Convolutional conditional neural processes for local climate downscaling

A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep-learning techniques to be applied to off-the-grid spatio-temporal data. In contrast to existing methods that map from low-resolution model output to high-resolution predictions at a discrete set of locations, this model outputs a stochastic process that can be queried at an arbitrary latitude–longitude coordinate. The convCNP model is shown to outperform an ensemble of existing downscaling techniques over Europe for both temperature and precipitation taken from the VALUE intercomparison project. The model also outperforms an approach that uses Gaussian processes to interpolate single-site downscaling models at unseen locations. Importantly, substantial improvement is seen in the representation of extreme precipitation events. These results indicate that the convCNP is a robust downscaling model suitable for generating localised projections for use in climate impact studies.

Towards the Circular Soil Concept: Optimization of Engineered Soils for Green Infrastructure Application

At conventional construction sites, the removal of soil and other excavated materials causes enormous mass movement, with a significant climate impact and contribution to global CO2 release. This study aimed to generate a Circular Soil concept for reusing excavated materials by creating engineered soils for landscape construction at large building sites. Engineered soils act as a substitute for natural soils and fulfill vital technical and soil functions when installing an urban green infrastructure (GI). In a field study, the vegetation performance on engineered soils was evaluated to establish a methodological approach, to assess the applicability of the Circular Soil concept. First, the technical specifications (grain-size distribution) were modeled for intensive green roof and turfgrass applications. Then, the soil components were optimized, mixed, installed and tested for greenery purposes, focusing on plant growth performance indicators (vitality, projective cover ratio and grass-herb ratio) to assess the vegetation performance. The results showed that the engineered soils match the performance of the reference soil alternatives. In conclusion, the Circular Soil concept has a high potential to contribute considerably to sustainable on-site soil management and the circular economy. It can be applied on a larger scale for urban GI development and sustainable resources management in the landscaping and construction sector.

What Is the Impact of Mass Timber Utilization on Climate and Forests?

As the need to address climate change grows more urgent, policymakers, businesses, and others are seeking innovative approaches to remove carbon dioxide emissions from the atmosphere and decarbonize hard-to-abate sectors. Forests can play a role in reducing atmospheric carbon. However, there is disagreement over whether forests are most effective in reducing carbon emissions when left alone versus managed for sustainable harvesting and wood product production. Cross-laminated timber is at the forefront of the mass timber movement, which is enabling designers, engineers, and other stakeholders to build taller wood buildings. Several recent studies have shown that substituting mass timber for steel and concrete in mid-rise buildings can reduce the emissions associated with manufacturing, transporting, and installing building materials by 13%-26.5%. However, the prospect of increased utilization of wood products as a climate solution also raises questions about the impact of increased demand for wood on forest carbon stocks, on forest condition, and on the provision of the many other critical social and environmental benefits that healthy forests can provide. A holistic assessment of the total climate impact of forest product demand across product substitution, carbon storage in materials, current and future forest carbon stock, and forest area and condition is challenging, but it is important to understand the impact of increased mass timber utilization on forests and climate, and therefore also on which safeguards might be necessary to ensure positive outcomes. To thus assess the potential impacts, both positive and negative, of greater mass timber utilization on forests ecosystems and emissions associated with the built environment, The Nature Conservancy (TNC) initiated a global mass timber impact assessment (GMTIA), a five-part, highly collaborative research program focused on understanding the potential benefits and risks of increased demand for mass timber products on forests and identifying appropriate safeguards to ensure positive outcomes.

Climate impact of an optimised gas treatment on old landfills

It is a well-established fact that the quality and quantity of landfill gas (LFG) start declining after a landfill is closed to further waste intake. Conventional gas treatment and utilisation systems such as flares and gas-driven engines require a certain quality of LFG: specifically, a sufficient methane concentration. Various measures are utilised to maintain the necessary quality of LFG, including a turn-down of gas extraction rates and a shutdown of low-quality gas wells, resulting in a decline of LFG production. This, however, does not have to be the case. The low calorific value (LCV) LFG capture and treatment technology developed by e-flox and referred to in this article as ‘LCV LFG System’ can significantly increase the collection rate and the amount of treated methane in an old landfill. This article introduces such new treatment measures, describes gas capture calculation methodologies and presents actual results based on a medium-sized landfill in Germany. The study demonstrates, among other things, that the LCV LFG system can reduce the CO2 avoidance costs to roughly 10 €/tCO2eq. We present this new technology as a quick and straightforward measure of dealing with the climate issues related to methane emissions of old landfills.

Does Climate Change Influence Russian Agriculture? Evidence from Panel Data Analysis

Agriculture is one of the economic sectors primarily affected by climate change. This impact is very uneven, especially for countries with large territories. This paper examines the contribution of climate change to the improvement in agricultural productivity in Russia over the past two decades. Several ensembles of fixed effects regressions on yields and gross harvests of grain, fruits, and berries, potato, and vegetables were evaluated for a sample of 77 Russian regions over the 2002–2019 period. In contrast to similar studies of the climate impact on Russian agriculture, we considered a larger set of variables, including both Russian and global climate trends, technological factors, and producer prices. Russian weather trends such as winter softening and increase in summer heat have a significant but opposite effect on yields. An interesting finding is a significant and mostly positive influence of global climatic variables, such as the CO2 concentration, El Niño and La Niña events on both harvests and yields. Although technological factors are the main drivers of growth in Russian agricultural performance over the past 20 years, we found a strong positive effect on yield and gross harvest only for mineral fertilizers. The influence of the other variables is mixed, which is mainly due to data quality and aggregation errors.

Evaluation of CMIP6 Climate Models for Climate Change Impact Assessments in Upper Awash Basin, Ethiopia

Identifying GCMs that represent the climate of a specific area is crucial for climate change studies. However, the uncertainties in GCMs caused by computational constraints, such as coarser resolution, physical parameterizations, initializations, and model structures, make it imperative to identify a representative individual or group of GCM for a climate impact study. An advanced envelope-based multi-criteria selection approach was used to identify a subset of the most appropriate future GCMs in the Upper Awash Basin. The skill accounting is based on (1) the range of projected mean changes of climate variables, (2) range of variability in climate extremes and, (3) model run performance to represent historical climate data. Statistical downscaling and bias correction were made for the selected model runs. The downscaled and bias-corrected monthly values for precipitation are expected to increase from 0.42% to 2.82% in mid-century and 0.15% to 3.79% by the end century considering the SSP4.5 scenario. For SSP8.5, it increases from 1.45% to 5.51% and 2.57% to 9.78% in the respective periods. Likewise, under the SSP4.5 forcing scenario, the monthly average air temperature projected to be warmer, which increased from 0.68°C to 1.55°C during mid-century and 0.09°C to 1.92°C end-of-century. Meanwhile, for SSP8.5, the projection indicates an increment of 0.19°C to 1.98°C under mid-century and 2.37°C to 7.00°C end-century. The projected change of future precipitation and temperature in the study basin increases the precipitation intensities, wet days and dry spells due to high-temperature increment.