José M. Gutiérrez

The different phases of the Coupled Model Intercomparison Project (CMIP) provide ensembles of past, present, and future climate simulations crucial for climate change impact and adaptation activities. These ensembles are produced using multiple Global Climate Models (GCMs) from different modeling centres with some shared building blocks and inter-dependencies. Applications typically follow the ‘model democracy’ approach which might have significant implications in the resulting products (e.g. large bias and low spread). Thus, quantifying model similarity within ensembles is crucial for interpreting model agreement and multi-model uncertainty in climate change studies. The classical methods used for assessing GCM similarity can be classified into two groups. The a priori approach relies on expert knowledge about the components of these models, while the a posteriori …

Despite the sophistication of global climate models (GCMs), their coarse spatial resolution limits their ability to resolve important aspects of climate variability and change at the local scale. Both dynamical and empirical methods are used for enhancing the resolution of climate projections through downscaling, each with distinct advantages and challenges. Dynamical downscaling is physics based but comes with a large computational cost, posing a barrier for downscaling an ensemble of GCMs large enough for reliable uncertainty quantification of climate risks. In contrast, empirical downscaling, which encompasses statistical and machine learning techniques, provides a computationally efficient alternative to downscaling GCMs. Empirical downscaling algorithms can be developed to emulate the behavior of dynamical models directly, or through frameworks such as perfect prognosis in which relationships …

We review how the international modelling community, encompassing Integrated Assessment models, global and regional Earth system and climate models, and impact models, have worked together over the past few decades, to advance understanding of Earth system change and its impacts on society and the environment, and support international climate policy. We then recommend a number of priority research areas for the coming~ 6 years (ie until~ 2030), a timescale that 75 matches a number of newly starting international modelling activities and encompasses the IPCC 7th Assessment Report

Assessing the regional responses to different Global Warming Levels (GWLs; e.g. + 1.5, 2, 3 and 4 ºC) is one of the most important challenges in climate change sciences since the Paris Agreement goal of keeping global temperature increase well below 2 °C with respect to the pre-industrial period. Regional responses to global warming were typically analyzed using global projections from Global Climate Models (GCMs) and, more recently, using higher resolution Regional Climate Models (RCMs) over limited regions. For instance, the IPCC AR6 WGI Atlas provides results of the regional response to different GWLs for several climate variables from both GCMs and RCMs. These results are calculated under the assumption that the regional signal to global warming is consistent between the GCMs and the nested RCMs. In the present study we investigate the above assumption by evaluating the consistency of …

Pierce's Disease (PD) is a vector-borne disease caused by the bacterium Xylella fastidiosa, which poses a significant threat to grapevines worldwide. Despite its importance, the risk of future PD establishment in Europe remains unclear due to previous incomplete methodologies followed by conflicting results. Here we present a comprehensive approach considering the compound effect of climate change on the pathosystem. Within the general trend of progressively increasing PD risk, we identified the +3°C scenario as a turning point for potential spreading beyond Mediterranean regions, representing a serious risk for French and Italian viticulture. Our innovative methodology reveals PD risk as a multi-factor multi-scale process, showing contrasting spatial patterns and different risk velocities across regions, as well as a high timing uncertainty. By overcoming previous limitations, our findings contribute to a better understanding of the potential spread of PD in Europe, supporting informed decision-making for disease management and prevention.

Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC), including the first global stocktake under the Paris Agreement that will conclude at COP28 in December 2023. Evidence-based decision-making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles. We follow methods as close as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. We compile monitoring datasets to produce estimates for key climate indicators related to forcing of the climate system: emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, surface temperature changes, the Earth's energy imbalance, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. The purpose of this effort, grounded in an open data, open science approach, is to make annually updated reliable global climate indicators available in the public domain (https://doi.org/10.5281/zenodo.8000192, Smith et al., 2023a). As they are traceable to IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its …

El Consejo Superior de Investigaciones Científicas (CSIC) presenta el martes 7 de marzo la Plataforma Temática Interdisciplinar (PTI) Clima y Servicios Climáticos, un nuevo instrumento del organismo que aglutina a equipos científicos especializados en atmósfera y clima y que tiene el objetivo de abordar de forma coordinada uno de los grandes retos globales marcados por Naciones Unidas: el Objetivo de Desarrollo Sostenible (ODS) número 13, centrado en la acción por el clima. La presentación de esta nueva PTI tendrá lugar en la sede de la Delegación del Gobierno en Cantabria, ubicada en Santander, y contará con las intervenciones de la delegada del Gobierno en Cantabria, Ainoa Quiñones; la presidenta del CSIC, Eloísa del Pino; el vicepresidente de Investigación Científica y Técnica del CSIC, José María Martell; el delegado de la Agencia Estatal de Meteorología (AEMET) en Cantabria, José Luis Arteche; el director del Instituto de Física de Cantabria (IFCA-CSIC-UC) y uno de los coordinadores de la PTI Clima, José Manuel Gutiérrez; y la investigadora de la AEMET Yolanda Luna.

Regional climate models (RCMs) are essential tools for simulating and studying regional climate variability and change. However, their high computational cost limits the production of comprehensive ensembles of regional climate projections covering multiple scenarios and driving Global Climate Models (GCMs) across regions. RCM emulators based on deep learning models have recently been introduced as a cost-effective and promising alternative that requires only short RCM simulations to train the models. Therefore, evaluating their transferability to different periods, scenarios, and GCMs becomes a pivotal and complex task in which the inherent biases of both GCMs and RCMs play a significant role. Here we focus on this problem by considering the two different emulation approaches proposed in the literature (PP and MOS, following the terminology introduced in this paper). In addition to standard evaluation techniques, we expand the analysis with methods from the field of eXplainable Artificial Intelligence (XAI), to assess the physical consistency of the empirical links learnt by the models. We find that both approaches are able to emulate certain climatological properties of RCMs for different periods and scenarios (soft transferability), but the consistency of the emulation functions differ between approaches. Whereas PP learns robust and physically meaningful patterns, MOS results are GCM-dependent and lack physical consistency in some cases. Both approaches face problems when transferring the emulation function to other GCMs, due to the existence of GCM-dependent biases (hard transferability). This limits their applicability to …

2 The three Special Reports are: Global Warming of 1.5 C: An IPCC Special Report on the impacts of global warming of 1.5 C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty (SR1. 5); Climate Change and Land: An IPCC Special Report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (SRCCL); IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC).

Deep learning (DL) has emerged as a promising tool to downscale climate projections at regional‐to‐local scales from large‐scale atmospheric fields following the perfect‐prognosis approach. Given their complexity, it is crucial to properly evaluate these methods, especially when applied to changing climatic conditions where the ability to extrapolate/generalize is key. In this work, we intercompare several DL models extracted from the literature for the same challenging use‐case (downscaling temperature in the CORDEX North America domain) and expand standard evaluation methods building on eXplainable Artificial Intelligence (XAI) techniques. Specifically, we introduce two novel XAI‐based diagnostics—Aggregated Saliency Map and Saliency Dispersion Maps—and show how they can be used to unravel the internal behavior of these models, aiding in their design and evaluation. This work advocates for …

Global Climate Models (GCMs) are the primary tool to simulate climate evolution and assess the impacts of climate change. However, they often operate at a coarse spatial resolution that limits their accuracy in reproducing local-scale phenomena. Statistical downscaling methods leveraging deep learning offer a solution to this problem by approximating local-scale climate fields from coarse variables, thus enabling regional GCM projections. Typically, climate fields of different variables of interest are downscaled independently, resulting in violations of fundamental physical properties across interconnected variables. This study investigates the scope of this problem and, through an application on temperature, lays the foundation for a framework introducing multi-variable hard constraints that guarantees physical relationships between groups of downscaled climate variables.

Sea surface temperature (SST) and sea surface air temperature (SSAT) are commonly used as proxies for investigating the impact of climate change on oceans. These variables have been warming since pre-industrial times and are expected to continue to warm in the future under all Shared Socioeconomic Pathways (SSPs). However, they are warming at different rates in a spatially heterogeneous way, even with some cooling spots. In this work, we provide a general overview on the regional scaling of SST and SSAT with global warming, based on a 26-member CMIP6 ensemble. We use global warming level (GWL) as a climate change dimension, and analyze the slope of the linear fit (β) between decadal sea temperature anomalies and the corresponding GWLs during the 21st century. This analysis is done globally, regionally, and also grid-point by grid-point. The results show that SST and SSAT scale linearly with GWL at global scale, with scaling factors 0.71±0.001 K/K and 0.86±0.001 K/K, respectively. These results are quite robust, with small differences between seasons, SSPs and horizontal model resolutions. However, large differences appear at regional scale, and the scaling of the two temperatures are strongly affected by sea-ice. The lowest values are obtained for the Southern Ocean region, β= 0.54±0.005 K/K, projecting the mean SST to increase half as fast as the global mean temperature. These results provide relevant information for a refinement of ocean reference regions, taking into account the spatial homogeneity in terms of regional response to global warming.

Deep Learning (DL) has recently emerged as a promising Empirical Statistical Downscaling perfect-prognosis technique (ESD-PP), to generate high-resolution fields from large-scale climate variables. Here, we analyze two state-of-the-art DL topologies for ESD-PP of different levels of complexity over North America. Besides classical validation leaning on accuracy metrics (eg, Root Mean Squared Error (RMSE)), we evaluate several interpretability techniques to gain understanding on the inner functioning of the DL models deployed. Taking as reference the RMSE both topologies show similar values. Nonetheless, by analyzing the resulting interpretability maps, we find that the simplest model fails to capture a realistic physics-based input-output link, whilst the complex one describes a local pattern, characteristic of downscaling. In climate change scenarios, where weather extremes are exacerbated, erroneous patterns can lead to highly biased projections. Therefore, including interpretability techniques as a diagnostic of model functioning in the evaluation process can help us to better select and design them.

This study provides a long-term (1891–2014) global assessment of precipitation trends using data from two station-based gridded datasets and climate model outputs evolved through the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively). Our analysis employs a variety of modeling groups that incorporate low- and high-top level members, with the aim of assessing the possible effects of including a well-resolved stratosphere on the model’s ability to reproduce long-term observed annual precipitation trends. Results demonstrate that only a few regions show statistically significant differences in precipitation trends between observations and models. Nevertheless, this pattern is mostly caused by the strong interannual variability of precipitation in most of the world regions. Thus, statistically significant model-observation differences on trends (1891–2014) are found at …

This Summary, based on the IPCC Sixth Assessment Report, is tailored to the actuarial community. It has been co-developed by the authors of the IPCC report and a team of actuaries and catastrophe experts from the IAA. The scientific data and conclusions are attributed alone to the IPCC, while the need for emphasis on some risks, and the comments about actuarial practices have been provided by the IAA team.

In the recent past, an increase in computation resources led to the development of regional climate models with increasing domains and resolutions, spanning 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 encompasses 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 a reference. Overall, the gains for maximum temperature are more relevant in comparison to minimum temperature, partially due to known problems derived from the snow–albedo–atmosphere feedback. For more local scales, areas near the coast reveal higher added value in comparison with the interior, which displays limited gains and sometimes notable detrimental effects with values around −30 %. At the same time, the added value for temperature extremes reveals a similar range, although with larger gains in coastal regions and in locations from the interior …

The collaboration between the Coordinated Regional Climate Downscaling Experiment (CORDEX) and the Earth System Grid Federation (ESGF) provides open access to an unprecedented ensemble of regional climate model (RCM) simulations, across the 14 CORDEX continental-scale domains, with global coverage. These simulations have been used as a new line of evidence to assess regional climate projections in the latest contribution of the Working Group I (WGI) to the IPCC Sixth Assessment Report (AR6), particularly in the regional chapters and the Atlas. Here, we present the work done in the framework of the Copernicus Climate Change Service (C3S) to assemble a consistent worldwide CORDEX grand ensemble, aligned with the deadlines and activities of IPCC AR6. This work addressed the uneven and heterogeneous availability of CORDEX ESGF data by supporting publication in CORDEX …

This work presents a comprehensive assessment of the suitability of random forests, a well‐known machine learning technique, for the statistical downscaling of precipitation. Building on the experimental and validation framework proposed in the Experiment 1 of the COST action VALUE—the largest, most exhaustive intercomparison study of statistical downscaling methods to date—we introduce and thoroughly analyze a posteriori random forests (AP‐RFs), which use all the information contained in the leaves to reliably predict the shape and scale parameters of the gamma probability distribution of precipitation on wet days. Therefore, as opposed to traditional random forests, which typically provide deterministic predictions, our AP‐RFs allow realistic stochastic precipitation samples to be generated for wet days. Indeed, as compared to one particular implementation of a generalized linear model that exhibited an …

Reconstruction of Gene Regulatory Networks (GRNs) of gene expression data with Probabilistic Network Models (PNMs) is an open problem. Gene expression datasets consist of thousand of genes with relatively small sample sizes (i.e. are large-p-small-n). Moreover, dependencies of various orders coexist in the datasets. On the one hand transcription factor encoding genes act like hubs and regulate target genes, on the other hand target genes show local dependencies. In the field of Undirected Network Models (UNMs)—a subclass of PNMs—the Glasso algorithm has been proposed to deal with high dimensional microarray datasets forcing sparsity. To overcome the problem of the complex structure of interactions, modifications of the default Glasso algorithm have been developed that integrate the expected dependency structure in the UNMs beforehand. In this work we advocate the use of a simple score …

Historical and future projected changes in climatic patterns over the largest irrigated basin in the world, the Indus River Basin (IRB), threaten agricultural production and food security in Pakistan, in particular for vulnerable farming communities. To build a more detailed understanding of the impacts of climate change on agriculture s in the IRB, the present study analyzes (1) observed trends in average temperature, precipitation and related extreme indicators, as well as seasonal shifts over a recent historical period (1997–2016); and (2) statistically downscaled future projections (up to 2100) from a set of climate models in conjunction with crop-specific information for the four main crops of the IRB: wheat, cotton, rice and sugarcane. Key findings show an increasing trend of about over 0.1 °C/year in observed minimum temperature across the study area over the historical period, but no significant trend in maximum temperature. Historical precipitation shows a positive annual increase driven mainly by changes in August and September. Future projections highlight continued warming resulting in critical heat thresholds for the four crops analyzed being increasingly exceeded into the future, in particular in the Kharif season. Concurrently, inter-annual rainfall variability is projected to increase up to 10–20% by the end of the 21st century, augmenting uncertainty of water availability in the basin. These findings provide insight into the nature of recent climatic shifts in the IRB and emphasize the importance of using climate impact assessments to develop targeted investments and efficient adaptation measures to ensure resilience of agriculture in Pakistan …