An analysis of ways to decarbonize conference travel after COVID-19

Nature Nature Research 583 (2020) 356-360


Milan Klower, Deborah Hopkins, Myles Allen, James Higham

Constraining projections using decadal predictions

Geophysical Research Letters American Geophysical Union 47:18 (2020) e2020GL087900


Daniel J Befort, Christopher O'Reilly, Antje Weisheimer


There is increasing demand for robust, reliable and actionable climate information for the next 1 to 50 years. This is challenging for the scientific community as the longest initialized predictions are limited to 10 years (decadal predictions). Thus, to provide seamless information for the upcoming 50 years, information from decadal predictions and uninitialized projections need to be merged. In this study, the ability to obtain valuable climate information beyond decadal time-scales by constraining uninitialized projections using decadal predictions is assessed. The application of this framework to surface temperatures over the North Atlantic Subpolar Gyre region, shows that the constrained uninitialized sub-ensemble has higher skill compared to the overall projection ensemble also beyond ten years when information from decadal predictions is no longer available. Though showing the potential of such a constraining approach to obtain climate information for the near-term future, its utility depends on the added value of initialization.

Seasonal forecasts of the 20th century

Bulletin of the American Meteorological Society American Meteorological Society 101:8 (2020) E1413-E1426


Antje Weisheimer, Daniel Befort, David Macleod, Timothy Palmer, Chris O’Reilly, Kristian Strømmen


New seasonal retrospective forecasts for 1901-2010 show that skill for predicting ENSO, NAO and PNA is reduced during mid-century periods compared to earlier and more recent high-skill decades.

Forecasts of seasonal climate anomalies using physically-based global circulation models are routinely made at operational meteorological centers around the world. A crucial component of any seasonal forecast system is the set of retrospective forecasts, or hindcasts, from past years which are used to estimate skill and to calibrate the forecasts. Hindcasts are usually produced over a period of around 20-30 years. However, recent studies have demonstrated that seasonal forecast skill can undergo pronounced multi-decadal variations. These results imply that relatively short hindcasts are not adequate for reliably testing seasonal forecasts and that small hindcast sample sizes can potentially lead to skill estimates that are not robust. Here we present new and unprecedented 110-year-long coupled hindcasts of the next season over the period 1901 to 2010. Their performance for the recent period is in good agreement with those of operational forecast models. While skill for ENSO is very high during recent decades, it is markedly reduced during the 1930s to 1950s. Skill at the beginning of the 20th Century is, however, as high as for recent high-skill periods. Consistent with findings in atmosphere-only hindcasts, a mid-century drop in forecast skill is found for a range of atmospheric fields including large-scale indices such as the NAO and the PNA patterns. As with ENSO, skill scores for these indices recover in the early 20th Century suggesting that the mid-century drop in skill is not due to lack of good observational data.

A public dissemination platform for our hindcast data is available and we invite the scientific community to explore them.

Beyond skill scores: exploring sub‐seasonal forecast value through a case‐study of French month‐ahead energy prediction

Quarterly Journal of the Royal Meteorological Society Wiley 146:733 (2020) 3623-3637


Joshua Dorrington, Isla Finney, Tim Palmer, Antje Weisheimer


We quantify the value of sub‐seasonal forecasts for a real‐world prediction problem: the forecasting of French month‐ahead energy demand. Using surface temperature as a predictor, we construct a trading strategy and assess the financial value of using meteorological forecasts, based on actual energy demand and price data. We show that forecasts with lead times greater than two weeks can have value for this application, both on their own and in conjunction with shorter‐range forecasts, especially during boreal winter. We consider a cost/loss framework based on this example, and show that, while it captures the performance of the short‐range forecasts well, it misses the marginal value present in medium‐range forecasts. We also contrast our assessment of forecast value to that given by traditional skill scores, which we show could be misleading if used in isolation. We emphasise the importance of basing assessment of forecast skill on variables actually used by end‐users.

FAIR v1.3: a simple emissions-based impulse response and carbon cycle model



Christopher J Smith, Piers M Forster, Myles Allen, Nicholas Leach, Richard J Millar, Giovanni A Passerello, Leighton A Regayre