Regional Climate Change Projections Research Articles

Integrating remote sensing modeling for drought assessment and vegetation monitoring in El Tarf, Algeria: a 40-year analysis

The escalating frequency and intensity of drought events, exacerbated by anthropogenic climate change, present critical challenges to hydrological resources, ecosystem resilience, and agricultural productivity. In North Africa, and specifically Algeria, comprehending the spatiotemporal patterns of drought is essential for sustainable water resource management and ecological conservation. This investigation focuses on the El Tarf region, located in northeastern Algeria, recognized for its ecological diversity and agricultural relevance. Employing a synergistic methodology, this study integrates the Standardized Precipitation Evapotranspiration Index (SPEI) for long-term drought severity evaluation with the Normalized Difference Vegetation Index (NDVI) to monitor vegetation phenology and health over time. Utilizing the computational power of Google Earth Engine, we process extensive temporal datasets of SPEI spanning from 1980 to 2023, complemented by high-resolution satellite imagery for NDVI computation. This comprehensive approach facilitates an in-depth analysis of drought dynamics, their severity, and impacts on vegetative cover in El Tarf over the past four decades. Our findings indicate significant long-term drought trends, with notable dry spells occurring between 1994 and 2003. Severe drought events were particularly pronounced in December 2022 (SPI = -2.50) and May 2002 (SPI = -2.2), correlating with substantial precipitation deficits. Although areas experiencing extreme drought have diminished, there has been an increase in moderate drought occurrences, suggesting an uptick in drought frequency, which poses risks to hydrological resources and ecosystem health. These observed patterns align with regional climate change projections, highlighting an urgent necessity for adaptive management strategies in agriculture and water resources.

Global warming significantly increases the risk of Pierce’s disease epidemics in European vineyards

Pierce’s disease (PD) is a vector-borne disease caused by the bacteria Xylella fastidiosa, which affects grapevines in the Americas. Currently, vineyards in continental Europe, the world’s largest producer of quality wine, have not yet been affected by PD. However, climate change may alter this situation. Here we incorporate the latest regional climate change projections into a climate-driven epidemiological model to assess the risk of PD epidemics in Europe for different levels of global warming. We found a significant increase in risk above +2∘C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$+\\,2\\,^{\\circ }\\hbox {C}$$\\end{document} in the main wine-producing regions of France, Italy and Portugal, in addition to a critical tipping point above +3∘C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$+\\,3\\,^{\\circ }\\hbox {C}$$\\end{document} for the possible spread of PD beyond the Mediterranean. The model identifies decreasing risk trends in Spain, as well as contrasting patterns across the continent with different velocities of risk change and epidemic growth rates. Although there is some uncertainty in model projections over time, spatial patterns of risk are consistent across different climate models. Our study provides a comprehensive analysis of the future of PD at multiple spatial scales (country, Protected Designation of Origin and vineyard), revealing where, why and when PD could become a new threat to the European wine industry.

High-resolution dynamic downscaling of historical and future climate projections over Central Asia

Climate change poses various challenges for agriculture and water management practices in Central Asia (CA). Central to these challenges are cryosphere dynamics, fragile mountain ecosystems, and ongoing natural hazards that highlight the need for robust projections of regional climate change. For the first time, dynamic downscaling was conducted in Central Asia at a spatial resolution of 5 km. This produced a regional dataset that incorporated periods between 1980 and 2000 and 2076 to 2096. Results show that dynamic downscaling significantly improves the simulation of temperature and precipitation across CA compared to General Circulation Models (GCMs) and other Regional Climate Models (RCMs) due to better representation of topography and related meteorological fields. Our analysis shows that there will be a significant warming trend in Central Asia with a projected increase of 6°C under the Shared Socioeconomic Pathway (SSP) scenario SSP5-8.5 from 2076 to 2096. Pronounced warming is detected over mountainous areas of CA from autumn to spring, which can be explained by the snow-albedo feedback. Precipitation increases are projected from winter to spring and decreases are projected from summer to autumn.

Exploring warm extremes in South America: insights into regional climate change projections through dry-bulb and wet-bulb temperatures

Exploring warm extremes in South America: insights into regional climate change projections through dry-bulb and wet-bulb temperatures

Zonally asymmetric component of summer surface temperature trends caused by intraseasonal time-scale processes

Recent years have witnessed extreme heatwaves in Europe and western North America. This study shows that these regions stand out in the zonally asymmetric component of the long-term trend of boreal summer surface temperature, and that intraseasonal timescale processes play an important role in shaping the zonally asymmetric trend pattern. However, these two regions have warmed by different mechanisms. Over Europe, the warming is mostly caused by the positive trend of the net (downward minus upward) surface shortwave radiation weighted by its intraseasonal timescale connection with the skin temperature. The long-term warming in western North America has been caused by the declining surface latent heat flux (weakened evaporative cooling) weighted by its intraseasonal connection with the skin temperature. These mechanisms are consistent with those identified in earlier studies of individual extreme events in the two regions, indicating that part of the long trends are a manifestation of extreme events. The overall findings indicate that to make accurate projections of regional climate change using climate model simulations, it is critical to ensure that the models also accurately simulate intraseasonal variability.

Critical climate thresholds for fire in wet, temperate forests

Climate models predict more frequent droughts and more severe fire weather. Wildfires in wet forests, while historically uncommon, can have catastrophic impacts on forest values and services. Therefore, it is important to ask whether climate change is increasing the frequency of fires in wet forests. Long-term fire histories and weather records were compared in wet Eucalyptus forests supplying water to Melbourne, Australia, to identify the combination of dryness and fire weather under which stand-replacing fires occur. The effect of climate change on stand replacement frequency was predicted using down-scaled regional climate change projections for RCP4.5 and RCP8.5.An index of surface soil dryness (SSD), which is a proxy for live and dead fuel moisture, and daily maximum vapor pressure deficit (Dmax), which is a proxy for daily fire weather, were used to rank each day of each fire season from 1900/01 to 2019/20, within 898 km2 of water supply catchments. The two indices were compared for days with and without stand-replacing fire activity within the study area. Threshold values for the combination of both indices were identified. Damaging fires occurred on about 10 % of days falling above this threshold. The two worst fires, between them burning 86 % of the wet forest in the study area, occurred on days with moderate rather than severe SSD (within the top 2 % of days but only about half of the maximum SSD) but with the most extreme Dmax (highest and second highest ranked out of 43,920 days).The frequency of long fire seasons (SSD > 65 mm for 30 or more days) increased from 1 in ∼30 years during the 20th century to 1 in 4 years in the past 15 years, due to a doubling in the number of warm dry days (Dmax > 2.0 kPa) per fire season. The frequency of extreme fire weather days (Dmax > 5.5 kPa) was also higher in the past 15 years than for most of the previous century. These observations suggest that fire frequency and severity are likely to increase in these wet forests, potentially threatening a suite of ecosystem services. Based on regional climate change projections, increases in the frequency of stand replacing fire will be driven more by increases in maximum temperature than by reductions in rainfall. Under RCP4.5, stand replacing fire frequency in the study area could increase from 1 in ∼140 years historically to 1 in ∼40 years by 2050 and 1 in ∼22 years by 2090 (1 in ∼6 years under RCP8.5), which would result in widespread loss of these iconic forests, including Eucalyptus regnans, the world’s tallest Angiosperm.

Wheat Self-Sufficiency in Turkey: Production and Climate Change in Focus

Abstract: Due to the fact that they can be preserved for extended periods of time and are utilized in virtually all cuisines, wheat and wheat products are the most popular grains to grow and produce. Beyond the current climate change impacts, closed national borders and growing limits for import-export products throughout the pandemic phase have caused governments to question their ability to produce enough food to meet their own needs in the short term. One of the objectives of this research is to determine what variables affect wheat self-sufficiency in Türkiye, which is one of the world's major wheat suppliers, and to develop recommendations for wheat production areas in the face of climate change's predicted impacts. With respect to Türkiye, wheat self-sufficiency data from the Turkish Statistical Institute (2000 to 2020) and regional climate change projections data from the General Directorate of Meteorology for the years 2050 and 2080 were used to identify the most significant variables, as well as the relationship between those variables and self-sufficiency. The findings indicate that wheat production is the most essential component in achieving wheat self-sufficiency and that climate change has a significant impact on wheat productivity and the areas where wheat is grown. Following this, the study concludes by detailing prospective wheat production regions in current great plain areas in the context of regional climate change projections, as well as critical policies for sustainable wheat cultivation.

AIRCC-Clim: A user-friendly tool for generating regional probabilistic climate change scenarios and risk measures

Complex physical models are the most advanced tools available for producing realistic simulations of the climate system. However, such levels of realism imply high computational cost and restrictions on their use for policymaking and risk assessment. Two central characteristics of climate change are uncertainty and that it is a dynamic problem in which international actions can significantly alter climate projections and information needs, including partial and full compliance of global climate goals. Here we present AIRCC-Clim, a simple climate model emulator that produces regional probabilistic climate change projections of monthly and annual temperature and precipitation, as well as risk measures, based both on standard and user-defined emissions scenarios for six greenhouse gases. AIRCC-Clim emulates 37 atm-ocean coupled general circulation models with low computational and technical requirements for the user. This standalone, user-friendly software is designed for a variety of applications including impact assessments, climate policy evaluation and integrated assessment modelling.

A perfect model study on the reliability of the added small-scale information in regional climate change projections

The issue of the added value (AV) of high resolution regional climate models is complex and still strongly debated. Here, we approach AV in a perfect model framework within a 16-member single model initial condition ensemble with the regional climate model RACMO2 embedded in the global climate model EC-Earth2.3. In addition, we also used an ensemble produced by a pseudo global warming (PGW) approach. Results for winter temperature and precipitation are investigated from two different perspectives: (1) a signal-to-noise perspective analysing the systematic response to changing emission forcings versus internal climate variability, and (2) a prediction perspective aimed at predicting a 30-year future climate state. Systematic changes in winter temperature and precipitation contain fine-scale response patterns, but in particular for precipitation these patterns are small compared to internal variability. Therefore, single members of the ensemble provide only limited information on these systematic patterns. However, they can be estimated more reliably from PGW members because of the stronger constraints on internal variability. From the prediction perspective, we analysed AV of fine-scale information by comparing three prediction pairs. This analysis shows that there is AV in the fine-scale information for temperature, yet for precipitation adding fine-scale changes generally deteriorates the predictions. Using only the large-scale change (without fine scales) from a single ensemble member as a delta change on top of the present-day climate state, already provides a robust estimate of the future climate state and therefore can be used as a simple benchmark to measure added value.

Impacts of Different Land Use Scenarios on Future Global and Regional Climate Extremes

Land use and land cover change (LULCC) alters the character of the land surface and directly impacts the climate. The impacts of LULCC on historical and future climate have been largely investigated, mostly using simulations with or without land use change. However, it is still not clear to what extent the projections of future climate change depend on the choice of land use scenario, which can provide important guidance on using land use and land management as a tool for regional climate mitigation. Here, using ten Earth system models participating in future land use policy sensitivity experiments in Land Use Model Intercomparison Project (LUMIP), we assessed the impact of two different land use scenarios (SSP1-2.6 and SSP3-7.0) on extreme climate. The results demonstrate that the use of different land use change scenarios has a substantial effect on the projections of regional climate extreme changes. Our study also reveals that, compared with other anthropogenic forcings, land use change has a considerable contribution to regional temperature extreme changes, with the contribution ranging from −14.0% to 10.3%, and the contribution to regional precipitation extreme change is larger, with a range of −118.4~138.8%. The global climate effects of land use change are smaller in magnitude than regional effects, with a small (5%) contribution to temperature extreme change. We also found a large spread in the model’s responses to LULCC, especially for precipitation extremes, suggesting that observation-based studies on reducing models’ uncertainties are needed to obtain more robust future projections of regional climate change. Our study highlights the essential role of land use and land management strategies in future regional climate mitigation.

Non-Hydrostatic Regcm4 (Regcm4-NH): Evaluation of Precipitation Statistics at the Convection-Permitting Scale over Different Domains

Recent studies over different geographical regions of the world have proven that regional climate models at the convection-permitting scale (CPMs) improve the simulation of precipitation in many aspects, such as the diurnal cycle, precipitation frequency, intensity, and extremes at daily—but even more at hourly—time scales. Here, we present an evaluation of climate simulations with the newly developed RegCM4-NH model run at the convection-permitting scale (CP-RegCM4-NH) for a decade-long period, over three domains covering a large European area. The simulations use a horizontal grid spacing of ~3 km and are driven by the ERA-Interim reanalysis through an intermediate driving RegCM4-NH simulation at ~12 km grid spacing with parameterized deep convection. The km-scale simulations are evaluated against a suite of hourly observation datasets with high spatial resolutions and are compared to the coarse-resolution driving simulation in order to assess improvements in precipitation from the seasonal to hourly scale. The results show that CP-RegCM4-NH produces a more realistic representation of precipitation than the coarse-resolution simulation over all domains. The most significant improvements were found for intensity, heavy precipitation, and precipitation frequency, both on daily and hourly time scales in all seasons. In general, CP-RegCM4-NH tends to correctly produce more intense precipitation and to reduce the frequency of events compared to the coarse-resolution one. On the daily scale, improvements in CP simulations are highly region dependent, with the best results over Italy, France, and Germany, and the largest biases over Switzerland, the Carpathians, and Greece, especially during the summer seasons. At the hourly scale, the improvement in CP simulations for precipitation intensity and spatial distribution is clearer than at the daily timescale. In addition, the representation of extreme events is clearly improved by CP-RegCM4-NH, particularly at the hourly time scale, although an overestimation over some subregions can be found. Although biases between the model simulations at the km-scale and observations still exist, this first application of CP-RegCM4-NH at high spatial resolution indicates a clear benefit of convection-permitting simulations and encourages further assessments of the added value of km-scale model configurations for regional climate change projections.

TopoCLIM: rapid topography-based downscaling of regional climate model output in complex terrain v1.1

Abstract. This study describes and evaluates a new downscaling scheme that specifically addresses the need for hillslope-scale atmospheric-forcing time series for modelling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope in that it does not rely directly on surface observations and is able to generate the full suite of model forcing variables required for hydrological and land surface modelling in hourly time steps. It achieves this by utilizing the previously published TopoSCALE scheme to generate synthetic observations of the current climate at the hillslope scale while accounting for a broad range of surface–atmosphere interactions. These synthetic observations are then used to debias (downscale) CORDEX climate variables using the quantile mapping method. A further temporal disaggregation step produces sub-daily fields. This approach has the advantages of other empirical–statistical methods, including speed of use, while it avoids the need for ground data, which are often limited. It is therefore a suitable method for a wide range of remote regions where ground data is absent, incomplete, or not of sufficient length. The approach is evaluated using a network of high-elevation stations across the Swiss Alps, and a test application in which the impacts of climate change on Alpine snow cover are modelled.

Mediterranean viticulture in the context of climate change

The exposure of viticulture to climate change and extreme weather conditions makes the winemaking sector particularly vulnerable, being one of its major challenges in the current century. While grapevine is considered a highly tolerant crop to several abiotic stresses, Mediterranean areas are frequently affected by adverse environmental factors, namely water scarcity, heat and high irradiance, and are especially vulnerable to climate change. Due to the high socio-economic value of this sector in Europe, the study of adaptation strategies to mitigate the negative climate change impacts are of main importance for its sustainability and competitiveness. Adaptation strategies include all the set of actions and processes that can be performed in response to climate change. It is crucial to improve agronomic strategies to offset the loss of productivity and likely changes in production and fruit quality. It is important to look for new insights concerning response mechanisms to these stresses to advance with more effective and precise measures. These measures should be adjusted to local terroirs and regional climate change projections for the sustainable development of the winemaking sector. This review describes the direct climate change impacts (on phenology, physiology, yield and berry quality), risks, and uncertainties for Mediterranean viticulture, as well as a set of canopy, soil and water management practices that winegrowers can use to adapt their vines to warmer and drier conditions.

Fire hazard forecast by the regional climate change projection using the ETA model: a case study in Bahia, Brazil

This article proposes a method for predicting fire occurrence, considering regional climate change projection using the Eta model, with a 20 km resolution, for the RCP4.5 and RCP8.5 scenarios. Fire occurrence in the state of Bahia was calculated as a function of the three main sensitivity factors on a daily time-scale: days without precipitation, precipitation, and maximum temperature. Historical fire occurrences from 1998 to 2018 and meteorological data from 1960 to 2018 were obtained from official institutes, and weather forecast parameters from 2018 to 2050 were downscaled from the web platform PROJETA. The correlations between the meteorological factors and fire occurrence were calculated for the historical data and a weight factor corresponding to a control simulation. Afterwards, a correction factor was determined, based on the historical fire occurrence data used for the forecast in the two scenarios. The results indicate that between 2018 and 2050, risk of fire will have an average increase of 27% at the RCP4.5 and 38% at the RCP8.5 scenario.

Synoptic circulation changes over Central Europe from 1900 to 2100: Reanalyses and Coupled Model Intercomparison Project phase 6

AbstractWhile the evidence for anthropogenic climate change continues to strengthen, and concerns about severe weather events are increasing, global projections of regional climate change are still uncertain due to model‐dependent changes in large‐scale atmospheric circulation, including over North Atlantic and Europe. Here, the Jenkinson–Collison classification of daily circulation patterns is used to evaluate past and future changes in their seasonal frequencies over Central Europe for the 1900–2100 period. Three reanalyses and eight global climate models from the Coupled Model Intercomparison Project phase 6, were used based on daily mean sea‐level pressure data. Best agreement in deriving relative frequencies of the synoptic types was found between the reanalyses. Global models can generally capture the interannual variability of circulation patterns and their climatological state, especially for the less frequent synoptic types. Based on historical data and the shared socioeconomic pathway 5 scenario, the evaluated trends show more robust signals during summer, given their lesser internal variability. Increasing frequencies were found for circulation types characterized by weak pressure gradients, mainly at the expense of decreasing frequencies of westerlies. Our findings indicate that given a high‐emission scenario, these signals will likely emerge from past climate variability towards the mid‐21st century for most altered circulation patterns.

On the dependency of GCM-based regional surface climate change projections on model biases, resolution and climate sensitivity

We investigate the dependency of projected regional changes in surface air temperature (SAT) and precipitation on the model biases, resolution and global temperature sensitivity in two global climate model (GCM) ensembles. End of twenty-first century changes under high end scenarios normalized in units of per degree of global warming (PDGW) are examined for CMIP5 (RCP8.5) and CMIP6 (SSP5-8.5) ensembles of comparable size over 26 sub-continental scale regions, for December–January–February (DJF) and June–July–August (JJA). A brief analysis is also carried out for the scenario SSP3-7.0, which shows results essentially in line with the SSP5-8.5 ones. We find that the average regional change patterns are very similar between the CMIP5 and CMIP6 ensembles, both for SAT and precipitation, with spatial correlations exceeding 0.84. Also similar are the regional bias patterns over most regions analyzed, suggesting that these two generations of models still share some common systematic errors. A statistically significant relationship between projected regional changes and biases is found in 27% of regional cases for both SAT and precipitation; between regional changes and model resolution in 2% of cases for SAT and 12% of cases for precipitation; and between regional changes and global temperature sensitivity in 19% of cases for SAT and 14% of cases for precipitation. Therefore, we assess that the GCM resolution does not appear to be a significant factor in affecting the sub-continental scale projected changes, at least for the resolution range in the CMIP5 and CMIP6 models, while global temperature sensitivity and especially model biases play a more important role. These dependencies are not always consistent between the CMIP5 and CMIP6 ensembles. Overall, in our assessment the CMIP6 ensemble does not appear to provide substantially different, and presumably improved, regional surface climate change information compared to CMIP5 despite the use of more comprehensive models and somewhat higher resolution.

The impact of land use on future water balance - A simple approach for analysing climate change effects

Regional climate change projections for Europe agree in predicting a statistically significant warming in all seasons. The most significant climate change effect is its impact on water cycle through altering precipitation patterns and evapotranspiration processes at multiple scales. The anticipated changes in the distribution and precipitation amounts together with continuously increasing temperatures may induce a higher rate of water consumption in plants, which can generate changes in soil moisture, groundwater, and the water cycle. Thus, climate change can cause changes in the water balance equations structure. A Thornthwaite-type monthly step water balance model was established to compare the water balance in three different surface land cover types: (i) a natural forested area; (ii) a parcel with mixed surface cover; (iii) an agricultural area. The key parameter of the model is the water storage capacity of the soil. Maximal rooting depth of the given area is also determinable during the calibration process using actual evapotranspiration (AET) and soil physical data. The locally calibrated model was employed for assessing future AET and soil moisture of selected land cover types using data from four bias-corrected regional climate models. The projections demonstrate increasing actual evapotranspiration values in each surface cover type at the end of the 21st century. Regarding the 10th percentile minimum soil moisture values, the forested area displayed an increasing trend, while the agricultural field and mixed parcel showed a strong decrease. The 30-year monthly means of evapotranspiration shows the maximum values in June and July, while the minimum soil moisture in September. Water stress analysis indicates water stress is expected to occur only in the agricultural field during the 21st century. The comparison of the three surface covers reveals that forest has the greatest soil water storage capacity due to the highest rooting depth. Thus, according to the projections for 21st century, less water stress is predicted to occur at the forested area compared to the other two surface covers which shows shallow rooting depth.

Long-term adaptation of European viticulture to climate change: an overview from the H2020 Clim4Vitis action

Climate change is a major challenge to viticulture worldwide. The adaptation potential of the different strategies to cope with climate change still embraces many uncertainties (e.g., unpredictable social-economic developments and land-use changes), particularly in the long-term. However, adaptation strategies adjusted to local terroirs and regional climate change projections will contribute to the sustainable development of the winemaking sector. The Clim4Vitis action (https://clim4vitis.eu/) recommends some guidelines for long-term adaptation (Figure 1).

Recent increasing frequency of compound summer drought and heatwaves in Southeast Brazil

An increase in the frequency of extremely hot and dry events has been experienced over the past few decades in South America, and particularly in Brazil. Regional climate change projections indicate a future aggravation of this trend. However, a comprehensive characterization of drought and heatwave compound events, as well as of the main land–atmosphere mechanisms involved, is still lacking for most of South America. This study aims to fill this gap, assessing for the first time the historical evolution of compound summer drought and heatwave events for the heavily populated region of Southeast Brazil and for the period of 1980–2018. The main goal is to undertake a detailed analysis of the surface and synoptic conditions, as well as of the land–atmosphere coupling processes that led to the occurrence of individual and compound dry and hot extremes. Our results confirm that the São Paulo, Rio de Janeiro and Minas Gerais states have recorded pronounced and statistically significant increases in the number of compound summer drought and heatwave episodes. In particular, the last decade was characterized by two austral summer seasons (2013/14 and 2014/15) with outstanding concurrent drought and heatwave conditions stemmed by severe precipitation deficits and a higher-than-average occurrence of blocking patterns. As result of these land and atmosphere conditions, a high coupling (water-limited) regime was imposed, promoting the re-amplification of hot spells that resulted in mega heatwave episodes. Our findings reveal a substantial contribution of persistent dry conditions to heatwave episodes, highlighting the vulnerability of the region to climate change.

Correlations Between Sea‐Level Components Are Driven by Regional Climate Change

AbstractThe accurate quantification of uncertainties in regional sea‐level projections is essential for guiding policy makers. As climate models do not currently simulate total sea level, these uncertainties must be quantified through summation of uncertainties in individual sea‐level components. This summation depends on the correlation between the components, which has previously been prescribed or derived from each individual component's dependence on global mean surface temperature. In this study, we quantify, for the first time, regional correlations between sea‐level components based on regional climate change projections. We compute regional sea‐level projections consistent with climate projections from an ensemble of 14 Earth System Models. From the multi‐model spread, we estimate the uncertainty in the regional climate's response to greenhouse forcing. To quantify the total uncertainty, we add the uncertainty in the response of sea‐level components to this regional climate change. This approach reveals how regional climate processes impose correlations between sea‐level components, affecting the total uncertainty. One example is an anti‐correlation between North Atlantic sterodynamic change and Antarctic dynamic mass loss, suggesting a teleconnection established by the large‐scale ocean circulation. We find that prescribed correlations, applied in the fifth assessment report of the Intergovernmental Panel on Climate Change, lead to a global overestimation in the uncertainty in regional sea‐level projections on the order of 20%. Regionally, this overestimation exceeds 100%. We conclude that accurate uncertainty estimates of regional sea‐level change must be based on projections of regional climate change and cannot be derived from global indicators such as global mean surface temperature.