Climate Model Simulations Research Articles

Cfr (v2024.1.26): a Python package for climate field reconstruction

Abstract. Climate field reconstruction (CFR) refers to the estimation of spatiotemporal climate fields (such as surface temperature) from a collection of pointwise paleoclimate proxy datasets. Such reconstructions can provide rich information on climate dynamics and provide an out-of-sample validation of climate models. However, most CFR workflows are complex and time-consuming, as they involve (i) preprocessing of the proxy records, climate model simulations, and instrumental observations; (ii) application of one or more statistical methods; and (iii) analysis and visualization of the reconstruction results. Historically, this process has lacked transparency and accessibility, limiting reproducibility and experimentation by non-specialists. This article presents an open-source and object-oriented Python package called cfr that aims to make CFR workflows easy to understand and conduct, saving climatologists from technical details and facilitating efficient and reproducible research. cfr provides user-friendly utilities for common CFR tasks such as proxy and climate data analysis and visualization, proxy system modeling, and modularized workflows for multiple reconstruction methods, enabling methodological intercomparisons within the same framework. The package is supported with extensive documentation of the application programming interface (API) and a growing number of tutorial notebooks illustrating its usage. As an example, we present two cfr-driven reconstruction experiments using the PAGES 2k temperature database applying the last millennium reanalysis (LMR) paleoclimate data assimilation (PDA) framework and the graphical expectation–maximization (GraphEM) algorithm, respectively.

Projecting Changes in the Drivers of Compound Flooding in Europe Using CMIP6 Models

AbstractWhen different flooding drivers co‐occur, they can cause compound floods. Despite the potential impact of compound flooding, few studies have projected how the joint probability of flooding drivers may change. Furthermore, existing projections may not be very robust, as they are based on only 5 to 6 climate model simulations. Here, we use a large ensemble of simulations from the Coupled Model Intercomparison Project 6 (CMIP6) to project changes in the joint probability of extreme storm surges and precipitation at European tide gauges under a medium and high emissions scenario, enabled by data‐proximate cloud computing and statistical storm surge modeling. We find that the joint probability will increase in the northwest and decrease in most of the southwest of Europe. Averaged over Europe, the absolute magnitude of these changes is 36%–49% by 2080, depending on the scenario. The large‐scale changes in the joint probability of extreme storm surges and precipitation are similar to those in the joint probability of extreme wind speeds and precipitation, but locally, differences can exceed the changes themselves. Due to internal climate variability and inter‐model differences, projections based on simulations of only 5 to 6 randomly chosen CMIP6 models have a probability of higher than 10% to differ qualitatively from projections based on all CMIP6 simulations in multiple regions, especially under the medium emissions scenario and earlier in the twenty‐first century. Therefore, our results provide a more robust and less uncertain representation of changes in the potential for compound flooding in Europe than previous projections.

Interchangeability of multi-decade skin and surface air temperature trends over land in models

Satellite land surface temperature (Ts ) records have now reached 20+ year length, but their trends may differ from historical records built from in-situ measurements of near-surface air temperature (Tas ). In the ERA5 reanalysis, 60° S–60° N land Ts and Tas trends can differ by up to ±0.06 °C decade−1 over 20 years, depending on the period, or more on smaller spatial scales. Here I use 1979–1998 outputs from ACCESS1-0 climate model simulations with prescribed land Ts to understand changes in Ts and Tas . CO2’s effective radiative forcing causes adjustments that warm Tas relative to Ts . In ACCESS1-0, vegetation enhances the adjustments to CO2 over land. Meanwhile, feedbacks in ACCESS1-0 oppose the adjustments, resulting in small long-term net effects on global temperature estimates. In coupled simulations from other models, there is no agreement on whether Ts or Tas warms more and the most extreme case shows global long-term differences of just 5% between land Ts or land Tas trends. The results contrast with over-ocean behavior where adjustments and feedbacks reinforce each other, and drive larger long-term Tas warming relative to Ts across all models.

Bias Correction of Monthly Precipitation from different General Circulation Models Using Cumulative Density Function in Raipur District, Chhattisgarh, India

Accurate representation of precipitation patterns is crucial for understanding and adapting to these impacts. General Circulation Models (GCMs) are essential for projecting future climate scenarios but often exhibit biases in simulating precipitation, undermining the reliability of their outputs. This study focused on bias correction of monthly precipitation data from different GCMs using Cumulative Density Functions (CDFs). Bias correction techniques were employed to align model-simulated precipitation with observed data, revealing significant improvements in the accuracy of future precipitation projections. The study area, Raipur, characterized by diverse topography, served as the location for analysis. Three GCMs were selected based on their availability and participation in the CMIP6 experiment. The bias correction process involved the calculation of CDFs and equiprobability transformations, resulting in a closer match between model predictions and observations. Results showed substantial variability in monthly precipitation values across different climate models and scenarios, with distinct seasonal patterns observed. Inter-model discrepancies underscored the complexities of precipitation simulations, highlighting the need for careful interpretation of model outputs. Continued research efforts were crucial for improving the accuracy and reliability of climate model simulations for informed decision-making and planning in climate-sensitive sectors.

Stable isotope evidence for long-term stability of large-scale hydroclimate in the Neogene North American Great Plains

Abstract. The Great Plains of North America host a stark climatic gradient, separating the humid and well-watered eastern US from the semi-arid and arid western US, and this gradient shapes the region's water availability, its ecosystems, and its economies. This climatic boundary is largely set by the influence of two competing atmospheric circulation systems that meet over the Great Plains – the wintertime westerlies bring dominantly dry air that gives way to moist, southerly air transported by the Great Plains low-level jet in the warmer months. Climate model simulations suggest that, as CO2 rises, this low-level jet will strengthen, leading to greater precipitation in the spring but less in the summer and, thus, no change in mean annual precipitation. Combined with rising temperatures that will increase potential evapotranspiration, semi-arid conditions will shift eastward, with potentially large consequences for the ecosystems and inhabitants of the Great Plains. We examine how hydroclimate in the Great Plains varied in the past in response to warmer global climate by studying the paleoclimate record within the Ogallala Formation, which underlies nearly the entire Great Plains and provides a spatially resolved record of hydroclimate during the globally warmer late Miocene. We use the stable isotopes of oxygen (δ18O) as preserved in authigenic carbonates hosted within the abundant paleosol and fluvial successions that comprise the Ogallala Formation as a record of past hydroclimate. Today, and coincident with the modern aridity gradient, there is a sharp meteoric water δ18O gradient with high (−6 ‰ to 0 ‰) δ18O in the southern Great Plains and low (−12 ‰ to −18 ‰) δ18O in the northern plains. We find that the spatial pattern of reconstructed late Miocene precipitation δ18O is indistinguishable from the spatial pattern of modern meteoric water δ18O. We use a recently developed vapor transport model to demonstrate that this δ18O spatial pattern requires air mass mixing over the Great Plains between dry westerly and moist southerly air masses in the late Miocene – consistent with today. Our results suggest that the spatial extents of these two atmospheric circulation systems have been largely unchanged since the late Miocene and any strengthening of the Great Plains low-level jet in response to warming has been isotopically masked by proportional increases in westerly moisture delivery. Our results hold implications for the sensitivity of Great Plains climate to changes in global temperature and CO2 and also for our understanding of the processes that drove Ogallala Formation deposition in the late Miocene.

Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?

Abstract. Snow dynamics play a critical role in the climate system, as they affect the water cycle, ecosystems, and society. In climate modelling, the representation of the amount and extent of snow on the land surface is crucial for simulating the mass and energy balance of the climate system. Here, we evaluate simulations of daily snow depths against 83 station observations in southern Germany in an elevation range of 150 to 1000 m over the time period 1987–2018. Two simulations stem from high-resolution regional climate models – the Weather Research & Forecasting (WRF) model at 1.5 km resolution and the COnsortium for Small scale MOdelling model in CLimate Mode (COSMO-CLM; abbreviated to CCLM hereafter) at 3 km resolution. Additionally, the hydrometeorological snow model Alpine MUltiscale Numerical Distributed Simulation ENgine (AMUNDSEN) is run at point scale at the locations of the climate stations, based on the atmospheric output of CCLM. To complement the comparison, the ERA5-Land dataset (9 km), a state-of-the-art reanalysis land-surface product, is also compared. All four simulations are driven by the atmospheric boundary conditions of ERA5. Due to an overestimation of the snow albedo, the WRF simulation features a cold bias of 1.2 °C, leading to the slight overestimation of the snow depth in low-lying areas, whereas the snow depth is underestimated at snow-rich stations. The number of snow days (days with a snow depth above 1 cm) is reproduced well. The WRF simulation can recreate extreme snow depths, i.e. annual maxima of the snow depth, their timings, and inter-station differences, and thereby shows the best performance of all models. The CCLM reproduces the climatic conditions with very low bias and error metrics. However, all snow-related assessments show a strong systematic underestimation, which we relate to deficiencies in the snow module of the land-surface model. When driving AMUNDSEN with the atmospheric output of the CCLM, the results show a slight tendency to overestimate snow depth and number of snow days, especially in the northern parts of the study area. Snow depth extremes are reproduced well. For ERA5-Land (ERA5L), the coarser spatial resolution leads to larger differences between the model elevation and the station elevation, which contributes to a significant correlation of climatic biases with the elevation bias. In addition, the mean snow depth and number of snow days are strongly overestimated, with conditions that are too snowy in the late winter. Extreme snow depth conditions are reproduced well in the low-lying areas, whereas strong deviations occur with more complex topography. In sum, due to the high spatial resolution of convection-permitting climate models, they show the potential to reproduce the winter climate (temperature and precipitation) in southern Germany. However, different sources of uncertainties, i.e. the spatial resolution, the snow albedo parametrisation, and other parametrisations within the snow model, prevent their further use in a straightforward manner for impact research. Hence, careful evaluation is needed before any impact-related interpretation of the simulations, such as in the context of climate change research.

Improving land surface feedbacks to the atmosphere in convection-permitting climate simulations for Europe

We investigated positive temperature (warm) and negative precipitation (dry) biases in convection-permitting model (CPM) simulations for Europe (2.2 km grid spacing) that were considerably larger than in equivalent regional climate model (RCM) simulations (12 km grid spacing). We found that improvements in dry biases could be made by (1) using a more complex runoff scheme which takes into account topography and groundwater, (2) delaying the onset of water stress in vegetation to enhance transpiration, (3) changing the microphysics scheme to CASIM (Cloud AeroSol Interacting Microphysics) which also decreases heavy rainfall and increases light rainfall. Increasing soil moisture to the critical point can remove dry precipitation biases in southern Europe but not in northern areas, indicating that soil moisture limitation is a key contributor to precipitation biases in the south only. Instead, in the north, changing the cloud scheme of the model has more impact on precipitation biases. We found that the more intense and intermittent nature of rainfall in the CPM, which is more realistic leads to different canopy interception compared to the RCM. This can impact canopy evaporation, evapotranspiration and feed back on precipitation. Increasing rainfall storage in the canopy only leads to small improvements in warm biases, since it still fills rapidly with intense CPM rainfall, suggesting the need for an additional moisture store via improved groundwater modelling or surface pooling. Overall, this work highlights the challenge of correctly capturing land surface feedbacks in CPMs, which play an important role in future climate projections in some regions.

Projected Changes in Mean and Extreme Precipitation over Northern Mexico

Abstract Northern Mexico is home to more than 32 million people and is of significant agricultural and economic importance for the country. The region includes three distinct hydroclimatic regions, all of which regularly experience severe dryness and flooding and are highly susceptible to future changes in precipitation. To date, little work has been done to characterize future trends in either mean or extreme precipitation over northern Mexico. To fill this gap, we investigate projected precipitation trends over the region in the NA-CORDEX ensemble of dynamically downscaled simulations. We first verify that these simulations accurately reproduce observed precipitation over northern Mexico, as derived from the Multi-Source Weighted-Ensemble Precipitation (MSWEP) product, demonstrating that the NA-CORDEX ensemble is appropriate for studying precipitation trends over the region. By the end of the century, simulations forced with a high-emissions scenario project that both mean and extreme precipitation will decrease to the west and increase to the east of the Sierra Madre highlands, decreasing the zonal gradient in precipitation. We also find that the North American monsoon, which is responsible for a substantial fraction of the precipitation over the region, is likely to start later and last approximately three weeks longer. The frequency of extreme precipitation events is expected to double throughout the region, exacerbating the flood risk for vulnerable communities in northern Mexico. Collectively, these results suggest that the extreme precipitation-related dangers that the region faces, such as flooding, will increase significantly by the end of the century, with implications for the agricultural sector, economy, and infrastructure. Significance Statement Northern Mexico regularly experiences severe flooding and its important agricultural sector can be heavily impacted by variations in precipitation. Using high-resolution climate model simulations that have been tested against observations, we find that these hydroclimate extremes are likely to be exacerbated in a warming climate; the dry (wet) season is projected to receive significantly less (more) precipitation (approximately ±10% by the end of the century). Simulations suggest that some of the changes in precipitation over the region can be related to the North American monsoon, with the monsoon starting later in the year and lasting several weeks longer. Our results also suggest that the frequency of extreme precipitation will increase, although this increase is smaller than that projected for other regions, with the strongest storms becoming 20% more frequent per degree of warming. These results suggest that this region may experience significant changes to its hydroclimate through the end of the century that will require significant resilience planning.

Can we rely on drought‐ending “miracles” in the Colorado River Basin?

AbstractUnexpected and large spring precipitation events in the Colorado River Basin (CRB) that significantly alleviated an otherwise severe water shortage have been observed for over a century, such as the “Miracle May” of 2015. Although these events are often termed as “drought‐busting” or “miracle events” by water managers and the media, they have not been extensively researched or characterized. In this collaborative study with water managers across the CRB, we propose a definition for these hard‐to‐predict, ultra‐high precipitation events occurring during the late‐snow or snowmelt season. This characterization provides a framework for quantifying the frequency and intensity of extreme dry‐to‐wet springtime transitions. Despite limitations of climate model simulations due to uncertainties and the inhomogeneous qualities, our findings suggest that such transitions may become less frequent and less intense in a warming climate. In view of the potentially wetter but less‐snowy climate in the basin, the need for future research to more quantitatively assess these “miracle events” is emphasized.

Atmospheric Rivers in the Eastern and Midwestern United States Associated With Baroclinic Waves

AbstractAtmospheric rivers (ARs) significantly impact the hydrological cycle and associated extremes in western continental regions. Recent studies suggest ARs also influence water resources and extremes in continental interiors. AR detection tools indicate that AR conditions are relatively frequent in areas east of the Rocky Mountains. The origin of these ARs, whether from synoptic‐scale waves or mesoscale processes, is unclear. This study uses meteorological composite maps and transects of AR conditions during the four seasons. The analysis reveals that ARs east of the Rockies are associated with long‐wave, baroclinic Rossby waves. This result demonstrates that eastern North American ARs are dynamically similar to their western coastal counterparts, though mechanisms for vertical moisture flux differ between the two. These findings provide a foundation for understanding future climate change and ARs in this region and offer new methods for evaluating climate model simulations.

Towards spatio-temporal comparison of simulated and reconstructed sea surface temperatures for the last deglaciation

Abstract. An increasing number of climate model simulations is becoming available for the transition from the Last Glacial Maximum to the Holocene. Assessing the simulations' reliability requires benchmarking against environmental proxy records. To date, no established method exists to compare these two data sources in space and time over a period with changing background conditions. Here, we develop a new algorithm to rank simulations according to their deviation from reconstructed magnitudes and temporal patterns of orbital and millennial-scale temperature variations. The use of proxy forward modeling allows for accounting for non-climatic processes that affect the temperature reconstructions. It further avoids the need to reconstruct gridded fields or regional mean temperature time series from sparse and uncertain proxy data. First, we test the reliability and robustness of our algorithm in idealized experiments with prescribed deglacial temperature histories. We quantify the influence of limited temporal resolution, chronological uncertainties, and non-climatic processes by constructing noisy pseudo-proxies. While model–data comparison results become less reliable with increasing uncertainties, we find that the algorithm discriminates well between simulations under realistic non-climatic noise levels. To obtain reliable and robust rankings, we advise spatial averaging of the results for individual proxy records. Second, we demonstrate our method by quantifying the deviations between an ensemble of transient deglacial simulations and a global compilation of sea surface temperature reconstructions. The ranking of the simulations differs substantially between the considered regions and timescales, which suggests that optimizing for agreement with the temporal patterns of a small set of proxies might be insufficient for capturing the spatial structure of the deglacial temperature variability. We attribute the diversity in the rankings to more regionally confined temperature variations in reconstructions than in simulations, which could be the result of uncertainties in boundary conditions, shortcomings in models, or regionally varying characteristics of reconstructions such as recording seasons and depths. Future work towards disentangling these potential reasons can leverage the flexible design of our algorithm and its demonstrated ability to identify varying levels of model–data agreement. Additionally, the algorithm can be applied to variables like oxygen isotopes and climate transitions such as the penultimate deglaciation and the last glacial inception.

Change in the distribution of heavy 1 h precipitation due to temperature changes in measured values, model reanalyses and model simulations of future climate

This article presents main results on how the 1 h precipitation distribution changes, focused on high percentiles, with increasing air temperature based on the data from the Czech territory. The air temperature, the dew point temperature (both measured at 2 m above ground) and the temperature of lifting condensation level are used as temperature characteristics. The change in the distribution of 1 h precipitation measurements is compared with the results of reanalyses based on simulations of numerical weather prediction model ALADIN-CZ and with the results of future climate simulations by climate model ALADIN-CLIMAT-CZ. In general, the increase in precipitation appears clearly in the very upper part of precipitation distribution for high percentiles. Values of the upper percentiles of precipitation increase up to a certain threshold temperature and then decrease, which is also found in other studies. This also applies to the simulations of future climate. The main difference in measured and model results appears at the highest temperature values, which may be implied by the very different number of the model and the measured data.

Exacerbated summer European warming not captured by climate models neglecting long-term aerosol changes

AbstractIn much of western-central Europe, summer temperatures have surged three times faster than the global mean warming since 1980, yet this is not captured by most climate model simulations. Here we disentangle this warming into thermodynamic and circulation-induced contributions, and show that the latter is the main reason why numerically simulated warming is weaker than observed. Crucially, regional climate models from the Coordinated Regional Downscaling Experiment with constant aerosol forcings systematically show the strongest discrepancies from observations: in these simulations, the regional brightening and associated thermodynamic warming due to aerosol reductions is not represented. We estimate an effect of ~0.5 °C over western-central Europe for our model ensemble, and the discrepancy to climate models with evolving aerosols increases in future projections. To better reap the benefits of regional high-resolution simulations, it is thus imperative to represent the relevant external forcings and associated responses across the entire climate model chain.

Amundsen Sea Ice Loss Contributes to Australian Wildfires.

Wildfires in Australia have attracted extensive attention in recent years, especially for the devastating 2019-2020 fire season. Remote forcing, such as those from tropical oceans, plays an important role in driving the abnormal weather conditions associated with wildfires. However, whether high latitude climate change can impact Australian fires is largely unclear. In this study, we reveal a robust relationship between Antarctic sea ice concentration (SIC), primarily over the Amundsen Sea region, with Australian springtime fire activity, by using reanalysis data sets, AMIP simulation results, and a state-of-the-art climate model simulation. Specifically, a diminished Amundsen SIC leads to the formation of a high-pressure system above Australia as a result of the eastward propagation of Rossby waves. Meanwhile, two strengthened meridional cells originating from the tropic and polar regions also enhance subsiding airflow in Australia, resulting in prolonged arid and high-temperature conditions. This mechanism explains about 28% of the variability of Australian fire weather and contributed more than 40% to the 2019 extreme burning event, especially in the eastern hotspots. These findings contribute to our understanding of polar-low latitude climate teleconnection and have important implications for projecting Australian fires as well as the global environment.

Recent reductions in aerosol emissions have increased Earth’s energy imbalance

The Earth’s energy imbalance is the net radiative flux at the top-of-atmosphere. Climate model simulations suggest that the observed positive imbalance trend in the previous two decades is inconsistent with internal variability alone and caused by anthropogenic forcing and the resulting climate system response. Here, we investigate anthropogenic contributions to the imbalance trend using climate models forced with observed sea-surface temperatures. We find that the effective radiative forcing due to anthropogenic aerosol emission reductions has led to a 0.2 ± 0.1 W m−2 decade−1 strengthening of the 2001–2019 imbalance trend. The multi-model ensemble reproduces the observed imbalance trend of 0.47 ± 0.17 W m−2 decade−1 but with 10-40% underestimation. With most future scenarios showing further rapid reductions of aerosol emissions due to air quality legislation, such emission reductions may continue to strengthen Earth’s energy imbalance, on top of the greenhouse gas contribution. Consequently, we may expect an accelerated surface temperature warming in this decade.

Climate Model Simulations of the Effects of Orbital Parameters on Glacier Equilibrium Line Altitude

AbstractThe effects of obliquity and precession on conditions favorable for Northern Hemisphere glaciation are explored using an energy balance and mass balance model of equilibrium line altitude (ELA), the height on a glacier where accumulation and ablation are in balance annually. Climate forcing for the ELA model is obtained from idealized single‐forcing orbital simulations with two atm‐ocean general circulation models, Geophysical Fluid Dynamics Laboratory (GFDL) CM2.1 and National Center for Atmospheric Research (NCAR) Community Earth System Model version 1.2. Over Scandinavia and Baffin Island, the respective locations in which the Scandinavian and Laurentide ice sheets are thought to have originated, low obliquity and perihelion at the boreal winter solstice are associated with lower ELA values, as would be expected from the orbital theory of the ice ages. Linear reconstructions of ELA variations over the past 800 kyr indicate that precession dominated ELA variations in Scandinavia and Baffin Island in the GFDL model, and in Scandinavia in the NCAR model. Obliquity and precession played equal roles in Baffin Island in the NCAR model. A decomposition of the ELA responses finds that the effects of ablation on ELA are much larger than the effects of precipitation. Overall, the findings of this study point to precession being a more important factor in glacial inception than obliquity, which contrasts with previous findings in which obliquity had a slightly larger effect on positive degree days (PDDs), a simple metric for ablation. This is likely due to differences in seasonality of melt from the ELA model and PDDs.

Links of atmospheric circulation to cold days in simulations of EURO-CORDEX climate models for central Europe

Despite ongoing climate change and warming, extreme cold events still negatively affect human society. Since cold air incursions are related to specific circulation patterns, the main aims of this study are (1) to validate how well current EURO-CORDEX regional climate models (RCMs) reproduce these synoptic links and (2) to assess possible future changes in atmospheric circulation conducive to cold events. Using anomalies of daily minimum temperature, we define cold days (CDs) in central Europe and analyse their characteristics over the historical (1979−2020) and future (2070−2099) periods. We classify wintertime atmospheric circulation by applying a novel technique based on Sammon mapping to the state-of-the-art ERA5 reanalysis output. We discover that circulation types (CT) conducive to CDs are characterised by easterly advection and/or clear-sky anticyclonic conditions. While the RCM ensemble generally reproduces these synoptic links relatively well, we observe biases in the occurrence of CDs in individual simulations. These biases can be attributed to inadequately reproduced frequencies of CTs conducive to CDs (primarily propagating from driving data), as well as to deviations in the conduciveness within these CTs (primarily originating in the RCMs). Interestingly, two competing trends are identified for the end of the twenty-first century: (1) most RCMs project an increased frequency of CTs conducive to CDs, suggesting more frequent CDs, while (2) the same CTs are projected to warm faster compared to their counterparts, suggesting weaker CDs. The interplay between these opposing trends contributes to the overall uncertainty surrounding the recurrence and severity of future winter extremes in central Europe.

Sea-ice variations and trends during the Common Era in the Atlantic sector of the Arctic Ocean

Abstract. Sea ice is crucial in regulating the heat balance between the ocean and atmosphere and quintessential for supporting the prevailing Arctic food web. Due to limited and often local data availability back in time, the sensitivity of sea-ice proxies to long-term climate changes is not well constrained, which renders any comparison with palaeoclimate model simulations difficult. Here we compiled a set of marine sea-ice proxy records with a relatively high temporal resolution of at least 100 years, covering the Common Era (past 2k years) in the Greenland–North Atlantic sector of the Arctic to explore the presence of coherent long-term trends and common low-frequency variability, and we compared those data with transient climate model simulations. We used cluster analysis and empirical orthogonal functions to extract leading modes of sea-ice variability, which efficiently filtered out local variations and improved comparison between proxy records and model simulations. We find that a compilation of multiple proxy-based sea-ice reconstructions accurately reflects general long-term changes in sea-ice history, consistent with simulations from two transient climate models. Although sea-ice proxies have varying mechanistic relationships to sea-ice cover, typically differing in habitat or seasonal representation, the long-term trend recorded by proxy-based reconstructions showed a good agreement with summer minimum sea-ice area from the model simulations. The short-term variability was not as coherent between proxy-based reconstructions and model simulations. The leading mode of simulated sea ice associated with the multidecadal to centennial timescale presented a relatively low explained variance and might be explained by changes in solar radiation and/or inflow of warm Atlantic waters to the Arctic Ocean. Short variations in proxy-based reconstructions, however, are mainly associated with local factors and the ecological nature of the proxies. Therefore, a regional or large-scale view of sea-ice trends necessitates multiple spatially spread sea-ice proxy-based reconstructions, avoiding confusion between long-term regional trends and short-term local variability. Local-scale sea-ice studies, in turn, benefit from reconstructions from well-understood individual research sites.

Powering aircraft with 100 % sustainable aviation fuel reduces ice crystals in contrails

Abstract. Powering aircraft by sustainable aviation fuels (SAFs) is a pathway to reduce the climate impact of aviation by lowering aviation lifecycle CO2 emissions and by reducing ice crystal numbers and radiative forcing from contrails. While the effect of SAF blends on contrails has been measured previously, here we present novel measurements on particle emission and contrails from 100 % SAF combustion. During the ECLIF3 (Emission and CLimate Impact of alternative Fuels) campaign, a collaboration between the Deutsches Zentrum für Luft- und Raumfahrt (DLR), Airbus, Rolls-Royce, and Neste, the DLR Falcon 20 research aircraft performed in situ measurements following an Airbus A350-941 source aircraft powered by Rolls-Royce Trent XWB-84 engines in 1 to 2 min old contrails at cruise altitudes. Apparent ice emission indices of 100 % HEFA-SPK (hydro-processed esters and fatty acids–synthetic paraffinic kerosene) were measured and compared to Jet A-1 fuel contrails at similar engine and ambient ice-supersaturated conditions within a single flight. A 56 % reduction in ice particle numbers per mass of burned fuel was measured for 100 % HEFA-SPK compared to Jet A-1 under engine cruise conditions. The measured 35 % reduction in soot particle numbers suggests reduced ice activation by the low-sulfur HEFA fuel. Contrail properties are consistently modeled with a contrail plume model. Global climate model simulations for the 2018 fleet conservatively estimate a 26 % decrease in contrail radiative forcing and stronger decreases for larger particle reductions. Our results indicate that higher hydrogen content fuels as well as clean engines with low particle emissions may lead to reduced climate forcing from contrails.

Seasonal pumped hydropower storage role in responding to climate change impacts on the Brazilian electrical sector

Since Brazil's major energy resources are renewable and directly related to climate factors, it is among the countries most likely of being affected by climate change. Given Brazil's high hydropower storage capacity and the strong seasonal patterns of its renewable resources, introducing Seasonal Pumped Hydropower Storage (SPHS) can help mitigate these challenges. To this end, a methodology is proposed that links the dynamic system-optimization model – MESSAGEix - to regional climate model simulations, called the Brazilian Electricity System MESSAGEix Model (BESMM). This model, with its detailed hydropower representation, is capable of integrating data from three climate change scenarios with the country's energy system. Climate change introduces a new dimension to this approach, as there is evidence of increasing the seasonal imbalance of variable renewable resources in Brazil. BESMM results suggest that SPHS can play a fundamental role in achieving a 100 % renewable matrix by 2100 in RCP 2.6 scenario, as well as enhancing the renewable energy endowment in scenarios RCP 4.5 and RCP 8.5. A reduction of up to 68 % of CO2 emissions is predicted in scenarios incorporating SPHS, compared to scenarios without SPHS.