ALBERT

Description: Optical images of the Earth at very high spatial resolutions (VHR, typically 〈 5 m) are seeing rapid growth in volumes over the past 5 years, due in part to the fast-expanding constellations of CubeSats. Special preprocessing of these VHR images is required to ensure their geometric and radiometric consistency for quantitative analyses for a wide range of Earth and environmental sciences and applications. Here we describe a hierarchical normalization framework (HiNF) to achieve and evaluate geometric and radiometric normalization of these VHR images towards producing analysis ready data (ARD) of optical CubeSat images. We demonstrated HiNF at a spatially heterogeneous and temporally dynamic wetland site in northeastern Germany by generating a stack of temporally consistent ~ biweekly 5-m images over 8 years (2013–2020) at visible and near infrared bands (VNIR). The HiNF combined images from rigorously calibrated multispectral sensors onboard large satellites (Landsat-7/8 and Sentinel-2) and less well calibrated sensors onboard RapidEye (SmallSats) and PlanetScope (CubeSats). A two-stage radiometric normalization procedure produced two levels of image normalization and resulted in more normalized images that passed the quality control in time series compared to common one-stage procedures. The outcome of this novel procedure allows for downstream applications to balance between the quality and the quantity of available normalized CubeSat images in a time series. The HiNF provides a new approach to quantitative evaluations of radiometric normalizations using daily MODIS imagery as bridging benchmark data. The quantitative evaluations showed the HiNF resulted in greater normalization efficacy in the visible bands than in the NIR over the predominantly wetland area. The two normalization levels yielded statistically similar efficacy for the NIR band and the widely-used normalized difference vegetation index according to the Chow test (at significance level of 0.05) but less so for the visible bands. The HiNF facilitates generating ARD of optical CubeSat images and assuring their qualities through its demonstrated efficacy and its quantitative evaluation approach. Such ARD-quality time series of VHR images from CubeSats allow for improved analyses and quantitative applications of this new stream of multispectral images at spatial scales that are better related to ground measurements and environmental management in terrestrial ecosystems.

Description: Peatlands are areas with naturally accumulated thick layers of dead organic materials. While peatlands cover about 3% of the world’s land area, their carbon storage is estimated equivalent to ~30% of all soil carbon, ~75% of all atmospheric carbon, and as much carbon as all terrestrial biomass. Drained peatlands due to past human uses can emit carbon and be a key source of greenhouse gases while rewetted peatlands usually have significantly reduced CO2 emissions and can even become a carbon sink. However, quantifying the potential and limitations of reducing emissions by peatland rewetting is challenging. Carbon fluxes on peatlands are both spatially complex and temporally dynamic owing to their microtopography, changing water levels and associated vegetation status. Here we demonstrated the generation of temporally consistent ~biweekly 5-m images over 8 years (2013-2020) at visible and near infrared bands (VNIR) to track the temporal trajectories of vegetation and surface water and estimate cover-specific carbon fluxes at a rewetted peatland site in northeastern Germany (Figure 1). To ensure temporally-consistent multispectral images for the subsequent analyses of vegetation/water covers, we set up a two-stage normalization procedure that normalized the images from RapidEye (SmallSats) and PlanetScope (CubeSats) to rigorously calibrated multispectral sensors onboard large satellites (Landsat-7/8 and Sentinel-2). The two-stage normalization procedure produced two levels of image normalization that allows for downstream applications to balance between the quality and the quantity of available normalized CubeSat images in a time series. A quantitative evaluation approach using daily MODIS images as bridging benchmark data revealed that the temporal consistency in CubeSat images was comparable to that in Landsat and Sentinel-2 images, which confirmed the efficacy of the normalization procedure. The temporal information in the stack of normalized 5-m images helped us estimate the vegetation types and the changes in vegetation/surface water covers throughout the 8 years. The within-year time series of CubeSat images at the three visible and one near-infrared bands showed discernible differences among vegetation types at this peatland sites, which promises systemic mapping of vegetation compositions in peatlands using very-high-resolution CubeSat imagery time series over heterogeneous peatlands. We aggregated vegetation and surface water covers within each year into three condition categories at the peatland site, always emergent vegetation, always surface water, and alternating between vegetation and water. The estimated areas of the three condition categories closely covary with the measured water table depths at the site (Figure 2). The substantial areal expansion of always emergent vegetation at the site, that are captured by the CubeSat imagery time series, aligns well with the timing of three drought events (2016, 2018 and 2019) in this region. These surface covers and conditions at both high temporal and spatial resolutions from CubeSat images allow us to disaggregate ecosystem-scale measurements of CO2 and CH4 fluxes by the eddy covariance (EC) tower at the site into cover-specific fluxes. We attribute CO¬2 and CH4 fluxes measured by EC over 8 years to the three surface condition categories through a nonparametric approach to flux decomposition using annual maps of surface condition categories and half-hourly EC-measurement footprints. The disaggregated carbon fluxes improve our upscaled estimates of carbon emission/sequestration over rewetted peatland sites. Such spatial-temporally-resolved carbon fluxes in dynamic and heterogeneous peatlands will contribute to better informed restoration and protection of peatlands.

Description: Extreme events such as storms are difficult to predict more than a week in advance. Additionally, the climate change signal in variables such as wind speed and precipitation is low compared to the noise of weather which further complicates the attribution of a single event to climate change. The damage done by mid-latitude storms, however, makes it imperative to study how they change with climate change. The mid-latitude cyclone Eunice hit the South of the UK on February 18, 2022 and caused the first red weather warning ever issued in London by the UK Met Office. Here, we assess changes to Eunice due to anthropogenic forcing. As climate models typically perform poorly at representing storms, we use the ECMWF ensemble prediction system as its initialised numerical models show skill in the prediction of the storm. Using changed boundary conditions for the greenhouse gases and 3D ocean conditions, we create counterfactual scenarios for storm Eunice for a pre-industrial climate and for an increased CO2 climate for three initialisation dates (eight, four, and two days before the event). We compare distributions of wind gusts between pre-industrial, current, and future climates by initialisation date. We also compare storm tracks qualitatively in the three experiments using a storm tracking algorithm. Atmospheric conditions are currently unchanged in the counterfactual simulations, with possible implications for the estimated climate change signal. We explore this through tge use of multiple initialisation dates but further work is needed to create realistic initial conditions for the atmosphere.

Description: The relationship between cumulative CO2 emissions and CO2-induced warming is determined by the Transient Climate Response to Emissions (TCRE), but total anthropogenic warming also depends on non-CO2 forcing, complicating the interpretation of emissions budgets based on CO2 alone. An alternative is to frame emissions budgets in terms of CO2-forcing-equivalent (CO2-fe) emissions—the CO2 emissions that would yield a given total anthropogenic radiative forcing pathway. Unlike conventional “CO2-equivalent” emissions, these are directly related to warming by the TCRE and need to fall to zero to stabilize warming: hence, CO2-fe emissions generalize the concept of a cumulative carbon budget to multigas scenarios. Cumulative CO2-fe emissions from 1870 to 2015 inclusive are found to be 2,900 ± 600 GtCO2-fe, increasing at a rate of 67 ± 9.5 GtCO2-fe/yr. A TCRE range of 0.8–2.5°C per 1,000 GtC implies a total budget for 0.6°C of additional warming above the present decade of 880–2,750 GtCO2-fe, with 1,290 GtCO2-fe implied by the Coupled Model Intercomparison Project Phase 5 median response, corresponding to 19 years' CO2-fe emissions at the current rate. ©2018. American Geophysical Union. All Rights Reserved.

Description: Economically viable and reliable building systems and tool sets are being sought, examined and tested for extraterrestrial infrastructure buildup. This project focused on a unique architecture weaving the robotic building construction technology with designs for assisting rapid buildup of initial operational capability Lunar and Martian bases. The project aimed to study new methodologies to construct certain crucial infrastructure elements in order to evaluate the merits, limitations and feasibility of adapting and using such technologies for extraterrestrial application. Current extraterrestrial settlement buildup philosophy holds that in order to minimize the materials needed to be flown in, at great transportation costs, strategies that maximize the use of locally available resources must be adopted. Tools and equipment flown as cargo from Earth are proposed to build required infrastructure to support future missions and settlements on the Moon and Mars.

Description: Reduced-complexity climate models (RCMs) are critical in the policy and decision making space, and are directly used within multiple Intergovernmental Panel on Climate Change (IPCC) reports to complement the results of more comprehensive Earth system models. To date, evaluation of RCMs has been limited to a few independent studies. Here we introduce a systematic evaluation of RCMs in the form of the Reduced Complexity Model Intercomparison Project (RCMIP). We expect RCMIP will extend over multiple phases, with Phase 1 being the first. In Phase 1, we focus on the RCMs' global-mean temperature responses, comparing them to observations, exploring the extent to which they emulate more complex models and considering how the relationship between temperature and cumulative emissions of CO2 varies across the RCMs. Our work uses experiments which mirror those found in the Coupled Model Intercomparison Project (CMIP), which focuses on complex Earth system and atmosphere–ocean general circulation models. Using both scenario-based and idealised experiments, we examine RCMs' global-mean temperature response under a range of forcings. We find that the RCMs can all reproduce the approximately 1 ∘C of warming since pre-industrial times, with varying representations of natural variability, volcanic eruptions and aerosols. We also find that RCMs can emulate the global-mean temperature response of CMIP models to within a root-mean-square error of 0.2 ∘C over a range of experiments. Furthermore, we find that, for the Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP)-based scenario pairs that share the same IPCC Fifth Assessment Report (AR5)-consistent stratospheric-adjusted radiative forcing, the RCMs indicate higher effective radiative forcings for the SSP-based scenarios and correspondingly higher temperatures when run with the same climate settings. In our idealised setup of RCMs with a climate sensitivity of 3 ∘C, the difference for the ssp585–rcp85 pair by 2100 is around 0.23∘C(±0.12 ∘C) due to a difference in effective radiative forcings between the two scenarios. Phase 1 demonstrates the utility of RCMIP's open-source infrastructure, paving the way for further phases of RCMIP to build on the research presented here and deepen our understanding of RCMs.

Description: Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state-of-the-art knowledge in an internally self-consistent modeling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain-specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low-emissions SSP1-1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850–1900, using an observationally based historical warming estimate of 0.8°C between 1850–1900 and 1995–2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above preindustrial in the long-term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections.

Description: The achievement of several sustainable development goals and the Paris Climate Agreement depends on rapid progress towards sustainable food and land systems in all countries. We have built a flexible, collaborative modeling framework to foster the development of national pathways by local research teams and their integration up to global scale. Local researchers independently customize national models to explore mid-century pathways of the food and land use system transformation in collaboration with stakeholders. An online platform connects the national models, iteratively balances global exports and imports, and aggregates results to the global level. Our results show that actions toward greater sustainability in countries could sum up to 1 Mha net forest gain per year, 950 Mha net gain in the land where natural processes predominate, and an increased CO2 sink of 3.7 GtCO2e yr−1 over the period 2020–2050 compared to current trends, while average food consumption per capita remains above the adequate food requirements in all countries. We show examples of how the global linkage impacts national results and how different assumptions in national pathways impact global results. This modeling setup acknowledges the broad heterogeneity of socio-ecological contexts and the fact that people who live in these different contexts should be empowered to design the future they want. But it also demonstrates to local decision-makers the interconnectedness of our food and land use system and the urgent need for more collaboration to converge local and global priorities.

Description: There is an urgent need for countries to transition their national food and land-use systems toward food and nutritional security, climate stability, and environmental integrity. How can countries satisfy their demands while jointly delivering the required transformative change to achieve global sustainability targets? Here we present a collaborative approach developed with the FABLE -Food, Agriculture, Biodiversity, Land, and bioEnergy- Consortium to reconcile both global and national elements for developing national food and land use system pathways. This approach includes three key features: (1) global targets, (2) country-driven multi-objective pathways, and (3) multiple iterations of pathway refinement informed by both national and international impacts. This approach strengthens policy coherence and highlights where greater national and international ambition is needed to achieve global goals (e.g. the SDGs). We discuss how this could be used to support future climate and biodiversity negotiations and what further developments would be needed.