High-resolution Global Climate Model Research Articles

Using high resolution climate models to explore future changes in post-tropical cyclone precipitation

One of the most costly effects of climate change will be its impact on extreme weather events, including tropical cyclones (TCs). Understanding these changes is of growing importance, and high resolution global climate models are providing potential for such studies, specifically for TCs. Beyond the difficulties associated with TC behavior in a warming climate, the extratropical transition (ET) of TCs into post-tropical cyclones (PTCs) creates another challenge when understanding these events and any potential future changes. PTCs can produce excessive rainfall despite losing their original tropical characteristics. The present study examines the representation of PTCs and their precipitation in three high resolution (25–50 km) climate models: CNRM, MRI, and HadGEM. All three of these models agree on a simulated decrease in TC and PTC events in the future warming scenario, yet they lack consistency in simulated regional patterns of these changes, which is further evident in regional changes in PTC-related precipitation. The models also struggle with their represented intensity evolution of storms during and after the ET process. Despite these limitations in simulating intensity and regional characteristics, the models all simulate a shift toward more frequent rain rates above 10 mm h−1 in PTCs. These high rain rates become 4%–12% more likely in the warmer climate scenario, resulting in a 5%–12% increase in accumulated rainfall from these rates.

Technology to aid the analysis of large-volume multi-institute climate model output at a central analysis facility (PRIMAVERA Data Management Tool V2.10)

Abstract. The PRIMAVERA project aimed to develop a new generation of advanced and well-evaluated high-resolution global climate models. As part of PRIMAVERA, seven different climate models were run in both standard and higher-resolution configurations, with common initial conditions and forcings to form a multi-model ensemble. The ensemble simulations were run on high-performance computers across Europe and generated approximately 1.6 PiB (pebibytes) of output. To allow the data from all models to be analysed at this scale, PRIMAVERA scientists were encouraged to bring their analysis to the data. All data were transferred to a central analysis facility (CAF), in this case the JASMIN super-data-cluster, where it was catalogued and details made available to users using the web interface of the PRIMAVERA Data Management Tool (DMT). Users from across the project were able to query the available data using the DMT and then access it at the CAF. Here we describe how the PRIMAVERA project used the CAF's facilities to enable users to analyse this multi-model dataset. We believe that PRIMAVERA's experience using a CAF demonstrates how similar, multi-institute, big-data projects can efficiently share, organise and analyse large volumes of data.

Tropical Pacific Quasi-Decadal Variability Suppressed by Submesoscale Eddies

Abstract Tropical Pacific quasi-decadal (TPQD) climate variability is characterized by quasi-decadal sea surface temperature (SST) variations in the central Pacific (CP). This low-frequency climate variability is suggested to influence extreme regional weather and substantially impact global climate patterns and associated socioeconomics through teleconnections. Previous studies mostly attributed the TPQD climate variability to basin-scale air–sea coupling processes. However, due to the coarse resolution of the majority of the observations and climate models, the role of subbasin-scale processes in modulating the TPQD climate variability is still unclear. Using a long-term high-resolution global climate model, we find that energetic small-scale motions with horizontal scales from tens to hundreds of kilometers (loosely referred to as equatorial submesoscale eddies) act as an important damping effect to retard the TPQD variability. During the positive TPQD events, compound increasing precipitation and warming SST in the equatorial Pacific intensifies the upper ocean stratification and weakens the temperature fronts along the Pacific cold tongue. This suppresses submesoscale eddy growth as well as their associated upward vertical heat transport by inhibiting baroclinic instability (BCI) and frontogenesis; conversely, during the negative TPQD events, the opposite is true. Using a series of coupled global climate models that participated in phase 6 of the Coupled Model Intercomparison Project with different oceanic resolutions, we show that the amplitude of the TPQD variability becomes smaller as the oceanic resolution becomes finer, providing evidence for the impacts of submesoscale eddies on damping the TPQD variability. Our study suggests that explicitly simulating equatorial submesoscale eddies is necessary for gaining a more robust understanding of low-frequency tropical climate variability. Significance Statement Submesoscale ocean eddies inhibit the development of quasi-decadal climate variability in the equatorial central Pacific, according to a high-resolution global climate simulation.

A seamless approach for evaluating climate models across spatial scales

In regions of the world where topography varies significantly with distance, most global climate models (GCMs) have spatial resolutions that are too coarse to accurately simulate key meteorological variables that are influenced by topography, such as clouds, precipitation, and surface temperatures. One approach to tackle this challenge is to run climate models of sufficiently high resolution in those topographically complex regions such as the North American Regionally Refined Model (NARRM) subset of the Department of Energy’s (DOE) Energy Exascale Earth System Model version 2 (E3SM v2). Although high-resolution simulations are expected to provide unprecedented details of atmospheric processes, running models at such high resolutions remains computationally expensive compared to lower-resolution models such as the E3SM Low Resolution (LR). Moreover, because regionally refined and high-resolution GCMs are relatively new, there are a limited number of observational datasets and frameworks available for evaluating climate models with regionally varying spatial resolutions. As such, we developed a new framework to quantify the added value of high spatial resolution in simulating precipitation over the contiguous United States (CONUS). To determine its viability, we applied the framework to two model simulations and an observational dataset. We first remapped all the data into Hierarchical Equal-Area Iso-Latitude Pixelization (HEALPix) pixels. HEALPix offers several mathematical properties that enable seamless evaluation of climate models across different spatial resolutions including its equal-area and partitioning properties. The remapped HEALPix-based data are used to show how the spatial variability of both observed and simulated precipitation changes with resolution increases. This study provides valuable insights into the requirements for achieving accurate simulations of precipitation patterns over the CONUS. It highlights the importance of allocating sufficient computational resources to run climate models at higher temporal and spatial resolutions to capture spatial patterns effectively. Furthermore, the study demonstrates the effectiveness of the HEALPix framework in evaluating precipitation simulations across different spatial resolutions. This framework offers a viable approach for comparing observed and simulated data when dealing with datasets of varying spatial resolutions. By employing this framework, researchers can extend its usage to other climate variables, datasets, and disciplines that require comparing datasets with different spatial resolutions.

Mechanisms of projected sea-level trends and variability in the Southeast Asia region based on MPI-ESM-ER

The low-lying and densely populated Southeast Asia (SEA) region is threatened by sea-level change. To better understand the mechanism of sea-level change in this region, the sea-level trends and variability in the SEA region are investigated over the historical period 1950–2014 and during 2015–2099 under Shared Socio-economic Pathway5-8.5 scenario forcing, based on the output of the high-resolution (~ 0.1° for ocean) global climate model MPI-ESM-ER. The results confirm that the SEA sterodynamic sea level and its components (i.e., thermosteric, halosteric, and manometric sea level) are rising with accelerations, which are superimposed on El Niño-Southern Oscillation (ENSO) driven variabilities. To understand changes of thermosteric and halosteric sea level, regional-mean ocean heat and freshwater contents are analysed based on physical processes of ocean transports and air-sea fluxes. We show that ENSO variability impacts the thermosteric sea level mainly through lateral ocean heat transports, while it impacts the halosteric sea level mainly through surface freshwater flux. In the projection, a decreased volume transport of the relatively cold water (with respect to the SEA average) from the Pacific warms the SEA region. At the same time, a freshening of the transported saline water (with respect to the SEA average) results in an increased ocean freshwater transport into the SEA region. Locally, the pathway of volume transport from the Pacific to the SEA region is shifting northward, which results in a weakened Indonesian throughflow and an enhanced South China Sea throughflow, both leading to changes of regional sea-level pattern.

Intermodel Biases of the Western North Pacific Monsoon Trough in CMIP6 Models

Abstract The impact of climate change on tropical cyclone (TC) activity is often assessed by various downscaling approaches, statistical–dynamical frameworks, and high-resolution global climate models using the projected changes of environmental factors. Uncertainty in simulating and projecting TC-relevant, large-scale circulation is closely linked to the projection of TC activity in a warming climate. Based on the model output in phase 6 of the Coupled Model Intercomparison Project (CMIP6), this study examines the intermodel biases in simulating the western North Pacific monsoon trough (MT), which is one of the most important large-scale circulation systems for TC activity, especially TC formation. It is found that most CMIP6 models can successfully simulate the climatological mean structure of the MT, although considerable biases remain in its exact location and its simulated historical changes. The mean latitude of the simulated MT spreads between 10° and 20°N, with noticeable differences in its orientation. The multimodel ensemble mean indicates that the MT exhibits no significant long-term zonal and poleward shifts in the future scenarios, consistent with the projection in the selected models in which the simulated MT resembles the observed spatiotemporal characteristics of the counterpart. Further analysis suggests that the intermodel bias in the simulated MT location is closely related to the east–west contrast of sea surface temperature (SST) anomalies in the tropical Pacific. More attention is required on improving the simulation of the basinwide SST distribution and its associated MT to reduce the uncertainty in predicting the future location of TC formation.

Modeling the Production of Heinrich Layers With a Sediment‐Enabled Iceberg Model

AbstractIn the North Atlantic, relatively coarse grained sediments can be found periodically throughout sediment cores spanning the Last Glacial Period. These sediments were rafted by icebergs released from the Laurentide Ice Sheet (LIS) in so‐called Heinrich Events. These “Heinrich Layers” coincide with records of global climate change, suggesting that the impact of these events was propagated beyond the North Atlantic. In order to best understand the climate context and significance of Heinrich Events, it is important to constrain the mechanism for their release from the LIS and the nature of the ice sheet itself. One approach for investigating the source of Heinrich Events is to understand the sediment load of icebergs involved, information that would inform interpretations of how those icebergs were produced. By simulating Heinrich Events in a high resolution global climate model (20–40 times the resolution of previous studies), this work investigates the processes involved in the deposition of Heinrich Layers in the North Atlantic. In these simulations, the same volume of sediment is distributed differently through the same volume of icebergs, producing profoundly different sediment records. Due to the high resolution of the model, these simulated sedimentary layers can be inspected in great detail, revealing nuances of the deposit. Only when sediment is distributed throughout the entire iceberg does the model produce a sediment pattern in agreement with observations, yet icebergs with this sediment distribution are not observed in the modern‐day.

Northward Shifts of the Sahara Desert in Response to Twenty-First-Century Climate Change

Abstract The spatial extent of the Sahara (the largest nonpolar desert) has significant impacts on the livelihood of people residing in its surrounding areas. Despite the fact that climate change would foreseeably impact the location and size of the desert, its future responses (i.e., advance or retreat) are rarely explored in previous studies. Here, through the development of an ensemble Bayesian discriminant analysis approach, we use 10 of the latest high-resolution GCM (global climate model) simulations to document robust annual and seasonal responses of the Sahara Desert to twenty-first-century climate change, with the consideration of modeling uncertainties. We find northward shifts of the Sahara/Sahel and eastern expansion of the nondesert zone under both SSP2–4.5 and SSP5–8.5 scenarios, the former more pronounced in the wet season and the latter in the dry season. Countries located near the Mediterranean may thus experience higher risks of drought, while the projected retreat of the Saharan southern boundary will be beneficial to the local water availability of proximal countries. Significance Statement Given that sub-Saharan Africa is one of the most vulnerable regions to climate change, the Sahara’s expansion would bring unexpected health risks to billions of people. It is thus vital to understand its robust response to global warming. However, previous studies are merely focused on using a simple precipitation threshold as the definition criterion to estimate the varying size of the Sahara Desert. In addition, significant uncertainty in precipitation projections also limits relevant investigations of the Sahara’s future responses. Here, by developing an ensemble Bayesian discriminant analysis approach, we could provide an objective basis for desert identification under large intermodel uncertainty. Further, we find significant northward shifts of both the Sahara and the Sahel, which may induce higher risks of drought over the northwest of North Africa.

How well do high-resolution Global Climate Models (GCMs) simulate tropical cyclones in the Bay of Bengal?

Using six HighResMIP multi-ensemble GCMs (both the atmosphere-only and coupled versions) at 25 km resolution, the Tropical Cyclone (TC) activity over the Bay of Bengal (BoB) is examined in the present (1950–2014) climate. We use the Genesis Potential Index (GPI) to study the large-scale environmental conditions associated with the TC frequency in the models. Although the models struggle to reproduce the observed frequency and intensity of TCs, most models can capture the bimodal characteristics of the seasonal cycle of cyclones over the BoB (with fewer TCs during the pre-monsoon [April–May] than the post-monsoon [October–November] season). We find that GPI can capture the seasonal variation of the TC frequency over the BoB in both the observations and models. After calibrating the maximum sustained windspeeds in the models with IBTrACS, we find that like the observations the proportion of strong cyclones is also higher in the pre-monsoon than the post-monsoon. However, the inter-seasonal contrast of the proportion of strong cyclones between the pre-monsoon and post-monsoon seasons is reduced in almost all the models compared to the observations. The windshear term in GPI contributes the most to the model biases in all models during the post-monsoon season. This bias is caused by weakening of upper-level (200 hPa) easterlies in analysed models. During the pre-monsoon season, the environmental term in GPI dominating the model biases varies from model to model. When comparing the atmosphere-only and coupled versions of the models, a reduction of 0.5 °C in the sea surface temperature (SST) and a lowering of TC frequency occur in almost all the coupled models compared to their atmosphere-only counterparts.

Increases in extreme precipitation over the Northeast United States using high-resolution climate model simulations

Extreme precipitation is among the most destructive natural disasters. Simulating changes in regional extreme precipitation remains challenging, partially limited by climate models’ horizontal resolution. Here, we use an ensemble of high-resolution global climate model simulations to study September–November extreme precipitation over the Northeastern United States, where extremes have increased rapidly since the mid-1990s. We show that a model with 25 km horizontal resolution simulates much more realistic extreme precipitation than comparable models with 50 or 100 km resolution, including frequency, amplitude, and temporal variability. The 25 km model simulated trends are quantitatively consistent with observed trends over recent decades. We use the same model for future projections. By the mid-21st century, the model projects unprecedented rainfall events over the region, driven by increasing anthropogenic radiative forcing and distinguishable from natural variability. Very extreme events (>150 mm/day) may be six times more likely by 2100 than in the early 21st century.

Clear-air turbulence trends over the North Atlantic in high-resolution climate models

Clear-air turbulence (CAT) has a large impact on the aviation sector. Our current understanding of how CAT may increase with climate change in future is largely based on simulations from CMIP3 and CMIP5 global climate models (GCMs). However, these models have now been superseded by high-resolution CMIP6 GCMs, which for the first time have grid lengths at which individual turbulence patches may start to be resolved. Here we use a multi-model approach to quantify projected moderate CAT changes over the North Atlantic using CMIP6 models. The influence of the model resolution on CAT projections is analysed. Twenty-one CAT diagnostics are used, in order to represent uncertainties in CAT production mechanisms. Each diagnostic responds differently in time, but the majority display an increase in moderate CAT between 1950 and 2050. Although winter is historically the most turbulent season, there is strong multi-model agreement that autumn and summer will have the greatest overall relative increase in CAT frequency. By 2050, summers are projected to become as turbulent as 1950 winters and autumns. The global-mean seasonal near-surface temperature is used as a comparative metric. For every 1 °C of global near-surface warming, autumn, winter, spring, and summer are projected to have an average of 14%, 9%, 9%, and 14% more moderate CAT, respectively. Our results confirm that the aviation sector should prepare for a more turbulent future.

Climate Change Impacts on Streamflow in the Krishna River Basin, India: Uncertainty and Multi-Site Analysis

In Peninsular India, the Krishna River basin is the second largest river basin that is overutilized and more vulnerable to climate change. The main aim of this study is to determine the future projection of monthly streamflows in the Krishna River basin for Historic (1980–2004) and Future (2020–2044, 2045–2069, 2070–2094) climate scenarios (RCP 4.5 and 8.5, respectively), with the help of the Soil Water and Assessment Tool (SWAT). SWAT model parameters are optimized using SWAT-CUP during calibration (1975 to 1990) and validation (1991–2003) periods using observed discharge data at 5 gauging stations. The Cordinated Regional Downscaling EXperiment (CORDEX) provides the future projections for meteorological variables with different high-resolution Global Climate Models (GCM). Reliability Ensemble Averaging (REA) is used to analyze the uncertainty of meteorological variables associated within the multiple GCMs for simulating streamflow. REA-projected climate parameters are validated with IMD-simulated data. The results indicate that REA performs well throughout the basin, with the exception of the area near the Krishna River’s headwaters. For the RCP 4.5 scenario, the simulated monsoon streamflow values at Mantralayam gauge station are 716.3 m3/s per month for the historic period (1980–2004), 615.6 m3/s per month for the future1 period (2020–2044), 658.4 m3/s per month for the future2 period (2045–2069), and 748.9 m3/s per month for the future3 period (2070–2094). Under the RCP 4.5 scenario, lower values of about 50% are simulated during the winter. Future streamflow projections at Mantralayam and Pondhugala gauge stations are lower by 30 to 50% when compared to historic streamflow under RCP 4.5. When compared to the other two future periods, trends in streamflow throughout the basin show a decreasing trend in the first future period. Water managers in developing water management can use the recommendations made in this study as preliminary information and adaptation practices for the Krishna River basin.

A potential explanation for the global increase in tropical cyclone rapid intensification

Tropical cyclone rapid intensification events often cause destructive hurricane landfalls because they are associated with the strongest storms and forecasts with the highest errors. Multi-decade observational datasets of tropical cyclone behavior have recently enabled documentation of upward trends in tropical cyclone rapid intensification in several basins. However, a robust anthropogenic signal in global intensification trends and the physical drivers of intensification trends have yet to be identified. To address these knowledge gaps, here we compare the observed trends in intensification and tropical cyclone environmental parameters to simulated natural variability in a high-resolution global climate model. In multiple basins and the global dataset, we detect a significant increase in intensification rates with a positive contribution from anthropogenic forcing. Furthermore, thermodynamic environments around tropical cyclones have become more favorable for intensification, and climate models show anthropogenic warming has significantly increased the probability of these changes.

Using high-resolution global climate models from the PRIMAVERA project to create a European winter windstorm event set

Abstract. PRIMAVERA (process-based climate simulation: advances in high-resolution modelling and European climate risk assessments) was a European Union Horizon 2020 project whose primary aim was to generate advanced and well-evaluated high-resolution global climate model datasets for the benefit of governments, business and society in general. Following consultation with members of the insurance industry, we have used a PRIMAVERA multi-model ensemble to generate a European winter windstorm event set for use in insurance risk analysis, containing approximately 1300 years of windstorm data. The data are available at https://doi.org/10.5281/zenodo.6492182. To create the storm footprints for the event set, the storms in the PRIMAVERA models are identified through tracking. A method is developed to separate the winds from storms occurring in the domain at the same time. The wind footprints are bias corrected and converted to 3 s gusts onto a uniform grid using quantile mapping. The distribution of the number of model storms per season as a function of estimated loss is consistent with re-analysis, as are the total losses per season, and the additional event set data greatly reduce uncertainty on return period magnitudes. The event set also reproduces the temporally clustered nature of European windstorms. Since the event set is generated from global climate models, it can help to quantify the non-linear relationship between large-scale climate indices such as the North Atlantic Oscillation (NAO) and windstorm damage. Although we find only a moderate positive correlation between extended winter NAO and storm damage in northern European countries (consistent with re-analysis), there is a large change in risk of extreme seasons between negative and positive NAO states. The intensities of the most severe storms in the event set are, however, sensitive to the gust conversion and bias correction method used, so care should be taken when interpreting the expected damages for very long return periods.

Dependence of Convective Cloud Properties and Their Transport on Cloud Fraction and GCM Resolution Diagnosed from a Cloud-Resolving Model Simulation

The scale-aware convective parameterization for high resolution global climate models must satisfy the requirement that the parameterized subgrid convective transport diminishes as the model resolution increases to convection-resolving resolutions. A major assumption in current scale-aware convection schemes is that the differences between convective cloud properties and their environmental counterparts are independent of cloud fraction. This study examines convective cloud vertical velocity, moist static energy (MSE), moisture, and the vertical eddy transport of MSE and moisture for different averaging subdomain sizes and fractional convective cloudiness using a cloud resolving model simulation of a midlatitude mesoscale convective system. Results show that convective cloud fraction, mass flux, and vertical transport of MSE and moisture increase with decreasing subdomain size. The differences between convective cloud properties in both updrafts and downdrafts and their environment depend on both cloud fraction and the averaging subdomain size. For a given subdomain size, the differences increase with cloud fraction, in contrast to the assumption used in current scale-aware convection parameterization schemes. A consequence of this is that the parameterized convective eddy transport reaches maximum at a higher cloud fraction than believed in previous studies. This has implications on how fast the subgrid convective transport should diminish as GCM resolution increases.

Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050

Context. Sites for next-generation telescopes are chosen decades before the first light of a telescope. Site selection is usually based on recent measurements over a period that is too short to account for long-term changes in observing conditions such as those arising from anthropogenic climate change. For astronomical facilities with a typical lifetime of 30 yr, it is therefore essential to be aware of climate evolution to optimise observing time. Aims. In this study, we analyse trends in astronomical observing conditions for eight sites. Most sites either already host telescopes that provide in situ measurements of weather parameters or are candidates for hosting next-generation telescopes. For a fine representation of orography, we use the highest resolution global climate model (GCM) ensemble available provided by the high-resolution model intercomparison project and developed as part of the European Union Horizon 2020 PRIMAVERA project. Methods. We evaluate atmosphere-only and coupled PRIMAVERA GCM historical simulations against in situ measurements and the fifth generation atmospheric reanalysis (ERA5) of the European centre for medium-range weather forecasts for the period 1979–2014. The projections of changes in current site conditions are then analysed for the period 2015–2050 using PRIMAVERA future climate simulations. Results. Over most sites, we find that PRIMAVERA GCMs show a good agreement in temperature, specific humidity, and precipitable water vapour compared to in situ observations and ERA5. The ability of PRIMAVERA to simulate those variables increases confidence in their projections. For those variables, the model ensemble projects an increasing trend for all sites, which will result in progressively poorer astronomical observing conditions compared to current conditions. On the other hand, no significant trends are projected for relative humidity, cloud cover, or astronomical seeing and PRIMAVERA does not simulate these variables well compared to observations and reanalyses. Therefore, there is little confidence in these projections. Conclusions. Our results show that climate change will negatively impact the quality of astronomical observations and is likely to increase time lost due to bad site conditions. We stress that it is essential for astronomers to include long-term climate projections in their process for site selection and monitoring. We show that high-resolution GCMs can be used to analyse the effect of climate change on site characteristics of next-generation telescopes.

Representation of Lake–Atmosphere Interactions and Lake-Effect Snowfall in the Laurentian Great Lakes Basin among HighResMIP Global Climate Models

Abstract Credible modeling, tools, and guidance, regarding the changing Laurentian Great Lakes and the climatic impacts, are needed by local decision-makers to inform their management and planning. The present study addresses this need through a model evaluation study of the representation of lake–atmosphere interactions and resulting lake-effect snowfall in the Great Lakes region. Analysis focuses on an extensive ensemble of 74 historical simulations generated by 23 high-resolution global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP). The model assessment addresses the modeling treatment of the Great Lakes, the spatial distribution and seasonality of climatological snowfall, the seasonal cycle of lake-surface temperatures and overlake turbulent fluxes, and the lake-effect ratio between upwind and downwind precipitation. A deeper understanding of model performance and biases is achieved by partitioning results between HighResMIP GCMs that are 1) coupled to 1D lake models versus GCMs that exclude lake models, 2) between prescribed-ocean model configurations versus fully coupled configurations, and 3) between deep Lake Superior versus relatively shallow Lake Erie. While the HighResMIP GCMs represent the Great Lakes by a spectrum of approaches that include land grid cells, ocean grid cells (with lake surface temperature and ice cover boundary conditions provided by the Met Office Hadley Center Sea Ice and Sea Surface Temperature Dataset), and 1D lake models, the current investigation demonstrates that none of these rudimentary approaches adequately represent the complex nature of seasonal lake temperature and ice cover evolution and its impact on lake–atmosphere interactions and lake-effect precipitation in the Great Lakes region. Significance Statement The purpose of this study is to evaluate the capability of high-resolution global climate models to simulate lake–atmosphere interactions and lake-effect snowfall in the Great Lakes region, given the critical influence of the lakes on regional climate and vast societal and environmental impacts of lake-effect snowfall. It is determined that the models inadequately represent lake temperatures and ice cover, often leading to insufficient annual snowfall in the lake-effect zones. More advanced, three-dimensional lake models need to be coupled to climate models to support greater credibility in regional lake and climate simulations and future climate projections.

Projected increases in potential groundwater recharge and reduced evapotranspiration under future climate conditions in West Africa

Study regionWest Africa, with a focus between 5°W and 5°E. Study focusThe effects of changing climate and CO2 concentration (RCP8.5 between 2000 and 2100) on the West African monsoon are examined using the UPSCALE high-resolution (25 km) global climate model ensembles for present and future climate, combined with the JULES land-surface model. New hydrological insightsFuture climate is predicted to have an enhanced summertime Saharan heat low, changing large-scale circulation, causing monsoon rainfall generally to increase. The monsoon progresses further inland and occurs later in the year. UPSCALE rainfall projections indicate that the eastern Sahel becomes wetter (+ 12.2%) but the western Sahel drier (− 13.5%). Future evapotranspiration is reduced across most of West Africa due to the CO2 fertilisation effect causing lower transpiration. Potential groundwater recharge (soil drainage at the bottom of a 3 m deep soil column), is predicted to increase from 0% to 16% of rainfall under present climate, to 1–20% in the future, doubling from ~ 5% to ~ 10% in northern Ghana and the eastern Sahel. Potential recharge increases largely due to increased soil hydraulic conductivity, caused by higher soil moisture resulting from increased rainfall and reduced transpiration. Other factors have only a minor influence on the water balance and potential recharge, including rainfall intensity and land use type. A predicted increase in future potential groundwater recharge is significant as development of groundwater resources is seen as a key means to meet growing water demand in West Africa.

Seamless Projections of Global Storm Surge and Ocean Waves Under a Warming Climate

AbstractFor coastal adaptation purposes, it is important to estimate the climate related changes in extreme sea levels due to storm surges and ocean waves, in addition to mean sea level rise. This study provides the first consistent and continuous estimation of projected changes in global storm surges and ocean waves from the past to the warmer future, based on an extremely high resolution global climate model. The spatial pattern in the trend of annual maximum sea surface heights and wave heights is predominantly driven by changes in tropical cyclone (TC) frequency. In the western North Pacific, future TC frequencies are projected to decrease, and the annual maximum sea surface heights and wave heights show decreasing trends (−20 cm/century and −200 cm/century). Although highly intense tropical cyclones are enhanced in the warmer climate, highly extreme storm surges and wave heights do not necessarily increase due to the large natural variability.

Ranking of CMIP6 based High-resolution Global Climate Models for India using TOPSIS

ABSTRACT Changing temperature and precipitation are the prominent candle bearers of climate change. Global Climate Models (GCM) simulate and predict historical, present, and future climate scenarios. This study focused on selecting the best performing GCM under High-resolution Model Intercomparison Project (HighResMIP) suitable for the Indian subcontinent for predicting daily maximum (Tasmax) and minimum (Tasmin) temperature. Entropy-TOPSIS Multicriteria Decision-Making (MCDM) approach was employed for the relative ranking of GCM models. This study compared 64 years (1951–2014) historical data of 11 high-resolution GCMs under three different nominal resolutions viz., 25 km, 50 km, and 100 km. India Meteorological Department (IMD) gridded data was used as observed data for the performance measure. The data were evaluated using three factors, i.e. Intra- and inter-annual variation of temperature, tempo-spatial extreme events study, and using selected performance indicators. The predictive ability of the GCM models spatially in different Koppen climate zones of India is also reported in this study. This study highlighted Hadgem3-Gc31 and ECMWF-IFS models for Tasmax; NICAM16-8S and MPI-ESM1-2 models for Tasmin are suitable for simulating Indian temperature. The outcome of this study will be helpful to the climate researchers and decision-makers to choose the best suitable CMIP6 GCM model at a regional level.