Anthropogenic Climate Change Signal Research Articles

The impacts of climate change on offshore wind park projects: from changes in resource to project profitability

Offshore wind power generation is projected to increase 15-fold over the next two decades. This generation is, however, weather dependent. The compounding of the anthropogenic climate change signal with high spatial and temporal wind variability can lead to large uncertainties in the projected impacts of climate change on wind resources, and further down the line in the economics of a project. In this study, we showcase a methodology to analyze the impact of climate change on the economic indicators of an offshore wind farm project. Projections of changes in wind resources are obtained using an ensemble of statistically downscaled Coupled Model Intercomparison Project 6 (CMIP6) for different emission scenarios and time periods. A series of assumptions about the features of a representative wind farm and its key economic parameters are made to compute two economic indicators: the Internal Rate of Return (IRR) and the Levelized Cost of Energy (LCOE). The IRR is an estimate of the profitability of potential investments and the LCOE is the cost over the lifetime of an investment compared to the expected energy production. We find that the effect of changes of resource on IRR and LCOE depends on the region, emissions scenario, and projection period. In general, and conditioned on the assumptions underlying this study, impacts on IRR are more significant -i.e., with differences between -0.5% and -1% in Brazil- than on LCOE. We conclude that it is especially important to take into account climate change when making investment decisions based on a project’s expected profitability.

Spatiotemporal characteristics of the time of emergence for anthropogenic tropospheric temperature changes based on the CMIP6 multi-model results

In the 20th century, with the intensification of human activities, the Earth is experiencing unprecedented warming. However, there are certain differences in the sensitivity of temperature changes to anthropogenic forcings in different regions and at different altitudes of the troposphere. The time of emergence (TOE) is the key point at which the anthropogenic climate change signal exceeds from the internal climate variability serving as a noise. It is a crucial variable for climate change detection, climate prediction and risk assessment. Here, we systematically analyzed the spatiotemporal characteristics of the TOE of temperature changes over the past century by calculating the SNR based on the selected CMIP6 multi-model outputs. The results show that the temperature TOE, particularly in the lower and middle troposphere, shows distinct latitude dependence, displaying an ‘M-type’ distribution from the Antarctic to the Arctic: it first appears in low-latitudes, followed by high-latitudes, and last appears in the two mid latitude bands. For the tropics, the TOE of tropospheric temperatures becomes earlier with increasing altitude: the TOE of air temperatures at the surface, mid-tropospheric 500 hPa and upper-tropospheric 200 hPa occurs in 1980 ± 15, 1965 ± 20, and 1930 ± 30, respectively. The TOEs of tropospheric temperatures in eastern equatorial Pacific are 10–30 years later than those in the western equatorial Pacific. For the regional TOEs of surface air temperature diverse differences exist on land and ocean in various latitudes of two hemispheres.

The emergence of a climate change signal in long-term Irish meteorological observations

Detecting the emergence of a forced anthropogenic climate change signal from observations is critical for informing adaptation responses. By regressing local variations in climate onto annual Global Mean Surface Temperature (GMST), we track the emergence of an anthropogenic signal in long-term quality assured observations of temperature and precipitation for the island of Ireland, a sentinel location on the western European Atlantic seaboard. Analysis of station based observations, together with island scale composite series is undertaken for annual and seasonal means, together with 16 indices of extremes, with the derived signal-to-noise ratio classified as normal, unusual or unfamiliar relative to early industrial climate. More than half of indices show the emergence of at least unusual conditions relative to early industrial climate. The increase in annual mean temperature has led to the emergence of unfamiliar climate at six of eleven stations. Warming at the island scale is estimated at 0.88 °C per degree warming in GMST. While many stations show the emergence of unusual climate for spring, summer and autumn mean temperature, no forced signal of change is found for winter mean temperature. Changes in cool/warm days and nights are unfamiliar relative to early industrial climate. However, no anthropogenic signal is found for the hottest day annually or in summer – an extreme often associated with climate change in public consciousness. Increases in annual precipitation totals have emerged as unusual for western stations with large increases in winter totals per degree warming in GMST (e.g., 25.2% and 19.7% at Malin Head and Markree, respectively), indicating heightened flood risk with continued warming. By contrast, summer precipitation shows no significant relationship with GMST. Increases in rainfall intensity have emerged as unusual for 30% of stations, with increases consistent with the Clausius-Clapeyron relationship. Our analysis shows that an emerging climate change signal is discernible for Ireland, a location strongly influenced by climate variability.

Evaluation of climate variability and change in ACCESS historical simulations for CMIP6

We analyse and document the historical simulations performed by two versions of the Australian Community Climate and Earth System Simulator (ACCESS-CM2 and ACCESS-ESM1.5) for the Coupled Model Intercomparison Project Phase 6 (CMIP6). Three ensemble members from each model are used to compare the simulated seasonal-mean climate, climate variability and climate change with observations over the historical period. Where appropriate, we also compare the ACCESS model results with the results from 36 other CMIP6 models. We find that the simulations of the winter and summer mean climates (over the global domain) by the two ACCESS models are similar to or better than most of the other CMIP6 models for surface temperature, precipitation and surface specific humidity. For sea-level pressure, both ACCESS models perform worse than most other models. The spatial structures of the prominent climate variability modes (ENSO, IOD, IPO and AMO) also compare favourably with the corresponding observed structures. However, the results for the simulation of the models’ temporal variability are mixed. In particular, whereas ACCESS-ESM1.5 simulates ENSO events with ~3-year periods (that are closer to the observed periods of 3–7 years), the ACCESS-CM2 simulates ENSO events having quasi-biennial periods. However, ACCESS-CM2 has a much smaller bias (−0.1 W m−2) in present-day top-of-the-atmosphere energy balance than ACCESS-ESM1.5 (−0.6 W m−2). The ACCESS models simulate the anthropogenic climate change signal in historical global-mean surface temperature reasonably well, although the simulated signal variances are ~10% weaker than the observed signal variance (a common bias in most CMIP6 models). Both models also well simulate the major features of observed surface temperature changes, as isolated using a multiple regression model. Despite some identified biases, the two ACCESS models provide high-quality climate simulations that may be used in further analyses of climate variability and change.

Anthropogenic climate change contribution to wildfire-prone weather conditions in the Cerrado and Arc of deforestation

2015 saw the strongest El Niño event in the historical record, resulting in extreme drought conditions in Brazil. As drought conditions may also lead to greater fire danger, this study uses the 2015 fire in Brazil as a case study to examine whether and to what extent human-induced climate change has contributed to the fire weather conditions in the Cerrado and the southern Amazonia transitional forests known as the Arc of deforestation. Our results show that anthropogenic climate change is indeed a driver of meteorological conditions conducive to strong fire weather in these two regions, measured by fire weather index (FWI), especially on shorter timescales of daily and weekly. The anthropogenic climate change signal of FWI on short timescales corresponds to a similar order of increase in the FWI sub-indices (initial spread index and fine fuel moisture code) that can rapidly change due to the influence of the instantaneous weather conditions. For both regions the changes in fire weather in response to anthropogenic climate change are dominated by the combination of temperature and relative humidity responses. High FWI is more likely to occur under El Niño conditions, less likely under La Niña conditions, although the impacts of El Niño vs La Niña conditions are not symmetric when compared with El Niño Southern Oscillation neutral states. To summarize, both human-induced climate change and the presence of El Niño increased the likelihood of occurrence for the strong fire weather condition in 2015. Our results suggest that local and regional adaptation measures, such as improved drought monitoring and warning systems, could help with effective planning of fire prevention, firefighting actions, and disaster preparedness.

The Impact of Human‐Induced Climate Change on Regional Drought in the Horn of Africa

AbstractA severe drought hit the Greater Horn of Africa (GHA) in 2014, but it remains unclear whether this extreme event was attributable to anthropogenic climate change or part of longer‐term natural cycles. Precipitation patterns are known to be changing across the GHA, but trajectories in land surface variables are much less well known. We simulated the GHA land surface environment to assess the balance between natural cycles and human‐induced climate change. Using a new form of event attribution study where we focused on both climate variables and also directly simulated land surface variables, we combined publicly volunteered distributed computing with land surface simulations to quantify land surface responses. Uncertainty was quantified both for climate model and land surface model outputs. We identified two distinct “drought trajectories” in the GHA bimodal seasonality area during the March–May (Long Rains season) of 2014. Human‐induced climate change may have resulted in regions from Lake Nalubaale (Lake Victoria) to Northern Kenya receiving less precipitation in this season and having up to 20% higher probability of drought‐level evapotranspiration rates (increasing drought). In contrast, the simulated anthropogenic climate change signal for this season induced somewhat wetter conditions and up to 20% lower probability of drought‐level evapotranspiration in Eastern Ethiopia, Southern Somalia, and coastal Kenya (decreasing drought). Uncertainties in our modeling system varied by region and variable of focus, but broadly we found that land surface simulation uncertainty neither added significantly to climate model uncertainty nor significantly reduced it.

How much of Typhoon Morakot's extreme rainfall is attributable to anthropogenic climate change?

Typhoon Morakot (2009), which made landfall in Taiwan during August 7–9, 2009, produced the highest rainfall and became the deadliest typhoon ever recorded in Taiwan since 1958. To assess the role of anthropogenic climate change in the typhoon‐related torrent, we compare the water budget between a pair of cloud‐resolving sensitivity experiments. The pair consists of a control simulation that reproduces Typhoon Morakot (2009) in current climate and a sensitivity simulation in which the same storm is placed in a slightly different climate background where the late 20th century anthropogenic climate change signal is removed. The anthropogenic signal is estimated with the CMIP5 experiments of 18 models for the period of 1985–2005, during which the global warming trend is discernible. In climate states that differ merely by a 20‐year mean anthropogenic change, Morakot (2009) yields 3.4–3.6% more total rainfall in the control experiment than its sensitivity counterpart within a radius of 300–500 km from the storm centre. Water budget analysis indicates that the increase in typhoon rainfall is mainly resulted from the enhanced convergence of vapour flux. The enhancement is, in turn, contributed by the increased tropospheric moisture due to global warming and, to a lesser extent, by a more active secondary circulation in the storm that is associated with the anthropogenic climate change.

Attribution of Global Soil Moisture Drying to Human Activities: A Quantitative Viewpoint

AbstractAnthropogenic impacts on widespread global soil moisture (SM) drying in the root zone layer during 1948–2005 were evaluated based on the Global Land Data Assimilation System version 2 (GLDAS‐2) and global climate models from the Coupled Model Intercomparison Project Phase 5 using trend analysis and optimal fingerprint methods. Both methods show agreement that natural forcing alone cannot drive significant SM drying. There is a high probability (≥90%) that the anthropogenic climate change signal is detectable in global SM drying. Specifically, anthropogenic greenhouse gas forcing can lead to global SM drying by 2.1 × 10−3 m3/m3, which is comparable to the drying trend seen in Global Land Data Assimilation System version 2 (2.4 × 10−3 m3/m3) over the past 58 years. Global SM drying is expected to continue in the future, given continuous greenhouse gas emissions.

Bjerknes Compensation in the CMIP5 Climate Models

This study examines the atmospheric and oceanic heat transports in preindustrial control and historical runs of 15 fully coupled global climate models from the CMIP5 project. The presence of Bjerknes compensation is confirmed in all models by the strong anticorrelation and approximately equal magnitude of the anomalies of these heat transports. Previous studies of Bjerknes compensation in the absence of external forcing have all shown the strongest compensation at high latitudes, where the warm ocean meets the cold Arctic atmosphere. In this study, however, it is found that many of the 15 models have a second and often dominant peak of compensation in the northern midlatitudes, where strong air–sea interaction is often associated with the midlatitude storm tracks. It has also been suggested that variations in heat transport in the ocean lead those in the atmosphere, but this work has found no clear and robust support for this, as only half the models show such a relationship. In the historical simulations where external forcings are applied, Bjerknes compensation continues to be present, but many models show pronounced trends in the heat transports. All of the models show multidecadal variability in heat transports in both preindustrial control and historical simulations. Any anthropogenic climate change signal could potentially be masked or amplified by the natural variability governed by Bjerknes compensation. Given its presence in the CMIP5 models, which are the basis of so much policy and adaptation planning, an improved understanding of Bjerknes compensation may have socioeconomic relevance for the future.

Role of Natural Climate Variability in the Detection of Anthropogenic Climate Change Signal for Mean and Extreme Precipitation at Local and Regional Scales

Abstract Climate change will impact both mean and extreme precipitation, having potentially significant consequences on water resources. The implementation of efficient adaptation measures must rely on the development of reliable projections of future precipitation and on the assessment of their related uncertainty. Natural climate variability is a key uncertainty component, which can result in apparent decadal trends that may be greater or lower than the long-term underlying anthropogenic climate change trend. The goal of the present study is to assess how natural climate variability affects the ability to detect the climate change signal for mean and extreme precipitation. Annual and seasonal total precipitation are used as indicators of the mean, whereas annual and seasonal maximum daily precipitation are used as indicators of extremes. This is done using the CanESM2 50-member and CESM1 40-member large ensembles of simulations over the 1950–2100 period. At the local scale, results indicate that natural climate variability will dominate the uncertainty for annual and seasonal extreme precipitation going up to the end of the century in many parts of the world. The climate change signal can, however, be reliably detected much earlier at the regional scale for extreme precipitation. In the case of annual and seasonal total precipitation, the climate change signal can be reliably detected at the local scale without resorting to a regional analysis. Nonetheless, natural climate variability can impede the detection of the anthropogenic climate change signal until the middle to late century in many parts of the world for mean and extreme precipitation.

Anthropogenic impact on Antarctic surface mass balance, currently masked by natural variability, to emerge by mid-century

Global and regional climate models robustly simulate increases in Antarctic surface mass balance (SMB) during the twentieth and twenty-first centuries in response to anthropogenic global warming. Despite these robust model projections, however, observations indicate that there has been no significant change in Antarctic SMB in recent decades. We show that this apparent discrepancy between models and observations can be explained by the fact that the anthropogenic climate change signal during the second half of the twentieth century is small compared to the noise associated with natural climate variability. Using an ensemble of 35 global coupled climate models to separate signal and noise, we find that the forced SMB increase due to global warming in recent decades is unlikely to be detectable as a result of large natural SMB variability. However, our analysis reveals that the anthropogenic impact on Antarctic SMB is very likely to emerge from natural variability by the middle of the current century, thus mitigating future increases in global sea level.

Are Greenhouse Gas Signals of Northern Hemisphere winter extra-tropical cyclone activity dependent on the identification and tracking algorithm?

For Northern Hemisphere extra-tropical cyclone activity, the dependency of a potential anthropogenic climate change signal on the identification method applied is analysed. This study investigates the impact of the used algorithm on the changing signal, not the robustness of the climate change signal itself. Using one single transient AOGCM simulation as standard input for eleven state-of-the-art identification methods, the patterns of model simulated present day climatologies are found to be close to those computed from re-analysis, independent of the method applied. Although differences in the total number of cyclones identified exist, the climate change signals (IPCC SRES A1B) in the model run considered are largely similar between methods for all cyclones. Taking into account all tracks, decreasing numbers are found in the Mediterranean, the Arctic in the Barents and Greenland Seas, the mid-latitude Pacific and North America. Changing patterns are even more similar, if only the most severe systems are considered: the methods reveal a coherent statistically significant increase in frequency over the eastern North Atlantic and North Pacific. We found that the differences between the methods considered are largely due to the different role of weaker systems in the specific methods.

Analyses of annual and seasonal maximum daily rainfall accumulations for Ukraine, Moldova, and Romania

AbstractAnnual and seasonal maximum daily rainfall time series from 44 rain gages in Romania, Ukraine, and Moldova are used to examine questions related to the validity of the stationarity assumption, the dependence of extreme rainfall on elevation, and its upper tail properties during the 20th century. Analyses are performed both at the annual and seasonal scales. The validity of the stationarity assumption is examined by testing the records for abrupt and slowly varying changes. The presence of change‐points in the mean and variance of the daily maximum rainfall distribution is assessed using the nonparametric Pettitt test, while the presence of monotonic patterns is examined using the nonparametric Mann‐Kendall and Spearman tests. These results indicate that most of the violations of the stationarity assumption are related to abrupt rather than slowly varying changes, and that it is difficult to detect an anthropogenic climate change signal in these records. The generalized extreme value (GEV) distribution is used as modelling framework to investigate the dependence of extreme rainfall on elevation and its upper tail properties. Both at the annual and seasonal scales there is no strong dependence of the parameters of the GEV distribution on elevation. These records point to an unbounded‐above and heavy‐tail behaviour, and discussion of these upper tail properties in light of mixtures of different rainfall‐generating mechanisms is provided. Copyright © 2011 Royal Meteorological Society

Analyses of extreme flooding in Austria over the period 1951–2006

AbstractAnalyses of extreme flooding in Austria is performed using daily discharge time series from 27 stations over the period 1951‐2006. The main research questions revolve around: (1) temporal non‐stationarities in the flood record, (2) upper tail and scaling properties of the flood peak records, and (3) relation between magnitude and frequency of flooding and the North Atlantic Oscillation (NAO). Two datasets are derived from the daily discharge time series: annual maximum daily discharge and peaks‐over‐threshold (POT) data. The validity of the stationarity assumption in the annual maximum discharge record is assessed by investigating the presence of abrupt and slowly varying changes using nonparametric tests. The time series are tested for abrupt changes both in the mean and variance of the flood peak distributions by means of the Pettitt test. The presence of monotonic trends is investigated by means of the Mann‐Kendall and Spearman tests. Violations of the stationarity assumption are associated with abrupt rather than gradual changes. These step changes generally involve river regulation through construction of dams or other major engineering works. It is not possible to make conclusive statements about the presence of an anthropogenic climate change signal in the flood peak record. Similar conclusions are obtained when focussing on the frequency of POT floods. The Generalised Extreme Value distribution is used to study the upper tail and scaling properties of annual maximum daily discharge records. The location and scale parameters exhibit power‐law behaviour as a function of drainage area. The shape parameters indicate that the flood peak distributions for Austria have a heavy tail. Non‐stationary modelling of the annual maximum daily discharge and POT time series is used to explore the relation between flood magnitude and frequency and NAO. The results indicate that NAO is a significant covariate in explaining the magnitude and frequency of occurrence of flooding over a large part of Austria. Copyright © 2011 Royal Meteorological Society

Adaptation of the optimal fingerprint method for climate change detection using a well-conditioned covariance matrix estimate

The “optimal fingerprint” method, usually used for detection and attribution studies, requires to know, or, in practice, to estimate the covariance matrix of the internal climate variability. In this work, a new adaptation of the “optimal fingerprints” method is presented. The main goal is to allow the use of a covariance matrix estimate based on an observation dataset in which the number of years used for covariance estimation is close to the number of observed time series. Our adaptation is based on the use of a regularized estimate of the covariance matrix, that is well-conditioned, and asymptotically more precise, in the sense of the mean square error. This method is shown to be more powerful than the basic “guess pattern fingerprint”, and than the classical use of a pseudo-inverted truncation of the empirical covariance matrix. The construction of the detection test is achieved by using a bootstrap technique particularly well-suited to estimate the internal climate variability in real world observations. In order to validate the efficiency of the detection algorithm with climate data, the methodology presented here is first applied with pseudo-observations derived from transient regional climate change scenarios covering the 1960-2099 period. It is then used to perform a formal detection study of climate change over France, analyzing homogenized observed temperature series from 1900 to 2006. In this case, the estimation of the covariance matrix is only based on a part of the observation dataset. This new approach allows the confirmation and extension of previous results regarding the detection of an anthropogenic climate change signal over the country.

Realistic greenhouse gas forcing and seasonal forecasts

This paper investigates the improvement of seasonal forecasts by including realistically varying greenhouse gas (GHG) concentrations. Forecasts starting every May and November are compared over the period 1958 until 2001. One set has constant GHG concentrations while an other has a realistic GHG trend. The large scale temperature trends derived at different lead times are compared between the forecast sets and observations over the entire 44 years. It is shown that after a few months the anthropogenic climate change signal is lost by up to 70% although it was present in the initial conditions. The differences in trends vary with lead times, seasons and regions. Strongest effects are found in the Tropics and the Summer Hemispheres, in particular the Northern one. On the local scale, the improvement is not widespread in trends and very weak in predicting detrended interannual variability. Both sets exhibit a strong absolute temperature bias.

Reevaluating the Causes of Observed Changes in Indian Ocean Water Masses

Abstract The consistency between observed changes in Subantarctic Mode Water (SAMW) properties at 32°S in the Indian Ocean and model simulations is explored using the Third Hadley Centre Coupled Ocean–Atmosphere GCM (HadCM3). Hydrographic data collected in 2002 show that the water mass is warmer and saltier on isopycnals than in 1987, in contrast to the isopycnal freshening observed between 1962 and 1987. The response of HadCM3 under a range of forcing scenarios is explored and the observed freshening is only seen in experiments that include greenhouse gas forcing; however, there is no subsequent return to more saline conditions in 2002. The response of the model to greenhouse gas forcing is dominated by a persistent freshening trend, the simulated water mass variability agrees well with that suggested by the limited observations. Comparing model isopycnal changes from the forced experiments with a control run shows that the changes from the 1960s to 2002 are best explained by internal variability. This is in contrast to earlier work, which attributed the observed isopycnal freshening to anthropogenic forcing. Although the model shows that at present an anthropogenic climate change signal is not detectable in SAMW, the model water mass freshens on isopycnals during the twenty-first century under increased greenhouse gas forcing. This is consistent with recent heat content observations, which suggest that the salting is unlikely to persist. In HadCM3, this freshening is due to an increasing surface heat flux and Ekman heat and freshwater flux into the water mass formation region. This paper emphasizes the importance of higher-frequency observations of SAMW if detection and attribution statements are to be made.

Climate change signal analysis for Northeast Asian surface temperature

Climate change detection, attribution, and prediction were studied for the surface temperature in the Northeast Asian region using NCEP/NCAR reanalysis data and three coupled-model simulations from ECHAM4/OPYC3, HadCM3, and CCCma GCMs (Canadian Centre for Climate Modeling and Analysis general circulation model). The Bayesian fingerprint approach was used to perform the detection and attribution test for the anthropogenic climate change signal associated with changes in anthropogenic carbon dioxide (CO2) and sulfate aerosol (SO 4 2− ) concentrations for the Northeast Asian temperature. It was shown that there was a weak anthropogenic climate change signal in the Northeast Asian temperature change. The relative contribution of CO2 and SO 4 2− effects to total temperature change in Northeast Asia was quantified from ECHAM4/OPYC3 and CCCma GCM simulations using analysis of variance. For the observed temperature change for the period of 1959–1998, the CO2 effect contributed 10%–21% of the total variance and the direct cooling effect of SO 4 2− played a less important role (0%–7%) than the CO2 effect. The prediction of surface temperature change was estimated from the second CO2+SO 4 2− scenario run of ECHAM4/OPYC3 which has the least error in the simulation of the present-day temperature field near the Korean Peninsula. The result shows that the area-mean surface temperature near the Korean Peninsula will increase by about 1.1° by the 2040s relative to the 1990s.

Comments on “Twentieth-Century Trends of Arctic Precipitation from Observational Data and a Climate Model Simulation”

Recently Kattsov and Walsh (2000, hereafter KW) investigated Arctic precipitation changes during the twentieth century. They compared observationally based precipitation data over the Northern Hemisphere subpolar and polar region with climate model output derived from a single long-term ECHAM4/T42 experiment forced with observed SST data. In addition to the SST forcing the model run is also subject to increasing greenhouse gas concentrations according to historical measurements. With regard to the area-averaged annual sums of total preciptation, a clear positive trend is common to both datasets, observed and simulated, particularly during the first half of the twentieth century. Assuming that the model’s ability to reproduce the observed time series is not due to chance, this result suggests that SST and/or CO2 changes may be responsible for this increase in Arctic precipitation. However, the authors point to some uncertainty factors, mainly concerning the accuracy of the observational data (cf. Cullather et al. 2000) and some unrealistic features in the Global Ice and Sea Surface Temperature data version 2.2 (GISST2.2) sea ice data as lower boundary condition of the model experiment. Here we want to present some additional results based on Monte Carlo experiments with the same model to pick up another aspect that generally limits the reliability of intercomparison studies between observations and single model simulations, namely the discrimination of externally induced and internal chaotic variability components. Time–space variations of observed climatological variables are composed of natural variability induced by internal stochastic processes as well as external factors like the solar cycle, vulcanic eruptions, etc. (Fig. 1). A potential external either natural or anthropogenic climate change signal is hardly distinguishable from noise since natural fluctuations of the real climate system are largely unknown and cannot be separated without additional assumptions. Similar considerations ap-

ARE NATURAL CLIMATE FORCINGS ABLE TO COUNTERACT THE PROJECTED ANTHROPOGENIC GLOBAL WARMING

A two-dimensional global climate model is used to assess the climatic changes associated with the new IPCC SRES emissions scenarios and to determine which kind of changes in total solar irradiance and volcanic perturbations could mask the projected anthropogenic global warming associated to the SRES scenarios. Our results suggest that only extremely unlikely changes in total solar irradiance and/or volcanic eruptions would be able to overcome the simulated anthropogenic global warming over the century. Nevertheless, in the critical interval of the next two decades the externally-driven natural climate variability might possibly confuse the debate about temperature trends and impede detection of the anthropogenic climate change signal.