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Forests are currently a substantial carbon sink globally. Many climate change mitigation strategies leverage forest preservation and expansion, but rely on forests storing carbon for decades to centuries. Yet climate-driven disturbances pose critical risks to the long-term stability of forest carbon. We quantify the climate drivers that influence wildfire and climate stress-driven tree mortality, including a separate insect-driven tree mortality, for the contiguous United States for current (1984-2018) and project these future disturbance risks over the 21st century. We find that current risks are widespread and projected to increase across different emissions scenarios by a factor of >4 for fire and >1.3 for climate-stress mortality. These forest disturbance risks highlight pervasive climate-sensitive disturbance impacts on US forests and raise questions about the risk management approach taken by forest carbon offset policies. Our results provide US-wide risk maps of key climate-sensitive disturbances for improving carbon cycle modeling, conservation and climate policy.

Carbon offsets are widely used by individuals, corporations, and governments to mitigate their greenhouse gas emissions on the assumption that offsets reflect equivalent climate benefits achieved elsewhere. These climate-equivalence claims depend on offsets providing real and additional climate benefits beyond what would have happened, counterfactually, without the offsets project. Here, we evaluate the design of California's prominent forest carbon offsets program and demonstrate that its climate-equivalence claims fall far short on the basis of directly observable evidence. By design, California's program awards large volumes of offset credits to forest projects with carbon stocks that exceed regional averages. This paradigm allows for adverse selection, which could occur if project developers preferentially select forests that are ecologically distinct from unrepresentative regional averages. By digitizing and analyzing comprehensive offset project records alongside detailed forest inventory data, we provide direct evidence that comparing projects against coarse regional carbon averages has led to systematic over-crediting of 30.0 million tCO2 e (90% CI: 20.5-38.6 million tCO2 e) or 29.4% of the credits we analyzed (90% CI: 20.1%-37.8%). These excess credits are worth an estimated $410 million (90% CI: $280-$528 million) at recent market prices. Rather than improve forest management to store additional carbon, California's forest offsets program creates incentives to generate offset credits that do not reflect real climate benefits.

MetSim is a freely available, open source Python based model for simulation and disaggregation of meteorological variables with applications in the environmental and Earth sciences. MetSim can be used to generate spatially distributed sub-daily timeseries of incoming shortwave radiation, outgoing longwave radiation, air pressure, specific humidity, relative humidity, vapor pressure, precipitation, and air temperature given daily timeseries of minimum temperature, maximum temperature, and precipitation. Based on previously developed algorithms, we demonstrate that MetSim is able to closely reproduce their results while providing a number of advantages and improvements. We implemented automated testing to decrease errors during development and to improve reproducibility, modularized the algorithms to allow for extensions and modifications, and implemented robust single and multi-node parallelism. We describe the overall architecture, algorithms, and capabilities of MetSim by describing its four major modules. These are split into a model driver, solar geometry module, meteorological simulation module, and temporal disaggregation module. We also describe the available options and parameters that MetSim exposes to its users and analyze MetSim's scalability for large datasets. [ABSTRACT FROM AUTHOR]