Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization

International audienceBy coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850–near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: – How does the Earth system respond to forcing? – What are the origins and consequences of systematic model biases? – How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs

Measuring Winds From Space to Reduce the Uncertainty in the Southern Ocean Carbon Fluxes: Science Requirements and Proposed Mission

Strong winds in Southern Ocean storms drive air-sea carbon and heat fluxes. These fluxes are integral to the global climate system and the wind speeds that drive them are increasing. The current scatterometer constellation measuring vector winds remotely undersamples these storms and the higher winds within them, leading to potentially large biases in Southern Ocean wind reanalyses and the fluxes that derive from them. This observing system design study addresses these issues in two ways. First, we describe an addition to the scatterometer constellation, called Southern Ocean Storms -- Zephyr, to increase the frequency of independent observations, better constraining high winds. Second, we show that potential reanalysis wind biases over the Southern Ocean lead to uncertainty over the sign of the net winter carbon flux. More frequent independent observations per day will capture these higher winds and reduce the uncertainty in estimates of the global carbon and heat budgets

Potential climatic transitions with profound impact on Europe

We discuss potential transitions of six climatic subsystems with large-scale impact on Europe, sometimes denoted as tipping elements. These are the ice sheets on Greenland and West Antarctica, the Atlantic thermohaline circulation, Arctic sea ice, Alpine glaciers and northern hemisphere stratospheric ozone. Each system is represented by co-authors actively publishing in the corresponding field. For each subsystem we summarize the mechanism of a potential transition in a warmer climate along with its impact on Europe and assess the likelihood for such a transition based on published scientific literature. As a summary, the ‘tipping’ potential for each system is provided as a function of global mean temperature increase which required some subjective interpretation of scientific facts by the authors and should be considered as a snapshot of our current understanding. <br/

Time scales of climate response

A coupled atmosphere–ocean general circulation model (AOGCM) is integrated to a near-equilibrium state with the normal, half-normal, and twice-normal amounts of carbon dioxide in the atmosphere. Most of the ocean below the surface layers achieves 70 % of the total response almost twice as fast when the changes in radiative forcing are cooling as compared to the case when they are warming the climate system. In the cooling case, the time to achieve 70 % of the equilibrium response in the midoceanic depths is about 500–1000 yr. In the warming case, this response time is 1300–1700 yr. In the Pacific Ocean and the bottom half of the Atlantic Ocean basins, the response is similar to the global response in that the cooling case results in a shorter response time scale. In the upper half of the Atlantic basin, the cooling response time scales are somewhat longer than in the warming case due to changes in the oceanic thermohaline circulation. In the oceanic surface mixed layer and atmosphere, the response time scale is closely coupled. In the Southern Hemisphere, the near-surface response time is slightly faster in the cooling case. However in the Northern Hemisphere, the near-surface response times are faster in the warming case by more than 500 yr at times during the integrations. In the Northern Hemisphere, both the cooling and warming cases have much shorter response time scales than found in the Southern Hemisphere. Oceanic mixing of heat is the key in determining these time scales. It is shown that the model’s simulation of present-day radiocarbon and chlorofluorocarbon (CFC) distributions compares favorably to the observations indicating that the quantitative time scales may be realistic. 1