Climate Model Simulations Research Articles

Regional climate models and bias correction methods for rainfall-runoff modeling in Katar watershed, Ethiopia

Systematic errors in regional climate models (RCMs) hinder their implementation and lead to uncertainties in regional hydrological climate change studies. As a result, checking the accuracy of climate model simulations and applying bias correction are preliminary methods for achieving consistent findings. Therefore, identifying suitable RCM models for bias correction is important for providing reliable inputs for evaluating climate change impacts. The impacts of bias correction methods on streamflow were assessed on the Katar catchment within the Lake Ziway subbasin using coordinated regional climate downscaling experiments with a spatial resolution of 50 km (CORDEX-44) RCMs through the Integrated Hydrological Modelling System (IHMS) version 6.3. This study evaluated fourteen RCM models under five precipitation and three temperature bias correction methods for the Katar catchment. Statistical approaches, such as bias (PBIAS), the root mean square error (RMSE), the mean absolute error (MAE), the coefficient of variation (CV), the coefficient of determination (R2), and the relative volume error (RVE), are used for performance analysis. GERICS-MPI, RAC4-NOAA-2G, and CCLM4-NCCR-AFR-22 have better performances for both rainfall and temprature. The empirical cumulative distribution function (ECDF) method performed best in removing bias from the frequency-based statistics of rainfall and streamflow, followed by the power transformation (PT), distribution mapping (DM), local intensity scaling (LOCI), and linear scaling (LS) methods. Specifically, for temperature, the VARI and DM methods perform better in frequency-based statistics than the LS method. The performance of hydrological modeling is strongly affected by the selection of rainfall bias correction methods. In addition, the effect of the temperature bias correction method was not significant. The adequacy of the BCM depends on the RCM models and regional context. Therefore, the BCM implementation procedure can be adapted from region to region. This study revealed that the performance of the RCM models differed and that the errors in the RCM model outputs were reduced by the use of bias correction methods.

Assessment of the hydrological impact of an hourly precipitation distribution correction method in the SASER modeling system

The SASER (SAfran-Surfex-Eudyssée-Rapid) hydrological modeling system is used for simulating the continental hydrological cycle. The validation of the simulated streamflow showed that the results were generally accurate but with a tendency towards underestimating peak values. This underestimation has several causes, but in this study, we addressed the known fact that the precipitation forcing data produced by SAFRAN is good at the daily time scale, as it ingests daily precipitation data, but its performance in interpolating daily to hourly precipitation data using relative humidity as a weight is poor. Our objective was to improve the SAFRAN hourly precipitation distribution and evaluate its hydrological impact on SURFEX LSM simulations. We used a correction method to adjust the hourly precipitation distribution of SAFRAN based on a more realistic distribution obtained from a regional climate model simulation forced by ERA-Interim (CNRM-ALADIN). At the grid point scale, the correction preserves SAFRAN’s accumulated precipitation on a time window of a few days, while reflecting the RCM’s hourly variability of precipitation. We evaluated the correction method across different time windows and found that the 7-day correction showed the highest performance. Our results show that hourly precipitation patterns have a strong influence on surface runoff at the event and grid-point scales. However, at the long-term and catchment scale, the sensitivity to the use of the new precipitation forcing dataset was limited.

Arctic amplification–induced decline in West and South Asia dust warrants stronger antidesertification toward carbon neutrality

Dust loading in West and South Asia has been a major environmental issue due to its negative effects on air quality, food security, energy supply and public health, as well as on regional and global weather and climate. Yet a robust understanding of its recent changes and future projection remains unclear. On the basis of several high-quality remote sensing products, we detect a consistently decreasing trend of dust loading in West and South Asia over the last two decades. In contrast to previous studies emphasizing the role of local land use changes, here, we attribute the regional dust decline to the continuous intensification of Arctic amplification driven by anthropogenic global warming. Arctic amplification results in anomalous mid-latitude atmospheric circulation, particularly a deepened trough stretching from West Siberia to Northeast India, which inhibits both dust emissions and their downstream transports. Large ensemble climate model simulations further support the dominant role of greenhouse gases induced Arctic amplification in modulating dust loading over West and South Asia. Future projections under different emission scenarios imply potential adverse effects of carbon neutrality in leading to higher regional dust loading and thus highlight the importance of stronger anti-desertification counter-actions such as reforestation and irrigation management.

Climate change impacts on the tourism sector of the Spanish Mediterranean coast: Medium-term projections for a climate services tool

The Mediterranean Sea is a climate change hotspot since it provides a magnified warming signal. Heavily populated areas (e.g., Spanish Mediterranean coasts) are vulnerable to negative socio-economic impacts. This is particularly important for climate-related economic sectors such as coastal tourism, the focus of this paper. To promote a sustainable development of these activities and provide key information to stakeholders, it is necessary to anticipate changes in climate. Thus, it is fundamental to use climate modelling tools which account for air-sea interactions, which largely determine the climate signal of the Mediterranean coasts. In this paper, a set of regional air-sea coupled climate model simulations from Med-CORDEX are used to (i) study the climatic conditions on the Spanish Mediterranean coasts in the next decade(s) and (ii) to assess the possibility of extending the coastal tourist season towards spring-fall. We show that climate conditions are getting warmer and drier in the area, especially in summer. Heat waves and heavy precipitation will become more frequent. Thermal discomfort will increase in summer and summer conditions are extending towards spring and fall. Our work remarks the urgent need of adaptation measures of the sector, including the extension of the high tourist season to spring-fall, especially in the long term. We make a special effort to compile a set of adaptation measures for stakeholders. This study is part of the project ECOAZUL-MED, which aims to create a climate service tool to optimize the management of relevant sectors of the blue economy in the Spanish Mediterranean coasts.

Intercomparison of tropical Indian Ocean circulation in ocean reanalysis and evaluation in CMIP6 climate models

In the present study, we assess the Tropical Indian Ocean (TIO) circulation features from the available ocean reanalysis products and the latest version of Coupled Model Intercomparison Project (CMIP6) climate model simulations. We considered the following reanalysis products; Ocean Reanalysis System 5 (ORAS5), Estimating the Circulation and Climate of the Ocean (ECCO), Global Ocean Data Assimilation System (GODAS), Ensemble Coupled Data Assimilation (ECDA), the Bluelink Reanalysis (BRAN) and Simple Ocean Data Assimilation (SODA) and compared them against in-situ observations and the satellite-derived Ocean Surface Current Analyses Real-time (OSCAR). The reanalysis products underestimate the strength and location of the Wyrtki Jets. The BRAN reanalysis performed well compared to the other products in representing the TIO surface zonal currents, followed by ORAS5. The vertical extension of subsurface zonal currents in the equatorial Indian Ocean and their seasonal maxima are well captured in ORAS5. Thus, our analysis suggests that the ORAS5 is a qualitative product to estimate the TIO circulation. We further evaluated the TIO current patterns simulated by CMIP6 models with in-situ data/ ORAS5. The majority of the models show discrepancies in simulating equatorial and south equatorial current systems with a mean bias of 0.1cms−1 and 0.2cms−1, respectively. NorESM2-MM, NorESM2-LM, CanESM5, CESM2-WACCM-FV2, and E3SM-1-ECA models showed a superior skill in reproducing the TIO circulation compared to the rest of the models. Our analysis highlights the importance of assessing various reanalysis products and coupled climate models in representing the circulation of the TIO and, consequently, their role in depicting regional weather and climate.

Spatiotemporal changes in future precipitation of Afghanistan for shared socioeconomic pathways

Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these changes at regional scales. This assessment is particularly critical for Afghanistan, where agriculture serves as the primary livelihood for the population. New global climate model (GCM) simulations have recently been released for the recently established shared socioeconomic pathways (SSPs). This requires evaluating projected precipitation changes under these new scenarios and subsequent policy updates. This research employed six GCMs from the CMIP6 to project spatial and temporal precipitation changes across Afghanistan under all SSPs, including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The employed GCMs were bias-corrected using the Global Precipitation Climatological Center's (GPCC) monthly gridded precipitation data with a 1.0° spatial resolution. Subsequently, the climate change factor was calculated to assess precipitation changes for both the near future (2020–2059) and the distant future (2060–2099). The bias-corrected projections' multi-model ensemble (MME) revealed increased precipitation across most of Afghanistan for SSPs with higher emissions scenarios. The bias-corrected simulations showed a substantial increase in summer precipitation of around 50%, projected under SSP1-1.9 in the southwestern region, while a decline of over 50% is projected in the northwestern region until 2100. The annual precipitation in the northwest region was projected to increase up to 15% for SSP1-2.6. SSP2-4.5 showed a projected annual precipitation increase of around 20% in the southwestern and certain eastern regions in the far future. Furthermore, a substantial rise of approximately 50% in summer precipitation under SSP3-7.0 is expected in the central and western regions in the far future. However, it is crucial to note that the projected changes exhibit considerable uncertainty among different GCMs.

Possible role of anthropogenic climate change in the record-breaking 2020 Lake Victoria levels and floods

Abstract. Heavy rainfall in eastern Africa between late 2019 and mid 2020 caused devastating floods and landslides throughout the region. These rains drove the levels of Lake Victoria to a record-breaking maximum in the second half of May 2020. The combination of high lake levels, consequent shoreline flooding, and flooding of tributary rivers caused hundreds of casualties and damage to housing, agriculture, and infrastructure in the riparian countries of Uganda, Kenya, and Tanzania. Media and government reports linked the heavy precipitation and floods to anthropogenic climate change, but a formal scientific attribution study has not been carried out so far. In this study, we characterize the spatial extent and impacts of the floods in the Lake Victoria basin and then investigate to what extent human-induced climate change influenced the probability and magnitude of the record-breaking lake levels and associated flooding by applying a multi-model extreme event attribution methodology. Using remote-sensing-based flood mapping tools, we find that more than 29 000 people living within a 50 km radius of the lake shorelines were affected by floods between April and July 2020. Precipitation in the basin was the highest recorded in at least 3 decades, causing lake levels to rise by 1.21 m between late 2019 and mid 2020. The flood, defined as a 6-month rise in lake levels as extreme as that observed in the lead-up to May 2020, is estimated to be a 63-year event in the current climate. Based on observations and climate model simulations, the best estimate is that the event has become more likely by a factor of 1.8 in the current climate compared to a pre-industrial climate and that in the absence of anthropogenic climate change an event with the same return period would have led lake levels to rise by 7 cm less than observed. Nonetheless, uncertainties in the attribution statement are relatively large due to large natural variability and include the possibility of no observed attributable change in the probability of the event (probability ratio, 95 % confidence interval 0.8–15.8) or in the magnitude of lake level rise during an event with the same return period (magnitude change, 95 % confidence interval 0–14 cm). In addition to anthropogenic climate change, other possible drivers of the floods and their impacts include human land and water management, the exposure and vulnerability of settlements and economic activities located in flood-prone areas, and modes of climate variability that modulate seasonal precipitation. The attribution statement could be strengthened by using a larger number of climate model simulations, as well as by quantitatively accounting for non-meteorological drivers of the flood and potential unforced modes of climate variability. By disentangling the role of anthropogenic climate change and natural variability in the high-impact 2020 floods in the Lake Victoria basin, this paper contributes to a better understanding of changing hydrometeorological extremes in eastern Africa and the African Great Lakes region.

Future flood envelope curves for the estimation of design flood magnitudes for highway bridges at river crossings

Creager flood envelope curves, which serve as the upper bound/limit of observed extreme flows, are commonly used by practitioners to estimate design flood magnitudes, which in the case of most river-crossing highway bridges is 75-year flood magnitude in Canada. This study proposes a novel framework for climate change adaption of Creager curves for estimating future design floods. These curves, for the current period, are assessed considering 417 observation stations, located in seven major Canadian river basins (i.e., Fraser, Nelson, Mackenzie, Yukon, Churchill, St Lawrence and St John). The Creager coefficient C, which defines flood envelope curves, varies between 1 and 45 across the studied river basins. To adapt Creager curves for future changes in streamflow, a correction factor, RC, which is the ratio of future to current period C values, is proposed. These factors are obtained for observation sites, using streamflow data from an ensemble of Regional Climate Model (RCM) simulations for current and future periods, through two Regional Frequency Analysis approaches. The first approach, considering only the RCM cells where the stations are located, suggests RC in the 0.3–1.6 range, with southeasterly basins showing values < 1. The second approach, considering all RCM cells for a given region, yields a wider range for RC and adds useful information in that RC values can also be established at ungauged locations. From a practical viewpoint, the proposed framework for estimating future design floods is robust and transferrable to other basins, but can benefit using streamflow projections from other models for better uncertainty quantification.

Reassessing the Climate Change Narrative

We note that the atmosphere has distinct tropical and extratropical regimes. The tropical regime is significantly dependent on the greenhouse effect and is characterized by temperatures that are largely horizontally homogenized. The extratropical regime is dominated by large scale unstable convective eddies that transport heat between the tropics and the poles (leaving the poles warmer than they otherwise would be) and serve to determine the temperature difference between the tropics and the poles. Changes in tropical temperature and in the tropics-to-pole temperature difference both contribute to changes in global mean temperature. It turns out that changes in global mean temperature associated with major climate change (i.e., the last glacial maximum and the warm period of the Eocene about 50 million years ago) were associated primarily with changes in the tropics-to-pole temperature differences. By contrast, changes in global mean temperature over the past 150 years or so are almost entirely associated with changes in tropical temperature. Thus, there is no intrinsic amplification associated with a change in the tropics-to-pole temperature difference. However, model simulations of climate behave differently from both observations and from each other. In particular, they all show more significant contributions for the tropics-to-pole temperature difference – sometimes much more significant. They also show excessive tropical warming.

On the uncertainty of long-period return values of extreme daily precipitation

Methods for calculating return values of extreme precipitation and their uncertainty are compared using daily precipitation rates over the Western U.S. and Southwestern Canada from a large ensemble of climate model simulations. The roles of return-value estimation procedures and sample size in uncertainty are evaluated for various return periods. We compare two different generalized extreme value (GEV) parameter estimation techniques, namely L-moments and maximum likelihood (MLE), as well as empirical techniques. Even for very large datasets, confidence intervals calculated using GEV techniques are narrower than those calculated using empirical methods. Furthermore, the more efficient L-moments parameter estimation techniques result in narrower confidence intervals than MLE parameter estimation techniques at small sample sizes, but similar best estimates. It should be noted that we do not claim that either parameter fitting technique is better calibrated than the other to estimate long period return values. While a non-stationary MLE methodology is readily available to estimate GEV parameters, it is not for the L-moments method. Comparison of uncertainty quantification methods are found to yield significantly different estimates for small sample sizes but converge to similar results as sample size increases. Finally, practical recommendations about the length and size of climate model ensemble simulations and the choice of statistical methods to robustly estimate long period return values of extreme daily precipitation statistics and quantify their uncertainty.

Strengthened impact of boreal winter North Pacific Oscillation on ENSO development in warming climate

The North Pacific Oscillation (NPO), an important mode of atmospheric variability, is a crucial trigger for the development of El Niño-Southern Oscillation (ENSO) via the seasonal footprinting mechanism. How the NPO effect on ENSO changes in response to greenhouse warming remains unclear, however. Here, using climate model simulations under high-emission scenarios, we show that greenhouse warming leads to an enhanced influence of NPO on ENSO as is manifested by enhanced responses of winter sea surface temperature (SST), precipitation and wind anomalies in the equatorial Pacific to the preceding winter NPO. The strengthened NPO impact is also reflected in an increased frequency of NPO events that are followed by ENSO events. Warmer background SST enhances the wind-evaporation-SST feedback over the subtropical North Pacific due to a nonlinear SST-evaporation relationship. This strengthens the NPO-generated surface zonal wind anomalies over the equatorial western-central Pacific, which trigger ENSO. Increased impact of winter NPO on ENSO could enable prediction of interannual variability at longer leads.

Cooler Arctic surface temperatures simulated by climate models are closer to satellite-based data than the ERA5 reanalysis

Many climate models simulate near-surface air temperatures that are too low in the Arctic compared to the observation-based ERA5 reanalysis data, a bias that was noted in the Inter-governmental Panel on Climate Change Sixth Assessment Report (IPCC AR6). Here we present a high-resolution, satellite-derived dataset of near-surface air temperatures for the Arctic sea-ice region (1982–2020). We use it as a benchmark to reevaluate climate reanalyses and model simulations in CMIP6 (Coupled Model Intercomparison Project 6). We find that the CMIP6 simulations in the central Arctic, with generally thicker ice and snow, align well with satellite observations, with an annual mean bias of less than ± 1 °C over sea ice. By contrast, climate reanalyses like ERA5 exhibit widespread warm biases exceeding 2 °C in the same region. We conclude that reliance on ERA5 reanalysis as a reference may have led to an underestimation of climate model reliability in the Arctic region.

A doubled increasing trend of evapotranspiration on the Tibetan Plateau

Estimation of evapotranspiration (ETa) change on the Tibetan Plateau (TP) is essential to address the water requirement of billions of people surrounding the TP. Existing studies have shown that ETa estimations on the TP have a very large uncertainty. In this article, we discuss how to more accurately quantify ETa amount and explain its change on the TP. ETa change on the TP can be quantified and explained based on an ensemble mean product from climate model simulations, reanalysis, as well as ground-based and satellite observations. ETa on the TP experienced a significant increasing trend of around 8.4 ± 2.2 mm (10 a)−1 (mean ± one standard deviation) during 1982–2018, approximately twice the rate of the global land ETa (4.3 ± 2.1 mm (10 a)−1). Numerical attribution analysis revealed that a 53.8% TP area with the increased ETa was caused by increased temperature and 23.1% part was due to soil moisture rising, because of the warming, melting cryosphere, and increased precipitation. The projected future increase in ETa is expected to cause a continued acceleration of the water cycle until 2100.

A Glimpse into the Future: The 2023 Ocean Temperature and Sea Ice Extremes in the Context of Longer-Term Climate Change

Abstract In the year 2023, we have seen extraordinary extrema in high sea surface temperature (SST) in the North Atlantic and in low sea ice extent in the Southern Ocean, outside the 4σ envelope of the 1982–2011 daily time series. Earth’s net global energy imbalance (12 months up to September 2023) amounts to +1.9 W m−2 as part of a remarkably large upward trend, ensuring further heating of the ocean. However, the regional radiation budget over the North Atlantic does not show signs of a suggested significant step increase from less negative aerosol forcing since 2020. While the temperature in the top 100 m of the global ocean has been rising in all basins since about 1980, specifically the Atlantic basin has continued to further heat up since 2016, potentially contributing to the extreme SST. Similarly, salinity in the top 100 m of the ocean has increased in recent years specifically in the Atlantic basin, and in addition in about 2015 a substantial negative trend for sea ice extent in the Southern Ocean began. Analyzing climate and Earth system model simulations of the future, we find that the extreme SST in the North Atlantic and the extreme in Southern Ocean sea ice extent in 2023 lie at the fringe of the expected mean climate change for a global surface-air temperature warming level (GWL) of 1.5°C, and closer to the average at a 3.0°C GWL. Understanding the regional and global drivers of these extremes is indispensable for assessing frequency and impacts of similar events in the coming years.

Towards variance-conserving reconstructions of climate indices with Gaussian process regression in an embedding space

Abstract. We present a new framework for the reconstruction of climate indices based on proxy data such as tree rings. The framework is based on the supervised learning method Gaussian Process Regression (GPR) and aims at preserving the amplitude of past climate variability. It can adequately handle noise-contaminated proxies and variable proxy availability over time. To this end, the GPR is formulated in a modified input space, termed here embedding space. We test the new framework for the reconstruction of the Atlantic multi-decadal variability (AMV) in a controlled environment with pseudo-proxies derived from coupled climate-model simulations. In this test environment, the GPR outperforms benchmark reconstructions based on multi-linear principal component regression. On AMV-relevant timescales, i.e. multi-decadal, the GPR is able to reconstruct the true amplitude of variability even if the proxies contain a realistic non-climatic noise signal and become sparser back in time. Thus, we conclude that the embedded GPR framework is a highly promising tool for climate-index reconstructions.

Last Glacial Maximum climate and atmospheric circulation over the Australian region from climate models

Abstract. The Last Glacial Maximum (LGM; ∼21 kyr ago) was the most recent time that the Earth experienced global maximum ice volume and minimum eustatic sea level. The climate changes over the Australian region at the LGM remain uncertain, including the extent of cooling in the arid interior, changes in the regional atmospheric circulations such as the tropical monsoon and mid-latitude westerlies, and changes in the balance between precipitation and evaporation. In this study, 13 climate model simulations that were included in the Paleoclimate Modelling Intercomparison Project (PMIP) Phases 3 and 4 are used to investigate regional climate (temperature, precipitation, and wind) over Australia at the LGM. The model simulations are compared with existing proxy records and other modelling studies. All models simulate consistent annual and seasonal cooling over the Australian region (defined as 0–45° S, 110–160° E) at the LGM compared to pre-industrial, with a multi-model mean 2.9 °C decrease in annual average surface air temperature over land at the LGM compared to pre-industrial. Models simulate a range of LGM precipitation anomalies over the region. Simulated precipitation changes over tropical Australasia appear to be driven by changes in circulation and moisture transport, which vary greatly between models. Surface moisture balance calculated from precipitation minus evaporation shows little change over much of the Australian land area at the LGM. Changes in the strength and position of the mid-latitude westerlies are uncertain, with wide model disagreement. These results indicate that climate model simulations do not show a robust response in either tropical or mid-latitude circulation to LGM boundary conditions, suggesting that caution is required when interpreting model output in this region. Further analysis based on model evaluation and quantitative model–proxy comparison is required to better understand the drivers of LGM climate and atmospheric circulation changes in this region.

On the impact of net-zero forcing Q-flux change

Numerical climate model simulations suggest that global warming is enhanced or hampered by the spatial pattern of the warming itself. This phenomenon is known as the “pattern effect” and has in recent years become the most promising explanation for the change over time of climate sensitivity in climate models. Under historical global warming, different patterns of surface-temperature change have emerged, notably a yet unexplained cooling in the Southern Ocean and the East Pacific. Historical climate model simulations notoriously fail to reproduce this cooling, which may contribute to the deviation of the simulated global-mean warming from the observed record. Here we qualitatively investigate the potential impact of historical and other surface-temperature pattern changes by changing the ocean heat transport convergence (Q-flux) in a slab-ocean model. The Q-flux changes are always implemented such that in the global mean they impose no net forcing. Consistent with earlier studies we find that the impact of a negative Q-flux change in the Southern Ocean has a stronger effect than in other regions because of a feedback loop between sea-surface temperatures (SSTs) and clouds in the Southern Ocean and the stably stratified regions in the tropics. The SST-cloud feedback loop facilitates the expansion of the Antarctic sea ice, indeed taking the model into a Snowball-Earth state. The intensity of this effect is found to be model dependent, especially due to differences in the cloud parametrisation. In experiments with deactivated sea ice the impact of the negative Q-flux change is much weaker.

Atmospheric teleconnections between the Arctic and the Baltic Sea region as simulated by CESM1-LE

Abstract. This paper examines teleconnections between the Arctic and the Baltic Sea region and is based on two cases of Community Earth System Model version 1 large ensemble (CESM-LE) climate model simulations: the stationary case with pre-industrial radiative forcing and the climate change case with RCP8.5 radiative forcing. The stationary control simulation's 1800-year long time series were used for stationary teleconnection and a 40-member ensemble from the period 1920–2100 is used for teleconnections during ongoing climate change. We analyzed seasonal temperature at a 2 m level, sea-level pressure, sea ice concentration, precipitation, geopotential height, and 10 m level wind speed. The Arctic was divided into seven areas. The Baltic Sea region climate has strong teleconnections with the Arctic climate; the strongest connections are with Svalbard and Greenland region. There is high seasonality in the teleconnections, with the strongest correlations in winter and the lowest correlations in summer, when the local meteorological factors are stronger. North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) climate indices can explain most teleconnections in winter and spring. During ongoing climate change, the teleconnection patterns did not show remarkable changes by the end of the 21st century. Minor pattern changes are between the Baltic Sea region temperature and the sea ice concentration. We calculated the correlation between the parameter and its ridge regression estimation to estimate different Arctic regions' collective statistical connections with the Baltic Sea region. The seasonal coefficient of determination, R2, was highest for winter: for T2 m, R2=0.64; for sea level pressure (SLP), R2=0.44; and for precipitation (PREC), R2=0.35. When doing the same for the seasons' previous month values in the Arctic, the relations are considerably weaker, with the highest R2=0.09 being for temperature in the spring. Hence, Arctic climate data forecasting capacity for the Baltic Sea region is weak. Although there are statistically significant teleconnections between the Arctic and Baltic Sea region, the Arctic impacts are regional and mostly connected with climate indexes. There are no simple cause-and-effect pathways. By the end of the 21st century, the Arctic ice concentration has significantly decreased. Still, the general teleconnection patterns between the Arctic and the Baltic Sea region will not change considerably by the end of the 21st century.

Risk analysis for preventive conservation of heritage collections in Mediterranean museums: case study of the museum of fine arts in Alexandria (Egypt)

The impacts of climate change on heritage collections in Mediterranean museums are serious and lead to accelerated material degradation, loss of value, increasing conservation costs and climatisation. Climate change scenarios and simulation models have been developed to predict the extreme and average future environmental conditions and to assess the future long-term risks caused by global warming for museum buildings and their collections in Mediterranean countries, with Egypt being particularly at risk. This paper presents the results of the risk analysis of the indoor and outdoor environments in Alexandria Museum of Fine Arts (AMFA) in Egypt to provide an overview of the current situation of conservation and museum management and to provide evidence-based data to support decision-making regarding preventive conservation given the museum's limited funding, capacity and resources. Unfortunately, the air quality in the museum cannot be considered satisfactory and specific measures need to be taken to improve the level of air quality and museum and building management. The results enabled an assessment of indoor air quality and provided information on potential risks to the museum building and collections, including variations in temperature (T) and relative humidity (RH), concentrations of NO2, SO2, O3, CO2, acetic and formic acid and lighting conditions, as well as the location and management of the museum. The results necessitate the development of a preventive conservation plan to address the challenges associated with high T/RH fluctuations and pollution pressure. This requires more regular use of the HVAC system within certain T/RH set points as well as minimising light exposure and the use of UV-filtering glazing. Care should be taken to ensure that housekeeping and emergency preparedness reduce the damping and salt florescence in the museum building. However, dealing with the impact of climate change on indoor and outdoor environments and museum collections in Mediterranean museums requires a holistic and adaptive approach that includes joint collaboration, research, training and strategic planning to ensure the long-term preservation of valuable cultural heritage collections in different climates with customised adaptations based on local environmental conditions, resources and needs. Resilience planning should be region-specific and take into account the potential impacts of extreme weather events, sea level rise and other climate-related challenges.

A Standardized Benchmarking Framework to Assess Downscaled Precipitation Simulations

Abstract Presently, there is no standardized framework or metrics identified to assess regional climate model precipitation output. Because of this, it can be difficult to make a one-to-one comparison of their performance between regions or studies, or against coarser-resolution global climate models. To address this, we introduce the first steps toward establishing a dynamic, yet standardized, benchmarking framework that can be used to assess model skill in simulating various characteristics of rainfall. Benchmarking differs from typical model evaluation in that it requires that performance expectations are set a priori. This framework has innumerable applications to underpin scientific studies that assess model performance, inform model development priorities, and aid stakeholder decision-making by providing a structured methodology to identify fit-for-purpose model simulations for climate risk assessments and adaptation strategies. While this framework can be applied to regional climate model simulations at any spatial domain, we demonstrate its effectiveness over Australia using high-resolution, 0.5° × 0.5° simulations from the CORDEX-Australasia ensemble. We provide recommendations for selecting metrics and pragmatic benchmarking thresholds depending on the application of the framework. This includes a top tier of minimum standard metrics to establish a minimum benchmarking standard for ongoing climate model assessment. We present multiple applications of the framework using feedback received from potential user communities and encourage the scientific and user community to build on this framework by tailoring benchmarks and incorporating additional metrics specific to their application. Significance Statement We introduce a standardized benchmarking framework for assessing the skill of regional climate models in simulating precipitation. This framework addresses the lack of a uniform approach in the scientific community and has diverse applications in scientific research, model development, and societal decision-making. We define a set of minimum standard metrics to underpin ongoing climate model assessments that quantify model skill in simulating fundamental characteristics of rainfall. We provide guidance for selecting metrics and defining benchmarking thresholds, demonstrated using multiple case studies over Australia. This framework has broad applications for numerous user communities and provides a structured methodology for the assessment of model performance.