bluewave

The early evolution suggests east based or east based transitioning to basin wide. So that would probably favor another warmer than average winter. Could be looking at a record 9 warmer winers in a row since 15-16. Don’t think we ever had a cold modoki El Niño with well above normal snowfall begin this east based before. But moderate to stronger basin wide or east based El Niño’s have had variable snowfall. Generally below normal to normal with a few snowier seasons in the mix. A higher end El Niño could have a big impact on global climate after the 15-16 super El Niño represented a big step up in global temperatures.

The only certainty with our winters since 09-10 has been no specific analogs from before this era have been useful for a seasonal forecast due to changes in the global climate.

It also looks like the anomalous warm pool south of Hawaii may have played a role. Notice the classic La Niña VP anomalies north of Australia. But the forcing south of Hawaii has more of a Nino-like look. So the trough and STJ near California were enhanced producing a local Nino-like effect. But SE Ridge in the Eastern US was pure La Niña.

April finished 3 warmest behind 1998 and 1983. But those months were at the tail end rather than the beginning of the event. So this is the first time Nino 1+2 was this warm during a spring ahead of an El Niño. https://psl.noaa.gov/enso/dashboard.html

They had more breaks of sun than some of the surrounding sites. You can see them running close on the high for the month so far. Each day has been slightly different with cloud over from station to station with the cold pool. Data for May 1, 2023 through May 4, 2023 Click column heading to sort ascending, click again to sort descending. Name Station Type Highest Max Temperature ESTELL MANOR COOP 65 NEWARK LIBERTY INTL AP WBAN 64 HARRISON COOP 64 Newark Area ThreadEx 64 HIGHTSTOWN 2 W COOP 64 PHILADELPHIA/MT. HOLLY WFO COOP 63 Atlantic City Area ThreadEx 63 EB FORSYTHE NEW JERSEY RAWS 63 PENNSAUKEN 1N COOP 63 New Brunswick Area ThreadEx 63 MARGATE COOP 63 MILLVILLE MUNICIPAL AIRPORT WBAN 63 ATLANTIC CITY INTL AP WBAN 63 SEABROOK FARMS COOP 63 LONG BRANCH-OAKHURST COOP 63 FREEHOLD-MARLBORO COOP 63 NEW BRUNSWICK 3 SE COOP 63

The April Central Pacific Trade wind index still came in positive. So we are closer to the El Niño years which took longer for the trades to relax. Developing El Niño Aprils https://www.cpc.ncep.noaa.gov/data/indices/cpac850 850 MB TRADE WIND INDEX(175W-140W)5N 5S CENTRAL PACIFIC ANOMALY 2023….+1.1 2018…..+1.7 2015….-1.6 2014….-0.1 2009….+1.2 2006…+1.5 2004…..+1.2 2002….+0.4 1997….-1.8 1994...+0.5 1991…+0.5 1986….+0.9 1982….-1.5.

Maybe some more small hail today with the near record cold 850mb temperatures.

The subsurface favors this remaining east based warming for the current time. So 1+2 should probably remain near or above +2. Models really can’t reliably forecast long range whether this remains east based or becomes more basin wide. Trades would need to relax near the Dateline for 3.4 to begin to warm above neutral. That’s why Nino 3.4 is still neutral while 1+2 is over +2. The 16 and 97 events were approaching moderate in 3.4 when they first went above +2 in 1+2. Since 1+2 is such a small area, need Nino 3 to also warm closer to +2 to impact the actual forcing. East based forcing usually takes 1+2 and 3 to be in tandem. Still to early to know how this plays out since we haven’t seen the trades relax near the Dateline yet. That’s what is needed for 3.4 to get to moderate to strong levels by the fall. The subsurface below 3.4 is weaker than the last 2 super events in 2015 and 1997. Those years were already producing strong WWBs near the Dateline by March. But the upper ocean temperature anomalies from 180-100W are still impressive for this time of year. https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ocean/index/heat_content_index.txt Equtorial Upper 300m temperature Average anomaly based on 1981-2010 Climatology (deg C) YR MON 180W-100W April 2023…+1.20 April 2015….+1.74 April 2014…..+1.41 April 1997……+2.17

Looks like a small hail sounding with some CAPE and low freezing level.

The 30 year means keep going up every 10 years so it takes a smaller departure to finish near the top. https://www.ncei.noaa.gov/access/us-climate-normals/#dataset=normals-monthly&timeframe=30&location=NY&station=USW00094728 https://www.ncdc.noaa.gov/IPS/lcd/lcd.html;jsessionid=ED181C50757CEAA76F64228C9949C424?_page=0&state=NY&_target1=Next+> Time Series Summary for NY CITY CENTRAL PARK, NY - Month of Apr Click column heading to sort ascending, click again to sort descending. Rank Year Mean Avg Temperature Departure 1 2010 57.9 +5.4 2 2023 57.6 +3.9 3 2017 57.2 +4.2 4 1941 56.8 +6.7 5 2002 56.1 +3.6 - 1981 56.1 +4.1 - 1921 56.1 +6.1

Heaviest thunderstorms in a while here.

Yeah, need the trades to relax near the Dateline for the El Niño to fully develop. Unusually warm SSTs from IO to WPAC may be playing a role. So still a big disconnect between Nino 3.4 and 1.2. When Nino 1.2 first warmed to +2 to +3 in 1997, Nino 3.4 was +0.5 to +1.0. This time, Nino 3.4 is still neutral due to the stronger trades and a much less impressive subsurface.

Yeah, pretty far west for a developing El Niño this time of year. So the subsurface below Nino 3.4 is much less impressive than the super years like 97-98 and 15-16. But Nino 1+2 is near record levels for April. Sustained Nino 1+2 values above +2 usually are accompanied by more warming in 3.4 than we have currently seen. So no analogs at all for this type of El Niño evolution both in regard to the WWB activity and faster 1+2 warming. Tough to guess how this will ultimately end up in terms of strength and location of warmest anomalies. https://psl.noaa.gov/enso/dashboard.html https://www.cpc.ncep.noaa.gov/data/indices/

Very close..

Near record upper low for this time of year.

January 15th to February 15th 2016 was probably the closest we got to super modoki forcing with the record Nino 4 SSTs. But it wasn’t as far west as the 09-10 true modoki. 97-98 was our last super El Nino with east based forcing.

Tough to verify day 10 snowstorms during the winter when that warm pool ridge east of New England keeps popping up the closer in we get. Someone at one of the weather model centers should come here and discuss this frequent model error. The GFS has had even more of a cold bias than the Euro at day 10. So the long range GFS has even more virtual snow than the Euro.

It had already migrated those years due to the much stronger CP WWBs. So we continue to get moderate WWBs pretty far west of the Dateline alternating with periods of stronger trades like we are getting now. This results in a slower surface warming around 3.4. The lack of any stronger EPAC WWBs like we had in March has allowed 1.2 cool a bit relative to what it was. So the El Niño development will come down to how strong the WWBs can get near the Dateline. Since there is only so much 1.2 warming can do without being connected to a stronger subsurface signature and WWB pattern further west.

Why Adjust Temperatures? There are a number of folks who question the need for adjustments at all. Why not just use raw temperatures, they ask, since those are pure and unadulterated? The problem is that (with the exception of the newly created Climate Reference Network), there is really no such thing as a pure and unadulterated temperature record. Temperature stations in the U.S. are mainly operated by volunteer observers (the Cooperative Observer Network, or co-op stations for short). Many of these stations were set up in the late 1800s and early 1900s as part of a national network of weather stations, focused on measuring day-to-day changes in the weather rather than decadal-scale changes in the climate. Nearly every single station in the network in the network has been moved at least once over the last century, with many having 3 or more distinct moves. Most of the stations have changed from using liquid in glass thermometers (LiG) in Stevenson screens to electronic Minimum Maximum Temperature Systems (MMTS) or Automated Surface Observing Systems (ASOS). Observation times have shifted from afternoon to morning at most stations since 1960, as part of an effort by the National Weather Service to improve precipitation measurements. All of these changes introduce (non-random) systemic biases into the network. For example, MMTS sensors tend to read maximum daily temperatures about 0.5 C colder than LiG thermometers at the same location. There is a very obvious cooling bias in the record associated with the conversion of most co-op stations from LiG to MMTS in the 1980s, and even folks deeply skeptical of the temperature network like Anthony Watts and his coauthors add an explicit correction for this in their paper. Time of observation changes from afternoon to morning also can add a cooling bias of up to 0.5 C, affecting maximum and minimum temperatures similarly. The reasons why this occurs, how it is tested, and how we know that documented time of observations are correct (or not) will be discussed in detail in the subsequent post. There are also significant positive minimum temperature biases from urban heat islands that add a trend bias up to 0.2 C nationwide to raw readings. Because the biases are large and systemic, ignoring them is not a viable option. If some corrections to the data are necessary, there is a need for systems to make these corrections in a way that does not introduce more bias than they remove. What are the Adjustments? Two independent groups, the National Climate Data Center (NCDC) and Berkeley Earth (hereafter Berkeley) start with raw data and use differing methods to create a best estimate of global (and U.S.) temperatures. Other groups like NASA Goddard Institute for Space Studies (GISS) and the Climate Research Unit at the University of East Anglia (CRU) take data from NCDC and other sources and perform additional adjustments, like GISS’s nightlight-based urban heat island corrections. Time of Observation (TOBs) Adjustments Temperature data is adjusted based on its reported time of observation. Each observer is supposed to report the time at which observations were taken. While some variance of this is expected, as observers won’t reset the instrument at the same time every day, these departures should be mostly random and won’t necessarily introduce systemic bias. The major sources of bias are introduced by system-wide decisions to change observing times, as shown in Figure 3. The gradual network-wide switch from afternoon to morning observation times after 1950 has introduced a CONUS-wide cooling bias of about 0.2 to 0.25 C. The TOBs adjustments are outlined and tested in Karl et al 1986 and Vose et al 2003, and will be explored in more detail in the subsequent post. The impact of TOBs adjustments is shown in Figure 6, below. Pairwise Homogenization Algorithm (PHA) Adjustments The Pairwise Homogenization Algorithm was designed as an automated method of detecting and correcting localized temperature biases due to station moves, instrument changes, microsite changes, and meso-scale changes like urban heat islands. The algorithm (whose code can be downloaded here) is conceptually simple: it assumes that climate change forced by external factors tends to happen regionally rather than locally. If one station is warming rapidly over a period of a decade a few kilometers from a number of stations that are cooling over the same period, the warming station is likely responding to localized effects (instrument changes, station moves, microsite changes, etc.) rather than a real climate signal. To detect localized biases, the PHA iteratively goes through all the stations in the network and compares each of them to their surrounding neighbors. It calculates difference series between each station and their neighbors (separately for min and max) and looks for breakpoints that show up in the record of one station but none of the surrounding stations. These breakpoints can take the form of both abrupt step-changes and gradual trend-inhomogenities that move a station’s record further away from its neighbors. The figures below show histograms of all the detected breakpoints (and their magnitudes) for both minimum and maximum temperatures. While fairly symmetric in aggregate, there are distinct temporal patterns in the PHA adjustments. The single largest of these are positive adjustments in maximum temperatures to account for transitions from LiG instruments to MMTS and ASOS instruments in the 1980s, 1990s, and 2000s. Other notable PHA-detected adjustments are minimum (and more modest maximum) temperature shifts associated with a widespread move of stations from inner city rooftops to newly-constructed airports or wastewater treatment plants after 1940, as well as gradual corrections of urbanizing sites like Reno, Nevada. The net effect of PHA adjustments is shown in Figure 8, below. The PHA has a large impact on max temperatures post-1980, corresponding to the period of transition to MMTS and ASOS instruments. Max adjustments are fairly modest pre-1980s, and are presumably responding mostly to the effects of station moves. Minimum temperature adjustments are more mixed, with no real century-scale trend impact. These minimum temperature adjustments do seem to remove much of the urban-correlated warming bias in minimum temperatures, even if only rural stations are used in the homogenization process to avoid any incidental aliasing in of urban warming, as discussed in Hausfather et al. 2013. The PHA can also effectively detect and deal with breakpoints associated with Time of Observation changes. When NCDC’s PHA is run without doing the explicit TOBs adjustment described previously, the results are largely the same (see the discussion of this in Williams et al 2012). Berkeley uses a somewhat analogous relative difference approach to homogenization that also picks up and removes TOBs biases without the need for an explicit adjustment. With any automated homogenization approach, it is critically important that the algorithm be tested with synthetic data with various types of biases introduced (step changes, trend inhomogenities, sawtooth patterns, etc.), to ensure that the algorithm will identically deal with biases in both directions and not create any new systemic biases when correcting inhomogenities in the record. This was done initially in Williams et al 2012 and Venema et al 2012. There are ongoing efforts to create a standardized set of tests that various groups around the world can submit homogenization algorithms to be evaluated by, as discussed in our recently submitted paper. This process, and other detailed discussion of automated homogenization, will be discussed in more detail in part three of this series of posts.

Do you have the subsurface charts for this week during other developing El Niño years? I know the subsurface is well behind the some of strongest El Niño years at this point. The 180-100 west reading is around +1 this month. 2015 was +1.74 and 1997 was +2.17. https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ocean/index/heat_content_index.txt

The Pine Barrens is a great location for radiational cooling. But the temperature patterns are the same there across the winter months as around NYC. Faster warming in December and February than January. BNL https://www.bnl.gov/weather/4cast/extremes.php https://www.bnl.gov/weather/4cast/monthlymeantemps.htm Average temperatures December 1951-1980….32.8…..1991-2020…..36.2….+3.4 January……1951-1980….28.2….1991-2020……30.3…..+2.1 February…..1951-1980….29.2….1991-2020……32.5…..+3.3 Minimum temperatures January 1951-1980…19.1…1991-2020…22.4..+3.3