HOTSSea v1: a NEMO-based physical Hindcast of the Salish Sea (1980–2018) supporting ecosystem model development DOI Creative Commons
Greig Oldford, Tereza Jarníková, Villy Christensen

et al.

Geoscientific model development, Journal Year: 2025, Volume and Issue: 18(2), P. 211 - 237

Published: Jan. 20, 2025

Abstract. Decadal-scale oceanographic, environmental, and ecological changes have been reported in the Salish Sea, an ecologically productive inland sea northeast Pacific that supports economies cultures of millions people. However, there are substantial data gaps related to physical water properties make it difficult evaluate trends pathways effects between ocean productivity marine ecosystems. With aim addressing these gaps, we present Hindcast Sea (HOTSSea) v1, a 3D oceanographic model developed using Nucleus for European Modelling Ocean (NEMO) engine, with temporal coverage from 1980–2018. We used experimental approach incrementally assess sensitivity atmospheric reanalysis products boundary forcings horizontal discretisation grid (∼ 1.5 km). Biases inherited were quantified, simple temperature bias correction factor applied at one was found substantially improve skill. Evaluation salinity indicates performance is best Strait Georgia. Relatively large biases occur near-surface waters, especially subdomains topography narrower than grid's resolution. demonstrated simulates anomalies secular warming trend over entire column general agreement observations. HOTSSea v1 provided first look spatially temporally heterogenous throughout northern central part domain where observations sparse. Overall, despite relatively coarse discretisation, performs well representing spatial–temporal scales needed support research decadal-scale climate on ecosystems, fish, fisheries. conclude by underscoring need further extend hindcast capture regime shift occurred 1970s.

Language: Английский

Convection‐permitting modeling with regional climate models: Latest developments and next steps DOI Creative Commons
Philippe Lucas‐Picher, Daniel Argüeso, Erwan Brisson

et al.

Wiley Interdisciplinary Reviews Climate Change, Journal Year: 2021, Volume and Issue: 12(6)

Published: Aug. 16, 2021

Abstract Approximately 10 years ago, convection‐permitting regional climate models (CPRCMs) emerged as a promising computationally affordable tool to produce fine resolution (1–4 km) decadal‐long simulations with explicitly resolved deep convection. This explicit representation is expected reduce projection uncertainty related convection parameterizations found in most models. A recent surge CPRCM decadal over larger domains, sometimes covering continents, has led important insights into advantages and limitations. Furthermore, new observational gridded datasets spatial temporal (~1 km; ~1 h) resolutions have leveraged additional knowledge through evaluations of the added value CPRCMs. With an improved coordination frame ongoing international initiatives, production ensembles provide more robust projections better identification their associated uncertainties. review paper presents overview methodology latest research on current future climates. Impact studies that are already taking advantage these highlighted. ends by proposing next steps could be accomplished continue exploiting full potential article categorized under: Climate Models Modeling > Earth System

Language: Английский

Citations

228

Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979–2100) DOI Creative Commons
Zhongfeng Xu, Ying Han, Chi‐Yung Tam

et al.

Scientific Data, Journal Year: 2021, Volume and Issue: 8(1)

Published: Nov. 4, 2021

Dynamical downscaling is an important approach to obtaining fine-scale weather and climate information. However, dynamical simulations are often degraded by biases in the large-scale forcing itself. We constructed a bias-corrected global dataset based on 18 models from Coupled Model Intercomparison Project Phase 6 (CMIP6) European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset. The data have ERA5-based mean interannual variance, but with non-linear trend ensemble of CMIP6 models. spans historical time period 1979–2014 future scenarios (SSP245 SSP585) 2015–2100 horizontal grid spacing (1.25° × 1.25°) at six-hourly intervals. Our evaluation suggests that better quality than individual terms climatological mean, variance extreme events. This will be useful projections Earth's climate, atmospheric environment, hydrology, agriculture, wind power, etc. Machine-accessible metadata file describing reported data: https://doi.org/10.6084/m9.figshare.16802326

Language: Английский

Citations

180

Climate Change and Rainfall Intensity–Duration–Frequency Curves: Overview of Science and Guidelines for Adaptation DOI Creative Commons
Jean‐Luc Martel, François Brissette, Philippe Lucas‐Picher

et al.

Journal of Hydrologic Engineering, Journal Year: 2021, Volume and Issue: 26(10)

Published: Aug. 3, 2021

One of the most important impacts a future warmer climate is projected increase in frequency and intensity extreme rainfall events. This increasing trend seen both observational record model projections. However, thorough review recent scientific literature paints complex picture which intensification extremes depends on multitude factors. While some indices follow Clausius-Clapeyron relationship scaling an ∼7% per 1°C warming, there substantial evidence that this frequency, with longer return period events seeing larger increases, leading to super cases. The now well documented at daily scale but less clear subdaily scale. In years, simulations finer spatial temporal resolution, including convection-permitting models, have provided more reliable projections rainfall. Recent analyses indicate may also as function duration, such shorter-duration, will likely see largest increases climate. has broad implications design use intensity–duration–frequency (IDF) curves, for overall magnitude steepening can be predicted. paper presents overview measures been adopted by various governing bodies adapt IDF curves changing Current vary from multiplying historical simple constant percentage modulating correction factors based periods them temperature increases. All these current fail recognize possible and, perhaps importantly, toward shorter-duration significantly impact stormwater runoff cities small rural catchments. discusses remaining gaps offers technical recommendations practitioners how improve resilience.

Language: Английский

Citations

132

The pseudo-global-warming (PGW) approach: methodology, software package PGW4ERA5 v1.1, validation, and sensitivity analyses DOI Creative Commons
Roman Brogli, Christoph Heim,

Jonas Mensch

et al.

Geoscientific model development, Journal Year: 2023, Volume and Issue: 16(3), P. 907 - 926

Published: Feb. 6, 2023

Abstract. The term “pseudo-global warming” (PGW) refers to a simulation strategy in regional climate modeling. consists of directly imposing large-scale changes the system on control (usually representing current conditions) by modifying boundary conditions. This differs from traditional dynamic downscaling technique where output global model (GCM) is used drive models (RCMs). PGW are usually derived transient simulation. approach offers several benefits, such as lowering computational requirements, flexibility design, and avoiding biases models. However, implementing non-trivial, care must be taken not deteriorate physics when To simplify preparation simulations, we present detailed description methodology provide companion software PGW4ERA5 facilitating simulations. In describing methodology, particular attention devoted adjustment pressure geopotential fields. Such an required ensuring consistency between thermodynamical (temperature humidity) one hand dynamical other hand. It demonstrated that this important extratropics highly essential tropical subtropical regions. We show projections simulations prepared using presented closely comparable for most climatological variables.

Language: Английский

Citations

48

Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review DOI
Yongjian Sun, Kefeng Deng, Kaijun Ren

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2024, Volume and Issue: 208, P. 14 - 38

Published: Jan. 9, 2024

Language: Английский

Citations

36

Correction of ERA5 Wind for Regional Climate Projections of Sea Waves DOI Open Access
Alvise Benetazzo, Silvio Davison, Francesco Barbariol

et al.

Water, Journal Year: 2022, Volume and Issue: 14(10), P. 1590 - 1590

Published: May 16, 2022

This paper proposes a method to infer the future change in wind-wave climate using reanalysis wind corrected statistically match data from regional model (RCM). The is applied sea surface speed of ERA5 European Centre for Medium-Range Weather Forecasts. correction determined quantile mapping between and RCM at any given point geographical space. issues that need be addressed better understand apply are discussed. Corrected fields eventually used force spectral wave numerical simulate significant height. strategy implemented over Adriatic Sea (a semi-enclosed basin Mediterranean Sea) includes present-day period (1981–2010) near-future (2021–2050) under two IPCC RCP4.5 RCP8.5 concentration scenarios. Evaluation against observations waves gives confidence reliability proposed approach. Results confirm evolution toward an overall decrease storm severity basin, especially its northern area. It expected methodology may other reanalyses, RCMs (including multi-model ensembles), or seas with similar characteristics.

Language: Английский

Citations

48

Downscaling of environmental indicators: A review DOI
Shiting Li, Chao Xu, Meirong Su

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 916, P. 170251 - 170251

Published: Jan. 21, 2024

Language: Английский

Citations

11

Diffusion model-based probabilistic downscaling for 180-year East Asian climate reconstruction DOI Creative Commons

Fenghua Ling,

Zeyu Lu, Jing‐Jia Luo

et al.

npj Climate and Atmospheric Science, Journal Year: 2024, Volume and Issue: 7(1)

Published: June 13, 2024

Abstract As our planet is entering into the “global boiling” era, understanding regional climate change becomes imperative. Effective downscaling methods that provide localized insights are crucial for this target. Traditional approaches, including computationally-demanding dynamical models or statistical frameworks, often susceptible to influence of uncertainty. Here, we address these limitations by introducing a diffusion probabilistic model (DPDM) meteorological field. This can efficiently transform data from 1° 0.1° resolution. Compared with deterministic schemes, it not only has more accurate local details, but also generate large number ensemble members based on probability distribution sampling evaluate uncertainty downscaling. Additionally, apply 180-year dataset monthly surface variables in East Asia, offering detailed perspective scale over past centuries.

Language: Английский

Citations

10

Statistical downscaling and projection of future temperatures across the Loess Plateau, China DOI Creative Commons
Xuewei Fan, Lin Jiang, Jiaojiao Gou

et al.

Weather and Climate Extremes, Journal Year: 2021, Volume and Issue: 32, P. 100328 - 100328

Published: May 7, 2021

The Loess Plateau in China is one of the most erosive regions world, especially under warming climate conditions, which are aggravating evapotranspiration and water scarcity. Thus, there a need to better understand historical future change patterns Plateau, global models (GCMs) key tool achieve this. Because mismatch spatial resolution between GCMs requirements regional applications, Statistical Downscaling Model (SDSM) combination with two bias-correction methods was employed for first time downscale modeled values from Phase 5 Coupled Intercomparison Project daily maximum temperature (TMAX), mean (TMEAN), minimum (TMIN) over Plateau. After evaluation model capability, bias-corrected downscaled temperatures forced by GCM outputs period 2010–2099 were then projected. results show that SDSM cumulative density function matching technique produced more accurate estimates than integration delta correction, reduced root square errors (and associated standard deviations) 59.2% (88.6%), 45.3% (78.8%), 48.8% (43.4%) TMAX, TMEAN, TMIN, respectively. projected will increase entire plateau relative 1961–1990 period, greatest changes northern eastern regions. Another finding can reduce uncertainties projection obtain reliable projections.

Language: Английский

Citations

45

A copula post-processing method for wind power projections under climate change DOI Creative Commons
Sogol Moradian, Salem Gharbia, G. Iglesias

et al.

Energy Conversion and Management X, Journal Year: 2024, Volume and Issue: 23, P. 100660 - 100660

Published: July 1, 2024

Wind energy plays a pivotal role in the ongoing effort to reduce carbon emissions sector. With increasing evidence of climate change, there is growing concern regarding planning and operation wind resources. Accurate forecasts are essential understand frequency distribution speed data given area and, consequently, estimate production. This paper aims analyze resources under assess their potential, create zoning maps for production island Ireland. For this objective, from 31 general circulation models (GCMs) two change scenarios were utilized both hindcast forecast periods 1981–2010 2021–2050, respectively. The GCM outputs first bias-corrected then post-processed using various (non–)parametric statistical distributions 3 Copula families. results indicate an expected decrease average region up ∼ 21 % by 2050, contingent on consideration target point. Ultimately, study concludes presenting power density specifically region, offering valuable insights sustainable planning.

Language: Английский

Citations

8