Climate Variability-Observations, Reconstructions, and Model Simulations for the Atlantic-European and Alpine Region from 1500-2100 AD

A detailed analysis is undertaken of the Atlantic-European climate using data from 500-year-long proxy-based climate reconstructions, a long climate simulation with perpetual 1990 forcing, as well as two global and one regional climate change scenarios. The observed and simulated interannual variability and teleconnectivity are compared and interpreted in order to improve the understanding of natural climate variability on interannual to decadal time scales for the late Holocene. The focus is set on the Atlantic-European and Alpine regions during the winter and summer seasons, using temperature, precipitation, and 500 hPa geopotential height fields. The climate reconstruction shows pronounced interdecadal variations that appear to “lock” the atmospheric circulation in quasi-steady long-term patterns over multi-decadal periods controlling at least part of the temperature and precipitation variability. Different circulation patterns are persistent over several decades for the period 1500 to 1900. The 500-year-long simulation with perpetual 1990 forcing shows some substantial differences, with a more unsteady teleconnectivity behaviour. Two global scenario simulations indicate a transition towards more stable teleconnectivity for the next 100 years. Time series of reconstructed and simulated temperature and precipitation over the Alpine region show comparatively small changes in interannual variability within the time frame considered, with the exception of the summer season, where a substantial increase in interannual variability is simulated by regional climate models.     

Multi-year variability or unidirectional trends? Mapping long-term precipitation and temperature changes in continental Southeast Asia using PRECIS regional climate model

The subject of change detection in climate time series has recently received greater interest as the perception of a human-induced change in the climate is now widely accepted. However, changes in regional precipitation and temperature remain uncertain. This study characterizes projected fine-scale changes in precipitation and temperature in continental Southeast Asia over the period 1960?C2049. Twenty four annual variables were derived from grid-based daily precipitation and temperature produced by the PRECIS regional climate model under A2 and B2 scenarios. These time series, capturing precipitation intensities (classified as low, medium and high), seasonality and extremes in precipitation and temperature, were subjected to the modified Mann-Kendall trend detection test accounting for long-term persistence. The results indicate that temperature increases over the whole region with steeper trends in higher latitudes. Increases in annual precipitation, mainly restricted to Myanmar and the Gulf of Thailand, result from increases in high precipitation during the wet season. Decreases are observed mainly over the sea and caused by a reduction of low precipitation. Changes in the occurrence of the monsoon affect the low-latitude sea areas only. By showing that significant precipitation change are minor over land areas, these results challenge most of the previous studies that suggested significant precipitation changes over Southeast Asia, often mixing up multi-decadal variability and long-term unidirectional trends. Significant changes in precipitation and temperature may induce higher agricultural yields as steepest temperature and precipitation increases will predominantly affect the coldest and driest land areas of the region.     

Signatures of a universal spectrum for atmospheric interannual variability in some disparate climatic regimes

Summary Atmospheric flows exhibit long-range spatiotemporal correlations manifested as the fractal geometry to the global cloud cover pattern concomitant with inverse power law form for power spectra of temporal fluctuations on all space-tie scales ranging from turbulence (centimetersseconds) to climate (kilometers-years). Long-range spatiotemporal correlations are ubiquitous to dynamical systems in nature and are identified as signatures ofself-organized criticality. Standard models in meteorological theory cannot explain satisfactorily the observed self-organized criticality in atmospheric flows. Mathematical models for simulation and prediction of atmospheric flows are nonlinear and do not possess analytical solutions. Finite precision computer realizations of nonlinear models give unrealistic solutions because ofdeterministic chaos, a direct consequence of round-off error growth in iterative numerical computations. Recent studies show that roundoff error doubles on an average for each iteration of iterative computations. Round-off error propagates to the main stream computation and gives unrealistic solutions in numerical weather prediction (NWP) and climate models which incorporate thousands of iterative computations in long-term numerical integration schemes. An alternative non-deterministic cell dynamical system model for atmospheric flows described in this paper predicts the observed self-organized criticality as intrinsic to quantumlike mechanics governing flow dynamics. The model provides universal quantification for self-organized criticality in terms of the statistical normal distribution. Model predictions are in agreement with a majority of observed spectra of time series of several standard climatological data sets representative of disparate climatic regimes. Universal spectrum for natural climate variability rules out linear trends. Man-made greenhouse gas related atmospheric warming will result in intensification of natural climate variability, seen immediately in high frequency fluctuations such as QBO and ENSO and even shorter timescales. Model concepts and results of analyses are discussed with reference to possible prediction of climate change.With 11 Figures     

气候变化趋势分析中自相关的检验与去除

受资料本身、分析方法及未来排放情景假设等因素影响, 气候变暖幅度尚存在较大的不确定性。从分析方法入手, 探讨气象观测序列可能存在的自相关及其对气候变化趋势分析的不确定性影响。引入了Durbin-Watson一阶自相关检验方法对气象观测序列进行检验, 并用Cochrane-Orcutt方法去除存在的自相关。分析发现:浙江省平湖市气温序列存在的自相关放大了该站气温的升温趋势, 并且虚高了气温变化趋势的显著性水平。因此, 对资料序列进行自相关检验与去除是十分必要的。     

Natural variability of summer rainfall over China in HadCM3

Summer rainfall over China has shown decadal variability in the past half century, which has resulted in major north–south shifts in rainfall with important implications for flooding and water resource management. This study has demonstrated how multi-century climate model simulations can be used to explore interdecadal natural variability in the climate system in order to address important questions around recent changes in Chinese summer rainfall, and whether or not anthropogenic climate change is playing a role. Using a 1,000-year simulation of HadCM3 with constant pre-industrial external forcing, the dominant modes of total and interdecadal natural variability in Chinese summer rainfall have been analysed. It has been shown that these modes are comparable in magnitude and in temporal and spatial characteristics to those observed in the latter part of the twentieth century. However, despite 1,000 years of model simulation it has not been possible to demonstrate that these modes are related to similar variations in the global circulation and surface temperature forcing occurring during the latter half of the twentieth century. This may be in part due to model biases. Consequently, recent changes in the spatial distribution of Chinese summer rainfall cannot be attributed solely to natural variability, nor has it been possible to eliminate the likelihood that anthropogenic climate change has been the driving factor. It is more likely that both play a role.     

Communicating global climate change using simple indices: an update

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

Trends and Variability of Sea Surface Temperature in the Northwest Atlantic from Three Historical Gridded Datasets

ABSTRACT

Historical variability in sea surface temperature (SST) in the North Atlantic (NA) is examined using trend and Empirical Orthogonal Function (EOF) analyses of annual and summer means from three interpolated monthly datasets: Hadley Centre Sea Ice and Sea Surface Temperature (HadISST1), Extended Reconstruction of SST (ERSST), and Centennial in situ Observation-Based Estimates (COBE). Comparisons with time series of upper-ocean temperature from four monitoring sites off Atlantic Canada reveal substantial similarity in the interannual to multi-decadal variability but notable differences in the longer-term trends. The magnitude of decadal-scale variability is comparable to, or greater than, the long-term changes in all of the datasets; together with the trend discrepancies, this needs to be considered in climate change applications. Averaged over the NA, the annual means have a long-term increasing trend and a pronounced multi-decadal variation, resembling those in global mean (land-ocean) surface temperature and the Atlantic Multi-decadal Oscillation (AMO). There is remarkable similarity in the spatial and temporal variability of the three leading EOF modes from the different gridded datasets, with the first highly correlated with the AMO, the second modestly correlated with the winter North Atlantic Oscillation, and the third apparently related to ocean circulation variability. Trends since 1981 are generally two to three times larger than those since 1900 and 1950, which is at least partly related to the phase of the AMO. Trends in the summer means are generally larger than in the annual means. Overall, the results provide support for both anthropogenic global warming and decadal-scale natural variations making important contributions to ocean climate variability in the Northwest Atlantic.     

Tropical cyclones,climate change,and scientific uncertainty: what do we know,what does it mean,and what should be done?

The question of whether and to what extent global warming may be changing tropical cyclone (TC) activity is of great interest to decision makers. The presence of a possible climate change signal in TC activity is difficult to detect because interannual variability necessitates analysis over longer time periods than available data allow. Projections of future TC activity are hindered by computational limitations and uncertainties about changes in regional climate, large scale patterns, and TC response. This review discusses the state of the field in terms of theory, modeling studies and data. While Atlantic TCs have recently become more intense, evidence for changes in other basins is not persuasive, and changes in the Atlantic cannot be clearly attributed to either natural variability or climate change. However, whatever the actual role of climatic change, these concerns have opened a “policy window” that, if used appropriately, could lead to improved protection against TCs.     

Does temperature contain a stochastic trend: linking statistical results to physical mechanisms

By construction, the time series for radiative forcing that are used to run the 20c3m experiments, which are implemented by climate models, impart non-stationary movements (either stochastic or deterministic) to the simulated time series for global surface temperature. Here, we determine whether stochastic or deterministic trends are present in the simulated time series for global surface temperature by examining the time series for radiative forcing. Statistical tests indicate that the forcings contain a stochastic trend against the alternative hypothesis that the series are trend stationary with a one-time structural change. This result is consistent with the economic processes that impart a stochastic trend to anthropogenic emissions and the physical processes that integrate emissions in the atmosphere. Furthermore, the stochastic trend in the aggregate measure of radiative forcing also is present in the simulated time series for global surface temperature, which is consistent with the relation between these two variables that is represented by a zero dimensional energy balance model. Finally, we propose that internal weather variability imposed on the stochastic trend in radiative forcings is responsible for statistical results, which gives the impression that global surface temperature is trend stationary with a one-time structural change. We conclude that using the ideas of stochastic trends, cointegration, and error correction can generate reliable conclusions regarding the causes of changes in global surface temperature during the instrumental temperature record.     

Global and regional variability in a coupled AOGCM

 The variability of near surface temperature on global and regional spatial scales and interannual time scales from a 1000 year control integration of the Hadley Centre coupled model (HADCM2-CTL) are compared with the observational record of surface temperature. The model succeeds in reproducing the observed patterns of natural variability, with high variability over the northern continents and low variability over much of the tropics. The model global mean variability has similar strength to observed global mean variability on time scales less than 20 years. The warming seen in the historical record is outside the range of natural variability as simulated in HADCM2-CTL. The model has El-Ni?o/Southern Oscillation (ENSO)-like behaviour with a central Pacific, peak to peak, strength of approximately 3 K. Changes in near surface temperature in the central Pacific are strongly correlated with changes in near surface temperature over most of the tropics, large regions of the extra-tropics and changes in tropical ocean upper 250 m heat content. Tropospheric temperature changes and tropical surface pressure changes are also strongly correlated with changes in the central Pacific surface temperature. Oceanic regions show significant departures from an AR1 or first order Markov behaviour in the Northwest Atlantic, Northwest Pacific and Arctic oceans. The Northwest Atlantic region has large amounts of variability over periods greater than 50 years. This variability is associated with a jump in the strength of North Atlantic meridional stream function. The spectra of the Western European and Continental US land regions are not significantly different from an AR1 process. The flow through the Drake Passage has an interannual standard deviation of approximately 2.5 Sv with significant departures from an AR1 process at time scales greater than 40 years. Winter northern hemispheric 500 hPa geopotential height shows some evidence of multiple regimes but no year to year persistence of these regimes. Received: 31 January 1996/Accepted: 22 July 1996     

Timescales associated with climate change and their relevance in adaptation strategies

Anthropogenic greenhouse gas emissions that induce changes in the Earth’s climate affect particular variables and locations differently. A key part of this difference is the timescale at which this change takes place, which will eventually have important consequences for adaptation requirements. This idea of timescale associated with climate change has been used several times in the past to estimate the urgency of adaptation in particular regions. The definition of climate-change timescale is, however, not unique. For example, we can think of it in terms of an expected trend (e.g. in temperature) reaching a given threshold, or think of it in terms of the time it may take this trend to become statistically significant. We may also wonder about the validity of this speculation given that, due to natural variability, the expected trend may in fact not be realized. In this article we explore alternative ways of defining the timescale of climate-change, compare their properties, and illustrate them with an example for the case of projected surface temperature over North America. It is shown that these timescales are analytically related but may differ substantially in magnitude under certain conditions. In particular, it is shown that climate change impact on vulnerable systems may arrive before statistical detection of the variable’s trend takes place. This fact may have implications on how climate change impacts are seen by those with diverging interests.     

Multi-sliding time windows based changing trend of mean temperature and its association with the global-warming hiatus

Based on three global annual mean surface temperature time series and three Chinese annual mean surface air temperature time series, climate change trends on multiple timescales are analyzed by using the trend estimation method of multi-sliding time windows. The results are used to discuss the so-called global-warming hiatus during 1998–2012. It is demonstrated that different beginning and end times have an obvious effect on the results of the trend estimation, and the implications are particularly large when using a short window. The global-warming hiatus during 1998–2012 is the result of viewing temperature series on short timescales; and the events similar to it, or the events with even cold tendencies, have actually occurred many times in history. Therefore, the global-warming hiatus is likely to be a periodical feature of the long-term temperature change. It mainly reflects the decadal variability of temperature, and such a phenomenon in the short term does not alter the overall warming trend in the long term.     

Probabilistic projections of regional temperature and precipitation extending from observed time series

Probabilistic projections of change in regional temperature and precipitation previously derived allow for the range of sensitivities to global warming simulated by CMIP3 models. However, the changes were relative to an idealized base climate for 1980–1999, disregarding observed trends, such as those in rainfall in some Australian regions. Here we propose a method that represents projections for both forced change and decadal means as time series that extend from the observed series, illustrated using data for central Victoria. The main idea is to estimate the time-evolving underlying (or forced) past climate then convert this to a series of absolute values, by using the mean of the full observational record. We again use the pattern scaling assumption, and combine the CMIP3 sensitivities used for future change with a global warming series beginning at 1900. Like the confidence interval of regression theory, the analysis gives an estimate of the range of the underlying climate at each decade. This range can be augmented to allow for natural variability. A Bayesian theory can be applied to combine the model-based sensitivity with that estimated from observations. The time series are modified and the persistence of current observed anomalies considered, ultimately merging the probabilistic projections with the observed record. For some other cases, such as rainfall in southwest and north Australia and temperature in the state of Iowa, the two sensitivity estimates appear less compatible, and possible additional forcings are considered. Examples of the potential use of such time series are presented.     

Climatic change of inland river basin in an arid area: a case study in northern Xinjiang, China

We examined climate variability at two timescales for northern Xinjiang, China: one is of the past 500?years using dendrochronology data and the other is of the past 50?years using meteorological station data. The regression models built from the 50-year period were used to reconstruct the climate of the 500-year period. The results indicate that climate underwent many alternating warm–cold and wet–dry periods in the past 500?years. For the 50-year period, we applied the Mann–Kendall jump test to data from 48 meteorological stations to identify possible transition points of temperature and precipitation. For this period, we also analyzed the impacts of latitude, altitude, slope aspect, and human activities on climate variability, aiming to recognize major factors that influence regional climate variability. The results show a warming and wetting trend in the recent 50?years in northern Xinjiang. We determined that natural pattern variability is dominant in the long-term climate variability in the region, but human impacts are non-negligible in the past 50?years. Regional climate variability may be associated with or driven by latitude, altitude, ecosystems, topography, and human activities. The study provides an empirical evidence of the unique regional characteristics of inland river basin in an arid area over the global climate change background.     

Analysis of temperature series in Europe in relation to the detection of the enhanced greenhouse effect

Summary A method is developed for analysing climate series. It is based on the assumption that climate undergoes abrupt changes by natural means. It is a generalization of an existing method for dividing a series into two parts. It is assumed that increasing concentrations of greenhouse gases will lead to a gradual climate change (trend) and that this change will be superimposed upon the natural abrupt changes (jumps). On the basis of these facts, jumps in the direction of a climate change resulting from the increased concentrations of greenhouse gases are expected to be stronger than those in the opposite direction and previous jumps in the same direction. Different criteria are used to support this assumption. The method of analysis is applied to time series of summer and winter temperatures of 13 European stations.The largest increases in temperature do not occur in the recent past; they occur around 1910 in winter and about 1930 in summer. As the test for detection of the enhanced greenhouse effect is made stricter, the assumption put forward becomes weaker. Most time series do not have significant trends within various sub-periods. Differences in variability between successive sub-periods are generally not significant. There is agreement between the results reported here and others in the literature. So far, there is no definite evidence that the increasing concentration of greenhouse gases is affecting the climate of Europe.With 6 Figures     

Multi-fingerprint detection and attribution analysis of greenhouse gas, greenhouse gas-plus-aerosol and solar forced climate change

 A multi-fingerprint analysis is applied to the detection and attribution of anthropogenic climate change. While a single fingerprint is optimal for the detection of climate change, further tests of the statistical consistency of the detected climate change signal with model predictions for different candidate forcing mechanisms require the simultaneous application of several fingerprints. Model-predicted climate change signals are derived from three anthropogenic global warming simulations for the period 1880 to 2049 and two simulations forced by estimated changes in solar radiation from 1700 to 1992. In the first global warming simulation, the forcing is by greenhouse gas only, while in the remaining two simulations the direct influence of sulfate aerosols is also included. From the climate change signals of the greenhouse gas only and the average of the two greenhouse gas-plus-aerosol simulations, two optimized fingerprint patterns are derived by weighting the model-predicted climate change patterns towards low-noise directions. The optimized fingerprint patterns are then applied as a filter to the observed near-surface temperature trend patterns, yielding several detection variables. The space-time structure of natural climate variability needed to determine the optimal fingerprint pattern and the resultant signal-to-noise ratio of the detection variable is estimated from several multi-century control simulations with different CGCMs and from instrumental data over the last 136 y. Applying the combined greenhouse gas-plus-aerosol fingerprint in the same way as the greenhouse gas only fingerprint in a previous work, the recent 30-y trends (1966–1995) of annual mean near surface temperature are again found to represent a significant climate change at the 97.5% confidence level. However, using both the greenhouse gas and the combined forcing fingerprints in a two-pattern analysis, a substantially better agreement between observations and the climate model prediction is found for the combined forcing simulation. Anticipating that the influence of the aerosol forcing is strongest for longer term temperature trends in summer, application of the detection and attribution test to the latest observed 50-y trend pattern of summer temperature yielded statistical consistency with the greenhouse gas-plus-aerosol simulation with respect to both the pattern and amplitude of the signal. In contrast, the observations are inconsistent with the greenhouse-gas only climate change signal at a 95% confidence level for all estimates of climate variability. The observed trend 1943–1992 is furthermore inconsistent with a hypothesized solar radiation change alone at an estimated 90% confidence level. Thus, in contrast to the single pattern analysis, the two pattern analysis is able to discriminate between different forcing hypotheses in the observed climate change signal. The results are subject to uncertainties associated with the forcing history, which is poorly known for the solar and aerosol forcing, the possible omission of other important forcings, and inevitable model errors in the computation of the response to the forcing. Further uncertainties in the estimated significance levels arise from the use of model internal variability simulations and relatively short instrumental observations (after subtraction of an estimated greenhouse gas signal) to estimate the natural climate variability. The resulting confidence limits accordingly vary for different estimates using different variability data. Despite these uncertainties, however, we consider our results sufficiently robust to have some confidence in our finding that the observed climate change is consistent with a combined greenhouse gas and aerosol forcing, but inconsistent with greenhouse gas or solar forcing alone. Received: 28 April 1996 / Accepted: 27 January 1997     

未来50年中国气候变化趋势的初步研究

文章比较了各种气候模式对温室效应的估计，及其可能对中国气候的影响。分析和预测了太阳活动与火山活动的长期变化，在此基础上估计了未来可能产生的自然气候变化。结果表明，在未来50年中太阳活动和火山活动均可能使气候变冷。因此，可能在一定程度上抵消因温室效应加剧而产生的变暖。但在2010年之后，温室效应可能逐步占据主导地位，到2030年全球平均气温可能比1961～1990年平均上升0.6℃以上，东亚地区的增温，可能比全球平均稍强。气候变暖后，东亚地区降水可能增加。但在我国北方，夏季干旱程度可能加大。     

CLIMATE CHANGE: LONG-TERM TRENDS AND SHORT-TERM OSCILLATIONS

Identifying the Northern Hemisphere (NH) temperature reconstruction and instrumental data for the past 1000 years shows that climate change in the last millennium includes long-term trends and various oscillations. Two long-term trends and the quasi-70-year oscillation were detected in the global temperature series for the last 140 years and the NH millennium series. One important feature was emphasized that temperature decreases slowly but it increases rapidly based on the analysis of different series. Benefits can be obtained of climate change from understanding various long-term trends and oscillations. Millennial temperature proxies from the natural climate system and time series of nonlinear model system are used in understanding the natural climate change and recognizing potential benefits by using the method of wavelet transform analysis. The results from numerical modeling show that major oscillations contained in numerical solutions on the interdecadal timescale are consistent with that of natural proxies. It seems that these oscillations in the climate change are not directly linked with the solar radiation as an external forcing. This investigation may conclude that the climate variability at the interdecadal timescale strongly depends on the internal nonlinear effects in the climate system.     

Quantifying the contributions of anthropogenic and natural forcings to climate changes over arid-semiarid areas during 1946–2005

In this study, the contributions from changes in man-made greenhouse gases (GHG), anthropogenic aerosols (AA), and land use (LU), as well as natural solar and volcanic (NAT) forcing changes, to observed changes in surface air temperature (T) and precipitation (P) over global land, especially over arid-semiarid areas, during 1946–2005 are quantified using observations and climate model simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Results show that the anthropogenic (ANT) forcings dominate the ubiquitous surface warming seen in observations and lead to slight increases in precipitation over most land areas, while the NAT forcing leads to small cooling over land. GHG increases are the primary factor responsible for the anthropogenic climate change, while the AA forcing offsets a large part of the GHG-induced warming and P changes. The LU forcing generally contributes little to the T and P changes from 1946 to 2005 over most land areas. Unlike the consistent temperature changes among most model simulations, precipitation changes display a large spread among the models and are incomparable with the observations in spatial distributions and magnitude, mainly due to its large internal variability that varies among individual model runs. Using an optimal fingerprinting method, we find that the observed warming over land during 1946–2005 can be largely attributed to the ANT forcings, and the combination of the ANT and NAT forcings can explain about 85~95% of the observed warming trend over global land as well as over most arid-semiarid regions such as Northern China. However, the anthropogenic influences on precipitation over the past 60 years are generally undetectable over most land areas, including most arid-semiarid regions. This indicates that internal variability is still larger than the forced change for land precipitation.     

Northern hemisphere winter atmospheric climate: modes of natural variability and climate change

Under anthropogenic climate change it is possible that the increased radiative forcing and associated changes in mean climate may affect the “dynamical equilibrium” of the climate system; leading to a change in the relative dominance of different modes of natural variability, the characteristics of their patterns or their behavior in the time domain. Here we use multi-century integrations of version three of the Hadley Centre atmosphere model coupled to a mixed layer ocean to examine potential changes in atmosphere-surface ocean modes of variability. After first evaluating the simulated modes of Northern Hemisphere winter surface temperature and geopotential height against observations, we examine their behavior under an idealized equilibrium doubling of atmospheric CO₂. We find no significant changes in the order of dominance, the spatial patterns or the associated time series of the modes. Having established that the dynamic equilibrium is preserved in the model on doubling of CO₂, we go on to examine the temperature pattern of mean climate change in terms of the modes of variability; the motivation being that the pattern of change might be explicable in terms of changes in the amount of time the system resides in a particular mode. In addition, if the two are closely related, we might be able to assess the relative credibility of different spatial patterns of climate change from different models (or model versions) by assessing their representation of variability. Significant shifts do appear to occur in the mean position of residence when examining a truncated set of the leading order modes. However, on examining the complete spectrum of modes, it is found that the mean climate change pattern is close to orthogonal to all of the modes and the large shifts are a manifestation of this orthogonality. The results suggest that care should be exercised in using a truncated set of variability EOFs to evaluate climate change signals.