Skill Comparison of Some
Dynamical and Empirical Downscaling Methods for Southern Africa from a
Seasonal Climate Modelling Perspective
Report no 1334/1/06
2006
EXECUTIVE SUMMARY
1. Introduction and
Objectives
The main emphasis of the project was to assess the ability of an
advanced state-of-the-art, albeit computationally expensive, method of
downscaling large-scale climate predictions to regional and local scale
as a seasonal rainfall forecasting tool for southern Africa in order to
improve seasonal outlook information for hydrological purposes.
Downscaling the large scale to more localized seasonal rainfall over
southern Africa had been shown to be feasible, but further research in
downscaling, with both improved spatial and temporal resolution, was
required. The main aims of the project were to:
- Set base-line forecast skill levels, using statistical
models;
- Compile an appropriate general circulation model (GCM)
climatology of a sufficiently large ensemble;
- Nest dynamical regional climate models in the GCM simulated
large scale fields;
- Compare the nested scheme’s forecast skill with
the base-line skill levels.
2. Results and Conclusions
Ultimately, various downscaling techniques and raw GCM output were
compared to one another over the 10-year period from 1991/92 to 2000/01
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) was nested into the large-scale fields of a coarser GCM. The
study concluded by investigating the internal variability of the RCM.
The study addressed the performance of a number of simulation systems
(no forecast lead-time) of varying complexity. These systems’
performance was tested for the December-January-February (DJF) rainfall
for both homogeneous regions and for 963 stations over South Africa,
and compared with each other over a 10-year test period from 1991/92 to
2000/01. For the most part the simulation methods outscored the
baseline method that used sea-surface temperature (SST) anomalies to
simulate rainfall, thereby 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. Moreover, advantages in statistically
post-processing output from GCMs as well as output from regional
climate models (RCMs) were demonstrated. Skill should further improve
with an increased number of ensemble members. However, multiple
realizations are not required to describe the internal variability of
the nested system, which suggests that increasing the ensemble size
would mainly contribute to probabilistic forecast skill.
3. Recommendations
The potential for using dynamical and statistical downscaling methods
and their combination for modelling South African seasonal regional
rainfall variability was demonstrated. In addition to expanding on the
number of ensemble members, the test period of 10 years should be
increased in order to test the robustness of the results presented here
since this test period may be too short to unequivocally demonstrate
which simulation method is the best. An increased ensemble size can
also be considered to test the probabilistic skill levels of these
systems and how they can be used in an operational seasonal forecasting
environment that demands a description of forecast uncertainties.