Introduction to Climate Models

A set of three reanalyses and eight Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) are used, based on daily Sea Level Pressure (SLP) data.

This study investigates the connection of springtime NAH SST anomaly and the Eurasian SAT variation in thirty-two coupled climate models that participated in the fifth phase of the Coupled Model Intercomparison Project (CMIP5).

This study aims at comparing current and future maize and sorghum climate suitability and yield using five state-of-art climate models in the Representative Concentration Pathways (RCP8.

For future projections, the mean of two best performing climate models was used under two emissions scenarios, Representative Concentration Pathways (RCP) 4.

The data were obtained from two CMIP5 global climate models CSIRO-Mk3-6-0 and GFDL-ESM2G and the CMIP5 multimodel ensemble, in the historical experiment and two representative concentration pathways: RCP2.

The aim of this study is twofold: (1) to evaluate and to adapt a reanalysis system “Systeme d’Analyse Fournissant des Renseignements Adaptes a la Nivologie” (SAFRAN) to map the meteorological variables on the Tensift catchment (Morocco) between 2004 and 2014; (2) to project temperature and precipitation for the 2041–2060 horizon at high-resolution based on the Euro-CORDEX database at 12 km resolution (using two Representative Concentration Pathway -RCPs- scenarios and four Regional Climate Models), on the SAFRAN reanalysis and on a network of meteorological stations.

The ability of 31 climate models for simulating the precipitation, maximum temperature, and minimum temperature was evaluated for the studied area, and a multi-model ensemble was employed.

Here, we use a multi-model ensemble of statistically bias-corrected and downscaled climate models, as well as the corresponding parent models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), to drive a statistical panel model of U.

In the climate models that are used to investigate future climate changes, lakes are greatly simplified and sometimes absent.

The results indicate that climate change substantially affects N2O emission and highlight the urgent need to incorporate this strong feedback into most climate models for convincing projection of future climate change.

The objective calibration method originally performed on regional climate models is applied to a fine horizontal resolution Numerical Weather Prediction (NWP) model over a mainly continental domain covering the Alpine Arc.

These results will allow researchers to make informed decisions regarding the most ideal product for their application and aid in the improvement of mesoscale and submesoscale SSS products, which can lead to the refinement of numerical weather prediction (NWP) and climate models.

To date, the assessment of hydrological climate change impacts, not least on pluvial flooding, has been severely limited by i) the insufficient spatial resolution of regional climate models (RCMs) as well as ii) the simplified description of key processes, e.

Analysis of climate change impacts across the Arctic using dynamical models has almost exclusively been limited to outputs from global climate models or coarser regional climate models.

Here we evaluate simulated TWSA from 25 climate models (from the Climate Model Intercomparison Project 6) through comparison with TWSA from GRACE satellite data (2003 – 2014) in 14 river basins globally and assess causes of discrepancies by examining precipitation (P), evapotranspiration (ET), and runoff (Roff) fluxes during recharge (increasing TWS) and discharge (decreasing TWS) cycles.

The ISIMIP2b protocol (phase 2b of the Intersectoral Impact Model Intercomparison Project), which provides simulated impacts from 1 to 8 sectoral impact models and four CMIP5 (5 th phase of the Coupled Model Intercomparison Project) climate models, is used in this study.

This study investigates the occurrence of the Weddell Sea Polynya (WSP) under an idealized climate change scenario by evaluating simulations from climate models of different ocean resolutions.

Global climate models are not able to reproduce the nearshore surface winds, and only a few downscaling studies have been performed by using relatively coarse-grid atmospheric models forced by idealized climate change scenarios.

  The amount of dust emitted increased notably following European settlement, and climate models predict future increases in dust emission in response to climate warming.

 Regional climate models (RCMs) are a useful tool for trying to predict the impacts of climate change on hydrological events, although their application may lead to significant differences when different models are adopted.

The difference between tropical east and west Pacific sea-surface temperatures affects local and global climate, and most climate models suggest a weaker future gradient despite a recent observed increase.

<p>Sea surface temperature (SST) reconstructions based on isoprenoid glycerol dialkyl glycerol tetraether (isoGDGT) distributions from the Eocene southwest (sw) Pacific Ocean are unequivocally warmer than can be reconciled with state-of-the-art fully coupled climate models.

Second, we evaluate the cascade of uncertainty of snow depth projections resulting from impact model parameters, greenhouse gas emission scenarios, climate models and their internal variability, and downscaling processes.

This approach was used to account for the uncertainty on the unknown future greenhouse gas emissions trajectories, and differences coming from climate models (GCMs and RCMs).

5 was used to dynamically downscale near-future climate projections from two global climate models to a 50-km horizontal resolution over the upper reaches of the Yangtze River (UYRB).

Our confidence in future climate projection depends on the ability of climate models to simulate the current climate, and model performance in simulating atmospheric circulation affects the ability to simulate extreme events.

Precipitation extremes in China are determined for the 21st century in six simulations using a regional climate model, RegCM4, and 17 global climate models that participated in CMIP5.

The ENEA-REG system is made up of two interchangeable regional climate models as atmospheric components (RegCM, REGional Climate Model, and WRF, Weather Research and Forecasting), a river model (Hydrological Discharge, HD), and an ocean model (Massachusetts Institute of Technology General Circulation Model, MITgcm); processes taking place at the land surface are represented within the atmospheric models with the possibility to use several land surface schemes of different complexity.

High resolution regional climate models are needed to understand how climate change will impact extreme precipitation.

However, sector-specific regional impact studies, based on the latest high-resolution regional climate models (RCMs), are urgently required for developing successful local adaptation strategies for beekeeping and to preserve biodiversity under future climate change scenarios.

This study assessed the impacts of climate change on terrestrial hydrological components and Crop Water Use (CWU) over the Chesapeake Bay watershed using a combination of Global Climate Models (GCMs) and a land surface model.

The biophysical impacts of these changes on the regional climate in Europe are currently extensively investigated within the WCRP CORDEX Flagship Pilot Study (FPS) LUCAS – "Land Use and Climate Across Scales" using an ensemble of different Regional Climate Models (RCMs) coupled with diverse Land Surface Models (LSMs).

Since regional climate models&#8217; spatiotemporal resolution often is too coarse to deal with such local issues, the regional climate model REMO is improved within the frame of the project in cooperation with the Climate Service Center Germany (GERICS).

We investigate a set of 9 simulations of 3 different RCMs driven by 3 different global climate models which allow us to analyse the influence of driving data on the RCM&#8217;s outputs.

Future near-term (2021–2050) and long-term (2071–2100) climate scenarios were developed from six statistically bias corrected Regional Climate Models (RCMs) under two Representative Concentration Pathways (RCPs) scenarios: RCP4.

5 emission scenarios using an ensemble of five bias-corrected Regional Climate Models.

This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso (200 km) and larger (500 km) scales, roughly four hundred hours of flight time by four heavily instrumented research aircraft, four global-ocean class research vessels, an advanced ground-based cloud observatory, a flotilla of autonomous or tethered measurement devices operating in the upper ocean (nearly 10000 profiles), lower atmosphere (continuous profiling), and along the air-sea interface, a network of water stable isotopologue measurements, complemented by special programmes of satellite remote sensing and modeling with a new generation of weather/climate models.

Using the data of historical monitor since the 1950s, supplementary field research of mangrove wetland sediment rates measured, satellite remote sensing, digital elevation model, global climate models, and ArcGIS, we investigated the Dongzhaigang mangrove area changes, related causes, and the impacts of future sea level rise under greenhouse gas emission scenarios, as representative concentration pathways (RCPs) 2.

Projections of future sea-level changes are usually based on global climate models (GCMs).

Confidence in these projections depends on the ability of climate models to simulate the various components of future sea level rise.

While the resolution of the global models is limited, the effect of global warming is robust across greenhouse gas concentration scenarios, climate models and hydrological models.

Furthermore, probability of early summer droughts was estimated based on two climate models, MPI-ESM and HadGEM2 and two greenhouse gas concentration scenarios, RCP4.

This suggests a need for coupled social-climate models that capture pervasive real-world asymmetries in the population distribution of the consequences of anthropogenic climate change and climate (in)action.

<p>The long-term response of the Atlantic meridional overturning circulation (AMOC) to anthropogenic climate change remains poorly understood in part, due to the computational expenses associated with running fully-coupled climate models to equilibrium.

Second, we apply runoff data obtained from combining five different global circulation models and two regional climate models to estimate future reservoir inflow at three CO2 emissions scenarios (RCP2.

An ensemble of projections for the future Irish climate were generated by downscaling from five different global climate models (GCMs), each based on four Representative Concentration Pathways (RCPs; RCP2.

Using the methodology of morphing, the weather files were constructed based on the typical meteorological year (TMY) data and predicted data generated from eight typical global climate models (GCMs) for three representative concentration pathways (RCP2.

Future rainfall erosivity is predicted using an average composite of 19 Global Climate Models (GCMs) from the Coupled Model Inter-comparison Projects (CMIP5) WorldClim dataset across three Representative Concentration Pathways (RCP2.

A ROM (REMO-OASIS-MPIOM) climate simulation in uncoupled and coupled atmosphere–ocean mode, at 25 km horizontal resolution, and a multi-model ensemble built with a set of regional climate models from the CORDEX-Africa experiment at 0.

In this work, we used a set of convection-permitting (4 km horizontal resolution) regional climate models to explore their ability to reproduce the synoptic forcings that trigger deep convection over La Plata basin.

High-quality land use and land cover (LULC) information is of crucial importance for the performance of regional climate models (RCMs), in particular at high spatial resolutions down to convection permitting scales below 4 km.

This synchronization, which we refer to as the boundary current synchronization (BCS), is reproduced in global climate models with high spatial resolution.

On the overall, we find that the regression method performs better to constraint the equilibrium climate sensitivity and that the major source of uncertainties comes from the differences in the simulation of the pattern effect among climate models rather than the uncertainties on the historical forcing.

Low‐cloud based emergent constraints have the potential to substantially reduce uncertainty in Earth’s equilibrium climate sensitivity, but recent work has shown that previously developed constraints fail in the latest generation of climate models, suggesting that new approaches are needed.

<p>Global Climate Models (GCMs) generally exhibit significant biases in the representation of large-scale atmospheric circulation.

With the increase of the amount of observed/simulated large-scale atmospheric data including climate models, e.

The representation of ocean heat uptake in Simple Climate Models 7 used for policy advice on climate change mitigation strategies is often based on 8 variants of the one-dimensional Vertical Advection/Diffusion equation (VAD) for 9 some averaged form of potential temperature.

The representation of ocean heat uptake in Simple Climate Models used for policy advice on climate change mitigation strategies is often based on variants of the one-dimensional Vertical Advection/Diffusion equation (VAD) for some averaged form of potential temperature.

The use of higher resolution regional climate models (RCMs) to downscale global climate model simulations is of high importance.

In this study, we used the cumulative distribution function transform to conduct a bias correction for simulations from different regional climate models (RCMs) driven by one global climate model (HadGEM2-ES).

The ability of climate models to simulate 20th century global mean sea level (GMSL) and regional sea-level change has been demonstrated.

Critically, paleoclimate models offer the opportunity to provide mean sea-level surface temperature to derive an elevation estimate where proxy-based values may not be available.

Climate models predict an increase in extent, frequency, and duration of marine hypoxia events in the twenty first century.

This paper aims to study long term changes in spring bloom dynamics in the Dutch coastal waters, using historical coastal in-situ data and satellite observations as well as projected future solar radiation and air temperature trajectories from regional climate models as driving forces covering the twenty-first century.

We use a suite of state-of-the-art climate models, with each model consisting of a single-model initial-condition large ensemble (SMILE), yielding at least 15 individual realizations of equally likely evolutions of future climate state for each climate model.

The Multi-Model Large Ensemble Archive (MMLEA) is a collection of CMIP5-generation single model initial condition large ensembles (SMILEs) and thus provides estimates of internal variability from several independently developed coupled climate models.

<p>Downscaling data from General Circulation Models (GCMs) with Regional Climate Models (RCMs) is a computationally expensive process, even more so running at the convection permitting scale (CP).

Therefore, we examine the results of 360 years of climate model data from two different climate models (COSMO-CLM driven by EC-EARTH and ALARO-0 driven by CNRM ARPEGE) both at convection-permitting scale (CPS, ~ 3 km resolution) and non-convection-permitting scale (non-CPS, 12.

This research attempts to assess the combined effects of (i) climate change (CC), (ii) land use/land cover change (LULCC), and (iii) development (Dev) conditions on water resources and hydropower generation (HPGen) using Regional Climate Models (RCMs) from Coordinated Regional Downscaling Experiment (CORDEX) under the Representative Concentrated Pathways (RCP): RCP4.

LandCover6k exemplifies many of the qualities that Smith (2021) promotes, and our experience as a transdisciplinary research group using archaeology and pollen data to improve land-cover change and climate models can be used to amplify some of his points and to challenge others.

In this study, we applied three bias-corrected global climate models (GCMs) to conduct 9km-resolution regional climate simulations in CA for the present (1986–2005) and future (2031–2050) periods.

In this study, we applied three bias-corrected global climate models (GCMs) to conduct 9 km-resolution regional climate simulations in CA for the reference (1986–2005) and future (2031–2050) periods.

Global climate models (GCMs) consistently underestimate the response of September Arctic sea-ice area (SIA) to warming.

State-of-the-art climate models simulate a large spread in the projected decline of Arctic sea-ice area (SIA) over the 21st century.

, also Greenland blocking, and its representation in climate models is crucial in determining future GrIS accumulation zone melt and thus global sea level rise.

374 results are fundamental for improving the boundary conditions of numerical ice sheet, ocean, and coupled climate models, which are critically required for understanding past ice sheet and global sea level response during warm climate intervals.

The current study downscaled two global climate models on the mid-term, noted the 2040s (2041–2050) and long-term, noted 2090s (2091–2099) under two shared socioeconomic pathways (SSP) scenarios, SSP 3–7.

The comparison of observation and model historical data enables identification of the best-performing global climate models (GCMs), which are then employed in the projection analysis under two Shared Socioeconomic Pathways (SSPs): SSP2-4.

Climate models project a strong increase in Arctic precipitation as well as an increase in the ratio of liquid to solid precipitation for the 21 st century.

Study focus In this study, variations of the future streamflow in the Pra River Basin (PRB), are projected using the Soil and Water Analysis Tool (SWAT) model with bias-corrected climate data from regional climate models (RCMs) for the near 21 st century (2010–2039), the mid 21 st century (2040–2069), and the end of the 21 st century (2070–2099), under two Representative Concentration Pathways (RCP4.

The regional coupled model presents a global oceanic component with increased horizontal resolution along the northwestern African coast, and its performance over the CCUS is assessed against relevant reanalysis data sets and compared with an ensemble of global climate models (GCMs) and an ensemble of atmosphere-only regional climate models (RCMs) in order to assess the role of the horizontal resolution.

ROM presents a global oceanic component with increased horizontal resolution along the northwestern African coast, and its performance over the CCUS is assessed against relevant reanalysis data sets and compared with an ensemble of global climate models (GCMs) and an ensemble of atmosphere-only regional climate models (RCMs) in order to assess the role of the horizontal resolution.

This is at odds with state-of-the-art climate models, which typically simulate a receding Antarctic sea ice cover in response to increasing greenhouse forcing.

Here we analyzed future trends in Arctic surface wind speed and its relationship with sea ice cover among CMIP5 global climate models.

To this end, information from Antarctic ice core records (snow accumulation and δ 18 O and tree-ring width records situated in the mid-latitudes of the Southern Hemisphere are combined with the physics of climate models using a data assimilation method.

To this end, information from Antarctic ice core records (snow accumulation and δ18O\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta ^{{18}}\hbox{O}$$\end{document}) and tree-ring width records situated in the mid-latitudes of the Southern Hemisphere are combined with the physics of climate models using a data assimilation method.

Here, we propose to improve this by analyzing the contribution of LWS to regional sea-level change by considering five Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models forced by three different Representative Concentration Pathway (RCP) greenhouse gas emission scenarios.

<p>The interaction between ice sheets and the solid Earth plays an important role for ice-sheet stability and sea-level change and hence for global climate models.

Paleoclimate modelling has long been regarded as a robust out-of-sample test bed of the climate models used to project future climate changes.

Firstly, a large set of global climate models (GCMs) associated with the Daily Bias Correction (DBC) method are employed to project future climate scenarios, while the Cellular Automaton–Markov (CA–Markov) model is used to project future Land Use/Cover Change (LUCC) scenarios.

The methodology involved evaluating the performance of CMIP5 Global Climate Models (GCMs) in simulating the sea level rise in the Arabian Sea using various statistical methods.

the inception of the next glaciation, formation and depth of permafrost, glacial troughs, sub-glacial channels, sea-level rise, orbital forcing) and their future evolutions, ii) effects on the host rocks and the barrier system(s) as well as iii) the uncertainties related to these effects but also to general climate models and predictions.

This study uses five CMIP6 global climate models (GCMs) to drive the variable infiltration capacity (VIC) model, and then simulates the hydrological response of the upper and middle Huaihe River Basin (UMHRB) under future shared socioeconomic pathway scenarios (SSPs).

<p>Reliable projections of extremes in near-surface air temperature (SAT) by climate models become more and more important&#160;as global warming is leading to significant increases in the hottest days and decreases in coldest nights around the world with considerable impacts on various sectors, such as agriculture, health and tourism.

To this end, </span><span>an offline</span><span> d</span><span>ata assimilation procedure is used to combine the information from the physics of the </span><span>climate </span><span>system, </span><span>as </span><span>included in climate models, and </span><span>paleoclimate records, in particular tree-ring width and ice cores records (snow accumulation and </span><span>&#948;</span>^{<span>18</span>}<span>O)</span><span>.

A set of three reanalyses and eight Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) are used, based on daily Sea Level Pressure (SLP) data.

For each 1 °C of tropical mean warming, global climate models project extreme TW (the annual maximum of daily mean or 3-hourly values) to increase roughly uniformly between 20° S and 20° N latitude by about 1 °C.