Abstract

Data on zonally averaged atmospheric angular momentum and high cloud cover percent are analyzed for the periods April–October 1979 and November 1982–October 1983. The dominant periodicity in both momentum and cloud datasets was the so called “30–60 day atmospheric oscillation” in tropical and subtropical belts. In lag correlations between high cloud belts, both the periodicity and a latitudinally varying correlation structure were evident. In the 1979 period (Northern Hemisphere summer) the cloud-cloud correlations had nodes near 17°S, 5°N, 24°N and 36°N, i.e., anomalously high/low zonal mean convection between 5 and 24°N coincided with anomalously low/high zonal mean convection between 17°S and 5°N, and between 24 and 36°N. In April–October 1983, a similar periodicity and phase structure were present, but not as well defined. The principal node in the northern Hemisphere summer, near 5°N, appears to lie between the belt of maximum cloud cover for the period (which is between 5 and 9°N) and the equator. In an analysis of the period November 1982–April 1983 (Southern Hemisphere summer), the principal node was located in the Southern Hemisphere. Lag correlations between high cloud belts and momentum belts showed strong correlations with the 30–60 day oscillation present. Anomalously high/low zonal mean high cloudiness in the tropics is accompanied by anomalously high/low zonal mean momentum in the tropics, with the latter anomalies subsequently propagating into midlatitudes.

Abstract

The Sacramento Basin is used as the focus for a case study testing whether general circulation models (GCMS) are capable of simulating the large-scale and synoptic-scale processes important in studies of regional water resources. Output from a variety of GCMs developed at GISS and NCAR were examined, but only results from Community Climate Model (CCM) simulations are presented since they are typical. The stationary waves, jet streams, and storm tracks in the North Pacific-North America region in the CCM simulators show major differences from the observations, both in the mean and in their interannual variations. In addition, although the stationary wave and jet stream patterns associated with individual storms in the basin exhibit robust differences from mean fields in the observations, these differences are not captured in the models. Consequently, the larger-scale fields necessary for driving nested models and impact models for the basin, or for western North America in general, are problematic in these models.

The model deficiencies persist at resolutions as high as T106. Also, the use of time series of observed ocean boundary conditions does little to improve model deficiencies. Consequently, the deficiencies in the model large-scale circulation features can be attributed to the model subgrid-scale parameterizations, underscoring the need to improve model parameterizations.

The problem of detection of climate change and attribution of causes of change has been formalized as a series of discrete probability judgements in an expert elicitation protocol. Here results are presented from the protocol for 19 experts, highlighting areas of convergence and divergence among experts. There is broad agreement among the experts that the global mean surface air temperature, vertical pattern of temperature change, geographical pattern of temperature change, and changes in diurnal temperature are the important lines of evidence for climate change detection and attribution. For the global mean and vertical pattern lines of evidence, the majority of experts (90%) reject the null hypothesis (no climate change) at the 5% significance level, thereby lending strong support to detection of climate change. For these lines of evidence the median probability of detection at the 5% significance level across experts exceeds 0.9. For the geographical pattern and diurnal cycle lines of evidence, there is far less agreement and fewer than half the experts support detection at even the 10% level of significance. On attribution there is a broad consensus that greenhouse forcing is responsible for about half the warming in global mean temperature in the past century. This result is fairly robust to uncertainties assessed in the relevant forcings by this set of experts. For the other lines of evidence, greenhouse forcing makes smaller fractional contributions with more spread among expert assessments. The near consensus of the experts on detection of climate change and attribution to greenhouse gases rests on the evidence of change in global mean surface air temperature. For the other lines of evidence, there is either significant expert disagreement on detection (the geographical pattern and diurnal cycle), or attribution of change is predominantly to causes other than greenhouse gas forcing (the vertical pattern).

Abstract

There has been much recent published research about a putative “pause” or “hiatus” in global warming. We show that there are frequent fluctuations in the rate of warming around a longer-term warming trend, and that there is no evidence that identifies the recent period as unique or particularly unusual. In confirmation, we show that the notion of a pause in warming is considered to be misleading in a blind expert test. Nonetheless, the most recent fluctuation about the longer-term trend has been regarded by many as an explanatory challenge that climate science must resolve. This departs from long-standing practice, insofar as scientists have long recognized that the climate fluctuates, that linear increases in CO₂ do not produce linear trends in global warming, and that 15-yr (or shorter) periods are not diagnostic of long-term trends. We suggest that the repetition of the “warming has paused” message by contrarians was adopted by the scientific community in its problem-solving and answer-seeking role and has led to undue focus on, and mislabeling of, a recent fluctuation. We present an alternative framing that could have avoided inadvertently reinforcing a misleading claim.

Abstract

Synoptic weather systems form an important part of the physical link between remote large-scale climate drivers and regional rainfall. A synoptic climatology of daily rainfall events is developed for the Central Wheatbelt of southwestern Australia over the April–October growing season for the years 1965–2009. The climatology reveals that frontal systems contribute approximately one-half of the rainfall in the growing season while cutoff lows contribute about a third. The ratio of frontal rainfall to cutoff rainfall varies throughout the growing season. Cutoff lows contribute over 40% of rainfall in the austral autumn and spring, but this falls to about 20% in August when frontal rainfall climbs to more than 60%. The number of cutoff lows varies markedly from one growing season to another, but does not exhibit a significant long-term trend. The mean rainfall per cutoff system is also highly variable, but has gradually declined over the analysis period, particularly in the past decade. The decline in rainfall per frontal system is less significant. Cutoff low rainfall has contributed more strongly in percentage terms to the recent decline in rainfall in the Central Wheatbelt than the frontal component and accounts for more than half of the overall trend. Atmospheric blocking is highly correlated with rainfall in the region where cutoff low rainfall makes its highest proportional contribution. Hence, the decline in rain from cutoff low systems is likely to have been associated with changes in blocking and the factors controlling blocking in the region.

Abstract

The authors undertake a multiscale spectral reexamination of the variability of the Pacific–South American (PSA) pattern and the mechanisms by which this variability occurs. Time scales from synoptic to interannual are investigated, focusing on the means by which tropical variability is communicated to the midlatitudes and on in situ forcing within the midlatitude waveguides. Particular interest is paid to what fraction of the total variability associated with the PSA, occurring on interannual time scales, is attributable to tropical forcing relative to that occurring on synoptic and intraseasonal time scales via internal waveguide dynamics. In general, it is found that the eastward-propagating wave train pattern typically associated with the PSA manifests across time scales from synoptic to interannual, with the majority of the variability occurring on synoptic-to-intraseasonal time scales largely independent of tropical convection. It is found that the small fraction of the total variance with a tropical signal occurs via the zonal component of the thermal wind modulating both the subtropical and polar jets. The respective roles of the Hadley circulation and stationary Rossby wave sources are also examined. Further, a PSA-like mode is identified in terms of the slow components of higher-order modes of tropospheric geopotential height. This study reestablishes the multiscale nonlinear nature of the PSA modes arising largely as a manifestation of internal midlatitude waveguide dynamics and local disturbances.

Abstract

The analysis of climatological data often involves statistical significance testing at many locations. While the field significance approach determines if a field as a whole is significant, a multiple testing procedure determines which particular tests are significant. Many such procedures are available, most of which control, for every test, the probability of detecting significance that does not really exist. The aim of this paper is to introduce the novel “false discovery rate” approach, which controls the false rejections in a more meaningful way. Specifically, it controls a priori the expected proportion of falsely rejected tests out of all rejected tests; additionally, the test results are more easily interpretable. The paper also investigates the best way to apply a false discovery rate (FDR) approach to spatially correlated data, which are common in climatology. The most straightforward method for controlling the FDR makes an assumption of independence between tests, while other FDR-controlling methods make less stringent assumptions. In a simulation study involving data with correlation structure similar to that of a real climatological dataset, the simple FDR method does control the proportion of falsely rejected hypotheses despite the violation of assumptions, while a more complicated method involves more computation with little gain in detecting alternative hypotheses. A very general method that makes no assumptions controls the proportion of falsely rejected hypotheses but at the cost of detecting few alternative hypotheses. Despite its unrealistic assumption, based on the simulation results, the authors suggest the use of the straightforward FDR-controlling method and provide a simple modification that increases the power to detect alternative hypotheses.

Abstract

This work identifies and documents a suite of large-scale drivers of rainfall variability in the Australian region. The key driver in terms of broad influence and impact on rainfall is the El Niño–Southern Oscillation (ENSO). ENSO is related to rainfall over much of the continent at different times, particularly in the north and east, with the regions of influence shifting with the seasons. The Indian Ocean dipole (IOD) is particularly important in the June–October period, which spans much of the wet season in the southwest and southeast where IOD has an influence. ENSO interacts with the IOD in this period such that their separate regions of influence cover the entire continent. Atmospheric blocking also becomes most important during this period and has an influence on rainfall across the southern half of the continent. The Madden–Julian oscillation can influence rainfall in different parts of the continent in different seasons, but its impact is strongest on the monsoonal rains in the north. The influence of the southern annular mode is mostly confined to the southwest and southeast of the continent. The patterns of rainfall relationship to each of the drivers exhibit substantial decadal variability, though the characteristic regions described above do not change markedly. The relationships between large-scale drivers and rainfall are robust to the selection of typical indices used to represent the drivers. In most regions the individual drivers account for less than 20% of monthly rainfall variability, though the drivers relate to a predictable component of this variability. The amount of rainfall variance explained by individual drivers is highest in eastern Australia and in spring, where it approaches 50% in association with ENSO and blocking.

Abstract

The causes of rainfall variations in southeastern Australia associated with three key El Niño years (1982, 1997, and 2002) are explored. Whereas 1982 and 2002 were exceptionally dry years, 1997 had near-average rainfall. These variations in rainfall can be explained by changes in the behavior of cutoff low pressure systems. Although each year had a similar number of cutoff low events, 1997 had higher rainfall per cutoff low event when compared with the other years. In particular, rain in 1997 is attributable to five large wet events from cutoff low pressure systems. In each of these wet events, the moist air originated from the marine boundary layer off the coast of northeastern Australia. Cutoff lows in 1982 and 2002 were much drier and did not draw in moist air from the northeastern coast. In typical classifications, 1982 and 1997 are grouped together as “canonical” El Niños whereas 2002 is a Modoki El Niño. The results presented here imply that these groupings are not definitive in explaining variations in southeastern Australian rainfall.

Abstract

Changes in the metastability of the Southern Hemisphere 500-hPa circulation are examined using both cluster analysis techniques and split-flow blocking indices. The cluster methodology is a purely data-driven approach for parameterization whereby a multiscale approximation to nonstationary dynamical processes is achieved through optimal sequences of locally stationary fast vector autoregressive factor (VARX) processes and some slow (or persistent) hidden process switching between them. Comparison is made with blocking indices commonly used in weather forecasting and climate analysis to identify dynamically relevant metastable regimes in the 500-hPa circulation in both reanalysis and Atmospheric Model Intercomparison Project (AMIP) datasets. The analysis characterizes the metastable regime in both reanalysis and model datasets prior to 1978 as positive and negative phases of a hemispheric midlatitude blocking state with the southern annular mode (SAM) associated with a transition state. Post-1978, the SAM emerges as a true metastable state replacing the negative phase of the hemispheric blocking pattern. The hidden state frequency of occurrences exhibits strong trends. The blocking pattern dominates in the early 1980s, and then gradually decreases. There is a corresponding increase in the SAM frequency of occurrence. This trend is largely evident in the reanalysis summer and spring but was not evident in the AMIP dataset. Further comparison with the split-flow blocking indices reveals a superficial correspondence between the cluster hidden state frequency of occurrences and split-flow indices. Examination of composite states shows that the blocking indices capture splitting of the zonal flow whereas the cluster composites reflect coherent block formation. Differences in blocking climatologies from the respective methods are discussed.