Climate models have gotten steadily more sophisticated over the past 5 decades, representing a wider range of timescales and spatial scales and capturing increasing degrees of complexity and interconnections among different components of the climate system. Climate models use mathematical tools to represent physical processes like evaporation of water from the ocean’s surface, moisture transport in the atmosphere, or mixing of heat in the ocean; the better the math is at mimicking the processes, the more accurately the models can explain past variations and predict future conditions.
Toward this end, in 2003 the U.S. Climate Variability and Predictability (U.S. CLIVAR) national research program, with funding from the National Oceanic and Atmospheric Administration (NOAA) and the National Science Foundation (NSF), assembled a group of climate process teams (CPTs) to focus on improving global climate models. Each team comprised 7–12 principal investigators from academia, partners from modeling centers, and several postdoctoral researchers (some of whom were embedded at modeling centers).
Each CPT tackled a particular physical process (e.g., mixing by internal waves in the ocean or formation of clouds in the atmosphere) and how it is represented in one or more global climate models. The CPTs have a universal mission: improving the representation of physical processes in climate and weather models to help make better predictions of the Earth system. Over the years, CPTs have made significant advances in model performance, allowing us to better represent, understand, and predict climate change.
Funding for these teams was renewed in 2010, but now this funding is running out. Yet we still require improvements to climate models to more accurately predict how environmental conditions will vary in coming years and decades. Thus, the CPTs’ vital work is not finished, and this effort must continue to receive support.
Past CPTs have produced important improvements in global climate models. New convective parameterizations [Bretherton and Park, 2009], improved ocean model representations of shear-driven mixing [Jackson et al., 2008], bottom boundary mixing [Legg et al., 2006], and mixed-layer submesoscale restratification [Fox-Kemper et al., 2008] are all now included in one or more state-of-the-art global climate models.
By focusing in depth on a single problem for a finite time, CPTs have accelerated scientific understanding of particular processes. Successful examples of this in the oceanographic community include a more complete picture of the ocean internal wave energy distribution [MacKinnon et al., 2017] and new research into ocean submesoscale processes that are not typically resolved by global climate models [Boccaletti et al., 2007].
Boundary layer cloud processes have proven to be very difficult to parameterize in global climate models, yet they play a critical role in modulating Earth’s climate, making their accurate representation in climate models necessary to understanding Earth’s climate and response to forcing [Bretherton and Park, 2009; Guo et al., 2015]. But thanks to CPTs, targeted studies have now led to a firmer grasp of the role of clouds in Earth’s energy balance.
CPTs also led focused research efforts to improve representation of sea ice and iceberg processes in climate models that have led to better climate predictions of iceberg calving size distribution over the Antarctic Peninsula [Stern et al., 2016].
The CPTs were instrumental in helping the involved scientific communities to develop strong and enduring links between academia and modeling centers, allowing better use of resources and expertise. Waterhouse et al. , for example, synthesized ocean mixing data from a variety of observing platforms over a long period to provide an observational benchmark for improved mixing parameterizations in global ocean models. This synthesis product has the potential to increase our understanding of global ocean processes, such as the meridional overturning circulation, along with the heat and energy balance of the global climate.
Currently funded CPTs are coming to an end, and members of the U.S. CLIVAR Process Study and Model Improvement Panel perceived a need to review the benefits the teams provide and to devise a plan for future efforts. The panel decided to seek input from the observational, modeling, and theoretical communities on how best to achieve a translation of process understanding into climate model improvements.
To collect feedback on the utility of CPTs, the panel sent surveys to representatives of U.S. modeling centers, process studies, recent satellite missions, recent CPTs, and U.S. CLIVAR working groups. The results of these surveys confirmed broad community interest for a scoping workshop to identify processes for which newly available observational data and understanding could inform future model improvements. Subsequently, a workshop was held at Princeton University in October 2015 that brought together 90 leaders from the community to discuss a path forward.
All of the outreach and information gathered from the community emphasized that CPT activities have advanced climate models further than would have been possible with traditional funding mechanisms and smaller groups of principal investigators working on such projects. This is evident in a comprehensive U.S. CLIVAR white paper [Subramanian et al., 2016] that shows the need for launching a new CPT-like effort and addresses the questions of what form such an effort ought to take, which areas need to be tackled, and how such an effort might be implemented.
The white paper recommends that CPT activities continue in the future, drawing on feedback from the surveys and the workshop. The community’s consensus is that new activities should retain many successful aspects of the past CPTs. These include the formation of teams involving modelers, observationalists, and theoreticians. Team members should be drawn from modeling centers as well as academia, and funds should support postdocs dedicated to the task.
The white paper also lends strong support to approaches involving multiple modeling centers and multiple agencies that are well suited to delivering sustainable and comprehensive improvements to climate models. New developments should enlarge the scope of such activities to consider not only teams built around the theme of improving the representation of a specific process but also new teams focused on coupled processes and model component interactions to address specific biases or climate phenomena. New activities should also consider the emerging computational and expanded observational capabilities.
The U.S. CLIVAR survey demonstrates that the climate science community broadly supports future mechanisms to facilitate the translation of process understanding into improvements in climate models over the coming decade. We encourage our colleagues to form the cross-institutional collaborations among modelers, theoreticians, and observationalists that will enable these model improvements, and we hope funding agencies will continue to welcome these team efforts.
Boccaletti, G., R. Ferrari, and B. Fox-Kemper (2007), Mixed layer instabilities and restratification, J. Phys. Oceanogr., 37, 2228–2250, https://doi.org/10.1175/JPO3101.1.
Bretherton, C. S., and S. Park (2009), A new moist turbulence parameterization in the Community Atmosphere Model, J. Clim., 22, 3422–3448, https://doi.org/10.1175/2008JCLI2556.1.
Fox-Kemper, B., R. Ferrari, and R. Hallberg (2008), Parameterization of mixed layer eddies: I. Theory and diagnosis, J. Phys. Oceanogr., 38, 1145–1165, https://doi.org/10.1175/2007JPO3792.1.
Guo, H., et al. (2015), CLUBB as a unified cloud parameterization: Opportunities and challenges, Geophys. Res. Lett., 42, 4540–4547, https://doi.org/10.1002/2015GL063672.
Jackson, L., R. Hallberg, and S. Legg (2008), A parameterization of shear-driven turbulence for ocean climate models, J. Phys. Oceanogr., 38, 1033–1053, https://doi.org/10.1175/2007JPO3779.1.
Legg, S., R. W. Hallberg, and J. B. Girton (2006), Comparison of entrainment in overflows simulated by z-coordinate, isopycnal and non-hydrostatic models, Ocean Modell., 11, 69–97, https://doi.org/10.1016/j.ocemod.2004.11.006.
MacKinnon, J., et al. (2017), Climate process team on internal-wave driven ocean mixing, Bull. Am. Meteorol. Soc., https://doi.org/10.1175/BAMS-D-16-0030.1, in press.
Stern, A. A., A. Adcroft, and O. Sergienko (2016), The effects of Antarctic iceberg calving-size distribution in a global climate model, J. Geophys. Res. Oceans, 121, 5773–5788, https://doi.org/10.1002/2016JC011835.
Subramanian, A., et al. (2016), Translating process understanding to improve climate models, U.S. CLIVAR White Pap. 2016-3, 48 pp., U.S. Clim. Var. and Predict. Program, Washington, D. C., https://doi.org/10.5065/D63X851Q.
Waterhouse, A. F., et al. (2014), Global patterns of diapycnal mixing from measurements of the turbulent dissipation rate, J. Phys. Oceanogr., 44, 1854–1872, https://doi.org/10.1175/JPO-D-13-0104.1.
—Caroline C. Ummenhofer (email: firstname.lastname@example.org), Department of Physical Oceanography, Woods Hole Oceanographic Institution, Mass.; Aneesh Subramanian, Scripps Institution of Oceanography, University of California, San Diego, La Jolla; and Sonya Legg, Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, N.J.
Editor’s Note, 3 April 2018: For further information on CPTs and current information on the status of funding opportunities, please see https://usclivar.org/climate-process-teams.
Ummenhofer, C. C.,Subramanian, A., and Legg, S. (2017), Maintaining momentum in climate model development, Eos, 98, https://doi.org/10.1029/2017EO086501. Published on 15 November 2017.
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