bluewave

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

Blocking with closed lows this time of year usually means rainfall opportunities.

Maybe the blocking allowed for heavier rains around NYC instead of all the 2.00”+ amounts going NE. But the models like the Euro and RGEM did a good job indicating that there would be 2.00”+ amounts where the best convection set up.

That’s a pretty good method to use for determining the strongest the El Niño can get from the May reading especially during the strongest years. It’s probably why model forecasts issued after the spring are more reliable. I filled in some of the May to fall-winter monthly peaks in Nino 3.4 with El Niño years. The maximum range was in 2015 at +0.57C. But years that finish in the weak, moderate, and strong range usually need more than just the May Nino 3.4 reading to guess the final number. We saw the uncertainties in years like 14-15 and 09-10. So at least we can venture a guess at what the higher range might be rather than the lower. https://www.cpc.ncep.noaa.gov/data/indices/ersst5.nino.mth.91-20.ascii Super years bolded 2015-05….28.85…..+0.92…..2015-11….29.42…+2.72…super + 0.57 increase 2014-05….28.25……+0.32…..2014-11…27.46….+0.75…weak 2009-05…27.99…….+0.06….2009-12…28.34….+1.74…strong 2006-05…27.85……-0.09……2006-12...27.74…..+1.14… moderate 2004-05….28.00..+0.06……..2004-12…27.34…..+0.74…weak 2002-05…28.24….+0.31…….2002-11…..28.17……+1.47…. moderate 1997-05….28.58…..+0.64……1997-11…..29.12……+2.41…super 0.54 increase 1994-05….28.24…+0.31……..1994-12….27.85…….+1.25…moderate 1991-05….28.20….+0.26…….1992-02….28.53…….+1.78…strong 1987-05…28.56….+0.62…….1987-09…..28.36…….+1.65…strong 1986-05…27.40…..-0.53…….1987-02……27.88…..+1.13…..moderate 1982-05….28.39...+0.50…….1983-01…….28.89…+2.35….super +0.50 increase 1972-05….28.32….+0.38……1972-12…….28.69…..+2.09….super +0.37 increase

April has never averaged warmer than May before. But the monthly max has on a few occasions. Too difficult these days to get a May under 60° which would be necessary. Monthly Mean Avg Temperature for NEWARK LIBERTY INTL AP, NJ Click column heading to sort ascending, click again to sort descending. Year Apr May Season 2023 59.0 M 59.0 2010 57.9 66.2 62.1 1994 57.4 63.7 60.6 2017 57.2 61.1 59.2 1985 57.0 67.1 62.1 1974 56.5 62.7 59.6 1941 56.2 63.1 59.7 2002 56.0 60.9 58.5 2006 55.7 63.8 59.8 2011 55.5 65.6 60.6 Monthly Highest Max Temperature for NEWARK LIBERTY INTL AP, NJ Click column heading to sort ascending, click again to sort descending. Year Apr May Season 2002 97 90 97 1990 94 83 94 2023 93 M 93 2009 93 87 93 1976 93 83 93 2010 92 95 95 1994 92 95 95 1974 91 94 94 1960 91 83 91 1942 91 93 93 1941 91 95 95 1977 90 91 91 1962 90 98 98 Time Series Summary for NEWARK LIBERTY INTL AP, NJ - Month of May Click column heading to sort ascending, click again to sort descending. Year Mean Avg Temperature Missing Count 2022 66.2 0 2021 64.3 0 2020 60.8 0 2019 63.5 0 2018 66.9 0 2017 61.1 0 2016 62.6 0 2015 68.2 0 2014 64.1 0 2013 63.3 0 2012 66.4 0 2011 65.6 0 2010 66.2 0 2009 63.3 0 2008 60.5 0 2007 65.1 0 2006 63.8 0 2005 59.1 0 2004 66.3 0 2003 58.9 0 2002 60.9 0 2001 64.0 0 2000 64.2 0 1999 63.2 0 1998 64.9 0 1997 59.2 0 1996 61.6 0 1995 62.7 0 1994 63.7 0 1993 67.0 0 1992 61.6 0 1991 68.9 0

It seems related to the MJO activity since early March. So it looks like Nino 1.2 has already peaked for the present time. So not sure how this will affect the overall development going forward. It will all come down to how the WWBs respond after the current pick up in the trades. The subsurface has also become less impressive in Nino 1.2 the last few weeks.

Lowest -AO since last December. https://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/norm.daily.ao.nao.pna.aao.gdas.120days.csv 21Apr2023-3.4547

Several more freezes possible for the higher elevations next week.

Less ice means more lake effect snow for the Great Lakes. https://glisa.umich.edu/resources-tools/climate-impacts/great-lakes-ice-coverage/ The number of days per winter with lake ice coverage has declined since the start of record in 1973. 1 In most areas, ice cover declines were a sudden shift as opposed to a gradual decline. For Lakes Michigan, Erie, and Ontario the shift occurred in the mid-1980s, but for Lakes Superior and Huron the shift occurred during the 1997/98 winter. 2 3 Ice cover has decreased the most in the north (i.e., Lake Superior, Northern Lake Michigan and Huron) and in coastal areas Ice cover on the Great Lakes will likely continue to decrease in the future, however, these decreases are expected to be interrupted by high-ice winters associated with cold air outbreaks. Reduced ice cover results in more winter lake-effect precipitation and increased winter wave activity. https://glisa.umich.edu/resources-tools/climate-impacts/lake-effect-snow-in-the-great-lakes-region/ Overall, snowfall has increased in northern lake-effect zones in the Great Lakes basin even as snowfall totals in Illinois, Indiana, and Ohio have declined with rising temperatures. Warmer Great Lakes surface water temperatures and declining Great Lakes ice cover have likely driven the observed increases in lake-effect snow.

Yeah, most of the rise has been since 1980 as emissions rapidly increased and aerosols declined. But you can see how it’s been an uneven rate of warming across the US. Places like BTV other locations around the Northeast have seen a faster rate of increase. Also a faster rate of increase around International Falls and slower to the SW over South Dakota. There has been a localized slowing of the rate across the corn belt especially with high temperatures. This is due to the rapid expansion of agriculture and associated irrigation. So it has prevented that region from experiencing the peak summer highs during the dust bowl. https://site.extension.uga.edu/climate/2021/01/how-temperature-and-precip-have-changed-over-the-past-60-years-by-county/ https://www.nature.com/articles/s41467-020-16676-w Model devegetation simulations, that represent the wide-spread exposure of bare soil in the 1930s, suggest human activity fueled stronger and more frequent heatwaves through greater evaporative drying in the warmer months. This study highlights the potential for the amplification of naturally occurring extreme events like droughts by vegetation feedbacks to create more extreme heatwaves in a warmer world.

Looks like only slow warming in 3.4 for the present time. These WWBs west of the Dateline have been much weaker so far than the years which had a trimonthly peak of +2.0 or greater. Notice how much stronger the trades have been than 72, 82,97, and 15. So the subsurface is much less impressive as GaWx posted a while back. Nino 1+2 is much warmer than the super years at this time but 3.4 and 4 is cooler. Need the WWBs west of the Dateline to increase to get to a Nino 3.4 +1.5 strong level. But we saw how years like 09 that got off to a later start in summer still made it to +1.6. The big question is how the WWBs will respond going forward as the models don’t really handle them well beyond 8-15 days. That’s one reason the model forecasts have been biased warm with the exception of 2016 with recent El Niño forecasts.