Search

Drought is a complex climatic phenomenon characterized by water scarcity recognized as the most widespread and insidious natural hazard, posing significant challenges to ecosystems and human society. In this study, we propose a new daily based index for characterizing droughts, which involves standardizing precipitation and/or precipitation minus potential evapotranspiration data. The performance of this new index is assessed with data from the evaluation runs of the Coordinated Regional Climate Downscaling Experiment for the European domain and the observational data from the Iberian Gridded Dataset, covering the period from 1989 to 2009. Comparative assessments are conducted against the daily Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and a simple Z-Score standardization of climatic variables. Seven different accumulation periods are considered (7, 15, 30, 90, 180, 360, and 720 days) with three drought levels: moderate, severe, and extreme. The evaluation focuses mainly on the direct comparison amongst indices, added value assessment using the Distribution Added Value and a simple bias difference for drought characteristics. Results reveal that not only does the new index allow the characterization of flash droughts, but also demonstrates added value when compared to SPI and SPEI, especially for longer accumulation periods. In comparison to the Z-Score, the new index shows slightly greater gains, particularly for extreme drought events at lower accumulation periods. Furthermore, an assessment of the spatial extent of drought for the 2004–2005 event is performed using the observational dataset. All three indices generally provide similar representations, except for the Z-Score, which exhibits limitations in capturing extreme drought events at lower accumulation periods. Overall, the findings suggest that the new index offers improved performance and adds value comparatively to similar indices with a daily time step. [ABSTRACT FROM AUTHOR]

Deep learning (DL) methods have recently garnered attention from the climate change community, as an innovative approach for downscaling climate variables from Earth System and Global Climate Models (ESGCMs) with horizontal resolutions still too coarse to represent regional-to-local-scale phenomena. In the context of the Coupled Model Intercomparison Project phase 6 (CMIP6), ESGCMs simulations were conducted for the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC), at resolutions ranging from 0.70° to 3.75°. Here, four Convolutional Neural Network (CNN) architectures were evaluated for their ability to downscale, to a resolution of 0.1°, seven CMIP6 ESGCMs over the Iberian Peninsula - a known climate change hotspot, due to its increased vulnerability to projected future warming and drying conditions. The study is divided into three stages: (1) evaluating the performance of the four CNN architectures in predicting mean, minimum, and maximum temperatures, as well as daily precipitation, trained using ERA5 data, and compared with the Iberia01 observational dataset; (2) downscaling the CMIP6 ESGCMs using the trained CNN architectures and further evaluating the ensemble against Iberia01; and (3) constructing a multi-model ensemble of CNNbased downscaled projections for temperature and precipitation over the Iberian Peninsula at 0.1° resolution throughout the 21st century, under four Shared Socioeconomic Pathway (SSP) scenarios. Upon validation and satisfactory performance evaluation, the DL downscaled projections demonstrate overall agreement with the CMIP6 ESGCM ensemble in terms of temperature and precipitation projections. Moreover, the advantages of using a high-resolution DL downscaled ensemble of ESGCM climate projections are evident, offering substantial added value in representing regional climate change over Iberia. Notably, a clear warming trend is observed, consistent with previous studies in this area, with projected temperature increases ranging from 2°C to 6°C depending on the climate scenario. Regarding precipitation, robust projected decreases are observed in western and southwestern Iberia, particularly after 2040. These results may offer a new tool for providing regional climate change information for adaptation strategies based on CMIP6 ESGCMs prior to the next phase of the European branch from the Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX) experiments. [ABSTRACT FROM AUTHOR]

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

In the recent past, the increase of computation resources led to the appearance of regional climate models with increasing domains and resolutions, spamming larger temporal periods. A good example is the World Climate Research Program - Coordinated Regional Climate Downscaling Experiment for the European domain (EURO-CORDEX). This set of regional models encompass the entire European continent, for a 130-year common period until the end of the 21st century, while having a 12 Km horizontal resolution. Such simulations are computationally demanding, while at the same time, not always showing added value. This study considers a recently proposed metric in order to assess the added value of the EURO-CORDEX Hindcast (1989-2008) and Historical (1971-2005) simulations, for the maximum and minimum temperature over the Iberian Peninsula. This approach allows an evaluation of the higher against the driving lower resolutions relative to the performance of the whole or partial probability density functions, by having an observational regular gridded dataset as reference. Overall, the gains for maximum temperature are more relevant in comparison to minimum temperature, partially owed to known problems derived from the snow-albedo-atmosphere feedback. For more local scales, areas near the coast reveal significant added value in comparison with the interior, which displays limited gains and sometimes significant detrimental effects around −30 %. Nevertheless, the added value for temperature extremes reveals a similar range, although with stronger gains in coastal regions and in locations from the interior for maximum temperature, contrasting with the losses for locations in the interior of the domain for the minimum temperature. [ABSTRACT FROM AUTHOR]

Over the years higher resolution Regional Climate Model simulations have emerged owing to the large increase in computational resources. The 12 Km resolution from the Coordinated Regional Climate Downscaling Experiment for the European domain (EURO-CORDEX) is a reference, which includes a larger multi-model ensemble at a continental scale while spanning at least a 130-year period. These simulations are computationally demanding but not always revealing added value. In this study, a recently developed regular gridded dataset (Iberia0.1) and a new metric for added value quantification, the distribution added value (DAV), are used to assess the precipitation of all available EURO-CORDEX Hindcast (1989-2008) and Historical (1971-2005) simulations. This approach enables a direct assessment between the higher resolution regional model runs against their forcing Global model or ERA-Interim reanalysis, with respect to their PDFs. This assessment is performed for the Iberian Peninsula. Overall, important gains are found for most cases, particularly in precipitation extremes. Most Hindcast models reveal gains above 15 %, namely for wintertime, while for precipitation extremes values above 20 % are reached for the summer and autumn. As for the Historical models, although most pairs display gains, regional models forced by 2 GCMs reveal losses, sometimes around −5 % or stronger, for the entire year. However, the spatialization of the DAV is clear in terms of added value for precipitation, particularly precipitation extremes with significant gains, above 100 %. [ABSTRACT FROM AUTHOR]

The complexity of the state-of-the-art climate models requires high computational resources and imposes rather simplified parameterization of inland waters. The effect of lakes and reservoirs on the local and regional climate is commonly parameterized in regional or global climate modeling as a function of surface water temperature estimated by atmosphere-coupled one-dimensional lake models. The latter typically neglect one of the major transport mechanisms specific to artificial reservoirs: heat and mass advection due to in- and outflows. Incorporation of these essentially two-dimensional processes into lake parameterizations requires a trade-off between computational efficiency and physical soundness, which is addressed in this study. We evaluated the performance of the two most used lake parameterization schemes and a machine learning approach on high-resolution historical water temperature records from 24 reservoirs. Simulations were also performed at both variable and constant water level to explore the thermal structure differences between lakes and reservoirs. Our results highlight that surface water temperatures in reservoirs differ significantly from those found in lakes, reinforcing the need to include anthropogenic inflow and outflow controls in regional and global climate models. Our findings also highlight the efficiency of the machine learning approach, which may overperform process-based physical models both in accuracy and in computational requirements, if applied to reservoirs with long-term observations available. A relationship between mean water retention times and the importance of inflows and outflows is established: reservoirs with the retention time shorter than ~100 days, if simulated without in- and outflow effects, tend to exhibit a statistically significant deviation in the computed surface temperatures regardless of their morphological characteristics. [ABSTRACT FROM AUTHOR]

In the context of the first phase of the Euro-CORDEX Flagship Plot Study (FPS) Land Use and Climate Across Scales (LUCAS), we investigate the afforestation impact on the seasonal cycle of soil temperature over the European continent with an ensemble of ten regional climate models (RCMs). For this purpose, each ensemble member performed two idealized land cover experiments in which Europe is covered either by forests or grasslands. The multi-model mean exhibits a reduction of the annual amplitude of soil temperature (AAST) over all European regions, although this not a robust feature among the models. In Mediterranean, the simulated AAST response to afforestation is between -4 K and +2 K while in Scandinavia the inter-model spread ranges from -7 K to +1 K. We then examine the role of changes in the annual amplitude of ground heat flux (AAGHF) and summer soil moisture content (SMC) in determining the effect of afforestation on AAST response. In contrast with the diverging results in AAST, all the models consistently indicate a widespread AAGHF decrease and summer SMC decline due to afforestation. The AAGHF changes effectively explain the largest part of the inter-model variance in AAST response in most regions, while the changes in summer SMC determine the sign of AAST response within a group of three simulations sharing the same land surface model. Finally, we pair FLUXNET sites to compare the simulated results with observation-based evidence of the impact of forest on soil temperature. In line with models, observations indicate a summer ground cooling in forested areas compared to open lands. The vast majority of models agree with the sign of the observed reduction in AAST, although with a large variation in the magnitude of changes. Overall, we aspire to emphasize the effects of afforestation on soil temperature profile with this study, given that changes in the seasonal cycle of soil temperature potentially perturb crucial biochemical processes. Such perturbations can be of societal relevance as afforestation is proposed as a climate change mitigation strategy. [ABSTRACT FROM AUTHOR]