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The impact of climate change on crops and agricultural yield is an actual threat while being a challenging issue due to the high complexity of factors that intervene at the local scale of the crop. Assessing it, requires the use of coupled models climate-phenology, meanwhile methods to identify management and genotypes suitable for local future conditions, in order to sustain adaptation strategies. We present the implementation and use of a new integrated climate-phenology adaptation support modeling system based on regional CORDEX climate models and the CERES Maize model from DSSAT platform, with new modules for optimal management and genotype identification using a hybrid method: deterministic modeling and -ML/ genetic algorithms. It was run as a regional pilot over Romania, operating in real-time in interaction with users, performing agro-climate projections (combination of fertilization, sowing date, soil) and providing best crop management simulated under climate change projections. Multi-model ensemble simulations for two climate scenarios RCP4.5 and RCP8.5 and twelve management scenarios show new results for the region. For the actual genotype we find a projected mean decrease in yield in both climate scenarios for all sowing dates and fertilization levels tested, response shown to be sensitive to initial soil parameters. This response was linked to two factors: a shorter growing season by up to 10 % and a loss of fertilization efficiency in a warmer climate. A warning points to results showing a narrowing of agro-management opportunities for crop yield but in opposite it is shown a significant role of optimal genotype-range identification that may provide crop solutions under climate change even in extreme years. Identifying best genotype under warmer climate along sets of six cross-parameter simulations show systematic lower values of the maximal yields, but emphasizes genotype windows of increases in the intermediate yield values in scenarios compared to actual climate. The highest harvest sensitivity to genotype is shown to be to changes in the thermal time to juvenil respectively to maturity stage under warmer climate. The results sustain using a deterministic coupled modeling system combined with data-driven modeling for identifying optimal adaptation including fertilization paths that contribute to climate change mitigation. [ABSTRACT FROM AUTHOR]

As aviation's contribution to anthropogenic climate change is increasing, industry aims at reducing the aviation climate effect. However, the large contribution of non-CO2 effects to the total climate effect of aviation and their large variability for each individual flight inhibit finding appropriate guidance. Here, we present a method for the simplified calculation of CO2 equivalent emissions, expressed using the physical climate metrics ATR100 or AGWP100, from CO2 and non-CO2 effects for a given flight, exclusively based on the aircraft seat category as well as the origin and destination airports. The simplified calculation method estimates non-CO2 climate effects of air traffic as precisely as possible, without detailed information on the actual flight route, actual fuel burn, and current weather situation. For this purpose, we evaluate a global data set containing detailed flight trajectories, flight emissions, and climate responses, and derive a set of regression formulas for climate effects, which we call climate effect functions, as well as regression formulas for fuel consumption and NOx emissions. Compared to previous studies, this method is available for a larger number of aircraft types, including most commercial airliners with seat capacities starting from 101 passengers, and delivers more specific results through a clustering approach. The climate effects calculated using the climate effect functions derived in this study exhibit a mean absolute relative error of 15.0 % and a root mean square error of 1.24 nK with respect to results from the climate response model AirClim. The climate effect functions are designed for climate footprint assessments, but would not create an incentive in an emission trading system, for which detailed information on the current weather as well as the actual flight route and profile would be required. [ABSTRACT FROM AUTHOR]

While the IPCC's physical science report usually assesses a handful of future scenarios, the IPCC Sixth Assessment Working Group III report (AR6 WGIII) on climate mitigation assesses hundreds to thousands of future emissions scenarios. A key task is to assess the global-mean temperature outcomes of these scenarios in a consistent manner, given the challenge that the emission scenarios from different integrated assessment models come with different sectoral and gas-to-gas coverage and cannot all be assessed consistently by complex Earth System Models. In this work, we describe the "climate assessment" workflow and its methods, including infilling of missing emissions and emissions harmonisation as applied to 1,202 mitigation scenarios in AR6 WGIII. We evaluate the global-mean temperature projections and effective radiative forcing characteristics (ERF) of climate emulators FaIRv1.6.2, MAGICCv7.5.3, and CICERO-SCM, discuss overshoot severity of the mitigation pathways using overshoot degree years, and look at an interpretation of compatibility with the Paris Agreement. We find that the lowest class of emission scenarios that limit global warming to "1.5°C (with a probability of greater than 50%) with no or limited overshoot" includes 90 scenarios for MAGICCv7.5.3, and 196 for FaIRv1.6.2. For the MAGICCv7.5.3 results, "limited overshoot" typically implies exceedance of median temperature projections of up to about 0.1°C for up to a few decades, before returning to below 1.5°C by or before the year 2100. For more than half of the scenarios of this category that comply with three criteria for being "Paris-compatible", including net-zero or net-negative greenhouse gas (GHG) emissions, are projected to see median temperatures decline by about 0.3-0.4°C after peaking at 1.5-1.6°C in 2035-2055. We compare the methods applied in AR6 with the methods used for SR1.5 and discuss the implications. This article also introduces a 'climate-assessment' Python package which allows for fully reproducing the IPCC AR6 WGIII temperature assessment. This work can be the start of a community tool for assessing the temperature outcomes related to emissions pathways, and potential further work extending the workflow from emissions to global climate by downscaling climate characteristics to a regional level and calculating impacts. [ABSTRACT FROM AUTHOR]

Air pollution is a major sustainability challenge - and future anthropogenic precursor and greenhouse gas emissions will greatly affect human well-being. While mitigating climate change can reduce air pollution both directly and indirectly, distinct policy levers can affect these two interconnected sustainability issues across a wide range of scenarios. We help to assess such issues by presenting a public Tool for Air Pollution Scenarios (TAPS) that can flexibly construct and assess a variety of climate and air quality emissions pathways through its coupling with socioeconomic modeling of climate change mitigation. In this study, we develop and implement TAPS with three components: recent global and fuel-specific anthropogenic emissions inventories, scenarios of emitting activities to 2100 from the MIT Economic Projection and Policy Analysis model (EPPA), and emissions intensity trends based on recent scenario data from the Greenhouse Gas - Air Pollution Interactions and Synergies (GAINS) model. An initial application shows that in scenarios with less climate and pollution policy ambition, near-term air quality improvements from existing policies are eclipsed by long-term emissions increases - particularly from industrial processes that combine sharp production growth with less stringent pollution controls in developing regions. Additional climate actions would substantially reduce fossil fuel related air pollutant emissions (such as sulfur and nitrogen oxides), while further pollution controls would lead to larger reductions for ammonia and organic carbon. Future TAPS applications could explore diverse regional and global policies that affect these emissions, using pollutant emissions results to drive global atmospheric chemical transport models to study the scenarios' health impacts. [ABSTRACT FROM AUTHOR]

Cities concentrate people, wealth, emissions, and infrastructures, thus representing a challenge and an opportunity for climate change mitigation and adaptation. This places an urgent demand for accurate urban climate projections to help organizations and individuals making climate smart-decisions. However, most of the state-of-the-art global and regional climate models have an oversimplified representation of (or completely neglect) urban climate processes. Here, we use the city of Paris as a case study to show that this is the case for the fifth (and latest) generation reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5) and for simulations employing the widely used bulk bare rock approach to urban climate parameterization. Subsequently, we leveraged on the hourly resolution of ERA5 and the Satellite Application Facility Land Surface Analysis (LSA-SAF) land surface temperature product to demonstrate the significant added value of employing the SURFEX land-surface model coupled to Town Energy Balance (TEB) urban canopy model in simulating the Parisian Surface Urban Heat Island (SUHI) during daytime and the urban heat island during both daytime and nighttime. Our results showed the significant added value of SURFEX-TEB in reproducing the observed daytime and nighttime Parisian urban heat island effect. An annual average bias magnitude reduction of 0.5 °C was observed for daytime and around 1.5 °C for nighttime when compared to ERA5 and bare rock approach. Also, SURFEX-TEB revealed an overall better performance in reproducing the observed daytime SUHI, whilst the added value of SURFEX-TEB was lower during nighttime (but still slightly better than ERA5 and the bare rock approach), due to the lack of land-atmosphere feedbacks in the proposed offline framework. Finally, the offline SURFEX-TEB framework applied here demonstrates the ability to simulate the urban climate, which is an asset to build urban climate projections that allow the development of mitigation and adaptation strategies. [ABSTRACT FROM AUTHOR]

Producing targeted climate information at the local scale, including major sources of climate change projection uncertainty for diverse emissions scenarios, is essential to support climate change mitigation and adaptation efforts. Here, we present the first chain of computationally efficient Earth System Model (ESM) emulators allowing to rapidly translate greenhouse gas emission pathways into spatially resolved annual-mean temperature anomaly field time series, accounting for both forced climate response and natural variability uncertainty at the local scale. By combining the global-mean, emissions-driven emulator MAGICC with the spatially resolved emulator MESMER, ESM-specific as well as constrained probabilistic emulated ensembles can be derived. This emulation chain can hence build on and extend large multi-ESM ensembles such as the ones produced within the 6th phase of the Coupled Model Intercomparison Project (CMIP6). The main extensions are threefold. (i) A more thorough sampling of the forced climate response and the natural variability uncertainty is possible with millions of emulated realizations being readily created. (ii) The same uncertainty space can be sampled for any emission pathway, which is not the case in CMIP6, where some of the most societally relevant strong mitigation scenarios have been run by only a small number of ESMs. (iii) Other lines of evidence to constrain future projections, including observational constraints, can be introduced, which helps to refine projected future ranges beyond the multi-ESM ensemble's estimates. In addition to presenting results from the coupled MAGICC-MESMER emulator chain, we carry out an extensive validation of MESMER, which is trained on and applied to multiple emission pathways for the first time in this study. The newly developed MAGICC-MESMER coupled emulator will allow unprecedented assessments of the implications of manifold emissions pathways at regional scale. [ABSTRACT FROM AUTHOR]