Combining climatological and participatory approaches for assessing changes in extreme climatic indices at regional scale

This paper combines the climatological and societal perspectives for assessing future climatic extremes over Kangasabati River basin in India using an ensemble of four high resolution (25 km) regional climate model (RCM) simulations from 1970 to 2050. The relevant extreme indices and their thresholds are defined in consultation with stakeholders and are then compared using RCM simulations. To evaluate the performance of RCM in realistically representing atmospheric processes in the basin, model simulations driven with ERAInterim global re-analysis data from 1989 to 2008 are compared with observations. The models perform well in simulating seasonality, interannual variability and climatic extremes. Future climatic extremes are evaluated based on RCM simulations driven by GCMs, for present (1970–1999) and for the SRES A1B scenario for future (2021–2050) period. The analysis shows an intensification of majority of extremes as projected by future ensemble mean. The study suggests that there is a marked consistency in stakeholder observed changes in climate extremes and future predicted trends.     

Sensitivity of the southern annular mode to greenhouse gas emission scenarios

The leading mode of southern hemisphere (SH) climatic variability, the southern annular mode (SAM), has recently seen a shift towards its positive phase due to stratospheric ozone depletion and increasing greenhouse gas (GHG) concentrations. Here we examine how sensitive the SAM (defined as the leading empirical orthogonal function of SH sea level pressure anomalies) is to future GHG concentrations. We determine its likely evolution for three intergovernmental panel on climate change (IPCC) special report on emission scenarios (SRES) for austral summer and winter, using a multi-model ensemble of IPCC fourth assessment report models which resolve stratospheric ozone recovery. During the period of summer ozone recovery (2000–2050), the SAM index exhibits weakly negative, statistically insignificant trends due to stratospheric ozone recovery which offsets the positive forcing imposed by increasing GHG concentrations. Thereafter, positive SAM index trends occur with magnitudes that show sensitivity to the SRES scenario utilised, and thus future GHG emissions. Trends are determined to be strongest for SRES A2, followed by A1B and B1, respectively. The winter SAM maintains a similar dependency upon GHG as summer, but over the entire twenty-first century and to a greater extent. We also examine the influence of ozone recovery by comparing results to models that exclude stratospheric ozone recovery. Projections are shown to be statistically different from the aforementioned results, highlighting the importance of ozone recovery in governing SAM-evolution. We therefore demonstrate that the future SAM will depend both upon GHG emissions and stratospheric ozone recovery.     

Projections of uncertainties in climate change scenarios into expected winter wheat yields

Summary The crop model CERES-Wheat in combination with the stochastic weather generator were used to quantify the effect of uncertainties in selected climate change scenarios on the yields of winter wheat, which is the most important European cereal crop. Seven experimental sites with the high quality experimental data were selected in order to evaluate the crop model and to carry out the climate change impact analysis. The analysis was based on the multi-year crop model simulations run with the daily weather series prepared by the stochastic weather generator. Seven global circulation models (GCMs) were used to derive the climate change scenarios. In addition, seven GCM-based scenarios were averaged in order to derive the average scenario (AVG). The scenarios were constructed for three time periods (2025, 2050 and 2100) and two SRES emission scenarios (A2 and B1). The simulated results showed that: (1) Wheat yields tend to increase (40 out of 42 applied scenarios) in most locations in the range of 7.5–25.3% in all three time periods. In case of the CCSR scenario that predicts the most severe increase of air temperature, the yields would be reduced by 9.6% in 2050 and by 25.8% in 2100 if the A2 emission scenario would become reality. Differences between individual scenarios are large and statistically significant. Particularly for the time periods 2050 and 2100 there are doubts about the trend of the yield shifts. (2) The site effect was caused by the site-specific soil and climatic conditions. Importance of the site influence increases with increasing severity of imposed climatic changes and culminates for the emission scenario A2 and the time period 2100. The sustained tendency benefiting two warmest sites has been found as well as more positive response to the changed climatic conditions of the sites with deeper soil profiles. (3) Temperature variability proved to be an important factor and influenced both mean and standard deviation of the yields. Change of temperature variability by more than 25% leads to statistically significant changes in yield distribution. The effect of temperature variability decreases with increased values of mean temperature. (4) The study proved that the application of the AVG scenarios – despite possible objections of physical inconsistency – might be justifiable and convenient in some cases. It might bring results comparable to those derived from averaging outputs based on number of scenarios and provide more robust estimate than the application of only one selected GCM scenario.     

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     

Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 2: Precipitation mean state and seasonal cycle in South America

Evaluating the response of climate to greenhouse gas forcing is a major objective of the climate community, and the use of large ensemble of simulations is considered as a significant step toward that goal. The present paper thus discusses a new methodology based on neural network to mix ensemble of climate model simulations. Our analysis consists of one simulation of seven Atmosphere–Ocean Global Climate Models, which participated in the IPCC Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three SRES scenarios: A2, A1B and B1. Our statistical method based on neural networks and Bayesian statistics computes a transfer function between models and observations. Such a transfer function was then used to project future conditions and to derive what we would call the optimal ensemble combination for twenty-first century climate change projections. Our approach is therefore based on one statement and one hypothesis. The statement is that an optimal ensemble projection should be built by giving larger weights to models, which have more skill in representing present climate conditions. The hypothesis is that our method based on neural network is actually weighting the models that way. While the statement is actually an open question, which answer may vary according to the region or climate signal under study, our results demonstrate that the neural network approach indeed allows to weighting models according to their skills. As such, our method is an improvement of existing Bayesian methods developed to mix ensembles of simulations. However, the general low skill of climate models in simulating precipitation mean climatology implies that the final projection maps (whatever the method used to compute them) may significantly change in the future as models improve. Therefore, the projection results for late twenty-first century conditions are presented as possible projections based on the “state-of-the-art” of present climate modeling. First, various criteria were computed making it possible to evaluate the models’ skills in simulating late twentieth century precipitation over continental areas as well as their divergence in projecting climate change conditions. Despite the relatively poor skill of most of the climate models in simulating present-day large scale precipitation patterns, we identified two types of models: the climate models with moderate-to-normal (i.e., close to observations) precipitation amplitudes over the Amazonian basin; and the climate models with a low precipitation in that region and too high a precipitation on the equatorial Pacific coast. Under SRES A2 greenhouse gas forcing, the neural network simulates an increase in precipitation over the La Plata basin coherent with the mean model ensemble projection. Over the Amazonian basin, a decrease in precipitation is projected. However, the models strongly diverge, and the neural network was found to give more weight to models, which better simulate present-day climate conditions. In the southern tip of the continent, the models poorly simulate present-day climate. However, they display a fairly good convergence when simulating climate change response with a weak increase south of 45°S and a decrease in Chile between 30 and 45°S. Other scenarios (A1B and B1) strongly resemble the SRES A2 trends but with weaker amplitudes.     

Effects of climate change on the intensity and frequency of heavy snowfall events in the Pyrenees

The intensity and frequency of heavy snowfall events in the Pyrenees were simulated using data from the HIRHAM regional climate model for a control period (1960?C1990) and two greenhouse emission scenarios (SRES B2 and A2) for the end of the twenty-first century (2070?C2100). Comparisons between future and control simulations enabled a quantification of the expected change in the intensity and frequency of these events at elevations of 1,000, 1,500, 2,000 and 2,500?m a.s.l. The projected changes in heavy snowfall depended largely on the elevation and the greenhouse gas emission scenario considered. At 1,000?m a.s.l., a marked decrease in the frequency and intensity of heavy snowfall events was projected with the B2 and A2 scenarios. At 1,500?m a.s.l., a decrease in the frequency and intensity is only expected under the higher greenhouse gas emission scenario (A2). Above 2,000?m a.s.l., no change or heavier snowfalls are expected under both emission scenarios. Large spatial variability in the impacts of climate change on heavy snowfall events was found across the study area.     

Uncertainty in climate change projections: the role of internal variability

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

Extremes of near-surface wind speed over Europe and their future changes as estimated from an ensemble of RCM simulations

In this study, we analyse the uncertainty of the effect of enhanced greenhouse gas conditions on windiness projected by an ensemble of regional model simulations driven by the same global control and climate change simulations. These global conditions, representative for 1961–1990 and 2071–2100, were prepared by the Hadley Centre based on the IPCC SRES/A2 scenario. The basic data sets consist of simulated daily maximum and daily mean wind speed fields (over land) from the PRUDENCE data archive at the Danish Meteorological Institute. The main focus is on the results from the standard 50 km-resolution runs of eight regional models. The best parameter for determining possible future changes in extreme wind speeds and possible change in the number of storm events is maximum daily wind speed. It turned out during this study that the method for calculating maximum daily wind speed differs among the regional models. A comparison of simulated winds with observations for the control period shows that models without gust parameterisation are not able to realistically capture high wind speeds. The two models with gust parametrization estimate an increase of up to 20% of the number of storm peak (defined as gusts?≥?8 Bft in this paper) events over Central Europe in the future. In order to use a larger ensemble of models than just the two with gust parameterisation, we also look at the 99th percentile of daily mean wind speed. We divide Europe into eight sub-regions (e.g., British Isles, Iberian Peninsula, NE Europe) and investigate the inter-monthly variation of wind over these regions as well as differences between today’s climate and a possible future climate. Results show differences and similarities between the sub-regions in magnitude, spread, and seasonal tendencies. The model ensemble indicates a possible increase in future mean daily wind speed during winter months, and a decrease during autumn in areas influenced by North Atlantic extra-tropical cyclones.     

Climate change scenarios for precipitation extremes in Portugal

Precipitation indices are commonly used as climate change indicators. Considering four Climate Variability and Predictability-recommended indices, this study assesses possible changes in their spatial patterns over Portugal under future climatic conditions. Precipitation data from the regional climate model Consortium for Small-Scale Modelling–Climate version of the Local Model (CCLM) ensemble simulations with ECHAM5/MPI-OM1 boundary conditions are used for this purpose. For recent–past, medians and probability density functions of the CCLM-based indices are validated against station-based and gridded observational dataset from ENSEMBLES-based (gridded daily precipitation data provided by the European Climate Assessment & Dataset project) indices. It is demonstrated that the model is able to realistically reproduce not only precipitation but also the corresponding extreme indices. Climate change projections for 2071–2100 (A1B and B1 SRES scenarios) reveal significant decreases in total precipitation, particularly in autumn over northwestern and southern Portugal, though changes exhibit distinct local and seasonal patterns and are typically stronger for A1B than for B1. The increase in winter precipitation over northeastern Portugal in A1B is the most important exception to the overall drying trend. Contributions of extreme precipitation events to total precipitation are also expected to increase, mainly in winter and spring over northeastern Portugal. Strong projected increases in the dry spell lengths in autumn and spring are also noteworthy, giving evidence for an extension of the dry season from summer to spring and autumn. Although no coupling analysis is undertaken, these changes are qualitatively related to modifications in the large-scale circulation over the Euro-Atlantic area, more specifically to shifts in the position of the Azores High and associated changes in the large-scale pressure gradient over the area.     

Assessing future changes in seasonal climatic extremes in the Ganges river basin using an ensemble of regional climate models

Using an ensemble of four high resolution (~25 km) regional climate models, this study analyses the future (2021–2050) spatial distribution of seasonal temperature and precipitation extremes in the Ganges river basin based on the SRES A1B emissions scenario. The model validation results (1989–2008) show that the models simulate seasonality and spatial distribution of extreme temperature events better than precipitation. The models are able to capture fine topographical detail in the spatial distribution of indices based on their ability to resolve processes at a higher regional resolution. Future simulations of extreme temperature indices generally agree with expected warming in the Ganges basin, with considerable seasonal and spatial variation. Significantly warmer summers in the central part of the basin along with basin-wide increase in night temperature are expected during the summer and monsoon months. An increase in heavy precipitation indices during monsoon, coupled with extended periods without precipitation during the winter months; indicates an increase in the incidence of extreme events.     

Triggering of El Niño by westerly wind events in a coupled general circulation model

Two ten-members ensemble experiments using a coupled ocean-atmosphere general circulation model are performed to study the dynamical response to a strong westerly wind event (WWE) when the tropical Pacific has initial conditions favourable to the development of a warm event. In the reference ensemble (CREF), no wind perturbation is introduced, whereas a strong westerly wind event anomaly is introduced in boreal winter over the western Pacific in the perturbed ensemble (CWWE). Our results demonstrate that an intense WWE is capable of establishing the conditions under which a strong El Niño event can occur. First, it generates a strong downwelling Kelvin wave that generates a positive sea surface temperature (SST) anomaly in the central-eastern Pacific amplified through a coupled ocean-atmosphere interaction. This anomaly can be as large as 2.5°C 60 days after the WWE. Secondly, this WWE also initiates an eastward displacement of the warm-pool that promotes the occurrence of subsequent WWEs in the following months. These events reinforce the initial warming through the generation of additional Kelvin waves and generate intense surface jets at the eastern edge of the warm-pool that act to further shift warm waters eastward. The use of a ten-members ensemble however reveals substantial differences in the coupled response to a WWE. Whereas four members of CWWE ensemble develop into intense El Niño warming as described above, four others display a moderate warming and two remains in neutral conditions. This diversity between the members appears to be due to the internal atmospheric variability during and following the inserted WWE. In the four moderate warm cases, the warm-pool is initially shifted eastward following the inserted WWE, but the subsequent weak WWE activity (when compared to the strong warming cases) prevents to further shift the warm-pool eastwards. The seasonal strengthening of trade winds in June–July can therefore act to shift warm waters back into the western Pacific, reducing the central-eastern Pacific warming. This strong sensitivity of the coupled response to WWEs may therefore limit the predictability of El Niño events, as the high frequency wind variability over the warm pool region remains largely unpredictable even at short time lead.     

Climatic changes associated with a global “2°C-stabilization” scenario simulated by the ECHAM5/MPI-OM coupled climate model

In this study, concentrations of the well-mixed greenhouse gases as well as the anthropogenic sulphate aerosol load and stratospheric ozone concentrations are prescribed to the ECHAM5/MPI-OM coupled climate model so that the simulated global warming does not exceed 2°C relative to pre-industrial times. The climatic changes associated with this so-called “2°C-stabilization” scenario are assessed in further detail, considering a variety of meteorological and oceanic variables. The climatic changes associated with such a relatively weak climate forcing supplement the recently published fourth assessment report by the IPCC in that such a stabilization scenario can only be achieved by mitigation initiatives. Also, the impact of the anthropogenic sulphate aerosol load and stratospheric ozone concentrations on the simulated climatic changes is investigated. For this particular climate model, the 2°C-stabilization scenario is characterized by the following atmospheric concentrations of the well-mixed greenhouse gases: 418 ppm (CO₂), 2,026 ppb (CH₄), and 331 ppb (N₂O), 786 ppt (CFC-11) and 486 ppt (CFC-12), respectively. These greenhouse gas concentrations correspond to those for 2020 according to the SRES A1B scenario. At the same time, the anthropogenic sulphate aerosol load and stratospheric ozone concentrations are changed to the level in 2100 (again, according to the SRES A1B scenario), with a global anthropogenic sulphur dioxide emission of 28 TgS/year leading to a global anthropogenic sulphate aerosol load of 0.23 TgS. The future changes in climate associated with the 2°C-stabilization scenario show many of the typical features of other climate change scenarios, including those associated with stronger climatic forcings. That are a pronounced warming, particularly at high latitudes accompanied by a marked reduction of the sea-ice cover, a substantial increase in precipitation in the tropics as well as at mid- and high latitudes in both hemispheres but a marked reduction in the subtropics, a significant strengthening of the meridional temperature gradient between the tropical upper troposphere and the lower stratosphere in the extratropics accompanied by a pronounced intensification of the westerly winds in the lower stratosphere, and a strengthening of the westerly winds in the Southern Hemisphere extratropics throughout the troposphere. The magnitudes of these changes, however, are somewhat weaker than for the scenarios associated with stronger global warming due to stronger climatic forcings, such as the SRES A1B scenario. Some of the climatic changes associated with the 2°C-stabilization are relatively strong with respect to the magnitude of the simulated global warming, i.e., the pronounced warming and sea-ice reduction in the Arctic region, the strengthening of the meridional temperature gradient at the northern high latitudes and the general increase in precipitation. Other climatic changes, i.e., the El Niño like warming pattern in the tropical Pacific Ocean and the corresponding changes in the distribution of precipitation in the tropics and in the Southern Oscillation, are not as markedly pronounced as for the scenarios with a stronger global warming. A higher anthropogenic sulphate aerosol load (for 2030 as compared to the level in 2100 according to the SRES A1B scenario) generally weakens the future changes in climate, particularly for precipitation. The most pronounced effects occur in the Northern Hemisphere and in the tropics, where also the main sources of anthropogenic sulphate aerosols are located.     

A coupled model study on the intensification of the Asian summer monsoon in IPCC SRES Scenarios

The Asian summer monsoon is an important part of the climate system. Investigating the response of the Asian summer monsoon to changing concentrations of greenhouse gases and aerosols will be meaningful to understand and predict climate variability and climate change not only in Asia but also globally. In order to diagnose the impacts of future anthropogenic emissions on monsoon climates, a coupled general circulation model of the atmosphere and the ocean has been used at the Max-Planck-Institute for Meteorology. In addition to carbon dioxide, the major well mixed greenhouse gases such as methane, nitrous oxide, several chlorofluorocarbons, and CFC substitute gases are prescribed as a function of time. The sulfur cycle is simulated interactively, and both the direct aerosol effect and the indirect cloud albedo effect are considered. Furthermore, changes in tropospheric ozone have been pre-calculated with a chemical transport model and prescribed as a function of time and space in the climate simulations. Concentrations of greenhouse gases and anthropogenic emissions of sulfur dioxide are prescribed according to observations （1860-1990） and projected into the future （1990-2100） according to the Scenarios A2 and B2 in Special Report on Emissions Scenarios （SRES, Nakcenovic et al., 2000） developed by the Intergovernmental Panel on Climate Change （IPCC）. It is found that the Indian summer monsoon is enhanced in the scenarios in terms of both mean precipitation and interannual variability. An increase in precipitation is simulated for northern China but a decrease for the southern part. Furthermore, the simulated future increase in monsoon variability seems to be linked to enhanced ENSO variability towards the end of the scenario integrations.     

Climatic variability within an equilibrium greenhouse simulation

A simulation of the possible consequences of a doubling of the CO₂ content of the atmosphere has been performed with a low resolution global climatic model. The model included the diurnal and seasonal cycles, computed sea ice amount and cloud cover, and used implied oceanic heat fluxes to represent transport processes in the oceans. A highly responsive 2-layer soil moisture formulation was also incorporated. Twenty year equilibrated simulations for control (1 × CO₂) and greenhouse (2 × CO₂) conditions were generated. The major emphasis of the analysis presented here is on the intra-annual and interannual variability of the greenhouse run with respect to the control run. This revealed considerable differences from the time-averaged results with occasions of marked positive and negative temperature deviations. Of particular interest were the periods of negative temperature departures compared to the control run which were identified, especially over the Northern Hemisphere continents. Temporal and spatial precipitation and soil moisture anomalies also occurred, some of which were related to the surface temperature changes. Substantial sea surface temperature anomalies were apparent in the greenhouse run, indicating that a source of climatic forcing existed in addition to that due to doubling of the CO₂. Comparison of the intra-annual and interannual variability of the control run with that of the greenhouse run suggests that, in many situations, it will be difficult to identify a greenhouse signal against the intrinsic natural variability of the climatic system.     

Evaluating uncertainties in regional climate simulations over South America at the seasonal scale

This work focuses on the evaluation of different sources of uncertainty affecting regional climate simulations over South America at the seasonal scale, using the MM5 model. The simulations cover a 3-month period for the austral spring season. Several four-member ensembles were performed in order to quantify the uncertainty due to: the internal variability; the definition of the regional model domain; the choice of physical parameterizations and the selection of physical parameters within a particular cumulus scheme. The uncertainty was measured by means of the spread among individual members of each ensemble during the integration period. Results show that the internal variability, triggered by differences in the initial conditions, represents the lowest level of uncertainty for every variable analyzed. The geographic distribution of the spread among ensemble members depends on the variable: for precipitation and temperature the largest spread is found over tropical South America while for the mean sea level pressure the largest spread is located over the southeastern Atlantic Ocean, where large synoptic-scale activity occurs. Using nudging techniques to ingest the boundary conditions reduces dramatically the internal variability. The uncertainty due to the domain choice displays a similar spatial pattern compared with the internal variability, except for the mean sea level pressure field, though its magnitude is larger all over the model domain for every variable. The largest spread among ensemble members is found for the ensemble in which different combinations of physical parameterizations are selected. The perturbed physics ensemble produces a level of uncertainty slightly larger than the internal variability. This study suggests that no matter what the source of uncertainty is, the geographical distribution of the spread among members of the ensembles is invariant, particularly for precipitation and temperature.     

Assessing the strength of regional changes in near-surface climate associated with a global warming of 2°C

In this study, the strength of the regional changes in near-surface climate associated with a global warming of 2°C with respect to pre-industrial times is assessed, distinguishing between 26 different regions. Also, the strength of these regional climate changes is compared to the strength of the respective changes associated with a markedly stronger global warming of 4.5°C. The magnitude of the regional changes in climate is estimated by means of a normalized regional climate change index, which considers changes in the mean as well as changes in the interannual variability of both near-surface temperature and precipitation. The study is based on two sets of four ensemble simulations with the ECHAM5/MPI-OM coupled climate model, each starting from different initial conditions. In one set of simulations (1860–2200), the greenhouse gas concentrations and sulphate aerosol load have been prescribed according to observations until 2000 and according to the SRES A1B scenario after 2000. In the other set of simulations (2020–2200), the greenhouse gas concentrations and sulphate aerosol load have been prescribed in such a way that the simulated global warming does not exceed 2°C with respect to pre-industrial times. The study reveals the strongest changes in near-surface climate in the same regions for both scenarios, i.e., the Sahara, Northern Australia, Southern Australia and Amazonia. The regions with the weakest changes in near-surface climate, on the other hand, vary somewhat between the two scenarios except for Western North America and Southern South America, where both scenarios show rather weak changes. The comparison between the magnitude of the regional changes in near-surface climate for the two scenarios reveals relatively strong changes in the 2°C-stabilization scenario at high northern latitudes, i.e., Northeastern Europe, Alaska and Greenland, and in Amazonia. Relatively weak regional climate changes in this scenario, on the other hand, are found for Eastern Asia, Central America, Central South America and Southern South America. The ratios between the regional changes in the near-surface climate for the two scenarios vary considerably between different regions. This illustrates a limitation of obtaining regional changes in near-surface climate associated with a particular scenario by means of scaling the regional changes obtained from a widely used “standard” scenario with the ratio of the changes in the global mean temperature projected by these two scenarios.     

The Potential Impact of Global Warming on Hail Losses to Winter Cereal Crops in New South Wales

This study was undertaken to determine the impact of potential global warming on the magnitude of hail losses to winter cereal crops within two areas situated on the western slopes of New South Wales, Australia. A model relating historical crop hail losses to climatic variables was developed for each area. These models included seasonal measures of vertical instability, low-level moisture and the height of the freezing level. In both areas, windshear was not found to be an important factor influencing seasonal crop hail losses. The two crop hail loss models were then used in conjunction with upper-air climatic data from three single mixed-layer global climate models (GCMs). Each GCM was run for 1 × CO₂ conditions and for 2 × CO₂ conditions. The enhanced greenhouse effect on climatic variables was taken to be the difference between their values for these two runs. Changes to climatic variables were then translated directly into changes in the percentage value of the winter cereal crop lost due to hail. In both areas, the three GCMs agreed concerning the direction of change in each of the variables used in the crop hail loss model. GCM simulations of the greenhouse effect resulted in a decline in winter cereal crop hail losses, with the exception of one GCM simulation at one location where losses increased slightly. None of the changes due to the enhanced greenhouse effect, however, were significant owing to a large observed seasonal variability of crop hail losses. Also, the simulated seasonal variability of crop hail losses did not change significantly due to the enhanced greenhouse effect. These results depended on two important assumptions. Firstly, it was assumed that the dominant relationships between climatic variables and crop hail losses in the past would remain the same in a future climate. Secondly, it was assumed that the single mixed-layer GCMs used in the study were correctly predicting climate change under enhanced greenhouse conditions.     

The sensitivity of the Indian summer monsoon to a global warming of 2��C with respect to pre-industrial times

In this study the potential future changes in different aspects of the Indian summer monsoon associated with a global warming of 2°C with respect to pre-industrial times are assessed, focussing on the role of the different mechanisms leading to these changes. In addition, these changes as well as the underlying mechanisms are compared to the corresponding changes associated with a markedly stronger global warming exceeding 4.5°C, associated with the widely used SRES A1B scenario. The study is based on two sets of four ensemble simulations with the ECHAM5/MPI-OM coupled climate model, each starting from different initial conditions. In one set of simulations (2020?C2200), greenhouse gas concentrations and sulphate aerosol load have been prescribed in such a way that the simulated global warming dioes not exceed 2°C with respect to pre-industrial times. In the other set of simulations (1860?C2200), greenhouse gas concentrations and sulphate aerosol load have been prescribed according to observations until 2000 and according to the SRES A1B scenario after 2000. The study reveals marked changes in the Indian summer monsoon associated with a global warming of 2°C with respect to pre-industrial conditions, namely an intensification of the summer monsoon precipitation despite a weakening of the large-scale monsoon circulation. The increase in the monsoon rainfall is related to a variety of different mechanisms, with the intensification of the atmospheric moisture transport into the Indian region as the most important one. The weakening of the large-scale monsoon circulation is mainly caused by changes in the Walker circulation with large-scale divergence (convergence) in the lower (uppper) troposphere over the Indian Ocean in response to enhanced convective activity over the Indian Ocean and the central and eastern Pacific and reduced convective activity over the western tropical Pacific. These changes in the Walker circulation induce westerly (easterly) wind anomalies at lower (upper) level in the Indian region. The comparison with the changes in the Indian summer monsoon associated with a global warming of 4.5°C reveals that both the intensification of the monsoon precipitation and the weakening of the large-scale monsoon circulation (particularly in the lower troposphere) are relatively strong (with respect to the magnitude of the projected global warming by the end of the twentieth century for the two scenarios) in the scenario with a global warming of 2°C. The relatively strong intensification of the monsoon rainfall is related to rather strong increases in evaporation over the Arabian Sea and the Bay of Bengal, while a rather weak amplification of the meridional temperature gradient between the Indian Ocean and the land areas to the north contributes to the relatively strong reduction of the large-scale monsoon flow.     

Impacts of Anthropogenic Forcings and El Nio on Chinese Extreme Temperatures

This study investigates the potential influences of anthropogenic forcings and natural variability on the risk of summer extreme temperatures over China.We use three multi-thousand-member ensemble simulations with different forcings(with or without anthropogenic greenhouse gases and aerosol emissions) to evaluate the human impact,and with sea surface temperature patterns from three different years around the El Ni ?no–Southern Oscillation(ENSO) 2015/16 event(years 2014,2015 and 2016) to evaluate the impact of natural variability.A generalized extreme value(GEV) distribution is used to fit the ensemble results.Based on these model results,we find that,during the peak of ENSO(2015),daytime extreme temperatures are smaller over the central China region compared to a normal year(2014).During 2016,the risk of nighttime extreme temperatures is largely increased over the eastern coastal region.Both anomalies are of the same magnitude as the anthropogenic influence.Thus,ENSO can amplify or counterbalance(at a regional and annual scale) anthropogenic effects on extreme summer temperatures over China.Changes are mainly due to changes in the GEV location parameter.Thus,anomalies are due to a shift in the distributions and not to a change in temperature variability.     

Assessment of different metrics for physical climate feedbacks

We quantify the feedbacks from the physical climate system on the radiative forcing for idealized climate simulations using four different methods. The results differ between the methods and differences are largest for the cloud feedback. The spatial and temporal variability of each feedback is used to estimate the averaging scale necessary to satisfy the feedback concept of one constant global mean value. We find that the year-to-year variability, combined with the methodological differences, in estimates of the feedback strength from a single model is comparable to the model-to-model spread in feedback strength of the CMIP3 ensemble. The strongest spatial and temporal variability is in the short-wave component of the cloud feedback. In our simulations, where many sources of natural variability are neglected, long-term averages are necessary to get reliable feedback estimates. Considering the large natural variability and relatively small forcing present in the real world, as compared to the forcing imposed by doubling CO₂ concentrations in the simulations, implies that using observations to constrain feedbacks is a challenging task and requires reliable long-term measurements.