A series of Pacific storm systems will continue to impact the western U.S. into this weekend with periods of strong to damaging winds, high surf, heavy lower elevation rain, and heavy mountain snow. Severe thunderstorms are expected Saturday from north-central Louisiana to west-central Alabama. Damaging winds, large hail, and strong tornadoes will be possible. Read More >
NOAA's CPC Winter 2024-25 Outlook
for the Upper Mississippi River Valley
Released: November 21, 2024
NOAA's Climate Prediction Center (CPC) forecast for the upcoming winter months of December 2024 to February 2025. While there is a 57% chance that a weak La Niña will develop and impact the weather across much of the United States, its impacts in the Upper Mississippi River Valley can be highly variable with both temperatures and precipitation.
|
Temperatures: Equal chances for warmer- (warmest third), near-, and colder-than-normal (coldest third) across the Upper Mississippi River Valley. Why equal chances? Mixed Temperature Signals
|
 CPC's Winter 2024-25 U. S. Temperature Outlook |
|
|
|
Precipitation: Wetter-than-normal is favored (33-40%) across the Upper Mississippi River Valley. This does not necessarily imply that this winter will end up being snowier than normal. Locally, wetter-than-normal (not just a hundredth of an inch wetter than normal, but among the wettest third of the winters from 1991-2020) is slightly favored (33-40%) across the Upper Mississippi River Valley. This forecast was based on trends with La Niña since the early 1990s and over the past 15 winters.
|
 CPC's Winter 2024-25 U. S. Precipitation Outlook |
An above-normal precipitation forecast doesn't necessary mean an above-normal snow season (July-June).
|
|
La Niña is most likely to emerge in October-December 2024 (57% chance) and is expected to persist through January-March 2025.
Over the past month, ENSO-neutral continued, as evidenced by overall near-average sea surface temperatures (SSTs) observed across the central and eastern equatorial Pacific Ocean [Fig. 1]. Similar to last month, the latest weekly Niño indices ranged from +0.2°C (Niño-4) to -0.3°C (Niño-3.4; [Fig. 2]). Below-average subsurface temperatures persisted [Fig. 3] across the east-central and eastern equatorial Pacific Ocean [Fig. 4]. For the monthly average, low-level wind anomalies were easterly over a small region of the east-central equatorial Pacific, and upper-level wind anomalies were near average. Convection was suppressed over the Date Line and was weakly enhanced over eastern Indonesia [Fig. 5]. The traditional and equatorial Southern Oscillation indices were positive. Collectively, the coupled ocean-atmosphere system reflected ENSO-neutral.
The IRI plume predicts a weak and a short duration La Niña, as indicated by the Niño-3.4 index values less than -0.5°C [Fig. 6]. The latest North American Multi-Model Ensemble (NMME) forecasts are cooler than the IRI plume and predict a weak La Niña. Due to this guidance and La Niña-like atmospheric circulation anomalies over the tropics, the team still favors onset of La Niña, but it is likely to remain weak and have shorter duration than other historical episodes. A weak La Niña would be less likely to result in conventional winter impacts, though predictable signals could still influence the forecast guidance (e.g., CPC's seasonal outlooks). In summary, La Niña is most likely to emerge in October-December 2024 (57% chance) and is expected to persist through January-March 2025 [Fig. 7].
This discussion is a consolidated effort of the National Oceanic and Atmospheric Administration (NOAA), NOAA's National Weather Service, and their funded institutions. Oceanic and atmospheric conditions are updated weekly on the Climate Prediction Center web site (El Niño/La Niña Current Conditions and Expert Discussions). Additional perspectives and analysis are also available in an ENSO blog. A probabilistic strength forecast is available here. The next ENSO Diagnostics Discussion is scheduled for 12 December 2024.
In the Upper Mississippi River Valley, these winters can be highly variable with both temperatures and precipitation.
Besides La Niña, this winter will also be affected by:
CPC 2024-25 U.S. Winter Outlook:
A slowly-developing La Nina is favored to influence conditions for the upcoming winter across most of the country, according to NOAA’s U.S. Winter Outlook released on November 21 by the Climate Prediction Center — a division of NOAA’s National Weather Service. This outlook is for December 2024 through February 2025 and contains information on likely conditions throughout the country for temperature, precipitation, and drought.
This winter, NOAA predicts wetter-than-average conditions for the entire northern tier of the continental U.S., particularly in the Pacific Northwest and the Great Lakes region, along with northern and western Alaska. Meanwhile, drier-than-average conditions are expected from the Four Corners region of the Southwest to the Southeast, Gulf Coast and lower mid-Atlantic states.
“This winter, an emerging La Nina is anticipated to influence the upcoming winter patterns, especially our precipitation predictions,” said Jon Gottschalck, chief of the Operational Prediction Branch of the Climate Prediction Center.
La Nina conditions are expected to develop later this fall and typically lead to a more northerly storm track during the winter months, leaving the southern tier of the country warmer and drier. As a result, NOAA forecasters, in collaboration with the National Integrated Drought Information System (NIDIS), expect drought conditions to persist and worsen across the central and southern Plains of the U.S.
“Unfortunately, after a brief period in the spring of 2024 with minimal drought conditions across the country, more than a quarter of the land mass in the continental U.S. is currently in at least a moderate drought,” said Brad Pugh, operational drought lead with NOAA’s Climate Prediction Center. “And the winter precipitation outlook does not bode well for widespread relief.”
Here are some additional resources
The CPC winter forecasts to the right show the most likely outcome where there is greater confidence, but this is not the only possible outcome. Equal chance display areas where confidence is low, so there is an equal chance of it being among the warmest third, near-normal, or among the coldest third. |
CPC's Winter 2024-25 U. S. Temperature Outlook |
The CPC winter forecasts to the right show the most likely outcome where there is greater confidence, but this is not the only possible outcome. Equal chance display areas where confidence is low, so there is an equal chance of it being among the wettest third, near-normal, or among the driest third. |
CPC's Winter 2024-25 U. S. Precipitation Outlook |
|
 CPC's Winter 2024-25 U. S. Drought Outlook |
Winter forecasting tools: Here’s what’s new at NOAA |
|
About NOAA's seasonal outlooks |
|
|
NOAA’s seasonal outlooks provide the likelihood that temperatures and total precipitation amounts will be above-, near- or below-average, and how drought conditions are anticipated to change in the months ahead. The outlook does not project seasonal snowfall accumulations as snow forecasts are generally not predictable more than a week in advance. NOAA’s Climate Prediction Center updates the three-month outlook monthly. Seasonal outlooks help communities prepare for what is likely to come in the months ahead and minimize weather's impacts on lives and livelihoods. Resources such as drought.gov and climate.gov provide comprehensive tools to better understand and plan for climate-driven hazards. Empowering people with actionable forecasts, seasonal predictions and winter weather safety tips is key to NOAA’s effort to build a more Weather- and Climate-Ready Nation. |
|
Additional Resources |
|
|
Here are some additional resources |
|
Local Winter Outlook:
Locally, there are equal chances for warmer- (warmest third), near-, and colder-than-normal (coldest third) across the Upper Mississippi River Valley.
Why equal chances? Mixed Temperature Signals
Uncertainties with temperatures during La Niña winters
Since the early 1990s, La Niña (weak, moderate, & strong) impacts on local winter temperatures have changed. We are seeing more warm La Niña winters (5 out of the 6 warmest third of winters during La Niña have occurred since 1990), but these winters have also become more variable too. From 1949-1990 (12 events), 6 were among the coldest third, 5 were near-normal, & 1 was among the warmest third. Since 1991 (13 events), 5 were among the warmest third, 5 were among the coldest third, & 3 were near-normal.
From 1949 through 2001, weak La Niñas (sea surface temperatures in the ENSO 3.4 region of -0.5 to -0.9°C) were either among the coldest third (4 events) or near normal (3 events). Since then, 3 events were among the warmest third, 1 event was near normal, and 1 event was among the coldest third.
Climate Trends over the Past 15 Years - Mainly Near Normal or Among the Warmest Third
Since the winter of 2009-10, a vast majority of the winters have either been near-normal or among the warmest third of all winters.
For La Crosse, this accounts for 37 out of 45 winter months (82.2%) and 12 out of 15 meteorological winters (80%).
Locally, wetter-than-normal (not just a hundredth of an inch wetter than normal, but among the wettest third of the winters from 1991-2020) is slightly favored (33-40%) across the Upper Mississippi River Valley. This forecast was based on trends with La Niña since the early 1990s and over the past 15 winters.
Since the early 1990s, La Niña (weak, moderate, & strong) impacts on local winter precipitation have changed. We have been seeing more wetter La Niña winters and less drier La Niña winters. From 1949-1990 (12 total), 6 were among the driest third, 3 were among wettest third, & 3 were near normal. Since 1991 (13 events), 6 were among the wettest third, 4 were near normal, & 3 were among the driest third.
Climate Trends over the Past 15 Years
Since the winter of 2009-10, a vast majority of the winters have either been near-normal or among the wettest third of all winters. For La Crosse, this accounts for 38 out of 45 winter months (84.4%) and 11 out of 15 meteorological winters (73.3%). For Rochester, this accounts for 39 out of 45 winter months (86.7%) and 13 out of 15 meteorological winters (86.7%).
An above-normal precipitation forecast doesn't necessary mean an above-normal snow season (July-June).
Over the past 15 snow seasons, the trends in the signals for seasonal snow (July-June) are highly mixed.
For La Crosse, there has been no clear signal over the past 15 snow seasons. 6 out of 15 snow seasons (40%) had near normal seasonal snowfall, 5 out of 15 snow seasons (33.3%) were among the snowiest third of snow seasons, and 4 out of 15 snow seasons (26.7%) were among the lowest third of seasonal snowfall.
For Rochester, there has been a clear trend toward snowier snow seasons during the past 15 snow seasons. 9 out of 15 snow seasons (60%) were among the snowiest third of snow seasons, 3 out of 15 snow seasons (20%) were among the lowest third of seasonal snowfall, and 3 out of 15 snow seasons (20%) were near normal.
What is La Niña?:
What is La Niña?
Author: Mike Halpert
October 23, 2017
La Niña literally means "the little girl." in Spanish. La Niña is also sometimes called El Viejo (Old Man), anti-El Niño, or simply "a cold event" or "a cold episode". La Niña refers to abnormally cold water temperatures across the central and eastern equatorial waters (5°N-5°S, 120°-170
During La Niña, the surface winds across the entire tropical Pacific are stronger than usual, and most of the tropical Pacific Ocean is cooler than average. This results in more upwelling of cold water off the Peruvian coast which results in even colder waters in the central and eastern equatorial waters. Rainfall increases over Indonesia (where waters remain warm) and decreases over the central tropical Pacific (which is cool). Over Indonesia, there is more rising air motion and lower surface pressure. There is more sinking air motion over the cooler waters of the central and eastern Pacific.
 |
Generalized Walker Circulation (December-February) anomaly during La Niña events, overlaid on a map of average sea surface temperature anomalies. Anomalous ocean cooling (blue-green) in the central and eastern Pacific Ocean and warming over the western Pacific Ocean enhance the rising branch of the Walker circulation over the Maritime Continent and the sinking branch over the eastern Pacific Ocean. Enhanced rising motion is also observed over northern South America, while anomalous sinking motion is found over eastern Africa. NOAA Climate.gov drawing by Fiona Martin.
La Niña episodes typically last 9-12 months. They both tend to develop during the spring (March-June), reach peak intensity during the late autumn or winter (November-February), and then weaken during the spring or early summer (March-June). it is common for La Niña to last for two years or more. The longest La Niña lasted 33 months.
During La Niña winters, cooler-than-normal temperatures are typically found across western and central Canada, Japan, eastern China, southern Brazil, parts of western and southern Africa, and Madagascar. Wetter-than-normal conditions are found in Indonesia, western and central Canada, and southeast Africa. Meanwhile, drier-than-normal conditions are seen across central South America.
 |
There has been a fair amount of variability in the winter temperature and precipitation patterns during La Niña, but also that there are some clear tendencies for above or below normal temperature or precipitation in some regions.
 |
Another way to examine the common features of La Niña winters is to create a composite map (an average of all of these individual maps). This will highlight those regions that often have temperature or precipitation anomalies of the same sign. For temperature, there’s a strong tendency for temperatures to be below average across some of the West and North, particularly in the Northern Plains, with a weaker signal for above-average temperatures in the Southeast, as shown in the image below.
 |
Winter temperature differences from average (degrees F) during La Niña winters dating back to 1950. Temperatures tend to be colder than average across the northern Plains and warmer than average across the southern tier of the United States. NOAA Climate.gov image using data from ESRL and NCEI.
 |
Winter precipitation differences from average (inches) during La Niña winters dating back to 1950. Precipitation tends to be below-average across the southern tier of the United States and wetter than average across the Pacific Northwest and Ohio Valley. NOAA Climate.gov image using data from ESRL and NCEI.
The precipitation pattern, presented above, shows negative anomalies (indicating below-normal rainfall) across the entire southern part of the country with a weaker signal of above-average precipitation in the Ohio Valley and in the Pacific Northwest and the northern Rockies.
However, these figures are based on about 20 different La Niña episodes, many of them from the 1950s, 1960s, and 1970s, and we have not removed the longer-term trends from the temperature and precipitation data used here. The trend is an important component of seasonal temperature forecasts. It’s fairly trivial to break the sample size in half and compare the temperature patterns for the older half to the more recent half. That provides a significantly different picture, with the average of the latest events much warmer than the earlier ones. We can see this by comparing the right image below (more recent events) with the one to the left of it (older events).
 |
Comparison of winter temperature differences from average (degrees F) between the earliest and most recent ten La Niña winters dating back to 1950. Temperatures tend to be warmer across much of the country during the most recent ten La Niña events as compared to the earliest ten La Niña events. NOAA Climate.gov image using data from ESRL and NCEI.
This picture is consistent with long-term warming trends in the United States. These historical relationships along with guidance provided by a suite of computer models play a strong role in the final outlooks. Differences between the two periods for the precipitation composites are much smaller and therefore are not shown here.
(1) The terciles, technically, are the 33.33 and 66.67 percentile positions in the distribution. In other words, they are the boundaries between the lower and middle thirds of the distribution, and between the middle and upper thirds. These two boundaries define three categories: below-normal, near-normal, and above-normal. In the maps, the CPC forecasts show the probability of the favored category only when there is a favored category; otherwise, they show EC (“equal chances”). Often, the near-normal category remains at 33.33%, and the category opposite the favored one is below 33.33% by the same amount that the favored category is above 33.33%. When the probability of the favored category becomes very large, such as 70% (which is very rare), the above rule for assigning the probabilities for the two non-favored categories becomes different.
La Niña Winter Temperatures
Author: Tom Di Liberto (October 6, 2017)
|
When La Niña develops across the tropical central/eastern Pacific Ocean, it can affect areas thousands of miles away, including the United States. The effects are usually strongest in Northern Hemisphere winter. However, no two La Niña winters will have identical temperature and precipitation patterns across the United States. The series of maps to the right shows temperature patterns across the continental United States compared to the 1981-2010 average for every winter season—December through February—since 1950 that coincided with La Niña conditions in the equatorial Pacific Ocean. The years are ranked by how far below average the temperatures were in the central/eastern tropical Pacific: strong (at least -1.5° Celsius colder than average), moderate (between -1° and -1.5°C), and weak (between -0.5° and -1°C colder-than-average). In general, the stronger the La Niña, the more reliable the impacts on the United States. The typical U.S. impacts are warmer- and drier-than-average conditions across the southern tier of the United States, colder-than-average conditions across the north-central Plains, and wetter-than-average conditions in the Pacific Northwest stretching into northern California. However, as is evident in these maps, there is a great deal of variability even among strong La Niña events. For example, 8 of the 11 strong and moderate events show the cool conditions in the Northern Great Plains, which is most winters, but not all. This “failure” of the typical pattern occurs because La Niña is never the only thing that influences the climate over the United States during the winter. Other climate phenomena, such as the Arctic Oscillation or the Madden Julian Oscillation, as well as the random nature of weather, can also play a large part in how winter turns out. |
Winter (December-February) temperature during strong, moderate, and weak La Niñas since 1950 (Winter 2017-18 not included) |
Midwest La Niña Winter (DJF) Average Temperature Departures (25 Winters since 1949-50)
 |
 |
 |
 |
| 1949-1950 | 1954-1955 | 1955-1956 | 1964-1965 |
 |
 |
 |
 |
| 1970-1971 | 1971-1972 | 1973-1974 | 1974-1975 |
 |
 |
 |
 |
| 1975-1976 | 1983-1984 | 1984-1985 | 1988-1989 |
 |
 |
 |
 |
| 1995-1996 | 1998-1999 | 1999-2000 | 2000-2001 |
 |
 |
 |
 |
| 2005-2006 | 2007-2008 | 2008-2009 | 2010-2011 |
 |
 |
 |
 |
| 2011-2012 | 2017-2018 | 2020-2021 | 2021-2022 |
 |
|||
| 2022-2023 |
La Niña Winter Precipitation
Author: Tom Di Liberto (October 12, 2017)
|
When La Niña develops across the tropical central/eastern Pacific Ocean, it can affect areas thousands of miles away, including the United States. The effects are usually strongest in Northern Hemisphere winter. However, no two La Niña winters will have identical precipitation patterns. This series of maps shows precipitation patterns across the continental United States compared to the 1981-2010 average for every winter season—December through February—since 1950 that coincided with La Niña conditions in the equatorial Pacific Ocean. The years are ranked by how far below average the temperatures were in the central/eastern tropical Pacific: strong (at least -1.5° Celsius colder than average), moderate (between -1° and -1.5°C), and weak (between -0.5° and -1°C colder than average). In general, the stronger the La Niña, the more reliable the impacts on the United States. The typical U.S. impacts are warmer- and drier-than-average conditions across the southern tier of the United States, colder-than-average conditions across the north-central Plains, and wetter-than-average conditions in the Ohio Valley and Pacific Northwest/Northern California. However, as is evident in these maps, there is a great deal of variability even among strong La Niña events. And some impacts are more reliable than others. For example, 9 of the 11 strong and moderate events show wetter-than-average conditions in the Pacific Northwest—though the intensity of the anomaly varies—which is most winters, but not all. And 6 of the 11 events produced wet conditions in the Ohio Valley, which is slightly more than half, but far from a guarantee. This “failure” of the typical pattern occurs because La Niña is never the only thing that influences the climate over the United States during the winter. Other climate phenomena, such as the Arctic Oscillation or the Madden Julian Oscillation, as well as the random nature of weather, can also play a large part in how winter turns out. |
Winter (December-February) precipitation during strong, moderate, and weak La Niñas since 1950 (Winter 2017-18 not included) |
Midwest La Niña Winter Winter (DJF) Precipitation Departures (25 Winters since 1949-50)
 |
 |
 |
 |
| 1949-1950 | 1954-1955 | 1955-1956 | 1964-1965 |
 |
 |
 |
 |
| 1970-1971 | 1971-1972 | 1973-1974 | 1974-1975 |
 |
 |
 |
 |
| 1975-1976 | 1983-1984 | 1984-1985 | 1988-1989 |
 |
 |
 |
 |
| 1995-1996 | 1998-1999 | 1999-2000 | 2000-2001 |
 |
 |
 |
 |
| 2005-2006 | 2007-2008 | 2008-2009 | 2010-2011 |
 |
 |
 |
 |
| 2011-2012 | 2017-2018 | 2020-2021 | 2021-2022 |
 |
|||
| 2022-2023 |
La Niña Seasonal Snow
Author: Tom Di Liberto (October 24, 2024)
A quick reminder on what La Niña typically means for the atmosphere over North America during winter. During La Niña, the jet stream, that river of air 30-40,000 feet in the atmosphere that serves as a storm highway, shifts northward across the eastern Pacific Ocean. This causes a ripple effect on the atmosphere across North America. A high-pressure system tends to set up south of Alaska in the north Pacific Ocean and acts like an atmospheric boulder, forcing storms up and around. Downstream over the eastern U.S., the jet stream then dips south in response.
The end result is colder temperatures across western Canada and northwest/northcentral U.S. and warmer and drier-than-average conditions over the southern U.S. Wetter conditions also prevail in the Pacific Northwest and Ohio River Valley as storms follow around the blocking high in the Pacific or across more northern areas near the Great Lakes.
Some patterns jump out when looking at how snowfall differed from normal (the 1991-2020 average) for all La Niña winters from 1959-2024. La Niña winters tended to be banner years for snow across western Canada, the Pacific Northwest, and the northern Rockies. Snowfall also was above-average across the Great Lakes into northern New England. On the flip side, the southern tier of the United States observes below-average snowfall amounts.
 January–March snowfall during all 22 La Niña winters from 1959–2024 compared to the average for all January–March periods from 1991–2020. The long-term trend in snowfall over this period has been removed, meaning the maps better show the influence of La Niña on its own. NOAA Climate.gov map, based on ERA5 reanalysis data and analysis by Michelle L’Heureux. |
Using averages, though, can have a drawback. For one, a couple BIG snow years could make the overall average look snowier than what we typically experience. To deal with that, we can look at the 22 La Niña events on record and count those with below-average snowfall. Using this metric, the signal for above-average snow is particularly robust across the Pacific Northwest and Idaho, as out of 22 La Niña events, less than seven winters had below-average snow. Meanwhile, bad news for snow-lovers in the Mid-Atlantic: more than 15 La Niña winters had below-average snow.
|
|
This winter, if a La Niña forms, we’re expecting it to be a weak event. If you remember, a weaker La Niña means a weaker punch on the atmosphere and a less consistent impact on climate across North America. In the nine previous weak La Niña events, the pattern of snow was similar to that of all La Niña events with above-average snowfall observed, on-average, across the northwest and north central U.S. with below-average snowfall farther south.
 January–March snowfall during 9 weak La Niña winters from 1959–2024 compared to the average for all January–March periods from 1991–2020. The long-term trend in snowfall over this period has been removed, meaning the maps better show the influence of weak La Niñas by themselves. NOAA Climate.gov map, based on ERA5 reanalysis data and analysis by Michelle L’Heureux. |
But there were some exceptions. The north-central U.S. including the Dakotas and Minnesota had an even snowier signal during weak La Niñas than the average of all La Niñas. Meanwhile, the Pacific Northwest wasn’t as snowy as compared to all La Niña events, and snowfall was actually well-below average (not above-average like in the all-La Niña event case) just over the border in southwestern Canada.
The count of how many (out of nine) weak La Nina events had below-average snowfall also showed similar patterns, with some bad news for those in Virginia, Maryland, and Washington, D.C., where every single weak La Niña winter had below-average snow.
 This map shows how many of the 9 historical weak La Niña winters from 1959–2024 had below-average snowfall from January–March. Red colors mean those places had below-average snowfall more than half the time. Gray colors mean those places had below-average snowfall less than half the time. The long-term trend in snowfall over this period has been removed to better show the influence of weak La Niñas by themselves. NOAA Climate.gov map, based on ERA5 reanalysis data and analysis by Michelle L’Heureux. |
La Niña isn’t the only story when it comes to snowfall across North America. An important note: the previous maps based on La Niña events have also had the long-term trends removed (footnote 3). We do that to see what impact La Niña had on snowfall by itself. In other words, how La Niña winters would have played out if there was no long-term change in snowfall to complicate things. But, there is a long-trend that has to be considered.
 Changes in January–March snowfall across North America from 1959–2024. Most of the contiguous United States has seen a decline of snowfall (brown) over the period thanks to long-term warming. Farther north, where winter temperatures have room to warm and remain below freezing, snowfall has increased in places (blue). Such increases are consistent with global increases in atmospheric water vapor as a result of warming-driven evaporation. NOAA Climate.gov map, based on ERA5 reanalysis data and analysis by Michelle L’Heureux. |
Human-caused climate change is making things warmer. And across many regions, winter is the fastest-warming season. Not surprisingly, over most of the contiguous United States, January-March snowfall has trended downwards. Less snow doesn’t necessarily mean less precipitation, though. In fact, for much of the Great Lakes and Northeast, precipitation has increased in winter. It just means that “would-be” snow is falling as rain due to warming temperatures.
What about areas farther north like Alaska? Well, an overall warmer atmosphere means the air can hold more moisture. When the atmosphere is wrung out, it means more precipitation. In places in the far north where temperatures are still cold enough for snow, that extra moisture translates into an increase in snow.
Good question. Just because snow is trending lower, or there is a La Niña, doesn’t mean there can’t be a big snowstorm in any given winter. It just might be harder for that to happen in some places. Basically, I’m telling you there’s a chance. Good luck, snow lovers!
Midwest La Niña Seasonal Snow Departures (25 Winters since 1949-50)
 |
 |
 |
 |
| 1949-1950 | 1954-1955 | 1955-1956 | 1964-1965 |
 |
 |
 |
 |
| 1970-1971 | 1971-1972 | 1973-1974 | 1974-1975 |
 |
 |
 |
 |
| 1975-1976 | 1983-1984 | 1984-1985 | 1988-1989 |
 |
 |
 |
 |
| 1995-1996 | 1998-1999 | 1999-2000 | 2000-2001 |
 |
 |
 |
 |
| 2005-2006 | 2007-2008 | 2008-2009 | 2010-2011 |
 |
 |
 |
 |
| 2011-2012 | 2017-2018 | 2020-2021 | 2021-2022 |
 |
|||
| 2022-2023 |
With that said, ERA5 is a newer dataset, it is “reanalysis,” which means that a very short-range weather model is used to produce snowfall from in situ observations (from the ECMWF website, it outputs the “mass of snow that has fallen to the earth’s surface”). Essentially a reanalysis is predicting what observed snowfall would have looked like based on past observational inputs from satellites, stations, buoys, and other observing systems. Therefore, we recommend you treat some of the finer details with a healthy degree of suspicion and try to corroborate them in other datasets. Hopefully this blog post will motivate the creation of additional snowfall datasets and scientists will explore how well ERA5 compares with these other snowfall measurements.
Another aspect to keep in mind is that ERA5 snowfall is “Snowfall toward earth’s surface” which means measurements are not subjected to influence from pavement, canopy, surface winds, etc. which tend to reduce amounts actually measured at the surface (not to mention human error using a ruler).
La Niña AWSSI
Author: Midwestern Regional Climate Center
|
Winter seasons have significant societal impacts across all sectors ranging from direct human health and mortality to commerce, transportation, and education. The question “How severe was this winter?” does not have a simple answer. At the very least, the severity of a winter is related to the intensity and persistence of cold weather, the amount of snow, and the amount and persistence of snow on the ground. The Accumulated Winter Season Severity Index (AWSSI) was developed to objectively quantify and describe the relative severity of the winter season. |
|
Goals of the AWSSI
|
Limitations
|
How does the AWSSI accumulate?
First measurable snowfall (>= 0.1 inch) The winter season ends at the last occurrence of any of the following: Daily scores are calculated based on scores assigned to temperature, snowfall, and snow depth thresholds. The daily scores are accumulated through the winter season, allowing a running total of winter severity in the midst of a season as well as a final, cumulative value characterizing the full season. Accumulations of the temperature and snow components of the index are computed separately and then added together for the total index. This allows a comparison of the relative contribution of each to the total score. The AWSSI has been processed for 52 locations across the continental U.S. to provide a variety of locations in different climate regimes for analysis. The AWSSI is calculated for each season from 1950â€1951 to 2012â€2013. The seasonal data is then subject to quality control, and seasons missing data that would contribute 5% or more of the seasons AWSSI are removed. Averages and standard deviations are calculated for running accumulations of daily temperature and snow scores as well as the total AWSSI. The AWSSI data is gathered every hour throughout the day. Quintiles of AWSSI scores were determined for each location. Descriptive categories were assigned to each quintile as follows:
|
Annotated Scoring Page Sample: |
CONUS AWSSI during La Niña (24 Winters since 1954-55)
 |
 |
 |
 |
| 1954-1955 | 1955-1956 | 1964-1965 | 1970-1971 |
 |
 |
 |
 |
| 1971-1972 | 1973-1974 | 1974-1975 | 1975-1976 |
 |
 |
 |
 |
| 1983-1984 | 1984-1985 | 1988-1989 | 1995-1996 |
 |
 |
 |
 |
| 1998-1999 | 1999-2000 | 2000-2001 | 2005-2006 |
 |
 |
 |
 |
| 2007-2008 | 2008-2009 | 2010-2011 | 2011-2012 |
 |
 |
 |
 |
| 2017-2018 | 2020-2021 | 2021-2022 | 2022-2023 |
 |
 |
 |
 |
| 1954-1955 | 1955-1956 | 1964-1965 | 1970-1971 |
 |
 |
 |
 |
| 1971-1972 | 1973-1974 | 1974-1975 | 1975-1976 |
 |
 |
 |
 |
| 1983-1984 | 1984-1985 | 1988-1989 | 1995-1996 |
 |
 |
 |
 |
| 1998-1999 | 1999-2000 | 2000-2001 | 2005-2006 |
 |
 |
 |
 |
| 2007-2008 | 2008-2009 | 2010-2011 | 2011-2012 |
 |
 |
 |
 |
| 2017-2018 | 2020-2021 | 2021-2022 | 2022-2023 |
 |
 |
 |
 |
| 1954-1955 | 1955-1956 | 1964-1965 | 1970-1971 |
 |
 |
 |
 |
| 1971-1972 | 1973-1974 | 1974-1975 | 1975-1976 |
 |
 |
 |
 |
| 1983-1984 | 1984-1985 | 1988-1989 | 1995-1996 |
 |
 |
 |
 |
| 1998-1999 | 1999-2000 | 2000-2001 | 2005-2006 |
 |
 |
 |
 |
| 2007-2008 | 2008-2009 | 2010-2011 | 2011-2012 |
 |
 |
 |
 |
| 2017-2018 | 2020-2021 | 2021-2022 | 2022-2023 |
Information Sheet (2-page) - pdf
The Accumulated Winter Season Severity Index (AWSSI)
Barbara E. Mayes Boustead, Steven D. Hilberg, Martha D. Shulski, and Kenneth G. Hubbard. Journal of Applied Meteorology and Climatology, Vol. 54, No. 8, August 2015: 1693-1712.
Abstract | Full Text | PDF (2545 KB)
An Accumulated Winter Season Severity Index. Barbara Mayes Boustead, NOAA/NWS, Valley, NE; and S. Hilberg, M. D. Shulski, and K. G. Hubbard. Presented at 93rd Annual Meeting of the American Meteorological Society, January 2013.
An Accumulated Winter Season Severity Index (AWSSI). Webinar for the NWS Central Region, February 2017.
For more information, please contact Barb Mayes Boustead (barbara.mayes@noaa.gov) at the National Weather Service or Steve Hilberg (hberg@illinois.edu).
La Niña Severe Weather:
La Niña Severe Weather:
Author: Michael K. Tippett and Chiara Lepore
April 27, 2017
ENSO shifts the atmospheric circulation (notably, the jet stream) in ways that affect winter temperature and precipitation over the U.S. The changes in spring (March-May) are similar to those during winter, but somewhat weaker.
These shifts would also be expected to impact thunderstorm activity: El Niño tends to shift the jet stream farther south over the U.S., which blocks moisture from the Gulf of Mexico, reducing the fuel for thunderstorms. On the other hand, La Niña is associated with a more wavy and northward shifted jet stream, which might be expected to enhance severe weather activity in the south and southeast. Indeed, historic tornado outbreaks in 1974, 2008, and 2011 started during La Niña conditions.
However, tornado and severe weather activity is more variable (“noisier” and harder to predict) than ordinary weather (think temperature and precipitation), and any ENSO signal is harder to see. Until recently, the only solid evidence showing that more tornadoes occur during La Niña conditions was for winter (January-March), when the ENSO signal is strongest, but average tornado activity is relatively low (Cook & Schaefer, 2008).
A clearer picture of the impact of ENSO emerges when we look at the ingredients that are conducive to tornado and thunderstorm occurrence (Allen et al., 2015a). Instead of only looking at individual weather events, it’s important to consider the environmental cues for the outbreak of severe weather.
Two important ingredients for tornadoes are atmospheric instability (e.g., warm, moist air near the surface and cool dry air aloft) and vertical wind shear (winds at different altitudes blowing in different directions or speeds). Measures of these tornado-friendly ingredients can be combined into indexes that are less noisy than actual tornado reports and let us see how the phases of ENSO make the environment more or less favorable for severe weather (footnote 1).
In much of the U.S., La Niña conditions are associated with increases in these environmental factors and in tornado and hail reports. The largest signal is present in the south and southeast (including parts of Texas, Oklahoma, Kansas, Louisiana, Arkansas, and Missouri), except in Florida where the opposite relation is observed. Positive values indicate increased activity, and negative values indicate decreased activity compared to the long-term average (1979-2015).
 |
Figure 1. Sea surface temperature pattern showing the warm phase of the Pacific Decadal Oscillation (top). The status of the PDO between 1950 and this year, shown at bottom, indicates a predominantly positive phase from about 1978 to 1998 and a negative phase since 1999. Image Credit: Climate Impacts Group, University of Washington.
In the South and Southeast, where the signal is strongest, we see a clear shift in activity with the ENSO phase, but with a tremendous range of variability, meaning some El Niño years still have high severe weather activity, and some La Niña years are relatively inactive. While increased tornado activity is generally associated with La Nina conditions, blaming this year’s high activity on the weak La Nina conditions would be exaggerating the strength of the historical relationship (footnote 2).
 |
Regional (100-90W, 31-36N) totals of March-May tornado reports, hail events, a tornado environment index (TEI), and a hail environment index (HEI) expressed as a percentage of their 1979-2015 average and conditioned on the ONI. Box edges mark the 25th and 75th percentiles, and whiskers extend 1 and a half times the interquartile range. Figure by climate.gov; data from the authors.
1: The tornado environment index (TEI) and hail environment index (HEI) are functions of monthly averages of convective precipitation, convective available potential energy, and storm-relative helicity. TEI and HEI are calibrated to match the recent climatology of tornado numbers and hail events. See Tippett et al. (2012) and Allen et al. (2015b) for more details.
2: Inside baseball: Further details of the ENSO relation
Overall, La Nina conditions are associated with enhanced U.S. tornado activity, but more detailed aspects of ENSO may also be relevant (Lee et al., 2012). Gulf of Mexico sea surface temperature is close to the part of the U.S. most strongly impacted by severe weather: warm Gulf of Mexico surface water in spring enhances low-level moisture transport and southerly flow and is associated with enhanced US tornado and hail activity (Molina et al., 2016). The Gulf of Mexico sea surface temperature is negatively correlated with the tropical Pacific sea surface temperature, meaning when the tropical Pacific is cooler than average (La Niña), the Gulf of Mexico is usually warmer than average.
Seasonal (May-July) averages of Gulf of Mexico SST can be predicted with some skill (Jung and Kirtman, 2016). Atmospheric angular momentum is related to ENSO and also shows the impact of tropical forcing on tornado activity (Gensini and Marinaro, 2016).
Local La Niña Statistics:
Past La Niña Winters Statistics for the Local Area:
In the tables below, red represents a value in the upper third of winters, blue represents a value in the lower third of winters, and black represents a near-normal. For La Crosse, the temperature and precipitation data extend back to the 1872-73 winter and snowfall back to 1895-96. For Rochester, the temperature and precipitation data extend back to the 1886-87 winter and snowfall back to 1908-09.
| La Niña Monthly Mean Avg Temperature for La Crosse, WI | ||||
| Year | Dec | Jan | Feb | Season |
| 1949-1950 | 24.6 | 15.7 | 20.6 | 20.3 |
| 1954-1955 | 24.4 | 17.2 | 18.2 | 20.0 |
| 1955-1956 | 15.4 | 15.5 | 17.4 | 16.1 |
| 1964-1965 | 17.4 | 11.0 | 13.9 | 14.1 |
| 1970-1971 | 19.4 | 7.4 | 17.0 | 14.5 |
| 1971-1972 | 24.5 | 11.1 | 16.1 | 17.2 |
| 1973-1974 | 18.9 | 18.0 | 19.1 | 18.6 |
| 1974-1975 | 26.5 | 18.1 | 18.7 | 21.2 |
| 1975-1976 | 23.7 | 13.1 | 29.0 | 21.8 |
| 1983-1984 | 6.4 | 14.1 | 30.3 | 16.6 |
| 1984-1985 | 21.5 | 10.9 | 16.4 | 16.3 |
| 1988-1989 | 21.8 | 24.2 | 12.8 | 19.8 |
| 1995-1996 | 21.0 | 11.9 | 19.9 | 17.5 |
| 1998-1999 | 29.2 | 14.3 | 31.2 | 24.7 |
| 1999-2000 | 27.4 | 18.1 | 30.6 | 25.2 |
| 2000-2001 | 8.6 | 20.6 | 16.3 | 15.1 |
| 2005-2006 | 19.7 | 31.1 | 21.9 | 24.3 |
| 2007-2008 | 19.4 | 14.6 | 14.9 | 16.3 |
| 2008-2009 | 14.1 | 8.8 | 22.8 | 15.0 |
| 2010-2011 | 16.7 | 14.1 | 20.2 | 16.9 |
| 2011-2012 | 28.1 | 23.8 | 29.1 | 27.0 |
| 2017-2018 | 21.9 | 18.8 | 21.0 | 20.6 |
| 2020-2021 | 27.6 | 23.4 | 12.8 | 21.3 |
| 2021-2022 | 28.7 | 12.8 | 18.3 | 19.9 |
| 2022-2023 | 21.5 | 24.4 | 26.1 | 24.0 |
| Dec | Jan | Feb | Season | |
| 1991-2020 Normal | 25.1 | 18.9 | 23.3 | 22.5 |
| La Niña Mean | 21.1 | 16.5 | 20.6 | 19.4 |
| La Niña Max | 29.2 (1998-1999) |
31.1 (2005-2006) |
31.2 (1998-1999) |
27.0 (2011-2012) |
| La Niña Min | 6.4 (1983-1984) |
7.4 (1970-1971) |
12.8 (1988-1989 & 2020-2021) |
14.1 (1964-1965) |
| La Niña Monthly Total Precipitation for La Crosse, WI | ||||
| Year | Dec | Jan | Feb | Season |
| 1949-1950 | 1.20 | 1.34 | 1.43 | 3.97 |
| 1954-1955 | 0.47 | 0.30 | 0.57 | 1.34 |
| 1955-1956 | 0.78 | 0.46 | 0.43 | 1.67 |
| 1964-1965 | 0.86 | 0.81 | 0.72 | 2.39 |
| 1970-1971 | 0.97 | 1.52 | 2.06 | 4.55 |
| 1971-1972 | 2.55 | 0.62 | 0.50 | 3.67 |
| 1973-1974 | 1.37 | 0.40 | 1.65 | 3.42 |
| 1974-1975 | 1.39 | 1.50 | 1.71 | 4.60 |
| 1975-1976 | 0.70 | 0.69 | 0.64 | 2.03 |
| 1983-1984 | 0.68 | 0.28 | 0.92 | 1.88 |
| 1984-1985 | 2.42 | 0.88 | 1.27 | 4.57 |
| 1988-1989 | 0.78 | 0.41 | 0.40 | 1.59 |
| 1995-1996 | 0.82 | 3.03 | 0.41 | 4.26 |
| 1998-1999 | 0.30 | 2.84 | 0.78 | 3.92 |
| 1999-2000 | 0.67 | 1.43 | 0.91 | 3.01 |
| 2000-2001 | 1.90 | 1.19 | 0.99 | 4.08 |
| 2005-2006 | 0.56 | 0.47 | 0.71 | 1.74 |
| 2007-2008 | 2.64 | 1.30 | 1.14 | 5.08 |
| 2008-2009 | 2.32 | 0.74 | 0.97 | 4.03 |
| 2010-2011 | 2.39 | 0.79 | 1.12 | 4.30 |
| 2011-2012 | 1.41 | 1.08 | 1.44 | 3.93 |
| 2017-2018 | 0.53 | 1.38 | 1.22 | 3.13 |
| 2020-2021 | 0.36 | 0.89 | 0.73 | 1.98 |
| 2021-2022 | 1.72 | 0.61 | 0.34 | 2.67 |
| 2022-2023 | 2.38 | 2.17 | 2.19 | 6.74 |
| Dec | Jan | Feb | Season | |
| 1991-2020 Normal | 1.49 | 1.25 | 1.19 | 3.93 |
| La Niña Mean | 1.29 | 1.09 | 1.01 | 3.38 |
| La Niña Max | 2.64 (2007-2008) |
3.03 (1995-1996) |
2.19 (2022-2023) |
6.74 (2022-2023) |
| La Niña Min | 0.30 (1998-1999) |
0.28 (1983-1984) |
0.34 (2021-2022) |
1.34 (1954-1955) |
| Monthly Total Snowfall for La Crosse, WI | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|
Year |
Sep |
Oct |
Nov |
Dec |
Jan |
Feb |
Mar |
Apr |
May |
Season |
| 1949-1950 | 0.0 | 0.0 | 0.0 | 7.5 | 7.0 | 12.7 | 8.1 | 0.2 | 0.0 | 35.5 |
| 1954-1955 | 0.0 | T | 6.7 | 5.3 | 4.8 | 3.3 | 8.8 | T | T | 28.9 |
| 1955-1956 | 0.0 | T | 4.8 | 8.0 | 5.8 | 3.8 | 22.4 | 4.5 | 0.0 | 49.3 |
| 1964-1965 | 0.0 | T | 4.7 | 6.7 | 12.7 | 6.6 | 15.0 | 1.6 | 0.0 | 47.3 |
| 1970-1971 | 0.0 | T | 1.1 | 13.5 | 26.5 | 20.4 | 5.6 | 0.5 | 0.0 | 67.6 |
| 1971-1972 | 0.0 | 0.0 | 3.8 | 12.0 | 10.2 | 8.0 | 11.9 | 3.0 | 0.0 | 48.9 |
| 1973-1974 | 0.0 | 0.0 | T | 9.8 | 2.0 | 14.9 | 7.6 | 0.7 | 0.0 | 35.0 |
| 1974-1975 | T | 0.0 | 2.2 | 10.1 | 18.8 | 20.1 | 18.1 | 3.9 | 0.0 | 73.2 |
| 1975-1976 | 0.0 | 0.0 | 1.7 | 1.0 | 8.0 | 2.7 | 6.0 | 0.0 | T | 19.4 |
| 1983-1984 | 0.0 | 0.0 | 4.0 | 9.2 | 3.5 | 1.7 | 11.0 | T | 0.0 | 29.4 |
| 1984-1985 | 0.0 | T | 2.9 | 13.3 | 9.4 | 5.3 | 14.5 | T | 0.0 | 45.4 |
| 1988-1989 | 0.0 | T | 7.6 | 5.2 | 3.8 | 7.9 | 19.3 | T | T | 43.8 |
| 1995-1996 | 0.0 | 0.5 | 6.1 | 8.8 | 35.0 | 1.2 | 5.8 | 3.3 | 0.0 | 60.7 |
| 1998-1999 | 0.0 | 0.0 | 0.3 | 4.1 | 31.9 | 2.4 | 5.3 | 0.0 | 0.0 | 44.0 |
| 1999-2000 | 0.0 | 0.3 | 0.0 | 4.9 | 9.4 | 5.4 | 3.7 | 1.9 | 0.0 | 25.6 |
| 2000-2001 | 0.0 | T | 2.8 | 25.5 | 4.3 | 6.2 | 8.1 | 0.1 | 0.0 | 47.0 |
| 2005-2006 | 0.0 | 0.0 | 6.1 | 12.9 | 1.6 | 11.4 | 7.8 | T | 0.0 | 39.8 |
| 2007-2008 | 0.0 | 0.0 | 1.7 | 24.2 | 18.3 | 15.0 | 7.9 | 0.8 | 0.0 | 67.9 |
| 2008-2009 | 0.0 | T | 4.0 | 32.7 | 10.1 | 7.7 | 1.2 | T | 0.0 | 55.7 |
| 2010-2011 | 0.0 | 0.0 | T | 32.3 | 11.3 | 10.8 | 5.6 | 4.7 | 0.0 | 64.7 |
| 2011-2012 | 0.0 | T | 0.7 | 4.3 | 13.2 | 2.8 | 0.5 | T | 0.0 | 21.5 |
| 2017-2018 | 0.0 | T | T | 4.4 | 10.8 | 9.5 | 6.1 | 19.0 | 0.0 | 49.8 |
| 2020-2021 | 0.0 | 2.3 | 1.0 | 4.6 | 9.0 | 12.1 | 4.0 | T | 0.0 | 33.0 |
| 2021-2022 | 0.0 | 0.0 | 0.8 | 15.0 | 9.5 | 3.9 | 0.3 | 1.0 | 0.0 | 30.5 |
| 2022-2023 | 0.0 | T | 3.2 | 17.0 | 10.2 | 11.5 | 12.2 | 9.6 | 0.0 | 63.7 |
| Sep | Oct | Nov | Dec | Jan | Feb | Mar | Apr | May | Season | |
| 1991-2020 Normals | 0.0 | 0.3 | 3.4 | 10.9 | 11.8 | 9.7 | 7.3 | 2.9 | 0.0 | 46.3 |
| La Niña Mean | 0.0 | 0.2 | 3.0 | 11.7 | 11.5 | 8.3 | 8.7 | 3.2 | 0.1 | 45.1 |
| La Niña Max | T (1974-1975) |
2.3 (2020-2021) |
7.6 (1988-1989) |
32.7 (2008-2009) |
35.0 (1995-1996) |
20.4 (1970-1971) |
22.4 (1955-1956) |
19.0 (2017-2018) |
T (3 years) |
73.2 (1974-1975) |
| La Niña Min | 0.0 (24 Years) |
0.0 (11 Years) |
0.0 (1949-50 & 1999-2000) |
1.0 (1975-1976) |
1.6 (2005-2006) |
1.2 (1995-1996) |
0.3 (2021-2022) |
0.0 (1975-1976 & 1998-1999) |
0.0 (22 Years) |
19.4 (1975-1976) |
| La Niña Monthly Mean Avg Temperature for Rochester, MN | ||||
|---|---|---|---|---|
|
Year |
Dec |
Jan |
Feb |
Season |
| 1949-1950 | 20.9 | 10.3 | 14.3 | 15.2 |
| 1954-1955 | 22.4 | 14.6 | 15.2 | 17.5 |
| 1955-1956 | 12.8 | 12.4 | 13.1 | 12.7 |
| 1964-1965 | 15.4 | 9.7 | 11.6 | 12.2 |
| 1970-1971 | 18.0 | 5.8 | 16.4 | 13.3 |
| 1971-1972 | 20.0 | 6.5 | 12.4 | 13.0 |
| 1973-1974 | 14.8 | 12.8 | 15.8 | 14.4 |
| 1974-1975 | 22.3 | 14.6 | 15.4 | 17.5 |
| 1975-1976 | 22.5 | 12.7 | 28.2 | 21.0 |
| 1983-1984 | 2.9 | 12.4 | 25.4 | 13.3 |
| 1984-1985 | 19.1 | 10.2 | 14.8 | 14.7 |
| 1988-1989 | 19.2 | 21.7 | 9.3 | 17.0 |
| 1995-1996 | 18.1 | 9.6 | 17.0 | 14.9 |
| 1998-1999 | 25.0 | 11.1 | 26.6 | 20.7 |
| 1999-2000 | 23.5 | 14.4 | 26.3 | 21.3 |
| 2000-2001 | 6.1 | 17.4 | 11.2 | 11.6 |
| 2005-2006 | 17.2 | 28.1 | 19.6 | 21.7 |
| 2007-2008 | 17.0 | 12.8 | 13.3 | 14.4 |
| 2008-2009 | 12.5 | 8.5 | 20.7 | 13.7 |
| 2010-2011 | 14.7 | 11.4 | 18.0 | 14.6 |
| 2011-2012 | 26.1 | 22.8 | 27.7 | 25.5 |
| 2017-2018 | 17.9 | 14.6 | 15.3 | 16.0 |
| 2020-2021 | 24.7 | 20.7 | 8.3 | 17.9 |
| 2021-2022 | 24.9 | 9.4 | 13.9 | 16.1 |
| 2022-2023 | 17.2 | 19.3 | 19.9 | 18.8 |
| Dec | Jan | Feb | Season | |
| 1991-2020 Normals | 20.8 | 14.7 | 18.7 | 18.1 |
| La Niña Mean | 18.2 | 13.8 | 17.2 | 16.4 |
| La Niña Max | 26.1 (2011-2012) |
28.1 (2005-2006) |
28.2 (1975-1976) |
25.5 (2011-2012) |
| La Niña Min | 2.9 (1983-1984) |
5.8 (1970-1971) |
8.3 (2020-2021) |
11.6 (2000-2001) |
| La Niña Monthly Total Precipitation for Rochester, MN | ||||
|---|---|---|---|---|
|
Year |
Dec |
Jan |
Feb |
Season |
| 1949-1950 | 0.73 | 1.55 | 1.33 | 3.61 |
| 1954-1955 | 0.56 | 0.40 | 1.14 | 2.10 |
| 1955-1956 | 1.23 | 0.57 | 0.52 | 2.32 |
| 1964-1965 | 0.84 | 0.45 | 1.34 | 2.63 |
| 1970-1971 | 0.82 | 1.12 | 2.21 | 4.15 |
| 1971-1972 | 0.98 | 0.71 | 0.29 | 1.98 |
| 1973-1974 | 0.99 | 0.36 | 0.73 | 2.08 |
| 1974-1975 | 0.56 | 1.91 | 0.76 | 3.23 |
| 1975-1976 | 1.21 | 0.38 | 0.49 | 2.08 |
| 1983-1984 | 1.00 | 0.11 | 1.96 | 3.07 |
| 1984-1985 | 1.79 | 0.63 | 0.57 | 2.99 |
| 1988-1989 | 1.11 | 0.41 | 0.42 | 1.94 |
| 1995-1996 | 0.62 | 2.00 | 0.18 | 2.80 |
| 1998-1999 | 0.28 | 2.07 | 1.13 | 3.48 |
| 1999-2000 | 0.49 | 1.30 | 0.45 | 2.24 |
| 2000-2001 | 1.64 | 0.91 | 1.06 | 3.61 |
| 2005-2006 | 0.59 | 0.30 | 0.40 | 1.29 |
| 2007-2008 | 1.21 | 0.67 | 0.56 | 2.44 |
| 2008-2009 | 1.52 | 0.64 | 0.79 | 2.95 |
| 2010-2011 | 3.68 | 0.84 | 0.77 | 5.29 |
| 2011-2012 | 1.21 | 0.57 | 1.63 | 3.41 |
| 2017-2018 | 0.51 | 1.42 | 1.08 | 3.01 |
| 2020-2021 | 0.20 | 1.14 | 0.65 | 1.99 |
| 2021-2022 | 1.41 | 0.83 | 0.41 | 2.65 |
| 2022-2023 | 1.62 | 2.41 | 2.54 | 6.57 |
| Dec | Jan | Feb | Season | |
| 1991-2020 Normals | 1.28 | 0.99 | 1.02 | 3.29 |
| La Niña Mean | 1.07 | 0.95 | 0.94 | 2.96 |
| La Niña Max | 3.68 (2010-2011) |
2.41 (2022-2023) |
2.54 (2022-2023) |
6.57 (2022-2023) |
| La Niña Min | 0.20 (2020-2021) |
0.11 (1983-84) |
0.18 (1995-96) |
1.29 (2005-06) |
| Monthly Total Snowfall for Rochester, MN | ||||||||||
| Season | Sep | Oct | Nov | Dec | Jan | Feb | Mar | Apr | May | Season |
| 1949-1950 | 0.0 | T | 4.9 | 4.7 | 14.4 | 14.9 | 11.8 | 0.4 | T | 51.1 |
| 1954-1955 | 0.0 | 0.8 | 7.2 | 5.2 | 4.6 | 4.7 | 7.3 | T | 0.0 | 29.8 |
| 1955-1956 | 0.0 | 0.7 | 4.3 | 17.4 | 5.7 | 5.7 | 18.8 | 7.4 | 0.0 | 60.0 |
| 1964-1965 | 0.0 | T | 4.8 | 6.4 | 11.1 | 8.8 | 12.2 | 5.2 | T | 48.5 |
| 1970-1971 | 0.0 | 0.1 | 7.6 | 10.8 | 16.2 | 16.0 | 9.7 | 1.6 | 0.0 | 62.0 |
| 1971-1972 | 0.0 | 0.0 | 7.8 | 8.4 | 11.0 | 3.5 | 7.8 | 2.7 | 0.0 | 41.2 |
| 1973-1974 | 0.0 | T | 0.3 | 16.3 | 2.1 | 12.3 | 11.2 | 0.3 | 0.0 | 42.5 |
| 1974-1975 | 0.0 | 0.0 | 2.9 | 8.3 | 14.1 | 10.2 | 15.0 | 2.5 | 0.0 | 53.0 |
| 1975-1976 | 0.0 | T | 7.8 | 1.9 | 6.6 | 1.6 | 10.3 | T | 0.2 | 28.4 |
| 1983-1984 | 0.0 | T | 14.0 | 16.2 | 2.7 | 12.0 | 16.1 | 5.0 | 0.0 | 66.0 |
| 1984-1985 | 0.0 | T | 3.7 | 14.4 | 12.2 | 9.3 | 25.2 | 3.8 | 0.0 | 68.6 |
| 1988-1989 | 0.0 | T | 10.8 | 4.8 | 4.7 | 9.3 | 21.2 | 0.1 | 0.2 | 51.1 |
| 1995-1996 | T | 2.3 | 4.0 | 11.8 | 30.2 | 1.9 | 9.3 | 2.1 | 0.0 | 61.6 |
| 1998-1999 | 0.0 | 0.0 | 0.7 | 5.3 | 29.4 | 5.4 | 6.7 | T | 0.0 | 47.5 |
| 1999-2000 | 0.0 | 0.5 | T | 7.6 | 19.3 | 7.0 | 3.3 | 2.1 | 0.0 | 39.8 |
| 2000-2001 | 0.0 | T | 5.4 | 35.3 | 7.3 | 7.4 | 10.2 | T | 0.0 | 65.6 |
| 2005-2006 | 0.0 | 0.0 | 5.5 | 12.1 | 0.8 | 8.2 | 11.1 | 0.0 | 0.0 | 37.7 |
| 2007-2008 | 0.0 | 0.0 | 0.9 | 13.1 | 13.0 | 7.3 | 9.0 | 0.8 | T | 44.1 |
| 2008-2009 | 0.0 | 0.1 | 4.4 | 28.6 | 9.8 | 8.0 | 1.2 | 0.6 | 0.0 | 52.7 |
| 2010-2011 | 0.0 | 0.0 | 1.3 | 41.3 | 9.8 | 9.3 | 4.6 | 4.2 | T | 70.5 |
| 2011-2012 | 0.0 | T | 0.2 | 8.4 | 8.1 | 3.9 | T | T | 0.0 | 20.6 |
| 2017-2018 | 0.0 | 2.6 | 0.7 | 7.2 | 16.2 | 9.5 | 7.0 | 17.0 | 0.0 | 60.2 |
| 2020-2021 | 0.0 | 4.2 | 4.4 | 3.9 | 11.6 | 8.7 | 8.9 | T | 0.0 | 41.7 |
| 2021-2022 | 0.0 | 0.0 | 0.6 | 13.1 | 8.7 | 6.4 | 0.6 | 0.9 | 0.0 | 30.3 |
| 2022-2023 | 0.0 | 0.1 | 6.4 | 18.3 | 11.5 | 13.6 | 10.3 | 4.2 | 0.0 | 64.4 |
| Sep | Oct | Nov | Dec | Jan | Feb | Mar | Apr | May | Season | |
| 1991-2020 Normals | 0.0 | 0.9 | 4.5 | 12.4 | 12.2 | 10.7 | 8.6 | 3.3 | 0.5 | 53.1 |
| La Niña Mean | 0.0 | 0.7 | 4.6 | 12.8 | 11.2 | 8.2 | 10.4 | 3.2 | 0.0 | 49.6 |
| La Niña Max | T (1995-1996) |
4.2 (2020-2021) |
14.0 (1983-1984) |
41.3 (2010-2011) |
30.2 (1995-1996) |
16.0 (1970-1971) |
25.2 (1984-1985) |
17.0 (2017-2018) |
0.2 (1975-1976 & 1988-1989) |
70.5 (2010-2011) |
| La Niña Min | 0.0 (24 Years) |
0.0 (8 Years) |
T (1999-2000) |
1.9 (1975-1976) |
0.8 (2005-2006) |
1.6 (1975-1976) |
T (2010-2011) |
0.0 (2005-2006) |
0.0 (19 Years) |
20.6 (2011-2012) |
Arctic Oscillation:
Climate Variability: Arctic Oscillation (AO)
Author: LuAnn Dahlman
August 30, 2009
The Arctic Oscillation (AO) refers to an atmospheric circulation pattern over the mid-to-high latitudes of the Northern Hemisphere. The most obvious reflection of the phase of this oscillation is the north-to-south location of the storm-steering, mid-latitude jet stream. Thus, the AO can have a strong influence on weather and climate in major population centers in North America, Europe, and Asia, especially during winter.
The AO's positive phase is characterized by lower-than-average air pressure over the Arctic paired with higher-than-average pressure over the northern Pacific and Atlantic Oceans. The jet stream is farther north than average under these conditions, and storms can be shifted northward of their usual paths. Thus, the mid-latitudes of North America, Europe, Siberia, and East Asia generally see fewer cold air outbreaks than usual during the positive phase of the AO.
Conversely, AO's negative phase has higher-than-average air pressure over the Arctic region and lower-than-average pressure over the northern Pacific and Atlantic Oceans. The jet stream shifts toward the equator under these conditions, so the globe-encircling river of air is south of its average position. Consequently, locations in the mid-latitudes are more likely to experience outbreaks of frigid, polar air during winters when the AO is negative. In New England, for example, higher frequencies of coastal storms known as "Nor'easters" are linked to AO's negative phase.
 This graph shows monthly values for the Arctic Oscillation index. |
AO phases are analogous to the Southern Hemisphere's Antarctic Oscillation (AAO), a similar pattern of air pressure and jet stream anomalies in the Southern Hemisphere. Viewed from above either pole, these patterns show a characteristic ring-shape or "annular" pattern; thus, AO and AAO are also referred to as the Northern Annular Mode (NAM) and Southern Annular Mode (SAM), respectively.
Monthly and Daily values for the Arctic Oscillation Index are available from NOAA's Climate Prediction Center.
References
Thompson, D.W.J., S. Lee, and M.P. Baldwin, 2002: Atmospheric Processes Governing the Northern Hemisphere Annular Mode/North Atlantic Oscillation. From the AGU monograph on the North Atlantic Oscillation, 293, 85-89.
Thompson, D.W.J., and J.M. Wallace, 2001: Regional Climate Impacts of the Northern Hemisphere Annular Mode. Science, 293, 85-89.
Thompson, D.W.J., and J.M. Wallace, 2000: Annular modes in the extratropical circulation. Part I: Month-to-month variability. J. Climate, 13, 1000-1016.
Thompson, D.W.J., and J.M. Wallace 1998: The Arctic Oscillation signature in wintertime geopotential height and temperature fields. Geophys. Res. Lett. 25, 1297-1300.
Madden Julian Oscillation:
Climate Variability: Madden-Julian Oscillation (MJO)
Author: Jon Gottschalck
December 31, 2014
While the Madden-Julian Oscillation (MJO) is a lesser-known phenomenon, it can have dramatic impacts in the mid-latitudes. Several times a year the MJO is a strong contributor to various extreme events in the United States, including Arctic air outbreaks during the winter months across the central and eastern portions of the United States.
Imagine ENSO as a person riding a stationary exercise bike in the middle of a stage all day long. His unchanging location is associated with the persistent changes in tropical rainfall and winds that we have previously described as being linked to ENSO. Now imagine another bike rider entering the stage on the left and pedaling slowly across the stage, passing the stationary bike (ENSO), and exiting the stage at the right. This bike rider we will call the MJO and he/she may cross the stage from left to right several times during the show.
So, unlike ENSO, which is stationary, the MJO is an eastward moving disturbance of clouds, rainfall, winds, and pressure that traverses the planet in the tropics and returns to its initial starting point in 30 to 60 days, on average. This atmospheric disturbance is distinct from ENSO, which once established, is associated with persistent features that last several seasons or longer over the Pacific Ocean basin. There can be multiple MJO events within a season, and so the MJO is best described as intraseasonal tropical climate variability (i.e. varies on a week-to-week basis).
The MJO was first discovered in the early 1970s by Dr. Roland Madden and Dr. Paul Julian when they were studying tropical wind and pressure patterns. They often noticed regular oscillations in winds (as defined from departures from average) between Singapore and Canton Island in the west-central equatorial Pacific (Madden and Julian, 1971; 1972; Zhang, 2005).
The MJO consists of two parts, or phases: one is the enhanced rainfall (or convective) phase and the other is the suppressed rainfall phase. Strong MJO activity often dissects the planet into halves: one half within the enhanced convective phase and the other half in the suppressed convective phase. These two phases produce opposite changes in clouds and rainfall and this entire dipole (i.e., having two main opposing centers of action) propagates eastward. The location of the convective phases is often grouped into geographically based stages that climate scientists number 1-8 as shown in Figure 1.
 |
Figure 1: Difference from average rainfall for all MJO events from 1979-2012 for November-March for the eight phases described in the text. The green shading denotes above-average rainfall, and the brown shading shows below-average rainfall. To first order, the green shading areas correspond to the extent of the enhanced convective phase of the MJO and the brown shading areas correspond to the extent of the suppressed convective phase of the MJO. Note eastward shifting of shaded areas with each successive numbered phase as you view the figure from top to bottom.
For the MJO to be considered active, this dipole of enhanced/suppressed convective phases must be present and shifting eastward with time. An animated illustration that depicts the global scale and eastward propagation of these two phases of the MJO is shown here (Fig. 2: animation).
 |
Figure 2. An animation illustrating the organization of the MJO into its enhanced and suppressed convective phases during an MJO event during the spring of 2005. The green shading denotes conditions favorable for large-scale enhanced rainfall, and the brown shading shows conditions unfavorable for rainfall. The MJO becomes organized during late March through May as the green shading covers one half of the planet, and brown shades the other half all along as these areas move west to east with time. Notice how the shading returns to the same location on the order of about 45 days.
Let’s dig a little deeper and look at some of the characteristics within these two convective phases (Figure 3). In the enhanced convective phase, winds at the surface converge, and the air is pushed up throughout the atmosphere. At the top of the atmosphere, the winds reverse (i.e., diverge). Such rising air motion in the atmosphere tends to increase condensation and rainfall.
 |
Figure3: The surface and upper-atmosphere structure of the MJO for a period when the enhanced convective phase (thunderstorm cloud) is centered across the Indian Ocean and the suppressed convective phase is centered over the west-central Pacific Ocean. Horizontal arrows pointing left represent wind departures from average that are easterly, and arrows pointing right represent wind departures from average that are westerly. The entire system shifts eastward over time, eventually circling the globe and returning to its point of origin. Climate.gov drawing by Fiona Martin.
In the suppressed convective phase, winds converge at the top of the atmosphere, forcing air to sink and, later, to diverge at the surface (Rui and Wang, 1990). As air sinks from high altitudes, it warms and dries, which suppresses rainfall.
It is this entire dipole structure, illustrated in Figure 3, that moves west to east with time in the Tropics, causing more cloudiness, rainfall, and even storminess in the enhanced convective phase, and more sunshine and dryness in the suppressed convective phase.
The changes in rainfall and winds described above impact both the Tropics and the Extratropics, which makes the MJO important for extended-range weather and climate prediction over the U.S. and many other areas. The MJO can modulate the timing and strength of monsoons (e.g., Jones and Carvalho, 2002; Lavender and Matthews, 2009), influence tropical cyclone numbers and strength in nearly all ocean basins (e.g., Maloney and Hartmann, 2000), and result in jet stream changes that can lead to cold air outbreaks, extreme heat events, and flooding rains over the United States and North America (Higgins et al. 2000, Cassou, 2008, Lin et al. 2009, Zhou et al., 2012, Riddle et al., 2013, Johnson et al., 2014).
The MJO can produce impacts similar to those of ENSO, but which appear only in weekly averages before changing, rather than persisting and therefore appearing in seasonal averages as is the case for ENSO.
Future posts will focus on the details of how we monitor and assess the strength of the MJO, provide details on impacts and the reasons for those impacts, and describe the current state of MJO predictability. Realtime MJO information that is updated daily or weekly can be found on the NOAA CPC MJO webpage.
Madden R. and P. Julian, 1971: Detection of a 40-50 day oscillation in the zonal wind in the tropical Pacific, J. Atmos. Sci., 28, 702-708.
Madden R. and P. Julian, 1972: Description of global-scale circulation cells in the tropics with a 40-50 day period. J. Atmos. Sci., 29, 1109-1123.
Cassou, C., 2008: Intraseasonal interaction between the Madden Julian Oscillation and the North Atlantic Oscillation. Nature, 455, 523-527 doi:10.1038/nature07286 Letter
Higgins, W., J. Schemm, W. Shi, and A. Leetmaa, 2000: Extreme precipitation events in the western United States related to tropical forcing. J. Climate, 13, 793-820.
Nathaniel C. Johnson, Dan C. Collins, Steven B. Feldstein, Michelle L. L’Heureux, and Emily E. Riddle, 2014: Skillful Wintertime North American Temperature Forecasts out to 4 Weeks Based on the State of ENSO and the MJO*. Wea. Forecasting, 29, 23–38.
Jones, C. and L. Carvalho, 2002: Active and Break phases in the South American Monsoon System. J. Climate, 15, 905-914.
Lavender, S. and A. Matthews, 2009: Response of the West African monsoon to the Madden-Julian Oscillation, J. Climate, 22, 4097-4116.
Maloney E. and D. Hartmann, 2000: Modulation of hurricane activity in the Gulf of Mexico by the Madden-Julian Oscillation. Science, 287, 2002-2004.
Riddle, E. E., M. B. Stoner, N. C. Johnson, M. L. L’Heureux, D. C. Collins, and S. B. Feldstein, 2013: The impact of the MJO on clusters of wintertime circulation anomalies over the North American region. Climate Dyn., 40, 1749–1766.
Zhang, C., 2005: Madden-Julian Oscillation. Reviews of Geophysics, 43, 1-36.
Zhou S., M. L’Heureux, S. Weaver, and A. Kumar, 2012: A composite study of MJO influence on the surface air temperature and precipitation over the Continental United States. Climate Dyn., 38, 1459-1471.
North Atlantic Oscillation:
Climate Variability: North Atlantic Oscillation (NAO)
The North Atlantic Oscillation (NAO) index is based on the surface sea-level pressure difference between the Subtropical (Azores) High and the Subpolar Low. The positive phase of the NAO reflects below-normal heights and pressure across the high latitudes of the North Atlantic and above-normal heights and pressure over the central North Atlantic, the eastern United States and western Europe. The negative phase reflects an opposite pattern of height and pressure anomalies over these regions. Both phases of the NAO are associated with basin-wide changes in the intensity and location of the North Atlantic jet stream and storm track, and in large-scale modulations of the normal patterns of zonal and meridional heat and moisture transport, which in turn results in changes in temperature and precipitation patterns often extending from eastern North America to western and central Europe.
During particularly prolonged periods dominated by one particular phase of the NAO, abnormal height and temperature patterns are also often seen extending well into central Russia and north-central Siberia.
The NAO exhibits considerable interseasonal and interannual variability, and prolonged periods (several months) of both positive and negative phases of the pattern are common.
The NAO index is obtained by projecting the NAO loading pattern to the daily anomaly 500 millibar height field over 0-90°N. The NAO loading pattern has been chosen as the first mode of a Rotated Empirical Orthogonal Function (EOF) analysis using monthly mean 500 millibar height anomaly data from 1950 to 2000 over 0-90°N latitude.
 |
For more information, please visit the NCEI and Climate Prediction Center NAO pages.
Past Winters:
Below are the temperature, precipitation, and snow data for La Crosse, WI, and Rochester, MN.
|
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Caption (source) |
Caption (source) |
Caption (source) |
Caption (source) |
 |
Media use of NWS Web News Stories is encouraged! Please acknowledge the NWS as the source of any news information accessed from this site. |
|
Follow us on Twitter
Follow us on Facebook
Follow us on YouTube
ARX RSS Feed
![[Fig. 1]](https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_advisory/figure01.gif){kind=link}
![[Fig. 2]](https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_advisory/figure02.gif){kind=link}
![[Fig. 3]](https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_advisory/figure03.gif){kind=link}
![[Fig. 4]](https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_advisory/figure04.gif){kind=link}
![[Fig. 5]](https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_advisory/figure05.gif){kind=link}
![[Fig. 6]](https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_advisory/figure06.gif){kind=link}
![[Fig. 7]](https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_advisory/figure07.gif){kind=link}