Le 4 juillet 2018 : Flood damage costs under the sea level rise with warming of 1.5 °C and 2 °C
http://iopscience.iop.org/article/10.1088/1748-9326/aacc76
Abstract
We estimate a median global sea level rise up to 52 cm (25–87 cm, 5th–95th percentile) and up to 63 cm (27−112 cm, 5th—95th percentile) for a temperature rise of 1.5 °C and 2.0 °C by 2100 respectively. We also estimate global annual flood costs under these scenarios and find the difference of 11 cm global sea level rise in 2100 could result in additional losses of US$ 1.4 trillion per year (0.25% of global GDP) if no additional adaptation is assumed from the modelled adaptation in the base year. If warming is not kept to 2 °C, but follows a high emissions scenario (Representative Concentration Pathway 8.5), global annual flood costs without additional adaptation could increase to US$ 14 trillion per year and US$ 27 trillion per year for global sea level rise of 86 cm (median) and 180 cm (95th percentile), reaching 2.8% of global GDP in 2100. Upper middle income countries are projected to experience the largest increase in annual flood costs (up to 8% GDP) with a large proportion attributed to China. High income countries have lower projected flood costs, in part due to their high present-day protection standards. Adaptation could potentially reduce sea level induced flood costs by a factor of 10. Failing to achieve the global mean temperature targets of 1.5 °C or 2 °C will lead to greater damage and higher levels of coastal flood risk worldwide.
https://www.nature.com/articles/s41467-018-05442-8
Abstract
ENSO
Le 29/08/2018 : climate.gov/enso
Coral Reef Watch
Le 29/08/2018 : coralreefwatch.noaa.gov
Climate Prediction Center
Le 29/08/2018 : cpc.ncep.noaa.gov
Polar Science Center
Le 29/08/2018 : psc.apl.uw.edu
Historique des Climactualités (l'Arctique est mentionné en premier ; en bleu les valeurs minimales, en jaune les maximales)
Juin 2018 : 9.19 + 14.59 = 23.78
Mai 2018 : 11.02 + 10.65 = 21.67
Avril 2018 : 12.82 + 6.33 = 18.15
Mars 2018 : 13.87 + 3.50 = 17.37
Février 2018 : 13.68 + 2.31 = 15.99
Janvier 2018 : 12.68 + 3.46 = 16.14
Décembre 2017 : 11.76 + 7.13 = 18.89
Novembre 2017 : 10.07 + 13.25 = 23.32
Octobre 2017 : 7.82 + 17.27 = 25.09
Septembre 2017 : pas de stats
http://iopscience.iop.org/article/10.1088/1748-9326/aacc76
Abstract
We estimate a median global sea level rise up to 52 cm (25–87 cm, 5th–95th percentile) and up to 63 cm (27−112 cm, 5th—95th percentile) for a temperature rise of 1.5 °C and 2.0 °C by 2100 respectively. We also estimate global annual flood costs under these scenarios and find the difference of 11 cm global sea level rise in 2100 could result in additional losses of US$ 1.4 trillion per year (0.25% of global GDP) if no additional adaptation is assumed from the modelled adaptation in the base year. If warming is not kept to 2 °C, but follows a high emissions scenario (Representative Concentration Pathway 8.5), global annual flood costs without additional adaptation could increase to US$ 14 trillion per year and US$ 27 trillion per year for global sea level rise of 86 cm (median) and 180 cm (95th percentile), reaching 2.8% of global GDP in 2100. Upper middle income countries are projected to experience the largest increase in annual flood costs (up to 8% GDP) with a large proportion attributed to China. High income countries have lower projected flood costs, in part due to their high present-day protection standards. Adaptation could potentially reduce sea level induced flood costs by a factor of 10. Failing to achieve the global mean temperature targets of 1.5 °C or 2 °C will lead to greater damage and higher levels of coastal flood risk worldwide.
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| Figure 1. (a) Global temperature pathways of CMIP5 models (table S1) from RCP2.6 and RCP4.5 satisfying 1.5 and 2.0 °C by end-of-century (relative to 1986−2005, plus 0.61 °C to show relative to pre-industrial) and RCP8.5 scenario. (b) Global sea-level projections estimated using process-based approach for 1.5 °C and 2.0 °C temperature pathways and RCP8.5_J14. Line and shaded areas are median and 17th−84th percentile range respectively. |
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Le 14 août 2018 : A novel probabilistic forecast system predicting anomalously warm 2018-2022 reinforcing the long-term global warming trendhttps://www.nature.com/articles/s41467-018-05442-8
Abstract
In a changing climate, there is an ever-increasing societal demand for accurate and reliable interannual predictions. Accurate and reliable interannual predictions of global temperatures are key for determining the regional climate change impacts that scale with global temperature, such as precipitation extremes, severe droughts, or intense hurricane activity, for instance. However, the chaotic nature of the climate system limits prediction accuracy on such timescales. Here we develop a novel method to predict global-mean surface air temperature and sea surface temperature, based on transfer operators, which allows, by-design, probabilistic forecasts. The prediction accuracy is equivalent to operational forecasts and its reliability is high. The post-1998 global warming hiatus is well predicted. For 2018–2022, the probabilistic forecast indicates a warmer than normal period, with respect to the forced trend. This will temporarily reinforce the long-term global warming trend. The coming warm period is associated with an increased likelihood of intense to extreme temperatures. The important numerical efficiency of the method (a few hundredths of a second on a laptop) opens the possibility for real-time probabilistic predictions carried out on personal mobile devices.
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| Attribution of observed global-mean surface air temperature (GMT) and sea surface temperature (SST). a, b The total (red) annual, (purple) 5-year and (blue) 10-year variations in GMT and SST measured from 1880 are decomposed (through an attribution method based on multivariate linear regression onto volcanic eruptions, aerosol concentration, and greenhouse gas concentration2) into c, d a forced contribution and e, f a residual. g, h Relative variance of forced and residual GMT and SST changes as a function of the duration of these changes. Variations are mainly controlled by the residual, rather than forcing on interannual to decadal timescales. The observed GMT are from NASA GISS temperature data, and SST is from the NOAA ERSSTv5 record |
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ENSO
Le 29/08/2018 : climate.gov/enso
The odds of El Niño emerging in the tropical Pacific by fall have dropped slightly to 60% (from 65%), but remain at 70% by winter.
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GISS L-OTI anomalies de températures vs 1951-1980
Le 29/08/2018 : data.giss.nasa.gov
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| Note: Gray areas signify missing data. Note: Ocean data are not used over land nor within 100km of a reporting land station. |
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Le 29/08/2018 : coralreefwatch.noaa.gov
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| NOAA Coral Reef Watch's most recent Four-Month Coral Bleaching Heat Stress Outlook is below. This figure shows the distribution of the lowest heat stress levels predicted by at least 60% of the model ensemble members. In other words, there is a 60% chance that the displayed heat stress levels will occur. |
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Climate Prediction Center
Le 29/08/2018 : cpc.ncep.noaa.gov
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Polar Science Center
Le 29/08/2018 : psc.apl.uw.edu
Average Arctic sea ice volume in July 2018 was 17,200 km3. This value is the 6th lowest on record about 1800 km3 above the June record that was set in 2017 with 15,400 km3 . Ice volume was 42% below the maximum in 1979 and 26% below the mean value for 1979-2017. June 2018 ice volume falls close to the long term trend line. [à actualiser, car concerne le mois précédent]
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| Fig.1 Arctic sea ice volume anomaly from PIOMAS updated once a month. Daily Sea Ice volume anomalies for each day are computed relative to the 1979 to 2017 average for that day of the year. Tickmarks on time axis refer to 1st day of year. The trend for the period 1979- present is shown in blue. Shaded areas show one and two standard deviations from the trend. Error bars indicate the uncertainty of the monthly anomaly plotted once per year. |
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Arctic Data archive system (ADS)
Le 29/08/2018 : ads.nipr.ac.jp |
| Arctic Sea Ice Extent [million km2] |
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| Antarctic Sea Ice Extent [million km2] |
Août 2018 : 4,8 + 17,7 = 22,5
Juillet 2018 : 6.67 + 16.44 = 23.11Juin 2018 : 9.19 + 14.59 = 23.78
Mai 2018 : 11.02 + 10.65 = 21.67
Avril 2018 : 12.82 + 6.33 = 18.15
Mars 2018 : 13.87 + 3.50 = 17.37
Février 2018 : 13.68 + 2.31 = 15.99
Janvier 2018 : 12.68 + 3.46 = 16.14
Décembre 2017 : 11.76 + 7.13 = 18.89
Novembre 2017 : 10.07 + 13.25 = 23.32
Octobre 2017 : 7.82 + 17.27 = 25.09
Septembre 2017 : pas de stats
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| urtikan |
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| canalblog |
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| terresacree |
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| matyvanille |
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