Climate variability and projected change in the western United States: regional downscaling and drought statistics

Climate change in the twenty-first century, projected by a large ensemble average of global coupled models forced by a mid-range (A1B) radiative forcing scenario, is downscaled to Climate Divisions across the western United States. A simple empirical downscaling technique is employed, involving model-projected linear trends in temperature or precipitation superimposed onto a repetition of observed twentieth century interannual variability. This procedure allows the projected trends to be assessed in terms of historical climate variability. The linear trend assumption provides a very close approximation to the time evolution of the ensemble-average climate change, while the imposition of repeated interannual variability is probably conservative. These assumptions are very transparent, so the scenario is simple to understand and can provide a useful baseline assumption for other scenarios that may incorporate more sophisticated empirical or dynamical downscaling techniques. Projected temperature trends in some areas of the western US extend beyond the twentieth century historical range of variability (HRV) of seasonal averages, especially in summer, whereas precipitation trends are relatively much smaller, remaining within the HRV. Temperature and precipitation scenarios are used to generate Division-scale projections of the monthly palmer drought severity index (PDSI) across the western US through the twenty-first century, using the twentieth century as a baseline. The PDSI is a commonly used metric designed to describe drought in terms of the local surface water balance. Consistent with previous studies, the PDSI trends imply that the higher evaporation rates associated with positive temperature trends exacerbate the severity and extent of drought in the semi-arid West. Comparison of twentieth century historical droughts with projected twenty-first century droughts (based on the prescribed repetition of twentieth century interannual variability) shows that the projected trend toward warmer temperatures inhibits recovery from droughts caused by decade-scale precipitation deficits.     

An assessment of potential change in wildfire activity in the Russian boreal forest zone induced by climate warming during the twenty-first century

The problem of forest fires is very important for Russia. In this paper we consider this problem in the connection with the projection of significant climate change. An approach to determine the magnitude of change in wildfire risk in Russia under the influence of climate warming is discussed. Observations for the European part of Russia and for Siberia have been used in this analysis. A statistical correlation between drought indices calculated by use of monthly sums of temperature and precipitation and the frequency of fire danger was obtained for the forest zone of Russia. The change in fire danger potential was evaluated using temperature and precipitation monthly means at the nodes of a regular spatial grid. Climate change scenarios were obtained from Global Climate Models (GCM) ensemble projections. The maximum increases (about 12–30%) of the number of days with fire danger conditions during the twenty-first century fire season were obtained for the southern forest zone boundary in both the European region of Russia and in Siberia. In the Baikal and Primoriye Regions, fire danger distributions in the twenty-first century are not projected to change significantly.     

Global warming and 21st century drying

Global warming is expected to increase the frequency and intensity of droughts in the twenty-first century, but the relative contributions from changes in moisture supply (precipitation) versus evaporative demand (potential evapotranspiration; PET) have not been comprehensively assessed. Using output from a suite of general circulation model (GCM) simulations from phase 5 of the Coupled Model Intercomparison Project, projected twenty-first century drying and wetting trends are investigated using two offline indices of surface moisture balance: the Palmer Drought Severity Index (PDSI) and the Standardized Precipitation Evapotranspiration Index (SPEI). PDSI and SPEI projections using precipitation and Penman-Monteith based PET changes from the GCMs generally agree, showing robust cross-model drying in western North America, Central America, the Mediterranean, southern Africa, and the Amazon and robust wetting occurring in the Northern Hemisphere high latitudes and east Africa (PDSI only). The SPEI is more sensitive to PET changes than the PDSI, especially in arid regions such as the Sahara and Middle East. Regional drying and wetting patterns largely mirror the spatially heterogeneous response of precipitation in the models, although drying in the PDSI and SPEI calculations extends beyond the regions of reduced precipitation. This expansion of drying areas is attributed to globally widespread increases in PET, caused by increases in surface net radiation and the vapor pressure deficit. Increased PET not only intensifies drying in areas where precipitation is already reduced, it also drives areas into drought that would otherwise experience little drying or even wetting from precipitation trends alone. This PET amplification effect is largest in the Northern Hemisphere mid-latitudes, and is especially pronounced in western North America, Europe, and southeast China. Compared to PDSI projections using precipitation changes only, the projections incorporating both precipitation and PET changes increase the percentage of global land area projected to experience at least moderate drying (PDSI standard deviation of ≤?1) by the end of the twenty-first century from 12 to 30 %. PET induced moderate drying is even more severe in the SPEI projections (SPEI standard deviation of ≤?1; 11 to 44 %), although this is likely less meaningful because much of the PET induced drying in the SPEI occurs in the aforementioned arid regions. Integrated accounting of both the supply and demand sides of the surface moisture balance is therefore critical for characterizing the full range of projected drought risks tied to increasing greenhouse gases and associated warming of the climate system.     

Hydrologic effects of climate change in the Yukon River Basin

A monthly water balance (WB) model was developed for the Yukon River Basin (YRB). The WB model was calibrated using mean monthly values of precipitation and temperature derived from the Precipitation-elevation Regression on Independent Slopes Model (PRISM) data set and by comparing estimated mean monthly runoff with runoff measured at Pilot Station, Alaska. The calibration procedure used the Shuffled Complex Evolution global search. Potential hydrologic effects of climate change were assessed for the YRB by imposing changes in precipitation and temperature derived from selected Inter-governmental Panel for Climate Change (IPCC) climate simulations. Scenarios from five general circulation model (GCM) simulations were used to provide a range of potential changes. Results from the scenarios indicate an increase in annual runoff in the twenty-first century for the YRB with simulated increases in precipitation having the greatest effect on increases in runoff. Simulated increases in temperature were found to alter the timing of snow accumulation and melt.     

Going to the Extremes

Projections of changes in climate extremes are critical to assessing the potential impacts of climate change on human and natural systems. Modeling advances now provide the opportunity of utilizing global general circulation models (GCMs) for projections of extreme temperature and precipitation indicators. We analyze historical and future simulations of ten such indicators as derived from an ensemble of 9 GCMs contributing to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR4), under a range of emissions scenarios. Our focus is on the consensus from the GCM ensemble, in terms of direction and significance of the changes, at the global average and geographical scale. The climate extremes described by the ten indices range from heat-wave frequency to frost-day occurrence, from dry-spell length to heavy rainfall amounts. Historical trends generally agree with previous observational studies, providing a basic sense of reliability for the GCM simulations. Individual model projections for the 21st century across the three scenarios examined are in agreement in showing greater temperature extremes consistent with a warmer climate. For any specific temperature index, minor differences appear in the spatial distribution of the changes across models and across scenarios, while substantial differences appear in the relative magnitude of the trends under different emissions rates. Depictions of a wetter world and greater precipitation intensity emerge unequivocally in the global averages of most of the precipitation indices. However, consensus and significance are less strong when regional patterns are considered. This analysis provides a first overview of projected changes in climate extremes from the IPCC-AR4 model ensemble, and has significant implications with regard to climate projections for impact assessments. An erratum to this article is available at . An erratum to this article can be found at     

The first multi-model ensemble of regional climate simulations at kilometer-scale resolution part 2: historical and future simulations of precipitation

This paper presents the first multi-model ensemble of 10-year, “convection-permitting” kilometer-scale regional climate model (RCM) scenario simulations downscaled from selected CMIP5 GCM projections for historical and end of century time slices. The technique is to first downscale the CMIP5 GCM projections to an intermediate 12–15 km resolution grid using RCMs, and then use these fields to downscale further to the kilometer scale. The aim of the paper is to provide an overview of the representation of the precipitation characteristics and their projected changes over the greater Alpine domain within a Coordinated Regional Climate Downscaling Experiment Flagship Pilot Study and the European Climate Prediction system project, tasked with investigating convective processes at the kilometer scale. An ensemble of 12 simulations performed by different research groups around Europe is analyzed. The simulations are evaluated through comparison with high resolution observations while the complementary ensemble of 12 km resolution driving models is used as a benchmark to evaluate the added value of the convection-permitting ensemble. The results show that the kilometer-scale ensemble is able to improve the representation of fine scale details of mean daily, wet-day/hour frequency, wet-day/hour intensity and heavy precipitation on a seasonal scale, reducing uncertainty over some regions. It also improves the representation of the summer diurnal cycle, showing more realistic onset and peak of convection. The kilometer-scale ensemble refines and enhances the projected patterns of change from the coarser resolution simulations and even modifies the sign of the precipitation intensity change and heavy precipitation over some regions. The convection permitting simulations also show larger changes for all indices over the diurnal cycle, also suggesting a change in the duration of convection over some regions. A larger positive change of frequency of heavy to severe precipitation is found. The results are encouraging towards the use of convection-permitting model ensembles to produce robust assessments of the local impacts of future climate change.

     

Non-parametric trend analysis of the aridity index for three large arid and semi-arid basins in Iran

Currently, an important scientific challenge that researchers are facing is to gain a better understanding of climate change at the regional scale, which can be especially challenging in an area with low and highly variable precipitation amounts such as Iran. Trend analysis of the medium-term change using ground station observations of meteorological variables can enhance our knowledge of the dominant processes in an area and contribute to the analysis of future climate projections. Generally, studies focus on the long-term variability of temperature and precipitation and to a lesser extent on other important parameters such as moisture indices. In this study the recent 50-year trends (1955–2005) of precipitation (P), potential evapotranspiration (PET), and aridity index (AI) in monthly time scale were studied over 14 synoptic stations in three large Iran basins using the Mann–Kendall non-parametric test. Additionally, an analysis of the monthly, seasonal and annual trend of each parameter was performed. Results showed no significant trends in the monthly time series. However, PET showed significant, mostly decreasing trends, for the seasonal values, which resulted in a significant negative trend in annual PET at five stations. Significant negative trends in seasonal P values were only found at a number of stations in spring and summer and no station showed significant negative trends in annual P. Due to the varied positive and negative trends in annual P and to a lesser extent PET, almost as many stations with negative as positive trends in annual AI were found, indicating that both drying and wetting trends occurred in Iran. Overall, the northern part of the study area showed an increasing trend in annual AI which meant that the region became wetter, while the south showed decreasing trends in AI.     

Long-term precipitation variability in the Ionian Islands, Greece (Central Mediterranean): climatic signal analysis and future projections

The present study examines the variability of the precipitation regime across the Ionian Islands complex, Greece (Central Mediterranean), for a period spanning more than one century. Significant negative long-term linear trends in the annual precipitation totals are observed, more pronounced in the southern parts of the studied area, while a climatic discontinuity possibly occurred during the 1970s, manifested first in the southern Ionian. Statistically significant nonlinear trends and subdecadal intermittent oscillations were detected using Monte Carlo singular spectral analysis. The correlation of the winter precipitation variability at Ionian complex with the North Atlantic Oscillation anomalies was also investigated and extended in the frequency domain. Finally, future projections were performed using an ensemble of Regional Climatic Models. Model simulations suggested a decrease of the order of ～20% or more in the mean annual precipitation of the area by the end of the century.     

Past and Future Changes in the Climate of Hong Kong

Over the years, the Hong Kong Observatory has carried out scientific studies to evaluate the observed climate trends and project the future climate in Hong Kong. Analysis of the meteorological observations at the observatory's headquarters in Tsim Sha Tsui since 1885 reveals that the temperature rise in Hong Kong during the past 124 years is in accord with the global rising trend. The accelerated rising trend in the mean temperature in last few decades may be attributed to the anthropogenic influences, especially urbanization. A similar increasing trend is also observed for rainfall. Other observations such as increasing cloud amount and decreasing total global solar radiation are all consistent with the global trend. Studies of past occurrences of extreme temperature and rainfall have also been carried out. The results indicate that cold episodes have become rarer while very hot days and heavy rain events are becoming more frequent. The observatory also makes use of the data from the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) and employs statistical downscaling techniques to carry out projections of temperature and precipitation in the 21st century. It is found that the rise in temperature in Hong Kong will be slightly higher than the global mean in the 21st century. The annual rainfall in Hong Kong is also expected to rise by the end of the 21st century, so is its year-to-year variability.     

Observed and projected climate change in the European region during the twentieth and twenty-first centuries according to Feddema

Climate change in the European region during the twentieth and twenty-first centuries is analyzed according to Feddema’s method. Precipitation and air temperature data from the twentieth century are taken from the Climatic Research Unit, while data for the twenty-first century are taken from the ENSEMBLES climate change project. The latter were bias-corrected to ensure homogeneity across the twentieth and twenty-first centuries. Climate classes based on monthly and annual values of potential evapotranspiration, precipitation and their ratio, are defined for 30-year averages, from which trend and spatial agreement analysis are calculated. There are separate classes for annual values and for intra-annual variation. The results indicate that the change of annual climate characteristics will be much more intense in the twenty-first than it was in the twentieth century. The dominant process in the projections is warming, mostly via cold to cool (about 45% of grid points) in north Europe and cool to warm (about 8% of grid points) transformations. The second most important process is the drying of moderately moist classes affecting about 10% of the grid points in south Europe. Changes in intra-annual variability classes are more common than changes in the annual ones during the twentieth century. The chance of increase in intra-annual temperature variation from high to extreme is about 5% during the course of the twentieth century, and about 10% in the following century.     

Extreme climate events in China: IPCC-AR4 model evaluation and projection

Observations from 550 surface stations in China during 1961–2000 are used to evaluate the skill of seven global coupled climate models in simulating extreme temperature and precipitation indices. It is found that the models have certain abilities to simulate both the spatial distributions of extreme climate indices and their trends in the observed period. The models’ abilities are higher overall for extreme temperature indices than for extreme precipitation indices. The well-simulated temperature indices are frost days (Fd), heat wave duration index (HWDI) and annual extreme temperature range (ETR). The well-simulated precipitation indices are the fraction of annual precipitation total due to events exceeding the 95th percentile (R95T) and simple daily intensity index (SDII). In a general manner, the multi-model ensemble has the best skill. For the projections of the extreme temperature indices, trends over the twenty-first century and changes at the end of the twenty-first century go into the same direction. Both frost days and annual extreme temperature range show decreasing trends, while growing season length, heat wave duration and warm nights show increasing trends. The increases are especially manifested in the Tibetan Plateau and in Southwest China. For extreme precipitation indices, the end of the twenty-first century is expected to have more frequent and more intense extreme precipitation. This is particularly visible in the middle and lower reaches of the Yangtze River, in the Southeast coastal region, in the west part of Northwest China, and in the Tibetan Plateau. In the meanwhile, accompanying the decrease in the maximum number of consecutive dry days in Northeast and Northwest, drought situation will reduce in these regions.     

Future evolution of extreme precipitation in the Mediterranean

Mediterranean basins can be impacted by severe floods caused by extreme rainfall, and there is a growing awareness about the possible increase in these heavy rainfall events due to climate change. In this study, the climate change impacts on extreme daily precipitation in 102 catchments covering the whole Mediterranean basin are investigated using nonstationary extreme value model applied to annual maximum precipitation in an ensemble of high-resolution regional climate model (RCM) simulations from the Euro-CORDEX experiment. Results indicate contrasted trends, with significant increasing trends in Northern catchments and conversely decreasing trends in Southern catchments. For most cases, the time of signal emergence for these trends is before the year 2000. The same spatial pattern is obtained under the two climate scenarios considered (RCP4.5 and RCP8.5) and in most RCM simulations, suggesting a robust climate change signal. The strongest multi-model agreement concerns the positive trends, which can exceed +?20% by the end of the twenty-first century in some simulations, impacting South France, North Italy, and the Balkans. For these areas, society-relevant strong impacts of such Mediterranean extreme precipitation changes could be expected in particular concerning flood-related damages.     

GCM-based regional temperature and precipitation change estimates for Europe under four SRES scenarios applying a super-ensemble pattern-scaling method

Seasonal GCM-based temperature and precipitation projections for the end of the 21st century are presented for five European regions; projections are compared with corresponding estimates given by the PRUDENCE RCMs. For most of the six global GCMs studied, only responses to the SRES A2 and B2 forcing scenarios are available. To formulate projections for the A1FI and B1 forcing scenarios, a super-ensemble pattern-scaling technique has been developed. This method uses linear regression to represent the relationship between the local GCM-simulated response and the global mean temperature change simulated by a simple climate model. The method has several advantages: e.g., the noise caused by internal variability is reduced, and the information provided by GCM runs performed with various forcing scenarios is utilized effectively. The super-ensemble method proved especially useful when only one A2 and one B2 simulation is available for an individual GCM. Next, 95% probability intervals were constructed for regional temperature and precipitation change, separately for the four forcing scenarios, by fitting a normal distribution to the set of projections calculated by the GCMs. For the high-end of the A1FI uncertainty interval, temperature increases close to 10°C could be expected in the southern European summer and northern European winter. Conversely, the low-end warming estimates for the B1 scenario are ~ 1°C. The uncertainty intervals of precipitation change are quite broad, but the mean estimate is one of a marked increase in the north in winter and a drastic reduction in the south in summer. In the RCM simulations driven by a single global model, the spread of the temperature and precipitation projections tends to be smaller than that in the GCM simulations, but it is possible to reduce this disparity by employing several driving models for all RCMs. In the present suite of simulations, the difference between the mean GCM and RCM projections is fairly small, regardless of the number or driving models applied.     

Evaluation of the twenty-first century RCM simulations driven by multiple GCMs over the Eastern Mediterranean–Black Sea region

In this study, human-induced climate change over the Eastern Mediterranean–Black Sea region has been analyzed for the twenty-first century by performing regional climate model simulations forced with large-scale fields from three different global circulation models (GCMs). Climate projections have been produced with Special Report on Emissions Scenarios A2, A1FI and B1 scenarios, which provide greater diversity in climate information for future period. The gradual increases for temperature are widely apparent during the twenty-first century for each scenario simulation, but ECHAM5-driven simulation generally has a weaker signal for all seasons compared to CCSM3 simulations except for the Fertile Crescent. The contrast in future temperature change between the winter and summer seasons is very strong for CCSM3-A2-driven and HadCM3-A2-driven simulations over Carpathians and Balkans, 4–5 °C. In addition, winter runoff over mountainous region of Turkey, which feeds many river systems including the Euphrates and Tigris, increases in second half of the century since the snowmelt process accelerates where the elevation is higher than 1,500 m. Moreover, analysis of daily temperature outputs reveals that the gradual decrease in daily minimum temperature variability for January during the twenty-first century is apparent over Carpathians and Balkans. Analysis of daily precipitation extremes shows that positive trend is clear during the last two decades of the twenty-first century over Carpathians for both CCSM3-driven and ECHAM5-driven simulations. Multiple-GCM driven regional climate simulations contribute to the quantification of the range of climate change over a region by performing detailed comparisons between the simulations.     

Storm surge frequency reduction in Venice under climate change

Increased tidal levels and storm surges related to climate change are projected to result in extremely adverse effects on coastal regions. Predictions of such extreme and small-scale events, however, are exceedingly challenging, even for relatively short time horizons. Here we use data from observations, ERA-40 re-analysis, climate scenario simulations, and a simple feature model to find that the frequency of extreme storm surge events affecting Venice is projected to decrease by about 30% by the end of the twenty-first century. In addition, through a trend assessment based on tidal observations we found a reduction in extreme tidal levels. Extrapolating the current +17 cm/century sea level trend, our results suggest that the frequency of extreme tides in Venice might largely remain unaltered under the projected twenty-first century climate simulations.     

Changes in frost, snow and Baltic sea ice by the end of the twenty-first century based on climate model projections for Europe

Changes in indices related to frost and snow in Europe by the end of the twenty-first century were analyzed based on experiments performed with seven regional climate models (RCMs). All the RCMs regionalized information from the same general circulation model (GCM), applying the IPCC-SRES A2 radiative forcing scenario. In addition, some simulations used SRES B2 radiative forcing and/or boundary conditions provided by an alternative GCM. Ice cover over the Baltic Sea was examined using a statistical model that related the annual maximum extent of ice to wintertime coastal temperatures. Fewer days with frost and snow, shorter frost seasons, a smaller liquid water equivalent of snow, and milder sea ice conditions were produced by all model simulations, irrespective of the forcing scenario and the driving GCM. The projected changes have implications across a diverse range of human activities. Details of the projections were subject to differences in RCM design, deviations between the boundary conditions of the driving GCMs, uncertainties in future emissions and random effects due to internal climate variability. A larger number of GCMs as drivers of the RCMs would most likely have resulted in somewhat wider ranges in the frost, snow and sea ice estimates than those presented in this paper.     

SST and circulation trend biases cause an underestimation of European precipitation trends

Clear precipitation trends have been observed in Europe over the past century. In winter, precipitation has increased in north-western Europe. In summer, there has been an increase along many coasts in the same area. Over the second half of the past century precipitation also decreased in southern Europe in winter. An investigation of precipitation trends in two multi-model ensembles including both global and regional climate models shows that these models fail to reproduce the observed trends. In many regions the model spread does not cover the trend in the observations. In contrast, regional climate model (RCM) experiments with observed boundary conditions reproduce the observed precipitation trends much better. The observed trends are largely compatible with the range of uncertainties spanned by the ensemble, indicating that the boundary conditions of RCMs are responsible for large parts of the trend biases. We find that the main factor in setting the trend in winter is atmospheric circulation, for summer sea surface temperature (SST) is important in setting precipitation trends along the North Sea and Atlantic coasts. The causes of the large trends in atmospheric circulation and summer SST are not known. For SST there may be a connection with the well-known ocean circulation biases in low-resolution ocean models. A quantitative understanding of the causes of these trends is needed so that climate model based projections of future climate can be corrected for these precipitation trend biases.     

Evaluating climate change at the Croatian Adriatic from observations and regional climate models’ simulations

The 2m temperature (T2m) and precipitation from five regional climate models (RCMs), which participated in the ENSEMBLES project and were integrated at a 25-km horizontal resolution, are compared with observed climatological data from 13 stations located in the Croatian coastal zone. The twentieth century climate was simulated by forcing RCMs with identical boundary conditions from the ERA-40 reanalysis and the ECHAM5/MPI-OM global climate model (GCM); climate change in the twenty-first century is based on the A1B scenario and assessed from the GCM-forced RCMs’ integrations. When forced by ERA-40, most RCMs exhibit cold bias in winter which contributes to an overestimation of the T2m annual cycle amplitude and the errors in interannual variability are in all RCMs smaller than those in the climatological mean. All models underestimate observed warming trends in the period 1951–2010. The largest precipitation biases coincide with locations/seasons with small observed amounts but large precipitation amounts near high orography are relatively well reproduced. When forced by the same GCM all RCMs exhibit a warming in the cold half-year and a cooling (or weak warming) in the warm period, implying a strong impact of GCM boundary forcing. The future eastern Adriatic climate is characterised by a warming, up to +5 °C towards the end of the twenty-first century; for precipitation, no clear signal is evident in the first half of the twenty-first century, but a reduction in precipitation during summer prevails in the second half. It is argued that land-sea contrast and complex coastal configuration of the Croatian coast, i.e. multitude of island and well indented coastline, have a major impact on small-scale variability. Orography plays important role only at small number of coastal locations. We hypothesise that the parameterisations related to land surface processes and soil hydrology have relatively stronger impact on variability than orography at those locations that include a relatively large fraction of land (most coastal stations), but affecting less strongly locations at the Adriatic islands.     

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.     

A transient climate change simulation with greenhouse gas and aerosol forcing: experimental design and comparison with the instrumental record for the twentieth century

 The Canadian Centre for Climate Modelling and Analysis (CCCma) global coupled model is used to investigate the potential climate effects of increasing greenhouse gas (GHG) concentrations and changes in sulfate aerosol loadings. The forcing scenario adopted closely resembles that of Mitchell et al. for both the greenhouse gas and aerosol components. Its implementation in the model and the resulting changes in forcing are described. Five simulations of 200 years in length, nominally for the years 1900 to 2100, are available for analysis. They consist of a control simulation without change in forcing, three independent simulations with the same greenhouse gas and aerosol changes, and a single simulation with greenhouse gas only forcing. Simulations of the evolution of temperature and precipitation from 1900 to the present are compared with available observations. Temperature and precipitation are primary climate variables with reasonable temporal and spatial coverage in the observational record for the period. The simulation of potential climate change from the present to the end of the twenty-first century, based on projected GHG and aerosol forcing changes, is discussed in a companion paper. For the historical period dealt with here, the GHG and aerosol forcing has changed relatively little compared to the forcing changes projected to the end of the twenty-first century. Nevertheless, the forced climate signal for temperature in the model is reasonably consistent with the observed global mean temperature from the instrumental record. This is true also for the trend in zonally averaged temperature as a function of latitude and for some aspects of the geographical and regional distributions of temperature. Despite the modest change in overall forcing, the difference between GHG+aerosol and GHG-only forcing is discernible in the temperature response for this period. Changes in precipitation, on the other hand, are much less evident in both the instrumental and simulated record. There is an apparent increasing trend in average precipitation in both the observations and the model results over that part of the land for which observations are available. Regional and geographical changes and trends (which are less affected by sampling considerations), if they exist, are masked by the large natural variability of precipitation in both model and observations. Received: 24 September 1998 / Accepted: 8 October 1999