Northern Hemisphere midlatitude cyclone variability in GCM simulations with different ocean representations

Abstract : The impact of different ocean models or sea surface temperature (SST) and sea-ice concentrations on cyclone tracks in the Northern Hemisphere midlatitudes is determined within a hierarchy of model simulations. A reference simulation with the coupled atmosphere ocean circulation model ECHAM/HOPE is compared with simulations using ECHAM and three simplified ocean and sea-ice representations: (1) a variable depth mixed layer (ML) ocean, (2) forcing by varying SST and sea-ice, and (3) with climatological SST and sea-ice; the latter two are from the coupled ECHAM/HOPE integration. The reference simulation reproduces the observed cyclone tracks. The cyclones are tracked automatically by a standard routine and the variability of individual cyclone trajectories within the storm tracks is determined by a cluster approach. In the forced simulation with varying SST, the geographical distribution and the statistics of the cyclones are not altered compared to the coupled reference simulation. In the ML- and the climatological simulation, deviations of the mean cyclone distribution are found which occur mainly in the North Pacific, and can partially be traced back to missing El Niño/Southern Oscillation (ENSO) variability. The climatological experiment is superior to the ML-experiment. The variability of the individual cyclone trajectories, as determined by the cluster analysis, reveals the same types and frequencies of propagation directions for all four representations of the lower boundary. The largest discrepancies for the cluster occupations are found for the climatological and the ML-simulatio

Low-frequency variability in idealised GCM experiments with circumpolar and localised storm tracks

International audienceIdealised global circulation model simulations with circumpolar and localised (one and two) storm tracks are re-analysed to determine scaling, intermittency and phase-space structures. In a hundred year experiment with a circumpolar storm track, the spectrum S(f ) of the first principal component of the zonal wind fluctuations shows the following power law regimes: (a) a short-term memory between f- -4 and f -2 up to 50 days and (b) a long-term memory f -1 from 50 to 400 days and f -0.24 beyond 400 days, similar to observed maritime single station near-surface air temperature data. In the presence of localised storm tracks, the wave number two dominates the dynamics and a long-term memory cannot be detected. The recurrence plot is introduced as a novel tool to comprehensively visualise the evolution of the dynamical system in terms of state separations (distances) in phase space. The patterns allow for a qualitative interpretation of the underlying local phenomena in phase space, such as waves, analogs, extremes, and global regimes. Attractor dimensions are, in general, larger than 10, but they appear to be lower in the wave-dominated regimes of the double storm track experiment

Variability regimes of simulated Atlantic MOC

The spectral variability structure of the meridional overturning circulation (MOC) of the Atlantic Ocean is determined in 500 year simulations with state-of-the-art coupled atmosphere-ocean general circulation models (GFDL and ECHAM5/MPIOM). The power spectra of the monthly stream function are compared with trend-eliminating detrended fluctuation analysis (DFA2). The shapes of the spectra differ substantially between latitudes, depth and the two models with constant (white) behaviour for high frequencies as a single common feature. The most frequent property of the spectra is power-law scaling, S(f) ∼ f −β , with nontrivial exponents, mostly β ≈ 1, in agreement with 1/f or flicker noise; this is mainly found in the interannual to decadal frequency range (1/f spectra observed for sea surface temperature fluctuations are explained by a stochastically forced ocean energy balance model with vertical diffusion). For lowest frequencies, some spectra show stationary long term memory, while others reveal spectra increasing with frequency. None of the spectra can be considered uniquely as red noise explained by an ocean integrating a white stochastic atmospheric forcing

Fluctuation regimes of soil moisture in ERA-40 re-analysis data

Soil moisture variability is analysed in the re-analysis data ERA-40 of the European Centre for Medium-Range Weather Forecasts (ECMWF) which includes four layers within 189 cm depth. Short-term correlations are characterised by an e-folding time scale assuming an exponential decay, whilst long-term memory is described by power law decays with exponents determined by detrended fluctuation analysis. On a global scale, the short-term variability varies congruently with long-term memory in the surface layer. Key climatic regions (Europe, Amazon and Sahara) reveal that soil moisture time series are non-stationary in arid regions and in deep layers within the time horizon of ERA-40. The physical processes leading to soil moisture variability are linear according to an analysis of volatility (the absolute differences), which is substantiated by surrogate data analysis preserving the long-term memory

Avalanches, breathers, and flow reversal in a continuous Lorenz-96 model

For the discrete model suggested by Lorenz in 1996, a one-dimensional long-wave approximation with nonlinear excitation and diffusion is derived. The model is energy conserving but non-Hamiltonian. In a low-order truncation, weak external forcing of the zonal mean flow induces avalanchelike breather solutions which cause reversal of the mean flow by a wave-mean flow interaction. The mechanism is an outburst-recharge process similar to avalanches in a sandpile model

Return levels of temperature extremes in southern Pakistan

Southern Pakistan (Sindh) is one of the hottest regions in the world and is highly vulnerable to temperature extremes. In order to improve rural and urban planning, it is useful to gather information about the recurrence of temperature extremes. In this work, return levels of the daily maximum temperature Tmax are estimated, as well as the daily maximum wet-bulb temperature TWmax extremes. We adopt the peaks over threshold (POT) method, which has not yet been used for similar studies in this region. Two main datasets are analyzed: temperatures observed at nine meteorological stations in southern Pakistan from 1980 to 2013, and the ERA-Interim (ECMWF reanalysis) data for the nearest corresponding locations. The analysis provides the 2-, 5-, 10-, 25-, 50-, and 100-year return levels (RLs) of temperature extremes. The 90 % quantile is found to be a suitable threshold for all stations. We find that the RLs of the observed Tmax are above 50 °C at northern stations and above 45 °C at the southern stations. The RLs of the observed TWmax exceed 35 °C in the region, which is considered as a limit of survivability. The RLs estimated from the ERA-Interim data are lower by 3 to 5 °C than the RLs assessed for the nine meteorological stations. A simple bias correction applied to ERA-Interim data improves the RLs remarkably, yet discrepancies are still present. The results have potential implications for the risk assessment of extreme temperatures in Sindh

World's greatest observed point rainfalls: Jennings (1950) scaling law

The observed relation of worldwide precipitation maxima P versus duration d follows the Jennings scaling law, P ≈ d b, with scaling coefficient b ≈ 0.5. This scaling is demonstrated to hold for single-station rainfall extending over three decades. A conceptual stochastic rainfall model that reveals similar scaling behavior is introduced as a first-order autoregressive process [AR(1)] to represent the lower tropospheric vertical moisture fluxes, whose upward components balance the rainfall while the downward components are truncated and defined as no rain. Estimates of 40-yr ECMWF Re-Analysis (ERA-40) vertical moisture flux autocorrelations (at grids near the rainfall stations) provide estimates for the truncated AR(1). Subjected to maximum depth-duration analysis, the scaling coefficient b ≈ 0.5 is obtained extending for about two orders of magnitude, which is associated with a wide range of vertical moisture flux autocorrelations 0.1 < a < 0.7. [ABSTRACT FROM AUTHOR

Return interval distribution of extreme events and long term memory

The distribution of recurrence times or return intervals between extreme events is important to characterize and understand the behavior of physical systems and phenomena in many disciplines. It is well known that many physical processes in nature and society display long range correlations. Hence, in the last few years, considerable research effort has been directed towards studying the distribution of return intervals for long range correlated time series. Based on numerical simulations, it was shown that the return interval distributions are of stretched exponential type. In this paper, we obtain an analytical expression for the distribution of return intervals in long range correlated time series which holds good when the average return intervals are large. We show that the distribution is actually a product of power law and a stretched exponential form. We also discuss the regimes of validity and perform detailed studies on how the return interval distribution depends on the threshold used to define extreme events.Comment: 8 pages, 6 figure

Atmospheric bias teleconnections in boreal winter associated with systematic sea surface temperature errors in the tropical Indian Ocean

Coupled climate models suffer from significant sea surface temperature (SST) biases in the tropical Indian Ocean (TIO), leading to errors in global climate predictions. In this study, we investigate the local and remote effects of the TIO SST bias on the simulated atmospheric circulation and spatio-temporal variability – bias teleconnections. A set of century-long simulations forced by idealized SST perturbations, which resemble various (monopolar or dipolar, positive or negative) TIO SST biases in coupled climate models, are conducted with an intermediate-complexity atmospheric model. Bias teleconnections with a focus on boreal wintertime are analysed using the normal-mode function (NMF) decomposition, which can differentiate between balanced and unbalanced flows across spatial scales. The results show that the atmospheric circulation biases caused by the TIO SST bias have the Gill–Matsuno-type pattern in the tropics and Rossby-wave-train structure in the extratropics, similar to the steady-state response to tropical heating perturbations. The teleconnections between the tropical and extratropical biases are set up by Rossby wave activity flux emanating from the subtropics. Over 90 % of the bias variance (i.e. the square of the bias amplitude) is contained in zonal wavenumbers k≤5. The northward shift of the SST bias away from the Equator weakens the amplitude but does not change the spatial structure of the atmospheric response. Besides, the positive SST bias produces stronger bias teleconnections than the negative one of the same size and magnitude. In the NMF framework, the change in the spatial variance of the time-mean state (i.e. energy) is equal to the sum of the bias variance and the covariance between the circulation bias and the reference state (i.e. bias covariance). Due to the TIO SST biases, the global unbalanced zonal-mean (k=0) flow energy decreases, whereas its balanced counterpart increases. These changes primarily arise from the strong bias covariance. For k&gt;0, both the global unbalanced and the tropical balanced energies increase in the case of a monopolar SST bias and decrease in the case of a dipolar SST bias. The increase appears mainly as the bias variance, whereas the decrease is associated with a strong negative bias covariance at k=1 and 2. In contrast, the extratropical balanced wave energy decreases (increases) when the TIO SST bias is positive (negative), which is mainly associated with the bias covariance at k=1. The change in the interannual variance (IAV) is contingent upon the sign of the TIO SST bias. A positive bias reduces, whereas a negative one increases, the IAV in both balanced and unbalanced flows. Geographically, large IAV changes are observed in the tropical Indo-West Pacific region, Australia, South and Northeast Asia, the Pacific-North America region, and Europe, where the background IAVs are strong.</p