data set, HadISST1, and the nighttime marine air temperature (NMAT) data set, HadMAT1. HadISST1 replaces the global sea ice and sea surface temperature (GISST) data sets and is a unique combination of monthly globally complete fields of SST and sea ice concentration on a 1 ° latitude-longitude grid from 1871. The companion HadMAT1 runs monthly from 1856 on a 5 ° latitude-longitude grid and incorporates new corrections for the effect on NMAT of increasing deck (and hence measurement) heights. HadISST1 and HadMAT1 temperatures are reconstructed using a two-stage reducedspace optimal interpolation procedure, followed by superposition of quality-improved gridded observations onto the reconstructions to restore local detail. The sea ice fields are made more homogeneous by compensating satellite microwave-based sea ice concentrations for the impact of surface melt effects on retrievals in the Arctic and for algorithm deficiencies in the Antarctic and by making the historical in situ concentrations consistent with the satellite data. SSTs near sea ice are estimated using statistical relationships between SST and sea ice concentration. HadISST1 compares well with other published analyses, capturing trends in global, hemispheric, and regional SST well,

Estimating the mean and the covariance matrix of an incomplete dataset and filling in missing values with imputed values is generally a nonlinear problem, which must be solved iteratively. The expectation maximization (EM) algorithm for Gaussian data, an iterative method both for the estimation of mean values and covariance matrices from incomplete datasets and for the imputation of missing values, is taken as the point of departure for the development of a regularized EM algorithm. In contrast to the conventional EM algorithm, the regularized EM algorithm is applicable to sets of climate data, in which the number of variables typically exceeds the sample size. The regularized EM algorithm is based on iterated analyses of linear regressions of variables with missing values on variables with available values, with regression coefficients estimated by ridge regression, a regularized regression method in which a continuous regularization parameter controls the filtering of the noise in the data. The regularization parameter is determined by generalized cross-validation, such as to minimize, approximately, the expected mean squared error of the imputed values. The regularized EM algorithm can estimate, and exploit for the imputation of missing values, both synchronic and diachronic covariance matrices, which may contain information on spatial covariability, stationary temporal covariability, or cyclostationary temporal covariability. A test of the regularized EM algorithm with simulated surface temperature data demonstrates that the algorithm is applicable to typical sets of climate data and that it leads to more accurate estimates of the missing values than a conventional non-iterative imputation technique.

Global sea surface temperature (SST) evolution is analyzed by constructing predictive models that best describe the dataset’s statistics. These inverse models assume that the system’s variability is driven by spatially coherent, additive noise that is white in time and are constructed in the phase space of the dataset’s leading empirical orthogonal functions. Multiple linear regression has been widely used to obtain inverse stochastic models; it is generalized here in two ways. First, the dynamics is allowed to be nonlinear by using polynomial regression. Second, a multilevel extension of classic regression allows the additive noise to be correlated in time; to do so, the residual stochastic forcing at a given level is modeled as a function of variables at this level and the preceding ones. The number of variables, as well as the order of nonlinearity, is determined by optimizing model performance. The two-level linear and quadratic models have a better El Niño–Southern Oscillation (ENSO) hindcast skill than their one-level counterparts. Estimates of skewness and kurtosis of the models ’ simulated Niño-3 index reveal that the quadratic model reproduces better the observed asymmetry between the positive El Niño and negative La Niña events. The benefits of the quadratic model are less clear in terms of its overall, cross-validated hindcast skill; this model outperforms, however, the linear one in predicting the magnitude of extreme SST anomalies. Seasonal ENSO dependence is captured by incorporating additive, as well as multiplicative forcing with a 12-month period into the first level of each model. The quasi-quadrennial ENSO oscillatory mode is robustly simulated by all models. The “spring barrier ” of ENSO forecast skill is explained by Floquet and singular vector analysis, which show that the leading ENSO mode becomes strongly damped in summer, while nonnormal optimum growth has a strong peak in December.

[1] The recent destructive Atlantic hurricane seasons and several recent publications have sparked debate over whether warming tropical sea surface temperatures (SSTs) are causing more intense, longer-lived tropical cyclones. This paper investigates worldwide tropical cyclone frequency and intensity to determine trends in activity over the past twenty years during which there has been an approximate 0.2°–0.4°C warming of SSTs. The data indicate a large increasing trend in tropical cyclone intensity and longevity for the North Atlantic basin and a considerable decreasing trend for the Northeast Pacific. All other basins showed small trends, and there has been no significant change in global net tropical cyclone activity. There has been a small increase in global Category 4–5 hurricanes from the period 1986–1995 to the period 1996– 2005. Most of this increase is likely due to improved observational technology. These findings indicate that other important factors govern intensity and frequency of tropical

The parameter estimation problem for the coupled ocean–atmosphere system in the tropical Pacific Ocean is investigated using an advanced sequential estimator [i.e., the extended Kalman filter (EKF)]. The intermediate coupled model (ICM) used in this paper consists of a prognostic upper-ocean model and a diagnostic atmospheric model. Model errors arise from the uncertainty in atmospheric wind stress. First, the state and parameters are estimated in an identical-twin framework, based on incomplete and inaccurate observations of the model state. Two parameters are estimated by including them into an augmented state vector. Model-generated oceanic datasets are assimilated to produce a time-continuous, dynamically consistent description of the model’s El Niño–Southern Oscillation (ENSO). State estimation without correcting erroneous parameter values still permits recovering the true state to a certain extent, depending on the quality and accuracy of the observations and the size of the discrepancy in the parameters. Estimating both state and parameter values simultaneously, though, produces much better results. Next, real sea surface temperatures observations from the tropical Pacific are assimilated for a 30-yr period (1975–2004). Estimating both the state and parameters by the EKF method helps to track the observations better, even when the ICM is not capable of simulating all the details of the observed state. Furthermore, unobserved ocean

Abstract. The amplitude of the El Niño-Southern Oscillation (ENSO) would be normally distributed if the coupled Pacific ocean-atmosphere were a linear system forced by Gaussian weather noise. Moment estimates of skewness and kurtosis demonstrate that this is not the case for monthly mean anomalies in Pacific sea surface temperatures during 1950-97. The noted predominance of El Niño events compared to La Niña events is related to the high skewness in the eastern Pacific. Skewness and kurtosis both exhibit an intriguing geographical variation from positive in the eastern to negative in the western Pacific. We have also examined Niño-3 indices generated by three climate models having widely different complexity. These exhibit a wide range of skewness and kurtosis values rather different from those found for the observations. Skewness and kurtosis can be used to diagnose non-linear processes and provide powerful tools for validating models, and for testing observed sea-surface temperatures for the presence of possible climate change. 1.

Modern climate dynamics uses a two-fisted approach in attacking and solving the problems of atmospheric and oceanic flows. The two fists are: (i) observational analyses; and (ii) simulations of the geofluids, including the coupled atmosphere–ocean system, using a hierarchy of dynamical models. These models represent interactions between many of processes that act on a broad range of spatial and time scales, from a few to tens of thousands of kilometers, and from diurnal to multidecadal, respectively. The evolution of virtual climates simulated by the most detailed and realistic models in the hierarchy is typically as difficult to interpret as that of the actual climate system, based on the available observations thereof. Highly simplified models of weather and climate, though, help gain a deeper understanding of a few isolated processes, as well as clues on how the interaction between these processes and the rest of the climate system may participate in shaping climate variability. Finally, models of intermediate complexity, which resolve well a subset of the climate system and parameterise the remainder of the processes or scales of motion, serve as a conduit between the models at the two ends of the hierarchy. We present here a methodology for constructing intermediate models based almost entirely on the observed evolution of selected climate fields, without reference to dynamical equations that may govern this evolution; these models parameterise unresolved processes as multivariate stochastic forcing. This methodology may be applied with equal success to actual observational data sets, as well as to data sets resulting from a high-end model simulation. We illustrate this methodology by its applications to: (i) observed and simulated low-frequency variability of atmospheric flows in the Northern Hemisphere; (ii) observed evolution of tropical sea-surface temperatures; and (iii) observed air–sea

The possible role that tropical Pacific SSTs played in driving the megadroughts over North America during the medieval period is addressed. Fossil coral records from the Palmyra Atoll are used to derive tropical Pacific SSTs for the period from A.D. 1320 to A.D. 1462 and show overall colder conditions as well as extended multidecadal La Niña–like states. The reconstructed SSTs are used to force a 16-member ensemble of atmosphere GCM simulations, each with different initial conditions, with the atmosphere coupled to a mixed layer ocean outside of the tropical Pacific. Model results are verified against North American tree ring reconstructions of the Palmer Drought Severity Index. A singular value decomposition analysis is performed using the soil moisture anomaly simulated by another 16-member ensemble of simulations forced by global observed SSTs for 1856–2004 and tree ring reconstructions of the Palmer Drought Severity Index for the same period. This relationship is used to transfer the modeled medieval soil moisture anomaly (relative to the modern simulation) into a model-estimated Palmer Drought Severity Index. The model-estimated Palmer Drought Severity Index reproduces many aspects of both the interannual and decadal variations of the tree ring reconstructions, in addition to an overall drier climate that is drier than the tree ring records suggest. The model-estimated Palmer Drought Severity Index simulates two previously

A prime challenge for ENSO seasonal forecast models is to predict boreal summer ENSO conditions at lead. August-September ENSO has a strong influence on Atlantic hurricane activity, Northwest Pacific typhoon activity and tropical precipitation. However, summer ENSO skill is low due to the spring predictability barrier between March and May. A 'Consolidated' ENSO-CLIPER seasonal prediction model is presented to address this issue with promising initial results. Consolidated CLIPER comprises the ensemble of 18 model variants of the statistical ENSO-CLIPER (CLImatology and PERsistence) prediction model. Assessing August-September ENSO skill using deterministic and probabilistic skill measures applied to crossvalidated hindcasts 1952-2002 and using deterministic skill measures applied to replicated realtime forecasts 1900-1950, shows that the consolidated CLIPER model consistently outperforms the standard CLIPER model at leads from 2 to 6 months for all the main ENSO indices (3, 3.4 and 4). The consolidated CLIPER August-September 1952-2002 hindcast skill is also positive to 97.5 % confidence at leads out to 4 months (early April) for all ENSO indices. Optimisation of the consolidated CLIPER model may lead to further skill improvements. 2

The causes and global context of the North American drought between 1998 and 2004 are examined using atmospheric reanalyses and ensembles of atmosphere model simulations variously forced by global SSTs or tropical Pacific SSTs alone. The drought divides into two distinct time intervals. Between 1998 and 2002 it coincided with a persistent La Niña–like state in the tropical Pacific, a cool tropical troposphere, polewardshifted jet streams, and, in the zonal mean, eddy-driven descent in midlatitudes. During the winters reduced precipitation over North America in the climate models was sustained by anomalous subsidence and reductions of moisture convergence by the stationary flow and transient eddies. During the summers reductions of evaporation and mean flow moisture convergence drove the precipitation reduction, while transient eddies acted diffusively to oppose this. During these years the North American drought fitted into a global pattern of circulation and hydroclimate anomalies with noticeable zonal and hemispheric symmetry. During the later period of the drought, from 2002 to 2004, weak El Niño conditions prevailed and, while the global climate adjusted accordingly, western North America remained, uniquely among midlatitude regions, in drought. The ensemble mean of the climate model simulations did not simulate the continuation of the drought in these years, suggesting that the termination of the drought was largely unpredictable in