Home  About us  Contact
 

Climate Predictions (climate + prediction)

Distribution by Scientific Domains
Earth and Environmental Science80%

Terms modified by Climate Predictions

  • climate prediction center
    		•

    Selected Abstracts

    The economic value of ensemble forecasts as a tool for risk assessment: From days to decades

    THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 581 2002
    T. N. Palmer
    Abstract Despite the revolutionary development of numerical weather and climate prediction (NWCP) in the second half of the last century, quantitative interaction between model developers and forecast customers has been rather limited. This is apparent in the diverse ways in which weather forecasts are assessed by these two groups: root-mean-square error of 500 hPa height on the one hand; pounds, euros or dollars saved on the other. These differences of approach are changing with the development of ensemble forecasting. Ensemble forecasts provide a qualitative tool for the assessment of weather and climate risk for a range of user applications, and on a range of time-scales, from days to decades. Examples of the commercial application of ensemble forecasting, from electricity generation, ship routeing, pollution modelling, weather-risk finance, disease prediction and crop yield modelling, are shown from all these time-scales. A generic user decision model is described that allows one to assess the potential economic value of numerical weather and climate forecasts for a range of customers. Using this, it is possible to relate analytically, potential economic value to conventional meteorological skill scores. A generalized meteorological measure of forecast skill is proposed which takes the distribution of customers into account. It is suggested that when customers' exposure to weather or climate risk can be quantified, such more generalized measures of skill should be used in assessing the performance of an operational NWCP system. Copyright © 2002 Royal Meteorological Society. [source]

    A nonlinear dynamical perspective on model error: A proposal for non-local stochastic-dynamic parametrization in weather and climate prediction models,

    THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 572 2001
    T. N. Palmer
    Abstract Conventional parametrization schemes in weather and climate prediction models describe the effects of subgrid-scale processes by deterministic bulk formulae which depend on local resolved-scale variables and a number of adjustable parameters. Despite the unquestionable success of such models for weather and climate prediction, it is impossible to justify the use of such formulae from first principles. Using low-order dynamical-systems models, and elementary results from dynamical-systems and turbulence theory, it is shown that even if unresolved scales only describe a small fraction of the total variance of the system, neglecting their variability can, in some circumstances, lead to gross errors in the climatology of the dominant scales. It is suggested that some of the remaining errors in weather and climate prediction models may have their origin in the neglect of subgrid-scale variability, and that such variability should be parametrized by non-local dynamically based stochastic parametrization schemes. Results from existing schemes are described, and mechanisms which might account for the impact of random parametrization error on planetary-scale motions are discussed. Proposals for the development of non-local stochastic-dynamic parametrization schemes are outlined, based on potential-vorticity diagnosis, singular-vector analysis and a simple stochastic cellular automaton model. [source]

    Performance comparison of some dynamical and empirical downscaling methods for South Africa from a seasonal climate modelling perspective

    INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 11 2009
    Willem A. Landman
    Abstract The ability of advanced state-of-the-art methods of downscaling large-scale climate predictions to regional and local scale as seasonal rainfall forecasting tools for South Africa is assessed. Various downscaling techniques and raw general circulation model (GCM) output are compared to one another over 10 December-January-February (DJF) seasons from 1991/1992 to 2000/2001 and also to a baseline prediction technique that uses only global sea-surface temperature (SST) anomalies as predictors. The various downscaling techniques described in this study include both an empirical technique called model output statistics (MOS) and a dynamical technique where a finer resolution regional climate model (RCM) is nested into the large-scale fields of a coarser GCM. The study addresses the performance of a number of simulation systems (no forecast lead-time) of varying complexity. These systems' performance is tested for both homogeneous regions and for 963 stations over South Africa, and compared with each other over the 10-year test period. For the most part, the simulations method outscores the baseline method that uses SST anomalies to simulate rainfall, therefore providing evidence that current approaches in seasonal forecasting are outscoring earlier ones. Current operational forecasting approaches involve the use of GCMs, which are considered to be the main tool whereby seasonal forecasting efforts will improve in the future. Advantages in statistically post-processing output from GCMs as well as output from RCMs are demonstrated. Evidence is provided that skill should further improve with an increased number of ensemble members. The demonstrated importance of statistical models in operation capacities is a major contribution to the science of seasonal forecasting. Although RCMs are preferable due to physical consistency, statistical models are still providing similar or even better skill and should still be applied. Copyright © 2008 Royal Meteorological Society [source]

    Identification of three dominant rainfall regions within Indonesia and their relationship to sea surface temperature

    INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 12 2003
    Edvin Aldrian
    Abstract The characteristics of climatic rainfall variability in Indonesia are investigated using a double correlation method. The results are compared with empirical orthogonal function (EOF) and rotated EOF methods. In addition, local and remote responses to sea-surface temperature (SST) are discussed. The results suggest three climatic regions in Indonesia with their distinct characteristics. Region A is located in southern Indonesia from south Sumatera to Timor island, southern Kalimantan, Sulawesi and part of Irian Jaya. Region B is located in northwest Indonesia from northern Sumatra to northwestern Kalimantan. Region C encompasses Maluku and northern Sulawesi. All three regions show both strong annual and, except Region A, semi-annual variability. Region C shows the strongest El Niño,southern oscillation (ENSO) influence, followed by Region A. In Region B, the ENSO-related signal is suppressed. Except for Region B, there are significant correlations between SST and the rainfall variabilities, indicating a strong possibility for seasonal climate predictions. March to May is the most difficult season to predict the rainfall variability. From June to November, there are significant responses of the rainfall pattern to ENSO in Regions A and C. A strong ENSO influence during this normally dry season (June to September) is hazardous in El Niño years, because the negative response means that higher SST in the NIÑO3 of the Pacific region will lower the rainfall amount over the Indonesian region. Analyses of Indonesian rainfall variability reveal some sensitivities to SST variabilities in adjacent parts of the Indian and Pacific Oceans. Copyright © 2003 Royal Meteorological Society [source]

    Tree species range shifts at a continental scale: new predictive insights from a process-based model

    JOURNAL OF ECOLOGY, Issue 4 2008
    Xavier Morin
    Summary 1Climate change has already caused distribution shifts in many species, and climate predictions strongly suggest that these will accelerate in the future. Obtaining reliable predictions of species range shifts under climate change is thus currently one of the most crucial challenges for both ecologists and stakeholders. 2Here we simulate the distributions of 16 North American tree species at a continental scale for the 21st century according to two IPCC storylines, using a process-based species distribution model that for the first time allows identification of the possible causes of distribution change. 3Our projections show local extinctions in the south of species ranges (21% of the present distribution, on average), and colonizations of new habitats in the north, though these are limited by dispersal ability for most species. Areas undergoing local extinctions are slightly larger under climate scenario A2 (+3.2 C, +22% on average) than B2 (+1.0 C, +19% on average). This small difference is caused by nonlinear responses of processes (leaves and flowers phenological processes in particular) to temperature. We also show that local extinction may proceed at a slower rate than forecasted so far. 4Although predicted distribution shifts are very species-specific, we show that the loss of habitats southward will be mostly due to increased drought mortality and decreased reproductive success, while northward colonizations will be primarily promoted by increased probability of fruit ripening and flower frost survival. 5Synthesis. Our results show that different species will not face the same risks due to climate change, because their responses to climate differ as well as their dispersal rate. Focusing on processes, our study therefore tempers the alarming conclusions of widely used niche-based models about biodiversity loss, mainly because our predictions take into account the local adaptation and trait plasticity to climate of the species. [source]