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Este documento presenta un análisis comparativo de los resultados de un modelo de simulación de clima, datos de reanálisis regionales y datos locales de precipitación y temperatura estacional de veintitrés estaciones meteorológicas de Guatemala, para detectar señales de la habilidad del modelo a reproducir el clima estacional en un periodo de 3 años (1998-2000). La simulación se realizó con un modelo climático regional (MCR), para su reducción de escala dinámica, las condiciones de frontera se obtuvieron de los datos de reanálisis ERA-Interim. El modelo utilizado fue RegCM, versión 4, y se comparó con los datos de precipitación y temperatura de la Base de datos CRU a nivel regional centroamericano y a nivel nacional con tres instituciones que generan datos globales (CRU, TRMM y GPCP) y los datos locales. Los esquemas convectivos utilizados fueron el esquema de Grell sobre tierra y Emanuel sobre el océano, con 50 km de resolución espacial. Los ajustes realizados a las parametrizaciones generaron buen desempeño a nivel regional Centroamericano y a nivel Guatemala a pesar de perder habilidad en algunas regiones y meses. El modelo reproduce adecuadamente el comportamiento de la precipitación estacional en la mayor parte de la temporada lluviosa. Subestima la temperatura a nivel regional, pero a nivel Guatemala muestra buen ajuste. La comparación con los datos locales observados muestra que el modelo se ajusta para el periodo en estudio; pero, es necesario realizar más experimentos con distintas resoluciones espaciales y temporales y evaluar la persistencia del modelo.

In the paper is presented a heating system installed in church and the interior climate generated. Thermal Comfort is the purpose of each designer, since the design stage and has to be ensure for the churchgoers, but even for the interior finishes. The heating system that uses hydronic radiators is evaluated trough the CFD modelling, in order to evaluate pro and contra arguments. The simulation has been made in a 3d simulation software environment, in Autodesk CFD with good results.

A study of recent climate simulation during the period 1979–2006 is carried out over Bangladesh with Meteorological Research Institute (MRI) global 20 km mesh Atmospheric General Circulation Model (AGCM), called MRI-AGCM. Skills of the model in simulating rainfall and temperature as well as frequency and intensity of extreme events are investigated at various temporal and spatial scales with a focus on mean surface air temperature and rainfall over Bangladesh. Initial and lateral boundary conditions are provided based on the IPCC SRES A1B scenario during the period 1979–2006. The MRI-AGCM shows good performance in simulating meteorological parameters such as rainfall and temperature and exhibits seasonal and inter-annual variations over the South Asian Association for Regional Cooperation (SAARC) region including Bangladesh with some persistent biases being present in summer monsoon rainfall. The model successfully simulates temperature over this region with a systematic cold bias due to the East-West temperature gradient. Simulated mean surface air temperature is compared with NCEP/NCAR reanalysis and CRU mean surface air temperature during the period 1979–2006 (28 years) for summer and winter seasons. Outputs from the MRI-AGCM show some fine scale structures that are missed by the NCEP/NCAR reanalysis products due to its coarse resolution. In general, the MRI-AGCM show a predominant cold bias of a few degrees (greater in summer than in winter) throughout the SAARC domain, the MRI-AGCM simulated rainfall shows good agreement with that of CRU and considerable discrepancies are found both with Global Precipitation Climatology Project (GPCP) and NCEP rainfall fields in the summer monsoon season. However, model simulated rainfall is relatively in good agreement with NCEP than GPCP. The statistical analysis such as correlation, bias and RMSE are derived between model and all observed data including station data, CRU, NCEP/NCAR and GPCP. Spatial and temporal patterns of summer monsoon rainfall of MRI-AGCM show almost similar pattern with different magnitudes. The high resolution MRI-AGCM rainfall is quantitatively in good agreement with GPCP, NCEP and CRU rainfall.

The comparison of modern climate models with reanalysis and observational data indicates significant success in general atmospheric circulation modeling and reveals the problems of climate simulation for some regions which are often associated with errors in the modeling of the vertical troposphere structure. The results of simulation of inversions and low- and upper-level tropospheric jet streams in Russia were analyzed and verified using the INMCM climate model based on radiosonde data. It is shown that the model accurately reproduces the frequency of inversions and jet streams in various regions of Russia. At the same time, climatology of such characteristics as surface inversion fraction, jet stream height, and some others is simulated less accurately, and the verification results vary greatly for different points. It is demonstrated that the INM-CM5.0 model can be successfully used to assess possible changes in the Novorossiysk bora based on the link between the large-scale flow and the bora frequency.

The influence of external forcings on surface temperature is not fully understood at decadal to multi-centennial timescales. This study uses simulation results from the Community Earth System Model (CESM) forced by total solar irradiation (TSI), greenhouse gases (GHGs) concentrations, volcanic eruptions (VOL), and land use and land covers (LULC) to investigate the possible impacts of external forcing on decadal to multi-centennial timescales over the past 2000 years. The simulated global annual mean surface temperature (GMST) variations show a good coherence with observations over the past 150 years and proxy-based reconstructions over the past 2000 years. Ensemble empirical mode decomposition (EEMD) is used to isolate the GMST variations on decadal to multi-centennial timescales. We found that the VOL forcing has a significant effect on GMST at decadal to multi-decadal timescales, but its effect weakens gradually as the timescale lengthens. The TSI forcing is a major factor of GMST on multi-decadal to sub-millennial timescales, and its effect strengthens as the timescale lengthens. The GHGs is the dominant driver of recent warming, but its effect is weak in the pre-industrial times. The LULC could lead to a slight cooling trend over the past 2000 years but has no large impact on GMST at decadal to multi-centennial timescales. This study reports a 2000-year length climate simulation and may contribute to better understanding external drivers of GMST at different timescales.

Seasonal prediction and climate change modeling are carried out on the basis of the programs incorporating the global model of the atmosphere, ocean model, sea ice and land models, etc. Each component of a coupled model has to provide a fairly complete representation of the physical processes in the appropriate medium, while the other components are replaced by reanalysis data. In this paper we present the results of a study aimed at improving the quality of the climate simulation with the SL-AV global atmospheric model where both ocean sur face temperature and ice concentration are prescribed. We also discuss some probtems in tuning the parameterizations of subgrid-scale physical processes and their modifications.

The development of model systems requires a detailed assessment of standing genetic variation across natural populations. The Brachypodium species complex has been promoted as a plant model for grass genomics with translation to small grain and biomass crops. To capture the genetic diversity within this species complex, thousands of Brachypodium accessions from around the globe were collected and genotyped by sequencing. Overall, 1897 samples were classified into two diploid or allopolyploid species, and then further grouped into distinct inbred genotypes. A core set of diverse B. distachyon diploid lines was selected for whole genome sequencing and high resolution phenotyping. Genome-wide association studies across simulated seasonal environments was used to identify candidate genes and pathways tied to key life history and agronomic traits under current and future climatic conditions. A total of 8, 22, and 47 QTL were identified for flowering time, early vigor, and energy traits, respectively. The results highlight the genomic structure of the Brachypodium species complex, and the diploid lines provided a resource that allows complex trait dissection within this grass model species.

Development of the multiscale version of the global atmosphere model SL-AV required many improvements in the dynamical core, replacement or refinement of parameterization algorithms and complex tuning of the model. These modifications were initially tested with the experiments on modern climate simulation and then incorporated into the model configuration for medium-range numerical weather prediction. The impact of these model improvements on forecast quality is studied in this paper. The increase in accuracy of model climate characteristics has led to the reduction of forecast errors. The comparison of quality for numerical forecasts starting from the initial data of Hydrometcenter of Russia and ECMWF is carried out. The effect of replacing the initial data turned out to be comparable to the effect of multi-year works on model development. This shows the importance and necessity of development and improvement of the Hydrometcenter of Russia data assimilation system.

A high-resolution (4 km) regional climate simulation conducted with the Weather Research and Forecast (WRF) model is used to investigate potential impacts of global warming on skiing conditions in the interior western United States (IWUS). Recent past and near-future climate conditions are compared. The past climate period is from November 1981 to October 2011. The future climate applies to a 30-year period centered on 2050. A pseudo global warming approach is used, with the driver re-analysis dataset perturbed by the CMIP5 ensemble mean model guidance. Using the 30-year retrospective simulation, a vertical adjustment technique is used to determine meteorological parameters in the complex terrain where ski areas are located. For snow water equivalent (SWE), Snow Telemetry sites close to ski areas are used to validate the technique and apply a correction to SWE in ski areas. The vulnerability to climate change is assessed for 71 ski areas in the IWUS considering SWE, artificially produced snow, temperature, and rain. 20 of the ski areas will tend to have fewer than 100 days per season with sufficient natural and artificial snow for skiing. These ski areas are located at either low elevations or low latitudes making these areas the most vulnerable to climate change. Throughout the snow season, natural SWE decreases significantly at the low elevations and low latitudes. At higher elevations changes in SWE are predicted to not be significant in the mid-season. In mid-February, SWE decreases by 11.8% at the top elevations of ski areas while it decreases by 25.8% at the base elevations.

Against flood disasters to be intensified in a future climate, we are required to implement adaptation strategies on a limited budget. In urban areas, heavy rainfall-based floods are classified into two types: pluvial and fluvial floods. It is well known that fluvial floods cause deeper inundation and stronger fluid force while pluvial ones occur more frequently. Such hydrodynamic characteristics have been intensively discussed in a literature; however, their impact and the resulting damage have not yet been examined in a comprehensive manner due to small samples of storm events in one region that leads to high uncertainty in frequency analysis. In the context of climate change impact assessment on extreme events, considerable ensembles of climate data have become available, contributing to smaller uncertainty in frequency analysis of flood damages. This study presents a case study of frequency estimation of fluvial and pluvial floods in an urban area set in Nagoya City, Japan. We applied a large ensemble climate simulation database, d4PDF, to a combined pluvial and fluvial flood model, from which we derived flood risk curves for each type of flooding. The results indicated that pluvial flooding presents comparable economic risk to fluvial flooding (16% and 17% lesser damage at 50- and 100-year return periods, respectively) despite its significantly shallower flood depths (area with flood depth over 45 cm was only 10.5% and 5.4%, respectively). This is because pluvial floods widely occur over the city, including areas further away from the river. Furthermore, probably similar with other mega cities with long history, fluvial flood risk has been managed by settling the central economic district (originally the Nagoya Castle founded several centuries ago) on higher altitudes. The results suggest that pluvial flooding could have comparable economic risks to fluvial flooding in urban areas where major economic assets are widely sprawled over the city as well as historical countermeasures are implemented against fluvial flooding. Pluvial floods, countermeasures against which tend to be smaller than fluvial floods, should be managed at a comparable level in urban areas.

Sociopolitical values are an important driver of climate change beliefs, attitudes, and policy preferences. People with “individualist‐hierarchical” values favor individual freedom, competition, and clearly defined social hierarchies, while “communitarian‐egalitarians” value interdependence and equality across gender, age, heritage, and ethnicity. In the US, individualist‐hierarchs generally perceive less risk from climate change and express lower support for actions to mitigate it than communitarian‐egalitarians. Exposure to scientific information does little to change these views. Here, we ask if a widely used experiential simulation, World Climate, can help overcome these barriers. World Climate combines an engaging role‐play with an interactive computer model of the climate system. We examine pre‐ and post‐World Climate survey responses from 2,080 participants in the US and use a general linear mixed model approach to analyze interactions among participants' sociopolitical values and gains in climate change knowledge, affect, and intent to take action. As expected, prior to the simulation, participants holding individualist‐hierarchical values had lower levels of climate change knowledge, felt less urgency, and expressed lower intent to act than those holding communitarian‐egalitarian values. However, individualist‐hierarchs made significantly larger gains across all constructs, particularly urgency, than communitarian‐egalitarians. Participants' sociopolitical values also shifted: those with individualistic‐hierarchical values before the simulation showed a substantial, statistically significant shift toward a communitarian‐egalitarian worldview. Simulation‐based experiences like World Climate may help reduce polarization and build consensus towards science‐based climate action.

A dominant paradigm for mid-latitude air-sea interaction identifies the synoptic-scale atmospheric “noise” as the main driver for the observed ocean surface variability. While this conceptual model successfully holds over most of the mid-latitude ocean surface, its soundness over frontal zones (including western boundary currents; WBC) characterized by intense mesoscale activity, has been questioned in a number of studies suggesting a driving role for the small scale ocean dynamics (mesoscale oceanic eddies) in the modulation of air-sea interaction. In this context, climate models provide a powerful experimental device to inspect the emerging scale-dependent nature of mid-latitude air-sea interaction. This study assesses the impact of model resolution on the representation of air-sea interaction over the Gulf Stream region, in a multi-model ensemble of present-climate simulations performed using a common experimental design. Lead-lag correlation and covariance patterns between sea surface temperature (SST) and turbulent heat flux (THF) are diagnosed to identify the leading regimes of air-sea interaction in a region encompassing both the Gulf Stream system and the North Atlantic subtropical basin. Based on these statistical metrics it is found that coupled models based on “laminar” (eddy-parameterised) and eddy-permitting oceans are able to discriminate between an ocean-driven regime, dominating the region controlled by the Gulf Stream dynamics, and an atmosphere-driven regime, typical of the open ocean regions. However, the increase of model resolution leads to a better representation of SST and THF cross-covariance patterns and functional forms, and the major improvements can be largely ascribed to a refinement of the oceanic model component. The authors of this study wish to thank two reviewers for their many insightful comments. AB, PA, ES, RH, JG-S, DP, ESG, MJR, CR, JS, PV acknowledge PRIMAVERA funding received from the European Commission under Grant Agreement 641727 of the Horizon 2020 research programme. JG-S was additionally supported by the Spanish ‘Ramón y Cajal’ programme (RYC-2016-21181). The authors declare that they have no conflict of interest. The datasets used in this work are cited in this manuscript with appropriate doi’s in publicly available archives.

In this evaluation study, the coupled atmosphere–ocean Adriatic Sea and Coast (AdriSC) climate model, which was implemented to carry out 31-year evaluation and climate projection simulations in the Adriatic and northern Ionian seas, is briefly presented. The kilometre-scale AdriSC atmospheric results, derived with the Weather Research and Forecasting (WRF) 3 km model for the 1987–2017 period, are then thoroughly compared to a comprehensive publicly and freely available observational dataset. The evaluation shows that overall, except for the summer surface temperatures, which are systematically underestimated, the AdriSC WRF 3 km model has a far better capacity to reproduce surface climate variables (and particularly the rain) than the WRF regional climate models at 0.11∘ resolution. In addition, several spurious data have been found in both gridded products and in situ measurements, which thus should be used with care in the Adriatic region for climate studies at local and regional scales. Long-term simulations with the AdriSC climate model, which couples the WRF 3 km model with a 1 km ocean model, might thus be a new avenue to substantially improve the reproduction, at the climate scale, of the Adriatic Sea dynamics driving the Eastern Mediterranean thermohaline circulation. As such it may also provide new standards for climate studies of orographically developed coastal regions in general.

Simulations of the CMIP6 historical period 1850–2014, characterized by the emergence of anthropogenic climate drivers like greenhouse gases, are presented for different configurations of the NASA Goddard Institute for Space Studies (GISS) Earth System ModelE2.1. The GISS‐E2.1 ensembles are more sensitive to greenhouse gas forcing than their CMIP5 predecessors (GISS‐E2) but warm less during recent decades due to a forcing reduction that is attributed to greater longwave opacity in the GISS‐E2.1 pre‐industrial simulations. This results in an atmosphere less sensitive to increases in opacity from rising greenhouse gas concentrations, demonstrating the importance of the base climatology to forcing and forced climate trends. Most model versions match observed temperature trends since 1979 from the ocean to the stratosphere. The choice of ocean model is important to the transient climate response, as found previously in CMIP5 GISS‐E2: the model that more efficiently exports heat to the deep ocean shows a smaller rise in tropospheric temperature. Model sea level rise over the historical period is traced to excessive drawdown of aquifers to meet irrigation demand with a smaller contribution from thermal expansion. This shows how fully coupled models can provide indirect observational constraints upon forcing, in this case, constraining irrigation rates with observed sea level changes. The overall agreement of GISS‐E2.1 with observed trends is familiar from evaluation of its predecessors, as is the conclusion that these trends are almost entirely anthropogenic in origin. We thank two anonymous reviewers for their comments that substantially improved this article. Development of GISS‐E2.1 was supported by the NASA Modeling, Analysis, and Prediction (MAP) Program. CMIP6 simulations with GISS‐E2.1 were made possible by the NASA High‐End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center. We thank Ellen Salmon and the NCCS staff for hosting and providing convenient access to the model output. Peer Reviewed "Article signat per 47 autors/es: Ron L. Miller, Gavin A. Schmidt, Larissa S. Nazarenko, Susanne E. Bauer, Maxwell Kelley, Reto Ruedy, Gary L. Russell , Andrew S. Ackerman, Igor Aleinov, Michael Bauer, Rainer Bleck, Vittorio Canuto, Grégory Cesana, Ye Cheng, Thomas L. Clune, Ben I. Cook, Carlos A. Cruz, Anthony D. Del Genio, Gregory S. Elsaesser, Greg Faluvegi, Nancy Y. Kiang, Daehyun Kim, Andrew A. Lacis , Anthony Leboissetie,r Allegra N. LeGrande, Ken K. Lo, John Marshall, Elaine E. Matthews, Sonali McDermid, Keren Mezuman, Lee T. Murray, Valdar Oinas, Clara Orbe, Carlos Pérez García‐Pando, Jan P. Perlwitz, Michael J. Puma, David Rind, Anastasia Romanou, Drew T. Shindell, Shan Sun, Nick Tausnev, Kostas Tsigaridis, George Tselioudis, Ensheng Weng, Jingbo Wu, Mao‐Sung Yao"

This chapter describes the observed change in temperatures since preindustrial times, the impacts of extreme temperatures on the environment and society, and the indices and metrics commonly used for evaluating temperature change. The methods for observational analysis, climate modeling, and regional climate downscaling are presented to help readers understand the state-of-the-art techniques for climate simulation and projection. This chapter reviews mitigation and adaptation strategies employed by major US cities and introduces the methods for strategy evaluation and optimization that can be employed by governments or stakeholders to plan and initiate appropriate climate adaptations and resiliency measures.

This paper summarizes the current state of reproducibility and replicability in the fields of climate and paleoclimate science, including brief histories of their development and applications in climate science, new and recent approaches towards improvement of reproducibility and replicability, and challenges. Recommendations for addressing those challenges include: development of searchable, auto-updated, interlinked, multi-archive public paleoclimate repositories for raw and processed digital datasets; cross-center standardized code base cases, improved data storage techniques, and a focus on replicability for climate simulation storage and access; and support of the development and community awareness of findable, accessible, interoperable and reusable (FAIR) principles by funding agencies and publishers. This paper is largely based on the May 2018 presentations of a panel of researchers to the Committee on Reproducibility and Replicability in Science, part of the National Academies of Science, Engineering, and Medicine. The commentary and recommendations made here are in alignment with those of its Consensus Study Report on Reproducibility and Replicability in Science (2019).

In the autumn, the French Mediterranean area is frequently exposed to heavy precipitation events whose daily accumulation can exceed 300 mm. One of the key processes contributing to these precipitation amounts is the deep convection, which can be resolved explicitly by state-or-the-art convection-permitting model to reproduce heavy rainfall events that are comparable to observations. However, this approach has never been used in climate simulation for the Mediterranean coastal region. In this research, we investigate the added values of using three ensembles of climate simulations at convection-permitting resolution (approx. 3 km) in replicating extreme precipitation events in both daily and shorter time scale over the South of France. These three convection-permitting simulations are performed with the Weather Research and Forecasting Model (WRF). They are forced by three EURO-CORDEX simulations, which are also downscaled with WRF at the resolution of 0.11° (approx. 12 km). We found that a convection-permitting approach provides a more realistic representation of extreme daily and 3-hourly rainfall simulations in comparison with EURO-CORDEX simulations. Their similarity with observations allows a use for climate change studies and its impacts.

The sensitivity of the hydrological system to climate change and the role of hydrological systems in the environment have motivated researchers to study the impacts of climate change on hydrology. Modelling the hydrological impacts of climate change is generally done in various stages and has uncertainty associated with each of them. These include scenario uncertainty in climate scenario selection, model uncertainty in climate simulation by global climatic models (GCMs), uncertainties while downscaling GCMs, biases in downscaled data, erroneous input to the hydrological model, and uncertainty in the structure and parameterisation of the hydrological model. The present paper aims at reviewing the uncertainties involved at each stage of climate change impact assessment of hydrology. In the near future, climate scenario uncertainties would be smaller than those associated with the choice of GCMs. Multi-model ensemble approach takes better account of uncertainties involved with GCMs. Moreover, considering a range of possible climate scenarios is recommended than using a single best or average case climate scenario. GCMs shall be downscaled by statistical or dynamical methods (regional climatic models (RCMs)) before using them for regional studies. Bias correction methods can considerably improve the RCM simulations. Evaluation of model performance is recommended for regional-scale studies for the preparation of adaptation strategies. Taking into account the uncertainties associated with climate impact studies can help formulate effective adaptation strategies.

Crop growth in land surface models normally requires high-temporal-resolution climate data (3-hourly or 6-hourly), but such high-temporal-resolution climate data are not provided by many climate model simulations due to expensive storage, which limits modeling choices if there is an interest in a particular climate simulation that only saved monthly outputs. The Community Land Surface Model (CLM) has proposed an alternative approach for utilizing monthly climate outputs as forcing data since version 4.5, and it is called the anomaly forcing CLM. However, such an approach has never been validated for crop yield projections. In our work, we created anomaly forcing datasets for three climate scenarios (1.5 ∘C warming, 2.0 ∘C warming, and RCP4.5) and validated crop yields against the standard CLM forcing with the same climate scenarios using 3-hourly data. We found that the anomaly forcing CLM could not produce crop yields identical to the standard CLM due to the different submonthly variations, crop yields were underestimated by 5 %–8 % across the three scenarios (1.5, 2.0 ∘C, and RCP4.5) for the global average, and 28 %–41 % of cropland showed significantly different yields. However, the anomaly forcing CLM effectively captured the relative changes between scenarios and over time, as well as regional crop yield variations. We recommend that such an approach be used for qualitative analysis of crop yields when only monthly outputs are available. Our approach can be adopted by other land surface models to expand their capabilities for utilizing monthly climate data.

Climate simulation-based scenarios are routinely used to characterize a range of plausible climate futures. Despite some recent progress on bending the emissions curve, RCP8.5, the most aggressive scenario in assumed fossil fuel use for global climate models, will continue to serve as a useful tool for quantifying physical climate risk, especially over near- to midterm policy-relevant time horizons. Not only are the emissions consistent with RCP8.5 in close agreement with historical total cumulative CO 2 emissions (within 1%), but RCP8.5 is also the best match out to midcentury under current and stated policies with still highly plausible levels of CO 2 emissions in 2100.

Numerical models are being used for the simulation of recent climate conditions as well as future projections. Due to the complexity of the Earth&rsquo, s climate system and processes occurring at sub-grid scales, model results often diverge from the observed values. Different methods have been developed to minimize such biases. In the present study, the recently introduced &ldquo, triangular irregular networks (TIN)-Copula&rdquo, method was used for the bias correction of modelled monthly total and extreme precipitation in Cyprus. The method was applied to a 15-year historical period and two future periods of the same duration. Precipitation time-series were derived from a 12-km resolution EURO-CORDEX regional climate simulation. The results show that the TIN-Copula method significantly reduces the positive biases between the model results and observations during the historical period of 1986&ndash, 2000, for both total and extreme precipitation (&gt, 80%). However, the level of improvement differs temporally and spatially. For future periods, the model tends to project significantly higher total precipitation rates prior to bias correction, while for extremes the differences are smaller. The adjustments slightly affect the overall climate change signal, which tends to be enhanced after bias correction, especially for total precipitation and for the autumn period.

During spring 2011, an extreme flood occurred along the Richelieu River located in southern Quebec, Canada. The Richelieu River is the last section of the complex Richelieu basin, which is composed of the large Lake Champlain located in a valley between two large mountains. Previous attempts in reproducing the Richelieu River flow relied on the use of simplified lumped models and showed mixed results. In order to prepare a tool to assess accurately the change of flood recurrences in the future, a state‐of‐the‐art distributed hydrological model was applied over the Richelieu basin. The model setup comprises several novel methods and data sets such as a very high resolution river network, a modern calibration technique considering the net basin supply of Lake Champlain, a new optimization algorithm, and the use of an up‐to‐date meteorological data set to force the model. The results show that the hydrological model is able to satisfactorily reproduce the multiyear mean annual hydrograph and the 2011 flow time series when compared with the observed river flow and an estimation of the Lake Champlain net basin supply. Many factors, such as the quality of the meteorological forcing data, that are affected by the low density of the station network, the steep terrain, and the lake storage effect challenged the simulation of the river flow. Overall, the satisfactory validation of the hydrological model allows to move to the next step, which consists in assessing the impacts of climate change on the recurrence of Richelieu River floods.

The Greenland ice sheet (GrIS) has been losing mass in the last several decades, with a current contributing of around 0.7 mm per year to global mean sea level rise (SLR). Projections of future melt rates are often derived from standalone ice sheet models, forced by data from global or regional climate models. In many cases, the surface mass balance parameterization relies on simplified schemes that relate melt with surface temperature.In this study, we present a mass and energy conserving, 350-year simulation with the Community Earth System Model version 2.1 (CESM2.1) bidirectionally coupled to the Community Ice Sheet Model version 2.1 (CISM2.1). In this simulation, the carbon dioxide concentration is initially increasing by 1% per year from pre-industrial levels (287 ppmv), to a quadrupling (1140 ppmv) and stabilization after year 140. The model simulates a global warming of 5.3 K and 8.5 K with respect to preindustrial by years 131-150 and 331-150, respectively, and a strong decline in the North Atlantic Meridional Overturning Circulation that is initiated before GrIS runoff substantially increases. 91% of the total GrIS contribution to global mean sea level rise (SLR, 1140 mm) is simulated in the two centuries following CO2 stabilization, as the mass loss increases from 2.2 mm SLR per year in 131-150 to 6.6 mm SLR per year in 331-351. This increase is caused by melt acceleration as the ablation areas expand, and Greenland summer surface temperatures predominantly approach melt conditions when the global warming exceeds a certain threshold (around 4.2 K). This enhances the albedo and turbulent heat fluxes contribution to total melt energy.

Researchers apply a superparameterization technique to boost the accuracy and efficiency of climate predictions generated by the Energy Exascale Earth System Model.

Research in recent years has highlighted the significance of ARs to the occurrence and modulation of weather and hydrology extremes, as well as the critical role they play in helping establish the Earth’s climate—namely its water and energy budgets—through their decisive effect on the poleward transport of water vapor. With this in mind, it is imperative that ARs—and their associated dynamics, thermodynamics, and hydrodynamics—be accurately represented in our weather and climate models. This chapter reviews modeling and model-evaluation studies of ARs, including both weather forecast and climate simulation/projection applications. Specifically, research performed to date on predictive aspects of ARs for short- to extended-range (i.e., 1- to 15-day) weather forecasts is discussed and summarized. In addition, this chapter reviews research and model-evaluation studies that examine and quantify regional and global climate model representations of ARs. By extension, the more the fidelity and systematic biases in climate models are understood and quantified, the more the uncertainty in climate model projection of ARs can be characterized. Thus, this chapter also reviews the research that has examined contemporary sets of climate projections to quantify how ARs and related fields are expected to change. Finally, this chapter summarizes gaps in understanding and research of AR simulations, multi-time-scale forecasting, and climate projections.

Offshore wind energy is regarded as a key alternative to fossil fuels in many parts of the world. Its exploitation is based on the sound evaluation of wind resources. This study used data from a meteorological mast, a floating light detection and ranging (LiDAR) device, and the Modern-Era Retrospective Analysis for Research and Applications, a reanalysis data set established by the NASA Center for Climate Simulation, to evaluate wind resources of the Changhua-South Offshore Wind Farm. The average wind speeds evaluated at a height of 105 m in the studied wind farm were 7.97 and 8.02 m/s according to the data obtained from the floating LiDAR device and a mast, respectively. The full-load hours were 3320.5 and 3296.5 h per year when data from the LiDAR device and mast were used, respectively. The estimated annual energy production (AEP) with a probability of 50% (P50) reached 314 GWh/y, whereas the AEPs with a probability of 75% (P75) and with a probability of 90% (P90) were 283 GWh/y and 255 GWh/y, respectively. The estimated AEP of P75 was 90% of the AEP of P50, whereas the estimated AEP of P90 was 81% of the AEP of P50. This difference might need to be considered when assessing the risk of financing a wind project.

In this study, the performance of the Regional Climate Modeling system version 4 (RegCM4) is evaluated over Indian regions through a 30-year climate simulation. The main focus is given on the model bias, correlation between the model simulated and observed results, and the standard error in the model simulations towards the 30-year mean temperature and precipitation climatology and their inter-annual and inter-decadal variability over India and the subregions. We find that the model-simulated temperature climatology has ~ 2 °C cold bias and the precipitation climatology has ~ 0.8 mm/day dry bias over India. Major cold bias (~ 3 °C) is noticed over South Peninsular India in the temperature climatology and a significant dry bias (~ 3 mm/day) is seen over Northeast India in the precipitation climatology. The performance skill of the model is found best over Northwest India for both temperature and precipitation, while it is found poor over South Peninsular India for temperature, and Northeast India for precipitation. Our overall results indicate that the RegCM4 reproduced the temperature climatology reasonably well over Indian regions, while it underestimated the precipitation climatology over many regions particularly during monsoon season. We find that less availability of moisture in the atmosphere and perhaps a weaker convection due to weaker upward motion of air may lead to decrease the precipitation in the model.

Regional climate models have been used since 1989 in order to improve climate simulation in regions where mesoscale forcings modulate the regional climate. These models are driven by time-dependent lateral boundary conditions from global climate models or reanalysis, and this process is called dynamical downscaling. Here, we review the evolution of regional climate modeling, as well as present the studies developed for South America.