Variability of seasonal-mean fields arising from intraseasonal variability. part 2, application to nh winter circulations

A method for studying patterns of interannual variability arising from intraseasonal variability has been applied to the extratropical Northern Hemisphere wintertime 500 hPa geopotential height, using data from the NCEP-NCAR. These patterns describe the effects predominantly of intraseasonal variability and blocking. Removing this component from the sample interannual covariance matrix, one can define a residual, or slow, component of interannual variability that is more closely related to external forcings and very slowly varying (interannual/supra-annual) internal dynamics. For the Northern Hemisphere NCEP-NCAR reanalysis data, there are considerable differences between the intraseasonal patterns and the total patterns. The intraseasonal patterns are more spatially localized and more closely related to known intraseasonal variability, especially blocking events and the Madden-Julian Oscillation. Although the slow patterns and the total patterns look similar, they have some important differences. The slow patterns are more closely related to the slowly varying external forcing and very low-frequency internal dynamics than those derived by the sample covariance matrix. This is evidenced by the fact that the principal component time series of the slow patterns have a larger proportion of variability related to these factors. Where tropical SST forcing is important, the slow patterns tended to be more highly correlated with the interannual variations in the forcing. Three slow modes, related to the Tropical Northern Hemisphere, East Atlantic and Western Pacific teleconnections, are all significantly related to tropical SST variability associated predominantly with the El Nino-Southern Oscillation, in the case of the first two, and Indian Ocean variability, in the third case. The derived slow patterns and intraseasonal patterns may help to better understand the long-range predictability, uncertainty, and forcing of climate variables, for the wintertime circulation.     

Slow and Intraseasonal Modes of the Boreal Winter Atmospheric Circulation Simulated by CMIP5 Models

Abstract The authors evaluate the performance of models from Coupled Model Intercomparison Project Phase 5（CMIP5）in simulating the historical（1951-2000）modes of interannual variability in the seasonal mean Northern Hemisphere（NH）500 hPa geopotential height during winter（December-January-February,DJF）.The analysis is done by using a variance decomposition method,which is suitable for studying patterns of interannual variability arising from intraseasonal variability and slow variability（time scales of a season or longer）.Overall,compared with reanalysis data,the spatial structure and variance of the leading modes in the intraseasonal component are generally well reproduced by the CMIP5 models,with few clear differences between the models.However,there are systematic discrepancies among the models in their reproduction of the leading modes in the slow component.These modes include the dominant slow patterns,which can be seen as features of the Pacific-North American pattern,the North Atlantic Oscillation/Arctic Oscillation,and the Western Pacific pattern.An overall score is calculated to quantify how well models reproduce the three leading slow modes of variability.Ten models that reproduce the slow modes of variability relatively well are identified.     

Estimating the potential predictability of Australian surface maximum and minimum temperature

A study is made of the potential predictability of seasonal means in Australian surface maximum and minimum temperature using monthly data from December 1950 to November 2000. Because the usual assumption of stationarity cannot be applied to the observations at all stations and for all seasons, a modification to an existing methodology is proposed. Here, we show that, to a first order, monthly mean variances within a season can be modeled by a linear relationship, and inter-monthly correlations can be assumed to be stationary. The intraseasonal component of variability can then be estimated using monthly data. Removing the intraseasonal variance from the total interannual variance allows an estimate of the potential predictability to be made. Surface maximum and minimum temperature has high potential predictability over most of northern Australia in the four main seasons. However, there is high potential predictability only in some of the four seasons for the centre and south of Australia. Surface minimum temperature is generally more potentially predictable than surface maximum temperature. The spatial and temporal patterns of potential predictability are generally consistent with published patterns of hindcast skill from a statistical forecast scheme. A comparison between the intraseasonal variance of Australian surface maximum and minimum temperature estimated using the stationary variance assumption and the linear assumptions showed qualitatively and quantitatively similar patterns of distribution.     

Teleconnections and predictive characteristics of Australian seasonal rainfall

A new methodology is proposed that allows patterns of interannual covariability, or teleconnections, between the intraseasonal and slow components of seasonal mean Australian rainfall and the corresponding components in the Southern Hemisphere atmospheric circulation to be estimated. In all seasons, the dominant rainfall–circulation teleconnections in the intraseasonal component are shown to have the characteristic features associated with well-known intraseasonal dynamical and statistical atmospheric modes and their relationship with rainfall. Thus, for example, there are patterns of interannual covariability that reflect rainfall relationships with the intraseasonal Southern Annular Mode, the Madden-Julian Oscillation and wavenumber 3 and 4 intraseasonal modes of variability. The predictive characteristics of the atmospheric circulation–rainfall relationship are shown to reside with the slow components. In all seasons, we find rainfall–circulation teleconnections in the slow components related to the El Niño-Southern Oscillation. Each season also has a coupled mode, with a statistically significant trend in the time series of the atmospheric component that appears to be related to recent observed trends in rainfall. The slow Southern Annular Mode also features in association with southern Australian rainfall, especially during austral winter and spring. There is also evidence of an influence of Indian Ocean sea surface temperature variability on rainfall in southeast Australia during austral winter and spring.     

Relationships between Interannual and Intraseasonal Variations of the Asian-Western Pacific Summer Monsoon Hindcasted by BCC_CSM1.1（m）

Using hindcasts of the Beijing Climate Center Climate System Model, the relationships between interannual variability （IAV） and intraseasonal variability （ISV） of the Asian-western Pacific summer monsoon are diagnosed. Predictions show reasonable skill with respect to some basic characteristics of the ISV and IAV of the western North Pacific summer monsoon （WNPSM） and the Indian summer monsoon （ISM）. However, the links between the seasonally averaged ISV （SAISV） and seasonal mean of ISM are overestimated by the model. This deficiency may be partially attributable to the overestimated frequency of long breaks and underestimated frequency of long active spells of ISV in normal ISM years, although the model is capable of capturing the impact of ISV on the seasonal mean by its shift in the probability of phases. Furthermore, the interannual relationships of seasonal mean, SAISV, and seasonally averaged long-wave variability （SALWV; i.e., the part with periods longer than the intraseasonal scale） of the WNPSM and ISM with SST and low-level circulation are examined. The observed seasonal mean, SAISV, and SALWV show similar correlation patterns with SST and atmospheric circulation, but with different details. However, the model presents these correlation distributions with unrealistically small differences among different scales, and it somewhat overestimates the teleconnection between monsoon and tropical central-eastern Pacific SST for the ISM, but underestimates it for the WNPSM, the latter of which is partially related to the too-rapid decrease in the impact of E1 Nifio-Southern Oscillation with forecast time in the model.     

Understanding the West African monsoon variability and its remote effects: an illustration of the grid point nudging methodology

General circulation models still show deficiencies in simulating the basic features of the West African Monsoon at intraseasonal, seasonal and interannual timescales. It is however, difficult to disentangle the remote versus regional factors that contribute to such deficiencies, and to diagnose their possible consequences for the simulation of the global atmospheric variability. The aim of the present study is to address these questions using the so-called grid point nudging technique, where prognostic atmospheric fields are relaxed either inside or outside the West African Monsoon region toward the ERA40 reanalysis. This regional or quasi-global nudging is tested in ensembles of boreal summer simulations. The impact is evaluated first on the model climatology, then on intraseasonal timescales with an emphasis on North Atlantic/Europe weather regimes, and finally on interannual timescales. Results show that systematic biases in the model climatology over West Africa are mostly of regional origin and have a limited impact outside the domain. A clear impact is found however on the eddy component of the extratropical circulation, in particular over the North Atlantic/European sector. At intraseasonal timescale, the main regional biases also resist to the quasi-global nudging though their magnitude is reduced. Conversely, nudging the model over West Africa exerts a strong impact on the frequency of the two North Atlantic weather regimes that favor the occurrence of heat waves over Europe. Significant impacts are also found at interannual timescale. Not surprisingly, the quasi-global nudging allows the model to capture the variability of large-scale dynamical monsoon indices, but exerts a weaker control on rainfall variability suggesting the additional contribution of regional processes. Conversely, nudging the model toward West Africa suppresses the spurious ENSO teleconnection that is simulated over Europe in the control experiment, thereby emphasizing the relevance of a realistic West African monsoon simulation for seasonal prediction in the extratropics. Further experiments will be devoted to case studies aiming at a better understanding of regional processes governing the monsoon variability and of the possible monsoon teleconnections, especially over Europe.     

Future changes in daily summer temperature variability: driving processes and role for temperature extremes

Anthropogenic greenhouse gas emissions are expected to lead to more frequent and intense summer temperature extremes, not only due to the mean warming itself, but also due to changes in temperature variability. To test this hypothesis, we analyse daily output of ten PRUDENCE regional climate model scenarios over Europe for the 2071–2100 period. The models project more frequent temperature extremes particularly over the Mediterranean and the transitional climate zone (TCZ, between the Mediterranean to the south and the Baltic Sea to the north). The projected warming of the uppermost percentiles of daily summer temperatures is found to be largest over France (in the region of maximum variability increase) rather than the Mediterranean (where the mean warming is largest). The underlying changes in temperature variability may arise from changes in (1) interannual temperature variability, (2) intraseasonal variability, and (3) the seasonal cycle. We present a methodology to decompose the total daily variability into these three components. Over France and depending upon the model, the total daily summer temperature variability is projected to significantly increase by 20–40% as a result of increases in all three components: interannual variability (30–95%), seasonal variability (35–105%), and intraseasonal variability (10–30%). Variability changes in northern and southern Europe are substantially smaller. Over France and parts of the TCZ, the models simulate a progressive warming within the summer season (corresponding to an increase in seasonal variability), with the projected temperature change in August exceeding that in June by 2–3 K. Thus, the most distinct warming is superimposed upon the maximum of the current seasonal cycle, leading to a higher intensity of extremes and an extension of the summer period (enabling extreme temperatures and heat waves even in September). The processes driving the variability changes are different for the three components but generally relate to enhanced land–atmosphere coupling and/or increased variability of surface net radiation, accompanied by a strong reduction of cloudiness, atmospheric circulation changes and a progressive depletion of soil moisture within the summer season. The relative contribution of these processes differs substantially between models.     

Year-to-Year and interdecadal variability in the activity of intraseasonal fluctuations in the Northern Hemisphere wintertime circulation

Summary Interannual variability in the activity of fluctuations with subseasonal time scales is investigated based upon observed data of the extratropical Northern Hemisphere circulation over the recent 38 winters. Their activity is represented in the root mean square (RMS) field of filtered geopotential height in which the fluctuations with time scales between 10 days and a season are retained. The singular value decomposition (SVD) was applied to the covariance matrix between the seasonal mean and RMS fields for the 500-hPa height.The leading SVD mode for the north Pacific represents the strong relationship between the polarity of the Pacific/North American (PNA) pattern in the seasonal-mean anomalies and the amplitude of a meridionally-oriented dipole-like oscillation within the season. It tends to be more active when the seasonal-mean jet stream is strongly diffluent over the central Pacific than when the jet is extended zonally across the Pacific. The leading SVD mode for the north Atlantic is indicative of stronger intraseasonal fluctuations near Greenland in the presence of anticyclonic seasonal-mean anomalies associated with the North Atlantic Oscillation (NAO).The intraseasonal variability in the extratropics is strongly correlated with the underlying sea surface temperature (SST) anomalies, and that in the north Pacific also exhibits significant but rather weak correlation with SST anomalies in the equatorial Pacific. The activity of the atmospheric intraseasonal fluctuations is found to be modulated in accordance with interdecadal variability in the seasonal-mean circulation and SST.On leave from Department of Earth & Planetary Physics, University of Tokyo.With 12 Figures     

Modes of variability of Southern Hemisphere atmospheric circulation estimated by AGCMs

The seasonal mean variability of the atmospheric circulation is affected by processes with time scales from less than seasonal to interannual or longer. Using monthly mean data from an ensemble of Atmospheric General Circulation Model (AGCM) realisations, the interannual variability of the seasonal mean is separated into intraseasonal, and slowly varying components. For the first time, using a recently developed method, the slowly varying component in multiple AGCM ensembles is further separated into internal and externally forced components. This is done for Southern Hemisphere 500?hPa geopotential height from five AGCMs in the CLIVAR International Climate of the Twentieth Century project for the summer and winter seasons. In both seasons, the intraseasonal and slow modes of variability are qualitatively well reproduced by the models when compared with reanalysis data, with a relative metric finding little overall difference between the models. The Southern Annular Mode (SAM) is by far the dominant mode of slowly varying internal atmospheric variability. Two slow-external modes of variability are related to El Ni?o-Southern Oscillation (ENSO) variability, and a third is the atmospheric response to trends in external forcing. An ENSO-SAM relationship is found in the model slow modes of variability, similar to that found by earlier studies using reanalysis data. There is a greater spread in the representation of model slow-external modes in winter than summer, particularly in the atmospheric response to external forcing trends. This may be attributable to weaker external forcing constraints on SH atmospheric circulation in winter.     

Daily atmospheric variability in the South American monsoon system

The space–time structure of the daily atmospheric variability in the South American monsoon system has been studied using multichannel singular spectrum analysis of daily outgoing longwave radiation. The three leading eigenmodes are found to have low-frequency variability while four other modes form higher frequency oscillations. The first mode has the same time variability as that of El Nino-Southern Oscillation (ENSO) and exhibits strong correlation with the Pacific sea surface temperature (SST). The second mode varies on a decadal time scale with significant correlation with the Atlantic SST suggesting an association with the Atlantic multidecadal oscillation (AMO). The third mode also has decadal variability but shows an association with the SST of the Pacific decadal oscillation (PDO). The fourth and fifth modes describe an oscillation that has a period of about 165 days and is associated with the North Atlantic oscillation (NAO). The sixth and seventh modes describe an intraseasonal oscillation with a period of 52 days which shows strong relation with the Madden-Julian oscillation. There exists an important difference in the variability of convection between Amazon River Basin (ARB) and central-east South America (CESA). Both regions have similar variations due to ENSO though with higher magnitude in ARB. The AMO-related mode has almost identical variations in the two regions, whereas the PDO-related mode has opposite variations. The interseasonal NAO-related mode also has variations of opposite sign with comparable magnitudes in the two regions. The intraseasonal variability over the CESA is robust while it is very weak over the ARB region. The relative contributions from the low-frequency modes mainly determine the interannual variability of the seasonal mean monsoon although the interseasonal oscillation may contribute in a subtle way during certain years. The intraseasonal variability does not seem to influence the interannual variability in either region.     

Variability of seasonal-mean fields arising from intraseasonal variability. Part 3: Application to SH winter and summer circulations

A study has been made, using the National Centers for Environmental Prediction and National Center for Atmospheric Research re-analysis 500 hPa geopotential height data, to determine how intraseasonal variability influences, or can generate, coherent patterns of interannual variability in the extratropical summer and winter Southern Hemisphere atmospheric circulation. In addition, by separating this intraseasonal component of interannual variability, we also consider how slowly varying external forcings and slowly varying (interannual and longer) internal dynamics might influence the interannual variability of the Southern Hemisphere circulation. This slow component of interannual variation is more likely to be potentially predictable. How sea surface temperatures are related to the slow components is also considered. The four dominant intraseasonal modes of interannual variability have horizontal structures similar to those seen in both well-known intraseasonal dynamical modes and statistical modes of intraseasonal variability. In particular, they reflect intraseasonal variability in the high latitudes associated with the Southern Annular Mode, and wavenumber 4 (summer) and wavenumber 3 (winter) patterns associated with south Pacific regions of persistent anomalies and blocking, and possibly variability related to the Madden-Julian Oscillation (MJO). The four dominant slow components of interannual variability, in both seasons, are related to high latitude variability associated with the Southern Annular Mode, El Nino Southern Oscillation (ENSO) variability, and South Pacific Wave variability associated with Indian Ocean SSTs. In both seasons, there are strong linear trends in the first slow mode of high latitude variability and these are shown to be related to similar trends in the Indian Ocean. Once these are taken into account there is no significant sea surface temperature forcing of these high latitude modes. The second and third ENSO related slow modes, in each season, have high correlations with tropical sea surface temperature variability in the Pacific and Indian Oceans, both contemporaneously and at one season lag. The fourth slow mode has a characteristic South Pacific wave structure of either a wavenumber 4 (summer) or wavenumber 3 (winter) pattern, with strongest loadings in the South Pacific sector, and an association simultaneously with a dipole SST temperature gradient in the subtropical Indian Ocean.     

Daily modes of South Asian summer monsoon variability in the NCEP climate forecast system

The leading modes of daily variability of the Indian summer monsoon in the climate forecast system (CFS), a coupled general circulation model, of the National Centers for Environmental Predictions (NCEP) are examined. The space?Ctime structures of the daily modes are obtained by applying multi-channel singular spectrum analysis (MSSA) on the daily anomalies of rainfall. Relations of the daily modes to intraseasonal and interannual variability of the monsoon are investigated. The CFS has three intraseasonal oscillations with periods around 106, 57 and 30?days with a combined variance of 7%. The 106-day mode has spatial structure and propagation features similar to the northeastward propagating 45-day mode in the observations except for its longer period. The 57-day mode, despite being in the same time scale as of the observations has poor eastward propagation. The 30-day mode is northwestward propagating and is similar to its observational counterpart. The 106-day mode is specific to the model and should not be mistaken for a new scale of variability in observations. The dominant interannual signal is related to El Ni?o-Southern Oscillation (ENSO), and, unlike in the observations, has maximum variance in the eastern equatorial Indian Ocean. Although the Indian Ocean Dipole (IOD) mode was not obtained as a separate mode in the rainfall, the ENSO signal has good correlations with the dipole variability, which, therefore, indicates the dominance of ENSO in the model. The interannual variability is largely determined by the ENSO signal over the regions where it has maximum variance. The interannual variability of the intraseasonal oscillations is smaller in comparison.     

Monsoon intraseasonal oscillation and land�Catmosphere interaction in an idealized model

A simple coupled model is used in a zonally-symmetric configuration to investigate the effect of land?Catmosphere coupling on the Asian monsoon intraseasonal oscillation. The atmospheric model is a version of the Quasi-equilibrium Tropical Circulation Model with a prognostic atmospheric boundary layer, as well as two free-tropospheric modes in momentum, and one each in moisture and temperature. The land model is the simple one-layer model SLand. The complete nonlinear version and a linear version of the model are used to understand how land?Catmosphere interaction influences the northward-propagating intraseasonal oscillation that has been documented in the atmospheric model (Bellon and Sobel in J Geophys Res 113, 2008a, J Atmos Sci 65:470?C489, 2008b). Our results show that this interaction damps the intraseasonal variability in most cases. The small heat capacity of land surfaces is the main factor that intervenes directly in the dynamics of the intraseasonal oscillation and explains the damping of intraseasonal variability. But in a few peculiar cases, the small heat capacity of land can also cause a strong interaction between the intraseasonal oscillation and the mean state via the nonlinearity of precipitation, that enhances the monsoon intraseasonal variability. High land albedo indirectly influences the intraseasonal variability by setting the seasonal mean circulation to conditions unfavorable for the monsoon intraseasonal oscillation.     

Interannual spring Wyrtki jet variability and its regional impacts

The role of spring Wyrtki jets in modulating the equatorial Indian Ocean and the regional climate is an unexplored problem. The source of interannual variability in the spring Wyrtki jets is explored in this study. The relationship between intraseasonal and interannual variability from 1958 to 2008 and its relation with Indian Summer Monsoon is further addressed. Analysis reveals that the interannual variability in spring Wyrtki jets is controlled significantly by their intraseasonal variations. These are mostly defined by a single intraseasonal event of duration 20 days or more which either strengthens or weakens the seasonal mean jet depending on its phase. The strong spring jets are driven by such intraseasonal westerly wind bursts lasting for 20-days or more, whereas the weak jets are driven by weaker intraseasonal westerlies. During the years of strong jets, the conventional westward phase propagation of Wyrtki jets is absent and instead there is an eastward phase propagation indicating the possible role of Madden Julian Oscillation (MJO) in strengthening the spring Wyrtki jets. These strong intraseasonal westerly wind bursts with eastward phase propagation during strong years are observed mainly in late spring and have implications on June precipitation over the Indian and adjoining land mass. Anomalously strong eastward jets accumulate warm water in the eastern equatorial Indian Ocean (EIO), leading to anomalous positive upper ocean heat content and supporting more local convection in the east. This induces subsidence over the Indian landmass and alters monsoon rainfall by modulating monsoon Hadley circulation. In case of weak current years such warm anomalies are absent over the eastern EIO. Variations in the jet strength are found to have strong impact on sea level anomalies, heat content, salinity and sea surface temperature over the equatorial and north Indian Ocean making it a potentially important player in the north Indian Ocean climate variability.     

Modes of interannual variability of Southern Hemisphere atmospheric circulation in CMIP3 models: assessment and projections

An assessment is made of the modes of interannual variability in the seasonal mean summer and winter Southern Hemisphere 500 hPa geopotential height in the twentieth century in models from the Coupled Model Intercomparison Project phase 3 (CMIP3) dataset. The analysis is done for both the intraseasonal and slow components of the geopotential height. When the CMIP3 models are assessed against reanalysis data, the spatial structure and variance of the leading modes in the intraseasonal component are generally well reproduced. There are systematic differences between the models in their reproduction of the leading modes in the slow component. An overall score using the leading modes in the slow component allows a categorisation of CMIP3 model performance. Using an ensemble from four models that suitably reproduce the twentieth century modes, modes of variability in the slow-internal and slow-external components are estimated. The leading mode of the slow-external component is shown to be related to observed changes in greenhouse gas concentrations. In this ensemble, there is little change in the leading modes in the intraseasonal component in the twenty-first century. Larger changes in variance, and subtle changes in regional-scale structure, are found for the leading modes in the slow-internal component. These are related to changes in the slowly varying dynamics of the Southern Annular Mode and the El Niño-Southern Oscillation. By far the biggest change is in the leading mode of the slow-external component. The spatial structure becomes uniform in the twenty-first century, and the variance increases with increasing greenhouse gas concentrations.     

Probabilities and distribution of monsoon rainfall in normal, flood and drought years over India

Summary The paper deals with the variability of summer-monsoon rainfall during normal, flood and drought years over India. During flood years the monsoon rainfall increases mostly all over parts of the country and large area less than 100 cm isohytel covers Orissa and adjoining Madhya Pradesh. During drought years the rainfall amount decreases over the entire country and isohytel of 100 cm shrinks to almost a point. The variability of monsoon rainfall from flood to normal to drought years depends upon the number of depression/low-pressure area which form over the North Bay and move inland. To understand the intraseasonal and interannual variability of the monsoon rainfall, daily and seasonal anomalies has been performed by using the Empirical Orthogonal Function analysis. Further Empirical Orthogonal Function (EOF) analysis is carried out on these data to find out the nature of rainfall distribution in different monsoon categories namely normal, flood and drought years. This technique thus serves to identify spatial and temporal patterns characteristics of possible physical significance. Received July 25, 2000/Revised September 26, 2000     

The mid-latitude horizontal and vertical structure of the zonally asymmetric intraseasonal and interannual ozone variability in boreal winters

 A singular value decomposition (SVD) is used to calculate SVD-selected fields of ozone and geopotential, which exhibit maximum covariance, from the observed zonally asymmetric total ozone field and that of the three-dimensional geopotential field thus leaving almost purely dynamical induced variations in the remaining ozone field. This procedure was applied to Total Ozone Mapping Spectrometer data (TOMS) and to geopotential values from the National Centers for Environmental Prediction in the boreal mid-latitudes in the winter months of 1979–1992. Intraseasonal variability (December–February) and trend-eliminated interannual winter mean variability of total ozone and geopotential are analyzed. The first four modes of SVD analysis explain more than 70% of the covariance for the intraseasonal variability and more than 80% of that for the interannual variability. The vertical structure of geopotential regression maps reveals a clear wave-1 pattern for modes one and two and a wave-2 pattern for modes three and four. These patterns show differently but generally westward tilted phases and are more complex at heights below 70 hPa. Further a linear transport model of a conservative tracer was applied to each individual geopotential mode found by the SVD analysis in connection with an observed height and latitude dependent zonal mean ozone distribution. The model results of total ozone reproduce the spatial patterns of the SVD-selected total ozone field quite well whereas their magnitudes are variously underestimated. This method allows us to assess the vertical distribution of the contribution of single modes to the total ozone variability. Maximum contributions are found between 150 and 70 hPa. Smaller amplitude maxima are found around 10 hPa, which result from contributions of horizontal advection of ozone alone. These results reflect an expected dynamical link between the variability of the zonally asymmetric parts of geopotential and ozone. Received: 7 November 1997 / Accepted: 10 June 1998     

CoPIVEP: a theory-based analysis of coupled processes and interannual variability in the Equatorial Pacific in four coupled GCMs

 The interannual variability over the tropical Pacific and a possible link with the mean state or the seasonal cycle is examined in four coupled ocean-atmosphere general circulation models (GCM). Each model is composed of a high-resolution ocean GCM of either the tropical Pacific or near-global oceans coupled to a moderate-resolution atmospheric GCM, without using flux correction. The oceanic subsurface is considered to describe the mean state or the seasonal cycle through the analytical formulations of some potential coupled processes. These coupled processes characterise the zonal gradient of sea surface temperature (hereafter SST), the oceanic vertical gradient of temperature and the equatorial upwelling. The simulated SST patterns of the mean state and the interannual signals are generally too narrow. The grid of the oceanic model could control the structure of the SST interannual signals while the behaviour of the atmospheric model could be important in the link between the oceanic surface and the subsurface. The first SST EOFs are different between the coupled models, however, the second SST EOFs are quite similar and could correspond to the return to the normal state while that of the observations (COADS) could favour the initial anomaly. All the models seem to simulate a similar equatorial wave-like dynamics to return to the normal state. The more the basic state is unstable from the coupled processes point of view, the more the interannual signal are high. It seems that the basic state could control the intensity of the interannual variability. Two models, which have a significant seasonal variation of the interannual variance, also have a significant seasonal variation of the instability with a few months lag. The potential seasonal phase locking of the interannual fluctuations need to be examined in more models to confirm its existence in current tropical GCMs. Received: 30 July 1999 / Accepted: 25 April 2000     

A scenario of European climate change for the late twenty-first century: seasonal means and interannual variability

A scenario of European climate change for the late twenty-first century is described, using a high-resolution state-of-the-art model. A time-slice approach is used, whereby the atmospheric general circulation model, HadAM3P, was integrated for two periods, 1960–1990 and 2070–2100, using the SRES A2 scenario. For the first time an ensemble of such experiments was produced, along with appropriate statistical tests for assessing significance. The focus is on changes to the statistics of seasonal means, and includes analysis of both multi-year means and interannual variance. All four seasons are assessed, and anomalies are mapped for surface air temperature, precipitation and snow mass. Mechanisms are proposed where these are dominated by straightforward local processes. In winter, the largest warming occurs over eastern Europe, up to 7°C, mean snow mass is reduced by at least 80% except over Scandinavia, and precipitation increases over all but the southernmost parts of Europe. In summer, temperatures rise by 6–9°C south of about 50°N, and mean rainfall is substantially reduced over the same area. In spring and autumn, anomalies tend to be weaker, but often display patterns similar to the preceding season, reflecting the inertia of the land surface component of the climate system. Changes in interannual variance are substantial in the solsticial seasons for many regions (note that for precipitation, variance estimates are scaled by the square of the mean). In winter, interannual variability of near-surface air temperature is considerably reduced over much of Europe, and the relative variability of precipitation is reduced north of about 50°N. In summer, the (relative) interannual variance of both variables increases over much of the continent.     

Application of Markov models to area-average daily precipitation series and interannual variability in seasonal totals

This paper examines the success of various Markov-chain models of daily precipitation series in reproducing the characteristics of area-average rainfall in Britain. The first model considered is the standard twos-tate first-order Markov renewal process coupled to an amount model using the incomplete -probability distribution. We find that variability of seasonal totals and autocorrelation of daily amounts are both too small in this model, compared with observations. These are serious deficiencies, often overlooked, and possibly related. We proceed to consider models involving Markov chains of higher (temporal) order and many states, both of which generalizations may increase autocorrelation. A second-order two-state model is no better than the first-order, but a first-order many-state model captures a high fraction of the seasonal variability, because use of many states improves the model's representation of spells of heavy precipitation, which appear to have a considerable influence on the seasonal variance. Better still is a second-order many-state model, a type which, to our knowledge, has not previously been investigated. We suggest that the best model would have a continuum of states, rather than a discrete set. Our conclusion is that a large proportion of seasonal variability may be explained in terms of the average daily structure, but there may be a residual component caused by processes operating on longer time-scales and possibly predictable with reference to these. Reproduction of long-period (e.g. monthly or seasonal) variance and of the structure of daily autocorrelation provide crucial tests of stochastic weather generators, and we recommend that models which fail to simulate these statistics realistically be used only with great caution.