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  • American Meteorological Society  (35)
  • Sage Publications  (31)
  • Copernicus
  • 2010-2014  (22)
  • 2005-2009  (31)
  • 2000-2004  (17)
  • 1990-1994  (10)
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  • 11
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    CORRIGENDUM (2002)
    American Meteorological Society
    Details
    Publication Date: 2002-03-01
    Print ISSN: 0894-8755
    Electronic ISSN: 1520-0442
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 12
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    Forecast Calibration and Combination: A Simple Bayesian Approach for ENSO (2004)
    American Meteorological Society
    Details
    Publication Date: 2004-04-01
    Print ISSN: 0894-8755
    Electronic ISSN: 1520-0442
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 13
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    Simple Nonparametric Techniques for Exploring Changing Probability Distributions of Weather (2005)
    American Meteorological Society
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    Publication Date: 2005-11-01
    Description: Anthropogenic influences are expected to cause the probability distribution of weather variables to change in nontrivial ways. This study presents simple nonparametric methods for exploring and comparing differences in pairs of probability distribution functions. The methods are based on quantiles and allow changes in all parts of the probability distribution to be investigated, including the extreme tails. Adjusted quantiles are used to investigate whether changes are simply due to shifts in location (e.g., mean) and/or scale (e.g., variance). Sampling uncertainty in the quantile differences is assessed using simultaneous confidence intervals calculated using a bootstrap resampling method that takes account of serial (intraseasonal) dependency. The methods are simple enough to be used on large gridded datasets. They are demonstrated here by exploring the changes between European regional climate model simulations of daily minimum temperature and precipitation totals for winters in 1961–90 and 2071–2100. Projected changes in daily precipitation are generally found to be well described by simple increases in scale, whereas minimum temperature exhibits changes in both location and scale.
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    Topics: Geography , Geosciences , Physics
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  • 14
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    Observed Trends and Teleconnections of the Siberian High: A Recently Declining Center of Action (2005)
    American Meteorological Society
    Details
    Publication Date: 2005-05-01
    Description: This study investigates variability in the intensity of the wintertime Siberian high (SH) by defining a robust SH index (SHI) and correlating it with selected meteorological fields and teleconnection indices. A dramatic trend of –2.5 hPa decade−1 has been found in the SHI between 1978 and 2001 with unprecedented (since 1871) low values of the SHI. The weakening of the SH has been confirmed by analyzing different historical gridded analyses and individual station observations of sea level pressure (SLP) and excluding possible effects from the conversion of surface pressure to SLP. SHI correlation maps with various meteorological fields show that SH impacts on circulation and temperature patterns extend far outside the SH source area extending from the Arctic to the tropical Pacific. Advection of warm air from eastern Europe has been identified as the main mechanism causing milder than normal conditions over the Kara and Laptev Seas in association with a strong SH. Despite the strong impacts of the variability in the SH on climatic variability across the Northern Hemisphere, correlations between the SHI and the main teleconnection indices of the Northern Hemisphere are weak. Regression analysis has shown that teleconnection indices are not able to reproduce the interannual variability and trends in the SH. The inclusion of regional surface temperature in the regression model provides closer agreement between the original and reconstructed SHI.
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    Topics: Geography , Geosciences , Physics
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  • 15
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    Atlantic Atmosphere–Ocean Interaction: A Stochastic Climate Model–Based Diagnosis (2005)
    American Meteorological Society
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    Publication Date: 2005-04-01
    Description: A simple linear stochastic climate model of extratropical wintertime ocean–atmosphere coupling is used to diagnose the daily interactions between the ocean and the atmosphere in a fully coupled general circulation model. Monte Carlo simulations with the simple model show that the influence of the ocean on the atmosphere can be difficult to estimate, being biased low even with multiple decades of daily data. Despite this, fitting the simple model to the surface air temperature and sea surface temperature data from the complex general circulation model reveals an ocean-to-atmosphere influence in the northeastern Atlantic. Furthermore, the simple model is used to demonstrate that the ocean in this region greatly enhances the autocorrelation in overlying lower-tropospheric temperatures at lags from a few days to many months.
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    Topics: Geography , Geosciences , Physics
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  • 16
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    The Variability of Seasonality (2005)
    American Meteorological Society
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    Publication Date: 2005-01-01
    Description: Seasons are the complex nonlinear response of the physical climate system to regular annual solar forcing. There is no a priori reason why they should remain fixed/invariant from year to year, as is often assumed in climate studies when extracting the seasonal component. The widely used econometric variant of Census Method II Seasonal Adjustment Program (X-11), which allows for year-to-year variations in seasonal shape, is shown here to have some advantages for diagnosing climate variability. The X-11 procedure is applied to the monthly mean Niño-3.4 sea surface temperature (SST) index and global gridded NCEP–NCAR reanalyses of 2-m surface air temperature. The resulting seasonal component shows statistically significant interannual variations over many parts of the globe. By taking these variations in seasonality into account, it is shown that one can define less ambiguous ENSO indices. Furthermore, using the X-11 seasonal adjustment approach, it is shown that the three cold ENSO episodes after 1998 are due to an increase in amplitude of seasonality rather than being three distinct La Niña events. Globally, variations in the seasonal component represent a substantial fraction of the year-to-year variability in monthly mean temperatures. In addition, strong teleconnections can be discerned between the magnitude of seasonal variations across the globe. It might be possible to exploit such relationships to improve the skill of seasonal climate forecasts.
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    Topics: Geography , Geosciences , Physics
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  • 17
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    Granger Causality of Coupled Climate Processes: Ocean Feedback on the North Atlantic Oscillation (2006)
    American Meteorological Society
    Details
    Publication Date: 2006-04-01
    Description: This study uses a Granger causality time series modeling approach to quantitatively diagnose the feedback of daily sea surface temperatures (SSTs) on daily values of the North Atlantic Oscillation (NAO) as simulated by a realistic coupled general circulation model (GCM). Bivariate vector autoregressive time series models are carefully fitted to daily wintertime SST and NAO time series produced by a 50-yr simulation of the Third Hadley Centre Coupled Ocean–Atmosphere GCM (HadCM3). The approach demonstrates that there is a small yet statistically significant feedback of SSTs on the NAO. The SST tripole index is found to provide additional predictive information for the NAO than that available by using only past values of NAO—the SST tripole is Granger causal for the NAO. Careful examination of local SSTs reveals that much of this effect is due to the effect of SSTs in the region of the Gulf Steam, especially south of Cape Hatteras. The effect of SSTs on NAO is responsible for the slower-than-exponential decay in lag-autocorrelations of NAO notable at lags longer than 10 days. The persistence induced in daily NAO by SSTs causes long-term means of NAO to have more variance than expected from averaging NAO noise if there is no feedback of the ocean on the atmosphere. There are greater long-term trends in NAO than can be expected from aggregating just short-term atmospheric noise, and NAO is potentially predictable provided that future SSTs are known. For example, there is about 10%–30% more variance in seasonal wintertime means of NAO and almost 70% more variance in annual means of NAO due to SST effects than one would expect if NAO were a purely atmospheric process.
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    Topics: Geography , Geosciences , Physics
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  • 18
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    Toward an Integrated Seasonal Forecasting System for South America (2006)
    American Meteorological Society
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    Publication Date: 2006-08-01
    Description: This study proposes an objective integrated seasonal forecasting system for producing well-calibrated probabilistic rainfall forecasts for South America. The proposed system has two components: (i) an empirical model that uses Pacific and Atlantic sea surface temperature anomalies as predictors for rainfall and (ii) a multimodel system composed of three European coupled ocean–atmosphere models. Three-month lead austral summer rainfall predictions produced by the components of the system are integrated (i.e., combined and calibrated) using a Bayesian forecast assimilation procedure. The skill of empirical, coupled multimodel, and integrated forecasts obtained with forecast assimilation is assessed and compared. The simple coupled multimodel ensemble has a comparable level of skill to that obtained using a simplified empirical approach. As for most regions of the globe, seasonal forecast skill for South America is low. However, when empirical and coupled multimodel predictions are combined and calibrated using forecast assimilation, more skillful integrated forecasts are obtained than with either empirical or coupled multimodel predictions alone. Both the reliability and resolution of the forecasts have been improved by forecast assimilation in several regions of South America. The Tropics and the area of southern Brazil, Uruguay, Paraguay, and northern Argentina have been found to be the two most predictable regions of South America during the austral summer. Skillful rainfall forecasts are generally only possible during El Niño or La Niña years rather than in neutral years.
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    Topics: Geography , Geosciences , Physics
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  • 19
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    A Multimodel Assessment of Future Projections of North Atlantic and European Extratropical Cyclones in the CMIP5 Climate Models* (2013)
    American Meteorological Society
    Details
    Publication Date: 2013-08-06
    Description: The response of North Atlantic and European extratropical cyclones to climate change is investigated in the climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). In contrast to previous multimodel studies, a feature-tracking algorithm is here applied to separately quantify the responses in the number, the wind intensity, and the precipitation intensity of extratropical cyclones. Moreover, a statistical framework is employed to formally assess the uncertainties in the multimodel projections. Under the midrange representative concentration pathway (RCP4.5) emission scenario, the December–February (DJF) response is characterized by a tripolar pattern over Europe, with an increase in the number of cyclones in central Europe and a decreased number in the Norwegian and Mediterranean Seas. The June–August (JJA) response is characterized by a reduction in the number of North Atlantic cyclones along the southern flank of the storm track. The total number of cyclones decreases in both DJF (−4%) and JJA (−2%). Classifying cyclones according to their intensity indicates a slight basinwide reduction in the number of cyclones associated with strong winds, but an increase in those associated with strong precipitation. However, in DJF, a slight increase in the number and intensity of cyclones associated with strong wind speeds is found over the United Kingdom and central Europe. The results are confirmed under the high-emission RCP8.5 scenario, where the signals tend to be larger. The sources of uncertainty in these projections are discussed.
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    Topics: Geography , Geosciences , Physics
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  • 20
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    Simple Uncertainty Frameworks for Selecting Weighting Schemes and Interpreting Multimodel Ensemble Climate Change Experiments (2013)
    American Meteorological Society
    Details
    Publication Date: 2013-06-15
    Description: Future climate change projections are often derived from ensembles of simulations from multiple global circulation models using heuristic weighting schemes. This study provides a more rigorous justification for this by introducing a nested family of three simple analysis of variance frameworks. Statistical frameworks are essential in order to quantify the uncertainty associated with the estimate of the mean climate change response. The most general framework yields the “one model, one vote” weighting scheme often used in climate projection. However, a simpler additive framework is found to be preferable when the climate change response is not strongly model dependent. In such situations, the weighted multimodel mean may be interpreted as an estimate of the actual climate response, even in the presence of shared model biases. Statistical significance tests are derived to choose the most appropriate framework for specific multimodel ensemble data. The framework assumptions are explicit and can be checked using simple tests and graphical techniques. The frameworks can be used to test for evidence of nonzero climate response and to construct confidence intervals for the size of the response. The methodology is illustrated by application to North Atlantic storm track data from the Coupled Model Intercomparison Project phase 5 (CMIP5) multimodel ensemble. Despite large variations in the historical storm tracks, the cyclone frequency climate change response is not found to be model dependent over most of the region. This gives high confidence in the response estimates. Statistically significant decreases in cyclone frequency are found on the flanks of the North Atlantic storm track and in the Mediterranean basin.
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    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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