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.
Numerical models have been developed to investigate and understand responses of biogeochemical cycle to global changes. Steady state, when a system is in dynamic equilibrium, is generally required to initialize these model simulations. However, the spin‐up process that is used to achieve steady state pose a great burden to computational resources, limiting the efficiency of global modeling analysis on biogeochemical cycles. This study introduces a new Semi‐Analytical Spin‐Up (SASU) to tackle this grand challenge. We applied SASU to Community Land Model version 5 and examined its computational efficiency and accuracy. At the Brazil site, SASU is computationally 7 times more efficient than (or saved up to 86% computational cost in comparison with) the traditional native dynamics (ND) spin‐up to reach the same steady state. Globally, SASU is computationally 8 times more efficient than the accelerated decomposition spin‐up and 50 times more efficient than ND. In summary, SASU achieves the highest computational efficiency for spin‐up on site and globally in comparison with other spin‐up methods. It is generalizable to wide biogeochemical models and thus makes computationally costly studies (e.g., parameter perturbation ensemble analysis and data assimilation) possible for a better understanding of biogeochemical cycle under climate change.
Subseasonal prediction fills the gap between weather forecasts and seasonal outlooks. There is evidence that predictability on subseasonal timescales comes from a combination of atmosphere, land, and ocean initial conditions. Predictability from the land is often attributed to slowly varying changes in soil moisture and snow pack, while predictability from the ocean is attributed to sources such as the El Niño Southern Oscillation. Here we use a unique set of subseasonal reforecast experiments to quantify the respective roles of atmosphere, land, and ocean initial conditions on subseasonal prediction skill over land. The majority of prediction skill for global surface temperature in weeks 3-4 comes from the atmosphere, while ocean initial conditions become important after week 4. In the CESM2 subseasonal prediction system, the land initial state does not contribute to surface temperature prediction skill in weeks 3-6 and climatological land conditions lead to higher skill, challenging our current understanding. However, land-atmosphere coupling is important in week 1. Results are similar over most land regions except South America, where ocean initialization is more important. Subseasonal precipitation prediction skill (weeks 3-6) also comes primarily from the atmosphere initial condition, except for a few regions for which the ocean state becomes important.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.