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PowerPoint Presentation

Asia: Monthly Climate Outlook January to October Issued: April 2022 Overview Current Status Outlooks Annex 1 – Supplemental Information Climate Outlook Asia: January to October Overview Asia Current Status and Outlook – Temperature Asia Current Status and Outlook – Rainfall Global Outlook

PowerPoint Presentation

Likely Climate Outlook Asia: January to October Overview 4 Global Outlook - Temperature Outlook: 3-Month Outlook May to July - Temperature With the backdrop of a warming climate and the loss of the cooling influence of La Niña, most land areas are likely to be warmer than normal over the next few

News

2016: one of the warmest two years on record

to the annual average for 2016, which was about 1.1C above the long term average from 1850 to 1900. However, the main contributor to warming over the last 150 years is human influence on climate from increasing greenhouse gases in the atmosphere.” The estimated figure of 0.77°C ±0.1 °C above

PowerPoint Presentation

El Niño–Southern Oscillation (ENSO) prediction indicating the possibility of change to neutral conditions over the next three months, La Niña is having less of a cooling influence on the forecast. In the context of climate change, this means that most of the the world’s land area is likely to see

Microsoft Word - 2023_08_storm_babet_v1.docx

Mountains of Northern Ireland and parts of the West Midlands, East Anglia and south-east England, and over 100mm in the wetter locations (in some places over 150mm). The process used to generate the maps attempts to take topographical influences into account, and this shows a significant area of eastern

metoffice_weatherwarriors_firstexplorations_22-04_weather-action-plan.pdf

manage and mitigate the risks in the ways you have identified? • Could climate change have an influence on these events in the future? Using the action plan template (found on page 4), ask everyone to use their notes to complete the template. To help groups consider the services they could use

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Urban heat forecasts could be enhanced with machine learning

with citizen observations and urban land cover data to aid the machine learning, allows the temperature forecast to learn from previous events and account for natural and man-made influences on the temperature, which can vary significantly within relatively short distances in cities. Lead author

guide_co_production-of-services.pdf

of climate basics and skills for effective communication of climate information as well as an appreciation of how climate services can translate into actionable decisions and influence performancecritical sectors and users. The initiative is also pioneering a new regular Climate Café platform

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