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Donat, Markus G.

Publications and source records attributed to Donat, Markus G..

Initialized Earth system prediction from subseasonal to decadal timescales

Initialized Earth system predictions are made by starting a numerical prediction model in a state as consistent as possible to observations, and running it forward in time for up to ten years. Skillful predictions at time slices from subseasonal to seasonal (S2S), seasonal to interannual (S2I) and seasonal to decadal (S2D) offer information useful for various stakeholders, from agriculture to water resource management, and human and infrastructure safety. In this Review, we examine the processes influencing predictability, and discuss estimates of skill across S2S, S2I and S2D timescales. There are encouraging signs that skillful predictions can be made: at S2S timescales, there has been some skill in predicting the Madden-Julian Oscillation and North Atlantic Oscillation; at S2I in predicting the El Niño-Southern Oscillation; and at S2D, in predicting variability in North Atlantic sea surface temperatures. However, challenges remain, and future work must prioritise reducing model error, more effectively communicating forecasts to users, and increasing process and mechanistic understanding that could increase predictive skill and, in turn, confidence. As numerical models progress towards Earth system models, initialized predictions are expanding to include prediction of sea-ice, air pollution, terrestrial and ocean biochemistry which can bring clear benefit to society and various stakeholders.

climate prediction↗

Global Climate

The 2019 global lower tropospheric temperature (LTT), which encompasses the atmosphere from the surface to ~10 km, ranked second warmest in seven datasets and first or third in the remaining two (Fig. 2.7). These records extend back to 1958 using radiosonde (balloon-borne instrumentation) data and one reanalysis dataset (JRA55), which demonstrate reasonable agreement with the 40+ year satellite record (since late 1978) and two other reanalysis datasets (since 1979 and 1980, ERA5 and MERRA2, respectively). Aweak El Niño contributed to increased global temperatures as 2019 values were +0.44° to +0.68°C higher than the 1981–2010 average (depending on the dataset), being just slightly cooler (~0.07°C on average) than the record warm year of 2016. At least fourof the five globally complete datasets (ERA5, MERRA2, JRA55, RSS, UAH) recorded each of the four months—June, September, November, and December—as experiencing their warmest monthly global LTT.

58 GEOSCIENCES↗