Climate Prediction Skill Research Articles

Climate Model Errors over the South Indian Ocean Thermocline Dome and Their Effect on the Basin Mode of Interannual Variability

AbstractAn open-ocean thermocline dome south of the equator is a striking feature of the Indian Ocean (IO) as a result of equatorial westerly winds. Over the thermocline dome, the El Niño–forced Rossby waves help sustain the IO basin (IOB) mode and offer climate predictability for the IO and surrounding countries. This study shows that a common equatorial easterly wind bias, by forcing a westward-propagating downwelling Rossby wave in the southern IO, induces too deep a thermocline dome over the southwestern IO (SWIO) in state-of-the-art climate models. Such a deep SWIO thermocline weakens the influence of subsurface variability on sea surface temperature (SST), reducing the IOB amplitude and possibly limiting the models’ skill of regional climate prediction. To the extent that the equatorial easterly wind bias originates from errors of the South Asian summer monsoon, improving the monsoon simulation can lead to substantial improvements in simulating and predicting interannual variability in the IO.

Prospects for decadal climate prediction in the Mediterranean region

AbstractThe Mediterranean region stands as one of the most sensitive to climate change, both in terms of warming and drying. On shorter time‐scales, internal variability has substantially affected the observed climate and in the next decade might enhance or compensate long‐term trends. Here we compare the multi‐model climate predictions produced within the framework of the CMIP5 (Coupled Model Intercomparison Project Phase 5) project with historical simulations to assess the level of multi‐year climate prediction skill in the Mediterranean region beyond that originating from the model accumulated response to the external radiative forcings. We obtain a high and significant skill in predicting 4‐year averaged annual and summer mean temperature over most of the study domain and in predicting precipitation for the same seasons over northern Europe and sub‐Saharan Africa. A lower skill is found during the winter season but still positive for temperature. Although most of this high skill originates from the model response to the external radiative forcings, the initialization contributes to the temperature skill over the Mediterranean Sea and surrounding land areas. The high and significant correlations between the observed Mediterranean temperatures and the observed Atlantic multidecadal oscillation (AMO) in the summer and annual means are captured by the CMIP5 ensemble which suggests that the added skill is related to the ability of the CMIP5 ensemble to predict the AMO. Such a link to the AMO seems restricted to western Africa and summer means only for the precipitation case.

Interdecadal change in the Northern Hemisphere seasonal climate prediction skill: part II. predictability and prediction skill

The interdecadal change in seasonal predictability and numerical models’ seasonal forecast skill in the Northern Hemisphere are examined using both observations and the seasonal hindcast from six coupled atmosphere-ocean climate models from the 21 period of 1960–1980 (P1) to that of 1981–2001 (P2). It is shown that the one-month lead seasonal forecast skill of the six models’ multi-model ensemble is significantly increased from P1 to P2 for all four seasons. We identify four possible reasons accounting for the interdecadal change of the seasonal forecast skill. Firstly, the numerical model’s ability to simulate the mean state, the time variability and the spatial structures of the sea surface temperature and precipitation over the tropical Pacific is improved in P2 compared to P1. Secondly, an examination of the potential predictability of the atmosphere, estimated by the ratio of the total variance to the variance due to the internal dynamics of the model atmosphere, reveals that the atmospheric potential predictability is significantly increased after 1980s which is mainly due to an increased influence of El Nino-Southern Oscillation signal over the North Pacific and North American regions. Thirdly, the long-term climate trends in the atmosphere are found to contribute, to some extent, to the increased seasonal forecast skill especially over the Eurasian regions. Finally, the improved ocean observations in P2 may provide better initial conditions for the coupled models’ seasonal forecast.

Interdecadal change in the Northern Hemisphere seasonal climate prediction skill: part I. The leading forced mode of atmospheric circulation

Using observations and 1-month lead hindcast data from six coupled atmosphere–ocean climate models, this study investigates the interdecadal change in the leading maximum covariance analysis mode (MCA1) of atmospheric circulation in response to the changes in the El Nino and Southern Oscillation (ENSO) occurred around late 1970s. We focus on boreal winter climate variability and predictability over the North Pacific–North American (NPNA) region using December–January–February prediction initiated from November 1st in the period of 1960–1980 (P1) and 1981–2001 (P2). Observed analysis reveals that ENSO variability, the related tropical convective activity, and thus the MCA1 are considerably enhanced from P1 to P2. As a result, surface climate anomalies over the NPNA are more significantly correlated with the MCA1 in P2 than P1, particularly over North America. The six coupled models and their multi-model ensemble not only are capable of capturing the interdecadal change of the MCA1 and its relationship with surface air temperature and precipitation over the NPNA regions but also have significantly higher forecast skills for the MCA1 and the surface climate anomalies in P2 than P1. However, models have systematic biases in the spatial distribution of the MCA1. It is demonstrated that the interdecadal change in the MCA1 should contribute to the improved forecast skill of the NPNA climate during recent epoch.

Seasonal Hydrological Forecasts for Watersheds over the Southeastern United States for the Boreal Summer and Fall Seasons

Abstract The authors evaluate the skill of a suite of seasonal hydrological prediction experiments over 28 watersheds throughout the southeastern United States (SEUS), including Florida, Georgia, Alabama, South Carolina, and North Carolina. The seasonal climate retrospective forecasts [the Florida Climate Institute–Florida State University Seasonal Hindcasts at 50-km resolution (FISH50)] is initialized in June and integrated through November of each year from 1982 through 2001. Each seasonal climate forecast has six ensemble members. An earlier study showed that FISH50 represents state-of-the-art seasonal climate prediction skill for the summer and fall seasons, especially in the subtropical and higher latitudes. The retrospective prediction of streamflow is based on multiple calibrated rainfall–runoff models. The hydrological models are forced with rainfall from FISH50, (quantile based) bias-corrected FISH50 rainfall (FISH50_BC), and resampled historical rainfall observations based on matching observed analogs of forecasted quartile seasonal rainfall anomalies (FISH50_Resamp). The results show that direct use of output from the climate model (FISH50) results in huge biases in predicted streamflow, which is significantly reduced with bias correction (FISH50_BC) or by FISH50_Resamp. On a discouraging note, the authors find that the deterministic skill of retrospective streamflow prediction as measured by the normalized root-mean-square error is poor compared to the climatological forecast irrespective of how FISH50 (e.g., FISH50_BC, FISH50_Resamp) is used to force the hydrological models. However, our analysis of probabilistic skill from the same suite of retrospective prediction experiments reveals that, over the majority of the 28 watersheds in the SEUS, significantly higher probabilistic skill than climatological forecast of streamflow can be harvested for the wet/dry seasonal anomalies (i.e., extreme quartiles) using FISH50_Resamp as the forcing. The authors contend that, given the nature of the relatively low climate predictability over the SEUS, high deterministic hydrological prediction skills will be elusive. Therefore, probabilistic hydrological prediction for the SEUS watersheds is very appealing, especially with the current capability of generating a comparatively huge ensemble of seasonal hydrological predictions for each watershed and for each season, which offers a robust estimate of associated forecast uncertainty.

Dependence of the climate prediction skill on spatiotemporal scales: Internal versus radiatively-forced contribution

[1] This article aims at quantifying the improvement in climate prediction skill as a function of temporal (from monthly to decadal) and spatial scales (from grid point to global) when initializing a perturbed parameter ensemble of the Hadley Centre Climate Model. The focus is on near-surface temperature and precipitation in the Tropical band, the Northern and Southern hemispheres. For temperature, the forecast system reproduces the dominant impact of the external forcing at global spatial scale and at decadal time scales. There are significant improvements with initialization for the first 40 forecast months in the global and tropical domains. In the Northern (Southern) hemisphere, the initialization increases the skill in the first 12 (20) months on regional but not hemispheric scales. The initialization has a stronger impact in the model variants with a weaker global-mean temperature trend. For precipitation, the initialization corrects the negative correlation found at global and tropical scales.

Relating the strength of the tropospheric biennial oscillation (TBO) to the phase of the Interdecadal Pacific Oscillation (IPO)

The phase of the Interdecadal Pacific Oscillation (IPO) can influence the Tropospheric Biennial Oscillation (TBO) such that a shift from a negative to positive phase of the IPO was associated with a weakening of the TBO after the mid‐1970s. Here it is shown that the most recent transition in the late 1990s of the IPO from positive to negative was associated with a larger‐amplitude TBO in the Indo‐Pacific region, thus confirming and strengthening the previous results by extending them to the opposite phase of the IPO. A major contributor to this change in the TBO has been an ongoing increase of sea surface temperatures (SSTs) in the Indian Ocean that contributes to stronger trade winds in the Pacific, one of the processes previously identified with strengthening the TBO. Such modulation of interannual variability by decadal timescale processes has implications for understanding possible skill of decadal climate predictions.

Identifying the causes of the poor decadal climate prediction skill over the North Pacific

While the North Pacific region has a strong influence on North American and Asian climate, it is also the area with the worst performance in several state‐of‐the‐art decadal climate predictions in terms of correlation and root mean square error scores. The failure to represent two major warm sea surface temperature events occurring around 1963 and 1968 largely contributes to this poor skill. The magnitude of these events competes with the largest observed temperature anomalies in the twenty‐first century that might be associated with the long‐term warming. Understanding the causes of these major warm events is thus of primary concern to improve prediction of North Pacific, North American and Asian climate. The 1963 warm event stemmed from the propagation of a warm ocean heat content anomaly along the Kuroshio‐Oyashio extension. The 1968 warm event originated from the upward transfer of a warm water mass centered at 200 m depth. For being associated with long‐lived ocean heat content anomalies, we expect those events to be, at least partially, predictable. Biases in ocean mixing processes present in many climate prediction models seem to explain the inability to predict these two major events. Such currently unpredictable warm events, if occurring again in the next decade, would substantially enhance the effect of long‐term warming in the region.

Does Nudging Squelch the Extremes in Regional Climate Modeling?

Abstract An important question in regional climate downscaling is whether to constrain (nudge) the interior of the limited-area domain toward the larger-scale driving fields. Prior research has demonstrated that interior nudging can increase the skill of regional climate predictions originating from historical data. However, there is concern that nudging may also inhibit the regional model’s ability to properly develop and simulate mesoscale features, which may reduce the value added from downscaling by altering the representation of local climate extremes. Extreme climate events can result in large economic losses and human casualties, and regional climate downscaling is one method for projecting how climate change scenarios will affect extreme events locally. In this study, the effects of interior nudging are explored on the downscaled simulation of temperature and precipitation extremes. Multidecadal, continuous Weather Research and Forecasting model simulations of the contiguous United States are performed using coarse reanalysis fields as proxies for global climate model fields. The results demonstrate that applying interior nudging improves the accuracy of simulated monthly means, variability, and extremes over the multidecadal period. The results in this case indicate that interior nudging does not inappropriately squelch the prediction of temperature and precipitation extremes and is essential for simulating extreme events that are faithful in space and time to the driving large-scale fields.

Comparing the UK Met Office Climate Prediction System DePreSys with idealized predictability in the HadCM3 model

AbstractThe initial condition effect on climate prediction skill over a 2‐year hindcast time‐scale has been assessed from ensemble HadCM3 climate model runs using anomaly initialization over the period 1990–2001, and making comparisons with runs without initialization (equivalent to climatological conditions), and to anomaly persistence. It is shown that the assimilation improves the prediction skill in the first year globally, and in a number of limited areas out into the second year. Skill in hindcasting surface air temperature anomalies is most marked over ocean areas, and is coincident with areas of high sea surface temperature and ocean heat content skill. Skill improvement over land areas is much more limited but is still detectable in some cases. We found little difference in the skill of hindcasts using three different sets of ocean initial conditions, and we obtained the best results by combining these to form a grand ensemble hindcast set.Results are also compared with the idealized predictability studies of Collins (Clim. Dynam. 2002; 19: 671–692), which used the same model. The maximum lead time for which initialization gives enhanced skill over runs without initialization varies in different regions but is very similar to lead times found in the idealized studies, therefore strongly supporting the process representation in the model as well as its use for operational predictions. The limited 12‐year period of the study, however, means that the regional details of model skill should probably be further assessed under a wider range of observational conditions. Copyright © 2011 Royal Meteorological Society and British Crown Copyright, the Met Office

The GloSea4 Ensemble Prediction System for Seasonal Forecasting

Abstract Seasonal forecasting systems, and related systems for decadal prediction, are crucial in the development of adaptation strategies to climate change. However, despite important achievements in this area in the last 10 years, significant levels of skill are only generally found over regions strongly connected with the El Niño–Southern Oscillation. With the aim of improving the skill of regional climate predictions in tropical and extratropical regions from intraseasonal to interannual time scales, a new Met Office global seasonal forecasting system (GloSea4) has been developed. This new system has been designed to be flexible and easy to upgrade so it can be fully integrated within the Met Office model development infrastructure. Overall, the analysis here shows an improvement of GloSea4 when compared to its predecessor. However, there are exceptions, such as the increased model biases that contribute to degrade the skill of Niño-3.4 SST forecasts starting in November. Global ENSO teleconnections and Madden–Julian oscillation anomalies are well represented in GloSea4. Remote forcings of the North Atlantic Oscillation by ENSO and the quasi-biennial oscillation are captured albeit the anomalies are weaker than those found in observations. Hindcast length issues and their implications for seasonal forecasting are also discussed.

Two Limits of Initial-Value Decadal Predictability in a CGCM

Abstract When the climate system experiences time-dependent external forcing (e.g., from increases in greenhouse gas and aerosol concentrations), there are two inherent limits on the gain in skill of decadal climate predictions that can be attained from initializing with the observed ocean state. One is the classical initial-value predictability limit that is a consequence of the system being chaotic, and the other corresponds to the forecast range at which information from the initial conditions is overcome by the forced response. These limits are not caused by model errors; they correspond to limits on the range of useful forecasts that would exist even if nature behaved exactly as the model behaves. In this paper these two limits are quantified for the Community Climate System Model, version 3 (CCSM3), with several 40-member climate change scenario experiments. Predictability of the upper-300-m ocean temperature, on basin and global scales, is estimated by relative entropy from information theory. Despite some regional variations, overall, information from the ocean initial conditions exceeds that from the forced response for about 7 yr. After about a decade the classical initial-value predictability limit is reached, at which point the initial conditions have no remaining impact. Initial-value predictability receives a larger contribution from ensemble mean signals than from the distribution about the mean. Based on the two quantified limits, the conclusion is drawn that, to the extent that predictive skill relies solely on upper-ocean heat content, in CCSM3 decadal prediction beyond a range of about 10 yr is a boundary condition problem rather than an initial-value problem. Factors that the results of this study are sensitive and insensitive to are also discussed.

Impact of atmosphere and sub‐surface ocean data on decadal climate prediction

We present a set of idealised model experiments that investigate the impact of assimilating different amounts of ocean and atmosphere data on decadal climate prediction skill. Assimilating monthly average sub‐surface temperature and salinity data successfully initialises the meridional overturning circulation and produces skillful predictions of global ocean heat content. However, when sea surface temperature data is assimilated alone the predictions have much less skill, particularly in the extra‐tropics. The upper 2000m temperature and salinity observations currently provided by the Argo array of floats are therefore potentially well suited to initialising decadal climate predictions. We note however that we do not attempt to simulate the actual distribution of Argo floats. Assimilating data beneath 2000m always reduces the RMSE, with the most significant improvements in the Southern Ocean. Furthermore, assimilating six hourly atmospheric observations significantly improves the forecast skill within the first year, but has little impact thereafter.

Initializing Decadal Climate Predictions with the GECCO Oceanic Synthesis: Effects on the North Atlantic

Abstract This study aims at improving the forecast skill of climate predictions through the use of ocean synthesis data for initial conditions of a coupled climate model. For this purpose, the coupled model of the Max Planck Institute (MPI) for Meteorology, which consists of the atmosphere model ECHAM5 and the MPI Ocean Model (MPI-OM), is initialized with oceanic synthesis fields available from the German contribution to Estimating the Circulation and Climate of the Ocean (GECCO) project. The use of an anomaly coupling scheme during the initialization avoids the main problems with drift in the climate predictions. Thus, the coupled model is continuously forced to follow the density anomalies of the GECCO synthesis over the period 1952–2001. Hindcast experiments are initialized from this experiment at constant intervals. The results show predictive skill through the initialization up to the decadal time scale, particularly over the North Atlantic. Viewed over the time scales analyzed here (annual, 5-yr, and 10-yr mean), greater skill for the North Atlantic sea surface temperature (SST) is obtained in the hindcast experiments than in either a damped persistence or trend forecast. The Atlantic meridional overturning circulation hindcast closely follows that of the GECCO oceanic synthesis. Hindcasts of global-mean temperature do not obtain greater skill than either damped persistence or a trend forecast, owing to the SST errors in the GECCO synthesis, outside the North Atlantic. An ensemble of forecast experiments is subsequently performed over the period 2002–11. North Atlantic SST from the forecast experiment agrees well with observations until the year 2007, and it is higher than if simulated without the oceanic initialization (averaged over the forecast period). The results confirm that both the initial and the boundary conditions must be accounted for in decadal climate predictions.

An ensemble approach for attribution of hydrologic prediction uncertainty

Hydrologic prediction errors arise from uncertainty in initial moisture states (mainly snowpack and soil moisture), in boundary forcings (primarily future precipitation and temperature), and from model structure and parameter uncertainty. We evaluate the relative importance of initial condition and boundary forcing uncertainties using a hindcast‐based framework that contrasts Ensemble Streamflow Prediction (ESP) with an approach that we term “reverse‐ESP”. In ESP, a hydrologic model with assumed perfect initial conditions (ICs) is forced by a forecast ensemble resampled from observed meteorological sequences; whereas reverse‐ESP combines an ensemble of resampled ICs with a perfect meteorological forecast. The framework shows that in northern California, US, ICs yield streamflow prediction skill for up to 5 months during the transition between the wet and dry seasons, whereas during the reverse transition, climate forecast information is critical. In southern Colorado, IC knowledge outweighs climate prediction skill for shorter periods due to a more uniform precipitation regime.

Measuring the potential predictability of ensemble climate predictions

In this study, ensemble predictions of the El Niño Southern Oscillation (ENSO) and the Arctic Oscillation (AO) were conducted using two coupled models and two atmospheric circulation models, respectively, as well as various ensemble schemes. Several measures of potential predictability including ensemble mean square (EM2), ensemble spread and the ratio of signal‐to‐noise were explored in terms of their ability of estimating a priori the predictive skill of the ENSO and AO ensemble predictions. The emphasis was put on examining the relationship between the measures of predictability that do not use observations and the model prediction skill of correlation and mean square error (MSE) that make use of observations. The relationship identified here offers a practical means of estimating the potential predictability and the confidence level of an individual prediction. It was found that the EM2 is a better indicator of the actual skill of ensemble ENSO and AO prediction than the ratio of signal‐to‐noise. When correlation‐based metrics are used, the prediction skill is likely to be a linear function of EM2, i.e., the larger the EM2 the higher skill the prediction; whereas when MSE‐based metrics are used, a “triangular relationship” is suggested between them, namely, that when EM2 is large, the prediction is likely to be reliable whereas when EM2 is small the prediction skill is highly variable. In contrast with ensemble weather prediction (NWP), the ensemble spread is not a good predictor in quantifying climate prediction skill in the models used in this study because the forced response may be much larger than the noise in the climate timescales compared to the NWP. A statistical framework was proposed to explain why EM2 is a good indicator of actual prediction skill in the ensemble climate predictions.

A Multimodel Analysis, Validation, and Transferability Study of Global Soil Wetness Products

Abstract Multimodel ensemble forecasting has been shown to offer a systematic improvement in the skill of climate prediction with atmosphere and ocean circulation models. However, little such work has been done for the land surface component, an important lower boundary for weather and climate forecast models. In this study, the authors examine and evaluate several methods of combining individual global soil wetness products from uncoupled land surface model calculations and coupled land–atmosphere model reanalyses to produce an ensemble analysis. Analyses are verified against observations from the Global Soil Moisture Data Bank (GSMDB) with skill measured by correlation coefficient and root-mean-square error (RMSE). A preliminary transferability study is conducted as well for investigating the feasibility of transferring ensemble regression parameters within two specific regions (Illinois and east-central China) and between these two regions of similar climate and land use. The results show that when sufficient validation data are available, one can use a seasonally dependent linear regression to improve the skill of any individual model simulation of soil wetness. Further improvements in skill can be achieved with more sophisticated ensembling methods, such as the regression-adjusted multimodel ensemble mean analysis and regression-adjusted multimodel analysis. However, all the ensembling schemes involving regression usually do not help improve the skill scores as far as the simulation of anomalies of soil wetness is concerned. In the absence of calibration data, the simple arithmetic ensemble mean across multiple soil wetness products generally does as well or better than the best individual model at any location in the representation of both soil wetness and its anomaly. Transferability from one subset of stations from the Illinois or east-central China dataset to another gives satisfactory results. However, results are poor when transferring regression weights between different regions, even with similar climate regimes and land cover. Such an exercise helps us to understand better the virtues and limitations of various ensembling techniques and enables progress toward creating an optimum, model-independent analysis from a practical point of view.

Evidence of Decadal Climate Prediction Skill Resulting from Changes in Anthropogenic Forcing

Abstract It is argued that simulations of the twentieth century performed with coupled global climate models with specified historical changes in external radiative forcing can be interpreted as climate hindcasts. A simple Bayesian method for postprocessing such simulations is described, which produces probabilistic hindcasts of interdecadal temperature changes on large spatial scales. Hindcasts produced for the last two decades of the twentieth century are shown to be skillful. The suggestion that skillful decadal forecasts can be produced on large regional scales by exploiting the response to anthropogenic forcing provides additional evidence that anthropogenic change in the composition of the atmosphere has influenced the climate. In the absence of large negative volcanic forcing on the climate system (which cannot presently be forecast), it is predicted that the global mean temperature for the decade 2000–09 will lie above the 1970–99 normal with a probability of 0.94. The global mean temperature anomaly for this decade relative to 1970–99 is predicted to be 0.35°C with a 5%–95% confidence range of 0.21°–0.48°C.

The Role of Sea Surface Temperatures in Interactions between ENSO and the North Pacific Oscillation

The North Pacific Oscillation (NPO) is a decadal to interdecadal fluctuation of sea surface temperatures (SSTs) in the North Pacific. Previous works have shown that during individual El Nino and La Nina winters, atmospheric circulation anomalies over North America are characteristically different for different phases of the NPO. Two physical mechanisms could account for this observed link between North Pacific SSTs and ENSO's effects over North America: 1) NPO SSTs could force changes in the overlying atmosphere that modulate ENSO's effects, and 2) the atmosphere could undergo internal variability that both modulates ENSO's effects and imprints a characteristic pattern of North Pacific SSTs. The first mechanism suggests methods for increasing the skill of seasonal climate predictions by incorporating the state of the North Pacific, using simple persistence of SSTs if nothing else. The second mechanism implies that North Pacific SSTs add no information that could be used to improve seasonal climate predictions of ENSO's effects. Analysis of a 300-yr run of a coupled ocean-atmosphere model shows that it exhibits links between NPO and ENSO similar to those observed. It is found that specifying NPO SSTs does not force these links. This suggests that the association found between NPO SSTs and ENSO's effects is primarily due to the fact that both are responding to the same internal atmospheric variability. In such a case, incorporating accurate predictions of NPO SSTs into ENSO prediction schemes would have little ability to improve forecasts of ENSO's effects.

A comparison of methods to determine the onset of the growing season in northern Nigeria

AbstractIn the sudan savanna of northern Nigeria, with its semi‐arid climate, the ability to determine effectively or predict the start of actual productive rains cannot be overemphasized. Several methods exist for calculating the date of onset of the rains that may be taken as the start of the growing season. Five methods currently in use, which are relatively easy to apply on a large scale, were selected for comparison. One is a traditional technique (Ramadan method), two use accumulated rainfall totals (Walter's and Sivakumar's methods), and two use rainfall–evapotranspiration relationships (Kowal's and Benoit's methods). For the period 1961–91, the traditional technique performed most poorly. Walter's method gave quite early onsets and Sivakumar's method gave very late onsets, thereby seriously shortening the growing season. Kowal's and Benoit's determinations fell most often in between the results of Walter's and Sivakumar's methods in their performance. However, although generally to a lesser extent than the other methods, they are still significantly affected by false starts. To avoid incorrectly predicting the growing season's onset as far as possible, but to prevent an unacceptable shortening of the growing season, a combination of Kowal's and Sivakumar's criteria was used to develop an improved technique. This proved to work well for determining the onset date in the study area. However, because some false starts remain when using average onset dates, it is suggested that an operational advisory team should be constituted by the government. This team would be responsible for calculating onset dates in any year, on‐line for the ongoing season, in a participatory approach with farmers, and for disseminating such dates to the farmers. This could be done for any place for which the appropriate data can be made available. In the future, improved climate prediction skill may replace the classical probabilistic approaches presently suffering from increasing rainfall variabilities. Copyright © 2002 Royal Meteorological Society.