Monthly Archives: November 2022

Europe monthly temperatures: Weather Averages for European Cities

Опубликовано: November 1, 2022 в 9:00 am

Автор: alexxlab

Категории: Miscellaneous

July Temperature Averages in European Cities

Average temperatures for July at major cities throughout Europe are listed below in degrees Celsius and Fahrenheit.

The cities range from Amsterdam to Zurich and from Reykjavík to İstanbul. They are listed alphabetically and you can jump ahead to the section for Amsterdam to Kiev, Leeds to Pristina and Reykjavík to Zurich.

Included in this list are Europe’s national capitals plus cities with a metropolitan population of over one million people.

The tables give the normal maximum and minimum monthly temperatures based on historical weather data collected over a long period, in most cases 30 years.

Average July temperatures
High °F	Low °F	City	High °C	Low °C
72	55	Amsterdam, Netherlands	22	13
78	58	Andorra la Vella, Andorra	26	15
89	73	Athens, Greece	32	23
82	65	Barcelona, Spain	28	19
83	64	Belgrade, Serbia	29	18
75	58	Berlin, Germany	24	14
71	53	Birmingham, United Kingdom	22	12
80	58	Bratislava, Slovakia	27	15
73	57	Brussels, Belgium	23	14
84	60	Bucharest, Romania	29	16
81	62	Budapest, Hungary	27	16
79	61	Chisinau, Moldova	26	16
75	55	Cologne, Germany	24	13
70	55	Copenhagen, Denmark	21	13
67	53	Dublin, Ireland	20	12
67	52	Glasgow, United Kingdom	19	11
72	55	Hamburg, Germany	22	13
73	55	Helsinki, Finland	23	13
85	69	İstanbul, Turkey	29	20
77	59	Kazan, Russia	25	15
76	59	Kiev, Ukraine	25	15

Average daily temperatures in July
High °F	Low °F	City	High °C	Low °C
66	52	Leeds, United Kingdom	19	11
74	57	Lille, France	23	14
82	64	Lisbon, Portugal	28	18
80	58	Ljubljana, Slovenia	27	15
74	57	London, United Kingdom	23	14
72	54	Luxembourg, Luxembourg	22	12
82	62	Lyon, France	28	17
88	65	Madrid, Spain	31	18
69	55	Manchester, United Kingdom	21	13
86	67	Marseille, France	30	19
85	64	Milan, Italy	29	18
72	55	Minsk, Belarus	22	13
81	69	Monaco, Monaco	27	20
74	56	Moscow, Russia	23	14
74	55	Munich, Germany	23	13
86	66	Naples, Italy	30	19
81	69	Nice, France	27	20
74	58	Nizhny & Novgorod, Russia	24	14
71	54	Oslo, Norway	22	12
77	60	Paris, France	25	16
89	69	Podgorica, Montenegro	32	20
74	53	Prague, Czech Republic	23	12
80	55	Pristina, Kosovo	26	13

July temperatures: daily averages
High °F	Low °F	City	High °C	Low °C
56	47	Reykjavík, Iceland	13	8
71	54	Riga, Latvia	22	12
89	62	Rome, Italy	32	17
85	64	Rostov-on-Don, Russia	29	18
72	56	Rotterdam, Netherlands	22	13
71	57	Saint Petersburg, Russia	22	14
79	61	Samara, Russia	26	16
83	64	San Marino, San Marino	29	18
78	55	Sarajevo, Bosnia & Herzegovina	26	13
86	61	Skopje, Macedonia	30	16
79	57	Sofia, Bulgaria	26	14
71	56	Stockholm, Sweden	22	13
70	54	Tallinn, Estonia	21	12
89	61	Tirana, Albania	32	16
82	62	Turin, Italy	28	17
78	56	Ufa, Russia	26	14
76	57	Vaduz, Liechtenstein	25	14
87	70	Valletta, Malta	31	21
80	61	Vienna, Austria	27	16
72	54	Vilnius, Lithuania	22	12
85	65	Volgograd, Russia	29	18
74	55	Warsaw, Poland	23	13
80	58	Zagreb, Croatia	27	15
77	55	Zurich, Switzerland	25	13

Europe continued to swelter in July

Following two months of intense heat in May and June, Europe continued to swelter in July with an intense and prolonged heatwave that started in Spain and Portugal before spreading further north and east, towards France, the United Kingdom, central Europe and Scandinavia. Dry conditions also continued throughout last month, exacerbated by the heat. As a result, some countries saw record high temperatures and recorded their driest July ever. The Copernicus Climate Change Service (C3S*) has been monitoring the situation.

Around mid-July, an area of high pressure settled over western Europe, bringing clear-sky, dry conditions and high insolation. This, combined with hot air flow from North Africa ahead of a trough moving north-eastwards from west of Portugal, led to heatwave conditions which affected most of western Europe.

Temperatures soared to above 40°C for at least one day in Spain, France and the United Kingdom. In particular, the UK saw a national daily maximum temperature record of 40.3°C, set at Coningsby in Lincolnshire on July 19 – the first time that the country ever recorded a temperature of over 40°C. In general, the data indicates that the previous UK temperature record of 38.7°C (recorded in July 2019) was met or exceeded at 46 stations across the country.

ERA5, ECMWF’s fifth generation atmospheric reanalysis of the global climate covering the period from January 1950 to the present, allows us to calculate the average number of July days where temperatures of over 40°C were recorded in the 1991-2020 reference period. Analyses carried out using ERA5 show no temperatures above this threshold for the UK in this reference period. However, for France and the Iberian Peninsula, July 2022 was not a record month in terms of the number of days with temperatures over 40°C.

Longevity v extremes

Western Europe in general saw a larger number of days with temperatures above 30°C, 35°C and 40°C than a typical July. While the heatwave was very intense in terms of daily temperature extremes in some regions of western Europe, reaching local and national records (as we have seen above), it was mainly the longevity of the period with daily maximum temperatures between 35°C and 40°C that characterised July, rather than the very extreme temperatures of above 40°C, particularly in southwestern Europe. The number of days above 30°C was also well above average but, as for the number of days above 35°C, generally not record-breaking. The low number of days above 35°C in southeastern Europe is also noteworthy, again indicating that the heat mainly affected the southwest.

As indicated in the animation below, throughout the month conditions alternated between above and below average temperatures over most of Europe. The very large positive anomalies that coincide with the heatwave sweeping over the west of Europe in the middle of the month are clearly visible.

ERA5 daily mean temperature anomalies over Europe in July relative to the daily average for the period 1991-2020 , with red showing warmer than average and blue colder than average conditions. Source: C3S/ECMWF. DOWNLOAD VIDEO

Final figures show that July was the warmest on record in southwestern Europe in terms of average maximum temperatures. Average minimum temperatures were also among the top three.¹

Monthly average temperature anomalies for south-west Europe in May-July 2022. Source: C3S/ECMWF. DOWNLOAD IMAGE

Looking at average daily temperatures (below) for the region and comparing them to the heatwaves in May and June, we see that temperatures reached record-highs for several days in July, whereas during the previous months it was only at the peak of the heatwaves that record maximum regional temperatures were recorded.

The run of record average temperatures in southwestern Europe in July bears comparison with the extended spell of high temperatures in August 2003 and the brief period of very high temperatures in August 2021.

Average daily maximum and minimum temperatures (March-August) over south-west Europe. Source: C3S/ECMWF. DOWNLOAD IMAGE

It is still too early to say if this will be a record summer both for Europe and its sub-regions in terms of average temperature (figures for August are needed to reach a final conclusion). However, unless temperatures in August are more than 0.45°C below average, which has only happened once during the 21th century, this summer will be one of the five hottest on record.

Widespread dry conditions

Europe experienced dry conditions during most of July, with much of the continent seeing rainfall and, in particular, soil moisture well below average (see below). The month began with conditions already dry, following a drier-than-average June over the UK, Ireland, Italy and most of the Iberian Peninsula, in addition to a large region stretching from the northern Balkans across eastern Europe and to north-western Russia.

Monthly soil moisture anomaly (%) for July. The anomalies are expressed as a percentage of the July average for the 1991–2020 reference period. Source: ERA5/C3S. DOWNLOAD IMAGE

Southern England experienced its driest July since records began in 1836. The UK as a whole saw just 56% (46.3mm) of its average rainfall for July, making it the driest July in over 20 years. This continued a run of months, with the exception of February, that were drier than average in 2022 so far.

A similar situation was observed in France, where July was the second driest month since records began. With a total rainfall of 9.7 millimetres, last month was the driest July since records began in 1959, according to the French meteorological and climate service. This represents a rainfall deficit of 85% compared to the average for the 1991-2020 reference period. The previous records dated back to July 2020 with 16.7 millimetres of precipitation, and July 1964 with 24.7 millimetres.

Persistent lack of precipitation since December 2021 has had a major impact in northern Italy, where the Po River completely disappeared in places. In late June, the flow measured at Pontelagoscuro, near Ferrara, fell below an average of 145 cubic metres per second (the historic average flow for June is 1,805 cubic metres per second). In mid-July at Cremona – roughly halfway along the Po – the water was more than 8 metres below “hydrographic zero”. These dry conditions adversely affected energy production, agriculture and river transport in the region.

The situation in the Po Valley later improved thanks to precipitation towards the end of the month, resulting in a water level increase of 40 centimetres for the river, although hydropower production in the region was still affected, as the Enel Green Power hydroelectric plant in Isola Serafini (Monticelli d’Ongina, Piacenza) remained offline.

Far-reaching adverse societal and environmental impacts

Reduced hydropower output was only one of the impacts of the high temperatures and dry conditions. July’s hot and dry conditions resulted in a wide range of adverse impacts, some of which were forecast and monitored by Copernicus services.

For example, heatwaves and exposure to extreme temperatures can have serious adverse health impacts and cause heat exhaustion and heat stroke. Moreover, high temperatures cause high levels of surface ozone, which can also have a significant impact on human health, ranging from headaches, asthma attacks and other respiratory problems to cardio-vascular illnesses. The Copernicus Atmosphere Monitoring Service (CAMS*) forecast very high levels of surface ozone pollution across a large region of Europe as temperatures soared in July.

The Copernicus Emergency Management Service (CEMS) repeatedly warned of increased fire danger due to the lack of rain and the resulting dry vegetation, combined with high temperatures. Wildfire activity in July resulted in many records being broken, in terms of the intensity and damage caused by the fires, but also in terms of the number of СEMS Rapid Mapping activations. CAMS is also monitoring fire activity in Europe, following its first report on wildfire activity in Europe and other parts of the northern hemisphere at the start of the boreal forest fire season.

Locations and fire radiative power (FRP) of active fires in southwest Europe in July 2022. Source: CAMS/ECMWF

In agriculture, the effects of the hot and dry conditions have been severely felt. In France, due to the 85% rainfall deficit mentioned above, 90 of the country’s 96 départements had been placed on drought alert to varying degrees by July 27, with 87 départements placing restrictions on irrigation for agriculture. These restrictions, and the hot dry conditions that caused them, had inevitable consequences in terms of agricultural output in France and elsewhere.

The Joint Research Centre (JRC), the European Commission’s in-house science service, issued a yield outlook for EU summer crops that was substantially reduced due to continued hot and dry conditions in large parts of Europe. At EU level, the yield forecasts for grain maize, sunflowers and soybeans were most markedly reduced (by 8 to 9%) and are now well below the 5-year average.

Climate data at your fingertips

C3S produces monthly climate bulletins with data on surface air temperature, sea ice and hydrological variables, which are published on its website. In addition, it provides monthly, seasonal, and yearly updates largely based on the ERA5 reanalysis dataset and makes this data freely available in the C3S Climate Data Store (CDS).

The July issue of the C3S Climate Bulletin reports that, globally, July 2022 was 0.38°C warmer than the 1991-2020 average for July and one of the three warmest Julys on record. The European-average temperature for July was 0.72°C above the 1991-2020 average, making it the joint sixth warmest July, with July 2006, in this data record. July 2022 was 1.0°C colder than the warmest July, which occurred in 2010.

As regards hydrology, the July Bulletin notes that July 2022 was drier than average for most of western Europe. Precipitation, soil moisture and relative humidity were all consistently below average from the Iberian Peninsula across the continent to the Black Sea, including the UK and Ireland and southern Scandinavia, and parts of western Russia and the Caucasus. France and parts of the UK experienced the driest July based on local precipitation records. Conversely, it was wetter than average over much of Scandinavia, Iceland and in regions of central and eastern Europe. Despite average precipitation, Türkiye experienced wetter soils, the legacy of June’s wetter-than-average conditions.

* C3S and CAMS are implemented by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission

1.: The South-West Europe region used to calculate the anomaly timeseries is defined as land bounded by longitudes 25°W and 15°E and latitudes 36°N and 45°N, excluding the region bounded by longitudes 0 to 12E and latitudes 36 to 38N”.

A new Weather pattern is developing over Northern Hemisphere, creating a weaker and more disrupted circulation as we head closer to Autumn

A weather pattern change is starting over the Northern Hemisphere, just in time for the final stage of the meteorological Summer. But as the pressure-systems shift, we will see a temperature drop over much of the United States, a heatwave over the Pacific Northwest, and a longwave moving over Europe.

First, we will quickly look at the Summer so far. But then, we will focus mainly on the changing pressure patterns over the Northern Hemisphere and how they will shape our weather as we step closer to the beginning of the meteorological Fall season.

HALFWAY THROUGH SUMMER

Meteorological Summer covers the 3-monthly period of June, July, and August. These are the warmest three months in the Nothern Hemisphere and represent the summer season in the statistics.

We are already past two-thirds of Summer, so we can look back at how the weather patterns have evolved so far.

The pressure pattern behind June and July has featured high-pressure zones over much of the United States and Europe. In addition, we can see a deep low-pressure system over Greenland and a high-pressure area expanding from Aleutians to northern Canada.

Below we have the temperature anomaly analysis for the same period. We can see that Summer so far has been warmer than normal in Europe and most of the United States, and northern Canada. Colder than average temperatures were present over Greenland and parts of eastern Canada.

Precipitation-wise, we see drier conditions in the southern United States, the midwest, and parts of western and northern Canada. But there was more rainfall in the southwestern United States, the Pacific Northwest, and over the Ohio river valley.

Over Europe, we see mostly drier conditions. The exceptions are certain regions with more convective activity/storms in western and central parts. Scandinavia has mostly seen more precipitation than usual.

But what was driving the Summer weather thus far, and how will it shape the rest of the season?

WEATHER DRIVERS

La Nina is one of the main factors behind the weather patterns this Summer. We can see it as an area of cold ocean anomalies in the tropical Pacific Ocean. The ocean temperature analysis by NOAA below nicely reveals the active La Nina phase.

But while the La Nina has a low-frequency background influence, many shorter-period atmospheric waves circulate the tropics.

ATMOSPHERIC WAVES

A lot of the global variability is driven by invisible tropical “waves” in the atmosphere. The largest and most dominant source of short-term variability from the tropical regions is the Madden-Julian Oscillation wave (MJO).

MJO is an eastward-moving disturbance of thunderstorms, clouds, rain, winds, and pressure anomalies. It moves across the Earth on the equator in about 30 to 60 days.

It also has the power to influence the weather patterns further north over the North Hemisphere, as there is a strong connection between the tropics and the global weather.

The MJO consists of two parts: one is the enhanced rainfall (wet) phase, and the other is the suppressed rainfall (dry) phase. Image by NOAA Climate.

The MJO wave was an important part of the Summer weather patterns so far, as it was active over June and July. The image below shows a strong, active phase of MJO in July, with upward motion over the western Pacific and suppressed activity over the Americas.

We have also produced a video animation showing the global precipitable water. You can see how the tropical regions connect with the mid-latitudes, creating “atmospheric rivers” and connecting to the global weather system.

Below is an ECMWF ensemble forecast of the MJO activity. It is quite easy to read, as we can see in which phases the wave is moving and on which day. If the wave is within the central circle, it is considered weak.

You can see past activity with the black lines. But the forecast shows little activity in the short-term forecast. This means that most of August will go by without a strong MJO influence.

But what will the rest of the Summer look like, and what does the forecast show for the start of the meteorological Fall season?

A PATTERN CHANGE

With the MJO out of the equation, for now, we can observe the North Hemisphere circulation by looking at the Arctic Oscillation (AO).

The Arctic Oscillation index explains the pressure pattern and circulation over the North Pole. A positive phase means a stronger and more stable lower level polar circulation, while a negative phase means high pressure over the polar regions and a disrupted flow.

This is much more profound during the Winter when the pressure patterns are stronger, and the Polar Vortex is active. A positive or negative phase of the AO can open or close “the gates of the Arctic”, releasing cold air into the United States and Europe.

The positive phase means stable circulation and contains cold air. Negative AO means the circulation is weak, and the jet stream is wavy, so cold air can escape the polar regions during the Winter.

While there are no cold air outbreaks during the Summer, the Arctic Oscillation is still used to indicate the weather pattern development and overall circulation. As seen in the forecast below, we can see that we are in a positive AO phase now, but the forecast calls for a negative phase.

We can see on the atmospheric pressure anomaly map below how the pattern will change. Currently, we have the positive mode (blue square), but high latitude blocking (red square) is set to appear by mid-month, creating a more disrupted hemispheric flow. Image by StratObserve.

Looking at the pressure anomaly forecast for this week, you can see the low-pressure zone over the polar regions. That is the positive AO mode, of course, more confined to the arctic circle in Summer. Around it, we have a strong high-pressure zone over northern Europe, creating a rex-block pattern.

Over North America, we can see a strong ridge over the western United States, expanding further north into Canada. In response, we have a low-pressure area (trough) standing over the eastern United States.

Looking at the 850mb temperature anomalies, we can first see a cold anomaly in the Arctic, thanks to the strong low-pressure system. Then, the strong warm anomalies enter western and northern Europe under the high-pressure zone.

An interesting temperature pattern has started to develop in the United States. Cooler air starts to come down into the eastern parts of the country under the low-pressure area. Warm anomalies are building in the northwestern United States and western Canada.

The surface temperature forecast across the United States shows cooler than normal morning temperatures over much of the eastern and southern United States. In Winter, such a pattern could create a decent cold air outbreak.

Precipitation-wise over North America, we can see more rainfall over the Midwest and the western United States. Over the southern United States, we mainly see drier conditions over the weekend and early next week.

WEATHER IN LATE AUGUST

Next week, the pressure pattern will change over the polar regions. This reflects the change in the Arctic Oscillation phase from positive to negative. You can see the high-pressure building into the polar region, replacing the prior low-pressure area.

Two strong ridges connect across the polar circle. One over the Arctic Ocean and the strengthening ridge over the western United States and Canada. A low-pressure zone amplifies over the eastern United States and western Europe.

Looking at the temperature anomalies, we can first see a cold anomaly extending across the United States as the low-pressure zone regresses westward. Strong heat domes build over the northwestern United States and Canada and over northern and southern Europe.

Over North America, we can see a strong contrast between western Canada and the United States. With the exception of the far northwest, we can see colder than normal daily temperatures over most of the United States.

Higher than normal temperatures are expected to develop under the ridge covering the northwestern United States and Canada. Temperatures over 37 degrees celsius (100F) are expected over the Northwestern United States and over 32 degrees celsius (90F) over southwestern Canada.

We are also in an ongoing Hurricane season. But looking at the development in the Tropics, we can see no real activity in the near future. The ECMWF ensemble forecast shows no tropical storm formation in the next 10 days.

6-10 DAY OFFICIAL WEATHER OUTLOOK

NOAA regularly releases its weekly forecasts for the United States. Below is the temperature forecast in the 6-10 day range, which covers mid-August. You can see the higher temperatures in the northwestern United States and a cooler than normal Summer over much of the central and eastern part of the country.

We also have the official precipitation outlook below, which calls for more precipitation over much of the United States, except for the Midwest and the north.

LATE-MONTH WEATHER PATTERN

In this period, the ECMWF pressure forecast shows a high-pressure area stabilizing over the polar circle and Canada. This is a typical disruption of circulation, with low-pressure zones circling the Aleutians, the United States, and northwestern Europe.

The 850mb temperature anomaly for late-month shows warm anomalies returning over central and western Europe. The heatwave continues over the Pacific Northwest while a cold anomaly drops further back into the central United States.

Below we have a surface temperature anomaly forecast over North America. The forecast calls for colder than normal air spreading into parts of the Midwest and reaching down towards the southern United States. Hotter conditions still prevail over the northwestern United States and southwestern Canada.

NOAA 8-14 DAY WEATHER OUTLOOK

This is also reflected in the official NOAA 8-14 day temperature outlook. NOAA is forecasting cooler than normal conditions over much of the central United States. Warmer weather is forecast to continue over the western United States, with temperatures rising again in the northeast.

NOAA precipitation outlook also calls for wetter conditions over the southern and eastern United States. The northern part of the country is forecast to receive less precipitation than normal under the influence of the ridge.

EXTENDED RANGE WEATHER

Looking further into the end of August and early September, we will use the ECMWF extended ensemble forecast.

The pressure pattern forecast for the month transition shows a high-pressure area remaining over Canada, also covering the western United States. That hints at possible lower pressure over the eastern United States. In addition, a high-pressure zone extends over northern Europe.

The temperature forecast for Europe shows warm anomalies over much of central and western Europe. But there is no strong warm signal, for now, that would hint at unusually warm/hot weather for the end of the month.

The temperature forecast for North America shows cooler than normal temperatures in the southern half of the United States. Warmer than normal weather is expected to continue over the northwestern United States and southwest Canada under high pressure.

Precipitation-wise, more rainfall is expected in the south-central United States, along with the active monsoon in the southwest. Drier conditions prevail over the northern United States and southern Canada.

Looking at the tropical development in this range, we have a low probability of tropical storm development. Some indication is shown along the east coast of the United States, but the probability remains very low at the moment.

FROM SUMMER TO AUTUMN

September marks the first month of the meteorological Fall. The meteorological season covers 3 months, September, October, and November. So, the transition from August to September marks the shift from meteorological Summer to Fall.

The extended forecast trend for early Sept shows continued high pressure over western Canada and a likely low-pressure response in the southern United States. This would promote a continued northerly flow into the southern United States.

Over Europe, we see the high-pressure zone easing away from northern Europe back into the North Atlantic. This could indicate a more unstable pattern over the continent.

Looking at the temperature forecast for Europe in this period, the warm anomalies over the central parts are almost gone. This confirms a more unstable pattern is likely over the continent, with a shift towards a more Autumn configuration.

Looking at the temperature forecast for North America, we see the warm anomalies remaining over the northwestern and northeastern United States. However, a cooler than normal area remains over the southern part of the country, linked to a likely low-pressure area.

The precipitation forecast shows more precipitation trending in the southern and western parts of the country. Drier conditions prevail in the Pacific Northwest of the United States and southwestern Canada.

In the early parts of September, we still see little in the way of any significant development. Some signals are from the east coast up into the northeast, but that is likely not a real or pure tropical low-pressure development.

But as we move into the Fall, what do the latest seasonal trends show for the upcoming weather season?

AUTUMN SEASON 2022 OUTLOOK

Below we have an analysis/forecast graphic by ECMWF, which shows the long-range forecast of the main ENSO region. The La Nina conditions remain over the Fall and Winter seasons. But a weakening of the La Nina is expected for early next year, with an El Nino hint for later in the year.

Another respected ENSO forecast comes from the Australian BOM office. They produced a graph that contains multiple forecasts from different global models. They all agree on a continued negative ENSO phase from Fall into Winter.

There are differences in the strength of the La Nina at this period, but the average forecast from all models agrees on an active moderate event.

A more direct ENSO influence can be found in North America. For this reason, we produced a graphic that combines 10 of the last La Nina Fall seasons, showing the average weather pattern during a La Nina Fall season.

On the pressure anomaly pattern, you can see three areas of high-pressure anomalies and a low-pressure zone over Alaska and western Canada.

High-pressure anomaly in the North Pacific is more of a classical La Nina signature in all seasons. But in autumn, the high-pressure area over the western United States is also often seen. There is also a high-pressure anomaly in the North Atlantic.

ECMWF FALL 2022 FORECAST

The pressure anomaly forecast shows the typical La Nina high-pressure system in the North Pacific, with another high over the western United States and the North Atlantic. This is a similar pattern to the historical analysis we have done above. It also has a low-pressure zone hinted over Alaska and western Canada.

Looking at the European sector, we have a high-pressure zone extending from the North Atlantic over Europe. As a response, the low-pressure zone is moved further up to the north.

When we look at the global air temperature anomaly distribution, we can see the warmer anomalies over the western United States and Europe. Northwestern Canada and Alaska are in a neutral zone, which will likely turn cooler in the next updates.

As seen in the historical analysis, you can also see weaker warm anomalies over the southeastern United States. Overall, this seems to be quite a typical La Nina Fall pattern, confirming its influence on global circulation.

Over Europe, we can see warmer than normal surface temperatures over much of the continent. This is due to a high-pressure area over the North Atlantic and extending into the continent.

Over North America, we see much warmer temperatures under a high-pressure system in the western United States. Warm anomalies extend into the northeastern United States and eastern Canada. But Alaska and northwestern Canada remain neutral to cool under the influence of the polar jet stream.

Precipitation-wise, we can see mostly drier conditions over Europe. More precipitation is forecast over northern Europe, under the influence of a low-pressure zone.

Over North America, we have wetter conditions over the northwestern United States, also expanding over western Canada. The Midwest and the southern United States show a dry signal, while we also see more precipitation over the southeast.

The La Nina influence is expected to grow later in the season, which means a low-pressure area will be further established over western Canada. This could mean early snowfall is likely for parts of the northwest and the northern United States.

Below is the official Fall 2022 outlook from NOAA/CPC for the United States. Temperature-wise, we see the warm west and far northeast standing out, as also seen in the ECMWF forecast.

We can also see a neutral temperature zone in the Midwest. That does indicate a chance of more cold fronts coming down from the northwestern United States and western Canada towards the central regions. It could mean earlier snowfall than normal in the northern United States.

Precipitation-wise, the NOAA Fall 2022 outlook shows drier conditions in the western and central United States. Wetter conditions are expected mostly on the east coast of the United States.

The typical issue with a La Nina is typically the support of drought conditions in the southern and western United States. Below we have the latest drought analysis from NOAA, which shows the current drought conditions across the United States.

Most of the western half of the United States is already under some drought conditions. The driest conditions prevail in the southern United States. Over the southwest, the monsoon has alleviated some of the drought conditions. However, some drought conditions are also seen in the northeast corner.

We will release regular weekly and monthly updates as fresh forecasts and data are available. So make sure to bookmark our page. Also, if you have seen this article in the Google App (Discover) feed, click the like button (♥) there to see more of our forecasts and our latest articles on weather and nature in general.

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An unusual ocean anomaly is growing in the North Pacific. How did it form, and what does it indicate for the weather seasons ahead?

Record June temperatures in western Europe – World Weather Attribution

June 2017 was marked by high temperatures across Western Europe, with heatwaves triggering national heat-health plans and wildfires requiring evacuations in Portugal and Spain.

Key findings

Record-breaking heat gripped most of Western Europe during June 2017, sending monthly mean temperatures about 3 degrees Celsius (4. 8 degrees Fahrenheit) above normal (1981-2010).
Scientists with World Weather Attribution (WWA) and partners in England, France and Switzerland conducted a multi-method analysis to assess whether and to what extent human-caused climate change played a role in the heat.
These high temperatures are no longer rare in the current climate, occurring roughly every 10 to 30 years depending on the country.
The team found that climate change made the intensity and frequency of such extreme heat at least twice as likely in Belgium, at least four times as likely in France, Switzerland, the Netherlands, and central England and at least 10 times as likely in Portugal and Spain.

Introduction

Heatwaves can have significant impacts on human health and wellbeing including exacerbating existing medical conditions, particularly in the elderly and very young, and increasing energy demand, which can lead to disruptive power outages. In France, the night of June 21 will go down as the hottest June night ever recorded for most of the country as the average nighttime temperature reached 26. 4 degrees Celsius (79.52 degrees Fahrenheit). The average monthly June temperature in France was the second warmest on record after June 2003 in the official “heat index” series. Level three of France’s four-level heatwave plan, put in place after the devastating 2003 heat wave, was triggered for Paris as temperatures in the French capital topped 37 degrees Celsius (96.8 degrees Fahrenheit) during the week of June 19. In the Netherlands, June 2017 is likely to be the hottest June ever observed while in Switzerland, June 2017 is the second warmest since 1864 when observationsbegan. In northern areas, the minimum nighttime temperatures reached record highs of 24-25 degrees Celsius (75.2-77 degrees Fahrenheit). A level 3 warning was activated from June 20-23. In Britain the UK Met Office reported that Wednesday, June 21 was the hottest June day in more than 40 years when temperatures reached 34.5 degrees Celsius at Heathrow.

A major forest fire in Spain forced more than 1,500 people from homes, campsites, and hotels, and encroached on a UNESCO World Heritage site housing endangered species. In Portugal, a deadly forest fire killed at least 64 people, injured 204, and displaced over 500 from their homes. Triggering the EU’s Civil Protection Mechanism, France, Italy and Spain have provided firefighting planes to help get Portugal’s “worst forest fire in more than a century” under control. The smoke of the wildfires in Portugal were catapulted high into the atmosphere and then transported several thousands of kilometers across Europe. Smoke from the fires could be seen all the way to the Swiss Alps from the station at Jungfraujoch (3580 m).

The World Weather Attribution team in partnership with experts from other European countries conducted an event attribution study to investigate whether and to what extent human-induced climate change played a role in the high June temperatures across different countries in Western Europe.

Figure 1. Left: first estimate of Tmean anomalies for June 2017 relative to 1981-2010, right: rank of the anomalies (dark red is highest value, bright red second highest, usually after 2003). Source: ERA-interim/ECMWF analysis/ECMWF forecast via KNMI Climate Explorer.

Return periods & trends in observations

First we analyze the observed June temperature record in several Western European countries to assess whether or not there is a trend toward increasing temperature. We also compute the return periods of average monthly June temperature as observed in 2017 to assess how rare this heat event is. For two countries, Portugal and Spain, we additionally analyze the average maximum June temperatures as in these two countries not only minimum and mean temperatures but also the maxima were exceptional. The data are taken as the average of the 0.5º CRU TS 4.00 analysis 1901-2015 over all grid points within the boundaries of Portugal (without the Azores and Madeira), Spain (without the Canary Islands), France (without overseas territories but including Corsica) and Belgium. This was extended to June 25 using E-OBS v15.0 to Dec 2016, the monthly updates to May 2017 and the daily updates for 1–26 June 2017, with a bias correction to CRU TS computed from the overlap period 1950–2015. The data for June 27–30 were taken from the ECMWF analysis (27) and forecasts (28–30) with a bias correction to the daily E-OBS analysis starting 1979 using ERA-interim.

For Switzerland we used the Swissmean 1.0 series (1854–now) based on homogenized station data. The Netherlands were represented by the Central Netherlands Temperature v1.1 (1906–now). As this does not include the coastal areas, we restricted the model analyses to the box 51.5–52.5 ºN, 4.5–6.5 ºE. The same holds for England, where we used the Central England Temperature and the box 51–54 ºN, 0–3 ºW to represent the temperature series in models. As the variability of the CET is much larger before 1900 (King et al, 2015) we start our analyses in that year. All these series were extended to June 2017 using E-OBS for June 1–26 and ECMWF forecasts for June 27–30 as discussed above (for the CNT these are for the underlying stations rather than a box).

Based on these series we expect the mean temperature of June 2017 to be the highest one in the series in Portugal, Spain, France and the Central Netherlands. Note the Météo France “heat index” series based on 30 non-public stations gives a second place, after 2003. In Switzerland it is expected to be the second-highest since observations began in 1864, after 2003. Based on the CRU TS data, in Belgium it will be about the fifth warmest. The Central England June temperature will likely end up as the fourth highest June value since 1900.

We analyzed the observational data by fitting all June temperature observations to a distribution that shifts proportional to the smoothed global mean temperature. This method assumes that global warming is the main factor affecting local temperatures on the 100-yr time scale. This assumption is correct almost everywhere over land, which is dominated by the day-to-day variations of the weather. Natural variability with time scales of more than a century has a very small amplitude over land (Suckling 2016). It also assumes that climate change is linear, i.e., that all temperatures go up the same amount. This is not the case in areas where soil moisture depletion is important (Seneviratne 2010) and this assumption is checked in regional climate scenarios provided by the CORDEX initiative. For the distribution we take a Normal distribution, which fits the monthly mean data well. This reduces the fit to a simple linear regression. All fits exclude the year being studied, June 2017. The fits are shown in Figure 2 as a function of the global mean temperature with the linear fit, and as a function of the return period for the climates of 1901 (blue) and 2017 (red).

The probability in the climate of 1901 requires an extrapolation of several orders of magnitude in probability in the southern countries. This extrapolation depends very strongly on the distribution that is assumed, the uncertainty of this assumption is not included in the margins quoted. The model results below with more data points show that the tail of the distribution is likely thinner than a Normal distribution, which implies that the fits here may well underestimate the Risk Ratio, i.e. the change in the likelihood of the event occurring due to climate change.

Figure 2. Fits to the June temperature time series as described in the text of Tmean of Portugal, Spain, France, Switzerland, Belgium, the Central Netherlands and Central England; Tmax in Portugal and Spain. The red lines indicate the current climate, the blue ones the climate of around 1901. The purple symbol and lines indicate June 2017 (not included in the fits).

The results for the observed temperature in 2017 and return periods (including 95% Confidence Intervals, CI) of this value in the current climate are shown in Tables 1 and 2. The return periods are not very high, around 10 years (10% chance per year) in the northern countries and Switzerland to around 20 years (5% chance per year) in France, Spain and Portugal. This means that we can expect another hot June like this before too long. The maximum temperature averaged over Spain was more exceptional, around 80 years, almost certainly more than 30 years (less than 3% chance every year).

Table 1. Observer/forecast value and return period of June 2017

Country (rank) Tmean	Observed / forecast Tmean, anomaly	Return period in current climate	95% CI on return period
Portugal (1)	23. 2 ºC 2.9 ºC	25 yr (4 %/yr)	10 … 110 yr
Spain (1)	22.7 ºC 3.0 ºC	35 yr (3 %/yr)	13 … 150 yr
France (1/2*)	19.9 ºC 2.9 ºC	30 yr (3 %/yr)	12 … 140 yr
Switzerland (2)	15.2 ºC 3.2 ºC	20 yr (5%/yr)	7 … 130 yr
Belgium (~5)	18.1 ºC 2.0 ºC	9 yr (11 %/yr)	2.5 … 45 yr
Central Netherlands (1)	18.5 ºC 2.8 ºC	20 yr (5 %/yr)	8 … 70 yr
Central England (~4)	16.4 ºC 1.9 ºC	15yr (7 %/yr)	7 … 50 yr
Tmax
Portugal (1)	30.2 ºC 4.2 ºC	20 yr (5 %/yr)	10 … 80 yr
Spain (1)	30.6 ºC 4.3 ºC	80 yr (1 %/yr)	30 … 500 yr

* The CRU TS 4.00 / E-OBS v15.0+ / ECMWF series gives the highest value, the Météo France “heat index” series based on 30 non-public stations the second-highest.

The fit also allows us to compare the probability in the climate around 1901 (i.e., with a global mean temperature as observed around 1901) with the climate of today. We emphasise again that this extrapolation is very uncertain in the southern countries and may well lead to an underestimation of the Risk Ratio.

Table 2. Risk Ratios estimated from observed trends.

Country Tmean	Risk Ratio (p1/p0)	95% CI on RR	∆T, change in magnitude	95% CI on ∆T
Portugal	2000	300 … 30000	2.4 ºC	1.8 … 3.0 ºC
Spain	9000	1000 … 300000	2.6 ºC	2.0 … 3.2 ºC
France	200	30 … 2000	1.8 ºC	1.2 … 2.4 ºC
Switzerland	150	30 … 900	2.4 ºC	1.5 … 3.1 ºC
Belgium	8	2.4 … 40	1.1 ºC	0.5 … 1. 8 ºC
Central Netherlands	30	6 … 250	1.6 ºC	0.9 … 2.3 ºC
Central England	4	1.3 … 13	0.7 ºC	0.1 … 1.2 ºC
Tmax
Portugal	1400	150 … 20000	3.3 ºC	2.4 … 4.1 ºC
Spain	25000	1500 … 1000000	3.2 ºC	2.5 … 3.9 ºC

Furthermore, the Risk Ratio estimates have large uncertainties due to the relatively small number of data points (for most countries 116). However, they are always (much) larger than one, showing that the probability of observing a temperature as high as in 2017 or higher has increased, in many regions strongly, over the past century.

Attribution — model results

The next step in our analysis is to assess whether and to what extent external drivers, in particular anthropogenic climate change, caused this positive June temperature trend in the observational data. Answering this question requires the use of climate models, in which the relative impact of various external “forcings” such as changes in solar insolation, volcanoes, and greenhouse gas concentrations can be quantified. We need to use climate models because the observational record can assess correlation (is there a trend?) but not causation (what caused the trend?).

For this analysis we looked at five different climate models and modelling initiatives respectively (weather@home, HadGEM3-A, EC-Earth 2.3, CMIP5 and CORDEX). CMIP5, the “5th Coupled Model Intercomparison Project” (Taylor et al. 2012), is a collection of coordinated experiments from climate models run at centers across the world and is used in the IPCC Fifth Assessment Report. The EC-Earth 2.3 model, which also forms part of the CMIP5 ensemble, was run 16 times at KNMI. The CORDEX ensemble of Regional Climate Model (RCM) runs downscales CMIP5 runs from 1950-2100 to much higher resolutions over Europe (11km). 11 bias-adjusted ensemble members of the European CORDEX branch EURO-CORDEX (www. euro-cordex.net) are used in this analysis. Two further ensembles of atmosphere-only models are used: the UK Met Office HadGEM3-A model at N219 (60km) and the very large ensemble of Oxford’s weather@home runs of HadRM3P simulations at (50km). These models are all run with and without (the “counterfactual”) human-caused emissions of greenhouse gases and aerosols, allowing us to isolate and quantify the influence of human-induced climate change on the likelihood of June warm spells within the climate models.

weather@home

Using the distributed computing framework known as weather@home (Massey et al. 2015), we simulate two different large ensembles of June 2017 using the Met Office Hadley Centre regional climate model HadRM3P at 50km resolution over Europe embedded in the atmosphere-only global circulation model HadAM3P. The first set of ensembles represents possible June weather under current climate conditions. This ensemble is called the “all forcings” scenario and includes human-caused climate change. The second set of ensembles represents possible June weather in a world as it might have been without anthropogenic climate drivers. This ensemble is called the “natural” or “counterfactual” scenario.

As weather@home is an atmosphere-only modelling framework, observed sea surface temperatures (SSTs) are necessary to drive the model. SSTs for the “natural” simulations are obtained by subtracting various estimates of the difference between pre-industrial and present-day conditions from CMIP5 (Schaller et al., 2014). As observed SSTs are not yet available at the time of writing, the model is forced using seasonal forecast SSTs from the Met Office forecasting system GloSEA5 (Haustein et al., 2016). While biased warm in most countries, the distribution of average June temperatures compares well with observed distributions of June temperatures in the analyzed countries apart from the southernmost, Spain and Portugal, where the bias is cold. While the distribution and variability in the model climatology compare well with observations, the difference in June temperatures between the all forcings and counterfactual simulations is only about 1 ºC suggesting the counterfactual simulations underestimate the trend from human-induced warming. Results from weather@home simulations thus are likely a very conservative estimate of the risk ratio. Return periods of June temperatures in the two ensembles as well as the climatology are shown in Figure 3 as well as the risk ratios and their change with return period. Table 3 shows the risk ratios for the observed June 2017 events.

It is to be expected that the changes in probability in the atmosphere-only simulations do not exactly compare with observations and coupled model simulations. While observations and coupled models include changes due to natural variability and, in case of the observations other forcings, the atmosphere-only simulations of an individual year allow the change in probability due to anthropogenic forcings alone to be quantified. Everything else, including SST patterns, is kept the same. However, compared to the trend from human-induced warming, these other influences are small (at least over Europe) and thus cannot explain the discrepancies to trends in observations.

Figure 3. Left-hand side: Return periods of June mean temperatures for the seven analyzed countries in three different weather@home ensembles: the “all-forcings” for 2017 (red), the “counterfactual” simulations for June 2017 (blue) and the climatology of average June temperatures from 1986-2015 (black). Right-hand side: risk ratios between the “all-forcings” and “counterfactual” simulations depending on the return time.

Table 3. Risk ratios from weather@home for the observed 2017 events in the seven analyzed countries. Changes in magnitude for all countries are approx. 1°C.

Tmean	Risk Ratio	95% CI
Portugal	8.5	5.6…12.8
Spain	8.7	5…17.2
France	2.7	2.1…3.6
Switzerland	2.3	1.9…2.9
Belgium	2.2	1.8…2.5
Central Netherlands	2. 5	2…3.2
Central England	2.8	2.3…3.3
Tmax
Portugal	3.8	2.9…4.9
Spain	5.1	2.8…13.2

CIMP5

For the CMIP5 analysis, we first checked which of the 15 models (Taylor et al. 2012; Table 4) with the required simulations for the analysis have a temperature anomaly distribution that is compatible with the observed distribution (following the method of Lewis and Karoly 2013; King et al. 2015). Temperature anomalies were extracted from the observational data (1961-1990 baseline) and compared with historical model simulations using the same baseline. The number of models used in each regional analysis is shown in Table 5. This evaluation process was done over seven regions for June mean temperature (Tmean) and over two regions (Spain and Portugal) for June average maximum temperature (Tmax).

The change in the likelihood of hot June temperatures (above the the observed June 2017 temperature anomalies) was computed between a natural model ensemble, based on historicalNat simulations, and an all-forcings current-world ensemble, based on RCP8. 5 for 2006-2026. The Risk Ratio for hot June temperatures in the current world relative to the natural world was calculated using all available model simulations (shown as the best estimate in Table 5) and on 10000 bootstrapped sub-ensembles so the sampling uncertainty could be quantified. The resulting 95% confidence intervals in the Risk Ratios are also shown in Table 5.

Changes in intensity of extreme heat events were estimated by finding the location of June 2017 in the statistical distribution from the current-world ensemble and finding the corresponding point in the natural-world ensemble. The best estimate of this change is shown in Table 5.

Table 4. CMIP5 model simulations used in this analysis

Model	Historical	HistoricalNat	RCP2.6	RCP4.5	RCP6.0	RCP8.5
ACCESS1-3	1,2,3	1		1		1
bcc-csm1-1	1,2,3	1	1	1	1	1
CanESM2	1,2,3,4,5	1,2,3,4,5	1,2,3,4,5	1,2,3,4,5		1,2,3,4,5
CCSM4	1,2,3,4,5,6	1,2,4,6	1,2,4,6	1,2,4,6	1,2,4,6	1,2,4,6
CESM1-CAM5	1,2,3	1,2,3	1,2,3	1,2,3	1,2,3	1,2,3
CNRM-CM5	1,2,3,4,5,6,7,8,9,10	1,2,4	1	1		1,2,4
CSIRO-Mk3-6-0	1,2,3,4,5,6,7,8,9,10	1,2,3,4,5	1,2,3,4,5	1,2,3,4,5	1,2,3,4,5	1,2,3,4,5
GFDL-CM3	1,2,3,4,5	1	1	1	1	1
GISS-E2-H	1,2,3,4,5	1,2	1	1,2	1	1,2
HadGEM2-ES	1,2,3,4,5	1,2,3,4	1,2,3,4	1,2,3,4		1,2,3,4
IPSL-CM5A-LR	1,2,3,4,5,6	1,2,3	1,2,3	1,2,3	1	1,2,3
IPSL-CM5A-MR	1,2,3	1	1	1	1	1
MIROC-ESM	1,2,3	1	1	1	1	1
MRI-CGCM3	1,2,3	1	1	1	1	1
NorESM1-M	1,2,3	1	1	1	1	1

Table 5. Risk Ratios (best estimate and 95% confidence intervals) for hot June temperatures in model simulations representing the current world compared with a counterfactual natural world.

Country	Number of models	Best estimate Risk Ratio	95% CI	Best estimate Change in Magnitude (degrees C)
Belgium	6	3	1.2 … 5.3	1.03
CET	9	5.7	3.6 … 9.5	1.35
France	13	6.3	2.8 … 20	1.32
Netherlands	14	5.5	2.7 … 11.3	1.18
Portugal T	13	15.6	5 … 78	1.56
Portugal TX	14	8.6	2.9 … 70	1.78
Spain T	11	50	10 … Inf	1.59
Spain TX	7	30	5 … Inf	1. 96
Switzerland	13	8	3.75 … 50	1.65

The likelihood of high June temperatures similar to those we have seen in 2017 is estimated for two future scenarios. Model ensembles representing global warming of 1.5-degrees C and 2-degrees C above a pre-industrial baseline were extracted from the model projections (following King et al. (2017)). These ensembles were used to examine the likelihood of high June temperatures at the policy-relevant Paris global warming targets (Figure 4).

Figure 4: Estimates of the likelihood of high June temperatures in each European region under each of the four scenarios for mean temperatures (T) and maximum temperatures (TX). Best estimates are shown with 90% confidence intervals in parentheses.

EC-Earth 2.3

EC-Earth 2.3 is a coupled atmosphere-ocean general circulation model ensemble with 16 members (Hazeleger et al. 2010), each producing a transient climate simulation from 1860 to 2100. The model resolution is T159 which translates to around 150 km in the European domain. The underlying scenarios are the historical CMIP5 protocols until the year 2005 and the RCP8.5 scenario (Taylor et al. 2012) from 2006 onwards. Up to about 2030, the historical and RCP8.5 temperature evolution is very similar. As for the observations, we fit all modelled June temperatures to a distribution that shifts proportionally to the modelled global mean temperature (smoothed per ensemble member). We use all years up to 2017 to investigate the event in the recent and past climate, and the entire ensemble up to 2100 to investigate the event in the future climate.

For the EC-Earth analysis we checked for each region separately that the model has a temperature distribution that is compatible with the observed one, after a bias correction in the June mean temperature. We do this by fitting the data to a Normal distribution and comparing the scale parameter, allowing for a bias in the mean. For the bias correction we compare the modelled mean June temperatures between 1900-2016 with the mean June temperature of CRU TS data, and add the difference to the model. After that the model data is fitted to a General Pareto Distribution (GPD), because this fits the highest temperatures better than a Normal distribution. The fits are shown in Figure 5 as a function of the model global mean temperature, and as a function of the return period for the climates of 1900 and 2017. The Risk Ratios between 2017 and 1900 are shown in Table 6. Upper bounds are not always well-defined because the return periods in the climate of 1900 are very high.

By the year 2100, under the assumption of the RCP8.5 scenario, for all regions the June 2017 temperatures will be normal, occurring almost every year.

Table 6. Risk Ratios calculated from EC-Earth model data, calculated with a GPD distribution

Tmean	RR	95% CI	∆T	95% CI
Portugal	16	8 … 2000	1.2	1.1 … 2.3
Spain	70	20 … ∞	1.7	1.5 … 2.1
France	50	20 … 900	1. 6	1.4 … 1.9
Switzerland	140	17 … ∞	1.6	1.2 … 2.0
Belgium	3	2 … 5	0.6	0.4 … 1.0
Central Netherlands	35	12 … 400	1.5	1.3 … 2.2
Central England	14	9 … 76	1.466	1.271 … 2.205
Tmax
Portugal	40	9 … ∞	1.536	1.223 … 1.803
Spain	170	27 … ∞	2.074	1.884 … 2.555

Figure 5. GPD Fits to the June temperature EC-Earth series as described in the text, for Tmean of Portugal, Spain, France, Switzerland, Belgium, the Central Netherlands and Central England; Tmax in Portugal and Spain. The red lines indicate the current climate, the blue ones the climate of 1900. The purple symbol and lines indicate the magnitude of the observed event of June 2017.

HadGEM3-A

The 15 HadGEM3-A coupled historical runs 1960–2015 (Christidis et al, 2013) (N216, about 60km) are evaluated for the separate regions. The model turns out to have a too high variability. Therefore we reject this model for this analysis and do not use this model for calculations on the Risk Ratio.

CORDEX

A subsample from the EURO-CORDEX ensemble (Jacob et al., 2014) was used in order to calculate the change in odds of an exceedance of the observed mean temperature in the different countries. Ten 11-km GCM-RCM model combinations were considered, made available through the Copernicus Climate Change Service project CLIM4ENERGY. Simulations were bias adjusted using the Watch Forcing Data Era Interim (WFDEI, Weedon et al., 2014) and applying the so-called CDFt method (Vrac et al., 2016). By construction, daily temperatures were corrected and verifications were made against station data (Vautard et al., 2017, in preparation).

Return periods were estimated by pooling the 10 models together and using data in 30-year periods, 1971-2000 for past periods and 2001-2030 for the “current period”. Through this pooling we obtain an ensemble of 300 simulations of the month June from which return periods of the events with the observed magnitude were calculated for each respective ensemble and sampling uncertainty was estimated using the same bootstrapping procedure as for weather@home simulations keeping the full time series in the random drawings.

For this ensemble we also used two future periods in order to understand how the likelihood of extreme June temperatures would evolve under the RCP8.5 concentration scenario.

In this ensemble, the high temperatures observed in Western Europe in 2017 would have been rare events with return periods close to a hundred years at the end of the 20th century (see Figure 6). In the current climate, the likelihood of events like the observed to occur generally increased by varying factors from 2 to 6 depending on the country corresponding to a 1-to-10 to 1-to-20 year event today (see Table 7). This factor, the Risk Ratio, would apply again comparing today’s climate with future periods (2021-2050 and 2041-2070), with a likelihood increase by typically a factor 2 for the near future and 4 for the middle of the century relative to the current climate. Therefore in the absence of climate policies, one would expect such events to occur about once in 3 years in several countries.

It is interesting to remark that in many of the countries, there is a relative divergence of the return period curves with the increase in risk being higher for rare events. This may be due to soil-atmosphere interactions inducing positive feedback between drought and heat waves (Seneviratne 2010).

Figure 6. Return periods of June mean temperatures for the seven analyzed countries in four different CORDEX ensemble simulations: 1971-2000 (blue), 2001-2030 (red), 2021-2050 (yellow) and 2041-2070 (purple).

Table 7. Risk Ratios for the EURO-CORDEX experiment

Country	Risk Ratio	Conf. Interval 5-95%
Portugal (1)	3.6	2.3 … 7.6
Spain (1)	5.0	2.7 … 21.5
France (1)	3.7	2.3 … 8.9
Switzerland (2)	6. 3	3.9 … 13.6
Belgium (4)	2.2	1.7 … 3.3
Central Netherlands (1)	3.5 (NL)	1.7 … 8.0

The same bias-adjusted EURO-CORDEX ensembles for RCPs 4.5 and 8.5 have been employed to provide a European-scale picture on the future (end of 21st Century) likelihood of mean June temperatures to exceed the maximum mean June temperature of a historical reference period (1981-2010). By definition, the latter has an empirical probability to occur once in 30 years in 1981-2010. Results reveal that already for the moderate RCP4.5 emission scenario probabilities are larger than 10% (i.e., at least once in 10 years) over the entire European continent (Figure 7, left panel). Regions along the Mediterranean coast and in Northern Europe can show probabilities exceeding 40%. Even higher values are obtained for RCP8.5 (Figure 7, right panel). Over large parts of the continent, the probability for mean June temperatures to exceed the reference period’s maximum is larger than 50% and can reach values of more than 80% in Southern Europe. Mean June temperatures that are exceptional in a today’s climate would appear regularly by the end of the century with potentially drastic impacts on human health.

Figure 7: Probability for mean June temperature in the period 2070-2099 to exceed the maximum mean June temperature of a reference period’s climate (here 1981-2010). Left panel: RCP4.5, right panel: RCP8.5. Shown are ensemble mean probabilities as provided by bias-adjusted EURO-CORDEX regional climate scenarios.

Synthesis

Figure 8 shows the estimates of the change in risk for the different methodologies with model results in red and observations in blue. The synthesis result in magenta does not include the CORDEX and weather@home results. In weather@home the warming trend is not compatible with the observed trend whereas the simulations in CORDEX are very different in terms of the actual time period simulated that a quantitative comparison is not meaningful.

Figure 9: Same as Figure 8 but for the average maximum temperatures in Spain and Portugal.

Figures 8 and 9 summarise all the results on the change in probability from the observed trend (blue) and the model ensembles (red). We computed a unweighted average of all results, but excluding the two model results where the trends are incompatible with the observations (CORDEX and weather@home) this is shown in magenta. In all seven countries the likelihood of hot Junes has increased significantly. In all countries the observed changes in the likelihood of the event, average June temperatures as high as 2017, are much larger than in the model simulations with the regional models in weather@home and CORDEX showing the lowest increase in risk. As June temperatures were more extreme in Portugal and Spain it is not surprising that increases in Risk Ratios are higher for these countries. In Gaussian distributed variables or GPD distributions with negative shape parameter the dependency on the exact threshold is strongest further in the tail of the distribution.

Still, the difference between the Risk Ratios derived from observations and most models is sizeable. As we verified that the variability is similar (we rejected a model with too high variability) and as we corrected for biases in the mean, this is mainly due to differing estimates of the effect of anthropogenic emissions on summer temperatures in Europe. This difference is especially large in Tmax in Portugal and Spain. The discrepancy was found for CMIP3 models in southern Europe (van Oldenborgh et al, 2009) and also present for 3-day extremes in the summer of 2015 (Sippel et al, 2016). The cause for the differing trends is unknown. A first possibility is inhomogeneous observations, although the time series for Switzerland and the Netherlands are based on homogenised series. Part of the discrepancy could be due to random weather fluctuations, even though the high value for 2017 that is not included in the trend estimate is evidence against that. There could be decadal or longer time scale variability, but the autocorrelations of the residuals are compatible with white noise after subtracting the trend as a factor times the smoothed global mean temperature (in Belgium there is a step downward around 1950, whilst the CNT and CET show relatively cool weather in the 1970s and 1980s connected to air pollution). Finally, there may be model deficiencies in this area that cause a trend underestimation. However, for the whole SREX MED (Mediterranean) region, observed trends agree well with CMIP5. Maybe all these explanations contribute somewhat.

In any case, despite the spread, we can conclude that the probability of a June temperature as high as observed in 2017 or higher has increased by a factor more than ten due to global warming in Portugal and Spain, and at least doubled in the other analyzed countries.

Conclusion

The model results indicate that past historical increases in greenhouse gases have raised the odds of a warm June in Belgium, France, the Netherlands, Portugal, Spain, Switzerland and central England considerably (see Table 8). The observed trend is compatible with the effects of human-induced emissions of greenhouse gases. Since past and projected future greenhouse gas increases will continue to raise the temperatures, the frequency of summer months like June 2017 should be expected to increase over the coming decades and what is still an unusually hot June today would be a normal June later this century.

Table 8. Synthesis estimates of Risk Ratio from pre-industrial to today for average June temperatures, as shown in Figure 8 and 9.

Country	Change in odds from pre-industrial to today
Belgium	4 (2…10)
France	40 (10…300)
The Netherlands (CNT region)	18 (6…100)
Portugal (Tmean)	80 (20…1800)
Portugal (Tmax)	70 (20…241000)
Spain (Tmean)	320 (60…∞)
Spain (Tmax)	500 (60…∞)
Switzerland	50 (10…16400)
United Kingdom (CET region)	7 (4…20)

As the intensity and frequency of extreme heat events increases, many countries in Europe have made efforts to reduce vulnerability to extreme heat. For example, following the deadly 2003 heat wave, France implemented a national heat wave plan that included health monitoring activities, public cooling centers, home visiting plans for the elderly living alone and other actions that reduced mortality in subsequent heat waves (Murray et al. 2012). While we won’t know the mortality and morbidity impacts of the current heat wave until pubic health records are made available, it’s likely that many impacts were avoided as a result of heat wave action plans (Fouillet et al., 2008). At the same time, efforts to optimize the performance of heat wave watch and warning plans will be key to managing the vulnerability of populations as the frequency of extreme heat wave events increases.

References

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Haustein, K., Otto, F.E.L., Uhe, P., Schaller, N., Allen, M.R., Hermanson, L., Christidis, N., McLean, P. and Cullen, H. (2016) Real-time extreme weather event attribution with forecast seasonal SSTs. Environmental Research Letters, 11(6): doi: 10.1088/1748–9326/11/6/064 006

King, A. D., Karoly, D.J. and Henley, B.J. (2017) Australian climate extremes at 1.5 and 2 degrees of global warming. Nature Climate Change, 7, 412–416. doi: 10.1038/nclimate3296

King, A. D., van Oldenborgh, G.J., Karoly, D.J., Lewis, S.C. and Cullen, H. (2015) Attribution of the record high central England temperature of 2014 to anthropogenic influences. Environmental Research Letters, 10(5): 054002. doi: 10.1088/1748-9326/10/5/054002

Lewis, S. C., and D. J. Karoly. (2013) Anthropogenic contributions to Australia’s record summer temperatures of 2013. Geophysical Research Letters, 40: 3705–3709. doi: 10.1002/grl.50673

Murray, V., McBean, G., Bhatt, M.,Borsch, S., Cheong, T.S., Erian, W. F., Llosa, S., Nadim, F., Nunez, M., Oyun, R. and Suarez, A. G. (2012) Chapter 9: Case studies. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press.

van Oldenborgh, G.J., Drijfhout, S., van Ulden, A., Haarsma, R., Sterl, A., C. Severijns, C., Hazeleger, W. and Dijkstra, H. (2009) Western Europe is warming much faster than expected. Climate of the Past, 5: 1–12. doi: 10.5194/cp-5-1-2009

Schaller, N., Otto, F.E.L., van Oldenborgh, G.J., Massey, N.R., Sparrow, S. and Allen, M.R. (2014) The heavy precipitation event of May–June 2013 in the upper Danube and Elbe basins [in Explaining Extreme Events of 2013 from a Climate Perspective]. Bulletin of the American Meteorological Society, 95(9): S69–S72.

Seneviratne, S.I., Corti, T., Davin, E.L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B. and J.Teuling, A.J. (2010) Investigating soil moisture – climate interactions in a changing climate: A review. Earth-Science Reviews, 99( 3–4): 125–161. doi: 10.1016/j.earscirev.2010.02.004

Sippel, S., Otto, F. E. L., Flach, M. and van Oldenborgh, G. J. (2016) The role of anthropogenic warming in 2015 Central European heat wave. Bulletin of the American Meteorological Society, 97, S51–S56, doi: 10.1175/BAMS-D-16-0150.1.

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Massey, N., Jones, R., Otto, F.E.L., Aina, T., Wilson, S., Murphy, J.M., Hassell, D., Yamazaki, Y.H. and Allen, M.R. (2015) weather@home— development and validation of a very large ensemble modelling system for probabilistic event attribution. Quarterly Journal of the Royal Meteorological Society, 141: 1528–1545. doi: 10.1002/qj.2455

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European heatwave update: drought and extended-range forecasts

This chart shows information regarding probabilities for the 7-day mean anomalies of air temperature at 2 m to be in the lower or upper parts of several selectable categories derived from the ECMWF ensemble (ENS).

Another heatwave is currently affecting most of Europe with various and very high intensity levels. This follows heatwave conditions across Europe earlier this summer, which have now contributed to large parts of Europe also experiencing severe drought conditions.

The drought that started in early summer as a consequence of low precipitation amounts over four consecutive seasons across Europe is persisting into late summer. The precipitation deficit is still severe, causing low soil moisture, high vegetation stress, low river flows and water shortages in many areas across Europe. The extended-range hydrological forecasts from the Copernicus Emergency Management Service show that this situation is likely to continue into the late summer for most of western Europe.

EFAS sub-seasonal forecast with the severe low flow signal in many parts of Europe in the next six weeks, with the inset hydrograph highlighting the evolution of weekly mean river discharge in the southern part of the UK.

The surge of heat of 11–13 August, mainly affecting northern Spain, western France, Ireland and UK, can be seen in the short-range Extreme Forecast Index (EFI) for 2-metre temperature. Looking at the forecast issued one week ahead (5 August), the EFI product captured well the spatial pattern of the heatwave and gave a reasonable early warning for the event. The EFI product also highlights the very warm sea in the western Mediterranean.

These charts aim to provide pointers to areas where anomalous values of 2 m temperature are likely to occur based on the ECMWF ensemble forecast (ENS) system. You can access animated EFI charts here, where you can select desired times and climatology quantiles using the drop down menu. Date/time can also be selected using the slider underneath the chart or the play/pause symbols at the bottom left of the chart.

Our extended-range forecasts provide a broader overview for the coming four to six weeks. These forecasts are generally provided as seven-day averages in terms of anomalies relative to what is expected for the time of year.

For example, the 2 m temperature: Weekly mean anomalies chart shows for this week temperatures up to 10°C higher than average in parts of northern Spain, western France and southern England. Much of northern Spain, France, the UK, Belgium, Luxemburg, the Netherlands and western Germany will be up to 6°C higher than average. Next week, these extremes will have mostly dissipated, although a large part of northern Europe is predicted to see temperatures 3°C higher than average. Over the following weeks, this area of warmer-than-average temperatures moves to western Europe where it remains into September.

Despite the continuing warm weather, drought conditions are expected to ease over the next few weeks. Our Precipitation: Weekly mean anomalies product shows that, while this week precipitation remains at 30% lower than average over much of northern Europe, and as much as 60% lower in some regions, next week will bring higher-than-average precipitation to a region stretching from Scandinavia, across much of the UK, through France to the Mediterranean Basin. Parts of eastern Europe will also see higher-than-average precipitation. In late August, Portugal, Spain and parts of western France will see two weeks of lower-than-average precipitation. By early September, however, precipitation will be around average for most parts of Europe.

Screenshot of chart showing the seven-day mean anomaly of precipitation (in mm) for Monday 15 August to 22 August 2022. It can be seen that conditions will be close to average for much of Europe, with some areas experiencing slightly higher-than-average precipitation.

Looking further ahead still, our long-range, or seasonal, forecasts, which provide information on conditions up to seven months ahead, show that warmer-than-average temperatures are likely to persist over much of Europe until October, with southern and northern regions experiencing warmer-than-average temperatures to the end of the year. Precipitation is likely to remain lower than average in Portugal, as well as western parts of Spain and France.

ECMWF is actively working on improving the seasonal forecasts of extreme weather, such as droughts and heatwaves, for example, by collaborating with scientists across Europe in the CAFE project. In addition, ECMWF is providing open-source tools to calculate and compute thermal indices to assess heat impacts for weather forecasts.

Further reading on the Copernicus Atmosphere Monitoring Service website: Intense wildfire activity in southwestern Europe amid heatwaves and dry conditions.

Climate of Europe: Temperature, climate graph, Climate tables for Europe

Albania
Andorra
Armenia
Austria
Azerbaijan
Belarus
Belgium
Bosnia and Herzegovina
Bulgaria
Croatia
Cyprus
Czech Republic
Denmark
Estonia
Faroe Islands
Finland
France
Georgia
Germany
Gibraltar
Greece
Guernsey
Hungary
Iceland
Ireland
Isle of Man
Italy
Jersey
Kazakhstan
Latvia
Liechtenstein
Lithuania
Luxemburg
Macedonia
Malta
Moldova
Monaco
Montenegro
Norway
Poland
Portugal
Republic of Kosovo
Romania
Russian Federation
San Marino
Serbia
Slovakia
Slovenia
Spain
Sweden
Switzerland
The Netherlands
Turkey
Ukraine
United Kingdom

Europe has many different climates, but is dominated by Dfb.

Classifications

Classification	Count	Köppen-Geiger	Examples
Warm humid continental climate	479929	Dfb	Astana, Karaganda, Ust-Kamenogorsk, Pavlodar, Hakodate
Oceanic climate	331986	Cfb	Melbourne, Johannesburg, Soweto, Port Elizabeth, Canberra
Hot-summer Mediterranean climate	75750	Csa	Istanbul, Allahabad, Lucknow, Varanasi, Izmir
Humid subtropical climate	38850	Cfa	Tokyo, Hanoi, Sydney, Yokohama, Durban
Warm-summer Mediterranean climate	31122	Csb	Cape Town, Mitchells Plain, Blue Downs, Bellville, Elsiesrivier
Hot humid continental climate	19719	Dfa	Sapporo, Almaty, Aktobe, Sinŭiju, Matsumoto
Warm humid continental climate	19424	Dsb	Hacıbektaş, Kaza, Trespone, Gemur, Ağrı
Subarctic climate	17738	Dfc	Stepantsminda, Kumlistsikhe, Karzok, Soldeu, Perisher Valley
Cold semi-arid climates	9020	BSk	Baku, Ulaanbaatar, Yerevan, Asmara, Taraz
Hot humid continental climate	5475	Dsa	Ankara, Shymkent, Taldykorgan, Kargil, Khovaling
Subarctic climate	1018	Dwc
Hot semi-arid climates	505	BSh	Delhi, New Delhi, Hyderabad, Ahmedabad, Pune
Tropical rainforest climate	496	Af	Singapore, Medan, Kuala Lumpur, Pekanbaru, Wonosobo
Tundra climate	384	ET
Tropical savanna climate	342	Aw	Mumbai, ಬೆಂಗಳೂರು, Bangkok, Bengaluru, Ho Chi Minh City
Subpolar oceanic climate	331	Cfc	Miena
Subarctic climate	259	Dfd
Tropical monsoon climate	241	Am	Jakarta, Sylhet, Chittagong, Semarang, Makassar
Cold desert climates	238	BWk	Aktau, Kyzylorda, Atyrau, Leh, Sumur
Hot desert climates	120	BWh	Cairo, Alexandria, Giza, Dubai, Jodhpur
Warm humid continental climate	108	Dwb
Subarctic climate	64	Dsc	Sarchu
Mediterranean climate	33	Cs
Tropical savanna climate	9	As
Dry-summer maritime subalpine climate	4	Csc

Display all climates

Astana

The climate in Astana is cold and temperate. Astana has a significant amount of rainfall during the year. This is true even for the driest month. This location is classified as Dfb by Köppen and Geiger. The average annual temperature in Astana is 4.2 °C | 39.6 °F. About 401 mm | 15.8 inch of precipitation falls annually.