A document by Harro Joseph Jongen. Click on the document to view its contents.

Comprehensive land models are subject to significant parametric uncertainty, which can be hard to quantify due to the large number of parameters and high model computational costs. We constructed a large parameter perturbation ensemble (PPE) for the Community Land Model vesion 5.1 with biogeochemistry configuration (CLM5.1-BGC). We performed more than 2000 simulations perturbing 211 parameters across six forcing scenarios. This provides an expansive dataset, which can be used to identify the most influential parameters on a wide range of output variables globally, by biome, or by plant functional type. We found that parameter effects can exceed scenario effects and that a small number of parameters explains a large fraction of variance across our ensemble. The most important parameters can differ regionally and also based on the forcing scenario. The software infrastructure developed for this experiment has greatly reduced the human and computer time needed for CLM PPEs, which can facilitate routine investigation of parameter sensitivity and uncertainty, as well as automated calibration.

Improved representation of urban processes in Earth System Models (ESMs) is a pressing need for climate modeling and climate-driven urban energy studies. Despite recent improvements to its fully coupled building energy model, the current Community Land Model Urban (CLMU) in the Community Earth System Model (CESM) lacks the infrastructure to model air-conditioning (AC) adoption explicitly. This undermines CESM’s fidelity in modeling urban climate and energy use, and limits its use in climate and energy risk assessments. Here, we establish an explicit-AC-adoption parameterization scheme in CESM that represents AC adoption explicitly through an AC adoption rate parameter in the Building Energy Model of CLMU, and build a present-day, global, survey-based, and spatially explicit AC adoption rate dataset at country and sub-country level that is integrated within CESM. The new dataset can be leveraged for other ESMs or global-scale models and analyses. The explicit AC adoption scheme and the AC adoption rate dataset significantly improve the accuracy of anthropogenic heat modeling due to AC in CESM. The new parameterization scheme makes it possible to evaluate the effects of changing AC adoption on global urban energy and climate using CESM. These developments enhance CESM in its use for climate impact assessments under future climate and socioeconomic development scenarios, and represent continued efforts in better representing urban processes and coupled human-urban-Earth dynamics in ESMs.

Urbanization (urban land change) alters local and regional climate through biophysical and biogeochemical processes and has broader climate impacts through atmospheric feedbacks. Despite its critical climate impacts, urban areas have rarely been explicitly represented in global-scale Earth system models, and physically-based transient urban representations are missing as well. The Community Earth System Model (CESM) has a physically based urban land parameterization – Community Land Model Urban (CLMU) – that is sufficiently detailed to represent the properties and processes in the urban environment. We improve this model by implementing a dynamic urban scheme to represent transient land use due to urbanization. Leveraging existing urbanization projection datasets, the new scheme allows urban extent to be updated annually during a climate simulation while conserving energy and mass balance during the transition. Land-only simulation results confirm the robustness of the new dynamic urban scheme and demonstrate the direct local climate effects induced by urban land expansion. In the appendix of this paper, we also document two recent improvements to the building energy scheme of CLMU.

A framework to enable Earth system predictability research on the subseasonal timescale is developed with the Community Earth System Model, version 2 (CESM2) using two model configurations that differ in their atmospheric components. One configuration uses the Community Atmosphere Model, version 6 (CAM6) with its top near 40 km, referred to as CESM2(CAM6). The other employs the Whole Atmosphere Community Climate Model, version 6 (WACCM6) whose top extends to ~ 140 km in the vertical and it includes fully interactive tropospheric and stratospheric chemistry (CESM2(WACCM6)). Both configurations were used to carry out subseasonal reforecasts for the time period 1999 to 2020 following the Subseasonal Experiment’s (SubX) protocol. CESM2(CAM6) and CESM2(WACCM6) show very similar subseasonal prediction skill of 2-meter temperature, precipitation, the Madden-Julian Oscillation (MJO), and North Atlantic Oscillation (NAO) to the Community Earth System Model, version 1 with the Community Atmosphere Model, version 5 (CESM1(CAM5)) and to operational models. CESM2(CAM6) and CESM2(WACCM6) reforecast sets provide a comprehensive dataset for predictability research of multiple Earth system components, including three-dimensional output for many variables, and output specific to the mesosphere and lower-thermosphere (MLT) region. We show that MLT variability can be predicted ~ 10 days in advance of sudden stratospheric warming events. Weekly real-time forecasts with CESM2(WACCM6) contribute to the multi-model mean ensemble forecast used to issue the NOAA weeks 3-4 outlooks. As a freely available community model, both CESM2 configurations can be used to carry out additional experiments to elucidate sources of subseasonal predictability.