The enhanced warming trend and precipitation decline in the Mediterranean region make it a climate change hotspot. We compare projections of multiple Coupled Model Intercomparison Project Phase 5 ...(CMIP5) and Phase 6 (CMIP6) historical and future scenario simulations to quantify the impacts of the already changing climate in the region. In particular, we investigate changes in temperature and precipitation during the 21st century following scenarios RCP2.6, RCP4.5 and RCP8.5 for CMIP5 and SSP1-2.6, SSP2-4.5 and SSP5-8.5 from CMIP6, as well as for the HighResMIP high-resolution experiments. A model weighting scheme is applied to obtain constrained estimates of projected changes, which accounts for historical model performance and inter-independence in the multi-model ensembles, using an observational ensemble as reference. Results indicate a robust and significant warming over the Mediterranean region during the 21st century over all seasons, ensembles and experiments. The temperature changes vary between CMIPs, CMIP6 being the ensemble that projects a stronger warming. The Mediterranean amplified warming with respect to the global mean is mainly found during summer. The projected Mediterranean warming during the summer season can span from 1.83 to 8.49 ∘C in CMIP6 and 1.22 to 6.63 ∘C in CMIP5 considering three different scenarios and the 50 % of inter-model spread by the end of the century. Contrarily to temperature projections, precipitation changes show greater uncertainties and spatial heterogeneity. However, a robust and significant precipitation decline is projected over large parts of the region during summer by the end of the century and for the high emission scenario (−49 % to −16 % in CMIP6 and −47 % to −22 % in CMIP5). While there is less disagreement in projected precipitation than in temperature between CMIP5 and CMIP6, the latter shows larger precipitation declines in some regions. Results obtained from the model weighting scheme indicate larger warming trends in CMIP5 and a weaker warming trend in CMIP6, thereby reducing the difference between the multi-model ensemble means from 1.32 ∘C before weighting to 0.68 ∘C after weighting.
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One of the key quality aspects in a probabilistic prediction is its reliability. However, this property is difficult to estimate in the case of seasonal forecasts due to the limited size of most of ...the hindcasts that are available nowadays. To shed light on this issue, this work presents a detailed analysis of how the ensemble size, the hindcast length and the number of points pooled together within a particular region affect the resulting reliability estimates. To do so, we build on 42 land reference regions recently defined for the IPCC‐AR6 and assess the reliability of global seasonal forecasts of temperature and precipitation from the European Center for Medium Weather Forecasts SEAS5 prediction system, which is compared against its predecessor, System4. Our results indicate that whereas longer hindcasts and larger ensembles lead to increased reliability estimates, the number of points that are pooled together within a homogeneous climate region is much less relevant.
Plain Language Summary
Seasonal climate forecasts provide information on the average conditions that can be expected for the next months (up to a year) and can help decision making in different socio‐economic sectors such as agriculture, energy and health (among others). However, predictability at this time‐scale is in general limited, so the actual usefulness of seasonal forecasts must be carefully evaluated before they are used in practical applications. In this aspect, reliability—which measures how well/bad the forecast probability for a particular event fits with its actual occurrence—is a key property. This work assesses the reliability of global seasonal forecasts of temperature and precipitation using the latest operational seasonal forecasting system from European Center for Medium Weather Forecasts. Our results show that reliability is generally better for temperature than for precipitation. Moreover, we demonstrate that reliability is sensitive to the number of retrospective forecasts (known as hindcast) and ensemble members (from which forecast probabilities are obtained) available. Finally, we also demonstrate that the new IPCC‐AR6 land reference regions are adequate for seasonal verification purposes. These findings are important for a fair interpretation of the reliability of seasonal forecasts which are obtained for specific regions/seasons/systems building on different experimental frameworks.
Key Points
KP1 The new SEAS5 from the European Center for Medium Weather Forecasts increases the reliability of the previous System4 for global seasonal predictions of temperature and precipitation
KP2 The reliability of probabilistic seasonal forecasts can vary substantially due to the ensemble size and the length of the available hindcast
KP3 The newly defined IPCC‐AR6 land reference regions are adequate for the verification of seasonal forecast reliability
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
A new global high-resolution coupled climate model, EC-Earth3P-HR has been developed by the EC-Earth consortium, with a resolution of approximately 40 km for the atmosphere and 0.25.sup." for the ...ocean, alongside with a standard-resolution version of the model, EC-Earth3P (80 km atmosphere, 1.0.sup." ocean). The model forcing and simulations follow the High Resolution Model Intercomparison Project (HighResMIP) protocol. According to this protocol, all simulations are made with both high and standard resolutions. The model has been optimized with respect to scalability, performance, data storage and post-processing. In accordance with the HighResMIP protocol, no specific tuning for the high-resolution version has been applied.
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Dynamical forecast systems have low to moderate skill in continental winter predictions in the extratropics. Here we assess the multimodel predictive skill over Northern Hemisphere high latitudes and ...midlatitudes using four state‐of‐the‐art forecast systems. Our main goal was to quantify the impact of the Arctic sea ice state during November on the sea level pressure (SLP), surface temperature, and precipitation skill during the following winter. Interannual variability of the November Barents and Kara Sea ice is associated with an important fraction of December to February (DJF) prediction skill in regions of Eurasia. We further show that skill related to sea ice in these regions is accompanied with enhanced skill of DJF SLP in western Russia, established by a sea ice‐atmosphere teleconnection mechanism. The teleconnection is strongest when atmospheric blocking conditions in Scandinavia/western Russia in November reduce a systematic SLP bias that is present in all systems.
Plain Language Summary
There is a broad range of stakeholders that could benefit from Northern Hemisphere, midlatitude winter climate predictions from dynamical forecast systems. However, a widespread use is currently hindered by important forecast system limitations. The results from this study suggest that autumnal Arctic sea ice state may have an important impact on winter climate forecast capacity in parts of Eurasia. We further show that large ensembles of simulations can be further exploited, by identifying the members with a better representation of certain processes, in this case the teleconnection between Arctic sea ice and the atmospheric circulation, to enhance the prediction skill of temperature and precipitation over the continents. Exploring this approach for other regions and seasons can provide a possible pathway toward more human‐relevant seasonal climate predictions.
Key Points
Climate forecast systems have limited predictive capacity at seasonal scales in the Northern Hemisphere midlatitudes
Autumnal Barents and Kara Sea ice is likely a source of winter climate predictability in large regions of northern Eurasia
Analysis of multimodel initialized predictions suggests that winter predictability in Eurasia is enhanced by a sea ice‐atmosphere linkage
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The distribution of data contributed to the Coupled Model Intercomparison Project Phase 6 (CMIP6) is via the Earth System Grid Federation (ESGF). The ESGF is a network of internationally distributed ...sites that together work as a federated data archive. Data records from climate modelling institutes are published to the ESGF and then shared around the world. It is anticipated that CMIP6 will produce approximately 20 PB of data to be published and distributed via the ESGF. In addition to this large volume of data a number of value-added CMIP6 services are required to interact with the ESGF; for example the citation and errata services both interact with the ESGF but are not a core part of its infrastructure. With a number of interacting services and a large volume of data anticipated for CMIP6, the CMIP Data Node Operations Team (CDNOT) was formed. The CDNOT coordinated and implemented a series of CMIP6 preparation data challenges to test all the interacting components in the ESGF CMIP6 software ecosystem. This ensured that when CMIP6 data were released they could be reliably distributed.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
In this paper, we present and evaluate the skill of an EC-Earth3.3 decadal prediction system contributing to the Decadal Climate Prediction Project – Component A (DCPP-A). This prediction system is ...capable of skilfully simulating past global mean surface temperature variations at interannual and decadal forecast times as well as the local surface temperature in regions such as the tropical Atlantic, the Indian Ocean and most of the continental areas, although most of the skill comes from the representation of the external radiative forcings. A benefit of initialization in the predictive skill is evident in some areas of the tropical Pacific and North Atlantic oceans in the first forecast years, an added value that is mostly confined to the south-east tropical Pacific and the eastern subpolar North Atlantic at the longest forecast times (6–10 years). The central subpolar North Atlantic shows poor predictive skill and a detrimental effect of initialization that leads to a quick collapse in Labrador Sea convection, followed by a weakening of the Atlantic Meridional Overturning Circulation (AMOC) and excessive local sea ice growth. The shutdown in Labrador Sea convection responds to a gradual increase in the local density stratification in the first years of the forecast, ultimately related to the different paces at which surface and subsurface temperature and salinity drift towards their preferred mean state. This transition happens rapidly at the surface and more slowly in the subsurface, where, by the 10th forecast year, the model is still far from the typical mean states in the corresponding ensemble of historical simulations with EC-Earth3. Thus, our study highlights the Labrador Sea as a region that can be sensitive to full-field initialization and hamper the final prediction skill, a problem that can be alleviated by improving the regional model biases through model development and by identifying more optimal initialization strategies.
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Near-term projections of climate change are subject to substantial uncertainty from internal climate variability. Here we present an approach to reduce this uncertainty by sub-selecting those ...ensemble members that more closely resemble observed patterns of ocean temperature variability immediately prior to a certain start date. This constraint aligns the observed and simulated variability phases and is conceptually similar to initialization in seasonal to decadal climate predictions. We apply this variability constraint to large multi-model projection ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6), consisting of more than 200 ensemble members, and evaluate the skill of the constrained ensemble in predicting the observed near-surface temperature, sea-level pressure, and precipitation on decadal to multi-decadal timescales.
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Despite the wealth of existing climate forecast data, only a small part is effectively exploited for sectoral applications. A major cause of this is the lack of integrated tools that allow the ...translation of data into useful and skillful climate information. This barrier is addressed through the development of an R package. Climate Services Toolbox (CSTools) is an easy-to-use toolbox designed and built to assess and improve the quality of climate forecasts for seasonal to multi-annual scales. The package contains process-based, state-of-the-art methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination, and multivariate verification, as well as basic and advanced tools to obtain tailored products. Due to the modular design of the toolbox in individual functions, the users can develop their own post-processing chain of functions, as shown in the use cases presented in this paper, including the analysis of an extreme wind speed event, the generation of seasonal forecasts of snow depth based on the SNOWPACK model, and the post-processing of temperature and precipitation data to be used as input in impact models.
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One of the challenges in studying desert dust aerosol along with its numerous interactions and impacts is the paucity of direct in situ measurements, particularly in the areas most affected by dust ...storms. Satellites typically provide column-integrated aerosol measurements, but observationally constrained continuous 3D dust fields are needed to assess dust variability, climate effects and impacts upon a variety of socio-economic sectors. Here, we present a high-resolution regional reanalysis data set of desert dust aerosols that covers Northern Africa, the Middle East and Europe along with the Mediterranean Sea and parts of central Asia and the Atlantic and Indian oceans between 2007 and 2016. The horizontal resolution is 0.1∘ latitude × 0.1∘ longitude in a rotated grid, and the temporal resolution is 3 h. The reanalysis was produced using local ensemble transform Kalman filter (LETKF) data assimilation in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH) developed at the Barcelona Supercomputing Center (BSC). The assimilated data are coarse-mode dust optical depth retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue Level 2 products. The reanalysis data set consists of upper-air variables (dust mass concentrations and the extinction coefficient), surface variables (dust deposition and solar irradiance fields among them) and total column variables (e.g. dust optical depth and load). Some dust variables, such as concentrations and wet and dry deposition, are expressed for a binned size distribution that ranges from 0.2 to 20 µm in particle diameter. Both analysis and first-guess (analysis-initialized simulation) fields are available for the variables that are diagnosed from the state vector. A set of ensemble statistics is archived for each output variable, namely the ensemble mean, standard deviation, maximum and median. The spatial and temporal distribution of the dust fields follows well-known dust cycle features controlled by seasonal changes in meteorology and vegetation cover. The analysis is statistically closer to the assimilated retrievals than the first guess, which proves the consistency of the data assimilation method. Independent evaluation using Aerosol Robotic Network (AERONET) dust-filtered optical depth retrievals indicates that the reanalysis data set is highly accurate (mean bias = −0.05, RMSE = 0.12 and r = 0.81 when compared to retrievals from the spectral de-convolution algorithm on a 3-hourly basis). Verification statistics are broadly homogeneous in space and time with regional differences that can be partly attributed to model limitations (e.g. poor representation of small-scale emission processes), the presence of aerosols other than dust in the observations used in the evaluation and differences in the number of observations among seasons. Such a reliable high-resolution historical record of atmospheric desert dust will allow a better quantification of dust impacts upon key sectors of society and economy, including health, solar energy production and transportation. The reanalysis data set (Di Tomaso et al., 2021) is distributed via Thematic Real-time Environmental Distributed Data Services (THREDDS) at BSC and is freely available at http://hdl.handle.net/21.12146/c6d4a608-5de3-47f6-a004-67cb1d498d98 (last access: 10 June 2022).
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In orbit since late 2017, the Tropospheric Monitoring Instrument (TROPOMI) is offering new outstanding opportunities for better understanding the emission and fate of nitrogen dioxide (NO2) pollution ...in the troposphere. In this study, we provide a comprehensive analysis of the spatio-temporal variability of TROPOMI NO2 tropospheric columns (TrC-NO2) over the Iberian Peninsula during 2018–2021, considering the recently developed Product Algorithm Laboratory (PAL) product. We complement our analysis with estimates of NOx anthropogenic and natural soil emissions. Closely related to cloud cover, the data availability of TROPOMI observations ranges from 30 %–45 % during April and November to 70 %–80 % during summertime, with strong variations between northern and southern Spain. Strongest TrC-NO2 hotspots are located over Madrid and Barcelona, while TrC-NO2 enhancements are also observed along international maritime routes close the strait of Gibraltar, and to a lesser extent along specific major highways. TROPOMI TrC-NO2 appear reasonably well correlated with collocated surface NO2 mixing ratios, with correlations around 0.7–0.8 depending on the averaging time.We investigate the changes of weekly and monthly variability of TROPOMI TrC-NO2 depending on the urban cover fraction. Weekly profiles show a reduction of TrC-NO2 during the weekend ranging from -10 % to -40 % from least to most urbanized areas, in reasonable agreement with surface NO2. In the largest agglomerations like Madrid or Barcelona, this weekend effect peaks not in the city center but in specific suburban areas/cities, suggesting a larger relative contribution of commuting to total NOx anthropogenic emissions. The TROPOMI TrC-NO2 monthly variability also strongly varies with the level of urbanization, with monthly differences relative to annual mean ranging from -40 % in summer to +60 % in winter in the most urbanized areas, and from -10 % to +20 % in the least urbanized areas. When focusing on agricultural areas, TROPOMI observations depict an enhancement in June–July that could come from natural soil NO emissions. Some specific analysis of surface NO2 observations in Madrid show that the relatively sharp NO2 minimum used to occur in August (drop of road transport during holidays) has now evolved into a much broader minimum partly de-coupled from the observed local road traffic counting; this change started in 2018, thus before the COVID-19 outbreak. Over 2019–2021, a reasonable consistency of the inter-annual variability of NO2 is also found between both datasets.Our study illustrates the strong potential of TROPOMI TrC-NO2 observations for complementing the existing surface NO2 monitoring stations, especially in the poorly covered rural and maritime areas where NOx can play a key role, notably for the production of tropospheric O3.
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