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  • 1
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    El Niño–Southern Oscillation and Associated Climatic Conditions around the World during the Latter Half of the Twenty-First Century (2018)
    American Meteorological Society
    Details
    Publication Date: 2018-05-16
    Print ISSN: 0894-8755
    Electronic ISSN: 1520-0442
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 2
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    Using Natural Variability as a Baseline to Evaluate the Performance of Bias Correction Methods in Hydrological Climate Change Impact Studies (2016)
    American Meteorological Society
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    Publication Date: 2016-08-01
    Description: Postprocessing of climate model outputs is usually performed to remove biases prior to performing climate change impact studies. The evaluation of the performance of bias correction methods is routinely done by comparing postprocessed outputs to observed data. However, such an approach does not take into account the inherent uncertainty linked to natural climate variability and may end up recommending unnecessary complex postprocessing methods. This study evaluates the performance of bias correction methods using natural variability as a baseline. This baseline implies that any bias between model simulations and observations is only significant if it is larger than the natural climate variability. Four bias correction methods are evaluated with respect to reproducing a set of climatic and hydrological statistics. When using natural variability as a baseline, complex bias correction methods still outperform the simplest ones for precipitation and temperature time series, although the differences are much smaller than in all previous studies. However, after driving a hydrological model using the bias-corrected precipitation and temperature, all bias correction methods perform similarly with respect to reproducing 46 hydrological metrics over two watersheds in different climatic zones. The sophisticated distribution mapping correction methods show little advantage over the simplest scaling method. The main conclusion is that simple bias correction methods appear to be just as good as other more complex methods for hydrological climate change impact studies. While sophisticated methods may appear more theoretically sound, this additional complexity appears to be unjustified in hydrological impact studies when taking into account the uncertainty linked to natural climate variability.
    Print ISSN: 1525-755X
    Electronic ISSN: 1525-7541
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 3
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    Large-scale analysis of global gridded precipitation and temperature datasets for climate change impact studies (2020)
    American Meteorological Society
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    Publication Date: 2020-09-18
    Description: Currently, there are a large number of diverse climate datasets in existence, which differ, sometimes greatly, in terms of their data sources, quality control schemes, estimation procedures, and spatial and temporal resolutions. Choosing an appropriate dataset for a given application is therefore not a simple task. This study compares nine global/near-global precipitation and three global temperature datasets over 3138 North American catchments. The chosen datasets all meet the minimum requirement of having at least 30-years of available data, so they could all potentially be used as reference datasets for climate change impact studies. The precipitation datasets include two gauged-only products (GPCC and CPC-Unified), two satellite products corrected using ground-based observations (CHIRPS V2.0 and PERSIANN-CDR V1R1), four reanalysis products (NCEP-CFSR, JRA55, ERA-Interim and ERA5) and one merged product (MSWEP V1.2). The temperature datasets include one gauge-based (CPC-Unified) and two reanalysis (ERA-Interim and ERA5) products. High-resolution gauge-based gridded precipitation and temperature datasetswere combined as the reference dataset for this inter-comparison study. To assess dataset performance, all combinations were used as inputs to a lumped hydrological model. The results showed that all temperature datasets performed similarly, albeit with the CPC performance being systematically inferior to that of the other three. Significant differences in performance were, however, observed between the precipitation datasets. The MSWEP dataset performed best, followed by the gauge-based, reanalysis and satellite datasets categories. Results also showed that gauge-based datasets should be preferred in regions with good weather network density, but CHIRPS and ERA5 would be good alternatives in data sparse regions.
    Print ISSN: 1525-755X
    Electronic ISSN: 1525-7541
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 4
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    Relative importance of internal climate variability versus anthropogenic climate change in global climate change (2020)
    American Meteorological Society
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    Publication Date: 2020-10-22
    Description: : To better understand the role of internal climate variability (ICV) in climate change impact studies, this study quantifies the importance of ICV (defined as the inter-member variability of a single model initial-condition large ensemble (SMILE)) in relation to the anthropogenic climate change (ACC, defined as multi-model ensemble mean) in global and regional climate change using a criterion of Time of Emergence (ToE). The uncertainty of the estimated ToE is specifically investigated by using three SMILEs to estimate the ICV. The results show that using 1921-1940 as a baseline period, the annual mean precipitation ACC is expected to emerge within this century over extra-tropical regions as well as along the equatorial band. However, ToEs are unlikely to occur, even by the end of this century, over intra-tropical regions outside of the equatorial band. In contrast, annual mean temperature ACC have already emerged from the temperature ICV for most of the globe. Similar spatial patterns are observed at the seasonal scale, while a weaker ACC for boreal summer (Jun-Jul-Aug) precipitation and additional ICV for boreal winter (Dec-Jan-Feb) temperature translate to later ToEs for some regions. In addition, the uncertainty of ToE related to the choice of a SMILE is mostly less than 20 years for annual mean precipitation and temperature. However, it can be as large as 90 years for annual mean precipitation over some regions. Overall, results indicate that the choice of a SMILE is a significant source of uncertainty in the estimation of ToE and results based on only one SMILE should be interpreted with caution.
    Print ISSN: 0894-8755
    Electronic ISSN: 1520-0442
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 5
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    Evaluation of the South Pacific Convergence Zone in IPCC AR4 Climate Model Simulations of the Twentieth Century (2011)
    American Meteorological Society
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    Publication Date: 2011-03-15
    Description: Understanding how the South Pacific convergence zone (SPCZ) may change in the future requires the use of global coupled atmosphere–ocean models. It is therefore important to evaluate the ability of such models to realistically simulate the SPCZ. The simulation of the SPCZ in 24 coupled model simulations of the twentieth century is examined. The models and simulations are those used for the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC). The seasonal climatology and interannual variability of the SPCZ is evaluated using observed and model precipitation. Twenty models simulate a distinct SPCZ, while four models merge intertropical convergence zone and SPCZ precipitation. The majority of models simulate an SPCZ with an overly zonal orientation, rather than extending in a diagonal band into the southeast Pacific as observed. Two-thirds of models capture the observed meridional displacement of the SPCZ during El Niño and La Niña events. The four models that use ocean heat flux adjustments simulate a better tropical SPCZ pattern because of a better representation of the Pacific sea surface temperature pattern and absence of cold sea surface temperature biases on the equator. However, the flux-adjusted models do not show greater skill in simulating the interannual variability of the SPCZ. While a small subset of models does not adequately reproduce the climatology or variability of the SPCZ, the majority of models are able to capture the main features of SPCZ climatology and variability, and they can therefore be used with some confidence for future climate projections.
    Print ISSN: 0894-8755
    Electronic ISSN: 1520-0442
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 6
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    Postprocessing of Ensemble Weather Forecasts Using a Stochastic Weather Generator (2014)
    American Meteorological Society
    Details
    Publication Date: 2014-03-01
    Description: This study proposes a new statistical method for postprocessing ensemble weather forecasts using a stochastic weather generator. Key parameters of the weather generator were linked to the ensemble forecast means for both precipitation and temperature, allowing the generation of an infinite number of daily times series that are fully coherent with the ensemble weather forecast. This method was verified through postprocessing reforecast datasets derived from the Global Forecast System (GFS) for forecast leads ranging between 1 and 7 days over two Canadian watersheds in the Province of Quebec. The calibration of the ensemble weather forecasts was based on a cross-validation approach that leaves one year out for validation and uses the remaining years for training the model. The proposed method was compared with a simple bias correction method for ensemble precipitation and temperature forecasts using a set of deterministic and probabilistic metrics. The results show underdispersion and biases for the raw GFS ensemble weather forecasts, which indicated that they were poorly calibrated. The proposed method significantly increased the predictive power of ensemble weather forecasts for forecast leads ranging between 1 and 7 days, and was consistently better than the bias correction method. The ability to generate discrete, autocorrelated daily time series leads to ensemble weather forecasts’ straightforward use in forecasting models commonly used in the fields of hydrology or agriculture. This study further indicates that the calibration of ensemble forecasts for a period up to one week is reasonable for precipitation, and for temperature it could be reasonable for another week.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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