Shaun Lovejoy
McGill University

Biography

Following a B.A. and M.A. in theoretical physics from Trinity College, Cambridge, in 1981 Shaun Lovejoy earned his PhD in Physics from McGill University, Montreal (Canada). He is has been on faculty at McGill since 1985 and is currently a professor of physics.
 The beginning of Lovejoy’s career coincided with the nonlinear revolution. Following his discovery of wide range scaling (fractality) in cloud images (Science, 1982), he began a long collaboration with colleague Daniel Schertzer. Together, they developed and applied scaling ideas in the geosciences, contributing to the explosive growth of nonlinear geophysics including the modeling and empirical analyses and characterization of geosystems over wide ranges of scales. The most important advances of this period include cascades as the generic multifractal process, generalized (anisotropic) scale invariance, universal multifractals, (causal) space-time multifractal modeling of geofields and the establishment of wide range scaling in the horizon and vertical directions as well as in time. 
In 2013 Lovejoy showed that the conventional weather - climate dichotomy had to be replaced by the trichotomy of weather – macroweather - climate and in 2015 he pointed out that the standard scalebound paradigm underestimated atmospheric variability by a factor of a quadrillion (1015). He has also shown how scaling can be used to make more accurate macroweather (“long range”) forecasts (the STOChastic Seasonal and Interannual Prediction System; StocSIPS) as well as climate projections to 2100 that have much smaller uncertainties than conventional models. 

Many of these developments are reviewed in his monograph “The weather and climate: emergent laws and multifractal cascades”, (with D. Schertzer, 496pp, Cambridge University Press, 2013). In addition, an up to date non-specialist survey of this scaling paradigm in atmospheric and climate science “Weather, macroweather and the climate: our random yet predictable atmosphere” (334pp, Oxford University Press, 2019) has recently appeared. Lovejoy has published over 200 journal papers, 3 books and over 60 book chapters. Other application areas include hydrology, precipitation, floods and river networks, topography, geogravity, geomagnetism, volcanic activity, earthquakes, biogeosystems, macroevolution. Lovejoy´s work is widely cited (Google scholar: 17800 citations, h-index=64, ISI h-index: 46). 

In 1989, Lovejoy co-founded the Nonlinear Processes in Geophysics scientific division at the European Geosciences Union (EGU) and in 1994 he was founding co-editor of the joint AGU-EGU journal Nonlinear Processes in Geophysics. From its inception in 1997, was an active member of AGU’s Nonlinear Geophysics (NG) focus group of which he was Vice president (2006-2008), then President (2008-2012). From 2013-2016, he was president of EGU’s Nonlinear Processes in geophysics division. In 2015 he gave the NG focus group’s Lorenz Lecture and in 2016 he became an AGU fellow. In 2016, he was named Fessenden professor at McGill University. In 2019 he was awarded the EGU’s Richardson medal. 

Abstract: The Future of Climate Models

Since the first climate models in the 1970’s, algorithms and computer speeds have increased by a factor of ≈1017 allowing the simulation of more and more processes at finer and finer resolutions. Yet, the spread of the members of the multi-model ensemble (MME) of the Climate Model Intercomparison Project (CMIP) used in last year’s 6th IPCC Assessment Report was larger than ever: model uncertainty, in the sense of MME uncertainty, has increased. Even if the holy grail is still kilometric scale models, bigger may not be better. Why model structures that live for ≈ 15 minutes only to average them over factors of several hundred thousand in order to produce decadal climate projections?

I argue that alongside the development of “seamless” (unique) weather-climate models that chase ever smaller—and mostly irrelevant—details, the community should seriously invest in the development of stochastic macroweather models.  Such models exploit the statistical laws that are obeyed at scales longer than the lifetimes of planetary scale structures, beyond the deterministic prediction limit (≈ 10 days).  I argue that the conventional General Circulation Models and these new macroweather models are complementary in the same way that statistical mechanics and continuum mechanics are equally valid with the method of choice determined by the application.

Candidates for stochastic macroweather models are now emerging, those based on the Fractional Energy Balance Equation (FEBE) are particularly promising. The FEBE is an update and generalization of the classical Budyko-Sellers energy balance models, it respects the symmetries of scaling and energy conservation and it already allows for both state-of-the-art monthly and seasonal, interannual temperature forecasts and multidecadal projections. I demonstrate this with 21st century FEBE climate projections for global mean temperatures. Overall, the projections agree with the CMIP5 and CMIP6 multi-model ensembles and the FEBE parametric uncertainty is about half of the MME structural uncertainty. Without the FEBE, uncertainties are so large that climate policies (mitigation) are largely decoupled from climate consequences (warming) allowing policy makers too much “wiggle room”. The lower FEBE uncertainties will help avoid a looming “uncertainty crisis”. 

Both model types are complementary, a fact demonstrated by showing that CMIP global mean temperatures can be accurately projected using such stochastic macroweather models (validating both approaches). Unsurprisingly, they can therefore be combined to produce an optimum hybrid model in which the two model types are used as copredictors: when combined the various uncertainties are reduced even further.