Question 1 Given that the globe is warming, why does the DJF outlook favor below-average temperatures in the southeastern U. S.? Climate variability on seasonal time scales is sometimes/often larger than the climate change signal. Climate change signals (trends) are different regionally and during different seasons. Strong El Niño conditions, which are occurring this year in the tropical Pacific, favor below average DJF temperatures in the Southeast.

Question 2 I just noticed that CPC’s winter outlook, made in November, indicated above average temperatures across the Northern Plains during DJF. Instead, temperatures during the winter were colder than average. Why was CPC’s outlook wrong? How do winter “wild cards” like the AO/NAO influence a seasonal forecast? How good are CPC’s typical seasonal temperature and precipitation forecasts? These are probabilistic forecasts, which means they provide an estimate of how likely it is certain conditions will occur. The forecast says that there was an “increased likelihood” of above average temperatures, not that temperatures would BE above average. The greatest probability on the map was 50-60% (in the Northern Plains, which still means that there was a 40-50% chance that temperatures would NOT be above average. Winter wild cards like the Arctic Oscillation (related to pressure differences between the mid-latitudes and the polar region) or the North Atlantic Oscillation (related to the relative strengths of the North Atlantic subtropical high and polar low)) add uncertainty to seasonal forecasts. Since there is no reliable way to forecast the seasonal phase of these oscillations, forecasters acknowledge this uncertainty with modest probabilities (i.e. generally not more than 50%). The modest probabilities mean that there is almost an equal chance that overall seasonal conditions could be different from the forecast. But the forecasts are correct more often than not, as CPC’s seasonal temperature forecasts are about 20-25% better than a random forecast.

Question 3 The DJF precipitation outlook favored wetter-than-median conditions across the southern tier of the country because of El Niño. Which forecast tools indicated that? How does abnormally warm waters in the equatorial Pacific make it wetter than average across the southern states? An enhanced likelihood of above-average precipitation across the South was indicated by average conditions observed over multiple El Nino years and also indicated in model forecasts. Warmer than average Pacific sea surface temperatures lead to shifts in tropical convection and shifts in atmospheric heating across the central and eastern Pacific. These shifts lead to a stronger and more southward Pacific jet stream, which often leads to enhanced rainfall across the southern tier of the U.S.

Question 4 I’m not sure I believe in a weather forecast made past one week. Please explain how you are able to make forecasts for 6-10 and 8-14 days. What is the process? How good are these forecasts. Forecasts for these time ranges predict average conditions over 5 or 7 day periods. Weather forecasts for individual days become less skillful the farther out in time we go, but it is possible to predict average conditions. The forecasts are based on computer simulations (generally averages of a set of models or model runs), with the first step forecasting the anticipated circulation pattern. The circulation pattern is then related to surface temperature and precipitation patterns using advanced statistical tools such as analogs and regression techniques. There are also methods for calibrating the model output and these methods help improve the forecast. 6-10 day temperature forecasts are often quite accurate, more than 50% better than a random forecast, with the long term average around 30%. Week-2 temperature forecasts average about 20% better than random, while 6-10 day precipitation scores near 15% and week-2 precipitation about 10% better than chance.

Question 5 What is an “ensemble” and why is it better to use than individual model runs when making a forecast? How are they created and used in a climate forecast? What additional information can we get from the ensemble members? Ensembles are multiple runs of forecast models (can be from the same model or from different models) that attempt to give the range of possible solutions. Ensemble members are created by slightly changing the initial conditions. Since the true starting point (initial condition) is not known, this provides multiple solutions. Ensemble means (average of all members) generally provide better forecasts than individual model solutions. Use of ensembles enables us to derive probabilities for various forecast parameters. For example, calibrated model temperature and precipitation provide probabilistic information (i.e. 15 of 20 members indicating above average temperature would result in a 75% probability for that model solution). It is important however, for the model output to be calibrated since model biases can be large for longer range forecasts.

Question 6 What is the Arctic Oscillation and what are the associated impacts over the United States? How is it related to the North Atlantic Oscillation? How does it (and other teleconnections, like the PNA) influence these forecasts? The Arctic Oscillation (or AO) is a pressure seesaw between the polar region and the mid-latitudes. Higher-than-average pressure at the pole corresponds to the negative phase and tends to mean colder temperatures in the mid-latitudes.. Impacts favor cold over much of US during the negative (cold) phase and warmer-than-average temperatures during the positive (warm) phase. The Arctic Oscillation and other pressure patterns cause variations in climate in several locations at once, referred to as a teleconnection. Other teleconnection patterns such as the PNA are examined and subjectively considered when relevant. Teleconnections can provide a guide as to how robust or likely a model forecast might be. They can also be used to provide an idea about likely temperature and precipitation patterns associated with a particular pattern.