The tenth IMSC, Beijing, China, 2007 - International Meetings on ...

Insights from a Skill Analysis of the First Decade of Long-Lead U.S. Three-Month Temperature
and Precipitation Forecasts
Speaker: Robert E. Livezey
Robert E. Livezey
NWS Climate Services, Office of Services, NOAA
robert.e.livezey@noaa.gov
Marina Timofeyeva
University Corporation for Atmospheric Research
<strong>The</strong> first ten years (issued starting in mid-December 1994) of official, long-lead (out to
one year) U. S. three-month mean temperature and precipitation forecasts are verified using a
categorical skill score. Through aggregation of forecasts over overlapping three-month target
periods and/or multiple leads, we obtain informative results about skill improvements, skill
variability (by lead, season, location, variable, and situation), skill sources, and potential
forecast utility. <strong>The</strong> forecasts clearly represent advances over zero-lead forecasts issued prior
to 1995. But our most important result is that skill hardly varies by lead-time all the way out to
one year, except for cold-season forecasts under strong El Nino or La Nina (ENSO) conditions.
<strong>The</strong> inescapable conclusion is that this lead-independent skill comes from use of long-term
trends to make the forecasts and we show that these trends are almost entirely associated
with climate change. However, we also argue that climate change is not yet being optimally
taken into account. All other skill in the forecasts comes from exploitation of strong and
predictable ENSO episodes for winter forecasts, out to 6.5-months lead for precipitation and
beyond 8.5-months for temperature. Apparently other sources of skill supported by existing
research, including predictability inherent in weaker ENSO episodes and interactive feedbacks
between the extratropical atmosphere and underlying surfaces, do not materially contribute to
positive forecast performance. Compared to strong ENSO and climate change signals, other
sources are too weak, unreliable, or poorly understood to detect an impact. Another
consequence of the clear attribution of skill is that often-observed high regional/seasonal skills
imply that the forecasts can be unambiguously valuable to a wide range of users. With these
findings, steps (some immediate) can be taken to improve both the skill and usability of official
long-lead forecasts.
A Multiple Linear Regression Model for the Seasonal Prediction of Rainfall over Peninsular
India
Speaker: Lorna R. Nayagam
Lorna R. Nayagam
Department of Atmospheric Sciences, Cochin University of Science and Technology
lorna@cusat.ac.in
80