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The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data-model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model-data benchmarking; and data assimilation and ecological forecasting. This community-driven approach is a key to meeting the pressing needs of science and society in the 21st century.

Summary: Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear.We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for > 700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits.While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R2 (0.12–0.49, 0.15–0.42, and 0.25–0.56) and increased NRMSE (3.58–18.24%, 6.27–11.55%, and 7.0–33.12%) compared with nonspatial random cross‐validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability.These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications. [ABSTRACT FROM AUTHOR]

Hector is an open-source reduced-complexity climate–carbon cycle model that models critical Earth system processes on a global and annual basis. Here, we present an updated version of the model, Hector V3.2.0 (hereafter Hector V3), and document its new features, implementation of new science, and performance. Significant new features include permafrost thaw, a reworked energy balance submodel, and updated parameterizations throughout. Hector V3 results are in good general agreement with historical observations of atmospheric CO 2 concentrations and global mean surface temperature, and the future temperature projections from Hector V3 are consistent with more complex Earth system model output data from the sixth phase of the Coupled Model Intercomparison Project. We show that Hector V3 is a flexible, performant, robust, and fully open-source simulator of global climate changes. We also note its limitations and discuss future areas for improvement and research with respect to the model's scientific, stakeholder, and educational priorities. [ABSTRACT FROM AUTHOR]

Hector, an open-source reduced complexity climate-carbon cycle model. Hector is a computationally efficient source of climate information, capable of completing a run in a fraction of a second. Hector models critical Earth system processes on a global and annual basis. Here we present an updated version of the model, Hector V3. In this manuscript, we document Hector's new features, and implementation of new science (e.g., radiative forcing calculations, carbon cycle, etc.). Hector V3 results are in good agreement with historical observations of CO2 concentrations and global mean surface temperature, and its future temperature projections are consistent with more complex Earth System Model output data from the Sixth Coupled Model Intercomparison Project. We document that Hector V3 is a flexible, performant, and robust simulator of contemporary and 21st-century global climate, and in closing, discuss future areas of improvement and research with respect to the model's scientific, stakeholder, and educational priorities. [ABSTRACT FROM AUTHOR]

Biodiversity projections with uncertainty estimates under different climate, land-use, and policy scenarios are essential to setting and achieving international targets to mitigate biodiversity loss. Evaluating and improving biodiversity predictions to better inform policy decisions remains a central conservation goal and challenge. A comprehensive strategy to evaluate and reduce uncertainty of model outputs against observed measurements and multiple models would help to produce more robust biodiversity predictions. We propose an approach that integrates biodiversity models and emerging remote sensing and in-situ data streams to evaluate and reduce uncertainty with the goal of improving policy-relevant biodiversity predictions. In this article, we describe a multivariate approach to directly and indirectly evaluate and constrain model uncertainty, demonstrate a proof of concept of this approach, embed the concept within the broader context of model evaluation and scenario analysis for conservation policy, and highlight lessons from other modeling communities. [ABSTRACT FROM AUTHOR]

Ecosystem models are often calibrated and/or validated against derived remote sensing data products, such as MODIS leaf area index. However, these data products are generally based on their own models, whose assumptions may not be compatible with those of the ecosystem model in question, and whose uncertainties are usually not well quantified. Here, we develop an alternative approach whereby we modify an ecosystem model to predict full-range, high spectral resolution surface reflectance, which can then be compared directly against airborne and satellite data. Specifically, we coupled the two-stream representation of canopy radiative transfer in the Ecosystem Demography model (ED2) with a leaf radiative transfer model (PROSPECT 5) and a simple soil reflectance model. We then calibrated this model against reflectance observations from the NASA Airborne VIsible/InfraRed Imaging Spectrometer (AVIRIS) and survey data from 54 temperate forest plots in the northeastern United States. The calibration successfully constrained the posterior distributions of model parameters related to leaf biochemistry and morphology and canopy structure for five plant functional types. The calibrated model was able to accurately reproduce surface reflectance and leaf area index for sites with highly varied forest composition and structure, using a single common set of parameters across all sites. We conclude that having dynamic vegetation models directly predict surface reflectance is a promising avenue for model calibration and validation using remote sensing data. [ABSTRACT FROM AUTHOR]

Here we present results from the first phase of the Reduced Complexity Model Intercomparison Project (RCMIP). RCMIP is a systematic examination of reduced complexity climate models (RCMs), which are used to complement and extend the insights from more complex Earth System Models (ESMs), in particular those participating in the Sixth Coupled Model Intercomparison Project (CMIP6). In Phase 1 of RCMIP, with 14 participating models namely ACC2, AR5IR (2 and 3 box versions), CICERO-SCM, ESCIMO, FaIR, GIR, GREB, Hector, Held et al. two layer model, MAGICC, MCE, OSCAR and WASP, we highlight the structural differences across various RCMs and show that RCMs are capable of reproducing global-mean surface air temperature (GSAT) changes of ESMs and historical observations. We find that some RCMs are capable of emulating the GSAT response of CMIP6 models to within a root-mean square error of 0.2°C (of the same order of magnitude as ESM internal variability) over a range of scenarios. Running the same model configurations for both RCP and SSP scenarios, we see that the SSPs exhibit higher effective radiative forcing throughout the second half of the 21st Century. Comparing our results to the difference between CMIP5 and CMIP6 output, we find that the change in scenario explains approximately 46% of the increase in higher end projected warming between CMIP5 and CMIP6. This suggests that changes in ESMs from CMIP5 to CMIP6 explain the rest of the increase, hence the higher climate sensitivities of available CMIP6 models may not be having as large an impact on GSAT projections as first anticipated. A second phase of RCMIP will complement RCMIP Phase 1 by exploring probabilistic results and emulation in more depth to provide results available for the IPCC's Sixth Assessment Report author teams. [ABSTRACT FROM AUTHOR]

Long-term (1982-1995) observations of the ground thermal regime of a drained thaw-lake basin in the Pechora Lowlands of the Russian European north revealed a high spatial and temporal variability in the ground temperature response to artificial drainage. The thermal response was controlled by the atmospheric climate and by evolution of the landsurface following drainage. Observed changes in permafrost conditions were related to three climatic subperiods identified from air and ground temperature trends. The first (1982-1984) was characterized by gradual ground cooling associated with partial formation of permafrost patches under the initial stage of formation of marshy meadows. The second (1985-1987) involved strong ground cooling, resulting in the formation of a subsurface permafrost layer beneath most of the basin. The third (1988-1995) was marked by a gradual increase in annual mean ground temperature, promoting partial permafrost degradation under marshy meadows and willow stands. Initially, newly aggraded permafrost remained under peat mounds and tundra meadows. The spatial pattern of permafrost change can be attributed to heterogeneous landsurface evolution and variable snow thickness. Four distinct ground temperature regimes are distinguished: (i) thawed ground, (ii) deep permafrost, (iii) unstable permafrost and (iv) stable permafrost. [ABSTRACT FROM AUTHOR]