DroughtScape- Summer 2008

VegDRI Expands West Spring Rains Ease Drought But CA Still Dry Scholar Donates Books NDMC Welcomes Employees Bathke, Nothwehr Latest Workshop Info Up State Lawmakers to Focus on Drought Planning Decadal Variation -- Clues to Droughts and Floods

Convergence of atmospheric and North Atlantic CO2 trends on multidecadal timescales

The oceans’ carbon uptake substantially reduces the rate of anthropogenic carbon accumulation in the atmosphere1, and thus slows global climate change. Some diagnoses of trends in ocean carbon uptake have suggested a significant weakening in recent years2-8, while others conclude that decadal variability confounds detection of long-term trends9-11. Here, we study trends in observed surface ocean partial pressure of CO2 (pCO2) in three gyre-scale biomes of the North Atlantic, considering decadal to multidecadal timescales between 1981 and 2009. Trends on decadal timescales are of variable magnitudes and depend sensitively on the precise choice of years. As more years are considered, oceanic pCO2 trends begin to converge to the trend in atmospheric pCO2. North of 30oN, it takes 25 years for the influence of decadal-timescale climate variability to be overcome by a long-term trend that is consistent with the accumulation of anthropogenic carbon. In the permanently stratified subtropical gyre, warming has recently become a significant contributor to the observed increase in oceanic pCO2. This warming, previously attributed to both a multidecadal climate oscillation and anthropogenic climate forcing12,13, is beginning to reduce ocean carbon uptake

Evaluating Modeled Intra- to Multidecadal Climate Variability Using Running Mann–Whitney \u3cem\u3eZ\u3c/em\u3e Statistics

An analysis method previously used to detect observed intra- to multidecadal (IMD) climate regimes was adapted to compare observed and modeled IMD climate variations. Pending the availability of the more appropriate phase 5 Coupled Model Intercomparison Project (CMIP-5) simulations, the method is demonstrated using CMIP-3 model simulations. Although the CMIP-3 experimental design will almost certainly prevent these model runs from reproducing features of historical IMD climate variability, these simulations allow for the demonstration of the method and illustrate how the models and observations disagree. This method samples a time series’s data rankings over moving time windows, converts those ranking sets to a Mann–Whitney U statistic, and then normalizes the U statistic into a Z statistic. By detecting optimally significant IMD ranking regimes of arbitrary onset and varying duration, this process generates time series of Z values that are an adaptively low-passed and normalized transformation of the original time series. Principal component (PC) analysis of the Z series derived from observed annual temperatures at 92 U.S. grid locations during 1919–2008 shows two dominant modes: a PC1 mode with cool temperatures before the late 1960s and warm temperatures after the mid-1980s, and a PC2 mode indicating a multidecadal temperature cycle over the Southeast. Using a graphic analysis of a Z error metric that compares modeled and observed Z series, the three CMIP-3 model simulations tested here are shown to reproduce the PC1 mode but not the PC2 mode. By providing a way to compare grid-level IMD climate response patterns in observed and modeled data, this method can play a useful diagnostic role in future model development and decadal climate forecasting

The impact of resolution on the adjustment and decadal variability of the Atlantic Meridional Overturning Circulation in a coupled climate model

Variations in the Atlantic Meridional Overturning Circulation (MOC) exert an important influence on climate, particularly on decadal time scales. Simulation of the MOC in coupled climate models is compromised, to a degree that is unknown, by their lack of fidelity in resolving some of the key processes involved. There is an overarching need to increase the resolution and fidelity of climate models, but also to assess how increases in resolution influence the simulation of key phenomena such as the MOC. In this study we investigate the impact of significantly increasing the (ocean and atmosphere) resolution of a coupled climate model on the simulation of MOC variability by comparing high and low resolution versions of the same model. In both versions, decadal variability of the MOC is closely linked to density anomalies that propagate from the Labrador Sea southward along the deep western boundary. We demonstrate that the MOC adjustment proceeds more rapidly in the higher resolution model due the increased speed of western boundary waves. However, the response of the Atlantic Sea Surface Temperatures (SSTs) to MOC variations is relatively robust - in pattern if not in magnitude - across the two resolutions. The MOC also excites a coupled ocean-atmosphere response in the tropical Atlantic in both model versions. In the higher resolution model, but not the lower resolution model, there is evidence of a significant response in the extratropical atmosphere over the North Atlantic 6 years after a maximum in the MOC. In both models there is evidence of a weak negative feedback on deep density anomalies in the Labrador Sea, and hence on the MOC (with a time scale of approximately ten years). Our results highlight the need for further work to understand the decadal variability of the MOC and its simulation in climate models

Generation of SST anomalies in the midlatitudes

Analyses of monthly mean sea surface temperatures (SST) from a hierarchy of global cou- pled ocean-atmosphere models have been carried out with the focus on the midlatitudes (20N-45N). The spectra of the simulated SSTs have been tested against the null hypothe- sis of Hasselmann's stochastic climate model, which assumes an AR(1)-process for the SST variability. It has been found that the spectra of the SST variability in CGCl\/ls with fully dynamical ocean models are significantly different from the AR(1)-process, while the SST variability in an AGCM coupled to a slab ocean is consistent with an AR(1)-process. The deviation of the SST variability in CGCl\/ls with fully dynamical ocean models from the AR(1)-process are not characterized by spectral peaks but are due to a different shape of the spectra. This can be attributed to local air-sea interactions which can be simulated with an AGCM coupled to a slab ocean with dynamical varying mixed layer depth