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for the global latitude-longitude grid used in Numerical Weather Prediction [Buckeridge & Scheichl, 2010]. The key idea is to use conditional coarsening in the N-S direction while increasing the mesh spacing uniformly in the E-W direction.  The Figure below shows an example of a semi-coarsened mesh. Using

in the projected loss of ice from the Antarctic ice sheet (Figure 2a). These revised projections show good agreement with independent work carried out at the Met Office for UKCP18 5 . 1 Daangendorf, S. et al (2019), Nature Climate Change: Persistent acceleration in global sea-level rise since

saw extremely high levels of tropical cyclone activity in the Western North Pacific. This was found to be mainly caused by sea surface warming in the eastern and central Pacific, with human influence largely increasing the odds of this event. Zhang et al 2016 Funk, C., Shukla, S., Hoell, A. and Livneh

the Northern Hemisphere (90N-20N). The graphs below display the accuracy of these two forecasts, with the smaller the error being a better value forecast to airline operators. Performance measure: ≤ 2.85m/s for Wind and ≤ 0.56K for Temperature (based on 12-month mean values) Root Mean Square Vector

., Morton, D., Giglio, L., Chen, Y., van der Werf, G., Kasibhatla, P., DeFries, R., Collatz, G., Hantson, S., Kloster, S., Bachelet, D., Forrest, M., Lasslop, G., Li, F., Mangeon, S., Melton, J., Yue, C., Randerson, J. (2017): A human-driven decline in global burned area, Science, 356, 1356-1361

value forecast to airline operators. Performance measure: ≤ 2.85m/s for Wind and ≤ 0.56K for Temperature (based on 12-month mean values) Root Mean Square Vector Error 4.00 3.80 3.60 3.40 3.20 3.00 2.80 Northern Hemisphere T+24 Wind (m/s) at 250hPa Monthly Rolling 12-month mean Target Latest value

Hemisphere (90N-20N). The graphs below display the accuracy of these two forecasts, with the smaller the error being a better value forecast to airline operators. Performance measure: ≤ 2.85m/s for Wind and ≤ 0.56K for Temperature (based on 12-month mean values) Root Mean Square Vector Error 4.00 3.80