The impact of climate variability on the water cycle in desert ecosystems is controlled by biospheric feedback at interannual to millennial timescales. This paper describes a unique field dataset from weighing lysimeters beneath nonvegetated and vegetated systems that unequivocally demonstrates the role of vegetation dynamics in controlling water cycle response to interannual climate variabilit...

A predictability framework, based on relative entropy, is applied here to low-frequency variability in a standard T21 barotropic model on the sphere with realistic orography. Two types of realistic climatology, corresponding to different heights in the troposphere, are used. The two dynamical regimes with different mixing properties, induced by the two types of climate, allow the testing of the...

The effects of natural variability, especially El Niño-Southern Oscillation (ENSO) effects, have been the focus of several recent studies on the change of drought patterns with climate change. The interannual relationship between ENSO and the global climate is not stationary and can be modulated by the Pacific Decadal Oscillation (PDO). However, the global land distribution of the dry-wet chang...

Climate variations influence North Atlantic winter storm intensity and resultant variations in wave energy levels. A 60 year hindcast allows investigation of the influence of decadal climate variability on long-term trends of North Atlantic wave power, PW, spanning the 1948–2008 epoch. PW variations over much of the eastern North Atlantic are strongly influenced by the fluctuating North Atlanti...

A number of previous studies have identified changes in the climate occurring on decadal to multi-decadal time-scales. Recent studies also have revealed multi-decadal variability in the modulation of the magnitude of El Niño–Southern Oscillation (ENSO) impacts on rainfall and stream flow in Australia and other areas. This study investigates multidecadal variability of drought risk by analysing ...

Any initial value forecast of climate will be subject to errors originating from poorly known initial conditions, model imperfections, and by “chaos” in the sense that, even if the initial conditions were perfectly known, infinitesimal errors can amplify and spoil the forecast at some lead time. Here the latter source of error is examined using a “perfect model” approach whereby small perturbat...

Numerous attempts have been made to constrain climate sensitivity with observations [1-10] (with [6] as LC09, [8] as SB11). While all of these attempts contain various caveats and sources of uncertainty, some efforts have been shown to contain major errors and are demonstrably incorrect. For example, multiple studies [11-13] separately addressed weaknesses in LC09 [6]. The work of Trenberth et ...

We used the NASA Ocean Biogeochemical Model (NOBM) combined with remote sensing data via assimilation to evaluate the contribution of four phytoplankton groups to the total primary production. First, we assessed the contribution of each phytoplankton groups to the total primary production at a global scale for the period 1998–2011. Globally, diatoms contributed the most to the total phytoplankt...