bluewave

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Fairly typical 2010’s June for the temperature departures. We will finish with a very small + departure. These repeating monthly patterns allow for easier seasonal forecasting. The typical 2010’s winter starts out with a warm December. This is followed by a colder start to spring in March. Then a cooler beginning to summer during June. Fall gets off to a slow start with summer heat lingering heat into September and October. November can be the one cool fall month. June...EWR....NYC....LGA 2019....0.0.......0.0.......0.3....so far 2018...-0.3.....+0.6.....+0.7 2017..+0.3......+0.6....+1.4 2016..+0.5......+0.9....+1.4 2015...-0.4......-0.2.....-0.9 2014...+0.4.....+1.1....+0.3 2013...+0.9.....+1.3...+1.8 2012.....0.0.....-0.4....+0.7 2011...+2.1.....+0.9...+0.5 2010...+3.8....+3.3....+3.8

I think Europe will finally adopt air conditioning now. https://www.washingtonpost.com/world/2019/06/28/europes-record-heatwave-is-changing-stubborn-minds-about-value-air-conditioning/?utm_term=.ea277d2c3a0d Until now, fewer than 5 percent of all European households have been air-conditioned, compared with 90 percent in the United States. But Europe’s air-conditioner stock is estimated to roughly double within the next two decades, according to the International Energy Agency (IEA), as record heat becomes more frequent and prolonged because of climate change. On a continent that has long shrugged off air conditioning as unnecessary and where doctors still debate its potentially harmful side effects, this week’s quest to find cooler air may foreshadow a drastic change in Europeans’ relationship to the air conditioner. Dirk Trembich, the head of the Berliner Klima air-conditioning company, said interest began to surge in April 2018 — ahead of a record-hot summer. Demand still hasn’t faded.

They needed a clearing away from any trees for a proper weather station sitting. Too bad there wasn’t an open area in the park for NYS Mesonet to set up a new station. Proper sitting NYC incorrect sitting under trees

Not enough sunlight is getting through to the NYC ASOS. Someone should take some new photos. The last batch was posted 6 years ago and it was already in deep shade. http://www.weather2000.com/ASOS/NYC_ASOS.html

The sea ice north of Greenland is projected to be the last to go. https://ccin.ca/ccw/seaice/future

Yeah, the SAL appears to have enhanced the record heat potential. https://weather.com/forecast/regional/news/2019-06-27-florida-record-heat-june

The air mass originated over the Sahara desert. But it was locally enhanced by a downslope flow. https://mobile.twitter.com/mikarantane/status/1144192987003523072 https://mobile.twitter.com/mikarantane/status/1144592574449115137

Very impressive dipole pattern continues on the models into early July. Extent has pulled slightly ahead of 2012. Overall 2nd place for the date behind 2010. 2010.....9.501 2019.....9.660 2016.....9.665 2012.....9.678

Looks like Carpentras just broke the French national record. This is even more remarkable coming so early in the season. The previous record occurred in August 2003. https://mobile.twitter.com/mikarantane/status/1144582141675786241 https://mobile.twitter.com/EKMeteo

The real record breaking heat this June turned out to be near San Francisco and Miami. Our last June with monthly record heat was 2017 when LGA tied the June record high of 101. Time Series Summary for LA GUARDIA AP, NY - Month of Jun Click column heading to sort ascending, click again to sort descending. Rank Year Highest Max Temperature Missing Count 1 2017 101 0 - 1952 101 0 3 2008 100 0

The most impressive part of this late June heatwave was to our south. Miami tied their June highest temperature of 98 degrees. Time Series Summary for MIAMI INTERNATIONAL AP, FL - Month of Jun Click column heading to sort ascending, click again to sort descending. Rank Year Highest Max Temperature Missing Count 1 2019 98 4 - 2009 98 0 - 1985 98 0 - 1944 98 0 https://mobile.twitter.com/JohnMoralesNBC6/status/1144016117746327554?ref_src=twsrc^tfw|twcamp^tweetembed|twterm^1144016117746327554&ref_url=https%3A%2F%2Fwww.washingtonpost.com%2Fweather%2F2019%2F06%2F26%2Ftemperature-records-are-melting-away-miami%2F Unprecedented. That's how I can best describe the #heatwave in #Miami. Four records in 4 days (asterisks). Ten heat records this month. And setting the new record for highest number of days of 95°+ in the first half of a calendar year (8). The old record was 6. #climate

Quick 15 degree temperature jump to 80 as the marine layer clears out. http://wxweb.meteostar.com/meteogram/link.shtml?choice=Kmtp

That marine layer just doesn’t want to give up along the New England coast. A lingering piece of the pattern which resulted in our delayed start to summer. Eastern LI is still in the soup with MTP at only 65 degrees. https://www.wrh.noaa.gov/mesowest/getobext.php?sid=KMTP&table=1&num=168&banner=off

You can see why the Arctic Basin is at record low levels of extent. Blocking and record warmth focused over the Pacific sector. This is the opposite of 2012 when the harshest conditions were centered closer to the Atlantic regions. While 2019 has an Arctic Basin lead over 2012, the 2012 Atlantic extent was low enough to maintain a small overall advantage. NSIDC extent 6-26-19....9.819 6-26-12....9.712

June 1973? https://www.ncdc.noaa.gov/stormevents/eventdetails.jsp?id=10073922

5th latest day in the season to go above 90 degrees at Newark. 8th latest first day to reach 90 degrees at LGA. You can thank all the rain and the -NAO for the hold up. First/Last Summary for NEWARK LIBERTY INTL AP, NJ Each section contains date and year of occurrence, value on that date. Click column heading to sort ascending, click again to sort descending. Year First Value Last Value Difference 1935 07-11 (1935) 91 08-01 (1935) 92 20 1982 07-08 (1982) 94 07-26 (1982) 95 17 1972 07-02 (1972) 91 09-17 (1972) 93 76 1947 07-01 (1947) 92 08-26 (1947) 94 55 2019 06-26 (2019) 91 - - - First/Last Summary for LA GUARDIA AP, NY Each section contains date and year of occurrence, value on that date. Click column heading to sort ascending, click again to sort descending. Year First Value Last Value Difference 1978 07-21 (1978) 91 08-17 (1978) 90 26 1972 07-19 (1972) 90 09-17 (1972) 90 59 1985 07-14 (1985) 94 09-06 (1985) 92 53 1947 07-14 (1947) 90 08-26 (1947) 92 42 1960 07-09 (1960) 90 09-01 (1960) 90 53 1982 07-08 (1982) 93 07-26 (1982) 93 17 2014 07-02 (2014) 91 09-06 (2014) 91 65 1958 07-01 (1958) 93 08-15 (1958) 92 44 2019 06-26 (2019) 90 - - -

They should have left the thermometer up on the castle where it had been before 1996. Moving from the direct sunlight into the shade beneath the canopy created an artificial high temperature cooling trend. Notice how it’s the only station with a declining 90 degree day count since 1980 among EWR, LGA, and NYC.

First day above 85 at Islip since May 26th.

Well defined sea breeze front pushing north on Long Island.

2018 set the record for the most days with measurable rainfall in NYC. 2019 is currently in 3rd place for Jan-Jun. Time Series Summary for NY CITY CENTRAL PARK, NY - Jan through Dec Click column heading to sort ascending, click again to sort descending. Rank Year Number of Days Precipitation >= .01 Missing Count 1 2018 158 0 2 1996 152 0 3 1972 145 0 4 2003 142 0 5 2008 141 0 - 1950 141 0 Time Series Summary for NY CITY CENTRAL PARK, NY Click column heading to sort ascending, click again to sort descending. Rank Ending Date Number of Days Precipitation >= .01 Jan 1 to Jun 30 Missing Count 1 1950-06-30 84 0 2 1972-06-30 81 0 3 2019-06-30 79 5

Pretty impressive for NYC to have 10 months with over 5.00” of precipitation since February 2018. 2018 2.18 5.83 5.17 5.78 3.53 3.11 7.45 8.59 6.19 3.59 7.62 6.51 65.55 2019 3.58 3.14 3.87 4.55 6.82 5.32 M M M M M M 27.28

They were both the strongest of their class for the South Shore of Nassau. An intense line of severe thunderstorms oriented from north to south developed during Labor Day afternoon ahead of a strong approaching cold front. As the storms moved east at 40 to 50 mph, they produced high winds, large hail, and an isolated tornado. Wind gusts from 60 to 80 mph downed many trees and power lines throughout the area. The cost estimates of damage included above are preliminary figures submitted by the Nassau County Office of Emergency Management. In Richmond County, the following peak wind gusts were reported: 80 mph at Great Kills, the Verranzano Bridge, and in Richmond. High winds downed trees and caused a building to collapse in Richmond. One tree fell on and injured a man in Richmond. In New York County (Manhattan), high winds caused a building to collapse. In the Bronx, high winds downed a tree that fell on 3 people resulting in 1 death and 2 injuries in the courtyard of the Edenwald Houses at 1135 East 229th Street. In Kings County (Brooklyn), high winds downed and uprooted several large trees. One tree fell on and injured a person at East 229th Street. Five to 6-foot diameter trees were uprooted east of Coney Island in the Gerritsen Beach Section, where 3 funnel clouds were also sighted and a firefighter was injured from large hail. Large trees also fell on and damaged cars in Bensonhurst. In Queens County, a peak wind gust of 62 mph was measured at both LaGuardia Airport and at JFK Airport. In Nassau County, the following peak wind gusts were reported: 75 mph in Farmingdale, 60 mph in Port Washington and Mineola and 58 mph at Farmingdale Republic Airport. High winds downed large tree limbs at Rockville Center, Baldwin, and Oceanside and downed trees in Long Beach, Massapequa, and Valley Stream. One-inch diameter hail dented cars and covered the ground in Farmingdale. In Suffolk County, high winds overturned many boats in the Great South Bay, downed large trees in West Babylon and Rocky Point and downed large tree limbs in Wading River. One person died when a thunderstorm wind gust capsized a 19 foot sail boat in Great South Bay near Copiague. A Centerport woman, 36, and her daughter, 3, were injured when a tree fell on them in the parking lot of the Ground Round Restaurant and CVS on Fort Salonga Road. The following peak wind gusts were reported: 72 mph in Babylon and 65 mph in Fire Island. The NWS confirmed that an F2 tornado was responsible for significant damage that occurred in Lynbrook. Most of the village received damage from straight line winds up to 80 mph, that was associated with a severe squall line. Downed trees covered the village with some structural damage where the F2 tornado touched down. The major path of damage was from the northwest section of Lynbrook east-southeast to the southeast section of the village. Funnel clouds were observed from near the intersection of Marshall Ave. and Burtis Street and to the southeast. A tornado was first sighted by two eyewitnesses on Hampton Place. It rose and touched down several times: Second, near Winter Street and across Glover Circle; Third, along Peninsula Blvd. between Earle and Benton Avenues; and Fourth, as a weak F2 near the intersection of Rocklyn Ave. and Merrick Road. It moved across the Long Island Railroad Tracks and Sunrise Highway before it finally dissipated. More than three hundred trees were blown over, many on houses and cars. Six people received minor injuries. Four of these were in "The Fun Zone" on Rocklyn Avenue. One woman was slightly injured by a tree that fell on her car. One police officer was also injured.

http://www.realclimate.org/index.php/archives/2019/06/unforced-variations-vs-forced-responses/ Unforced Variations vs Forced Responses? Guest commentary by Karsten Haustein, U. Oxford, and Peter Jacobs (George Mason University). One of the perennial issues in climate research is how big a role internal climate variability plays on decadal to longer timescales. A large role would increase the uncertainty on the attribution of recent trends to human causes, while a small role would tighten that attribution. There have been a number of attempts to quantify this over the years, and we have just published a new study (Haustein et al, 2019) in the Journal of Climate addressing this question. Using a simplified climate model, we find that we can reproduce temperature observations since 1850 and proxy-data since 1500 with high accuracy. Our results suggest that multidecadal ocean oscillations are only a minor contributing factor in the global mean surface temperature evolution (GMST) over that time. The basic results were covered in excellent articles in CarbonBrief and Science Magazine, but this post will try and go a little deeper into what we found. Until recently, the hypothesis that there are significant natural (unforced) ocean cycles with an approximate periodicity of 60-70 years had been widely accepted. The so-called Atlantic Multidecadal Variability index (AMV, sometimes called the AMO instead), but also the Pacific Decadal Variability index (PDV) have been touted as major factors in observed multidecadal GMST fluctuations (for instance, here). Due to the strong co-variability between AMV and GMST, both, the Early 20th Century Warming (1915-1945) and the Mid-Century Cooling (1950-1980) have been attributed to low-frequency AMV variability, associated to a varying degree with changes in the Atlantic Meridional Overturning Circulation (AMOC). In particular, the uncertainty in quantifying the human-induced warming fraction in the early 20th Century was still substantial. Fig. 1: Matches of modeled temperature to the observations since 1850. Upper graph shows the global response model with ENSO (bold green) compared to HadOST (bold black). Lower graph is the same as above but with lowess smoothed observational data. The response model results (green thin lines) represent the parameter uncertainty for an associated TCR of 1.6K. The dashed thin line is the upper and lower (reasonable) bound for the effective aerosol forcing for 2017 (-0.5 and -1.0 W/m2), in contrast to the best estimate of -0.75 W/m2 used in the response model. The grey area indicates the 5-95th percentile of the total uncertainty. The two graphs are offset by 0.9°C without a particular baseline. Response model and observations are aligned for the 1901-2000 period. In contrast to those earlier studies, we were able to reproduce effectively all the observed multidecadal temperature evolution, including the Early Warming and the Mid-Century cooling, using known external forcing factors (solar activity, volcanic eruptions, greenhouse gases, pollution aerosol particles). Adding an El Niño signal, we virtually explain the entire observed record (Figure 1). Further, we were able to reproduce the temperature evolution separately over land and ocean, and between Northern and Southern Hemispheres (NH/SH). We found equally high fractions of explained variability associated with anthropogenic and natural radiative forcing changes in each case. Attributing 90% of the Early Warming to external forcings (50% of which is due to natural forcing from volcanoes and solar) is – in our view – a key leap forward. To date, no more than 50% had been attributed to external forcing (Hegerl et al. 2018). While there is less controversy about the drivers of the Mid-Century cooling, our response model results strongly support the idea that the trend was caused by increased levels of sulphate aerosols which temporarily offset greenhouse gas-induced warming. What does this mean? Some commentators have used the uncertainty in the attribution for the Early 20th Century warming as an excuse to not accept the far stronger evidence for the human causes of more recent trends (notably, Judith Curry). This was never very convincing, but is even further diminished given a viable attribution for the Early Warming now exists. Despite a number of studies that have already provided evidence – based on a solid physical underpinning – for a large external contribution to observed multidecadal ocean variability, most prominently the AMV (e.g. Mann et al., 2014; Clement et al., 2015, Stolpe et al. 2017), ideas such as the stadium wave (Wyatt and Curry, 2014) continue to be proposed. The problem is that most studies that argue for unforced low-frequency ocean oscillations do not accommodate time-varying external drivers such as anthropogenic aerosols. Our findings highlight that this non-linearity is a crucial feature of the historic forcing evolution. Any claim that these forcings were/are small has to be accompanied by solid evidence disproving the observed multidecadal variations in incoming radiation (e.g. Wild 2009). On the contrary, our findings confirm that the fraction of human-induced warming since the pre-industrial era is bascially all of it. Implications Fig 2. The residual observed variability in the NH. Model minus 30 year smooth observations (red). A revised AMV index is shown in black. Note that the rhs y-axis labels for the AMV SSTs is different. We conclude that the AMV time series (based on the widely accepted definition) almost certainly does not represent a simple internal mode of variability. Indeed, we think that the AMV definition is flawed and not a suitable method to extract whatever internal ocean signal there might be. Instead we recommend the use of an alternative index which we think will be closer to the internal signal, called the North Atlantic Variability Index (NAVI). It is essentially the AMV relative to the NH temperature (Figure 2). The resulting timeseries of the new NAVI index is a good representation of the AMOC decline, arguably the true internal component (although also forcing-related) in the North Atlantic. This implies that while the AMOC is an important player (see for instance, Stefan’s RC post), it is not driving alleged low-frequency North Atlantic ocean oscillations. The AMV should therefore not be used as predictor in attribution studies given that the multidecadal temperature swings are unlikely internally generated. Though we note that the projection of AMV on GMST is small in any case. What did our results rely on? Fig 3. NH response model results from 1500-2017 (bold red). NTrend proxy in orange and a subset of individual NH proxy reconstruction (thin brown lines). HadCRUT4 and Berkeley in grey and black for the 1850-2017 period. The response model is baselined to the initialisation data which corresponds to 1500 A.D. There are three novelties that led to our conclusions: (1) We differentiate between forcing factors such as volcanoes and pollution aerosols with regard to their transient climate response (TCR). For example, anthropogenic aerosols are primarily emitted over NH continents, i.e. they have a faster TCR which we explicitly account for in our analysis. (2) We use an updated aerosol emission dataset (CEDS, Hoesly et al., 2017, also used in CMIP6), resulting in a substantially different temporal evolution of historic aerosol emissions compared to the older dataset (Lamarque et al. 2010). The effective aerosol forcing is based on the most recent estimate by [9]. (3) The final change is related to the observational data. The HadISST1/2 (Kennedy et al. in prep) ocean temperature dataset (SST) has never been used in conjunction with land data. We have combined HadISST2 with Cowtan/Way over land (using air temperature over sea ice) and filled the missing years after 2010 with OSTIA SSTs (due to it being preliminary only). In addition, it has been known for quite some time now that there is a bias in virtually all SST dataset during the 2nd world war (Cowtan et al., 2018, and see also Kevin’s SkS post). We correct for that bias over ocean (1942-1945), which, in conjunction with warmer HadISST2 SSTs before the 1930s, significantly reduced previous discrepancies related to the Early Warming. Lastly, the fact that the model is initialised in 1500 A.D. ensures that the slow response to strong volcanic eruptions is sensibly accounted for (Figure 3), as it has shown to be important on centennial timescales (e.g. Gleckler et al. 2006). What about overfitting? In order to address this issue, we would like to point out that not a single parameter depends on regression. TCR and ECS span a wide range of accepted values and all we did is to estimate TCR based on the best fit of the final response model result with observations. We concede that the fast response time and the effective aerosol forcing are difficult to pin down given there is a wide range of published estimates available. However, it is worth mentioning that the results are not very sensitive to variations in both parameters (see thin lines in Figure 1). Instead, the overall uncertainty is dominated by the TCR and GHG forcing uncertainty. The story is more complex when it comes to the NH/SH and land/ocean-only results as we need to account for the different warming-ratios. Guided by climate model and observational data, we introduce a novel method that objectively estimates the required TCR factors. Conclusions: It was us. The findings presented in our paper highlight that we are now able to explain almost all the warming patterns since 1850, including the Early Warming period. We achieve this by separating different forcing factors, by including an updated aerosol dataset and by removing notable SST biases. We have avoided overfitting by virtue of a strict non-regression policy. We ask the different research communities to take these findings as food for thought, particularly with regard to the Early Warming. We most definitely believe that it is time to rethink the role of the AMV and recommend using our newly introduced NAVI definition instead. This will also help to understand contemporary AMOC changes and its relation to climate change better, and perhaps provide guidance as to which climate models best approximate internal ocean variability on longer timescales.

The big story this year is the record warmth and low sea ice extent in the Pacific sector. But you can see 2012 ahead of 2019 in the Atlantic sector. Overall, 2019 is a bit behind the pace of 2012 to date. https://mobile.twitter.com/AlaskaWx/status/1143202863633448960 The northern Bering & southern Chukchi Seas are baking. Large areas away from land with ocean surface temperatures more than 5C (9F) above the 1981-2010 average. Impacts to the climate system, food web, communities and commerce https://mobile.twitter.com/AlaskaWx/status/1142829854930296832 Chukchi and Beaufort Seas combined #seaice extent remains the lowest of record ( @NSIDC passive microwave data since 1979), 21 percent below the 1981-2010 median. Lower concentration of ice will be easily moved by winds this week.