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HOME > El Niño/La Niña > La Niña United States Temperature & Precipitation Probability Maps and Regional Statistics
 
 
Graphics:

Seasonal Probability Maps

Regional Statistics Maps

Discussions:

Introduction

The U.S. Probability Distribution Maps

Detailed Regional Statistics

The La Niña Cases

Definition of Above, Near, and Below Normal Categories

Estimation of Probabilities

Adjustments to Account for Long-Term Temperature and Precipitation Trends

La Niña Case Selection

Contact Richard Tinker [rtinker@ncep.noaa.gov], Climate Prediction Center Meteorologist, for further information or to report problems with this suite of products.

La Niña Seasonal U.S. Probability Maps
3-Month Period Temperature Precipitation
Raw Trend-Adjusted Raw Trend-Adjusted
July - September X X X X
August - October X X X X
September - November X X X X
October - December X X X X
November - January X X X X
December - February X X X X
January - March X X X X
February - April X X X X
March - May X X X X
April - June X X X X
May - July X X X X
 
La Niña Regional Statistics Maps
Region

(Click here for a map depicting the areas contained in each region)
Temperature Precipitation
Probabilities Means/Extremes Probabilities Means/Extremes
West Coast October - December

January - March

April - June
October - December

January - March

April-June
October - December

January - March

April - June
October - December

January - March

April - June
Southwest October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
Intermountain West October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
Central Rockies October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
North Rockies October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April-June
October - December

January - March

April-June
North High Plains October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
South High Plains October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
North Great Plains October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
Central Plains October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
South Great Plains October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
Great Lakes October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
Lower Midwest October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
Lower Southeast October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
Upper Southeast October - December

January - March

April - June
October - December

January - March

April - June
October - December January - March April - June October - December

January - March

April - June
Northeast October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June
October - December

January - March

April - June

Introduction

This suite of products examines the season-by-season and region-by-region variations in 3-month average temperatures and total precipitation observed in the U.S. during past moderate-to-strong La Niña episodes, and compares these against a complete 45-year (1952/1953 - 1996/1997) distribution of historical occurrences.

Some of the graphics utilize the actual observed ("raw") data recorded during past La Niña episodes, and some use historical data that have been adjusted for temperature and precipitation trends that began in the mid-1960's extrapolated to 1999/2000. The latter subset of products represents an attempt to quantitatively account for the combined effects of two significant climate signals: The La Niña episode, and long-term trends in average temperature and total precipitation. All of these products are contrasted against a 45-year climatology ending just before the exceptionally strong El Niño episode of 1997-1998.

In terms of appearance, the full suite of products can be divided into two sets: Maps of conditional probability distributions for the U.S., and detailed regional statistics. Below, we first describe what is shown in each of these product subsets, then go on to describe the methods used to generate the figures.

The U.S. Probability Distribution Maps

Probability distribution maps encompassing the contiguous 48 states are provided for overlapping three-month periods. There are 11 maps (one for each 3-month period except June - August) showing raw (non-adjusted) temperature probabilities, 11 showing raw precipitation probabilities, 11 showing trend-adjusted temperature probabilities, and 11 showing trend-adjusted precipitation probabilities, making a total of 44. In each of these maps, areally-averaged data dividing the country into 102 climate regions of approximately equal area were used, and the areas covered by each region are shaded as a unit.

All of these maps show the conditional probabilities that mean temperatures or total precipitation will rank among the highest and lowest one-third of the 45-year climatological record (terciles), given a moderate-to-strong La Niña episode (and also, for the trend-adjusted maps, given the continuation of long-term trends through the valid period). This information is depicted only for those regions where the probabilities for the tercile classes of above, near, and below normal departed sufficiently from a uniform distribution such that the chances of this departure being an accident were less than about 10%.

The probability for the class the distribution is skewed towards is color-coded. For example, when the highest probability in a given region is for the warmest (wettest) class, this probability is denoted by yellow and red (green) shades; when the highest probability is for the coldest (driest) class, it is indicated by blue (brown and orange) shades; etc. For each shaded climate region on each chart, the probability for the opposite tercile class (for example, the likelihood for the cold tercile in regions shaded red) is rounded to the nearest 10% and plotted as a value in the region. As an example, look at the January - March trend-adjusted temperature probabilities. The climate region in southeastern Colorado has a moderately dark orange shade and the number ‘10' plotted inside. The shading is in the yellow/red family of colors, so the distribution is skewed towards the warmer-than-normal class. From the color key, one can see that the region has a 65% to 75% probability of recording a January - March mean temperature in the top one-third of the 1953-1997 distribution. The ‘10' indicates the likelihood of the opposite tercile rounded to the nearest 10%, so there is a 5% to 15% probability of a below-normal January-March mean temperature. Finally, by adding the highest and lowest probabilities in these ranges together and subtracting the result from 100, one can determine the approximate probability for the near-normal category, which is 10% to 30%. Exact probabilities for each class are included in the detailed regional presentations described next.

Detailed Regional Statistics

These charts provide both alternative presentations of and supplementary information about both the raw and trend-adjusted La Niña temperature and precipitation signals. For each of the three non-overlapping 3-month periods that comprise three-quarters of a calendar year (October - December, January-March, and April-June) the contiguous U.S. was divided into 15 large regions. Four sets of graphics (2 each for temperature and precipitation) were produced for each of the 15 regions for each 3-month period. In contrast to the U.S. maps, these directly contrast the raw La Niña case data with the trend-adjusted data, so the user can readily see the influence of the long-term trend in modifying the La Niña signals. In addition to tercile probabilities and ranges, the means and extremes of both the historic and trend-adjusted La Niña occurrences are presented, along with the 45-year climatological average, for each of the 102 climate regions. The years of occurrence for the extremes (maxima and minima) are indicated only for the raw La Niña cases because they have no meaning for the trend-adjusted cases.

The La Niña Cases

For precipitation, cases were culled from the period 1930/1931 through 1998/1999, while for temperature cases since 1939/1940 were selected. Except for this difference between temperature and precipitation case selection, all graphics valid for the same 3-month period use the same set of La Niña cases. From 1950 to the present, the case selections conform exactly to CPC's official list of moderate to strong La Niñas with the exceptions of October-December 1954 and January-March 1955, which did not quantitatively satisfy the selection criteria, but otherwise behaved like moderate La Niñas.

Definition of Above, Near, and Below Normal Categories

The range of values that constituted the above, near, and below normal terciles were determined directly from 45 years of 3-month mean temperature and total precipitation data for each of 102 roughly equal-sized climate regions that comprise the lower 48 states. The periods July-September through October-December used 1952-1996 data while the remaining 3-month periods used 1953-1997. These 45 pieces of data (for a given climate region and time of year) were organized into the 15 highest, 15 lowest, and 15 middle values, with the exact tercile limits defined as the point halfway between the lowest value in one tercile and the highest value in the next lower tercile.

Estimation of Probabilities

The probabilities were developed for a particular season through a bootstrap technique that consists of building hypothetical sample La Niña cases by resampling (with replacement) a pool of all the months from the La Niña case years in that season. Statistical tests determined that this resampling technique increased confidence in the reliability of the results as though the number of cases was 30% larger than it actually is, thus helping to reduce sampling error and raggedness in the final products.

Adjustments to Account for Long-Term Temperature and Precipitation Trends

As indicated earlier, these maps were derived for two different sets of La Niña data, both of which are presented in this suite of products. The first is straightforward, using unadjusted (raw) data during La Niña case years. However, the probabilities derived from this data are insufficient for the estimation of current probabilities because the long-term trends imply a changing climate. CPC forecasters first encountered this difficulty in attempting to apply El Niño-based composites to winter/spring temperature predictions during 1996/1997.

A first attempt has been made to quantitatively take the long-term trend signals into account, guided by insight from other work done at CPC (especially Livezey and Smith (1999)). In the set of U.S. probability distributions that takes the trends into account, data for the La Niña case years were adjusted to linear extrapolations of long-term trends to 1999/2000. As an example, assume region x for some season averaged 50 degrees Fahrenheit in 1969. Assume further that there has been an upward trend of 0.5 degrees Fahrenheit per decade for this region and season since the mid-1960's from a base-period average estimated from the early 1940's to the mid-1960's. If you extrapolate this trend from 1966 to 2000, you get an accumulated change to the base climatological state of +1.7 degrees Fahrenheit. However, in 1969, three years' worth of trend would already have been incorporated into this value, or 0.15 degrees Fahrenheit. This is removed from the cumulative adjustment (since it's already in the observed value), resulting in an overall adjustment of 1.70 - 0.15 = 1.55 degrees which is added to the raw value (in this case, 50 degrees) to create an adjusted value of 51.55 degrees. Although this example cites 1966 as the start of the trend, there is uncertainty in this starting point, and this uncertainty has been built into the resampling by using a family of trends derived by making small adjustments to the start, hinge-point, and final years of data from which the signal is extracted. The starting point was juggled between 1939 and 1943 (1929 and 1933 for precipitation), the trend-start point between 1964 and 1968, and the final point between 1994 and 1998.

The results of incorporating these trend adjustments into the historical La Niña data amount to conditional forecasts that take both La Niña effects and the long-term trend effects into account. Although these data are modified from the real-world observations, the Climate Prediction Center feels they more accurately depict the true probability distributions than do the maps which ignore the trends. It is primarily the trend-adjusted maps on which our long-term seasonal outlooks have relied with respect to La Niña's expected influence on U.S. conditions during the next few seasons.

La Niña Case Selection

The years representing moderate to strong La Niñas change from period to period. This is because the part(s) of the year for which the central equatorial Pacific sea surface temperatures (SST's) are well below normal differs from episode to episode. The cases included in these products are those for which the average SST in a key area was near or greater than one degree Celsius below normal in at least one of the three months spanning a particular period, and near or greater than 0.8 degree Celsius below normal in the remaining months. The key area used for case selection is bounded by the International Dateline and 150 degrees west longitude, and 5 degrees north and 5 degrees south latitudes. This area was used because it approximates the region in the equatorial Pacific where the intensity and areal extent of tropical showers and thunderstorms (the major source of atmospheric energy in the tropics) are the most sensitive to relatively small changes in SST. Thus, the SST anomaly in this area should be a good index of how strong a La Niña's impact on the global atmosphere will be. The diagrams shown here reflect, complement, and extend the information recently presented by Livezey et al. (1997: J. Climate, 10, 1787-1820), which used similar selection criteria.


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Page last modified: January 24, 2005
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