The 4.2 ka event is not remarkable in the context of Holocene climate variability.

The "4.2 ka event" is a commonly described abrupt climate excursion that occurred about 4200 years ago. However, the extent to which this event is coherent across regional and larger scales is unclear. To objectively assess climate excursions in the Holocene we compile 1142 paleoclimate datasets that span all continents and oceans and include a wide variety of archive and proxy types. We analyze these data to determine the timing, significance and spatial imprint of climate excursions using an objective method that quantifies local, regional and global significance. Site-level excursions in temperature and hydroclimate are common throughout the Holocene, but significant global-scale excursions are rare. The most prominent excursion occurred 8200 years ago, when cold and dry conditions formed a large, significant excursion centered in the North Atlantic. We find additional significant excursions between 1600 and 1000 years ago, which agree with tree-ring data and annual-scale paleoclimate reconstructions, adding confidence and context to our findings. In contrast, although some datasets show significant climate excursions 4200 years ago, they do not occur in large, coherent spatial regions. Consequently, like most other periods in the Holocene, the "4.2 ka event" is not a globally significant climate excursion.

The Terrestrial Biosphere Model Farm.

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901-2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.

Land carbon models underestimate the severity and duration of drought's impact on plant productivity.

The ability to accurately predict ecosystem drought response and recovery is necessary to produce reliable forecasts of land carbon uptake and future climate. Using a suite of models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we assessed modeled net primary productivity (NPP) response to, and recovery from, drought events against a benchmark derived from tree ring observations between 1948 and 2008 across forested regions of the US and Europe. We find short lag times (0-6 months) between climate anomalies and modeled NPP response. Although models accurately simulate the direction of drought legacy effects (i.e. NPP decreases), projected effects are approximately four times shorter and four times weaker than observations suggest. This discrepancy between observed and simulated vegetation recovery from drought reveals a potential critical model deficiency. Since productivity is a crucial component of the land carbon balance, models that underestimate drought recovery time could overestimate predictions of future land carbon sink strength and, consequently, underestimate forecasts of atmospheric CO2.