Search Results

You are looking at 1 - 2 of 2 items for :

  • Author or Editor: Huug van den Dool x
  • Journal of Applied Meteorology and Climatology x
  • Refine by Access: All Content x
Clear All Modify Search
Anthony G. Barnston
,
Michael K. Tippett
,
Huug M. van den Dool
, and
David A. Unger

Abstract

Since 2002, the International Research Institute for Climate and Society, later in partnership with the Climate Prediction Center, has issued an ENSO prediction product informally called the ENSO prediction plume. Here, measures to improve the reliability and usability of this product are investigated, including bias and amplitude corrections, the multimodel ensembling method, formulation of a probability distribution, and the format of the issued product. Analyses using a subset of the current set of plume models demonstrate the necessity to correct individual models for mean bias and, less urgent, also for amplitude bias, before combining their predictions. The individual ensemble members of all models are weighted equally in combining them to form a multimodel ensemble mean forecast, because apparent model skill differences, when not extreme, are indistinguishable from sampling error when based on a sample of 30 cases or less. This option results in models with larger ensemble numbers being weighted relatively more heavily. Last, a decision is made to use the historical hindcast skill to determine the forecast uncertainty distribution rather than the models’ ensemble spreads, as the spreads may not always reproduce the skill-based uncertainty closely enough to create a probabilistically reliable uncertainty distribution. Thus, the individual model ensemble members are used only for forming the models’ ensemble means and the multimodel forecast mean. In other situations, the multimodel member spread may be used directly. The study also leads to some new formats in which to more effectively show both the mean ENSO prediction and its probability distribution.

Full access
Robert E. Livezey
,
Konstantin Y. Vinnikov
,
Marina M. Timofeyeva
,
Richard Tinker
, and
Huug M. van den Dool

Abstract

WMO-recommended 30-yr normals are no longer generally useful for the design, planning, and decision-making purposes for which they were intended. They not only have little relevance to the future climate, but are often unrepresentative of the current climate. The reason for this is rapid global climate change over the last 30 yr that is likely to continue into the future. It is demonstrated that simple empirical alternatives already are available that not only produce reasonably accurate normals for the current climate but also often justify their extrapolation to several years into the future. This result is tied to the condition that recent trends in the climate are approximately linear or have a substantial linear component. This condition is generally satisfied for the U.S. climate-division data. One alternative [the optimal climate normal (OCN)] is multiyear averages that are not fixed at 30 yr like WMO normals are but rather are adapted climate record by climate record based on easily estimated characteristics of the records. The OCN works well except with very strong trends or longer extrapolations with more moderate trends. In these cases least squares linear trend fits to the period since the mid-1970s are viable alternatives. An even better alternative is the use of “hinge fit” normals, based on modeling the time dependence of large-scale climate change. Here, longer records can be exploited to stabilize estimates of modern trends. Related issues are the need to avoid arbitrary trend fitting and to account for trends in studies of ENSO impacts. Given these results, the authors recommend that (a) the WMO and national climate services address new policies for changing climate normals using the results here as a starting point and (b) NOAA initiate a program for improved estimates and forecasts of official U.S. normals, including operational implementation of a simple hybrid system that combines the advantages of both the OCN and the hinge fit.

Full access