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Bony S.,University Pierre and Marie Curie | Bellon G.,Meteo - France | Klocke D.,European Center for Medium RangeWeather Forecasts | Sherwood S.,University of New South Wales | And 2 more authors.
Nature Geoscience

Predicting the response of tropical rainfall to climate change remains a challenge. Rising concentrations of carbon dioxide are expected to affect the hydrological cycle through increases in global mean temperature and the water vapour content of the atmosphere. However, regional precipitation changes also closely depend on the atmospheric circulation, which is expected to weaken in a warmer world. Here, we assess the effect of a rise in atmospheric carbon dioxide concentrations on tropical circulation and precipitation by analysing results from a suite of simulations from multiple state-of-the-art climate models, and an operational numerical weather prediction model. In a scenario in which humans continue to use fossil fuels unabated, about half the tropical circulation change projected by the end of the twenty-first century, and consequently a large fraction of the regional precipitation change, is independent of global surface warming. Instead, these robust circulation and precipitation changes are a consequence of the weaker net radiative cooling of the atmosphere associated with higher atmospheric carbon dioxide levels, which affects the strength of atmospheric vertical motions. This implies that geo-engineering schemes aimed at reducing global warming without removing carbon dioxide from the atmosphere would fail to fully mitigate precipitation changes in the tropics. Strategies that may help constrain rainfall projections are suggested. © 2013 Macmillan Publishers Limited. All rights reserved. Source

Arnold H.M.,University of Oxford | Moroz I.M.,Oxford Centre for Industrial and Applied Mathematics | Palmer T.N.,University of Oxford | Palmer T.N.,European Center for Medium RangeWeather Forecasts
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

Simple chaotic systems are useful tools for testing methods for use in numerical weather simulations owing to their transparency and computational cheapness. The Lorenz system was used here; the full system was defined as 'truth', whereas a truncated version was used as a testbed for parametrization schemes. Several stochastic parametrization schemes were investigated, including additive and multiplicative noise. The forecasts were started from perfect initial conditions, eliminating initial condition uncertainty. The stochastically generated ensembles were compared with perturbed parameter ensembles and deterministic schemes. The stochastic parametrizations showed an improvement in weather and climate forecasting skill over deterministic parametrizations. Including a temporal autocorrelation resulted in a significant improvement over white noise, challenging the standard idea that a parametrization should only represent subgridscale variability. The skill of the ensemble at representing model uncertainty was tested; the stochastic ensembles gave better estimates of model uncertainty than the perturbed parameter ensembles. The forecasting skill of the parametrizations was found to be linked to their ability to reproduce the climatology of the full model. This is important in a seamless prediction system, allowing the reliability of short-term forecasts to provide a quantitative constraint on the accuracy of climate predictions from the same system. © 2013 The Author(s) Published by the Royal Society. Source

Rodwell M.J.,European Center for Medium RangeWeather Forecasts | Richardson D.S.,European Center for Medium RangeWeather Forecasts | Hewson T.D.,European Center for Medium RangeWeather Forecasts | Haiden T.,European Center for Medium RangeWeather Forecasts
Quarterly Journal of the Royal Meteorological Society

A new equitable score is developed for monitoring precipitation forecasts and for guiding forecast system development. To accommodate the difficult distribution of precipitation, the score measures the error in 'probability space' through use of the climatological cumulative distribution function. For sufficiently skilful forecasting systems, the new score is less sensitive to sampling uncertainty than other established scores. It is therefore called here the 'Stable Equitable Error in Probability Space' (SEEPS). Weather is partitioned into three categories: 'dry', 'light precipitation' and 'heavy precipitation'. SEEPS adapts to the climate of the region in question so that it assesses the salient aspects of the local weather, encouraging 'refinement' and discouraging 'hedging'. To permit continuous monitoring of a system with resolution increasing in time, forecasts are verified against point observations. With some careful choices, observation error and lack of representativeness of model grid-box averages are found to have relatively little impact. SEEPS can identify key forecasting errors including the overprediction of drizzle, failure to predict heavy large-scale precipitation and incorrectly locating convective cells. Area averages are calculated taking into account the observation density. A gain of ~2 days, at lead times of 3-9 days, over the last 14 years is found in extratropical scores of forecasts made at the EuropeanCentre for Medium-Range Weather Forecasts (ECMWF). This gain is due to system improvements, not the increased amount of data assimilated. SEEPSmay also be applicable for verifying other quantities that suffer from difficult spatio-temporal distributions. © 2010 Royal Meteorological Society. Source

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