Monitoring and Prediction of Climate Extremes

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1 Monitoring and Prediction of Climate Extremes Stephen Baxter Meteorologist, Climate Prediction Center NOAA/NWS/NCEP Deicing and Stormwater Management Conference ACI-NA/A4A Arlington, VA May 19, 2017

2 What is Climate? In short: time-averaged weather We are interested in the total distribution of a variable over some period of time. A typical temperature distribution at a given point might look like this: Mean Tails

3 Why do we care? An understanding of climate tells one what to reasonably expect as a function of location and time of year. However, for some periods (weeks, months, etc.), the distribution of a given meteorological value is different than the long-term climatology. This is climate variability. The tendency for some periods to prefer a certain weather regime over another has societal ramifications.

4 CPC Mission Deliver real-time products and information that predict and describe climate variations on timescales from weeks to year(s) thereby promoting effective management of climate risk and a climate-resilient society. Focus: weeks, months, seasons, years (i.e. short term climate) Valuable resource for NOAA s efforts to deliver climate services Provides strong name recognition in international efforts Temperature Outlook

5 Climate Prediction Products Focus on Week-2 to seasonal-tointerannual 6-10 Day & 8-14 Day Precipitation & Temperature Outlooks Day 3-14 Hazards Outlooks (US, Global Tropics) Monthly & Seasonal Precipitation & Temperature Outlooks Seasonal Drought Outlook Seasonal Hurricane Outlooks (Atlantic and Eastern Pacific) Monthly ENSO Prediction Week 3-4 Temperature/ Precipitation Week 2 Heat Outlook Arctic Sea Ice Forecasts Tools used to develop prediction products Dynamical Models Statistical Models Analogs Canonical Correlation Analysis Regression * Dynamical Models Climate Forecast System Global Forecast System ECMWF

6 Climate Monitoring Products Daily and monthly data, time series, and maps for various climate parameters and compilation of data on historical and current atmospheric and oceanic conditions Primary modes of climate variability (ENSO, MJO, NAO, PNA, AO,...) Atmospheric Circulation (global troposphere and stratosphere) Storm Tracks and Blocking Monsoons Oceanic Conditions (global and coastal) Precipitation and Surface Temperature (global and US) Drought (US, North America; NIDIS) Climate Reanalysis

7 Patterns of Variability Climate variability can be decomposed into various leading patterns. These patterns help form building blocks from which the observed climate can be reconstructed. Each of these patterns should correspond to some physical mechanism within the Earth-ocean-atmosphere system. Some prominent examples on monthly/seasonal timescales: El Niño/La Niña (ENSO) North Atlantic/Arctic Oscillation (NAO/AO) North Pacific Oscillation/West Pacific pattern (NPO/WP)

8 El Niño-Southern Oscillation ENSO provides a good deal of predictable signal due to its longevity and well-understood atmospheric response. However, the climate footprint is somewhat muted over North America, and tends to be most reliable in the strongest warm events. The precipitation signal over the Southeast is known to be among the most reliable ENSO teleconnections. (The last two winters not withstanding?)

9 Winters of and These were ENSO events of nearly identical magnitude, but with vastly different outcomes over the U.S.

10 Precipitation Despite large differences in the temperature anomalies, the precipitation patterns are fairly comparable.

11 The Last Two Winters The last two winters featured opposite phases of ENSO (near record El Niño in , weak La Niña in ). The difference between the two is somewhat expected based on that ENSO difference.

12 Precipitation The difference in precipitation between the last two winters is generally not what one would expect given ENSO differences.

13 NAO/AO Measures the relative strength of the polar vortex or the Icelandic Low. In its negative phase, the average westerly winds in the midlatitudes are weaker, and there is more north-south transport of air masses. The best recent example of this is the winter. Going a bit farther back, the winter is another prominent example.

14 Winters of and

15 NPO/WP Similar to the NAO, but over the North Pacific. Negative phase is characterized by anomalously high pressure over the Bering Strait. This leads to the advection of Siberian air into North America. This figure highlights the role of the NPO/WP in the winter.

16 Winters of and

17 U.S. Cold Season Energy Use What if we defined the impact, then looked for associated atmospheric variability? Using population-weighted heating degree days over the contiguous U.S., then regressing onto the atmospheric mid-level circulation, yields interesting results:

18 Latest Seasonal Outlook for DJF Utah Below: 12% Near: 33% Above: 55% S. Texas Below: 24% Near: 33% Above: 43% Maine Below: 33% Near: 33% Above: 33% S. Texas Below: 31% Near: 33% Above: 36%

19 Long Range Forecast Tools At this long lead, the most reliable signal is El Niño. Slowly evolving sea-surface temperatures across the Northern Hemisphere may also be helpful, along with any decadal/secular trends.

20 Conflicting Signals El Niño footprint Long-term trends

21 Summary Prediction of climate extremes is possible based on the shift of the climatological distribution away from normal, increasing the space in the tails of the distribution. At leads of greater than 6 months, ENSO is the best predictor, along with decadal/secular trends. At shorter leads, some speculation about the mean state of the NAO/AO may be possible based on recent research. This is closely related to ongoing debate about the role of snow cover and sea ice in midlatitude winter climate. There is also ongoing research regarding the seasonal predictability of midlatitude variability in the North Pacific, unrelated to ENSO.

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