The Effect of Explicit Convection on Couplings between Rainfall, Humidity, and Ascent over Africa under Climate Change

AbstractThe Hadley circulation and tropical rain belt are dominant features of African climate. Moist convection provides ascent within the rain belt, but must be parameterized in climate models, limiting predictions. Here, we use a pan-African convection-permitting model (CPM), alongside a parameterized convection model (PCM), to analyze how explicit convection affects the rain belt under climate change. Regarding changes in mean climate, both models project an increase in total column water (TCW), a widespread increase in rainfall, and slowdown of subtropical descent. Regional climate changes are similar for annual mean rainfall but regional changes of ascent typically strengthen less or weaken more in the CPM. Over a land-only meridional transect of the rain belt, the CPM mean rainfall increases less than in the PCM (5% vs 14%) but mean vertical velocity at 500 hPa weakens more (17% vs 10%). These changes mask more fundamental changes in underlying distributions. The decrease in 3-hourly rain frequency and shift from lighter to heavier rainfall are more pronounced in the CPM and accompanied by a shift from weak to strong updrafts with the enhancement of heavy rainfall largely due to these dynamic changes. The CPM has stronger coupling between intense rainfall and higher TCW. This yields a greater increase in rainfall contribution from events with greater TCW, with more rainfall for a given large-scale ascent, and so favors slowing of that ascent. These findings highlight connections between the convective-scale and larger-scale flows and emphasize that limitations of parameterized convection have major implications for planning adaptation to climate change.

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Simulating future precipitation extremes in a complex Alpine catchment

Natural Hazards and Earth System Science ◽

10.5194/nhess-13-263-2013 ◽

2013 ◽

Vol 13 (2) ◽

pp. 263-277 ◽

Cited By ~ 9

Author(s):

C. Dobler ◽

G. Bürger ◽

J. Stötter

Keyword(s):

Climate Change ◽

Large Scale ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Precipitation Extremes ◽

Climate Projections ◽

Research Station ◽

Climate Change Scenarios ◽

Station Data

Abstract. The objectives of the present investigation are (i) to study the effects of climate change on precipitation extremes and (ii) to assess the uncertainty in the climate projections. The investigation is performed on the Lech catchment, located in the Northern Limestone Alps. In order to estimate the uncertainty in the climate projections, two statistical downscaling models as well as a number of global and regional climate models were considered. The downscaling models applied are the Expanded Downscaling (XDS) technique and the Long Ashton Research Station Weather Generator (LARS-WG). The XDS model, which is driven by analyzed or simulated large-scale synoptic fields, has been calibrated using ECMWF-interim reanalysis data and local station data. LARS-WG is controlled through stochastic parameters representing local precipitation variability, which are calibrated from station data only. Changes in precipitation mean and variability as simulated by climate models were then used to perturb the parameters of LARS-WG in order to generate climate change scenarios. In our study we use climate simulations based on the A1B emission scenario. The results show that both downscaling models perform well in reproducing observed precipitation extremes. In general, the results demonstrate that the projections are highly variable. The choice of both the GCM and the downscaling method are found to be essential sources of uncertainty. For spring and autumn, a slight tendency toward an increase in the intensity of future precipitation extremes is obtained, as a number of simulations show statistically significant increases in the intensity of 90th and 99th percentiles of precipitation on wet days as well as the 5- and 20-yr return values.

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Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

10.21203/rs.3.rs-725819/v1 ◽

2021 ◽

Author(s):

Antoine Doury ◽

Samuel Somot ◽

Sébastien Gadat ◽

Aurélien Ribes ◽

Lola Corre

Keyword(s):

Climate Change ◽

Network Architecture ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Future Climate ◽

Near Surface ◽

The Way

Abstract Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

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Do Convection-Permitting Regional Climate Models Improve Projections of Future Precipitation Change?

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-15-0004.1 ◽

2017 ◽

Vol 98 (1) ◽

pp. 79-93 ◽

Cited By ~ 121

Author(s):

Elizabeth J. Kendon ◽

Nikolina Ban ◽

Nigel M. Roberts ◽

Hayley J. Fowler ◽

Malcolm J. Roberts ◽

...

Keyword(s):

Climate Change ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Global Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Climate Projections ◽

Convective Storms ◽

Wide Range

Abstract Regional climate projections are used in a wide range of impact studies, from assessing future flood risk to climate change impacts on food and energy production. These model projections are typically at 12–50-km resolution, providing valuable regional detail but with inherent limitations, in part because of the need to parameterize convection. The first climate change experiments at convection-permitting resolution (kilometer-scale grid spacing) are now available for the United Kingdom; the Alps; Germany; Sydney, Australia; and the western United States. These models give a more realistic representation of convection and are better able to simulate hourly precipitation characteristics that are poorly represented in coarser-resolution climate models. Here we examine these new experiments to determine whether future midlatitude precipitation projections are robust from coarse to higher resolutions, with implications also for the tropics. We find that the explicit representation of the convective storms themselves, only possible in convection-permitting models, is necessary for capturing changes in the intensity and duration of summertime rain on daily and shorter time scales. Other aspects of rainfall change, including changes in seasonal mean precipitation and event occurrence, appear robust across resolutions, and therefore coarse-resolution regional climate models are likely to provide reliable future projections, provided that large-scale changes from the global climate model are reliable. The improved representation of convective storms also has implications for projections of wind, hail, fog, and lightning. We identify a number of impact areas, especially flooding, but also transport and wind energy, for which very high-resolution models may be needed for reliable future assessments.

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Quantifying the Likelihood of Regional Climate Change: A Hybridized Approach

Journal of Climate ◽

10.1175/jcli-d-11-00730.1 ◽

2013 ◽

Vol 26 (10) ◽

pp. 3394-3414 ◽

Cited By ~ 18

Author(s):

C. Adam Schlosser ◽

Xiang Gao ◽

Kenneth Strzepek ◽

Andrei Sokolov ◽

Chris E. Forest ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Climate Projections ◽

Massachusetts Institute Of Technology ◽

Adaptation To Climate Change ◽

Intergovernmental Panel ◽

Near Surface ◽

Institute Of Technology

Abstract The growing need for risk-based assessments of impacts and adaptation to climate change calls for increased capability in climate projections: specifically, the quantification of the likelihood of regional outcomes and the representation of their uncertainty. Herein, the authors present a technique that extends the latitudinal projections of the 2D atmospheric model of the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM) by applying longitudinally resolved patterns from observations, and from climate model projections archived from exercises carried out for the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC). The method maps the IGSM zonal means across longitude using a set of transformation coefficients, and this approach is demonstrated in application to near-surface air temperature and precipitation, for which high-quality observational datasets and model simulations of climate change are available. The current climatology of the transformation coefficients is observationally based. To estimate how these coefficients may alter with climate, the authors characterize the climate models’ spatial responses, relative to their zonal mean, from transient increases in trace-gas concentrations and then normalize these responses against their corresponding transient global temperature responses. This procedure allows for the construction of metaensembles of regional climate outcomes, combining the ensembles of the MIT IGSM—which produce global and latitudinal climate projections, with uncertainty, under different global climate policy scenarios—with regionally resolved patterns from the archived IPCC climate model projections. This hybridization of the climate model longitudinal projections with the global and latitudinal patterns projected by the IGSM can, in principle, be applied to any given state or flux variable that has the sufficient observational and model-based information.

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CECILIA Regional Climate Simulations for Future Climate: Analysis of Climate Change Signal

Advances in Meteorology ◽

10.1155/2015/354727 ◽

2015 ◽

Vol 2015 ◽

pp. 1-13 ◽

Cited By ~ 7

Author(s):

Michal Belda ◽

Petr Skalák ◽

Aleš Farda ◽

Tomáš Halenka ◽

Michel Déqué ◽

...

Keyword(s):

Climate Change ◽

Large Scale ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Global Scale ◽

Climate Change Signal ◽

Northwestern Part ◽

Climate Simulations ◽

Climate Analysis

Regional climate models (RCMs) are important tools used for downscaling climate simulations from global scale models. In project CECILIA, two RCMs were used to provide climate change information for regions of Central and Eastern Europe. Models RegCM and ALADIN-Climate were employed in downscaling global simulations from ECHAM5 and ARPEGE-CLIMAT under IPCC A1B emission scenario in periods 2021–2050 and 2071–2100. Climate change signal present in these simulations is consistent with respective driving data, showing similar large-scale features: warming between 0 and 3°C in the first period and 2 and 5°C in the second period with the least warming in northwestern part of the domain increasing in the southeastern direction and small precipitation changes within range of +1 to −1 mm/day. Regional features are amplified by the RCMs, more so in case of the ALADIN family of models.

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Adaptation to climate change and variability: a case of direct seeded rice in Andhra Pradesh, India

Journal of Water and Climate Change ◽

10.2166/wcc.2018.141 ◽

2018 ◽

Vol 10 (2) ◽

pp. 419-430 ◽

Cited By ~ 3

Author(s):

Krishna Reddy Kakumanu ◽

Gurava Reddy Kotapati ◽

Udaya Sekhar Nagothu ◽

Palanisami Kuppanan ◽

Suresh Reddy Kallam

Keyword(s):

Climate Change ◽

Water Supply ◽

Andhra Pradesh ◽

Large Scale ◽

Climate Models ◽

System Level ◽

Adaptation To Climate Change ◽

Active Involvement ◽

Climate Change And Variability ◽

Direct Seeded

Abstract Farmers, researchers and policy-makers are increasingly concerned about the potential impacts of climate change. Researchers are using various climate models to assess the impacts and identifying relevant alternative adaptation strategies to mitigate climate change. In India, rice is the major cereal crop grown and is influenced due to climate change and variability, inadequate water supply, labour shortage and methane emissions from rice ecosystems. This necessitates adoption action and upscaling of key adaption strategies like direct seeded rice (DSR) using validated data from rice growing areas in India. The study used experimental data of 2010–2014 and field survey data of DSR and non-DSR farmers collected during 2014. Results show that DSR method has incurred less tillage and labour costs by eluding puddling and transplantation by labour. Large-scale adoption of DSR was observed during 2012–2015 in Guntur district of Andhra Pradesh. This was mainly due to the delayed monsoon and water supply, reduction in cost of cultivation, capacity building of stakeholders and their active involvement in awareness and training programmes. The study has demonstrated that integrated extension approach in technology dissemination and scaling-out through stakeholder integration is crucial. However, a mission mode framework is needed for technology upscaling at system level.

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Km-resolution climate models: prospects and challenges

10.5194/dach2022-311 ◽

2022 ◽

Author(s):

Christoph Schär

Keyword(s):

Climate Change ◽

Graphics Processing Units ◽

Climate Models ◽

Global Climate ◽

Regional Climate ◽

Heavy Precipitation ◽

Wave Drag ◽

Global Climate Models ◽

Moist Convection ◽

Climate Change Projections

Currently major efforts are underway toward refining the horizontal grid spacing of climate models to about 1 km, using both global and regional climate models. There is the well-founded hope that this increase in resolution will improve climate models, as it enables replacing the parameterizations of moist convection and gravity-wave drag by explicit treatments. Results suggest that this approach has a high potential in improving the representation of the water cycle and extreme events, and in reducing uncertainties in climate change projections. The presentation will provide examples of these developments in the areas of heavy precipitation and severe weather events over Europe. In addition, it will be argued that km-resolution is a promising approach toward constraining uncertainties in global climate change projections, due to improvements in the representation of tropical and subtropical clouds. Work in the latter area has only recently started and results are highly encouraging. For a few years there have also been attempts to make km-resolution available in global climate models for decade-long simulations. Developing this approach requires a concerted effort. Key challenges include the exploitation of the next generation hardware architectures using accelerators (e.g. graphics processing units, GPUs), the development of suitable approaches to overcome the output avalanche, and the maintenance of the rapidly-developing model source codes on a number of different compute architectures. Despite these challenges, it will be argued that km-resolution GCMs with a capacity to run at 1 SYPD (simulated year per day), might be much closer than commonly believed. The presentation is largely based on a recent collaborative paper (Sch&#228;r et al., 2020, BAMS, https://doi.org/10.1175/BAMS-D-18-0167.1) and ongoing studies. It will also present aspects of a recent Swiss project in this area (EXCLAIM, https://exclaim.ethz.ch/).

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Empirical-Statistical Downscaling: Nonlinear Statistical Downscaling

10.1093/acrefore/9780190228620.013.770 ◽

2018 ◽

Author(s):

Aristita Busuioc ◽

Alexandru Dumitrescu

Keyword(s):

Climate Change ◽

Statistical Downscaling ◽

Large Scale ◽

Climate Models ◽

Dynamical Downscaling ◽

Global Climate ◽

Regional Climate ◽

Regional Climate Models ◽

Global Climate Models ◽

Local Climate

This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.The concept of statistical downscaling or empirical-statistical downscaling became a distinct and important scientific approach in climate science in recent decades, when the climate change issue and assessment of climate change impact on various social and natural systems have become international challenges. Global climate models are the best tools for estimating future climate conditions. Even if improvements can be made in state-of-the art global climate models, in terms of spatial resolution and their performance in simulation of climate characteristics, they are still skillful only in reproducing large-scale feature of climate variability, such as global mean temperature or various circulation patterns (e.g., the North Atlantic Oscillation). However, these models are not able to provide reliable information on local climate characteristics (mean temperature, total precipitation), especially on extreme weather and climate events. The main reason for this failure is the influence of local geographical features on the local climate, as well as other factors related to surrounding large-scale conditions, the influence of which cannot be correctly taken into consideration by the current dynamical global models.Impact models, such as hydrological and crop models, need high resolution information on various climate parameters on the scale of a river basin or a farm, scales that are not available from the usual global climate models. Downscaling techniques produce regional climate information on finer scale, from global climate change scenarios, based on the assumption that there is a systematic link between the large-scale and local climate. Two types of downscaling approaches are known: a) dynamical downscaling is based on regional climate models nested in a global climate model; and b) statistical downscaling is based on developing statistical relationships between large-scale atmospheric variables (predictors), available from global climate models, and observed local-scale variables of interest (predictands).Various types of empirical-statistical downscaling approaches can be placed approximately in linear and nonlinear groupings. The empirical-statistical downscaling techniques focus more on details related to the nonlinear models—their validation, strengths, and weaknesses—in comparison to linear models or the mixed models combining the linear and nonlinear approaches. Stochastic models can be applied to daily and sub-daily precipitation in Romania, with a comparison to dynamical downscaling. Conditional stochastic models are generally specific for daily or sub-daily precipitation as predictand.A complex validation of the nonlinear statistical downscaling models, selection of the large-scale predictors, model ability to reproduce historical trends, extreme events, and the uncertainty related to future downscaled changes are important issues. A better estimation of the uncertainty related to downscaled climate change projections can be achieved by using ensembles of more global climate models as drivers, including their ability to simulate the input in downscaling models. Comparison between future statistical downscaled climate signals and those derived from dynamical downscaling driven by the same global model, including a complex validation of the regional climate models, gives a measure of the reliability of downscaled regional climate changes.

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Assessing mean climate change signals in the global CORDEX-CORE ensemble

Climate Dynamics ◽

10.1007/s00382-020-05494-x ◽

2020 ◽

Author(s):

Claas Teichmann ◽

Daniela Jacob ◽

Armelle Reca Remedio ◽

Thomas Remke ◽

Lars Buntemeyer ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Climate Services ◽

Temperature And Precipitation ◽

Climate Change Information ◽

The World ◽

Mean Climate

Abstract The new Coordinated Output for Regional Evaluations (CORDEX-CORE) ensemble provides high-resolution, consistent regional climate change projections for the major inhabited areas of the world. It serves as a solid scientific basis for further research related to vulnerability, impact, adaptation and climate services in addition to existing CORDEX simulations. The aim of this study is to investigate and document the climate change information provided by the CORDEX-CORE simulation ensemble, as a part of the World Climate Research Programme (WCRP) CORDEX community. An overview of the annual and monthly mean climate change information in selected regions in different CORDEX domains is presented for temperature and precipitation, providing the foundation for detailed follow-up studies and applications. Initially, two regional climate models (RCMs), REMO and RegCM were used to downscale global climate model output. The driving simulations by AR5 global climate models (AR5-GCMs) were selected to cover the spread of high, medium, and low equilibrium climate sensitivity at a global scale. The CORDEX-CORE ensemble has doubled the spatial resolution compared to the previously existing CORDEX simulations in most of the regions (25$$\,\mathrm {km}$$ km (0.22$$^{\circ }$$ ∘ ) versus 50$$\,\mathrm {km}$$ km (0.44$$^{\circ }$$ ∘ )) leading to a potentially improved representation of, e.g., physical processes in the RCMs. The analysis focuses on changes in the IPCC physical climate reference regions. The results show a general reasonable representation of the spread of the temperature and precipitation climate change signals of the AR5-GCMs by the CORDEX-CORE simulations in the investigated regions in all CORDEX domains by mostly covering the AR5 interquartile range of climate change signals. The simulated CORDEX-CORE monthly climate change signals mostly follow the AR5-GCMs, although for specific regions they show a different change in the course of the year compared to the AR5-GCMs, especially for RCP8.5, which needs to be investigated further in region specific process studies.

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Assessing maximum potential water withdrawal for food production under climate change - an application in Spain

Journal of Water and Climate Change ◽

10.2166/wcc.2014.057 ◽

2014 ◽

Vol 5 (4) ◽

pp. 633-651

Author(s):

D. González-Zeas ◽

L. Garrote ◽

A. Iglesias

Keyword(s):

Climate Change ◽

Time Series ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Water Withdrawal ◽

Climate Change Projections ◽

Adaptation Measures ◽

Monthly Time Series

This paper provides and tests a methodology to compute surface water (SW) availability for irrigation on regulated systems at large scale, considering different alternatives of streamflow monthly time series derived from regional climate models. SW availability for consumptive use for a river basin is estimated through the concept of maximum potential water withdrawal (MPWW). MPWW is defined as the maximum demand that can be supplied at a given point in the river network under certain conditions: management restrictions (such as ecological flows), demand priorities, monthly distribution of demand and required reliability. Calculation was applied in 567 basins that cover the entirety of mainland Spain to evaluate adaptation needs for agriculture by comparing MPWW for irrigation in the current situation and under climate change projections. The results show that streamflow monthly time series obtained from the regional climate model simulations and bias corrected by University of New Hampshire/Global Runoff Data Centre (UNH/GRDC) dataset and Schreiber's formula provide MPWW values similar to those obtained with the observed data under current situations. Under climate change projections, the capability to satisfy water requirements for agricultural production is significantly reduced and adaptation measures are necessary to mitigate the expected long-term impact.

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