Thermohaline variability in the upper layers of the Arabian Sea

The Arabian Sea is an area of complex air-sea interaction processes with seasonal reversing monsoons. The associated thermohaline variability in the upper layers appears to control the large scale monsoon flow which is not yet completely understood. The variability in the thermohaline fields is known to occur in temporal domain ranging from intra-diurnal to inter-annual time scales and on spatial domains of few tens of kilometers to few thousands of kilometers. In the Arabian Sea though the surface temperature was routinely measured by both conventional measurements and satellites, the corresponding information on the subsurface thermohaline field is very sparse due to the lack cw adequate measurements. In such cases the numerical models offer promise in providing information on the subsurface features given an initial thermohaline field and surface heat flux boundary conditions. This thesis is an outcome of investigations carried out on the various aspects of the thermohaline variability on different time scales. In addition to the description of the mean annual cycle. the one dimensional numerical models of Miller (1976) and Price et a1 (1986) are utilised to simulate the observed mixed layer characteristics at selected locations in the Arabian Sea on time scales ranging from intra-diurnal to synoptic scales under variable atmospheric forcing.

The impact of North Atlantic Oscillation on the atmospheric pollutant variability in the Porto region, Portugal

The North Atlantic Oscillation (NAO) is an important large-scale atmospheric circulation that influences the European countries climate. This study evaluated NAO impact in air quality in Porto Metropolitan Area (PMA), Portugal, for the period 2002-2006. NAO, air pollutants and meteorological data were statistically analyzed. All data were obtained from PMA Weather Station, PMA Air Quality Stations and NOAA analysis. Two statistical methods were applied in different time scale : principal component and correlation coefficient. Annual time scale, using multivariate analysis (PCA, principal component analysis), were applied in order to identified positive and significant association between air pollutants such as PM10, PM2.5, CO, NO and NO2, with NAO. On the other hand, the correlation coefficient using seasonal time scale were also applied to the same data. The results of PCA analysis present a general negative significant association between the total precipitation and NAO, in Factor 1 and 2 (explaining around 70% of the variance), presented in the years of 2002, 2004 and 2005. During the same years, some air pollutants (such as PM10, PM2.5, SO2, NOx and CO) present also a positive association with NAO. The O3 shows as well a positive association with NAP during 2002 and 2004, at 2nd Factor, explaining 30% of the variance. From the seasonal analysis using correlation coefficient, it was found significant correlation between PM10 (0.72., p<0.05, in 2002), PM2.5 (0 74, p<0.05, in 2004), and SO2 (0.78, p<0.01, in 2002) with NAO during March-December (no winter period) period. Significant associations between air pollutants and NAO were also verified in the winter period (December to April) mainly with ozone (2005, r=-0.55, p.<0.01). Once that human health and hospital morbidities may be affected by air pollution, the results suggest that NAO forecast can be an important tool to prevent them, in the Iberian Peninsula and specially Portugal.

The mean evolution and variability of the Asian summer monsoon: Comparison of ECMWF and NCEP-NCAR reanalyses

The behavior of the Asian summer monsoon is documented and compared using the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA) and the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) Reanalysis. In terms of seasonal mean climatologies the results suggest that, in several respects, the ERA is superior to the NCEP-NCAR Reanalysis. The overall better simulation of the precipitation and hence the diabatic heating field over the monsoon domain in ERA means that the analyzed circulation is probably nearer reality. In terms of interannual variability, inconsistencies in the definition of weak and strong monsoon years based on typical monsoon indices such as All-India Rainfall (AIR) anomalies and the large-scale wind shear based dynamical monsoon index (DMI) still exist. Two dominant modes of interannual variability have been identified that together explain nearly 50% of the variance. Individually, they have many features in common with the composite flow patterns associated with weak and strong monsoons, when defined in terms of regional AIR anomalies and the large-scale DMI. The reanalyses also show a common dominant mode of intraseasonal variability that describes the latitudinal displacement of the tropical convergence zone from its oceanic-to-continental regime and essentially captures the low-frequency active/break cycles of the monsoon. The relationship between interannual and intraseasonal variability has been investigated by considering the probability density function (PDF) of the principal component of the dominant intraseasonal mode. Based on the DMI, there is an indication that in years with a weaker monsoon circulation, the PDF is skewed toward negative values (i,e., break conditions). Similarly, the PDFs for El Nino and La Nina years suggest that El Nino predisposes the system to more break spells, although the sample size may limit the statistical significance of the results.

The effect of doubled CO2 and model basic state biases on the monsoon-ENSO system. I: Mean response and interannual variability

The impact of doubled CO2 concentration on the Asian summer monsoon is studied using a coupled ocean-atmosphere model. Both the mean seasonal precipitation and interannual monsoon variability are found to increase in the future climate scenario presented. Systematic biases in current climate simulations of the coupled system prevent accurate representation of the monsoon-ENSO teleconnection, of prime importance for seasonal prediction and for determining monsoon interannual variability. By applying seasonally varying heat flux adjustments to the tropical Pacific and Indian Ocean surface in the future climate simulation, some assessment can be made of the impact of systematic model biases on future climate predictions. In simulations where the flux adjustments are implemented, the response to climate change is magnified, with the suggestion that systematic biases may be masking the true impact of increased greenhouse gas forcing. The teleconnection between ENSO and the Asian summer monsoon remains robust in the future climate, although the Indo-Pacific takes on more of a biennial character for long periods of the flux-adjusted simulation. Assessing the teleconnection across interdecadal timescales shows wide variations in its amplitude, despite the absence of external forcing. This suggests that recent changes in the observed record cannot be distinguished from internal variations and as such are not necessarily related to climate change.

On the natural variability of the pre-industrial European climate

We suggest that climate variability in Europe for the “pre-industrial” period 1500–1900 is fundamentally a consequence of internal fluctuations of the climate system. This is because a model simulation, using fixed pre-industrial forcing, in several important aspects is consistent with recent observational reconstructions at high temporal resolution. This includes extreme warm and cold seasonal events as well as different measures of the decadal to multi-decadal variance. Significant trends of 50-year duration can be seen in the model simulation. While the global temperature is highly correlated with ENSO (El Nino- Southern Oscillation), European seasonal temperature is only weakly correlated with the global temperature broadly consistent with data from ERA-40 reanalyses. Seasonal temperature anomalies of the European land area are largely controlled by the position of the North Atlantic storm tracks. We believe the result is highly relevant for the interpretation of past observational records suggesting that the effect of external forcing appears to be of secondary importance. That variations in the solar irradiation could have been a credible cause of climate variations during the last centuries, as suggested in some previous studies, is presumably due to the fact that the models used in these studies may have underestimated the internal variability of the climate. The general interpretation from this study is that the past climate is just one of many possible realizations and thus in many respects not reproducible in its time evolution with a general circulation model but only reproducible in a statistical sense.

Stratospheric circulation in seasonal forecasting models: implications for seasonal prediction

Accurate seasonal forecasts rely on the presence of low frequency, predictable signals in the climate system which have a sufficiently well understood and significant impact on the atmospheric circulation. In the Northern European region, signals associated with seasonal scale variability such as ENSO, North Atlantic SST anomalies and the North Atlantic Oscillation have not yet proven sufficient to enable satisfactorily skilful dynamical seasonal forecasts. The winter-time circulations of the stratosphere and troposphere are highly coupled. It is therefore possible that additional seasonal forecasting skill may be gained by including a realistic stratosphere in models. In this study we assess the ability of five seasonal forecasting models to simulate the Northern Hemisphere extra-tropical winter-time stratospheric circulation. Our results show that all of the models have a polar night jet which is too weak and displaced southward compared to re-analysis data. It is shown that the models underestimate the number, magnitude and duration of periods of anomalous stratospheric circulation. Despite the poor representation of the general circulation of the stratosphere, the results indicate that there may be a detectable tropospheric response following anomalous circulation events in the stratosphere. However, the models fail to exhibit any predictability in their forecasts. These results highlight some of the deficiencies of current seasonal forecasting models with a poorly resolved stratosphere. The combination of these results with other recent studies which show a tropospheric response to stratospheric variability, demonstrates a real prospect for improving the skill of seasonal forecasts.

Understanding the processes governing climate variability and change, climate predictability and the probability of extreme events

The purpose of Research Theme 4 (RT4) was to advance understanding of the basic science issues at the heart of the ENSEMBLES project, focusing on the key processes that govern climate variability and change, and that determine the predictability of climate. Particular attention was given to understanding linear and non-linear feedbacks that may lead to climate surprises,and to understanding the factors that govern the probability of extreme events. Improved understanding of these issues will contribute significantly to the quantification and reduction of uncertainty in seasonal to decadal predictions and projections of climate change. RT4 exploited the ENSEMBLES integrations (stream 1) performed in RT2A as well as undertaking its own experimentation to explore key processes within the climate system. It was working at the cutting edge of problems related to climate feedbacks, the interaction between climate variability and climate change � especially how climate change pertains to extreme events, and the predictability of the climate system on a range of time-scales. The statisticalmethodologies developed for extreme event analysis are new and state-of-the-art. The RT4-coordinated experiments, which have been conducted with six different atmospheric GCMs forced by common timeinvariant sea surface temperature (SST) and sea-ice fields (removing some sources of inter-model variability), are designed to help to understand model uncertainty (rather than scenario or initial condition uncertainty) in predictions of the response to greenhouse-gas-induced warming. RT4 links strongly with RT5 on the evaluation of the ENSEMBLES prediction system and feeds back its results to RT1 to guide improvements in the Earth system models and, through its research on predictability, to steer the development of methods for initialising the ensembles

Seasonal trends in the stable isotopic composition of snow and meltwater runoff in a subarctic catchment at Okstindan, Norway

Seasonal variations in the stable isotopic composition of snow and meltwater were investigated in a sub-arctic, mountainous, but non-glacial, catchment at Okstindan in northern Norway based on analyses of delta(18)O and deltaD. Samples were collected during four field periods (August 1998; April 1999; June 1999 and August 1999) at three sites lying on an altitudinal transect (740-970 m a.s.l.). Snowpack data display an increase in the mean values of delta(18)O (increasing from a mean value of - 13.51 to - 11.49% between April and August), as well as a decrease in variability through the melt period. Comparison with a regional meteoric water line indicates that the slope of the delta(18)O - deltaD line for the snowpacks decreases over the same period, dropping from 7.49 to approximately 6.2. This change points to the role of evaporation in snowpack ablation and is confirmed by the vertical profile of deuterium excess. Snowpack seepage data, although limited, also suggest reduced values of deltaD, as might be associated with local evaporation during meltwater generation. In general, meltwaters were depleted in delta(18)O relative to the source snowpack at the peak of the melt (June), but later in the year (August) the difference between the two was not statistically significant. The diurnal pattern of isotopic composition indicates that the most depleted meltwaters coincide with the peak in temperature and, hence, meltwater production.

Magnitude of climate variability during middle Pliocene warmth: a palaeoclimate modelling study

The nature and magnitude of climatic variability during the period of middle Pliocene warmth (ca 3.29–2.97 Ma) is poorly understood. We present a suite of palaeoclimate modelling experiments incorporating an advanced atmospheric general circulation model (GCM), coupled to a Q-flux ocean model for 3.29, 3.12 and 2.97 Ma BP. Astronomical solutions for the periods in question were derived from the Berger and Loutre BL2 astronomical solution. Boundary conditions, excluding sea surface temperatures (SSTs) which were predicted by the slab-ocean model, were provided from the USGS PRISM2 2°×2° digital data set. The model results indicate that little annual variation (0.5°C) in SSTs, relative to a ‘control’ experiment, occurred during the middle Pliocene in response to the altered orbital configurations. Annual surface air temperatures also displayed little variation. Seasonally, surface air temperatures displayed a trend of cooler temperatures during December, January and February, and warmer temperatures during June, July and August. This pattern is consistent with altered seasonality resulting from the prescribed orbital configurations. Precipitation changes follow the seasonal trend observed for surface air temperature. Compared to present-day, surface wind strength and wind stress over the North Atlantic, North Pacific and Southern Ocean remained greater in each of the Pliocene experiments. This suggests that wind-driven gyral circulation may have been consistently greater during the middle Pliocene. The trend of climatic variability predicted by the GCM for the middle Pliocene accords with geological data. However, it is unclear if the model correctly simulates the magnitude of the variation. This uncertainty is derived from, (a) the relative insensitivity of the GCM to perturbation in the imposed boundary conditions, (b) a lack of detailed time series data concerning changes to terrestrial ice cover and greenhouse gas concentrations for the middle Pliocene and (c) difficulties in representing the effects of ‘climatic history’ in snap-shot GCM experiments.

Atlantic climate variability and predictability: A CLIVAR perspective

Three interrelated climate phenomena are at the center of the Climate Variability and Predictability (CLIVAR) Atlantic research: tropical Atlantic variability (TAV), the North Atlantic Oscillation (NAO), and the Atlantic meridional overturning circulation (MOC). These phenomena produce a myriad of impacts on society and the environment on seasonal, interannual, and longer time scales through variability manifest as coherent fluctuations in ocean and land temperature, rainfall, and extreme events. Improved understanding of this variability is essential for assessing the likely range of future climate fluctuations and the extent to which they may be predictable, as well as understanding the potential impact of human-induced climate change. CLIVAR is addressing these issues through prioritized and integrated plans for short-term and sustained observations, basin-scale reanalysis, and modeling and theoretical investigations of the coupled Atlantic climate system and its links to remote regions. In this paper, a brief review of the state of understanding of Atlantic climate variability and achievements to date is provided. Considerable discussion is given to future challenges related to building and sustaining observing systems, developing synthesis strategies to support understanding and attribution of observed change, understanding sources of predictability, and developing prediction systems in order to meet the scientific objectives of the CLIVAR Atlantic program.

Predictability and skill of boreal winter forecasts made with the ECMWF Seasonal Forecasting System II

The performance of boreal winter forecasts made with the European Centre for Medium-Range Weather Forecasts (ECMWF) System 11 Seasonal Forecasting System is investigated through analyses of ensemble hindcasts for the period 1987-2001. The predictability, or signal-to-noise ratio, associated with the forecasts, and the forecast skill are examined. On average, forecasts of 500 hPa geopotential height (GPH) have skill in most of the Tropics and in a few regions of the extratropics. There is broad, but not perfect, agreement between regions of high predictability and regions of high skill. However, model errors are also identified, in particular regions where the forecast ensemble spread appears too small. For individual winters the information provided by t-values, a simple measure of the forecast signal-to-noise ratio, is investigated. For 2 m surface air temperature (T2m), highest t-values are found in the Tropics but there is considerable interannual variability, and in the tropical Atlantic and Indian basins this variability is not directly tied to the El Nino Southern Oscillation. For GPH there is also large interannual variability in t-values, but these variations cannot easily be predicted from the strength of the tropical sea-surface-temperature anomalies. It is argued that the t-values for 500 hPa GPH can give valuable insight into the oceanic forcing of the atmosphere that generates predictable signals in the model. Consequently, t-values may be a useful tool for understanding, at a mechanistic level, forecast successes and failures. Lastly, the extent to which t-values are useful as a predictor of forecast skill is investigated. For T2m, t-values provide a useful predictor of forecast skill in both the Tropics and extratropics. Except in the equatorial east Pacific, most of the information in t-values is associated with interannual variability of the ensemble-mean forecast rather than interannual variability of the ensemble spread. For GPH, however, t-values provide a useful predictor of forecast skill only in the tropical Pacific region.

Toward a combined seasonal weather and crop productivity forecasting system: Determination of the working spatial scale

A methodology is presented for the development of a combined seasonal weather and crop productivity forecasting system. The first stage of the methodology is the determination of the spatial scale(s) on which the system could operate; this determination has been made for the case of groundnut production in India. Rainfall is a dominant climatic determinant of groundnut yield in India. The relationship between yield and rainfall has been explored using data from 1966 to 1995. On the all-India scale, seasonal rainfall explains 52% of the variance in yield. On the subdivisional scale, correlations vary between variance r(2) = 0.62 (significance level p < 10(-4)) and a negative correlation with r(2) = 0.1 (p = 0.13). The spatial structure of the relationship between rainfall and groundnut yield has been explored using empirical orthogonal function (EOF) analysis. A coherent, large-scale pattern emerges for both rainfall and yield. On the subdivisional scale (similar to 300 km), the first principal component (PC) of rainfall is correlated well with the first PC of yield (r(2) = 0.53, p < 10(-4)), demonstrating that the large-scale patterns picked out by the EOFs are related. The physical significance of this result is demonstrated. Use of larger averaging areas for the EOF analysis resulted in lower and (over time) less robust correlations. Because of this loss of detail when using larger spatial scales, the subdivisional scale is suggested as an upper limit on the spatial scale for the proposed forecasting system. Further, district-level EOFs of the yield data demonstrate the validity of upscaling these data to the subdivisional scale. Similar patterns have been produced using data on both of these scales, and the first PCs are very highly correlated (r(2) = 0.96). Hence, a working spatial scale has been identified, typical of that used in seasonal weather forecasting, that can form the basis of crop modeling work for the case of groundnut production in India. Last, the change in correlation between yield and seasonal rainfall during the study period has been examined using seasonal totals and monthly EOFs. A further link between yield and subseasonal variability is demonstrated via analysis of dynamical data.

Probabilistic simulations of crop yield over western India using the DEMETER seasonal hindcast ensembles

Process-based integrated modelling of weather and crop yield over large areas is becoming an important research topic. The production of the DEMETER ensemble hindcasts of weather allows this work to be carried out in a probabilistic framework. In this study, ensembles of crop yield (groundnut, Arachis hypogaea L.) were produced for 10 2.5 degrees x 2.5 degrees grid cells in western India using the DEMETER ensembles and the general large-area model (GLAM) for annual crops. Four key issues are addressed by this study. First, crop model calibration methods for use with weather ensemble data are assessed. Calibration using yield ensembles was more successful than calibration using reanalysis data (the European Centre for Medium-Range Weather Forecasts 40-yr reanalysis, ERA40). Secondly, the potential for probabilistic forecasting of crop failure is examined. The hindcasts show skill in the prediction of crop failure, with more severe failures being more predictable. Thirdly, the use of yield ensemble means to predict interannual variability in crop yield is examined and their skill assessed relative to baseline simulations using ERA40. The accuracy of multi-model yield ensemble means is equal to or greater than the accuracy using ERA40. Fourthly, the impact of two key uncertainties, sowing window and spatial scale, is briefly examined. The impact of uncertainty in the sowing window is greater with ERA40 than with the multi-model yield ensemble mean. Subgrid heterogeneity affects model accuracy: where correlations are low on the grid scale, they may be significantly positive on the subgrid scale. The implications of the results of this study for yield forecasting on seasonal time-scales are as follows. (i) There is the potential for probabilistic forecasting of crop failure (defined by a threshold yield value); forecasting of yield terciles shows less potential. (ii) Any improvement in the skill of climate models has the potential to translate into improved deterministic yield prediction. (iii) Whilst model input uncertainties are important, uncertainty in the sowing window may not require specific modelling. The implications of the results of this study for yield forecasting on multidecadal (climate change) time-scales are as follows. (i) The skill in the ensemble mean suggests that the perturbation, within uncertainty bounds, of crop and climate parameters, could potentially average out some of the errors associated with mean yield prediction. (ii) For a given technology trend, decadal fluctuations in the yield-gap parameter used by GLAM may be relatively small, implying some predictability on those time-scales.

Seasonality and interannual variability of the westerly jet in the Tibetan Plateau region

In this study, 40-yr ECMWF Re-Analysis (ERA-40) data are used for the description of the seasonal cycle and the interannual variability of the westerly jet in the Tibetan Plateau region. To complement results based on the analysis of monthly mean horizontal wind speeds, an occurrence-based jet climatology is constructed by identifying the locations of the jet axes at 6-hourly intervals throughout 1958–2001. Thus, a dataset describing the highly transient and localized features of jet variability is obtained. During winter and summer the westerly jet is located, respectively, to the south and north of the Tibetan Plateau. During the spring and autumn seasons there are jet transitions from south to north and vice versa. The median dates for these transitions are 28 April and 12 October. The spring transition is associated with large interannual variations, while the fall transition occurs more reliably within a 3-week period. The strength of the jet exhibits a peculiar seasonal cycle. During northward migration in April/May, the jet intensity weakens and its latitudinal position varies largely. In some springs, there are several transitions and split configurations occur before the jet settles in its northern summer position. In June, a well-defined and unusually strong jet reappears at the northern flanks of the Tibetan Plateau. In autumn, the jet gradually but reliably recedes to the south and is typically more intense than in spring. The jet transitions between the two preferred locations follow the seasonal latitudinal migration of the jet in the Northern Hemisphere. An analysis of interannual variations shows the statistical relationship between the strength of the summer jet, the tropospheric meridional temperature gradient, and the all-India rainfall series. Both this analysis and results from previous studies point to the particular dynamical relevance of the onsetting Indian summer monsoon precipitation and the associated diabatic heating for the formation of the strong summer jet. Finally, an example is provided that illustrates the climatological significance of the jet in terms of the covariation between the jet location and the spatial precipitation distribution in central Asia.

Assessing and understanding the impact of stratospheric dynamics and variability on the earth system

Advances in weather and climate research have demonstrated the role of the stratosphere in the Earth system across a wide range of temporal and spatial scales. Stratospheric ozone loss has been identified as a key driver of Southern Hemisphere tropospheric circulation trends, affecting ocean currents and carbon uptake, sea ice, and possibly even the Antarctic ice sheets. Stratospheric variability has also been shown to affect short term and seasonal forecasts, connecting the tropics and midlatitudes and guiding storm track dynamics. The two-way interactions between the stratosphere and the Earth system have motivated the World Climate Research Programme's (WCRP) Stratospheric Processes and Their Role in Climate (SPARC) DynVar activity to investigate the impact of stratospheric dynamics and variability on climate. This assessment will be made possible by two new multi-model datasets. First, roughly 10 models with a well resolved stratosphere are participating in the Coupled Model Intercomparison Project 5 (CMIP5), providing the first multi-model ensemble of climate simulations coupled from the stratopause to the sea floor. Second, the Stratosphere Historical Forecasting Project (SHFP) of WCRP's Climate Variability and predictability (CLIVAR) program is forming a multi-model set of seasonal hindcasts with stratosphere resolving models, revealing the impact of both stratospheric initial conditions and dynamics on intraseasonal prediction. The CMIP5 and SHFP model-data sets will offer an unprecedented opportunity to understand the role of the stratosphere in the natural and forced variability of the Earth system and to determine whether incorporating knowledge of the middle atmosphere improves seasonal forecasts and climate projections. Capsule New modeling efforts will provide unprecedented opportunities to harness our knowledge of the stratosphere to improve weather and climate prediction.