. Furthermore, it is found that Clausius–Clapeyron predicted change in extreme precipitation is a better predictor than directly using the change in mean precipitation, particularly between 60°N and 60°S. This could explain why extreme precipitation changes may be more detectable then mean changes.