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Chaotic dynamics is ubiquitous in nature. Traditionally, a good model representation of a certain system can help in predicting this system's future behavior. However, for a complex system, a physics-based model may not be easy to construct given the complexity of a system, in particular, those that exhibit chaotic behavior. Furthermore, due to the aperiodic nature of the motion and finite precision, a model-based prediction may only have relatively high accuracy over a short-time horizon, before significant growth of error occurs in the prediction. In this article, the authors explore an alternate modeling approach, which is based on data-driven modeling, to explore forecasting viability for systems that display chaotic dynamics. Specifically, a deep recurrent neural network architecture, a neural machine, is constructed for forecasting temporal evolution of different chaotic systems. Data obtained from simulations with well-known nonlinear dynamical system prototypes serve as training data for the chosen neural network. In practice, this simulation data may be replaced with field data. The trained system is studied to examine the forecasting ability. Two ordinary differential dynamical systems, namely the Lorenz'63 system and the Lorenz'96 system, and a partial differential system, the Kuramoto–Sivashinsky equation, are studied, and the numerical experiments conducted are presented here to demonstrate the forecasting viability of the constructed neural network. [ABSTRACT FROM AUTHOR]

Identification of the driver of coupled anomalies in the climate system is of great importance for a better understanding of the system and for its use in predictive efforts with climate models. The present analysis examines the robustness of a physical method proposed three decades ago to identify coupled anomalies as of atmospheric or oceanic origin by analyzing 850 mb vorticity and sea surface temperature anomalies. The method is then used as a metric to assess the coupling in climate simulations and a 30-year hindcast from models of the CMIP5 project. Analysis of the frequency of coupled anomalies exceeding one standard deviation from uncoupled NCEP/NCAR and ERA-Interim and partially coupled CFSR reanalyses shows robustness in the main results: anomalies of oceanic origin arise inside the deep tropics and those of atmospheric origin outside of the tropics. Coupled anomalies occupy similar regions in the global oceans independently of the spatiotemporal resolution. Exclusion of phenomena like ENSO, NAO, or AMO has regional effects on the distribution and origin of coupled anomalies; the absence of ENSO decreases anomalies of oceanic origin and favors those of atmospheric origin. Coupled model simulations in general agree with the distribution of anomalies of atmospheric and oceanic origin from reanalyses. However, the lack of the feedback from the atmosphere to the ocean in the AMIP simulations reduces substantially the number of coupled anomalies of atmospheric origin and artificially increases it in the tropics while the number of those of oceanic origin outside the tropics is also augmented. Analysis of a single available 30-year hindcast surprisingly indicates that coupled anomalies are more similar to AMIP than to coupled simulations. Differences in the frequency of coupled anomalies between the AMIP simulations and the uncoupled reanalyses, and similarities between the uncoupled and partially coupled reanalyses, support the notion that the nature of the coupling between the ocean and the atmosphere is transmitted into the reanalyses via the assimilation of observations. [ABSTRACT FROM AUTHOR]

The breeding method has been widely used in studies of data assimilation, predictability and instabilities. The bred vectors (BVs), which are the nonlinear difference between the control and perturbed runs, represent the time-evolving rapidly growing errors in dynamic systems. The Lorenz (1963) model (hereafter Lorenz63 model) has chaotic dynamics similar to weather and climate. This study investigates the features of BVs of the Lorenz63 model and its impact on regime prediction of the Lorenz63 model. The results show that the Lorenz63 model has two different BVs for each breeding cycle, and the two BVs approach being identical when growth rate is high. The duration of the current and next regime is associated with the relative directions between the BV with high growth rate and the model trajectory. [ABSTRACT FROM AUTHOR]

The atmospheric carbon dioxide (CO2) record displays a prominent seasonal cycle that arises mainly from changes in vegetation growth and the corresponding CO2 uptake during the boreal spring and summer growing seasons and CO2 release during the autumn and winter seasons. The CO2 seasonal amplitude has increased over the past five decades, suggesting an increase in Northern Hemisphere biospheric activity. It has been proposed that vegetation growth may have been stimulated by higher concentrations of CO2 as well as by warming in recent decades, but such mechanisms have been unable to explain the full range and magnitude of the observed increase in CO2 seasonal amplitude. Here we suggest that the intensification of agriculture (the Green Revolution, in which much greater crop yield per unit area was achieved by hybridization, irrigation and fertilization) during the past five decades is a driver of changes in the seasonal characteristics of the global carbon cycle. Our analysis of CO2 data and atmospheric inversions shows a robust 15 per cent long-term increase in CO2 seasonal amplitude from 1961 to 2010, punctuated by large decadal and interannual variations. Using a terrestrial carbon cycle model that takes into account high-yield cultivars, fertilizer use and irrigation, we find that the long-term increase in CO2 seasonal amplitude arises from two major regions: the mid-latitude cropland between 25° N and 60° N and the high-latitude natural vegetation between 50° N and 70° N. The long-term trend of seasonal amplitude increase is 0.311 ± 0.027 per cent per year, of which sensitivity experiments attribute 45, 29 and 26 per cent to land-use change, climate variability and change, and increased productivity due to CO2 fertilization, respectively. Vegetation growth was earlier by one to two weeks, as measured by the mid-point of vegetation carbon uptake, and took up 0.5 petagrams more carbon in July, the height of the growing season, during 2001-2010 than in 1961-1970, suggesting that human land use and management contribute to seasonal changes in the CO2 exchange between the biosphere and the atmosphere. [ABSTRACT FROM AUTHOR]

The effect of vegetation feedback on decadal-scale Sahel rainfall variability is analyzed using an ensemble of climate model simulations in which the atmospheric general circulation model ICTPAGCM ('SPEEDY') is coupled to the dynamic vegetation model VEGAS to represent feedbacks from surface albedo change and evapotranspiration, forced externally by observed sea surface temperature (SST) changes. In the control experiment, where the full vegetation feedback is included, the ensemble is consistent with the observed decadal rainfall variability, with a forced component 60 % of the observed variability. In a sensitivity experiment where climatological vegetation cover and albedo are prescribed from the control experiment, the ensemble of simulations is not consistent with the observations because of strongly reduced amplitude of decadal rainfall variability, and the forced component drops to 35 % of the observed variability. The decadal rainfall variability is driven by SST forcing, but significantly enhanced by land-surface feedbacks. Both, local evaporation and moisture flux convergence changes are important for the total rainfall response. Also the internal decadal variability across the ensemble members (not SST-forced) is much stronger in the control experiment compared with the one where vegetation cover and albedo are prescribed. It is further shown that this positive vegetation feedback is physically related to the albedo feedback, supporting the Charney hypothesis. [ABSTRACT FROM AUTHOR]

The most important anthropogenic influences on climate are the emission of greenhouse gases and changes in land use, such as urbanization and agriculture. But it has been difficult to separate these two influences because both tend to increase the daily mean surface temperature. The impact of urbanization has been estimated by comparing observations in cities with those in surrounding rural areas, but the results differ significantly depending on whether population data or satellite measurements of night light are used to classify urban and rural areas. Here we use the difference between trends in observed surface temperatures in the continental United States and the corresponding trends in a reconstruction of surface temperatures determined from a reanalysis of global weather over the past 50 years, which is insensitive to surface observations, to estimate the impact of land-use changes on surface warming. Our results suggest that half of the observed decrease in diurnal temperature range is due to urban and other land-use changes. Moreover, our estimate of 0.27 °C mean surface warming per century due to land-use changes is at least twice as high as previous estimates based on urbanization alone. [ABSTRACT FROM AUTHOR]

Presents results from mechanistic experiments to clarify the origin and maintenance of an extratropical climate extreme of drought. How during April and May 1998, sea surface temperature anomalies combined with atmospheric circulation to establish the drought affecting Oklahoma and Texas; Maintenance of the drought by lower evaporation and precipitation; End of the drought with weather systems that were able to penetrate the region and overwhelm the soil-moisture feedback; Potential of numerical models to make predictions of regional climate.

Assimilation methods, meant to constrain divergence of model trajectory from reality using observations, do not exactly satisfy the physical laws governing the model state variables. This allows mismatches in the analysis in the vicinity of observation locations where the effect of assimilation is most prominent. These mismatches are usually mitigated either by the model dynamics in between the analysis cycles and/or by assimilation at the next analysis cycle. However, if the observations coverage is limited in space, as it was in the ocean before the Argo era, these mechanisms may be insufficient to dampen the mismatches, which we call shocks, and they may remain and grow. Here we show through controlled experiments, using real and simulated observations in two different ocean models and assimilation systems, that such shocks are generated in the ocean at the lateral boundaries of the moored buoy network. They thrive and propagate westward as Rossby waves along these boundaries. However, these shocks are essentially eliminated by the assimilation of near-homogenous global Argo distribution. These findings question the fidelity of ocean reanalysis products in the pre-Argo era. For example, a reanalysis that ignores Argo floats and assimilates only moored buoys, wrongly represents 2008 as a negative Indian Ocean Dipole year. [ABSTRACT FROM AUTHOR]

We do not deny the obvious importance of global warming and decrease in diurnal temperature range (DTR) due to greenhouse effects, which are present in both surface-station observations and the NCEP/NCAR 50-year reanalysis (NNR). Moreover, the NNR shows the largest warming trend over the past two decades, as reported in the surface-station data, suggesting that the NNR captures the dominant greenhouse-warming effect. [ABSTRACT FROM AUTHOR]